IEEE open journal of signal processing最新文献_第2页

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

Editorial: Special Issue on the ICASSP 2023 Signal Processing Grand Challenges 编辑：ICASSP 2023 信号处理大挑战特刊

IF 2.9

IEEE open journal of signal processing Pub Date : 2024-06-24 DOI: 10.1109/OJSP.2024.3397168

Alexander Bertrand;Ozlem Kalinli

引用次数: 0

Universal Fourier Attack for Time Series 时间序列的通用傅里叶攻击

IF 2.9

IEEE open journal of signal processing Pub Date : 2024-06-14 DOI: 10.1109/OJSP.2024.3402154

Elizabeth Coda;Brad Clymer;Chance DeSmet;Yijing Watkins;Michael Girard

引用次数: 0

Sparse DOA Estimation With Polarimetric Arrays 利用极坐标阵列进行稀疏 DOA 估算

IF 2.9

IEEE open journal of signal processing Pub Date : 2024-06-07 DOI: 10.1109/OJSP.2024.3411468

Augusto Aubry;Marco Boddi;Antonio De Maio;Massimo Rosamilia

引用次数: 0

Multi-Channel Low-Rank Convolution of Jointly Compressed Room Impulse Responses 联合压缩室内脉冲响应的多通道低库卷积

IF 2.9

IEEE open journal of signal processing Pub Date : 2024-06-05 DOI: 10.1109/OJSP.2024.3410089

Martin Jälmby;Filip Elvander;Toon van Waterschoot

{"title":"Multi-Channel Low-Rank Convolution of Jointly Compressed Room Impulse Responses","authors":"Martin Jälmby;Filip Elvander;Toon van Waterschoot","doi":"10.1109/OJSP.2024.3410089","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3410089","url":null,"abstract":"The room impulse response (RIR) describes the response of a room to an acoustic excitation signal and models the acoustic channel between a point source and receiver. RIRs are used in a wide range of applications, e.g., virtual reality. In such an application, the availability of closely spaced RIRs and the capability to achieve low latency are imperative to provide an immersive experience. However, representing a complete acoustic environment using a fine grid of RIRs is prohibitive from a storage point of view and without exploiting spatial proximity, acoustic rendering becomes computationally expensive. We therefore propose two methods for the joint compression of multiple RIRs, based on the generalized low-rank approximation of matrices (GLRAM), for the purpose of efficiently storing RIRs and allowing for low-latency convolution. We show how one of the components of the GLRAM decomposition is virtually invariant to the change of position of the source throughout the room and how this can be exploited in the modeling and convolution. In simulations we show how this offers high compression, with less quality degradation than comparable benchmark methods.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"850-857"},"PeriodicalIF":2.9,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10549780","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141543936","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