检索结果-内蒙古大学图书馆

7th International Conference on Advances in Computing, Communications and Informatics, ICACCI 2018

作者： Kiran, Kadambari Sai Mandal, Anupam Prasanna Kumar, K.R. Mitra, Pabitra Veni, S. Dept. of Electronics and Communication Engineering Amrita Vishwa Vidyapeetham India Center for Artificial Intelligence and Robotics CV Raman Nagar Bangalore India Dept. of Computer Science and Engineering Indian Institute of Technology Kharagpur India

ISBN: (纸本)9781538653142

In this paper, different dictionary learning and sparse coding algorithms are studied namely k-medoid, sparse non-negative matrix factorization (sNMF), active newton set algorithm (ASNA) and supervised non-negative matrix factorization (sup. NMF) for spoken word recognition task. The number of dictionary atoms per word class determined as 100 based on our proposed approach is empirically verified to be optimal considering tradeoff between accuracy and time of computation. The recognition task involved classification (matching) of sparsely coded word templates generated using learned dictionaries. A classification method based on the sum of non-zero weights of the coded representation is proposed for word recognition. The method does not require building class specific models making it apt for template based recognition. Moreover, it does not require signal recovery unlike most of the existing template based methods that performs matching on templates derived from recovered signal. The representation generated using magnitude short-term fourier transform (STFT) features, sNMF for dictionary learning and sup. NMF for sparse coding in conjunction with our proposed method of classification is found to exhibit an accuracy of 88.82% on clean speech, the highest among those studied. This outperformed the accuracy figures of the corresponding methods based on signal recovery such as signal-distortion-ratio. For noisy speech at different signal-to-noise ratio (SNR) levels, the combined dictionary comprising of separately trained speech and noise dictionaries provided the best recognition accuracy of 35.5% at zero dB SNR. © 2018 IEEE.

关键词： Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

Spoken dialogue-based remote control of educational mobile robot with mecanum wheels 17

Spoken dialogue-based remote control of educational mobile r...

引用

17th IEEE International Conference on Emerging eLearning Technologies and Applications, ICETA 2019

作者： Chivarov, Nayden Chikurtev, Denis Pleva, Matus Ondas, Stanislav Juhar, Jozef Liao, Yuan-Fu Institute of Information and Communication Technologies Bulgarian Academy of Sciences BAS Acad. G. Bonchev St. Sofia1113 Bulgaria Robotics Lab European Polytechnical University 23 Sv. Sv. Kiril i Metodiy Str. Pernik2300 Bulgaria Department of Electronics and Multimedia Communications Faculty of Electrical Engineering and Informatics Technical University of Kosice Letna 9 Kosice04200 Slovakia Department of Electronic Engineering College of Electrical Engineering &computer Science National Taipei University of Technology Zhongxiao E. Rd. 406 Taipei10608 Taiwan

ISBN: (纸本)9781728149677

This paper describes a new platform of educational mobile robot with Mecanum wheels and voice enabled dialogue-based human robot interaction model was established. Moreover, new trends in using deep neural networks for cloud-and edge-based speech recognition was described in detail. Finally, the progress of both the cloudand edge-based speech recognition was reported. Especially, promising usability test result on resisting room reverberation and background noise had been achieved by the proposed convolutional neural network (CNN)-based wake-up word (WUW) spotting system. © 2019 IEEE.

关键词： Mobile robots

来源：评论

学校读者我要写书评

暂无评论

MetaSleepLearner: A pilot study on fast adaptation of bio-signals-based sleep stage classifier to new individual subject using Meta-Learning

arXiv

引用

arXiv 2020年

作者： Banluesombatkul, Nannapas Ouppaphan, Pichayoot Leelaarporn, Pitshaporn Lakhan, Payongkit Chaitusaney, Busarakum Jaimchariyatam, Nattapong Chuangsuwanich, Ekapol Chen, Wei Phan, Huy Dilokthanakul, Nat Wilaiprasitporn, Theerawit Bio-inspired Robotics and Neural Engineering Lab School of Information Science and Technology Vidyasirimedhi Institute of Science & Technology Rayong Thailand Department of Otolaryngology Faculty of Medicine Chulalongkorn University Thailand Excellence Center for Sleep Disorders King Chulalongkorn Memorial Hospital Thai Red Cross Society Bangkok Thailand Division of Pulmonary and Critical Care Medicine Faculty of Medicine Chulalongkorn University Thailand Head of Excellence Center for Sleep Disorders King Chulalongkorn Memorial Hospital Thai Red Cross Society Bangkok Thailand The Computer Engineering Department Chulalongkorn University Bangkok Thailand The Center for Intelligent Medical Electronics Department of Electronic Engineering School of Information Science and Technology Fudan University Shanghai200433 China The School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom

