An improved pattern recovery approach that integrates the Hopfield neural network (HNN) with the iterative signal reconstruction and Parzen window-based classification is proposed. The HNN is observed as a form of associative memory network, used for various pattern recognition and optimization tasks. However, when the input pattern is highly damaged with a very limited set of available samples, the Hopfield network fails to perform the retrieval. The convex optimization-based gradient descent algorithm is considered for pattern recovery of damaged inputs in order to provide an improved pattern approximation for further processing within the HNN, enabling successful network performance. Additionally, in the case of grayscale images, the Parzen window approach is used to classify the probability density functions (pdfs) of the training set and to choose those being comparable to the pdf of the input pattern, therefore refining the selection of patterns and providing better convergence to the exact retrieval. The theoretical considerations are verified experimentally, showing the high performance of the proposed approach when only 10 % of the pixels are available for binary patterns and 40 % of pixels for grayscale patterns.
{"title":"Enhancing Hopfield network performance for pattern retrieval using sparse recovery algorithm and Parzen estimator","authors":"Djordje Stanković , Andjela Draganić , Cornel Ioana , Irena Orović","doi":"10.1016/j.dsp.2024.104814","DOIUrl":"10.1016/j.dsp.2024.104814","url":null,"abstract":"<div><div>An improved pattern recovery approach that integrates the Hopfield neural network (HNN) with the iterative signal reconstruction and Parzen window-based classification is proposed. The HNN is observed as a form of associative memory network, used for various pattern recognition and optimization tasks. However, when the input pattern is highly damaged with a very limited set of available samples, the Hopfield network fails to perform the retrieval. The convex optimization-based gradient descent algorithm is considered for pattern recovery of damaged inputs in order to provide an improved pattern approximation for further processing within the HNN, enabling successful network performance. Additionally, in the case of grayscale images, the Parzen window approach is used to classify the probability density functions (pdfs) of the training set and to choose those being comparable to the pdf of the input pattern, therefore refining the selection of patterns and providing better convergence to the exact retrieval. The theoretical considerations are verified experimentally, showing the high performance of the proposed approach when only 10 % of the pixels are available for binary patterns and 40 % of pixels for grayscale patterns.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"156 ","pages":"Article 104814"},"PeriodicalIF":2.9,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142446376","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}
Pub Date : 2024-10-10DOI: 10.1016/j.dsp.2024.104799
Efe Tarhan , Furkan Kokdogan , Sinan Gezici
We propose a visible light positioning (VLP) system with a single light emitting diode (LED) transmitter and an intelligent reflecting surface (IRS) for estimating the position of a receiver equipped with a single photo-detector. By performing a number of transmissions from the LED transmitter and optimizing the orientation vectors of the IRS elements for each transmission, position information is extracted by the receiver based on power measurements of the signals reflecting from the IRS. The theoretical limit and the maximum likelihood (ML) estimator are presented for the proposed setting. In addition, an algorithm, named IRS focusing, is proposed for determining the orientations of the IRS elements during the localization process. The effectiveness of the proposed localization approach is demonstrated through simulations. Furthermore, extensions are provided to apply the proposed approach in the presence of partial prior information about the receiver position and when the IRS is located at the LED transmitter.
