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machine learning and BCI

Machine learning and BCI

引用

international Winter Workshop on Brain-Computer Interface (BCI)

作者： Klaus-Robert Müller Machine Learning Group Berlin Germany Dept. of Brain and Cognitive Engineering Korea University Seoul Korea

Summary from only given. A main motivation for multimodal imaging has been the possibility to enhance medical diagnosis[1]. Beyond this original medical motivation the fusion of multiple modalities has created successful interesting research opportunities that have furthered our understanding of the brain and cognition[15]. In BCI recently multimodal fusion concepts have received great attention under the label hybrid BCI[13]. Fusing information has also been a very common practice in the sciences and engineering [17]. Recently a family of novel multimodal data analysis methods have emerged that can extract nonlinear relations between data[1,2,5-10]. They are rooted in the modern machine learning and signal processing techniques that are now available for analysing EEG, for decoding mental states etc[3,11,12,14,16]. The talk will first discuss recent multimodal analysis techniques such as SPoC[5-7]. Furthermore if time permits we will discuss a novel reliable method for estimating the Hurst exponent, a quantity that has recently become popular for describing network properties and is being used for diagnostic purposes[4]. Both nonlinear techniques allow for a better and more reliable and robust analysis of complex phenomena in neurophysiological data.

关键词： Decision support systems Abstracts

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Smart traffic optimization using image processing

Smart traffic optimization using image processing

引用

IEEE international conference in MOOC Innovation and Technology in Education (MITE)

作者： Pranav Maheshwari Deepanshu Suneja Praneet Singh Yogeshwar Mutneja Robotics Institute Carnegie Mellon University Pittsburgh U.S.A. Infosys Limited Bangalore India T.C.S Limited New Delhi India

Currently the traffic control system in place in our country is non-flexible and non-adaptive to the ever growing number of vehicles on the road. It does not take into account the changing density of traffic during the different hours of the day. Consequently the roads get congested frequently and intersections get blocked. Time and fuel, two highly important resources get wasted in this inefficient working of the present-day system. In this article, we propose a dynamic system that overcomes all these drawbacks. Our system uses cameras installed at the red lights and intersections to monitor the traffic dynamically. It then processes this information using image processing, computes the volume of the real time traffic, sets the timer of the signal accordingly. Simultaneously, it monitors if there is any scope of congestion at the intersection and adjusts the timer to prevent it. The entire system works autonomously and has a quick turnaround time, saving critical resources at every junction. The system also has the potential to adopt machine learning techniques in order to recognize the different emerging patterns of the future traffic and reach an optimal solution.

关键词： Robots Automobiles Gray-scale Image edge detection

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machine learning tools in machinery faults diagnosis: A review

Machine learning tools in machinery faults diagnosis: A revi...

引用

3rd international conference on Materials Engineering and Automatic Control, ICMEAC 2014

作者： Hui, K.H. Hee, Lim Meng Leong, M. Salman RAZAK School of Engineering and Advanced Technology Universiti Teknologi Malaysia Malaysia Universiti Teknologi Malaysia Malaysia

ISBN: (纸本)9783038351405

machinery faults can be detected by various signal processing tools;however, they require human expertise to achieve maximum success. machine learning tools can help to achieve automatic machinery-faults diagnosis. This paper provides a brief review of the most common machine learning tools. © (2014) Trans Tech Publications, Switzerland.

关键词： Fault detection

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Human activity recognition framework in monitored environments

Human activity recognition framework in monitored environmen...

引用

3rd international conference on Pattern Recognition Applications and Methods, ICPRAM 2014

作者： León, O. Cuéllar, M.P. Delgado, M. Le Borgne, Y. Bontempi, G. Dept. Computer Science and Artificial Intelligence University of Granada C/. Pdta. Daniel Saucedo Aranda s.n. Granada Spain Machine Learning Group Universit Libre de Bruxelles Campus LaPlaine Bruxelles Belgium

ISBN: (纸本)9789897580185

This work addresses the problem of the recognition of human activities in Ambient Assisted Living (AAL) scenarios. The ultimate goal of a good AAL system is to learn and recognise behaviours or routines of the person or people living at home, in order to help them if something unusual happens. In this paper, we explore the advances in unobstrusive depth camera-based technologies to detect human activities involving motion. We explore the benefits of a framework for gesture recognition in this field, in contrast to raw signal processing techniques. For the framework validation, Hidden Markov Models and Dynamic Time Warping have been implemented for the action learning and recognition modules as a baseline due to their well known results in the field. The results obtained after the experimentation suggest that the depth sensors are accurate enough and useful in this field, and also that the preprocessing framework studied may result in a suitable methodology. Copyright © 2014 SCITEPRESS.

