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Various Modulation Types Classification on the Air by Convolutional Neural Network 7

Various Modulation Types Classification on the Air by Convol...

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7th International Conference on Future Networks and Distributed Systems, ICFNDS 2023

作者： Hammadi, Yousif I Alsaeedi, Mokhalad Abdulameer Kadhim Mahmood, Omar Abdulkareem Sameen, Aws Zuhair Muthanna, Mohammed Saleh Ali Aziz, Ahmed Bilad Alrafidain University College Diyala Iraq Department Of Communications Engineering College Of Engineering University Of Diyala Baqubah32001 Iraq Department Of Medical Instrumentation Engineering Techniques College Of Medical Techniques Al-Farahidi University Baghdad Iraq Institute Of Computer Technologies And Information Security Southern Federal University Taganrog344006 Russia Department Of Computer Science Faculty Of Computer And Artificial Intelligence Benha University Egypt Department Of International Business Management Tashkent State University Of Economics Tashkent Uzbekistan

ISBN: (纸本)9798400709036

Deep learning (DL), a relatively recent AI technique, has been successfully applied to the problem of automated modulation categorization (AMC), with promising results. An essential part of developing the spectrum-sensing capabilities needed by software-defined radio is carrying out radio signal classification jobs, which we did in this research by introducing a better deep neural architecture. Different detection techniques were utilized in the literature;however, DL neural networks are recently utilized in this field. Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) which is one variation of the RNN network, and other techniques are some of the employed DL architectures in the field of AMC. In this paper, Convolutional Neural Network (CNN) model has been built to automatically classify ten-modulation formats correctly. The suggested CNN network is constructed mainly of six-Convolutional layers. The overall number of trainable parameters in the proposed network is 1,757,962. Thus, the network accuracy, which was achieved during the training reached more than 90%. Python programming packages have been used to implement the suggested approach using *** platform, which provides wide-range of facilities. © 2023 ACM.

关键词： Network architecture

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Enhanced SNR Based Admission Control Algorithm for Vehicular Ad-Hoc Network 7

Enhanced SNR Based Admission Control Algorithm for Vehicular...

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7th International Conference on Future Networks and Distributed Systems, ICFNDS 2023

作者： Sameen, Aws Zuhair Alwan, Mohammed Hasan Kadhum, Mayssam Saad Hammadi, Yousif I Alsaeedi, Mokhalad Abdulameer Kadhim Aziz, Ahmed Muthanna, Mohammed Saleh Ali Department Of Medical Instrumentation Engineering Techniques College Of Medical Techniques Al-Farahidi University Baghdad10015 Iraq Department Of Communications Engineering College Of Engineering University Of Diyala Baqubah32001 Iraq Bilad Alrafidain University College Diyala32001 Iraq Department Of Computer Science Faculty Of Computer And Artificial Intelligence Benha University Egypt Department Of International Business Management Tashkent State University Of Economics Tashkent Uzbekistan Institute Of Computer Technologies And Information Security Southern Federal University Taganrog344006 Russia

ISBN: (纸本)9798400709036

Vehicular Ad-hoc Network (VANET) becomes a fundamental subcategory of mobile ad-hoc networks that provides vehicles to communicate with each other and with roadside infrastructure smartly. Various VANET performance challenges are considered in terms of Quality of Service (QoS) due to vehicle speed and transmission rates. Different problems cause performance degradation as performance anomaly where high rates of vehicles have to meet the speed of the low rates vehicle while transmitting data. In this paper, a QoS-aware Signal to Noise Ratio (SNR) admission control mechanism (QASAC) is proposed to handle the performance anomaly problem while maintaining the QoS levels for different traffic types. QASAC has been compared against the latest SNR based admission control mechanism. QASAC has enhanced the performance of data delivery up to 23% in terms of data dropping rates for high priority traffic. © 2023 ACM.

