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MPHR: A Robust Algorithm for Estimating Nodal Head in Water Networks

IEEE signal processing LETTERS 2025年 32卷 856-860页

作者： Maggang, Amin A. Tay, David B. Gong, Jinzhe Josey, Brendan M. Deakin Univ Sch IT Geelong Vic 3220 Australia Deakin Univ Sch Engn Geelong Vic 3220 Australia

The monitoring of the pressure, in terms of nodal head values, is an important task in water distribution networks, to ascertain the integrity of the components, e.g. pipes. The placement of sensors at desired locations, in realistic situations, is a challenging problem due to various practical constraints. In practice, nodal heads are estimated at locations with no sensors. In this work, we propose an algorithm, termed mean percolation head reconstruction (MPHR), for estimating the nodal heads. The iterative algorithm is inspired by concepts from graph signal processing (GSP), and consists of two stages. The first stage is equivalent to the percolation of known values across the network, and the second stage performs some form of constrained consensus computations. The algorithm is robust against uncertainty in the network parameters and suboptimal placement of sensors. Extensive simulations have demonstrated the superiority of MPHR compared to other proposed reconstruction algorithms.

关键词： signal processing algorithms Sensors Magnetic heads Pressure sensors Head Distribution networks Vectors Uncertainty Training signal processing Graph signal processing (GSP) graph signal reconstruction nodal head estimation water distribution network

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Windowed Quantum Phase Estimation: signal processing Approach to a Quantum Algorithm

Windowed Quantum Phase Estimation: Signal Processing Approac...

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

作者： Xue Wen Samsung Research China Beijing

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Quantum phase estimation (QPE) is a key ingredient to some of the most important results of quantum computing. In this paper we examine the QPE algorithm through the looking glass of signal processing. This perspective helps to reveal a relation between the distribution of quantum phase estimates and the shape of the discrete Fourier transform (DFT) of a pure complex sinusoid. Guided by spectral shaping theory, we design a class of quantum circuits that are the quantum equivalents to classical windowing operators, for a family of window functions that have compact support in the DFT. These circuits have complexity linear in the number of target qubits, strictly lower than QPE itself. Using these circuits we are able to reshape QPE's performance characteristics. In particular, the asymptotic marginal cost for 2x reduction in fail rate (2x improvement in reliability) is reduced from one qubit to 1/3 qubit or lower, which can help reduce the qubit budget for a preset accuracy-reliability target, especially in high-reliability scenarios. This makes a demonstration of the value of signal processing principles in designing quantum algorithms.

关键词： Quantum algorithm Shape Qubit Discrete Fourier transforms Phase estimation signal processing algorithms Quantum mechanics signal processing Windows Speech processing

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Fitting Multiple Machine Learning Models With Performance Based Clustering

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IEEE signal processing LETTERS 2025年 32卷 816-820页

作者： Lorasdagi, Mehmet E. Koc, Ahmet B. Koc, Ali T. Kozat, Suleyman S. Bilkent Univ Dept Elect & Elect Engn TR-06800 Ankara Turkiye

Traditional machine learning approaches assume that data comes from a single generating mechanism, which may not hold for most real life data. In these cases, the single mechanism assumption can result in suboptimal performance. We introduce a clustering framework that eliminates this assumption by grouping the data according to the relations between the features and the target values, and we obtain multiple separate models to learn different parts of the data. We further extend our framework to applications having streaming data where we produce outcomes using an ensemble of models. For this, the ensemble weights are updated based on the incoming data batches. We demonstrate the performance of our approach over the widely-studied real life datasets, showing significant improvements over the traditional single-model approaches.

