m e d i c a l j o u r n a l a r m e d f o r c e s i n d i a 7 2 ( 2 0 1 6 ) 2 7 0 -2 7 6 a r t i c l e i n f o The use of these devices has made our life simple in household work as well as in offices.However the prolonged use of these devices is not without any complication. Computer and visual display terminals syndrome is a constellation of symptoms ocular as well as extraocular associated with prolonged use of visual display terminals. This syndrome is gaining importance in this modern era because of the widespread use of technologies in day-to-day life. It is associated with asthenopic symptoms, visual blurring, dry eyes, musculoskeletal symptoms such as neck pain, back pain, shoulder pain, carpal tunnel syndrome, psychosocial factors, venous thromboembolism, shoulder tendonitis, and elbow epicondylitis. Proper identification of symptoms and causative factors are necessary for the accurate diagnosis and management. This article focuses on the various aspects of the computer vision display terminals syndrome described in the previous literature. Further research is needed for the better understanding of the complex pathophysiology and management. #
Over the last few years several models have been suggested for analytical design of tools used in electrochemical machining of complex shaped workpieces. However, little success has been achieved in this direction. This is due to complex nature of interaction between the electrochemical machining parameters and lack of clear understanding of the mechanism of metal removal. This paper reports about the application of Finite Element Technique (FET) to design analysis of ECM tools. The results obtained have been compared with the experimental data and a good correlation has been observed. The paper also discusses in detail the computational aspects.
Many problems in voice recognition and audio processing involve feature extraction from raw waveforms. The goal of feature extraction is to reduce the dimensionality of the audio signal while preserving the informative signatures that, for example, distinguish different phonemes in speech or identify particular instruments in music. If the acoustic variability of a data set is described by a small number of continuous features, then we can imagine the data as lying on a low dimensional manifold in the high dimensional space of all possible waveforms. Locally linear embedding (LLE) is an unsupervised learning algorithm for feature extraction in this setting. In this paper, we present results from the exploratory analysis and visualization of speech and music by LLE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.This conference paper is available at ScholarlyCommons: http://repository.upenn.edu/cis_papers/3 EXPLORATORY ANALYSIS AND VISUALIZATION OF SPEECH AND MUSIC BY LOCALLY LINEAR EMBEDDING Viren Jain and Lawrence K. SaulDepartment of Computer and Information Science University of Pennsylvania, Philadelphia, PA 19104 {viren,lsaul}@seas.upenn.edu ABSTRACTMany problems in voice recognition and audio processing involve feature extraction from raw waveforms. The goal of feature extraction is to reduce the dimensionality of the audio signal while preserving the informative signatures that, for example, distinguish different phonemes in speech or identify particular instruments in music. If the acoustic variability of a data set is described by a small number of continuous features, then we can imagine the data as lying on a low dimensional manifold in the high dimensional space of all possible waveforms. Locally linear embedding (LLE) is an unsupervised learning algorithm for feature extraction in this setting. In this paper, we present results from the exploratory analysis and visualization of speech and music by LLE.
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