Empirical Analysis of Signature-Based Sign Language Recognition

The significance of automated SLR (Sign Language Recognition) proved not only in the deaf community but in various other spheres of life. The automated SLR are mainly based on the machine learning methods.PSL (Pakistani Sign Language)is an emerging area in order to benefit a big community in this region of the world. This paper presents recognition of PSL using machine learning methods. We propose an efficient and invariant method of classification of signs of PSL. This paper also presents a thorough empirical analysis of signature-based classification methods. Six different signatures are analyzed for two distinct groups of signs having total of forty five signs. Signs of PSL are close enough in terms of inter-sign similarity distancetherefore, it is a challenge to make the classification. Methodical empirical analysis proves that proposed method deals with these challenges adequately and effectivel

Estimating User Preferences by Managing Contextual History in Context Aware Systems

382-385В ходе исследования было проведено картографирование участка площади водосбора р. Солонка в пределах г. Казани, построена цифровая модель рельефа данной территории и рассмотрены ее основные параметры. Проведен анализ структуры данного участка водосборного бассейна реки Солонка. Выявлено, что основная часть территории (40 %) представляет собой обрабатываемые сельскохозяйственные земли. Также были установлены местоположения основных тальвегов временных водотоков

WaveletBased Despeckling of Synthetic Aperture Radar Images Using Adaptive and Mean Filters

Abstract—In this paper we introduced new wavelet based algorithm for speckle reduction of synthetic aperture radar images, which uses combination of undecimated wavelet transformation, wiener filter (which is an adaptive filter) and mean filter. Further more instead of using existing thresholding techniques such as sure shrinkage, Bayesian shrinkage, universal thresholding, normal thresholding, visu thresholding, soft and hard thresholding, we use brute force thresholding, which iteratively run the whole algorithm for each possible candidate value of threshold and saves each result in array and finally selects the value for threshold that gives best possible results. That is why it is slow as compared to existing thresholding techniques but gives best results under the given algorithm for speckle reduction. Keywords—Brute force thresholding, Directional smoothing, Direction dependent mask, undecimated wavelet transformation