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International Technical Conference o Circuits/Systems, computers and Communications (ITC-CSCC)

作者： Sakorn Mekruksavanich Ponnipa Jantawong Datchakorn Tancharoen Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Faculty of Engineering and Technology Panyapiwat Institute of Management Nonthaburi Thailand Department of Mathematics Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand

The usage of precision livestock has grown due to the need for higher efficiency and productivity in response to the high demand for food. To ensure sustainable development and quality control of the inputs required by the industry, it is essential to monitor and classify the behavior of cattle. Sensor-based monitoring systems provide accurate information by capturing raw data and identifying behavior through machine learning and deep learning algorithms. This approach has allowed farmers to better understand the individual needs of their animals. This study presents a deep residual neural network for the classification of cattle behavior. The performance of the ResNeXt model was evaluated using a public real-world dataset collected from sensors attached to the neck of six different Japanese black beef cows. The experimental results showed that the presented ResNeXt model achieved the highest average accuracy of 94.96% and the highest average F1-score of 93.66%. Compared to other baseline deep learning models and the current state-of-the-art model for cattle behavior classification, the presented model outperformed them and achieved better performance.

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An Artificial Intelligence-Based Bio-Medical Stroke Prediction and Analytical System Using a Machine Learning Approach

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Computational Intelligence and Neuroscience 2022年第0期2022卷 5489084页

作者： Pitchai, R. Dappuri, Bhasker Pramila, P.V. Vidhyalakshmi, M. Shanthi, S. Alonazi, Wadi B. Almutairi, Khalid M. A. Sundaram, R.S. Beyene, Ibsa Department of Computer Science and Engineering B v Raju Institute of Technology Telangana Narsapur502313 India Department of Electronics and Communication Engineering CMR Engineering College Kandlakoya Telangana 501401 India Department of Computer Science Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai600124 India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Ramapuram Tamilnadu Chennai600089 India Department of Computer Science and Engineering Kongu Engineering College Tamil Nadu Perundurai638060 India Health Administration Department College of Business Administration King Saud University P. O Box: 71115 Riyadh11587 Saudi Arabia Department of Community Health Sciences College of Applied Medical Sciences King Saud University P. O. Box 10219 Riyadh11433 Saudi Arabia Department of Health Sciences University of Texas TX United States Department of IT Mettu University Mettu Ethiopia

Stroke-related disabilities can have a major negative effect on the economic well-being of the person. When left untreated, a stroke can be fatal. According to the findings of this study, people who have had strokes generally have abnormal biosignals. Patients will be able to obtain prompt therapy in this manner if they are carefully monitored;their biosignals will be precisely assessed and real-time analysis will be performed. On the contrary, most stroke diagnosis and prediction systems rely on image analysis technologies such as CT or MRI, which are not only expensive but also hard to use. In this study, we develop a machine learning algorithm for the prediction of stroke in the brain, and this prediction is carried out from the real-time samples of electromyography (EMG) data. The study uses synthetic samples for training the support vector machine (SVM) classifier and then the testing is conducted in real-time samples. To improve the accuracy of prediction, the samples are generated using the data augmentation principle, which supports training with vast data. The simulation is conducted to test the efficacy of the model, and the results show that the proposed classifier achieves a higher rate of classification accuracy than the existing methods. Furthermore, it is seen that the rate of precision, recall, and f-measure is higher in the proposed SVM than in other methods. © 2022 R. Pitchai et al.

关键词： Forecasting

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On the Feasibility of RIS-enabled Cooperative Driving

On the Feasibility of RIS-enabled Cooperative Driving

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IEEE Vehicular Networking Conference (VNC)

作者： Michele Segata Paolo Casari Marios Lestas Dimitrios Tyrovolas Taqwa Saeed George Karagiannidis Christos Liaskos Department of Information Engineering and Computer Science University of Trento Italy Dept. of Electrical Engineering Frederick University Cyprus Dept. of Electrical Engineering Aristotle University of Thessaloniki Greece School of Information Technology Halmstad University Sweden Cyber Security Systems and Applied AI Research Center Lebanese American University (LAU) Lebanon Dept. of Computer Science and Engineering University of Ioannina & FORTH Greece

