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Identity Recognition Using Wavelet-based Neural Networks: Review and Comparative Study

Identity Recognition Using Wavelet-Based Neural Networks: Re...

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Decision Aid Sciences and Application (DASA), international conference on

作者： Eliganti Ramalakshmi Dama Pranati Sreya Tanguturi Poojitha Department of Information Technology Chaitanya Bharathi Institute of Technology Hyderabad India

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

Wavelet neural networks are a unique combination of wavelet analysis and neural network architectures, thus providing the benefits of advanced signal processing. Unlike traditional methods, WNNs offer a multi-resolution approach that outshines for nonstationary signals; therefore, these are very useful in applications related to areas such as biomedical signal analysis, image processing, and time-series forecasting. Architecture of WNNs enables the replacement of activation functions with wavelet functions for the extraction of localized features, making both processing and interpretation of data much more efficient. Some of the recent improvements in designing WNNs improve its computational efficiency to suit the requirements of real-time applications and also to incorporate it into deep learning frameworks, but still, one of the main challenges faced in WNNs such as computational complexity and scalability need to be addressed. As the historical development, core contributions, and evolving architecture of WNNs is done, this paper represents those along with filling up certain existing research gaps. This research paper addresses future directions regarding hybrid models that combine synergies of CNNs with RNNs as well as WNNs and therefore posits opening its way for enhanced performance across various domains. Researching such an area ensures and guarantees further expansion of applications in WNNs besides strengthening their importance in signal processing in the modern arena.

关键词： Wavelet transforms Neural networks Computer architecture Thermal sensors Wavelet analysis Feature extraction Real-time systems Computational efficiency internet of Things Noise measurement

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A Traffic Sign Detection Technique Using Road Scene images and GPS/GIS Information

A Traffic Sign Detection Technique Using Road Scene Images a...

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IEEE international conference on systems, Man and Cybernetics

作者： Sung-Chi Yang Huei-Yung Lin Yu-Hsiang Fan Shih-Han Wei Department of Electrical Engineering National Chung Cheng University Chiayi Taiwan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

With the advancement of computational intelligence, autonomous driving has become the future development trend of the automotive industry. Since the safety is commonly considered as the first priority of self-driving and driver assistance systems, the understanding of transportation infrastructure is an essential problem. In this paper, a technique for traffic sign detection and recognition is proposed. Different from the general image-based methods, we also incorporate the vehicle position and geographic information to improve the accuracy. based on the approximate location of a traffic sign obtained from GPS and GIS, it can be used to increase the confidence level of network detection results. In the experiments, the training datasets are derived from Google Street View images and collected with an in-vehicle camera. The performance evaluation compared to the image-only methods has demonstrated the effectiveness of the proposed approach.

关键词： Training Performance evaluation Accuracy Roads Cameras internet Safety Global Positioning System Geographic information systems Vehicles

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High-Frequency Vision-based Tactile Sensor with Embedded Transparent Piezoelectric Module for Humanoid Robotic Perception

High-Frequency Vision-Based Tactile Sensor with Embedded Tra...

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IEEE international conference on Micro Electro Mechanical systems

作者： Zhengyi Xie Chunpeng Jiang Haoxiang Jiang Yimeng Sun Bin Yang Jingquan Liu National Key Laboratory of Advanced Micro and Nano Manufacture Technology Shanghai Jiao Tong University Shanghai China Department of Micro/Nano Electronics School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Zhangjiang Laboratory China Shanghai University of Electric Power China

ISBN: (数字)9798331508890

ISBN: (纸本)9798331508906

This paper reports a novel composite high-frequency vision-based tactile sensor with an embedded transparent MEMS-based piezoelectric module. Taking advantage of the transparency of the piezoelectric module, the sensor can simultaneously capture high-quality image signals and piezoelectric signals. By combining high-frequency piezoelectric signals with the interpolation algorithm, the sensor enhances the frame rate of image signals, enabling the acquisition of high-frequency, high-quality image signals. Additionally, this integrated approach offers advantages in terms of miniaturization and low cost. This research provides a new direction for the development of humanoid robotic perception and has broad application potential.

