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检索条件"任意字段=Conference on Photonic Devices and Algorithms for Computing V"

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Demonstrating Post-Quantum Remote Attestation for RISC-v devices

Demonstrating Post-Quantum Remote Attestation for RISC-V Dev...

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Design, Automation and Test in Europe conference and Exhibition

作者： Maximilian Barger Marco Brohet Francesco Regazzoni Vrije Universiteit Amsterdam The Netherlands University of Amsterdam The Netherlands Università della Svizzera italiana Switzerland

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

The rapid proliferation of Internet of Things (IoT) devices has revolutionized many aspects of modern computing. Experience has shown that these devices often have severe security problems and are common targets for malware. One approach to ensure that only trusted software is executed on these devices is Remote Attestation (RA), which allows a verifier to attest the integrity of software running on such a prover device. As malware is typically not trusted, an infected device will fail to generate a valid signature, which allows the verifier to detect the presence of malware on the prover. To achieve its security guarantees, RA requires a trust anchor, often found in the form of dedicated hardware on the prover. For IoT and embedded devices such hardware has only recently become largely deployed. Current RA protocols rely on classical asymmetric signatures that are vulnerable to quantum attacks, which are expected to become feasible in the near future. In this work we present SPRAv, a software-based RA system that leverages the Physical Memory Protection (PMP) primitive of RISC-v to achieve its security guarantees and employs quantum-safe cryptographic algorithms to ensure resistance against quantum attacks in the future. Our evaluation shows that it is feasible to deploy this solution on RISC-v devices without incurring a prohibitive overhead or the need for additional hardware, paving the way towards quantum-resistant functionalities also in IoT.

关键词： Resistance Performance evaluation Protocols Costs Hardware Malware Internet of Things

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Preserving the Privacy in IoT Medical data in Edge computing using Modified AES Model with Discrete-Time Alternating Quantum Walk

Preserving the Privacy in IoT Medical data in Edge Computing...

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Evolutionary algorithms and Soft computing Techniques (EASCT), International conference on

作者： Dileep M R vineeth L Sashikanth Reddy Avula Navaneeth A v Department of Master of Computer Applications Nitte Meenakshi Institute of Technology Bengaluru India

The rapid growth of IoT edge devices has caused in an unprecedented explosion of data volume. Extremely often, the created data is gathered and disseminated via edge-based IoT structures. When hostile IoT devices make requests to steal private data from a cloud storage system via edge nodes, the data-sharing privacy exposure issue becomes even more concerning. We provide evolutionary privacy protection learning algorithms for an IoT data sharing scheme based on edge computing as a solution to the stated problem. Deep learning of medical models in an IoT-based healthcare scheme is described here via a federated learning approach. Using a combination of the alternating quantum walk (AQW) and the Advanced encryption standard (AES), this research suggests a new method of picture encryption. A keystream generator associated with AQW limits is used to build a likelihood distribution matrix, and the study makes use of quantum features to enhance the AES algorithm. To unlock the AES method, we must first extract certain singular values from the matrix. Elements of the probability distribution matrix stand in for Rcon in the AES algorithm. The AES algorithm's S-box and ShiftRow transformation mapping rules are then jumbled based on the increasing size of the clone probability distribution matrix. The preprocessing is done with a likelihood distribution matrix and a plaintext XOR operation, and the encryption is done with a modified version of the AES algorithm. To prevent an adversary from concluding confidential medical data via assaults like a perfect reconstruction attack, etc., this encryption technique may be applied to better safeguard local models.

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Novel Modeling Methodology for Memristive devices for Circuit Design and Simulation

Novel Modeling Methodology for Memristive Devices for Circui...

引用

International conference on Electrical Engineering, computing Science and Automatic Control (CCE)

作者： Jesus Jimenez-Leon Arturo Sarmiento-Reyes Pedro Rosales-Quintero Electronics Department National Institute for Astrophysics Optics and Electronics Puebla Mexico

ISBN: (数字)9798350377545

ISBN: (纸本)9798350377552

This work introduces a methodology for obtaining the high-level model of memristive devices. The methodology uses the I- v curve points of the device. Model equations are derived from the calculation of a state-variable of a first-order differential equation that describes the dynamics of the device. As a demostration, the procedure has been applied to three classical memristor models from which new modeling equations have been developed. Comparison between the resulting model and the initial data points is carried out by measuring the Mean Absolute Percentage Error (MAPE), resulting in 4%, 2.8% and 20.1% when compared to original Strukov's, Biolek's and Chang's models, respectively. This methodology simplifies the simulation process by maintaining a first-order state variable for the model, and avoiding highly non-linear equations such as those introduced by window funcions in the port equation.

关键词： Biological system modeling Software algorithms Measurement uncertainty Memristors Programming Mathematical models Data models Software Circuit synthesis Integrated circuit modeling

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Human-Machine Interaction in Industrial Automation: Gesture-Based PLC Control

Human-Machine Interaction in Industrial Automation: Gesture-...

