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47th IEEE International Conference on Acoustics, Speech and signal processing (ICASSP)

作者： Sun, Yuchen Huang, Kejun Univ Florida Dept CISE Gainesville FL 32611 USA

ISBN: (纸本)9781665405409

We propose a new algorithm called higher-order QR iteration (HOQRI) for computing the Tucker decomposition of large and sparse tensors. Compared to the celebrated higher-order orthogonal iterations (HOOI), HOQRI relies on a simple orthogonalization step in each iteration rather than a more sophisticated singular value decomposition step as in HOOI. More importantly, when dealing with extremely large and sparse data tensors, HOQRI completely eliminates the intermediate memory explosion by defining a new sparse tensor operation called TTMcTC. Furthermore, HOQRI is shown to monotonically improve the objective function, thus enjoying the same convergence guarantee as that of HOOI. Numerical experiments on synthetic and real data showcase the effectiveness of HOQRI.

关键词： Tensors Conferences signal processing algorithms signal processing Linear programming Explosions Acoustics

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Compressing Cipher Images by Using Semi-tensor Product Compressed Sensing and Pre-mapping

Compressing Cipher Images by Using Semi-tensor Product Compr...

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Data Compression Conference (DCC)

作者： Zhang, Bo Xiao, Di Huang, Hui Liang, Jia Army Engn Univ Commun NCO Acad Chongqing 400035 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China

ISBN: (纸本)9781665478939

As a new signal processing technology, compressed sensing (CS) has been showed to be a promising solution for compressing cipher images. However, the previous CS-based schemes are unsatisfactory in terms of ratio-distortion (R-D) performance. In order to solve this problem, an image encryption-then-compression (ETC) scheme by using semi-tensor product CS (STP-CS) and pre-mapping is proposed in this paper. In the proposed scheme, the original image is encrypted by using the scrambling operation. After image encryption, the cipher image is compressed through three steps. Firstly, the original image is compressed by using STP-CS. Secondly, the CS samples are processed by using pre-mapping operation. Thirdly, the resultant CS samples are quantized and encoded into bits. For image signal recovery, an iterative bivariate shrinkage (IBS) algorithm is proposed. Compared with the existing CS-based image ETC schemes, the proposed scheme has better R-D performance.

关键词： Ciphers Image coding Simulation signal processing algorithms signal processing Reconstruction algorithms Iterative algorithms

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Performance Optimization for Wireless Semantic Communications over Energy Harvesting Networks 47

Performance Optimization for Wireless Semantic Communication...

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47th IEEE International Conference on Acoustics, Speech and signal processing (ICASSP)

作者： Chen, Mingzhe Wang, Yining Poor, H. Vincent Princeton Univ Dept Elect & Comp Engn Princeton NJ 08544 USA Beijing Univ Posts & Telecommun Beijing Lab Adv Informat Network Beijing 100876 Peoples R China

ISBN: (纸本)9781665405409

In this paper, the optimization of semantic communications over energy harvesting networks is studied. In the considered model, a set of users use semantic communication techniques and the harvested energy to transmit text data to a base station (BS). Here, semantic communication techniques enable each user to transmit the meaning of the original data (called semantic information) thereby reducing its transmission delay and energy consumption. The BS will recover the data using the received semantic information. To further improve communication efficiency, each user can transmit only partial semantic information to the BS. Therefore, each user needs to jointly determine the partial semantic information to be transmitted and the resource block (RB) that is used for semantic information transmission. This problem is formulated as an optimization problem whose goal is to maximize the sum of all users' similarities that capture the differences between the original data that each user needs to transmit and the data recovered by the BS. To solve this problem, a value decomposition based deep Q network is proposed, which enables the users to jointly find the semantic information transmission and the RB allocation schemes that maximize the sum of all users' similarities. Simulation results demonstrate that the proposed method can improve sum of all users' similarities by up to threefold compared to the independent reinforcement learning.

