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International Conference on Artificial Intelligence and Data Sciences (AiDAS)

作者： Lam Xin Mi Saw Seow Hoon Khor Siak Wang Saw Seow Hui Dept. of Information Systems Faculty of Information and Communication Technology UTAR Perak Dept. of Allied Health Sciences Dr. WLT-CRCD UTAR Sg. Long Faculty of Science UTAR Perak Department of Computer Science Faculty of Information and Communication Technology UTAR Perak

The absence of an independent learning platform to address femoral shaft fractures poses challenges in understanding and classifying these fractures. The existing anatomy platforms, such as 3D interactive anatomical atlases, are insufficient in providing comprehensive information on the fractures and the high cost of these interactive anatomical atlases can be a barrier for learning to proceed due to the needs of the actual bones and human models. This study aims to develop an accessible, cost-effective educational 3D mobile application that caters to the learning needs associated with human femoral shaft fractures. Although femoral fractures are relatively uncommon, the increased cases of fractures necessitate awareness and preparedness among individuals as well as medical professionals. The proposed mobile application will enhance users' understanding of anatomical structures in femoral shaft fractures and contribute to improved patient care as well as enhance medical education.

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The DEVStone Metric: Performance Analysis of DEVS Simulation Engines

arXiv

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arXiv 2023年

作者： Cárdenas, Román Henares, Kevin Arroba, Patricia Risco-Martín, José L. Wainer, Gabriel A. Integrated Systems Laboratory Universidad Politécnica de Madrid Madrid Spain Dept. of Systems and Computer Engineering Carleton University OttawaON Canada Dpt. of Computer Architecture and Automation Universidad Complutense de Madrid Madrid Spain Center for Computational Simulation Universidad Politécnica de Madrid Madrid Spain

The DEVStone benchmark allows us to evaluate the performance of discrete-event simulators based on the DEVS formalism. It provides model sets with different characteristics, enabling the analysis of specific issues of simulation engines. However, this heterogeneity hinders the comparison of the results among studies, as the results obtained on each research work depend on the chosen subset of DEVStone models. We define the DEVStone metric based on the DEVStone synthetic benchmark and provide a mechanism for specifying objective ratings for DEVS-based simulators. This metric corresponds to the average number of times that a simulator can execute a selection of 12 DEVStone models in one minute. The variety of the chosen models ensures we measure different particularities provided by DEVStone. The proposed metric allows us to compare various simulators and to assess the impact of new features on their performance. We use the DEVStone metric to compare some popular DEVS-based simulators. © 2023, CC BY-SA.

关键词： Discrete event simulation

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Self-Supervised Motion Segmentation with Confidence-Aware Loss Functions for Handling Occluded Pixels and Uncertain Optical Flow Predictions

Self-Supervised Motion Segmentation with Confidence-Aware Lo...

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

作者： Chung-Yu Chen Bo-Yun Lai Ying-Shiuan Huang Wen-Chieh Lin Chieh-Chih Wang Dept. of Computer Science National Yang Ming Chiao Tung Univ. College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Mechanical and Mechatronics Systems Research Laboratories Industrial Technology Research Institute Hsinchu Taiwan

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In driving scenarios, motion segmentation is a crucial and fundamental component that is needed for many tasks. Recently, a self-supervised multitasking framework was proposed for driving scenarios. It simultaneously trains motion segmentation, optical flow, dept., and ego-motion models without annotated data. The self-supervised architecture derives training signals from training data via loss functions. If these loss functions lack robustness, they may result in model inaccuracies. To reduce the bad influences of occlusion and optical flow estimation errors on motion segmentation, we propose two loss functions: (1) Soft-Per-Pixel-Minimum (Soft-PPM) loss that excludes occluded pixels while balancing the contribution of each frame on the loss function temporally; (2) Flow difference loss that excludes pixels with unclear motion states to diminish the effect of optical flow estimation errors. Our loss function design is based on the key insight that information such as dept. and optical flow can be used to train motion segmentation models and act as a reliable measure for pixels during training. Our approach can improve segmentation accuracy for both moving and static objects and has achieved IoU scores on moving and static classes comparable to the state-of-the-art methods on the KITTI dataset.

