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共 729 条记录，以下是661-670 订阅

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Ensemble learning to EEG-based Brain Computer Interfaces with Applications on P300-Spellers

Ensemble Learning to EEG-based Brain Computer Interfaces wit...

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IEEE international conference on Systems, Man, and Cybernetics (SMC)

作者： Barsim, Karim Said Zheng, Wangbo Yang, Bin Stuttgart Univ Inst Signal Proc & Syst Theory Stuttgart Germany

ISBN: (纸本)9781538666500

Brain-Computer Interfaces (BCI) are systems in which the electrical activity of an animal brain becomes the main controller of an external electronic device capable of reading and processing electroencephalographic (EEG) signals. One early application of such systems is the attention-based spellers utilizing the P300 visually evoked potential in a framework known as the oddball paradigm. In this paper, we propose novel variants of machine learning model ensembles in addressing the task of P300 detection and attended target recognition in attention-based speller systems. Proposed ensembles adopted Bootstrap aggregation (Bagging) of calibrated Support Vector machines (SVM) as well as data-driven learners. The latter is dominantly represented by Convolutional Neural Networks (CNN) with several variants, including what is referred to as Inception, Xception, and Interleaved Group Convolutions (IGC) modules. The proposed models are evaluated on a publicly available EEG dataset developed specifically for BCI applications and published in public contests, namely the 2rd dataset of the 3rd BCI competitions. The proposed models consistently outperform all previous works on the same dataset and show the highest 5- and 15-trial recognition rates of 76.5% and 98.5%, respectively, for both subjects in the dataset jointly. Additionally, we introduce in this work the first study on inter-subject evaluation under similar training protocols, reaching between 30 %-40 % recognition rates for either subject. We further investigate the effect of reducing the training data on the performance of the proposed models showing possibilities of reduced training time for a target recognition rate.

关键词： Ensemble learning Electroencephalography (EEG) Brain Computer Interfaces (BCI) Support Vector machines (SVM) Convolutional Neural Networks (CNN) Interleaved Group Convolutions (IGC)

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Poster Abstract: Comparison of Classifiers for Prediction of Human Actions in a Smart Home 3

Poster Abstract: Comparison of Classifiers for Prediction of...

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3rd ACM/IEEE international conference on Internet of Things Design and Implementation (IoTDI)

作者： Alhafidh, Basman M. Hasan Daood, Amar, I Allen, William H. Florida Inst Technol Dept Elect & Comp Engn Melbourne FL 32901 USA Florida Inst Technol Sch Comp Melbourne FL 32901 USA

ISBN: (纸本)9781538663127

There is a strong interest in IoT-based systems that monitor and control smart home environments by accurately predicting the needs of the human occupants. Past research has focused on the accuracy of prediction of a user's future action. However, much of that work uses synthetic datasets which do not always reflect the real-world interactions that occur between an individual and the home environment. In addition, a focus on prediction accuracy often comes at the cost of slower processing time. This paper focuses on the prediction of future human actions in an intelligent environment with the goal of achieving both high accuracy and real-time performance. We performed experiments using the MavPad dataset, which was gathered from a fully-instrumented home environment, and compared several different machine learning algorithms that included both single and ensemble classifiers. The results show that using a Support Vector machine approach achieved the best results when using a group of sensors within a local zone around the user and the Random Forest classifier achieved higher performance when using sensors that are distributed across the entire home environment.

关键词： Smart Home System IoT Ensemble Classifier machine learning Algorithms Prediction System

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3rd international conference on machine learning and Intelligent Communications, MLICOM 2018

3rd International Conference on Machine Learning and Intelli...

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3rd international conference on machine learning and Intelligent Communications, MLICOM 2018

ISBN: (纸本)9783030005566

The proceedings contain 66 papers. The special focus in this conference is on machine learning and Intelligent Communications. The topics include: Smartphone application identification by convolutional neural network;joint D2D cooperative relaying and friendly jamming selection for physical layer security;variable tap-length blind equalization for underwater acoustic communication;random sequence generation algorithm for multi-chaotic systems;vulAware: Towards massive-scale vulnerability detection in cyberspace;Optimization in cognitive radio networks with SWIPT-based DF relay;Real-valued orthogonal sequences for ultra-low overhead channel estimation in MIMO-FBMC systems;a novel spectrum allocation scheme in femtocell networks using improved graph theory;distributed channel allocation for wireless mesh networks based on hypergraph interference model;a research of network applications classification based on deep learning;physical violence detection with movement sensors;Joint resource allocation for wireless energy harvesting based on DF relaying;Optimal power splitting of cognitive radio networks with SWIPT-enabled relay;deflection angle detection of the rotor and signal processing for a novel rotational gyroscope;Reusing wireless power transfer for backscatter-assisted cooperation in WPCN;Channel estimation in next generation LEO satellite communicastion systems;A novel approach of semi-blind frequency selection for HF regional emergency maneuver communication;a network coding optimization algorithm for reducing encoding nodes;backscatter-assisted relaying in wireless powered communications network;energy-efficient power allocation scheme based on discrete-rate adaptive modulation in distributed antenna system;real-time drone detection using deep learning approach;a method of balanced sleep scheduling in renewable wireless sensor networks;Pedestrian walking model for floor plan building based on crowdsourcing PDR data.

