Digital Signal Processing最新文献

Continuous discrete minimum error entropy Kalman filter in non-Gaussian noises system 非高斯噪声系统中的连续离散最小误差熵卡尔曼滤波器

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2024-10-31 DOI: 10.1016/j.dsp.2024.104846

引用次数: 0

Interpretable ADMM-CSNet for interrupted sampling repeater jamming suppression 用于抑制中断采样中继器干扰的可解释 ADMM-CSNet

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2024-10-31 DOI: 10.1016/j.dsp.2024.104850

{"title":"Interpretable ADMM-CSNet for interrupted sampling repeater jamming suppression","authors":"","doi":"10.1016/j.dsp.2024.104850","DOIUrl":"10.1016/j.dsp.2024.104850","url":null,"abstract":"<div><div>Interrupted sampling repeater jamming (ISRJ) is a category of coherent jamming that greatly influences radars' detection performance. Since the ISRJ has greater power than true targets, ISRJ signals can be removed in the time domain. Due to frequency band loss, grating lobes will be produced if pulse compression (PC) is performed directly, which may generate false targets. Compressive sensing (CS) is an effective method to restore the original PC signal. However, it is challenging for classic CS approaches to manually select the optimization parameters (<em>e.g.</em>, penalty parameters, step sizes, etc.) in different ISRJ backgrounds. In this article, a network method based on the Alternating Direction Method of Multipliers (ADMM), named ADMM-CSNet, is introduced to solve the problem. Based on the strong learning capacity of the deep network, all parameters in the ADMM are learned from radar data utilizing back-propagation rather than manually selecting in traditional CS techniques. Compared with classic CS approaches, a higher ISRJ removal signal restoration accuracy is reached faster. Simulation experiments indicate the proposal performs effectively and accurately for ISRJ removal signal reconstruction.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Self-learning based joint multi image super-resolution and sub-pixel registration 基于自学习的多图像联合超分辨率和子像素配准

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2024-10-31 DOI: 10.1016/j.dsp.2024.104837

{"title":"Self-learning based joint multi image super-resolution and sub-pixel registration","authors":"","doi":"10.1016/j.dsp.2024.104837","DOIUrl":"10.1016/j.dsp.2024.104837","url":null,"abstract":"<div><div>Multi Image Super-resolution (MISR) refers to the task of enhancing the spatial resolution of a stack of low-resolution (LR) images representing the same scene. Although many deep learning-based single image super-resolution (SISR) technologies have recently been developed, deep learning has not been widely exploited for MISR, even though it can achieve higher reconstruction accuracy because more information can be extracted from the stack of LR images. One of the primary obstacles encountered by deep networks when addressing the MISR problem is the variability in the number of LR images that act as input to the network. This impedes the feasibility of adopting an end-to-end learning approach, because the varying number of input images makes it difficult to construct a training dataset for the network. Another challenge arises from the requirement to align the LR input images to generate high-resolution (HR) image of high quality, which requires complex and sophisticated methods.</div><div>In this paper, we propose a self-learning based method that can simultaneously perform super-resolution and sub-pixel registration of multiple LR images. The proposed method trains a neural network with only the LR images as input and without any true target HR images; i.e., the proposed method requires no extra training dataset. Therefore, it is easy to use the proposed method to deal with different numbers of input images. To our knowledge this is the first time that a neural network is trained using only LR images to perform a joint MISR and sub-pixel registration. Experimental results confirmed that the HR images generated by the proposed method achieved better results in both quantitative and qualitative evaluations than those generated by other deep learning-based methods.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Dynamic mode decomposition-based technique for cross-term suppression in the Wigner-Ville distribution 基于动态模式分解的维格纳-维尔分布交叉项抑制技术

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2024-10-29 DOI: 10.1016/j.dsp.2024.104833

