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2019 European Microwave Conference in Central Europe, EuMCE 2019

作者： Fiser, Ondrej Ley, Sebastian Helbig, Marko Sachs, Jurgen Kantova, Michaela Vrba, Jan Department of Biomedical Technology CTU Prague Czech Republic Faculty of Computer Science and Automation TU Ilmenau Germany Department of Electrical Engineering and Information Technology TU Ilmenau Germany Department of Electromagnetic Field CTU Prague Czech Republic

ISBN: (纸本)9782874870675

The temperature dependence of the dielectric parameters of tissues and tissue mimicking phantoms is very important for the non-invasive temperature measurement in medical applications using microwaves. We investigated and proposed a procedure of the measurement of this dependence in the temperature range 25 - 50 °C. Our proposed procedure was verified using distilled water. The results were compared to the literature model in the frequency range 150 - 3000 MHz. Using this method, the temperature dependence of dielectric parameters of new tissue mimicking phantom based on agar, PE powder and TX-151 was measured in the temperature range 25 -50 °C. © 2019 EuMCE.

关键词： Temperature measurement

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Observer-based controller design for the minimally invasive surgery

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Current Directions in Biomedical engineering 2018年第1期4卷 41-44页

作者： Smolinski, Eike Benkmann, Alexander Drewelow, Wolfgang Jeinsch, Torsten Cappius, Hans-Joachim Westerhoff, Peter Institute of Automation Faculty of Computer Science and Electrical Engineering University of Rostock Rostock Germany World of Medicine GmbH Berlin 10587 Germany

Due to its advantages like minimal trauma and a low risk of infections, the minimally invasive surgery (MIS) has become the standard operation technique for many surgical procedures. During this type of intervention, one of the main tasks from a technical point of view is the control of the pressure in the operation area in order to give the surgeon sufficient space to perform his actions. Because no invasive measurements are available, an extended Kalman filter incorporating the peristaltic behavior of the used medical device was developed to estimate the pressure in the operation area. This contribution focuses on the observer design while a separately designed PI controller using loop shaping was employed for validation. The proposed observer-based control structure was tested with a simulator deploying the principle of physical equivalence. © 2018 Eike Smolinski et al.

关键词： Disturbance rejection

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Real-time deep learning for multi-label retinal disease diagnosis with embedded system

Computational and Structural Biotechnology Reports

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Computational and Structural Biotechnology Reports 2025年 2卷

作者： Feudjio Ghislain Saha Tchinda Beaudelaire Romain Atangana Tchiotsop Daniel Research Unit of Automation and Applied Computer (UR-AIA) Electrical Engineering Department of IUT-FV University of Dschang P.O. Box: 134 Bandjoun Cameroon Research Unit of Condensed Matter of Electronics and Signal Processing (UR‑MACETS) Department of Physics Faculty of Sciences University of Dschang P.O. Box 67 Dschang Cameroon Department of Computer Science Higher Teacher Training College University of Bertoua-Cameroon P.O.Box 652 Bertoua Cameroon

Background Ophthalmological consultations are essential for the early detection of retinal pathologies. The development of advanced diagnostic devices that combine portability, ease of access, and energy efficiency offers significant benefits, especially in rural or peri-urban areas, which often struggle with limited medical resources and time constraints. Method In this study, we develop a classification platform for various retinal diseases using a Raspberry Pi 4 board. The system relies on a lightweight deep learning model based on convolutional neural networks (CNNs). To enhance the model's efficiency, we integrate biorthogonal wavelet transforms, which facilitate the effective extraction of relevant features from the input images. This approach reduces computational complexity while maintaining the quality of the extracted information. The model, optimized for real-time deployment, was trained on a dataset containing 4303 retinal images, representing four classes of pathologies. Results The proposed embedded model achieves an accuracy of 0.9806 across the four classes of pathologies collected from the two public databases ODIR (Ocular Disease Intelligence Recognition) and RFMiD (Retinal Fundus Multi-Disease Image Dataset). Conclusions Compared to several state-of-the-art methods published to date, the outstanding performance of the proposed embedded system demonstrates its potential as a valuable tool for clinicians to diagnose various ocular diseases in underprivileged healthcare settings.

关键词： Embedded board Real-time classification Lightweight CNN model Wavelet transforms

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Corrigendum to “Optimization and control of solar-wind islanded hybrid microgrid by using heuristic and deterministic optimization algorithms and fuzzy logic controller” [Energy Rep. 10 (2023) 3272–3288]

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Energy Reports 2023年 10卷 3847-3847页

作者： Sk.A. Shezan Md. Fatin Ishraque GM Shafiullah Innobent Kamwa Liton Chandra Paul SM Muyeen Ramakrishna NSS Mohammed Zeehan Saleheen Polamarasetty P. Kumar School of Industrial Automation and Electrical Engineering Engineering Institute of Technology Melbourne VIC Australia School of Engineering and Energy Murdoch University Australia Department of Electrical Electronic and Communication Engineering Pabna University of Science and Technology Pabna Bangladesh Department of Electrical Engineering and Computer Engineering Laval University Quebec QC G1V 0A6 Canada Department of Electrical Engineering Qatar University Doha 2713 Qatar Department of Electrical and Electronics GMR Institute of Technology Rajam Andhra Pradesh 532127 India Department of Data Science & AI Faculty of IT Monash University Australia

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Simulation of a 7R manipulator inverse calibration by using an optimized neural network based on beetle antennae search algorithm

Simulation of a 7R manipulator inverse calibration by using ...

