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5th International Conference on Cognitive Systems and Signal Processing, ICCSIP 2020

作者： Huang, Haiming Liang, Xiaotao Zhuo, Yufan Wu, Linyuan Wen, Zhenkun Sun, Fuchun College of Electronic and Information Engineering Shenzhen University Shenzhen518060 China Peng Cheng Lab Shenzhen GD518055 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Computer Science and Technology Tsinghua University Beijing100084 China

ISBN: (纸本)9789811623356

Robot gripper is used in many different places at present, but traditional gripper is difficult to grasp and sort soft and fragile fruits and vegetables effectively. Based on manipulator technology, this paper presents a design scheme of pneumatic multi-fingered gripper. This design combines the vacuum chunk and soft fingers to make the gripper precisely grasp the target object without damaging the surface of the object. The flexible fingers can be made quickly by using a compound injection molding method. Combined with the STM32 microcontroller and the Universal Robots 5 (UR Arm), the operation system of the soft gripper is realized, and the man-machine interaction presents a good performance. Through the grasping experiment, it is proved that the prototype can grasp, transport, and place soft and fragile objects stably and accurately. Overall, this design implements the combination of pneumatic unit and soft gripper, and provides a new orientation to the research of soft robot. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Grippers

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HoloLens 2 Technical Evaluation as Mixed Reality Guide

arXiv

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arXiv 2022年

作者： Guo, Hung-Jui Prabhakaran, Balakrishnan Software Engineering Department The Univerity of Texas DallasTX United States Computer Science Department The Univerity of Texas DallasTX United States

Mixed Reality (MR) is an evolving technology lying in the continuum spanned by related technologies such as Virtual Reality (VR) and Augmented Reality (AR), and creates an exciting way of interacting with people and the environment. This technology is fast becoming a tool used by many people, potentially improving living environments and work efficiency. Microsoft HoloLens [1] has played an important role in the progress of MR, from the first generation to the second generation. In this paper, we systematically evaluate the functions of applicable functions in HoloLens 2 [2]. These evaluations can serve as a performance benchmark that can help people who need to use this instrument for research or applications in the future. The detailed tests and the performance evaluation of the different functionalities show the usability and possible limitations of each function. We mainly divide the experiment into the existing functions of the HoloLens 1, the new functions of the HoloLens 2, and the use of research mode. This research results will be useful for MR researchers who want to use HoloLens 2 as a research tool to design their own MR applications. © 2022, CC BY.

关键词： Mixed reality

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XVAE-mViT: Explinable Hybrid Artificial Intelligence Framework for Predicting COVID-19 from Chest X-Ray and CT Scans

XVAE-mViT: Explinable Hybrid Artificial Intelligence Framewo...

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International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)

作者： Daniel Addo Mugahed A. Al-antari Shijie Zhou Kwabena Sarpong Ertan Bütün Muhammed Talo Humam AbuAlkebash Chiagoziem C. Ukwuoma School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Department of Artificial Intelligence DaeyangAI Center Sejong University Seoul Republic of Korea Department of Computer Engineering Firat University Elazığ Turkey Department of Software Engineering Firat University Elazig Turkey Department of Mechatronics Engineering Kocaeli University Kocaeli Turkey The College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan China

The COVID-19 virus has rapidly spread as a global pandemic, causing significant impacts on public health, economies, and daily life worldwide. Accurately and quickly predicting COVID-19 is crucial to maintaining stronger healthcare systems. This paper introduces a novel hybrid model of artificial intelligence that combines the benefits of the Variational Auto-Encoder (VAE) with the attention mechanism based on the Vision Transformer (Vi $T$ ). The novel encoder network is structured with four sequential blocks, each involving residual connections of two multiscale kernel depth-wise separable convolution (MKnDSC) modules. The mobile Vi $T$ is coupled with the V AE to serve as the classification head for predicting COVID- 19 using chest X-ray (CXR) and computed tomography (CT) scan modalities. We achieved promising classification results with overall accuracies of 96.16% and 95.42% using CXR and CT images, respectively. The proposed hybrid AI framework appears to be a practical solution, especially considering its lightweight structure of 2.15 million parameters and 0.68 FLOPs.