关键词： Vehicular ad hoc networks

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Self-aware Hierarchical Neuromorphic Architecture

Self-aware Hierarchical Neuromorphic Architecture

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International Joint Conference on Neural Networks (IJCNN)

作者： Zilu Wang Xin Yao School of Mechanical Engineering and Automation Harbin Institute of Technology (Shenzhen) Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Faculty of Business Lingnan University Hongkong China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

We introduce a neuromorphic architecture devised by mapping biologically inspired concepts onto a computational framework for self-awareness, offering a new way to embed inherent autonomy directly within hardware. Leveraging this architecture as a blueprint, we have designed a memristor-based circuit capable of autonomous decision-making, employing a bio-inspired hierarchical design approach. The core of this approach involves translating the functions of biological neurons and synapses into memristor-based circuits, which act as efficient computational units at the bottom layer of the circuit. It ensures that the developed circuit strikes a balance between functional flexibility and computational efficiency. Experimental results show that our implemented circuit provides efficient computational capabilities for effective decision-making, thanks to its memristor-based bio-inspired hierarchical implementation. Furthermore, its neuromorphic architecture inspired by self-awareness facilitates the easy simulation of reliable autonomous behaviors. Through the analysis of hardware overhead and power consumption, our circuit proves to be hardware-friendly. Our work represents progress towards developing memristor-based neuromorphic circuits characterized by high computational performance and autonomous functionality.

关键词： Power demand Neuromorphics Microprocessors Decision making Neurons computer architecture Hardware

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A Multi Soft-sensor Approach for the Development of Emergencies Detection Units on the Edge

A Multi Soft-sensor Approach for the Development of Emergenc...

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Workshop on Metrology for Industry 4.0 and IoT

作者： Franklin Oliveira Daniel G. Costa Flávio Assis Ivanovitch Silva Graduate Program in Mechatronics Federal University of Bahia Salvador Brazil INEGI Faculty of Engineering University of Porto Porto Portugal Department of Computer Engineering and Automation Federal University of Rio Grande do Norte Natal Brazil

The adoption of multi-sensor hardware units to detect different types of emergencies in complex scenarios such as Industry 4.0 and Smart Cities is a recent development trend, which have demanded research efforts to optimize the way these units are assembled, configured, and deployed. However, although their expected advantages, usual multi-sensors emergency-oriented approaches may be constrained by the tight association between physical sensor devices and the emergency detection process. Therefore, this paper proposes an innovative approach to concurrently detect multiple emergencies exploiting the concept of soft-sensor modules, which are implemented on Emergency Detection Units (EDUs) as concurrent tasks along with a high-priority emergency alerting task. The soft-sensor concept defines physical sensors as ordinary input data streams, which will be processed according to the defined algorithms by each particular soft-sensor, allowing detection decisions to be performed on the edge. In order to support the practical exploitation of this concept, a formal specification based on Petri Nets is defined, modeling the concurrent operation of the tasks within an EDU. A proof of concept is also designed based on the affordable Raspberry Pi Pico microcontroller, demonstrating how the proposed approach would behave when two soft-sensors are implemented in a typical detection unit.

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Convolutional Neural Network Based Deep Neural Network Model for Electrocardiogram Records Classification 7

Convolutional Neural Network Based Deep Neural Network Model...

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7th International Conference on Future Networks and Distributed Systems, ICFNDS 2023

作者： Khudhur, Ahmed Mahmood Alwan, Mohammed Hasan Al Omairi, Qayssar Mahmood, Omar Abdulkareem Hammadi, Yousif I Muthanna, Mohammed Saleh Ali Aziz, Ahmed Muthanna, Ammar Information Technology Department College Of Computer Science And Information Technology Kirkuk University Iraq Department Of Communications Engineering College Of Engineering University Of Diyala Baqubah32001 Iraq Department Of Computer Engineering College Of Engineering University Of Diyala Iraq Bilad Alrafidain University College Diyala32001 Iraq Institute Of Computer Technologies And Information Security Southern Federal University Taganrog344006 Russia Department Of Computer Science Faculty Of Computer And Artificial Intelligence Benha University Egypt Department Of International Business Management Tashkent State University Of Economics Tashkent Uzbekistan Department Of Telecommunication Networks And Data Transmission The Bonch Bruevich Saint Petersburg State University Of Telecommunications Russia Department Of Applied Probability And Informatics Peoples Friendship University Of Russia Russia

ISBN: (纸本)9798400709036

Cardiovascular diseases, also known as CVDs, currently rank as the primary incidence of mortality. The present approach for identifying illnesses involves the analysis of the Electrocardiogram (ECG), an electronic diagnostic device utilized to capture the rhythm of the heart. Regrettably, the process of seeking out specialists to conduct analysis on a substantial volume of electrocardiogram (ECG) data results in a significant depletion of medical capabilities. Hence, the utilization of deep learning algorithms for the identification of ECG characteristics has progressively gained prominence. Nevertheless, there exist certain limitations associated with these conventional approaches, which necessitate the need for manual characteristic identification, intricate models, and extensive training duration. This study presents a novel and effective approach for categorizing the five different types of heartbeats in the MIT-BIH Arrhythmia database. The proposed method involves the utilization of a 16-layer deep one-dimensional convolutional neural network, which exhibits both robustness and efficiency in its classification performance. Thus, the designed model is consisting of five-groups, first to fourth groups are the main feature extraction and mapping blocks, while the fifth group is fully connected and classification layers. The findings indicate that the model presented in this study has superior performance in terms of accuracy, precision, and F1-score. The proper classification of medical cases has a significant impact on the conservation of medical resources, hence positively influencing clinical practice. Additionally, the model training phase is characterized by its efficiency, since it does not significantly deplete resources. © 2023 ACM.

