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

共 1067 条记录，以下是211-220 订阅

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A Highly Sensitive and Ultra-Low Loss photonic Crystal Fiber-Based Gas and Chemical Sensor 1

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3rd International conference on Communication, devices and computing, ICCDC 2021

作者： Hussayeen Khan Anik, M. Mahmud, Sakib Chakma, Pratyayee Talukder, Hriteshwar Chakrabarti, Kisalaya Biswas, Shovasis Kumar Department of Electrical and Electronic Engineering Shahjalal University of Science & Technology Sylhet Bangladesh Department of Electronics and Communication Engineering Haldia Institute of Technology West Bengal Haldia721657 India Department of Electrical and Electronic Engineering Independent University Bangladesh Dhaka1229 Bangladesh

ISBN: (数字)9789811691546

ISBN: (纸本)9789811691539

A Hexagonal-shaped photonic crystal fiber (PCF) with a circular analyte core bordered by six elliptical-shaped analyte channels is exemplified in this paper for gas and chemical sensing purposes. Fused silica is used as the pedestal fiber background of the sensor. The finite element method (FEM) is applied for numerical inspections. The sensor reveals maximum relative sensitivity of 85.55% for Benzene at 1.3 μm wavelength. The sensor displays ultra-low confinement loss of 1.309 × 10–11 dB/m for Benzene at 1.3 μm with a sensing range of 0.8–1.5 μm. The maximum v parameter value is of 2.235 for this sensor which secures single-mode propagation of light. This work also focuses on other vital properties just as, nonlinearity, effective area, mode field radius, and beam divergence for the usefulness of the sensor. Considering this sensor’s relative sensitivity, confinement loss, and other properties, this PCF can be a very favorable candidate in the field of gas and chemical sensing. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： photonic crystal fibers

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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...

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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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Hybrid Cryptographic Algorithm to Secure Internet of Things

Hybrid Cryptographic Algorithm to Secure Internet of Things

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International conference on ent computing and Control Systems (ICICCS)

作者： G Amala Nikitha G. Jaspher W. Kathrine Christina Rajesh Duthie v. Ebenezer Salaja Silas Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

Small IoT devices can only support light protocols since they lack significant processing or storage power. Lightweight encryption standards or techniques will be utilized for encryption and decryption to prevent hackers from accessing the data in order to increase the security of IoT devices’ network connections and commands. The encryption has to be robust enough to safeguard and lightweight enough to enable light processing hardware. There aren’t many standards for lightweight cryptography that are used to safeguard devices in an existing network. If the network is not secured properly, an intruder can get in and take control of the devices without making a visible appearance. A hybrid lightweight cryptographic methodology have been proposed to address this problem. LWC algorithms are designed to provide strong security while minimizing the computational and memory resources required for encryption and decryption. This is particularly important for IoT devices, which often have limited processing power, storage capacity, and battery life. LWC algorithms offer a range of benefits for IoT devices, including improved security, reduced power consumption, and increased battery life. They are also designed to be easy to implement and integrate with existing IoT platforms and protocols. Proposed system’s analytical work implementation will combine Salsa20 and AES algorithms to provide greater security and making it well-suited for real-time cryptography.

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RISC-v Architecture based Hardware Accelerator for kNN

RISC-V Architecture based Hardware Accelerator for kNN

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International conference on Circuits, Communication, Control and computing

作者： Shreya P Manchala Shreeya Ranganath v. Anandi Shreesha T P Satwik Kamath Soham Bhattacharya Ramaiah Institute of Technology Rowan University USA

ISBN: (数字)9798331528539

ISBN: (纸本)9798331528546

The demand for smarter devices has pushed embedded systems to their limits, particularly in computationally intensive tasks like image recognition and natural language processing. Hardware accelerators have emerged as a solution, offering significant performance and energy efficiency gains. However, traditional accelerators tied to specific processor architectures suffer from limitations like reduced flexibility and vendor lock-in. This project proposes a hardware accelerator for the K-Nearest Neighbor (kNN) algorithm, leveraging the open-source RISC-v architecture. RISC-v addresses the computational complexity, performance bottlenecks, and energy efficiency challenges of existing kNN implementations. The accelerator is designed with key features such as a customized instruction set, parallelism, pipelining, reduced power consumption, and optimized area utilization. The project aims for efficient use of flip-flops (FFs) and look-up tables (LUTs), ensuring optimal resource *** utilizing RISC-v's flexibility, this project aims to create an efficient hardware accelerator for kNN, enabling real-time processing of large datasets, minimizing energy use, and ensuring scalability across different applications.

