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Gait-Based User Identification for Smart Home: When Machine Learning Meets FBG Accelerometers

IEEE Transactions on Consumer Electronics 2024年

作者： Alhussein, Ghada Chi, Hao Ran Alberto, Nelia Antunes, Paulo Hadjileontiadis, Leontios Radwan, Ayman Domingues, M. Fatima Instituto de Telecomunicações-University of Aveiro Aveiro Portugal Instituto de Telecomunicações I3N and Dep. of Physics-University of Aveiro Aveiro Portugal Khalifa University of Science and Technology Department of Biomedical Engineering and Biotechnology Abu Dhabi United Arab Emirates Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki Greece Instituto de Telecomunicações Instituto Superior Tècnico -University of Lisbon Lisbon Portugal

Advancements in fiber optic sensor technology and telecommunications have paved the way for innovative health monitoring systems. However, previous works in this area have often been limited by the lack of comprehensive datasets, hindering the development of accurate and robust solutions. This paper addresses this gap by presenting a novel dataset and approach to gait-based user identification using a set of four optical Fiber Bragg Grating (FBG) sensor-based accelerometers, integrated into smart home environments. By computing features such as entropy, mean, standard deviation, kurtosis, and skewness from raw signals, and employing three unsupervised machine learning models-K-means, DB-scan, and Gaussian Mixture Model (GMM)-we achieve high accuracy in distinguishing individuals. Our dataset results for each sensor are as follows: for Sensor 1, K-means with a Silhouette Score of 0.3038 and Davies-Bouldin Index of 1.1238;for Sensor 2, K-means with a Silhouette Score of 0.3543 and Davies-Bouldin Index of 0.9659;for Sensor 3, DB-scan with a Silhouette Score of 0.2650 and Davies-Bouldin Index of 0.6855;and for Sensor 4, K-means with a Silhouette Score of 0.3949 and Davies-Bouldin Index of 1.0438. This approach not only enhances user identification but also facilitates personalized healthcare applications and unobtrusive monitoring. © 1975-2011 IEEE.

关键词： Smart homes

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Human Behaviour Impact to Use of Smartphones with the Python Implementation Using Naive Bayesian 11

Human Behaviour Impact to Use of Smartphones with the Python...

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11th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2020

作者： Talha, Iftakhar Mohammad Salehin, Imrus Debnath, Susanta Chandra Saifuzzaman, Mohd. Moon, Ms. Nazmun Nessa Nur, Fernaz Narin Daffodil International University Dept. of Computer Science and Engineering Sobhanbag Mirpur Rd Dhaka1207 Bangladesh Notre Dame University Dep. of Computer Science and Engineering 2/A Toyenbee Circular Rd Dhaka1000 Bangladesh

ISBN: (纸本)9781728168517

A change of behavior in special groups and many sustainable smart populations increasing day by day for excessive uses of smartphones. In recent years, the use of smartphones and mental imbalances have become a major problem with increasing negative effects. In our study, we find out the major problem of the negative side and its different sources like mental imbalance, stress, dep.ession, loneliness, etc. Bayes' theorem and classifier, support vector machine, special data set of human behavior, and probability are used to calculate accuracy. For collecting data from three major sections, we use the physical methods, virtual methods, and medical reports. So, a vast data set is trained by data to compare method, and also probability is used for predicting the validity of the data model. Naive Bayes' theorem accurate 71% positive which is indicated the negative impact of human behavior. Based on the SVM classifier, we separate the barrier between the impact of positive and negative data. In SVM, we set up a parameter to measure negative and positive values. Python library function is a major component to calculate all instructions and also use for data training. Finally, we compare the results obtained by our proposed specialization with the results obtained from the three baseline landmarks. © 2020 IEEE.

关键词： Smartphones

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The Realization Factors of Taiwan's Low Altitude Airspace Economy and UAM: Analysis and Evaluation

The Realization Factors of Taiwan's Low Altitude Airspace Ec...

