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International Conference on Information, Intelligence, Systems and Applications (IISA)

作者： Dimitrios Meimetis Ioannis Daramouskas Vaios Lappas Isidoros Perikos Vaggelis Kapoulas Michael Paraskevas Department of Aerospace Science & Technology NKUA Athens Greece Computer Technology Institute and Press “Diophantus” Patras Greece Applied Mechanics Lab UoP Patras Greece Computer Engineering and Informatics Department UoP Patras Greece Electrical and Computer Engineering Dept. Computer Technology Institute and Press “Diophantus” University of Peloponnese Patras Greece

ISBN: (数字)9798350368833

ISBN: (纸本)9798350368840

Unmanned aerial vehicles (UAVs) have garnered significant attention from the research community during the last decade, due to their diverse capabilities and potential applications. One of the most critical functions that drones must execute efficiently is navigation in real-world environments. This paper presents a decentralized approach for enabling unmanned aerial vehicles (UAVs) to navigate safely in unknown environments and avoid obstacles. Leveraging the Optimal Reciprocal Collision Avoidance (ORCA) algorithm, implemented in the Robot Operating System (ROS), our method facilitates conflict detection and resolution in 2D environments. Through simulations using ROS, Gazebo, and Iris drones, we validate the effectiveness of our approach in scenarios with initial trajectory conflicts. Our work addresses the pressing need for UAVs to autonomously plan and execute safe flights, laying the groundwork for enhanced UAV capabilities in various real-world applications. The simulation results demonstrate the efficiency and robustness of our approach.

关键词： Simulation Autonomous aerial vehicles Aircraft navigation Sensor systems Robustness Real-time systems Trajectory Safety Sensors Drones

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Towards the Selection of Distance Metrics for k-NN Classifier in Students' Performance Prediction Modeling

Towards the Selection of Distance Metrics for k-NN Classifie...

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Computing (ICOCO), IEEE International Conference on

作者： Ijaz Khan Mohammed Hazli Mohamed Zabil Abdul Rahim Ahmad Nafaa Jabeur Department of Computing College of Computing and Informatics' Univsiti Tenaga Nasional Kajang Malaysia Information Technology Dept. Buraimi University College Al-Buraimi Oman Deparmtne of Computing and Informatics' Universiti Tenaga Nasional (UniTen) Kajang Malaysia Computer Science Dept. German University of Technology Muscat Oman

This paper investigates the impact of changing distance metrics on the performance of the k-NN classifier. The study investigates the variation in models performance with changing distance metric and value of k in the context of students' performance prediction models. The research utilizes datasets specifically designed for students' performance prediction modeling. Starting with a I-NN model, the experiments increment the value of k by 2 until the size of the dataset is reached. The experiments are repeated with different distance metrics derived from Minkowski derivation, including Euclidean, Manhattan, and Chebyshev. The findings indicate that there is no unanimously dominant distance metric for every dataset. However, the Euclidean and Manhattan distance metrics emerge effective, while Chebyshev exhibits lower performance. The research concludes Euclidean and Manhattan distance metrics as appropriate metrics for students' performance prediction modeling using the k-NN classifier.

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Automatic Parkinson's tremor assessment with data analysis from daily activities 6

Automatic Parkinson's tremor assessment with data analysis f...

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6th South-East Europe Design Automation, computer Engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2021

作者： Fiska, Vasiliki Gakilazou, Vasiliki Katertsidis, Nikolaos Tzallas, Alexandros T. Giannakeas, Nikolaos Tsipouras, Markos G. Dept. of Electrical and Computer Engineering Univ. of Western Macedonia Kozani Greece Dept. of Informatics and Telecommunications Univ. of Ioannina Arta Greece Science and Technology Park of Epirus UNIVEYE IKE Ioannina Greece

