http://localhost:8080/xmlui/handle/123456789/338 2026-06-23T06:32:28Z 2026-06-23T06:32:28Z Shelke, S. V. Chandwadkar, D. M. http://localhost:8080/xmlui/handle/123456789/2884 2020-12-17T07:34:09Z 2016-04-01T00:00:00Z

Title: Handwritten Devnagri Character Recognition Authors: Shelke, S. V.; Chandwadkar, D. M. Abstract: Recognition of handwritten characters has been a popular research area for many years. Devnagari script is a major script of India and is widely used for various languages. In this paper we propose a system to recognize devnagri handwritten characters. Total 60 devnagri characters (50 letters and 10 digits) are taken in to consideration. 60 samples of each character i.e. total 3600 samples are used for features extraction. Classification is done by four different classifiers which are Multilayer perceptron, K-Nearest Neighbour, Naive Bayes classifier and Classification tree. Performance of different classifiers is compared.98.9 % accuracy is obtained by Multilayer perceptron

2016-04-01T00:00:00Z Shelke, Shraddha V. Chandwadkar, Dinesh M. http://localhost:8080/xmlui/handle/123456789/2350 2019-08-20T05:53:15Z 2016-04-01T00:00:00Z

Title: Handwritten Devnagri Character Recognition Authors: Shelke, Shraddha V.; Chandwadkar, Dinesh M. Abstract: Recognition of handwritten characters has been a popular research area for many years. Devnagari script is a major script of India and is widely used for various languages. In this paper we propose a system to recognize devnagri handwritten characters. Total 60 devnagri characters (50 letters and 10 digits) are taken in to consideration. 60 samples of each character i.e. total 3600 samples are used for features extraction. Classification is done by four different classifiers which are Multilayer perceptron, K-Nearest Neighbour, Naive Bayes classifier and Classification tree. Performance of different classifiers is compared.98.9 % accuracy is obtained by Multilayer perceptron

2016-04-01T00:00:00Z . Morade, Sunil S Patnaik, Suprava http://localhost:8080/xmlui/handle/123456789/2349 2019-08-20T05:43:28Z 2016-02-01T00:00:00Z

Title: Visual Lip Reading using 3D-DCT and 3D-DWT and LSDA Authors: . Morade, Sunil S; Patnaik, Suprava Abstract: Human uses visual information while trying to understand speech, especially in noisy conditions or in situations where the audio signal is not available. Lip reading is the technique of a comprehensive understanding the underlying speech by processing on the movement of lips. However, the recognition of lip motion is a difficult task since the region of interest (ROI) is nonlinear and noisy. In proposed method lip reading system we have used two stage feature extraction model which is precised, discriminative and computation efficient. The first stage 3D Discrete Wavelet Transform (3D-DWT) or 3D Discrete Cosine Transform (3D-DCT) is used and the second stage is Locality Sensitive Discriminant Analysis (LSDA) to trim down the feature dimensions. These features make a novel lip reading system with small feature vector size. In addition to the novel feature extraction technique, the performance of Naive Bayes and SVM classifier is compared. CUAVE database of 0 to 9 utterances in English is used for experimentation. Results of 3 dimension transform with LSDA are compared with 2 dimension transform with LSDA. Experimental results show that 3D-DWT+LSDA feature mining are compared with 3D-DWT with PCA or LDA. 3D-DWT+LSDA result is also compared with 3D-DCT + LSDA

2016-02-01T00:00:00Z Dherage, Vinay Sunil Morade, Sunil Sudam http://localhost:8080/xmlui/handle/123456789/2348 2019-08-20T05:37:32Z 2016-01-01T00:00:00Z

Title: Train control system using can protocol Authors: Dherage, Vinay Sunil; Morade, Sunil Sudam Abstract: This system aims to train running automatically without any human operators. Also take care of Safety and security , Provides information to avoid train to train collisions, over speeding problem, signaling errors .by this system passengers will get the train location, speed and direction in real time by using there mobile phone. CAN protocol interconnect all the train compartments in the network to ensure safety and security of passengers during disasters occurring within trains such as bomb blasts and fire outbreaks. The CAN node is used to ensure the safety and security of the passenger. In this system (i) a passenger can query about the location of a train via SMS. (ii) Centrally controlled route server. (iv) automatic speed adjustment (v) audio speakers to inform the passengers about the approaching station (vi)provide alert messages during a crisis situation.(vii) Emergency push button to stop the train in critical situation

2016-01-01T00:00:00Z