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2018 5th international conference on signal processing and Integrated Networks, SPIN 2018

2018 5th International Conference on Signal Processing and I...

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5th international conference on signal processing and Integrated Networks, SPIN 2018

ISBN: (纸本)9781538630457

the proceedings contain 181 papers. the topics discussed include: facial expression recognition with LDPP & LTP using deep belief network;facial expression recognition using Bandlet transform and centre symmetric - local binary pattern singing voice synthesis system for Carnatic music;automated polyp classification of gastroenteric lesion in colonoscopy videos;enhancement of underwater images using white balancing and Rayleigh-stretching;adaptive Volterra modeling for nonlinear systems based on LMS variants;web-based innovators marketplace on data jackets as communication support system;a novel machine learning approach to detect phishing websites;correlation based evaluation for search tools;a novel method for direction and deformation independent iris localization;beamforming and combining in multi-user large MIMO systems;automatic key frame extraction from videos for efficient mouse pain scoring;exploring dynamic factor graphs for forecasting building electrical load;an efficient character segmentation approach for handwritten Hindi text;and 3D Smith-chart applications for passive negative resistance in RF and microwave circuits.

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Noise Robust Fundamental Frequency Estimation of Speech using CNN-based discriminative modeling 5

Noise Robust Fundamental Frequency Estimation of Speech usin...

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5th international conference on Advanced Informatics - Concepts, theory and Applications (ICAICTA)

作者： Kawamura, Tomonori Kai, Atsuhiko Nakagawa, Seiichi Shizuoka Univ Grad Sch Integrated Sci & Technol Hamamatsu Shizuoka Japan Chubu Univ Kasugai Aichi Japan

ISBN: (纸本)9781538648049

the fundamental frequency (F0) is a quantity representing the pitch of periodic signal and its estimation for time-variant quasiperiodic acoustic signal is one of common problems in speech processing studies. the correct estimation of this contributes to the improvement of speech processing systems such as, analysis of prosody, test-to-speech system and speech recognition system. While many algorithms have been proposed and they exhibit excellent performance for clean environment, it is a very difficult task for noisy environment. It is generally known that machine learning approach is effective as a discriminative model for handling data in which noise is mixed. In this paper, we propose a robust fundamental frequency estimation method for noisy speech signal by using convolutional neural network (CNN) which is a type of deep neural network (DNN). In our proposed method, convolution layer and pooling layer serve as an approximator of autocorrelation analysis and followed by discriminative modeling for classifying quantized F0 state. this process acquires a discriminator that extracts noise robust FO features. Experimental result showed that our method outperforms convolutional methods based on autocorrelation analysis and its combination with DNN modeling.

关键词： Speech processing Fundamental frequancy estimation convolutional neural network

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Electric Vehicles Driving Range and Energy Consumption Investigation: A Comparative Study of machine learning Techniques

Electric Vehicles Driving Range and Energy Consumption Inves...

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international conference of signal processing and Intelligent Systems (ICSPIS)

作者： Abdollah Amirkhani Arman Haghanifar Mohammad R. Mosavi Iran University of Science and Technology Tehran Iran

ISBN: (数字)9781728153506

ISBN: (纸本)9781728153513

Electric vehicles are the next generation of cars which are pollutant-free, resulting in the elimination of many environmental and healthcare problems caused by fossil-fueled vehicles. On the other hand, mass production and wide adoption of these vehicles are facing significant barriers; long battery charging time and limited trip distance per charge are the most important ones to mention. Due to the development of fast DC chargers, the former problem is resolved to a certain extent, while the latter is still a topic of interest. In this article, using a publicly available dataset, driving range estimation of a specific electric vehicle model is scrutinized. At first, multiple regression models are trained based on the features like the average speed, type of the route and driving style; then the driving range prediction accuracy is investigated. On the next step, sensitivity analysis is performed on the energy consumption rate, and the results are discussed. At the final phase, the effect of each feature on the energy consumption rate is highlighted, and the deviation between experimental rates and the factory-defined rates are explained in details.

