Pub Date : 2024-10-14DOI: 10.1109/LSP.2024.3479923
Haitao Zhao;Chunxi Zhao;Tianyu Zhang;Bo Xu;Jinlong Sun
Integrated sensing and communication in 6G, particularly for air-ground surveillance using automatic dependent surveillance-broadcast (ADS-B) and multi-lateration (MLAT) systems, is gaining significant research interest. This letter investigates the problem of optimal anchor station selection for tracking aerial vehicles, and proposes a novel heuristic learning scheme termed as fishing net-like optimization (FNO). Specifically, we perform constrained random walk steps on a two-dimensional surface to optimize the initial anchor stations’ parameters. FNO also incorporates with new evaluation strategies and acceleration techniques to accelerate the convergence speed. Experimental results demonstrate that FNO can achieve better selection of the anchor stations, and the accuracy of the chosen MLAT can be improved by ten times or more with the anchors optimization.
{"title":"Fishing Net Optimization: A Learning Scheme of Optimizing Multi-Lateration Stations in Air-Ground Vehicle Networks","authors":"Haitao Zhao;Chunxi Zhao;Tianyu Zhang;Bo Xu;Jinlong Sun","doi":"10.1109/LSP.2024.3479923","DOIUrl":"https://doi.org/10.1109/LSP.2024.3479923","url":null,"abstract":"Integrated sensing and communication in 6G, particularly for air-ground surveillance using automatic dependent surveillance-broadcast (ADS-B) and multi-lateration (MLAT) systems, is gaining significant research interest. This letter investigates the problem of optimal anchor station selection for tracking aerial vehicles, and proposes a novel heuristic learning scheme termed as fishing net-like optimization (FNO). Specifically, we perform constrained random walk steps on a two-dimensional surface to optimize the initial anchor stations’ parameters. FNO also incorporates with new evaluation strategies and acceleration techniques to accelerate the convergence speed. Experimental results demonstrate that FNO can achieve better selection of the anchor stations, and the accuracy of the chosen MLAT can be improved by ten times or more with the anchors optimization.","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":"142524124","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}
Pub Date : 2024-10-14DOI: 10.1109/LSP.2024.3480043
Muhammad Bilal Akram Dastagir;Dongsoo Han
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.
量子计算与深度学习相结合，可利用叠加和纠缠等原理来增强复杂的数据驱动任务。噪声中量子（NISQ）时代为混合量子-经典架构应对这一挑战提供了机遇。尽管取得了重大进展，但这些混合模型的实际应用仍然有限。这封信提出了一种新型混合量子-经典深度学习架构，它整合了量子卷积神经网络（QCNN）和长短期记忆（LSTM）网络，并通过簇态信号处理（Cluster State Signal Processing）进行了增强。此外，这封信还利用 Cirq 平台--在量子计算机和模拟器上开发和模拟噪声中间量级量子（NISQ）电路的 Python 框架--解决了利用高维信号数据进行室内-室外检测的问题。该方法解决了噪声和退相干问题。初步结果表明，QCNN-LSTM 模型在准确性和效率方面优于纯量子模型和混合模型。这验证了混合架构的实际优势，为室内外检测等复杂数据分类的进步铺平了道路。
{"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}
Pub Date : 2024-10-14DOI: 10.1109/LSP.2024.3480032
Yanbo Gao;Xianye Wu;Shuai Li;Xun Cai;Chuankun Li
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.
{"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}
Pub Date : 2024-10-14DOI: 10.1109/LSP.2024.3479918
T. Averty;A. O. Boudraa;D. Daré-Emzivat
Most natural matrices that incorporate information about a graph are the adjacency and the Laplacian matrices. These algebraic representations govern the fundamental concepts and tools in graph signal processing even though they reveal information in different ways. Furthermore, in the context of spectral graph classification, the problem of cospectrality may arise and it is not well handled by these matrices. Thus, the question of finding the best graph representation matrix still stands. In this letter, a new family of representations that well captures information about graphs and also allows to find the standard representation matrices, is introduced. This family of unified matrices well captures the graph information and extends the recent works of the literature. Two properties are proven, namely its positive semidefiniteness and the monotonicity of their eigenvalues. Reported experimental results of spectral graph classification highlight the potential and the added value of this new family of matrices, and evidence that the best representation depends upon the structure of the underlying graph.