我们提出了一种可见光定位（VLP）系统，该系统由一个发光二极管（LED）发射器和一个智能反射面（IRS）组成，用于估计装有单个光电探测器的接收器的位置。通过 LED 发射器进行多次发射，并优化每次发射的 IRS 元件方向向量，接收器可根据 IRS 反射信号的功率测量值提取位置信息。针对提议的设置，提出了理论极限和最大似然 (ML) 估计器。此外，还提出了一种名为 IRS 聚焦的算法，用于在定位过程中确定 IRS 元件的方向。通过模拟演示了所提出的定位方法的有效性。此外，还提供了扩展功能，以便在接收器位置存在部分先验信息以及 IRS 位于 LED 发射器时应用所提出的方法。
{"title":"IRS aided visible light positioning with a single LED transmitter","authors":"Efe Tarhan , Furkan Kokdogan , Sinan Gezici","doi":"10.1016/j.dsp.2024.104799","DOIUrl":"10.1016/j.dsp.2024.104799","url":null,"abstract":"<div><div>We propose a visible light positioning (VLP) system with a single light emitting diode (LED) transmitter and an intelligent reflecting surface (IRS) for estimating the position of a receiver equipped with a single photo-detector. By performing a number of transmissions from the LED transmitter and optimizing the orientation vectors of the IRS elements for each transmission, position information is extracted by the receiver based on power measurements of the signals reflecting from the IRS. The theoretical limit and the maximum likelihood (ML) estimator are presented for the proposed setting. In addition, an algorithm, named IRS focusing, is proposed for determining the orientations of the IRS elements during the localization process. The effectiveness of the proposed localization approach is demonstrated through simulations. Furthermore, extensions are provided to apply the proposed approach in the presence of partial prior information about the receiver position and when the IRS is located at the LED transmitter.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"156 ","pages":"Article 104799"},"PeriodicalIF":2.9,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142433291","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}
Pub Date : 2024-10-09DOI: 10.1016/j.dsp.2024.104812
Qipeng Wang , Zhanchao Liu , Zekun Wu , Jingsong Wang , Chunyu Qu , Jianli Li
The small volume, high precision, and low cost of Nuclear Magnetic Resonance (NMR) sensors make them one of the best choices for future miniaturized and chip-scale Inertial Navigation System (INS). Due to technical and process limitations, NMR sensors inevitably exhibit random drift. To suppress these errors, a drift suppression method based on Signal Stability Detection and Adaptive Kalman Filter (SSD-AKF) for NMR sensors is proposed. Firstly, a state space model for the Kalman filter is established based on an Auto Regressive Moving Average (ARMA) sequence model. Secondly, to address the issue of reduced filtering accuracy caused by unstable signal noise in innovation-based AKF, an adaptive filtering method aided by a signal stability detection is proposed. The proposed method utilizes the standard deviation of prior information to assess the stability of the signal. Based on this assessment, the adaptive filter adjusts the gain matrix, ultimately enhancing the stability of the filter. The dynamic experimental results show that the proposed method can effectively improve filter performance and reduce sensor drift.
{"title":"Drift suppression method based on signal stability detection and adaptive Kalman filter for NMR sensor","authors":"Qipeng Wang , Zhanchao Liu , Zekun Wu , Jingsong Wang , Chunyu Qu , Jianli Li","doi":"10.1016/j.dsp.2024.104812","DOIUrl":"10.1016/j.dsp.2024.104812","url":null,"abstract":"<div><div>The small volume, high precision, and low cost of Nuclear Magnetic Resonance (NMR) sensors make them one of the best choices for future miniaturized and chip-scale Inertial Navigation System (INS). Due to technical and process limitations, NMR sensors inevitably exhibit random drift. To suppress these errors, a drift suppression method based on Signal Stability Detection and Adaptive Kalman Filter (SSD-AKF) for NMR sensors is proposed. Firstly, a state space model for the Kalman filter is established based on an Auto Regressive Moving Average (ARMA) sequence model. Secondly, to address the issue of reduced filtering accuracy caused by unstable signal noise in innovation-based AKF, an adaptive filtering method aided by a signal stability detection is proposed. The proposed method utilizes the standard deviation of prior information to assess the stability of the signal. Based on this assessment, the adaptive filter adjusts the gain matrix, ultimately enhancing the stability of the filter. The dynamic experimental results show that the proposed method can effectively improve filter performance and reduce sensor drift.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"156 ","pages":"Article 104812"},"PeriodicalIF":2.9,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427691","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}
Pub Date : 2024-10-09DOI: 10.1016/j.dsp.2024.104804
Ayse Elif Canbilen , Ibrahim Develi , Seyfettin Sinan Gültekin
The direct down-conversion principle, which has generally been used in the design of multiple-input multiple-output (MIMO) schemes, including space modulation techniques (SMTs), is attractive to researchers because of its low cost, low power consumption with fewer components, flexible and simple structure. However, hardware imperfections such as in-phase (I) and quadrature-phase (Q) imbalance (IQI) negatively affect the performance of the systems with direct down-conversion in practice. On the other hand, cooperative communication is a promising technology that can be utilized in the design of future wireless networks due to its significant advantages such as increasing system reliability, extending network coverage, reducing channel degradation, and providing high quality of service. In this study, SMT-based methods are integrated into cooperative systems, and a flexible and comprehensive model is presented that is applicable to many channel structures. Specifically, the error performance analysis of space shift keying (SSK), spatial modulation (SM), and quadrature SM (QSM) systems in the presence of IQI in decode-and-forward (DF) cooperative communication is carried out by analytical derivations and computer simulations over generalized Beckmann fading channels. The obtained results show that the performance of SMT-based DF cooperative systems is superior to the conventional schemes, and the effects of receiver IQI can be eliminated by optimal detector designs.