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3rd international Symposium on signal processing and Intelligent Recognition Systems, SIRS 2017

3rd International Symposium on Signal Processing and Intelli...

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3rd international Symposium on signal processing and Intelligent Recognition Systems, SIRS 2017

ISBN: (纸本)9783319679334

The proceedings contain 41 papers. The special focus in this conference is on signal processing and Intelligent Recognition Systems. The topics include: Removal of BW and respiration noise in abdECG for f ECG extraction;early stage detection of diabetic retinopathy using an optimal feature set;exploring cepstral coefficient based sleep stage scoring method for single-channel EEG signal using machine learning technique;non linear tracking using unscented kalman filter;an analysis on the influence that the position and number of control points have on MLS registration of medical images;component characterization of western and indian classical music;an experimental setup of DSA algorithm suitable for high bandwidth data transfer using USRP and GNU radio companion;influence of filter bank structure on the statistical significance of coefficients in cepstral analysis for acoustic signals;particle filtering technique for fast fading shadow power estimation in wireless communication;object detection and localization using compressed sensing;an improved approach for securing document images using dual cover;dependency of various color and intensity planes on CNN based image classification;epigraphic document image enhancement using retinex method;improved microaneurysm detection in fundus images for diagnosis of diabetic retinopathy;swarm robots in a closed loop visual odometry system by using visible light communication;an adaptive neuro-fuzzy inference system based situation awareness assessment in VLC enabled connected cars;identifying issues in estimating parameters from speech under lombard effect and a model for an emotional respondent robot.

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Metric learning Based Rolling Bearing Faults Diagnosis with Curvelet Transform

Metric Learning Based Rolling Bearing Faults Diagnosis with ...

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2019 3rd international conference on machine Vision and Information Technology (CMVIT 2019)

作者： Ziming Lu Jiang-Wen Xiao Zhengyi Huang School of Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control(Huazhong University of Science and Technology) Ministry of Education

Rolling bearing faults are among the primary causes of breakdown in mechanical equipment. Aiming at the vibration signals of rolling bearing which are non-stationary and easy to be disturbed by noise, a novel fault diagnosis method based on curvelet transform and metric learning is proposed. This method consists of 3 parts. The first one is feature engineering which includes reshaping the original timing features of rolling bearings, employing curvelet transform to transform reshaped features and making its coefficients as the new features. Curvelet transform can analyse the original signal from many angles. The second one is employing metric learning to map these new features into special embedding space. The last one is applying KNN classifier to detect the rolling bearing faults. Metric learning can effectively improve the performance of KNN by learning a mapping matrix to modify the distribution of samples. The proposed method overcomes the problems such as the subjectivity and blindness of manual feature extraction, poor coupling in each stage and sensitive to the effect of noise. Extensive simulations based on several data-sets show that the our method has better performance on bearing fault diagnosis than traditional methods.

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Deflection angle detection of the rotor and signal processing for a novel rotational gyroscope 3rd

Deflection angle detection of the rotor and signal processin...

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3rd international conference on machine learning and Intelligent Communications, MLICOM 2018

作者： Chen, Dianzhong Zhang, Zhongzhao Communication Research Center Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9783030005566

Differential capacitance detection, a common high resolution proof mass displacement detection scheme, is adopted in the gyroscope to measure the rotor deflection angle by installing an electrode with four poles under the rotor disk, which forms four detection capacitors and opposite ones form a differential capacitance detection pair. Theoretical inference explains the approximately proportional relationship between the capacitance difference and the rotor deflection angle. Simulation in Ansys Maxwell verifies the inference and confirms the differential capacitance detection range of the rotor deflection angle to 0–1°, limited by linearity. A signal processing system is constructed, obtaining a DC output voltage proportional to the measured input angular speed. Experiment shows the fabricated gyroscope with the designed differential capacitance detection pairs exhibits excellent performance with the resolution and the bias stability of 0.1 °/s and 0.5 °/h, respectively. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018.

关键词： Gyroscopes

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Assessment of machine learning Algorithms for the Purpose of Primary Sjogren's Syndrome Grade Classification from Segmented Ultrasonography Images 3rd

Assessment of Machine Learning Algorithms for the Purpose of...

