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Hybrid stochastic Divider-Based Delta-Sigma Data Convertor Using Linearity Enhancement of Multi-channel ADC with Robust Control Mechanism

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CIRCUITS SYSTEMS AND signal processing 2025年

作者： Chandran, G. Jyothish Nishanth, P. R. Ashwin, P. V. APJ Abdul Kalam Technol Univ Saintgits Coll Engn Kottayam 686532 Kerala India

Delta-sigma data converters oversample the input signal and perform noise shaping to produce a high-resolution digital output. Many existing methods implement a continuous-time delta-sigma modulator (CTDSM) for multi-channel ADCs, but these systems often suffer from flaws such as inadequate state resetting, which leads to inter-channel crosstalk and linearity issues. To overcome these limitations, a novel Hybrid stochastic Divider Delta-Sigma Modulator is proposed to enhance multi-channel ADC performance. This approach integrates a Hysteresis Split Source Comparison Quantizer and a Distributed stochastic Quantized neural Network to automatically reset states, effectively reducing crosstalk across channels and eliminating linearity issues using Linear Ensemble Half-band Filtering. Furthermore, existing CTDSM control mechanisms struggle with poor performance due to threshold-based methods, resulting in quantization errors, slope overload distortion, and high latency. To address these challenges, a Robust Feedback Compression Controller is introduced, optimizing multi-ADC operation by mitigating high latency through a Tunable Column Bit Compression mechanism. Additionally, a Scalable Extrapolation GAN Controller is employed to predict and correct quantization errors, improving speed and efficiency.

关键词： Analog-to-digital converter Continuous-time delta-sigma modulator Distributed mechanism Quantization error GAN controller Half-band filtering

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A Comprehensive Analysis of Traditional and Advanced Approaches of image Restoration Techniques

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Micro and Nanosystems 2025年第1期17卷 58-69页

作者： Ramesh, Chiluka Rao, D. Venkata Department of ECE Malineni Lakshmaiah Women’s Engineering College Pulladigunta Guntur India Department of ECE VR Siddhartha Engineering College A.P Vijayawada India

Introduction: Tremendous developments in multimedia technology have promoted a massive amount of research in image and video processing. As imaging technologies are rapidly increasing, it is becoming essential to use images in almost every application in our day-to-day life. Materials and methods: This paper presents a comparative analysis of various image restoration approaches, ranging from fundamental methods to advanced techniques. These approaches aim to improve the quality of images that have been degraded during acquisition or transmission. A brief overview of the image restoration approaches is mentioned in the paper, which are as follows: (i) Wiener Filter: The Wiener filter is a classical approach used for image restoration. It is a linear filter that minimizes the mean square error between the original image and the restored image. (ii) Inverse Filter: The inverse filter is another traditional restoration technique. It attempts to invert the degradation process to recover the original image. However, inverse filtering is highly sensitive to noise and tends to amplify noise artifacts. (iii) Linear and Nonlinear Filtering: These methods involve applying linear or nonlinear filters to the degraded image to enhance its quality. Linear filters, such as Gaussian filters, can effectively reduce noise but may blur the image. Nonlinear filters, such as median filters, can preserve edges while reducing noise. (iv) Compressive Sensing and Restoration Approaches: Compressive sensing is a signal processing technique that exploits the sparsity of signals or images to reconstruct them from fewer measurements. CS-based restoration methods aim to recover high-quality images from compressed or incomplete measurements. (v) neural Networks Approaches: With the advancements in deep learning, neural networks have been widely used for image restoration tasks. Convolutional neural networks (CNNs) and generative adversarial networks (GANs) have shown promising results in restorin

关键词： Median filters

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Analysis of conventional and modern contrast enhancement mechanisms

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第34期83卷 81057-81089页

作者： Agarwal, Archana Gupta, Shailender Vashishath, Munish JC Bose Univ Sci & Technol YMCA Faridabad Haryana India

Contrast enhancement is a crucial aspect of image processing, as it improves visual quality by adjusting the brightness and contrast of an image. This paper comprehensively explores contrast enhancement techniques, classified into three categories: image processing (IP) based methods Deep Learning (DL) based approaches, and Generative Adversarial Network (GAN) methods. The paper also details various quality evaluation methods for enhanced images and compares different algorithms. The performance of the presented algorithms is evaluated using metrics such as Structural Similarity Index Measurement (SSIM), Absolute Mean Brightness Error (AMBE), Average Information Content (AIC), Contrast Improvement Index (CII), Mean Square Error (MSE), Peak signal to Noise Ratio (PSNR), Universal Quality Index (UQI), and Color Enhancement Factor (CEF). The comparative analysis aims to provide insights into improving image quality, information content and error production within each category, facilitating informed decision-making in selecting contrast enhancement techniques for diverse applications.

