Search Results for author: Nikil Dutt

Found 25 papers, 1 papers with code

Context-Aware Stress Monitoring using Wearable and Mobile Technologies in Everyday Settings

no code implementations14 Dec 2023 Seyed Amir Hossein Aqajari, Sina Labbaf, Phuc Hoang Tran, Brenda Nguyen, Milad Asgari Mehrabadi, Marco Levorato, Nikil Dutt, Amir M. Rahmani

We achieved the F1-score of 70\% with a Random Forest classifier using both PPG and contextual data, which is considered an acceptable score in models built for everyday settings.

Reducing Intraspecies and Interspecies Covariate Shift in Traumatic Brain Injury EEG of Humans and Mice Using Transfer Euclidean Alignment

no code implementations3 Oct 2023 Manoj Vishwanath, Steven Cao, Nikil Dutt, Amir M. Rahmani, Miranda M. Lim, Hung Cao

We tested the robustness of this transfer learning technique on various rule-based classical machine learning models as well as the EEGNet-based deep learning model by evaluating on different datasets, including human and mouse data in a binary classification task of detecting individuals with versus without traumatic brain injury (TBI).

Binary Classification EEG +1

Active Reinforcement Learning for Personalized Stress Monitoring in Everyday Settings

no code implementations28 Apr 2023 Ali Tazarv, Sina Labbaf, Amir Rahmani, Nikil Dutt, Marco Levorato

Most existing sensor-based monitoring frameworks presume that a large available labeled dataset is processed to train accurate detection models.

Active Learning reinforcement-learning

Demand Layering for Real-Time DNN Inference with Minimized Memory Usage

no code implementations8 Oct 2022 Mingoo Ji, Saehanseul Yi, Changjin Koo, Sol Ahn, Dongjoo Seo, Nikil Dutt, Jong-Chan Kim

In our approach, a DNN is loaded and executed in a layer-by-layer manner, minimizing the memory usage to the order of a single layer.

Edge-centric Optimization of Multi-modal ML-driven eHealth Applications

no code implementations4 Aug 2022 Anil Kanduri, Sina Shahhosseini, Emad Kasaeyan Naeini, Hamidreza Alikhani, Pasi Liljeberg, Nikil Dutt, Amir M. Rahmani

Smart eHealth applications deliver personalized and preventive digital healthcare services to clients through remote sensing, continuous monitoring, and data analytics.

Efficient Personalized Learning for Wearable Health Applications using HyperDimensional Computing

no code implementations1 Aug 2022 Sina Shahhosseini, Yang Ni, Hamidreza Alikhani, Emad Kasaeyan Naeini, Mohsen Imani, Nikil Dutt, Amir M. Rahmani

Considering the significant role of wearable devices in monitoring human body parameters, on-device learning can be utilized to build personalized models for behavioral and physiological patterns, and provide data privacy for users at the same time.

BIG-bench Machine Learning Privacy Preserving

Online Learning for Orchestration of Inference in Multi-User End-Edge-Cloud Networks

no code implementations21 Feb 2022 Sina Shahhosseini, Dongjoo Seo, Anil Kanduri, Tianyi Hu, Sung-soo Lim, Bryan Donyanavard, Amir M. Rahmani, Nikil Dutt

To this end, we propose a reinforcement-learning-based computation offloading solution that learns optimal offloading policy considering deep learning model selection techniques to minimize response time while providing sufficient accuracy.

Cloud Computing Decision Making +2

Novel Blood Pressure Waveform Reconstruction from Photoplethysmography using Cycle Generative Adversarial Networks

no code implementations24 Jan 2022 Milad Asgari Mehrabadi, Seyed Amir Hossein Aqajari, Amir Hosein Afandizadeh Zargari, Nikil Dutt, Amir M. Rahmani

Continuous monitoring of blood pressure (BP)can help individuals manage their chronic diseases such as hypertension, requiring non-invasive measurement methods in free-living conditions.

Generative Adversarial Network Translation

Personalized Stress Monitoring using Wearable Sensors in Everyday Settings

no code implementations31 Jul 2021 Ali Tazarv, Sina Labbaf, Stephanie M. Reich, Nikil Dutt, Amir M. Rahmani, Marco Levorato

Since stress contributes to a broad range of mental and physical health problems, the objective assessment of stress is essential for behavioral and physiological studies.

Heart Rate Variability Photoplethysmography (PPG)

NeuroXplorer 1.0: An Extensible Framework for Architectural Exploration with Spiking Neural Networks

no code implementations4 May 2021 Adarsha Balaji, Shihao Song, Twisha Titirsha, Anup Das, Jeffrey Krichmar, Nikil Dutt, James Shackleford, Nagarajan Kandasamy, Francky Catthoor

Recently, both industry and academia have proposed many different neuromorphic architectures to execute applications that are designed with Spiking Neural Network (SNN).

