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FML-Vit: A Lightweight Vision Transformer Algorithm for Human Activity Recognition Using FMCW Radar

IEEE SENSORS JOURNAL 2024年第22期24卷 38518-38526页

作者： Ding, Minhao Dongye, Guangxin Lv, Ping Ding, Yipeng Cent South Univ Sch Elect Informat Changsha 410083 Peoples R China

In recent years, human activity recognition (HAR) using frequency module continuous wave (FMCW) radar is an effective tool that has been widely used in the fields of healthcare, smart driving, and smart living due to its convenience, inexpensiveness, and accuracy. Past studies have mainly investigated the improvement of the accuracy of HAR models while neglecting the deployment of the models. Therefore, we propose a model named FMCW lightweight vision transformer (FML-Vit) for HAR, primarily consisting of the FML-Vit block and FML-Vit subsample modules. The FML-Vit block, by incorporating a cascaded linear self-attention mechanism in place of the traditional multi-head attention mechanism, can transform the time complexity from O(K-2) to O(K). The FML-Vit subsampling modules perform dimension reduction and feature reallocation, while the context broadcasting (CB) module is used to reduce the density in the original attention maps, thereby increasing both the capacity and generalizability of the ViT. The proposed algorithm is compared with nine different state-of-the-art methods on self-datasets and open-source datasets. The results demonstrate that FML-Vit outperforms other current lightweight networks with the fastest inference.

关键词： Radar Transformers Accuracy Computational modeling Sensors Inference algorithms Human activity recognition Attention mechanisms Spectrogram signal processing algorithms Cascaded linear multihead attention mechanism context broadcasting (CB) frequency module continuous wave (FMCW) lightweight vison transformer (FML-Vit)

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Loaded Reciprocal Filter for OFDM-based Passive Radar signal processing

Loaded Reciprocal Filter for OFDM-based Passive Radar Signal...

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IEEE Radar Conference (RadarConf)

作者： Rodriguez, Javier Trujillo Colone, Fabiola Lombardo, Pierfrancesco Sapienza Univ Rome Dept Informat Engn Elect & Telecommun DIET Rome Italy

ISBN: (纸本)9781728153681

The Range-Doppler maps of Passive Bistatic Radar (PBR) are often evaluated by means of the suboptimal batches algorithm. For OFDM waveforms of opportunity, a typical choice is to fragment the signal in batches length equal to the OFDM symbol length. This is shown to provide signal-to-Noise Ratio (SNR) losses for both Matched Filter (MF) and Reciprocal Filter (RF), especially for long target ranges. We investigate the case of "non-OFDM fragmentation", with special attention to the case of batch length much longer than a single OFDM symbol that can potentially reduce the losses at long ranges. While this is effective for the MF, we show that the RF filter in this case still provides high Peak-toSidelobes Ratio (PSLR) values but at the expense of significant SNR losses. Therefore, we propose the Loaded Reciprocal Filter (LRF) as a modified version of the RF for the non-OFDM fragmentation case, which is shown to provide a trade-off between SNR losses and PSLR. Finally, a simulated case study is presented for a DVB-T-based PBR, which clearly shows the effectiveness of the proposed LRF scheme when using the nonOFDM fragmentation.

关键词： Radio frequency Passive radar Matched filters Passive filters OFDM Symbols signal processing algorithms

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Structured Directional Pruning via Perturbation Orthogonal Projection

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 5439-5453页

作者： Liu, Xiaofeng Wang, Qing Shao, Yunfeng Geng, Yanhui Li, Yinchuan Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Huawei Noahs Ark Lab Beijing 100085 Peoples R China

Despite the great potential of artificial intelligence (AI), which promotes machines to mimic human intelligence in performing tasks, it requires a deep/extensive model with a sufficient number of parameters to enhance the expressive ability. This aspect often hinders the application of AI on resource-constrained devices. Structured pruning is an effective compression technique that reduces the computation of neural networks. However, it typically achieves parameter reduction at the cost of non-negligible accuracy loss, necessitating fine-tuning. This paper introduces a novel technique called Structured Directional Pruning (SDP) and its fast solver, Alternating Structured Directional Pruning (AltSDP). SDP is a general energy-efficient coarse-grained pruning method that enables efficient model pruning without requiring fine-tuning or expert knowledge of the desired sparsity level. Theoretical analysis confirms that the fast solver, AltSDP, achieves SDP asymptotically after sufficient training. Experimental results validate that AltSDP reaches the same minimum valley as the vanilla optimizer, namely stochastic gradient descent (SGD), while maintaining a constant training loss. Additionally, AltSDP achieves state-of-the-art pruned accuracy integrating pruning into the initial training process without the need for fine-tuning. Consequently, the newly proposed SDP, along with its fast solver AltSDP, can significantly facilitate the development of shrinking deep neural networks (DNNs) and enable the deployment of AI on resource-constrained devices.

