Research

We live in a unique time in the history of humanity where we are experiencing a technology revolution. Not only that this revolution is reshaping our daily life but it is opening doors for research and innovations that can profoundly impact lives. Recent advances in fields of embedded and mobile computing, sensors and open source software have created a unique opportunity to impact almost every sector. Abundance of data around us is changing the way interact with our world and the way we deal with real life challenges in various fields.

Bringing Artificial Intelligence to Embedded Computing Paradigm

Smart Empower Innovation Labs brings over 20 years of collective cutting-edge academic and industrial research and development in fields of machine learning, data science, and embedded computing. We provide expert-level solutions by taking an interdisciplinary R&D approach to the traditionally disjoint fields of: Machine Learning & Embedded Computing. Applying cutting-edge techniques and proprietary machine learning and embedded computing algorithms, we bring artificial intelligence to the embedded paradigm and the IOT revolution. We unlock potentials of artificial intelligence in various industries.

Our research  expertise spans wide range of industries such as

  • Oil & Gas and energy sector.
  • Environmental monitoring and logging.
  • Healthcare.
  • IOT security.

Peer-reviewed Publications

A list of latest peer-reviewed publications for our ongoing  R&D activities to bring Artificial Intelligence to the Embedded Computing Paradigm.

  1. “A Multi-Scale Best Basis Sparse Learning Framework for Efficient IoT Big Data Applications,” International Journal of Computer Science and Communication Security, Vol 8,2018.
  2. “Appearance-based Salient Feature Extraction in Medical Images Using Sparse Contourlet-based Representation”, International Journal of Image, Graphics and Signal Processing(IJIGSP), Sep 2017.
  3. “Multiscale Sparse Appearance Modeling and Simulation of Pathological Deformations”, ICTACT Journal on Image and Video Processing (Volume:8,Issue 1), Sep. 2017.