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Investigation on Analog and Digital Modulations Recognition Using Machine Learning Algorithms

World Journal of engineering and Technology 2024年第4期12卷 867-884页

作者： Jean Ndoumbe Ivan Basile Kabeina Gaelle Patricia Talotsing Soubiel-Noël Nkomo Biloo Laboratory of Computer Engineering Data Science and Artificial Intelligence National Higher Polytechnic School of Douala Douala Cameroon Mobile Computing and Networking Research Laboratory Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada

In the field of radiocommunication, modulation type identification is one of the most important characteristics in signal processing. This study aims to implement a modulation recognition system on two approaches to machine learning techniques, the K-Nearest Neighbors (KNN) and Artificial Neural Networks (ANN). From a statistical and spectral analysis of signals, nine key differentiation features are extracted and used as input vectors for each trained model. The feature extraction is performed by using the Hilbert transform, the forward and inverse Fourier transforms. The experiments with the AMC Master dataset classify ten (10) types of analog and digital modulations. AM_DSB_FC, AM_DSB_SC, AM_USB, AM_LSB, FM, MPSK, 2PSK, MASK, 2ASK, MQAM are put forward in this article. For the simulation of the chosen model, signals are polluted by the Additive White Gaussian Noise (AWGN). The simulation results show that the best identification rate is the MLP neuronal method with 90.5% of accuracy after 10 dB signal-to-noise ratio value, with a shift of more than 15% from the k-nearest neighbors’ algorithm.

关键词： Automatic Recognition Artificial Neural Networks K-Nearest Neighbors Machine Learning Analog Modulations Digital Modulations

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Day-ahead regional solar power forecasting with hierarchical temporal convolutional neural networks using historical power generation and weather data

arXiv

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arXiv 2024年

作者： Perera, Maneesha De Hoog, Julian Bandara, Kasun Senanayake, Damith Halgamuge, Saman Department of Mechanical Engineering School of Electrical Mechanical and Infrastructure Engineering The University of Melbourne Melbourne Australia School of Computing and Information Systems Melbourne Centre for Data Science The University of Melbourne Melbourne Australia

Regional solar power forecasting, which involves predicting the total power generation from all rooftop photovoltaic (PV) systems in a region holds significant importance for various stakeholders in the energy sector to ensure a stable electricity supply. However, the vast amount of solar power generation and weather time series from geographically dispersed locations that need to be considered in the forecasting process makes accurate regional forecasting challenging. Therefore, previous studies have limited the focus to either forecasting a single time series (i.e., aggregated time series) which is the addition of all solar generation time series in a region, disregarding the location-specific weather effects or forecasting solar generation time series of each PV site (i.e., individual time series) independently using location-specific weather data, resulting in a large number of forecasting models. In this work, we propose two new deep-learning-based regional forecasting methods that can effectively leverage both types of time series (aggregated and individual) with weather data in a region. We propose two hierarchical temporal convolutional neural network architectures (HTCNN A1 and A2) and two new strategies to adapt HTCNNs for regional solar power forecasting. In the first strategy, we explore generating a regional forecast using a single HTCNN. In the second, we divide the region into multiple sub-regions based on weather information and train separate HTCNNs for each sub-region;the forecasts of each sub-region are then added to generate a regional forecast. The proposed work is evaluated using a large dataset collected over a year from 101 locations across Western Australia to provide a day ahead forecast at an hourly time resolution which involves forecasting a horizon of 18 hours. We compare our approaches with well-known alternative methods, including long-short term memory networks and convolution neural networks, and show that proposed HTCNN-based appro

关键词： Time series

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Collusion Resistant Identity-based Proxy Re-encryption Scheme on Lattice 6

Collusion Resistant Identity-based Proxy Re-encryption Schem...

