Sagar Dhakal Data-verified
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Researcher
faculty
Research Areas
Biography and Research Information
OverviewAI-generated summary
Sagar Dhakal's research interests encompass a range of topics, including the application of advanced computational methods and the study of energy systems. He has investigated the use of Spiking Neural Networks for classifying electrocardiogram (ECG) data and detecting anomalies, as well as explored voltage control and braking systems for Doubly Fed Induction Generators (DFIG) during fault conditions. Additionally, Dhakal has contributed to the understanding of astrophysical phenomena, specifically probing the magneto-ionic medium of the Milky Way using pulsars. His work also touches upon environmental sustainability, with a review on greening small hydropower systems. Dhakal has collaborated with researchers such as Tolga Ensarı and Sachin Bhandari at Arkansas Tech University, with whom he shares publications. His scholarly output is reflected in an h-index of 5, with a total of 11 publications and 207 citations.
Metrics
- h-index: 5
- Publications: 11
- Citations: 210
Selected Publications
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Spiking Neural Networks for ECG Classification and Anomaly Detection (2025)
Collaboration Network
Top Collaborators
- Probing the magneto-ionic medium of the Milky Way using pulsars
- Probing the magneto-ionic medium of the Milky Way using pulsars
- Greening small hydropower: A brief review
- Greening small hydropower: A brief review
- Voltage Control and Braking System of a DFIG during a Fault
- Voltage Control and Braking System of a DFIG during a Fault
- Voltage Control and Braking System of a DFIG during a Fault
- Voltage Control and Braking System of a DFIG during a Fault
- Spiking Neural Networks for ECG Classification and Anomaly Detection
- Spiking Neural Networks for ECG Classification and Anomaly Detection
- Spiking Neural Networks for ECG Classification and Anomaly Detection
- Spiking Neural Networks for ECG Classification and Anomaly Detection
- Spiking Neural Networks for ECG Classification and Anomaly Detection
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