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International conference on computing and Networking Technology (ICCNT)

作者： Yashika Achirangshu Patra Nitai Sunder Das Department of CSE C V Raman Global University Odisha India Department of EE C V Raman Global University Odisha India Department of ME C V Raman Global University Odisha India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

As modern science and technology rapidly evolve and lifestyles change, people increasingly seek intelligence in their daily lives and work. The trend that has emerged as the primary impetus for advancing natural human-machine interaction (HMI) is the integration of machine learning algorithms. Gesture recognition through computer vision is essential for developing novel human-computer interaction methods, enabling innovative ways for people to interact with computers. This paper presents an approach to human-machine interaction through hand gesture detection to control the Programmable Logic Controller (PLC), to make it a hands-off operation that is user-friendly and secure, thus enhancing the efficiency and usability of the industrial automation system. Furthermore, using Mitsubishi IQ-R PLC and MR-J4W3 Servo Drive, motion can be controlled through a Human Machine Interface. The system employs Multilayer Perceptron (MLP) for recognizing hand signs based on detected key points. The model used is Long-Short-Term Memory (LSTM), which gives an accuracy $92 \%$ for the detection of hand gestures.

关键词： Human computer interaction Automation Machine learning algorithms Programmable logic devices Multilayer perceptrons Market research Logic Usability Servomotors Long short term memory

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Wearable Device for visually Impaired using Deep Learning

Wearable Device for Visually Impaired using Deep Learning

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Applied Artificial Intelligence and computing (ICAAIC), International conference on

作者： P Mohanraj T Rajasekar N Sivaelango v Sri Karthickraja N vignesh Electronics and Communication Engineering Sri Ramakrishna Engineering College Coimbatore India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

This research introduces a novel wearable technology specifically to assist blind and visually impaired people in navigating their surroundings safely and independently. The proposed device integrates Raspberry Pi 4, cutting-edge object detection algorithms, voice output, and sensor feedback systems to provide comprehensive ambient awareness. Real-time video feeds captured by a Raspberry Pi camera module enable robust item detection and categorization. The Raspberry Pi 4 coupled with efficient deep learning algorithms facilitates real-time inference on resourceconstrained platforms. Additionally, water and ultrasonic sensors have been seamlessly integrated to detect wet surfaces and obstacles, respectively. Upon detecting hazards, the proposed system delivers prompt haptic and audio feedback, allowing users to avoid obstacles and make informed decisions. Extensive field testing and user trials validate the efficacy and versatility of this wearable technology in diverse real-world scenarios. This innovation enables a significant advancement in assistive technology, offering enhanced freedom and safety to visually impaired individuals. Future research directions may focus on algorithm optimization, sensor integration, and user-centered design enhancements to augment user experience and functionality.

关键词： Deep learning Navigation Assistive technologies Real-time systems Inference algorithms Hazards Wearable devices

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Implementation of Smart Security System in Agriculture fields Using Embedded Machine Learning

Implementation of Smart Security System in Agriculture field...

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Applied Intelligence and Sustainable computing (ICAISC), International conference on

作者： v viswanatha A C Ramachandra Puneet T Hegde M v Raghunatha Reddy vivek Hegde vageesh Sabhahit Department of ECE Nitte Meenakshi Institute of technology Bengaluru India

Tiny Machine Learning (TinyML), a branch of machine learning that focuses on the effectiveness of machine learning on extremely constrained edge machines, is flourishing. Deep learning techniques are being used more frequently lately in a variety of data-intensive and time-sensitive Internet of Things (IoT) apps. Because MCUs lack resources like RAM, deploying new methods like Deep Neural Networks (DNN) models on them has proven challenging. However, recent developments in the TinyML space promise to create a completely new class of peripheral apps. By eliminating the need for the cloud’s omnipresent computing support, which uses power and presents risks to data security and privacy, TinyML paves the way for the development of original apps and services. Traditional machine learning needs a lot of processing capacity to predict a scenario. This computational capacity will be moved from high-end systems to low-end devices thanks to the TinyML method for machine learning on small devices. To keep the precision of the learning models, enable resource-efficient small edge devices to manage the training and deployment process, maximize computing capacity, and enhance dependability are some of the challenges presented by this change. Here in this paper, we propose a efficient method to detect animals near farmland for security purposes using TinyML and compared with many algorithms and their effectiveness.

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A Review on Underwater Image Enhancement Techniques

A Review on Underwater Image Enhancement Techniques

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International conference on Control Communication & computing India (ICCC)

作者： v R Amala S M Alex Raj Department of Electronics and Communication Engg. College of Engineering Trivandrum Thiruvananthapuram India

Underwater imaging is an innovative research area that has more benefits to researchers and scholars for mine detection, navigation, autonomous underwater imaging devices, etc. Underwater photos suffer greatly from scattering, light absorption, color cast, and the presence of artifacts, which causes the image to become increasingly blurry. At this juncture, color correction and image enhancement are highly recommended for getting a precise image with the right information. This paper summarizes the recent algorithms and their approaches in the area of underwater imaging enhancement techniques. An outline of the developing applications of underwater image processing is also presented.

