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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Emmanuel Adetiba Jamiu R. Olasina Obiseye Obiyemi Dept. of Electrical and Information Engineering Covenant Applied Informatics and Communication Africa Center of Excellence Covenant University Ota Nigeria HRA Institute for Systems Science Durban University of Technology Durban South Africa Dept. of Computer Engineering Federal Polytechnic Ilaro Ilaro Nigeria Dept. of Electrical Power Engineering Space Science Centre Durban University of Technology Durban South Africa

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

Access to well-curated large datasets remains a significant bottleneck in AI-based research within wireless communication. Rapid advancements in neighbouring fields, such as computer vision and natural language processing, are largely due to the availability of extensive open-access datasets. However, similar progress has not been observed in wireless communication. To address this gap, we curated a comprehensive dataset for fifteen digital modulation schemes, including 4QAM, 16QAM, 64QAM, 256QAM, 8PSK, 16PSK, 32PSK, 64PSK, 128PSK, 256PSK, CPFSK, DBPSK, DQPSK, GFSK, and GMSK. Our dataset considers Rayleigh and Rician channel models under Additive White Gaussian Noise (A WGN) with SNRs ranging from -20dB to + 20dB in 5dB increments. The data samples were converted to constellation signal images and carefully pre-processed. Named RadioModRec, this dataset provides a valuable resource for researchers to train and evaluate AI models. It is freely accessible on Kaggle, promoting further innovation in the wireless communication domain.

关键词： Wireless communication Technological innovation Transfer learning Stochastic processes Rician channels Market research Natural language processing Optimization Residual neural networks Radio spectrum management

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Integrated Photonic Accelerator Based on Optical Spectrum Slicing for Convolutional Neural Networks

arXiv

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arXiv 2023年

作者： Tsirigotis, Aris Sarantoglou, George Deligiannidis, Stavros Sozos, Kostas Bogris, Adonis Mesaritakis, Charis Dept. Information and Communication Systems Engineering University of the Aegean Palama 2 str. Karlovasi Samos83200 Greece Dept. Informatics and Computer Engineering University of West Attica Ag. Spiridonos Egaleo12243 Greece

In this work we numerically analyze a passive photonic integrated neuromorphic accelerator based on hardware-friendly optical spectrum slicing nodes. The proposed scheme can act as a fully analogue convolutional layer, preprocessing information directly in the optical domain. The proposed scheme allows the extraction of meaningful spatio-temporal features from the incoming data, thus when used prior to a simple fully connected digital single layer network it can boost performance with negligible power consumption. Numerical simulations using the MNIST dataset confirmed the acceleration properties of the proposed scheme, where 10 neuromorphic nodes can replace the convolutional layers of a sophisticated LeNet-5 network, thus reducing the number of total floating point operations per second (FLOPS) by 98% while offering a 97.2% classification accuracy. ©2022 The Author(s) Copyright © 2023, The Authors. All rights reserved.

关键词： Convolution

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Low-Latency and Energy-Efficient Task Scheduling for End-Edge-Cloud Collaborative Computing

Low-Latency and Energy-Efficient Task Scheduling for End-Edg...

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IEEE International Conference on systems, Man and Cybernetics

作者： Jingyao Li Haitao Yuan Yaofei Ma Jing Bi Jinhong Yang Jia Zhang School of Automation Science and Electrical Engineering Beihang University Beijing China College of Computer Beijing University of Technology Beijing China CSSC Systems Engineering Research Institute Beijing China Dept. of Computer Science Southern Methodist University Dallas TX USA

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Mobile edge computing (MEC) is a new paradigm that improves the quality of service compared with traditional cloud computing. In MEC, computational tasks are submitted by numerous end users and are partially offloaded to edge servers or a central cloud. However, the characteristics of tasks are different from each other, and the limited resources of computational nodes are also heterogeneous, which brings great challenges to computation offloading and resource allocation for MEC. This work establishes an end-edge-cloud collaborative computing network, which consists of end devices, edge servers, and a central cloud. Task execution location and CPU running frequency determine the execution time and energy consumption to finish the tasks. Considering the aforementioned factors, a multi-objective constrained optimization problem is formulated. To solve the problem, an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) with self-adaptive crossover and mutation rates is proposed, which is called Improved NSGA-II with _Self-adaptive Crossover and Mutation (INSCM). The total execution time and energy consumption can be jointly minimized with our proposed INSCM. Numerous experiments are carried out to test the performance of INSCM. Simulation results show that INSCM effectively improves the performance of NSGA-II and surpasses random offloading and NSGA-III, which shows practical use in real-life scenarios.