关键词： Performance evaluation Machine learning algorithms Power demand Embedded systems Heuristic algorithms Computer architecture Real-time systems Energy efficiency Table lookup Hardware acceleration

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All-Optical computing for Super-Resolution Direction of Arrival Estimation

All-Optical Computing for Super-Resolution Direction of Arri...

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Asia Communications and photonics conference and Exhibition (ACP)

作者： Sheng Gao Hang Chen Haiou Zhang Zhi Sun Yuan Shen Xing Lin Department of Electronic Engineering Tsinghua University Beijing China

ISBN: (数字)9798350379266

ISBN: (纸本)9798350379273

Direction of arrival estimation technology is the foundation for communications, radar, navigation, etc. However, conventional electronic devices for DOA estimation require expensive RF circuits, high-precision analogue-to-digital converters and complex digital signal processing algorithms. Here, we propose a super-resolution diffractive neural network (S-DNN) to directly process electromagnetic (EM) waves for super-resolution DOA estimation over a broadband frequency range. The multilayer meta-structures of the S-DNN produces super-oscillatory angular responses in local angular range, which can perform all-optical DOA estimation with angular resolution beyond the diffraction limit. Space-time multiplexing of passive and reconfigurable S-DNNs is used to achieve high-resolution DOA estimation over a wide field of view. Experiments show that the angular resolution of the S-DNN is four times higher than the diffraction limited resolution. The fabricated S-DNN can be used for superresolution DOA estimation of multiple coherent sources over a 5 GHz frequency bandwidth, with an estimation delay that is in principle two to four orders of magnitude lower than commercial devices. We also use the all-optical DOA estimation capability of the S-DNN to provide angular direction to reconfigurable intelligent surface, to achieve low-latency and low-power integrated sensing and communication. Our work is an important step towards enabling various wireless sensing and communication tasks using photonic computing processors that outperform electronic computing in both computational paradigm and performance.

关键词： Wireless communication Radio frequency Wireless sensor networks Direction-of-arrival estimation Program processors Diffraction Superresolution Neural networks Estimation photonics

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Optimization of multi-objectives for Adaptive Reverse Recommendation method on edge devices 13

Optimization of multi-objectives for Adaptive Reverse Recomm...

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13th International conference on computing Communication and Networking Technologies, ICCCNT 2022

作者： Dcosta, Winston Meena, S.M. Gurlahosur, Sunil v Kulkarni, Uday KLE Technological University School of Computer Science and Engineering Hubballi India

ISBN: (数字)9781665452625

ISBN: (纸本)9781665452625

The Neural Architecture Search (NAS) enables Deep Learning engineers to automatically explore the wide variety of all possible neural architectures to design the solution for a given problem. Though the NAS methods automatically generate the best possible architecture, it is time-consuming, and resource constraint since the search space consists of the best architectures(good accuracy) and includes non performing architectures as well. Researchers have proposed various NAS methods especially based on evolutionary algorithms which consists of diverse search space due to the presence of genetic operations leading to expensive search time cost. These NAS methods also deal with multiple objectives like FLOPS, latency and power usage, and model accuracy, for image classification tasks. Though the generated architectures from NAS have multi-objective, they require significant computing resources and are not suitable for running on edge devices with limited computational resources. The proposed method includes a novel model optimization method designed for the neural architecture generated from NAS methods to make it smaller in size, memory efficient, and faster at inference with minimal loss of accuracy. We optimized the models designed for standard datasets and decreased the parameters by 7.82% for CIFAR-10, 2.95% for CIFAR-100, 6.36% for STL-10, and 3.57% for Food-101 datasets. The resulting models have achieved an accuracy of 97.4% for CIFAR-10, 86% for CIFAR-100, 96.7% for STL-10, and 87.4% for Food-101 datasets. © 2022 IEEE.

关键词： Multiobjective optimization

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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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Machine Learning to Enhance the v2v Communication-based IoD Using Companding Dataset

Machine Learning to Enhance the V2V Communication-based IoD ...