关键词： Electrocardiograms

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Network Traffic Prediction with Decomposition and Multi-Scale Autocorrelation in Large-Scale Cloud Data Centers

Network Traffic Prediction with Decomposition and Multi-Scal...

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IEEE International Conference on Networking, Sensing and Control

作者： Meijia Wang Haitao Yuan Zhenwei Kuang Hanbo Ma Jing Bi Jia Zhang MengChu Zhou School of Automation Science and Electrical Engineering Beihang University Beijing China Faculty of Information Technology Beijing University of Technology Beijing China Dept. of Computer Science Southern Methodist University Dallas TX USA Dept. of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9798350365221

ISBN: (纸本)9798350365238

As the Internet and big data technologies advance, a tremendous amount of data is generated daily. Efficient network operations are essential for handling such data. Accurately predicting future network traffic in real-time enables prompt response from cloud infrastructure and efficient traffic scheduling and allocation, ultimately reducing costs, preventing economic losses, and optimizing the performance of downstream facilities. However, predicting network traffic in large-scale data centers is challenging due to the multidimensional, nonlinear, and high-volatility nature of the time series. Traditional prediction methods, e.g., regression algorithms, struggle to capture non-linear features effectively. Many deep learning models face issues such as gradient explosion or vanishing during their training. Current commonly used prediction methods fail to fully uncover vital information about the frequency domain features in the time series. To do so, this work proposes a novel network traffic prediction model that combines a Savitzky Golay filter, sequence decomposition, multi-scale attention, a temporal convolutional network, an autocorrelation mechanism, and bidirectional long short-term memory. It can accurately predict future network traffic by adaptively extracting important features without requiring excessive feature engineering of the original data. It performs end-to-end sequence prediction, feature selection, and automatic learning of data features and temporal dependencies to achieve accurate time series prediction and avoid redundant data processing. Experiments involving ablation studies and comparisons with advanced prediction models are performed with the Google cluster trace. Experimental results show that the proposed model improves the prediction accuracy by at least 38.52% over the state-of-the-art models.

关键词： Data centers Biological system modeling Time series analysis Telecommunication traffic Predictive models Feature extraction Prediction algorithms Autocorrelation Data mining Long short term memory

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Energy-Saving Practice of Variable Frequency Drive Technology in Data Center HVAC System 5

Energy-Saving Practice of Variable Frequency Drive Technolog...

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5th International Conference on Power and Energy Technology, ICPET 2023

作者： Ai, Chuanxian Yang, Jinpeng Chen, Rui Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming China The National Computer Network Emergency Response Technical Team Coordination Center of China Yunnan Branch Kunming China Guizhou Power Grid Co. Ltd. China Southern Power Grid Co. Ltd Power Dispatch Control Center Guiyang China

ISBN: (纸本)9798350339673

The variable-frequency drive technology (VFD) can modify the motor speed or torque by varying the frequency and voltage of the AC motor input power according to the load demand as a way to save energy. To analyse the energy saving effect of variable-frequency drive technology applied to data centre heating, ventilation, and air conditioning (HVAC) systems, this paper applies VFD technology to data centre fluorine pump-compressor composite air conditioning systems, implements monitoring and analysis of the operational energy efficiency of this system before and after retrofit, and combines the actual application of this technology. The findings indicate that the VFD technology can substantially reduce the number of compressor starts and stops, reduce the compressor start-up inrush current, and improve the power system's stability. Furthermore, utilising this technology, the HVAC system can effectively synchronise with the load and fluctuations in outdoor temperature and humidity, resulting in accurate temperature regulation and optimal utilisation of outdoor cooling resources. This approach can lead to significant energy conservation of up to 42.95% when compared to extreme hot and cold weather conditions. The findings of the study indicate that the utilisation of VFD technology exhibits commendable adaptability and yields favourable outcomes in regions with varying temperature ranges. The minimum PUE value recorded was 1.228. © 2023 IEEE.

