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information Technology (ACIT)

作者： Rabia Emhamed Al Mamlook Ahmad Nasayreh Hasan Gharaibeh Mohammad Aljaidi Qais Al-Na'amneh Dalia Alzu'bi Hanin Bzizi Abdelrahman Abdeltawab Dep. of Business Administration Trine University Indiana USA Department of Industrial Engineering University Zawia Tripoli Libia Dep. Computer Science Yarmouk University Irbid Jordan Dep. of Computer Science Zarqa University Zarqa Jordan Dep. of Cyber Security Applied Science Private University Amman Jordan Dep. Information and Systems Engineering Concordia University Montreal Canada Dep of Biomedical Science Western Michigan University Kalamazoo MI USA Faculty of pharmacy Bani Sweif university Bani Suwayf Egypt

This paper searches into the convergence of such ad-vanced techniques with architectures like DenseNet201, VGG16, InceptionResNetV2, and NasNetMobile. Our focus centers on harnessing deep learning capabilities for the precise detection of eye ailments. Preliminary findings spotlight a notable uplift in diagnostic precision. Among the tested algorithms, the Nas-NetMobile architecture emerged superior, boasting an impressive accuracy rate of 92% on the chosen dataset. Through this work, we extend a significant advancement in the realm of automated eye disease diagnosis, potentially equipping medical professionals with sharper, swifter diagnostic tools. The outcomes presented herein could play a seminal role in sculpting more sophisticated and precise diagnostic approaches within ophthalmology.

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A Multivariate Functional Analysis of Inter-Hands Spatiotemporal Coordination in Human Bimanual Tasks and its Implications for Robotics

A Multivariate Functional Analysis of Inter-Hands Spatiotemp...

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International Conference on Autonomous Robots and Agents, ICARA

作者： Marco Baracca Andre Meixner Giuseppe Averta Paolo Salaris Tamim Asfour Matteo Bianchi Dept. of Information Engineering Research Center “E. Piaggio” University of Pisa Pisa Italy Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany Dep. of Control and Computer Engineering Politecnico di Torino Turin Italy

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Dual-arm manipulation is a key enabler for significantly enhancing the interaction between humans and robots, and their capabilities to purposefully shape the surrounding environment. However, the spatiotemporal coordination between the motion of the hands required for this type of actions makes their planning not trivial. A proper definition of these coordination patterns moving from the human example could simplify their translation on the robot side, fostering the generation of effective bimanual tasks. In this work, we propose Multivariate functional Principal Component Analysis (MfPCA) as a mathematical tool to encode inter-hands temporal kinematic covariations in terms of principal spatiotemporal coordination patterns in the Cartesian domain. We compared these patterns extracted from a dataset of human bimanual tasks with those resulting from the usage of classical fPCA, applied indep.ndently to each hand (univariate fPCA). We found that MfPCA allows for a better classification of the tasks, with respect to a state of the art taxonomy. For what concerns motion planning, MfPCA and fPCA yield similar accuracy in the reconstruction of the motion, but with a smaller number of principal components needed in the MfPCA case. These results, although preliminary, can open interesting perspectives for the usage of MfPCA for human-like bimanual motion planning and control of robotic manipulators, as well as for action recognition, to enable a more effective human-robot interaction.

关键词： Hands Translation Shape Robot kinematics Taxonomy Human-robot interaction Functional analysis Planning Spatiotemporal phenomena Principal component analysis

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Recurrent Penalized Splines for Real-Time Forecasting of the Parameters of Technical systems

Recurrent Penalized Splines for Real-Time Forecasting of the...

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International Conference on Industrial engineering, Applications and Manufacturing (ICIEAM)

作者： Elena Kochegurova Anastasiia Kaida Maria Galkina Information Systems and Technologies Dep. Tomsk Polytechnic University Tomsk Russian Federation School of Nuclear Science and Engineering Tomsk Polytechnic University Tomsk Russian Federation

Forecasting time series (TS) from their historical data is used in many preventive monitoring and decision support systems in industry. For systems operating in real-time, an urgent problem is to reduce forecasting time without compromising other quality indexes (accuracy and trend continuation). The purpose of this paper is to describe a forecasting model based on a recurrent penalized P-spline. The spline model has a compact computational scheme where the efficiency is achieved by using a short input of historical data a simple mathematical description. The model was compared to 11 most well-known forecasting algorithms. The performance of the proposed model compared quite well to the competitive forecasting algorithms. The model was used to forecast a real-world TS of a technological parameter of a boiler plant.