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IEEE International Conference on e-Business engineering (ICEBE)

作者： Tung-Ling Wu Chih-Ming Kung Guan-Ting Lin Kuen-Yu Tsai Guang-Yun Meng Yu-Che Huang Wan-Shu Cheng Institute of Transportation at Ministry of Transportation and Communications Taiwan R.O.C. Dep. Of Information Technology&Communication Shin Chin Univeristy Kaohsiung Campus Taiwan R.O.C. Dep. of Computer Science and Information Management Providence University Taichung Taiwan R.O.C. Department of Electrical Engineering National Taiwan Univeristy Taipei Taiwan R.O.C. Executive Officer Center for Research on High-Performance Remote Sensing and Urban Informatics NTU Taiwan R.O.C.

ISBN: (数字)9798350365856

ISBN: (纸本)9798350365863

Low altitude airspace economy applications will be of highly effective in a wide range of civil activities and will be a critical element in the smart cities of the future. In view of the main features of unmanned aerial vehicle (UAV), such as automatic piloting system, access to uninhabited areas, low cost compared to road and manned aircraft, shorter delivery time, and the ability to carry heavy payloads, it is important to recognize that UAVs can be used for a wide range of civil activities. With the development of these applications in full swing around the world, in addition to advanced communications technology and other related infrastructure, business demand and a favorable investment climate, clarity, definiteness and feasible regulations in the Taiwanese environment are also essential. Based on the premise of local economic strength, the technical level of the telecommunication industry, and commercial demands, Taiwan should establish a rational standard for the UAV regulatory system as soon as possible, so as to facilitate the development of UAM and to ensure the international competitiveness of the related technologies for market development,

关键词： Industries Technological innovation Transportation Autonomous aerial vehicles Regulation Communications technology Aircraft Standards Drones Investment

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Predicting the dep.ession Level of Excessive Use of Mobile Phone: Decision Tree and Linear Regression Algorithm 2

Predicting the Depression Level of Excessive Use of Mobile P...

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2nd International Conference on Sustainable engineering and Creative Computing, ICSECC 2020

作者： Salehin, Imrus Talha, Iftakhar Mohammad Moon, Nazmun Nessa Saifuzzaman, Mohd Nur, Fernaz Narin Akter, Mariom Dept. of Computer Science and Engineering Daffodil International University Sobhanbag Mirpur Rd Dhaka1207 Bangladesh Dep. of Computer Science and Engineering Notre Dame University Bangladesh 2/A Toyenbee Circular Rd Dhaka1000 Bangladesh

ISBN: (纸本)9781728175881

In this study, we apply advanced machine learning and regression analysis to find out the dep.ession level. The use of extreme mobile phones will impact human behavior. dep.ession prediction is the main factor in our research. The dep.essive brain emits cortisol, cytokines, and serotonin;these three components are most important for the brain to maintain emotion and behavior. We measure the dep.ession level using machine learning and data mining algorithms. We have done the whole work in the research area of medical science and information technology and also built up a collaboration. In this study, we focus on the strength of the algorithm and calculate the accuracy with python programming. The result expresses that smart mobile devices change the human brain day by day if they spend more time around 10 to 13 hours a day. Finally, we observed that a man or woman is slowly going through a dep.ession for the impact of the excessive mobile operates. In our study, a strength calculation method is a novel approach to finding out dep.ession level using Probability, Linear regression, Decision tree, and Naive Bayes. For the accuracy of our work, we have used three types of algorithms to find the optimal ratio and percentage © 2020 IEEE.

关键词： Decision trees

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Convolutional Neural Network Based Deep Neural Network Model for Electrocardiogram Records Classification 23

Convolutional Neural Network Based Deep Neural Network Model...

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Proceedings of the 7th International Conference on Future Networks and Distributed systems

作者： Ahmed Mahmood Khudhur Mohammed Hasan Alwan Qayssar Al Omairi Omar Abdulkareem Mahmood Yousif I Hammadi Mohammed Saleh Ali Muthanna Ahmed Aziz Ammar Muthanna Information Technology Department Kirkuk University Iraq Department of Communications Engineering University of Diyala Iraq Department of Computer Engineering University of Diyala Iraq Dep. of Medical Instruments Engineering Techniques Bilad Alrafidain University College Iraq Institute of Computer Technologies and Information Security Southern Federal University Russia Department of computer science Benha university Egypt and International Business Management Department Tashkent State University of Economics Uzbekistan Department of Telecommunication Networks and Data Transmission The Bonch Bruevich Saint Petersburg State University of Telecommunications Russia and Department of Applied Probability and Informatics Peoples Friendship University of Russia Russia

ISBN: (纸本)9798400709036

Сardiovascular 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 dep.etion 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 dep.ete resources.

