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A Reliable Method for Detecting Road Regions from a Single Image Based on Color Distribution and Vanishing Point Location

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Procedia Computer Science 2015年 58卷 2-9页

作者： Neethu John B. Anusha Krishnan Kutty Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore Tamilnadu 641112 India Centre for Research in Engineering Sciences and Technology KPIT Technologies Ltd Hinjewadi Pune 411057 India

Numerous advanced driver assistance systems (ADAS) are gaining popularity even in the mid to low end segment cars. Vision based technologies assisted by the use of cameras cater to a lot of these ADAS systems. These systems enhance the safety of the driver, passenger and pedestrians on the road. In a typical image taken from an on-board camera of a car, it is the road region that occupies most of the pixels. Therefore, an approach for detecting and eliminating road regions from an optical image is proposed which in turn speeds up computations for further object/ROI detection. Our proposed road detection algorithm works in two stages: i) vanishing point detection; and ii) road region identification. Experimental results show that this approach performs better with real-road images of varying texture, colour and shapes.

关键词： Blob detection likelihood estimation road detection texture-orientation vanishing point estimation.

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Background Modelling from a Moving Camera

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Procedia Computer Science 2015年 58卷 289-296页

作者： Amitha Viswanath Reena Kumari Behera Vinuchackravarthy Senthamilarasu Krishnan Kutty Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore Tamil Nadu641112 India Centre for Research in Engineering Sciences and Technology KPIT Technologies Ltd. Hinjewadi Pune 411057 India

In video analytics based systems, an efficient method for segmenting foreground objects from video frames is the need of the hour. Currently, foreground segmentation is performed by modelling background of the scene with statistical estimates and comparing them with the current scene. Such methods are not applicable for modelling the background of a moving scene, since the background changes between the scenes. The scope of this paper includes solving the problem of background modelling for applications involving moving camera. The proposed method is a non-panoramic background modelling technique that models each pixel with a single Spatio-Temporal Gaussian. Experimentation on various videos promises that the proposed method can detect foreground objects from the frames of moving camera with negligible falsealarms.

关键词： Video Analytics Moving Camera Non- Panoramic Background Modelling Spatial-Temporal Gaussian Distribution

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Audio Data Authentication with PMU Data and EWT

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Procedia Technology 2015年 21卷 596-603页

作者： Premjith B. Neethu Mohan Prabaharan Poornachandran Soman K.P. Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetha Coimbatore – 641112 India Amrita Center for Cyber Security Systems and Networks Amrita Vishwa Vidyapeetham Kollam

Digital forensics has become a flourishing research area. Electrical Network Frequency (ENF) plays an important role in assessing the authenticity of a digital recording such as audio. ENF criterion is a tool for extracting the embedded power line frequency from the recording. A cross correlation between a reference PMU data and extracted ENF signal can be done in order to determine the authenticity of an audio signal. In this paper, Empirical Wavelet Transform (EWT) is used for extracting the ENF from an audio signal. EWT decomposes signal into N modes. Hilbert Transform is used to compute the instantaneous frequency and amplitude of the extracted mode corresponding to ENF. EWT method is not able to capture the weak harmonics in a signal. This problem is resolved by fixing the frequency domain boundaries of each mode.

关键词： Digital Forensics Electrical Network Frequency Empirical Wavelet Transform Hilbert Transform

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Variance Based Offline Power Disturbance Signal Classification Using Support Vector Machine and Random Kitchen Sink

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Procedia Technology 2015年 21卷 163-170页

作者： Aneesh C. Hisham P.M. Sachin Kumar S. Maya P. K.P. Soman Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore – 641 112 India Dept of Electrical and Electronics Engineering Amrita Vishwa Vidyapeetham Coimbatore – 641 112 India

In this paper, five classes of different power quality disturbances such as swell, sag, harmonics, sag with harmonics and swell with harmonics are synthesized using MATLAB/SIMULINK software which is further decomposed into 8 intrinsic mode functions using variational mode decomposition (VMD). VMD is an adaptive signal processing method that decomposes the signal in to several intrinsic mode functions (IMF) or components. The variance calculated from each of the mode is taken as feature representation. It is found that sines and cosines of variance vector of eight different IMF candidates of a signal acts as feature vector that can accurately extract salient and unique nature of the power disturbances. The classification is performed using Support Vector Machines (SVM) and Random Kitchen Sink (RKS) algorithm. The classification results in a highest accuracy of 94.44% for RKS method when compared to SVM.