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Consistent Event-Triggered Consensus on Complete Graphs

Consistent Event-Triggered Consensus on Complete Graphs

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IEEE Conference on Decision and control

作者： Duarte J. Antunes David Meister Toru Namerikawa Frank Allgöwer W.P.M.H. Heemels Dep. of Mechanical Eng. Eindhoven Control Systems Technology Group University of Technology the Netherlands Institute for Systems Theory and Automatic Control University of Stuttgart Germany School of Integrated Design Engineering Graduate School of Science and Technology Keio University Japan

This paper starts by considering an optimal control formulation of the consensus problem on complete graphs with a cost capturing disagreement and agents modeled by integrators. An optimal control policy for this problem is shown to be the well-known consensus algorithm by which each agent resets its state to the average of its and other agents' state values received at every time step. The framework is extended to the case where agents can only exchange information periodically, with a period larger than one. Then an event-triggered control strategy is proposed that results in a better cost than that of the optimal periodic one with the same average transmission rate, that is, it is consistent. According to this strategy, each agent distributedly transmits its state if the error between its current state and a common consensus estimate based on previously transmitted agents' data exceeds a threshold. Simulation results are presented to illustrate the proposed strategy.

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Solutions for Digital Interaction of a Resilient Energy Community in a Service-oriented Framework 12

Solutions for Digital Interaction of a Resilient Energy Comm...

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12th International Conference and Exposition on Electrical and Power engineering, EPE 2022

作者： Sanduleac, Mihai Ionescu, Constantin Mandis, Alexandru Gropa, Victor Efremov, Cristina Sanduleac, Vlad Department of Power Systems Faculty of Energy Engineering University Politehnica of Bucharest Bucharest Romania Dep. of Energy Generation and Use Faculty of Energy Engineering University Politehnica of Bucharest Bucharest Romania Faculty of Energetics and Electrical Engineering Technical University of Moldova Chisinau Moldova Department of Computer Science and Engineering Faculty of Automatic Control and Computers University Politehnica of Bucharest Romania

ISBN: (数字)9781665489942

ISBN: (纸本)9781665489942

Energy communities are emerging entities which need their own information and Communication System. Resilience is a key metric of such communities, and it has to be implemented for both energy supply versus public network outages and for its information system versus cyber-attacks, acting as cyber-citadels which have to resist external malicious attacks while having also digital interactions with external entities. The paper presents solutions for a resilient energy community with appropriate implementations of digital interaction for data exchange, in order to acquire external energy service for specialized companies and to deliver also information and communication-related services for external users. Principles of a Contractual Data Protection Regulation are presented, as an adaptation of data protection tailored for automated energy services and based on secure data exchange. The principles are verified in a demonstration project which has also external entities with digital interaction. © 20XX IEEE.

关键词： Cybersecurity

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Analysis of voltage mode quadrature oscillator based on fin field-effect transistor

Journal of Engineering Research (Kuwait)

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Journal of engineering Research (Kuwait) 2024年

作者： Mohammed, Arsen Ahmed Demi̇rel, Hüseyi̇n Engineering Faculty Electrical dep. Kirkuk Iraq Yıldırım Beyazıt University Business School Dept. of Management Information Systems Ankara 06760 Turkey

This study introduces a new voltage mode sinusoidal quadrature oscillator (V-M.S.Q.O.) that employs multioutput Z copy voltage differencing transconductance amplifiers (M-Zc-VDTA) as an active filter based on FinFET transistor. The suggested circuits have low passive and active sensitivities, and they may be constructed as integrated circuits using only two M-Zc-VDTAs and three grounded capacitors. The circuits produce two (VM) sinusoidal quadrature oscillator voltage results by adjusting the electronic base current and frequency of the oscillation condition separately. FinFET technology lowers power consumption while improving circuit performance. They can also produce amplitude shift keying-amplitude modulation signals for communication electronics systems and provide electrical basis control of output current amplitude. Using the Cadence Virtuoso tool with a 7 nm parameter, simulation results were obtained for the suggested circuits. © 2024 The Authors

