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Automatic Modulation Recognition Based on CNN and GRU

Tsinghua science and Technology 2022年第2期27卷 422-431页

作者： Fugang Liu Ziwei Zhang Ruolin Zhou the Department of Electronics and Information Engineering Heilongjiang University of Science and TechnologyHarbin 150022China the Department of Electrical and Computer Engineering University of MassachusettsDartmouthMA 02747USA

Based on a comparative analysis of the Long Short-Term Memory(LSTM)and Gated Recurrent Unit(GRU)networks,we optimize the structure of the GRU network and propose a new modulation recognition method based on feature extraction and a deep learning ***-order cumulant,Signal-to-Noise Ratio(SNR),instantaneous feature,and the cyclic spectrum of signals are extracted firstly,and then input into the Convolutional Neural Network(CNN)and the parallel network of GRU for *** modulation modes of communication signals are recognized *** results show that the proposed method can achieve high recognition rate at low SNR.

关键词： modulation recognition deep learning Gated Recurrent Unit(GRU) Convolutional Neural Network(CNN)

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Visual Programming Concept for Infineon XMC Series Microcontrollers 47

Visual Programming Concept for Infineon XMC Series Microcont...

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47th ICT and Electronics Convention, MIPRO 2024

作者： Vulić, Lukrecia Kaučić, Luka Aleksi, Ivan Matić, Tomislav Computer Science And Information Technology Osijek Faculty Of Electrical Engineering Osijek Croatia

ISBN: (纸本)9798350382495

In today's modern world, efficient programming is a necessity. To speed up code generation nowadays, programming code is generated using different graphical tools. Although efficient, this technology is scarcely used in embedded system development. In this paper, we present a procedure for block-based visual programming with applications in embedded systems development. We present several examples of blocks using the Blockly open-source visual programming tool and test the implementation on selected Infineon XMC-based boards. The presented programming solution shows successful implementation of block-based visual programming in embedded systems development. © 2024 IEEE.

关键词： Embedded systems

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Current Trends, Challenges and Techniques in XAI Field;A Tertiary Study of XAI Research 47

Current Trends, Challenges and Techniques in XAI Field;A Ter...

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47th ICT and Electronics Convention, MIPRO 2024

作者： Brdnik, Saša Šumak, Boštjan University Of Maribor Institute Of Informatics Faculty Of Electrical Engineering And Computer Science Slovenia

ISBN: (纸本)9798350382495

This tertiary systematic literature review examines 29 systematic literature reviews and surveys in Explainable Artificial Intelligence (XAI) to uncover trends, limitations, and future directions. The study explores current explanation techniques, providing insights for researchers, practitioners, and policymakers interested in enhancing AI transparency. Notably, the increasing number of systematic literature reviews (SLRs) in XAI publications indicates a growing interest in the field. The review offers an annotated catalogue for human-computer interaction-focused XAI stakeholders, emphasising practitioner guidelines. Automated searches across ACM, IEEE, and science Direct databases identified SLRs published between 2019 and May 2023, covering diverse application domains and topics. While adhering to methodological guidelines, the SLRs often lack primary study quality assessments. The review highlights ongoing challenges and future opportunities related to XAI evaluation standardisation, its impact on users, and interdisciplinary research on ethics and GDPR aspects. The 29 SLRs, analysing over two thousand papers, include five directly relevant to practitioners. Additionally, references from the SLRs were analysed to compile a list of frequently cited papers, serving as recommended foundational readings for newcomers to the field. © 2024 IEEE.

关键词： Artificial intelligence

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Heart Arrhythmia Disease Prediction Using Logistic Regression Algorithms 2

Heart Arrhythmia Disease Prediction Using Logistic Regressio...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Logesh, K. Priya, V. Devi Kuppam Engineering College Department Of Computer Science And Engineering Andhra Pradesh India

ISBN: (纸本)9798350381665

Heart disease arrhythmia is a significant medical concern affecting millions of individuals worldwide. Early and accurate prediction of arrhythmias can play a crucial role in improving patient outcomes and reducing the burden on healthcare systems. The primary objective of this research is to compare the performance of these machine learning models in predicting heart disease arrhythmias. The models are trained and evaluated using standard accuracy metrics. The achieved accuracy rates for each model are as follows Naïve Bayes - 80%, Support Vector Machine (SVM) - 81%, Decision Tree - 83%, and Logistic Regression - 87%. The results suggest that Logistic Regression performs the best among the tested models, achieving an accuracy rate of 87%. This indicates that the selected features and the algorithm's underlying assumptions align well with the characteristics of heart disease arrhythmia data. While all models show promising accuracy rates, Logistic Regression stands out as a robust choice for arrhythmia prediction. © 2024 IEEE.

