Welcome to the Embedded Signal Processing Lab (ESP) at Texas A&M University.

We have several openings! If you are interested in joining our team, please visit the openings page.

Prospective students interested to work with us, please read through this page.

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The Embedded Signal Processing lab formed to investigate how embedded processing and sensing systems employing advanced signal processing techniques can improve medical care and enhance lives. Injuries, wounds, diseases, and learning disabilities deny people the freedom and opportunities they crave. By partnering with world class medical research teams, we have access to data and expertise which allows us to help return some of these freedoms. We are developing platforms to monitor the progression of disease, provide feedback to aid in rehabilitation, and even identify actions and postures which can lead to injury.

We explore theoretical properties of our problems and platforms. These problems include power optimization techniques, developing compact models to represent our problems, new techniques for classification in resource constrained environments, and signal processing methodologies for reducing data and identifying key signals. Our ultimate objective is to bridge the gap between theory and implementation.

This research requires an inherently multi-disciplinary approach, exploiting ideas from fields as diverse as pattern recognition, signal processing, and embedded system design. In most cases, we build our systems from scratch which involves hardware and software design. We use the systems to collect data. The design techniques mostly are derived from case study on data, and by exploiting specific properties of the signal processing.

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We have an opening for a research engineer acting as the principal for the firmware component/hardware of various embedded devices, mostly in the form of wearables. Please contact me or apply directly: https://lnkd.in/eNvj5sg Ad: http://tiny.cc/8yugoz #HIRINGNOW #WearableTech

Personalization of activity recognition leveraging deep learning and uncertainty in IEEE TBME journal “Personalizing Activity Recognition Models through Quantifying Different Types of Uncertainty using Wearables”

Paper: http://tiny.cc/63i8hz

#Wearables #DeepLearning #AI

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Our latest work on motion artifact rejection techniques published in JBHI journal “Robust Interbeat Interval and Heart Rate Variability Estimation Method using Wearable Sensors” @IEEE_JBHI

Paper: http://tiny.cc/yc72hz

#Wearables #WearableTech

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