关键词： Training Perturbation methods Artificial intelligence Filters Accuracy signal processing algorithms Vectors Tuning Artificial neural networks Software Pruning group Lasso shrinking deep neural networks (DNN) tiny artificial intelligence (tiny-AI)

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Learning Domain-Invariant Model for WiFi-Based Indoor Localization

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第12期23卷 13898-13913页

作者： Wang, Guanzhong Zhang, Dongheng Zhang, Tianyu Yang, Shuai Sun, Qibin Chen, Yan Univ Sci & Technol China Sch Cyber Sci & Technol Hefei 230027 Peoples R China Inst Artificial Intelligence Hefei Peoples R China Hefei Comprehens Natl Sci Ctr Hefei Peoples R China

WiFi-based indoor localization has gained widespread attention due to the pervasive availability of WiFi Access Points (APs). While signal processing-based methods can achieve decimeter-level localization, their performance is constrained by the limited spatial resolution of WiFi systems, especially in complex environments with strong interference. By contrast, deep learning-based methods have achieved impressive performance even in complex environments, which however often fail to generalize to new environments. In this paper, we propose a novel framework to learn domain-invariant model for WiFi-based indoor localization, which maintains impressive performance across different environments. The key insight is to design a deep learning-based WiFi localization system through the perspective of signal processing. Specifically, we let the neural network estimate APs-centered polar coordinates to avoid fitting the coordinates of APs strongly correlated with the environment, enabling us to obtain the domain-invariant model. To unleash the potential of neural networks in regressing high-precision parameters, we design a beamforming layer to integrate the knowledge of signal processing. Furthermore, we propose a multi-task learning scheme to further improve localization accuracy. Extensive experiments on diverse datasets have demonstrated that the localization performance of our method outperforms state-of-the-art methods and demonstrates superiority under cross-domain conditions.

关键词： Location awareness Wireless fidelity Accuracy Performance evaluation signal processing algorithms Learning systems Feature extraction Channel state information deep learning indoor localization WiFi

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A Comprehensive Review of Recent Advances on Intelligence algorithms and Information Engineering Applications

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IEEE ACCESS 2024年 12卷 135886-135912页

作者： Zeng, Yang Liao, Bolin Li, Zhan Hua, Cheng Li, Shuai Jishou Univ Coll Comp Sci & Engn Jishou 416000 Peoples R China Jishou Univ Sch Commun & Elect Engn Jishou 416000 Peoples R China Southwest Jiaotong Univ Inst Smart City & Intelligent Transportat Chengdu 610031 Peoples R China Univ Oulu Fac Informat Technol & Elect Engn Oulu 90570 Finland VTT Technol Res Ctr Finland Oulu 90570 Finland

The advent of artificial intelligence (AI) has catalyzed transformative changes across diverse domains, establishing itself as a disruptive technology. Traditional methods often fall short in solving intricate information engineering problems, whereas AI methods can offer superior efficiency, speed, and accuracy. This paper provides a comprehensive overview of the research and practical applications of artificial intelligence in addressing information engineering challenges over the past two decades. It focuses on four critical information engineering fields: intelligent control, pattern recognition and intelligent systems, intelligent signal processing and communication, and optimization algorithms. This paper covers the backgrounds, driving forces, technologies, and applications of these fields, as well as the applications of AI in these areas. We believe that this study will be a valuable resource for both novice and seasoned researchers interested in leveraging AI to tackle information engineering challenges, offering a comprehensive understanding of AI integration and its potential in these areas.

关键词： Mathematical models Neural networks Biological neural networks Convergence signal processing algorithms Reviews Linear systems Artificial intelligence Intelligent control Optimization methods neural networks intelligent control intelligent optimization

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Energy-Efficient Sensor Scheduling for State Estimation Over Homogeneous Multi-Hop Networks

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IEEE signal processing LETTERS 2025年 32卷 411-415页

作者： Li, Yao Song, Rui Zheng, Jianyong Guan, Xinping Shanghai Univ Sch Future Technol Shanghai 200444 Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