关键词： Forecasting

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Smartcart - Automatic Billing Cart for Shopping 10

Smartcart - Automatic Billing Cart for Shopping

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Joyce, Alice Antony, Angaline Joseph, John Johnson, Sneha Uma, E.S. Sahrdaya College of Engineering and Technology Department of Computer Science and Engineering Thrissur India

ISBN: (纸本)9798350384369

This paper presents a revolutionary project to transform shopping experiences in malls through a cost-effective automated billing system. By eliminating specialized baskets and intricate hardware, we significantly reduce implementation costs. A user-friendly mobile app offers real-time purchase tracking, secure UPI payments, and personalized experiences, simplifying mall operations for a mutually beneficial retail evolution. Our innovative approach addresses traditional retail challenges, ushering in a new era of efficiency and satisfaction. The sys- tem promises increased customer satisfaction and operational effectiveness, marking a significant step towards the evolution of shopping centers. © 2024 IEEE.

关键词： Customer satisfaction

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Vehicle Identity Anonymity Framework with Accountability for VANET Environment

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Journal of Internet Technology 2024年第2期25卷 267-280页

作者： Lo, Nai-Wei Chuang, Chi-Ying Luo, Jia-Ning Yang, Chong-Long Department of Information Management National Taiwan University of Science and Technology Taiwan Department of Computer Science and Information Engineering National Defense University Taiwan

In recent years, there has been rapid development in vehicle safety technology, with the emergence of various active safety systems including blind spot information systems, adaptive cruise control, and front collision warning systems. Simultaneously, car manufacturers and technology companies are actively exploring technologies in the realm of autonomous driving. To facilitate such applications, vehicles are required to communicate with each other, exchanging vital information such as position, speed, and acceleration. However, this exchange of information poses a potential risk of drivers’ personal data being compromised. For safety purposes, vehicles must undergo appropriate authentication before engaging in communication with other vehicles. Ensuring this authentication process maintains the anonymity of the vehicles is crucial. Yet, striking a balance between protecting vehicle anonymity and enabling vehicle identification when necessary remains a challenging issue. This paper introduces a multi-tier Vehicular Ad-Hoc Network (VANET) framework designed to uphold the conditional anonymity and traceability of vehicles. The implementation of a group signature mechanism facilitates anonymous authentication, thereby enabling the realization of conditional anonymity and traceability. Moreover, comprehensive simulations and security analyses were conducted to validate the effectiveness of the proposed framework, demonstrating its efficiency while incorporating robust safety considerations. © 2024 Taiwan Academic Network Management Committee. All rights reserved.

关键词： Vehicular ad hoc networks

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Aerator Automation and Pond Monitoring System 5

Aerator Automation and Pond Monitoring System

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Krishna, K. Rama Shaik, Nagur Vali Mandapaka, Nandini Department of Computer Science and Engineering Velagapudi Ramakrishna Siddhartha Engineering College India

ISBN: (纸本)9798350361155

The primary task in maintaining Fish ponds includes monitoring and maintaining the water level and water quality which impacts on Fish growth. Our project introduces a Smart Pond System that modernizes aquaculture management by using advanced sensors for Dissolved Oxygen (DO), pH, and temperature. These sensors gather real-time data, helping us understand the pond's health. A central brain, Arduino, processes this information and takes necessary action. If oxygen, pH or temperature strays from ideal levels, the system can automatically control the aerator to fix it. Remote Monitoring allows farmers to get timely notifications and sensor readings from the Pond. Weekly and monthly measurement charts of water Quality Parameters (temperature, pH, and DO levels) can easily identifies changes in Water Quality, so that farmer can take necessary actions in-case of spreading any diseases in ponds. With this integration system, farmers could maintain suitable conditions for Aqua Culture. © 2024 IEEE.