{"title":"Dynamic mode decomposition-based technique for cross-term suppression in the Wigner-Ville distribution","authors":"","doi":"10.1016/j.dsp.2024.104833","DOIUrl":"10.1016/j.dsp.2024.104833","url":null,"abstract":"<div><div>This paper presents a new method for time-frequency representation (TFR) using dynamic mode decomposition (DMD) and Wigner-Ville distribution (WVD), which is termed as DMD-WVD. The proposed method helps in removing cross-term in WVD-based TFR. In the suggested method, the DMD decomposes the multi-component signal into a set of modes where each mode is considered as mono-component signal. The analytic modes of these obtained mono-component signals are computed using the Hilbert transform. The WVD is computed for each analytic mode and added together to obtain cross-term free TFR based on the WVD. The effectiveness of the proposed method for TFR is evaluated using Rényi entropy (RE). Experimental results for synthetic signals namely, multi-component amplitude modulated signal, multi-component linear frequency modulated (LFM) signal, multi-component nonlinear frequency modulated (NLFM) signal, multi-component signal consisting of LFM and NLFM mono-component signal, multi-component signal consisting of sinusoidal and quadratic frequency modulated mono-component signals, and synthetic mechanical bearing fault signal and natural signals namely, electroencephalogram (EEG) and bat echolocation signals are presented in order to show the effectiveness of the proposed method for TFR. It is clear from the results that the proposed method suppresses cross-term effectively as compared to the other existing methods namely, smoothed pseudo WVD (SPWVD), empirical mode decomposition (EMD)-WVD, EMD-SPWVD, variational mode decomposition (VMD)-WVD, VMD-SPWVD, and DMD-SPWVD.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

DuINet: A dual-branch network with information exchange and perceptual loss for enhanced image denoising DuINet：用于增强图像去噪的信息交换和感知损失双分支网络

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2024-10-28 DOI: 10.1016/j.dsp.2024.104835

{"title":"DuINet: A dual-branch network with information exchange and perceptual loss for enhanced image denoising","authors":"","doi":"10.1016/j.dsp.2024.104835","DOIUrl":"10.1016/j.dsp.2024.104835","url":null,"abstract":"<div><div>Image denoising is a fundamental task in image processing and low-level computer vision, often necessitating a delicate balance between noise removal and the preservation of fine details. In recent years, deep learning approaches, particularly those utilizing various neural network architectures, have shown significant promise in addressing this challenge. In this study, we propose DuINet, a novel dual-branch network specifically designed to capture complementary aspects of image information. DuINet integrates an information exchange module that facilitates effective feature sharing between the branches, and it incorporates a perceptual loss function aimed at enhancing the visual quality of the denoised images. Extensive experimental results demonstrate that DuINet surpasses existing dual-branch models and several state-of-the-art convolutional neural network (CNN)-based methods, particularly under conditions of severe noise where preserving fine details and textures is critical. Moreover, DuINet maintains competitive performance in terms of the LPIPS index when compared to deeper or larger networks such as Restormer and MIRNet, underscoring its ability to deliver high visual quality in denoised images.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Full-duplex cooperative relaying systems for simultaneous wireless information and power transfer with non-orthogonal multiple access 利用非正交多址同时进行无线信息和功率传输的全双工合作中继系统

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2024-10-28 DOI: 10.1016/j.dsp.2024.104817

引用次数: 0

Multimodal evaluation of customer satisfaction from voicemails using speech and language representations 利用语音和语言表征对语音邮件中的客户满意度进行多模态评估

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2024-10-28 DOI: 10.1016/j.dsp.2024.104820

{"title":"Multimodal evaluation of customer satisfaction from voicemails using speech and language representations","authors":"","doi":"10.1016/j.dsp.2024.104820","DOIUrl":"10.1016/j.dsp.2024.104820","url":null,"abstract":"<div><div>Customer satisfaction (CS) evaluation in call centers is essential for assessing service quality but commonly relies on human evaluations. Automatic evaluation systems can be used to perform CS analyses, enabling the evaluation of larger datasets. This research paper focuses on CS analysis through a multimodal approach that employs speech and language representations derived from the real-world voicemails. Additionally, given the similarity between the evaluation of a provided service (which may elicit different emotions in customers) and the automatic classification of emotions in speech, we also explore the topic of emotion recognition with the well-known corpus IEMOCAP which comprises 4-classes corresponding to different emotional states. We incorporated a language representation with word embeddings based on a CNN-LSTM model, and three different self-supervised learning (SSL) speech encoders, namely Wav2Vec2.0, HuBERT, and WavLM. A bidirectional alignment network based on attention mechanisms is employed for synchronizing speech and language representations. Three different fusion strategies are also explored in the paper. According to our results, the GGF model outperformed both, unimodal and other multimodal methods in the 4-class emotion recognition task on the IEMOCAP dataset and the binary CS classification task on the KONECTADB dataset. The study also demonstrated superior performance of our methodology compared to previous works on KONECTADB in both unimodal and multimodal approaches.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

