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IEEE/SP Workshop on Statistical Signal Processing (SSP)

作者： Thien Huynh-The Quoc-Viet Pham Thai-Hoc Vu Daniel Benevides da Costa Van-Phuc Hoang Department of Computer and Communication Engineering HCMC University of Technology and Education Vietnam Institute of System Integration Le Quy Don Technical University Vietnam School of Computer Science and Statistics Trinity College Dublin Ireland Department of Electrical Electronic and Computer Engineering University of Ulsan Republic of Korea Technology Innovation Institute Abu Dhabi United Arab Emirates

The paper presents an intelligent spectrum sensing approach for next-generation wireless networks by exploiting deep learning, in which we develop a deep convolutional network (ConvNet) to automatically identify Fifth Generation New Radio (5G NR) and Long-Term Evolution (LTE) signals under standards-specified channel models with diversified RF impairments. In particular, we design a semantic segmentation ConvNet to detect and localize the spectral content of 5G NR and LTE in a synthetic signal featured by spectrum occupancy. A received signal is first converted by a short-time Fourier transform and represented as a wideband spectrogram image which is then passed through the ConvNet, incorporated by DeepLabv3+ and ResNet18 to improve the accuracy of pixel-wise segmentation to further increase the accuracy of signal identification. In the simulations, our ConvNet achieves around 95% mean accuracy and 91% mean intersection-over-union (IoU) at medium SNR level and demonstrates robustness under various practical channel impairments.

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Detection of Human Stress Using Optimized Feature Selection and Classification in ECG Signals

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Mathematical Problems in engineering 2023年第1期2023卷

作者： Narmadha, G. Deivasigamani, S. Vellaisamy, Muthukumar Freitas, Lídio Inácio Ahmad, Badlishah R. Sakthivel, B. Department of Electrical and Electronics Engineering Sethu Institute of Technology Tamil Nadu Virudhunagar626115 India Faculty of Engineering Technology and Built Environment UCSI University Cheras Kuala Lumpur56000 Malaysia School of Engineering Faculty of Computing and Engineering Quest International University Ipoh Perak30250 Malaysia Department of Mechanical Engineering Dili Institute of Technology Dili Timor-leste Computer and Communication System University Malaysia Perlis Arau Malaysia Department of Electronics and Communication Engineering PSY Engineering College Tamil Nadu Sivagangai630561 India

An autonomic nervous system (ANS) of humans is majorly affected by psychological stress. The changes in ANS may cause several chronic diseases in humans. The electrocardiogram (ECG) signal is used to observe the variation in ANS. Numerous techniques are presented for an ECG stress signal handling feature extraction and classification. This work managed a heart rate variability feature acquired from smaller peak waveforms such as P, Q, S, and T waves. Also, the R peak is detected, which is a significant part of the ECG waveform. In this work, the proposed stress classification work has been categorized into two main processes: feature selection (FS) and classification. The main aim of the proposed work is to propose an optimized FS and classifier model for the detection of stress in ECG signals. The Metaheuristics model of the African vulture optimization (AVO) technique is presented to perform an FS. This selection is made to choose the required features and minimize the data for classification. The AVO-based modified Elman recurrent neural network (MERNN) technique is proposed to perform an efficient classification. The AVO is used for fine-tuning the weight of the MERNN technique. The experimental result of this technique is evaluated in terms of Recall (91.56%), Accuracy (92.43%), Precision (92.78%), and F1 score (95.86%). Thus, the proposed performance achieved a superior result than the conventional techniques. © 2023 Narmadha G et al.

关键词： Classification (of information)

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AI-aided power electronic converters automatic online real-time efficiency optimization method

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Fundamental Research 2025年第3期5卷 1111-1116页

作者： Tang, Yuanhong Cao, Di Xiao, Jian Jiang, Chenying Huang, Qi Li, Yunwei Chen, Zhe Blaabjerg, Frede Hu, Weihao Power System Wide-area Measurement and Control Sichuan Provincial Key Laboratory School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu 611731 China MORNSUN Guangzhou Science & Technology Co. Ltd. Guangzhou 510663 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu 611731 China Yangtze Delta Region Institute (Quzhou) University of Electronic Science and Technology of China Quzhou 324000 China Department of Chengdu State Grid Corporation of China Chengdu 610599 China Southwest University of Science and Technology Mianyang 621010 China Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6G 2V4 Canada Department of Energy Technology Aalborg University Aalborg 9220 Denmark