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Modelling Value-oriented Legal Reasoning in LOGIKEY

arXiv

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arXiv 2020年

作者： Benzmüller, Christoph Fuenmayor, David Lomfeld, Bertram University of Bamberg AI Systems Engineering Freie Universität Berlin Dep. of Mathematics and Computer Science Germany Université du Luxembourg Dep. of Computer Science Freie Universität Berlin Dep. of Mathematics and Computer Science Germany Freie Universität Berlin Dep. of Law Germany

The logico-pluralist LOGIKEY knowledge engineering methodology and framework is applied to the modelling of a theory of legal balancing in which legal knowledge (cases and laws) is encoded by utilising context-dep.ndent value preferences. The theory obtained is then used to formalise, automatically evaluate, and reconstruct illustrative property law cases (involving appropriation of wild animals) within the Isabelle/HOL proof assistant system, illustrating how LOGIKEY can harness interactive and automated theorem proving technology to provide a testbed for the development and formal verification of legal domain-specific languages and theories. Modelling value-oriented legal reasoning in that framework, we establish novel bridges between latest research in knowledge representation and reasoning in non-classical logics, automated theorem proving, and applications in legal *** Codes 68T01 68T27 68T30 03Axx 03B16 03B35 03B45 03B60 03B70 Copyright © 2020, The Authors. All rights reserved.

关键词： Theorem proving

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Reconfigurable Intelligent Surfaces and DF-relay Improved Spectral Efficiency in Cognitive Radio Networks

Reconfigurable Intelligent Surfaces and DF-relay Improved Sp...

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IEEE Conference on Vehicular Technology (VTC)

作者： Abderahmane El Mettiti Mohamed Saber Abdellah Chehri Hasna Chaibi Abdel Badaoui Rachid Saadane EHTP LRIT-GSCM Faculty of Sciences University Mohamed V Laboratory Engineering System SIRC/LAGeS EHTP Casablanca Morocco Dep. Math. and Computer Science Royal Military College Kingston Canada Genuis Laboratory Supmti Rabat Morocco Laboratory LASTIMI Mohammed V University Casablanca Morocco

Cognitive radio (CR) is considered a primary technology for spectrum usage efficiency and dynamic spectrum management. Spectrum sensing is an essential cognitive radio cycle function. Recently, reconfigurable intelligent surfaces (RIS) technology has emerged as a promising new enabler of 6G wireless communication, with the ability to control signal propagation and increase signal coverage and spectrum management. This paper investigates how integrating RISs with CR can improve spectral efficiency. First, we optimize the RIS parameters by identifying the optimal transmit powers and number of RIS antennas. Then, we compare the novel RIS technology with the traditional decode-and-forward (DF) relaying technology. Next, we investigate how adopting RIS can enhance spectral efficiency. Finally, we demonstrate how integrating the novel technology of reconfigurable intelligent surfaces with CR can significantly increase spectrum detection, the most crucial step in the cognitive radio process. Simulation studies indicate that RIS is more energy-efficient than SISO and DF-relay communications in various setups. Moreover, by boosting spectrum sensing capability in CR situations, RISs can significantly enhance the efficiency of frequency spectrum usage.

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Learning Based Model Predictive Control for Quadcopters with Dual Gaussian Process

arXiv

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arXiv 2021年

作者： Liu, Yuhan Tóth, Roland The Harbin Institute of Technology Department of Control Science and Engineering Harbin China The Eindhoven University of Tech. Dep. of Electrical Eng. Control Systems Group Eindhoven Netherlands The Institute for Computer Science and Control Budapest Hungary

An important issue in quadcopter control is that an accurate dynamic model of the system is nonlinear, complex, and costly to obtain. This limits achievable control performance in practice. Gaussian process (GP) based estimation is an effective tool to learn unknown dynamics from input/output data. However, conventional GP-based control methods often ignore the computational cost associated with accumulating data during the operation of the system and how to handle forgetting in continuous adaption. In this paper, we present a novel Dual Gaussian Process (DGP) based model predictive control strategy that improves the performance of a quadcopter during trajectory tracking. The bio-inspired DGP structure is a combination of a long-term GP and a short-term GP, where the long-term GP is used to keep the learned knowledge in memory and the short-term GP is employed to rapidly compensate unknown dynamics during online operation. Furthermore, a novel recursive online update strategy for the short-term GP is proposed to successively improve the learnt model during online operation in an efficient way. Numerical simulations are used to demonstrate the effectiveness of the proposed strategy. © 2021, CC BY-NC-SA.