关键词： Hidden Markov models

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Unsupervised relevance analysis for feature extraction and selection: A distance-based approach for feature relevance

Unsupervised relevance analysis for feature extraction and s...

引用

3rd international conference on Pattern Recognition Applications and Methods, ICPRAM 2014

作者： Peluffo, Diego H. Lee, John A. Verleysen, Michel Rodríguez, José L. Castellanos-Domínguez, Germán Machine Learning Group ICTEAM Université catholique de Louvain Place du Levant 3 B-1348 Louvain-la-Neuve Belgium Molecular Imaging Radiotherapy and Oncology IREC Université catholique de Louvain Avenue Hippocrate 55 B-1200 Bruxelles Belgium Grupo de Automática Antigua estacíon del Ferrocarril Universidad Autónoma de Manizales Manizales Colombia Signal Processing and Recognition Group Universidad Nacional de Colombia 7 Via al Magdalena Manizales Colombia

ISBN: (纸本)9789897580185

The aim of this paper is to propose a new generalized formulation for feature extraction based on distances from a feature relevance point of view. This is done within an unsupervised framework. To do so, it is first outlined the formal concept of feature relevance. Then, a novel feature extraction approach is introduced. Such an approach employs the M-norm as a distance measure. It is demonstrated that under some conditions, this method can readily explain literature methods. As another contribution of this paper, we propose an elegant feature ranking approach for feature selection followed from the spectral analysis of the data variability. Also, we provide a weighted PCA scheme revealing the relationship between feature extraction and feature selection. To assess the behavior of the studied methods within a pattern recognition system, a clustering stage is carried out. Normalized mutual information is used to quantify the quality of resultant clusters. Proposed methods reach comparable results with respect to literature methods. Copyright © 2014 SCITEPRESS.

关键词： Extraction

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A Time Effective Approach for Vector Similarity Measurement Based on Vector Addition

A Time Effective Approach for Vector Similarity Measurement ...

引用

3rd international conference on Civil and Architecture Engineering (ICCAE’2014);3rd international conference on Automation, Robotics and Mechanical Engineering (ICAMR’2014);3rd international conference on machine learning and Computer Science (IMLCS’2014);3rd international conference on Management, Humanity and Economics (ICMHE’2014);3rd international conference on Power, Control and VLSI Engineering (ICPCVE’2014)

作者： Wasiq Khan Ping Jiang Mohammed Bilal Speech Signal Processing University of Bradford Department of computer science intelligent systemsUniversity of HullUnited Kingdom Data Mining University of LeedsUnited Kingdom

Pattern matching is one of the principal methodologies in speech signal processing and isolated word recognition. There are a number of similarity measures to find the similarity between the target words in an acquired speech by matching with template. The retrieved similarity result is dependent both on the feature vectors extraction technique and the distance metric produced by similarity measure. Short Time Fourier Transform is used for spectrogram re- trieval which is used for performance comparison of different simi- larity measures. In this paper, a novel similarity measure is proposed which is based on vector addition. It outperforms in terms of execu- tion time as compared to other similarity measures. We have done a detailed study of the performance of different similarity measurement techniques for a number of spectrograms on a large database of speech recordings. Manhattan distance, Euclidean distance, vector cosine angle distance, Bhattacharyya coefficients and posterior prob- ability measure are used for the performance comparison. The results show that this similarity measure outperforms as compare to all above similarity measures in terms of time complexity and similarity performance.

关键词： signal processing Speech Recognition Pattern Matching PPM Word Identification.

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Support Vector machine-Based Fault Diagnosis of Power Transformer Using k Nearest-Neighbor Imputed DGA Dataset

Support Vector Machine-Based Fault Diagnosis of Power Transf...

引用

The 3rd international conference on signal and Image processing(CSIP 2014) & The 6th international conference on Computational Intelligence and Software Engineering(CiSE 2014)

作者： Zahriah Binti Sahri Rubiyah Binti Yusof Fakulti Teknologi Maklumat dan Komunikasi Universiti Teknikal Malaysia Melaka Malaysia Japan International Institute of Technology Universiti Tekonologi MalaysiaKuala Lumpur Malaysia

Missing values are prevalent in real-world datasets and they may reduce predictive performance of a learning algorithm. Dissolved Gas Analysis(DGA), one of the most deployable methods for detecting and predicting incipient faults in power transformers is one of the casualties. Thus, this paper proposes filling-in the missing values found in a DGA dataset using the k-nearest neighbor imputation method with two different distance metrics: Euclidean and Cityblock. Thereafter, using these imputed datasets as inputs, this study applies Support Vector machine(SVM) to built models which are used to classify transformer faults. Experimental results are provided to show the effectiveness of the proposed approach.

