Browsing by Author "Mitra, A."

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    Analysis and Enhancement of Spectral Efficiency of UWOC System for IoUT Applications
    (Institute of Electrical and Electronics Engineers Inc., 2023) Mitra, A.; Kumar, A.; Krishnan, P.
    Underwater wireless optical communication (UWOC) has engrossed significant attention in diverse fields, such as defence, military applications, environmental monitoring, and scientific research. The growing number of interconnected devices in underwater environments has increased the importance of UWOC capacity analysis. It will be instrumental in underwater optical wireless sensor networks (UOWSNs) and the Internet of Underwater Things (IoUT). Understanding the concept of underwater channel capacity is crucial as it determines the maximum volume of reliable information that can be sent through the underwater communication channel. This work focuses on analyzing the performance of a UWOC system that facilitates communication between a surface source (ship) and an autonomous underwater vehicle (AUV) as the intended recipient. The investigation considers explicitly heterodyne detection and models the channel with Exponential Generalized Gamma (EGG) distribution. The study presents closed-form expressions for average channel capacity, employing Meijer's G function and derived average spectral efficiency (ASE) based on the derived channel capacity. Additionally, the paper delves into the influence of various factors, including pointing errors, bubble levels, water types, beam waist, and aperture radius, on the system's overall performance. © 2023 IEEE.
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    Automatic seizure detection by modified line length and Mahalanobis distance function
    (2018) Pathak, A.; Ramesh, A.; Mitra, A.; Majumdar, K.
    Automatic seizure detection with high accuracy and in linear time has profound implications on therapeutic intervention mechanisms. In this work taking into account 12 popular seizure detection algorithms we have shown that line length is one feature that is extractable in linear time from EEG signals and capable of automatic seizure onset detection with highest accuracy among linear time extractable features. Also line length is less prone to give false positives. The detection accuracy has been ascertained by ROC curve analysis on Freiburg Seizure Prediction Project data containing intracranial EEG recordings of 87 seizures from 21 patients with sufficient interictal signals. Next, we have modified the classical line length feature extraction algorithm to improve its accuracy without any additional computational burden. Finally, we have applied both classical line length (LL) and modified line length (MLL) on all focal channels and detected seizures on multidimensional focal channel signals by Mahalanobis distance function (MDF). Both detected 73 out of 87 seizures. Area under the ROC curve (AUC), detection delay and false positive for LL and MLL are 0.951, 11.903 s, 0.201/h and 0.954, 11.698 s, 0.198/h respectively. Since LL has already been incorporated into an FDA approved commercially available closed loop intervention system, even this minute improvement may have significant healthcare implications. 2018 Elsevier Ltd
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    Automatic seizure detection by modified line length and Mahalanobis distance function
    (Elsevier Ltd, 2018) Pathak, A.; Ramesh, A.; Mitra, A.; Majumdar, K.
    Automatic seizure detection with high accuracy and in linear time has profound implications on therapeutic intervention mechanisms. In this work taking into account 12 popular seizure detection algorithms we have shown that line length is one feature that is extractable in linear time from EEG signals and capable of automatic seizure onset detection with highest accuracy among linear time extractable features. Also line length is less prone to give false positives. The detection accuracy has been ascertained by ROC curve analysis on Freiburg Seizure Prediction Project data containing intracranial EEG recordings of 87 seizures from 21 patients with sufficient interictal signals. Next, we have modified the classical line length feature extraction algorithm to improve its accuracy without any additional computational burden. Finally, we have applied both classical line length (LL) and modified line length (MLL) on all focal channels and detected seizures on multidimensional focal channel signals by Mahalanobis distance function (MDF). Both detected 73 out of 87 seizures. Area under the ROC curve (AUC), detection delay and false positive for LL and MLL are 0.951, 11.903 s, 0.201/h and 0.954, 11.698 s, 0.198/h respectively. Since LL has already been incorporated into an FDA approved commercially available closed loop intervention system, even this minute improvement may have significant healthcare implications. © 2018 Elsevier Ltd