关键词： Optical losses Training computer vision Estimation error Motion segmentation Training data Loss measurement Data models Robustness Optical flow

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Sixteen years of sound and music computing: A look into the history and trends of the conference and community 17

Sixteen years of sound and music computing: A look into the ...

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17th Sound and Music Computing Conference, SMC 2020

作者： Mauro, Davide Andrea Avanzini, Federico Baraté, Adriano Ludovico, Luca Andrea Ntalampiras, Stavros Dimitrov, Smilen Serafin, Stefania Dept. of Computer & Information Technology Marshall University United States Dept. of Computer Science University of Milano Italy Dept. of Architecture Design and Media Technology Aalborg University Copenhagen Denmark

ISBN: (纸本)9788894541502

This contribution provides an overview of the Sound and Music Computing conference and community over the course of its sixteen years. As a sequel to a previous corresponding contribution investigating the community ten years ago, here we analyze the proceedings of the past editions, as well as the changes in the organization of the conference itself. The analysis reveals the growth of the SMC community in terms of attendees and countries represented at the conference, highlights the changes in trends and topics, and provides insights on the directions of the conference. A reflection is made with regards to the SMC roadmap originally conceived in 2004. Motivated by similar initiatives in "sister"communities, this resource is made available to the community at http://***/. Copyright © 2020 Davide Andrea Mauro et al.

关键词： computer programming

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Mathematical Model of Asset and Liability Management in the Presence of Normative and Internal Constraints

Mathematical Model of Asset and Liability Management in the ...

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International Conference on Control systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Aleksandra Zhukova Anna Flerova Aleksey Chernov Nikolay Pilnik Dept. of Mathematical Modelling of Economic Systems FRC CSC of RAS MIPT Moscow Russian Federation Dept. of Mathematical Modelling of Economic Systems FRC CSC RAS Moscow Russian Federation MIPT-School of Applied Mathematics and Computer Science Moscow Institute of Physics and Technology Moscow Russian Federation

In this paper we present the mathematical formulation of the problem of planning the Asset Liability Management if the Presence of Normative and Internal Constraints. As a nonlinear optimal control problem, it has specifics due to the state constraints. The maximum principle involves verification of regularity property in order to apply computational methods. We reformulate the problem as the problem with convex objective function and linear, but mixed system of constraints. This enables application of optimization methods to compute the optimal controls, and study the dependence of controls on the parameters that describe the external environments and the management objective.

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Physics-Based Spatio-Temporal Modeling With Machine Learning for the Prediction of Oceanic Internal Waves

Physics-Based Spatio-Temporal Modeling With Machine Learning...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Song Wu Xiaojiang Zhang Wei Dong Senzhang Wang Xiaoyong Li SenLiang Bao Kai Li dept. College of Computer Science and Technology National University of Defense Technology Changsha China dept. College of Meteorology and Oceanography National University of Defense Technology Changsha China School of computer science and engineering Central South University Changsha China dept. College of Systems Engineering National University of Defense Technology Changsha China

Accurately predicting the occurrence of oceanic internal waves in the northeastern South China Sea is of great importance to marine ecosystems, and economy. The traditional physics-based models for monitoring the occurrence of internal waves require complex parameterization, and the partial differential equations (PDEs) are relatively difficult to solve. The emergence of integrating physical knowledge and data-driven models brings light to solving the problem, which improves interpretability and meets the physical consistency. It not only inherits the advantages of machine learning in massive data processing but also makes up for the “black box” characteristics. In this paper, we propose a physics-based spatio-temporal data analysis model based on the widely used LSTM framework to achieve oceanic internal wave prediction. The results show higher prediction accuracy compared with the traditional LSTM model, and the introduction of physical laws can improve data utilization while enhancing interpretability.

