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Available online ,
doi: 10.11999/JEIT240782
Abstract:
Objective In 2017, the PFP algorithm was introduced as an ultra-lightweight block cipher to address the demand for efficient cryptographic solutions in constrained environments, such as the Internet of Things (IoT). With a hardware footprint of approximately 1355 GE and low power consumption, PFP has attracted attention for its ability to deliver high-speed encryption with minimal resource usage. Its encryption and decryption speeds outperform those of the internationally recognized PRESENT cipher by a factor of 1.5, making it highly suitable for real-time applications in embedded systems. While the original design documentation asserts that PFP resists various traditional cryptographic attacks, including differential, linear, and impossible differential attacks, the possibility of undiscovered vulnerabilities remains unexplored. This study evaluates the algorithm’s resistance to related-key differential attacks, a critical cryptanalysis method for lightweight ciphers, to determine the actual security level of the PFP algorithm using formal cryptanalysis techniques. Methods To evaluate the security of the PFP algorithm, Satisfiability Modulo Theories (SMT) is used to model the cipher’s round function and automate the search for distinguishers indicating potential design weaknesses. SMT, a formal method increasingly applied in cryptanalysis, facilitates automated attack generation and the detection of cryptographic flaws. The methodology involved constructing mathematical models of the cipher’s rounds, which are tested for differential characteristics under various key assumptions. Two distinguisher models are developed: one based on single-key differentials and the other on related-key differentials, the latter being the focus of this analysis. These models automated the search for weak key differentials that could enable efficient key recovery attacks. The analysis leveraged the nonlinear substitution-permutation structure of the PFP round function to systematically identify vulnerabilities. The results are examined to estimate the probability of key recovery under different attack scenarios and assess the effectiveness of related-key differential cryptanalysis against the full-round PFP cipher. Results and Discussions The SMT-based analysis revealed a critical vulnerability in the PFP algorithm. A related-key differential characteristic with a probability of 2–62 is identified, persisting through 32 encryption rounds. This characteristic indicates a predictable pattern in the cipher’s behavior under related-key conditions, which can be exploited to recover the secret key. Such differentials are particularly concerning as they expose a significant weakness in the cipher’s resistance to related-key attacks, a critical threat in IoT applications where keys may be reused or related across multiple devices or sessions.Based on this finding, a key recovery attack is developed, requiring only 263 chosen plaintexts and 248 full-round encryptions to retrieve the 80-bit master key. The efficiency of this attack demonstrates the vulnerability of the PFP cipher to practical cryptanalysis, even with limited computational resources. The attack’s relatively low complexity suggests that PFP may be unsuitable for applications demanding high security, particularly in environments where adversaries can exploit related-key differential characteristics. Moreover, these results indicate that the existing resistance claims for the PFP cipher are insufficient, as they do not account for the effectiveness of related-key differential cryptanalysis. This challenges the assertion that the PFP algorithm is secure against all known cryptographic attacks, emphasizing the need for thorough cryptanalysis before lightweight ciphers are deployed in real-world scenarios.(Fig. 2 : Related-key differential characteristic with probability 2–62 in 32 rounds; Table 1 : Attack complexity and resource requirements for related-key recovery.) Conclusions In conclusion, this paper presents a cryptographic analysis of the PFP lightweight block cipher, revealing its vulnerability to related-key differential attacks. The proposed key recovery attack demonstrates that, despite its efficiency in hardware and speed, PFP fails to resist attacks exploiting related-key differential characteristics. This weakness is particularly concerning for IoT applications, where key reuse or related keys across devices is common. These findings highlight the need for further refinement in lightweight cipher design to ensure robust resistance against advanced cryptanalysis techniques. As lightweight ciphers continue to be deployed in security-critical systems, it is essential that designers consider all potential attack vectors, including related-key differentials, to strengthen security guarantees. Future work should focus on enhancing the cipher’s security by exploring alternative key-schedule designs or increasing the number of rounds to mitigate the identified vulnerabilities. Additionally, this study emphasizes the effectiveness of SMT-based formal methods in cryptographic analysis, providing a systematic approach for identifying previously overlooked weaknesses in cipher designs.
Available online ,
doi: 10.11999/JEIT240719
Abstract:
Objective Vehicular networks, as key components of intelligent transportation systems, are encountering increasing spectrum resource limitations within their dedicated 25 MHz communication band, as well as challenges from electromagnetic interference in typical communication environments. To address these issues, this paper integrates cognitive radio technology with radar sensing and introduces Hybrid-Reconfigurable Intelligent Surface (H-RIS) to jointly optimize radar sensing, data transmission, and computation. This approach aims to enhance spectrum resource utilization and the Joint Throughput Capacity (JTC) of vehicular networks. Methods A phased optimization approach is adopted to alternately optimize power allocation, time allocation, and reflection components in order to identify the best solution. The data transmission capacity of secondary users is characterized by defining a performance index for JTP. The problem is tackled through a two-stage optimization strategy where power allocation, time allocation, and reflection element optimization are solved iteratively to achieve the optimal solution. First, a joint optimization problem for sensing, communication, and computation is formulated. By jointly optimizing time allocation, H-RIS reflection element coefficients, and power allocation, the goal is to maximize the joint throughput capacity. The block coordinate descent method decomposes the optimization problem into three sub-problems. In the optimization of reflection element coefficients, a stepwise approach is employed, where passive reflection elements are fixed to optimize active reflection elements and vice versa. Results and Discussions The relationship between joint throughput and the number of iterations for the proposed Alternating Optimization Iterative Algorithm (AOIA) is shown (Figure 4 ). The results indicate that both algorithms converge after a finite number of iterations. The correlation between the target secondary user’s joint throughput and radar power is presented (Figure 5 ). In the H-RIS-assisted Integrated Sensing Communication and Computation Vehicle-to-Everything (ISCC-V2X) scenario, the joint throughput of the Aimed Secondary User (ASU) is maximized through optimal power configuration (Figure 5 ). The comparison of the target secondary user’s joint throughput with radar system power for the proposed algorithm and baseline schemes is shown (Figure 6 ), demonstrating that the proposed method significantly outperforms random Reconfigurable Intelligent Surfaces (RIS) and No-RIS schemes under the same parameter settings. Furthermore, the proposed H-RIS optimization scheme outperforms both Random H-RIS and traditional passive optimization RIS in terms of joint throughput.The relationship between the target secondary user’s joint throughput and the number of H-RIS reflection elements is illustrated (Figure 7 ). The results show that the proposed scheme provides a significant performance improvement over both Random RIS and No-RIS schemes under the same parameter settings. The relationship between the transmit power of the target secondary user’s joint throughput and the transmit power of the ASU is depicted (Figure 9 ), highlighting that joint throughput increases with transmit power in all scenarios. The relationship between joint throughput and the number of active reflection elements for the proposed algorithm and other benchmark schemes is shown (Figure 10 ), demonstrating that joint throughput increases with the number of active reflection elements in H-RIS scenarios, with the proposed scheme exhibiting a faster growth rate than Random H-RIS. The relationship between ASU joint throughput, radar sensing time, and radar power is presented (Figure 11 ), revealing that an optimal joint time and power allocation strategy exists. This strategy maximizes ASU joint throughput while ensuring H-RIS presence and sufficient protection for the primary user. Conclusion To address the challenges of spectrum resource scarcity and low data transmission efficiency in vehicular networks, this paper focuses on improving the joint throughput of intelligent vehicle users, enhancing spectrum utilization, and achieving efficient data transmission in the H-RIS-assisted ISCC-V2X scenario. A joint optimization method for vehicular network perception, communication, and computation based on H-RIS is explored. The introduction of H-RIS aims to enhance data transmission efficiency while considering the interests of both primary and secondary users. The joint optimization problem for the target secondary user’s perception, communication, and computation is analyzed. First, the joint allocation scenario for the H-RIS-assisted ISCC-V2X system is constructed, introducing the signal model, radar perception model, communication model, and computation model. Using these models, a joint optimization problem is formulated. Through alternating optimization, the optimal H-RIS reflection element coefficients, time allocation vector, and power allocation vector are derived to maximize the joint throughput. Simulation results demonstrate that the incorporation of H-RIS significantly improves the joint throughput of the target secondary user. Furthermore, an optimal power allocation scheme is identified that maximizes the joint throughput. When both time allocation and power allocation are considered jointly, simulations show the existence of an optimal scheme that maximizes the joint throughput of the target secondary user.
Available online ,
doi: 10.11999/JEIT240866
Abstract:
Objective : To address the balance between efficient energy utilization and information freshness in UAV-assisted data collection for the Internet of Things (IoT) using Reconfigurable Intelligent Surfaces (RIS). Methods : A data collection optimization policy based on deep reinforcement learning is proposed. Considering flight energy consumption, communication complexity, and Age of Information (AoI) constraints, a joint optimization scheme is designed using a Double Deep Q-Network (DDQN). The scheme integrates UAV trajectory planning, IoT device scheduling, and RIS phase adjustment, mitigating Q-value overestimation observed in traditional Q-learning methods. Results and Discussions : The proposed method enables the UAV to dynamically adjust its trajectory and communication strategy based on real-time environmental conditions, enhancing data transmission efficiency and reducing energy consumption. Simulation results demonstrate superior convergence compared with traditional methods (Fig. 3 ). The UAV trajectory shows that the proposed method effectively accomplishes the data collection task (Fig. 4 ). Furthermore, rational allocation of energy and communication resources allows dynamic adaptation to varying communication environment parameters, ensuring an optimal balance between energy consumption and AoI (Fig. 5 )(Fig. 6 ). Conclusions : The deep reinforcement learning-based optimization policy for UAV-assisted IoT data collection with RIS effectively resolves the trade-off between energy utilization and information freshness. This robust solution improves data collection efficiency in dynamic communication environments.
Available online ,
doi: 10.11999/JEIT240741
Abstract:
Objective Federated Learning (FL) represents a distributed learning framework with significant potential, allowing users to collaboratively train a shared model while retaining data on their devices. However, the substantial differences in computing, storage, and communication capacities across FL devices within complex networks result in notable disparities in model training and transmission latency. As communication rounds increase, a growing number of heterogeneous devices become stragglers due to constraints such as limited energy and computing power, changes in user intentions, and dynamic channel fluctuations, adversely affecting system convergence performance. This study addresses these challenges by jointly incorporating assistance mechanisms and reducing device overhead to mitigate the impact of stragglers on model accuracy and training latency. Methods This paper designs a FL architecture integrating joint edge-assisted training and adaptive sparsity and proposes an adaptively sparse FL optimization algorithm based on edge-assisted training. First, an edge server is introduced to provide auxiliary training for devices with limited computing power or energy. This reduces the training delay of the FL system, enables stragglers to continue participating in the training process, and helps maintain model accuracy. Specifically, an optimization model for auxiliary training, communication, and computing resource allocation is constructed. Several deep reinforcement learning methods are then applied to obtain the optimized auxiliary training decision. Second, based on the auxiliary training decision, unstructured pruning is adaptively performed on the global model during each communication round to further reduce device delay and energy consumption. Results and Discussions The proposed framework and algorithm are evaluated through extensive simulations. The results demonstrate the effectiveness and efficiency of the proposed method in terms of model accuracy and training delay.The proposed algorithm achieves an accuracy rate approximately 5% higher than that of the FL algorithm on both the MNIST and CIFAR-10 datasets. This improvement results from low-computing-power and low-energy devices failing to transmit their local models to the central server during multiple communication rounds, reducing the global model’s accuracy (Table 3 ).The proposed algorithm achieves an accuracy rate 18% higher than that of the FL algorithm on the MNIST-10 dataset when the data on each device follow a non-IID distribution. Statistical heterogeneity exacerbates model degradation caused by stragglers, whereas the proposed algorithm significantly improves model accuracy under such conditions (Table 4 ).The reward curves of different algorithms are presented (Fig. 7 ). The reward of FL remains constant, while the reward of EAFL_RANDOM fluctuates randomly. ASEAFL_DDPG shows a more stable reward curve once training episodes exceed 120 due to the strong learning and decision-making capabilities of DDPG and DQN. In contrast, EAFL_DQN converges more slowly and maintains a lower reward than the proposed algorithm, mainly due to more precise decision-making in the continuous action space and an exploration mechanism that expands action selection (Fig. 7 ).When the computing power of the edge server increases, the training delay of the FL algorithm remains constant since it does not involve auxiliary training. The training delay of EAFL_RANDOM fluctuates randomly, while the delays of ASEAFL_DDPG and EAFL_DQN decrease. However, ASEAFL_DDPG consistently achieves a lower system training delay than EAFL_DQN under the same MEC computing power conditions (Fig. 9 ).When the communication bandwidth between the edge server and devices increases, the training delay of the FL algorithm remains unchanged as it does not involve auxiliary training. The training delay of EAFL_RANDOM fluctuates randomly, while the delays of ASEAFL_DDPG and EAFL_DQN decrease. ASEAFL_DDPG consistently achieves lower system training delay than EAFL_DQN under the same bandwidth conditions (Fig. 10 ). Conclusions The proposed sparse-adaptive FL architecture based on an edge-assisted server mitigates the straggler problem caused by system heterogeneity from two perspectives. By reducing the number of stragglers, the proposed algorithm achieves higher model accuracy compared with the traditional FL algorithm, effectively decreases system training delay, and improves model training efficiency. This framework holds practical value, particularly for FL deployments where aggregation devices are selected based on statistical characteristics, such as model contribution rates. Straggler issues are common in such FL scenarios, and the proposed architecture effectively reduces their occurrence. Simultaneously, devices with high model contribution rates can continue participating in multiple rounds of federated training, lowering the central server’s frequent device selection overhead. Additionally, in resource-constrained FL environments, edge servers can perform more diverse and flexible tasks, such as partial auxiliary training and partitioned model training.
