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Multi-modal signal Based Childhood Rolandic Epilepsy Detection 6th

Multi-modal Signal Based Childhood Rolandic Epilepsy Detecti...

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6th international conference on Cognitive Systems and signal processing, ICCSIP 2021

作者： Wu, Yixian Hu, Dinghan Jiang, Tiejia Gao, Feng Cao, Jiuwen Machine Learning and I-health International Cooperation Base of Zhejiang Province Hangzhou Dianzi University Hangzhou310018 China Artificial Intelligence Institute Hangzhou Dianzi University Zhejiang Hangzhou310018 China Department of Neurology The Children’s Hospital Zhejiang University School of Medicine National Clinical Research Center for Child Health Zhejiang Hangzhou China

ISBN: (纸本)9789811692468

Electroencephalogram (EEG) is the common signal used in epilepsy analysis but suffered from the inconvenient issue in data acquisition, especially when applied to children with Rolandic epilepsy. In this paper, we present a study on multi-modal signal based epileptic seizure detection for children suffered from Rolandic epilepsy, where EEG combined with the synchronized surveillance video is included for analysis. the Mel frequency cepstrum coefficient (MFCC) and linear prediction cepstrum coefficient (LPCC) features are taken to characterize EEG. the spatiotemporal interest points (STIP) are extracted from video sequences to construct a bag of words model, which are based on the descriptors of histograms of oriented gradient (HOG) and the histograms of oriented optical flow (HOF) in the neighborhood. the histograms of word frequency (HWF) features are obtained for video representation. Direct feature fusion on the EEG based MFCC+LPCC and video based HWF is applied for classifier training. Data of 13 children with Rolandic epilepsy recorded from the Children’s Hospital, Zhejiang University School of Medicine (CHZU) are applied in the experiment. the results show that the model trained on the multi-modal features of EEG and video can achieve the highest overall accuracy of 98.2%. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Electroencephalography

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Unified Analysis of Algorithms for Dictionary learning

Unified Analysis of Algorithms for Dictionary Learning

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Computing and Big Data (ICCBD), international conference on

作者： Min Xiang School of Mathematical Sciences Zhejiang University Hangzhou China

the advantages of data sparse representation have generated a lot of interest in recent years. the goal of dictionary learning is to seek an over-complete dictionary adaptively by data, from which every signal can be represented approximately by a linear combination of only a few columns of the dictionary. this problem is often solved by solving two optimization processes iteratively: sparse approximation and dictionary update. We analyze these two processes respectively. the drawback of the greedy algorithm for sparse approximation is discussed in this paper, and we give a new forward-backward index updating strategy for sparse approximation, which makes use of previous sparse patterns in iterations. A unified analysis of the framework for dictionary update is given, and we conclude that the dictionary update of existing algorithms all can be regarded as the projected gradient algorithm on Euclidean space or Oblique manifold with different choices of step size. Based on our analysis, a new algorithm is proposed for dictionary learning with a convergent cost function sequence. Numerical simulations show that our algorithm can be applied to refine the solutions produced by existing dictionary learning algorithms.

关键词： Manifolds Greedy algorithms Dictionaries machine learning Big Data Approximation algorithms Sparse representation

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Research on Calculation of Feeding Frequency of Fish for Precise Feeding Based on Deep learning

Research on Calculation of Feeding Frequency of Fish for Pre...

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IEEE international conference on Information Communication and signal processing (ICICSP)

作者： Yufeng Zhang Junchao Yang Lulu Jia Jinhui Yang School of artificial intelligence Chongqing Technology and Business University Chongqing China School of environment and resources Chongqing Technology and Business University Chongqing China National Research Base of Intelligent Manufacturing Service Chongqing Technology and Business University Chongqing China

In Recirculating Aquaculture System (RAS), feeding frequency is an important factor affecting fish growth, and precise feeding according to the state of fish is the key to improving the aquaculture efficiency. the current problems in detecting the feeding frequency of fish include low efficiency, high technical requirements, and the influenced by the aquaculture environment. In this paper, we adopt a computer vision method to calculate feeding frequency to achieve objective with high accuracy. Firstly, we process the video of the fish according to the inter-frame difference method to obtain the image of the feeding state of the fish. then we propose a modified VGG16 model to determine the feeding state of the fish, transform it into a 0–1 classification problem and calculate the feeding frequency of the fish. the feeding frequency and the growth status of the fish are then used to develop an intelligent feeding strategy to improve the growth rate of the fish and the conversion rate of the bait. Tests have shown that the accuracy of identifying feeding state by the modified VGG16 model can reach 92.4%. the method has a positive effect on the development of recirculating aquaculture system.

关键词： Technical requirements Computer vision Time-frequency analysis Computational modeling Transforms signal processing Fish

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PbV mSp: A priority-based VM selection policy for VM consolidation in green cloud computing

PbV mSp: A priority-based VM selection policy for VM consoli...

