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Multivariate Fast Iterative filtering for the Decomposition of Nonstationary Signals

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2022年 70卷 1521-1531页

作者： Cicone, Antonio Pellegrino, Enza Univ Aquila DISIM I-67100 Laquila Italy INAF Ist Astrofis & Planetol Spaziali I-00133 Rome Italy Ist Nazl Geofis & Vulcanol I-00143 Rome Italy Univ Aquila DIIIE I-67040 Laquila Italy

In this work, we present a new technique for the decomposition of multivariate data, which we call Multivariate Fast Iterative filtering (MvFIF) algorithm. We study its properties, proving rigorously that it converges in finite time when applied to the decomposition of any kind of multivariate signal. We test MvFIF performance using a wide variety of artificial and real multivariate signals, showing its ability to: separate multivariate modulated oscillations;align frequencies along different channels;produce a quasi-dyadic filterbank when decomposing white Gaussian noise;decompose the signal in a quasi-orthogonal set of components;being robust to noise perturbation, even when the number of channels is increased considerably. Finally, we compare it and its performance with the main methods developed so far in the literature, proving that MvFIF produces, without any a priori assumption on the signal under investigation and in a fast and reliable manner, a uniquely defined decomposition of any multivariate signal.

关键词： Signal processing algorithms filtering Iterative algorithms Optimization Frequency modulation Convergence Wavelet transforms Adaptive signal processing filtering algorithms array signal processing iterative methods

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SoK: Applications of Sketches and Rollups in Blockchain Networks

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2024年第3期21卷 3194-3208页

作者： Kotzer, Arad Gandelman, Daniel Rottenstreich, Ori Technion Israel Inst Technol IL-3200003 H_efa Israel Technion Israel Inst Technol Taub Dept Comp Sci IL-3200003 Haifa Israel Florida State Univ Dept Comp Sci Tallahassee FL USA Technion Israel Inst Technol Viterbi Dept Elect & Comp Engn H_efa Israel

Blockchain networks suffer from a critical scalability problem that is often expressed in two dimensions: the size of the network state and the computational overhead in processing transactions as part of the network update. This paper surveys two major families of solutions known as sketches and rollups that both rely on aggregation methods. Sketches are popular hash-based data structures used to represent a large amount of data while supporting particular queries such as on set membership, cardinality estimation and identification of large elements. Rollups play in a different form and refer to aggregation of the execution of multiple transactions to allow high transaction rates and low fees. The design of popular blockchain networks such as Bitcoin and Ethereum makes use of sketches for various tasks such as summarization of transaction blocks or declaring the interests of light nodes. In addition, rollups are implemented in several forms to allow high transaction rates through offloading parts of the execution out of the blockchain while keeping its security. This paper overviews the basics of sketches and rollups and provides a comprehensive survey on the wide range of existing applications of the two families in blockchains.

关键词： Blockchains Task analysis Fingerprint recognition filtering algorithms Security Scalability Bitcoin Blockchain scalability sketches bloom filter rollups

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SlidingConv: Domain-Specific Description of Sliding Discrete Cosine Transform Convolution for Halide

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IEEE ACCESS 2024年 12卷 7563-7583页

作者： Kanetaka, Yamato Takagi, Hiroyasu Maeda, Yoshihiro Fukushima, Norishige Nagoya Inst Technol Fac Engn Dept Civil Engn Showa Ku Nagoya Aichi 4668555 Japan Yamaha Corp Hamamatsu Shizuoka 4300904 Japan Tokyo Univ Sci Fac Engn Dept Elect Engn NiijukuKatsushika Ku Tokyo 1258585 Japan