关键词： Missing Values,Dissolved Gas Analysis,Support Vector machine,k-Nearest Neighbors

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Stable direct adaptive neural control of an electrically driven dual-axis motion platform using backstepping technique

Stable direct adaptive neural control of an electrically dri...

引用

3rd IFAC conference on Intelligent Control and Automation Science, ICONS 2013

作者： Elkoteshy, Y. Yang, S.Y. Jiao, L.C. School of International Education Xidian University Xi'an 710071 China Key Laboratory of Intelligent Perception and Image Understanding Xidian University Ministry of Education of China Xi'an 710071 China

ISBN: (纸本)9783902823458

In this paper, the adaptive backstepping neural control (ABNC) is applied to an electrically driven dual-axis motion platform. The Dynamic model of the electrically driven dual-axis motion is obtained by coupling the dynamics of the dual-axis motion platform with the actuators (DC motors) dynamics. Thus, it is more realistic to select the actuators input voltages to be the control inputs instead of input torques, unlike the case when the actuators dynamics are not included. Unlike the existing ABNC techniques, single hidden layer feedforward neural networks with additive hidden nodes (SLFNN) are used to approximate the unknown nonlinear functions in the actual and virtual control laws where the networks parameters are adjusted based on extreme learning machine (ELM) algorithm. In ELM-based SLFNN, the hidden layer parameters are randomly selected and only the output layer weights linking the hidden layer with the output layer are needed to be updated. The adaptive update laws for the output layer weights are derived based on Lyapounov stability theory for guaranteeing semi-global boundedness of all signals in the closed-loop system. The simulation study illustrates the effectiveness of the proposed controller and shows that the system outputs track the desired trajectories with small tracking errors. Copyright © 2013 IFAC.

关键词： DC motors

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Image Analysis for Soil Dye Tracer Infiltration Studies

Image Analysis for Soil Dye Tracer Infiltration Studies

引用

3rd international conference on Image processing Theory, Tools and Applications (IPTA)

作者： Widemann, Baltasar Trancon y Bogner, Christina Univ Bayreuth Bayreuth Germany

ISBN: (纸本)9781467325851;9781467325837

Flow processes in soils are closely related to groundwater quality often affected by human activities. Because hydrological models usually lack explanatory power, direct visualization of flow paths in dye tracer infiltration studies has become a standard tool in soil hydrology. These experiments provide images of dye-stained paths in soils and help evaluating the vulnerability or understanding the general hydrological functioning of a given site. Extracting relevant information demands expertise in hydrology as well as in image analysis and statistics. To our knowledge, no agreed and effective method to analyze large collections of such images exists in the soil hydrology community. In this paper we propose a general framework consisting of index functions and visual tools to support the expert in his/her evaluation of dye tracer infiltration images.

关键词： Soil hydrology Dye tracer study Feature extraction machine learning Classification Visualization

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A NEW APPROACH FOR EPILEPTIC SEIZURE DETECTION USING EXTREME learning machine

A NEW APPROACH FOR EPILEPTIC SEIZURE DETECTION USING EXTREME...

引用

3rd international conference on Bio-Inspired Systems and signal processing (BIOsignalS 2010)

作者： Song, Yuedong Azad, Sarita Lio, Pietro Univ Cambridge Comp Lab 15 JJ Thomson Ave Cambridge CB3 0FD England Univ Cambridge Dept Appl Math & Theoret Phys Cambridge England

ISBN: (纸本)9789896740184

In this paper, we investigate the potential of discrete wavelet transform (DWT), together with a recently developed machine learning algorithm referred to as Extreme learning machine (ELM), to the task of classifying EEG signals and detecting epileptic seizures. EEG signals are decomposed into frequency sub-bands using DWT, and then these sub-bands are passed to an ELM classifier. A comparative study on system performance is conducted between ELM and back-propagation neural networks (BPNN). Results show that the ELM classifier not only achieves better classification accuracy, but also needs much less learning time compared to the BPNN classifier. It is also found that the length of the EEG segment used. affects the prediction performance of classifiers.

关键词： Epileptic seizure detection Electroencephalogram (EEG) Discrete Wavelet Transform Extreme learning machine (ELM)

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