Future cooperative autonomous vehicles will require high-performance communication means to support functions such as cooperative maneuvering and cooperative perception. The high-bandwidth requirements of these functions can be met through mmWave communications, whose utilization is often hindered by the harsh propagation conditions of typical vehicular environments. A solution to this problem is the use of reconfigurable intelligent surfaces (RISs), which enable the reflection of signals in a configurable direction, and have recently gained attention in the vehicular domain. In this paper, we provide an initial feasibility study, highlighting the challenges ahead and the performance RISs need to deliver in order to enable this type of communications. Specifically, we utilize CoopeRIS, a simulation framework for RISs integrated into the Plexe/Veins/SUMO ecosystem that we develop as further contribution and will release to the public.

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Identification of Building Damages using Satellite Images based on CNN- Recurrent Neural Network Approach

Identification of Building Damages using Satellite Images ba...

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International Conference on Communication and Electronics Systems (ICCES)

作者： Ganesh K. Chavhan Sumeshwar Singh Ankush Sahebrao Doke Daniel Pilli S. Kailasavalli Manasi Vyankatesh Ghamande Department of Geography K. J. Somaiya College of Arts Commerce and Science Kopargaon Ahmednagar Maharashtra India Department of Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Department of Geography Mahatma Phule Nutan Maha Vidyalaya Mirajgaon Maharashtra India MBA Department KLBS Koneru Lakshmaiah Educational Foundation Vijayawada Andhra Pradesh India Mathematics PSNA College of Engineering and Technology Dindigul Tamilnadu India Engineering and Applied Science Vishwakarma Institute of Information Technology Pune Maharashtra India

Recently, numerous disaster-related studies have integrated Remote-Sensing (RS) technology alongside picture processing and conventional methods. One example is identifying earthquake damage to buildings in orthophotos. Common approaches to creating damage maps from RS pictures include automatic and visual methods. However, because of the need for manual sampling, the visual method is laborious. By extracting the defect features, the automatic technique can identify the damaged structure. Challenges to the widespread use of automatic methods are presented by the great variety of design approaches and the dynamic nature of real-world situations, such as variations in shadow and light. To better train neural networks with less interference from measurement noise, principal component analysis (PCA) is used. After PCA is applied to the DI values, an ensemble of neural networks is utilized to provide predictions about the location and severity of damage. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are two widely used methods in machine learning, and the CRNN approach combines the two (RNNs). As compared to the CNN and RNN models, the proposed technique performs admirably.

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Generalised Image Outpainting with U-Transformer

arXiv

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arXiv 2022年

作者： Gao, Penglei Yang, Xi Zhang, Rui Goulermas, John Y. Geng, Yujie Yan, Yuyao Huang, Kaizhu Department of Computer Science University of Liverpool United Kingdom Department of Intelligent Science Xi’an Jiaotong-Liverpool University China Department of Foundational Mathematics Xi’an Jiaotong-Liverpool University China Institute of Applied Physical Sciences and Engineering Duke Kunshan University China

In this paper, we develop a novel transformer-based generative adversarial neural network called U-Transformer for generalised image outpainting problem. Different from most present image outpainting methods conducting horizontal extrapolation, our generalised image outpainting could extrapolate visual context all-side around a given image with plausible structure and details even for complicated scenery, building, and art images. Specifically, we design a generator as an encoder-to-decoder structure embedded with the popular Swin Transformer blocks. As such, our novel neural network can better cope with image long-range dependencies which are crucially important for generalised image outpainting. We propose additionally a U-shaped structure and multi-view Temporal Spatial Predictor (TSP) module to reinforce image self-reconstruction as well as unknown-part prediction smoothly and realistically. By adjusting the predicting step in the TSP module in the testing stage, we can generate arbitrary outpainting size given the input sub-image. We experimentally demonstrate that our proposed method could produce visually appealing results for generalized image outpainting against the state-of-the-art image outpainting approaches. © 2022, CC BY-NC-SA.