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6th International Conference on Computer Information Science and Application Technology, CISAT 2023

作者： Deng, Xiaohong Xie, Hua Xiong, Weizhi School of Electronics and Information Engineering Gannan University of Science and Technology Ganzhou341000 China College of Information Science Jiangxi University of Science and Technology Ganzhou341000 China Key Laboratory of Cloud Computing and Big Data Ganzhou341000 China

ISBN: (纸本)9781510668546

Proxy re-encryption plays an important role for data security in cloud computing and big data, but the traditional reencryption scheme based on the classical number theory problem cannot resist quantum attack, and the lattice based cryptosystem has been proved to have the ability to resist quantum attack, so the lattice based re-encryption scheme has always been a research hotspot. In view of the problem that collusion-resistance and multi-hop can not be satisfied at the same time in existing schemes, this paper proposes a new identity-based proxy re-encryption scheme on lattice. Firstly, the scheme uses trapdoors as private keys to achieve one-way anti-collusion. In addition, we constructs a variant of learning with errors (LWE), which uses a convert algorithm to transform the user matrix and replace the vector with a uniform matrix, so that the scheme can meet the requirements of multi-hop and encrypt multiple bits of messages at one time. Theoretical analysis and experimental simulation prove that the security of the new scheme can be reduced to a decision LWE problem, which can reach the IND-sID-CPA security in the standard model, and can improve the encryption efficiency while ensuring the security. © 2023 SPIE.

关键词： Cryptography

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Enabling Protection for Critical Infrastructure through Security and Privacy in the Industrial Internet of Things 9

Enabling Protection for Critical Infrastructure through Secu...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Yasotha, B. Vijayanandh, T. Thirumoorthy, P. Priya, M.Sangeetha Sasidaran, S. Prakalya, S.B. Srmist Department of Data Science and Business Systems Kattankulathur India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Avadi Tamil Nadu Chennai India Dhanalakshmi College of Engineering Department of Electrical and Electronics Engineering Chennai India Chennai India Panimalar Engineering College Department of Information Technology Chennai600123 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350377972

Industrial Internet of Things can improve critical infrastructure in energy, transportation, and manufacturing. However, IIoT device and system integration opened security weaknesses that bad actors may exploit, causing huge disruptions and financial losses. IoT network security and privacy must be prioritized to protect key infrastructure. IIoT security issues and solutions are examined in this research. First, we take a look at the state of security and the unique risks that industrial IoT devices are up against, including hacking, illegal access, and cyber-physical assaults. Then, to lessen the impact of these dangers, we investigate cutting-edge security solutions, such as authentication, encryption, and access control systems. Building a robust security architecture specifically for IIoT applications is one of the main goals of the project. To improve safety and anonymity, this system uses cutting-edge innovation like blockchain, AI, and edge computing. We also talk about how privacy-preserving methods like differential privacy and anonymization are important for keeping sensitive information safe while allowing data sharing for analysis and operations. We assess the efficacy of the suggested security protocols and procedures using a battery of case studies and simulations. Reducing the attack surface and improving the resilience of IIoT systems can be achieved by a multi-layered security approach that includes effective privacy protections. We conclude by outlining our research's larger implications, which include the importance of standardization, regulatory compliance, and stakeholder collaboration in ensuring the safe and privacy-protecting deployment of IIoT technologies in vital infrastructure. Protecting vital infrastructure from cyber threats is our ultimate goal, and our work helps build IIoT systems that are more secure and dependable. © 2024 IEEE.

关键词： Industrial internet of things (IIoT)

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Type-Representation Enhanced Event Detection via Structure Encoder 1

Type-Representation Enhanced Event Detection via Structure E...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Feng, Yuming Huang, Linmeng Liang, Haozhe Jin, Xiaolong Guan, Saiping Guo, Jiafeng School of Computer Science and Technology University of Chinese Academy of Sciences China Institute of Computing Technology Chinese Academy of Sciences Key Lab of Network Data Science and Technology China Institute of Systems Engineering Academy of Military Science Beijing China

ISBN: (纸本)9798350358780

Event detection, aiming to detect whether a sentence contains the type of events of interest, is one of the important tasks in information Extraction. There exist many methods for event detection, which usually treat event detection as a classification problem. These existing methods suffer from the imbalanced training data of different event types. To relieve this problem, this paper proposes a new Type-Representation-Enhanced Event Detection (TREED) method, which introduce hierarchical event type representations to enhance classification accuracy. Specifically, TREED first models the hierarchical relationship among event types in the dataset;Next, upon interactions between the representations of trigger words and event types, it obtains the new representations of trigger words integrating event type information. Finally, TREED classifies the events based on the obtained more accurate trigger word representations. Experimental results on a commonly used benchmark dataset, ACE-2005, demonstrate that the proposed model can improve both accuracy and recall of the event detection task. © 2023 IEEE.