{"title":"Error Performance of DF Cooperative SMTs with I/Q Imbalance over Beckmann Fading Channels","authors":"Ayse Elif Canbilen , Ibrahim Develi , Seyfettin Sinan Gültekin","doi":"10.1016/j.dsp.2024.104804","DOIUrl":"10.1016/j.dsp.2024.104804","url":null,"abstract":"<div><div>The direct down-conversion principle, which has generally been used in the design of multiple-input multiple-output (MIMO) schemes, including space modulation techniques (SMTs), is attractive to researchers because of its low cost, low power consumption with fewer components, flexible and simple structure. However, hardware imperfections such as in-phase (I) and quadrature-phase (Q) imbalance (IQI) negatively affect the performance of the systems with direct down-conversion in practice. On the other hand, cooperative communication is a promising technology that can be utilized in the design of future wireless networks due to its significant advantages such as increasing system reliability, extending network coverage, reducing channel degradation, and providing high quality of service. In this study, SMT-based methods are integrated into cooperative systems, and a flexible and comprehensive model is presented that is applicable to many channel structures. Specifically, the error performance analysis of space shift keying (SSK), spatial modulation (SM), and quadrature SM (QSM) systems in the presence of IQI in decode-and-forward (DF) cooperative communication is carried out by analytical derivations and computer simulations over generalized Beckmann fading channels. The obtained results show that the performance of SMT-based DF cooperative systems is superior to the conventional schemes, and the effects of receiver IQI can be eliminated by optimal detector designs.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"156 ","pages":"Article 104804"},"PeriodicalIF":2.9,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427695","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}
Pub Date : 2024-10-06DOI: 10.1016/j.dsp.2024.104795
Boumegouas Rahil, Djendi Mohamed
This brief proposes a double-channel direct affine projection sign (DC-DAPS) algorithm for blind speech enhancement applications, especially in the presence of strong acoustic noise, and intensive non-Gaussian impulsive interferences. We also derive a full analysis of the stability of the proposed DC-DAPS algorithm. The performance evaluation of the proposed algorithm confirms its accuracy even under different noisy scenarios, and demonstrates that our approach outperforms the double-channel direct affine projection (DC-DAP), and the double-channel direct normalized least mean square (DC-DNLMS) algorithms. Simulation results showed that the proposed DC-DAPS algorithm could effectively achieve improved performance in combating acoustic noise and impulsive interferences, and speeding up the convergence rate even with highly correlated input signals such as the speech signal.
{"title":"A robust double-channel affine projection sign algorithm for blind acoustic interferences cancellation","authors":"Boumegouas Rahil, Djendi Mohamed","doi":"10.1016/j.dsp.2024.104795","DOIUrl":"10.1016/j.dsp.2024.104795","url":null,"abstract":"<div><div>This brief proposes a double-channel direct affine projection sign (DC-DAPS) algorithm for blind speech enhancement applications, especially in the presence of strong acoustic noise, and intensive non-Gaussian impulsive interferences. We also derive a full analysis of the stability of the proposed DC-DAPS algorithm. The performance evaluation of the proposed algorithm confirms its accuracy even under different noisy scenarios, and demonstrates that our approach outperforms the double-channel direct affine projection (DC-DAP), and the double-channel direct normalized least mean square (DC-DNLMS) algorithms. Simulation results showed that the proposed DC-DAPS algorithm could effectively achieve improved performance in combating acoustic noise and impulsive interferences, and speeding up the convergence rate even with highly correlated input signals such as the speech signal.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"156 ","pages":"Article 104795"},"PeriodicalIF":2.9,"publicationDate":"2024-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531112","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}
Pub Date : 2024-10-05DOI: 10.1016/j.dsp.2024.104811
Serkan Keser , Esra Gezer