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Blockchain and Deep Learning Development of Smart Charging of Electric Vehicles to Meet the Demand Side Management

Blockchain and Deep Learning Development of Smart Charging o...

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Sustainable Computing and Data Communication Systems (ICSCDS), International Conference on

作者： C. P. Shirley S.V. Evangelin Sonia V. Sathya N. Manikandan M.K. Vidhyalakshmi Ch. Venkata Krishna Reddy Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India Department of Software Engineering Periyaar Maniammai Institute of Science and Technology Thanjavur Tamil Nadu India Department of Business Administration Kalasalingam Business School Kalasalingam Academy of Research and Education Krishnankoil Srivilliputhur Tamil Nadu India Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Hyderabad Telangana India

The use of Electrical Vehicles (EVs) is increasing rapidly nowadays. This results in the efficient utilization of electrical energy. This leads to making the grid more economically reliable. Thus the smart charging of electric vehicles is done through blockchain technology with deep learning to meet the demand side management. This helps in the determination of the charging system with higher accuracy the systems. This indudes real-time implementation with higher precision control. This helps to obtain the exact optimal solution for dynamic functions. The deep earning helps to obtain real-time implementation in the system. This also helps to obtain the optimum charging cost consumed at a specific period. The charging station energy management system is implemented based on blockchain technology. This helps to meet the demand side management in the system. They are accompanied by centralized and decentralized energy models in the architecture. This also helps in the reduction of peer-to-peer transactions proceeding from the electricity grid to electric vehicles.

关键词： Deep learning Demand side management Electric vehicles Real-time systems Power grids Blockchains Security

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Smart Diagnostic System For Early Detection And Prediction Of Polycystic Ovary Syndrome

Smart Diagnostic System For Early Detection And Prediction O...

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International Conference on computer Communication and Informatics (ICCCI)

作者： K P Harish M.N Dhivyanchali K.Nirmala Devi N. Krishnamoorthy R Dhana Sree R Dharanidharan Department of Computer Science & engineering Kongu Engineering College (of Affiliation) Erode India Swamy Vivekanandha Medical College Hospital and Research Institute Elayampalayam Tiruchengode Tamilnadu INDIA Department of Software Systems and Engineering School of Information Technology and Engineering Vellore Institute of Technology Vellore Campus Tamilnadu

PCOS are the serious condition which affects female ovaries during their reproductive age of 15 to 45. This disease affects 5 to 10% of reproductive-age females. Although it is difficult to fully resolve this issue,the PCOS affected women can be mitigated through proper exercise, by taking proper nutritious diet and maintaining the healthy BMI. Until they take a pregnancy test, the majority of women are unaware of the disease. The clinical dataset has the 541 instances and 45 attributes of unbalanced classes of 0 and 1 (no and yes) which has 364 instances of 0 (no) class and 177 instances of 1 (yes) class. Preprocessing is done for the unbalanced dataset by filling the null values and changing the datatype of all attributes to numeric datatypes. The unbalanced dataset is balanced by the balancing techniques of SMOTE and Random Over Sampling. Comparing the both balanced techniques through the accuracy the random oversampling gives the *** supervised learning algorithms are Decision tree, KNN,Random Forest,AdaBoost, Logistic regression,Gradient boosting, cat boosting, XGBoosting, Linear SVM, Radial SVM, Linear discriminant analysis and Quadratic discriminant analysis are *** supervised learning algorithms are trained and tested by splitting the dataset to 70% for training and 30% for testing. The ensemble stacking techniques are used by implementing the all models at the cross validation of *** xgboost gives the accuracy of 96% for the balanced dataset.

关键词： Support vector machines Training Stacking Null value Boosting Pregnancy test Regression tree analysis

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Smart Farmer Education System for Disease Identification, Severity Assessment and Infestation Management (Brinjal)

Smart Farmer Education System for Disease Identification, Se...