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ASME 2019 International Mechanical engineering Congress and Exposition, IMECE 2019

作者： Wang, Yuxiang Chen, Zhangwei Zu, Hongfei Zhang, Xiang State Key Laboratory of Fluid Power and Mechatronics Systems Zhejiang University Hangzhou China Faculty of Mechanical Engineering&Automation Zhejiang Sci-Tech University Hangzhou China Institute of Computer Science Hangzhou Dianzi University Hangzhou China

ISBN: (纸本)9780791883518

This paper proposes an intelligent technique by combining multilayer feed-forward neural networks (MLFNN) and beetle antennae search (BAS) algorithm for the inverse calibration of a 7R manipulator. First of all, the MLFNN is optimized by BAS. Then, the optimized BAS-MLFNN method is employed for the nonlinear mapping from the true angles of the joint to the errors of the joint. The true angle values of the joints are selected as the inputs and the joint errors as the outputs. The simulation of calibration is implemented on a Kuka’s LWR manipulator. Numerical simulation results show that compared with the commonly used BPNN, the proposed BAS-MLFNN method possesses better performance in terms of higher precision. Copyright © 2019 ASME.

关键词： Manipulators

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Reviewing continual learning from the perspective of human-level intelligence

arXiv

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arXiv 2021年

作者： Chang, Yifan Li, Wenbo Peng, Jian Tang, Bo Kang, Yu Lei, Yinjie Gui, Yuanmiao Zhu, Qing Liu, Yu Li, Haifeng The State Key Laboratory of Fire Science Department of Automation and Institute of Advanced Technology University of Science and Technology of China Hefei China Institute of Intelligent Machines Chinese Academy of Sciences Hefei230031 China The Department of Electrical and Computer Engineering Mississippi State University United States College of Electronics and Information Engineering Sichuan University Chengdu China Faculty of Geosciences and Environmental Engineering The Southwest Jiaotong University Chengdu China Institute of Remote Sensing and Geographic Information System Peking Universit Beijing China School of Geosciences and Info-Physics Central South University South Lushan Road Changsha410083 China

Humans continual learning (CL) ability is closely related to Stability Versus Plasticity Dilemma that describes how humans achieve ongoing learning capacity and preservation for learned information. The notion of CL has always been present in artificial intelligence (AI) since its births. This paper proposes a comprehensive review of CL. Different from previous reviews that mainly focus on the catastrophic forgetting phenomenon in CL, this paper surveys CL from a more macroscopic perspective based on the Stability Versus Plasticity mechanism. Analogous to biological counterpart, "smart" AI agents are supposed to i) remember previously learned information (information retrospection);ii) infer on new information continuously (information prospection:);iii) transfer useful information (information transfer), to achieve high-level CL. According to the taxonomy, evaluation metrics, algorithms, applications as well as some open issues are then introduced. Our main contributions concern i) rechecking CL from the level of artificial general intelligence;ii) providing a detailed and extensive overview on CL topics;iii) presenting some novel ideas on the potential development of CL. © 2021, CC BY.

关键词： Petroleum reservoir evaluation

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Fast generation of high fidelity RGB-D images by deep-learning with adaptive convolution

arXiv

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arXiv 2020年

作者： Xian, Chuhua Zhang, Dongjiu Dai, Chengkai Wang, Charlie C.L. Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information School of Computer Science and Engineering South China University of Technology Guangzhou Guangzhou510006 China Faculty of Industrial Design Engineering Delft University of Technology Netherlands Department of Mechanical and Automation Engineering Chinese University of Hong Kong Hong Kong

Using the raw data from consumer-level RGB-D cameras as input, we propose a deep-learning based approach to efficiently generate RGB-D images with completed information in high resolution. To process the input images in low resolution with missing regions, new operators for adaptive convolution are introduced in our deep-learning network that consists of three cascaded modules - the completion module, the refinement module and the super-resolution module. The completion module is based on an architecture of encoder-decoder, where the features of input raw RGB-D will be automatically extracted by the encoding layers of a deep neural-network. The decoding layers are applied to reconstruct the completed depth map, which is followed by a refinement module to sharpen the boundary of different regions. For the super-resolution module, we generate RGB-D images in high resolution by multiple layers for feature extraction and a layer for up-sampling. Benefited from the adaptive convolution operators proposed in this paper, our results outperform the existing deep-learning based approaches for RGB-D image complete and super-resolution. As an end-to-end approach, high fidelity RGB-D images can be generated efficiently at the rate of 22 frames per second. Copyright © 2020, The Authors. All rights reserved.