关键词： Wireless communication Energy consumption Simulation Semantics signal processing algorithms Information processing Resource management

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Doppler Velocity De-Aliasing Based on Lag-1 Cross-Correlation for Dual-Polarization Weather Radar

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2024年 21卷 1页

作者： Dong, Xichao Zhao, Xiaomeng Chen, Zhiyang Li, Yinghe Wang, Sihan Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Beijing Key Lab Embedded Real Time Informat Proc Beijing 100081 Peoples R China Beijing Inst Technol Chongqing Innovat Ctr Chongqing Key Lab Novel Civilian Radar Chongqing 401120 Peoples R China Aerosp Newsky Technol Co Ltd Wuxi 214000 Peoples R China

Alternate transmission and alternate reception (ATAR) mode, as a crucial polarization mode, enables a single channel to be utilized in dual-polarization weather radars, which reduces the weight, power, and cost of the radar. The ATAR mode has a broad application based on high-altitude platforms with limited load and power. The interval between co-polarization signals is twice the pulse repetition time (PRT) in the ATAR mode. Thus, the Nyquist velocity interval is half of the one existing in the traditional radars. This hampers the application of the ATAR mode in severe weather observations. In this letter, a velocity de-aliasing algorithm is proposed, where lag-1 cross-correlations between adjacent radials are utilized to calculate the velocity estimates. This extends the Nyquist velocity interval of the ATAR model twofold compared to that of the traditional algorithm, which uses lag-1 autocorrelations between adjacent radials. In addition, the constraints of the proposed algorithm are analyzed. The proposed algorithm is validated via X-band PWR data, and the results show that compared to the true value, the biases of the de-aliased velocities in the ATAR mode are within +/- 1 m/s for 96.9% of the range bins. This method is also suitable for use with the alternate transmission and simultaneous reception (ATSR) mode.

关键词： Radar Meteorology Doppler effect Estimation signal processing algorithms Nickel Meteorological radar Alternate transmission and alternate reception (ATAR) polarimetric weather radar velocity de-aliasing

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Exact Equality of the MSEs for Two Types of Nonlinear Adaptive Systems: Saturation and Dead-Zone Types

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IEEE ACCESS 2024年 12卷 181704-181717页

作者： Miyoshi, Seiji Yamaguchi, Yua Kansai Univ Fac Engn Sci Dept Elect Elect & Informat Engn Suita Osaka 5648680 Japan Nidec Adv Technol Corp Muko Kyoto 6170003 Japan

Adaptive signal processing systems, commonly utilized in applications such as active noise control and acoustic echo cancellation, often encompass nonlinearities due to hardware components such as loudspeakers, microphones, and amplifiers. Examining the impact of these nonlinearities on the overall performance of adaptive systems is critically important. In this study, we employ a statistical-mechanical method to investigate the behaviors of adaptive systems, each containing an unknown system with a nonlinearity in its output. We specifically address two types of nonlinearity: saturation and dead-zone types. We analyze both the dynamic and steady-state behaviors of these systems under the effect of such nonlinearities. Our findings indicate that when the saturation value is equal to the dead-zone width, the mean square errors (MSEs) in steady states are identical for both nonlinearity types. Furthermore, we derive a self-consistent equation to obtain the saturation value and dead-zone width that maximize the steady-state MSE. We theoretically clarify that these values depend on neither the step size nor the variance of background noise.

关键词： Adaptive systems Adaptation models Adaptive signal processing Adaptive equalizers Noise Vectors Steady-state signal processing algorithms Mathematical models Finite impulse response filters Adaptive filter adaptive signal processing system identification LMS algorithm saturation-type nonlinearity dead-zone-type nonlinearity statistical-mechanical analysis

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ADAPTIVE NODE PARTICIPATION FOR STRAGGLER-RESILIENT FEDERATED LEARNING 47

ADAPTIVE NODE PARTICIPATION FOR STRAGGLER-RESILIENT FEDERATE...

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47th IEEE International Conference on Acoustics, Speech and signal processing (ICASSP)

作者： Reisizadeh, Amirhossein Tziotis, Isidoros Hassani, Hamed Mokhtari, Aryan Pedarsani, Ramtin MIT Cambridge MA 02139 USA UT Austin Austin TX USA UPenn Philadelphia PA USA UC Santa Barbara Santa Barbara CA USA

ISBN: (纸本)9781665405409

Federated learning is prone to multiple system challenges including system heterogeneity where clients have different computation and communication capabilities. Such heterogeneity in clients' computation speeds has a negative effect on the scalability of federated learning algorithms and causes significant slow-down in their runtime due to the existence of stragglers. In this chapter, we propose a novel straggler-resilient federated learning method that incorporates statistical characteristics of the clients' data to adaptively select the clients in order to speed up the learning procedure. The key idea of our algorithm is to start the training procedure with faster nodes and gradually involve the slower nodes in the model training once the statistical accuracy of the data corresponding to the current participating nodes is reached. The proposed approach reduces the overall runtime required to achieve the statistical accuracy of data of all nodes, as the solution for each stage is close to the solution of the subsequent stage with more samples and can be used as a warm-start. Our numerical experiments demonstrate significant speedups in wall-clock time of our straggler-resilient method compared to other federated learning benchmarks.