filtering is a fundamental tool in image processing, and its acceleration affects many applications. Therefore, various algorithmic and hardware accelerations have been proposed for filtering. Recursive processing using infinite impulse response (IIR) filtering is an efficient algorithm, and various hardware acceleration methods have been applied to IIR filtering. In addition, a domain-specific language (DSL) of RecFilter was proposed to generate efficient IIR code for various hardware applications as an extension of image processing language, Halide. Recursive filters based on sliding discrete cosine transform (SDCT) have been the most efficient approximations in recent years. For hardware acceleration, parallelization of recursive filters is challenging. One of the most efficient methods is tile-based parallelization. However, even if a function is optimized and modularized, it is not sufficiently optimized for applications where various pre/post-processing steps are coupled before and after filtering. Additionally, multiplatform deployment requires reimplementation of the code. In this study, we extended Halide for SDCT convolutions to realize efficient computing of image processing applications with filtering, named SlidingConv. The experimental results showed that SlidingConv is faster than the hand-tuned CPU code and 1/1900 of the hand-tuned code length, running more efficiently than de facto libraries like OpenCV. To verify its efficiency, we deployed the code on various hardware (x86/64 CPU with AVX2/AVX-512, ARM CPU, and GPU). In addition, we verified that the proposed method can accelerate image processing with pre/post-processing for filtering. Our code is available at https://***/SlidingConv/.

关键词： filtering IIR filters Finite impulse response filters Kernel Discrete cosine transforms filtering algorithms Image processing parallel recursive filtering sliding DCT domain-specific language Halide

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Set-Type Belief Propagation With Applications to Poisson Multi-Bernoulli SLAM

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 1989-2005页

作者： Kim, Hyowon Garcia-Fernandez, Angel F. Ge, Yu Xia, Yuxuan Svensson, Lennart Wymeersch, Henk Chungnam Natl Univ Dept Elect Engn Daejeon 34134 South Korea Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ England Univ Antonio Nebrija ARIES Res Ctr Madrid 28015 Spain Chalmers Univ Technol Dept Elect Engn S-41258 Gothenburg Sweden Linkoping Univ Dept Elect Engn S-58183 Linkoping Sweden

Belief propagation (BP) is a useful probabilistic inference algorithm for efficiently computing approximate marginal probability densities of random variables. However, in its standard form, BP is only applicable to the vector-type random variables with a fixed and known number of vector elements, while certain applications rely on random finite sets (RFSs) with an unknown number of vector elements. In this paper, we develop BP rules for factor graphs defined on sequences of RFSs where each RFS has an unknown number of elements, with the intention of deriving novel inference methods for RFSs. Furthermore, we show that vector-type BP is a special case of set-type BP, where each RFS follows the Bernoulli process. To demonstrate the validity of developed set-type BP, we apply it to the Poisson multi-Bernoulli (PMB) filter for simultaneous localization and mapping (SLAM), which naturally leads to a set-type BP PMB-SLAM method, which is analogous to a vector type SLAM method, subject to minor modifications.

关键词： Vectors Simultaneous localization and mapping filtering algorithms Radio frequency filtering theory Target tracking Random variables Belief propagation multi-target tracking Poisson multi-Bernoulli filter random finite sets simultaneous localization and mapping

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Modified Huber M-Estimate Function-Based Distributed Constrained Adaptive filtering Algorithm Over Sensor Network

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IEEE SENSORS JOURNAL 2022年第20期22卷 19567-19582页

作者： Xu, Wenjing Zhao, Haiquan Zhou, Lijun Southwest Jiaotong Univ Key Lab Magnet Suspens Technol & Maglev Vehicle Minist Educ Chengdu 610031 Peoples R China Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China

Most of the existing distributed adaptive filtering algorithms over wireless sensor networks (WSNs) are developed, aiming to solve unconstrained network optimization problems. However, in practice, the weight coefficients of the filter may need to satisfy a set of linear equations. Thus, a distributed adaptive algorithm that can solve the sensor network optimization problem under constraints is needed. Considering the possible impulsive interference in the observed signals, a novel robust distributed constrained adaptive algorithm called diffusion constrained least mean M-estimate (D-CLMM) is proposed by using the modified Huber function (MHF), which endows the network robustness to impulsive noise. The transient, steady-state performances and stability of the proposed D-CLMM are studied with the aid of some commonly used assumptions and verified by computer simulations. Moreover, the effectiveness of D-CLMM is verified in distributed parameter estimation and beamforming applications in non-Gaussian noise environments.

