IEEE open journal of signal processing最新文献_第6页

Multilinear Kernel Regression and Imputation via Manifold Learning 多线性核回归和通过 Manifold Learning 进行推算

IF 2.9

IEEE open journal of signal processing Pub Date : 2024-08-15 DOI: 10.1109/OJSP.2024.3444707

Duc Thien Nguyen;Konstantinos Slavakis

{"title":"Multilinear Kernel Regression and Imputation via Manifold Learning","authors":"Duc Thien Nguyen;Konstantinos Slavakis","doi":"10.1109/OJSP.2024.3444707","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3444707","url":null,"abstract":"This paper introduces a novel kernel regression framework for data imputation, coined multilinear kernel regression and imputation via the manifold assumption (MultiL-KRIM). Motivated by manifold learning, MultiL-KRIM models data features as a point-cloud located in or close to a user-unknown smooth manifold embedded in a reproducing kernel Hilbert space. Unlike typical manifold-learning routes, which seek low-dimensional patterns via regularizers based on graph-Laplacian matrices, MultiL-KRIM builds instead on the intuitive concept of tangent spaces to manifolds and incorporates collaboration among point-cloud neighbors (regressors) directly into the data-modeling term of the loss function. Multiple kernel functions are allowed to offer robustness and rich approximation properties, while multiple matrix factors offer low-rank modeling, dimensionality reduction and streamlined computations, with no need of training data. Two important application domains showcase the functionality of MultiL-KRIM: time-varying-graph-signal (TVGS) recovery, and reconstruction of highly accelerated dynamic-magnetic-resonance-imaging (dMRI) data. Extensive numerical tests on real TVGS and synthetic dMRI data demonstrate that the “shallow” MultiL-KRIM offers remarkable speedups over its predecessors and outperforms other “shallow” state-of-the-art techniques, with a more intuitive and explainable pipeline than deep-image-prior methods.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"1073-1088"},"PeriodicalIF":2.9,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10637724","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Occlusion-Informed Radar Detection for Millimeter-Wave Indoor Sensing 毫米波室内传感的遮挡感应雷达探测

IF 2.9

IEEE open journal of signal processing Pub Date : 2024-08-15 DOI: 10.1109/OJSP.2024.3444709

Ahmed Murtada;Bhavani Shankar Mysore Rama Rao;Moein Ahmadi;Udo Schroeder

{"title":"Occlusion-Informed Radar Detection for Millimeter-Wave Indoor Sensing","authors":"Ahmed Murtada;Bhavani Shankar Mysore Rama Rao;Moein Ahmadi;Udo Schroeder","doi":"10.1109/OJSP.2024.3444709","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3444709","url":null,"abstract":"The emergence of Multiple-Input Multiple-Output (MIMO) millimeter-wave (mmWave) radar sensors has prompted interest in indoor sensing applications, including human detection, vital signs monitoring, and real-time tracking in crowded environments. These sensors, equipped with multiple antenna elements, offer high angular resolution, often referred to as imaging radars for their capability to detect high-resolution point clouds. Employing radar systems with high-angular resolution in occlusion-prone scenarios often results in sparse signal returns in range profiles. In extreme cases, only one target return may be observed, as the resolution grid size becomes significantly smaller than the targets, causing portions of the targets to consistently occupy the full area of a test cell. Leveraging this structure, we propose two detectors to enhance the detection of non-occluded targets in such scenarios, thereby providing accurate high-resolution point clouds. The first method employs multiple hypothesis testing over each range profile where the range cells within are considered mutually occluding. The second is formulated based on binary hypothesis testing for each cell, considering the distribution of the signal in the other cells within the same range profile. Numerical analysis demonstrates the superior performance of the latter method over both the classic detection and the former method, especially in low Signal-to-Noise Ratio (SNR) scenarios. Our work showcases the potential of occlusion-informed detection in imaging radars to improve the detection probability of non-occluded targets and reduce false alarms in challenging indoor environments.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"976-990"},"PeriodicalIF":2.9,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10637692","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142246512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE Signal Processing Society Information 电气和电子工程师学会信号处理学会信息

IF 2.9

IEEE open journal of signal processing Pub Date : 2024-08-14 DOI: 10.1109/OJSP.2023.3347994

引用次数: 0

Self-Supervised Learning-Based Time Series Classification via Hierarchical Sparse Convolutional Masked-Autoencoder 通过层次稀疏卷积掩码自动编码器进行基于自监督学习的时间序列分类

IF 2.9

IEEE open journal of signal processing Pub Date : 2024-07-30 DOI: 10.1109/OJSP.2024.3435673