关键词： Signal encoding

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SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation

SpreadFGL: Edge-Client Collaborative Federated Graph Learnin...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Luying Zhong Yueyang Pi Zheyi Chen Zhengxin Yu Wang Miao Xing Chen Geyong Min College of Computer and Data Science Fuzhou University China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University China Engineering Research Center of Big Data Intelligence Ministry of Education China School of Computing and Communications University of Lancaster UK School of Engineering Computing and Mathematics University of Plymouth UK Department of Computer Science University of Exeter UK

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Federated Graph Learning (FGL) has garnered widespread attention by enabling collaborative training on multiple clients for semi-supervised classification tasks. However, most existing FGL studies do not well consider the missing inter-client topology information in real-world scenarios, causing insufficient feature aggregation of multi-hop neighbor clients during model training. Moreover, the classic FGL commonly adopts the FedAvg but neglects the high training costs when the number of clients expands, resulting in the overload of a single edge server. To address these important challenges, we propose a novel FGL framework, named SpreadFGL, to promote the information flow in edge-client collaboration and extract more generalized potential relationships between clients. In SpreadFGL, an adaptive graph imputation generator incorporated with a versatile assessor is first designed to exploit the potential links between subgraphs, without sharing raw data. Next, a new negative sampling mechanism is developed to make SpreadFGL concentrate on more refined information in downstream tasks. To facilitate load balancing at the edge layer, SpreadFGL follows a distributed training manner that enables fast model convergence. Using real-world testbed and benchmark graph datasets, extensive experiments demonstrate the effectiveness of the proposed SpreadFGL. The results show that SpreadFGL achieves higher accuracy and faster convergence against state-of-the-art algorithms.

关键词： Training Accuracy Collaboration Benchmark testing Imputation Generators Topology

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Distributed solar generation forecasting using attention-based deep neural networks for cloud movement prediction

arXiv

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arXiv 2024年

作者： Perera, Maneesha De Hoog, Julian Bandara, Kasun Halgamuge, Saman Department of Mechanical Engineering School of Electrical Mechanical and Infrastructure Engineering The University of Melbourne Melbourne Australia School of Computing and Information Systems Melbourne Centre for Data Science The University of Melbourne Melbourne Australia

Accurate forecasts of distributed solar generation are necessary to reduce negative impacts resulting from the increased uptake of distributed solar photovoltaic (PV) systems. However, the high variability of solar generation over short time intervals (seconds to minutes) caused by cloud movement makes this forecasting task difficult. To address this, using cloud images, which capture the second-to-second changes in cloud cover affecting solar generation, has shown promise. Recently, deep neural networks with attention that focus on important regions of an image have been applied with success in many computer vision applications. However, their use for forecasting cloud movement has not yet been extensively explored. In this work, we propose an attention-based convolutional long short-term memory network to forecast cloud movement and apply an existing self-attention-based method previously proposed for video prediction to forecast cloud movement. We investigate and discuss the impact of cloud forecasts from attention-based methods towards forecasting distributed solar generation, compared to cloud forecasts from non-attention-based methods. We further provide insights into the different solar forecast performances that can be achieved for high and low altitude clouds. We find that for clouds at high altitudes, the cloud predictions obtained using attention-based methods result in solar forecast skill score improvements of 5.86% or more compared to non-attention-based methods. © 2024, CC BY.