关键词： Training Runtime Scalability Conferences signal processing algorithms signal processing Benchmark testing

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Fast Iterative Wiener Filter-Based ISAR Imaging and Cross-Range Scaling With Periodically Gapped CPI

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Wang, Yuanyuan Dai, Fengzhou Liu, Qian Liang, Yi Lu, Xiaofei Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China Jiuquan Satellite Launch Ctr Jiuquan 732750 Peoples R China

In this article, the problem of inverse synthetic aperture radar (ISAR) high-resolution imaging and cross-range scaling with periodically gapped coherent processing interval (CPI) caused by the radar performing multiple tasks time divisionally is addressed. In the case of periodically gapped CPI, the traditional fast Fourier transform (FFT) based ISAR imaging method is no longer applicable, and sparse reconstruction is an effective approach to solve this problem. Sparse Bayesian learning (SBL) is the most robust and accurate reconstruction method among all sparse reconstruction algorithms, and it is also an implementation of optimal estimation of the random signal model under the minimum mean square error (MMSE) criterion. Based on the fact that the noise variance of radar observation can be estimated with secondary data, and the amplitudes of the target scatterers can be regarded as unknown deterministic variables, we propose another implementation method for the MMSE estimator, i.e., iterative Wiener filter (IWF), for ISAR imaging and cross-range scaling of periodically gapped data. Compared with SBL, the proposed method has the advantages of lower computational complexity, faster convergence, and higher reconstruction accuracy. Further, we design two fast IWF algorithms based on the triangular-circulant low displacement rank decomposition by utilizing the Toeplitz-block-Toeplitz (TBT) structure, in which almost all operations except matrix decomposition can be calculated using FFT, which can efficiently and accurately process two different types of periodically gapped echo data. Finally, the performance of the algorithm was verified with both simulation and measured data.

关键词： Radar imaging Imaging Radar signal processing algorithms Matrix decomposition Matching pursuit algorithms Scattering Cross-range scaling fast algorithms inverse synthetic aperture radar (ISAR) imaging iterative Wiener filter (IWF) periodically gapped coherent processing interval (CPI)

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A half-duplex joint communications and sensing system using ZP-OFDM 2

A half-duplex joint communications and sensing system using ...

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2nd IEEE International Symposium on Joint Communications and Sensing (JC and S)

作者： Dokhanchi, Sayed Hossein Barreto, Andre Noll Fettweis, Gerhard Barkhausen Inst Dresden Germany

ISBN: (纸本)9781665405799

In this paper, we investigate a joint communications and sensing (JC&S) system using zero-padded orthogonal frequency division multiplexing (ZP-OFDM) to simplify the hardware design at the receiver of a monostatic radar. This is possible because, unlike with cyclic-prefix OFDM (CP-OFDM) radar, ZP-OFDM radar does not need full duplexing, as there is no self-interference during the guard interval (GI), which can be used for radar detection. We derive the required receiver signal processing for radar detection in that case, and show that when the self-interference in CP-OFDM JC&S is high, the ZP-OFDM is beneficial. This advantage depends on the target distance, because it influences the relative amount of energy from the reflected energy that can be captured in the GI. We show then that ZP-OFDM can be a cost-effective and efficient alternative to CP-OFDM under certain conditions.