关键词： Micromechanical devices Human computer interaction Interpolation Visualization image resolution Heuristic algorithms Multimodal sensors Tactile sensors Humanoid robots signal resolution

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Analysis of Visual Sensory System based Edge Computing Approach in IOT 29

Analysis of Visual Sensory System based Edge Computing Appro...

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29th IEEE conference on signal Processing and Communications Applications (SIU)

作者： Simsek, Emrah Ozyer, Baris Ataturk Univ Bilgisayar Muhendisligi Bolumu Erzurum Turkey

ISBN: (纸本)9781665436496

internet of Things technologies, have wide application area in daily life with Industry 4.0, interact with artificial intelligence, cloud computing and embedded systems. internet of Things technology can be used in more comprehensive applications, supported by visual sensory systems. Due to the high processing, storage and communication requirements of visual sensory systems, problem-specific data processing architectures are used in the internet of Things. Edge Computing means the methods process in the edge of the network instead of the server to reduce data transmission, processing and storage requirements. In this study, the storage, processing and transmission requirements of edge computing-based data processing architectures for the basic problems of visual perception such as object detection and object recognition are analyzed. The solution of the object detection or object recognition problems in the analysis was carried out in different scenarios. In these scenarios, the requirements are determined by sharing the methods used to solve the problem between the server and the IoT device. In the analysis, the requirements for pre and post processing methods, feature extraction methods and classification algorithms are used. According to the analysis results, it has been determined the scenario in image features are transmitted to the servers minimizes the general requirements.

关键词： internet of Things Edge Computing Visual Sensing Object Detection

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PA-SIE: Pixel-Attack Methodology Factored Selective image Encryption Scheme for Low Resource Edge Computing systems

PA-SIE: Pixel-Attack Methodology Factored Selective Image En...

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international conference on Big Data and Smart Computing (BIGCOMP)

作者： Harshita Gupta Mayank Kabra Asmita Zjigyasu Madhav Rao Thapar University International Institute of Information Technology Bangalore

The transmission of image data over the internet is inevitable considering its effective use-cases in tele-healthcare, online-banking, remote-education, e-commerce, email exchanges, and many others. However secure communication of the image data is still evolving and not standardized yet, but it is desirable owing to various cyber attacks especially adversarial attack leading to the loss of critical information. One of the primary challenges on adopting secure communication is to balance computational and communication cost for encrypting the data but also attain high security. Applying encryption across all pixelated data is likely to increase the communication overhead and reduce the throughput which otherwise becomes bottleneck for some of real-time applications. The large size of ciphertext makes the system highly inefficient. This research proposes an approach based on one-pixel attack factored selective image encryption scheme (PA-SIE) to reduce the size of the ciphertext while ensuring the security of the image data. The proposed approach identifies the sensitive pixels in the image by using the differential evolution algorithm and encrypts them using a preferred encryption scheme, while leaving the remaining pixels unencrypted. To the best of our knowledge, this is the first study to propose this approach for balancing cost and security in image data transmission. This approach significantly reduces the cost of data transfer while ensuring the secure transmission of sensitive image data. The proposed PA-SIE approach offers maximum communication latency improvement and energy savings of 84.65 %, when compared with full Elliptic Curve Cryptography (ECC) applied encrypted image transmission for web traffic over the HTTPS protocol. The proposed approach is a step towards ensuring secure and efficient transmission of image data over the internet from low resource and bandwidth constrained edge-computing systems.

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Design and Analysis of Amplitude-Phase-Form Detection in Residual Phase Noise 14

Design and Analysis of Amplitude-Phase-Form Detection in Res...