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International Conference on Industrial and Information Systems, ICIIS

作者： Keshara Bandara Samitha Vidhanaarachchi A.A.E. Nanayakkara Kavindu Kulasinghe Sanka Rajapakshe Asela Wickramasinghe Kanishka Yapa Department Of Information Technology Sri Lanka Institute Of Information Technology Malabe Sri Lanka Department Of Computer Science and software Engineering Sri Lanka Institute Of Information Technology Malabe Sri Lanka Department of Soil Science Faculty of Agriculture University of Ruhuna Mapalana Kamburupitiya Sri Lanka Department of Computer Systems Engineering Sri Lanka Institute Of Information Technology Malabe Sri Lanka

The agriculture industry is one of the most significant sources of foreign exchange and employment in the Sri Lankan market. Therefore, small crops play a crucial role in ensuring the food security of the population as they are integral components of Sri Lankan cuisine. However, industry experts have identified inefficient disease and pest management as a hindrance to production. This issue requires immediate attention and proper action, which can be effectively addressed through the utilization of ICT technologies. Consequently, this research paper proposes a smart system that aims to assist farmers and agriculture professionals by providing them with farmer education, disease and infestation management tools, as well as progression level calculations. The implementation of this system aims to uplift the agriculture industry in Sri Lanka. The developed ‘AI-based mobile application system for Brinjal Diseases,’ which is based on analyzing the eating patterns of leafhopper damage, presents an innovative approach to identify the most significant threats to Brinjal, specifically focusing on leafhopper damage. This proposed system effectively informs various stakeholders, including the Area Agricultural Research Officer, Seed Certification and Plant Protection Center (SCPPC), Agricultural Service Center, Office of the Register of Pesticides (ROP), Plant Protection Service (PPS), as well as farmers, regarding the dispersion and prevention of brinjal diseases and pest infections.

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Prediction of Epileptic Seizures using Support Vector Machine and Regularization

Prediction of Epileptic Seizures using Support Vector Machin...

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2020 IEEE Region 10 Symposium, TENSYMP 2020

作者： Rafid Ahmad, Shaikh Rezwan Sayeed, Samee Mohammad Ahmed, Zaziba Siddique, Nusayer Masud Parvez, Mohammad Zavid Brac University Software Engineering and Hci Research Group Department of Computer Science and Engineering Bangladesh

ISBN: (纸本)9781728173665

Epilepsy is a neurological disorder that causes abnormal behavior and recurrent seizures due to unusual brain activity. This study has attempted to predict seizures in epileptic patients through the process of feature extraction from EEG signals during preictal/ictal and interictal periods, classification and regularization. EEG signals from various parts of the brain from 10 epileptic patients are considered. Fast Fourier Transform (FFT) is used to determine the three features-the phase angle, the amplitude and the power spectral density of the signals. To classify the signals, these features are then used along with Support Vector Machine (SVM) as the classifier. Furthermore, regularization is used to make better predictions i.e. increase prediction accuracy and decrease the rate of false alarm. Finally, the proposed approach is tested on CHB-MIT Scalp EEG data set and it is able to predict epileptic seizures 25 minutes on average before the onset of the seizure with 100% accuracy and a low false-alarm rate of 0.46 per hour. This study intends to contribute to the development of better and advanced seizure predicting devices in the medical field. © 2020 IEEE.

关键词： Support vector machines

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Design of Vehicle-cloud Collaborative Framework for 5G Cross-border Multi-PLAN Scenarios

Design of Vehicle-cloud Collaborative Framework for 5G Cross...

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International Conference on Network and Information Systems for computers (ICNISC)

作者： Guotao Du Jinlong Chu Jing Ma Shiduo Ning School of Mechanical Engineering Dalian University of Technology DUT Dalian China Department of Computer Science & Software Engineering School of Engineering Computer & Mathematical Sciences Auckland University of Technology Auckland New Zealand

ISBN: (纸本)9781665453523

The 5G-MOBIX project applies 5G network communication technology and defines five user stories and specific user scenarios in order to solve the cross-border problem of 5G driverless vehicles. Combined with the remote control user scenario in the remote driving user case, this paper explains the interconnect delay problem between PLMNS in this scenario. From the perspective of infrastructure, that is, the cooperation between vehicle infrastructure and cloud infrastructure, the CCAM architecture of remote control user story simulation test scenario is designed. The proposed solution of direct interconnection is verified and tested, and the cross-border test is simulated at the test site in China. The final test results show that the solution is feasible.