In this letter, anestimation-oriented power-constrained sensor scheduling problem over multi-hop sensor networks is studied. Two different online scheduling schemes for multi-hop transmission, i.e., global-delay-based scheduling (GS) and covariance-based scheduling (CS) are proposed, respectively. We propose a stochastic triggering scheme to satisfy power constraints. A Markovian model is adopted to formulate the state transition relationship in the scheduling process. In order to calculate the switching threshold and selection probabilities, an algorithm for parameters determination is further properly designed to overcome the coupling property between hops. We have explicitly analyzed the performance of GS by figuring out the upper and lower bounds of cost. Moreover, the superiority and optimality of CS have been theoretically proved by using the optimal state distribution method. Numerical simulations and comparisons with existing methods have been conducted illustratively to verify the correctness and effectiveness of our proposed schemes, algorithms and results.

关键词： Spread spectrum communication Schedules Prediction algorithms Switches Processor scheduling Wireless sensor networks Lower bound signal processing algorithms Relays Costs Multi-hop networks sensor scheduling state estimation

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A CSI Entropy-Based Estimation Algorithm and Modified Recurrent Neural Networks for Time-Varying Non-Stationary Channels

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第5期73卷 6614-6625页

作者： Mai, Zhiyuan Chen, Yueyun Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China Univ Sci & Technol Beijing Shunde Innovat Sch Beijing 528399 Guangdong Peoples R China

Accurate channel state information (CSI) estimation is helpful for reliable transmission in high mobility communication scenarios, in which the channel being highly time-varying and non-stationary. Existing long short-term memory (LSTM) for channel estimation experiences accuracy degradation as the channel deteriorates, resulting in an unsatisfiable reliable requirement. In this paper, we propose a modified neural network architecture based on LSTM to exploit the channel's temporal correlation inherent for fast and accurate CSI acquisition, named CeLSTM. In the CeLSTM, we design a CSI entropy rectifier linear unit (CeRLU) for mapping the calculated CSI entropy into recurrent neural networks, which measures the temporal coherence of historically estimated CSI to facilitate the accurate estimation of current CSI. The CeLSTM learns nonlinear channel statistical correlation characteristics and conducts self-corrects from received signals by offline and online learning, respectively, to estimate CSI in dynamic environments. Simulation results show that CeLSTM improves the CSI estimation accuracy by 45% and 60% on average compared with the minimum mean square error (MMSE) and LSTM, respectively. Benefiting from CeRLU, the estimation time-consuming based on CeLSTM is 60% lower than LSTM and exhibits good robustness through MSE performance analysis under various time-varying channels.

关键词： Channel estimation Estimation Training signal processing algorithms Correlation Wireless communication Time-varying channels channel state information entropy recurrent neuron networks time-varying non-stationary channels

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DISH: A Distributed Hybrid Optimization Method Leveraging System Heterogeneity

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 4007-4021页

作者： Niu, Xiaochun Wei, Ermin Northwestern Univ Dept Ind Engn & Management Sci Evanston IL 60201 USA Northwestern Univ Dept Elect & Comp Engn Evanston IL 60201 USA

We study distributed optimization problems over multi-agent networks, including consensus and network flow problems. Existing distributed methods neglect the heterogeneity among agents' computational capabilities, limiting their effectiveness. To address this, we propose DISH, a distributed hybrid method that leverages system heterogeneity. DISH allows agents with higher computational capabilities or lower computational costs to perform local Newton-type updates while others adopt simpler gradient-type updates. Notably, DISH covers existing methods like EXTRA, DIGing, and ESOM-0 as special cases. To analyze DISH's performance with general update directions, we formulate distributed problems as minimax problems and introduce GRAND (gradient-related ascent and descent) and its alternating version, Alt-GRAND, for solving these problems. GRAND generalizes DISH to centralized minimax settings, accommodating various descent ascent update directions, including gradient-type, Newton-type, scaled gradient, and other general directions, within acute angles to the partial gradients. Theoretical analysis establishes global sublinear and linear convergence rates for GRAND and Alt-GRAND in strongly-convex-nonconcave and strongly-convex-PL settings, providing linear rates for DISH. In addition, we derive the local superlinear convergence of Newton-based variations of GRAND in centralized settings to show the potentials and limitations of Newton's method in distributed settings. Numerical experiments validate the effectiveness of our methods.