关键词： Extrapolation

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Toward Process Controlled Medical Robotic System

arXiv

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arXiv 2023年

作者： Liu, Yihao Kheradmand, Amir Armand, Mehran The Biomechanical- and Image-Guided Surgical Systems Laboratory Laboratory for Computational Sensing & Robotics Department of Computer Science Johns Hopkins University BaltimoreMD21218 United States The Department of Neurology Department of Neuroscience Laboratory for Computational Sensing & Robotics Johns Hopkins University BaltimoreMD21218 United States The Biomechanical- and Image-Guided Surgical Systems Laboratory Laboratory for Computational Sensing & Robotics Department of Orthopaedic Surgery Department of Mechanical Engineering Department of Computer Science Applied Physics Laboratory Johns Hopkins University BaltimoreMD21218 United States

Medical errors, defined as unintended acts either of omission or commission that cause the failure of medical actions, are the third leading cause of death in the United States. Medical errors can include communication breakdowns, diagnostic errors, poor judgment, and inadequate skills. The application of autonomy and robotics can alleviate some causes of medical errors by improving accuracy and providing means to accurately follow planned procedures. However, for the robotic applications to improve safety, they must maintain constant operating conditions in the presence of disturbances, and provide reliable measurements, evaluation, and control for each state of the procedure. This article addresses the need for process control in medical robotic systems, and proposes a standardized design cycle toward its automation. Monitoring and controlling the changing conditions in a medical or surgical environment necessitates a clear definition of workflows and their procedural dependencies. We propose integrating process control into medical robotic workflows using hierarchical Finite State Machines (hFSM) to identify changes in states of the system and environment, and execute possible operations or transitions to new states. Therefore, the system translates clinician experiences and procedure workflows into machine-interpretable languages. The design cycle using hFSM formulation can be a deterministic process, which opens up possibilities for higher-level automation in medical robotics. Shown in our work, with a standardized design cycle and software paradigm, we pave the way toward controlled workflows that can be automatically generated. Additionally, a modular design for a robotic system architecture that integrates hFSM can provide easy software and hardware integration. This article discusses the system design, software implementation, and example application to Robot-Assisted Transcranial Magnetic Stimulation (RATMS) and robot-assisted femoroplasty. We also provide

关键词： Navigation systems

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Findings of the Shared Task on Multilingual Coreference Resolution

arXiv

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arXiv 2022年

作者： Žabokrtský, Zdeněk Konopík, Miloslav Nedoluzhko, Anna Novák, Michal Ogrodniczuk, Maciej Popel, Martin Pražák, Ondřej Sido, Jakub Zeman, Daniel Zhu, Yilun Charles University Faculty of Mathematics and Physics Institute of Formal and Applied Linguistics Prague Czech Republic University of West Bohemia Faculty of Applied Sciences Department of Computer Science and Engineering Pilsen Czech Republic Institute of Computer Science Polish Academy of Sciences Warsaw Poland Georgetown University Department of Linguistics WashingtonDC United States

This paper presents an overview of the shared task on multilingual coreference resolution associated with the CRAC 2022 workshop. Shared task participants were supposed to develop trainable systems capable of identifying mentions and clustering them according to identity coreference. The public edition of CorefUD 1.0, which contains 13 datasets for 10 languages, was used as the source of training and evaluation data. The CoNLL score used in previous coreference-oriented shared tasks was used as the main evaluation metric. There were 8 coreference prediction systems submitted by 5 participating teams;in addition, there was a competitive Transformer-based baseline system provided by the organizers at the beginning of the shared task. The winner system outperformed the baseline by 12 percentage points (in terms of the CoNLL scores averaged across all datasets for individual languages). Copyright © 2022, The Authors. All rights reserved.