Available online ,
doi: 10.11999/JEIT240677
Abstract:
Objective: As a vital component of the global communication network, satellite communication attracts significant attention for its capacity to provide seamless global coverage and establish an integrated space-ground information network. Time-Hopping (TH), a widely used technique in satellite communication, is distinguished by its strong anti-jamming capabilities, flexible spectrum utilization, and high security levels. In an effort to enhance data transmission security, a system utilizing randomly varying TH patterns has been developed. To tackle the challenge of limited transmission power, symbols are distributed across different time slots and repeatedly transmitted according to random TH patterns. At the receiver end, a coherent combining strategy is implemented for signals originating from multiple time slots. To minimize Signal-to-Noise Ratio (SNR) loss during this combining process, precise estimation of TH patterns and multi-hop carrier phases is essential. The randomness of the TH patterns and multi-hop carrier phases further complicates parameter estimation by increasing its dimensionality. Additionally, the low transmission power leads to low-SNR conditions for the received signals in each time slot, complicating parameter estimation even more. Traditional exhaustive search methods are hindered by high computational complexity, highlighting the pressing need for low-complexity multidimensional parameter estimation techniques tailored specifically for TH communication systems. Methods: Firstly, a TH communication system featuring randomly varying TH patterns is developed, where the time slot index of the signal in each time frame is determined by the TH code. Both parties involved in the communication agree that this TH code will change randomly within a specified range. Building on this foundation, a mathematical model for estimating TH patterns and multi-hop carrier phases is derived from the perspective of maximum likelihood estimation, framing it as a multidimensional nonlinear optimization problem. Moreover, guided by a coherent combining strategy and constrained by low SNR conditions at the receiver, a Cross-Entropy (CE) iteration aided algorithm is proposed for the joint estimation of TH patterns and multi-hop carrier phases. This algorithm generates multiple sets of TH code and carrier phase estimates randomly based on a predetermined probability distribution. Using the SNR loss of the combined signal as the objective function, the CE method incorporates an adaptive importance sampling strategy to iteratively update the probability distribution of the estimated parameters, facilitating rapid convergence towards optimal solutions. Specifically, in each iteration, samples demonstrating superior performance are selected according to the objective function to calculate the probability distribution for the subsequent iteration, thereby enhancing the likelihood of reaching the optimal solution. Additionally, to account for the randomness inherent in the iterations, a global optimal vector set is established to document the parameter estimates that correspond to the minimum SNR loss throughout the iterative process. Finally, simulation experiments are conducted to assess the performance of the proposed algorithm in terms of iterative convergence speed, parameter estimation error, and the combined demodulation Bit Error Rate (BER). Results and Discussions: The estimation errors for the TH code and carrier phase were simulated to evaluate the parameter estimation performance of the proposed algorithm. With an increase in SNR, the accuracy of TH code estimation approaches unity. When a small phase quantization bit width is applied, the Root Mean Square Error (RMSE) of the carrier phase estimation is primarily constrained by the grid search step size. Conversely, as the phase quantization bit width increases, the RMSE gradually converges to a fixed value. Regarding the influence of phase quantization on combined demodulation, as the phase quantization bit width increases, nearly theoretical BER performance can be achieved. A comparison between the proposed algorithm and the exhaustive search method reveals that the proposed algorithm significantly reduces the number of search trials compared to the grid search method, with minimal loss in BER performance. An increase in the variation range of the TH code necessitates a larger number of candidate groups for the CE method to maintain a low combining SNR loss. However, with a greater TH code variation range, the number of search iterations and its growth rate in the proposed algorithm are significantly lower than those in the exhaustive search method. Regarding transmission power in the designed TH communication method, as the number of hops in the multi-hop combination increases, the required SNR per hop decreases for the same BER performance, indicating that maximum transmission power can be correspondingly reduced. Conclusions: A TH communication system with randomly varying TH patterns tailored for satellite communication applications has been designed. This includes the presentation of a multi-hop signal coherent combining technique. To address the multidimensional parameter estimation challenge associated with TH patterns and multi-hop carrier phases under low SNR conditions, a CE iteration-aided algorithm has been proposed. The effectiveness of this algorithm is validated through simulations, and its performance regarding iterative convergence characteristics, parameter estimation error, and BER performance has been thoroughly analyzed. The results indicate that, in comparison to the conventional grid search method, the proposed algorithm achieves near-theoretical optimal BER performance while maintaining lower complexity.
Available online ,
doi: 10.11999/JEIT240560
Abstract:
Objective Recent advances in remote sensing imaging technology have made oriented object detection in remote sensing images a prominent research area in computer vision. Unlike traditional object detection tasks, remote sensing images, captured from a wide-range bird's-eye view, often contain a variety of objects with diverse scales and complex backgrounds, posing significant challenges for oriented object detection. Although current approaches have made substantial progress, existing networks do not fully exploit the contextual information across multi-scale features, resulting in classification and localization errors during detection. To address this, a context-aware multiple receptive field fusion network is proposed, which leverages the contextual correlation in multi-scale features. By enhancing the feature representation capabilities of deep networks, the accuracy of oriented object detection in remote sensing images can be improved. Methods For input remote sensing images, ResNet-50 and a feature pyramid network are first employed to extract features at different scales. The features from the first four layers are then enhanced using a receptive field expansion module. The resulting features are processed through a high-level feature aggregation module to effectively fuse multi-scale contextual information. After obtaining enhanced features at different scales, a feature refinement region proposal network is designed to revise object detection proposals using refined feature representations, resulting in more accurate candidate proposals. These multi-scale features and candidate proposals are then input into the Oriented R-CNN detection head to obtain the final object detection results. The receptive field expansion module consists of two submodules: a large selective kernel convolution attention submodule and a shift window self-attention enhancement submodule, which operate in parallel. The large selective kernel convolution submodule introduces multiple convolution operations with different kernel sizes to capture contextual information under various receptive fields, thereby improving the network’s ability to perceive multi-scale objects. The shift window self-attention enhancement submodule divides the feature map into patches according to predefined window and step sizes and calculates the self-attention-enhanced feature representation of each patch, extracting more global information from the image. The high-level feature aggregation module integrates rich semantic information from the feature pyramid network with low-level features, improving detection accuracy for multi-scale objects. Finally, a feature refinement region proposal network is designed to reduce location deviation between generated region proposals and actual rotating objects in remote sensing images. The deformable convolution is employed to capture geometric and contextual information, refining the initial proposals and producing the final oriented object detection results through a two-stage region-of-interest alignment network. Results and Discussions The effectiveness and robustness of the proposed network are demonstrated on two public datasets: DIOR-R and HRSC2016. For DIOR-R dataset, the AP50, AP75 and AP50:95 metrics are used for evaluation. Quantitative and qualitative comparisons (Fig. 7 ) demonstrate that the proposed network significantly enhances feature representation for different remote sensing objects, distinguishing objects with similar appearances and localizing objects at various scales more accurately. For the HRSC2016 dataset, the mean Average Precision (mAP) is used, and both mAP(07) and mAP(12) are computed for quantitative comparison. The results (Fig. 7 , Table 2 ) further highlight the network’s effectiveness in improving ship detection accuracy in remote sensing images. Additionally, ablation studies (Table 3 ) demonstrate that each module in the proposed network contributes to improved detection performance for oriented objects in remote sensing images. Conclusions This paper proposes a context-aware multi-receptive field fusion network for oriented object detection in remote sensing images. The network includes a receptive field expansion module that enhances the perception ability for remote sensing objects of different sizes. The high-level feature aggregation module fully utilizes high-level semantic information, further improving localization and classification accuracy. The feature refinement region proposal network refines the first-stage proposals, resulting in more accurate detection. The qualitative and quantitative results on the DIOR-R and HRSC2016 datasets demonstrate that the proposed network outperforms existing approaches, providing superior detection results for remote sensing objects of varying scales.
Available online ,
doi: 10.11999/JEIT240348
Abstract:
Objective Sensor arrays are widely used to capture the spatio-temporal information of incident signal sources, with their configurations significantly affecting the accuracy of Direction Of Arrival (DOA) estimation. The Degrees Of Freedom (DOF) of conventional Uniform Linear Array (ULA) are limited by the number of physical sensors, and dense array deployments lead to severe mutual coupling effects. Emerging sparse arrays offer clear advantages by reducing hardware requirements, increasing DOF, mitigating mutual coupling, and minimizing system redundancy through flexible sensor deployment, making them a viable solution for high-precision DOA estimation. Among various sparse array designs, the Coprime Array (CA)—consisting of two sparse ULAs with coprime inter-element spacing and sensor counts—has attracted considerable attention due to its reduced mutual coupling effects. However, the alternately deployed subarrays result in a much lower number of Continuous Degrees Of Freedom (cDOF) than anticipated, which degrades the performance of subspace-based DOA estimation algorithms that rely on spatial smoothing techniques. Although many studies have explored array configuration optimization and algorithm design, real-time application demands indicate that optimizing array configurations is the most efficient approach to improve DOA estimation performance. Methods This study examines the weight functions of CA and identifies a significant number of redundant virtual array elements in the difference coarray. Specifically, all virtual array elements in the difference coarray exhibit weight functions of two or more, a key factor reducing the available cDOF and DOF. To address this deficiency, the conditions for generating redundant virtual array elements in the cross-difference sets of subarrays are analyzed, and two types of coprime arrays with translated subarrays, namely, CATrS-I and CATrS-II are devised. These designs aim to increase available cDOF and DOF and enhance DOA estimation performance. Firstly, without altering the number of physical sensors, the conditions for generating redundant virtual array elements in the cross-difference sets are modified by translating any subarray of CA to an appropriate position. Then, the precise range of translation distances is determined, and the closed-form expressions for cDOF and DOF, the hole positions in the difference coarray, and weight functions of CATrS-I and CATrS-II are derived. Finally, the optimal configurations of CATrS-I and CATrS-II are obtained by solving an optimization problem that maximizes cDOF and DOF while maintaining a fixed number of physical sensors. Results and Discussions Theoretical analysis shows that the proposed CATrS-I and CATrS-II can reduce the weight functions of most virtual array elements in the difference coarray to 1, thus increasing the available cDOF and DOF while maintaining the same number of physical sensors. Comparisons with several previously developed sparse arrays highlight the advantages of CATrS-I and CATrS-II. Specifically, the Augmented Coprime Array (ACA), which doubles the number of sensors in one subarray, and the Reference Sensor Relocated Coprime Array (RSRCA), which repositions the reference sensor, achieve only a limited reduction in redundant virtual array elements, particularly those associated with small virtual array elements. As a result, their mutual coupling effects are similar to those of the original CA. In contrast, the proposed CATrS-I and CATrS-II significantly reduce both the number of redundant virtual array elements and the weight functions corresponding to small virtual array elements by translating one subarray to an optimal position. This adjustment effectively mitigates mutual coupling effects among physical sensors. Numerical simulations further validate the superior DOA estimation performance of CATrS-I and CATrS-II in the presence of mutual coupling, demonstrating their superiority in spatial spectrum and DOA estimation accuracy compared to existing designs. Conclusions Two types of CATrS are proposed for DOA estimation by translating the subarrays of CA to appropriate distances. This design effectively reduces the number of redundant virtual array elements in the cross-difference sets, leading to a significant increase in cDOF and DOF, while mitigating mutual coupling effects among physical sensors. The translation distance of the subarray is analyzed, and the closed-form expressions for cDOF and DOF, the hole positions in the difference coarray, and the weight functions of virtual array elements are derived. Theoretical analysis and simulation results demonstrate that the proposed CATrS-I and CATrS-II offer superior performance in terms of cDOF, DOF, mutual coupling suppression, and DOA estimation accuracy. Future research will focus on further reducing redundant virtual array elements in the self-difference sets by disrupting the uniform deployment of subarrays and extending these ideas to more generalized and complex sparse array designs to further enhance array performance.