关键词： Data models Clustering algorithms Vectors signal processing algorithms Wind turbines Training Predictive models Data mining Convergence Wind speed Clustering expectation-maximization streaming data ensemble methods

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Simple One-Step Multi-View Clustering With Fast Similarity and Cluster Structure Learning

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IEEE signal processing LETTERS 2025年 32卷 1850-1854页

作者： Xia, Xianxian Huang, Dong Yang, Chen-Min He, Chaobo Wang, Chang-Dong South China Agr Univ Coll Math & Informat Guangzhou 510642 Peoples R China South China Normal Univ Sch Comp Sci Guangzhou 510635 Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Guangdong Key Lab Big Data Anal & Proc Guangzhou 510006 Peoples R China

Multi-view clustering (MVC) is essential for integrating heterogeneous data from multiple sources. However, many existing approaches are hindered by high computational complexity and the separate optimization of similarity and cluster structures. In light of these challenges, this paper presents a novel anchor-based MVC method termed simple one-step multi-view clustering with fast similarity and cluster structure learning (SONIC), which models adaptive anchor learning, multi-view similarity structure learning, and discrete cluster structure learning in a joint framework. In particular, we employ the anchor-based multi-view similarity learning to capture the consensus manifold structure latent in multiple views, thereby constructing a unified bipartite graph with adaptive anchor learning and view weighting. Then we impose a low-rank constraint on the bipartite graph structure to directly reveal the desired number of clusters without additional post-processing. An efficient alternating minimization algorithm is developed to optimize the model, resulting in a computational complexity that scales linearly with the number of samples. Extensive experiments on eight benchmark datasets demonstrate the superior performance of SONIC in both clustering quality and computational efficiency.

关键词： Optimization Bipartite graph Similarity learning Linear programming Adaptation models Vectors signal processing algorithms Laplace equations Clustering algorithms Training Consensus information data clustering lare-scale clustering linear time multi-view clustering

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Switching Games for Image Compression

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IEEE signal processing LETTERS 2025年 32卷 1016-1020页

作者： Huhtanen, Marko Univ Oulu Fac Informat Technol & Elect Engn Oulu 090570 Finland

To compress an image, a technique based on optimal scalings with diagonal matrices is described. To start the process, an initial image of high compression ratio is required. Such an image can be produced, for example, with the 2D FFT or 2D DCT of the original image. Principal component analysis is a special case of this compression technique where the initial image is extremely rough, consisting of the first 2D Fourier basis function only. This initial image is then optimally orthogonalized and expanded by iteratively applying diagonal matrices from the left and right to attain double orthogonality. The process can be viewed as a continuous version of Berlekamp's switching game.

关键词： Image coding Switches Principal component analysis Discrete cosine transforms Games Eigenvalues and eigenfunctions Approximation algorithms Solids signal processing algorithms Matrix decomposition Lossy compression double orthogonality principal component analysis 2D FFT 2D DCT

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Stochastic Analysis of FxLMS Algorithm for Feedback Active Noise Control

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IEEE signal processing LETTERS 2025年 32卷 416-420页

作者： Wang, Cong Wu, Ming Zhou, Shuang Yang, Jun Chinese Acad Sci Inst Acoust Key Lab Noise & Vibrat Res Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Feedback active noise control (ANC) systems are effective in reducing predictable noise, e.g. periodic, narrowband and colored noise. There are still few studies on the theoretical analysis of feedback ANC systems, and are limited to idealized signals such as sinusoidal or Gaussian signals. This paper presents the stochastic analysis of a feedback ANC system based on the filtered-x least mean square (FxLMS) algorithm, which is not relying on a specific noise model and perfect secondary path. The equations for the mean and mean-square convergence behavior are derived. Extensive simulations of sinusoidal, band-limited white noise, and hybrid signals illustrate the accuracy of the analysis.

关键词： Noise Vectors Mathematical models Convergence Delays Adaptive filters Stochastic processes signal processing algorithms Feedforward systems White noise Feedback active noise control (ANC) FxLMS steady-state and transient behavior stochastic analysis

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A New Iterative Weighted Least Squares Algorithm for 1-D SA Localization

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IEEE signal processing LETTERS 2025年 32卷 1740-1744页

作者： Zou, Yanbin Xiao, Yangpeng Wu, Wenbo Sun, Yimao Liu, Huaping Shantou Univ Dept Elect & Informat Engn Shantou 515063 Peoples R China Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China Oregon State Univ Sch Elect Engn & Comp Sci Corvallis OR 97331 USA

Three-dimensional (3-D) target localization using one-dimensional (1-D) space angle (SA) measurements from linear arrays has recently gained significant attention. Each 1-D SA measurement defines a conical surface, and the intersection of multiple such surfaces determines the target's location in 3-D space. However, state-of-the-art methods for solving the 1-D SA localization problem are often either suboptimal or computationally intensive. In this paper, we propose a novel iterative weighted least squares (IWLS) algorithm to address the problem. To provide deeper insights, we present a geometric interpretation of the iterative process, highlighting its physical significance. Furthermore, we analyze the computational complexity of the proposed algorithm and compare it with existing methods. Simulation results demonstrate that the proposed algorithm not only achieves higher estimation accuracy but also requires less computational time compared to state-of-the-art approaches.

