Academy of Technology

About: Academy of Technology is a based out in . It is known for research contribution in the topics: Laser & Photoluminescence. The organization has 916 authors who have published 1064 publications receiving 7813 citations.

Fractionation of organic carbon based on susceptibility to oxidation

Abstract: A new method is proposed for fractionation of soil organic carbon, based on its susceptibility to oxidation with KMnO4 solutions of various concentrations. This method can be used for: characterizing the organic matter of various soils, evaluation of qualitative changes of organic matter under the influence of fertilization and crop rotation, and for evaluation of susceptibility to oxidation of various substrates of humification

Unknown Bounds on Performance in Nakagami Channels

Abstract: Optimum performance of communication in Nakagami channels is reexamined. With novel functionals of m/\gamma -distribution, analyses of signal interference and of coherent binary detection are greatly simplified. The general error-rate formula holds for coherent and noncoherent detection of PSK or FSK signals. A lower bound on error rates is established for some diversity systems.

Classification of EEG signal using wavelet transform and support vector machine for epileptic seizure diction

Abstract: Feature extraction and classification of electroencephalogram (EEGs) signals for (normal and epileptic) is a challenge for engineers and scientists. Various signal processing techniques have already been proposed for classification of non-linear and non- stationary signals like EEG. In this work, SVM (support vector machine) based classifier was employed to detect epileptic seizure activity from background electro encephalographs (EEGs). Five types of EEG signals (healthy subject with eye open condition, eye close condition, epileptic, seizure signal from hippocampal region) were selected for the analysis. Signals were preprocessed, decomposed by using discrete wavelet transform DWT till 5th level of decomposition tree. Various features like energy, entropy and standard deviation were computed and consequently used for classification of signals. The results show the promising classification accuracy of nearly 91.2% in detection of abnormal from normal EEG signals. This proposed classifier can be used to design expert system for epilepsy diagnosis purpose in various hospitals.