Akash Kulkarni
Verified @kletech.ac.in
Scopus Publications
Scholar Citations
Scholar h-index
Scopus Publications
Nalini C. Iyer, P. C. Nissimagoudar, Preeti Pillai, H. M. Gireesha, Akash Kulkarni, and Aditya Okade
Springer International Publishing
Nalini C. Iyer, Akash Kulkarni, Raghavendra Shet, and U. Keerthan
Springer Singapore
Raghavendra M. Shet, Nalini C. Iyer, P. C. Nissimagoudar, Akash Kulkarni, J. Abhiram, and S. K. Amarnath
Springer Singapore
Nalini C. Iyer, R. M. Shet, P. C. Nissimagoudar, H. M. Gireesha, Venkatesh Mane, Akash Kulkarni, Ajit Bijapur, A. Akshata, and P. Neha
Springer Singapore
Subrahmanya Gunaga, Nalini C Iyer, and Akash Kulkarni
ACM
Localization is an estimate of vehicle position for a given environment. This work focuses on the state estimation of a vehicle for localization functionality using the Schmidt Kalman filter for fused sensor data. The Kalman filter provides an efficient approach in reducing the errors presented by the sensors. Further, computational complexity is reduced through pre-processed initialization in the Schmidt Kalman filter. The two sensors used are GPS (Global Positioning System) and IMU (Inertial Measurement Unit), where GPS provides the position, and IMU provides acceleration/direction. Since GPS data has a dependency on the external environmental factors resulting in discontinuities, it is augmented with similar data until the corrected GPS data is resumed. The error in the position determined by GPS can be as high as 12m. This work presents a method for fusing sensor data using the Schmidt Kalman filter in a practical scenario.
Akash Kulkarni
IEEE
Graphics Processing Units (GPU)s have become an integral part for high-end applications. The paper proposes a solution to leverage the GPU driver developer to identify regressions when upgrading driver features and an automatic testing infrastructure to identify compatibility problems.
S.D. Thepade, J.H. Dewan, and A.A. Kulkarni
Institution of Engineering and Technology
Today, enormous amount of multimedia data is generated, transmitted and stored on the internet which has opened new research dimensions for computing field. In recent work, hybrid wavelet transforms (HWT) generated with various constituent transforms is proven to be better than individual orthogonal transforms [7]. Later work as proved that HWT generated with varying proportions of constituent transforms gives better compression quality as compared to equal proportions of constituent orthogonal transforms in HWT depending upon the compression ratio [1, 2]. Here the appraise of the effect of color spaces on image compression using HWT generated with varying proportions of constituent transforms and constituent transforms is presented. The experimentation is done on the test bed having 15 images of varied sizes and eight compression ratios (60% to 95%). The results show that for higher compression ratio of 95%, the LUV color space gives better compression quality as compared to other considered color spaces with HWT generated with 4:1 proportion of Cosine- Sine constituent transforms. For 65% to 90% compression ratios, HWT generated from 1:1 proportion of Cosine-Kekre constituent transform with RGB color space gives less average mean square error (MSE). For lower compression ratio of 60%, HWT generated with 1:4 proportion of Cosine-Kekre constituent transform gives better image compression quality with RGB color space.