Energy losses during the conversion and supply of electric power are considered a significant issue and cannot be estimated. Improvement in the efficiency of energy conversion systems is highly restricted because of their internal nonlinearity and complexity. Thus, inspired by the successful utilization of robotic chemists, we demonstrate a pioneering concept of artificial intelligence (AI)-aided automatic online real-time optimization of a power electronics converter using a dual active bridge (DAB) converter as an example. An optimal modulation strategy was obtained through repeated automatic exploration experiments on a practical DAB converter platform. Specifically, the DAB experimental platform operated autonomously around the clock for approximately 71 h. It performed 120,000 consecutive experiments (12,000 episodes) within a six-variable experimental space driven by a deep deterministic policy gradient (DDPG) algorithm. The proposed AI-aided automatic online real-time optimization method achieved significantly improved efficiency of power conversion and supply. Consequently, zero carbon emissions may be obtained in the future. © 2023

关键词： Artificial intelligence Automatic online real-time exploration DC-DC converter Dual active bridge converter Energy conversion system Power electronics

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Satellite-pose estimation using IMU sensor data and Kalman filter with RF-433 Mhz powered communication and helical antenna design for ground station

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Measurement: Sensors 2024年 35卷

作者： Paneru, Biplov Adhikari, Ankit Paneru, Bishwash Shah, Krishna Bikram Chhetri Sapkota, Sanjog Poudyal, Ramhari Poudyal, Khem Narayan Department of Electronics and Communication Engineering Nepal Engineering College Affiliated to Pokhara University Bhaktapur Nepal Department of Mechanical and Aerospace Engineering Institute of Engineering Pulchowk Campus Affiliated to Tribhuvan University Lalitpur Nepal Department of Applied Sciences and Chemical Engineering Institute of Engineering Pulchowk Campus Affiliated to Tribhuvan University Lalitpur Nepal Department of Computer Science Engineering Nepal Engineering College Affiliated to Pokhara University Nepal Nepal Research and Collaboration Center Nepal Department of Information System Engineering HIST Engineering College Affiliated to Purbanchal University Nepal

Space research relies heavily on satellites, and technological developments are critical to the success of these missions. In order to estimate satellite pose, a simulation of an IMU sensor using Matplotlib is presented in this study. A roll, pitch, and yaw value analysis is performed on data obtained from the BNO-055 IMU sensor. A prototype ground station's helical antenna is used to investigate these parameters. For effective communication between the satellite body and ground station setup, a low-cost transceiver module RF-433Mhz module is connected to the ground station. IMUsensors are essential for inertial measurements in spacecraft, which support ground station monitoring. This study makes use of a four-turn helical antenna that is built for deployment and designed using the 4nec2 simulation program for RF-based satellite-ground station communication. The antenna's VSWR is 1.432, and its directivity is 18.581. Matplotlib is utilized to model variations in roll, pitch, and yaw values. Roll, pitch, and yaw ranges that are observed are −0.375 to 0.25, −0.375 to 0.5625, and −0.3125 to 0.1875, in that order. These ranges are useful in effectively visualizing the body pose of the CubeSat when connecting via PySerial to a Matplotlib-developed program. © 2024 The Authors

关键词： Kalman filters

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Generation of Multimode Orbital Angular Momentum Waves Using Wearable Array Antenna

Generation of Multimode Orbital Angular Momentum Waves Using...

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IEEE Symposium on Wireless Technology and Applications (ISWTA)

作者： Shehab Khan Noor Mohamed Nasrun Osman Mohd Najib Mohd Yasin Arif Mawardi Ismail Tanvir Islam Nurulazlina Ramli Advanced Communication Engineering (ACE) Centre of Excellence Faculty Electronic Engineering Technology University Malaysia Perlis Kangar Perlis Malaysia Department of Electrical and Computer Engineering University of Houston Houston TX USA Centre for Advanced Electrical & Electronic System (CAEES) Faculty of Engineering Built Environment and Information Technology SEGi University Petaling Jaya Selangor Malaysia

