Modifications of secondary reflector antenna patterns due to shaped beams from flanged primary horn feeds

With the advent of satellite communication and radio astronomy, the need for large and efficient reflector antennas had triggered a widespread investigation in reflector feed design techniques. Major improvements sought are reduction in spill-over, cross polarization losses and the enhancement of aperture efficiency. The search for such a feed culminated in the corrugated horn. The main idea behind the present work is to use the H-plane sectoral horns fitted with,corrugated flanges as feeds of a paraboloid and see how the secondary pattern of the reflector antenna varies with different parameters of the feed. An offset paraboloid is used as the secondary reflector in order to avoid the adverse effect of aperture ‘blocking by the feed horn structure on the secondary radiation pattern. The measurements were repeated for three different H-plane sectoral horns with the same set of corrugated flanges at various X-band frequencies. The following parameters of the whole system are studied: (a) Beam shaping. (b) Gain. (c) Variation of VSWR and (d) Cross polarization

PERFORMANCE OF DIFFERENT CLASSIFIERS IN SPEECH RECOGNITION

Speech is the most natural means of communication among human beings and speech processing and recognition are intensive areas of research for the last five decades. Since speech recognition is a pattern recognition problem, classification is an important part of any speech recognition system. In this work, a speech recognition system is developed for recognizing speaker independent spoken digits in Malayalam. Voice signals are sampled directly from the microphone. The proposed method is implemented for 1000 speakers uttering 10 digits each. Since the speech signals are affected by background noise, the signals are tuned by removing the noise from it using wavelet denoising method based on Soft Thresholding. Here, the features from the signals are extracted using Discrete Wavelet Transforms (DWT) because they are well suitable for processing non-stationary signals like speech. This is due to their multi- resolutional, multi-scale analysis characteristics. Speech recognition is a multiclass classification problem. So, the feature vector set obtained are classified using three classifiers namely, Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Naive Bayes classifiers which are capable of handling multiclasses. During classification stage, the input feature vector data is trained using information relating to known patterns and then they are tested using the test data set. The performances of all these classifiers are evaluated based on recognition accuracy. All the three methods produced good recognition accuracy. DWT and ANN produced a recognition accuracy of 89%, SVM and DWT combination produced an accuracy of 86.6% and Naive Bayes and DWT combination produced an accuracy of 83.5%. ANN is found to be better among the three methods.

Heavy metal tolerance patterns of total heterotrophic bacteria isolated from the soils of Mahatma Gandhi University campus, Kottayam, Kerala

Industrialization of our society has led to an increased production and discharge of both xenobiotic and natural chemical substances. Many of these chemicals will end up in the soil. Pollution of soils with heavy metals is becoming one of the most severe ecological and human health hazards. Elevated levels of heavy metals decrease soil microbial activity and bacteria need to develop different mechanisms to confer resistances to these heavy metals. Bacteria develop heavy-metal resistance mostly for their survivals, especially a significant portion of the resistant phenomena was found in the environmental strains. Therefore, in the present work, we check the multiple metal tolerance patterns of bacterial strains isolated from the soils of MG University campus, Kottayam. A total of 46 bacterial strains were isolated from different locations of the campus and tested for their resistant to 5 common metals in use (lead, zinc, copper, cadmium and nickel) by agar dilution method. The results of the present work revealed that there was a spatial variation of bacterial metal resistance in the soils of MG University campus, this may be due to the difference in metal contamination in different sampling location. All of the isolates showed resistance to one or more heavy metals selected. Tolerance to lead was relatively high followed by zinc, nickel, copper and cadmium. About 33% of the isolates showed very high tolerance (>4000μg/ml) to lead. Tolerance to cadmium (65%) was rather low (<100 μg/ml). Resistance to zinc was in between 100μg/ml - 1000μg/ml and the majority of them shows resistance in between 200μg/ml - 500μg/ml. Nickel resistance was in between 100μg/ml - 1000μg/ml and a good number of them shows resistance in between 300μg/ml - 400μg/ml. Resistance to copper was in between <100μg/ml - 500μg/ml and most of them showed resistance in between 300μg/ml - 400μg/ml. From the results of this study, it was concluded that heavy metal-resistant bacteria are widely distributed in the soils of MG university campus and the tolerance of heavy metals varied among bacteria and between locations