Ting Yu;Kele Xu;Xu Wang;Bo Ding;Dawei Feng

{"title":"Self-Supervised Learning-Based Time Series Classification via Hierarchical Sparse Convolutional Masked-Autoencoder","authors":"Ting Yu;Kele Xu;Xu Wang;Bo Ding;Dawei Feng","doi":"10.1109/OJSP.2024.3435673","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3435673","url":null,"abstract":"In recent years, the use of time series analysis has become widespread, prompting researchers to explore methods to improve classification. Time series self-supervised learning has emerged as a significant area of study, aiming to uncover patterns in unlabeled data for richer information. Contrastive self-supervised learning, particularly, has gained attention for time series classification. However, it introduces inductive bias by generating positive and negative samples. Another approach involves Masked Autoencoders (MAE), which are effective for various data types. However, due to their reliance on the Transformer architecture, they demand significant computational resources during the pre-training phase. Recently, inspired by the remarkable advancements achieved by convolutional networks in the domain of time series forecasting, we aspire to employ convolutional networks utilizing a strategy of mask recovery for pre-training time series models. This study introduces a novel model termed Hierarchical Sparse Convolutional Masked-Autoencoder, “HSC-MAE”, which seamlessly integrates convolutional operations with the MAE architecture to adeptly capture time series features across varying scales. Furthermore, the HSC-MAE model incorporates dedicated decoders that amalgamate global and local information, enhancing its capacity to comprehend intricate temporal patterns. To gauge the effectiveness of the proposed approach, an extensive array of experiments was conducted across nine distinct datasets. The experimental outcomes stand as a testament to the efficacy of HSC-MAE in effectively mitigating the aforementioned challenges.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"964-975"},"PeriodicalIF":2.9,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10614789","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142099791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detection of Radar Pulse Signals Based on Deep Learning 基于深度学习的雷达脉冲信号检测

IF 2.9

IEEE open journal of signal processing Pub Date : 2024-07-30 DOI: 10.1109/OJSP.2024.3435703

Fengyang Gu;Luxin Zhang;Shilian Zheng;Jie Chen;Keqiang Yue;Zhijin Zhao;Xiaoniu Yang

{"title":"Detection of Radar Pulse Signals Based on Deep Learning","authors":"Fengyang Gu;Luxin Zhang;Shilian Zheng;Jie Chen;Keqiang Yue;Zhijin Zhao;Xiaoniu Yang","doi":"10.1109/OJSP.2024.3435703","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3435703","url":null,"abstract":"Radar is widely used in aviation, meteorology, and military fields, and radar pulse signal detection has become an indispensable and essential function of cognitive radio systems as well as electronic warfare systems. In this paper, we propose a deep learning-based radar signal detection method. Firstly, we propose a detection method based on raw in-phase and quadrature (IQ) input, which utilizes a convolutional neural network (CNN) to automatically learn the features of radar pulse signals and noises, to accomplish the detection task. To further reduce the computational complexity, we also propose a hybrid detection method that combines compressed sensing (CS) and deep learning, which reduces the length of the signal by compressed downsampling, and then feeds the compressed signal to the CNN for detection. Extensive simulation results show that our proposed IQ-based method outperforms the traditional short-time Fourier transform method as well as three existing deep learning-based detection methods in terms of probability of detection. Furthermore, our proposed IQ-CS-based method can achieve satisfactory detection performance with significantly reduced computational complexity.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"991-1004"},"PeriodicalIF":2.9,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10614929","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142246552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Difference Frequency Gridless Sparse Array Processing 差频无网格稀疏阵列处理

IF 2.9

IEEE open journal of signal processing Pub Date : 2024-07-09 DOI: 10.1109/OJSP.2024.3425284

Yongsung Park;Peter Gerstoft

引用次数: 0

Fully Quantized Neural Networks for Audio Source Separation 用于音源分离的全量化神经网络

IF 2.9

IEEE open journal of signal processing Pub Date : 2024-07-09 DOI: 10.1109/OJSP.2024.3425287

Elad Cohen;Hai Victor Habi;Reuven Peretz;Arnon Netzer

{"title":"Fully Quantized Neural Networks for Audio Source Separation","authors":"Elad Cohen;Hai Victor Habi;Reuven Peretz;Arnon Netzer","doi":"10.1109/OJSP.2024.3425287","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3425287","url":null,"abstract":"Deep neural networks have shown state-of-the-art results in audio source separation tasks in recent years. However, deploying such networks, especially on edge devices, is challenging due to memory and computation requirements. In this work, we focus on quantization, a leading approach for addressing these challenges. We start with a theoretical and empirical analysis of the signal-to-distortion ratio (SDR) in the presence of quantization noise, which presents a fundamental limitation in audio source separation tasks. These analyses show that quantization noise mainly affects performance when the model produces high SDRs. We empirically validate the theoretical insights and illustrate them on audio source separation models. In addition, the empirical analysis shows a high sensitivity to activations quantization, especially to the network's input and output signals. Following the analysis, we propose Fully Quantized Source Separation (FQSS), a quantization-aware training (QAT) method for audio source separation tasks. FQSS introduces a novel loss function based on knowledge distillation that considers quantization-sensitive samples during training and handles the quantization noise of the input and output signals. We validate the efficiency of our method in both time and frequency domains. Finally, we apply FQSS to several architectures (CNNs, LSTMs, and Transformers) and show negligible degradation compared to the full-precision baseline models.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"926-933"},"PeriodicalIF":2.9,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10591369","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141966239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient Key-Based Adversarial Defense for ImageNet by Using Pre-Trained Models 利用预训练模型为 ImageNet 提供基于密钥的高效对抗性防御

