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Stochastic Halfspace Approximation Method for Convex Optimization With Nonsmooth Functional Constraints

IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2025年第1期70卷 479-486页

作者： Singh, Nitesh Kumar Necoara, Ion Natl Univ Sci & Technol Politehn Bucharest Automat Control & Syst Engn Dept Bucharest 060042 Romania Romanian Acad Gheorghe Mihoc Caius Iacob Inst Math Stat & Appl M Roma TX USA

In this work, we consider convex optimization problems with smooth objective function and nonsmooth functional constraints. We propose a new stochastic gradient algorithm, called the stochastic halfspace approximation method (SHAM), to solve this problem, where at each iteration we first take a gradient step for the objective function and then we perform a projection step onto one halfspace approximation of a randomly chosen constraint. We propose various strategies to create this stochastic halfspace approximation and we provide a unified convergence analysis that yields new convergence rates for the SHAM algorithm in both optimality and feasibility criteria evaluated at some average point. In particular, we derive convergence rates of order O(1/root k) , when the objective function is only convex, and O(1/k) when the objective function is strongly convex. The efficiency of SHAM is illustrated through detailed numerical simulations.

关键词： Convergence Convex functions Linear programming Approximation algorithms signal processing algorithms Optimization Systems engineering and theory Convex optimization nonsmooth functional constraints stochastic halfspace approximation stochastic projection gradient methods

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Three-Dimensional Localization of Mixed Near-Field and Far-Field Sources Based on a Unified Exact Propagation Model

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 245-258页

作者： Fang, Jiaxiong Chen, Hua Liu, Wei Yang, Songjie Yuen, Chau So, Hing Cheung Ningbo Univ Fac Elect Engn & Comp Sci Ningbo 315211 Peoples R China Hong Kong Polytech Univ Dept Elect & Elect Engn Hong Kong Peoples R China Univ Elect Sci & Technol China Natl Key Lab Wireless Commun Chengdu 610054 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

In applications like speaker localization using a microphone array, the collected signals are typically a mixture of far-field (FF) and near-field (NF) sources. To find the positions of both NF and FF sources, a three-dimensional spatial-temporal localization algorithm based on a unified exact propagation geometry is developed in this paper, which avoids approximating the spatial phase difference with the first-order and second-order Taylor expansions applied to FF and NF sources, respectively. Our scheme utilizes cross-correlation to produce virtual observations for establishing a third-order parallel factor data model with the use of spatial and temporal information. The array's steering vectors can be extracted by trilinear decomposition. The amplitude and phase information of the whole array elements is jointly exploited to classify the source types and obtain the location estimates via a least squares method. Moreover, the proposed algorithm is computationally efficient since no spectral searches, high-order statistics calculations or parameter pairing procedures are required. The deterministic Cram & eacute;r-Rao bound is also derived as a performance benchmark, and numerical results are provided to demonstrate the effectiveness of the developed method.

关键词： Noise measurement Location awareness Classification algorithms Geometry Three-dimensional displays signal processing algorithms Phased arrays Multiple signal classification Apertures Microphone arrays Array signal processing source localization spatial-temporal near-field far-field

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Traffic Speed Estimation Device Based on Dual-Axis Magneto-Impedance Sensors for Complex Traffic Conditions

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年

作者： Yao, Ruixuan Uchiyama, Tsuyoshi Nagoya Univ Dept Elect Nagoya 4648603 Japan

The development of autonomous driving has created new demands for real-time road surveillance. Portable, high-precision, low-power road infrastructure is needed to enable real-time communication of collected vehicle data with passing vehicles. Vehicle recognition and speed estimation are key challenges in this context. We propose a traffic detection device using dual-axis magneto-impedance (MI) sensors installed roadside to detect vehicles in adjacent lanes. Experiments were conducted by driving various vehicle types under a variety of complex road and traffic conditions, analyzing how different measurement conditions and driving patterns affect magnetic signatures. We identified the magnetic signatures most strongly correlated with vehicle recognition and speed estimation. We developed corresponding solutions to address issues as repeated misrecognition and the influence of non-adjacent lanes, which are frequently highlighted in related works. Ultimately, we achieved an overall recognition accuracy of 96.66% in random measurements on complex traffic conditions, reducing recognition errors related to these issues to 3.22%. Additionally, we proposed two speed estimation methods: the time-domain magnetic signature differential method and the dominant frequency method, utilizing multiple sensor nodes and a single node, respectively. Both speed estimation methods were analyzed and compared through controlled experiments, and their performance was evaluated in random measurement experiments. Both methods showed similar estimation accuracy on straight roads, with 100% and 98.8% of results falling within a +/- 5 km/h error range, respectively. In low-speed curved sections where multiple sensor nodes could not be deployed, we achieved speed estimation using a single sensor by analyzing frequency-domain signals. Finally, under complex traffic conditions, the device demonstrated a minimum Mean Absolute Error (MAE) of only 2.09 km/h for speed estimation.

