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31 results on '"Deshpande, Ashwini M."'

2. Performance Analysis of Neural Machine Translation Models for ASL to ASL Gloss Conversion

Author

Waghmare, Prachi P., Deshpande, Ashwini M., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Sharma, Harish, editor, Shrivastava, Vivek, editor, Bharti, Kusum Kumari, editor, and Wang, Lipo, editor

Published
2023
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3. A Comparative Investigation of Deep Feature Extraction Techniques for Video Summarization

Author

Kadam, Bhakti D., Deshpande, Ashwini M., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Bansal, Hari Om, editor, Ajmera, Pawan K., editor, Joshi, Sandeep, editor, Bansal, Ramesh C., editor, and Shekhar, Chandra, editor

Published
2023
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4. A Deep Learning Framework for Real-Time Indian Sign Language Gesture Recognition and Translation to Text and Audio

Author

Deshpande, Ashwini M., Inamdar, Gayatri, Kankaria, Riddhi, Katage, Siddhi, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Pati, Bibudhendu, editor, Panigrahi, Chhabi Rani, editor, Mohapatra, Prasant, editor, and Li, Kuan-Ching, editor

Published
2023
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6. Comparative Analysis of Least Squares Method and Extended Kalman Filter for Position Estimation in GPS Receiver

Author

Kavathekar, Jyoti S., Deshpande, Ashwini M., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Merchant, S. N., editor, Warhade, Krishna, editor, and Adhikari, Debashis, editor

Published
2021
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7. A comprehensive experimental evaluation of remote sensing hyperspectral image denoising methods for mixed noise.

Author

Joglekar, Maitreyi and Deshpande, Ashwini M.

Subjects
*BURST noise, *RANDOM noise theory, *REMOTE sensing, *DATA quality, *NOISE, *DEEP learning
Abstract

Hyperspectral Imagery (HSI) often suffers from degradation caused by different types of noise, including Gaussian noise, impulse noise, and stripe noise. Restoring the quality of HSI is a complex task, particularly due to the high dimensionality of HSI datasets. In recent years, there has been a growing utilization of low-rank tensor-based and deep learning-based approaches for denoising HSI data, especially in scenarios involving mixed noise. In this research paper, we assess the performance of two low-rank-based methods and one deep learning-based method when applied to benchmark HSI datasets with a focus on the removal of mixed noise. We quantitatively evaluate the restored datasets using a range of quality metrics and subject them to qualitative visual assessment. Our experimental findings reveal that the low-rank-based methods exhibit promising results in effectively mitigating mixed noise in HSI data. These methods offer a viable solution for addressing the challenge of noise removal in hyperspectral imagery, contributing to improved data quality and usability. [ABSTRACT FROM AUTHOR]

Published
2024
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9. Video-Based Marathi Sign Language Recognition and Text Conversion Using Convolutional Neural Network

Author

Deshpande, Ashwini M., Kalbhor, Snehal R., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Hitendra Sarma, T., editor, Sankar, V., editor, and Shaik, Rafi Ahamed, editor

Published
2020
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10. Human Head Pose and Eye State Based Driver Distraction Monitoring System

Author

Modak, Astha, Paradkar, Samruddhi, Manwatkar, Shruti, Madane, Amol R., Deshpande, Ashwini M., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Chaudhuri, Bidyut B., editor, Nakagawa, Masaki, editor, Khanna, Pritee, editor, and Kumar, Sanjeev, editor

Published
2020
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18. Project Based Learning Approach in Digital Signal Processing Course for Increasing Learners' Cognitive and Behavioral Engagement to Promote Self-Learning.

Author

Deshpande, Ashwini M.

Subjects
DIGITAL signal processing, STUDENT engagement, OUTCOME-based education, LEARNING, AUTODIDACTICISM
Abstract

Project-based learning (PjBL) is one of the most predominantly practiced forms of active learning pedagogy in higher education. PjBL framework provides an authenticated platform/forum for 21st-century learners to acquire skills for solving real-life complex engineering problems through collaborative and iterative learning. The work presented in this paper describes a PjBL approach implemented as an instructional pedagogy and for a summative assessment of an undergraduate (UG) course on Digital Signal Processing (DSP). Students from Electronics and Telecommunication Department study this course in Third Year Engineering as a core course. The projects are chosen to make students identify and resolve real-life signal processing problems utilizing their subject knowledge and programming skills. PjBL implementation framework and rubrics-based evaluation devised for their summative assessment are discussed along with a study case. Student feedback analysis on the attainment of course outcomes and skills enhanced after completion of the course project is discussed. Through this analysis, it has been found that the effectiveness of the PjBL pedagogy employed has led to increasing students' engagement and interest in learning signal processing topics. It has also grown their self-learning ability and created awareness about the practical applications of the Digital Signal Processing course. [ABSTRACT FROM AUTHOR]

Published
2022
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19. Towards Enhancing Students' Performance Through Outcome-Based Education: A Learner-Centric Pedagogical Approach.