Identifying bio-signals based-sleep stages requires time-consuming and tedious labor of skilled clinicians. Deep learning approaches have been introduced in order to challenge the automatic sleep stage classification conundrum. However, the difficulties can be posed in replacing the clinicians with the automatic system due to the differences in many aspects found in individual bio-signals, causing the inconsistency in the performance of the model on every incoming individual. Thus, we aim to explore the feasibility of using a novel approach, capable of assisting the clinicians and lessening the workload. We propose the transfer learning framework, entitled MetaSleepLearner, based on Model Agnostic Meta-Learning (MAML), in order to transfer the acquired sleep staging knowledge from a large dataset to new individual subjects (source code is available at https://***/IoBTVISTEC/MetaSleepLearner). The framework was demonstrated to require the labelling of only a few sleep epochs by the clinicians and allow the remainder to be handled by the system. Layer-wise Relevance Propagation (LRP) was also applied to understand the learning course of our approach. In all acquired datasets, in comparison to the conventional approach, MetaSleepLearner achieved a range of 5.4% to 17.7% improvement with statistical difference in the mean of both approaches. The illustration of the model interpretation after the adaptation to each subject also confirmed that the performance was directed towards reasonable learning. MetaSleepLearner outperformed the conventional approaches as a result from the fine-tuning using the recordings of both healthy subjects and patients. This is the first work that investigated a non-conventional pre-training method, MAML, resulting in a possibility for human-machine collaboration in sleep stage classification and easing the burden of the clinicians in labelling the sleep stages through only several epochs rather than an entire recording. © 2020, CC BY-N

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Low-power wireless sensor network system for early diagnostic of subacute rumen acidosis in cows

Low-power wireless sensor network system for early diagnosti...

引用

IEEE Workshop on Advances in Information, Electronic and Electrical engineering (AIEEE)

作者： Anatolijs Zabasta Nadezda Kunicina Kristaps Vitols Ilmars Duritis Uldis Grunde Janis Judvaitis Modris Greitans Ilga Sematovica Aija Malniece Ilja Galkins Institute of Electrical Engineering and Electronics EEF Riga Technical University Riga Latvia Faculty of Veterinary Medicine Latvia University of Life Sciences and Technologies Jelgava Latvia Robotics and Machine Perception laboratory Institute of Electronics and Computer Science Riga Latvia Cyber-Physical Systems laboratory Institute of Electronics and Computer Science Riga Latvia

High-yielding cow rations under intensive production conditions contribute to the development of subacute rumen acidosis (SARA), leading to pathologies such as rumenitis, laminitis, reproductive disorders, loss of productivity and reduced longevity. The aim of this work is to develop a reticulo-ruminal long-acting cyber-physical system for monitoring rumen parameters. Three scientific institutions in cooperation with two farmers conduct the research in order to create a prototype of a low-power wireless sensor network system for early diagnostic of subacute rumen acidosis of dairy cows. The new system architecture includes, reticulo-ruminal bolus with pH and temperature sensors, a microcontroller, a radio transmitter and a power supply module. The system includes a base station for data collection from boluses, an MQTT broker, a web server and a database. Data communication solution has been developed and tested in the laboratory, and micro-controllers have been selected and adapted for data processing. In addition, research is under way to create an autonomous long-term power supply system. Work shall be conducted in two directions: (a) stand-alone battery-powered electricity supply system; (b) an autonomous power supply system based on the generation of an electrostatic generator. The results of the initial stage of the research are discussed in this paper.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Fault Detection and Isolation for a 3-DOF Helicopter with Sliding Mode Strategies 15

Fault Detection and Isolation for a 3-DOF Helicopter with Sl...

