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Communication-Efficient Secure Distributed Estimation With Noisy Measurement Against FDI Attack IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-16 Zhanxi Zhang, Lijuan Jia, Senran Peng, Zi-Jiang Yang, Ran Tao
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Improved Algorithm for Efficient Computation of Slepian Functions over Arbitrary Regions on the Sphere IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-16 Haipeng Yu, Guobin Chang, Shubi Zhang
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Semantic-aware Adaptive Prompt Learning for Universal Multi-source Domain Adaptation IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-16 Yuxiang Yang, Yun Hou, Lu Wen, Pinxian Zeng, Yan Wang
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Multi-Organ Registration With Continual Learning IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-16 Wangbin Ding, Haoran Sun, Chenhao Pei, Dengqiang Jia, Liqin Huang
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Energy-Efficient Data Collection in Molecular Nanonetworks: An Optimization Framework IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-16 Farzad H. Panahi, Fereidoun H. Panahi
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Multi-Level Signal Fusion for Enhanced Weakly-Supervised Audio-Visual Video Parsing IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-16 Xin Sun, Xuan Wang, Qiong Liu, Xi Zhou
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Color Image Denoising Using Reduced Biquaternion U-Network IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-16 Bofan Nie, Shan Gai, Gonghe Xiong
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Geometric Warping Error aware Spatial-Temporal Enhancement for DIBR oriented View Synthesis IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-16 Dewei Wang, Rui Peng, Shuai Li, Yanbo Gao, Chuankun Li
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Towards Generated Image Provenance Analysis Via Conceptual-Similar-Guided-SLIP Retrieval IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-16 Xiaojie Xia, Liuan Wang, Jun Sun, Akira Nakagawa
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Point Spatio-Temporal Pyramid Network for Point Cloud Video Understanding IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-16 Zhiqiang Shen, Longguang Wang, Yulan Guo, Qiong Liu, Xi Zhou
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Navigating Uncertainty: Semantic-Powered Image Enhancement and Fusion IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-16 Jiaxin Yao, Yongqiang Zhao, Seong G. Kong, Xun Zhang
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KLANN: Linearising Long-Term Dynamics in Nonlinear Audio Effects Using Koopman Networks IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-16 Ville Huhtala, Lauri Juvela, Sebastian J. Schlecht
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Hierarchical Image Feature Compression for Machines Via Feature Sparsity Learning IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-16 Ding Ding, Zhenzhong Chen, Zizheng Liu, Xiaozhong Xu, Shan Liu
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Sparseness and Correntropy-Based Block Diagonal Representation for Robust Subspace Clustering IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-16 Yesong Xu, Ping Hu, Jiashu Dai, Nan Yan, Jun Wang
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Generalized Zadoff-Chu Sequences With Low PMEPR Property IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-16 Zhi Gu, Zhengchun Zhou, Avik Ranjan Adhikary, Pingzhi Fan, Yang Yang
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SSFE-M: A Self-Supervised Feature Extraction Model for Enhanced Camera Calibration IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-16 Neng Zhang, Ebroul Izquierdo
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Time of Arrival Estimation for Backscatter UWB IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-16 Chen He, Peng Wu, Luyang Han
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Leveraging Non-Causal Knowledge Via Cross-Network Knowledge Distillation for Real-Time Speech Enhancement IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-16 Hyun Joon Park, Wooseok Shin, Jin Sob Kim, Sung Won Han
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3D Lane Detection With Attention in Attention IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-15 Yinchao Gu, Chao Ma, Qian Li, Xiaokang Yang
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Enhancing Adversarial Transferability via Information Bottleneck Constraints IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-15 Biqing Qi, Junqi Gao, Jianxing Liu, Ligang Wu, Bowen Zhou
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Sr-LDA:Sparse and Reduced-rank Linear Discriminant Analysis for High Dimensional Matrix IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-10 Yao Wang, Cheng Wang, Binyan Jiang
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Open-Set Jamming Pattern Recognition via Generated Unknown Jamming Data IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-10 Guoqiang Wang, Yulong Gao
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Reverse Attention-Based Multi-Feature Interaction Network for Finger Vein Image Quality Evaluation IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-09 Yunhao Chi, Lu Yang, Fanchang Hao, Haiying Liu
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Channel Modeling for Receptors Reversible Reactive Receiver With Continuous Release of Ligands IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-09 Zhuo Sun, Yingqi Wang, Xu Bao
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Noise Morphing for Audio Time Stretching IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-08 Eloi Moliner, Leonardo Fierro, Alec Wright, Matti S. Hämäläinen, Vesa Välimäki
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Self-Knowledge Distillation-Based Staged Extraction and Multiview Collection Network for RGB-D Mirror Segmentation IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-08 Han Zhang, Xiaoxiao Ran, Wujie Zhou
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Unimodular Waveform Design for Dual-Function Radar-Communication Systems Under Per-User MUI Energy Constraint IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-08 Ye Yuan, Yuankai Wang, Kai Zhong, Jinfeng Hu, Dongxu An
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Hard-Soft Pseudo Labels Guided Semi-Supervised Learning for Point Cloud Classification IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-08 Yuan He, Guyue Hu, Shan Yu
