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检索条件"任意字段=IEEE International Conference on Signal Image Technology and Internet Based Systems"

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Robust and Efficient Adversarial Defense in SNNs via image Purification and Joint Detection

Robust and Efficient Adversarial Defense in SNNs via Image P...

引用

2025 ieee international conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： Chen, Weiran Xu, Qi School of Microelectronics University of Science and Technology of China Anhui Hefei China

ISBN: (纸本)9798350368741

Spiking neural networks (SNNs) leverage neural spikes to provide solutions for low-power intelligent applications on neuromorphic hardware. Although the spiking mechanism significantly enhances computational efficiency, especially in energy-constrained environments, they still lack resistance to noise perturbations and adversarial attacks. In this paper, we propose a defense framework based entirely on SNNs and design a fast and efficient training algorithm. The framework is divided into an image purification module and an adversarial detection module. The image purification module is employed for the extraction of noise and the reconstruction of input images. The adversarial detection module is utilized to differentiate between clean and adversarial images, thereby further enhancing defense performance. Meanwhile, our approach is highly flexible and can be seamlessly integrated with other defense strategies. Experimental results demonstrate that the proposed methodology outperforms state-of-the-art baselines in terms of defense effectiveness, training time and resource consumption. © 2025 ieee.

关键词： adversarial detection image purification low-resource robustness spiking neural networks

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signal Deinterleaving based on U-Net Networks 8

Signal Deinterleaving Based on U-Net Networks

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8th international conference on Computer and Communication systems, ICCCS 2023

作者： Kang, Zhi Zhong, Yi Wu, Yaoyun Cai, Yihong Institute of Electronic Equipment Southwest China Research Chengdu China

ISBN: (纸本)9781665456128

signal deinterleaving is a key part of electronic warfare radar signal processing. As signal environments become more complex, classical sorting methods are being put to the test. This paper draws on the idea of using fully convolutional network to achieve image segmentation, proposes a new signal deinterleaving method based on U-Net network, and introduces the algorithm principle and network training method in detail. Simulation results show that if the PDW sequence of a single target can be fully intercepted, signal sorting based on U-Net segmentation network can be well realized in a complex signal environment as long as the target signal parameters are separable in any dimension. © 2023 ieee.

关键词： Electronic warfare

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Invasive Ductal Carcinoma Detection using Convolutional Neural Network Architecture from Breast Histopathology images 2

Invasive Ductal Carcinoma Detection using Convolutional Neur...

引用

2nd international conference on Intelligent Cyber Physical systems and internet of Things (ICoICI)

作者： Kaur, Gurjot Sharma, Neha Chitkara Univ Inst Engn & Technol Rajpura Punjab India

ISBN: (纸本)9798331540661;9798331540678

The purpose of the work is to create a robust convolutional neural network-based classification model to distinguish images of invasive ductal carcinoma (IDC) breast disease. This collection includes histopathological images from breast tissue samples some with IDC-positive and others IDC-negative. First in data preparation are resizing images to 50x50 pixels and arranging them for training and testing. The sample consists of 3,747 IDC-positive images and 21,031 IDC-negative images for 24,678 images overall. The model's CNN architecture consisted of many layers: convolutional layers, max-pooling layers, batch normalization, and dropout layers aimed at reducing overfitting. Trained with binary cross-entropy loss function and an Adam optimizer running at 0.0001 learning rate. After 40 iterations on the training set, the model's accuracy on the testing set was 94.73%, and on the training, set was 99.12%. The performance of the model was assessed using several parameters including accuracy, precision, recall, and F1-score. The categorization report shows that both IDC-positive and IDC-negative classes have excellent recall and accuracy. Moreover, confirming the ability of the model to classify IDC-positive and IDC-negative samples is the confusion matrix. This unique CNN-based classification model of breast histomorphology images seems to do remarkably well in identifying IDC, showing both excellent accuracy and the capacity to distinguish between positive and negative IDC samples. The model could enable pathologists to identify breast cancer, therefore enhancing the accuracy and effectiveness of breast cancer screening campaigns. Future directions of this work could concentrate on improving the performance of the model with larger datasets and investigating alternative image augmentation techniques that will generalize its capabilities on diverse input datasets.

关键词： Invasive Ductal Carcinoma (IDC) Breast Histopathology images Convolutional Neural Network (CNN) Medical image Classification

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Design of Park Risk Monitoring System based on internet of Things 9

Design of Park Risk Monitoring System Based on Internet of T...

