IEEE Signal Processing Letters最新文献_第2页

Fishing Net Optimization: A Learning Scheme of Optimizing Multi-Lateration Stations in Air-Ground Vehicle Networks 渔网优化：优化空地车辆网络中多测站的学习方案

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-10-14 DOI: 10.1109/LSP.2024.3479923

Haitao Zhao;Chunxi Zhao;Tianyu Zhang;Bo Xu;Jinlong Sun

引用次数: 0

Towards Hybrid Quantum-Classical Deep Learning Architecture for Indoor-Outdoor Detection Using QCNN-LSTM and Cluster State Signal Processing 利用 QCNN-LSTM 和群集态信号处理实现用于室内外检测的混合量子-经典深度学习架构

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-10-14 DOI: 10.1109/LSP.2024.3480043

Muhammad Bilal Akram Dastagir;Dongsoo Han

{"title":"Towards Hybrid Quantum-Classical Deep Learning Architecture for Indoor-Outdoor Detection Using QCNN-LSTM and Cluster State Signal Processing","authors":"Muhammad Bilal Akram Dastagir;Dongsoo Han","doi":"10.1109/LSP.2024.3480043","DOIUrl":"https://doi.org/10.1109/LSP.2024.3480043","url":null,"abstract":"Quantum computing, combined with deep learning, leverages principles like superposition and entanglement to enhance complex data-driven tasks. The Noisy Intermediate-Scale Quantum (NISQ) era presents opportunities for hybrid quantum-classical architectures to address this challenge. Despite significant progress, practical applications of these hybrid models are limited. This letter proposes a novel hybrid quantum-classical deep learning architecture, integrating Quantum Convolutional Neural Networks (QCNNs) and Long-Short-Term Memory (LSTM) networks, enhanced by Cluster State Signal Processing. Furthermore, this letter addresses indoor-outdoor detection using high-dimensional signal data, utilizing the Cirq platform—a Python framework for developing and simulating Noisy Intermediate Scale Quantum (NISQ) circuits on quantum computers and simulators. The approach addresses noise and decoherence issues. Preliminary results show that the QCNN-LSTM model outperforms pure quantum and hybrid models in accuracy and efficiency. This validates the practical benefits of hybrid architectures, paving the way for advancements in complex data classification like indoor-outdoor detection.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Color and Geometric Contrastive Learning Based Intra-Frame Supervision for Self-Supervised Monocular Depth Estimation 基于色彩和几何对比学习的帧内监督，实现自我监督式单目深度估算

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-10-14 DOI: 10.1109/LSP.2024.3480032

Yanbo Gao;Xianye Wu;Shuai Li;Xun Cai;Chuankun Li

{"title":"Color and Geometric Contrastive Learning Based Intra-Frame Supervision for Self-Supervised Monocular Depth Estimation","authors":"Yanbo Gao;Xianye Wu;Shuai Li;Xun Cai;Chuankun Li","doi":"10.1109/LSP.2024.3480032","DOIUrl":"https://doi.org/10.1109/LSP.2024.3480032","url":null,"abstract":"In recent years, self-supervised monocular depth estimation has become popular due to its advantage in estimating the depth without the need of groundtruth depth labels. Instead, it takes an inter-frame supervision using depth based view synthesis to reconstruct temporal adjacent frames to indirectly supervise the generated depth. However, such supervision weakens the depth estimation at temporal incoherent regions containing small changes among consecutive frames. To overcome the above problem, we propose a color and geometric contrastive learning based intra-frame supervision framework to enhance self-supervised monocular depth estimation. Color-contrastive learning is proposed to guide the network to learn color invariant features considering color information is irrelevant to depth data. To improve the local details of the learned feature, a pixel-level contrastive learning is further used to optimize the learning. In view that the depth estimation, as a pixel-level task, is sensitive to the geometric transformation, geometric-contrastive learning is developed using an inverse geometric transformation to learn features that are equivariant to the geometric data augmentation. A local plane guidance layer (LPG) with contrastive learning is further used to decompose the geometric information and enhance the geometric contrastive learning. Experiments demonstrate that the proposed method achieves the best result compared to the state-of-the-art methods in all tested quality metrics, with the largest improvement of 22.8% over baseline Monodepth2 and 3.2% over Monovit, in terms of SqRel reduction.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A New Family of Graph Representation Matrices: Application to Graph and Signal Classification 新的图形表示矩阵系列：图形和信号分类的应用

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-10-14 DOI: 10.1109/LSP.2024.3479918