Author

Ohatkar, Sharada N. and Deshpande, Ashwini M.

Subjects
OUTCOME-based education, DIGITAL signal processing, FIELD days (Education), DIGITAL communications, ACTIVE learning
Abstract

Outcome-based education (OBE) is the buzzword these days in the education field which boosts students' creativity through their active engagement in learning course contents. Their participatory learning in the OBE model is well supported by making them aware of learning goals and course outcomes. Due to this, the students develop themselves nicely in their working surroundings along with peers. This helps develop their interpersonal skills along with improving academic performance. This paper primarily outlines the implementation of a few key standards of OBE in teaching-learning of two fundamental courses in Electronics and Telecommunication Engineering. It then covers how OBE, and its consequences demand paradigm shifts in instructional design and evaluation practices. The discussion on OBE for courses on Digital Communication and Digital Signal Processing is explored with Program-Outcomes (POs), Program-Specific-Outcomes (PSOs), besides Course-Outcomes (COs). Analysis of POs along with COs attainment for these two courses reflects the importance of the OBE framework. The inclusion of engineering pedagogy and active learning strategies in this framework leads to enhancing students' creativity leading to their growth in their professional careers. [ABSTRACT FROM AUTHOR]

Published
2022
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20. Outcome-Based Education: A Learner-Centric Pedagogical Framework with Case Studies in Digital Communication and Signal Processing Courses.

Author

Ohatkar, Sharada N. and Deshpande, Ashwini M.

Abstract

Outcome-based education (OBE) is the buzzword these days in the education field which boosts students’ creativity through their active engagement in learning course contents. Their participatory learning in the OBE model is well supported by making them aware of learning goals and course outcomes. Due to this, the students develop themselves nicely in their working surroundings along with peers. This helps develop their interpersonal skills along with improving academic performance. This paper primarily outlines the implementation of a few key standards of OBE in teaching-learning of two fundamental courses in Electronics and Telecommunication Engineering. It then covers how OBE, and its consequences demand paradigm shifts in instructional design and evaluation practices. The discussion on OBE for courses on Digital Communication and Digital Signal Processing is explored with Program Outcomes (POs), Program-Specific Outcomes (PSOs), and Course Outcomes (COs). Analysis of PO and CO attainment for these two courses reflects the importance of the OBE framework. The inclusion of engineering pedagogy and active learning strategies in this framework leads to enhancing students’ creativity leading to their growth in their professional careers. [ABSTRACT FROM AUTHOR]

Published
2022
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22. SHADOW DETECTION FROM AERIAL IMAGERY WITH MORPHOLOGICAL PREPROCESSING AND PIXEL CLUSTERING METHODS.

Author

Deshpande, Ashwini M., Gaikwad, Manali, Patki, Saudamini, Rathi, Aditee, and Roy, Sampa

Subjects
GAUSSIAN mixture models, PIXELS, PUBLIC spaces, AKAIKE information criterion, URBAN planning, HUMAN migrations, EMERGENCY management
Abstract

Building extraction from aerial imagery facilitates many geospecialized tasks like Urban Planning, Map Generation and Disaster Management. Well planned cities ensure good sanitation, lesser pollution and hence, a better standard of living for its citizens. This is essential for developing countries which face a major crisis of urban migration and space crunch, and where planned cities would be a move towards smart living. The objective of this work is to segment building footprints from aerial images. Traditional pixel clustering algorithms like K-means, Color Quantization (CQ) and Gaussian Mixture Model (GMM) are implemented with inclusion of preprocessing steps for improved performance. These techniques are compared based on performance and time taken. The number of clusters/components are selected on the basis of Silhouette Score and Akaike Information Criterion/Bayesian Information Criterion (AIC/BIC). A commonly encountered problem in building segmentation is misclassification of pixels due to shadows. This challenge is dealt by masking shadows using morphological operations as a part of preprocessing. [ABSTRACT FROM AUTHOR]

Published
2021
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