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Innovation and Intelligence for informatics, Computing, and Technologies (3ICT), International Conference o

作者： Hassaan Th. H. Thabet Aqeel A. Al-Hilali Haitham B. Qasim Thabit H. Thabit Firas M. Mahmood Dept. of Medical Instruments Techniques Engineering Al-Hadba University College Mosul Iraq Dept. of Medical Instruments Engineering Techniques Al-Farahidi University Baghdad Iraq Dept. of Computer Science Al-Esraa University College Baghdad Iraq Dept. of Computer Engineering and Information Ninevah University Mosul Iraq Dept. of Informatics and Software Engineering Cihan University-Erbil Iraq

The presence of water in human life is crucial, and God has blessed humans much with its availability. This paper tries to reduce the amount of water that people use for washing in all aspects of daily life and instead utilize it to irrigate gardens and maintain hygienic facilities. The importance of water as a fundamental ingredient and the range of social and age groups that regularly take advantage of the washing process made it an ideal location for water waste during the washing process. This is why the authors choose this region to conduct their research. The monitoring and management of water usage during the washing process are simulated using PLC techniques.

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Delayed dynamic-feedback controller design for multi-frequency vibration suppression

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IFAC-PapersOnLine 2024年第27期58卷 196-201页

作者： Adrian Saldanha Adam Peichl Wim Michiels Tomas Vyhlidal Department of Computer Science KU Leuven Celestijnenlaan 200A 3001 Leuven Belgium Dept. Instrumentation and Control Engineering Faculty of Mechanical Engineering and Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague 6 Czechia

We present a methodology for designing a dynamic controller with delayed output feedback for achieving non-collocated vibration suppression with a focus on the multi-frequency case. To synthesize the delay-based controller, we first remodel the system of equations as a delay-differential algebraic equation (DDAE) in such a way that existing tools for design of a static output feedback controller can be easily adapted. The problem of achieving non-collocated vibration suppression with sufficient damping is formulated as a constrained optimization problem of minimizing the spectral abscissa in the presence of zero-location constraints, with the constraints exhibiting polynomial dependence on its parameters. We transform the problem into an unconstrained one using elimination, following which we solve the resulting non-convex, non-smooth optimization problem.

关键词： vibration suppression time-delay DDAE

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Normal Vector Direction-based 3D LiDAR Point Cloud Planar Surface Removal for Object Cluster Minimization in Human Activity Monitoring System

Normal Vector Direction-based 3D LiDAR Point Cloud Planar Su...

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IEEE Instrumentation and Measurement Technology Conference

作者： Nova Eka Budiyanta Eko Mulyanto Yuniarno Tsuyoshi Usagawa Mauridhi Hery Purnomo Dept. of Electrical Engineering Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Electrical Engineering Dept. of Computer Engineering Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Computer Science and Electrical Engineering Graduate School of Science and Technology Kumamoto University Kumamoto Japan Dept. of Electrical Engineering Dept. of Computer Engineering Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Institut Teknologi Sepuluh Nopember University Center of Excellence on Artificial Intelligence for Healthcare and Society (UCE AIHeS) Surabaya Indonesia

The use of 3-Dimensional Light Detection and Ranging (3D LiDAR) point cloud as the alternative data to reduce privacy exposure in monitoring systems has been carried out in several studies. Unfortunately, various challenges in using point clouds intersect with the amount of data and computational costs. Several studies attempted to optimize the point cloud processing approach by segmenting the ground plane to get the object clusters separated. However, many unnecessary points can still burden the computation process. Since the ground plane mainly represents the horizontal planar plane on the x, y axis, this study tried to reduce the points on the vertical planar plane on the x, z and y, z axes with the x, y horizontal planar plane as well based on the surface normal vector direction of each point. The proposed approach has successfully reduced the raw point cloud by 79.29% removing the point cloud that indicates the planar surface of the three axes while maintaining the essential object of the monitoring system on the KITTI raw dataset. Therefore, the object cluster can be minimized, supporting the computational costs for further research in human activity monitoring systems.

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Enhanced Driver Stress Prediction from Multiple Biosignals via CNN Encoder-Decoder Model

Enhanced Driver Stress Prediction from Multiple Biosignals v...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Vishal Singh Roha Maggie Ezzat Gaber Gendy Kaveti Pavan Thomas M. Deserno Nagarajan Ganapathy Mehmet R. Yuce Dept. of Electrical & Computer Systems Monash University Melbourne Australia Dept. of Biomedical Engineering Indian Institute of Technology Hyderabad Hyderabad India PLRI Lab for Medical Informatics Technical University of Braunschweig Brunswick Germany

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

In this work, we present PhysioFuseNet, a novel framework designed to enhance driver stress state classification. PhysioFuseNet integrates a CNN-based encoder-decoder model with multimodal biosignal fusion. Using a driving simulator, different multimodal signals were acquired, namely electrocardiography, electrodermal activity, photoplethysmography, and respiration rate from (N = 25) healthy subjects. The experiment is of 35 minutes duration and contains different stress states (baseline (5 minutes), while normal, cognitive, and emotional sessions for 10 minutes). Multimodal features are extracted and employed in an encoder-decoder network. Extracted encoder features are combined through intermediate fusion and fed to support vector machine (SVM) and random forest (RF) classifiers. Experimental results demonstrate the efficacy of our approach, outperforming previous methods by achieving accuracies of 0.95 and 0.94 for SVM and RF, respectively. Notably, the framework excels in classifying emotional and cognitive stress states. In summary, the proposed framework could be useful in stress assessment in real-time and clinical conditions.