T. Averty;A. O. Boudraa;D. Daré-Emzivat

引用次数: 0

Adversarial Embedding Steganography via Progressive Probability Optimizing and Discarded Stego Recycling 通过渐进概率优化和丢弃式偷窃回收实现逆向嵌入式隐写术

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-10-10 DOI: 10.1109/LSP.2024.3478109

Fan Wang;Zhangjie Fu;Xiang Zhang;Junjie Lu

{"title":"Adversarial Embedding Steganography via Progressive Probability Optimizing and Discarded Stego Recycling","authors":"Fan Wang;Zhangjie Fu;Xiang Zhang;Junjie Lu","doi":"10.1109/LSP.2024.3478109","DOIUrl":"https://doi.org/10.1109/LSP.2024.3478109","url":null,"abstract":"Adversarial embedding for image steganography is a novel technology to effectively enhance the steganographic security of the traditional steganographic algorithms. However, the existing schemes still have room for further improvement in the design of optimization strategy and the steganographic post-processing of optimization failure. In this paper, we design the progressive probability optimizing strategy (PPO). It dynamically selects more efficient gradients to guide the optimization of the probability optimization in a progressive manner. Moreover, we propose a discarded stego recycling mechanism (DSR) to re-select the stego from the discarded stego set that have failed to deceive the target steganalyzer after the optimzation fails. In such way, the statistical distribution of the stego can still further approximate the cover, thus further improving the steganographic security on re-trained steganalyzers in adversary-aware scenario. Comprehensive experiments show that compared with the existing advanced schemes, the proposed method boosts the security improvement against both the re-trained hand-crafted feature-based and deep leanring-based steganalysis models.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient Vibrotactile Codec Based on Nbeats Network 基于 Nbeats 网络的高效振动触觉编解码器

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-10-09 DOI: 10.1109/LSP.2024.3477251

Yiwen Xu;Dongfang Chen;Ying Fang;Yang Lu;Tiesong Zhao

引用次数: 0

Adaptive Pattern-Coupled Sparse Bayesian Learning for Channel Estimation in OTFS Systems 自适应模式耦合稀疏贝叶斯学习用于 OTFS 系统中的信道估计

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-10-09 DOI: 10.1109/LSP.2024.3477254

Zhuo Chen;Xiaoming Niu;Jian Ding;Hong Wu;Zhiyang Liu

引用次数: 0

Edge-Aware Attention Transformer for Image Super-Resolution 用于图像超分辨率的边缘感知注意力变换器

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-10-09 DOI: 10.1109/LSP.2024.3477298

Haoqian Wang;Zhongyang Xing;Zhongjie Xu;Xiangai Cheng;Teng Li

{"title":"Edge-Aware Attention Transformer for Image Super-Resolution","authors":"Haoqian Wang;Zhongyang Xing;Zhongjie Xu;Xiangai Cheng;Teng Li","doi":"10.1109/LSP.2024.3477298","DOIUrl":"https://doi.org/10.1109/LSP.2024.3477298","url":null,"abstract":"In this study, we explore poor edge reconstruction in image super-resolution (SR) tasks, emphasizing the significance of enhancing edge details identified through visual analysis. Existing SR networks typically optimize their network architectures, enabling complete feature extraction from feature maps. This is because the management of spatial and channel information during SR is often pivotal to the network's feature extraction capacity. Despite continuous improvements, directly comparing SR and high-resolution (HR) images through differential mapping reveals the suboptimal performance of these methods in edge reconstruction. In this paper, we introduce a edgey-aware attention transformer (EAT), which focuses on edge reconstruction while maintaining the effective original low frequency information retrieval. Our framework utilizes deformable convolution (DC) to adaptively extract edge features. Then feature enhancement techniques are employed to intensify edge-sensitive features. Furthermore, extensive experiments demonstrate our EAT's exceptional quantitative and visual results, which surpass most benchmarks. This validates the EAT's effectiveness when compared to state-of-the-art models. The code is available at \u0000<uri>https://github.com/ImWangHaoqian/EAT</uri>\u0000.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrated Sensing and Communications Waveform Design for OTFS and FTN Fusion 用于 OTFS 和 FTN 融合的综合传感与通信波形设计

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-10-09 DOI: 10.1109/LSP.2024.3478112

Xiaolong Yang;Bingrui Zhang;Mu Zhou;Ming Gao

引用次数: 0

Radar-Based Crowd Counting in Real-World Environments With Spatiotemporal Transformer 利用时空变换器在真实世界环境中进行基于雷达的人群计数

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-10-09 DOI: 10.1109/LSP.2024.3477263

Jae-Ho Choi;Kyung-Tae Kim

引用次数: 0