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Computationally Efficient Structural Health Monitoring Using Graph signal processing

IEEE SENSORS JOURNAL 2024年第7期24卷 11895-11905页

作者： Cheema, Muhammad Asaad Sarwar, Muhammad Zohaib Gogineni, Vinay Chakravarthi Cantero, Daniel Rossi, Pierluigi Salvo Norwegian Univ Sci & Technol Dept Elect Syst N-7491 Trondheim Norway Norwegian Univ Sci & Technol Dept Struct Engn N-7491 Trondheim Norway Univ Southern Denmark Maersk Mc Kinney Moller Inst Appl AI & Data Sci Unit DK-5230 Odense Denmark

Structural health monitoring (SHM) of bridges is crucial for ensuring safety and long-term durability, however, standard damage-detection algorithms are computationally intensive. This article proposes a computationally efficient algorithm based on graph signal processing (GSP) to leverage the underlying network structure in the data. Under the assumption that damages impact both spatial and temporal structures of the sensor data, the algorithm combines spatial and temporal information from accelerometers by computing the smoothness of graph signals expanded along time. The Kullback-Leibler (KL) divergence is used as dissimilarity metric to distinguish between healthy condition and presence of a damage, while Tukey's method for outliers removal and sequential detection via exponential weighted moving average (EWMA) are then employed for performance improvement. The proposed GSP-based SHM system is appealing in terms of simplicity and low-complexity and is also suitable for real-time monitoring. The effectiveness in terms of detection performance is validated both on synthetically generated data and real-world measurements.

关键词： Sensors Bridges Monitoring signal processing signal processing algorithms Laplace equations Feature extraction Finite-element model (FEM) graph signal processing (GSP) joint graph Laplacian Kullback-Leibler (KL) divergence KW51 bridge structural health monitoring (SHM)

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Analyzing Risk Level from RoI and VoI for Robust Data Privacy using Fuzzy Inference System

Analyzing Risk Level from RoI and VoI for Robust Data Privac...

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International Conference on Robotics,Electrical and signal processing Techniques (ICREST)

作者： Rabab Khan Rongon M. Akhtaruzzaman Krishna Das Shekh Tanjil Sharif Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh Quantum Robotics and Automation Research Group (QRARG) Dhaka Bangladesh International University of Business Agriculture and Technology (IUBAT) Dhaka Bangladesh

ISBN: (数字)9798331530952

ISBN: (纸本)9798331530969

In the modern digital environment, securing data privacy has become increasingly critical as organizations confront rising threats from cyber-attacks and data breaches. To safeguard sensitive information, businesses must invest in robust data protection measures, yet balancing these investments with their potential benefits remains challenging. This research addresses this issue by proposing a novel framework that integrates Return on Investment (RoI) and Value of Information (VoI) within a Fuzzy Inference System (FIS) to assess data privacy risks effectively. RoI measures the financial return relative to the cost of data protection investments, indicating the economic value derived from such measures. VoI, on the other hand, evaluates the worth of the information protected by these investments, reflecting its impact on decision-making and operational efficiency. By employing a FIS, which accommodates uncertainties and imprecise data, this framework provides a nuanced approach to risk assessment, enabling organizations to optimize their data privacy strategies. The integration of RoI and VoI within the FIS allows for a comprehensive evaluation of risk levels, facilitating more informed decision-making and improved risk management. This research aims to enhance data privacy practices by offering a methodical and adaptable tool for evaluating and balancing investments in data protection.

关键词： Fuzzy logic Analytical models Uncertainty Decision making Data protection signal processing algorithms Organizations Mathematical models Risk management Investment

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Supervised Dimension Reduction Through Linear Projection

Supervised Dimension Reduction Through Linear Projection

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

作者： Biao Chen Joshua Kortje Department of EECS Syracuse University Syracuse NY USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This paper proposes two linear projection approaches for supervised dimension reduction using only the first and second-order statistics for the binary classification problem. They are derived under the general Gaussian model by maximizing the Kullback-Leibler divergence between the two classes in the projected sample. They subsume existing linear projection approaches developed under simplifying assumptions of Gaussian distributions, i.e., when these distributions share an equal mean or covariance matrix. Experiments are conducted to validate the proposed solutions and demonstrate their effectiveness for supervised dimension reduction.

关键词： Dimensionality reduction signal processing algorithms Gaussian distribution signal processing Acoustics Numerical models Speech processing Covariance matrices

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Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a Novel Learning Algorithm

Preconditioned Sharpness-Aware Minimization: Unifying Analys...