关键词： Energy consumption Cloud computing Time-frequency analysis Multi-access edge computing Simulation Collaboration Servers Optimization Sorting Genetic algorithms

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Biomass Fuel Classification from Agricultural Residues Using Machine Learning

Biomass Fuel Classification from Agricultural Residues Using...

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IEEE Region 10 Humanitarian Technology Conference (R10-HTC)

作者： Nikhil Pachauri Himanshu Gupta Om Prakash Verma Sushil Kumar Singh Deepak Verma Ravikumar RN Department of Mechatronics Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Instrumentation and Control Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India Department of Instrumentation and Control Engineering Dr B R Ambedkar National Institute of Technology Jalandhar Jalandhar India Department of Computer Engineering of Computer Engineering Marwadi University Rajkot Gujarat India Dept. of Computer Engineering Marwadi University Rajkot Gujarat India

ISBN: (数字)9798350340204

ISBN: (纸本)9798350340211

This work incorporates an adaptive learning-based boosting (ADboost) ML classifier to classify four types of fuel: agricultural residue, coals, wood, and produced biomass. Further, the ADboost’s hyperparameters, such as learning rate, maximum number of splits, and minimum leaf size are adjusted using teaching learning-based optimization (TLBO), resulting in TADboost. The performance of TADboost is compared against various popular ML (NN, BAG, NB, and SVM) models. Simulated result reveals that the suggested classifiers outperform other compared ML classifiers for fuel classification with the classification accuracy, precision, recall, F1-score, and kappa as 0.9659, 0.9671, 0.9449, 0.9558, and 0.9482, respectively.

关键词： Training Support vector machines Accuracy Biological system modeling Simulation Coal Linear programming Biomass Security Optimization

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Tcn-Inception: Temporal Convolutional Network and Inception Modules for Sensor-Based Human Activity Recognition

SSRN

引用

SSRN 2024年

作者： Helmi, Ahmed M. Al-Qaness, Mohammed A.A. Dahou, Abdelghani Trouba, Nafissa Toureche Elaziz, Mohamed Abd College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua321004 China School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China Mathematics and Computer Science department University of Ahmed DRAIA Adrar01000 Algeria Department of Mathematics Faculty of Science Zagazig University Zagazig44519 Egypt Computer Engineering Dept. Engineering and Information Technology College Buraydah Private Colleges Buraydah51418 Saudi Arabia Computer and Systems Engineering Dept. Faculty of Engineering Zagazig University Zagazig44519 Egypt

HighlightsTCN-InceptionTemporal Convolutional Network and Inception modules for Sensor-based Human Activity RecognitionMohammed A. A. Al-qaness,Abdelghani Dahou,Nafissa Toureche Trouba,Mohamed Abd Elaziz,Ahmed M. Helmi• Employing the Inception modules with Temporal Convolutional Network and using residual connections for capturing complex temporal patterns in HAR signals.• Extraction of multi-scale features and capturing of extended temporal dependencies using the novel model• Consideration of training efficiency and model dept. through the strategic use of residual connections and batch normalization.• Studying four different complex datasets, with variant set of activities, and carrying out an ablation study for more detailed analysis of the model. © 2024, The Authors. All rights reserved.

关键词： Internet of things

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Swarm Model for Path Tracking with Reference Motion Profile: a Diffeomorphism-based Approach

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IFAC-PapersOnLine 2023年第2期56卷 10721-10726页

作者： A. Bono B. Chen L. D'Alfonso G. Fedele Dept. of Computer Engineering Modeling Electronics and Systems Università della Calabria Via Pietro Bucci Cubo 42-C Rende (CS) 87036 Italy Dept. of Electronic and Electrical Engineering University College London London WC1E 6BT U.K.

