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Early career representative column: Recap of commissions HGE meet the experts session

URSI Radio science Bulletin 2023年第378期2021卷 91-92页

作者： Nosherwan Shoaib Miroslav Hanzelka Claudia Martinez Bruce Fritz Chaouki Kasmi Research Institute for Microwave and Millimeter-Wave Studies (RIMMS) School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST) H-12 Islamabad Pakistan

Commission H led a joint session with Commissions E and G at the 2022 AT-AP-RASC meeting entitled "Meet the Experts." The session was designed especially to give early career scientists an opportunity to hear the career perspectives from three established researchers in their respective fields. Each expert gave a 20 minute presentation about their experiences and thoughts on the present state of the field, as well as where the future of the research field is headed. The formal presentations were then followed by an hour of questions from the audience.

关键词： engineering profession Microwave technology Technological innovation Microwave imaging Magnetosphere Tomography Storms

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Expression of Concern for: Applying Machine Learning Techniques to Increase Real-Time Data Analysis Accuracy

Expression of Concern for: Applying Machine Learning Techniq...

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Emerging research in Computational science (ICERCS), International Conference on

作者： Preeti Naval Mahesh T R Ajay Kumar Lovish Dhingra Janakiraman V Chitrakant O. Banchhor Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India

ISBN: (数字)9798350359763

ISBN: (纸本)9798350359770

facts analysis examines, transforms, and models record sets to uncover valuable data and help make higher selections. Machine mastering techniques allow computer systems to automatically learn from records to make predictions primarily based on previously discovered patterns. By using these strategies, businesses can grow actual-time records analysis accuracy and see tendencies faster, allowing them to promptly respond to adjustments inside the surroundings. This paper will speak about using system studying techniques for actual-time statistics evaluation and the benefits of using such strategies. Mainly, techniques that include supervised studying, unsupervised learning, and deep studying will be discussed. Moreover, use instances in which system mastering may be applied, including economic fraud detection software programs, digital advertising and marketing, and predictive preservation, will be explored. Eventually, the outcomes of using system mastering strategies for data analysis, including statistics privateness and safety issues, can be mentioned.

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Realizing RF Wavefront Copying with RIS for Future Extended Reality Applications

arXiv

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arXiv 2024年

作者： Tsimpoukis, Stavros Tyrovolas, Dimitrios Ioannidis, Sotiris Akyildiz, Ian F. Karagiannidis, George K. Liaskos, Christos Institute of Computer Science Foundation for Research and Technology Hellas Greece Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki54124 Greece Dept. of Electrical and Computer Engineering Technical University of Crete Chania Greece Truva Inc. AlpharettaGA30022 United States Lebanon Lebanon Computer Science Engineering Department University of Ioannina Ioannina Greece

Lately a new approach to Extended Reality (XR), denoted as XR-RF, has been proposed which is realized by combining Radio Frequency (RF) Imaging and programmable wireless environments (PWEs). RF Imaging is a technique that aims to detect geometric and material features of an object through RF waves. On the other hand, the PWE focuses on the the conversion of the wireless RF propagation in a controllable, by software, entity through the utilization of Reconfigurable Intelligent Surfaces (RISs), which can have a controllable interaction with impinging RF waves. In that sense, this dynamic synergy leverages the potential of RF Imaging to detect the structure of an object through RF wavefronts and the PWE’s ability to selectively replicate those RF wavefronts from one spatial location to wherever an XR-RF mobile user is presently located. Then the captured wavefront, through appropriate hardware, is mapped to the visual representation of the object through machine learning models. As a key aspect of the XR-RF’s system workflow is the wavefront copying mechanism, this work introduces a new PWE configuration algorithm for XR-RF. Moreover, it is shown that the waveform replication process inevitably yields imprecision in the replication process. After statistical analysis, based on simulation results, it is shown that this imprecision can be effectively modeled by the gamma distribution. © 2024, CC BY.

