Recognizing emotion from speech has become one the active research themes in speech processing and in applications based on human-computer interaction. This paper conducts an experimental study on recognizing emotions from human speech. The emotions considered for the experiments include neutral, anger, joy and sadness. The distinuishability of emotional features in speech were studied first followed by emotion classification performed on a custom dataset. The classification was performed for different classifiers. One of the main feature attribute considered in the prepared dataset was the peak-to-peak distance obtained from the graphical representation of the speech signals. After performing the classification tests on a dataset formed from 30 different subjects, it was found that for getting better accuracy, one should consider the data collected from one person rather than considering the data from a group of people.

Real-time detection of moving objects involves memorisation of features in the template image and their comparison with those in the test image. At high sampling rates, such techniques face the problems of high algorithmic complexity and component delays. We present a new resistive switching based threshold logic cell which encodes the pixels of a template image. The cell comprises a voltage divider circuit that programs the resistances of the memristors arranged in a single node threshold logic network and the output is encoded as a binary value using a CMOS inverter gate. When a test image is applied to the templateprogrammed cell, a mismatch in the respective pixels is seen as a change in the output voltage of the cell. The proposed cell when compared with CMOS equivalent implementation shows improved performance in area, leakage power, power dissipation and delay.

Background: Incorrect snake identification from the observable visual traits is a major reason for death resulting from snake bites in tropics. So far no automatic classification method has been proposed to distinguish snakes by deciphering the taxonomy features of snake for the two major species of snakes i.e. Elapidae and Viperidae. We identify 38 different taxonomically relevant features to develop the Snake database from 490 sample images of Naja Naja (Spectacled cobra), 193 sample images of Ophiophagus Hannah (King cobra), 88 images of Bungarus caeruleus (Common krait), 304 sample images of Daboia russelii (Russell's viper), 116 images of Echis carinatus (Saw scaled viper) and 108 images of Hypnale hypnale (Hump Nosed Pit Viper). Results: Snake identification performances with 13 different types of classifiers and 12 attribute elevator demonstrate that 15 out of 38 taxonomically relevant features are enough for snake identification. Interestingly, these features were almost equally distributed from the logical grouping of top, side and body views of snake images, and the features from the bottom view of snakes had the least role in the snake identification. Conclusion: We find that only few of the taxonomically relevant snake features are useful in the process of snake identification. These discriminant features are essential to improve the accuracy of snake identification and classification. The presented study indicate that automated snake identification is useful for practical applications such as in medical diagnosis, conservation studies and surveys by interdisciplinary practitioners with little expertise in snake taxonomy.

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