This paper presents a novel strategy to control a multi-agent system along a given reference path while ensuring compliance with a given time profile for the movements along the route in question. The proposed protocol is based on a three-step methodology. In a first step, each agent state is augmented with an artificial variable that defines the movement of the agents along the given path. The extended agent state is then mapped into a virtual frame that takes into account the displacement of its position with respect to the reference path and the control over the additional artificial variable. Finally, in a third step, the control law designed in the artificial frame is translated into an action in the real frame using the theory of diffeomorphisms. The proposed control strategy ensures finite-time convergence of the entire multi-agent system on the reference path, while achieving error bounding for each agent evolution with respect to a given reference motion profile. Numerical simulations are performed to illustrate the described results.

关键词： Multi-agent system Finite-time control Distributed path tracking Swarm of agents Diffeomorphism-based control

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Automated MIMO Motion Feedforward control: Efficient Learning through Data-Driven Gradients via Adjoint Experiments and Stochastic Approximation

arXiv

引用

arXiv 2022年

作者： Aarnoudse, Leontine Oomen, Tom Dept. of Mechanical Engineering Control Systems Technology Eindhoven University of Technology Eindhoven Netherlands Delft Center for Systems and Control Delft University of Technology Delft Netherlands

Parameterized feedforward control is at the basis of many successful control applications with varying references. The aim of this paper is to develop an efficient data-driven approach to learn the feedforward parameters for MIMO systems. To this end, a cost criterion is minimized using a stochastic gradient descent algorithm, in which both the search direction and step size are determined through system experiments. In particular, the search direction is chosen as an unbiased estimate of the gradient which is obtained from a single experiment, regardless of the size of the MIMO system. The approach is illustrated using a simulation example, in which it is shown to be superior to a deterministic method in terms of convergence speed and thus experimental cost. Copyright © 2022, The Authors. All rights reserved.

关键词： Feedforward control

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An Investigation of Running Load Comparisons of ARCore on Native Android and Unity for Outdoor Navigation System Using Smartphone

An Investigation of Running Load Comparisons of ARCore on Na...

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Vocational Education and Electrical engineering, International Conference on

作者： Komang Candra Brata Nobuo Funabiki Sritrusta Sukaridhoto Evianita Dewi Fajrianti Mustika Mentari Dept. of Information and Communication Systems Okayama University Okayama Japan Dept. of Informatics Engineering Universitas Brawijaya Malang Indonesia Dept. of Informatic and Computer Politeknik Elektronika Negeri Surabaya Surabaya Indonesia Dept. of Information Technology State Polytechnic of Malang Malang Indonesia

In recent years, the utilization of location-based augmented reality (LAR) has gained popularity across various domains, including tourism, education, gaming, and advertising. The advancements in AR technology have made it easier to develop immersive LAR applications. ARCore, a widely adopted AR development library, enables the creation of location-based AR apps on Android devices. However, the implementation of ARCore in real-time scenarios that demand significant computational resources, like outdoor pedestrian navigation, can vary depending on the chosen development environment. This research aims to explore and compare the running load performance of ARCore implementation in location-based AR applications developed with Unity and Android Native, which are two prevalent AR development platforms. The study involves building a prototype LAR application using both Unity and native Android, followed by comprehensive running load performance evaluations. The findings reveal that while both Unity and native Android apps can deliver compelling location-based AR experiences, native Android apps demonstrate superior performance and resource efficiency in outdoor scenarios, whereas Unity offers the advantage of multi-platform support and ease of development for complex 3D assets. These insights provide valuable guidance to developers in selecting the most suitable approach for developing location-based AR applications for navigation, aligning with their specific needs and requirements.

关键词：

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Certain Investigation on IoT based Cotton Dust Pollution Monitoring in Textile Plants 8

Certain Investigation on IoT based Cotton Dust Pollution Mon...

