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Performance Evaluation of Coffee Bean Binary Classification Through Deep Learning Techniques 1

International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Islamy, Fajrul Muchtar, Kahlil Arnia, Fitri Dawood, Rahmad Febriana, Alifya Elwirehardja, Gregorius Natanael Pardamean, Bens Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department Master of Computer Science Program BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Coffee beans are one of the high-value commodities in Indonesia, but the sorting method for the quality of coffee beans still uses visual methods and sieves with mechanical machines. This study aims to provide an alternative to classifying coffee beans using Deep Learning algorithms, such as the ResNet-18 and MobileNetV2. The dataset used is USK-COFFEE which consists of two classes, the normal coffee bean class, and the defect coffee bean class. The models were trained using the balanced and imbalanced datasets, meaning that two scenarios were performed. From the results of this study, the accuracy value for the ResNet-18 model was 91.50% when trained on the balanced dataset and 90.87% on the imbalanced dataset. It outperformed the MobileNetV2 architecture, which only achieved 0.8125 and 0.8112 accuracy scores when trained on the balanced and imbalanced datasets, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： computer vision

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Performance Assessment of Multimode Network Designs for Improved Electromagnetic Compatibility

Performance Assessment of Multimode Network Designs for Impr...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Jain, Sandeep Kumar Pandey, Sri Prakash Prabha, S. Vivekananda Global University Department of Electrical Engineering Jaipur India College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

This paper offers a performance evaluation of multimode community designs for progressed electromagnetic compatibility (EMC). The EMC characteristics of multimode networks are tested using size and simulation approaches. Based on this analysis, it is found that the overall performance of those networks relies upon various factors, which include the form of issue used inside the layout, the length of interconnecting cables, and the running surroundings. The simulation effects also are compared with measured data to validate their accuracy. The paper also discusses the design issues to improve EMC performance in a multimode network and offers guidance on the choice of additives and strategies for EMC optimization. Eventually, this work presents layout engineers and researchers insight into using multimode networks for progressed EMC. Its technical abstract specializes in the performance evaluation of multimode community designs for progressed electromagnetic compatibility. In this take look, various network configurations have been evaluated, and their traits, consisting of engaging in paths, impedance, transmission loss, efficiency, and signal integrity, were studied. Researchers employed full-wave electromagnetic simulation strategies to obtain network characteristics and become aware of layout parameters that could doubtlessly enhance the machine's overall performance. The simulation outcomes show that introducing extra carrying out paths without or with integrated additives into the network layout can improve the machine noise rejection and sign integrity. The findings of this examination may additionally provide valuable insights into design concerns for enhancing EMC's overall performance in multimode network applications. © 2024 IEEE.

关键词： approaches characteristics multimode simulation strategies

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Predicting 3D forearm fracture angle from biplanar Xray images with rotational bone pose estimation 7

Predicting 3D forearm fracture angle from biplanar Xray imag...

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7th International Conference on Medical Imaging with Deep Learning, MIDL 2024

作者： Gu, Hanxue Colglazier, Roy Zhang, Jikai Lark, Robert Alman, Benjamin Mazurowski, Maciej Department of Electrical and Computer Engineering DukeNC27703 United States Department of Radiology DukeNC27703 United States Department of Orthopaedics DukeNC27703 United States Department of Computer Science DukeNC27703 United States

Two-dimensional X-ray images, while widely used, have limitations to reflect 3D information of the imaged objects. Several studies have tried to recover such information from multiple X-ray images of the same object. Still, those approaches often fail due to the unrealistic assumption that the target does not move between views and those two views are perfectly orthogonal. A problem where 3D information would be highly valuable but is very difficult to assess from 2D X-ray images is the measurement of the actual 3D fracture angles in the forearm. To address this problem, we propose a deep learning-based method that predicts the rotational movement and skeletal posture from biplanar X-ray images, offering a novel and precise solution. Our strategy comprises the following steps: (1) automatic segmentation of the ulna and radius bones of the forearm on two X-ray images by a neural network;(2) prediction of the rotational parameters of the bones by a pose prediction network;(3) automatic detection of fracture locations and assessment of the fracture angles on 2D images;and (4) reconstruction of the real 3D fracture angle by inferring it from the 2D fracture information and the skeleton pose parameters collected from the two images. Our experiments on X-ray images show that our method can accurately measure 2D fracture angles and infer the pose of the forearm bones. By simulating X-ray images for various types of fractures, we show that our method could provide more accurate measurements of fracture angles in 3D. We are the first attempt for the fully automatic fracture angle measurements on both 2D and 3D versions, and we show the robustness of our method even in extreme cases where the two views are highly nonorthogonal. © 2024 CC-BY 4.0, H. Gu, R. Colglazier, J. Zhang, R.L., B. Alman & M.M.

