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Spiking Generative Models Based on Variational Autoencoder and Adversarial Training

Spiking Generative Models Based on Variational Autoencoder a...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Wenchuan Zhang Jie Zhang Ricky Yuen-Tan Hou Weifeng Su Wentao Fan Department of Computer Science Guangdong Provincial/Zhuhai Key Laboratory IRADS Beijing Normal University-Hong Kong Baptist University United International College Zhuhai China Department of Computer Science and Technology Huaqiao University Xiamen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Deep neural networks (DNNs) have demonstrated exceptional performance across a variety of applications, yet they require substantial computing and power resources. In contrast, Spiking Neural Networks (SNNs) offer significant potential for energy-efficient computing due to their binary, event-driven properties. However, existing deep generative SNNs often struggle to produce high-quality low-dimensional representations in latent space, adversely affecting the quality of their generated samples. To address this limitation, we introduce a novel generative model that integrates the principles of variational autoencoders (VAEs) with adversarial training techniques. Our model consists of a generative module based entirely on an SNN-structured VAE and a discriminator employing artificial neural networks. This discriminator enhances the training process of our SNN-based generative model by applying adversarial principles similar to those used in Generative Adversarial Networks (GANs). Additionally, we have developed a conditional generation capability within our model, enabling the controlled production of specific images based on label inputs. Experimental evaluations on multiple datasets demonstrate that our model achieves superior image quality compared to existing top-performing SNN-based generative models. The source code of our model is accessible at https://***/zxj8806/SGM-VaGAN.

关键词： Training Adaptation models image synthesis Source coding Autoencoders Spiking neural networks Production signal processing Real-time systems Speech processing

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Camouflaged Human Detection: Comparative Analysis Using YoloV5s, Yolov5l, Yolov5m, Yolov5n, Yolov5x Model

Camouflaged Human Detection: Comparative Analysis Using Yolo...

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Artificial Intelligence and signal processing (AISP), INDIA, International conference on

作者： Sufiya Mulla Rajlaxmi Mandavkar Simran Jamadar Sneha Magdum Uma Gurav Dept of CSE (Data Science) KIT's College of Engineering (Autonomous) Kolhapur Maharashtra India Department of CSE AIML DS KIT's College of Engineering (Autonomous) Kolhapur Maharashtra India

ISBN: (数字)9798350350654

ISBN: (纸本)9798350350661

This research paper discusses about, The Camouflaged human or object Detection using different variations of YOLOV5. Our project deep dives into the analysis of YOLO(You Only Look Once) algorithm and it's different types for performance analysis. The proposed model, trained on the dataset with different regions and angles of images, utilizes YOLOv5 for efficient camouflaged human detection, with manual annotation of images used for training the dataset and calculated different outputs for performance analysis. There can be lot of applications of the Camouflaged object detection project. Even if it is Biodiversity conservation in Nature like detecting the animals or plants which gets camouflaged in the nature and are difficult to detect or detecting enemy military invasion. This algorithm can be combined with one or more other image processing algorithms also for better accuracy and easy implementation purpose. This research work is carried out on different variants of YOLOv5 algorithm. We have used the different variants like Yolov5l, Yolov5m, Yolov5n, Yolov5x enhancing accuracy and robustness. The performance analysis is carried out in terms of P curve (Precision), R curve(recall), PR curve(precision and recall) and F1 curve of each training set and also the confusion matrix for all the algorithms is calculated and it is observed that Yolov5l works better as compared to other YOLO variants.

关键词： YOLO Training Analytical models Accuracy image processing signal processing algorithms Manuals signal processing Robustness Performance analysis

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Digital Twin-Driven Bearing-Fault Detection in Induction Motor and Drives using Graph Sampling and Aggregation Network

Digital Twin-Driven Bearing-Fault Detection in Induction Mot...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Haraprasad Badajena Suryanarayan Majhi Bivash Chakraborty Mamata Jenamani Aurobinda Routray Ronit Dutta Advanced Technology Development Centre Indian Institute of Technology Kharagpur India Industrial and Systems Engineering Indian Institute of Technology Kharagpur India Dept. of Electrical Engineering Indian Institute of Technology Kharagpur India