ISBN: (纸本)9781665427425

A new approach for the assessment of motor symptoms caused by Parkinson's disease (PD), based on data recorded using a smart mobile phone, is presented in this manuscript. Data were obtained from the online platform ***, and were analyzed based on machine learning techniques to produce a comprehensive physician report, presenting motor symptoms in everyday life. The idea of this study is to equip PD patients with a smartphone, which will monitor signals in real time for a period of time in order to enhance the medical diagnosis routine of a physician, providing information about the overall picture of the patient's motor condition, resulting in the provision of individualized treatment to the patient. © 2021 IEEE

关键词： Patient treatment

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Fall Detection System for Elderly (FDS-E) using Low-cost Camera Based on LSTM and OpenPose

Fall Detection System for Elderly (FDS-E) using Low-cost Cam...

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IEEE Instrumentation and Measurement Technology Conference

作者： Herti Miawarni Tri Arief Sardjono Eko Setijadi Wijayanti 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 Biomedical Engineering Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Architecture Faculty of Engineering Universitas Diponegoro Semarang Indonesia 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

Recently, many countries have been dealing with an increasing amount of elderly who are living alone. Unfortunately, fall incidents in the elderly tend to happen, and without proper dependable care would lead to fatal injuries. The elderly must be closely monitored under constant observation. Accidental falls are a leading cause of injury, mortality, and mobility limitations in the elderly. The expenses of national healthcare systems are also significantly impacted by accidental falls. These findings highlight the importance of investing heavily in the study and advancement of fall detection and intervention technologies. It is essential to recognize them early and provide fast assistance by developing technologies that can simultaneously improve the standard and the safety of the elderly's living environment. In this paper, we present a computer vision algorithm for falling incident detection using a low-cost camera and deep learning model. Ultimately, this algorithm can be used for the cases of elderly, in which falling risk is high and fatal. Furthermore, one self-developed dataset was used to validate the suggested approach experimentally. We used an OpenPose-based feature using a low-cost camera input and then classified each event or activity as either fall or non-fall. Classification tasks are carried out by Long Short-Term Memory (LSTM) with multiple validation metrics. The LSTM in this work is also optimized using Bayesian method. Finally, our approach to recognize falls has accuracy of 99.5%, which is distinct from other past works.

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Implementation and Application of an Intelligibility Protocol for Interaction with an LLM

arXiv

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arXiv 2024年

作者： Srinivasan, Ashwin Bania, Karan Shreyas, V. Mestha, Harshvardhan Liu, Sidong Dept. of Computer Science & Information Systems BITS Pilani Goa Campus Goa India APPCAIR BITS Pilani Goa India Dept. of Chemistry BITS Pilani Goa Campus Goa India Dept. of Electrical and Electronics Engineering BITS Pilani Goa Campus Goa India Centre for Health Informatics Macquarie University Sydney Australia

Our interest is in constructing interactive systems involving a human-expert interacting with a machine learning engine on data analysis tasks. This is of relevance when addressing complex problems arising in areas of science, the environment, medicine and so on, which are not immediately amenable to the usual methods of statistical or mathematical modelling. In such situations, it is possible that harnessing human expertise and creativity to modern machine-learning capabilities of identifying patterns by constructing new internal representations of the data may provide some insight to possible solutions. In this paper, we examine the implementation of an abstract protocol developed for interaction between agents, each capable of constructing predictions and explanations. The PXP protocol, described in [12] is motivated by the notion of "two-way intelligibility" and is specified using a pair of communicating finite-state machines. While the formalisation allows the authors to prove several properties about the protocol, no implementation was presented. Here, we address this shortcoming for the case in which one of the agents acts as a "generator" using a large language model (LLM) and the other is an agent that acts as a "tester" using either a human-expert, or a proxy for a human-expert (for example, a database compiled using human-expertise). We believe these use-cases will be a widely applicable form of interaction for problems of the kind mentioned above. We present an algorithmic description of general-purpose implementation, and conduct preliminary experiments on its use in two different areas (radiology and drug-discovery). The experimental results provide early evidence in support of the protocol’s capability of capturing one- and two-way intelligibility in human-LLM in the manner proposed in [12]. © 2024, CC BY.