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

作者： Yilang Zhang Bingcong Li Georgios B. Giannakis Dept. of ECE Univ. of Minnesota Minneapolis MN USA Dept. of CS ETH Zürich Zürich Switzerland

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Targeting solutions over ‘flat’ regions of the loss landscape, sharpness-aware minimization (SAM) has emerged as a powerful tool to improve generalizability of deep neural network based learning. While several SAM variants have been developed to this end, a unifying approach that also guides principled algorithm design has been elusive. This contribution leverages preconditioning (pre) to unify SAM variants and provide not only unifying convergence analysis, but also valuable insights. Building upon preSAM, a novel algorithm termed infoSAM is introduced to address the so-called adversarial model degradation issue in SAM by adjusting gradients depending on noise estimates. Extensive numerical tests demonstrate the superiority of infoSAM across various benchmarks.

关键词： Degradation Noise Buildings signal processing algorithms Benchmark testing Minimization Numerical models Speech processing Convergence Guidelines

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The Robust Sparse Fourier Transform (RSFT) and Its Application in Radar signal processing

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2017年第6期53卷 2735-2755页

作者： Wang, Shaogang Patel, Vishal M. Petropulu, Athina Rutgers State Univ Dept Elect & Comp Engn Piscataway NJ 08854 USA

The Sparse Fourier Transform (SFT), designed for signals that contain a small number of frequencies, enjoys low complexity, and thus is ideally suited for big data applications. In this paper, we propose Robust Sparse Fourier Transform, (RSFT), which is a modification of SFT that extends the SFT advantages to real world, noisy settings. RSFT can accommodate off-grid frequencies in the data. Furthermore, by incorporating Neyman-Pearson detection in the SFT stages, frequency detection does not require knowledge of the exact sparsity of the signal, and is robust to noise. We analyze the asymptotic performance of RSFT, and study the computational complexity versus detection performance tradeoff. We show that, by appropriately choosing the detection thresholds, the optimal tradeoff can be achieved. We discuss the application of RSFT on short-range ubiquitous radar signal processing and demonstrate its feasibility via simulations.

关键词： OFDM signal processing algorithms Discrete Fourier transforms Frequency-domain analysis Frequency estimation Radar signal processing

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System design, signal-processing procedures, and preliminary results for the Canadian (London, Ontario) VHF atmospheric radar

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RADIO SCIENCE 1997年第2期32卷 687-706页

作者： Hocking, WK Department of Physics University of Western Ontario London Ontario Canada

Hardware, software, and design features of a new VKF atmospheric radar situated in Canada are described, with particular emphasis being placed on the flexibility which has been implemented at quite low cost. Called CLOVAR (Canadian (London, Ontario) VHF atmospheric radar), the instrument has now been operational since November 1993. It is located at 43 degrees 04.44'N, 81 degrees 20.20'W, operates at a frequency of 40.68 MHz, and is owned and operated by the nearby University of Western Ontario in London, Ontario, Canada. There are some unique features about this system, including its low-cost design, flexible beam-steering, and on-line software analysis procedures. In this paper we elaborate on these new developments and especially demonstrate the new signal processing algorithms currently in use. These new algorithms include procedures for rejection of signals due to aircraft, removal of instrumental drift, and full on-line spectral fitting of Gaussian functions. Typical data from the system are presented, including experimental data acquired with multibeam experiments, monthly mean vertical velocities, and some interesting results obtained during a solar eclipse. The radar can also function as an efficient meteor radar for determination of high-level winds, and this capability will also be briefly described. A special program of comparisons with colocated radiosonde flights is also discussed.

关键词： signal processing algorithms Radar antennas Switches Algorithm design and analysis Spaceborne radar

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TOOLS FOR REAL-TIME signal-processing RESEARCH

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IEEE COMMUNICATIONS MAGAZINE 1993年第11期31卷 64-74页

作者： SNYDER, JH QUACKENBUSH, SR MELCHNER, MJ KAPILOW, DA AT&T BELL LABS DEPT SPEECH CODING RESMURRAY HILLNJ 07974 AT&T BELL LABS INTERACT SYST RES DEPTMURRAY HILLNJ 07974

The use of digital signal processing (DSP) devices for real-time communication applications is discussed. The authors comment on distinguishing aspects of DSP architecture, describing not so much individual processors as those features common to DSPs and distinct from modern general-purpose processors. They describe three DSP32xx-based machines that support DSP algorithm implementation: SURF-board, HoBo, and DSP3. They also described rtpi, a source-code debugger for workstations and for the AT&T DSP32C signal-processor integrated circuit, and dspx, a collection of subroutines and host programs that provides an execution environment for DSPs akin to the UNIX environment. These tools facilitate the transfer of algorithms from mainframes or workstations to DSP hardware. Included are case studies of two real-time implementations: the low-delay CELP (LD-CELP) speech coder and the decoder side of the perceptual audio coder (PAC), an algorithm that compresses CD-quality audio into a 128-kb/s stream without perceptible distortion.< >