关键词： Wavefronts

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Enhancing LiDAR Scene Upsampling with Instance-aware Feature-embedding and Attention Mechanism

Enhancing LiDAR Scene Upsampling with Instance-aware Feature...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Wei-Jen Wang You-Sheng Do Wen-Chieh Lin Chieh-Chih Wang College of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Industrial Technology Research Institute Hsinchu Taiwan

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Scanning LiDAR is one of the widely used sensors in autonomous vehicles; however, the inherent sparsity of LiDAR point clouds often affects its performance. To address this issue, upsampling methods could be employed to enhance low-resolution LiDAR data. Although there have been methods on upsampling of single-object point clouds recently in computer vision, they tend to generate a considerable amount of artifacts when dealing with real-world LiDAR scenes consisting of multiple objects. In this paper, we propose a solution to tackle this problem by introducing an instance embedding auxiliary task and a context attention module. With our auxiliary learning architecture, the network can learn features that benefit both the primary upsampling task and the auxiliary instance embedding task. This training design enables the point generation process to be carried out separately and significantly reduces artifacts of the upsampling results on the SemanticKITTI dataset, particularly in areas surrounding instances. By leveraging these techniques to improve the model’s understanding of the relationship between objects and the background in LiDAR scenes, we achieve an overall 4% to 10% improvement in whole-scene upsampling.

关键词： Training Point cloud compression Laser radar Attention mechanisms Shape Refining Contrastive learning Feature extraction Sensors Intelligent robots

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Applying Machine Learning Techniques to Increase Real-Time Data Analysis Accuracy

Applying Machine Learning Techniques to Increase Real-Time D...

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Emerging research in Computational science (ICERCS), International Conference on

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Enhancing Thyroid Disease Diagnosis Through Machine Learning: A Classification-Based Approach

Enhancing Thyroid Disease Diagnosis Through Machine Learning...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Shanti Verma Hetal Modi Jaynesh H Desai Khyati Rami Lokmanya Institute of Management and Computer Application Gujarat Technological University Ahmedabad Gujarat Bhagwan Mahavir College of Computer Application Bhagwan Mahavir University Surat Gujarat Vidyadeep University Surat Gujarat Department of Computer Science SAL Institute of Technology & Engineering Research Ahmedabad Gujarat

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

Thyroid disease has recently adopted the related classification which has been worthy in the contemporary system of healthcare delivery because of the advances in diagnostics. This work proposes an automated diagnostic system for thyroid disease using machine learning approach to improve the diagnostic accuracy of the disease. The presented model also uses the features selection and classifiers like SVM and Random Forest for the classification of different thyroid disorders including hypothyroidism, hyperthyroidism, and euthyroidism. Missing values in the dataset are treated to provide actual worth in the results while imbalance is also dealt with. The process involves feeding its input a well-constructed data set after which it is checked for its performance through a validation process. The performance of the model is evaluated based on thyroid disease related parameters and the findings show a better performance compared to conventional diagnosis techniques. In this study, the authors also present their future work on how to possibly incorporate this machine learning framework into clinical practice although the study shows how this framework will help the healthcare workers to make better decisions in relation to thyroid diseases.

关键词： Support vector machines Pathology Accuracy Vectors Medical diagnosis Stress Random forests Monitoring Thyroid Diseases

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Characteristic Modes for Orthogonal Beamsteering Using ESPAR

Characteristic Modes for Orthogonal Beamsteering Using ESPAR

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Antennas and Propagation & USNC/URSI National Radio science Meeting

作者： Zixiang Han Yujie Zhang Shanpu Shen Tianrui Qiao Junhui Rao Jing Jin Qixing Wang Ross Murch Future Research Lab China Mobile Research Institute Beijing China Department of Electrical and Computer Engineering National University of Singapore Singapore Department of Electrical Engineering and Electronics University of Liverpool U.K. Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong SAR

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

A method based on the Theory of Characteristic Modes (TCM) for finding orthogonal beamsteering radiation patterns of electronically steerable parasitic array radiator (ESPAR) is proposed. This method can be used to design single-radio-frequency (RF) multiple-input multiple-output (MIMO) systems for Internet-of-Things (IoT) applications in next-generation wireless communication. It can directly generate a set of symmetrical and orthogonal pattern basis for beamsteering purpose and demodulated reference signal transmission. In addition, the input impedances of ESPAR are the same for all pattern basis so that impedance matching can be achieved by a fixed matching network. Simulation example of a 5-element circular dipole ESPAR is provided to demonstrate the effectiveness of the proposed method.

关键词： Wireless communication Radio frequency Simulation Impedance matching Scattering parameters Internet of Things MIMO communication

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Edge Computing for Distributed and Cooperative Media Algorithms in Connected Networks

Edge Computing for Distributed and Cooperative Media Algorit...