ISBN: (数字)9798350351354

ISBN: (纸本)9798350351361

In this paper, a flexible wearable antenna based on uniform circular array of four identical microstrip patch antennas on a felt textile substrate was designed to generate multiple orbital angular momentum (OAM) modes. The proposed antenna was simulated and analyzed to confirm the generation of OAM mode +1 and −1. The concept of reconfigurable method with 16 p-i-n diodes was implemented for the switching purpose. The proposed antenna covered overall bandwidth above 700 MHz for both the modes in free space while the obtained gain by mode 1 was 1.981 dBi and mode −1 was 1.993 dBi. Moreover, the mode purity analysis was carried out to verify the generation of OAM mode +1 and −1 with acceptable SAR value. The purity obtained was 47.78% for mode +1 and 47.91% for mode −1 at free space. This study shows that using textile antennas for modal multiplexing to produce multimode OAM waves is feasible in order to increase channel capacity and spectrum efficiency.

关键词： Space vehicles Wireless communication Orbital calculations Microstrip antenna arrays Switches Bandwidth Wearable antennas

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The Spreading Prediction and Severity Analysis of Blood Cancer Using Scale-Invariant Feature Transform

The Spreading Prediction and Severity Analysis of Blood Canc...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： Vazeer Ali Mohammed Mehmood Ali Mohammed Murtuza Ali Mohammed Rakesh Ramakrishnan J. Logeshwaran Department of Computer & Information System Lewis University Illinois USA Department of Information Technology University of The Cumberlands Williamsburg KY USA Department of Information Technology University of The Cumberlands Union City CA USA Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore Tamil Nadu INDIA

Blood cancer is an ever-growing global health concern. Early detection and accurate diagnosis are key to successful treatment and long-term prognosis of the disease. Despite various advances in medical technologies, current methods for blood cancer detection are far from perfect and often suffer from a lack of accuracy. This paper proposes the use of Scale-Invariant Feature Transform (SIFT) based approaches for predicting the spreading of blood cancer and assessing its severity. SIFT is a computer vision technique that extracts meaningful features from an image. Through SIFT, meaningful patterns in medical imaging datasets, such as those used for blood cancer detection, can be identified. The output from SIFT can then be used to build predictive models which can accurately predict the spread of the disease and its severity. Additionally, this paper explains how machine-learning algorithms and artificial neural networks can be used to further enhance the accuracy of these predictions. Finally, the proposed approach is validated using benchmark datasets and its accuracy is compared with existing methods for blood cancer prediction. The proposed model reached 89.71% accuracy, 89.25% precision, 90.71% recall and 89.40% f1-score results. The results show that prediction accuracy increases significantly when SIFT is used. It is concluded that SIFT can be a powerful tool for predicting blood cancer and estimating its severity.

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A Novel Predictive Analysis to Identify the Weather Impacts for Congenital Heart Disease Using Reinforcement Learning

A Novel Predictive Analysis to Identify the Weather Impacts ...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： Mehmood Ali Mohammed Rakesh Ramakrishnan Murtuza Ali Mohammed Vazeer Ali Mohammed J. Logeshwaran Department of Information Technology University of The Cumberlands Williamsburg KY USA Department of Information Technology University of The Cumberlands Union City CA USA Department of Computer & Information System Lewis University Illinois USA Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore Tamil Nadu INDIA

Reinforcement learning is a powerful approach for predictive analysis to identify the weather impacts for congenital heart disease. The key advantages of this method include the utilization of sensor data to predict the weather conditions on a daily basis and the ability to learn from feedback and adapt the predictions over time. In this paper, an innovation model has proposed by using reinforcement learning algorithm. It can gain important insights regarding the impact of weather on congenital heart disease. The predictive models developed using this approach have the potential to help medical professionals as well as the general public in better predicting and managing congenital heart disease. This could help reduce costs associated with care and ultimately improve health outcomes.

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Anaesthesia Monitoring: From Conventional to AI-Enabled Data Acquisition Methods

Anaesthesia Monitoring: From Conventional to AI-Enabled Data...