Endurance-Aware Mapping of Spiking Neural Networks to Neuromorphic Hardware

no code implementations9 Mar 2021 Twisha Titirsha, Shihao Song, Anup Das, Jeffrey Krichmar, Nikil Dutt, Nagarajan Kandasamy, Francky Catthoor

We propose eSpine, a novel technique to improve lifetime by incorporating the endurance variation within each crossbar in mapping machine learning workloads, ensuring that synapses with higher activation are always implemented on memristors with higher endurance, and vice versa.

graph partitioning

Personal Mental Health Navigator: Harnessing the Power of Data, Personal Models, and Health Cybernetics to Promote Psychological Well-being

no code implementations15 Dec 2020 Amir M. Rahmani, Jocelyn Lai, Salar Jafarlou, Asal Yunusova, Alex. P. Rivera, Sina Labbaf, Sirui Hu, Arman Anzanpour, Nikil Dutt, Ramesh Jain, Jessica L. Borelli

Traditionally, the regime of mental healthcare has followed an episodic psychotherapy model wherein patients seek care from a provider through a prescribed treatment plan developed over multiple provider visits.

Management Sociology

Detection of COVID-19 Using Heart Rate and Blood Pressure: Lessons Learned from Patients with ARDS

no code implementations12 Nov 2020 Milad Asgari Mehrabadi, Seyed Amir Hossein Aqajari, Iman Azimi, Charles A Downs, Nikil Dutt, Amir M Rahmani

In contrast, vital signs (e. g., heart rate) have been utilized to early-detect different respiratory diseases in ubiquitous health monitoring.

R-TOD: Real-Time Object Detector with Minimized End-to-End Delay for Autonomous Driving

2 code implementations23 Oct 2020 Wonseok Jang, Hansaem Jeong, Kyungtae Kang, Nikil Dutt, Jong-Chan Kim

For realizing safe autonomous driving, the end-to-end delays of real-time object detection systems should be thoroughly analyzed and minimized.

Autonomous Driving object-detection +1

Enabling Resource-Aware Mapping of Spiking Neural Networks via Spatial Decomposition

no code implementations19 Sep 2020 Adarsha Balaji, Shihao Song, Anup Das, Jeffrey Krichmar, Nikil Dutt, James Shackleford, Nagarajan Kandasamy, Francky Catthoor

With growing model complexity, mapping Spiking Neural Network (SNN)-based applications to tile-based neuromorphic hardware is becoming increasingly challenging.

Rolling Shutter Correction

The Causality Inference of Public Interest in Restaurants and Bars on COVID-19 Daily Cases in the US: A Google Trends Analysis

no code implementations27 Jul 2020 Milad Asgari Mehrabadi, Nikil Dutt, Amir M. Rahmani

Our results showed for states/territories with higher numbers of daily cases, the historical trends in search queries related to bars and restaurants, which mainly happened after re-opening, significantly affect the daily new cases, on average.

A Framework to Explore Workload-Specific Performance and Lifetime Trade-offs in Neuromorphic Computing

no code implementations1 Nov 2019 Adarsha Balaji, Shihao Song, Anup Das, Nikil Dutt, Jeff Krichmar, Nagarajan Kandasamy, Francky Catthoor

Our framework first extracts the precise times at which a charge pump in the hardware is activated to support neural computations within a workload.

BIG-bench Machine Learning

Mapping Spiking Neural Networks to Neuromorphic Hardware

no code implementations4 Sep 2019 Adarsha Balaji, Anup Das, Yuefeng Wu, Khanh Huynh, Francesco Dell'Anna, Giacomo Indiveri, Jeffrey L. Krichmar, Nikil Dutt, Siebren Schaafsma, Francky Catthoor

SpiNePlacer then finds the best placement of local and global synapses on the hardware using a meta-heuristic-based approach to minimize energy consumption and spike latency.


Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout

no code implementations18 Jul 2017 Anup Das, Paruthi Pradhapan, Willemijn Groenendaal, Prathyusha Adiraju, Raj Thilak Rajan, Francky Catthoor, Siebren Schaafsma, Jeffrey L. Krichmar, Nikil Dutt, Chris Van Hoof

The novelty of our approach lies in (1) encoding spatio-temporal properties of ECG signals directly into spike train and using this to excite recurrently connected spiking neurons in a Liquid State Machine computation model; (2) a novel learning algorithm; and (3) an intelligently designed unsupervised readout based on Fuzzy c-Means clustering of spike responses from a subset of neurons (Liquid states), selected using particle swarm optimization.

Clustering Heart rate estimation

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