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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Saxena, Ravi Raj Prabhakar, T.V. Kuri, Joy Murthy, Chandra R. Dept. of Electronic System Engineering Indian Institute of Science Bengaluru India Dept. of Electrical Communication Engineering Indian Institute of Science Bengaluru India

ISBN: (纸本)9798350368741

This work focuses on compressing vast amounts of time series data from IoT sensors while achieving low reconstruction error, at a low compression ratio (ratio of output data size to input data size), for efficient storage and transmission. We investigate two lossy compression techniques: Adaptive Piecewise Linear Approximation (APLA) and sparse encoding (SE). APLA reduces data volume by leveraging the temporal correlation in time series data, while SE captures small, complex variations that APLA might miss. We identify a signal characteristic parameter, denoted by σd, which is used to derive error bounds for SE using σd, the dictionary used, and the input compression parameters. We also introduce APLASE, a novel compression technique that combines the two techniques by applying SE to the reconstruction error generated by APLA. We evaluate APLA, SE and APLASE using data from the NASA flight data recorder database consisting of recordings from 116 sensors. Our results show that APLASE consistently outperforms both APLA and SE individually, as well as the recent QoZ algorithm, particularly at lower compression ratios. © 2025 IEEE.

关键词： Compression Piecewise Linear Approximation sparse encoding

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Application of optimized sparse encoding algorithm in data compression

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DIGITAL SIGNAL PROCESSING 2024年 151卷

作者： Song, Liqiang Ma, Weining Liu, Zhongxin Shi, Zhiyong Army Engn Univ PLA Dept Vehicle & Elect Engn Shijiazhuang Campus Shijiazhuang 050003 Peoples R China

Data transmission is crucial in the process of equipment monitoring. The compression algorithms are adopted to reduce the amount of data transmission. When sparse encoding algorithms are used for this purpose, despite extensive attempts to improve their performance, some problems still remain. The main issues pertain to: (1) Dictionary construction - As practical dictionary better matches the input signal, such a dictionary must be efficiently established;2) The setting of sparsity value - It is difficult to determine the size of sparsity values when prior knowledge is insufficient (when the parameter settings are too large, the cost of data transmission increases considerably, while excessively small values can result in poor reconstruction accuracy);and (3) How to implement engineering applications of data compression based on sparse encoding. To address these issues, in this work, an optimized sparse encoding algorithm is proposed by combining the non-negative Online Dictionary Learning (ODL) with the optimized Orthogonal Matching Pursuit (OMP) algorithm. First, a non-negative ODL algorithm is adopted for dictionary learning based on the training dataset to obtain an effective dictionary. Next, the optimized OMP algorithm is used to obtain the sparsity and the sparse coefficient matrix. Further analyses confirm that the reconstructed signal has a small reconstruction error. Finally, A lossy compression framework is proposed for the compressing equipment condition monitoring data using sparse encoding algorithms. Compared with the compression algorithms based on compressive sensing and DCT, the average compression ratio can reach 42.7 when the reconstruction accuracy is similar. In terms of comprehensive compression performance, the quality score is also higher compared to other algorithms.

关键词： sparse encoding Online dictionary learning Lossy compression Quantization encoding sparse transform

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A theory of capacity and sparse neural encoding

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NEURAL NETWORKS 2021年 143卷 12-27页

作者： Baldi, Pierre Vershynin, Roman Univ Calif Irvine Dept Comp Sci Irvine CA 92697 USA Univ Calif Irvine Dept Math Irvine CA 92717 USA

Motivated by biological considerations, we study sparse neural maps from an input layer to a target layer with sparse activity, and specifically the problem of storing K input-target associations (x, y), or memories, when the target vectors y are sparse. We mathematically prove that K undergoes a phase transition and that in general, and somewhat paradoxically, sparsity in the target layers increases the storage capacity of the map. The target vectors can be chosen arbitrarily, including in random fashion, and the memories can be both encoded and decoded by networks trained using local learning rules, including the simple Hebb rule. These results are robust under a variety of statistical assumptions on the data. The proofs rely on elegant properties of random polytopes and sub-gaussian random vector variables. Open problems and connections to capacity theories and polynomial threshold maps are discussed. (C) 2021 The Authors. Published by Elsevier Ltd.