关键词： Magnetic sensors Sensors Estimation Roads Accuracy Magnetic domains Real-time systems signal processing algorithms Magnetic analysis Intelligent sensors Magnetic sensor vehicle recognition speed estimation traffic surveillance

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A Dual Inexact Nonsmooth Newton Method for Distributed Optimization

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 188-203页

作者： Niu, Dunbiao Hong, Yiguang Song, Enbin Tongji Univ Coll Elect & Informat Engn Dept Control Sci & Engn Natl Key Lab Autonomous Intelligent Unmanned Syst Shanghai 201804 Peoples R China Sichuan Univ Coll Math Chengdu 610065 Sichuan Peoples R China

In this paper, we propose a novel dual inexact nonsmooth Newton (DINN) method for solving a distributed optimization problem, which aims to minimize a sum of cost functions located among agents by communicating only with their neighboring agents over a network. Our method is based on the Lagrange dual of an appropriately formulated primal problem created by introducing local variables for each agent and enforcing a consensus constraint among these variables. Due to the decomposed structure of the dual problem, the DINN method guarantees a superlinear (or even quadratic) convergence rate for both the primal and dual iteration sequences, achieving the same convergence rate as its centralized counterpart. Furthermore, by exploiting the special structure of the dual generalized Hessian, we design a distributed iterative method based on Nesterov's acceleration technique to approximate the dual Newton direction with suitable precision. Moreover, in contrast to existing second-order methods, the DINN method relaxes the requirement for the objective function to be twice continuously differentiable by using the linear Newton approximation of its gradient. This expands the potential applications of distributed Newton methods. Numerical experiments demonstrate that the DINN method outperforms the current state-of-the-art distributed optimization methods.

关键词： Convergence Jacobian matrices Vectors Newton method Linear programming Costs signal processing algorithms Approximation algorithms Distributed databases Cost function Inexact nonsmooth Newton method distributed optimization superlinear convergence rate communication cost

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Low-Power Real-Time Seizure Monitoring Using AI-Assisted Sonification of Neonatal EEG

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 2025年第1期13卷 80-89页

作者： Nguyen, Tien Daly, Aengus Gomez-Quintana, Sergi O'Sullivan, Feargal Temko, Andriy Popovici, Emanuel Univ Coll Cork Dept Elect & Elect Engn Coll Rd Cork T12 K8AF Ireland

Detecting seizures in neonates requires continuous electroencephalography (EEG) monitoring, a costly process that demands trained experts. Although recent advancements in machine learning offer promising solutions for automated seizure detection, the opaque nature of these algorithms poses significant challenges to their adoption in healthcare settings. A prior study demonstrated that integrating machine learning with sonification-an interpretation method that converts bio-signals into sound-can mitigate the black-box problem while enhancing seizure detection performance. This AI-assisted sonification algorithm can provide a valuable complementary tool in seizure monitoring besides the traditional visualization method. A low-power and affordable implementation of the algorithm is presented in this study using a microcontroller. To improve its practicality, we also introduce a real-time design that allows the sonification algorithm to function in parallel with data acquisition. The system consumes 12 mW in average, making it suitable for a battery-powered device.