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JASPER: A fast genome polishing tool that improves accuracy of genome assemblies

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PLoS Computational Biology 2023年第3期19卷 e1011032-e1011032页

作者： Guo, Alina Salzberg, Steven L. Zimin, Aleksey V. Department of Biomedical Engineering Johns Hopkins University BaltimoreMD United States Department of Applied Mathematics and Statistics Johns Hopkins University BaltimoreMD United States Center for Computational Biology Johns Hopkins University BaltimoreMD United States Department of Computer Science Johns Hopkins University BaltimoreMD United States Department of Biostatistics Johns Hopkins University BaltimoreMD United States

Advances in long-read sequencing technologies have dramatically improved the contiguity and completeness of genome assemblies. Using the latest nanopore-based sequencers, we can generate enough data for the assembly of a human genome from a single flow cell. With the long-read data from these sequences, we can now routinely produce de novo genome assemblies in which half or more of a genome is contained in megabase-scale contigs. Assemblies produced from nanopore data alone, though, have relatively high error rates and can benefit from a process called polishing, in which more-accurate reads are used to correct errors in the consensus sequence. In this manuscript, we present a novel tool for genome polishing called JASPER (Jellyfish-based Assembly Sequence Polisher for Error Reduction). In contrast to many other polishing methods, JASPER gains efficiency by avoiding the alignment of reads to the assembly. Instead, JASPER uses a database of k-mer counts that it creates from the reads to detect and correct errors in the consensus. Our experiments demonstrate that JASPER is faster than alignment-based polishers, and both faster and more accurate than other k-mer based polishing methods. We also introduce the idea of using a polishing tool to create population-specific reference genomes, and illustrate this idea using sequence data from multiple individuals from Tokyo, Japan. Copyright: © 2023 Guo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

关键词： Genes

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Programmable Mode/Wavelength DEMULTIPLEXER using Inverse-designed Low-loss Phase-change Material

Programmable Mode/Wavelength DEMULTIPLEXER using Inverse-des...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Ziyu Jiao Changming Wu Heshan Yu Ichiro Takeuchi Carlos Ríos Mo Li Department of Electrical and Computer Engineering University of Washington Seattle WA USA School of the Gifted Young University of Science and Technology of China Hefei P. R. China Department of Materials Science and Engineering University of Maryland College Park MD USA Institute for Research in Electronics and Applied Physics University of Maryland College Park MD USA Department of Physics University of Washington Seattle WA USA

We demonstrate a non-volatile optically programmable inverse-designed demultiplexer based on low-loss phase change material Sb 2 Se 3 integrated MMI that can be reconfigured both as mode and wavelength demultiplexers.

ISBN: (纸本)9781957171258

关键词： Demultiplexers Extinction ratios Multimode interference Photonic devices Refractive index Subwavelength structures

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A Hybrid Deep Neural Network with Attention Mechanism for Human Activity Recognition Based on Smartphone Sensors

A Hybrid Deep Neural Network with Attention Mechanism for Hu...

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International Conference on Information Technology (INCIT)

作者： Sakorn Mekruksavanich Datchakorn Tancharoen Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Faculty of Engineering and Technology Panyapiwat Institute of Management Nonthaburi Thailand Department of Mathematics Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand

Human activity recognition (HAR) is an area of study that seeks to automatically and precisely detect an individual’s behavior by analyzing bio-signal data. Bio-signal data can be acquired using sensing technology integrated into smartphones and other worn intelligent gadgets. Nevertheless, collecting sensor data tagged with activity information employing individual smartphones can result in data being analyzed in varying contexts, potentially compromising the accuracy of machine learning prediction methodologies. This paper introduces a novel HAR approach via smartphone sensors. The proposed method incorporates a hybrid deep neural network architecture enhanced with an attention mechanism. The deep learning model under consideration is referred to as the Att-CNN-BLSTM network. This particular network can autonomously extract significant features from smartphone sensor data. This feature extraction aims to effectively classify different human actions with a high degree of accuracy. In order to assess the efficacy of the hybrid model, a series of investigations were undertaken utilizing a publically accessible HAR dataset. This dataset was employed for training and testing purposes, employing a 5-fold cross-validation approach. We additionally carried out an analytical comparison utilizing state-of-the-art deep learning models from prior research. The findings from our experimentation demonstrate superior performance of the Att-CNN-BLSTM model over other advanced AI algorithms, with the highest accuracy rate of 90.54% and top F1-score of 86.88% attained.

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