关键词： signal processing algorithms Digital signal processing Hardware signal processing Digital signal processing chips Workstations Testing Erbium Trademarks Assembly

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RECURSIVE PRUNING OF THE 2-D DFT WITH 3-D signal-processing APPLICATIONS

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IEEE TRANSACTIONS ON signal processing 1993年第3期41卷 1340-1356页

作者： KNUDSEN, KS BRUTON, LT UNIV CALGARY DEPT ELECT & COMP ENGNCALGARY T2N 1N4ALBERTACANADA

A recursively pruned radix-(2 x 2) two-dimensional (2-D) fast Fourier transform (FFT) algorithm is proposed which reduces the number of operations involved in the calculation of the 2-D discrete Fourier transform (DFT). It is able to compute input and output data points having multiple and possibly irregularly shaped (that is, nonsquare) regions of support. The computational performance of the recursively pruned radix-(2 x 2) 2-D FFT algorithm is compared with that of pruning algorithms based on the one-dimensional (1-D) FFT. The proposed recursively pruned 2-D FFT algorithm is shown to yield significant computational savings when employed in the recently proposed combined 2-D DFT/1-D linear difference equation filter method to enhance three-dimensional spatially planar image sequences, and when employed in the MixeD moving object detection and trajectory estimation algorithm.

关键词： signal processing signal processing algorithms Discrete Fourier transforms Two dimensional displays Fast Fourier transforms Image sequences Least squares approximation Flow graphs Difference equations

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Achieving Robustness in Blind Modulo Analog-to-Digital Conversion

Achieving Robustness in Blind Modulo Analog-to-Digital Conve...

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

作者： Amir Weiss Faculty of Engineering BarIlan University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The need to digitize signals with intricate spectral characteristics often challenges traditional analog-to-digital converters (ADCs). The recently proposed modulo-ADC architecture offers a promising alternative by leveraging inherent features of the input signals. This approach can dramatically reduce the number of bits required for the conversion while maintaining the desired fidelity. However, the core algorithm of this architecture, which utilizes a prediction filter, functions properly only when the respective prediction error is bounded. In practice, this assumption may not always hold, leading to considerable instability and performance degradation. To address this limitation, we propose an enhanced modulo-unfolding solution without this assumption. We develop a reliable detector to successfully unfold the signals, yielding a robust solution. Consequently, the reinforced system maintains proper operation in scenarios where the original approach fails, while also reducing the quantization noise. We present simulation results that demonstrate the superior performance of our approach in a representative setting.

关键词： Quantization (signal) Simulation Noise Stability criteria signal processing algorithms Filtering algorithms Robustness Speech processing Analog-digital conversion Sensor arrays

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Sequential Decoding of Multiple Traces Over the Syndrome Trellis for Synchronization Errors

Sequential Decoding of Multiple Traces Over the Syndrome Tre...

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

作者： Anisha Banerjee Lorenz Welter Alexandre Graell I Amat Antonia Wachter-Zeh Eirik Rosnes Institute for Communications Engineering Technical University of Munich (TUM) Munich Germany Department of Electrical Engineering Chalmers University of Technology Gothenburg Sweden Simula UiB Bergen Norway

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Standard decoding approaches for convolutional codes, such as the Viterbi and BCJR algorithms, entail significant complexity when correcting synchronization errors. The situation worsens when multiple received sequences should be jointly decoded, as in DNA storage. Previous work has attempted to address this via separate-BCJR decoding, i.e., combining the results of decoding each received sequence separately. Another attempt to reduce complexity adapted sequential decoders for use over channels with insertion and deletion errors. However, these decoding alternatives remain prohibitively expensive for high-rate convolutional codes. To address this, we adapt sequential decoders to decode multiple received sequences jointly over the syndrome trellis. For the short blocklength regime, this decoding strategy can outperform separate-BCJR decoding under certain channel conditions, in addition to reducing decoding complexity. To mitigate the occurrence of a decoding timeout, formally called erasure, we also extend this approach to work bidirectionally, i.e., deploying two independent stack decoders that simultaneously operate in the forward and backward directions.

关键词： Convolutional codes Viterbi algorithm signal processing algorithms DNA signal processing Decoding Complexity theory Synchronization Speech processing Standards

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