引用

15th International Workshop on Variable Structure Systems, VSS 2018

作者： Perez, U. Capello, E. Punta, E. Perea, J. Fridman, L. Department of Control and Robotics Division of Electrical Engineering Engineering Faculty National Autonomous University of Mexico C.P. 04510 Mexico Politecnico di Torino Department of Mechanical and Aerospace Engineering Torino10129 Italy National Research Council of Italy CNR-IEIIT Institute of Electronics Computer and Telecommunication Engineering Torino Italy

ISBN: (纸本)9781538664391

This paper analyzes the problem of detection and isolation of faults in the actuators of a 3-Degree-of-Freedom (3-DOF) helicopter by a residual-based approach. A third-order sliding mode differentiator is designed for the evaluation of the residuals. Moreover, two sliding mode controllers are suitably designed to stabilize the angular positions and velocities: (i) a super twisting control system and (ii) a continuous twisting control system. The performance of the designed controllers and the effectiveness of the residual-based method are illustrated by simulations. © 2018 IEEE.

关键词： Controllers

来源：评论

学校读者我要写书评

暂无评论

Target localization intervention and prognosis evaluation for an individual with mild cognitive impairment

Brain‐X

引用

Brain‐X 2023年第3期1卷

作者： Weiping Wang Haiyan Zhao Chang He Yuanbo Cui Zhen Wang Alexander Hramov Ping Luan Xiong Luo Jipeng Ouyang Kurths Jürgen School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Shunde Graduate School University of Science and Technology Beijing Foshan Guangdong China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing China Contribution: Writing - review & editing Department of Neurology Peking University Third Hospital Beijing China Contribution: Resources Zhen Wang The Center for Optical Imagery Analysis and Learning and School of Mechanical Engineering Northwestern Polytechnical University Xi'an Shaanxi 710072 China. Contribution: Writing - original draft The 6th Research Institute of China Electronics Corporation Beijing China The Center for Optical Imagery Analysis and Learning and School of Mechanical Engineering Northwestern Polytechnical University Xi'an Shaanxi China Chang He School of Computer and Communication Engineering University of Science and Technology Beijing Beijing 100083 China. Contribution: Investigation Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Saratov State Medical University Saratov Russia Contribution: Data curation Guangdong Second Provincial General Hospital & Health Science Center Shenzhen Guangdong China Department of Neurology Shunde Hospital Southern Medical University Foshan Guangdong China Institute of Physics Humboldt-University Berlin Germany Potsdam Institute for Climate Impact Research Potsdam Germany

Currently, no specific treatments are available for Alzheimer's disease (AD). Mild cognitive impairment (MCI), the preclinical stage of AD, has a high possibility of reversing symptoms through neural regulation. A state dynamics model for single brain regions was developed to simulate blood oxygen level-dependent signals in a patient with early mild cognitive impairment. Subsequently, the analysis of functional connections was used to comprehensively consider multiple complex network centralities to locate the intervention targets, and a multiple brain region collaborative control scheme was designed. Finally, the reliability and effectiveness of the intervention were verified at the brain region and subnetwork levels. This technique provides a basis for future clinical diagnosis and treatment of AD and MCI.

关键词： adaptive pinning control Alzheimer's disease BOLD signals computational modeling Sub_network

来源：评论

学校读者我要写书评

暂无评论

Why is the Winner the Best?

Why is the Winner the Best?

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Robust lane marking detection algorithm using drivable area segmentation and extended SLT

arXiv

引用

arXiv 2019年

作者： Ozgunalp, Umar Fan, Rui Cheng, Shanshan Sun, Yuxiang Zuo, Weixun Zhu, Yilong Xue, Bohuan Zheng, Linwei Liang, Qing Liu, Ming Electrical and Electronics Engineering Cyprus International University North Cyprus Turkey Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong Hangzhou ATG Intelligent Technology Co. Ltd. Hangzhou China Guolu Gaoke Engineering Technology Institute Co. Ltd. Beijing China Robotics and Multiperception Laboratory Robotics Institute Hong Kong University of Science and Technology Hong Kong Hong Kong

