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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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image Encryption using Chaotic Techniques: A Survey Study

Image Encryption using Chaotic Techniques: A Survey Study

引用

2021 international conference in Advances in Power, signal, and Information technology, APSIT 2021

作者： Ayad, Jenan Hasan, Fadhil Sahib Ali, Alaa H. Hussein, Zaid Khudhur Abdulkareem, Hanan J. Jalil, Mustafa A. Ahmed, Ghaidaa Sadiq, Ahmed University of Technology Electrical Engineering Dep. Baghdad Iraq Electrical Engineering Dep. Mustansiriyah University Baghdad Iraq Technical Engineering Al-Esraa University College Medical Instrumentation Baghdad Iraq Al-Esraa University College Computer Technical Engineering Baghdad Iraq

ISBN: (纸本)9781665425063

The sharing of information through unsecured networks like the internet has increased fast recently. This information could be text, audio, image, or video. The security of data transmission is an essential issue. Encryption is one of the most effective techniques to assure safe data transmission. Videos and images size are very large and have a high correlation between neighboring pixels, so the classical encryption techniques such as RSA, DES, AES, etc., cannot be utilized for image encryption while chaotic image encryption schemes, which use a high level of unpredictability in key generation, can solve this problem. The chaos encryption main properties are pseudorandom number, sensitivity to initial conditions, and large key size. The goal of this study was to find the best chaotic map for picture encryption, this research looks into the use of chaos maps for image encryption, such as the Tent map, Logistic map, Sine map, and others. Arnold's Cat map, and also presents a comparison for their performance. © 2021 ieee.

关键词： Data transfer

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Sign Language Translation Using image Processing to Audio Conversion

Sign Language Translation Using Image Processing to Audio Co...

引用

ieee international conference on Intelligent Techniques in Control, Optimization and signal Processing (INCOS)

作者： V Subashini B Someshwaran S Sowmya S Ashwin Kumar Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

Sign language has long been a fundamental mode of communication for deaf and mute individuals, serving as a crucial tool for inclusivity and interaction. Nonetheless, communication barriers persist as many individuals outside of these communities struggle to comprehend and utilize sign language effectively. A ground-breaking system known as hand gesture recognition using image processing to audio conversion has emerged to address this issue. This innovative system aims to develop an application capable of translating sign language and hand gestures into text and audio, thereby facilitating communication between deaf and mute individuals and the wider society. This system enables the detection and classification of hand gestures by employing computer vision techniques, such as CNN algorithms for image processing and the MediaPipe framework for hand gesture identification in real-time video streams. Subsequently, the system utilizes audio signals to provide immediate feedback by converting the detected gestures into corresponding sounds. The feature extraction CNN algorithm is implemented in Python, while the execution takes place on a Raspberry Pi connected to an external camera utilizing OpenCV libraries. Through this comprehensive approach, the system endeavours to bridge the communication gap and enhance the inclusion of deaf and mute individuals in various social settings.

关键词： Sign language image recognition signal processing algorithms Streaming media signal processing Libraries Real-time systems

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Triple Asynchronous Fault Detection for Fuzzy Semi-Markov Jump systems

Triple Asynchronous Fault Detection for Fuzzy Semi-Markov Ju...

引用

international conference on Vision, image and signal Processing (ICVISP)

作者： Yijia Sun Linchuang Zhang College of Control Science and Engineering Bohai University Jinzhou China College of Information Science and Technology Institute of Ocean Research Bohai University Jinzhou China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

This paper investigates the problem of triple asynchronous event-triggered fault detection filtering for fuzzy semi-Markov jump systems. In practical applications, ensuring synchronous operation among the system, event generator and filter becomes challenging due to various factors such as interference and time delays. These factors complicate the coordination and performance of the system, especially when dealing with systems that operate in multiple modes. Moreover, the limited network resources present in real world scenarios further exacerbate the problem, necessitating the development of more efficient control and filtering schemes. To address these challenges, an asynchronous event-triggered scheme is proposed, along with a corresponding filter and premise variables asynchronous that are designed to handle the inherent delays and resource constraints. Two hidden semi-Markov models are introduced to describe the asynchronous phenomenon between plant, event generator and filter. Using Lyapunov stability theory, sufficient conditions are derived to guarantee that the system is not only stochastically stable and satisfies the $\mathcal{H}_{\infty}$ performance, but also ensures that the filter gains can be obtained. Finally, a modified DC motor equivalent circuit model is taken as an example to demonstrate the effectiveness of the proposed method.

关键词： Sufficient conditions Filtering Event detection Fault detection signal processing Electrical fault detection DC motors Filtering theory Generators Equivalent circuits

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An Auto-Encoder based Method for Camera Fingerprint Compression

An Auto-Encoder Based Method for Camera Fingerprint Compress...