关键词： Contrast enhancement Deep learning Generative adversarial learning Convolution neural network

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Human elbow flexion behaviour recognition based on posture estimation in complex scenes

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IET image processing 2023年第1期17卷 178-192页

作者： Gong, Faming Li, Yunjing Yuan, Xiangbing Liu, Xin Gao, Yating China Univ Petr East China Coll Comp Sci & Technol Qingdao 266580 Peoples R China Sinopec Gtp Offshore Oil Prod Plant Dongying Peoples R China

Aiming at the difficulty of recognising the smoking and making phone calls behaviours of people in the complex background of construction sites, a method of recognising human elbow flexion behaviour based on posture estimation is proposed. The human upper body key points needed are retrained based on AlphaPose to achieve human object localization and key points detection. Then, a mathematical model for human elbow flexion behaviour discrimination (HEFBD model) is proposed based on human key points, as well as locating the region of interest for small object detection and reducing the interference of complex background. A super-resolution image reconstruction method is used for pre-processing some blurred images. In addition, YOLOv5s is improved by adding a small object detection layer and integrating a convolutional block attention model to improve the detection performance. The detection precision of this method is improved by 5.6%, and the false detection rate caused by complex background is reduced by 13%, which outperforms other state-of-the-art detection methods and meets the requirement of real-time performance.

关键词： human upper body key points feature extraction posture estimation detection performance human key points video signal processing human elbow flexion behaviour discrimination complex background key points detection neural nets HEFBD model mathematical model super-resolution image reconstruction method Optical, image and video signal processing Computer vision and image processing techniques convolutional block attention model construction sites convolutional neural nets smoking human object localization object detection state-of-the-art detection methods image recognition making phone calls pose estimation object detection layer human elbow flexion behaviour recognition Video signal processing biomechanics image motion analysis image reconstruction image resolution complex scenes false detection rate detection precision

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A Deep stochastic Adaptive Fourier Decomposition Network for Hyperspectral image Classification

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IEEE TRANSACTIONS ON image processing 2024年 33卷 1080-1094页

作者： Cheng, Chunbo Zhang, Liming Li, Hong Dai, Lei Cui, Wenjing Hubei Polytechn Univ Sch Math & Phys Huangshi 435000 Hubei Peoples R China Univ Macau Fac Sci & Technol Macau Peoples R China Huazhong Univ Sci & Technol Sch Math & Stat Wuhan 430074 Hubei Peoples R China East China Univ Sci & Technol Dept Comp Sci & Engn Shanghai 200237 Peoples R China

Deep learning-based hyperspectral image (HSI) classification methods have recently shown excellent performance, however, there are two shortcomings that need to be addressed. One is that deep network training requires a large number of labeled images, and the other is that deep network needs to learn a large number of parameters. They are also general problems of deep networks, especially in applications that require professional techniques to acquire and label images, such as HSI and medical images. In this paper, we propose a deep network architecture (SAFDNet) based on the stochastic adaptive Fourier decomposition (SAFD) theory. SAFD has powerful unsupervised feature extraction capabilities, so the entire deep network only requires a small number of annotated images to train the classifier. In addition, we use fewer convolution kernels in the entire deep network, which greatly reduces the number of deep network parameters. SAFD is a newly developed signal processing tool with solid mathematical foundation, which is used to construct the unsupervised deep feature extraction mechanism of SAFDNet. Experimental results on three popular HSI classification datasets show that our proposed SAFDNet outperforms other compared state-of-the-art deep learning methods in HSI classification.