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Performance Evaluation of Supervisory Machine Learning algorithms for Cellular Networks Payload Enhancement in Downlink

Performance Evaluation of Supervisory Machine Learning Algor...

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International conference on computing and Networking Technology (ICCNT)

作者： B. Suresh K L v Sai Prakash Sakuru Department of Electronics & Communication National Institute of Technology Warangal Warangal TS India

Payload allocation in downlink cellular networks is vital for efficiently distributing data transmission to users/devices. The base stations (RRH + BBU) task allocates and assigns radio channels, time slots, and other resources to facilitate good data reception by users. The allocation process considers quality of service (QoS), traffic management, and scheduling algorithms. This study aims to optimize resource utilization, mitigate network congestion, and ensure fair and reliable data transmission to users with Supervisory Machine Learning approaches by considering the sizeable cellular traffic data to improve payload efficiency and network performance by dynamic resource allocation for advanced cellular technologies. This complete design is implemented in Google Colabortory (Colab), an open-source cloud platform. In this paper, XBOOST, LINEAR REGRESSION, GRADIENT BOOST, and RANDOM FOREST supervisory machine learning algorithms were compared for resource allocation and time complexity, and the results are presented.

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Revolutionizing Healthcare through IoT and Edge computing: A Evaluation of Optimization Techniques

Revolutionizing Healthcare through IoT and Edge Computing: A...

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Cognitive computing and Engineering Education (ICCCEE), International conference on

作者： vanshika Peshane Mirza Moiz Baig Shrikant v. Sonekar Supriya Sawwashere Department of Computer Science and Engineering JDCOEM Nagpur India

The purpose of this evaluation paper is to provide an overview of the current state of the art research on the implementation of optimization techniques in edge computing for smart hospitals, with a focus on the integration of Industry 4.0 concepts, ant colony optimization algorithms, and Internet of Things (IoT) devices. The paper begins by providing a background on the concept of edge computing and its potential in the healthcare industry, followed by a discussion of the current challenges and limitations in implementing edge computing in smart hospitals. The main body of the paper reviews recent studies that have applied optimization techniques, such as ant colony optimization, to various aspects of hospital operations, including resource allocation, patient scheduling, and facility management. The paper also examines the use of IoT devices and sensors to collect real-time data from the hospital environment, which is then processed by the edge computing system to optimize operations. The review concludes with an assessment of the current state of research in this field, highlighting areas of progress and opportunities for future research. Overall, this paper provides a comprehensive analysis of the use of optimization techniques in edge computing for smart hospitals, and demonstrates the potential of Industry 4.0 concepts, ant colony optimization, and IoT in revolutionizing the healthcare industry.

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Real-Time Monitoring and Anomaly Detection in Hospital IoT Networks Using Machine Learning 1

Real-Time Monitoring and Anomaly Detection in Hospital IoT N...

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1st International conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Ranjith Kumar, G. Govekar, Navnath Sopan Karthik, A. Nijhawan, Ginni Alawadi, Ahmed Hussien Asha, v. School of Computer Science and Artificial Intelligence Sr University Telangana Warangal India Navsahyadri Group of Institutions Department of Electrical Engineering Maharashtra Pune India Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara India The Islamic University Najaf Iraq New Horizon College of Engineering Department of Computer Applications Bangalore India

ISBN: (纸本)9798350330915

The study covers the vital aspect of strong abnormality detection systems on IoT hospital networks that do not have enough resources available. The study evaluated the effectiveness of four different algorithms- namely, QAE, WOA, PSO-DL, and DL-AD-in improving network security. It aimed at enhancing maximum intrusion detection but bearing in mind that healthcare IoT devices possess minimal computing capabilities. Real-Time Internet of Things 2022 (RT-IoT2022) dataset was used in the experiments, emulating cyber-attacks against smart health systems. This showed that QAE is compactly designed with good precision (0.92) and recall rates (0.88) due to the employing of quantization techniques. Competitive performance was obtained by WOA, which mimicked the social behavior in the nature resulting in average precision equal to 0.85 and average recall equal to 0.78. Particularly, the combined method of PSO-DL showed great performance where they had accuracy of 0.94, recall of 0.91 and f1-score of 0.92. DL-AD acted as a benchmark giving good accuracy results, with precision and recall averaging about 0.89 and 0.86, respectively. Compared with related works, the proposed algorithms, particularly QAE and PSO-DL addressed the specific issues associated with healthcare IoT networks as far as accuracy and recall are concerned with much better performance results in most cases. These offer very useful insights into a quickly changing terrain of healthcare IoT intrusion detection which ultimately leads to improved adaptability for future security measures. In this case, study stress on the use of customized approach for resource limitation health orientated IoT networks. © 2023 IEEE.

关键词： Anomaly Detection Healthcare IoT Intrusion Detection Particle Swarm Optimization Quantized Autoencoder Resource- Constrained Networks

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