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Surveying neuro-symbolic approaches for reliable artificial intelligence of things

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Journal of Reliable Intelligent Environments 2024年第3期10卷 257-279页

作者： Lu, Zhen Afridi, Imran Kang, Hong Jin Ruchkin, Ivan Zheng, Xi Faculty of Data Science City University of Macau Estrada de Coelho do Amaral 999078 China School of Computing Macquarie University Balaclava Rd SydneyNSW2109 Australia Computer Science University of California Los Angeles 404 Westwood Plaza Los AngelesCA90095 United States Electrical and Computer Engineering University of Florida 1889 Museum Rd GainesvilleFL32611 United States

The integration of Artificial Intelligence (AI) with the Internet of Things (IoT), known as the Artificial Intelligence of Things (AIoT), enhances the devices’ processing and analysis capabilities and disrupts such sectors as healthcare, industry, and oil. However, AIoT’s complexity and scale are challenging for traditional machine learning (ML). Deep learning offers a solution but has limited testability, verifiability, and interpretability. In turn, the neuro-symbolic paradigm addresses these challenges by combining the robustness of symbolic AI with the flexibility of DL, enabling AI systems to reason, make decisions, and generalize knowledge from large datasets better. This paper reviews state-of-the-art DL models for IoT, identifies their limitations, and explores how neuro-symbolic methods can overcome them. It also discusses key challenges and research opportunities in enhancing AIoT reliability with neuro-symbolic approaches, including hard-coded symbolic AI, multimodal sensor data, biased interpretability, trading-off interpretability, and performance, complexity in integrating neural networks and symbolic AI, and ethical and societal challenges. © The Author(s) 2024.

关键词： Deep learning

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Internet of Things IoMT data Aggregation Using Machine Learning

Internet of Things IoMT Data Aggregation Using Machine Learn...

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Device Intelligence, computing and Communication Technologies, (DICCT), International Conference on

作者： Lakshmi Sagar Pulabaigari C. Sivakumar Dept. of Computer Science and Engineering School of Computing Mohan Babu University Tirupati Andhra Pradesh India Dept. of Data Science School of Computing Mohan Babu University Tirupati Andhra Pradesh India

ISBN: (数字)9798350372847

ISBN: (纸本)9798350372854

The Internet of Things (IoT) has revolutionized the way data is handled and collected, allowing for large amounts of information to be revolutionized quickly and efficiently. This has paved the way for Machine Learning (ML) applications to be used on IoT datasets, such as 10MT data aggregation. This paper discusses the use of a ML approach for aggregation 10MT data in order to better understand trends and classify patterns in various sectors that would otherwise go undetected. It discusses the benefits and challenges of such a system, and how it could be used to improve the efficiency of industrial operations. It stresses the importance of data integrity in this process, as wrong or incomplete datasets can significantly reduce the accuracy and reliability of the results obtained. Furthermore, it discusses the need for suitable ML algorithms and models, such as clustering and random forest, to be applied to the collected data in order to produce robust and meaningful output. It observes the security and privacy challenges associated with such ML-based 10MT data aggregation systems, and the potential solutions available.

关键词： Machine learning algorithms data aggregation Market research Security Biomedical monitoring Wearable devices Medical diagnostic imaging

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Enhancing Effective Learning Capability of SOAR Agent based Episodic Memory

Enhancing Effective Learning Capability of SOAR Agent based ...

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Kaswan, Kuldeep Singh Naruka, Mahaveer Singh Dhatterwal, Jagjit Singh Galgotias University School of Computing Science and Engineering Greater Noida India G L Bajaj Institute of Technology and Management Greater Noida India Koneru Lakshmaiah Education Foundation Department of Artificial Intelligence & Data Science AP Vaddeswaram India

ISBN: (纸本)9798350322309

In order to increase the agent's intelligence, the fundamental goal of the research paper is to learn from its experiences. In order to solve a problem, learning is accomplished with the aid of episodic memory, which stores previous experiences as episodes. This study illustrates the improvement in intelligence with the use of the application of an example in soar. Working memory elements (WMEs), which are episodes (past experiences) in the form, are discussed in this paper as a means of enhancing intelligence. The purpose of this work is to demonstrate that knowledge always leads to performance improvement, meaning that as an agent studies more, performance will extend. © 2023 IEEE.

关键词： Image segmentation

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