Speaker identification is vital in various application domains, such as automation, security, and enhancing user experience. In the literature, convolutional neural network (CNN) or recurrent neural network (RNN) classifiers are generally used due to the one-dimensional time series of speech signals. However, new approaches using subspace classifiers are also crucial in speaker identification. In this study, in addition to the newly developed subspace classifiers for speaker identification, traditional classification algorithms, and various hybrid algorithms are analyzed in terms of performance. Stacked Features-Common Vector Approach (SF-CVA) and Hybrid CVA-Fisher Linear Discriminant Analysis (HCF) subspace classifiers are used for speaker identification for the first time in the literature. In addition, CVA is evaluated for the first time for speaker identification using hybrid deep learning algorithms. The study includes Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM), i-vector + Probabilistic Linear Discriminant Analysis (i-vector+PLDA), Time Delayed Neural Network (TDNN), AutoEncoder+Softmax (AE+Softmax), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Common Vector Approach (CVA), SF-CVA, HCF, and Alexnet classifiers for speaker identification. This study uses MNIST, TIMIT and Voxceleb1 databases for clean and noisy speech signals. Six different feature structures are tested in the study. The six different feature extraction approaches consist of Mel Frequency Cepstral Coefficients (MFCC)+Pitch, Gammatone Filter Bank Cepstral Coefficients (GTCC)+Pitch, MFCC+GTCC+Pitch+seven spectral features, spectrograms,i-vectors, and Alexnet feature vectors. High accuracy rates were obtained, especially in tests using SF-CVA. RNN-LSTM, i-vector+KNN, AE+Softmax, TDNN, and i-vector+HCF classifiers also gave high test accuracy rates.
{"title":"Comparative analysis of speaker identification performance using deep learning, machine learning, and novel subspace classifiers with multiple feature extraction techniques","authors":"Serkan Keser , Esra Gezer","doi":"10.1016/j.dsp.2024.104811","DOIUrl":"10.1016/j.dsp.2024.104811","url":null,"abstract":"<div><div>Speaker identification is vital in various application domains, such as automation, security, and enhancing user experience. In the literature, convolutional neural network (CNN) or recurrent neural network (RNN) classifiers are generally used due to the one-dimensional time series of speech signals. However, new approaches using subspace classifiers are also crucial in speaker identification. In this study, in addition to the newly developed subspace classifiers for speaker identification, traditional classification algorithms, and various hybrid algorithms are analyzed in terms of performance. Stacked Features-Common Vector Approach (SF-CVA) and Hybrid CVA-Fisher Linear Discriminant Analysis (HCF) subspace classifiers are used for speaker identification for the first time in the literature. In addition, CVA is evaluated for the first time for speaker identification using hybrid deep learning algorithms. The study includes Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM), i-vector + Probabilistic Linear Discriminant Analysis (i-vector+PLDA), Time Delayed Neural Network (TDNN), AutoEncoder+Softmax (AE+Softmax), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Common Vector Approach (CVA), SF-CVA, HCF, and Alexnet classifiers for speaker identification. This study uses MNIST, TIMIT and Voxceleb1 databases for clean and noisy speech signals. Six different feature structures are tested in the study. The six different feature extraction approaches consist of Mel Frequency Cepstral Coefficients (MFCC)+Pitch, Gammatone Filter Bank Cepstral Coefficients (GTCC)+Pitch, MFCC+GTCC+Pitch+seven spectral features, spectrograms,i-vectors, and Alexnet feature vectors. High accuracy rates were obtained, especially in tests using SF-CVA. RNN-LSTM, i-vector+KNN, AE+Softmax, TDNN, and i-vector+HCF classifiers also gave high test accuracy rates.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"156 ","pages":"Article 104811"},"PeriodicalIF":2.9,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142428065","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}
Pub Date : 2024-10-05DOI: 10.1016/j.dsp.2024.104803
Lifang He , Xinyu Xiong , Chaofan Li
This paper proposes a novel differential chaos shift keying (DCSK) system that utilizes carrier phase modulation in combination with time slot and code index modulation, referred to as CP-TCIM-DCSK system, to achieve high data rate transmission. In the proposed CP-TCIM-DCSK system, the carrier modulated by the carrier phase is used to transmit the information-bearing signal. In addition to physically transmitted information bits, extra information bits are conveyed through time slot and Walsh code modulation, as well as carrier phase modulation, making full use of the benefits of multidimensional modulation. To successfully estimate carrier phase bits and code index bits, an effective joint detection algorithm for carrier phase and code index tailored for the CP-TCIM-DCSK system is introduced. The theoretical Bit Error Rate (BER) expressions for the CP-TCIM-DCSK scheme are derived for both Additive White Gaussian Noise (AWGN) and multipath Rayleigh fading channels. Furthermore, a comparison of data rates, energy efficiency, and complexity between the CP-TCIM-DCSK system and the most advanced systems in the same category at present is conducted. The results validate the accuracy of theoretical analysis through simulation and demonstrate that the proposed scheme outperforms its competitive systems in terms of BER performance.
