IEEE Journal of Selected Topics in Signal Processing最新文献

{"title":"Complex-Valued Autoencoder-Based Neural Data Compression for SAR Raw Data","authors":"Reza Mohammadi Asiyabi;Mihai Datcu;Andrei Anghel;Adrian Focsa;Michele Martone;Paola Rizzoli;Ernesto Imbembo","doi":"10.1109/JSTSP.2025.3558651","DOIUrl":"https://doi.org/10.1109/JSTSP.2025.3558651","url":null,"abstract":"Recent advances in Synthetic Aperture Radar (SAR) sensors and innovative advanced imagery techniques have enabled SAR systems to acquire very high-resolution images with wide swaths, large bandwidth and in multiple polarization channels. The improvements of the SAR system capabilities also imply a significant increase in SAR data acquisition rates, such that efficient and effective compression methods become necessary. The compression of SAR raw data plays a crucial role in addressing the challenges posed by downlink and memory limitations onboard the SAR satellites and directly affects the quality of the generated SAR image. Neural data compression techniques using deep models have attracted many interests for natural image compression tasks and demonstrated promising results. In this study, neural data compression is extended into the complex domain to develop a Complex-Valued (CV) autoencoder-based data compression for SAR raw data. To this end, the basic fundamentals of data compression and Rate-Distortion (RD) theory are reviewed, well known data compression methods, Block Adaptive Quantization (BAQ) and JPEG2000 methods, are implemented and tested for SAR raw data compression, and a neural data compression based on CV autoencoders is developed for SAR raw data. Furthermore, since the available Sentinel-1 SAR raw products are already compressed with Flexible Dynamic BAQ (FDBAQ), an adaptation procedure applied to the decoded SAR raw data to generate SAR raw data with quasi-uniform quantization that resemble the statistics of the uncompressed SAR raw data onboard the satellites.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"19 3","pages":"572-582"},"PeriodicalIF":8.7,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10955162","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Protecting Images From Manipulations With Deep Optical Signatures","authors":"Kevin Arias;Pablo Gomez;Carlos Hinojosa;Juan Carlos Niebles;Henry Arguello","doi":"10.1109/JSTSP.2025.3554136","DOIUrl":"https://doi.org/10.1109/JSTSP.2025.3554136","url":null,"abstract":"Due to the advancements in deep image generation models, ensuring digital image authenticity, integrity, and confidentiality becomes challenging. While many active image manipulation detection methods embed digital signatures post-image acquisition, the vulnerabilities persist if unauthorized access occurs before this embedding or the embedding software is compromised. This work introduces an optics-based active image manipulation detection approach that learns the structure of a color-coded aperture (CCA), which encodes the light within the camera and embeds a highly reliable and imperceptible optical signature before image acquisition. We optimize our camera model with our proposed image manipulation detection network via end-to-end training. We validate our approach with extensive simulations and a proof-of-concept optical system. The results show that our method outperforms the state-of-the-art active image manipulation detection techniques.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"19 3","pages":"549-558"},"PeriodicalIF":8.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MIMO-Based Indoor Localisation With Hybrid Neural Networks: Leveraging Synthetic Images From Tidy Data for Enhanced Deep Learning","authors":"Manuel Castillo-Cara;Jesus Martínez-Gómez;Javier Ballesteros-Jerez;Ismael García-Varea;Raúl García-Castro;Luis Orozco-Barbosa","doi":"10.1109/JSTSP.2025.3555067","DOIUrl":"https://doi.org/10.1109/JSTSP.2025.3555067","url":null,"abstract":"Indoor localization determines an object's position within enclosed spaces, with applications in navigation, asset tracking, robotics, and context-aware computing. Technologies range from WiFi and Bluetooth to advanced systems like Massive Multiple Input-Multiple Output (MIMO). MIMO, initially designed to enhance wireless communication, is now key in indoor positioning due to its spatial diversity and multipath propagation. This study integrates MIMO-based indoor localization with Hybrid Neural Networks (HyNN), converting structured datasets into synthetic images using TINTO. This research marks the first application of HyNNs using synthetic images for MIMO-based indoor localization. Our key contributions include: (i) adapting TINTO for regression problems; (ii) using synthetic images as input data for our model; (iii) designing a novel HyNN with a Convolutional Neural Network branch for synthetic images and an MultiLayer Percetron branch for tidy data; and (iv) demonstrating improved results and metrics compared to prior literature. These advancements highlight the potential of HyNNs in enhancing the accuracy and efficiency of indoor localization systems.