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Class Based Thresholding in Early Exit Semantic Segmentation Networks IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-08 Alperen Görmez, Erdem Koyuncu
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Jamming Waveform Generation Method Based on Generative Adversarial Network Model IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-08 Yilin Jiang, Lisong Guan, Wenxuan Liu, Yuwei Yu
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The No-U-Turn Sampler as a Proposal Distribution in a Sequential Monte Carlo Sampler without Accept/Reject IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-08 Lee Devlin, Matthew Carter, Paul Horridge, Peter L. Green, Simon Maskell
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Hybrid ViT-CNN Network for Fine-Grained Image Classification IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-08 Ran Shao, Xiao-Jun Bi, Zheng Chen
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Instabilities in Convnets for Raw Audio IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-08 Daniel Haider, Vincent Lostanlen, Martin Ehler, Peter Balazs
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Message Passing Based Gaussian Mixture Model for DOA Estimation in Complex Noise Scenarios IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-08 Shanwen Guan, Xinhua Lu, Ji Li, Xiaonan Luo
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Selective HuBERT: Self-Supervised Pre-Training for Target Speaker in Clean and Mixture Speech IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-03 Jingru Lin, Meng Ge, Wupeng Wang, Haizhou Li, Mengling Feng
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An Underwater Image Enhancement Method Based on Balanced Adaption Compensation IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-03 Wenjia Ouyang, Junnan Liu, Yanhui Wei
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TPE-DF: Thumbnail Preserving Encryption via Dual-2DCS Fusion IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-03 Wenying Wen, Qiyu Jiang, Haigang Huang, Yushu Zhang, Yuming Fang
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Multiple Extended Target Joint Tracking and Classification Based on GPs and LMB Filter IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-01 Xuan Cheng, Hongbing Ji, Yongquan Zhang
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Training Neural Networks on Remote Edge Devices for Unseen Class Classification IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-01 Shayan Mohajer Hamidi
Conventionally, training a deep neural network (DNN) involves minimizing an empirical risk over a training dataset that comprises a certain number of classes. However, for training more versatile DNNs on edge devices, the training datasets are often updated to contain new classes that were not present in the original dataset. To this end, a naive approach could be to share the training samples corresponding
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Learning-Based Image Compression With Parameter-Adaptive Rate-Constrained Loss IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-01 Nilson D. Guerin, Renam Castro da Silva, Bruno Macchiavello
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One-Bit Aggregation for Over-the-Air Federated Learning Against Byzantine Attacks IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-01 Yifan Miao, Wanli Ni, Hui Tian
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Fast CU Partition for VVC Intra-Frame Coding Via Texture Complexity IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-01 Yun Song, Shisheng Cheng, Miaohui Wang, Xiangrong Peng
In versatile video coding (VVC), the quadtree with nested multi-type tree (QTMT) partition module significantly improves encoding performance compared to other coding tools. However, it also introduces notable computational complexity in intra-frame coding, occupying over 90% of the encoding time. This letter presents a fast coding unit (CU) partition method based on texture complexity to achieve a
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Light Field Image Restoration Via Latent Diffusion and Multi-View Attention IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-01 Shansi Zhang, Edmund Y. Lam
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GOENet: Group Operations Enhanced Binary Neural Network for Efficient Image Classification IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-01 Rui Ding, Yuxiao Wang, Haijun Liu, Xichuan Zhou
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Image Hiding and Restoration via Deep Moiré Networks IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-01 Xi Cheng, Zhenyong Fu
Moiré patterns arise when two repetitive textures are superimposed, which often degrade the image quality when taking photos. However, controllable moiré patterns can be useful in many practical applications e.g. image hiding. Existing moiré-based approaches heavily rely on manually designed filters, resulting in slow and inflexibility. Moreover, existing methods often struggle to recover image details
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Enhancing High-Resolution Image Compression Through Local-Global Joint Attention Mechanism IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-01 Zeyu Jiang, Xiaohong Liu, Aini Li, Guangyu Wang
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Optimal Parametric Estimation of Biased Sinusoidal Signals Using DREM IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-01 Dongxu Gao, Lijun Liu, Zhen Yu, Shihan Liu
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A Sequence-to-Sequence Model for Online Signal Detection and Format Recognition IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-04-01 Le Cheng, Hongna Zhu, Zhengliang Hu, Bin Luo
Signal detection and format recognition are critical and challenging tasks across civil and military sectors. However, they often encounter signal truncation issues during online signal processing, resulting in inaccurate predictions due to incomplete features. To address this issue, we herein propose the signal detection mask long short-term memory (SDM-LSTM) network module. The SDM-LSTM module facilitates
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Efficient Parallel Audio Generation Using Group Masked Language Modeling IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-03-27 Myeonghun Jeong, Minchan Kim, Joun Yeop Lee, Nam Soo Kim