关键词： Support vector machines Radio frequency Biological system modeling Anxiety disorders Predictive models Electrocardiography Feature extraction Photoplethysmography Real-time systems engineering in medicine and biology

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A Web and Mobile Application to Identify the Availability of Drinking Water Information During a Water Crisis

A Web and Mobile Application to Identify the Availability of...

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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Razeeth, Suhail Shihara, Fathima Pavithra, Kasuni Kaliyamoorthy, Maran Nawaz, Samsudeen Sabraz Kariyapper, R.K.A.R. South Eastern University of Sri Lanka Dept. of Information and Communication Technology Sri Lanka Uva Wellassa University of Sri Lanka Dept. of Computer Science and Informatics Sri Lanka Sri Sai Ram Institute of Management Studies Sri Sai Ram Engineering College India South Eastern University of Sri Lanka Department of Management and Information Technology Sri Lanka

ISBN: (纸本)9798350394580

Sri Lanka faces drinking water related issues often due to many reasons like weather changes, floods, droughts, and tsunamis, affecting the drinking water availability. In such instances, people find difficult to gather information regarding consumable drinking water availability. In order to facilitate the meeting of commercial water sellers and customers and to engage the general public and social services in addressing the drinking water problem, this study is focused on building both web and mobile applications. The web application was implemented with a selected set of functionalities such as social login, social sharing, geocoding, reverse geocoding, viewing, and filtering posts using Angular 6, *** web framework, and Leaflet. The mobile application was developed to direct the customers to the water availability location with a shortest path using the Dijkstra's algorithm. Case evaluation was conducted with the Rotaract Club members in Badulla and thinking aloud method was used to pick up the user's ideas while interacting with the system. It can be concluded that the created web and mobile applications would offer the public a clever way to draw attention to an issue with water availability, and the commercial water service providers are given a venue to market their services and locate potential customers. © 2023 IEEE.

关键词： crowdsourcing GIS mobile application water availability web application

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REVISITING SILHOUETTE AGGREGATION

arXiv

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arXiv 2024年

作者： Pavlopoulos, John Vardakas, Georgios Likas, Aristidis Department of Informatics Athens University of Economics and Business Archimedes Athena RC Greece Dept. of Computer Science & Engineering University of Ioannina IoanninaGR 45110 Greece

Silhouette coefficient is an established internal clustering evaluation measure that produces a score per data point, assessing the quality of its clustering assignment. To assess the quality of the clustering of the whole dataset, the scores of all the points in the dataset are typically (micro) averaged into a single value. An alternative path, however, that is rarely employed, is to average first at the cluster level and then (macro) average across clusters. As we illustrate in this work with a synthetic example, the typical micro-averaging strategy is sensitive to cluster imbalance while the overlooked macro-averaging strategy is far more robust. By investigating macro-Silhouette further, we find that uniform sub-sampling, the only available strategy in existing libraries, harms the measure’s robustness against imbalance. We address this issue by proposing a per-cluster sampling method. An experimental study on eight real-world datasets is then used to analyse both coefficients in two clustering tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Cluster analysis

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A Comparative Analysis between Sentiment Analysis Methods in Stock Prediction of IoT-based companies

A Comparative Analysis between Sentiment Analysis Methods in...

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IEEE World Symposium on Communication engineering (WSCE)

作者： Georgia E. Pasoi Kyriacos A. Talattinis Vasileios A. Memos Sotirios K. Goudos Panagiotis G. Sarigiannidis Yutaka Ishibashi Konstantinos E. Psannis Dept. of Applied Informatics University of Macedonia Thessaloniki Greece Dept. of Physics Aristotle University of Thessaloniki Thessaloniki Greece Dept. of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Dept. of Business Management Faculty of Business Administration Aichi Sangyo University Aichi Japan

ISBN: (数字)9798331542825

ISBN: (纸本)9798331542832

Predictions in financial markets have attracted much of scholars’ interest. Since the stock market is generally defined as dynamic and volatile due to the affect of the well-known "Internet of Things" (IoT). It is a fact that IoT significantly impacts the financial market and stocks in various ways. Therefore, numerous researchers have attempted to detect patterns regarding the way financial markets react to various external factors. In the last decade, the integration of machine learning and sentiment analysis has appeared to be an effective approach for improving IoT-based stock price predictions in the financial field. This paper intends to propose an approach involving financial news as a source of information for forecasting the stock movement of noteworthy IoT-related companies, employing Big Data Analytics (BDA), Machine Learning, and Sentiment Analysis methods as well. A comparative analysis is conducted between lexicon-based and machine learning-based methods of sentiment analysis about their performance, particularly in the domain of IoT-related stock market prediction.

