Devendrakula Vellalar Journalism Records and Ethnographic Theory

A man is strong in society because of his protective, mentality. Throughout human history, defensive behavious and instinct have kept humanity constantand grouring. In the battle for life that human face, revolutions are inevitable class conflicts. protests and revolutionsin human society bring about a dynamic transformation. The foundation of racial representation, tenacity and flight. Which in fact make thehuman race vibrant and multiplay, is formed by revolution, conflict, creative and critical literature. The literature has the ability to bring revolution more than any other weapon, according to the current educated culture

Multi-resolution texture classification based on local image orientation

The aim of this paper is to evaluate quantitatively the discriminative power of the image orientation in the texture classification process. In this regard, we have evaluated the performance of two texture classification schemes where the image orientation is extracted using the partial derivatives of the Gaussian function. Since the texture descriptors are dependent on the observation scale, in this study the main emphasis is placed on the implementation of multi-resolution texture analysis schemes. The experimental results were obtained when the analysed texture descriptors were applied to standard texture databases

Variation in phytoliths morphology of Erianthus arundinaceus

Five improved Erianthus arundinaceus clones were studied for variations in phytoliths present in leaves. Phytoliths were extracted by acid digestion and the morphology was studied by light microscopy. Phytoliths belonging to three classes viz., Eupanicoid, Festucoid and Chloridoid were identified in the samples. Variations in type and shape were observed among the clones studied. The results indicate that phytolith characteristics may be an important and reliable trait for clonal characterization in improved Erianthus clones and also there is a possibility of using this trait as a tool for the identification of species, hybrids and their parentage in sugarcane and their related genera

Factorized variational approximations for acoustic multi source localization

Estimation based on received signal strength (RSS) is crucial in sensor networks for sensor localization, target tracking, etc. In this paper, we present a Gaussian approximation of the Chi distribution that is applicable to general RSS source localization problems in sensor networks. Using our Gaussian approximation, we provide a factorized variational Bayes (VB) approximation to the location and power posterior of multiple sources using a sensor network. When the source signal and the sensor noise have uncorrelated Gaussian distributions, we demonstrate that the envelope of the sensor output can be accurately modeled with a multiplicative Gaussian noise model. In turn, our factorized VB approximations decrease the computational complexity and provide computational robustness as the number of targets increases. Simulations are provided to demonstrate the effectiveness of the proposed approximations

Scalable Data Parallel Algorithms for Texture Synthesis and Compression using Gibbs Random Fields

This paper introduces scalable data parallel algorithms for image processing. Focusing on Gibbs and Markov Random Field model representation for textures, we present parallel algorithms for texture synthesis, compression, and maximum likelihood parameter estimation, currently implemented on Thinking Machines CM-2 and CM-5. Use of fine-grained, data parallel processing techniques yields real-time algorithms for texture synthesis and compression that are substantially faster than the previously known sequential implementations. Although current implementations are on Connection Machines, the methodology presented here enables machine independent scalable algorithms for a number of problems in image processing and analysis. (Also cross-referenced as UMIACS-TR-93-80.

Adaptive Tracking of Angular Velocity for a Planar Rigid Body With Unknown Models for Inertia and Input Nonlinearity

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/57795/1/AdaptiveTrackingTACTTCST1D.pd

Joint acoustic-video fingerprinting of vehicles, part II

In this second paper, we first show how to estimate the wheelbase length of a vehicle using line metrology in video. We then address the vehicle fingerprinting problem using vehicle silhouettes and color invariants. We combine the acoustic metrology and classification results discussed in Part I with the video results to improve estimation performance and robustness. The acoustic video fusion is achieved in a Bayesian framework by assuming conditional independence of the observations of each modality. For the metrology density functions, Laplacian approximations are used for computational efficiency. Experimental results are given using field data