关键词： Machine learning

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10-point connector formed by two snap buttons

10-point connector formed by two snap buttons

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Chang-Ming Yang Shu-Cing Wu Rally Kao Kuo-Cheng Liao Ze-We Wu Shih-Hung Chen Chi-Chun Chen Ming Young Biomedical Corp Miaoli Taiwan Program Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology Taichung Taiwan Bonanza Biotechnology Co.Ltd Dept. of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung Taiwan Dept. of Electronic Engineering National Chin-Yi University of Technology Taichung Taiwan

With the rapid advancement of electronic textiles, the design and integration of connectors have emerged as pivotal challenges. This study emphasizes the development of a connection solution that amalgamates miniaturization, efficiency, and reliability, specifically tailored for Flexible Conductive Silicone Circuits. A diversified connection scheme is proposed, incorporating elements such as snaps, Pogo pins, and conductive hook-and-loop fasteners, thereby achieving an innovative and pragmatic design. Through meticulous manufacturing processes, including the utilization of hot-melt adhesive, PET films, conductive fabric, and other multilayer materials, a connection system with low impedance and commendable conductivity was successfully engineered. Impedance test outcomes have indicated that the resistance at each connection point is less than 0.7 ohms, substantiating its superior electrical properties. In terms of assembly, this research demonstrates the precise interconnection of the controller and conductive fabric circuit, ensured by metal pin technology, leading to connection stability. This solution not only furnishes appropriate connection technology for specific application scenarios but also opens new avenues and directions for connection design within the domain of electronic textiles.

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Robust Self-Guided Deep Image Prior

Robust Self-Guided Deep Image Prior

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Evan Bell Shijun Liang Qing Qu Saiprasad Ravishankar Dept. of Mathematics Michigan State University East Lansing MI Dept. of Biomedical Engineering Michigan State University East Lansing MI Dept. of Electrical and Computer Engineering University of Michigan Ann Arbor MI Dept. of Computational Mathematics Science and Engineering Michigan State University East Lansing MI

In this work, we study the deep image prior (DIP) for reconstruction problems in magnetic resonance imaging (MRI). DIP has become a popular approach for image reconstruction, where it recovers the clear image by fitting an overparameterized convolutional neural network (CNN) to the corrupted/undersampled measurements. To improve the performance of DIP, recent work shows that using a reference image as an input often leads to improved reconstruction results compared to vanilla DIP with random input. However, obtaining the reference input image often requires supervision and hence is difficult in practice. In this work, we propose a self-guided reconstruction scheme that uses no training data other than the set of undersampled measurements to simultaneously estimate the network weights and input (reference). We introduce a new regularization that aids the joint estimation by requiring the CNN to act as a powerful denoiser. The proposed self-guided method gives significantly improved image reconstructions for MRI with limited measurements compared to the conventional DIP and the reference-guided method while eliminating the need for any additional data.

关键词： Training Weight measurement Magnetic resonance imaging Supervised learning Training data Convolutional neural networks Task analysis

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Chaotic American zebra search optimization algorithm for benchmark challenges 15

Chaotic American zebra search optimization algorithm for ben...