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International Conference on Computing and Networking technology (ICCNT)

作者： V. Lilly Grace Anchal Gupta Rakesh Kumar Giri Tanuja Satish Dhope Mani Sarma Vittapu Dharmesh Dhabliya Department of computer science and engineering G.pullaiah college of engineering and technology Kurnool Andhra Pradesh India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Computer Science & Engineering Sunrise University Alwar India Electronics and Communication Bharati Vidyapeeth (Deemed to be University) College of Engineering Pune Maharashtra India Computer Science and Engineering Mallareddy Engineering College and Management Sciences Kistapur Village Medchal Hyderabad Telangana India Department of Information Technology Vishwakarma Institute of Information Technology Pune Maharashtra India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Side Computing is a rising technique used for connected networks’ allotted and cooperative media algorithms. Part Computing refers to processing operations being carried out near the source of the facts, generally close to the edge of the network, in place in a centralized cloud. It allows information to be processed quickly, securely, and reliably in remote or low-connectivity locations, allowing dispensed computing operations inside connected networks. This distributed machine enables latency when working with significant facts sets and enhances security and reliability. Cooperating media algorithms, including laptop vision, audio processing, or natural language processing, are frequently optimized using aspect Computing in related networks. The advantages of facet Computing allow for progressed scalability, green helpful resource utilization, and better latency performance, mainly in areas that suffer from restrained or negative networking situations and bandwidth. Moreover, aspect computing enables enhanced privacy, protection, and personal revel when handling sensitive information and data stored within the cloud.

关键词： Performance evaluation Portable computers Scalability Media Natural language processing Reliability Security Resource management Protection Edge computing

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Compatible Secure Key Distribution in Passive Optical Network based on Fiber Polarization Reciprocity

Compatible Secure Key Distribution in Passive Optical Networ...

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OptoElectronics and Communications Conference, OECC

作者： Da Lin Taihang Qiu Lei Deng Qi Yang Zhijun Yan Xiaoxiao Dai Xiaojing Gao Mengfan Cheng National Engineering Research Center for Next Generation Internet Access System School of Optical and Electronic Information Huazhong University of Science and Technology (HUST) Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China The School of Computer Science China University of Geosciences Wuhan China

In this work, we propose using Delta-Sigma modulation (DSM) techniques to improve the traditional channel reciprocal key distribution scheme in access network scenarios. The proposed approach relies only on digital fi... 详细信息

ISBN: (数字)9798350308396

ISBN: (纸本)9798350308402

关键词： Optical fiber polarization Point-to-multipoint communications Optical filters Optical polarization Filtering Delta-sigma modulation Passive optical networks

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Towards Reliable and Energy-Efficient Communication Protocols for Wireless Body Area Networks in Medical Applications

Towards Reliable and Energy-Efficient Communication Protocol...

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Automation and Computation (AUTOCOM), International Conference on

作者： Jayaprakash B J. Premkumar Neeraj Das Ankit Punia Subrat Kumar Nayak Varun Ojha School of Sciences Computer Science & IT JAIN (Deemed to be University) Bangalore Karnataka India Department of Biomedical Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Wireless body area networks are an attractive technology for measuring health data in medical applications. Several challenges exist in designing and implementing such reliable, energy-efficient communication protocols for these networks. This new strategy solution strategy is based on a hybrid communication protocol that simultaneously obtains the positive aspects of centralized and distributed structures. By centralizing network maintenance and control functions such as health monitoring of nodes and transmission control, the proposed protocol supports reliable data transmission with minimal energy consumption in WBANs. This reduces the overhead and collisions, giving the network better reliability. Unlike other protocols mentioned where the data flow from the sensor node to the central node is at least distributed, in our case, a few nodes send their data at a time to the Central Node. It enables low-power communication since nodes only need to wake up during their allotted transmission time slot. Through simulations, we demonstrate that the new protocol surpasses existing protocols in reliability and energy consumption performance. It can also help to minimize the energy consumption of WBAN nodes, which makes it an appropriate candidate for long-time medical monitoring applications. All in all, this work helps to pave the way for the efficient use of WBANs in a wide range of medical applications that require robustness and low-energy communications.

关键词： Wireless communication Biomedical equipment Wireless sensor networks Energy consumption Protocols Medical services Body area networks Reliability engineering Energy efficiency Reliability

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