Available online ,
doi: 10.11999/JEIT240395
Abstract:
Objective Ambient Backscatter Communication (AmBC) is an emerging, low-power, low-cost communication technology that utilizes ambient Radio Frequency (RF) signals for passive information transmission. It has demonstrated significant potential for various wireless applications. However, in AmBC systems, the reflected signals are often severely weakened due to double fading effects and signal obstruction from environmental obstacles. This results in a substantial reduction in signal strength, limiting both communication range and overall system performance. To address these challenges, Intelligent Reflecting Surface (IRS) technology has been integrated into AmBC systems. IRS can enhance reflection link gain by precisely controlling reflected signals, thereby improving system performance. However, the passive nature of both the IRS and tags makes accurate channel estimation a critical challenge. This study proposes an efficient channel estimation algorithm for IRS-assisted AmBC systems, aiming to provide theoretical support for optimizing system performance and explore the feasibility of achieving high-precision channel estimation in complex environments—key to the practical implementation of this technology. Methods This study develops a general IRS-assisted AmBC system model, where the system channel is divided into multiple subchannels, each corresponding to a specific IRS reflection element. To minimize the Mean Squared Error (MSE) in channel estimation, the Least Squares (LS) method is used as the estimation criterion. The joint optimization problem for channel estimation is explored by integrating various IRS reflection modes, including ON/OFF, Discrete Fourier Transform (DFT), and Hadamard modes. The communication channel is assumed to follow a Rayleigh fading distribution, with noise modeled as zero-mean Gaussian. Pilot signals are modulated using Quadrature Phase Shift Keying (QPSK). To thoroughly evaluate the performance of channel estimation, 1000 Monte Carlo simulations are conducted, with MSE and the Cramer-Rao Lower Bound (CRLB) serving as performance metrics. Simulation experiments conducted on the Matlab platform provide a comprehensive comparison and analysis of the performance of different algorithms, ultimately validating the effectiveness and accuracy of the proposed algorithm. Results and Discussions The simulation results demonstrate that the IRS-assisted channel estimation algorithm significantly improves performance. Under varying Signal-to-Noise Ratio (SNR) conditions, the MSE of methods based on DFT and Hadamard matrices consistently outperforms the ON/OFF method, aligning with the CRLB, thereby confirming the optimal performance of the proposed algorithms (Fig. 2, Fig. 3). Additionally, the MSE for direct and cascaded channels is identical when using the DFT and Hadamard methods, while the cascaded channel MSE for the ON/OFF method is twice that of the direct channel, highlighting the superior performance of the DFT and Hadamard approaches. As the number of IRS reflection elements increases, the MSE for the DFT and Hadamard methods decreases significantly, whereas the MSE for the ON/OFF method remains unchanged (Fig. 4, Fig. 5). This illustrates the ability of the DFT and Hadamard methods to effectively exploit the scalability of IRS, demonstrating better adaptability and estimation performance in large-scale IRS systems. Furthermore, increasing the number of pilot signals leads to a further reduction in MSE for the DFT and Hadamard methods, as more pilot signals provide higher-quality observations, thereby enhancing channel estimation accuracy (Fig. 6, Fig. 7). Although additional pilot signals consume more resources, their substantial impact on reducing MSE highlights their importance in improving estimation precision. Moreover, under high-SNR conditions, the MSE for all algorithms is lower than under low-SNR conditions, with the DFT and Hadamard methods showing more pronounced reductions (Fig. 4, Fig. 5). This indicates that the proposed methods enable more efficient channel estimation under better signal quality, further enhancing system performance. In conclusion, the channel estimation algorithms based on DFT and Hadamard matrices offer significant advantages in large-scale IRS systems and high-SNR scenarios, providing robust support for optimizing low-power, low-cost communication systems. Conclusions This paper presents a low-complexity channel estimation algorithm for IRS-assisted AmBC systems based on the LS criterion. Thechannel is decomposed into multiple subchannels, and the optimization of IRS phase shifts is designed to significantly enhance both channel estimation and transmission performance. Simulation results demonstrate that the proposed algorithm, utilizing the DFT and Hadamard matrices, achieves excellent performance across various SNR and system scale conditions. Specifically, the algorithm effectively reduces the MSE of channel estimation, exhibits higher estimation accuracy under high-SNR conditions, and shows low computational complexity and strong robustness in large-scale IRS systems. These results provide valuable insights for the theoretical modeling and practical application of IRS-assisted AmBC systems. The findings are particularly relevant for the development of low-power, large-scale communication systems, offering guidance on the design and optimization of IRS-assisted AmBC systems. Additionally, this work lays a solid theoretical foundation for the advancement of next-generation Internet of Things applications, with potential implications for future research on IRS technology and their integration with AmBC systems.
Available online ,
doi: 10.11999/JEIT240241
Abstract:
The 6th Generation (6G) mobile communication network is required to provide Ultra-Reliable and Low-Latency Communication(URLLC) services for large-scale nodes. Considering the multi-user massive Multiple-Input Multiple-Out(MIMO) technology-assisted URLLC downlink communication scenario, system performance is characterized based on the Finite BlockLength(FBL) regime theory, and an efficient power allocation algorithm is proposed to improve the users’ transmission rate under fairness issue. Specifically, the traditional MIMO systems utilize the global Singular Value Decomposition(SVD) linear precoding scheme, leading to high complexity and inability to guarantee the fairness of rates among users. To deal with these challenges, a precoding scheme based on the local SVD is proposed to effectively suppress inter-user interference and intra-user interference of MIMO system with relatively low complexity. Secondly, the optimization problem is formulated, where the power allocation factors are optimized to Maximize Minimum Rate (MMR) among users. In order to efficiently solve the non-convex problem containing high-dimensional variables which are coupled with each other, the Shannon capacity term in the objective function is relaxed by introducing auxiliary variables and piecewise McCormick envelopes, and it is transformed into convex functions, thereby reformulating the MMR problem. An optimization algorithm based on the Successive Convex Approximation (SCA) is proposed to solve the reformulated problem effectively. Simulation results validate the convergence and accuracy of the proposed optimization algorithm, and it is shown that the proposed optimization algorithm has advantages over the existing schemes in terms of system MMR performance and robustness.
The 6th Generation (6G) mobile communication network is required to provide Ultra-Reliable and Low-Latency Communication(URLLC) services for large-scale nodes. Considering the multi-user massive Multiple-Input Multiple-Out(MIMO) technology-assisted URLLC downlink communication scenario, system performance is characterized based on the Finite BlockLength(FBL) regime theory, and an efficient power allocation algorithm is proposed to improve the users’ transmission rate under fairness issue. Specifically, the traditional MIMO systems utilize the global Singular Value Decomposition(SVD) linear precoding scheme, leading to high complexity and inability to guarantee the fairness of rates among users. To deal with these challenges, a precoding scheme based on the local SVD is proposed to effectively suppress inter-user interference and intra-user interference of MIMO system with relatively low complexity. Secondly, the optimization problem is formulated, where the power allocation factors are optimized to Maximize Minimum Rate (MMR) among users. In order to efficiently solve the non-convex problem containing high-dimensional variables which are coupled with each other, the Shannon capacity term in the objective function is relaxed by introducing auxiliary variables and piecewise McCormick envelopes, and it is transformed into convex functions, thereby reformulating the MMR problem. An optimization algorithm based on the Successive Convex Approximation (SCA) is proposed to solve the reformulated problem effectively. Simulation results validate the convergence and accuracy of the proposed optimization algorithm, and it is shown that the proposed optimization algorithm has advantages over the existing schemes in terms of system MMR performance and robustness.
Available online ,
doi: 10.11999/JEIT240698
Abstract:
Objective In the Internet of Vehicles (IoV), where differentiated services coexist, the system is progressively evolving towards safety and collaborative control applications, such as autonomous driving. Current research primarily focuses on optimizing mechanisms for high reliability and low latency, with Quality of Service (QoS) parameters commonly used as benchmarks, while the timeliness of vehicle status updates receives less attention. Merely optimizing metrics like transmission delay and throughput is insufficient for ensuring that vehicles obtain status information in a timely manner. For example, in security-critical IoV applications, which require the exchange of state information between vehicles, meeting only the constraints of delay interruption probability or data transmission interruption does not fully address the high timeliness requirements of security services. To tackle this challenge and meet the stringent timeliness demands of security and collaborative applications, this paper proposes a user power control and resource allocation strategy aimed at ensuring information freshness. Methods This paper investigates user power control and resource allocation strategies to ensure information freshness. First, the problem of maximizing the Quality of Experience (QoE) for Vehicle-to-Infrastructure (V2I) users under the constraint of freshness in Vehicle-to-Vehicle (V2V) status updates is formulated based on the system model. Then, by incorporating the queue backlog constraint, equivalent to the Age of Information (AoI) violation constraint, the extreme value theory is applied to optimize the tail distribution of AoI. Furthermore, using the Lyapunov optimization method, the original problem is transformed into minimizing the Lyapunov drift plus a penalty function, based on which the optimal user transmission power is determined. Finally, a resource allocation strategy based on Genetic Algorithm Improved Particle Swarm Optimization (GA-PSO) is proposed, leveraging a hypergraph structure to determine the optimal user channel reuse mode. Results and Discussions Simulation analysis indicates the following: 1. The proposed algorithm employs a channel gain differential partitioning method to cluster V2V links, effectively reducing intra-cluster interference. By integrating GA-PSO, it accelerates the search for the optimal channel reuse pattern in three-dimensional matching, minimizing signaling overhead and avoiding local optima. Compared with benchmark algorithms, the proposed approach increases V2I channel capacity by 7.03% and significantly improves the average QoE for V2I users (Fig. 4 ). 2. As vehicle speed increases, the distance between vehicles also grows, leading to higher transmission power for V2V communication to maintain link reliability and timeliness. This power increase results in reduced V2I channel capacity, subsequently lowering the average QoE for V2I users. Simulation results show a nearly linear relationship between vehicle speed and average QoE for V2I users, suggesting a relatively uniform effect of speed on V2I link capacity (Fig. 5 ). 3. Under varying Vehicle User Equipment (VUE) densities, the extreme event control framework is used to compare the conditional Complementary Cumulative Distribution Function (CCDF) of AoI and V2V link beacon backlog. The equivalent queue constraint, derived using extreme value theory, effectively controls the occurrence of extreme AoI violations. The simulations show improved AoI tail distribution across different VUE densities (Fig. 6 and Fig. 7 ). 4. With decreasing vehicle speed, the CCDF tail distribution of AoI improves (Fig. 8 ). Reduced speed shortens the transmission distance, decreasing V2V link path loss. This lower path loss, combined with less restrictive VUE transmission power limits, increases the V2V link transmission rate. As beacon transmission rates increase, beacon backlog is reduced, and the probability of exceeding a fixed AoI threshold decreases, ensuring the freshness of V2V beacon transmissions. 5. A comparison of curves under identical beacon reach rates (Fig. 9 ) reveals that worst-case AoI consistently increases with rising beacon reach rates. At low beacon arrival rates, the average AoI is high. However, once the V2V beacon queue accumulates beyond a certain threshold, further increases in the update arrival rate also raise the average AoI. In summary, the proposed scheme optimizes both the AoI tail distribution and the QoE for V2I users. Conclusions This paper investigates resource allocation and power control in vehicular network communication scenarios. By simultaneously considering the constraints of transmission reliability and status update timeliness in V2V links, restricted by the Signal-to-Interference-plus-Noise Ratio (SINR) threshold and the AoI outage probability threshold, the proposed strategy ensures both link reliability and information freshness. An extreme control framework is applied to minimize the probability of extreme AoI outage events in V2V links, ensuring the timeliness of transmitted information and meeting service requirements. The Lyapunov optimization method is then used to transform the original problem, yielding the optimal transmission power for both V2I and V2V links. Additionally, a GA-PSO-based three-dimensional matching algorithm is developed to determine the optimal spectrum sharing scheme among V2I, V2V, and subchannels. Numerical results demonstrate that the proposed scheme optimizes the AoI tail distribution while enhancing the QoE for all V2I users.
Available online ,
doi: 10.11999/JEIT240847
Abstract:
Objective Unmanned Aerial Vehicle-Assisted Federal Edge Learning (UAV-Assisted FEL) communication addresses the data isolation problem and mitigates data leakage risks in terminal devices. However, eavesdroppers may exploit model updates in FEL to recover original private data, significantly threatening the system’s privacy and security. Methods To address this issue, this study proposes a secure aggregation and resource optimization scheme for UAV-Assisted FEL communication systems. Terminal devices train local models using local data and update parameters, which are transmitted to a global UAV. The UAV aggregates these parameters to generate new global model parameters. Eavesdroppers attempt to intercept the transmitted parameters to reconstruct the original data. To enhance security-privacy energy efficiency, the transmission bandwidth, CPU frequency, and transmit power of terminal devices, along with the CPU frequency of the UAV, are jointly optimized. An evolutionary Deep Deterministic Policy Gradient (DDPG) algorithm is proposed to solve this optimization problem. The algorithm intelligently interacts with the system to achieve secure aggregation and resource optimization while meeting latency and energy consumption requirements. Results and Discussions The simulation results validate the effectiveness of the proposed scheme. The experiments evaluate the effects of the scheme on key performance metrics, including system cost, secure transmission rate, and secure privacy energy efficiency, from multiple perspectives. As shown in (Fig. 2 ), with an increasing number of terminal devices, system cost, secure transmission rate, and secure privacy energy efficiency all increase. These results indicate that the proposed scheme ensures system security and enhances energy efficiency, even in multi-device scenarios. As shown in (Fig. 3 ), under varying global iteration counts, the system balances latency and energy consumption by either extending the duration to lower energy consumption or increasing energy consumption to reduce latency. The secure transmission rate rises with the number of global iterations, as fewer iterations allow the system to tolerate higher energy consumption and latency per iteration, leading to reduced transmission power from terminal devices to meet system constraints. Additionally, secure privacy energy efficiency improves with increasing global iterations, further demonstrating the scheme’s capacity to ensure system security and reduce system cost as global iterations increase. As shown in (Fig. 4 ), during UAV flight, secure privacy energy efficiency fluctuates, with higher secure transmission rates observed when the communication environment between terminal devices and the UAV is more favorable. As shown in (Fig. 5 ), the proposed scheme is compared with two baseline schemes: Scheme 1, which minimizes system latency, and Scheme 2, which minimizes system energy consumption. The proposed scheme significantly outperforms both baselines in cost overhead. Scheme 1 achieves a slightly higher secure transmission rate than the proposed scheme due to its focus on minimizing latency at the expense of higher energy consumption. Conversely, Scheme 2 shows a considerably lower secure transmission rate as it prioritizes minimizing energy consumption, resulting in lower transmission power and compromised secure transmission rates. The results indicate that the secure privacy energy efficiency of the proposed scheme significantly exceeds that of the baseline schemes, further demonstrating its effectiveness. Conclusions To enhance data transmission security and reduce system costs, this paper proposes a secure aggregation and resource optimization scheme for UAV-Assisted FEL. Under constraints of limited computational and communication resources, the scheme jointly optimizes the transmission bandwidth, CPU frequency, and transmission power of terminal devices, along with the CPU frequency of the UAV, to maximize the secure privacy energy efficiency of the UAV-Assisted FEL system. Given the complexity of the time-varying system and the strong coupling of multiple optimization variables, an advanced DDPG algorithm is developed to solve the optimization problem. The problem is first modeled as a Markov Decision Process, followed by the construction of a reward function positively correlated with the secure privacy energy efficiency objective. The proposed DDPG network then intelligently generates joint optimization variables to obtain the optimal solution for secure privacy energy efficiency. Simulation experiments evaluate the effects of the proposed scheme on key system performance metrics from multiple perspectives. The results demonstrate that the proposed scheme significantly outperforms other benchmark schemes in improving secure privacy energy efficiency, thereby validating its effectiveness.