关键词： Neural capacity sparse representations sparse encoding Neural maps Threshold functions

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Fast Beam Alignment for Millimeter Wave Communications: A sparse encoding and Phaseless Decoding Approach

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2019年第17期67卷 4402-4417页

作者： Li, Xingjian Fang, Jun Duan, Huiping Chen, Zhi Li, Hongbin Univ Elect Sci & Technol China Natl Key Lab Sci & Technol Commun Chengdu 611731 Sichuan Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Sichuan Peoples R China Stevens Inst Technol Dept Elect & Comp Engn Hoboken NJ 07030 USA

In this paper, we study the problem of beam alignment for millimeter wave (mmWave) communications, where a hybrid analog and digital beamforming structure is employed at the transmitter (i.e., base station), and an omni-directional antenna or an antenna array is used at the receiver (i.e., user). By exploiting the sparse scattering nature of mm Wave channels, the beam alignment problem is formulated as a sparse encoding and phaseless decoding problem. More specifically, the problem of interest involves finding a sparse sensing matrix and an efficient recovery algorithm to recover the support and magnitude of a sparse signal from compressive phaseless measurements. A sparse bipartite graph code algorithm is developed for sparse encoding and phaseless decoding. Our theoretical analysis shows that, in the noiseless case, our proposed algorithm can perfectly recover the support and magnitude of the sparse signal with a probability exceeding a pre-specified value from O(K-2) measurements, where K is the number of nonzero entries of the sparse signal. The proposed algorithm has a simple decoding procedure which is computationally efficient and noise robust. Simulation results show that our proposed method renders reliable beam alignment even in the low-signal-to-noise ratio regime and presents a clear performance advantage over existing methods.

关键词： Millimeter wave (mmWave) communications beam alignment sparse encoding phaseless decoding

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Security Detection of Building Structure Based on sparse encoding Deep Learning Algorithm

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INTERNATIONAL JOURNAL OF SECURITY AND ITS APPLICATIONS 2016年第12期10卷 129-140页

作者： Li, Zheng Sichuan Coll Architectural Technol Dept Informat Engn Deyang 618000 Peoples R China

Most health problems of building structures are accumulative damages which are difficult to detect, and it is more difficult to monitor the structure health due to the complexity of the practical structure and the environment noise, and the existing methods need lots of data for model training but it is very complicated to mark the data in practice. In order to solve above problems, the wireless sensor network is configured and the sparse encoding method is adopted to monitor the bridge structure health, and meanwhile the sparse encoding algorithm is adopted for training on the basis of the characteristic extraction of many unlabeled instances, thus to compress data dimensionality and preprocess unlabeled data. Then, the deep learning algorithm is adopted to predict the bridge structure health monitoring type, and meanwhile Hessian optimization is improved on the basis of the linear conjugate gradient in order to replace uncertain Hessian matrix by positive semidefinite Gaussian - Newton curvature matrix for secondary objective combination, thus to improve the efficiency of the deep learning algorithm. The experiment result shows that the security detection of the bridge structure based on deep learning algorithm can monitor the high-accuracy structure health conditions under the sparse encoding of the environment noise.