关键词： Energy consumption Electric vehicles Automobiles Data models Estimation Adaptation models signal processing

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Content-based Image Retrieval Using Color Difference Histogram in Image Textures

Content-based Image Retrieval Using Color Difference Histogr...

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international conference of signal processing and Intelligent Systems (ICSPIS)

作者： Armin Ajam Majid Forghani Mohammad M. AlyanNezhadi Hamed Qazanfari Zahra Amiri Shahrood Non-profit and Non-government Higher Edu. Inst. Shahrood Iran Institute of natural sciences and mathematics Ural Federal University Yekaterinburg Russia Image Processing and Data Mining Lab Shahrood University of Technology Shahrood Iran

ISBN: (数字)9781728153506

ISBN: (纸本)9781728153513

the aim of content-based image retrieval system is finding similar images to the query image from a database based on its visual content. In this paper, a novel retrieval system based on human vision is proposed. A factor that has a high impact on the search process is a set of features which are used in. the recent studies emerged that the human eye system considers the image content, texture, and color properties more than other features. therefore, to retrieve more precisely the images, features should be used that are close to the human eye system. In the current paper, at first, the texture is extracted from the images using the local binary patterns algorithm. After that, the color differences of two adjacent pixels with the same texture are calculated in the HSV color space. Afterward, the histogram is taken from the color difference values. the obtained features from the histogram can describe the visual content of the images in more detail. Finally, the effective features are selected based on their entropy value. the prominent advantage of the proposed method is the lack of implementation of segmentation, clustering, training, and any other method of machine learning, which requires a lot of processing and time. the method is evaluated on two standard Corel 10K and Corel 5K databases, and its retrieval rate is significantly improved compared to some recent methods.

关键词： Image color analysis Feature extraction Image retrieval Histograms Visualization machine learning Semantics

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DETECTION OF CRESCENT SAND DUNES CONTOURS IN SATELLITE IMAGES USING AN ACTIVE SHAPE MODEL WIth A CASCADE CLASSIFIER 5

DETECTION OF CRESCENT SAND DUNES CONTOURS IN SATELLITE IMAGE...

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5th international conference on Geoinformation Science / ISPRS conference on Multi-Dimensional and Multi-Scale Spatial Data Modeling (GeoAdvances)

作者： Azzaoui, M. A. Adnani, M. El Belrhiti, H. Chaouki, I. E. Masmoudi, L. Univ Mohammed V Agdal Lab Elect & Traitement Signal Geomat LETS Geomat Fac Sci Rabat 4 Ave Ibn Battouta BP 1014 RP Rabat Morocco Inst Agron & Vet Hassan II Dept Sci Fondamentales & Appliquees BP 6202 Rabat 10101 Morocco Ecole Natl Sci Appl Agadir BP 1136 Agadir Morocco

Crescent sand dunes called barchans are the fastest moving sand dunes in the desert, causing disturbance for infrastructure and threatening human settlements. their study is of great interest for urban planners and geologists interested in desertification (Hugenholtz et al., 2012). In order to study them at a large scale, the use of remote sensing is necessary. Indeed, barchans can be part of barchan fields which can be composed of thousands of dunes (Elbelrhiti et al.2008). Our region of interest is located in the south of Morocco, near the city of Laayoune, where barchans are stretching over a 400 km corridor of sand dunes. We used image processing techniques based on machine learning approaches to detect both the location and the outlines of barchan dunes. the process we developed combined two main parts: the first one consists of the detection of crescent shaped dunes in satellite images using a supervised learning method and the second one is the mapping of barchans contours (windward, brink and leeward) defining their 2D pattern. For the detection, we started by image enhancement techniques using contrast adjustment by histogram equalization along with noise reduction filters. We then used a supervised learning method: We annotated the samples and trained a hierarchical cascade classifier that we tested with both Haar and LBP features (Viola et Jones, 2001;Liao et al., 2007). then, we merged positive bounding boxes exceeding a defined overlapping ratio. the positive examples were then qualified to the second part of our approach, where the exact contours were mapped using an image processing algorithm: We trained an ASM (Active Shape Model) (Cootes et al., 1995) to recognize the contours of barchans. We started by selecting a sample with 100 barchan dunes with 30 landmarks (10 landmarks for each one of the 3 outlines). We then aligned the shapes using Procrustes analysis, before proceeding to reduce the dimensionality using PCA. Finally, we tested different de