Available online ,
doi: 10.11999/JEIT231120
Abstract:
Objective Under the hundreds of kilometers of transmission distance in low-orbit satellite communication, both power consumption and latency are significantly higher than in ground-based networks. Additionally, many data collection services exhibit short burst characteristics. Conventional resource reservation-based access methods have extremely low resource utilization, whereas dynamic application-based access methods incur large signaling overhead and fail to meet the latency and power consumption requirements for satellite Internet of Things (IoT). Random access technology, which involves competition for resources, can better accommodate the short burst data packet services typical of satellite IoT. However, as the load increases, data packet collisions at satellite access points lead to a sharp decline in actual throughput under medium and high loads. In terrestrial wireless networks, technologies such as near-far effect management and power control are commonly employed to create differences in packet reception power. However, due to the large number of terminals covered and the long distance between the satellite and the Earth, these techniques are unsuitable for satellite IoT, preventing the establishment of an adequate carrier-to-noise ratio. Developing separation conditions suitable for satellite IoT access scenarios is a key research focus. Considering the future development of spaceborne digital phased array technology, this paper leverages the data-driven beamforming capability of the on-board phased array and introduces the concept of spatial auxiliary channels. By employing a sum-and-difference beam design method, it expands the dimensions for separating collision signals beyond the time, frequency, and energy domains. This approach imposes no additional processing burdens on the terminal and aligns with the low power consumption and minimal control design principles for satellite IoT. Methods To address packet collision issues in hotspot areas of satellite IoT services, this study extends the conventional time-slot ALOHA access framework by introducing an auxiliary receiving beam alongside the random access of conventional receiving beams. The main and auxiliary beams simultaneously receive signals from the same terminal. By optimizing the main lobe gain of the auxiliary beam, a difference in the Signal-to-Noise Ratio (SNR) between the signals received by the main and auxiliary beams is established. This difference is then separated using Successive Interference Cancellation (SIC) technology, leveraging the correlation between the received signals of the auxiliary and main beams to support the separation of collision signals and ensure reliable reception of satellite IoT signals. Results and Discussions Firstly, the system throughput of the proposed scheme is simulated (Fig. 4 ). The theoretical throughput derived in the previous section is consistent with the simulation results. When the normalized load reached 1.8392, the maximum system throughput is 0.81085 packets/slot. Compared with existing methods such as SA, CRDSA, and IRSA, the proposed scheme demonstrated improved system throughput and packet loss rate performance in both peak and high-load regions, with a peak throughput increase of approximately 120%. Secondly, the influence of amplitude, phase, and angle measurement errors on system performance is evaluated. The angle measurement error had a greater effect on throughput performance than amplitude and phase errors. Amplitude and phase errors had a smaller effect on the main lobe gain but a larger effect on the sidelobe gain (Tables 3 -5 ). Therefore, angle measurement errors have a considerable effect on throughput improvement. Regarding beamwidth, as beamwidth increased, the roll-off of the corresponding difference beam with 10 array elements is gentler than that with 32 array elements. However, the peak gain of the auxiliary beam decreased, leading to reduced system throughput for configurations with larger main lobe widths. Conclusions This paper presents an auxiliary beam design strategy for power-domain signal separation in satellite IoT scenarios, aiming to improve system throughput and packet loss rate performance. The approach incorporates spatial domain processing and proposes the concept of auxiliary receiving beams. By generating a difference beam derived from the main beam and using it as the auxiliary beam, the scheme constructs the required SNR difference for power-domain signal separation, enhancing the probability of successfully receiving collided signals. Simulation results indicate that, compared with SA, the peak system throughput increased by 120%, with significant improvements observed. Furthermore, the scheme demonstrated robustness by tolerating moderate system and measurement errors, facilitating large-capacity random access for satellite IoT terminals.
Available online ,
doi: 10.11999/JEIT240733
Abstract:
Objective Mobile Edge Computing (MEC) is a distributed computing paradigm that brings computational resources closer to users, alleviating issues such as high latency and interference found in cloud computing. To enhance the offloading performance of MEC systems and promote green communication, Reconfigurable Intelligent Surface (RIS), a low-cost and easily deployable technology, offers a promising solution. RIS consists of numerous low-cost reflecting elements that can adjust phase shifts to alter the amplitude and phase of incident signals, thereby reconstructing the electromagnetic environment. This transforms traditional passive adaptation into active control. However, the signal reflected by RIS must pass through a two-stage cascaded channel, which is susceptible to multiplicative fading, leading to limited performance gains when direct links are unobstructed. To mitigate this, the concept of active RIS has been proposed, integrating signal amplification circuits into RIS elements, which not only reflect but also amplify signals, effectively overcoming this issue. Additionally, RIS can only transmit or reflect incident signals, limiting coverage to half-space: either the user and base station must be on the same side (reflecting RIS) or on opposite sides (transmitting RIS). This constraint limits deployment flexibility. To address this, Simultaneously Transmitting And Reflecting Reconfigurable Intelligent Surface (STAR-RIS) is proposed, combining both transmission and reflection functions, where part of the signal is reflected to the same side, and the rest is transmitted to the opposite side. To address the challenges in practical RIS-assisted MEC systems, the active Simultaneously Transmitting And Reflecting Reconfigurable Intelligent Surface (aSTAR-RIS) is integrated into the MEC system to overcome geographic deployment constraints and effectively mitigate the effects of multiplicative fading. Methods Considering the computational resources available at the MEC server, the energy consumption of the aSTAR-RIS, and the phase shift coupling constraints, the task offloading ratio, computational resource allocation, Multi-User Detection (MUD) matrix, aSTAR-RIS phase shift, and transmission power are jointly optimized, resulting in a multivariable coupled weighted total latency minimization problem. To solve this problem, an iterative algorithm combining Block Coordinate Descent (BCD) and Penalty Dual Decomposition (PDD) algorithms is proposed. In each iteration, the original problem is decomposed into two subproblems: one for optimizing computational resource allocation and task offloading ratio, and the other for designing the aSTAR-RIS phase shift, MUD matrix, and transmission power. For the first subproblem, the Lagrange multiplier method is used to incorporate constraints into the objective function and enable efficient optimization. The optimal Lagrange multiplier and resource allocation are found using the bisection method. The second subproblem involves handling the fractional objective function using the weighted minimum mean square error algorithm. From the first-order conditions, the optimal MUD matrix is derived. For the aSTAR-RIS phase shift optimization, a non-convex phase shift coupling constraint is decoupled using the PDD algorithm. Results And discussions as shown in (Fig. 2 ), with increasing iterations, the weighted total latency steadily decreases and stabilizes, validating the effectiveness of the proposed algorithm. A comparison with three benchmark schemes reveals that, although the proposed scheme converges more slowly, it achieves the lowest weighted total latency upon convergence, with a 12.66% reduction compared to the passive STAR-RIS scheme. This improvement is mainly due to the power amplification effect, which reduces the impact of multiplicative fading, thereby enhancing the received signal at the base station and reducing latency. As illustrated in (Fig. 3 ), the weighted total latency decreases as the number of aSTAR-RIS elements increases, allowing for more reflection paths and higher channel gain. For fewer elements, aSTAR-RIS shows a significant performance gain over STAR-RIS, but as the number of elements grows, the performance of both aSTAR-RIS and passive STAR-RIS converges, primarily due to thermal noise and power constraints. Moreover, compared to the benchmark scheme that optimizes for maximum rate, the proposed scheme shows significant advantages in reducing latency. As shown in (Fig. 4 ), when the aSTAR-RIS power overhead increases, the weighted total latency decreases, further showing the potential of aSTAR-RIS in improving communication performance via active amplification. Conclusions This paper investigates a task offloading scheme for an aSTAR-RIS-assisted MEC system, which optimizes the task offloading ratio, computational resource allocation, MUD matrix, aSTAR-RIS phase shift, and transmission power to minimize total user delay. The optimization problem is solved using an iterative approach, decomposing the problem into two subproblems and applying the Lagrange multiplier method, PDD, and BCD algorithms. Simulation results demonstrate that the proposed algorithm significantly outperforms benchmark schemes in terms of weighted total latency. The findings validate the effectiveness of aSTAR-RIS in MEC systems, highlighting its advantages over passive STAR-RIS in task offloading, resource optimization, and communication performance.
Available online ,
doi: 10.11999/JEIT240640
Abstract:
Objective Breathing rate is a vital physiological indicator of human health. Abnormal changes in this rate can signify diseases like chronic obstructive pulmonary disease, sleep apnea syndrome, and nocturnal hypoventilation syndrome. Timely and accurate detection of these changes can help identify health risks early, enable professional medical intervention, and optimize treatment timing, thereby improving overall health. However, current detection methods often face limitations due to noise interference and “blind spot” issues, which impact accuracy and robustness. To address these challenges, this paper employs Wi-Fi devices to measure indoor human breathing rates using Integrated Sensing And Communication (ISAC) technology. By combining Variational Modal Decomposition (VMD) and Hilbert-Huang Transform (HHT), a new breathing rate sensing algorithm is proposed. This approach aims to enhance detection accuracy and robustness, resolve the “blind spot” problem in existing technologies, and offer an efficient and reliable solution for health monitoring. Methods Wi-Fi links with high environmental sensitivity were selected to construct the Channel State Information (CSI) ratio model. Subcarriers of the filtered CSI ratio time series were projected, and amplitude and phase information were combined to generate a candidate set of breathing mode signals. For each subcarrier, the sequence with the highest short-term breath noise ratio, determined by periodicity, was identified as the final breath pattern. A threshold was then applied to select relevant subcarriers. Time-frequency analysis using VMD and HHT eliminated modal components unrelated to the human breath rate, and the remaining components were reconstructed. Principal Component Analysis (PCA) was applied for dimensionality reduction, selecting components accounting for over 99% of the variance. The ReliefF algorithm was subsequently used to reconstruct the breath signal into a fused signal, from which the breathing rate was calculated using a peak detection algorithm. Results and Discussions Experiments were conducted in two scenarios: a conference office and a corridor. In both setups, a pair of transceivers was deployed, with a 2-meter distance maintained between the transmitter and receiver. The transmitter used one omnidirectional antenna, and the receiver had three antennas positioned perpendicular to the ground. Participants were seated on the vertical bisector of the Line Of Sight (LOS) path, synchronizing their breathing with a metronome as CSI data were recorded. Each test lasted 1 minute, with a confirmed breathing rate of 16 bpm. System parameters used in the experiments are detailed in Table 1. In the conference office scenario, this paper collected data at various distances from the participant to the transceiver. As illustrated in Figure 9, the Mean Estimation Accuracy (MEA) of our algorithm remains above 97%, even when the participant is 5 meters away. In contrast, the MEA of the other two methods drops by 4% and 5%, respectively. As the sensing distance increases, the multipath effect intensifies, leading to a gradual weakening of the reflected signal and greater noise interference. This impact significantly challenges the breathing detection accuracy of the other methods. The algorithm presented in this paper incorporates a VMD-HHT time-frequency analysis step. This enhancement allows for effective signal decomposition and feature extraction, markedly improving the accuracy of detecting the target breathing signal. Moreover, the method exhibits strong adaptability and robustness, effectively addressing noise interference and multipath effects in complex environments, thus demonstrating more stable performance. In the corridor scenario, we evaluated the algorithm's performance at varying distances. The average absolute error of the algorithm was measured with distances ranging from 2 meters to 5 meters. At 2 meters, the Mean Absolute Error (MAE) recorded was 0.37 bpm, and even at 5 meters, the MAE only increased to 0.45 bpm, remaining below 0.5 bpm. As the distance between the target and transceiver increased from 3 to 5 meters, the MAE gradually rose. This trend is attributed to the further attenuation of the signal reflected from the human target, along with the escalating multipath and signal attenuation effects in the environment. Conclusions The experimental results indicate that the MEA of this sensing method exceeds 97% in both the conference office and corridor scenarios. This effectively addresses the "blind spot" issue present in current technologies. The enhanced accuracy and robustness of the algorithm outperform existing sensing schemes. Moreover, this method broadens the application of ISAC in breathing detection and opens new avenues for developing intelligent health management systems in the future.