关键词： signal processing algorithms Location awareness Three-dimensional displays Vectors Sun Noise measurement Iterative algorithms Convergence Planar arrays Training One-dimensional space angle (1-D SA) iterative weighted least squares (IWLS)

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Multi-Target Association for UAVs Based on Fused Topology and Visual Features

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IEEE signal processing LETTERS 2025年 32卷 1755-1759页

作者： Li, Xudong Wu, Lizhen Niu, Yifeng Yuan, Man Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha 410073 Peoples R China

In this letter, we propose a novel multi-target association algorithm that fuses topological and visual features, and this algorithm improves target discrimination. The fused features are first constructed, and then a similarity calculation method is proposed to aggregate neighbor similarities to the central node based on topological similarity, which can be iterated several times to enhance the differentiation between targets. The experimental results demonstrate the advantages of the proposed algorithm.

关键词： Feature extraction Visualization Topology Autonomous aerial vehicles Vectors signal processing algorithms Fuses Convolutional neural networks Training Target recognition Feature fusion topology features multi-target association UAV

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Robust Multitask Diffusion Bias Compensation M-Estimate algorithms for Distributed Adaptive Learning With Noisy Input

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IEEE signal processing LETTERS 2025年 32卷 1146-1150页

作者： Peng, Senran Jia, Lijuan Yang, Zi-Jiang Tao, Ran Wang, Yue Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Ibaraki Univ Coll Engn Dept Mech Syst Engn Ibaraki 3168511 Japan

This letter studies the issue of robust multitask distributed estimation under the error-in-variable (EIV) model where input noise and output impulsive noise are considered. In such cases, existing distributed algorithms suffer from severe performance degradation. To tackle this problem, a robust multitask diffusion bias-compensated least mean M-estimate (R-MD-BCLMM) is proposed. We adopt a new real-time input noise variance estimation method which utilizes piecewise linearity of the modified Huber function to resist input noises. To further improve network information exchange capability and estimation performance, a robust spatial average combination based multitask adaptive clustering strategy is proposed. Finally, simulations demonstrate that the proposed R-MD-BCLMM algorithm outperforms some state-of-the-art distributed algorithms.

关键词： Noise Estimation signal processing algorithms Clustering algorithms Noise measurement Vectors Real-time systems Cost function Adaptation models Modeling Multitask networks robust M-estimate bias compensation spatial average combination

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Optimizing a-DCF for Spoofing-Robust Speaker Verification

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IEEE signal processing LETTERS 2025年 32卷 1081-1085页

作者： Kurnaz, Oguzhan Mishra, Jagabandhu Kinnunen, Tomi H. Hanilci, Cemal Univ Eastern Finland Joensuu 80130 Finland Bursa Tech Univ TR-16310 Bursa Turkiye

Automatic speaker verification (ASV) systems are vulnerable to spoofing attacks. We propose a spoofing-robust ASV system optimized directly for the recently introduced architecture-agnostic detection cost function (a-DCF), which allows targeting a desired trade-off between the contradicting aims of user convenience and robustness to spoofing. We combine a-DCF and binary cross-entropy (BCE) with a novel straightforward threshold optimization technique. Our results with an embedding fusion system on ASVspoof2019 data demonstrate relative improvement of 13% over a system trained using BCE only (from minimum a-DCF of 0.1445 to 0.1254). Using an alternative non-linear score fusion approach provides relative improvement of 43% (from minimum a-DCF of 0.0508 to 0.0289).

关键词： Measurement Costs Training Cost function signal processing algorithms Error analysis Security Robustness Computer architecture Training data a-DCF spoofing-robust speaker verification

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