关键词： Optimization Convergence Newton method Eigenvalues and eigenfunctions Standards signal processing algorithms Noise measurement Distributed optimization heterogeneous systems hybrid methods Newton-type methods

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Taylor-Guided Iterative Gradient Projection Neural Network for Coal-Dust Scanning Electron Microscopy Super Resolution

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IEEE SENSORS JOURNAL 2024年第24期24卷 41323-41337页

作者： An, Xiaowei Wang, Zhuopeng Teng, Shenghua Liang, Quanquan Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao 266590 Peoples R China Shandong Univ Sci & Technol Coll Elect & Informat Engn Qingdao 266510 Peoples R China

This article proposes an interactive-interpretable network (IIN) to facilitate accurately zooming in the low-resolution scanning electron microscopy (SEM) image data which could preserve the intricate details of original image without long exposure of coal-dust specimens under intense energy radiation. By harnessing the interpretability benefits of traditional model-driven approaches, the proposed data-driven deep neural network facilitates an interactive super-resolution (SR) process unfolding as signal processing optimization procedures. According to the iterative proximal strategy, a deep unfolding way with proximal gradient projection is employed, in which each layer plays as a step to integrate deep networks into classic optimization with more obviously augmenting clarity and interpretability. Leveraging Taylor series approximation, the SR intermediates are decomposed into fundamental (low-order), derivative (high-order) components, and Remainder term, which are informative by intrinsic prior knowledge to elucidate varying image frequency details. Also, Taylor Remainder is treated as intermediate residual through the discrepancy measurement between intermediate high-resolution part and the whole-order information aggregation, which serves as a guidance for the following interactive refinement. Additionally, the reconstructed outputs undergo further synergy of the dual-model framework that could enhance final SR outcomes. Final experiments show that the proposed method with the interpretable and accurate merits, which outperforms other highly related SR methods from quantitative and qualitative perspectives.

关键词： Iterative methods Scanning electron microscopy Superresolution Optimization Sensors Transformers signal processing algorithms Deep learning Convolution Computational modeling Coal-dust interactive-interpretable network (IIN) scanning electron microscopy (SEM) super resolution (SR)

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4.6-km D-Band Photonic-Assisted Terahertz Wireless Communication Employing SIMO and MRC Technology

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IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES 2024年第11期72卷 6657-6668页

作者： Zhang, Qiutong Yu, Jianjun Zhao, Xianming Li, Weiping Wei, Yi Yang, Xiongwei Tan, Jingwen Zhang, Bing Wang, Kaihui Zhang, Ying Wang, Yanyi Zhou, Wen Liu, Bo Zhao, Feng Yu, Jianguo Fudan Univ State Key Lab ASIC & Syst Key Lab Informat Sci Electromagnet Waves MoE Shanghai 200433 Peoples R China Fudan Univ Sch Informat Sci & Technol Shanghai 200433 Peoples R China Harbin Inst Technol Inst Future Informat Technol Harbin 150001 Peoples R China Shanghai Univ Sch Commun & Informat Engn Shanghai 200444 Peoples R China Nanjing Univ Informat Sci & Technol Inst Opt & Elect Nanjing 210044 Peoples R China Xian Univ Posts & Telecommun Sch Elect Engn Xian 710121 Peoples R China Beijing Univ Posts & Telecommun Sch Elect Engn Beijing 100876 Peoples R China

Photon-assisted generation of terahertz (THz)signals can support larger modulation bandwidths in future ultrahigh-speed wireless communication systems. Single-input-multiple-output(SIMO)or multiple-input-multiple-output(MIMO) technology effectively uses the spatial dimension to improve the quality of the received signal, such as signal-to-noise ratio (SNR) and bit error rate (BER) ratio. Therefore, we experimentally demonstrate D-bandphoton-assisted THz 1 x 2 SIMO 4.6-km wireless communication. We use a photon-assisted THz technology to generate a 124.37-GHzD-band signal, which is received according to the maximum ratio combining (MRC) technology after 1 x 2 SIMO wireless transmission and uses fully blind digital signal processing (DSP)for carrier recovery algorithm without any training sequence. According to the experimental results, the maximum SNR diversity gain between 1 x 1 and 1 x 2 SIMO can be up to2.8 dB. After the MRC algorithm, the BER can be reduced by one order of magnitude, which is less than 15% of soft decision forward error correction (SD-FEC). This is the first experimental demonstration of high gain and long range in a D-band THz 1 x 2 SIMO system with a line rate of 43.5 Gb/s. These results will provide important assistance to our future efforts in photon-assisted THz wireless communications.

关键词： Terahertz communications Wireless communication signal processing algorithms Photonics Training Bandwidth Optical filters Diversity gain heterodyne detection multiple-input-multiple-output (MIMO)-maximum ratio combining (MRC) photon-assisted terahertz (THz) communication single-input-multiple-output (SIMO)

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