Available online ,
doi: 10.11999/JEIT240469
Abstract:
Objective In radar target tracking, tracking accuracy is often influenced by sensor measurement biases and measurement noise. This is particularly true when measurement biases change abruptly and measurement noise is unknown and time-varying. Ensuring effective target tracking under these conditions poses a significant challenge. An adaptive target tracking method is proposed, utilizing a marginalized cubature Kalman filter to address this issue. Methods (1) Initially, measurements taken at adjacent time points are differentiated to formulate the differential measurement equation, thereby effectively mitigating the influence of measurement biases that are either constant or change gradually between adjacent observations. Concurrently, the target states at these moments are expanded to create an extended state vector facilitating real-time filtering. (2) Following the differentiation of measurements, sudden changes in measurement biases may cause the differential measurement at the current moment to be classified as outliers. To identify the occurrence of these abrupt bias changes, a Beta-Bernoulli indicator variable is established. If such a change is detected, the differential measurement for that moment is disregarded, and the predicted state is adopted as the updated state. In the absence of any abrupt changes, standard filtering procedures are conducted. The Gaussian measurement noise, despite having unknown covariance, continues to follow a Gaussian distribution after differentiation, allowing its covariance matrix to be modeled using the inverse Wishart distribution. (3) A joint distribution is formulated for the target state, indicator variables, and the covariance matrix of the measurement noise. The approximate posteriors of each parameter are derived using variational Bayesian inference. (4) To mitigate the increased filtering burden arising from the high-dimensional extended state vector, the extended target state is marginalized, and a marginalized cubature Kalman filter for target tracking is implemented in conjunction with the cubature Kalman filtering method. Results and Discussions The target tracking performance is clearly illustrated, indicating that the proposed method accurately identifies abrupt measurement biases while effectively managing unknown time-varying measurement noise. This leads to a tracking performance that significantly exceeds that of the comparative methods. The findings further support the conclusions by examining the Root Mean Square Error (RMSE). Additionally, the stability of the proposed method is demonstrated. The results reveal that the computational load associated with the proposed method is greatly reduced through marginalization processing. This reduction occurs because, during the variational Bayesian iteration process, cubature sampling and integration are performed multiple times. Once the target state is marginalized, the dimensionality of the cubature sampling is halved, and the number of sampling points for each variational iteration is also reduced by half. As a result, the computational load during the nonlinear propagation of the sampling points decreases, with the amount of computation reduction increasing with the number of variational iterations. Furthermore, the results demonstrate that marginalization does not compromise tracking accuracy, thereby further validating the effectiveness of marginalization processing. This finding also confirms that marginalization processing can be extended to other nonlinear variational Bayesian filters based on deterministic sampling, providing a means to reduce computational complexity. Conclusions This paper proposes an adaptive marginalized cubature Kalman filter to improve target tracking in scenarios with measurement biases and unknown time-varying measurement noise. The approach incorporates measurement differencing to eliminate constant biases, constructs indicator variables to detect abrupt biases, and models the unknown measurement noise covariance matrix using the inverse Wishart distribution. A joint posterior distribution of the parameters is established, and the approximate posteriors are solved through variational Bayesian inference. Additionally, marginalization of the target state is performed before implementing tracking within the CKF framework, reducing the filtering burden. The results of our simulation experiments yield the following conclusions: (1) The proposed method demonstrates superior target tracking performance compared to existing techniques in scenarios involving abrupt measurement biases and unknown measurement noise; (2) The marginalization processing strategy significantly alleviates the filtering burden of the proposed filter, making it applicable to more complex nonlinear variational Bayesian filters, such as robust nonlinear random finite set filters, to reduce filtering complexity; (3) This filtering methodology can be extended to target tracking scenarios in higher dimensions.
Display Method:
2024, 46(12): 4335-4353.
doi: 10.11999/JEIT240574
Abstract:
With the rapid development of 6G technology and the evolution of the Industrial Internet of Things (IIoT), federated learning has gained significant attention in the industrial sector. This paper explores the development and application potential of federated learning in 6G-driven IIoT, analyzing 6G’s prospects and how its high speed, low latency, and reliability can support data privacy, resource optimization, and intelligent decision-making. First, existing related work is summarized, and the development requirements along with the vision for applying federated learning technology in 6G industrial IoT scenarios are outlined. Based on this, a new paradigm for industrial federated learning, featuring a hierarchical cross-domain architecture, is proposed to integrate 6G and digital twin technologies, enabling ubiquitous, flexible, and layered federated learning. This supports on-demand and reliable distributed intelligent services in typical Industrial IoT scenarios, achieving the integration of Operational and Communication Information Technology (OCIT). Next, the potential research challenges that federated learning might face towards 6G industrial IoT(6G IIoT-FL) are analyzed and summarized, followed by potential solutions or recommendations. Finally, relevant future directions worth attention in this field are highlighted in the study, with the aim of providing insights for subsequent research to some extent.
With the rapid development of 6G technology and the evolution of the Industrial Internet of Things (IIoT), federated learning has gained significant attention in the industrial sector. This paper explores the development and application potential of federated learning in 6G-driven IIoT, analyzing 6G’s prospects and how its high speed, low latency, and reliability can support data privacy, resource optimization, and intelligent decision-making. First, existing related work is summarized, and the development requirements along with the vision for applying federated learning technology in 6G industrial IoT scenarios are outlined. Based on this, a new paradigm for industrial federated learning, featuring a hierarchical cross-domain architecture, is proposed to integrate 6G and digital twin technologies, enabling ubiquitous, flexible, and layered federated learning. This supports on-demand and reliable distributed intelligent services in typical Industrial IoT scenarios, achieving the integration of Operational and Communication Information Technology (OCIT). Next, the potential research challenges that federated learning might face towards 6G industrial IoT(6G IIoT-FL) are analyzed and summarized, followed by potential solutions or recommendations. Finally, relevant future directions worth attention in this field are highlighted in the study, with the aim of providing insights for subsequent research to some extent.
2024, 46(12): 4354-4362.
doi: 10.11999/JEIT231366
Abstract:
To overcome the effect of channel estimation errors on the ineffectiveness of conventional optimal resource allocation algorithms, a robust resource allocation algorithm with imperfect Channel State Information(CSI) is proposed in Multiple-Input Single-Output(MISO) symbiotic radio systems. Considering the constraints of the minimum throughput of users, transmission time, maximum transmit power of the base station, and the reflection coefficients of users, based on bounded channel uncertainties, a robust throughput-maximization resource allocation problem is formulated by jointly optimizing transmission time, beamforming vectors, and reflection coefficients. The original problem is transformed into a convex problem by applying the Lagrange dual theory, the variable substitution, and the alternating optimizing methods. Simulation results verified that the throughput of the proposed algorithm is improved by 11.7% and the outage probability is reduced by 5.31% by comparing it with the non-robust resource allocation algorithm.
To overcome the effect of channel estimation errors on the ineffectiveness of conventional optimal resource allocation algorithms, a robust resource allocation algorithm with imperfect Channel State Information(CSI) is proposed in Multiple-Input Single-Output(MISO) symbiotic radio systems. Considering the constraints of the minimum throughput of users, transmission time, maximum transmit power of the base station, and the reflection coefficients of users, based on bounded channel uncertainties, a robust throughput-maximization resource allocation problem is formulated by jointly optimizing transmission time, beamforming vectors, and reflection coefficients. The original problem is transformed into a convex problem by applying the Lagrange dual theory, the variable substitution, and the alternating optimizing methods. Simulation results verified that the throughput of the proposed algorithm is improved by 11.7% and the outage probability is reduced by 5.31% by comparing it with the non-robust resource allocation algorithm.
2024, 46(12): 4363-4372.
doi: 10.11999/JEIT240407
Abstract:
The application of Deep Reinforcement Learning (DRL) in intelligent driving decision-making is increasingly widespread, as it effectively enhances decision-making capabilities through continuous interaction with the environment. However, DRL faces challenges in practical applications due to low learning efficiency and poor data-sharing security. To address these issues, a Directed Acyclic Graph (DAG)blockchain-assisted deep reinforcement learning Intelligent Driving Strategy Optimization (D-IDSO) algorithm is proposed. First, a dual-layer secure data-sharing architecture based on DAG blockchain is constructed to ensure the efficiency and security of model data sharing. Next, a DRL-based intelligent driving decision model is designed, incorporating a multi-objective reward function that optimizes decision-making by jointly considering safety, comfort, and efficiency. Additionally, an Improved Prioritized Experience Replay with Twin Delayed Deep Deterministic policy gradient (IPER-TD3) method is proposed to enhance training efficiency. Finally, braking and lane-changing scenarios are selected in the CARLA simulation platform to train Connected and Automated Vehicles (CAVs). Experimental results demonstrate that the proposed algorithm significantly improves model training efficiency in intelligent driving scenarios, while ensuring data security and enhancing the safety, comfort, and efficiency of intelligent driving.
The application of Deep Reinforcement Learning (DRL) in intelligent driving decision-making is increasingly widespread, as it effectively enhances decision-making capabilities through continuous interaction with the environment. However, DRL faces challenges in practical applications due to low learning efficiency and poor data-sharing security. To address these issues, a Directed Acyclic Graph (DAG)blockchain-assisted deep reinforcement learning Intelligent Driving Strategy Optimization (D-IDSO) algorithm is proposed. First, a dual-layer secure data-sharing architecture based on DAG blockchain is constructed to ensure the efficiency and security of model data sharing. Next, a DRL-based intelligent driving decision model is designed, incorporating a multi-objective reward function that optimizes decision-making by jointly considering safety, comfort, and efficiency. Additionally, an Improved Prioritized Experience Replay with Twin Delayed Deep Deterministic policy gradient (IPER-TD3) method is proposed to enhance training efficiency. Finally, braking and lane-changing scenarios are selected in the CARLA simulation platform to train Connected and Automated Vehicles (CAVs). Experimental results demonstrate that the proposed algorithm significantly improves model training efficiency in intelligent driving scenarios, while ensuring data security and enhancing the safety, comfort, and efficiency of intelligent driving.
2024, 46(12): 4373-4382.
doi: 10.11999/JEIT240377
Abstract:
The Flying Ad-hoc NETworks (FANETs) are widely used in emergency rescue scenarios due to their high mobility and self-organization advantages. In emergency scenarios, a large number of user paging requests lead to a challenging coordination between the surge in local traffic and the limited spectrum resources, significant channel interference issues in FANETs are resulted from. There is an urgent need to extend the high spectrum utilization advantage of Partially Overlapping Channels (POCs) to emergency scenarios. However, the adjacent channel characteristics of POCs leads to complex interference that is difficult to characterize. Therefore, partial overlapping channel allocation methods in FANETs are studied in this paper. By utilizing geometric prediction to reconstruct time-varying interference graphs and characterizing the POCs interference model with the interference-free minimum channel spacing matrix, a Dynamic Channel Allocation algorithm for POCs based on Upper Confidence Bounds (UCB-DCA) is proposed. This algorithm aims to solve for an approximately optimal channel allocation scheme through distributed decision-making. Simulation results demonstrate that the algorithm achieves a trade-off between network interference and channel switching times, and has good convergence performance.
The Flying Ad-hoc NETworks (FANETs) are widely used in emergency rescue scenarios due to their high mobility and self-organization advantages. In emergency scenarios, a large number of user paging requests lead to a challenging coordination between the surge in local traffic and the limited spectrum resources, significant channel interference issues in FANETs are resulted from. There is an urgent need to extend the high spectrum utilization advantage of Partially Overlapping Channels (POCs) to emergency scenarios. However, the adjacent channel characteristics of POCs leads to complex interference that is difficult to characterize. Therefore, partial overlapping channel allocation methods in FANETs are studied in this paper. By utilizing geometric prediction to reconstruct time-varying interference graphs and characterizing the POCs interference model with the interference-free minimum channel spacing matrix, a Dynamic Channel Allocation algorithm for POCs based on Upper Confidence Bounds (UCB-DCA) is proposed. This algorithm aims to solve for an approximately optimal channel allocation scheme through distributed decision-making. Simulation results demonstrate that the algorithm achieves a trade-off between network interference and channel switching times, and has good convergence performance.
2024, 46(12): 4383-4390.
doi: 10.11999/JEIT240518
Abstract:
Reconfigurable Intelligent Surfaces (RIS) is considered as one of the potential key technologies for 6G mobile communications, which offers advantages such as low cost, low energy consumption, and easy deployment. By integrating RIS technology into marine wireless channels, it has the capability to convert the unpredictable wireless transmission environment into a manageable one. However, current channel models are struggling to accurately depict the unique signal transmission mechanisms of RIS-enabled base station to ship channels in marine communication scenarios, resulting in challenges in achieving a balance between accuracy and complexity for channel characterization and theoretical establishment. Therefore, this paper develops a segmented channel modeling method for near-field RIS-enabled marine communications, and then proposed a multi-domain joint parameterized statistical channel model for RIS-enabled marine communications. This approach focus on addressing the technical bottleneck of existing RIS channel modeling methods that face difficulties in achieving a balance between accuracy and efficiency, ultimately facilitating the rapid development of the 6G mobile communication industry in China.