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15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Kamboj, Vikram Kumar Krishna, Ardhala Bala Verma, Chaman Raboaca, Maria Simona Filote, Constantin Stepanova, Maria Schulich School of Engineering University of Calgary AB Canada Malla Reddy Engineering College for Women Dept. of Electrcical and Electronics Engineering Hyderabad India Eötvös Loránd University Faculty of Informatics Dept. of Media and Edu. Informatics Budapest Hungary National Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department Ramnicu Valcea240050 Romania Stefan Cel Mare University of Suceava Faculty of Electrical Engineering and Computer Science Suceava720229 Romania Bauman Moscow State Technical University Engineering Business and Management Faculty Russia

ISBN: (纸本)9798350321388

In this research, we presented a modification to the recently developed swarm premised American zebra search optimization algorithm (AZOA) using chaos. The particular Singer mapping was proposed by the so-called chaotic AZOA and was already known to perform better in optimization. Twelve uni, multi modal benchmark functions, three-bar truss and ten bar truss optimal structural designs were tested. The chaotic AZOA with Singer map would perform more effectively, more consistently, and quicker than the classical AZOA and other recent metaheuristics in optimization, according to the results, which also supported the viability of the modifications. It was discussed if the chaotic AZAO could be optimized with the original AZOA, and the chaotic AZOA was suggested for use in applications for actual engineering challenges. © 2023 IEEE.

关键词： Mapping

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Co-Design of Approximate Multilayer Perceptron for Ultra-Resource Constrained Printed Circuits

arXiv

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arXiv 2023年

作者： Armeniakos, Giorgos Zervakis, Georgios Soudris, Dimitrios Tahoori, Mehdi B. Henkel, Jörg The School of Electrical and Computer Engineering National Technical University of Athens Athens15780 Greece Karlsruhe76131 Germany The Computer Engineering & Informatics Dept. University of Patras Patras26504 Greece Department of Computer Science Kit Karlsruhe76131 Germany

Printed Electronics (PE) exhibits on-demand, extremely lowcost hardware due to its additive manufacturing process, enabling machine learning (ML) applications for domains that feature ultra-low cost, conformity, and non-toxicity requirements that silicon-based systems cannot deliver. Nevertheless, large feature sizes in PE prohibit the realization of complex printed ML circuits. In this work, we present, for the first time, an automated printed-aware software/hardware co-design framework that exploits approximate computing principles to enable ultra-resource constrained printed multilayer perceptrons (MLPs). Our evaluation demonstrates that, compared to the state-of-the-art baseline, our circuits feature on average 6x (5.7x) lower area (power) and less than 1% accuracy loss. © 2023, CC BY-NC-ND.

关键词： Timing circuits

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The Correlation Between Architecture Smells, Package Stability and Reusability: A Case Study

The Correlation Between Architecture Smells, Package Stabili...

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sciences and Techniques of Automatic Control and computer Engineering MI-STA, IEEE International Maghreb Meeting of the Conference on

作者： Mrwan BenIdris Ehab A. Omar Elfallah Marwa Aslam Cheema Mohammed Salah Addin Alsma Tareq Ziad Kadiki Abdelfattah Elbarsha Dep. of Computer Science Faculty of Information Technology University of Benghazi Dept. of Software Engineering Faculty of Information Technology Libyan International Medical University Health Informatics Department Faculty of Public Health University of Benghazi

ISBN: (数字)9798350372632

ISBN: (纸本)9798350372649

the design decisions made in the architecture of a software system are essential to its maintainability, and thus its quality is of great importance. Architecture smells (ASs) can be used to identify any quality issues within the system. To further explore the relationships between ASs, stability, and reusability, Spearman correlation coefficients will be calculated. This paper seeks to examine the connection between architecture smells, software stability, and reusability. To do this, we utilized a dataset created by other researchers and conducted an empirical study of the associations between five types of architecture smells and software stability and reusability. This research is necessary as there is a lack of in-dept. research on architecture smells and their relationship to software stability and reusability. We found a relationship between the presence of Feature Concentration in package and the stability and reusability of the package. Also, package's stability is affected by both Cyclic Dependency and Unstable Dependency. A slight positive correlation has been found between God Class and stability, as well as a very weak negative correlation between Unstable Dependency and reusability. Finally, we found a slight negative correlation between package stability and reusability.

关键词： Correlation coefficient Correlation Codes Conferences computer architecture Software systems Software development management

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