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Bearing fault diagnosis is crucial for ensuring the reliability and safety of industrial systems, particularly in preventing operational failures and maintaining product quality. Traditional signal processing methods and deep learning algorithms, while useful, often overlook the complex structural relationships within sensor data, limiting their diagnostic effectiveness. To address this, we present a novel Digital Twin-Driven Fault Diagnosis Framework that integrates graph-based learning techniques with advanced signal analysis. Our approach employs xGBoost and GraphSAGE embeddings to capture both spatial and temporal correlations within the current signals. The raw sensor data is then processed using a sliding window technique and time-frequency domain features are extracted then transformed into a graph structure that represents the intricate relationship in the signal. GraphSAGE is then applied to these graph structures, generating embeddings that enhance fault detection accuracy. Additionally, xGBoost is utilized for classification, improving the overall robustness of the system. The proposed method, deployed on edge devices, delivers real-time diagnostics, providing a scalable and efficient solution for industrial applications. Experimental results using real-world datasets demonstrate that our method significantly outperforms state-of-the-art algorithms, improving detection accuracy and Area under the curve (AUC) scores up to 97% and 99%, respectively.

关键词： Fault diagnosis Time-frequency analysis Accuracy Correlation Fault detection image edge detection signal processing algorithms Feature extraction Real-time systems Speech processing

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DPGIR: SIFT Recovery from a Hazy image

DPGIR: SIFT Recovery from a Hazy Image

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2022 IEEE International conference on Multimedia and Expo, ICME 2022

作者： Huang, Shan Chang, Hao Wu, Wei Li, Zhu Xidian University State Key Laboratory of Integrated Services Networks China University of Missouri Department of Computer Science & Electrical Engineering Kansas City United States

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Scale-Invariant Feature Transform (SIFT) plays a significant role in vision applications. Indeed, even with the advent of deep learning, SIFT-based object re-identification has been found to be competitive in a variety of object and scene recognition challenges. This is a relatively robust method. But under severe imaging conditions like hazy, the haze greatly hinders SIFT detection, causing matching performance degradation. To solve the problem, in this paper we propose a deep learning SIFT recovery algorithm from a single hazy image with a two-task framework, namely, Difference of Gaussian (DoG) Pyramid and Gradient image Recovery (DPGIR). One task is to recover the dehazed DoG pyramid of a hazy image, and the other is to recover its dehazed Gaussian gradients. This scheme to set different objectives for SIFT detection and description leads to very robust performance. Compared with state-of-the-art methods, experimental results demonstrate that our proposed algorithm recovers more SIFT key points. © 2022 IEEE.

关键词： Deep learning

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A Survey of Security Testing Techniques for Deep Learning Frameworks

A Survey of Security Testing Techniques for Deep Learning Fr...

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IEEE International conference on signal and image processing (ICSIP)

作者： Hanqing Li xiang Li Yuanping Nie Jianwen Tian NKLSTISS Institute of Systems Engineering Academy of Military Sciences Beijing China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

Deep learning models are rapidly advancing and finding widespread applications in various critical domains. Ensuring the security of these models has garnered significant attention from both academia and industry. Deep learning frameworks serve as the foundation for constructing, executing, and optimizing deep learning models. As a crucial intermediary component connecting application scenarios and hardware platforms, these frameworks provide support for the development of deep learning applications by abstracting different hardware platforms and hiding underlying details, thereby simplifying application development. Consequently, they are regarded as pivotal infrastructure in the field of deep learning. In this context, if a framework itself possesses security vulnerabilities, the deep learning models built upon it may also be susceptible to security issues, posing a grave threat to the overall security of the deep learning system. Regrettably, compared to the current emphasis on the security of deep learning models, the attention given to the security of deep learning frameworks is significantly inadequate. This paper focuses on the research of deep learning framework testing techniques. Firstly, Introduction to the Fundamental Architecture of Deep Learning Models and Frameworks, along with Relevant Testing Techniques. Secondly, through a systematic analysis and comprehensive review of academic articles on the research progress of deep learning framework testing techniques over the past five years, this paper explores the categorization of framework testing techniques into three distinct levels: model-level, API-level, and kernel-level, based on the characteristics of the tested input objects. Finally, it summarizes the challenges and deficiencies encountered by current deep learning framework testing techniques, providing an outlook on future research directions that warrant exploration, with the aim of advancing the continuous development of deep learning framew

关键词： Deep learning Surveys Industries Systematics Reviews image processing Hardware

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Subspace Modeling Enabled High-Sensitivity x-Ray Chemical Imaging

Subspace Modeling Enabled High-Sensitivity X-Ray Chemical Im...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Jizhou Li Bin Chen Guibin Zan Guannan Qian Piero Pianetta Yijin Liu SLAC National Accelerator Laboratory Stanford University School of Data Science City University of Hong Kong Max-Planck-Institut für Informatik

