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

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Emergence-Based Access Control New Approach to Secure the Internet of Things 18

Emergence-Based Access Control New Approach to Secure the In...

引用

1st International conference on Digital Tools and Uses (DTU)

作者： Abou El Kalam, Anas Outchakoucht, Aissam Es-Samaali, Hamza Cadi Ayyad Univ Natl Sch Appl Sci Marrakech Morocco Hassan II Univ Natl High Sch Elect & Mech Marrakech Morocco IPI Paris France

ISBN: (纸本)9781450364515

The security is becoming the most important challenge that threatens the development of the internet of things. In this respect, access control is considered as the cornerstone and core element that should be well designed and implemented. However, this mission becomes more complex in IoT environments as they have additional and specific requirements such as heterogeneity, limited capacities of storage and computing as well as the huge number of the devices. Dealing with this issue, this paper proposes an 'emergence based' process aiming to benefit from this tremendous number of smart objects and extract the significant features that we cannot pick up in systems with smallest number. Then, it proposes an access control framework dedicated to IoT environments based on three extremely powerful concepts: Blockchain networks, Reputation systems and Reinforcement Learning algorithms.

关键词： Security Internet of Things Access Control Dynamic Policy Emergence Blockchain Reputation Reinforcement Learning

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A Review on Topologies for Grid Integrated Electric Vehicle with Battery Storage

A Review on Topologies for Grid Integrated Electric Vehicle ...

引用

2023 International conference on Circuit Power and computing Technologies, ICCPCT 2023

作者： Shyma, S. Brinda, R. Padma Suresh, L. Annamalai University Department of Electrical and Electronics Engineering Tamilnadu India Baselios Mathews II College of Engineering Department of Electrical and Electronics Engineering Sasthamcotta India

ISBN: (纸本)9798350333244

Renewable energy sources, in particular wind energy, are being more and more integrated into the network of charging places for electric vehicles. As a result, there have been significant worries over the power systems' ability to operate dependably and satisfactorily. Integrating energy storage devices for electric vehicles is one of the suggestions made to increase the performance and dependability of these systems. The newest developments, advantages, size considerations, efficiency, cost, and recycling of battery energy storage systems are all covered in depth in this paper. The benefits and disadvantages of the various battery energy storage technologies are thoroughly analyzed, explained, and contrasted. When it comes to grid applications, the economic advantages of battery sizing using probabilistic and optimization techniques offer a potential solution during the strategy by taking into account a variety of factors affecting the sizing of the battery, such as the deprivation rate, reliability, and battery location. The comparative analysis are carried out in the optimization technique to predict the best efficiency in battery energy storage system. © 2023 IEEE.

关键词： Genetic algorithms

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On Efficiency of Adaptation algorithms for Mobile Interfaces Navigation

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5th International conference on Universal Access in Human-Computer Interaction held at the HCI International 2009

作者： Glavinic, Vlado Ljubic, Sandi Kukec, Mihael Univ Zagreb Fac Elect Engn & Comp Unska 3 HR-10000 Zagreb Croatia Univ Rijeka Fac Engn HR-51000 Rijeka Croatia Coll Apll Sci HR-42000 Varazhdin Croatia

ISBN: (纸本)9783642027093

Many ubiquitous computing systems and applications, including mobile learning ones, can make use of personalization procedures in order to support and improve universal usability. In our previous work, we have created a GUI menu model for mobile device applications, where personalization capabilities are primarily derived from the use of adaptable and adaptive techniques. In this paper we analyze from a theoretical point of view the efficiency of the two adaptation approaches and related algorithms. A task simulation framework has been developed for comparison of static and automatically adapted menus in the mobile application environment. Algorithm functionality is evaluated according to adaptivity effects provided in various mend configurations and within several classes of randomly generated navigation tasks. Simulation results thus obtained support the usage of adaptivity, which provides a valuable improvement in navigation efficiency within menu-based mobile interfaces.

关键词： personalization adaptation algorithmics m-devices m-learning

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Empowering IoT devices with Energy-Efficient AI and Machine Learning 7

Empowering IoT Devices with Energy-Efficient AI and Machine ...

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7th International conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Anusha, P. Chouhan, Nitesh Chandhok, Gunita Arun Sugumaran, D. Aswal, Upendra Suganya, A. R.M.K. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India MLV Textile and Engineering College Department of IT Rajasthan Bhilwara India SIMATS Saveetha College of Liberal Arts and Sciences Department of Commerce General Chennai India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Information Technology Tami Nadu Chennai India Graphic Era Deemed to Be University Uttarakhand Dehradun India Karpagam College of Engineering Department of Information Technology Tamil Nadu Coimbatore India

ISBN: (纸本)9798350372816

Existing IoT devices frequently exhibit inefficiencies in energy consumption and decision-making processes, resulting in inadequate efficiency and environmental impact. The study describes a unique framework that uses artificial intelligence (AI) and machine learning (ML) methods to provide Internet of Things (IoT) devices with cognitive decision-making capabilities, hence improving energy efficiency and overall performance. The proposed method enables real-time autonomous decision-making in IoT contexts by collecting extensive data, preprocessing it, and designing features, then selecting, training, and deploying algorithms. Continuous learning mechanisms enable flexibility in changing situations, while thorough testing reveals considerable gains in energy efficiency, proposed system dependability, and user pleasure. The results show a 30% reduction in energy consumption, a 25% increase in resource utilization, a 5% improvement in proposed system uptime, and significant improvements in user satisfaction indicators. The proposed method is an exciting possibility for making IoT deployments more sustainable and efficient, paving the path for intelligent technology ecosystems. © 2024 IEEE.