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Complementary Split Square Ring Resonator-Based Double Negative Metamaterial for C, X, and Ku bands Satellite Applications 7

Complementary Split Square Ring Resonator-Based Double Negat...

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7th International Conference on Space Science and Communication, IconSpace 2021

作者： Hossain, Ismail Samsuzzaman, Md Baharuddin, Mohd Hafiz Bais, Badariah Alam, Touhidul Jit Singh, Mandeep Singh Selangor Malaysia Patuakhali Science And Technology University Dept. Of Computer And Communication Engineering Dumki Patuakhali Bangladesh Universiti Kebangsaan Malaysia Dept Of Electrical Electronic And Systems Engineering Selangor Malaysia

ISBN: (纸本)9781665425230

This study proposed an interlinked coupled split square ring resonator (SSRR) based metamaterial (MTM) unit cell. The double-negative (DNG) metamaterial characteristics are exhibited in C, X, and Ku microwave frequency bands. The proposed MTM unit cell contains six split square ring resonators (SSRR) where a horizontal metal bar is interlinked between the fourth and fifth SSRR and a vertical metal bar interlinks between the third and fifth SSRR. The perfect electric conductor (PEC) and Rogers-RT 5880 are used in the resonator and dielectric layer to design the MTM unit cell. The unit cell has a dimension of 10×10×1.57 mm3, and the thickness of the resonator layer and dielectric layer is 0.04 mm, 1.57 mm, respectively. The computer Simulation technology (CST Microwave Studio 2019) software is used to extract effective parameters then the finite element method (FEM) is used to verify the unit cell's performance. Due to symmetrical structure, negative permittivity, negative permeability, and negative refractive are achieved. As a result, the proposed unit cell can improve microwave devices' performance for C, X, and Ku bands, particularly satellite and radar communications. © 2021 IEEE.

关键词： Metamaterials

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Extended Adaptive Data-Rate (X-ADR) Technique for Optimal Resource Allocation in Smart City Applications

Extended Adaptive Data-Rate (X-ADR) Technique for Optimal Re...

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IEEE International Symposium on World of Wireless Mobile and Multimedia Networks (WoWMoM)

作者： Nikumani Choudhury Manik Gupta Moustafa M. Nasralla Satoshi Fujita Dept. of Computer Science and Information Systems Birla Institute of Technology & Science Pilani Hyderabad India Dept. of Communications & Networks Engineering Prince Sultan University Riyadh Saudi Arabia Graduate School of Advanced Science and Engineering Hiroshima University Japan

With the rapid growth of urbanization, the need for sustainable, energy-efficient, and smart solutions for home, industry, governance, traffic, and in general, the need for improved quality of life and health has risen. As an enabling technology, the Internet of Things (IoT) must facilitate several advanced applications with varied QoS requirements for smart cities. In this regard, Long Range (LoRa) technologies can be an ideal communication protocol for resource-constrained IoT devices. The LoRa Wide Area Network (LoRaWAN) defines physical layer options and the medium access control (MAC) sub-layer protocols for facilitating low-power, low-rate communications among battery-operated wireless IoT devices. With the enormous number of IoT devices and growing QoS requirements, it is imperative to optimally allocate resources (bandwidth, spreading factor, and transmit power) to these constrained devices so that network lifetime can be improved. The Adaptive Data-Rate (ADR) technique is used by the Network Servers (NS) to adapt the transmission parameters of the devices optimally. This is based on several received parameters from the end devices as well as the network settings. In this work, we extend the ADR technique to the LoRa gateways to consider the class of LoRa devices and the frequency of transmissions further to extend the energy efficiency and lifespan of IoT devices. Additionally, there is a considerable delay in adapting to optimal settings for the end-devices resulting in excess power dissipation. Further, a novel mechanism is proposed to address the congestion issue in the network using the Spreading Factor parameter (SF).