Resolving morphological chemical phase transformations at the nanoscale is of vital importance to many scientific and industrial applications across various disciplines. The TxM-xANES imaging technique, by combining full-field transmission x-ray microscopy (TxM) and x-ray absorption near edge structure (xANES), has been an emerging tool that operates by acquiring a series of microscopy images with multi-energy x-rays and fitting to obtain the chemical map. Its capability, however, is limited by the poor signal-to-noise ratios due to system errors and low exposure illuminations for fast acquisition. In this work, by exploiting the intrinsic properties and subspace modeling of the TxM-xANES imaging data, we introduce a simple and robust denoising approach to improve the image quality, which enables fast and high-sensitivity chemical characterization. Extensive experiments on both synthetic and real datasets demonstrate the superior performance of the proposed method.

关键词： Three-dimensional displays Microscopy Noise reduction signal processing algorithms signal processing Nanoscale devices Speech processing

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New Automatic Allred Scoring for Breast Cancer Nuclei Detection and Scoring from ER-IHC Stained images

New Automatic Allred Scoring for Breast Cancer Nuclei Detect...

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IEEE International conference on signal and image processing applications (ICSIPA)

作者： Bo-Yu Lin Sei-Ichiro Kamata Mohammad Faizal Ahmad Fauzi Wan Siti Halimatul Munirah Wan Ahmad Graduate School of Information Production and Systems Waseda University Kitakyushu Japan Faculty of Engineering Multimedia University Cyberjaya Malaysia International Medical University Kuala Lumpur Malaysia

ISBN: (数字)9798350352368

ISBN: (纸本)9798350352375

Allred scoring is an important system for quantitatively evaluating estrogen receptor (ER) status and assisting pathologists in recommending hormone therapy. While several automatic methods have been proposed to overcome the high cost of manual counting, they face challenges such as expensive segmentation annotation costs, reduced classification accuracy due to loss of contextual information, and long evaluation times. This paper proposes an Integrated Segmentation and Classification model for Allred Score (ISCAS-Net) using an encoder-decoder network to provide more contextual information from patches for classification. A three-stage training approach is introduced to train the integrated model with only nuclei classification annotations, including unsupervised segmentation training, CNN classification model training with ground truth generation, and classification decoder training. A novel automatic Allred scoring system based on this model offers improved performance and significantly reduced evaluation time. Experiments on 37 immunohistochemically (IHC)-stained breast cancer Whole Slide images (WSIs) show 83.78% agreement on hormone therapy recommendations and 59.46% agreement on Allred scores, both improving by 2.7% compared to the state-of-the-art (SoTA) method. Moreover, the processing time is reduced from 1.72 hours to 11 minutes per WSI. This system's faster scoring and improved performance have the potential to enhance clinical workflow and support more accurate hormone therapy recommendations.

关键词： Training image segmentation Accuracy Costs Annotations Medical treatment Biochemistry

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image Compression Algorithm Based on Region of Interest Extraction for Unmanned Aerial Vehicles Communication

Image Compression Algorithm Based on Region of Interest Extr...

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IEEE Information Technology and Mechatronics Engineering conference (ITOEC)

作者： Yanxia Liang Tong Jia xin Liu Huanhuan Zhang Shaanxi Key Laboratory of Information Communication Network and Security Xi’an University of Posts and Telecommunications Xi’an China College of Information Engineering Xi’an Eurasia University Xi’an China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

The flexibility and maneuverability of unmanned aerial vehicles (UAVs) have led to their widespread use, but the challenge of limited storage space and bandwidth in emergency scenarios necessitates efficient image compression techniques. In this research, we propose an image compression algorithm tailored for UAV communication, termed ROIE-UC (Region of Interest Extraction for UAV Communication). Firstly, image pixels are clustered into super pixel blocks using the Simple Linear Iterative Clustering (SLIC). Secondly, these super pixels are grouped into regions of interest (ROI) using the Density-Based Spatial Clustering of applications with Noise (DBSCAN). The image is then segmented into ROI and non-ROI areas based on these clusters. Lossless compression is applied to the ROI, while lossy compression with a high ratio is used for non-ROI regions. At the receiving end, the image is decompressed and reconstructed. Experimental results show that the ROIE-UC achieves a Peak signal-to-Noise Ratio (PSNR) of 46.37 dB and a Structural Similarity Index (SSIM) of 99.94% for the ROI part, with compression ratios of 27, 25, and 14 for the whole image. Compared to the JPEG, PSNR improved by up to 28.52% and SSIM by 1.51%, with a compression ratio increase from 14.52 to 27.56. The ROIE-UC also provides up to a 49.44% increase in PSNR over other methods. These results demonstrate that ROIE-UC is an efficient and effective solution for image processing and data compression in UAV applications.