关键词： Variational mode decomposition Random Kitchen Sink Power system Support vector machine variance

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Application and Analysis of Smart Meter Data along with RTL SDR and GNU Radio

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Procedia Technology 2015年 21卷 317-325页

作者： Aswathi M. Gandhiraj R. Soman K.P. Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore- 641 112 India Department of Electronics and Communication Engineering Amrita Vishwa Vidyapeetham Coimbatore- 641 112 India

Introduction of smart meter offered fine grained information about the energy consumption of customers. To categorize the appliances consuming the energy, the Electromagnetic Interferences emitted by them are measured using Realtek Software Defined Radio. To avoid the frequency range mismatch of the interferences and the input frequency range of Realtek Software Defined Radio, an up converter is used. The interferences can be processed by using a GNU radio. The time stamped signals are stored as DAT file format. Disaggregation of appliances consuming the power is attained by comparing the results with that of smart meter results. To achieve this task smart meters are employed to calculate the total energy consumption. Smart meter data will also be time stamped. The data collected by the smart meter is processed by Raspberry Pi and is stored in memory. Both the GNU radio data and smart meter data are compared to conclude final results. This algorithm is superior to the existing one as it helps to disintegrate the multiple appliances consuming power at the same time.

关键词： Smart meter GNU radio RTLSDR antenna.

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Apache Spark a Big Data Analytics Platform for Smart Grid

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Procedia Technology 2015年 21卷 171-178页

作者： Shyam R. Bharathi Ganesh H.B. Sachin Kumar S. Prabaharan Poornachandran Soman K.P. Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetha Coimbatore – 641112 India Amrita Center for Cyber Security Systems and Networks Amrita Vishwa Vidyapeetham Kollam India

Smart grid is a complete automation system, where large pool of sensors is embedded in the existing power grids system for controlling and monitoring it by utilizing modern information technologies. The data collected from these sensors are huge and have all the characteristics to be called as Big Data. The Smart-grid can be made more intelligent by processing and deriving new information from these data in real time. This paper presents Apache spark as a unified cluster computing platform which is suitable for storing and performing Big Data analytics on smart grid data for applications like automatic demand response and real time pricing.

关键词： Smart Grid Data Analytics Big Data Apache Spark

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Causes of blindness and vision impairment in 2020 and trends over 30 years, and prevalence of avoidable blindness in relation to VISION 2020: the Right to Sight: an analysis for the Global Burden of Disease Study (vol 2, pg 144, 2021)

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LANCET GLOBAL HEALTH 2021年第4期9卷 E408-E408页

作者： Steinmetz, Jaimie D. Vision and Eye Research Institute Anglia Ruskin University Cambridge United Kingdom Biomedical Informatics Seoul National University Hospital Seoul South Korea Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Institute of Health Services Research Yonsei University Seoul South Korea Department of Ophthalmology University of Washington Seattle WA United States Department of Mathematics Imperial College London London United Kingdom WHO Collaborating Centre for Public Health Education and Training Imperial College London London United Kingdom Department of Primary Care and Public Health Imperial College London London United Kingdom School of Population and Global Health University of Melbourne Carlton VIC Australia School of Optometry and Vision Science UNSW Sydney NSW Australia Brien Holden Vision Institute Sydney Australia Department of Ophthalmology University of Adelaide Adelaide SA Australia Department of Parasitology and Mycology Jahrom University of Medical Sciences Jahrom Iran Department of Clinical Sciences University of Sharjah Sharjah United Arab Emirates Ophthalmic Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Ophthalmology Shahid Beheshti University of Medical Sciences Tehran Iran Research Institute for Endocrine Sciences Shahid Beheshti University of Medical Sciences Tehran Iran Ophthalmic Epidemiology Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Health Services Management Department Qazvin University of Medical Sciences Qazvin Iran Department of Systems Populations and Leadership University of Michigan Ann Arbor MI United States Department of Medicine Sofia Ismail Memorial Medical Centre Bogura Bangladesh The Fred Hollows Foundation Sydney NSW Australia Berhan Public Health & Eye Care Consultancy Addis A