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CARDIOCARE platform: A beyond the state of the art approach for the management of elderly multimorbid patients with breast cancer therapy induced cardiac toxicity

CARDIOCARE platform: A beyond the state of the art approach ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Tsiouris, Kostas M. Kalliatakis, Grigoris Mazzocco, Ketti Seruga, Bostjan Marias, Kostas Karanasiou, Georgia Antoniades, Athos Papakonstantinou, Andri Conti, Constanza Tsiknakis, Manolis Sfakianakis, Stelios Lakkas, Lampros Filippatos, Gerasimos Bucur, Anca Fotiadis, Dimitrios I. Manikis, George Mauri, Davide Constantinidou, Anastasia Pacella, Elsa University of Ioannina Unit of Medical Technology and Intelligent Information Systems Ioannina Greece Foundation for Research and Technology - Hellas Computational Bio-Medicine Laboratory Institute of Computer Science Heraklion Greece European Institute of Oncology Cardiology Division Milan Italy Institute of Oncology Ljubljana Department of Medical Oncology Ljubljana Slovenia Stremble Ventures Ltd Limassol Cyprus Karolinska University Hospital Endocrine Tumors and Sarcoma Theme Cancer Dept. for Breast Cancer Sweden Istituto di Management Sanitario S.r.l Milano Italy Foundation for Research and Technology - Hellas Institute of Computer Science Heraklion Greece University of Ioannina 2nd Dept. of Cardiology School of Medicine Ioannina Greece National and Kapodistrian University of Athens Athens Greece Attikon University Hospital School of Medicine Dept. of Cardiology Athens Greece Philips Research Europe Eindhoven Netherlands University of Ioannina Dept. of Medical Oncology Ioannina Greece Bank of Cyprus Oncology Centre Nicosia Cyprus European Society of Cardiology Brussels Belgium

ISBN: (纸本)9798350337488

Breast cancer (BC) is the most common cancer in women in Europe and worldwide, with a high prevalence in middle-aged and older women. The last years, the evolution in the existing treatment approaches have contributed to improved clinical outcomes and survival rates. Nevertheless, BC therapy-related cardiotoxicity, poses a severe impact in the short- and long-term Quality of Life (QoL) and associated survival of the BC patients. This study demonstrates how the CARDIOCARE platform and the developed risk stratification models provides healthcare professionals with a valuable tool for effectively managing BC patients, preventing treatment induced cardiotoxicity and improving their QoL. This is accomplished through the integration of multi-source patient-specific data from patient-oriented mobile applications and wearable sensors, and by the employment of beyond the state-of-the-art data mining and machine learning approaches. © 2023 IEEE.

关键词： Breast cancer cardiotoxicity machine learning mobile application risk stratification models

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Antenna Position Estimation Results from In-Situ Measurement Data

Antenna Position Estimation Results from In-Situ Measurement...

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International Conference on Electromagnetics in Advanced Applications (ICEAA)

作者： Ridalise Raal Rina-Mari Weideman Dirk de Villiers Annie Cuyt Stefan J. Wijnholds Dept. of Electrical & Electronic Eng Stellenbosch University Stellenbosch South Africa Dept. of Computer Science University of Antwerp Antwerp Belgium University of Stirling Stirling Scotland UK Eindhoven University of Technology Eindhoven The Netherlands Dept. of Innovation & Systems Netherlands Institute for Radio Astronomy (ASTRON) Dwingeloo The Netherlands

In this paper, legacy data from an in-situ measurement campaign that was performed on a compact sub-array of Low Band Antennas (LBAs) of the LOFAR radio telescopes are used to illustrate the effectiveness of a recently developed antenna element position estimation method. The method allows recovery from aliasing after sub-sampling of odd harmonic signals transmitted from an Unmanned Aerial Vehicle (UAV) with a known position in the sky. This allows not just small errors due to element placement deviations from the designed positions to be detected, but also large errors due to the incorrect connection of antenna elements to the digital back-end, or malfunction of receiver amplifiers. The results look promising and can be improved by using an increased number of samples from a suitable UAV flight path, and by updating the data model to include calculation of the phase errors from the analog receiver signal paths.

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