关键词： data center energy saving HVAC system variable-frequency drive technology

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Long-term Water Quality Prediction based on Intelligent Optimization and Seasonal-trend Decomposition

Long-term Water Quality Prediction based on Intelligent Opti...

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IEEE International Conference on automation Science and engineering (CASE)

作者： Ziqi Wang Xiangxi Wu Jing Bi Haitao Yuan Jia Zhang MengChu Zhou Faculty of Information Technology Beijing Laboratory of Smart Environmental Protection Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China Department of Computer Science Lyle School of Engineering at Southern Methodist University Dallas TX USA Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Nowadays, the applications of water quality prediction in the field of regional water environment management are increasing. It refers to predicting the elemental values of the water environment in the future based on past monitoring data, which is essential to realize the real-time evaluation of water quality and dynamic control of pollution sources. However, the water environment indicators are affected by various elements, which have a large volatility and non-linear characteristics. In addition, most of the existing water quality predictions focus on single-step predictive modeling of single elements of the water environment and lack multi-step predictive analysis of multifactor data of the water environment. In this paper, a novel long-term prediction model based on genetic simulated annealing-based particle swarm optimization (GSPSO) with seasonal-trend decomposition using LOESS (STL) is proposed and named GSPSO-STL-Autoformer (GS-Autoformer). It realizes the multi-factor and long-term prediction of water quality time series data. Firstly, the Autoformer’s hyperparameters are optimized by the GSPSO to improve its convergence speed. Secondly, the multi-factor features are decomposed by the STL to make the model more focused on learning feature information of each component. Finally, the long-term prediction is realized by the Autoformer. Comparative experiments with state-of-the-art peers show that the GS-Autoformer can effectively improve the accuracy of multi-factor and long-term predictions.

关键词： Accuracy Time series analysis Water quality Simulated annealing Predictive models Water pollution Data models Predictive analytics Particle swarm optimization Water resources

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Latency-Minimized Computation Offloading in Vehicle Fog Computing with Improved Whale Optimization Algorithm

Latency-Minimized Computation Offloading in Vehicle Fog Comp...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Jing Bi Xiangdong Xue Haitao Yuan Jia Zhang Faculty of Information Technology Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China Dept. of Computer Science in Lyle School of Engineering Southern Methodist University Dallas TX USA

Fog computing provides lower latency and higher bandwidth compared to cloud computing and is widely used in Internet of Vehicles (IoV). Vehicles cannot compute all tasks locally due to their limited computing power and battery capacity. Thus, it is a useful way to offload some tasks of vehicles to other resource-rich servers. However, due to the high mobility of vehicles, there may be a failure of returning computing results. Thus, it is a challenge to minimize the latency of tasks while meeting the constraint of energy consumption. This work proposes a vehicle-fog offloading system that offloads tasks to fog servers or idle vehicles which is a probabilistic offloading problem. So this work proposes an improved optimization algorithm called an adaptive Lévy flight-based Whale optimization algorithm with Hierarchical learning (LWH) to solve this problem. Simulation experiments show that LWH has strong global search capability and outperforms its five typical and widely used algorithms.

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Network Anomaly Detection with Stacked Sparse Shrink Autoencoders and Improved XGBoost

Network Anomaly Detection with Stacked Sparse Shrink Autoenc...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Jing Bi Ziyue Guan Haitao Yuan Jia Zhang Faculty of Information Technology Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China Dept. of Computer Science in Lyle School of Engineering Southern Methodist University Dallas TX USA

Efficient and accurate identification of network anomalies is of great significance to the construction of network security systems in the information age. It is highly challenging to accurately detect abnormal behaviors in the increasing network data. Currently, classification methods based on feature extraction of autoencoders have been proven to be suitable for network anomaly detection. However, traditional detection models with autoencoders have poor detection accuracy in the face of massive network features. In addition, the hyperparameter optimization of their models cannot be effectively solved. For network anomaly detection, this work proposes a new network anomaly detection method named SAXP, which integrates Stacked sparse shrink Autoencoders and a XGBoost model based on genetic simulated annealing Particle swarm optimization (GSPSO). Specifically, features extracted by stacked sparse shrink autoencoders are introduced into the XG Boost model for classification, and GSPSO is used to optimize the hyperparameters of XGBoost. Experimental results based on two real-life data sets demonstrate that the proposed SAXP achieves higher recognition accuracy than several state-of-the-art algorithms.

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