引用

international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Kaixuan Zhang Zihan Liu Jiashang Hu Shilin Wang School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China

Camera fingerprint links a picture to its camera sensor, which is widely applied in sensor device identification, social network tracing and forgery detection. However, such fingerprints are in high dimensionality and cost substantial memory and computing resources, limiting their uses in real-time processing on embedded devices. In this paper, we introduce a new method to compress high-dimensional floating-point fingerprints to low-dimensional binary features to save storage as well as maintaining their representative abilities. Also, we present a much faster approach to sensor device matching with hamming distance, compared with the commonly used Peak to Correlation Energy (PCE) distance. Our method contains two stages. First, raw fingerprints are compressed into low-dimensional features with our proposed grouping strategy and auto-encoder based model. Then, the compressed floating-point features are further converted into more compact binary features. Experiments show that our method achieves superior performance over several competitive compression methods in both identification and verification tasks.

关键词： image coding Social networking (online) Neural networks Fingerprint recognition Feature extraction Cameras Real-time systems

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Restoration Method of Motion Blurred image based on Feature Fusion and Particle Swarm Optimization Algorithm

Restoration Method of Motion Blurred Image Based on Feature ...

引用

Distributed Computing and Electrical Circuits and Electronics (ICDCECE), ieee international conference on

作者： Feng Liu Physical Education Research Department Xinjiang University Urumqi China

The current computer situation is that people generally have home computers. With the rise and prosperity of the internet era such as short video, people have higher and higher requirements for high-definition status of pictures and videos. In addition, some people hope to use internet technology to repair and clarify ancient photos and sports videos. Therefore, in order to make the motion blurred image clearer, this paper studies the image restoration method with the help of feature fusion and particle swarm optimization algorithm. In this paper, we mainly use analytic hierarchy process (AHP) and experimental case analysis and comparison to restore blurred images in different motion states. The experimental results show that the maximum time difference of the proposed method for processing motion blurred images can be 13.1s, which is much smaller than the other two methods.

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Power System Fault Detection Using image Processing And Pattern Recognition 2

Power System Fault Detection Using Image Processing And Patt...

引用

2nd ieee international conference on Applied Electromagnetics, signal Processing, and Communication, AESPC 2021

作者： Balamurali Krishna, P. Sinha, Pampa Maharana, Manoj Kumar Jena, Chitralekha Pavan Kumar, A.V. Akkenaguntla, Karthik Kiit Deemed to be University School of Electrical Engineering India Madanapalle Institute of Technology Science Dept. of EEE Madanapalle India

ISBN: (纸本)9781665442992

image classification is a procedure of classifying the images depending on the datasets with which the model is trained. image classifiers are playing a significant role in today's automation world. This paper presents a method of detecting and classifying the faults in the electrical distribution system using this technology. An image classifier and Artificial Neural Networks (ANN) technologies are considered for detection and classification of faults in the system. The fault detection and classification algorithm takes fault voltage components for the estimation of a fault condition. An ieee 5-bus system is considered to test the performance of the developed algorithm and model. When fault occurs voltage waveforms contain significant changes with respect to fundamental frequency. The generated images under fault conditions are fed for image processing with ANN, and it is trained with sets of data obtained. The results shows that system performance is perfectly accurate with the usage of ANN based image classifier using teachable machine web-based software developed by Google. © 2021 ieee.

关键词： Neural networks

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A Deep Learning Approach for Tree Root Detection using GPR Spectrogram imagery 45

A Deep Learning Approach for Tree Root Detection using GPR S...

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45th international conference on Telecommunications and signal Processing (TSP)

作者： Lantini, Livia Massimi, Federica Tosti, Fabio Alani, Amir M. Benedetto, Francesco Univ West London Sch Comp & Engn London England Roma Tre Univ Signal Proc Telecommun & Econ Lab Rome Italy

ISBN: (数字)9781665469487

ISBN: (纸本)9781665469487

Monitoring and conservation of natural resources have become increasingly necessary, as the impact of new diseases has created major concerns for environmentalists and communities in recent years. Within this context, tree roots are one of the plants' most important and vulnerable organs as well as one of the most challenging ones to inspect. To that effect, the non- destructive testing (NDT) methods have become one of the preferred methods of tree roots assessment and monitoring, as opposed to other conventional and destructive techniques. Within this context, applications of ground penetrating radar (GPR) have proven to be accurate and efficient for tree roots' investigation and mapping. However, a major challenge for GPR detection of tree roots is the soil inhomogeneity. This study aims to mitigate the uncertainty in root detection by proposing a deep learning method based on the analysis of GPR spectrograms (i.e., a graphic representation of a signal's frequency spectrum with respect to time). In this study, the GPR signal is first processed in both the time and frequency domains to filter the existing noise-related information and hence, to produce spectrograms. Subsequently, an image-based deep learning framework is implemented, and the effectiveness in detecting tree roots is analysed in comparison with conventional feature- based machine learning classifiers. The preliminary results of this research demonstrate the potential of the proposed approach and pave the way for the implementation of new methodologies in assessing tree root systems.