关键词： Model predictive control

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Inverting gradients - how easy is it to break privacy in federated learning? 20

Inverting gradients - how easy is it to break privacy in fed...

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Proceedings of the 34th International Conference on Neural Information Processing systems

作者： Jonas Geiping Hartmut Bauermeister Hannah Dröge Michael Moeller Dep. of Electrical Engineering and Computer Science University of Siegen

ISBN: (纸本)9781713829546

The idea of federated learning is to collaboratively train a neural network on a server. Each user receives the current weights of the network and in turns sends parameter updates (gradients) based on local data. This protocol has been designed not only to train neural networks data-efficiently, but also to provide privacy benefits for users, as their input data remains on device and only parameter gradients are shared. But how secure is sharing parameter gradients? Previous attacks have provided a false sense of security, by succeeding only in contrived settings - even for a single image. However, by exploiting a magnitude-invariant loss along with optimization strategies based on adversarial attacks, we show that is is actually possible to faithfully reconstruct images at high resolution from the knowledge of their parameter gradients, and demonstrate that such a break of privacy is possible even for trained deep networks. We analyze the effects of architecture as well as parameters on the difficulty of reconstructing an input image and prove that any input to a fully connected layer can be reconstructed analytically indep.ndent of the remaining architecture. Finally we discuss settings encountered in practice and show that even aggregating gradients over several iterations or several images does not guarantee the user's privacy in federated learning applications.

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EarCV: An open-source, computer vision package for maize ear phenotyping

Plant Phenome Journal

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Plant Phenome Journal 2022年第1期5卷

作者： Gonzalez, Juan M. Ghosh, Nayanika Colantonio, Vincent Pereira, Francielly de Cássia Pinto, Ricardo A. Wasson, Chase Leach, Kristen A. Resende, Marcio F. R. Plant Cellular & Molecular Biology Dep. Univ. of Florida Gainesville 32611 FL United States Computer & Information Science & Engineering Dep. Univ. of Florida Gainesville 32611 FL United States Horticultural Sciences Dep. Univ. of Florida Gainesville 32611 FL United States Federal Univ. of Lavras Minas Gerais Brazil Horticultural Sciences Dep. Federal Univ. of Lavras 3200 E Canal St. S Belle Glade 33430 FL United States

Fresh market sweet corn (Zea mays L.) is a row crop commercialized as a vegetable, resulting in strict expectations for ear size, color, and shape. Ear phenotyping in breeding programs is typically done manually and can be subjective, time consuming, and unreliable. computer vision tools have enabled an inexpensive, high-throughput, and quantitative alternative to phenotyping in agriculture. Here we present a computer vision tool using open-source Python and OpenCV to measure yield component and quality traits relevant to sweet corn from photographs. This tool increases accuracy and efficiency in phenotyping through high-throughput, quantitative feature extraction of traits typically measured qualitatively. EarCV worked in variable lighting and background conditions, such as under full sun and shade and against grass and dirt backgrounds. The package compares ears in images taken at varying distances and accurately measures ear length and ear width. It can measure traits that were previously difficult to quantify such as color, tip fill, taper, and curvature. EarCV allows users to phenotype any number of ears, dried or fresh, in any orientation while tolerating some debris and silk noise. The tool can categorize husked ears according to the predefined USDA quality grades based on length and tip fill. We show that the information generated from this computer vision approach can be incorporated into breeding programs by analyzing hybrid ears, capturing heritability of yield component traits, and detecting phenotypic differences between cultivars that conventional yield measurements cannot. Ultimately, computer vision can reduce the cost and resources dedicated to phenotyping in breeding programs. © 2022 The Authors. The Plant Phenome Journal published by Wiley Periodicals LLC on behalf of American Society of Agronomy and Crop science Society of America.

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