{"title":"A New Family of Graph Representation Matrices: Application to Graph and Signal Classification","authors":"T. Averty;A. O. Boudraa;D. Daré-Emzivat","doi":"10.1109/LSP.2024.3479918","DOIUrl":"https://doi.org/10.1109/LSP.2024.3479918","url":null,"abstract":"Most natural matrices that incorporate information about a graph are the adjacency and the Laplacian matrices. These algebraic representations govern the fundamental concepts and tools in graph signal processing even though they reveal information in different ways. Furthermore, in the context of spectral graph classification, the problem of cospectrality may arise and it is not well handled by these matrices. Thus, the question of finding the best graph representation matrix still stands. In this letter, a new family of representations that well captures information about graphs and also allows to find the standard representation matrices, is introduced. This family of unified matrices well captures the graph information and extends the recent works of the literature. Two properties are proven, namely its positive semidefiniteness and the monotonicity of their eigenvalues. Reported experimental results of spectral graph classification highlight the potential and the added value of this new family of matrices, and evidence that the best representation depends upon the structure of the underlying graph.","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":"142524186","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}
Pub Date : 2024-10-10DOI: 10.1109/LSP.2024.3478109
Fan Wang;Zhangjie Fu;Xiang Zhang;Junjie Lu
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.
{"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}
Within the domain of multimodal communication, the compression of audio, image, and video information is well-established, but compressing haptic signals, including vibrotactile signals, remains challenging. Particularly with the enhancement of haptic signal sampling rate and degrees of freedom, there is a substantial increase in data volume. While existing algorithms have made progress in vibrotactile codecs, there remains significant room for improvement in compression ratios. We propose an innovative Nbeats Network-based Vibrotactile Codec (NNVC) that leverages the statistical characteristics of vibrotactile data. This advanced codec integrates the Nbeats network for precise vibrotactile prediction, residual quantization, efficient Run-Length Encoding, and Huffman coding. The algorithm not only captures the intricate details of vibrotactile signals but also ensures high-efficiency data compression. It exhibits robust overall performance in terms of Signal-to-Noise Ratio (SNR) and Peak Signal-to-Noise Ratio (PSNR), significantly surpassing the state-of-the-art.
{"title":"Efficient Vibrotactile Codec Based on Nbeats Network","authors":"Yiwen Xu;Dongfang Chen;Ying Fang;Yang Lu;Tiesong Zhao","doi":"10.1109/LSP.2024.3477251","DOIUrl":"https://doi.org/10.1109/LSP.2024.3477251","url":null,"abstract":"Within the domain of multimodal communication, the compression of audio, image, and video information is well-established, but compressing haptic signals, including vibrotactile signals, remains challenging. Particularly with the enhancement of haptic signal sampling rate and degrees of freedom, there is a substantial increase in data volume. While existing algorithms have made progress in vibrotactile codecs, there remains significant room for improvement in compression ratios. We propose an innovative Nbeats Network-based Vibrotactile Codec (NNVC) that leverages the statistical characteristics of vibrotactile data. This advanced codec integrates the Nbeats network for precise vibrotactile prediction, residual quantization, efficient Run-Length Encoding, and Huffman coding. The algorithm not only captures the intricate details of vibrotactile signals but also ensures high-efficiency data compression. It exhibits robust overall performance in terms of Signal-to-Noise Ratio (SNR) and Peak Signal-to-Noise Ratio (PSNR), significantly surpassing the state-of-the-art.","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":"142447070","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}
Pub Date : 2024-10-09DOI: 10.1109/LSP.2024.3477254
Zhuo Chen;Xiaoming Niu;Jian Ding;Hong Wu;Zhiyang Liu
The orthogonal time frequency space (OTFS) has emerged as a promising modulation waveform for high-mobility wireless communications owing to its robust advantages of resisting Doppler effects. However, due to the limit of the frame duration, the fractional Doppler shift appears, which is a challenge for channel estimation in OTFS systems. In this letter, we formulate the channel estimation problem as a block sparse signal recovery issue and propose an adaptive pattern-coupled sparse Bayesian learning (APCSBL) method. To be specific, we introduce a pattern-coupled hierarchical Gaussian prior model to characterize the dependencies among adjacent channel coefficients. On this basis, an adaptive hyperparameter strategy is presented, in which we appropriately utilize various coupling parameters further to characterize the strength of the correlation between adjacent elements. Then we exploit the expectation maximization (EM) algorithm to update the hidden variables and the channel vector. Simulation results demonstrate that the proposed algorithm outperforms existing methods and works for various environments.