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Eurocon

作者： Xiaoxiao Liu Sean McGrath Colin Flanagan Yiming Lei Liaoyuan Zeng Electronic and Computer Engineering University of Limerick Limerick Ireland Ministry of education engineering research center on mobile digital hospital system School of electronics Peking University Beijing China Department of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

It is widely recognized that the timely acquisition and presentation of accurate physiological data is a vital aid to the anaesthesiologist in monitoring and maintaining the well-being of a patient undergoing surgery. Collecting, storing, synchronizing, integrating, evaluating, and retrieving pertinent information from patient data remains very difficult due to limited computer power, the need for specialized software, inconsistency between monitoring devices, and inadequate data storage. This paper introduces the general steps of data acquisition in anaesthesia and summarizes some data acquisition systems. It also introduces some applications of the burgeoning field of artificial intelligence (AI) to data acquisition, illustrating how AI techniques may be used to advantage to assist the anaesthesiologist in monitoring and interpreting such data.

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Perioperative Anesthesia Data: Visualization, Effects, Analysis with Artificial Intelligence

Perioperative Anesthesia Data: Visualization, Effects, Analy...

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Electronic communications, Internet of Things and Big Data (ICEIB), International Conference on

作者： Xiaoxiao Liu Sean McGrath Colin Flanagan Yiming Lei Liaoyuan Zeng Electronic and Computer Engineering University of Limerick Limerick Ireland School of Mathematics and Information HeBei University HeBei China Ministry of Education Engineering Research Center Mobile Digital Hospital System School of Electronics Peking University Beijing China Department of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

A comprehensive understanding of physiological data is essential for anesthesiologists to monitor and maintain the health of surgical patients. Understanding anesthetic signals is essential to improve over the past ten decades to comprehend a variety of diseases, ensure the safety of patients, and accelerate their recovery. We identified and summarized four themes of artificial intelligence (AI) research in anesthesia: applications of AI in anesthesiology, signals monitored in different anesthesia stages, commonly monitored signs, and data standardization. The effect of various AI technologies on data analysis was studied, and the types, functions, sensors, characteristics, and intelligent analysis of physiological signals monitored during anesthesia are presented in this article. AI influences anesthesia practices in many ways, including preoperative data evaluation, anesthetic depth monitoring, and postoperative event prediction.

关键词： Temperature sensors Temperature measurement Sensor phenomena and characterization Anesthesia Safety Biomedical monitoring Artificial intelligence

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Optical coherent dot-product chip for sophisticated deep learning regression

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Light(Science & Applications) 2021年第12期10卷 2330-2341页

作者： Shaofu Xu Jing Wang Haowen Shu Zhike Zhang Sicheng Yi Bowen Bai Xingjun Wang Jianguo Liu Weiwen Zou State Key Laboratory of Advanced Optical Communication Systems and Networks Intelligent Microwave Lightwave Integration Innovation Center(imLic)Department of Electronic EngineeringShanghai Jiao Tong University800 Dongchuan RoadShanghai 200240China State Key Laboratory of Advanced Optical Communications System and Networks Department of ElectronicsSchool of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China Institution of Semiconductors Chinese Academy of SciencesBeijing 100083China

Optical implementations of neural networks(ONNs)herald the next-generation high-speed and energy-efficient deep learning computing by harnessing the technical advantages of large bandwidth and high parallelism of ***,due to the problems of the incomplete numerical domain,limited hardware scale,or inadequate numerical accuracy,the majority of existing ONNs were studied for basic classification *** that regression is a fundamental form of deep learning and accounts for a large part of current artificial intelligence applications,it is necessary to master deep learning regression for further development and deployment of ***,we demonstrate a silicon-based optical coherent dot-product chip(OCDC)capable of completing deep learning regression *** OCDC adopts optical fields to carry out operations in the complete real-value domain instead of in only the positive *** reusing,a single chip conducts matrix multiplications and convolutions in neural networks of any ***,hardware deviations are compensated via in-situ backpropagation control provided the simplicity of chip ***,the OCDC meets the requirements for sophisticated regression tasks and we successfully demonstrate a representative neural network,the AUTOMAP(a cutting-edge neural network model for image reconstruction).The quality of reconstructed images by the OCDC and a 32-bit digital computer is *** the best of our knowledge,there is no precedent of performing such state-of-the-art regression tasks on ONN *** is anticipated that the OCDC can promote the novel accomplishment of ONNs in modern AI applications including autonomous driving,natural language processing,and scientific study.

关键词： neural hardware coherent

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