IF 2.9

IEEE open journal of signal processing Pub Date : 2024-06-26 DOI: 10.1109/OJSP.2024.3419569

AprilPyone MaungMaung;Isao Echizen;Hitoshi Kiya

{"title":"Efficient Key-Based Adversarial Defense for ImageNet by Using Pre-Trained Models","authors":"AprilPyone MaungMaung;Isao Echizen;Hitoshi Kiya","doi":"10.1109/OJSP.2024.3419569","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3419569","url":null,"abstract":"In this paper, we propose key-based defense model proliferation by leveraging pre-trained models and utilizing recent efficient fine-tuning techniques on ImageNet-1 k classification. First, we stress that deploying key-based models on edge devices is feasible with the latest model deployment advancements, such as Apple CoreML, although the mainstream enterprise edge artificial intelligence (Edge AI) has been focused on the Cloud. Then, we point out that the previous key-based defense on on-device image classification is impractical for two reasons: (1) training many classifiers from scratch is not feasible, and (2) key-based defenses still need to be thoroughly tested on large datasets like ImageNet. To this end, we propose to leverage pre-trained models and utilize efficient fine-tuning techniques to proliferate key-based models even on limited compute resources. Experiments were carried out on the ImageNet-1 k dataset using adaptive and non-adaptive attacks. The results show that our proposed fine-tuned key-based models achieve a superior classification accuracy (more than 10% increase) compared to the previous key-based models on classifying clean and adversarial examples.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"902-913"},"PeriodicalIF":2.9,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10572223","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141964708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Overview of the First Pathloss Radio Map Prediction Challenge 首届无线电路径损耗地图预测挑战赛概述

IF 2.9

IEEE open journal of signal processing Pub Date : 2024-06-26 DOI: 10.1109/OJSP.2024.3419563

Çağkan Yapar;Fabian Jaensch;Ron Levie;Gitta Kutyniok;Giuseppe Caire

{"title":"Overview of the First Pathloss Radio Map Prediction Challenge","authors":"Çağkan Yapar;Fabian Jaensch;Ron Levie;Gitta Kutyniok;Giuseppe Caire","doi":"10.1109/OJSP.2024.3419563","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3419563","url":null,"abstract":"Pathloss quantifies the reduction in power density of a signal radiated from a transmitter. The attenuation is due to large-scale effects such as free-space propagation loss and interactions (e.g., penetration, reflection, and diffraction) of the signal with objects such as buildings, vehicles, trees, and pedestrians in the propagation environment. Many current or planned wireless communications applications require the knowledge (or a reliable approximation) of the pathloss on a dense grid (radio map) of the environment of interest. Deterministic simulation methods such as ray tracing are known to provide very good estimates of pathloss values. However, their high computational complexity makes them unsuitable for most of the applications envisaged. To promote research and facilitate a fair comparison among the recently proposed fast and accurate deep learning-based pathloss radio map prediction methods, we have organized the ICASSP 2023 First Pathloss Radio Map Prediction Challenge. In this overview paper, we describe the pathloss radio map prediction problem, provide a literature survey of the current state of the art, describe the challenge datasets, the challenge task, and the challenge evaluation methodology. Finally, we provide a brief overview of the submitted methods and present the results of the challenge.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"948-963"},"PeriodicalIF":2.9,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10572278","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141993995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SINR Analysis of Windowed OFDM in Power Line Communication Systems 电力线通信系统中窗口 OFDM 的 SINR 分析

IF 2.9

IEEE open journal of signal processing Pub Date : 2024-06-26 DOI: 10.1109/OJSP.2024.3419448

Fausto García-Gangoso;Fernando Cruz-Roldán

{"title":"SINR Analysis of Windowed OFDM in Power Line Communication Systems","authors":"Fausto García-Gangoso;Fernando Cruz-Roldán","doi":"10.1109/OJSP.2024.3419448","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3419448","url":null,"abstract":"The use of an accurate cyclic prefix length is crucial in orthogonal frequency division multiplexing (OFDM) to avoid intercarrier and intersymbol interference. Although there have been many works that analyse the interference of windowed OFDM, this study remains open in the context of power line communications (PLCs) taking into account the physical-layer (PHY) specifications of the standards. This paper focuses on obtaining a closed-form expression of the input-output relationship in windowed OFDM power line communication (PLC) systems under the condition of insufficient cyclic prefix, while incorporating various blocks deployed in the PHY under IEEE 1901 standards. The derived analysis is important for quantifying the undesired signal component in each subcarrier at a specific time, which renders the detection of the corresponding symbol more difficult. Moreover, a novel procedure is proposed that allows the use of a smaller number of redundant samples to avoid interference. This novel procedure, performed in the receiver after the windowing stage, replaces the overlap-and-add operations with multiplications, offering the advantage of requiring fewer samples from the time-domain received signal to recover each transmitted data symbol. Numerical results demonstrate the feasibility of interference-free transmission on channels with a larger number of samples, thereby yielding better results across various PLC scenarios.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"1052-1060"},"PeriodicalIF":2.9,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10572224","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142518177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0