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"19 3","pages":"559-571"},"PeriodicalIF":8.7,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10946146","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sign-Enhanced Semidefinite Programming Algorithm and its Application to Independent Component Analysis","authors":"Dahu Wang;Chang Liu","doi":"10.1109/JSTSP.2025.3552918","DOIUrl":"https://doi.org/10.1109/JSTSP.2025.3552918","url":null,"abstract":"Independent component analysis (ICA) is widely applied in remote sensing signal processing. Among various ICA algorithms, the modified semidefinite programming (MSDP) algorithm stands out. However, the efficacy and safety of MSDP depend on the distribution of data. Our research found that MSDP is better suited for handling data with a super-Gaussian distribution. As real-world data usually exhibit a combination of sub-Gaussian and super-Gaussian distributions, MSDP faces challenges in accurately extracting all independent components (ICs). To solve this problem, we conducted a comprehensive analysis of the MSDP algorithm and introduced an enhanced version, the sign-enhanced MSDP (SMSDP) algorithm. By incorporating the sign function into the projected Hessian matrix, SMSDP enables the algorithm to effectively extract ICs from data characterized by a mixture of sub-Gaussian and super-Gaussian distributions. Furthermore, we provided a detailed comparison with MSDP to illustrate why SMSDP can achieve more accurate eigenpairs. Some experiments have demonstrated the effectiveness of SMSDP. The experiments in blind separation of image/sound, radar clutter removal, and real hyperspectral feature extraction also show the superiority of SMSDP in improving the accuracy of IC extraction.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"19 3","pages":"536-548"},"PeriodicalIF":8.7,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IEEE Signal Processing Society Information","authors":"","doi":"10.1109/JSTSP.2025.3566919","DOIUrl":"https://doi.org/10.1109/JSTSP.2025.3566919","url":null,"abstract":"","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"19 3","pages":"C3-C3"},"PeriodicalIF":8.7,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11006306","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IEEE Signal Processing Society Publication Information","authors":"","doi":"10.1109/JSTSP.2025.3566895","DOIUrl":"https://doi.org/10.1109/JSTSP.2025.3566895","url":null,"abstract":"","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"19 3","pages":"C2-C2"},"PeriodicalIF":8.7,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11006281","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Model-Based Learning for Multi-Antenna Multi-Frequency Location-to-Channel Mapping","authors":"Baptiste Chatelier;Vincent Corlay;Matthieu Crussière;Luc Le Magoarou","doi":"10.1109/JSTSP.2025.3549952","DOIUrl":"https://doi.org/10.1109/JSTSP.2025.3549952","url":null,"abstract":"Years of study of the propagation channel showed a close relation between a location and the associated communication channel response. The use of a neural network to learn the location-to-channel mapping can therefore be envisioned. The Implicit Neural Representation (INR) literature showed that classical neural architecture are biased towards learning low-frequency content, making the location-to-channel mapping learning a non-trivial problem. Indeed, it is well known that this mapping is a function rapidly varying with the location, on the order of the wavelength. This paper leverages the model-based machine learning paradigm to derive a problem-specific neural architecture from a propagation channel model. The resulting architecture efficiently overcomes the spectral-bias issue. It only learns low-frequency sparse correction terms activating a dictionary of high-frequency components. The proposed architecture is evaluated against classical INR architectures on realistic synthetic data, showing much better accuracy. Its mapping learning performance is explained based on the approximated channel model, highlighting the explainability of the model-based machine learning paradigm.