关键词： Remote Sensing IKONOS High resolution satellite images Cascade classifiers Active Shape Model Local Binary Patterns Haar features SURF SVM barchans dunes desertification

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Prediction of Outpatient Visits for Upper Respiratory Tract Infections by machine learning of PM2.5 and PM10 Levels in Taiwan 5

Prediction of Outpatient Visits for Upper Respiratory Tract ...

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5th IEEE international conference on Consumer Electronics-Taiwan, ICCE-TW 2018

作者： Yang, Pei-Hsuan Hsieh, Mi-Tren Lin, Gen-Min Chen, Mei-Juan Yeh, Chia-Hung Huang, Zhi-Xiang Yang, Chieh-Ming Department of Electrical Engineering National Dong Hwa University Hualien Taiwan Department of Medicine Hualien Armed Forces General Hospital Hualien Taiwan Department of Electrical Engineering National Sun Yat-sen University Kaohsiung Taiwan Department of Electrical Engineering National Taiwan Normal University Taipei Taiwan

ISBN: (纸本)9781538663011

Particulate Matter (PM) 2.5 and PM10 are referred as a mixture of liquid droplets and solid particles in the air with diameters leq 2.5 mum and leq 10 mum, respectively. Both PM2.5 and PM10 can deposit on respiratory tract and trigger inflammatory reactions, which makes the respiratory tract predisposed to infections. the study used machine learning on daily PM2.5 and PM10 levels of consecutive 30 days from the open website datasets of Environment Protection Administration between Dec. 2008 and Dec. 2016 to predict the subsequent one-week outpatient visits for upper respiratory tract infections (URI) from the Centers for Disease Control (CDC) in Taiwan between Jan. 2009 and Dec. 2016. the weekly URI cases were classified by tertile as high, moderate, and low volumes. In general, both URI burden and PM levels peak in winter and spring seasons. the testing used the mid-month dataset of each season (Jan., Apr., Jul., and Oct.), and the training used the other months datasets. In the nationwide data analysis, PM2.5 and PM10 levels input to the multilayer perceptron (MLP) can precisely predict the degree of URI number for the elderly (89.05% and 88.32%, respectively) and the overall population (81.75% and 83.21%, respectively). In conclusion, machine learning of PM2.5 and PM10 levels could accurately predict the burden of outpatient visits for URI in Taiwan. © 2018 IEEE.

关键词： machine learning

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A Novel machine learning Approach to Detect Phishing Websites

A Novel Machine Learning Approach to Detect Phishing Website...

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international conference on signal processing and Integrated Networks (SPIN)

作者： Ishant Tyagi Jatin Shad Shubham Sharma Siddharth Gaur Gagandeep Kaur Deptt. of CSE&IT Jaypee Institute of Information Technology NOIDA India

Phishing can be described as a way by which someone may try to steal some personal and important information like login id's, passwords, and details of credit/debit cards, for wrong reasons, by appearing as a trusted body. Many websites, which look perfectly legitimate to us, can be phishing and could well be the reason for various online frauds. these phishing websites may try to obtain our important information through many ways, for example: phone calls, messages, and pop up windows. So, the need of the hour is to secure information that is sent online and one concrete way of doing so is by countering these phishing attacks. this paper is focused on various machine learning algorithms aimed at predicting whether a website is phishing or legitimate. machine learning solutions are able to detect zero hour phishing attacks and they are better at handling new types of phishing attacks, so they are preferred. In our implementation, we managed an accuracy of 98.4% in prediction a website to be phishing or legitimate.