关键词： Water levels

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Transmission of Electromagnetic Waves Through a Clay Material 1

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32nd International Conference on Organization and Technology of Maintenance, OTO 2023

作者： Mandrić, Vanja Rupčić, Slavko Vinko, Davor Bilandžija, Domagoj Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (数字)9783031514944

ISBN: (纸本)9783031514937

This paper deals with the simulation calculation of transmission (S21) and reflection (S22) parameters in a material parametrically based on clay (brick). The electromagnetic parameters of the clay that are the subject of the study are: relative permittivity Εr, loss tangent tan δ and electrical conductivity σ, while the only structural parameter is the thickness d of the sample. The values of all considered parameters are within the range of data from available literature and values that could occur due to external causes (e. g. an increase in clay moisture leads to an increase in electrical conductivity). The simulation model was carried out with the software tool ANSYS HFSS and validated with a laboratory measurement model. The laboratory model consists of a circular waveguide with an inner diameter of 150 mm and a total length of 700 mm, which is excited by 52 mm long probes placed 50 mm from the last wall of the waveguide. The lower cut-off frequency of this measurement system is 1.173 GHz, so all results (simulation and measurement) are limited by this lower cut-off frequency of the waveguide. All simulation calculations were performed for three clay sample thicknesses: 18 mm, 36 mm and 54 mm. These calculations were made with the aim of determining brick composites that have high attenuation of electromagnetic EM waves (low transmission parameter and high reflection parameter) in order to reduce the strength of EM fields inside the object made of such bricks. The measured results were used to verify the simulation model. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Circular waveguides

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A CNN-GA Hybrid Approach for Efficient Analysis of Size and Shape of Nanoparticle

A CNN-GA Hybrid Approach for Efficient Analysis of Size and ...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Sushma, K.S.N. Sarkar, Argha Sanyal, Gautam School Of Computer Science And Engineering Reva University Bengaluru India National Institute Of Technology Department Of Computer Science And Engineering Durgapur India

ISBN: (纸本)9798350371567

Addressing the challenge of manually analyzing nanoparticle characteristics, such as size and shape, researchers have increasingly turned to deep learning methodologies. This research paper introduces a novel approach, a synergistic combination of CNN-GA architecture, to efficiently analyze the physical properties of nanoparticles. The integration of CNN for feature extraction from image data and GA for fine-tuning hyperparameters and attributes proves to be a powerful tool. Unlike the traditional manual design of CNN architectures, this semi-automatic approach allows researchers to design robust architectures without necessitating in-depth CNN expertise, facilitating the achievement of benchmark results in nanoparticle characterization. © 2024 IEEE.

关键词： Genetic algorithms

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The Distributed Intelligent Collaboration to UAV-Assisted VEC: Joint Position Optimization and Task Scheduling

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IEEE Internet of Things Journal 2025年第12期12卷 21473-21487页

作者： Yi, Meng Lee, Vincent C.S. Yang, Peng Li, Peisong Zhang, Yifan Wei, Wei Gao, Honghao Southeast University School of Computer Science and Engineering Nanjing211189 China Monash University Faculty of IT Department of DS and AI ClaytonVIC3800 Australia Southeast University School of Computer Science and Engineering the Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing211189 China Xidian University Hangzhou Institute of Technology Hangzhou311200 China Southeast University-Monash University Joint Graduate School Suzhou215123 China Xi'an University of Technology School of Computer Science and Engineering Xi'an710048 China Shanghai University School of Computer Engineering and Science Shanghai200444 China

Deploying Unmanned Aerial Vehicles (UAVs) as aerial base stations enhances the coverage and performance of communication networks in Vehicular Edge Computing (VEC) scenarios. However, due to the limited communication range and energy capacity of UAVs, they cannot continuously cover entire areas or sustain long flights. Therefore, achieving full communication coverage of a target area with a minimal number of UAVs and efficient task offloading remains a significant challenge. To address this problem, the UAV-assisted Two-stage Intelligent Collaboration (UTIC) method is proposed in this paper to tackle the joint position optimization and task scheduling issue. Firstly, a UAV-assisted Two-stage Task Scheduling (UTTS) system model is designed to optimize the allocation process. Secondly, an Enhanced Particle Swarm Optimization (E-PSO) algorithm is designed to determine the optimal positions of UAVs, ensuring complete coverage of all mobile vehicles (MVs) with the minimum number of UAVs. Thirdly, Deep Deterministic Policy Gradient (DDPG) method is employed to find the optimal scheduling decisions for MVs, considering energy consumption, delay, and task priorities. Simulation results demonstrate that the proposed UTIC method can achieve nearly 20% reduction in UAV deployment and outperform three other classical reinforcement learning (RL) algorithms in terms of reducing system cost. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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