关键词： Interference cancellation OFDM Half-duplex system Radar detection signal processing algorithms Radar Receivers

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Particle Filter-Based Passive Islanding Detection Scheme for Renewable Energy Penetrated Distribution Systems

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IEEE ACCESS 2024年 12卷 147501-147515页

作者： Tahir Chauhdary, Sohaib Mumtaz, Faisal Ahmed Sher, Hadeed Almutairi, Sulaiman Z. Aljumah, Ali S. Faisal Muratza, Ali Alqahtani, Mohammed H. Dhofar Univ Coll Engn Dept Elect & Comp Engn Salalah 211 Oman Natl Univ Sci & Technol NUST USPCAS E Islamabad 44000 Pakistan Ghulam Ishaq Khan Inst Engn Sci & Technol Fac Elect Engn Topi 23640 Pakistan Prince Sattam Bin Abdulaziz Univ Coll Engn Elect Engn Dept Al Kharj 11942 Saudi Arabia Qassim Univ Coll Engn Dept Elect Engn Buraydah 51452 Qassim Saudi Arabia

Renewable energy penetrated distribution systems (REPDS) are the upcoming form of conventional distribution systems due to environmental benefits and power quality concerns. However, islanding detection is a challenging problem associated with these REPDSs. In this paper, the Particle filter (PF) is utilized to detect the islanding events in modern REPDS by utilizing a voltage signal at the point of common coupling (PCC). Initially, the PF is implemented on voltage signature at PCC for state estimation of fundamental and non-fundamental components. Then, the Particle filter estimated residuals (PFER) and non-fundamental $3<^>{\mathrm {rd}}$ harmonic component (NFTHC) indices are computed. The estimated PFER & NFTHC indices are matched with the pre-specified threshold setting to recognize the islanding conditions. Then, the tripping decision is taken by OR-operation of both the estimated PFER & NFTHC. The suggested method is competent in detecting islanding events in both balanced as well as unbalanced load/generation events. Moreover, the suggested method is also capable of discriminating islanding conditions from non-islanding events. Extensive simulation on MATLAB/Simulink-based IEEE 13-bus test bed and UL-1741 test bed are accomplished to authenticate the usefulness of the suggested scheme. The results illustrate that the suggested method performs well with 99% accuracy, low computational latency, and very low non-detection zone (NDZ). Furthermore, the time of action of the presented scheme is less than 2 milliseconds (m sec) deprived of any false operation.

关键词： Islanding Particle filters signal processing algorithms Heuristic algorithms Reliability Power harmonic filters Voltage measurement Particle measurements Indexes Atmospheric measurements Renewable energy sources Power distribution Islanding detection passive methods particle filters renewable energy penetrated distribution systems

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Synthesis of Large Sparse Sensor Arrays Utilizing Relaxed-Intensified Exploration Algorithm (RIEA) for Optimal UAVs Beamforming

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Zhou, Zhigang Zeng, Cao Lan, Lan Liao, Guisheng Zhu, Shengqi Chen, Baixiao Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Shaanxi Peoples R China

In this study, we present a novel relaxed-intensified exploration algorithm (RIEA) to synthesize large-aperture sensor arrays producing good array sparsity and optimal weight vector of the sparse sensor arrays for sensing unmanned aerial vehicles (UAVs) in airspace. The proposed algorithm is based on the compressed-sensing framework integrated with a kind of relaxed-intensified optimization thought, which comprises two core stages: the relaxed optimization stage and the intensified reoptimization stage. The relaxed-intensified exploration algorithm (RIEA) is tailored to accelerate array synthesis efficiency and promote global optimization. For the proposed algorithm, the ability to approach the global convergence is embodied in two key stages. The first stage aims to generate an optimal sparse sensor array with arbitrary upper mask constraints, whose upper mask is slightly relaxed to expand the solution space for further enhancing the array sparsity. Meanwhile, direction dimension reduction is further conducted to relax the radiating direction matrix for reducing massive computational cost. For the intensified reoptimization stage, the "relaxed" upper mask is first readjusted back to the strictly constrained strength and the weight vector of the designed sparse sensor array in the previous stage is then further optimized to approach the global optimal solution. Finally, the presence of element pattern for an individual sensor and array beam-scanning capability are also considered and investigated in synthesizing the sparse sensor arrays for precise positioning and sensing of UAVs. Several representative examples of the small/large-aperture sparse sensor arrays are performed to demonstrate the superiority, effectiveness, and robustness of the proposed RIEA.

关键词： Sensor arrays Phased arrays Optimization Autonomous aerial vehicles Vectors Array signal processing signal processing algorithms Layout Minimization Computational efficiency Array pattern synthesis dimension reduction lower time cost relaxed-intensified optimization thought sparse sensor arrays

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