关键词： Cameras

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LDCNN-DTI: A Novel Light Deep Convolutional Neural Network for Drug-Target Interaction Predictions

LDCNN-DTI: A Novel Light Deep Convolutional Neural Network f...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Shudong Wang Zhenzhen Du Mao Ding Renteng Zhao Alfonso Rodriguez-Paton Tao Song College of Computer Science and Technology China University of Petroleum Qingdao China School of Electrical Engineering and Automation Tiangong University Tianjin China The Second Hospital of Shandong University Jinan China Trinity earth Technology Co.Ltd Faculty of Computer Science Polytechnical University of Madrid Campus de Montegancedo Boadilla del Monte Madrid Spain

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

In computational drug discovery, accurately predicting drug-target interaction (DTI) is vital for drug repositioning and developing new drugs. With DTI data rapidly accumulated in recent years, it is recently hot to use deep learning technology to predict DTIs, but still a challenge to design light learning frameworks by using less protein descriptors. In this work, to address the challenge, a novel light deep convolutional neural network (namely LDCNN) is proposed to predict DTIs, in which a small number of protein descriptors are produced by convolving amino acid sequences of different lengths. As results, it is obtained that LDCNN can reduce the number of neurons in convolution layers and filters by 50%, with lose of AUC 1.3% and AUPR 4% comparing with DeepConv method. Our LDCNN models can give hints in designing light deep learning models for DTI prediction.

关键词： Drugs Neurons Biological neural networks Predictive models Feature extraction Diffusion tensor imaging Convolution

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A Parallel Uncertain Frequent Itemset Mining Algorithm with Spark

A Parallel Uncertain Frequent Itemset Mining Algorithm with ...

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IEEE International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)

作者： Jiaman Ding Haibin Li Yuanyuan Wang Lianyin Jia Jinguo You Yang Yang Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China College of Engineering and Computer Science Australian National University Canberra Australia

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126173

Frequent Itemset Mining (FIM) from large-scale databases has emerged as an important problem in the data mining and knowledge discovery research community. However, FIM suffers from three important limitations with the rapidly expanding of big data in all domains. First, it assumes that all items have the same importance. Second, it ignores the fact that data collected in a real-life environment is often inaccurate. Third, it is also a data-intensive and computation-intensive process which makes the FIM algorithm very time-consuming over large datasets. To address these issues, we propose a Parallel uncertain frequent itemset mining algorithm with spark (Pufim). Pufim firstly expresses item uncertainty by considering both the probability and weight, and calculates the maximum probability weight value of 1-items. Next, a distributed Pufim-tree structure is designed inspiring by FP-Tree for reducing the times of scanning the databases. Each node of Pufim-tree stores an item and its maximum probability weight value. Finally, experiments on publicly available UCI datasets demonstrate that Pufim achieves more prominent results than other related approaches across various metrics. In addition, the empirical study also shows Pufim has a good scalability.

关键词： Itemsets Data mining Probability Database systems

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MACHINE LEARNING APPROACHES FOR COVID-19 DETECTION FROM CHEST X-RAY IMAGING: A SYSTEMATIC REVIEW

arXiv

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arXiv 2022年

作者： Arteaga-Arteaga, Harold Brayan delaPava, Melissa Mora-Rubio, Alejandro Bravo-Ortíz, Mario Alejandro Alzate-Grisales, Jesus Alejandro Arias-Garzón, Daniel López-Murillo, Luis Humberto Buitrago-Carmona, Felipe Villa-Pulgarín, Juan Pablo Mercado-Ruiz, Esteban Orozco-Arias, Simon Hassaballah, M. de la Iglesia-Vaya, Maria Cardona-Morales, Oscar Tabares-Soto, Reinel Department of Electronics and Automation Universidad Autónoma de Manizales Manizales Colombia Department of Chemical Engineering Universidad Nacional de Colombia Manizales Colombia Department of Computer Science Universidad Autónoma de Manizales Manizales Colombia Department of Systems and Informatics Universidad de Caldas Manizales Colombia Faculty of Computers and Information South Valley University Qena Egypt Unidad Mixta de Imagen Biomédica FISABIO-CIPF Fundación para el Fomento de la Investigación Sanitario y Biomédica de la Comunidad Valenciana Valencia Spain

There is a necessity to develop affordable, and reliable diagnostic tools, which allow containing the COVID-19 spreading. Machine Learning (ML) algorithms have been proposed to design support decision-making systems to assess chest X-ray images, which have proven to be useful to detect and evaluate disease progression. Many research articles are published around this subject, which makes it difficult to identify the best approaches for future work. This paper presents a systematic review of ML applied to COVID-19 detection using chest X-ray images, aiming to offer a baseline for researchers in terms of methods, architectures, databases, and current limitations. © 2022, CC BY.

关键词： COVID-19

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