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international conference on signal processing and Information Security (ICSPIS)

作者： Riman Mandal Manash Kumar Mondal Sourav Banerjee Pushpita Chatterjee Wathiq Mansoor Utpal Biswas Department of Comp. Sc. and Engg. University of kalyani Kalyani India Department of Comp. Sc. and Engg. Kalyani Govt. Engineering College Kalyani India Department of Computer Science Tennessee State University Nashville TN USA Department of Electrical Engineering University of Dubai Dubai UAE

ISBN: (纸本)9781665492669

Cloud computing forms the backbone of the era of automation and the Internet of things (IoT). It offers computing and storage-based services on consumption-based pricing. Large-scale datacenters are used to provide these service and consumes enormous electricity. Datacenters contribute a large portion of the carbon footprint in the environment. through virtual machine (VM) consolidation, datacenter energy consumption can be reduced via efficient resource management. VM selection policy is used to choose the VM that needs migration. In this research, we have proposed PbV mSp: A priority-based VM selection policy for VM consolidation. the PbV mSp is implemented in cloudsim and evaluated compared with well-known VM selection policies like gpa, gpammt, mimt, mums, and mxu. the results show that the proposed PbV mSp selection policy has outperformed the exisitng policies in terms of energy consumption and other metrics.

关键词： Measurement Cloud computing Energy consumption Green products Information security Pricing signal processing

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Assistive Stick for Visually Impaired People

Assistive Stick for Visually Impaired People

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international conference on Multimedia, signal processing and Communication Technologies, (IMPACT)

作者： Danish Asad Khan Mohammed Asif Zamir Mohammad Sarosh Umar Zafaryab Haider D/O Computer Engineering Jamia Millia Islamia New Delhi India SJMSOM Indian Institute of Technology Bombay Mumbai India D/O Computer Engineering Aligarh Muslim University Aligarh India IIRU Aligarh Muslim University Aligarh India

ISBN: (纸本)9781665476485

Blind or visually impaired people have difficulty navigating the street and are also unaware of nearby landmarks. they need an assistant (e.g. a cane, human, dog, e.t.c.) to go from one place to another place. In this modern era, it is possible to develop smart solutions with the help of modern technologies like deep learning (DL), natural language processing (NLP), internet of things (IoT), etc. that can assist them without any human intervention. A smart stick is one of the solutions that can help them with navigation and get to know the surrounding places. this paper proposed a smart solution and developed a smart stick to help visually impaired people with the use of DL and NLP and devices like the Raspberry Pi, an ultrasonic sensor, and a camera. this smart stick can detect obstacles using an ultrasonic sensor within the range of 1.5 metres and alert the person by generating audio as a warning message. the stick is also able to capture images from its surroundings using the camera and can read the text present in the captured image. the text in the image is detected using a deep learning model called the EAST text detector and recognised using an OCR Engine tesseract. Furthermore, the recognised text is converted into audio so that the person can hear about their surroundings. All calculations and processing are done on the Raspberry Pi, and the generated audio can be heard by using a speaker(earphone or headphone). the proposed solution is successfully implemented and demonstrated, and it is found to be very efficient and cost-effective.

关键词： Deep learning Text recognition Navigation Optical character recognition signal processing Cameras Natural language processing

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Cooperative Computation Offloading for Video Analysis in Ultra-Dense LEO Satellite-Terrestrial Networks

Cooperative Computation Offloading for Video Analysis in Ult...

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IEEE international conference on Information Communication and signal processing (ICICSP)

作者： Qi Zhao Tianjiao Chen Jiang Liu Fangqi Liu Yuke Zhou Beijing Institute of Spacecraft Environment Engineering Beijing China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecom Beijing China State Key Laboratory of Networking and Switching Technology Purple Mountain Laboratories Beijing University of Posts and Telecom Beijing Nanjing China

Video analysis using artificial intelligence (AI) is widely adopted in various services. However, ground users with limited resources may not process such tasks locally. Fortunately, the ultra-dense low earth orbit (LEO) satellite networks allow multiple satellites to cooperatively handle these tasks to provide low-latency computing services. therefore, this paper considers a cooperative computation offloading scheme for video analysis in ultra-dense LEO satellite-terrestrial networks, allowing for flexible task scheduling and video quality selection. Considering the privacy of satellites and the dynamic network environment, the cooperative computation offloading problem is established as a distributed Markov decision process (MDP) to reduce the task delay while increasing the accuracy of video analysis. then, a multi-agent deep reinforcement learning (DRL) approach is proposed to obtain efficient offloading strategies. Finally, simulations are conducted to verify the feasibility and performance of the proposed scheme.

关键词： Privacy Satellites Processor scheduling Low earth orbit satellites signal processing algorithms Dynamic scheduling Quality assessment

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Decision Fusion Based Multi-type Shot Boundary Detection in Real Time

Decision Fusion Based Multi-type Shot Boundary Detection in ...

