23 citations · 67 across the 17 of their papers we have counts for
20 papers
Non-invasive Seizure Detection Using Wearable Wrist-worn Accelerometry and Deep Learning
Nilushika Udayangani Hewa Dehigahawattage, Kishor Nandakishor, Marimuthu Palaniswami
Seizure monitoring and detection are crucial for reducing the morbidity and mortality associated with seizures. Current epilepsy care, often involving expensive video-electroenceph…
Learning to Remember: Distilling Memory Retention for Compact Recurrent Neural Networks
Nilushika Udayangania, Kishor Nandakishora, Marimuthu Palaniswami
Deep learning models, particularly recurrent neural networks and their variants, such as long short-term memory, have significantly advanced time series analysis. These models capt…
Generative Diffusion Prior Distillation for Long-Context Knowledge Transfer
Nilushika Udayangani, Kishor Nandakishor, Marimuthu Palaniswami
While traditional time-series classifiers assume full sequences at inference, practical constraints (latency and cost) often limit inputs to partial prefixes. The absence of class-…
Task-Aligned Self-Supervised Learning for Medical Image Analysis: A Task-Oriented Review with Practical Design Guidelines
Chathura Wimalasiri, Yuchong Yao, Kishor Nandakishor +1
Self-supervised learning (SSL) is increasingly used in medical image analysis to reduce dependence on costly expert annotations by learning transferable representations from unlabe…
Learning to Reason: Temporal Saliency Distillation for Interpretable Knowledge Transfer
Nilushika Udayangani Hewa Dehigahawattage, Kishor Nandakishor, Marimuthu Palaniswami
Knowledge distillation has proven effective for model compression by transferring knowledge from a larger network called the teacher to a smaller network called the student. Curren…
NeuroSleepNet: An Explainable Multi-Head Attention-Based Framework With Spatial and Multi-Scale Independent Temporal Context Learning for Automatic Sleep Stage Scoring
Muhammad Sudipto Siam Dip, Mohammod Abdul Motin, Chandan Karmakar +2
Automatic sleep stage scoring is crucial for monitoring sleep quality and sleep-related disorders. However, most existing electroencephalogram (EEG) based deep learning frameworks…
MOMA:Distill from Self-Supervised Teachers
Yuchong Yao, Nandakishor Desai, Marimuthu Palaniswami
Contrastive Learning and Masked Image Modelling have demonstrated exceptional performance on self-supervised representation learning, where Momentum Contrast (i.e., MoCo) and Maske…
Masked Contrastive Representation Learning
Yuchong Yao, Nandakishor Desai, Marimuthu Palaniswami
Masked image modelling (e.g., Masked AutoEncoder) and contrastive learning (e.g., Momentum Contrast) have shown impressive performance on unsupervised visual representation learnin…
Scheduling IoT Applications in Edge and Fog Computing Environments: A Taxonomy and Future Directions
Mohammad Goudarzi, Marimuthu Palaniswami, Rajkumar Buyya
Fog computing, as a distributed paradigm, offers cloud-like services at the edge of the network with low latency and high-access bandwidth to support a diverse range of IoT applica…
Online Slice Reconfiguration for End-to-End QoE in 6G Applications
Dibbendu Roy, Aravinda S. Rao, Tansu Alpcan +3
End-to-end (E2E) quality of experience (QoE) for 6G applications depends on the synchronous allocation of networking and computing resources, also known as slicing. However, the re…
Achieving AI-enabled Robust End-to-End Quality of Experience over Radio Access Networks
Dibbendu Roy, Aravinda S. Rao, Tansu Alpcan +2
Emerging applications such as Augmented Reality, the Internet of Vehicles and Remote Surgery require both computing and networking functions working in harmony. The End-to-end (E2E…
A Distributed Deep Reinforcement Learning Technique for Application Placement in Edge and Fog Computing Environments
Mohammad Goudarzi, Marimuthu Palaniswami, Rajkumar Buyya
Fog/Edge computing is a novel computing paradigm supporting resource-constrained Internet of Things (IoT) devices by the placement of their tasks on the edge and/or cloud servers.…
Achieving QoS for Real-Time Bursty Applications over Passive Optical Networks
Dibbendu Roy, Aravinda S. Rao, Tansu Alpcan +2
Emerging real-time applications such as those classified under ultra-reliable low latency (uRLLC) generate bursty traffic and have strict Quality of Service (QoS) requirements. Pas…
A Distributed Application Placement and Migration Management Techniques for Edge and Fog Computing Environments
Mohammad Goudarzi, Marimuthu Palaniswami, Rajkumar Buyya
Fog/Edge computing model allows harnessing of resources in the proximity of the Internet of Things (IoT) devices to support various types of real-time IoT applications. However, du…
The effects of asymmetric volume conductor modeling on non-invasive fetal ECG extraction
Emerson Keenan, Chandan Kumar Karmakar, Marimuthu Palaniswami
Objective: Non-invasive fetal electrocardiography (NI-FECG) shows promise for capturing novel physiological information that may indicate signs of fetal distress. However, signific…
A Scalable Framework for Trajectory Prediction
Punit Rathore, Dheeraj Kumar, Sutharshan Rajasegarar +2
Trajectory prediction (TP) is of great importance for a wide range of location-based applications in intelligent transport systems such as location-based advertising, route plannin…
Fuzzy c-Shape: A new algorithm for clustering finite time series waveforms
Fateme Fahiman, Jame C. Bezdek, Sarah M. Erfani +2
The existence of large volumes of time series data in many applications has motivated data miners to investigate specialized methods for mining time series data. Clustering is a po…
Internet of Things (IoT): A Vision, Architectural Elements, and Future Directions
Jayavardhana Gubbi, Rajkumar Buyya, Slaven Marusic +1
Ubiquitous sensing enabled by Wireless Sensor Network (WSN) technologies cuts across many areas of modern day living. This offers the ability to measure, infer and understand envir…
Cramér-Rao Bounds for Polynomial Signal Estimation using Sensors with AR(1) Drift
Swarnendu Kar, Pramod K. Varshney, Marimuthu Palaniswami
We seek to characterize the estimation performance of a sensor network where the individual sensors exhibit the phenomenon of drift, i.e., a gradual change of the bias. Though esti…
Jeeva: Enterprise Grid-enabled Web Portal for Protein Secondary Structure Prediction
Chao Jin, Jayavardhana Gubbi, Rajkumar Buyya +1
This paper presents a Grid portal for protein secondary structure prediction developed by using services of Aneka, a .NET-based enterprise Grid technology. The portal is used by re…