Reconfigurable Intelligent Surfaces (RIS) is considered as one of the potential key technologies for 6G mobile communications, which offers advantages such as low cost, low energy consumption, and easy deployment. By integrating RIS technology into marine wireless channels, it has the capability to convert the unpredictable wireless transmission environment into a manageable one. However, current channel models are struggling to accurately depict the unique signal transmission mechanisms of RIS-enabled base station to ship channels in marine communication scenarios, resulting in challenges in achieving a balance between accuracy and complexity for channel characterization and theoretical establishment. Therefore, this paper develops a segmented channel modeling method for near-field RIS-enabled marine communications, and then proposed a multi-domain joint parameterized statistical channel model for RIS-enabled marine communications. This approach focus on addressing the technical bottleneck of existing RIS channel modeling methods that face difficulties in achieving a balance between accuracy and efficiency, ultimately facilitating the rapid development of the 6G mobile communication industry in China.
2024, 46(12): 4391-4398.
doi: 10.11999/JEIT240427
Abstract:
Edge computing provides computing resources and caching services at the network edge, effectively reducing execution latency and energy consumption. However, due to user mobility and network randomness, caching services and user tasks frequently migrate between edge servers, increasing system costs. The migration computation model based on pre-caching is constructed and the joint optimization problem of resource allocation, service caching and migration decision-making is investigated. To address this mixed-integer nonlinear programming problem, the original problem is decomposed to optimize the resource allocation using Karush-Kuhn-Tucker condition and bisection search iterative method. Additionally, a Joint optimization algorithm for Migration decision-making and Service caching based on a Greedy Strategy (JMSGS) is proposed to obtain the optimal migration and caching decisions. Simulation results show the effectiveness of the proposed algorithm in minimizing the weighted sum of system energy consumption and latency.
Edge computing provides computing resources and caching services at the network edge, effectively reducing execution latency and energy consumption. However, due to user mobility and network randomness, caching services and user tasks frequently migrate between edge servers, increasing system costs. The migration computation model based on pre-caching is constructed and the joint optimization problem of resource allocation, service caching and migration decision-making is investigated. To address this mixed-integer nonlinear programming problem, the original problem is decomposed to optimize the resource allocation using Karush-Kuhn-Tucker condition and bisection search iterative method. Additionally, a Joint optimization algorithm for Migration decision-making and Service caching based on a Greedy Strategy (JMSGS) is proposed to obtain the optimal migration and caching decisions. Simulation results show the effectiveness of the proposed algorithm in minimizing the weighted sum of system energy consumption and latency.
2024, 46(12): 4399-4408.
doi: 10.11999/JEIT240411
Abstract:
It can effectively overcome the limitations of the ground environment, expand the network coverage and provide users with convenient computing services, through constructing the air-ground integrated edge computing network with Unmanned Aerial Vehicle (UAV) as the relay. In this paper, with the objective of maximizing the task completion amount, the joint optimization problem of UAV deployment, user-server association and bandwidth allocation is investigated in the context of the UAV assisted multi-user and multi-server edge computing network. The formulated joint optimization problem contains both continuous and discrete variables, which makes itself hard to solve. To this end, a Block Coordinated Descent (BCD) based iterative algorithm is proposed in this paper, involving the optimization tools such as differential evolution and particle swarm optimization. The original problem is decomposed into three sub-problems with the proposed algorithm, which can be solved independently. The optimal solution of the original problem can be approached through the iteration among these three subproblems. Simulation results show that the proposed algorithm can greatly increase the amount of completed tasks, which outperforms other benchmark algorithms.
It can effectively overcome the limitations of the ground environment, expand the network coverage and provide users with convenient computing services, through constructing the air-ground integrated edge computing network with Unmanned Aerial Vehicle (UAV) as the relay. In this paper, with the objective of maximizing the task completion amount, the joint optimization problem of UAV deployment, user-server association and bandwidth allocation is investigated in the context of the UAV assisted multi-user and multi-server edge computing network. The formulated joint optimization problem contains both continuous and discrete variables, which makes itself hard to solve. To this end, a Block Coordinated Descent (BCD) based iterative algorithm is proposed in this paper, involving the optimization tools such as differential evolution and particle swarm optimization. The original problem is decomposed into three sub-problems with the proposed algorithm, which can be solved independently. The optimal solution of the original problem can be approached through the iteration among these three subproblems. Simulation results show that the proposed algorithm can greatly increase the amount of completed tasks, which outperforms other benchmark algorithms.
2024, 46(12): 4409-4421.
doi: 10.11999/JEIT240505
Abstract:
At the present stage, the satellite subsystems in Space Information Networks (SINs) have their own systems and are separated from each other, which makes the network appear closed and fragmented, forming a severe resource barrier and resulting in weak collaborative application ability of space resources and low network expansion ability. The traditional architecture design adopts the “completely subversive” idea of the current space networks, which greatly increases the difficulty of actual deployment. Therefore, based on the current status of satellite networks, the idea of “upgrading step by step” is adopted to promote the evolution of the existing network architecture, and a dynamic and scalable architecture model is proposed in SINs from the perspective of mission drive, so as to realize the efficient and dynamic sharing of space resources among subsystems and promote the dynamic and efficient aggregation of space resources according to the changes in mission requirements. Firstly, a phased network architecture model is proposed, aiming at compatibility and upgrading of the existing network architecture. Then, the design of the core component coordinator is introduced, including network structure and working protocol, superframe structure, and efficient network resource allocation strategy, to realize the efficient transmission of spatial data. The simulation results show that the proposed network architecture realizes the efficient sharing of network resources and greatly improves the utilization rate of network resources.
At the present stage, the satellite subsystems in Space Information Networks (SINs) have their own systems and are separated from each other, which makes the network appear closed and fragmented, forming a severe resource barrier and resulting in weak collaborative application ability of space resources and low network expansion ability. The traditional architecture design adopts the “completely subversive” idea of the current space networks, which greatly increases the difficulty of actual deployment. Therefore, based on the current status of satellite networks, the idea of “upgrading step by step” is adopted to promote the evolution of the existing network architecture, and a dynamic and scalable architecture model is proposed in SINs from the perspective of mission drive, so as to realize the efficient and dynamic sharing of space resources among subsystems and promote the dynamic and efficient aggregation of space resources according to the changes in mission requirements. Firstly, a phased network architecture model is proposed, aiming at compatibility and upgrading of the existing network architecture. Then, the design of the core component coordinator is introduced, including network structure and working protocol, superframe structure, and efficient network resource allocation strategy, to realize the efficient transmission of spatial data. The simulation results show that the proposed network architecture realizes the efficient sharing of network resources and greatly improves the utilization rate of network resources.
2024, 46(12): 4422-4431.
doi: 10.11999/JEIT240029
Abstract:
Aiming at the problem that current network topology deception methods only make decisions in the spatial dimension without considering how to perform spatio-temporal multi-dimensional topology deception in cloud-native network environments, a multi-stage Flipit game topology deception method with deep reinforcement learning to obfuscate reconnaissance attacks in cloud-native networks. Firstly, the topology deception defense-offense model in cloud-native complex network environments is analyzed. Then, by introducing a discount factor and transition probabilities, a multi-stage game-based network topology deception model based on Flipit is constructed. Furthermore under the premise of analyzing the defense-offense strategies of game models, a topology deception generation method is developed based on deep reinforcement learning to solve the topology deception strategy of multi-stage game models. Finally, through experiments, it is demonstrated that the proposed method can effectively model and analyze the topology deception defense-offense scenarios in cloud-native networks. It is shown that the algorithm has significant advantages compared to other algorithms.
Aiming at the problem that current network topology deception methods only make decisions in the spatial dimension without considering how to perform spatio-temporal multi-dimensional topology deception in cloud-native network environments, a multi-stage Flipit game topology deception method with deep reinforcement learning to obfuscate reconnaissance attacks in cloud-native networks. Firstly, the topology deception defense-offense model in cloud-native complex network environments is analyzed. Then, by introducing a discount factor and transition probabilities, a multi-stage game-based network topology deception model based on Flipit is constructed. Furthermore under the premise of analyzing the defense-offense strategies of game models, a topology deception generation method is developed based on deep reinforcement learning to solve the topology deception strategy of multi-stage game models. Finally, through experiments, it is demonstrated that the proposed method can effectively model and analyze the topology deception defense-offense scenarios in cloud-native networks. It is shown that the algorithm has significant advantages compared to other algorithms.
2024, 46(12): 4432-4440.
doi: 10.11999/JEIT240398
Abstract:
Establishing accurate short-term forecasting models for electrical load is crucial for the stable operation and intelligent advancement of power systems. Traditional methods have not adequately addressed the issues of data volatility and model uncertainty. In this paper, a multi-dimensional adaptive short-term forecasting method for electrical load based on Bayesian Autoformer network is proposed. Specifically, an adaptive feature selection method is designed to capture multi-dimensional features. By capturing multi-scale features and time-frequency localized information, the model is enhanced to handle high volatility and nonlinear features in load data. Subsequently, an adaptive probabilistic forecasting model based on Bayesian Autoformer network is proposed. It captures relationships of significant subsequence features and associated uncertainties in load time series data, and dynamically updates the probability prediction model and parameter distributions through Bayesian optimization. The proposed model is subjected to a series of experimental analyses (comparative analysis, adaptive analysis, robustness analysis) on real load datasets of three different magnitudes (GW, MW, and KW). The model exhibits superior performance in adaptability and accuracy, with average improvements in Root Mean Square Error (RMSE), Pinball Loss, and Continuous Ranked Probability Score (CRPS) of 1.9%, 24.2%, and 4.5%, respectively.
Establishing accurate short-term forecasting models for electrical load is crucial for the stable operation and intelligent advancement of power systems. Traditional methods have not adequately addressed the issues of data volatility and model uncertainty. In this paper, a multi-dimensional adaptive short-term forecasting method for electrical load based on Bayesian Autoformer network is proposed. Specifically, an adaptive feature selection method is designed to capture multi-dimensional features. By capturing multi-scale features and time-frequency localized information, the model is enhanced to handle high volatility and nonlinear features in load data. Subsequently, an adaptive probabilistic forecasting model based on Bayesian Autoformer network is proposed. It captures relationships of significant subsequence features and associated uncertainties in load time series data, and dynamically updates the probability prediction model and parameter distributions through Bayesian optimization. The proposed model is subjected to a series of experimental analyses (comparative analysis, adaptive analysis, robustness analysis) on real load datasets of three different magnitudes (GW, MW, and KW). The model exhibits superior performance in adaptability and accuracy, with average improvements in Root Mean Square Error (RMSE), Pinball Loss, and Continuous Ranked Probability Score (CRPS) of 1.9%, 24.2%, and 4.5%, respectively.
2024, 46(12): 4441-4450.
doi: 10.11999/JEIT240092
Abstract:
Synthetic Aperture Radar (SAR) is a microwave remote sensing imaging radar. In recent years, with the advancement of digital technology and radio frequency electronic technology, the jamming technology of SAR imaging is developed rapidly. The active jamming such as deception jamming based on Digital Radio Frequency Memory (DRFM) technology brings serious challenges to SAR imaging systems for civil use and military use. For research on SAR anti-jamming imaging against deception jamming, firstly, orthogonal waveform diversity design and waveform optimization is carried out for Orthogonal Frequency Division Multiplexing waveforms with Cyclic Prefixes (CP-OFDM). And the CP-OFDM wide band orthogonal waveform set with excellent autocorrelation peak sidelobe level and cross-correlation peak level is obtained. Then the sparse SAR imaging theory is introduced, which is combined with CP-OFDM. By using the sparse reconstruction method, the high-quality and high-precision imaging with anti-jamming capability is realized. Finally, simulation based on point targets, surface targets and real data is conducted, and it is proved that the method can completely remove the false targets generated by deception jamming, suppress sidelobes and achieve high-precision imaging.
Synthetic Aperture Radar (SAR) is a microwave remote sensing imaging radar. In recent years, with the advancement of digital technology and radio frequency electronic technology, the jamming technology of SAR imaging is developed rapidly. The active jamming such as deception jamming based on Digital Radio Frequency Memory (DRFM) technology brings serious challenges to SAR imaging systems for civil use and military use. For research on SAR anti-jamming imaging against deception jamming, firstly, orthogonal waveform diversity design and waveform optimization is carried out for Orthogonal Frequency Division Multiplexing waveforms with Cyclic Prefixes (CP-OFDM). And the CP-OFDM wide band orthogonal waveform set with excellent autocorrelation peak sidelobe level and cross-correlation peak level is obtained. Then the sparse SAR imaging theory is introduced, which is combined with CP-OFDM. By using the sparse reconstruction method, the high-quality and high-precision imaging with anti-jamming capability is realized. Finally, simulation based on point targets, surface targets and real data is conducted, and it is proved that the method can completely remove the false targets generated by deception jamming, suppress sidelobes and achieve high-precision imaging.
2024, 46(12): 4451-4458.
doi: 10.11999/JEIT240289
Abstract:
In the process of trajectory deception against the networked radar using an Unmanned Aerial Vehicle (UAV) cluster, false target points are generated by delaying and forwarding intercepted radar signals. Errors such as radar station location errors, UAV jitter errors, and forwarding delay errors can all cause these false target points to deviate from their intended positions, thereby degrading the effectiveness of the deception. Considering known radar measurement positions, UAV preset positions, deception distances, and a specific Space Resolution Cell (SRC) of the networked radar, the boundary condition of successfully deceiving networked radar by a UAV cluster is analyzed in this paper. The impact patterns of these errors on deception effectiveness are also summarized in the paper. The numerical simulation results show that when all three kinds of errors are present, the derived results can effectively evaluate the deception ability of the UAV cluster to the networked radar.
