Multi Utility E-Controlled cum Voice Operated Farm Vehicle
ABSTRACT This paper describes the design and construction of MUEVOFV.The vehicle will be used to explore ways of increasing theproductivity using expensive agricultural mobile machinery bytaking over some of the tasks of the operator, allowing him tocontrol the machinery from remote place i.e. E-control throughvoice commands; and to control several accessories of themachine simultaneously. The system was designed to satisfy theneeds of various farm operations in unknown agricultural fields.The controller has a layered architecture and supports two degreesof cooperation using sensor modules between the operator androbotic vehicle, direct and supervisory control. The vehicle'sposition and heading direction can be controlled by globalpositioning.
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ABSTRACT: In speech processing gender clustering and classification is the most outstanding and challenging task. In both gender clustering and classification, one the most vital processes carried out is the selection of features. In speech processing, pitch is the most often used feature for gender clustering and classification. It is essential to note that compared to a female speech the pitch value of a male speech is much different. Also, in terms of frequency there is a considerable dissimilarity between the male and female speech. In some situations, either the frequency of male is almost same as female or the frequency of female is same as male. It is difficult to find out the exact gender in such conditions. This paper focus on rectifying these practical obstacles by extracting three significant features, namely, energy entropy, zero crossing rate, and short time energy. Gender clustering is performed based on these features. However, by means of Euclidean distance, Mahalanobis distance, Manhattan distance & Bhattacharyya distance methods the clustering performance is analyzed. Using fuzzy logic, neural network, hybrid neuro-fuzzy, and support vector machine the gender classification is done. A benchmark dataset and real-time dataset is used for testing to make sure the reliability of the performance. The test results show the performance of various techniques and distance algorithms for different datasets Keywords Mahalanobis distance, Manhattan distance, Bhattacharyya distance, Neuro fuzzy, Support vector machine.International Journal of Computer Applications 08/2012; 51(20):9-17. DOI:10.5120/8156-1533 · 0.82 Impact Factor
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ABSTRACT: Nowadays classification of gender is one of the most important processes in speech processing. Usually gender classification is based on considering pitch as feature. The pitch value of female is higher than the male. In most of the recent research works gender classification process is performed using the abovementioned condition. In some cases the pitch value of male is higher and also pitch of some female is low, in that case this classification does not produce the exact required result. By considering the aforementioned problem we have here proposed a new method for gender classification method which considers three features. The new method uses fuzzy logic and neural network to identify the gender of the speaker. To train fuzzy logic and neural network, training dataset is generated by using the above three features. Then mean value is calculated for the obtained result from fuzzy logic and neural network. By using this threshold value, the proposed method identifies the speaker belongs to which gender. The implementation result shows the performance of the proposed technique in gender classification.
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ABSTRACT: In speech processing, gender clustering and classification plays a major role. In both gender clustering and classification, selecting the feature is an important process and the often utilized feature for gender clustering and classification in speech processing is pitch. The pitch value of a male speech differs much from that of a female speech. Normally, there is a considerable frequency value difference between the male and female speech. But, in some cases the frequency of male is almost equal to female or frequency of female is equal to male. In such situation, it is difficult to identify the exact gender. By considering this drawback, here three features namely; energy entropy, zero crossing rate and short time energy are used for identifying the gender. Gender clustering and classification of speech signal are estimated using the aforementioned three features. Here, the gender clustering is computed using Euclidean distance, Mahalanobis distance, Manhattan distance & Bhattacharyya distance method and the gender classification method is computed using combined fuzzy logic and neural network, neuro fuzzy and support vector machine and its performance are analyzed.