关键词： image segmentation Visualization image coding PSNR Wireless networks Bandwidth Autonomous aerial vehicles Real-time systems image reconstruction Videos

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Analyzing the Effect of Relaxing Natural Sounds on the Human Brain During Alpha Up-Regulation Neurofeedback Training

Analyzing the Effect of Relaxing Natural Sounds on the Human...

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IEEE International conference on signal and image processing applications (ICSIPA)

作者： Humaira Nisar Yin Ni Ong Danyal Mahmood Department of Electronic Engineering Faculty of Engineering and Green Technology Universiti Tunku Abdul Rahman Kampar Perak Malaysia Department of Biomedical Engineering School of Mechanical and Manufacturing Engineering National University of Sciences and Technology Islamabad Pakistan

ISBN: (数字)9798350352368

ISBN: (纸本)9798350352375

This study explores the effect of relaxing natural sounds on human participants undergoing alpha up-regulation Neurofeedback Training (NFT). Twenty-five participants” electroencephalographic (EEG) signals were recorded while they listened to natural soundscapes., also known as relaxing music during twenty alpha up-regulation NFT sessions. The effect of listening to those natural sounds is analyzed by looking into the alpha brainwave activity. Power Spectral Density was calculated using Welsh“s Method. The analysis focused on identifying alpha power modulation triggered by different relaxing natural sounds and examining the effectiveness of relaxing natural sounds during NFT sessions. The findings showed that certain relaxing music types., such as Rain Relax and Ambient Relax., resulted in higher alpha power levels., indicative of enhanced relaxation and calmness among participants. Whereas relaxing music sounds like Night Sound Relax and Thunder Relax demonstrated lower effectiveness in promoting relaxation based on lower average alpha power levels. Moreover., individual variability in response to music stimuli was also observed. In conclusion., the study highlights the effectiveness of specific relaxing music types in offering relaxation outcomes during NFT sessions. It also underscores the importance of personalized approaches based on individual responses to music stimuli for enhancing relaxation.

关键词： Training Brain Rain image processing Music Modulation Electroencephalography

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Road Identification Algorithm Based on Tire Acceleration signal Under Multi-Vehicle Conditions

Road Identification Algorithm Based on Tire Acceleration Sig...

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image processing and Computer applications (ICIPCA), IEEE International conference on

作者： Peiwen xia Jiayi Zhang Zheng Nie Wenxin Yao Zongqiao Han Zhichao xu Shun Zhang Nanjing University of Science and Technology Nanjing China Jiangsu Lubida IOT Technology Co. Ltd Nantong China

In this paper, an intelligent tire algorithm based on back propagation neural network was proposed to identify the type of road peak adhesion coefficient. In the drum test, eight different vehicle conditions were taken into account for each of three simulated road conditions. Tire pressure of 900 or 1000 kPa, speed of 30 or 40 km/h, and load of 500 or 1000 kg were the parameters for the vehicle conditions. The corresponding tire x-acceleration signal was obtained using the tire acceleration sensor. Then, the original signal data was denoised using wavelet transform. The signal data was grouped by tire rotation period and the anomalous data was eliminated using the isolated forest algorithm. Following that, wavelet transform was used once more to extract the time domain and frequency domain characteristics from each batch of data, producing 45-dimensional feature parameters. Principal components analysis was applied to reduce the dimension of feature parameters to obtain 8-dimensional principal components. Finally, to identify the type of road peak adhesion coefficient, the normalized principal components and vehicle condition parameters (tire pressure, speed, load) were used as input to build a four-layer back propagation neural network, while the adam algorithm was used to optimize the learning rate. 10-fold cross validation was used to examine the model effect. The prediction results show that the model has a high identification accuracy for three types of road, with an average accuracy of 99.53%. The prediction accuracy of the models developed in 10-fold cross-validation fluctuates between ±0.2%. Under different vehicle conditions, the proposed road identification perception algorithm establishes the direct correlation between tire x-axis acceleration and road adhesion coefficient. With its high dependability and real-time capabilities, this algorithm has a wide range of possible applications in the areas of automated driving and intelligent tires.

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