{"title":"A novel differential chaos shift keying system based on joint time slot and code index of carrier phase modulation","authors":"Lifang He , Xinyu Xiong , Chaofan Li","doi":"10.1016/j.dsp.2024.104803","DOIUrl":"10.1016/j.dsp.2024.104803","url":null,"abstract":"<div><div>This paper proposes a novel differential chaos shift keying (DCSK) system that utilizes carrier phase modulation in combination with time slot and code index modulation, referred to as CP-TCIM-DCSK system, to achieve high data rate transmission. In the proposed CP-TCIM-DCSK system, the carrier modulated by the carrier phase is used to transmit the information-bearing signal. In addition to physically transmitted information bits, extra information bits are conveyed through time slot and Walsh code modulation, as well as carrier phase modulation, making full use of the benefits of multidimensional modulation. To successfully estimate carrier phase bits and code index bits, an effective joint detection algorithm for carrier phase and code index tailored for the CP-TCIM-DCSK system is introduced. The theoretical Bit Error Rate (BER) expressions for the CP-TCIM-DCSK scheme are derived for both Additive White Gaussian Noise (AWGN) and multipath Rayleigh fading channels. Furthermore, a comparison of data rates, energy efficiency, and complexity between the CP-TCIM-DCSK system and the most advanced systems in the same category at present is conducted. The results validate the accuracy of theoretical analysis through simulation and demonstrate that the proposed scheme outperforms its competitive systems in terms of BER performance.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"156 ","pages":"Article 104803"},"PeriodicalIF":2.9,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427692","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}
Pub Date : 2024-10-05DOI: 10.1016/j.dsp.2024.104810
Zonghao Li, Hui Ma, Zishuo Guo
Synthetic Aperture Radar (SAR) imagery has a wide range of applications in search and rescue ships lost contact and military reconnaissance. When detecting multi-scale targets, better determination of the target edge is conducive to improving the detection accuracy of the model, but most of the existing methods lack research on this aspect. To fix the problems mentioned earlier, this paper suggests using a SAR Ship target detection network called MAEE-Net. In this paper, a multi-input attention-based feature fusion module (MAFM) and an edge feature enhancement module (EFEM) are proposed. MAFM uses attention mechanism with multi-input and multiple-output to improve attention to shallow feature map target and suppress invalid information, so as to improve the information utilization rate of each layer. To make the network better at detecting the edges of ships, EFEM uses double-branched structure to carry out fine-grained information retention and edge feature extraction. PIoU v2 is introduced to enhance multi-target processing capability. Experiments were carried out on SSDD dataset and SAR-Ship-Dataset, the overall detection accuracy was as high as 98.6% and 94.7%. The detection accuracy was 93.5% and 99.3% on inshore and offshore sub-datasets of SSDD dataset. Experimental results on two datasets show that our model is impactful.