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9th international conference on Intelligent Computing and signal Processing, ICSP 2024

作者： Liu, Yulong Wang, Mingjun Ma, Xiaopan China Mobile Tietong Co. Ltd. Yulin Branch Shanxi Yulin719000 China School of Automation an Information Engineering University of Technology Shanxi Xi'an710048 China Xi'an Key Laboratory of Wireless Optical Communication and Network Research Shanxi Xi'an710048 China School of Physics and Telecommunications Engineering Shanxi University of Technology Shanxi Hanzhong723001 China

ISBN: (纸本)9798350376548

Aiming at the problems of single monitoring and decentralized management and control in park risk monitoring, a design of park risk monitoring system based on the internet of Things is proposed. Sensors and cameras are used to collect various hazards in the park, front-end pages and back-end servers are developed in the Nodejs environment combined with Web technology, and a data transmission model is established through NB-IoT and MQTT protocols, so as to realize the access, transmission, processing and visualization of various sensor data in the monitoring site. The test results show that the risk monitoring system realizes the risk prevention and early warning monitoring of risks in the park from three aspects: prevention, management and control, reduces the number of risks and effectively improves the monitoring level and management efficiency of risks in the park. © 2024 ieee.

关键词： Risk management

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Research on the Method of Stress Wave signal Generation for Aviation Bearing based on SAE-DeGAN

Research on the Method of Stress Wave Signal Generation for ...

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2024 ieee international conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2024

作者： Guo, Peipei Shan, Tianmin Jie, Zhenguo Wang, Han Shen, Yong Wang, Jinglin Avic Shanghai Aero Measurement-Controlling Research Institute Aviation Key Laboratory of Science and Technology on Fault Diagnosis and Health Management Shanghai China

ISBN: (纸本)9798350388855

To address the problem of insufficient stress wave datas samples for aviation bearing failures, this paper proposes a method to expand the stress wave signals of bearings by combining a Sparse Auto encoder (SAE) with Deep Convolution Generative Adversarial Network (DCGAN). The bearing failure stress wave signals are input into SAE for training to capture one-dimensional self-learning features in the hidden layer of the SAE network. This feature is then used to replace the random noise input to the generator of the DCGAN network. Simultaneously, the two-dimensional grayscale image of the bearing failure stress wave signal is input into the discriminator of the DCGAN network. The EMD(Earth Mover's Distance) distance between the histogram of the generated signal trained by the DCGAN model and the real signal is used to update the learning rate of the DCGAN network. Finally, the similarity and diversity of the generated signal and the real signal are compared. The results show that the bearing failure stress wave signals generated by SAE-DCGAN are highly similar to the real signals and increase the diversity of the bearing failure samples, effectively expanding the sample set of stress wave faults in aviation bearings. © 2024 ieee.

关键词： signal generators

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Design and Implementation of Battery Management and Wireless Charging in Electric Vehicles Using IoT 10

Design and Implementation of Battery Management and Wireless...

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10th international conference on Communication and signal Processing, ICCSP 2024

作者： Manikandan, S. Shyam Sundar, S. Kumaran, K. Thirumeni, Mariammal Rajalakshmi Engineering College Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350353068

The widespread adoption of electric vehicles (EVs) hinges on efficient battery management and convenient charging solutions. This paper presents the design and implementation of an IoT-based battery management system (BMS) integrated with wireless charging technology for EVs. The proposed system leverages sensor data acquisition, real-time monitoring, and cloud connectivity to ensure optimal battery health, extend lifespan, and enhance user experience. Wireless charging offers added convenience and eliminates cable handling, further improving charging experience. This paper details the hardware architecture, software development, and key features of the system, followed by an evaluation of its performance and discussion of potential applications. objects to be connected via the internet, bringing the world to the user's fingertips. Motivated by the aforementioned issues, this paper proposes the design and development of a wireless EV charging and battery monitoring system utilizing IoT technologies. © 2024 ieee.

关键词： internet of things

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An External Denoising Framework for Magnetic Resonance Imaging: Leveraging Anatomical Similarities Across Subjects with Fast Searches 8

An External Denoising Framework for Magnetic Resonance Imagi...

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8th international conference on signal and image Processing, ICSIP 2023

作者： Mei, Lifeng Liu, Sixing Tang, Chenhui Cai, Jiaqia Wang, Jingli Liu, Yilong Wu, Ed X. Lyu, Mengye Shenzhen Technology University College of Health Science and Environmental Engineering Shenzhen China Shenzhen Technology University College of Big Data and Internet Shenzhen China Guangzhou China The University of Hong Kong Laboratory of Biomedical Imaging and Signal Processing Department of Electrical and Electronic Engineering Hong Kong Hong Kong

ISBN: (纸本)9798350397932

External denoising, also known as reference-based denoising, utilizes information from clean reference images, yielding more robust results than internal denoising, especially in situations with high noise levels. The effectiveness of reference-based denoising relies on the similarity between reference and noisy images. In this paper, we introduce a novel external denoising framework for magnetic resonance imaging (MRI) that takes advantage of the inherent anatomical structure similarities across subjects. Our framework employs noise-resistant neural networks to extract deep features, which facilitate searches for high-quality reference images from a large external database. This versatile framework is compatible with all reference-based denoising algorithms, obviating the need for acquiring additional reference images from the subject under examination. © 2023 ieee.