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IEEE SSD International Multi-conference on Systems, Signals and devices

作者： O. Daoud Q. Hamarsheh A. Damati S. Al Jaafrah O. Qasem M. Al-Khawaldah Electrical and Computing Engineering School of Engineering and Computing American International University Al-Jahra Kuwait Electrical Engineering Department Faculty of Engineering and Technology Philadelphia University Amman Jordan Researcher

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

This paper investigates the improvement of vehicle-to-vehicle (v2v) communications in the Internet of Drones (IoD) scenario for the reduction of the Peak-to-Average Power Ratio (PAPR) problem in Orthogonal Frequency Division Multiplexing (OFDM) systems. Motivated by the recent research on machine learning and deep neural network, this study will attempt to optimize Quality of Service (QoS) by means of a multiprocessing approach based on Daubechies wavelets. The proposed scheme integrates high-performance PAPR reduction schemes, such as µ-law companding, hybrid precoding-companding, and neural network-based adaptive algorithms. Deep reinforcement learning-driven approach in clipping and filtering is further proposed, which can offer real-time adaptation for the high-demanding communications. Results show a considerable gain in PAPR, over 60% PAPR reduction under high SNR conditions. These findings show that IoD can sustain stable v2v connections in urban use cases, thereby providing a scalable solution for 5G and beyond networks.

关键词： 5G mobile communication vehicular ad hoc networks Peak to average power ratio Machine learning Quality of service Real-time systems Internet Reliability Drones Signal to noise ratio

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A Case Study of the Implementation Difficulties in the Pilot Low Cost Concentrating Solar Power Systems Deployment in Sub-Saharan Africa 6

A Case Study of the Implementation Difficulties in the Pilot...

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6th International conference on Contemporary computing and Informatics, IC3I 2023

作者： Babu Potluri, Sankar Goswami, Garima Yadav, Annu Ravivarman, P.G. Gajendran, P. Tiwaskar, S.A. S v R Engineering College Department of EEE Nandyal India Teerthankar Mahaveer University Department of EE Moradabad India School of COMPUTER Science and Applications IIMT University Meerut India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore India Vishwakarma Institute of Information Technology Computer Engineering Maharashtra Pune India

ISBN: (纸本)9798350304480

Among those most important resources for a particular country's expansion and expansion was electrical power. According to the most recent sustainability the growth paper, the continent continues to have a significant electrical power deficit. sunlight concentration appears to be a viable solution to make up the difference. This is due to the fact the majority of focusing solar energy plant elements are easily accessible and reasonably priced on their African marketplace, and authorized locals can construct the structures. Within Africa south of the Sahara, steer 50 nm-concentrating solar energy plants are being put into operation with promising outcomes that might be grew and utilized to generate large amounts of electrical power. One notable challenge was identified during an evaluation of a prototype focusing photovoltaic plant within the channel-region: the monitoring system's inability to lower the daily costs required to operate the monitoring control structure and enhance the numerous special machines focusing conduct. This essay emphasizes the current energy landscape and African study of concentrated sunlight is currently progressing. To demonstrate current study strategy about the development of inexpensive monitoring devices in regards to mathematical difficulty, energy use, and special machine alignment precision, the article also provides an in-depth examination of the most recent solar monitoring structures for essential receiver networks. © 2023 IEEE.

关键词： central receiver system CSP (Concentrated Solar Power) development electricity generation Low-Cost tracking algorithms

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Leveraging Adaptive Intelligence in Edge computing for Efficient Data Processing and Task Management

Leveraging Adaptive Intelligence in Edge Computing for Effic...

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Emerging Research in Computational Science (ICERCS), International conference on

作者： Lydia D Isaac v. Mohanraj Nisha Soms Department of Information Technology Sona College of Technology Salem India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

The swift progress of Internet of Things (IoT) devices and latency-sensitive submissions has driven the need for efficient data processing and task management in edge computing environments. Traditional edge computing frameworks often face challenges in optimizing resource utilization and handling dynamic workloads. The integration of adaptive intelligence in edge computing has arisen as a vital solution for augmenting data processing and task management efficiency in modern computing environments. This research introduces a novel algorithm, Edge Reinforcement Learning with Context-Aware Tasking (EDREL-CAT), designed to optimize task allocation and resource management at the edge. By utilizing reinforcement learning techniques coupled with context-aware algorithms, the proposed method aims to significantly improve task scheduling and processing efficiency while addressing the constraints inherent in edge computing. An all-inclusive simulation analysis is steered to evaluate the performance of EDREL-CAT against existing algorithms, including traditional scheduling methods and state-of-the-art approaches. The analysis employs various metrics, including task completion time, system throughput, task queue length and fairness index to provide a thorough comparison. The results demonstrate that EDREL-CAT outperforms existing solutions, confirming its potential as an effective tool for enhancing edge computing capabilities.

关键词： Performance evaluation Processor scheduling Simulation Reinforcement learning Data processing Throughput Real-time systems Resource management Internet of Things Edge computing

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