关键词： Electroencephalography Sonification Pediatrics Real-time systems Instruction sets signal processing algorithms Vocoders Monitoring Interpolation Detection algorithms Artificial intelligence biomedical monitoring biomedical signal processing electroencephalography embedded software Edge AI internet of medical things machine learning microprocessors real-time systems sonification signal processing algorithms

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Online Federated Reproduced Gradient Descent With Time-Varying Global Optima

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 1379-1393页

作者： Lin, Yifu Li, Wenling Song, Jia Li, Xiaoming Beihang Univ BUAA Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Beihang Univ BUAA Sch Astronaut Beijing 100191 Peoples R China Shenzhen Univ Res Inst Smart Cities Sch Architecture & Urban Planning Shenzhen 518060 Peoples R China

This paper addresses an online federated learning problem, where the time drift in data distribution leads to time-varying global optima. To adapt to the drift, this paper designs a random Fourier features (RFF) model combined with Reproducing Kernel Hilbert Space (RKHS) theory to tracking the global gradient. Meanwhile, the model also can mitigate gradient variance from local data and gradient bias due to data heterogeneity. Based on this model, the paper further proposes an online federated reproduced gradient descent (OFedRGD) algorithm. The Wasserstein distance is then employed as a distribution metric to analyze the regret by OFedRGD, which is composed of cumulative distribution drifts and cumulative gradient error caused by stochasticity and heterogeneity. Additionally, a set of CLEAR-datasets, including two online learning tasks, are used to test the proposed algorithm. The results show that the proposed algorithm can effectively improve classification accuracy in the two tasks by 5% and 16%, respectively, and its performance is less adversely affected by the degree of data dispersion.

关键词： Federated learning Optimization Adaptation models Data models signal processing algorithms Training Stochastic processes Probability distribution Vectors Servers Online federated learning random fourier feature reproducing kernel hilbert space time drift, gradient variance data heterogeneity reproduced gradient

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Solving Quadratic Systems With Full-Rank Matrices Using Sparse or Generative Priors

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 477-492页

作者： Chen, Junren Ng, Michael K. Liu, Zhaoqiang Univ Hong Kong Dept Math Hong Kong 852 Peoples R China Hong Kong Baptist Univ Dept Math Hong Kong 852 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China

The problem of recovering a signal x is an element of R-n from a quadratic system{yi=x(T)A(ix), i=1,...,m} with full-rank matrices A(i) frequently arises in applications such a sun assigned distance geometry and sub-wavelength imaging. With i.i.d. standard Gaussian matrices A(i) , this paper addresses the high-dimensional case where m << n by incorporating prior knowledge of x. First, we consider a k-sparse x and introduce the thresholded Wirtinger flow (TWF) algorithm that does not require the sparsity level k. TWF comprises two steps: the spectral initialization that identifies a point sufficiently closetox (up to a sign flip) when m=O(k(2)logn),and the thresholded gradient descent which, when provided a good initialization, produces a sequence linearly converging to x with m=O(k log n) measurements. Second, we explore the generative prior, assuming that x lies in the range of an L-Lipschitz continuous generative model with k-dimensional inputs in an l(2)-ball of radius r. With an estimate correlated with the signal, we develop the projected gradient descent (PGD)algorithm that also comprises two steps: the projected power method that provides an initial vector with O(root k log(L)/m) l(2)-error given m=O( k log(Lnr))measurements, and the projected gradient descent that refines the l(2)-error to O(delta) at a geometric rate when m=O(k log Lrn/delta(2)). Experimental results corroborate our theoretical findings and show that: (i)our approach for the sparse case notably outperforms the existing provable algorithm sparse power factorization;(ii) leveraging the generative prior allows for precise image recovery in the MNIST dataset from a small number of quadratic measurements.

关键词： Sparse matrices Complexity theory signal processing algorithms Vectors Phase measurement Compressed sensing Standards Particle measurements Image segmentation Image reconstruction Generative prior projected gradient descent projected power method quadratic system sparse prior wirtinger flow

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Light-Weight Low-Latency Reconfigurable CORDIC Architecture With a New Non-Power-of-2 Angle Set of Microrotations

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2025年

作者： Verma, Anu Das, Bishnu Prasad Indian Inst Technol IIT Roorkee Dept Elect & Commun Engn Roorkee 247667 Uttarakhand India