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"19 3","pages":"520-535"},"PeriodicalIF":8.7,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Purification of Contaminated Convolutional Neural Networks via Robust Recovery: An Approach With Theoretical Guarantee in One-Hidden-Layer Case","authors":"Hanxiao Lu;Zeyu Huang;Ren Wang","doi":"10.1109/JSTSP.2025.3549950","DOIUrl":"https://doi.org/10.1109/JSTSP.2025.3549950","url":null,"abstract":"Convolutional neural networks (CNNs), one of the key architectures of deep learning models, have achieved superior performance on many machine learning tasks such as image classification, video recognition, and power systems. Despite their success, CNNs can be easily contaminated by natural noises and artificially injected noises such as backdoor attacks. In this paper, we propose a robust recovery method to remove the noise from the potentially contaminated CNNs and provide an exact recovery guarantee on one-hidden-layer non-overlapping CNNs with the rectified linear unit (ReLU) activation function. Our theoretical results show that both CNNs' weights and biases can be exactly recovered under the overparameterization setting with some mild assumptions. The experimental results demonstrate the correctness of the proofs and the effectiveness of the method in both the synthetic environment and the practical neural network setting. Our results also indicate that the proposed method can be extended to multiple-layer CNNs and potentially serve as a defense strategy against backdoor attacks.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"19 3","pages":"507-519"},"PeriodicalIF":8.7,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reliable Automatic Modulation Classification via Grayscale Spectral Quotient Constellation Matrix and Deep Learning Models","authors":"Jiashuo He;Yuting Chen;Shanchuan Ying;Shuo Chang;Sai Huang;Zhiyong Feng","doi":"10.1109/JSTSP.2025.3547223","DOIUrl":"https://doi.org/10.1109/JSTSP.2025.3547223","url":null,"abstract":"Automatic modulation classification (AMC) is one of the crucial technologies for designing an intelligent and efficient transceiver for future wireless communications. However, the channel interferences can cause instability in traditional signal representations, such as inphase and quadrature (I/Q) sequence, and constellations, leading to poor generalization and significant classification performance degradation in new channel environments. Retraining the classifier to achieve robust and effective performance in such cases requires a large number of re-collected samples and consumes vast computational resources, which makes it costly and difficult to apply in practice. To solve this problem, we propose the grayscale spectral quotient constellation matrix (GSQCM)-based AMC methods using deep learning (DL) in orthogonal frequency division multiplexing (OFDM) systems, which do not require retraining the classifier or performing equalization even for the unseen channel cases. Specifically, we first propose a novel method, named bidirectional and multi-step spectral cyclic division (BMSSCD), to generate the channel-robust spectral quotient signals in a length-extension manner. Then, we convert these generated signals into dimension-specific GSQCMs. Finally, the GSQCMs are used as the input to train our classifiers based on several classical DL models, such as AlexNet, VGGNet, GoogLeNet, and ResNet. It is noted that all of the DL-based classifiers are trained under additive white Gaussian noise (AWGN) channel but tested under Rician and Rayleigh multipath fading channels. Extensive simulations show that (i) the novel signal representation, i.e., GSQCM, is well suited as network input for the DL-based AMC methods to train the reliable classifiers, avoiding the model overfitting on the dataset collected under a specific channel condition, (ii) the proposed GSQCM-DL methods exhibit strong generalization, achieving robust and superior performance in comparison to some existing methods when the unseen propagation scenarios are considered.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"19 3","pages":"583-594"},"PeriodicalIF":8.7,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IEEE Signal Processing Society Publication Information","authors":"","doi":"10.1109/JSTSP.2025.3562641","DOIUrl":"https://doi.org/10.1109/JSTSP.2025.3562641","url":null,"abstract":"","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"19 2","pages":"C2-C2"},"PeriodicalIF":8.7,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10982379","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}