关键词： Ground penetrating radar (GPR) tree roots assessment deep learning spectrograms

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2021 ieee international conference on Electronic Communications, internet of Things and Big Data, ICEIB 2021

2021 IEEE International Conference on Electronic Communicati...

引用

2021 ieee international conference on Electronic Communications, internet of Things and Big Data, ICEIB 2021

ISBN: (纸本)9781665437554

The proceedings contain 82 papers. The topics discussed include: micro aerial vehicle transmission of flight dynamics telemetry;investigating the situational awareness with optical see-through augmented reality in IoT-based environments;network intrusion detection system with stream machine learning in fog layer and online labeling in cloud layer;performance evaluation and improvement of a cloud-native data analysis system application;research on hospital number arrangement and calling system based on multi-machine communication;research on hospital number arrangement and calling system based on multi-machine communication;research on strategy and technology of creating smart city in Yilan with IoT technology;wireless sensor network routing algorithm based on traffic prediction for internet of thing;beehive with low-energy of high-voltage shock against killer hornets;calculation method of loop resistance based on maximum temperature of electrical equipment;and development of mountable infrared-thermal image detector for safety hamlet.

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image Sharing Chain Detection VIA Sequence-To-Sequence Model

Image Sharing Chain Detection VIA Sequence-To-Sequence Model

引用

international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Jiaxiang You Yuanman Li Rongqin Liang Yuxuan Tan Jiantao Zhou Xia Li College of Electronics and Information Engineering Shenzhen University China Department of Computer and Information Science University of Macau

image sharing chain detection aims to recover the sharing history of an image downloaded from online social networks (OSNs), including the ever-shared OSNs and their orders, which is an important task in the multimedia forensics community. Most of the existing algorithms directly treat the sharing chain detection as a classification problem by simply assigning a unique label to each sharing chain. Such a strategy though seems straightforward, it ignores the inherent properties of the sharing chain which can be regarded as a time sequence that carries the sharing history of an online image. In this paper, we suggest a new sharing chain detection framework via Sequence-to-Sequence (Seq2Seq) model. Different from previous classification based approaches, our model detects the sharing chain of online image progressively via a decoder. This progressive manner can fully utilize the decoded chain, which is embedded into a series of learned representations. Experimental results show that our method can detect sharing chains involving up to three OSNs, and exhibits much better performance than conventional ones.

关键词： Social networking (online) Forensics signal processing algorithms signal processing Transformers Decoding Classification algorithms

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A Defect Identification Method for Synchronous Condenser based on Gramian Angular Fields Enhancement and Optimization Graph Convolutional Neural Network

A Defect Identification Method for Synchronous Condenser Bas...

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ieee international conference on Power, Intelligent Computing and systems (ICPICS)

作者： Wenbo Li Shuai Zhang Yong Yang Yongtao Jin Lin Zhao Hongkun Lu Bo Wei E. Energy Technology Co. Ltd. Electric Power Research Institute of State Grid Zhejiang Electric Power Co. Ltd. Hangzhou China E. Energy Technology Co. Ltd. Hangzhou China Electric Power Research Institute of State Grid Zhejiang Electric Power Co. Ltd. Hangzhou Zhejiang China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

To achieve intelligent and reliable recognition of the operating conditions of synchronous condenser equipment, a typical defect recognition method of synchronous condenser based on gramian angular fields enhancement and optimization graph convolutional neural network is proposed. Firstly, the one-dimensional voiceprint signal of the camera is mapped into a two-dimensional image by using the K-field method, and then the original data sets under different working conditions are constructed. Then, the Gram angular field map under different working conditions is divided into several superpixel blocks by using super pixel segmentation method, which are used as nodes and graph structure data are constructed according to color and texture characteristics. Finally, the constructed graph structure is input into the graph convolution neural network model with attention mechanism for training and learning, so as to realize accurate recognition of the state of synchronous condenser equipment under multi-typical working conditions. A simulation test system including typical defects of the synchronous condenser equipment under normal, unbalanced and low oil pressure conditions is established. The test results show the proposed method can effectively characterize the three typical working states of the synchronous condenser, and the defect recognition accuracy is significantly improved compared with the conventional method, and it has good stability. The recognition rate can still be more than 93 % in a noise environment of 5 dB, which is about 5.3% higher than that of the conventional method.

关键词： Employee welfare Training Accuracy Robustness Stability analysis Convolutional neural networks Time-domain analysis Spectrogram Optimization signal to noise ratio

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