关键词： Energy efficiency Intelligent decision-making IoT devices Performance improvement Resource allocation Sustainability

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Physical Aware Clustering Training Method for Integrated photonic Convolution Neural Network with Nonlinear Distributed Weights

Physical Aware Clustering Training Method for Integrated Pho...

引用

Asia Communications and photonics conference and Exhibition (ACP)

作者： Yue Jiang Wenjia Zhang Xuying Liu Wenyu Zhu Zuyuan He State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Jiao Tong University Shanghai China

The computing power of the photonic Convolution Neural Network (CNN) have achieved Tera-level operations per second (OPS) for supporting machine vision algorithms. However, restrained by the nonlinear characteristics of weighting devices, it is hard to realize convergent training of photonic CNN under poor controlling precision. In this paper we proposed a Physical Aware Clustering training method where the Physical Aware Cluster Quantizer is embed with the straight through estimator (STE) algorithm for integrated photonic CNN with quantized and nonlinear distributed weights. Experiment shows that, by employing the upgraded STE methods (namely, STE plus), the photonic CNN using micro ring weighting bank and PAM4 controlling modules achieves the nearly 99.3% accuracy ratio for Fashion MNIST recognition task, whereas only 41.5% of that by using baseline STE training algorithms with the same physical devices.

关键词： Training Time-frequency analysis Quantization (signal) Convolution Neural networks Clustering algorithms Prototypes

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KylinArm: An Arm Gesture Recognition System for Mobile devices 1

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23rd International conference on algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Zhao, Shikun Hong, Jingxuan Zhang, Zixuan Wang, Xuqiang Zhang, Jin Gong, Xiaoli Nankai Univ Coll Comp Sci Tianjin Peoples R China Tianjin Key Lab Brain Sci & Intelligent Rehabil Tianjin Peoples R China State Key Lab Highend Server & Storage Technol Jinan Peoples R China State Grid Tianjin Informat & Commun Co Tianjin Peoples R China

ISBN: (数字)9789819708017

ISBN: (纸本)9789819708000;9789819708017

Gesture-based Human-Computer Interaction (HCI) has become a primary means of device control due to its naturalness and humanized characteristics, making it applicable for tasks such as drone control and gaming. Gesture recognition using an inertial measurement unit (IMU) has emerged as a major trend in this field. However, due to the intricate nature of the arm structure and the diversity of gestures, relying on a single IMU system for gesture recognition results in limited accuracy. Modern mobile devices, such as smartphones and smartwatches, are equipped with IMUs that allow for convenient data acquisition methods and offer computing resources for deep learning model inference. In this paper, we propose a real-time arm gesture recognition method, called KylinArm, which achieves high-precision gesture recognition by coordinating 2 IMUs. The KylinArm method is optimized for mobile devices and based on a dual-branch 1D-CNN classifier. It supports the classification of 12 arm gestures with an optimized strategy for mobile devices that have limited computation resources and power supply. Additionally, we adopt an optimization method based on CORrelation ALignment (CORAL) to address the decreasing accuracy that occurs when new users are introduced. Finally, we evaluate KylinArm and test it in real scenes, achieving a recognition accuracy of over 98%.

关键词： Arm Gesture Recognition Inertial Measurement Unit Human-Computer Interaction Mobile devices Deep Learning

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photonic-aware Neural Networks for Packet Classification in beyond 5G Networks 13

Photonic-aware Neural Networks for Packet Classification in ...

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13th International conference on the Network of the Future, NoF 2022

作者： Paolini, Emilio Civerchia, Federico De Marinis, Lorenzo Valcarenghi, Luca Maggiani, Luca Andriolli, Nicola Scuola Superiore sant'Anna Pisa56124 Italy TeCIP Institute Departments of Excellence Robotics & AI Italy National Research Council of Italy CNR-IEIIT Pisa56122 Italy Sma-RTy Italiat SRL Carugate20061 Italy

