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Shockwave Signal Downsampling Rate Acquisition Based on Sparse Fourier Transform

IEEE ACCESS 2022年 10卷 44076-44087页

作者： Xu, Bo Zhang, Zhi Xie, Zhenglin Han, Tailin Liu, Hong Liu, Xuan Ju, Mingchi Changchun Univ Sci & Technol Sch Elect & Informat Engn Changchun 130022 Peoples R China Changchun Univ Sci & Technol Sch Optoelect Engn Changchun 130022 Peoples R China Changchun Univ Sci & Technol Changchun 130022 Peoples R China

The wireless distributed transient shockwave testing system based on Nyquist sampling needs to keep high sampling rate and front-end data processing rate, which leads to the shortening of the life cycle of wireless nodes. In order to solve the above problems, the shockwave signal acquisition technology based on Sparse Fourier Transform is proposed in this paper. The specific method is to use the sparse characteristics of the frequency domain of the transient shockwave signal to collect the frequency domain by downsampling, reduce the amount of acquired data. The simulation results of the above experiments are compared with the simulation results of Compressed Sensing low sampling observation. The experimental results show that the time-domain acquisition accuracy error of the proposed framework is less than 10(-4) when the Nyquist sampling rate is less than 8 times, and the mean relative error in time domain is only 0.01%. And compared with the acquisition framework based on Compressed Sensing theory, the reconstructed mean absolute error of the proposed method is reduced by 88.66% in the case of the same downsampling multiple. Moreover, this method does not need to store the high-dimensional random observation matrix similar to Compressed Sensing, which overcomes the defect of hardware implementation difficulty.

关键词： Frequency-domain analysis Signal processing algorithms approximation algorithms Wireless sensor networks Wireless communication Transient analysis Sparse matrices Wireless nodes transient shockwave signal sparse fourier transform compressed sensing downsampling

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Nuisance Parameter Estimation algorithms for Separable Nonlinear Models

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第11期52卷 7236-7247页

作者： Chen, Long Chen, Jia-Bing Chen, Guang-Yong Gan, Min Chen, C. L. Philip Univ Macau Fac Sci & Technol Macau Peoples R China Fujian Med Univ Union Hosp Dept Gastroenterol Fuzhou 350001 Peoples R China Fuzhou Univ Coll Comp & Data Sci Fuzhou 350116 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China

Many inverse problems in machine learning, system identification, and image processing include nuisance parameters, which are important for the recovering of other parameters. Separable nonlinear optimization problems fall into this category. The special separable structure in these problems has inspired several efficient optimization strategies. A well-known method is the variable projection (VP) that projects out a subset of the estimated parameters, resulting in a reduced problem that includes fewer parameters. The expectation maximization (EM) is another separated method that provides a powerful framework for the estimation of nuisance parameters. The relationships between EM and VP were ignored in previous studies, though they deal with a part of parameters in a similar way. In this article, we explore the internal relationships and differences between VP and EM. Unlike the algorithms that separate the parameters directly, the hierarchical identification algorithm decomposes a complex model into several linked submodels and identifies the corresponding parameters. Therefore, this article also studies the difference and connection between the hierarchical algorithm and the parameter-separated algorithms like VP and EM. In the numerical simulation part, Monte Carlo experiments are performed to further compare the performance of different algorithms. The results show that the VP algorithm usually converges faster than the other two algorithms and is more robust to the initial point of the parameters.

关键词： Jacobian matrices Autoregressive processes Convergence Monte Carlo methods Inverse problems Ear approximation algorithms Expectation-maximization (EM) algorithm hierarchical identification algorithm separable nonlinear optimization problem variable projection (VP) algorithm

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iPool--Information-Based Pooling in Hierarchical Graph Neural Networks

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第9期33卷 5032-5044页

作者： Gao, Xing Dai, Wenrui Li, Chenglin Xiong, Hongkai Frossard, Pascal Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China Ecole Polytech Fed Lausanne EPFL Signal Proc Lab LTS4 CH-1015 Lausanne Switzerland