关键词： computers 5G mobile communication Collaboration computer architecture Communications technology Delays Remote control

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Predicting Parkinson’s disease trajectory using clinical and functional MRI features: a reproduction and replication study

arXiv

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arXiv 2024年

作者： Germani, Elodie Bhagwat, Nikhil Dugré, Mathieu Gau, Rémi Montillo, Albert A. Nguyen, Kevin P. Sokolowski, Andrzej Sharp, Madeleine Poline, Jean-Baptiste Glatard, Tristan Univ Rennes Inria CNRS Inserm France Department of Neurology and Neurosurgery McGill University Montreal Canada Department of Computer Science and Software Engineering Concordia University Montreal Canada Lyda Hill Department of Bioinformatics University of Texas Southwestern Medical Center Dallas United States

Parkinson’s disease (PD) is a common neurodegenerative disorder with a poorly understood physiopathology and no established biomarkers for the diagnosis of early stages and for prediction of disease progression. Several neuroimaging biomarkers have been studied recently, but these are susceptible to several sources of variability related for instance to cohort selection or image analysis. In this context, an evaluation of the robustness of such biomarkers to variations in the data processing workflow is essential. This study is part of a larger project investigating the replicability of potential neuroimaging biomarkers of PD. Here, we attempt to fully reproduce (reimplementing the experiments with the same methods, including data collection from the same database) and replicate (different data and/or method) the models described in [1] to predict individual’s PD current state and progression using demographic, clinical and neuroimaging features (fALFF and ReHo extracted from resting-state fMRI). We use the Parkinson’s Progression Markers Initiative dataset (PPMI, ***), as in [1] and aim to reproduce the original cohort, imaging features and machine learning models as closely as possible using the information available in the paper and the code. We also investigated methodological variations in cohort selection, feature extraction pipelines and sets of input features. Different criteria were used to evaluate the reproduction attempt and compare the results with the original ones. Notably, we obtained significantly better than chance performance using the analysis pipeline closest to that in the original study (R2 > 0), which is consistent with its findings. In addition, we performed a partial reproduction using derived data provided by the authors of the original study, and we obtained results that were close to the original ones. The challenges encountered while attempting to reproduce (fully and partially) and replicating the original work are likely exp

关键词： Neurodegenerative diseases

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FGNN-Based Improved Resource Distribution Framework for V2X Wireless Networks

FGNN-Based Improved Resource Distribution Framework for V2X ...

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IEEE Conference on Vehicular Technology (VTC)

作者： Syed Muhammad Waqas Yazhe Tang Fakhar Abbas Mehboob Hussain Yawar Abbas Bangash School of Computer Science and Technology Xi'an Jiaotong University Xi'an P.R. China Centre for Trusted Internet and Community National University of Singapore (NUS) Singapore School of Information Science and Technology Southwest Jiaotong University Chengdu China Department of Computer Software Engineering National University of Sciences and Technology (NUST) Pak.

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

Recently, deep learning has emerged as a promising approach for solving challenging resource distribution (RD) problems in vehicle-to-everything (V2X) wireless networks. How-ever, existing neural network architectures lack scalability, in-terpretability, and generalization. To address these limitations, in this study, we propose a new flexible graph neural network (FGNN)-based resource distribution framework for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) user selection and power management in V2X networks with several next- generation access points (APs) and a cluster of V2V and V2I communication users. In the proposed framework, we formu-lated an optimization problem with each V2V and V2I user with the least power constraint that adapts to V2X wireless network settings through training inactive users. Likewise, we consider the situation when every V2I user shares the band with a different group of V2V users. Moreover, we introduce a parameterization of the RD framework strategy employing a flexible graph neural network (FGNN) context derived from instantaneous channel conditions to learn the low-dimension features of every user/vehicle. To assess the execution of the framework, we conduct simulation experiments comparing it with baseline methods in terms of efficiency, sum rate, and fairness.

关键词： Training Vehicular and wireless technologies Wireless networks Scalability Power system management Vehicular ad hoc networks Graph neural networks

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