关键词： Sensors Optimization Wireless sensor networks Adaptive algorithms filtering algorithms Adaptive systems Robustness Distributed constrained adaptive filtering M-estimate non-Gaussian noise performance analysis robust algorithm wireless sensor network (WSN)

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Extended Target Tracking Utilizing Machine-Learning Software-With Applications to Animal Classification

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IEEE SIGNAL PROCESSING LETTERS 2024年 31卷 376-380页

作者： Malmstrom, Magnus Kullberg, Anton Skog, Isaac Axehill, Daniel Gustafsson, Fredrik Linkoping Univ S-58183 Linkoping Sweden Swedish Def Res Agcy FOI S-58183 Linkoping Sweden Uppsala Univ S-75310 Uppsala Sweden

This letter considers the problem of detecting and tracking objects in a sequence of images. The problem is formulated in a filtering framework, using the output of object-detection algorithms as measurements. An extension to the filtering formulation is proposed that incorporates class information from the previous frame to robustify the classification. Further, the properties of the object-detection algorithm are exploited to quantify the uncertainty of the bounding box detection in each frame. The complete filtering method is evaluated on camera trap images of the four large Swedish carnivores, bear, lynx, wolf, and wolverine. The experiments show that the class tracking formulation leads to a more robust classification.

关键词： Signal processing algorithms Classification algorithms Cameras Target tracking filtering algorithms Standards Loss measurement Multi-object tracking object detection environmental monitoring deep learning Kalman filters

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A Pre-Processing Signal Modeling Approach for Multichannel ATI Digitizers

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Mei, Sitao Zhao, Yijiu Shao, E. Wang, Yusen Zhang, Yi Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 518110 Peoples R China

Asynchronous time-interleaved (ATI) analog-to-digital converter (ADC) architecture utilizes pre-processing, low-pass filtering, and ADCs to simultaneously enhance the sampling rate and bandwidth of the acquisition system. We propose a novel approach to generate pre-processing signal in multichannel ATI architecture. The generated pre-processing signal is no longer limited to pulse wave or square wave with a fixed duty. We derive the condition for reconstructing input signal as the invertibility of the pre-processing matrix. Additionally, we analyzed the error sources in the multichannel ATI. Based on the spectral shift characteristics of the ATI, we propose an error coefficient estimation method. Using the estimated error coefficient, a digital filter can be designed to calibrate the system. A four-channel hardware prototype was developed to evaluate the performance of the proposed method, satisfactory results were obtained and the feasibility of the proposed approach was demonstrated.

关键词： Low-pass filters Calibration Mixers Estimation Frequency response filtering algorithms Time-frequency analysis Signal processing algorithms Hardware Fourier series Analog-to-digital converters (ADCs) asynchronous time-interleaved (ATI) frequency-interleaved (FI) wideband

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Kalman Filter Auto-Tuning With Consistent and Robust Bayesian Optimization

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第2期60卷 2236-2250页

作者： Chen, Zhaozhong Biggie, Harel Ahmed, Nisar Julier, Simon Heckman, Christoffer Univ Colorado Boulder Dept Comp Sci Boulder CO 80309 USA Univ Colorado Boulder Dept Smead Aerosp Engn Sci Boulder CO 80309 USA UCL Dept Comp Sci London WC1E 6BT England

The nonlinear and stochastic relationship between noise covariance parameter values and state estimator performance makes optimal filter tuning a very challenging problem. Popular optimization-based tuning approaches can easily get trapped in local minima, leading to poor noise parameter identification and suboptimal state estimation. Recently, black box techniques based on Bayesian optimization with Gaussian processes (GPBO) have been shown to overcome many of these issues, using normalized estimation error squared and normalized innovation error statistics to derive cost functions for Kalman filter auto-tuning. While reliable noise parameter estimates are obtained in many cases, GPBO solutions obtained with these conventional cost functions do not always converge to optimal filter noise parameters and lack robustness to parameter ambiguities in time-discretized system models. This article addresses these issues by making two main contributions. First, new cost functions are developed to determine if an estimator has been tuned correctly. It is shown that traditional chi-square tests are inadequate for correct auto-tuning because they do not accurately model the distribution of innovations when the estimator is incorrectly tuned. Second, the new metrics (formulated over multiple time discretization intervals) is combined with a student-t processes Bayesian optimization to achieve robust estimator performance for time discretized state space models. The robustness, accuracy, and reliability of our approach are illustrated on classical state estimation problems.