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14th IEEE international conference on Wireless Communications and signal Processing (WCSP)

作者： Wang, Qian Ma, Wenqiang Qian, Li Ping Wu, Yuan Kam, Pooi-Yuen Zhejiang Univ Technol Coll Informat Engn Hangzhou Peoples R China Univ Macau State Key Lab Internet Things Smart City Macau Peoples R China Univ Macau Dept Comp & Informat Sci Macau Peoples R China Chinese Univ Hong Kong Shenzhen Sch Sci & Engn Shenzhen Peoples R China

ISBN: (纸本)9781665450850

High-order modulations are widely used in wireless communications. And the existence of phase noise, especially in mm-wave or THz high-frequency systems, will greatly impair the transmission reliability. Most existing works still use the minimum Euclidean distance (MED) rule for performance analysis, which is actually a mismatched Gaussian receiver in phase noise. This paper thus proposes approximately optimum amplitude-phase-form detection schemes and analyzes the error performance in the presence of both additive Gaussian white noise (AWGN) and residual phase noise (RPN) due to imperfect carrier phase estimation. based on the matched maximum-likelihood (ML) receiver proposed in our earlier work, the joint amplitude and phase detection criteria in polar coordinates are given thoroughly. Then a suboptimal annular-sector (AS) detector is obtained, which corresponds to the separate amplitude and phase detection. This AS detector has low complexity to implement and does not depend on the specific channel information. And the optimal ML decision boundaries are explicitly shown, which tend to be circular as the signal-to-noise ratio (SNR) or RPN variance increases. Star-8QAM and Rect-8QAM are used here to illustrate the error performance by both theoretically derivation and detailed simulations. It is shown that the AS detector performs much better than the MED detector for high SNR or strong phase noise. Moreover, Star-8QAM with optimized ring ratio outperforms Rect-8QAM with optimized power ratio for a large range of RPN variances.

关键词： Phase noise Maximum-likelihood detection Polar coordinates Symbol error rate

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Recurrent Neural Network with Backstepping controller based Fractional-Order Chaotic System based Solution for Secure Communication and image Encryption

Recurrent Neural Network with Backstepping controller based ...

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IoT based Control Networks and Intelligent systems (ICICNIS), international conference on

作者： A. Narmada Anuj Jain Manoj Kumar Shukla Electronics and Communication Engineering Lovely Professional University Phagwara Punjab India School of Electronics and Electrical Engineering Lovely Professional University Phagwara Punjab India Symbiosis institute of Technology Symbiosis International (deemed) University Pune India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Synchronisation of Fractional-order Non-linear systems can be used by incorporating additional security attributes. In this work, a Recurrent Neural network with Backstepping controller is designed used for synchronizing two identical non-linear fractional-order chaotic systems in master-slave configuration. The controller is split into two parts. One is designed at the sender's end and the other at receiver section to obtain synchronisation between Master and Slave systems. Also, two signals are transmitted in which one has message signal embedded in it and the other is a complicated combination of master states which is used for synchronisation purpose at the receiver end so that information signal can be recovered. A complex key set is designed with system parameters, fractional orders, initial conditions, so that no intruder can decode the message signal and receiver system should also know the keyset. The efficacy of the proposed scheme is demonstrated with the help of results presented in the end.

关键词： Recurrent neural networks Backstepping Chaotic communication Transmitters Receivers Main-secondary Encryption Synchronization Intelligent systems Information entropy

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Research on Real-Time Data Transmission and signal Processing System of CIM Practical Training Teaching Platform based on 5G Network

Research on Real-Time Data Transmission and Signal Processin...

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image Processing and Computer Applications (ICIPCA), IEEE international conference on

作者： Xuemei Cheng Hao Zhang Shandong University of Engineering and Vocational Technology Jinan China Poly Shandong Real Estate Group Corporation Limited Jinan China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

In order to solve the problems of “digital signal processing”, such as many common knowledge points, difficult teaching, difficult visual teaching, and so on, a software platform for “signal processing” open teaching is built based on Visual C++6.0. This system is to cooperate with the teaching content of CIM, but also to improve the practical ability of students, so that they can more easily and quickly understand some of the more complex digital signal theory. The timing of the beam position signal is optimized based on the existing down sampling method, and the digital AGC and slow application signal processing modules are completed. We need to get turn-by-turn, quick application, and track closing data simultaneously. The experiment shows that the system can obtain the multi-frequency position information including the actual running state well. The advantages of this system are easy to control parameters, easy to use, and strong expansibility. This project plans to further improve the system in the future research work so as to achieve better teaching and practical application purposes.