关键词： Phishing Feature extraction Uniform resource locators machine learning algorithms Classification algorithms Prediction algorithms Forestry

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Deep Convolution Neural Network Based Speech Recognition for Chhattisgarhi

Deep Convolution Neural Network Based Speech Recognition for...

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international conference on signal processing and Integrated Networks (SPIN)

作者： Narendra D. Londhe Ghanahshyam B. Kshirsagar Hitesh Tekchandani Dept. of Electrical Engineering National Institute of Technology Raipur Chhattisgarh India Dept. of E&TC Engineering National Institute of Technology Raipur Chhattisgarh India

the existing ASR for Chhattisgarhi using conventional machine learning technique was implemented for speaker dependent speech recognition. However, the conventional machine learning based speech recognition is incapable to handle the spectral variations as well as the spectral correlation of acoustic signals. therefore, to overcome the aforementioned limitations, authors have implemented the deep convolution neural network (DCNN) based ASR for Chhattisgarhi dialect. Unlike other deep learning models, DCNN can efficiently handle the spectral variations and spectral correlation of speech signal with the less computational burden. the experiment of isolated Chhattisgarhi word recognition was implemented on self-recorded dataset acquired from 150 subjects from various geographical parts of Chhattisgarh state. the implemented algorithm is promisingly achieving 99.49% of accuracy for isolated word recognition. the different performance paraments are presented to validate the performed experiment.

关键词： Speech recognition Convolution machine learning Neural networks Hidden Markov models Training Mel frequency cepstral coefficient

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Centroid Selection in Kernel Extreme learning machine using K-means

Centroid Selection in Kernel Extreme Learning Machine using ...

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international conference on signal processing and Integrated Networks (SPIN)

作者： Mona Singhal Sanyam Shukla Department of Computer Science and Engineering Maulana Azad National Institute of Technology Bhopal Madhya Pradesh

Kernel Extreme learning machine (KELM) is used for classification, regression, clustering and feature selection with the help of kernel functions. Conventional KELM uses all training instances as centroids for classification problem while reduced KELM uses randomly choosen training instances as centroids. Furthermore, reduced KELM is used for reducing the computational complexity of conventional KELM. To further improve the computational complexity of KELM, K-means clustering algorithm for centroid selection in KELM is proposed in this paper. In this proposed approach, number of centroids are selected as 1/10 or 5/10 of the total number of training instances and then centroids are computed by using K-means algorithm. Experiments have been carried out by using 15 data sets to illustrate the effectiveness of the proposed method. the results obtained show the reduction in computational time and increment in G-mean which verify the proposed method as an efficient approach in comparison to earlier works.

关键词： Testing Kernel Training Clustering algorithms Mathematical model Computational complexity Partitioning algorithms

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Performance Improvement for Devanagari Script Input Based P300 Speller

Performance Improvement for Devanagari Script Input Based P3...

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international conference on signal processing and Integrated Networks (SPIN)

作者： Ghanahshyam B. Kshirsagar Narendra D. Londhe Dept. of Electrical Engineering National Institute of Technology Raipur Raipur C.G. India

the performance of P300 speller-based BCI systems are evaluated based on its classification accuracy as well as the time required to spell the target character which is known as information transfer-rate (ITR). though, previous works on English, Chinese and Devanagari script (DS) based P300 spellers have presented different approaches to improve the ITR. However, conventional machine learning approaches requires more number of trials for accurate detection of the target P300 component which increases the time to spell a character. the main aim of this work is to improve the information learning rate in DS based P300 speller by improving the classification accuracy in less number of trials. the deep convolution neural network was implemented for the detection of the target characters. the experimental results shown that the proposed work is able to provide higher accuracy for less number of trials which improved the ITR of existing DS based P300 speller.

关键词： Convolution Electroencephalography machine learning Feature extraction Support vector machines Biological neural networks

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