With the advent of data science, the analysis of network or graph data has become a very timely research problem. A variety of recent works have been proposed to generalize neural networks to graphs, either from a spectral graph theory or a spatial perspective. The majority of these works, however, focus on adapting the convolution operator to graph representation. At the same time, the pooling operator also plays an important role in distilling multiscale and hierarchical representations, but it has been mostly overlooked so far. In this article, we propose a parameter-free pooling operator, called iPool, that permits to retain the most informative features in arbitrary graphs. With the argument that informative nodes dominantly characterize graph signals, we propose a criterion to evaluate the amount of information of each node given its neighbors and theoretically demonstrate its relationship to neighborhood conditional entropy. This new criterion determines how nodes are selected and coarsened graphs are constructed in the pooling layer. The resulting hierarchical structure yields an effective isomorphism-invariant representation of networked data on arbitrary topologies. The proposed strategy achieves superior or competitive performance in graph classification on a collection of public graph benchmark data sets and superpixel-induced image graph data sets.

关键词： Convolution Clustering algorithms approximation algorithms Topology Computer architecture Task analysis Network topology Graph classification graph neural networks (GNNs) graph pooling hierarchical representation

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A Multiresolution Approach for Large Real-World Camera Placement Optimization Problems

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IEEE ACCESS 2022年 10卷 61601-61616页

作者： Puligandla, V. Anirudh Loncaric, Sven Univ Zagreb Fac Elect Engn & Comp Dept Elect Syst & Informat Proc Image Proc Grp Zagreb 10000 Croatia

There have been numerous attempts at solving the optimal camera placement problem across multiple applications. Exact linear programming-based, as well as, heuristic combinatorial optimization methods were shown to be successful in providing optimal or near-optimal solutions to this problem. Working over a discrete space model is the general practice when solving the camera placement problem. However, discretized environments often limit the methods' usage only to small-scale datasets due to resource and time constraints that grow exponentially with the number of 3D points collected from the discrete space. We propose a multi-resolution approach that enables the usage of existing optimization algorithms on large real-world problems modelled using high resolution 3D grids. Our method works by grouping together the given discrete set of possible camera locations into clusters of points, multiple times, resulting in multiple resolution levels. Camera placement optimization is repeated for all resolution levels while propagating the optimized solution from low to high resolutions. Our experiments on both simulated and real data with grids of varying sizes show that using our multi-resolution approach, existing camera placement optimization methods can be used even on high resolution grids consisting of hundreds of thousands of points. Our results also show that the strategy of grouping points together by exploiting underlying 3D geometry to optimize camera poses is not only significantly faster than optimizing on the entire set of samples but, it also provides better camera coverage.

关键词： Cameras Three-dimensional displays Solid modeling Optimization methods approximation algorithms Image resolution Surveillance Image decomposition integer linear programming heuristic algorithms pareto optimization

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lp-Norm Multiway Cut

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ALGORITHMICA 2022年第9期84卷 2667-2701页

作者： Chandrasekaran, Karthekeyan Wang, Weihang Univ Illinois Champaign IL 61820 USA

We introduce and study l(p)-NORM-MULTIWAY-CUT: the input here is an undirected graph with non-negative edge weights along with k terminals and the goal is to find a partition of the vertex set into k parts each containing exactly one terminal so as to minimize the l(p)-norm of the cut values of the parts. This is a unified generalization of min-sum multiway cut (when p = 1) and min-max multiway cut (when p = infinity), both of which are well-studied classic problems in the graph partitioning literature. We show that l(p)-NORM-MULTIWAY-CUT is NP-hard for constant number of terminals and is NP-hard in planar graphs. On the algorithmic side, we design an O(log(1.5) n log(0.5) k)-approximation for all p >= 1. We also show an integrality gap of Omega(k(1-1/p)) for a natural convex program and an O(k(1-1)(/p-epsilon))-inapproximability for any constant epsilon > 0 assuming the small set expansion hypothesis.