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3rd EAI international conference on Future Access Enablers of Ubiquitous and Intelligent Infrastructures (Fabulous)

作者： Vukicevic, Arso Zabotti, Alen de Vita, Salvatore Filipovic, Nenad BioIRC Bioengn Res & Dev Ctr Prvoslava Stojanovica 6 Kragujevac 34000 Serbia Univ Kragujevac Fac Engn Sestre Janjica 6 Kragujevac 34000 Serbia Azienda Osped Univ Santa Maria Della Misericordia Udine Udine Italy

ISBN: (纸本)9783319922133;9783319922126

Primary Sjogren's syndrome (pSS) is a chronic autoimmune disease that affects primarily women (9 females/1 male). Recently, a great interest has arisen for salivary gland ultrasonography (SGUS) as a valuable tool for the assessment of major salivary gland involvement in primary Sjogren's syndrome. The aim of this study was to assess accuracy of state of the art machine learning algorithms for the purpose of classifying pSS from SGUS images. The five-step procedure was carried out, including: image pre- processing, feature extraction, data set balancing and feature extraction, classifiers (K-Nearest Neighbour, Decision trees, Naive bayes, Discriminant analysis classifier, Random forest, Multilayer perceptron, Linear logistic regression) learning and their corresponding assessment. The preliminary results on the growing HarmonicSS cohort showed that Naive bayes (72.8% accuracy on training set, and 73.3% accuracy on test set) and Multilayer perceptron (85.0% accuracy in training stage, and 70.1% accuracy at test stage) are the most suitable for the purpose of pSS grade classification.

关键词： Sjogren's syndrome Classification Ultrasonography

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Performance Analysis of Supervised machine learning Techniques for Sentiment Analysis 3

Performance Analysis of Supervised Machine Learning Techniqu...

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3rd IEEE international conference on Sensing, signal processing and Security (ICSSS)

作者： Samal, Biswa Ranjan Behera, Anil Kumar Panda, Mrutyunjaya Utkal Univ PG Dept Comp Sci & Applicat Bhubaneswar 751004 Orissa India

ISBN: (纸本)9781509049295

Wide use of internet and web applications like, feedback collection systems are now making peoples smarter. In these applications, peoples used to give their feedback about the movies, products, services, etc through which they have gone, and this feedback are publicly available for future references. It is a tedious task for the machines to identify the feedback types, i:e positive or negative. And here machine learning Techniques plays vital roles to train the machine and make it intelligent so that the machine will be able to identify the feedback type which may give more benefits and features for those web applications and the users. There are many supervised machine learning techniques are available so it is a difficult task to choose the best one. In this paper, we have collected the movie review datasets of different sizes and have selected some of the widely used and popular supervised machine learning algorithms, for training the model. So that the model will be able to categorize the review. Python's NLTK package along with the WinPython and Spyder are used for processing the movie reviews. Then Python's sklearn package is used for training the model and finding the accuracy of the model.

关键词： Sentiment Analysis machine learning NLTK Naive Bayes LogisticRegression Linear Model

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End-to-end system for printed Amazigh script recognition in document images 3

End-to-end system for printed Amazigh script recognition in ...

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3rd international conference on Advanced Technologies for signal and Image processing (ATSIP)

作者： Aharrane, Nabil Dahmouni, Abdellatif Ensah, Karim El Moutaouakil Satori, Khalid USMBA Univ LIIAN Lab Fes Morocco Univ Mohammed 1 ENSAH Al Hoceima Morocco

ISBN: (纸本)9781538605516

in this work, we present an end-to-end system devoted to automatic recognition of printed Amazigh script in complex document images containing different languages such as Web images and natural scene images. To this end, text extraction from images is performed;the extracted text serves as input for a trained convolutional neural network (CNN) to identify its language. Finally, we proceed to the recognition of the Amazigh text script using a developed optical character recognition (OCR) system. The CNN reaches 99,12% of accuracy while the OCR system gets 99,93% The obtained results seem to be very satisfactory.

关键词： Amazigh OCR language identification deep learning convolutionnal neural network machine learning

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Identification and Counting of Mature Apple Fruit Based on BP Feed Forward Neural Network 3

Identification and Counting of Mature Apple Fruit Based on B...

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3rd IEEE international conference on Sensing, signal processing and Security (ICSSS)

作者： Lal, Shreya Behera, Santi Kumari Sethy, Prabira Kumar Rath, Amiya Kumar VSSUT Dept Comp Sci & Engn Burla 768018 Odisha India Sambalpur Univ Dept Elect Burla 768019 Odisha India

ISBN: (纸本)9781509049295

Classification of fruits is an onerous and tedious task because of countless number of fruits. The traditional approach for detection and classification of fruit and its maturity level is based on the naked eye observation by the experts, which is both time consuming and causes eye fatigue. Advance techniques in image processing and machine learning helps to automatic classify and count the fruits, accurately, quickly and non-destructively. A method to automatic detect and classify apple fruit maturity level, whether it is mature or immature based on its color features has been proposed. Images of the apple are resized and Image processing Techniques are applied for the extraction of apple color components (R, G, B). Artificial Neural Network is used as a classifier to identify and count the mature and immature applesusingcolor components. The proposed model has an accuracy of 98.1%, when all the three attributes are used as an input.

关键词： machine learning RGB classification features extraction Back Propagation Feed Forward Neural Network

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