In this paper, a robust lane detection algorithm is proposed, where the vertical road profile of the road is estimated using dynamic programming from the v-disparity map and, based on the estimated profile, the road area is segmented. Since the lane markings are on the road area and any feature point above the ground will be a noise source for the lane detection, a mask is created for the road area to remove some of the noise for lane detection. The estimated mask is multiplied by the lane feature map in a bird's eye view (BEV). The lane feature points are extracted by using an extended version of symmetrical local threshold (SLT), which not only considers dark light dark transition (DLD) of the lane markings, like (SLT), but also considers parallelism on the lane marking borders. The segmentation then uses only the feature points that are on the road area. A maximum of two linear lane markings are detected using an efficient 1D Hough transform. Then, the detected linear lane markings are used to create a region of interest (ROI) for parabolic lane detection. Finally, based on the estimated region of interest, parabolic lane models are fitted using robust fitting. Due to the robust lane feature extraction and road area segmentation, the proposed algorithm robustly detects lane markings and achieves lane marking detection with an accuracy of 91% when tested on a sequence from the KITTI dataset. Copyright © 2019, The Authors. All rights reserved.

关键词： Dynamic programming

来源：评论

学校读者我要写书评

暂无评论

Performance study of strongly coupled magnetic resonance

arXiv

引用

arXiv 2019年

作者： Wijerathna Basnayaka, Chathuranga M. Jayakody, Dushantha Nalin K. Sharma, Abhishek Wang, Hwang-Cheng Muthuchidambaranathan, P. Kaur, Kuljeet School of Postgraduate Studies and Research SRI LANKA Technological Campus Sri Lanka School of Computer Science and Robotics National Research Tomsk Polytechnic University Russia Dept. of Electronics and Communication Engineering LNM Institute of Information and Technology Jaipur India Department of Electronic Engineering National Ilan University Ilan Taiwan Dept. of Electronics and Communication Engineering National Institute of Technology Tiruchirappalli India Ecole de Technologie Superieure Universite du Quebec Montreal Canada

Strongly Coupled Magnetic Resonance (SCMR) uses electromagnetic resonance in order to efficiently transfer power wirelessly over mid-range distances. Since the energy exchange capability of resonant objects higher than non-resonant objects, strongly coupled systems are able to achieve more efficient energy transfer than other wireless power transfer systems. The paper presents detailed experimental and simulated analysis of the performance of the SCMR system. A prototype of the SCMR system was implemented and experiments were conducted to analyze the performance of the system. Finally, the resonant frequency of the system was experimentally verified and the factors influencing the wireless power transfer were also studied. Copyright © 2019, The Authors. All rights reserved.

关键词： Energy transfer

来源：评论

学校读者我要写书评

暂无评论

Wideband Cognitive Radio Networks Based Compressed Spectrum Sensing: A Survey

引用

Journal of Signal and Information Processing 2018年第2期9卷 122-151页

作者： Mohammed Abo-Zahhad Sabah M. Ahmed Mohammed Farrag Khaled Ali BaAli Electronics and Communication Engineering Department School of Electronics Communications and Computer Engineering Egypt-Japan University for Science and Technology Alexandria Egypt Electrical and Electronics Engineering Department Faculty of Engineering Assiut University Assiut Egypt Mechatronics and Robotics Engineering Department School of Innovative Design Engineering Egypt-Japan University for Science and Technology Alexandria Egyp Electrical Engineering Department King Khalid University (KKU) Abha KSA Electronics and Communication Department Faculty of Engineering and Petroleum Hadhramaut University Mukalla Republic of Yemen

Spectrum sensing is a core function at cognitive radio systems to have spectrum awareness. This could be achieved by collecting samples from the frequency band under observation to make a conclusion whether the band is occupied, or it is a spectrum hole. The task of sensing is becoming more challenging especially at wideband spectrum scenario. The difficulty is due to conventional sampling rate theory which makes it infeasible to sample such very wide range of frequencies and the technical requirements are very costly. Recently, compressive sensing introduced itself as a pioneer solution that relaxed the wideband sampling rate requirements. It showed the ability to sample a signal below the Nyquist sampling rate and reconstructed it using very few measurements. In this paper, we discuss the approaches used for solving compressed spectrum sensing problem for wideband cognitive radio networks and how the problem is formulated and rendered to improve the detection performance.

关键词： Cognitive Radio Spectrum Sensing Compressive Sensing Compressed Measurements Nyquist Sampling Rate

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：