关键词： Cadence virtuoso program Fin type Field Effect Transistor (FinFETs) Internal transconductances (gm) Quadrature sinusoidal oscillator (QSO) Voltage mode sinusoidal quadrature oscillator (VMQSO)

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TAXINOMISIS: A cloud – based platform for risk profiling and patient specific management of the carotid artery disease

TAXINOMISIS: A cloud – based platform for risk profiling an...

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

作者： Vassilis D. Tsakanikas Panagiotis K. Siogkas Vassiliki T. Potsika Antonis I. Sakellarios Dimitris S. Pleouras Vassiliki I. Kigka Themis P. Exarchos Igor B. Koncar Dimitrios I. Fotiadis Dep. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina Ioannina GR Greece Unit of Medical Technology and Intelligent Information Systems University of Ioannina Ioannina Greece Biomedical Research Institute Foundation for Research and Technology Hellas (FORTH)

Carotid Artery Disease is a complex multi-disciplinary medical condition causing strokes and several other disfunctions to individuals. Within this work, a cloud - based platform is proposed for clinicians and medical doctors that provides a comprehensive risk assessment tool for carotid artery disease. It includes three modeling levels: baseline data-driven risk assessment, blood flow simulations and plaque progression modeling. The proposed models, which have been validated through a wide set of studies within the TAXINOMISIS project, are delivered to the end users through an easy-to-use cloud platform. The architecture and the dep.oyment of this platform includes interfaces for handling the electronic patient record, the 3D arterial reconstruction, blood flow simulations and risk assessment reporting. TAXINOMISIS, compared with both similar software approaches and with the current clinical workflow, assists clinicians to treat patients more effectively and more accurately by providing innovative and validated *** Relevance - Asymptomatic carotid artery disease is a prevalent condition that affects a significant portion of the population, leading to an increased risk of stroke and other cardiovascular events. Early detection and appropriate treatment of this condition can significantly reduce the risk of adverse outcomes and improve patient outcomes. The development of a software tool to assist clinicians in the assessment and management of asymptomatic patients with carotid artery disease is therefore of great clinical relevance. By providing a comprehensive and reliable assessment of the disease and its risk factors, this tool will enable clinicians to make informed decisions regarding patient management and treatment. The impact of this tool on patient outcomes and the reduction of healthcare costs will be of great importance to both patients and the healthcare system.

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Smart Human Resource Management System to Maximize Productivity

Smart Human Resource Management System to Maximize Productiv...

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2020 International Computer Symposium, ICS 2020

作者： H.a.s.s, Hewage K.u, Hettiarachchi K.m.j.b, Jayarathna K.p.c., Hasintha Senarathne, A.N. Wijekoon, J. Sri Lanka Institute of Information Technology Dep. of Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dep. of Computer Systems Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dep. of Information Technolgy Malabe Sri Lanka

ISBN: (纸本)9781728192550

Human resource is one of the most valuable assets in an organization. They are bounded to develop the unique and dynamic aspects that strengthen their competitive advantage to persist in an always changing market environment. In order to recruit a quality candidate for an organization, reducing human involvement and verifying details of the candidate is important in recruitment process. Furthermore, having an idea about how well or poor the employees perform, and how likely the employee attrition can occur is vital in human resource management process. This paper is an attempt to introduce smart human resource management system that can maximize the productivity of an organizational environment using machine learning and blockchain technologies. The end goal of this research is a smart human resource management system that reduces human judgment, time in the candidate selection process and predicts employee performance and attrition to motivate current employers to maximize productivity with minimal financial loss in the workplace environment. Skill assessment and resume classification have been done using unsupervised learning algorithms and natural language processing after extracting raw data from employee resumes using Object Character Recognition. Candidate details verification is done by comparing the hashes of the records which are stored in the blockchain. Employee performance and attrition are predicted using supervised machine learning classification techniques with high accuracy and the result of the final performance is generated as a score for each employee considering the multiple attributes that has been standardized and regulated by some specifically considered e-competence frameworks. © 2020 IEEE.