In the process of trajectory deception against the networked radar using an Unmanned Aerial Vehicle (UAV) cluster, false target points are generated by delaying and forwarding intercepted radar signals. Errors such as radar station location errors, UAV jitter errors, and forwarding delay errors can all cause these false target points to deviate from their intended positions, thereby degrading the effectiveness of the deception. Considering known radar measurement positions, UAV preset positions, deception distances, and a specific Space Resolution Cell (SRC) of the networked radar, the boundary condition of successfully deceiving networked radar by a UAV cluster is analyzed in this paper. The impact patterns of these errors on deception effectiveness are also summarized in the paper. The numerical simulation results show that when all three kinds of errors are present, the derived results can effectively evaluate the deception ability of the UAV cluster to the networked radar.
A SAR Image Aircraft Target Detection and Recognition Network with Target Region Feature Enhancement
2024, 46(12): 4459-4470.
doi: 10.11999/JEIT240491
Abstract:
In Synthetic Aperture Radar (SAR) image aircraft target detection and recognition, the discrete characteristics of aircraft target images and the similarity between structures can reduce the accuracy of aircraft detection and recognition. A SAR image aircraft target detection and recognition network with enhanced target area features is proposed in this paper. The network consists of three parts: Feature Protecting Cross Stage Partial Darknet (FP-CSPDarnet) for protecting aircraft features, Feature Pyramid Net with Adaptive fusion (FPN-A) for adaptive feature fusion, and Detection Head for target area scattering feature extraction and enhancement (D-Head). FP-CSPDarnet can effectively protect the aircraft features in SAR images while extracting features; FPN-A adopts multi-level feature adaptive fusion and refinement to enhance aircraft features; D-Head effectively enhances the identifiable features of the aircraft before detection, improving the accuracy of aircraft detection and recognition. The experimental results using the SAR-ADRD dataset have demonstrated the effectiveness of the proposed method, with an average accuracy improvement of 2.0% compared to the baseline network YOLOv5s.
In Synthetic Aperture Radar (SAR) image aircraft target detection and recognition, the discrete characteristics of aircraft target images and the similarity between structures can reduce the accuracy of aircraft detection and recognition. A SAR image aircraft target detection and recognition network with enhanced target area features is proposed in this paper. The network consists of three parts: Feature Protecting Cross Stage Partial Darknet (FP-CSPDarnet) for protecting aircraft features, Feature Pyramid Net with Adaptive fusion (FPN-A) for adaptive feature fusion, and Detection Head for target area scattering feature extraction and enhancement (D-Head). FP-CSPDarnet can effectively protect the aircraft features in SAR images while extracting features; FPN-A adopts multi-level feature adaptive fusion and refinement to enhance aircraft features; D-Head effectively enhances the identifiable features of the aircraft before detection, improving the accuracy of aircraft detection and recognition. The experimental results using the SAR-ADRD dataset have demonstrated the effectiveness of the proposed method, with an average accuracy improvement of 2.0% compared to the baseline network YOLOv5s.
2024, 46(12): 4471-4482.
doi: 10.11999/JEIT231278
Abstract:
In active electrical scanning millimeter-wave security imaging, the uniform array antenna has the bottleneck of uncontrolled cost and high complexity, which is difficult to be widely applied in practices. To this end, a near-field focused sparse array design algorithm for high sparsity and low sidelobes is proposed in this paper. It applies an improved three dimensional (3D) time-domain imaging algorithm to achieve high-accuracy 3D reconstruction. Firstly, the near-field focusing sparse array antenna model is constructed by taking the near-field focusing position and peak sidelobe level as constraints, where the\begin{document}$ {\ell _p} $\end{document}
In active electrical scanning millimeter-wave security imaging, the uniform array antenna has the bottleneck of uncontrolled cost and high complexity, which is difficult to be widely applied in practices. To this end, a near-field focused sparse array design algorithm for high sparsity and low sidelobes is proposed in this paper. It applies an improved three dimensional (3D) time-domain imaging algorithm to achieve high-accuracy 3D reconstruction. Firstly, the near-field focusing sparse array antenna model is constructed by taking the near-field focusing position and peak sidelobe level as constraints, where the
2024, 46(12): 4483-4492.
doi: 10.11999/JEIT240370
Abstract:
To address the pulse dispersion issue in detecting frequency-shifted chirp signals with traditional Fractional Fourier Transform (FrFT), an adaptive FrFT detection method is proposed in this paper. Leveraging the structural model of short packets and the Neyman-Pearson detection model, an analytical method is derived to evaluate the false alarm probability and missed detection probability of signal frame detection using an evaluation function and a decision threshold. Incorporating the pulse characteristics of traditional FrFT for complete chirp signals, a correction scheme for the fractional Fourier integral operator is proposed, and the peak distribution function of the frequency-shifted chirp symbol is derived for the adaptive FrFT. Addressing the search time shift issue in the adaptive FrFT detection process, the peak size and distribution of the frequency-shifted chirp symbol are analyzed, and the superiority of the adaptive FrFT detection compared to traditional FrFT is demonstrated.
To address the pulse dispersion issue in detecting frequency-shifted chirp signals with traditional Fractional Fourier Transform (FrFT), an adaptive FrFT detection method is proposed in this paper. Leveraging the structural model of short packets and the Neyman-Pearson detection model, an analytical method is derived to evaluate the false alarm probability and missed detection probability of signal frame detection using an evaluation function and a decision threshold. Incorporating the pulse characteristics of traditional FrFT for complete chirp signals, a correction scheme for the fractional Fourier integral operator is proposed, and the peak distribution function of the frequency-shifted chirp symbol is derived for the adaptive FrFT. Addressing the search time shift issue in the adaptive FrFT detection process, the peak size and distribution of the frequency-shifted chirp symbol are analyzed, and the superiority of the adaptive FrFT detection compared to traditional FrFT is demonstrated.
Research on Combined Navigation Algorithm Based on Adaptive Interactive Multi-Kalman Filter Modeling
2024, 46(12): 4493-4503.
doi: 10.11999/JEIT240426
Abstract:
Practical applications struggle to obtain prior knowledge about inertial systems and sensors, affecting information fusion and positioning accuracy in combined navigation systems. To address the degradation of integrated navigation performance due to satellite signal quality changes and system nonlinearity in vehicle navigation, a Fuzzy Adaptive Interactive Multi-Model algorithm based on Multiple Kalman Filters (FAIMM-MKF) is proposed. It integrates a Fuzzy Controller based on satellite signal quality (Fuzzy Controller) and an Adaptive Interactive Multi-Model (AIMM). Improved Kalman filters such as Unscented Kalman Filter (UKF), Iterated Extended Kalman Filter (IEKF), and Square-Root Cubature Kalman Filter (SRCKF) are designed to match vehicle dynamics models. The method’s performance is verified through in-vehicle semi-physical simulation experiments. Results show that the method significantly improves vehicle positioning accuracy in complex environments with varying satellite signal quality compared to traditional interactive multi-model algorithms.
Practical applications struggle to obtain prior knowledge about inertial systems and sensors, affecting information fusion and positioning accuracy in combined navigation systems. To address the degradation of integrated navigation performance due to satellite signal quality changes and system nonlinearity in vehicle navigation, a Fuzzy Adaptive Interactive Multi-Model algorithm based on Multiple Kalman Filters (FAIMM-MKF) is proposed. It integrates a Fuzzy Controller based on satellite signal quality (Fuzzy Controller) and an Adaptive Interactive Multi-Model (AIMM). Improved Kalman filters such as Unscented Kalman Filter (UKF), Iterated Extended Kalman Filter (IEKF), and Square-Root Cubature Kalman Filter (SRCKF) are designed to match vehicle dynamics models. The method’s performance is verified through in-vehicle semi-physical simulation experiments. Results show that the method significantly improves vehicle positioning accuracy in complex environments with varying satellite signal quality compared to traditional interactive multi-model algorithms.
2024, 46(12): 4504-4512.
doi: 10.11999/JEIT240399
Abstract:
Wireless through-the-earth communication provides a solution for information transmission in heavily shielded space. The received current field signal has low Signal-to-Noise Ratio (SNR), is easily distorted, and is greatly affected by carrier frequency offset, making signal acquisition difficult. In this paper, a long synchronization signal frame structure is designed and a two-stage long correlation signal acquisition algorithm is proposed that combines coarse and fine frequency offset estimation. In the first stage, the training symbols in the received time-domain signal are used for coarse estimation of sampling interval deviation based on the maximum likelihood algorithm, and the coarse estimation value of the sampling point compensation interval is calculated. In the second stage, the coarse estimation value and the received SNR are combined to determine the traversal range of the fine estimation value of the sampling point compensation interval. A long correlation template signal with local compensation is designed to achieve accurate acquisition of the current field signal. The algorithm’s performance is verified in a heavily shielded space located 30.26 m below the ground. Experimental results show that compared to traditional sliding correlation algorithms, the proposed algorithm has a higher acquisition success probability.
Wireless through-the-earth communication provides a solution for information transmission in heavily shielded space. The received current field signal has low Signal-to-Noise Ratio (SNR), is easily distorted, and is greatly affected by carrier frequency offset, making signal acquisition difficult. In this paper, a long synchronization signal frame structure is designed and a two-stage long correlation signal acquisition algorithm is proposed that combines coarse and fine frequency offset estimation. In the first stage, the training symbols in the received time-domain signal are used for coarse estimation of sampling interval deviation based on the maximum likelihood algorithm, and the coarse estimation value of the sampling point compensation interval is calculated. In the second stage, the coarse estimation value and the received SNR are combined to determine the traversal range of the fine estimation value of the sampling point compensation interval. A long correlation template signal with local compensation is designed to achieve accurate acquisition of the current field signal. The algorithm’s performance is verified in a heavily shielded space located 30.26 m below the ground. Experimental results show that compared to traditional sliding correlation algorithms, the proposed algorithm has a higher acquisition success probability.
Electromagnetic Sensitivity Analysis of Curved Boundaries under the Method of Accompanying Variables
2024, 46(12): 4513-4521.
doi: 10.11999/JEIT240432
Abstract:
Sensitivity analysis an evaluation method for the influence with variations of the design parameters on electromagnetic performance, which is utilized to calculate sensitivity information. This information guides the analysis of structural models to ensure compliance with design specifications. In the optimization design of electromagnetic structures by commercial software, traditional algorithms are often employed, involving adjustments to the geometry. However, this approach is known to be extensive in terms of computational time and resource consumption. In order to enhance the efficiency of model design, a stable and efficient processing scheme is proposed in the paper, known as the Adjoint Variable Method (AVM). This method achieves estimation of 1st～2nd order sensitivity on parameter transformations with only two algorithmic simulation conditions required. The application of AVM has predominantly been confined to the sensitivity analysis of rectangular boundary parameters, with this paper making the first extension of AVM to the sensitivity analysis of arc boundary parameters. Efficient analysis of the electromagnetic sensitivity of curved structures is accomplished based on the conditions designed for three distinct scenarios: fixed intrinsic parameters, frequency-dependent objective functions, and transient impulse functions. Compared to the Finite-Difference Method (FDM), a significant enhancement in computational efficiency is achieved by the proposed method. The effective implementation of the method substantially expands the application scope of AVM to curved boundaries, which can be utilized in optimization problems such as the electromagnetic structures of plasma models and the edge structures of complex antenna models. When computational resources are limited, the reliability and stability of electromagnetic structure optimization can be ensured by the application of the proposed method.
Sensitivity analysis an evaluation method for the influence with variations of the design parameters on electromagnetic performance, which is utilized to calculate sensitivity information. This information guides the analysis of structural models to ensure compliance with design specifications. In the optimization design of electromagnetic structures by commercial software, traditional algorithms are often employed, involving adjustments to the geometry. However, this approach is known to be extensive in terms of computational time and resource consumption. In order to enhance the efficiency of model design, a stable and efficient processing scheme is proposed in the paper, known as the Adjoint Variable Method (AVM). This method achieves estimation of 1st～2nd order sensitivity on parameter transformations with only two algorithmic simulation conditions required. The application of AVM has predominantly been confined to the sensitivity analysis of rectangular boundary parameters, with this paper making the first extension of AVM to the sensitivity analysis of arc boundary parameters. Efficient analysis of the electromagnetic sensitivity of curved structures is accomplished based on the conditions designed for three distinct scenarios: fixed intrinsic parameters, frequency-dependent objective functions, and transient impulse functions. Compared to the Finite-Difference Method (FDM), a significant enhancement in computational efficiency is achieved by the proposed method. The effective implementation of the method substantially expands the application scope of AVM to curved boundaries, which can be utilized in optimization problems such as the electromagnetic structures of plasma models and the edge structures of complex antenna models. When computational resources are limited, the reliability and stability of electromagnetic structure optimization can be ensured by the application of the proposed method.