An optimization synchrosqueezed fractional wavelet transform for TFF analysis and its applications 用于 TFF 分析的优化同步queezed 小数小波变换及其应用

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2024-10-28 DOI: 10.1016/j.dsp.2024.104819

引用次数: 0

Spatial pilot reassignment algorithm for channel estimation stage of cell-free multi-ARS communication systems 无小区多ARS通信系统信道估计阶段的空间先导重分配算法

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2024-10-28 DOI: 10.1016/j.dsp.2024.104830

{"title":"Spatial pilot reassignment algorithm for channel estimation stage of cell-free multi-ARS communication systems","authors":"","doi":"10.1016/j.dsp.2024.104830","DOIUrl":"10.1016/j.dsp.2024.104830","url":null,"abstract":"<div><div>In this paper, we investigate the user throughputs of a Cell-Free (CF) system with multiple aerial relay stations (ARSs), where each ARS is defined as an unmanned aerial vehicle (UAV)-mounted relay station. The system operates under a decode-and-forward (DF) protocol and facilitates connectivity between a terrestrial base station (TBS) and terrestrial users. ARSs are equipped with multiple antennas and simultaneously serve users that are outfitted with single antennas and distributed in a specific area. Additionally, a small-cell (SC) system based on the CF structure, where each ARS serves one user with the best channel conditions, is also considered. We analyze system communication in two stages, including user-ARS links and ARS-TBS links, and then we derive expressions for the data rate of users and ARSs. Moreover, we propose the spatial pilot reassignment (SPR) algorithm to optimize pilot assignment, enhancing channel estimation over random pilot assignment methods. The user throughput is evaluated by altering several system parameters, including the with/without data power control, the number of users, the number of ARSs, and the time interval allocated for channel estimation. The results show that the SPR algorithm improves throughput by about 10% compared to the random pilot assignment method at a 90%-likely user throughput, which is equal to a cumulative distribution function value of 0.1.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 novel spatial pyramid-enhanced indoor visual positioning method 一种新颖的空间金字塔增强型室内视觉定位方法

IF 2.9 3区工程技术

Digital Signal Processing Pub Date : 2024-10-28 DOI: 10.1016/j.dsp.2024.104831

{"title":"A novel spatial pyramid-enhanced indoor visual positioning method","authors":"","doi":"10.1016/j.dsp.2024.104831","DOIUrl":"10.1016/j.dsp.2024.104831","url":null,"abstract":"<div><div>As a key application of Internet of Things (IoT) technology, visual localization plays an important role in everyday life. However, pedestrians in images can obstruct environmental features, negatively impacting the performance of visual localization systems. To address this issue, we propose a Spatial Pyramid-Enhanced MixVPR visual localization method (SPE-VL) that aims to enhance image feature descriptions through multi-scale spatial information, thereby mitigating the effects of pedestrian occlusion on localization accuracy. The SPE-VL method is divided into two main phases: sensor-based matching range constraint and image feature extraction and matching. In the matching range constraint phase, we propose a direction decision method based on a machine learning classifier that utilizes smartphone sensor data to restrict the direction of image matching, reducing the likelihood of mismatches. In the feature extraction and matching phase, we propose a Transformer-based feature cross-enhancement method that leverages local features and spatial contextual information to enhance features, improving both image retrieval accuracy and localization precision. Experimental results indicate that the SPE-VL method demonstrates higher localization accuracy and robustness compared to existing state-of-the-art methods, providing new insights and solutions for the application of visual localization in complex environments.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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