Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Anish C. Turlapaty"'
Publikováno v:
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6d153922ddba28bbcd06914b93206754
https://doi.org/10.1109/igarss46834.2022.9884594
https://doi.org/10.1109/igarss46834.2022.9884594
Publikováno v:
2022 IEEE International Conference on Signal Processing and Communications (SPCOM).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::37a7509e8b06e1fe3f84dead2d11419b
https://doi.org/10.1109/spcom55316.2022.9840794
https://doi.org/10.1109/spcom55316.2022.9840794
Variational Bayesian Estimation of Statistical Properties of Composite Gamma Log-Normal Distribution
Autor:
Anish C. Turlapaty
Publikováno v:
IEEE Transactions on Signal Processing. 68:6481-6492
An iterative variational Bayesian method is proposed for estimation of the statistical properties of the composite gamma log-normal distribution, specifically, the Nakagami parameter of the gamma component and the mean and variance parameters of the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8ed259c9da846c527b705b0a438ec4e6
https://doi.org/10.1109/tsp.2020.3037397
https://doi.org/10.1109/tsp.2020.3037397
Publikováno v:
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. 2021
Existing literature suggests that the probability density function (pdf) of surface Electromyography (sEMG) signals follows either a Gaussian or Laplacian model. In this paper, a Laplacian-Gaussian mixture model is proposed for the EMG signals extrac
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8818d0afc7517badc16ab5a5dbb2c135
https://pubmed.ncbi.nlm.nih.gov/34891384
https://pubmed.ncbi.nlm.nih.gov/34891384
Publikováno v:
2021 IEEE Applied Imagery Pattern Recognition Workshop (AIPR).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2f3cdce034bfd04e0fe551355d5056a1
https://doi.org/10.1109/aipr52630.2021.9762123
https://doi.org/10.1109/aipr52630.2021.9762123
Publikováno v:
2021 IEEE Applied Imagery Pattern Recognition Workshop (AIPR).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8e299733d8cc65fc2a102a447e4b08c5
https://doi.org/10.1109/aipr52630.2021.9762070
https://doi.org/10.1109/aipr52630.2021.9762070
Visible light communications (VLC) has emerged as a high-speed, low-cost, and green supplement for the existing radio frequency (RF) based infrastructures. However, the performance of VLC based systems is found to degrade significantly due to detrime
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1dd0d1c6c365870f5e29001a6fdc09a6
https://hdl.handle.net/11729/3107
https://hdl.handle.net/11729/3107
Publikováno v:
Biomedical Signal Processing and Control. 70:102948
The performance of a robotic exoskeleton depends upon the accuracy of control commands from the controller fed with Surface ElectroMyoGraphy (sEMG) input signals. The classification of hand gestures based on sEMG signals extracted from a human hand d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c62c90ee572990cb3c6b9da3d469f935
https://doi.org/10.1016/j.bspc.2021.102948
https://doi.org/10.1016/j.bspc.2021.102948
Publikováno v:
AIR
An exoskeleton robot performance can be improved by providing accurate control commands using information from the surface EMG signals. This paper proposes a classification of the hand movements based on sEMG signal. We explore the time domain and ti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::97ff4dfe4300b3c4f3935ba59d8e94ce
https://doi.org/10.1145/3352593.3352620
https://doi.org/10.1145/3352593.3352620
Autor:
Yuanwei Jin, Anish C. Turlapaty
Publikováno v:
IEEE Transactions on Signal Processing. 64:4663-4678
In this paper, we consider the problem of multi-parameter estimation in the presence of compound Gaussian clutter for cognitive radar by the variational Bayesian method. The advantage of variational Bayesian is that the estimation of multi-variate pa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c752a1092db1a951d16fd5082df5ff1c
https://doi.org/10.1109/tsp.2016.2573760
https://doi.org/10.1109/tsp.2016.2573760