ISBN: (数字)9781665472548

ISBN: (纸本)9781665472548

The benefits introduced by novel network technologies such as 5G and beyond, including low latency and support for billions of devices, have the potential to transform the lives of people. However, the features promised by these new technologies have also attracted malicious actors, with various motivations for attacking the network infrastructure, from cybercrime-based frauds to political goals. Thus, to enable the full potential of the emerging network technologies, it is necessary to take into accounts these attacks and develop tailored countermeasures. One future direction in mitigating the risks of potential attacks is the automatic classification of malicious packets, with the possibility to drop them if classified in the attack category. Hence, in this context, we propose a solution based on Neural Networks (NNs) to automatically classify packets into two classes, i.e., benign and attack, directly in the Radio Access Network (RAN), specifically inspecting packets when they are relayed at the next generation eNB (gNB)-Central Unit (CU) level. Since NNs can be computationally intensive algorithms, potentially increasing the latency of the network, we decide to leverage photonic-Aware Neural Network (PANN), photonic accelerators able to perform NN computations in the analog optical domain and with time-of-flight latency. We devised two different PANN architectures, considering different photonic constraints. The classification performance of the two architectures has been assessed on the CICIDS-2017 dataset and compared with electronic counterparts. Results proved that the F1-score loss due to underlying hardware constraints is negligible, paving the way for PANN applications in next generation networks. © 2022 IEEE.

关键词： 5G mobile communication systems

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Equivalence Checking of Quantum Circuits by Model Counting 12th

Equivalence Checking of Quantum Circuits by Model Counting

引用

12th International Joint conference on Automated Reasoning (IJCAR)

作者： Mei, Jingyi Coopmans, Tim Bonsangue, Marcello Laarman, Alfons Leiden Univ Leiden Netherlands

ISBN: (纸本)9783031635007;9783031635014

Verifying equivalence between two quantum circuits is a hard problem, that is nonetheless crucial in compiling and optimizing quantum algorithms for real-world devices. This paper gives a Turing reduction of the (universal) quantum circuits equivalence problem to weighted model counting (WMC). Our starting point is a folklore theorem showing that equivalence checking of quantum circuits can be done in the so-called Pauli-basis. We combine this insight with a WMC encoding of quantum circuit simulation, which we extend with support for the Toffoli gate. Finally, we prove that the weights computed by the model counter indeed realize the reduction. With an open-source implementation, we demonstrate that this novel approach can outperform a state-of-the-art equivalence-checking tool based on ZX calculus and decision diagrams.

关键词： Quantum computing Circuit equivalence Satisfiability #SAT Weighted model counting Pauli basis

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STEROCEN: Simulation and Training Environment for Resource Orchestration in Cloud-Edge Networks 15

STEROCEN: Simulation and Training Environment for Resource O...

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15th International conference on Networks of the Future

作者： Ferens, Mieszko Hortelano, Diego de Miguel, Ignacio Duran Barroso, Ramon J. Kosta, Sokol Aalborg Univ Copenhagen Denmark Univ Rey Juan Carlos Madrid Spain Univ Valladolid Valladolid Spain

ISBN: (纸本)9798350377774;9798350377767

Large scale deployment of Internet-of-Things (IoT) devices is projected to grow in the coming years. These devices are expected to be low-cost while supporting applications with growing computational demands. To enable the necessary computations, offloading of computational tasks to Edge and Cloud nodes is a fundamental technology. However, orchestration for such networks is a complex problem which affects both the network design and the decision system. To aid in solving this problem, simulation tools are essential for predicting the performance of networks in different conditions and under different orchestration policies. In this paper, we propose STEROCEN, a Cloud-Edge network resource orchestration simulation and training tool which allows for different configurations of up-to a four-layer network composed of: (i) end-device, (ii) Close Edge, (iii) Far Edge, and (iv) Cloud layers. Our tool collects delay metrics for flexibly defined applications, especially in regard to computation in the network nodes and including uncertainty in processing times. Additionally, the tool only needs the initial configuration and an independently defined orchestrator, allowing for testing of many strategies. As an example, we provide results of testing some Deep Reinforcement Learning (DRL) algorithms using the same training and simulation environment.

关键词： Simulator Orchestration Edge-Cloud computing Computation Offloading Reinforcement Learning IoT

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Analysis of fast prototyping of microcontroller-based ML software for acoustic signal classification 26

Analysis of fast prototyping of microcontroller-based ML sof...

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26th IEEE Signal Processing: algorithms, Architectures, Arrangements, and Applications, SPA 2023

作者： Marciniak, Filip Marciniak, Wojciech Marciniak, Tomasz Poznan University of Technology Faculty of Computing and Telecommunications Poznań60-965 Poland Poznan University of Technology DECYBEL Science Club Poznań60-965 Poland Poznan University of Technology Faculty of Automatic Control Robotics and Electrical Engineering Institute of Automatic Control and Robotics Division of Electronic Systems and Signal Processing Poznań60-965 Poland

ISBN: (纸本)9798350304985

The paper analyzes the preparation of software for acoustic signal classification with machine learning techniques for microcontrollers. The design process was tested for three types of devices: Nordic Thingy:53, *** and Arduino Nano 33 BLE Sense Lite. The classifier training process was carried out using the Edge Impulse platform. Experimental studies were carried out for the process of classifying sound signals generated by the vacuum cleaner motor. The results of the training and the model test were presented for different configurations. © 2023 Division of Signal Processing and Electronic Systems, Poznan University of Technology (DSPES PUT).

关键词： Microcontrollers

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