关键词： stochastic adaptive Fourier decomposition deep learning CNN HSIs classification

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Optimized theory-guided convolutional neural network for lung cancer classification using CT images with advanced FPGA implementation

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BIOMEDICAL signal processing AND CONTROL 2025年 106卷

作者： Manikandan, S. Karthigaikumar, P. Karpagam Coll Engn Dept Elect & Telecommun Engn Coimbatore Tamil Nadu India Karpagam Coll Engn Dept Elect & Commun Engn Coimbatore Tamil Nadu India

Lung carcinoma, commonly referred to as lung cancer is a severe disease with higher global mortality rate. The uncontrolled growth of cells in lung tissues is the reason for it. Detecting and treating lung cancer early is important for curing it, and diagnostic methods commonly include Computed Tomography (CT) scans and blood tests. However, accurately detecting and classifying pulmonary nodules in CT images remains a challenge due to the complexity of the data, higher computational demands,require for real-time processing. Existing systems often face limitations, such as high power consumption, prolonged processing times, and scalability issues, reducing their effectiveness in clinical environments. To overcome these challenges, this manuscript proposes an Optimized Theory-Guided Convolutional neural Network for Lung Cancer Classification utilizing CT images with Advanced FPGA Implementation (OTCNN-LCT-FPGA). Computed Tomography image (CTI) from the LIDC-IDRI dataset are pre-processed using Variational Bayesian Robust Adaptive Filtering (VBRAF) technique, which removes noise and converts RGB images into binary format. The pre-processed images are classified as benign or malignant using Theory-Guided Convolutional neural Network (TCNN). The Polar Coordinate Bald Eagle Search Algorithm (PBESA) is introduced to enhance the weight parameters of TCNN method while reducing resource utilization and increasing processing speed. The TCNN classifier is executed on Field-Programmable Gate Array (FPGA) to further decrease the computation time. The proposed OTCNN-LCT-FPGA method demonstrates significant improvements. How if, it achieves 6.26 %, 7.22 %, and 5.27 higher specificity and 2.96 %, 3.46 %, and 5.80 % higher F1-Score when compared to the existing methods, such as FCFNN-LCC, ISNeT-DLC-CT and DNNLCC-EOS respectively.

关键词： CT images Field-programmable gate array Lung cancer Variational based Bayesian robust Adaptive Filtering Polar Coordinate Bald Eagle Search Algorithm Theory-guided Convolutional neural Network

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Adaptive lightweight Transformer network for low-light image enhancement

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signal image AND VIDEO processing 2024年第6-7期18卷 5365-5375页

作者： Meng, De Lei, Zhichun Tianjin Univ Sch Microelect Tianjin 300072 Peoples R China

Low-light image enhancement methods based on deep learning have proven successful. In recent years, methods combining convolutional neural networks (CNN), multi-layer perceptron, and Retinex theory have achieved good results in enhancement tasks. The CNN has a limited receptive field, thus preventing it from modeling long-range pixel dependencies, and the Transformer is known for its capability to capture long-range dependencies but incurs significant computational costs. In this paper, we propose an adaptive lightweight Transformer network (ALT-Net) to restore images under normal lighting conditions by simulating the reverse-sequential execution of image signal processor (ISP) pipelines. Specifically, in the reverse direction, we use a lightweight encoder-decoder to achieve inverse mapping and add adaptive modules for correction. In the sequential direction, we add noise reduction modules and attention mechanisms to query key parameters in the ISP pipeline (white balance and color correction, etc.) to adjust the image. Compared with Retinexformer, ALT-Net improves the PSNR and SSIM on the LOL-v2-real dataset by 0.11 dB and 0.002 respectively. With only 80 k parameters, our ALT-Net achieves state-of-the-art performance in low-light image enhancement tasks, and it is more cost-effective than similar methods.

关键词： Low-light image enhancement image signal processor Transformer Light weight Attention mechanism

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SeisGAN: Improving Seismic image Resolution and Reducing Random Noise Using a Generative Adversarial Network

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MATHEMATICAL GEOSCIENCES 2024年第4期56卷 723-749页

作者： Lin, Lei Zhong, Zhi Cai, Chuyang Li, Chenglong Zhang, Heng China Univ Geosci Key Lab Theory & Technol Petr Explorat & Dev Hubei Wuhan Peoples R China Monash Univ Melbourne Australia