关键词： Angle measurement

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Guest Editorial Rate Splitting for Future Wireless Networks

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IEEE Journal on Selected Areas in Communications 2023年第5期41卷 1259-1264页

作者： Clerckx, Bruno Mao, Yijie Jorswieck, Eduard A. Yuan, Jinhong Love, David J. Erkip, Elza Niyato, Dusit Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom Graz8010 Austria ShanghaiTech University School of Information Science and Technology Shanghai201210 China Institute for Communications Technology TU Braunschweig Braunschweig38106 Germany University of New South Wales School of Electrical Engineering and Telecommunications SydneyNSW2052 Australia Purdue University Elmore Family School of Electrical and Computer Engineering West LafayetteIN47907 United States New York University Tandon School of Engineering Department of Electrical and Computer Engineering BrooklynNY11201 United States Nanyang Technological University School of Computer Science and Engineering Jurong West639798 Singapore

Rate splitting (RS) and rate splitting multiple access (RSMA) have emerged as a promising and powerful multiple access, interference management, and multi-user strategy for next-generation wireless systems and networks. This Special Issue is entirely dedicated to the theory, design, optimization, and applications of RS and RSMA in various network configurations. It starts with a guest editor-authored tutorial paper [A1] that delineates the basic principles and applications of RS and RSMA. The tutorial paper is then followed by 17 technical papers. © 1983-2012 IEEE.

关键词： Wireless networks

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Estimation of robust invariant set for switched linear systems using recursive state updating and robust invariant ellipsoid

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SICE Journal of Control, Measurement, and System Integration 2021年第1期14卷 97-106页

作者： Okajima, Hiroshi Fujinami, Kakeru Department of Computer Science and Electrical Engineering Kumamoto University Kumamoto Japan

This paper provides an analysis method of a robust invariant set for discrete-time linear switched systems with peak-bounded disturbances. In the case of a switched linear system, it is challenging to analyse the robust invariant set accurately than that of the linear time-invariant system. We propose a novel method to estimate a robust invariant set using a combination of a recursive state updating and an invariant ellipsoid for a common Lyapunov function. The effectiveness of the estimation accuracy by the proposed method is illustrated using numerical examples. © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Linear control systems

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Enhancing Acute Ischemic Stroke Diagnosis Using IoMT and Deep Learning Technologies

Enhancing Acute Ischemic Stroke Diagnosis Using IoMT and Dee...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： El-Sheikh, Mohamed M. Bayoumy, Aya Ahmed, Nada M. Mohamed Eldeeb, Amany Rabea, Fatma I. Nawar, Amira A. Khattap, Mohamed G. Galal Eldeen Mohamed Ali Hassan, Hend Mostafa, Mariam Ibrahim, Nayra Ramadan, Reem Refaat, Yomna Khaled, Mennatallah Maher, Ali Abd Elaziz, Mohamed Gamal Abdellatif Ibrahim, Ahmed Radiology and Medical Imaging Faculty of Applied Health Sciences Galala University Suez435611 Egypt Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Artificial Intelligence Science Program Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Air Defense College Department of Electronics and Electrical Communications Engineering Alexandria Egypt

ISBN: (纸本)9798350351408

This paper introduces an alternative technique for diagnosing Acute Ischemic Stroke within the IoMT environment. In the proposed approach, the collected data is transmitted to a cloud-based center where the technique utilizes EfficientNet, a deep learning model, designed to extract features from MRI images thereby enhancing the detection of acute ischemic infarctions. The performance of EfficientNet is compared against two other models, CNN and MobileNet, demonstrating its superior efficacy through metrics such as accuracy, precision, recall, and F1-score, which stand at 92.31%, 92.28%, 92.33%, and 92.30%, respectively. © 2024 IEEE.

关键词： Contrastive Learning

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Detecting the Underwater Distance and Swimming Direction of Tilapia using YOLO

Detecting the Underwater Distance and Swimming Direction of ...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Hsieh, Yi-Zeng Wu, Chia-Hsuan Chou, Cheng-Hou Teng, Chia-Ching Ho, Chih-Hsiang National Taiwan University of Science and Technology Department of Electrical Engineering Taipei City Taiwan National Central University Department of Computer and Science Information Engineering Taiwan Institute for Information Industry China

ISBN: (纸本)9798350324174

This paper uses an underwater unmanned vehicle to detect the distance and swimming direction of tilapia. As the underwater vehicle is equipped with a single camera system that lacks depth information, the YOLO3 architecture of deep learning is used to determine the relative distance of fish and further analyze the swimming direction of the fish group, which is of great help in analyzing fish group. © 2023 IEEE.