关键词： Multiway cut approximation algorithms

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Achieving Online Regression Performance of LSTMs With Simple RNNs

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第12期33卷 7632-7643页

作者： Vural, N. Mert Ilhan, Fatih Yilmaz, Selim F. Ergut, Salih Kozat, Suleyman Serdar Bilkent Univ Dept Elect & Elect Engn TR-06800 Ankara Turkey Univ Toronto Dept Comp Sci Toronto ON M5S Canada DataBoss AS TR-06800 Ankara Turkey Rumeli Univ Dept Comp Engn TR-34570 Istanbul Turkey

Recurrent neural networks (RNNs) are widely used for online regression due to their ability to generalize nonlinear temporal dependencies. As an RNN model, long short-term memory networks (LSTMs) are commonly preferred in practice, as these networks are capable of learning long-term dependencies while avoiding the vanishing gradient problem. However, due to their large number of parameters, training LSTMs requires considerably longer training time compared to simple RNNs (SRNNs). In this article, we achieve the online regression performance of LSTMs with SRNNs efficiently. To this end, we introduce a first-order training algorithm with a linear time complexity in the number of parameters. We show that when SRNNs are trained with our algorithm, they provide very similar regression performance with the LSTMs in two to three times shorter training time. We provide strong theoretical analysis to support our experimental results by providing regret bounds on the convergence rate of our algorithm. Through an extensive set of experiments, we verify our theoretical work and demonstrate significant performance improvements of our algorithm with respect to LSTMs and the other state-of-the-art learning models.

关键词： Training Signal processing algorithms Recurrent neural networks Logic gates Convergence approximation algorithms Time complexity Neural network training online gradient descent online learning recurrent neural networks (RNNs) regression

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Triple-D: Denoising Distant Supervision for High-Quality Data Creation 40

Triple-D: Denoising Distant Supervision for High-Quality Dat...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Zhu, Xinyi Zhang, Yongqi Chen, Lei Chen, Kai Guangzhou China The Hong Kong University of Science and Technology Hong Kong Hong Kong

ISBN: (纸本)9798350317152

Distant supervision is a technique that aims to create large amounts of training data at a low cost. This approach benefits various downstream systems, particularly in natural language processing and relation extraction tasks. However, due to its strong assumption that any sentence containing entities expresses the specific relation between them found in existing knowledge bases (KBs), distant supervision introduces considerable noise. Existing works attempt to denoise distant supervision data by either using the original text or replacing entities in the text with patterns representing the entity types as inputs. However, replacing a frequently repeating pattern will result in loss of context due to the excessively general semantics of the pattern. Furthermore, due to the lack of ground truth, denoising module often relies on parametric models that still learn distribution from noisy data, which further limits model performance. In this paper, we propose Triple-d, a technique for high-quality data creation through adaptive pattern replacement and a scalable non-parametric model. Specifically, we formulate the adaptive pattern replacement task as a maximum-profit bipartite graph problem and propose an approximation algorithm as a solution. Additionally, we design a non-parametric model with scalable instance normalization to efficiently estimate and eliminate the influence of each dimension in neighbors. Extensive experiments in the denoising task and a downstream relation extraction task on real-world datasets demonstrate the superior effectiveness and efficiency of Triple-d, highlighting its potential to improve the performance for high-quality data creation. © 2024 IEEE.

关键词： approximation algorithms

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A Formal Characterization of Black-Box System Safety Performance With Scenario Sampling

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IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第1期7卷 199-206页

作者： Weng, Bowen Capito, Linda Ozguner, Umit Redmill, Keith Ohio State Univ Dept Elect & Comp Engn Columbus OH 43210 USA

A typical scenario-based evaluation framework seeks to characterize a black-box system's safety performance (e.g., failure rate) through repeatedly sampling initialization configurations (scenario sampling) and executing a certain test policy for scenario propagation (scenario testing) with the black-box system involved as the test subject. In this letter, we first present a novel safety evaluation criterion that seeks to characterize the actual operational domain within which the test subject would remain safe indefinitely with high probability. By formulating the black-box testing scenario as a dynamic system, we show that the presented problem is equivalent to finding a certain almost robustly forward invariant set for the given system. Second, for an arbitrary scenario testing strategy, we propose a scenario sampling algorithm that is provably asymptotically optimal in obtaining the safe invariant set with arbitrarily high accuracy. Moreover, as one considers different testing strategies (e.g., biased sampling of safety-critical cases), we show that the proposed algorithm still converges to the unbiased approximation of the safety characterization outcome if the scenario testing satisfies a certain condition. Finally, the effectiveness of the presented scenario sampling algorithms and various theoretical properties are demonstrated in a case study of the safety evaluation of a control barrier function-based mobile robot collision avoidance system.