{"title":"Adaptive Pattern-Coupled Sparse Bayesian Learning for Channel Estimation in OTFS Systems","authors":"Zhuo Chen;Xiaoming Niu;Jian Ding;Hong Wu;Zhiyang Liu","doi":"10.1109/LSP.2024.3477254","DOIUrl":"https://doi.org/10.1109/LSP.2024.3477254","url":null,"abstract":"The orthogonal time frequency space (OTFS) has emerged as a promising modulation waveform for high-mobility wireless communications owing to its robust advantages of resisting Doppler effects. However, due to the limit of the frame duration, the fractional Doppler shift appears, which is a challenge for channel estimation in OTFS systems. In this letter, we formulate the channel estimation problem as a block sparse signal recovery issue and propose an adaptive pattern-coupled sparse Bayesian learning (APCSBL) method. To be specific, we introduce a pattern-coupled hierarchical Gaussian prior model to characterize the dependencies among adjacent channel coefficients. On this basis, an adaptive hyperparameter strategy is presented, in which we appropriately utilize various coupling parameters further to characterize the strength of the correlation between adjacent elements. Then we exploit the expectation maximization (EM) algorithm to update the hidden variables and the channel vector. Simulation results demonstrate that the proposed algorithm outperforms existing methods and works for various environments.","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":"142524115","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}
Pub Date : 2024-10-09DOI: 10.1109/LSP.2024.3477298
Haoqian Wang;Zhongyang Xing;Zhongjie Xu;Xiangai Cheng;Teng Li
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 https://github.com/ImWangHaoqian/EAT .
在这项研究中，我们探讨了图像超分辨率（SR）任务中的边缘重建问题，强调了通过视觉分析增强边缘细节的重要性。现有的 SR 网络通常会优化其网络架构，以便从特征图中完整提取特征。这是因为在 SR 过程中，空间和通道信息的管理往往对网络的特征提取能力至关重要。尽管不断改进，但通过差分映射直接比较 SR 和高分辨率（HR）图像发现，这些方法在边缘重建方面的性能并不理想。在本文中，我们介绍了一种边缘感知注意力转换器（EAT），它侧重于边缘重建，同时保持有效的原始低频信息检索。我们的框架利用可变形卷积（DC）自适应地提取边缘特征。然后采用特征增强技术来强化边缘敏感特征。此外，大量实验证明，我们的 EAT 在数量和视觉效果上都非常出色，超越了大多数基准测试。与最先进的模型相比，这验证了 EAT 的有效性。代码见 https://github.com/ImWangHaoqian/EAT。
{"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}
Pub Date : 2024-10-09DOI: 10.1109/LSP.2024.3478112
Xiaolong Yang;Bingrui Zhang;Mu Zhou;Ming Gao
In this letter, we propose an Integrated Sensing and Communications (ISAC) waveform design method based on the fusion of Orthogonal Time Frequency Space (OTFS) and Faster-Than-Nyquist (FTN). The objective is to maximize the communication data transmission rate while minimizing the sensing performance impact on the target parameter estimation. We first map the FTN symbols to OTFS waveform time domain for realizing symbol spacing compression and transmit them in time-varying channels. Then, an equalizer based on the Minimum Mean Square Error (MMSE) algorithm is used to eliminate the interference generated by the FTN. Simulation results show taking into account the system bit-error rate, the proposed method achieves an increase in the throughput as well as an improvement in the distance and velocity estimation of the target compared to the existing methods.
{"title":"Integrated Sensing and Communications Waveform Design for OTFS and FTN Fusion","authors":"Xiaolong Yang;Bingrui Zhang;Mu Zhou;Ming Gao","doi":"10.1109/LSP.2024.3478112","DOIUrl":"https://doi.org/10.1109/LSP.2024.3478112","url":null,"abstract":"In this letter, we propose an Integrated Sensing and Communications (ISAC) waveform design method based on the fusion of Orthogonal Time Frequency Space (OTFS) and Faster-Than-Nyquist (FTN). The objective is to maximize the communication data transmission rate while minimizing the sensing performance impact on the target parameter estimation. We first map the FTN symbols to OTFS waveform time domain for realizing symbol spacing compression and transmit them in time-varying channels. Then, an equalizer based on the Minimum Mean Square Error (MMSE) algorithm is used to eliminate the interference generated by the FTN. Simulation results show taking into account the system bit-error rate, the proposed method achieves an increase in the throughput as well as an improvement in the distance and velocity estimation of the target compared to the existing methods.","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":"142452740","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}
Pub Date : 2024-10-09DOI: 10.1109/LSP.2024.3477263
Jae-Ho Choi;Kyung-Tae Kim
With the advent of deep learning (DL) for signal processing, the deployment of DL for radar-based crowd counting has yielded significant performance enhancement. Despite these advancements, current methodologies predominantly undergo validation in controlled conditions with limited subject movement variability, posing a challenge for practical usage. Addressing this gap, this letter first attempts the application of radar-based crowd counting in an unregulated and dense setting, capturing the radar reflections of up to 31 subjects in real-world scenarios, such as queues at restaurant kiosks. Furthermore, to address the complexities of such a challenging condition, we introduce a novel radar crowd counting model that utilizes a spatiotemporal transformer. The expremental results demonstrate the potentiality of the proposed model as a robust crowd counting system under the full realistic scenarios, as well as establish its superiority over the conventional radar-based crowd counting models.
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