关键词： Fish

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CNN supported automated recognition of Covid-19 infection in chest X-ray images

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Materials Today: Proceedings 2022年 66卷 1201-1210页

作者： Padmakala, S. Revathy, S. Vijayalakshmi, K. Mathankumar, M. Department of Computer Science and Engineering St.Joseph's Insitute of Technology Chennai India Department of Electrical and Electronics Engineering Kumaraguru College of Technology Coimbatore India

Automatic recognition of lung system is use to identify normal and covid infected lungs from chest X-ray images of the people. In the year 2020, the coronavirus forcefully pushed the entire world into a freakish situation, the foremost challenge is to diagnosis the coronavirus. We have got standard diagnosis test called PCR test which is complex and costlier to check the patient's sample at initial stage. Keeping this in mind, we developed a work to recognize the chest X-ray image automatically and label it as Covid or normal lungs. For this work, we collected the dataset from open-source data repository and then pre-process each X-ray images from each category such as covid X-ray images and non-covid X-ray images using various techniques such as filtering, edge detection, segmentation, etc., and then the pre-processed X-ray images are trained using CNN-Resnet18 network. Using PyTorch python package, the resnet-18 network layer is created which gives more accuracy than any other algorithm. From the acquired knowledge the model is correctly classifies the testing X-ray images. Then the performance of the model is calculated and analyzed with various algorithms and hence gives that the resnet-18 network improves our model performance in terms of specificity and sensitivity with more than 90%. © 2022

关键词： Coronavirus

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Intelligent Digital Twin Communication Framework for Addressing Accuracy and Timeliness Tradeoff in Resource-Constrained Networks

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IEEE Transactions on Cognitive Communications and Networking 2024年第3期11卷 1954-1965页

作者： Cakir, Lal Verda Thomson, Craig J. Ozdem, Mehmet Canberk, Berk Nguyen, Van-Linh Duong, Trung Q. Edinburgh Napier University School of Computing Engineering and Built Environment EdinburghEH10 5DT United Kingdom BTS Group Istanbul Turkey Turk Telekom Turkey Department of Computer Science and Information Engineering Taiwan Memorial University of Newfoundland Faculty of Engineering and Applied Science St. John'sNLA1C 5S7 Canada Queen's University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom

The accuracy and timeliness tradeoff prevents Digital Twins (DTs) from realizing their full potential. High accuracy is crucial for decision-making, and timeliness is equally essential for responsiveness. Therefore, this tradeoff in DT communication must be addressed to achieve DT synchronization. Previous studies identified the issue but considered the problem as maximizing data transfer, which is infeasible due to resource constraints. To facilitate this, we quantify accuracy and timeliness as E and φ and define the problem as joint minimisation. We then introduce the Intelligent DT Communication (IDTC) Framework to solve the problem, which includes machine learning-based Predictive Synchronization (PS) and DT synchronization management (DTSYNC) protocol. Here, PS uses imputation and forecasting to generate future values, which are utilized to update DT at the projected time points. This mechanism of PS enables lowering E and φ of the communication. Subsequently, we utilize the DTSYNC to control synchronization and optimise the twining frequency ft. We evaluate the proposed framework using a public dataset and compare its performance with several state-of-the-art studies in a real-world scenario. Evaluation results indicate that IDTC outperforms the existing methods by 80% for E and 84% for φ while enabling ft adjustment, resulting in 3.8 times goodput improvement. © 2015 IEEE.

关键词： Adversarial machine learning

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SMCS TEAM: Open Course on Cyber Physical Systems Foundation and Design for Unmanned Aerial Vehicles (UAVs)

SMCS TEAM: Open Course on Cyber Physical Systems Foundation ...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Wan, Yan Fu, Shengli Xie, Junfei Lu, Kejie The University of Texas at Arlington Electrical Engineering ArlingtonTX75025 United States University of North Texas Electrical Engineering DentonTX United States San Diego State University Department of Electrical and Computer Engineering San DiegoCA92182 United States The University of Puerto Rico at Mayaguez Department of Computer Science and Engineering Mayaguez Puerto Rico

ISBN: (纸本)9781665410205

This abstract describes the project funded by the IEEE SMCS on Transforming Educational Assets and Materials (TEAM) in Systems, Man, and Cybernetics. The project develops an open course on Cyber Physical Systems (CPS) Foundation and Design for Unmanned Aerial Vehicles (UAVs). The course will be available to the public and serve the need of researchers, students and professionals who are interested in conducting UAVs related research. The open course contains integrated modules on control, communication and networking, computing, and artificial intelligence (AI) to provide trainees a comprehensive knowledge needed for UAVs. The course is self-paced and contains quizzes in each module for help students assess the quality of learning and also allow course designers to evaluate the effectiveness of the course materials for continuous improvement. The open course promotes CPS which is a SMCS technical field. It will also attract students and professionals to the SMC community. © 2024 IEEE.

关键词： Antennas

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