Hospital El Salvador: a novel paradigm of intensive care in response to COVID-19 in central America. Lancet Glob Health 2021;9: e241?42?In this Comment, the conflict of interest statement should have included the following: ?By virtue of their roles within a public hospital or the Ministry of Health, MB, LC, WH, and XS are government employees. The findings and conclusions in the Comment are only those of the authors.? This correction has been made as of Feb 26, 2021.

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Tamil characters classification using random Kitchen Sink Algorithm (RKS) 14

Tamil characters classification using random Kitchen Sink Al...

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2014 International Conference on Interdisciplinary Advances in Applied Computing, ICONIAAC 2014

作者： Karthieswari, R. Sreethivya, M. Deva Rajendra Kumar, D. Soman, K.P. Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9781450329088

Tamil is a classical south Indian language in India that contains more letters among other Indian languages. The motivation behind our work was to develop an efficient character recognition technique for analyzing the scanned Tamil documents. Initially character recognition was done with the help of frequently used Tamil characters. Based on that, we have created a new datasets by the process of segmentation using levelsets, binarization, and skew correction. In this paper we have recognized the Tamil characters by generating the random features using Random Kitchen Sinks. It gives the accuracy about 98.7%. Copyright 2014 ACM.

关键词： Kitchens

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AMRITA-CEN@FIRE-2014: Named entity recognition for Indian languages using rich features 6

AMRITA-CEN@FIRE-2014: Named entity recognition for Indian la...

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6th International Workshop of the Forum for Information Retrieval Evaluation, FIRE 2014

作者： Abinaya, N. John, Neethu Barathi Ganesh, H.B. Anand Kumar, M. Soman, K.P. Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9781450337557

This paper aims at implementing Named Entity Recognition (NER) for four languages such as English, Tamil, Hindi and Malayalam. The results obtained from this work are submitted to a research evaluation workshop Forum for Information Retrieval and Evaluation (FIRE 2014). This system detects three levels of named entity tags which are referred as nested named entities. It is a multi-label problem solved using chain classifier method. In this work, Conditional Random Field (CRF) and Support Vector Machine (SVM) are used for implementing NER system. In FIRE 2014, we developed a English NER system using CRF and other NER system for Tamil, Hindi and Malayalam are based on SVM. The FIRE estimated the average precision for all the four languages as 41.93 for outermost level and 33.25 for inner *** order to improve the performance of Indian languages, we implemented CRF based NER system for the same corpus in Tamil, Hindi and Malayalam. The average precision measure for these mentioned languages are 42.87 for outer level and 36.31 for inner level. The overall performance of the NER system improved by 2.24% for outer level and 9.20% for inner level. © 2015 ACM.

关键词： Support vector machines

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Machine learning approach for correcting preposition errors using SVD features 3

Machine learning approach for correcting preposition errors ...

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3rd International Conference on Advances in Computing, Communications and Informatics, ICACCI 2014

作者： Aravind, Anuja Kumar M., Anand Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9781479930791

Non-native English writers often make preposition errors in English language. The most commonly occurring preposition errors are preposition replacement, preposition missing and unwanted preposition. So, in this method, a system is developed for finding and handling the English preposition errors in preposition replacement case. The proposed method applies 2-Singular Value Decomposition (SVD2) concept for data decomposition resulting in fast calculation and these features are given for classification using Support Vector Machines (SVM) classifier which obtains an overall accuracy above 90%. Features are retrieved using novel SVD2 based method applied on trigrams which is having a preposition in the middle of the context. A matrix with the left and right vectors of each word in the trigram is computed for applying SVD2 concept and these features are used for supervised classification. Preliminary results show that this novel feature extraction and dimensionality reduction method is the appropriate method for handling preposition errors. © 2014 IEEE.

关键词： Support vector machines

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