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8th International Conference on Inventive Computation Technologies, ICICT 2025

作者： Sivanesan, P. Anand, J.V. Vaddanam, Trilok sai Irfan, Shaik Mohammad Department of Information Communication Technology School of Computing SASTRA University Tamilnadu Thanjavur 613401 India Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research Tamil Nadu Chennai 600116 India Dept. of Computer Science and Business Systems SASTRA University Tamilnadu Thanjavur India Dept. of ECE Siddhartha Academy of Higher Education Andhra Pradesh Vijayawada India

ISBN: (纸本)9798331512248

The objective of the work is to track the level of cotton dust in the textile sector. The IoT-enabled cotton dust monitoring system deploys three sensors: Sharp GP2Y1010AU0F;Shinyei PPD42NS;and Honeywell HPMA115S0-XXX measuring the dust levels. The Optical dust sensor GP2Y1010AU0F has a threshold of 1 mg/mS, which is placed inside the mask and when that threshold is crossed, buzzer alerts the worker with the air contaminants. It measures inhaled cotton dust and sends data to IoT, real-time concentration height of dust into the mobile devices. Experimental results show that Sharp GP2Y1010AU0F sensor has achieved award for acquiring the highest mean dust concentration of 95.167 mg/mS with a standard deviation of 3.014 among the three sensors, while Honeywell HPMA115S0-XXX and Shinyei PPD42NS recorded a mean of 87.833 mg/mS and 77.500 mg/mS, respectively. The observed response times of the sensors as follows for GP2Y1010AU0F-8s, HPMA115S0-XXX-6s, and PPD42NS- 5s, recording the fastest response by Honeywell HPMA115S0-XXX. A comparative study in accuracy, precision and sensitivity, with a response time, shows the merits and demerits for each sensor. Results emphasize the effectiveness of the system proposed for monitoring in terms of airborne cotton dust exposure in improving safety in textile workplaces. A meta-analysis is performed with Bayesian framework using JASP software. © 2025 IEEE.

关键词： Dust density concentration Meta-Analysis with Bayes factor

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Talk to your data: Enhancing Business Intelligence and Inventory Management with LLM-Driven Semantic Parsing and Text-to-SQL for Database Querying

Talk to your data: Enhancing Business Intelligence and Inven...

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Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Jerry Zhu Saad Ahmed Bazaz Srimonti Dutta Bhavaraju Anuraag Imran Haider Srijita Bandopadhyay Faculty of Mathematics University of Waterloo Ontario Canada Dept. of Computer Science National University of Computer and Emerging Sciences Islamabad Pakistan School of Agro and Rural Technology Indian Institute of Technology Guwahati Guwahati India Dept. of Electronics and Communication Engineering Vellore Institute of Technology India Dept. of Industrial and Systems Engineering Indian Institute of Technology Kharagpur Kharagpur India Dept. of Electronics and Communication Engineering University of Engineering and Management Kolkata India

ISBN: (数字)9798350369786

ISBN: (纸本)9798350369793

This paper delves into the potential of Large Language Models (LLMs) in revolutionizing business intelligence and inventory management through semantic parsing and text-to-SQL methodologies. It assesses various LLM models, such as DIN-SQL, DSP, NSQL, GPT, CoPilot, and LLaMa, elucidating their capabilities and contributions. Two critical analyses are presented here. The first compares cutting-edge LLM models using cosine similarity and cost efficiency metrics. The second analysis enhances GPT’s precision through prompt engineerings, like few-shot techniques, and explores frameworks like DIN-SQL, NSQL, and DSP. DIN-SQL substantially boosts accuracy, and NSQL demonstrates potential in specific scenarios. This research underscores the transformative potential of LLM-driven models in business intelligence and inventory management. DIN-SQL, in particular, emerges as a game-changer with the potential to reshape inventory management practices. GPT showcases its versatility through fine-tuning for tasks beyond conventional programming, while CoPilot offers a cost-effective alternative. This study emphasizes the importance of cost-effectiveness in real-world applications, with LLaMa and CoPilot being practical choices. NSQL, with its budget-friendly and semi-accurate solution, holds promise for semantic parsing in growing companies. These insights are a foundation for further innovation, promising unmatched efficiency and competitiveness across industries in the evolving Artificial intelligence landscape.

关键词： Industries Structured Query Language Technological innovation Costs Large language models Semantics Inventory management

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