关键词： Training Human computer interaction Visualization image processing Education signal processing Sampling methods Software Real-time systems Timing

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Detection and Classification of Monkeypox Disease with Chatbot

Detection and Classification of Monkeypox Disease with Chatb...

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Intelligent Data Communication Technologies and internet of Things (IDCIoT), international conference on

作者： P. Chandu P. Siddardha Priyadharshini K Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Monkeypox detection is essential for effective public health management and controlling its spread. Timely detection enables early intervention in outbreaks, reducing transmission risk. This project presents an innovative framework that enhances monkeypox detection and classification using deep learning and an interactive chatbot. It employs the VGG16 convolutional neural network (CNN) for accurate classification of monkeypox from medical images, utilizing performance metrics like precision, recall, and F1 score for reliability. Additionally, the system includes a neural network-based chatbot that provides interactive assistance, answers user questions, offers symptom information, and guides users through the diagnostic process. This chatbot improves user engagement and accessibility, linking clinical diagnostics with patient interaction. Built on the Flask framework, the system ensures a smooth, user-friendly interface for both image classification and chatbot functionalities.

关键词： Deep learning Measurement Neural networks Chatbots Real-time systems Convolutional neural networks Reliability Public healthcare Medical diagnostic imaging image classification

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4th international conference on Trends in Cognitive Computation Engineering, TCCE 2022

4th International Conference on Trends in Cognitive Computat...

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4th international conference on Trends in Cognitive Computation Engineering, TCCE 2022

ISBN: (纸本)9789811994821

The proceedings contain 43 papers. The special focus in this conference is on Trends in Cognitive Computation Engineering. The topics include: Descriptive Analysis for Electric Bus During Non-Operational Stage;the Effectiveness Level on the Electric Buses Operation: Case Study for Affordability and Accessibility;IoMT-based Android Application for Monitoring COVID-19 Patients Using Real-Time Data;A Cost-Effective Unmanned Ground Vehicle (UGV) Using Swarm Robotics technology for Surveillance and Future Combat;neural Network-based Obstacle and Pothole Avoiding Robot;a Comparative Study of Psychiatric Characteristics Classification for Predicting Psychiatric Disorder;Material Named Entity Recognition (MNER) for Knowledge-Driven Materials Using Deep Learning Approach;An Improved Optimization Algorithm-based Prediction Approach for the Weekly Trend of COVID-19 Considering the Total Vaccination in Malaysia: A Novel Hybrid Machine Learning Approach;analyzing the Effectiveness of Several Machine Learning Methods for Heart Attack Prediction;Ensemble Machine Learning Technique for Identifying COVID-19 from CT Scan images;solving the Royalty Payment Problem Through Shooting Method;ECG signal Classification Using Transfer Learning and Convolutional Neural Networks;partitional Technique for Searching Initial Cluster Centers in K-means Algorithm;a Novel Ensemble Methodology to Validate Fuzzy Clusters of Big Data;Model Analysis for Predicting Prostate Cancer Patient’s Survival: A SEER Case Study;quantum-Inspired Neural Network on Handwriting Datasets;an Efficient and Secure Data Deduplication Scheme for Cloud Assisted Storage systems with Access Control;priority-based Intelligent Reflecting Surface for Uplink 6G Communication;identifying Duplicate Questions Leveraging Recurrent Neural Network;stegoPix2Pix: image Steganography Method via Pix2Pix Networks;machine Learning-based Tomato Leaf Disease Diagnosis Using Radiomics Features;low-Cost Energy Efficient Encryption Algorithm for P

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