关键词： Human resource management

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Intelligent Recognition of Immune Cells: Analysis and Research

Intelligent Recognition of Immune Cells: Analysis and Resear...

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IEEE International Conference on e-Business engineering (ICEBE)

作者： Chih-En Li Wei-Lun Liu Kun-Yi Lai Yu-Hsiang Chiang Xiaowei Yan Hao-Xiang Chen Hsu-Ching Li Wan-Shu Cheng Department of Electrical Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan Institute for Systems Biology Seattle Washington U.S.A Dep. of Computer Science and Information Management Providence University Taichung Taiwan

Immunotherapy utilizes the potential of the patient's immune system to fight disease. T-lymphocytes have become the central focus of the immune system in the fight against cancer. One of the fundamental studies in immunotherapy is how to accurately identify various types of immune cells. Therefore, the study of immune-related cells will be an important reference for the treatment of COVID19 or other related diseases. In this paper, a series of experiments were conducted with COVID-19 patients as the starting point to design a strategy to identify the important subtypes of CD$4^{+}$T cells. In this paper, an intelligent machine learning algorithm is proposed according to the results of different experiments of linear and nonlinear dimension reduction algorithms, integrating the important characteristics of these two algorithms and improving the performance. Our experimental results show that the algorithm designed based on the front-end experiments can effectively distinguish the important lymphocytes. The proposed algorithm not only improves the performance, but also the dimension reduction procedure keeps the important features of both global and regional data. The experimental results of this paper will be beneficial for the subsequent analysis of the clustering in identifying important subtypes of CD$4^{+}$T cells. Most importantly, this wm significantly improve the procedure of manually integrating and labeling the recorded data after several tedious and monotonous analyses with external software.

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Secure Password Using EEG-based BrainPrint System: Unlock Smartphone Password Using Brain-Computer Interface Technology

Secure Password Using EEG-based BrainPrint System: Unlock Sm...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zuwaina Alkhyeli Ayesha Alshehhi Mazna Alhemeiri Salma Aldhanhani Khalil AlBalushi Fatima Ali AlNuaimi Abdelkader Nasreddine Belkacem Dep. Computer Science and Software Engineering CIT UAE University Al Ain UAE Dep. Information Systems and Security CIT UAE University Al Ain UAE Dep. Computer & Network Engineering CIT United Arab Emirates University Al Ain UAE TII Autonomous Robotics Research Center Abu Dhabi UAE

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

As security becomes a strong factor in daily activities, finding secure ways to unlock machines and smartphones is a challenge due to hardware limitations and the high risk of hacking. Considering the level of security and privacy in the digital world, attackers tend to be one step ahead. Therefore, this technical paper introduces a brain-computer interface (BCI) for increasing subject-based security using unique biometric features as a solution to build complex passwords. The BCI measures brain changes and extracts relevant bio-features from each subject using non-invasive electroencephalogram (EEG) tests. The proposed system allows users to gain access to their devices using brain waves (bypass) instead of inserting their password manually (normal path), which saves the user time and upgrades the level of privacy as no physical actions are required during this process. This system is also well suited for individuals with mobility impairments. We used the P300-based BCI controlling paradigm which dep.nds on reading the electrical brain activity of the user when observing a particular object. The other feature of the system is that it can extract unique features of each individual brain to produce a network that uniquely identifies them, which is used as a security layer. Users need to enter their unique network to access their device with failed attempts requiring an EEG test to identify the user. The system plays an active role in facilitating user processes for authentication while accessing devices. The system establishes an urgent call whenever the user’s brain currents command it to. The project outcomes were assessed by simulating the BCI before real-time implementation to determine errors and resolve the validity of the project scope.

关键词： Privacy Authentication Passwords Feature extraction Brain-computer interfaces Electroencephalography Security

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