关键词： Magnetic resonance imaging

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Deepfake image Detection Using Yolov8 7

Deepfake Image Detection Using Yolov8

引用

7th ieee Pune Section international conference, PuneCon 2024

作者： Sathe, Pranav Sabane, Varad Undale, Chaitanya Uttarkar, Atharva Chavhan, Vaibhav Sable, Nilesh P. Yenkikar, Anuradha Vishwakarma Institute of Information Technology Department of Cse - Artificial Intelligence Pune India

ISBN: (纸本)9798331527822

Due to growing number of fake media and the possible problems with misinformation and identity theft, deepfake image recognition has become a hot topic. In order to evaluate the efficacy of widely recognized deep learning models in detecting and classifying deepfake images, we compare and contrast YOLO (You Only Look Once) V8, CNN (Convolutional Neural Network), combination of LSTM (Long Short-Term Memory) with CNN and ResNet in this paper. The capacity to recognize photos that have been manipulated effectively is essential for maintaining trust regarding digital media and preventing the spread of deceptive data. The objective of our research is to explain each model's abilities, limitations and performance in the context of deepfake image recognition. We evaluated each model's accuracy, precision, and F1-scores using a dataset of 190402 images, half real and half fake that cover a wide spectrum of deep fake images. based on the evaluation metrics, each model was ranked in terms of its ability to separately describe and determine original versus generated photos with focus on detecting subtle changes eluding human senses. The comparison research shows each model's nuanced capabilities, offering light on the implications for applications in the real world like detecting and controlling the spread of deep fake information across numerous internet platforms. Our findings help towards building deep fake detection systems, further understanding the comparative performance of state of the art deep architectures. This study shall inform more effective deep fake detection systems that can guarantee trust and security in digital media environments. Accuracy achieved by model is 96%, precision score was 94%, recall value is 0.98, F1 score has a value of 0.95. © 2024 ieee.

关键词： Contrastive Learning

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Contrast and Colour Improvement based Haze Removal of Satellite images using Fusion Technique

Contrast and Colour Improvement based Haze Removal of Satell...

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2024 international conference on Intelligent systems for Cybersecurity, ISCS 2024

作者： Gireesh, N. Obulareddy, Vaishnavi Toufeer, Shaik Wafiya Kalyan, Pandipati Chowdary, P. Yaswanth Sree Vidyanikethan Engineering College Dept. of Ece Andhra Pradesh Tirupati517102 India

ISBN: (纸本)9798350375237

Satellite imagery plays a pivotal role in various domains, yet its utility can be hindered by atmospheric haze, leading to degraded contrast and color fidelity. This paper presents a pioneering methodology aimed at ameliorating these issues through a Fusion Technique. Leveraging Contrast Limited Adaptive Histogram Equalization (CLAHE) and Unsharp Mask Filtering, our approach systematically processes Hazy Satellite images to generate Contrast Enhanced Outputs, White Balanced Outputs, Chromatic Weight Maps, Saliency Weight Maps, and Luminance Weight Maps. Subsequently, a fusion mechanism amalgamates these components, yielding a Dehazed image of enhanced quality. Evaluation metrics including Peak signal-to-Noise Ratio (PSNR), Entropy, and absolute mean brightness error (AMBE) attest to the superior performance of our method. By addressing the challenges inherent in haze removal from satellite imagery, our approach not only enhances visual appeal but also enriches the informational content of the scenes captured, thus fostering advancements in satellite image processing and analysis. © 2024 ieee.

关键词： image enhancement

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Linear Frequency Modulated signal Denoising based on Symplectic Geometric Mode Decomposition and Block Thresholding 23

Linear Frequency Modulated Signal Denoising Based on Symplec...

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23rd ieee international conference on Communication technology, ICCT 2023

作者： Ni, Jincheng Zhao, Jin Wu, Xianliang AnHui University Key Lab of Intelligent Computing & Signal Processing Ministry of Education Heifei China

ISBN: (纸本)9798350325959

In this paper, a novel and comprehensive signal denoising method is proposed by combining Symplectic Geometric Modal Decomposition (SGMD) and Block Thresholding denoising. The proposed approach involves a three-step process: first, the signal is decomposed into a set of Symplectic Geometric Components (SGCs) using SGMD. Subsequently, each SGC is subjected to Block-Thresholding denoising. Finally, the denoised SGCs are recombined to obtain the denoised linear frequency modulation (LFM) signal. The experimental verification demonstrates the effectiveness of the SGMD-BT method in denoising LFM signals. This novel approach offers a fresh solution for the processing and analysis of LFM signals, holding significant application potential and research importance. © 2023 ieee.

关键词： Block Thresholding denoising signal denoising Symplectic Geometric Modal Decomposition

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