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IEEE international conference on Information Communication and signal processing (ICICSP)

作者： Huizhong Shi Yana Zhang Yanfang Li Department of Information and Communication Engineering Communication University of China Beijing China

With the development of mobile Internet and cloud computing, especially the popularization of lightweight handheld cameras, online videos are growing rapidly. For efficient re-editing of the existing videos, intelligent content analysis and automatic video processing become focused. Shot boundary detection (SBD) is a basic algorithm of intelligent video processing. From threshold-based boundary detection to deep learning-based boundary detection, it is difficult for one algorithm to work well on the multi-type shot boundary (a straight transition and various types of gradual transitions). What's more, the detection accuracy is limited by the abrupt changes in videos caused by fast motion and illumination variation. this paper proposes a multi-type shot boundary detection algorithm based on decision fusion (MSBD-DF). the distance clustering based on LAB histogram, instead of shot clustering, directly tells if it is a frame in a shot or in the transiting. Decision fusion further decides whether it is a boundary of a cut or a gradual transition. Also, a key-point matching based on ORB improves the detection robustness against the fast motion and illumination variation. the experimental results show that the F1 value of cut boundary detection reaches 96.45%, and concurrently the F1 value of gradual transition boundary detection reaches 92.31%. the average detection speed is 9.03ms/frame, being applicable to real-time video processing.

关键词： Histograms Semantics signal processing algorithms Lighting signal processing Feature extraction Real-time systems

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Data Streams Management: Multidimensional Summary with Big Data Tools

Data Streams Management: Multidimensional Summary with Big D...

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Computing and Big Data (ICCBD), international conference on

作者： Jean Gane Sarr Ndiouma Bame Aliou Boly Dept. Mathématiques et Informatique Cheikh Anta Diop University Dakar Senegal

Data streams are large, fast, varied and sometimes multidimensional. these characteristics are the reason why their processing and storage are a real challenge. In addition, they reduce the possibilities of querying them a posteriori. It therefore becomes necessary to set up summaries on these data streams for an analysis on the data already unloaded from the system. In this sense, many multidimensional summaries of data streams have been proposed but with some limits. thus, this article aims to present a new multidimensional summary approach for big data streams called StreamCubeCascadeMode as well as functions that create, load, and update the summary when new events arrive in the system. this proposal is realized by the construction of data storage structures called cubes. these cubes are calculated at the expiration of a time window taken from a Tilted-time window. the solution is implemented with the help of Big data tools which allows it to optimize storage and processing resources by having the ability to scale.

关键词： Geography Sentiment analysis Memory machine learning Big Data Data models Windows

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Cloud Computing, machine learning, and Secure Data Sharing enabled through Blockchain

Cloud Computing, Machine Learning, and Secure Data Sharing e...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Rajesh Singh Akkaraju Sailesh Chandra Babeetta Bbhagat Jeidy Panduro-Ramirez Anil Pandurang Gaikwad Bhasker Pant Uttaranchal Institute of Technology Uttaranchal University Uttarakhand India Doctoral Scholar School of Business and Management Christ (Deemed to be) University Bangalore Karnataka India Department of Computer Science & Information Technology Chhatrapati Shivaji Maharaj University Navi Mumbai Department of Business Universidad Tecnológica Del Perú Perú JSPM’S JSCOE-MCA Department Savitribai Phule Pune University Pune Maharashtra India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

Blockchain technologies are sweeping the globe. Cloud computing & secure data sharing have emerged as new technologies, owing to current advances in machine learning. Conventional machine learning algorithms need the collection & processing of training information on centralized systems. With the introduction of new decentralized machine learning algorithms & cloud computing, ML on-device information learning is now a reality. IoT gadgets may outsource training duties to cloud computing services to enable AI at the network’s perimeter. Furthermore, these dispersed edges intelligence architectures bring additional issues, also including consumer confidentiality & information safety. Blockchain has been proposed as a viable alternative to these issues. Blockchain, as a dispersed intelligent database, has evolved as a revolutionary innovation for the future phase of multiple industries’ uses due to its decentralized, accessible, & safe structure. this system also includes trustworthy automatic scripting running & unchangeable information recordings. As quantum technologies have proven more viable in the latest days, blockchain has faced prospective challenges from quantum computations. In this paper, we summarize the existing material in the study fields of blockchain-based cloud computing, machine learning, and secure data sharing, as well as a basic orientation to post-quantum blockchain to offer a summary of the existing state-of-the-art in these cutting-edge innovations.

关键词： Training Cloud computing Technological innovation Quantum computing machine learning algorithms machine learning Blockchains

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Automatic Recognition of Epileptiform EEG Abnormalities Using machine learning Approaches 5

Automatic Recognition of Epileptiform EEG Abnormalities Usin...

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5th international conference on Advanced Technologies for signal and Image processing (ATSIP)

作者： Ben Slimen, Itaf Seddik, Hassene Univ Tunis CEREP Res Lab ENSIT Tunis 1008 Tunisia

ISBN: (纸本)9781728175133

Epilepsy is one of the various neurological disorders with 1% of the world population. It is characterized by the anomalous of a large number of neurons. In this paper, a proposed automated system for seizure detection and diagnosis using EEG signals records. Seizures periods are generally characterized by epileptiform discharges with different changes including spike rate variation according to the shapes, spikes and the amplitude. the epileptiform is used as an indicator to anticipate the EEG signal class using machine learning methods. Based on EEG characterizes the proposed approach achieves a perfect classification rates with 99.8% using the Bonn database.

关键词： EEG spike number seizure detection KNN SVM RF

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