{"title":"MAEE-Net: SAR ship target detection network based on multi-input attention and edge feature enhancement","authors":"Zonghao Li, Hui Ma, Zishuo Guo","doi":"10.1016/j.dsp.2024.104810","DOIUrl":"10.1016/j.dsp.2024.104810","url":null,"abstract":"<div><div>Synthetic Aperture Radar (SAR) imagery has a wide range of applications in search and rescue ships lost contact and military reconnaissance. When detecting multi-scale targets, better determination of the target edge is conducive to improving the detection accuracy of the model, but most of the existing methods lack research on this aspect. To fix the problems mentioned earlier, this paper suggests using a SAR Ship target detection network called MAEE-Net. In this paper, a multi-input attention-based feature fusion module (MAFM) and an edge feature enhancement module (EFEM) are proposed. MAFM uses attention mechanism with multi-input and multiple-output to improve attention to shallow feature map target and suppress invalid information, so as to improve the information utilization rate of each layer. To make the network better at detecting the edges of ships, EFEM uses double-branched structure to carry out fine-grained information retention and edge feature extraction. PIoU v2 is introduced to enhance multi-target processing capability. Experiments were carried out on SSDD dataset and SAR-Ship-Dataset, the overall detection accuracy was as high as 98.6% and 94.7%. The detection accuracy was 93.5% and 99.3% on inshore and offshore sub-datasets of SSDD dataset. Experimental results on two datasets show that our model is impactful.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"156 ","pages":"Article 104810"},"PeriodicalIF":2.9,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427693","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}
Pub Date : 2024-10-04DOI: 10.1016/j.dsp.2024.104802
Hossein Shakibania , Sina Raoufi , Hassan Khotanlou
Low-light images, characterized by inadequate illumination, pose challenges of diminished clarity, muted colors, and reduced details. Low-light image enhancement, an essential task in computer vision, aims to rectify these issues by improving brightness, contrast, and overall perceptual quality, thereby facilitating accurate analysis and interpretation. This paper introduces the Convolutional Dense Attention-guided Network (CDAN), a novel solution for enhancing low-light images. CDAN integrates an autoencoder-based architecture with convolutional and dense blocks, complemented by an attention mechanism and skip connections. This architecture ensures efficient information propagation and feature learning. During training, we utilize a composite loss function that merges L2 and VGG losses for improved numeric and perceptual results. Furthermore, a dedicated post-processing phase refines color balance and contrast. Our approach demonstrates notable progress compared to state-of-the-art results in low-light image enhancement, showcasing its robustness across a wide range of challenging scenarios. Our model performs remarkably on benchmark datasets, effectively mitigating under-exposure and proficiently restoring textures and colors in diverse low-light scenarios. This achievement underscores CDAN's potential for diverse computer vision tasks, notably enabling robust object detection and recognition in challenging low-light conditions.
{"title":"CDAN: Convolutional dense attention-guided network for low-light image enhancement","authors":"Hossein Shakibania , Sina Raoufi , Hassan Khotanlou","doi":"10.1016/j.dsp.2024.104802","DOIUrl":"10.1016/j.dsp.2024.104802","url":null,"abstract":"<div><div>Low-light images, characterized by inadequate illumination, pose challenges of diminished clarity, muted colors, and reduced details. Low-light image enhancement, an essential task in computer vision, aims to rectify these issues by improving brightness, contrast, and overall perceptual quality, thereby facilitating accurate analysis and interpretation. This paper introduces the Convolutional Dense Attention-guided Network (CDAN), a novel solution for enhancing low-light images. CDAN integrates an autoencoder-based architecture with convolutional and dense blocks, complemented by an attention mechanism and skip connections. This architecture ensures efficient information propagation and feature learning. During training, we utilize a composite loss function that merges L2 and VGG losses for improved numeric and perceptual results. Furthermore, a dedicated post-processing phase refines color balance and contrast. Our approach demonstrates notable progress compared to state-of-the-art results in low-light image enhancement, showcasing its robustness across a wide range of challenging scenarios. Our model performs remarkably on benchmark datasets, effectively mitigating under-exposure and proficiently restoring textures and colors in diverse low-light scenarios. This achievement underscores CDAN's potential for diverse computer vision tasks, notably enabling robust object detection and recognition in challenging low-light conditions.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"156 ","pages":"Article 104802"},"PeriodicalIF":2.9,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142428119","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}
Pub Date : 2024-10-03DOI: 10.1016/j.dsp.2024.104809
Juan Li, Baoyi Cai
Monitoring water distribution networks (WDNs) requires careful consideration of sensor placement, which is essential for obtaining comprehensive information about the network. A natural graphical structure underlies WDN, making graph sampling theory advantageous for selecting monitoring nodes. However, graph sampling theory is only applied only to restrictive band-limited signals, while the pressure data of WDN is a restrictive non-band-limited signal. To address this issue, this paper presents an approximate conversion method for transforming non-band-limited signals into band-limited signals, accompanied by an optimal spectrum threshold formula. This formula is used to perform spectral screening in the graph frequency domain, effectively converting non-band-limited signals into band-limited signals that preserve the major frequency components while ignoring smaller-value frequency components. By sampling band-limited signal, we identify sampling nodes that perfectly recover the signal. These sampling nodes act as monitoring nodes that can perform a comprehensive inspection of the WDN and accurately locate leaks. The accuracy of our method in recovering the signal and locating the leak is demonstrated by comparing it with two existing sensor placement optimization methods.
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