We present a fast and high-quality codec language model for parallel audio generation. While SoundStorm, a state-of-the-art parallel audio generation model, accelerates inference speed compared to autoregressive models, it still suffers from slow inference due to iterative sampling. To resolve this problem, we propose Group-Masked Language Modeling (G-MLM) and Group Iterative Parallel Decoding (G-IPD)
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Synthetic Speech Detection Based on the Temporal Consistency of Speaker Features IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-03-27 Yuxiang Zhang, Zhuo Li, Jingze Lu, Wenchao Wang, Pengyuan Zhang
Current synthetic speech detection (SSD) methods perform well on specific datasets but require improvement in interpretability and robustness. One possible reason is the lack of interpretability analysis of synthetic speech defects. In this paper, the flaws in the temporal consistency (TC) of speaker features inherent in the speech synthesis process are analyzed. Differences in the TC of intra-utterance
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PD-CR: Patch-Based Diffusion Using Constrained Refinement for Image Restoration IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-03-26 Hyunjun Cho, Hong-Kyu Shin, Yurim Jang, Sung-Jea Ko, Seung-Won Jung
Diffusion models, which are state-of-the-art generative models, have been widely applied to image restoration tasks. However, most image restoration methods based on diffusion models require a large amount of computational memory, making it difficult to use them with high-resolution images. Although patch-based diffusion models have emerged to address this problem, these models are limited in effectively
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Sorting Convolution Operation for Achieving Rotational Invariance IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-03-26 Hanlin Mo, Guoying Zhao
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Masked and Permuted Implicit Context Learning for Scene Text Recognition IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-03-26 Xiaomeng Yang, Zhi Qiao, Jin Wei, Dongbao Yang, Yu Zhou
Scene Text Recognition (STR) is challenging because of various text styles, shapes, and backgrounds. Although the integration of linguistic information enhances models' performance, existing methods based on either permuted language modeling (PLM) or masked language modeling (MLM) have their drawbacks. PLM's autoregressive decoding lacks foresight into subsequent characters, while MLM overlooks inter-character
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Event-based Shutter Unrolling and Motion Deblurring in Dynamic Scenes IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-03-26 Yangguang Wang, Chenxu Jiang, Xu Jia, Yufei Guo, Lei Yu
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GPU Implementation of a Fast Multichannel Wiener Filter Algorithm for Active Noise Control IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-03-25 Junlin Liu, Hongling Sun, Ming Wu, Jun Yang
Most of the traditional active noise control (ANC) systems are implemented using digital signal processor (DSP), but the lack of computational power of DSP has been a limitation to the application and development of ANC technology. Currently, it is a feasible approach to utilize Graphics Processing Unit (GPU) to enhance the computational power of the ANC system. The advantage of GPU is parallel computation
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A Novel DFT-Based Algorithm for 2-D Multiple Sinusoidal Frequency Estimation IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-03-22 Veyis Solak, Ahmet Serbes
Frequency estimation of a two dimensional (2-D) multi-component sinusoidal signal in the presence of additive white Gaussian noise (AWGN) is a significant problem in various disciplines such as signal processing, radar/sonar, and wireless communications. This letter presents a novel, fast and accurate DFT-based algorithm for the frequency estimation of 2-D multi-component sinusoidal signals. We show
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MaGAT: Mask-Guided Adversarial Training for Defending Face Editing GAN Models From Proactive Defense IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-03-21 Shengwei Luo, Fangjun Huang
The malicious misuse of face editing technology has endangered individual privacy and reputation. Adversarial attack-based proactive defense has been proposed to against it, which could prevent facial images from being successfully manipulated by face editing GAN models. However, the malicious manipulators could defeat proactive defense through adversarial training. Therefore, studying the effectiveness
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RF-Based Drone Detection Enhancement via a Generalized Denoising and Interference-Removal Framework IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-03-20 Ziqi Wang, Zihan Cao, Julan Xie, Wei Zhang, Zishu He
Radio frequency-based (RF-based) detection methods are currently the main means of countering drones. However, these prevalent approaches frequently exhibit deficiencies in effectively addressing noise and interference, making them potentially unsuitable for application in realistic urban environments. This letter proposes a generalized RF signal-enhanced framework that explicitly addresses noise and
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Visual-Textual Cross-Modal Interaction Network for Radiology Report Generation IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-03-20 Wenfeng Zhang, Baoning Cai, Jianming Hu, Qibing Qin, Kezhen Xie
The radiology report generation task generates diagnostic descriptions from radiology images, aiming to alleviate the onerous task for radiologists and alerting them to abnormalities. However, the data bias problem poses a persistent challenge, since the abnormal regions usually occupy a small portion of radiology image, while the report generation process should pay greater attention to the abnormal
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Gram Matrix Completion for Cooperative Localization in Partially Connected Wireless Sensor Network IEEE Signal Process. Lett. (IF 3.9) Pub Date : 2024-03-20 Peiyue Jiang, Zehong Zhuang, Wei Xie
The performance of cooperative localization can be degraded in the partially connected wireless sensor network (WSN). In this letter, we address the localization problem based on the incomplete distance information. By considering the input of distance-based localization algorithms, we proposed a novel Gram matrix completion method. With the function of Gram matrix, we indirectly represent the measurement