2024, 46(12): 4522-4528.
doi: 10.11999/JEIT240417
Abstract:
Convolutional Neural Networks (CNNs) exhibit translation invariance but lack rotation invariance. In recent years, rotating encoding for CNNs becomes a mainstream approach to address this issue, but it requires a significant number of parameters and computational resources. Given that images are the primary focus of computer vision, a model called Offset Angle and Multibranch CNN (OAMC) is proposed to achieve rotation invariance. Firstly, the model detect the offset angle of the input image and rotate it back accordingly. Secondly, feed the rotated image into a multibranch CNN with no rotation encoding. Finally, Response module is used to output the optimal branch as the final prediction of the model. Notably, with a minimal parameter count of 8 k, the model achieves a best classification accuracy of 96.98% on the rotated handwritten numbers dataset. Furthermore, compared to previous research on remote sensing datasets, the model achieves up to 8% improvement in accuracy using only one-third of the parameters of existing models.
Convolutional Neural Networks (CNNs) exhibit translation invariance but lack rotation invariance. In recent years, rotating encoding for CNNs becomes a mainstream approach to address this issue, but it requires a significant number of parameters and computational resources. Given that images are the primary focus of computer vision, a model called Offset Angle and Multibranch CNN (OAMC) is proposed to achieve rotation invariance. Firstly, the model detect the offset angle of the input image and rotate it back accordingly. Secondly, feed the rotated image into a multibranch CNN with no rotation encoding. Finally, Response module is used to output the optimal branch as the final prediction of the model. Notably, with a minimal parameter count of 8 k, the model achieves a best classification accuracy of 96.98% on the rotated handwritten numbers dataset. Furthermore, compared to previous research on remote sensing datasets, the model achieves up to 8% improvement in accuracy using only one-third of the parameters of existing models.
2024, 46(12): 4529-4541.
doi: 10.11999/JEIT240502
Abstract:
To address the issues of significant target scale variation, edge discontinuity, and blurring in 360° omnidirectional images Salient Object Detection (SOD), a method based on the Adjacent Coordination Network (ACoNet) is proposed. First, an adjacent detail fusion module is used to capture detailed and edge information from adjacent features, which facilitates accurate localization of salient objects. Then, a semantic-guided feature aggregation module is employed to aggregate semantic feature information from different scales between shallow and deep features, suppressing the noise transmitted by shallow features. This helps alleviate the problem of discontinuous salient objects and blurred boundaries between the object and background in the decoding stage. Additionally, a multi-scale semantic fusion submodule is constructed to enlarge the receptive field across different convolution layers, thereby achieving better training of the salient object boundaries. Extensive experimental results on two public datasets demonstrate that, compared to 13 other advanced methods, the proposed approach achieves significant improvements in six objective evaluation metrics. Moreover, the subjective visualized detection results show better edge contours and clearer spatial structural details of the salient maps.
To address the issues of significant target scale variation, edge discontinuity, and blurring in 360° omnidirectional images Salient Object Detection (SOD), a method based on the Adjacent Coordination Network (ACoNet) is proposed. First, an adjacent detail fusion module is used to capture detailed and edge information from adjacent features, which facilitates accurate localization of salient objects. Then, a semantic-guided feature aggregation module is employed to aggregate semantic feature information from different scales between shallow and deep features, suppressing the noise transmitted by shallow features. This helps alleviate the problem of discontinuous salient objects and blurred boundaries between the object and background in the decoding stage. Additionally, a multi-scale semantic fusion submodule is constructed to enlarge the receptive field across different convolution layers, thereby achieving better training of the salient object boundaries. Extensive experimental results on two public datasets demonstrate that, compared to 13 other advanced methods, the proposed approach achieves significant improvements in six objective evaluation metrics. Moreover, the subjective visualized detection results show better edge contours and clearer spatial structural details of the salient maps.
2024, 46(12): 4542-4552.
doi: 10.11999/JEIT240087
Abstract:
In order to improve the accuracy of emotion recognition models and solve the problem of insufficient emotional feature extraction, this paper conducts research on bimodal emotion recognition involving audio and facial imagery. In the audio modality, a feature extraction model of a Multi-branch Convolutional Neural Network (MCNN) incorporating a channel-space attention mechanism is proposed, which extracts emotional features from speech spectrograms across time, space, and local feature dimensions. For the facial image modality, a feature extraction model using a Residual Hybrid Convolutional Neural Network (RHCNN) is introduced, which further establishes a parallel attention mechanism that concentrates on global emotional features to enhance recognition accuracy. The emotional features extracted from audio and facial imagery are then classified through separate classification layers, and a decision fusion technique is utilized to amalgamate the classification results. The experimental results indicate that the proposed bimodal fusion model has achieved recognition accuracies of 97.22%, 94.78%, and 96.96% on the RAVDESS, eNTERFACE’05, and RML datasets, respectively. These accuracies signify improvements over single-modality audio recognition by 11.02%, 4.24%, and 8.83%, and single-modality facial image recognition by 4.60%, 6.74%, and 4.10%, respectively. Moreover, the proposed model outperforms related methodologies applied to these datasets in recent years. This illustrates that the advanced bimodal fusion model can effectively focus on emotional information, thereby enhancing the overall accuracy of emotion recognition.
In order to improve the accuracy of emotion recognition models and solve the problem of insufficient emotional feature extraction, this paper conducts research on bimodal emotion recognition involving audio and facial imagery. In the audio modality, a feature extraction model of a Multi-branch Convolutional Neural Network (MCNN) incorporating a channel-space attention mechanism is proposed, which extracts emotional features from speech spectrograms across time, space, and local feature dimensions. For the facial image modality, a feature extraction model using a Residual Hybrid Convolutional Neural Network (RHCNN) is introduced, which further establishes a parallel attention mechanism that concentrates on global emotional features to enhance recognition accuracy. The emotional features extracted from audio and facial imagery are then classified through separate classification layers, and a decision fusion technique is utilized to amalgamate the classification results. The experimental results indicate that the proposed bimodal fusion model has achieved recognition accuracies of 97.22%, 94.78%, and 96.96% on the RAVDESS, eNTERFACE’05, and RML datasets, respectively. These accuracies signify improvements over single-modality audio recognition by 11.02%, 4.24%, and 8.83%, and single-modality facial image recognition by 4.60%, 6.74%, and 4.10%, respectively. Moreover, the proposed model outperforms related methodologies applied to these datasets in recent years. This illustrates that the advanced bimodal fusion model can effectively focus on emotional information, thereby enhancing the overall accuracy of emotion recognition.
2024, 46(12): 4553-4562.
doi: 10.11999/JEIT240428
Abstract:
In the era of big data, table widely exists in various document images, and table detection is of great significance for the reuse of table information. In response to issues such as limited receptive field, reliance on predefined proposals, and inaccurate table boundary localization in existing table detection algorithms based on convolutional neural network, a table detection network based on DINO model is proposed in this paper. Firstly, an image preprocessing method is designed to enhance the corner and line features of table, enabling more precise table boundary localization and effective differentiation between table and other document elements like text. Secondly, a backbone network SwTNet-50 is designed, and Swin Transformer Blocks (STB) are introduced into ResNet to effectively combine local and global features, and the feature extraction ability of the model and the detection accuracy of table boundary are improved. Finally, to address the inadequacies in encoder feature learning in one-to-one matching and insufficient positive sample training in the DINO model, a collaborative hybrid assignments training strategy is adopted to improve the feature learning ability of the encoder and detection precision. Compared with various table detection methods based on deep learning, our model is better than other algorithms on the TNCR table detection dataset, with F1-Scores of 98.2%, 97.4%, and 93.3% for IoU thresholds of 0.5, 0.75, and 0.9, respectively. On the IIIT-AR-13K dataset, the F1-Score is 98.6% when the IoU threshold is 0.5.
In the era of big data, table widely exists in various document images, and table detection is of great significance for the reuse of table information. In response to issues such as limited receptive field, reliance on predefined proposals, and inaccurate table boundary localization in existing table detection algorithms based on convolutional neural network, a table detection network based on DINO model is proposed in this paper. Firstly, an image preprocessing method is designed to enhance the corner and line features of table, enabling more precise table boundary localization and effective differentiation between table and other document elements like text. Secondly, a backbone network SwTNet-50 is designed, and Swin Transformer Blocks (STB) are introduced into ResNet to effectively combine local and global features, and the feature extraction ability of the model and the detection accuracy of table boundary are improved. Finally, to address the inadequacies in encoder feature learning in one-to-one matching and insufficient positive sample training in the DINO model, a collaborative hybrid assignments training strategy is adopted to improve the feature learning ability of the encoder and detection precision. Compared with various table detection methods based on deep learning, our model is better than other algorithms on the TNCR table detection dataset, with F1-Scores of 98.2%, 97.4%, and 93.3% for IoU thresholds of 0.5, 0.75, and 0.9, respectively. On the IIIT-AR-13K dataset, the F1-Score is 98.6% when the IoU threshold is 0.5.
2024, 46(12): 4563-4574.
doi: 10.11999/JEIT240388
Abstract:
With generative adversarial networks have attracted much attention because they provide new ideas for blind super-resolution reconstruction. Considering the problem that the existing methods do not fully consider the low-frequency retention characteristics during image degradation, but use the same processing method for high and low-frequency components, which lacks the effective use of frequency details and is difficult to obtain better reconstruction result, a frequency separation generative adversarial super-resolution reconstruction network based on dense residual and quality assessment is proposed. The idea of frequency separation is adopted by the network to process the high-frequency and low-frequency information of the image separately, so as to improve the ability of capturing high-frequency information and simplify the processing of low-frequency features. The base block in the generator is designed to integrate the spatial feature transformation layer into the dense wide activation residuals, which enhances the ability of deep feature representation while differentiating the local information. In addition, no-reference quality assessment network is designed specifically for super-resolution reconstructed images using Visual Geometry Group (VGG), which provides a new quality assessment loss for the reconstruction network and further improves the visual effect of reconstructed images. The experimental results show that the method has better reconstruction effect on multiple datasets than the current state-of-the-art similar methods. It is thus shown that super-resolution reconstruction using generative adversarial networks with the idea of frequency separation can effectively utilize the image frequency components and improve the reconstruction effect.
With generative adversarial networks have attracted much attention because they provide new ideas for blind super-resolution reconstruction. Considering the problem that the existing methods do not fully consider the low-frequency retention characteristics during image degradation, but use the same processing method for high and low-frequency components, which lacks the effective use of frequency details and is difficult to obtain better reconstruction result, a frequency separation generative adversarial super-resolution reconstruction network based on dense residual and quality assessment is proposed. The idea of frequency separation is adopted by the network to process the high-frequency and low-frequency information of the image separately, so as to improve the ability of capturing high-frequency information and simplify the processing of low-frequency features. The base block in the generator is designed to integrate the spatial feature transformation layer into the dense wide activation residuals, which enhances the ability of deep feature representation while differentiating the local information. In addition, no-reference quality assessment network is designed specifically for super-resolution reconstructed images using Visual Geometry Group (VGG), which provides a new quality assessment loss for the reconstruction network and further improves the visual effect of reconstructed images. The experimental results show that the method has better reconstruction effect on multiple datasets than the current state-of-the-art similar methods. It is thus shown that super-resolution reconstruction using generative adversarial networks with the idea of frequency separation can effectively utilize the image frequency components and improve the reconstruction effect.
2024, 46(12): 4575-4588.
doi: 10.11999/JEIT240299
Abstract:
As Moore’s Law comes to an end, it is more and more difficult to improve the chip manufacturing process, and chiplet technology has been widely adopted to improve the chip performance. However, new design parameters introduced into the chiplet architecture pose significant challenges to the computer architecture simulator. To fully support exploration and evaluation of chiplet architecture, System-level Exploration and Evaluation simulator for Chiplet (SEEChiplet), a framework based on gem5 simulator, is developed in this paper. Firstly, three design parameters concerned about chiplet chip design are summarized in this paper, including: (1) chiplet cache system design; (2) Packaging simulation; (3) Interconnection networks between chiplet. Secondly, in view of the above three design parameters, in this paper: (1) a new private last level cache system is designed and implemented to expand the cache system design space; (2) existing gem5 global directory is modified to adapt to new private Last Level Cache (LLC) system; (3) two common packaging methods of chiplet and inter-chiplet network are modeled. Finally, a chiplet-based processor is simulated with PARSEC 3.0 benchmark program running on it, which proves that SEEChiplet can explore and evaluate the design space of chiplet.
As Moore’s Law comes to an end, it is more and more difficult to improve the chip manufacturing process, and chiplet technology has been widely adopted to improve the chip performance. However, new design parameters introduced into the chiplet architecture pose significant challenges to the computer architecture simulator. To fully support exploration and evaluation of chiplet architecture, System-level Exploration and Evaluation simulator for Chiplet (SEEChiplet), a framework based on gem5 simulator, is developed in this paper. Firstly, three design parameters concerned about chiplet chip design are summarized in this paper, including: (1) chiplet cache system design; (2) Packaging simulation; (3) Interconnection networks between chiplet. Secondly, in view of the above three design parameters, in this paper: (1) a new private last level cache system is designed and implemented to expand the cache system design space; (2) existing gem5 global directory is modified to adapt to new private Last Level Cache (LLC) system; (3) two common packaging methods of chiplet and inter-chiplet network are modeled. Finally, a chiplet-based processor is simulated with PARSEC 3.0 benchmark program running on it, which proves that SEEChiplet can explore and evaluate the design space of chiplet.
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