关键词： Structure security Deep learning sparse encoding Wireless sensor Bridge structure

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A First Step Towards Even More sparse encodings of Probability Distributions 1

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30th International Conference on Inductive Logic Programming (ILP) held as part of the 1st International Joint Conference on Learning and Reasoning (IJCLR)

作者： Marwitz, Florian Andreas Braun, Tanya Moeller, Ralf Univ Lubeck Inst Informat Syst Lubeck Germany

ISBN: (数字)9783030974541

ISBN: (纸本)9783030974541;9783030974534

Real world scenarios can be captured with lifted probability distributions. However, distributions are usually encoded in a table or list, requiring an exponential number of values. Hence, we propose a method for extracting first-order formulas from probability distributions that require significantly less values by reducing the number of values in a distribution and then extracting, for each value, a logical formula to be further minimized. This reduction and minimization allows for increasing the sparsity in the encoding while also generalizing a given distribution. Our evaluation shows that sparsity can increase immensely by extracting a small set of short formulas while preserving core information.

关键词： Probabilistic graphical models sparse encoding Lifting

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sparse deep encoded features with enhanced sinogramic red deer optimization for fault inspection in wafer maps

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JOURNAL OF INTELLIGENT MANUFACTURING 2025年第5期36卷 3359-3397页

作者： Altantawy, Doaa A. Yakout, Mohamed A. Mansoura Univ Fac Engn Elect & Commun Engn Dept 60 El Gomhoria St Mansoura Egypt

Due to the complexity and dynamics of the semiconductor manufacturing processes, wafer bin maps (WBM) present various defect patterns caused by various process faults. The defect type detection on wafer maps provides information about the process and equipment in which the defect occurred. Recently, automatic inspection has played a vital role in meeting the high-throughput demand, especially with deep convolutional neural networks (DCNN) which shows promising efficiency. At the same time, the need for a large amount of labeled and balanced datasets limits the performance of such approaches. In addition, complex DCNN in recognition tasks can provide redundant features that cause overfitting and reduce interpretability. In this paper, a new hybrid deep model for wafer map fault detection to get over these challenges is proposed. Firstly, a new convolutional autoencoder (CAE) is employed as a synthetization model to fix the high imbalance problem of the dataset. Secondly, for efficient dimensionality reduction, an embedding procedure is applied to the synthesized maps to get sparse encoded wafer maps by reinforcing a sparsity regularization in an encoder-decoder network to form a sparsity-boosted autoencoder (SBAE). The sparse embedding of wafer maps guarantees more discriminative features with 50% reduction in spatial size compared to the original wafer maps. Then, the 2D encoded sparse maps are converted to 1D sinograms to be fed later into another aggressive feature reduction stage using a new modified red deer algorithm with a new tinkering strategy. The resultant feature pool is reduced to similar to 25 1D feature bases, i.e., similar to 1.5% of the initial size of the 2D wafer maps. Finally, for the prediction stage, a simple 1DCNN model is introduced. The proposed inspection model is tested via different experiments on real-world wafer map dataset (WM-811K). Compared to state-of-the-art techniques, the proposed model outperforms their performance even with smal

关键词： Wafer maps WM-811K Autoencoders sparse encoding 1DCNN Red deer optimization

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Large-scale stochastic sparse subspace representation with consensus anchor guidance

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APPLIED INTELLIGENCE 2025年第7期55卷 1-29页

作者： Yang, Ge Deng, Tingquan Yang, Ming Wang, Changzhong Harbin Engn Univ Coll Math Sci Harbin 150001 Peoples R China Bohai Univ Coll Math Jinzhou 121013 Peoples R China

Subspace clustering (SC) is a hotspot in data analysis and machine learning. There exists much literature addressing this topic and most of which cannot handle large scale data. Although anchor graph learning is introduced to SC, there is still a problem that anchors cannot preserve the subspace structure of original data and spectral clustering process is still implemented slowly. To address these issues, an Anchor Graph Regularization based Large-Scale Stochastic sparse Subspace Representation with Consensus Anchor Guidance (AGLS(4)RA) is proposed in this paper, which integrates three modules, including sparse self-representation, anchor graph regularization, and sparse coding into a unified framework. These modules are collaboratively worked to learn an optimal, high-quality anchor matrix under the row sparse constraint. Furthermore, the random sampling and label propagation techniques are also introduced to accelerate the clustering task. AGLS(4)RA is capable of processing data in linear time, which is beneficial to the execution of large-scale tasks. A series of comparative experiments on benchmark datasets verify the effectiveness of the proposed method.