Seismic images are essential for understanding the subsurface geological structure and resource distribution. However, the accuracy and certainty of geological analysis using seismic images are limited by the resolution and signal-to-noise ratio. Simultaneously improving resolution and suppressing random noise with traditional methods can be quite challenging. This research proposes a new approach called SeisGAN which leverages a generative adversarial network to address the challenge at hand. Due to the lack of high-resolution noiseless and low-resolution noisy seismic data, stochastic parameter control is employed to simulate a vast range of diverse, paired seismic data for SeisGAN training. The results on the synthetic dataset demonstrate that the proposed method is effective in enhancing the resolution and suppressing the random noise in the original images. Spectrum analysis shows that the proposed method increases the bandwidth of the original data, primarily at high frequencies. Ablation experiments reveal that, under similar conditions, SeisGAN outperforms traditional convolutional neural networks. Incorporating the VGG loss in the generator loss function improves the model's ability to recover high-frequency details. The application of the technique on two publicly available field seismic datasets indicates SeisGAN's excellent generalizability, despite being trained only on synthetic seismic data. Compared with bicubic interpolation and traditional noise suppression and resolution enhancement methods, SeisGAN is capable of effectively suppressing the random noise and enhancing the dominant frequency of field seismic data, making it easier to identify adjacent thin layers and fault features, even for small-scale faults. The zoomed images are clearer and easier to interpret. Furthermore, an example of automatic machine fault identification demonstrates the significant contribution of the SeisGAN-enhanced image to accurate fault recognition.

关键词： Seismic image processing Seismic resolution Seismic signal-to-noise ratio Deep learning

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Dynamic Tactical image Recognition and Analysis in Football Matches Using Convolutional neural Networks

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TRAITEMENT DU signal 2025年第1期42卷 583-592页

作者： Xie, Qi Baoji Univ Arts & Sci Sch Phys Educ Baoji 721000 Peoples R China

With the increasing complexity of modern football tactics, how to intelligently and accurately analyze tactical changes in real-time during matches has become an important research direction. Traditional manual tactical analysis methods are inefficient and susceptible to subjective bias. Therefore, using computer vision and deep learning technologies for tactical image recognition and analysis in football matches has gradually become a research hotspot. Convolutional neural Networks (CNNs), as a powerful image processing tool, have been widely applied in video analysis and player detection. However, multi-target motion prediction and tracking management in dynamic football match scenes still face significant challenges. Existing research mainly focuses on static image analysis or simple player tracking, but the high-frequency image updates, player interactions, and occlusion issues in football matches complicate multi-target tracking. While some deep learning-based methods for multi-target detection and tracking have made progress, challenges remain, such as handling high-density player targets and improving motion trajectory prediction accuracy. To address these shortcomings, this study proposes two core techniques based on CNNs: first, multi-target motion prediction, which accurately forecasts players' future positions based on historical motion data;second, multi-target tracking management, which uses deep learning to track and manage each player's movement trajectory in real-time. Through these two techniques, this research aims to improve the realtime and accuracy of tactical analysis in football matches, providing coaches and analysts with more scientific and efficient tactical decision-making support.

关键词： CNN football matches dynamic tactical image multi-target motion prediction multi-target tracking management computer vision

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Explainable audio CNNs applied to neural decoding: sound category identification from inferior colliculus

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signal image AND VIDEO processing 2024年第2期18卷 1193-1204页

作者： Ozcan, Fatma Alkan, Ahmet Kahramanmaras Sutcu Imam Univ Elect & Elect Engn Dept TR-46100 Kahramanmaras Turkiye

Recently, work has been done to understand aspects of how CI processes with sound. Here, we use neural temporal correlation in the inferior colliculus for identifying and categorising the sound that was used as a stimulus. The success of the classification gradually deteriorates for shorter durations. We tried to improve these success values with deep learning methods for audio, on processing windows of 62.5 ms, 250 ms and 1000 ms. We demonstrate that 62.5 ms could be an integration time for temporal correlation. The neural data contains sound features that can be easily processed with artificial neural networks dedicated to audio signals. Network architectures dedicated to audio classification, such as Yamnet, Vggish, Openl3, used in transfer learning, give quite quickly neural data classification results with very high accuracy, compared to image classification networks. In the case of unshuffled correlation images, we have the best accuracy. With noiseless shuffled correlation images, we have the best accuracy, such as for 1000 ms: 100%, for 250 ms: 96.7%, for 62.5 ms: 93.8%, obtained with the OpenL3 network. To evaluate the importance of the contributions of the input features of a neural network to its outputs, we use Explainable Artificial Intelligence. We then used three different explicability methods, such as Grad-CAM, LIME and Occlusion Sensitivity to obtain three sensitive maps. Network uses different regions corresponding to a very high or very low correlation to make its prediction.

关键词： Explanability Interpretation Pre-trained audio networks Temporal correlation Time resolution Multiunit activity

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