Trigonometric functions are widely used in various scientific and engineering applications, including digital signal processing. This article proposes a low-complexity low-latency reconfigurable CORDIC (COordinate Rotation Digital Computer) algorithm that computes trigonometric functions, which is based on a novel angle set of microrotations. The CORDIC algorithm is an iterative algorithm that reduces the performance due to the high latency of computation. The proposed pipelined scale-free CORDIC algorithm computes the functions in only four iterations with an optimized angle set of microrotations with high accuracy. It can be configured in two modes with different trajectories 1) rotation-mode;and 2) vectoring-mode for circular and hyperbolic trajectories. On-board FPGA implementation of the proposed architecture consumes 34.95% less register, 48.12% less occupied slices, 19.06% less LUTs, 66.51% less slice-latency product (SLP), 59.29% less LUT-latency product (LLP), and 71.23% less register-latency product (RLP) compared to the best existing state-of-the-art CORDIC architectures. Moreover, the latency of the proposed architecture is 72.34% less than the best of existing designs. Besides, the parasitic-extracted layout simulation results of the proposed design across different word lengths are reported in TSMC 65-nm CMOS technology for the validation of the proposed architecture.

关键词： Computer architecture Accuracy Hardware Trajectory Convergence Taylor series Pipelines Complexity theory Table lookup signal processing algorithms Coordinate rotation digital computer (CORDIC) reconfigurable CORDIC FPGA ASIC

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A Reliable Offloaded processing GNSS Receiver for Low-Power and Low-Accuracy Scenarios

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IEEE SENSORS JOURNAL 2025年第7期25卷 11532-11544页

作者： Su, Yu Jin, Tian Ju, Yi Beihang Univ Sch Elect & Informat Engn Beijing 100083 Peoples R China

Under the energy consumption constraints of mobile IoT terminals, global navigation satellite system (GNSS)-based tracking devices encounter significant challenges. Offloaded processing can offer a viable solution for reducing the energy consumption of GNSS tracking devices. This approach reduces the complexity of signal processing and utilizes remote servers for positioning. The signal tracking stage in the terminal is computationally intensive, and forwarding large amounts of raw data to the processing server consumes much power. To overcome these challenges, an offloaded processing GNSS receiver based on coarse acquisition results is introduced. Additionally, to improve the robustness of the measurements and the reliability of positioning, a fault detection and exclusion (FDE) algorithm based on geometric consistency and factor subset combination is proposed. The vehicle experiments demonstrate that the proposed algorithm can effectively detect and exclude faulty reconstructed pseudorange measurements by using a small number of subsets and can achieve a horizontal positioning accuracy of 33.17 m with 10 ms snapshot data.

关键词： Global navigation satellite system Receivers signal processing algorithms Servers Sensors Satellites Accuracy Internet of Things Energy consumption Codes Factor subset fault detection and exclusion (FDE) geometric consistency global navigation satellite system (GNSS)-based tracking offloaded processing GNSS receiver

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Near-Field High-Speed User Sensing in Wideband mmWave Communications: algorithms and Bounds

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 919-935页

作者： Miao, Hongxia Peng, Mugen Beijing Univ Posts & Telecommun Sch Informat & Commun Engn State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Integrated sensing and communications (ISAC) has been expected to be a key technique in the sixth-generation cellular networks. With the increase of carrier frequency (to millimeter-wave or Terahertz spectrum) and antenna array size (to extremely large-scale antenna) in wireless communications, the near-field area is enlarged and cannot be ignored. Accordingly, the channel model and its estimation algorithms are changed, which bring new chances in ISAC. However, the impact of both Doppler and spatial wideband effects, caused by high mobility and multicarriers, on sensing performance using communication signals is not well studied. In this study, these two effects are shown to be helpful in user sensing. First, the channel model is proposed for a high-speed moving user transmitting an orthogonal frequency division multiplex (OFDM) signal, where there are six unknown parameters. Then, the Cramer-Rao lower bounds (CRLB) for joint six parameter estimation is determined, where the impact of the near-field parameter and the velocity on the CRLB of positioning are discussed and quantified. Further, to compensate for the deficiency that the CRLB is tight only in high signal-to-noise-ratio (SNR) scenarios, we derive the Ziv-Zakai bound (ZZB) for positioning by exploiting the prior information on positioning parameters. Subsequently, a joint position and velocity parameter estimation algorithm is designed by first performing a discrete fractional Fourier transform on the received signal to obtain a coarse estimation and then refining it by Newton-based refinement. Numerical results coincide with our analysis.

关键词： OFDM Estimation signal processing algorithms Channel estimation Chirp Frequency estimation Training signal to noise ratio Sensors Matching pursuit algorithms Near-field ISAC high-speed moving CRLB ZZB

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