关键词： Measurement Sentiment analysis Recurrent neural networks Companies Lexicon Big Data Internet of Things Reliability Stock markets Software development management

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Velocity-aided Channel Estimation for Spatially Selective mmWave Massive MIMO Communications

Velocity-aided Channel Estimation for Spatially Selective mm...

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European Signal Processing Conference (EUSIPCO)

作者： Evangelos Vlachos Christos Mavrokefalidis Kostas Berberidis Industrial Systems Institute ATHENA Research Center Patras Greece Dept. of Computer Engineering and Informatics University of Patras Patras Greece

This paper focuses on addressing the challenge of estimating multiple-input multiple-output (MIMO) channels for wireless communication between a ground base-station and a moving vehicle. One recently recognised model for time-varying channels incorporates spatial selectivity, which is referred to as beam squint, and is particularly relevant in the millimeter-wave (mmWave) range. In such scenarios, it is essential to account for the beam squint when attempting to recover channel parameters using a training sequence. However, the use of a training sequence alone may be insufficient for this purpose. To overcome this issue, in this work, we propose a channel estimation approach that exploits information provided by the control module of the vehicle, namely its velocity. The estimation problem that is designed, regards the channel both in a parametric and a non-parametric form and the alternating direction method of multipliers is utilised to efficiently solve it. It is demonstrated via simulations that considerable gains can be achieved if information from the control unit of the vehicle can be appropriately introduced and exploited.

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CONMIQ Multicast: A Scalable Multicast Video Streaming in LTE Networks 4

CONMIQ Multicast: A Scalable Multicast Video Streaming in LT...

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4th International Conference on Vocational Education and Electrical engineering, ICVEE 2021

作者： Cholilur Rochman, Muhammad Iqbal Anistyasari, Yeni National Taiwan University of Science and Technology Dept. of Computer Science and Information Engineering Taipei Taiwan Universitas Negeri Surabaya Dept. of Informatics Surabaya Indonesia

ISBN: (纸本)9781665405997

Multicast transmission is a recurrent problem in wireless networking as the system has to cater to multiple numbers of users at the same time. Furthermore, video payload adds more complexity to the problem, i.e., video needs to be delivered in a timely and orderly manner. Using H.264/SVC standard for scalable video, scaling could be used to compromise the quality and the size of the video, yet it poses a complex dependency problem. We propose a multicast scheme named CONMIQ (Constrained Non-linear Model of Incremental Quality) that provides suboptimal video quality to all users and still fulfill resource requirement. The solution will be applied to LTE-A(Long-Term Evolution-Advanced) network, which provides robust user channel quality assessment and a better OFDMA channel. To find the maximum video quality, we model the video quality of each video block in a GOP (Group of Pictures) as a second-degree polynomial function, then solve the maximization problem on that function, along with the considerations of resource constraints, video block dependency, and the varying channel condition of subscribed users. The experimental results verify that our suboptimal solution outperforms naive approaches while still performing comparably well than the much slower optimal solution. © 2021 IEEE.

关键词： Video streaming

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Neuromorphic Camera Assisted High-Flow Imaging Cytometry for Particle Classification

Neuromorphic Camera Assisted High-Flow Imaging Cytometry for...

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Conference on Lasers and Electro-Optics Europe (CLEO EUROPE)

作者： Ioannis Tsilikas Stavros Deligiannidis Aris Tsirigotis Georgios N. Tsigaridas Adonis Bogris Charis Mesaritakis Dept. Physics National Technical University of Athens Zografou Athens Greece Dept. Information and Communication Systems Engineering University of the Aegean Karlovasi Samos Greece Dept. Informatics and Computer Engineering University of West Attica Egaleo Greece

ISBN: (纸本)9798350345995

In this work we combine a high flow cytometry experimental setup and a 10Kframe/sec capable neuromorphic event-based camera, followed by lightweight machine learning schemes, thus allowing the simultaneous imaging and real-time classification of test particles, moving at a speed of 0.8m/sec with an accuracy of 97.6%. The key advantage of the utilized microscopy system, is the use of an event-based camera, generating spiking events, triggered by pixel's contrast changes. This bio-inspired operation, contrary to conventional CMOS cameras [1], alleviates bandwidth constraints and can significantly boost frame-rate capabilities, thus capturing high speed events. Following this paradigm, medical imaging modalities, where the detection and analysis of fast-moving particles is a necessity, such as high-flow cytometry, can greatly proliferate from the proposed approach.

关键词： Biomedical imaging Cameras CMOS cameras Cytometry Machine learning Real time imaging

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