Department of Electrical Engineering

Machine Learning for Signal Processing

The research focus of the lab is utilization and development of techniques and methods from the domains of signal processing and machine learning with application to signal and image analysis in various domains.


  • Image analysis methods for detection of prostatic carcinoma
  • Methodologies for patient-specific seizure detection and prediction using machine learning and signal-derived dictionary approaches
  • Deep learning approaches for concealed objected detection
  • Deep learning based methodologies for diabetes forecasting
  • Methods for power quality enhancement

Ongoing projects

  1. Development of robust histopathology image analysis algorithm for the detection and staging of prostatic carcinoma
  2. Deep learning approach for concealed object detection in Millimeter-wave imaging system
  3. Enhanced Diabetes Forecasting Model Using EHR
  4. Patient-specific seizure prediction using signal derived dictionary approach


  1. Ali, A., Rehman, A., Almogren, A., Eldin, E., Kaleem, M., Application of Deep Learning Gated Recurrent Unit in Hybrid Shunt Active Power Filter for Power Quality Enhancement, Energies, Vol. 15, No. 7553, DOI=10.3390/en15207553, September 2022
  2. Qureshi, M., Kaleem, M., EEG-based Seizure Prediction with Machine Learning, Signal, Image and Video Processing, DOI=10.1007/s11760-022-02363-4, September 2022
  3. Cordes, D., Kaleem, M., Yang, Z., Zhuang, X., Curran, T., Sreenivasan, K., Mishra, V., Nandy, R., Walsh, R., Energy-Period Profiles of Brain Networks in Group fMRI Resting-State Data: A Comparison of Empirical Mode Decomposition With the Short-Time Fourier Transform and the Discrete Wavelet Transform, Frontiers in Neuroscience, Vol. 15, pp: 594, May 2021.
  4. Kaleem, M., Guergachi, A., Krishnan, S., Patient-Specific Seizure Detection in Long-Term EEG Using Wavelet Decomposition, Biomedical Signal Processing and Control, Vol. 46, No. 2018, pp: 157-165, July 2018.
  5. Kaleem, M., Gurve, D., Guergachi, A., Krishnan, S., Patient-Specific Seizure Detection in Long-Term EEG Using Signal-Derived Empirical Mode Decomposition (EMD)-based Dictio-nary Approach, Journal of Neural Engineering, Vol. 15, No. 5, pp: 1-14, July 2018.
  6. Cordes, D., Zhuang, X., Kaleem, M., Sreenivasan, K., Yang, Z., Mishra, V., Banks, S., Bluett, B., Cummings, J., Advances in functional magnetic resonance imaging data analysis methods using Empirical Mode Decomposition to investigate temporal changes in early Parkinson's disease, Alzheimer's & Dementia: Translational Research & Clinical Interventions (TRCI), Vol. 4, No. 2018, pp: 372-386, June 2018.
  7. Kaleem, M., Guergachi, A., Krishnan, S., Hierarchical decomposition based on a variation of empirical mode decomposition, Signal, Image and Video Processing, Vol. 11, No. 5, pp: 793-800, July 2017.


Dr Muhammad Farhat Kaleem

Muhammad Asim Butt

Khalid Ijaz


Iqra Naveed (PhD student)
Ayesha Ali (PhD student)
Muhammad Mateen Qureshi (MS student, graduated in 2022)

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