关键词： Safety Uncertainty System testing Monte Carlo methods Dynamics approximation algorithms Unmanned aerial vehicles Robot safety probability and statistical methods black-box system scenario sampling invariant set

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Online Koopman Operator Learning to Identify Cross-Coupling Effect of Piezoelectric Tube Scanners in Atomic Force Microscopes

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2022年第2期18卷 1111-1120页

作者： Huang, Xiang Zhou, Wei Li, Xiuting Zhu, LiMin Zhang, Hai-Tao Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Huazhong Agr Univ Coll Sci Wuhan 430070 Peoples R China Shanghai Jiao Tong Univ Sch Mech Engn State Key Lab Mech Syst & Vibrat Shanghai 200240 Peoples R China Shanghai Key Lab Networked Mfg & Enterprise Infor Shanghai Peoples R China

As one of the most significant applications of nanopositioning technology, the tremendous development of atomic force microscopes (AFMs) has been witnessed these years. Essentially, the scanning motions of AFMs are generally driven by piezoelectric tube scanners (PTSs), whose cross-coupling effect hinders their high speed and high-precision positioning. Therefore, it becomes an urgent yet challenging mission to establish a niche model for the nonlinear cross-coupling dynamics of the PTS. As an emergent pure data-driven learning approach, the Koopman operator sheds some light on the PTS modeling methodology, which is thus adopted in this article to approximate the nonlinear cross-coupling dynamics of PTSs in an infinite-dimensional space. Moreover, an online high-order extended dynamic mode decomposition algorithm is proposed for the finite-dimensional approximation of the Koopman operator online. The merit of the present model lies in updating the identified cross coupling upon the arrival of new data in an incremental way. Finally, experiments are conducted to approximate the X-Y axes cross coupling of PTSs of an NTMDT Prima AFM, which verifies the effectiveness and superiority of the proposed modeling algorithm. This article is expected to pave the way from the Koopman operator learning theory to real applications in dynamics modeling of abundant nanoscale measurement systems.

关键词： Hysteresis Heuristic algorithms Couplings Nanoscale devices Dynamics approximation algorithms Force Atomic force microscope (AFM) cross coupling modeling nonlinearity piezoelectric tube scanner (PTS)

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A Truthful Double Auction Mechanism for Multi-Resource Allocation in Crowd Sensing Systems

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2022年第5期15卷 2579-2590页

作者： Liu, Xi Liu, Jun Qujing Normal Univ Sch Informat Engn Qujing 655011 Peoples R China Qujing Normal Univ Inst Appl Math Qujing 655011 Peoples R China

As a novel sensing paradigm, crowd sensing systems have gained great attention and been widely adopted in the environmental monitoring and calculation areas. In crowd sensing systems, mobile users provide their multiple resources to the requesters to execute tasks. Existing studies focus on the divisible task or one-to-one mapping for single resource allocation. However, this assumption does not hold for crowd sensing systems. Owing to the task attribute, some tasks cannot be divided into multiple parts to run on different devices. In addition, a high performance mobile device can execute multiple tasks simultaneously. We address the problem of multi-resource allocation in crowd sensing systems for the auction-based model considering many-to-one mapping for indivisible tasks, where many-to-one mapping allows one mobile device to provide multiple resources to execute one or more tasks. In this article, we study, for the first time to the best of our knowledge, a truthful mechanism that stimulates mobile users and requesters to declare their true values. We design a truthful double auction mechanism together with a payment scheme tailored to fit it that would help researchers understand how a truthful double auction mechanism can be designed. In addition, we prove that our proposed mechanism maintains budget-balance, individual rationality, and computational tractability. Furthermore, we analyze the approximation ratio of our proposed approximation algorithm. Experimental results demonstrate that our proposed mechanism has high computation efficiency and good performance.

关键词： Task analysis Sensors Resource management Mobile handsets approximation algorithms Pricing Cameras Crowd sensing double auction mechanism design truthfulness

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