关键词： Fast subspace clustering sparse self-representation Anchor graph Manifold learning sparse encoding

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Adversarial Attacks on Convolutional Siamese Signature Verification Networks 17th

Adversarial Attacks on Convolutional Siamese Signature Verif...

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17th International Conference on Document Analysis and Recognition (ICDAR)

作者： Jahangir, Maham Malik, Muhammad Imran Shafait, Faisal Natl Univ Sci & Technol NUST Sch Elect Engn & Comp Sci SEECS Islamabad Pakistan Natl Ctr Artificial Intelligence NCAI Deep Learning Lab Islamabad Pakistan

ISBN: (纸本)9783031416842;9783031416859

A handwritten signature serves as an important biometric modality to identify individuals. The state-of-the-art methods for signature verification employ deep learning networks to perform the classification task. However, deep neural networks can be fooled by adversarial attacks that introduce small imperceptible perturbations to the input images. In this paper, we explore the vulnerability of signature verification systems against adversarial attacks. The state-of-the-art attacks developed by the machine learning community to fool image classifiers are unsuitable for attacking document classifiers as they are applied to the background of signature images making them quite perceptible. To overcome this challenge, we design an attack based on dictionary learning with the goal to perturb the foreground (strokes) of the signature image. The proposed method is evaluated in terms of attack success rate and imperceptibility. The experimental results on the benchmark CEDAR dataset using Siamese Deep Signet Model highlight the efficacy of the proposed approach as compared to other methods by achieving 95% and 98% attack success rates with our proposed approach.

关键词： Adversarial Attack sparse encoding Dictionary Learning Signature Verification

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sparse Reconstruction of Glucose Fluxes Using Continuous Glucose Monitors

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020年第5期17卷 1797-1809页

作者： Al-Matouq, Ali Ahmed Laleg-Kirati, Taous-Meriem Novara, Carlo Rabbone, Ivana Vincent, Tyrone Prince Sultan Univ Fac Engn Management POB 66863 Raffia St Riyadh 11586 Saudi Arabia King Abdullah Univ Sci & Technol Comp Elect & Math Sci & Engn CEMSE Div Thuwal 239556900 Saudi Arabia Politecn Torino I-10129 Turin Italy Univ Turin SSD Diabetol Endocrinol Pediat Osped Infantile Re AOU Citt Salute & Sci Torino I-10124 Turin Italy Colorado Sch Mines Dept Elect Engn & Comp Sci 1600 Illinois St Golden CO 80401 USA

A new technique for estimating postprandial glucose flux profiles without the use of glucose tracers is proposed. A sparse vector space representation is first found for the space of plausible glucose flux profiles using sparse encoding. A Lasso formulation is then used to estimate the glucose fluxes that combines (1) known patient model parameters;(2) the vector space of plausible glucose flux profiles;(3) continuous glucose monitor measurements taken during the meal;(4) amount of insulin injected;(5) amount of meal carbohydrates;and (6) an estimate of the initial conditions. Three glucose fluxes are then estimated, namely;glucose rate of appearance from the intestine;endogenous glucose production from the liver;insulin dependent glucose utilization;and other important state variables. The simulation results show that the technique is capable of estimating the glucose fluxes with high accuracy, even for complex meal scenarios. The experimental results indicate that the technique is capable of reproducing the triple tracer measurements for three T1DM undergoing the triple tracer protocol while estimating the missing measurements for a certain model parameter selection.

关键词： Sugar Insulin Plasmas Liver Intestines Mathematical model Glucose metabolism continuous glucose monitors type 1 diabetes meal tolerance test sparse encoding lasso estimation

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