Research Projects

Explore the exciting portfolio of amazing projects we've done

Machine Learning For Combating COVID-19

The spread of coronavirus in over 200 countries within a span of a few months necessitates the development of forecasting models that can assist governments around the globe in keeping an eye on their country’s situation forecast so that preventive measures can be timely taken. Since the epidemic spread is still in the early stages in some countries, forecasting models harnessing data from countries already hit by the epidemic will provide a realistic and timely intimation of risks of potential outbreaks in these countries. The proposed open-source data...

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  • PI: Prof. Dr. Faisal Shafait

iDoc: A portable, cost-effective diagnostic screening device for early dengue fever detection (2017)

The aim of this project is to develop a cost effective and portable embedded remote medical checkup device that provides an accurate and rapid solution for an early detection and diagnosis of Dengue Fever/ Dengue Hemorrhagic Fever/ Dengue Shock Syndrome. The device can also be used by the Lady Health Workers (LHW) and dispensers at the remote locations where the medical facilities are non-existent.

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  • PI: Prof. Dr. Faisal Shafait

The Fish Biodiversity Estimation by Low-Cost Non-Destructive Video Based Sampling (2016)

To employ underwater computer vision technology for an automated surveying of fish species. The project will form the basis for a non-invasive, low-cost surveying of Pakistan’s fish population, and benefit the preservation of endangered species as well as a more sustainable economic exploitation of Pakistan’s resources.

  • German PI: Dr. Adrian Ulges
  • Pakistani PI: Prof. Dr. Faisal Shafait

Forest Monitoring and Change Detection using Remote Sensing Data

To develop a system that is able to quantitatively monitor forests of Pakistan by deploying a small low-cost UAV to acquire temporal images and possibly fuse it with available satellite imagery to enable thorough assessments of local forest conditions e.g., degradation and deforestation caused by theft, urbanization or natural factors such as arid climate, heavy dependence on irrigation water, etc. by employing state-of-the-art machine learning techniques.

  • German PI: Prof. Dr. Norbert Wehn
  • Pakistani PI: Dr. M. Shahzad

Crop Monitoring via Remote Sensing from Earth Observation Satellites to UAV

To generate a crop monitoring system that enables the user to assess the crop’s phenological growth stages and based on extracted crop parameters, predict the crop yield productions. The developed system is intended to assist in filling gaps in policy makers information that is essential in ensuring sustainable management of crops and forecast yield predictions.

  • German PI: Prof. Dr. Xiao Xiang Zhu
  • Pakistani PI: Dr. M. Shahzad

IoT Enabled Surface Water Pollution Detection for Predictive Healthcare

To develop an Internet of Things (IoT) based system to monitor the quality of water in Pakistan, thus, providing a proper and near real time assessment of the water to the community in a ubiquitous fashion to overcome the limitations of conventional costly and inefficient methods of water quality monitoring. The purpose is to improve the performance as compared to the state-of-the-art.

  • German PI: Prof. Dr. Andreas Dengel
  • Pakistani PI: Dr. Rafia Mumtaz

Water resource estimation by Satellite Based Image analysis (WASABI)

In order to manage Pakistan’s water resources, it is necessary to enforce an even distribution of water throughout the year. WASABI is designed to predict flooding beforehand, supervise the volume of water bodies (including rivers, lakes, and reservoirs like dams, etc.), and predict the ratio at which the water level is declining or even increasing during some parts of the year, yielding accurate estimates about the future supply of water.

  • German PI: Prof. Dr. Adrian Ulges
  • Pakistani PI: Dr. M. Imran Malik

Wearable EEG for prediagnostic screening of Mental Diseases in Rural Areas

The aim is to develop a low cost, wearable device that can record several physiological parameters simultaneously, and which can communicate with a smartphone. The most relevant parameters to be handled are EEG, heart rate, and human body motion. The system will serve to reduce the workload of the neurologist who would not need to spend time on reading perfectly normal recordings.

  • German PI: Prof. Dr. Didier Stricker
  • Pakistani PI: Dr. Awais M. Kamboh

Reshaping Online Consumer Experience with Data Driven Personalized Chatbots-(2017)

To develop a bot that could augment the intelligence of a user in order to retrieve such content that could satisfy the potential needs of the user. The bot will be able to handle user queries in the real estate domain for Pakistani target market. The project also aims to develop an extensive database of the housing and property inventory existing in Pakistan.

  • PI: Prof. Dr. Faisal Shafait

Crop Health Monitoring and Early warning system using IoT enabled precision agriculture (2019)

To implement an extensible IoT-based crop health monitoring system which will help farmers to overcome conventional techniques of farming i.e. minimize manual and inefficient methods of crop monitoring and to use technology-based solutions for informed decisions related to crop under stress instead of their intuition; consequently this will improve crop yield and have a positive impact on Pakistan’s GDP which is based on farming.

  • PI: Dr. Rafia Mumtaz

Non-destructive approach for detecting Marine life in Fresh Waters of Pakistan - (2016)

The goal of the proposed project is to employ underwater computer vision technology for an automated surveying of fish species by providing state-of-the-art automatic, non-destructive, cost-effective, and reliable solution. It will be able to detect and classify the available species of fresh water fish and a database of the fish species will also be maintained.

  • PI: Dr. M. Imran Malik

Design and Development of Condition Monitoring Testbed based on Smart solar Microgrid

This project aims to develop the condition monitoring system as well as the intelligence behind the “smartness” of a smart grid. For this purpose, the model for test micro grid was developed which was able to give energy demand estimate in correlation with the weather conditions. Such technologies will be vital to achieve a smart and optimized solar micro grid for a marginalized community in Pakistan which can not only improve their quality of life, but also enhance their abilities and their contribution towards the country’s economy.

  • PI: Syed Muhammad Raza Kazmi

Cursive Urdu Script Recognition using Deep Learning- 2015 NVIDIA

The project aimed to perform OCR on an extensive dataset of printed and handwritten Urdu documents. The computational power of the GPU, managed by TensorFlow, enabled us to perform extensive research on training uni or multi-directional LSTM in order to successfully develop an OCR of complex-script documents.

  • PI: Prof. Dr. Faisal Shafait