关键词： Tuning Kalman filters Bayes methods filtering algorithms Noise measurement Technological innovation Nonlinear filters Bayesian optimization (BO) filter tuning Kalman filter nonparameteric regression

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A Two-Step Descriptor-Based Keypoint filtering Algorithm for Robust Image Matching

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷 1页

作者： Mousavi, Vahid Varshosaz, Masood Remondino, Fabio Pirasteh, Saied Li, Jonathan Southwest Jiaotong Univ Fac Geosci & Environm Engn Chengdu 610031 Sichuan Peoples R China KN Toosi Univ Technol Geomat Engn Fac Dept Photogrammetry & Remote Sensing Tehran *** Iran Bruno Kessler Fdn Opt Metrol Unit 3D I-38123 Trento Italy Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Geotech & Geomat Chennai 602105 Tamil Nadu India Univ Waterloo Dept Geog & Environm Management Waterloo ON N2L 3G1 Canada

Finding robust and correct keypoints in images remains a challenge, especially when repetitive patterns are present. In this article, we propose a universal two-step filtering method to solve the mismatch problem in repetitive patterns. Having applied a mean-shift clustering algorithm to remove obvious mismatches, the proposed confusion reduction (CR) method uses a novel confusion index (CI) in a gridding schema to identify and filter out the remaining confusing keypoints. In both steps, the descriptors' statistical properties are evaluated using kernel density estimation. Various synthetic and real stereo pairs, along with multiview image blocks, were used to assess the performance of the presented algorithm. The results were also compared with those obtained by several state-of-the-art mismatch removal methods. The experiments showed that, on average, the proposed strategy improves the accuracy of matching by 10% and the accuracy of photogrammetric blocks by 20%-30%.

关键词： Indexes Feature extraction Estimation Clustering algorithms Kernel filtering algorithms Detectors Descriptor image matching kernel density estimation keypoint filtering mean shift

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Semiglobal Safety-Filtered Extremum Seeking With Unknown CBFs

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2025年第3期70卷 1698-1713页

作者： Williams, Alan Krstic, Miroslav Scheinker, Alexander Univ Calif San Diego Dept Mech & Aerosp Engn San Diego CA 92093 USA Los Alamos Natl Lab Accelerator Operat & Technol Appl Electrodynam AOT Los Alamos NM 87545 USA

We introduce a safe extremum-seeking algorithm that achieves the minimization of an unknown objective function while ensuring that an unknown, yet measured, control barrier function (CBF) remains above an arbitrarily small negative value for all time. In other words, "practical safety" is maintained during the entire period of convergence to the constrained extremum. Our design is based on quadratic program (QP) CBF style filters for safety, which is applied in an average and estimated sense. Using nonsmooth analysis tools, we guarantee semiglobal practical asymptotic (SPA) stability of the global constrained optimum, practical convergence to the safe set if starting in a condition violating the CBF, and practical safety for all time-semiglobally-if starting in safe set. The safety result of this article is analogous with modern notions of SPA stability, guaranteeing that, for any small violation of safety, there exist design coefficients that guarantee that such a small violation is not exceeded. This article outlines a set of sufficient conditions on the barrier and objective functions, and by way of a Lyapunov argument, we demonstrate that nonconvex constrained optimization problems can be solved. We present these results in the setting of a static map and a dynamical system. A simulation example illustrates the results.

关键词： Safety Heuristic algorithms Asymptotic stability Optimization filtering algorithms Thermal stability Switches Convergence Approximation algorithms Stability criteria Constrained optimization extremum seeking (ES) safe control

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