Dr. Hafsa Qamar received her BS degree in electrical engineering from University of Engineering and Technology, Lahore, Pakistan, in 2015, followed by the M.S. degree in energy systems engineering from Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Swabi, Pakistan, in 2017. She carried out her M.S. research from University of Sannio, Italy on Erasmus Mundus scholarship. Dr. Hafsa received her Ph.D. degree in Electrical Engineering from Arizona State University, USA on Fulbright scholarship in May 2022. She published her work in high impact factor journals and reputed conferences in the USA. Dr. Hafsa was awarded with outstanding engineering graduate scholarship by Ira A. Fulton School of Engineering in Arizona State University. Her research interests include advanced pulse width modulation methods for improving the performance in EV/HEV drive trains and grid connected PV converters.

The Intelligent Machines and Sociotechnical Systems (iMaSS) lab at the Department of Electrical Engineering, LUMS is organizing a five-day workshop on the foundations of autonomous navigation and path planning in mobile robots. The objective of the workshop is to offer the participants a glimpse into the exciting field of robotics through hands on experience on DJI Robomaster EP Core, which is a state-of-the-art ground robot platform. The workshop is designed for all SSE students with an interest in robotics and a good command on any programming language such as C, C++, or Python. 

In this workshop, the students will learn how to navigate a ground robot in unknown environments with obstacles by using feedback from proximity sensors, and how to fuse feedback from vision and proximity sensors for target detection and manipulation. 

Dr. Attaullah Y. Memon received his BE degree from NUST, Pakistan in 1999, and the MS and PhD degrees from Michigan State University, USA, in 2004 and 2009, respectively, all in Electrical Engineering. He specializes in the areas of mathematical modeling and design of robust nonlinear controllers for dynamical electro-mechanical systems ranging from autonomous unmanned vehicles to mobile robotic platforms. His current research interests include design and development of unmanned underwater vehicles (UUVs), nonlinear robust and optimal control, robotics & automation, and hybrid control of networked and embedded control systems. He is the founding director of Robotics and Dynamic Systems (RDS) Research Laboratory at NUST-PNEC and is currently working as the Principal Investigator of various funded R&D projects in the fields of marine robotics and underwater technologies. Dr. Memon is winner of the Best Teacher Award for the years 2011 and 2016, and Best Researcher Award for the Year 2015. Since 2007, he is a professional member of IEEE Control Systems Society and Robotics and Automation Society.

Grid-connected rooftop and ground-mounted solar photovoltaics (PV) systems have gained attraction globally in recent years due to (a) reduced PV module prices, (b) maturing inverter technology, and (c) incentives through feed-in tariff (FiT) or net metering. The large penetration of grid-connected PVs coupled with nonlinear loads and bidirectional power flows impacts grid voltage levels and total harmonic distortion (THD) at the low-voltage (LV) distribution feeder. In this presentation, LV power quality issues with significant nonlinear loads were evaluated at the point of common coupling (PCC). Various cases of PV penetration (0 to 100%) were evaluated for practical feeder data in a weak grid environment and tested at the radial modified IEEE-34 bus system to evaluate total harmonic distortion in the current (THDi) and voltage (THDv) at PCC along with the seasonal variations. Results showed lower active, reactive, and apparent power losses of 1.9, 2.6, and 3.3%, respectively, with 50% solar PV penetration in the LV network as the voltage profile of the LV network was significantly improved compared to the base case of no solar. Further, with 50% PV penetration, THDi and THDv at PCC were noted as 10.2 and 5.2%, respectively, which is within the IEEE benchmarks at LV.

Final Thesis Defense Committee:

1.          Dr. Asghar Saqib (Professor, External Evaluator)

Department of Electrical Engineering, University of Engineering and Technology (UET), Lahore, Pakistan.

2.          Dr. Naveed-ul-Hassan (Associate Professor, Thesis Committee Member)

Syed Babar Ali School of Science and Engineering, Department of Electrical Engineering, Lahore University of Management Sciences (LUMS), Pakistan.

3.          Prof. Nauman Ahmad Zaffar (Associate Professor, Thesis Committee Member)

Syed Babar Ali School of Science and Engineering, Department of Electrical Engineering, Lahore University of Management Sciences (LUMS), Pakistan.

4.           Dr. Basit Shafiq (Associate Professor, Thesis Committee Member)

Syed Babar Ali School of Science and Engineering, Department of Computer Science, Lahore University of Management Sciences (LUMS), Pakistan.

5.          Dr. Hassan Abbas Khan (Associate Professor, PhD Supervisor)

Syed Babar Ali School of Science and Engineering, Department of Electrical Engineering, Lahore University of Management Sciences (LUMS), Pakistan.

Publications:

1. Ahsan, S., Niazi, K. A. K., Khan, H. A., & Yang, Y. (2018). Hotspots and performance evaluation of crystalline-silicon and thin-film photovoltaic modules. Microelectronics Reliability, 88, 1014-1018.  
2. Ahsan, S. M., & Khan, H. A. (2019). Performance comparison of CdTe thin film modules with c-Si modules under low irradiance. IET Renewable Power Generation, 13(11), 1920-1926.                
3. Ahsan, S. M., Khan, H. A., Hassan, N. U., Arif, S. M., & Lie, T. T. (2020). Optimized power dispatch for solar photovoltaic-storage system with multiple buildings in bilateral contracts. Applied Energy, 273, 115253.
4. Baig, M. Q., Khan, H. A., & Ahsan, S. M. (2020). Evaluation of solar module equivalent models under real operating conditions—A review. Journal of Renewable and Sustainable Energy, 12(1), 012701.  
5. Arif, S. M., Lie, T. T., Seet, B. C., Ahsan, S. M., & Khan, H. A. (2020). Plug-In Electric Bus Depot Charging with PV and ESS and Their Impact on LV Feeder. Energies, 13(9), 2139. 
6. Arif, S. M., Hussain, A., Lie, T. T., Ahsan, S. M., & Khan, H. A. (2020). Analytical Hybrid Particle Swarm Optimization Algorithm for Optimal Siting and Sizing of Distributed Generation in Smart Grid. Journal of Modern Power Systems and Clean Energy, 8(6), 1221-1230.
7. Ahsan, S. M., Khan, H. A., Ayyadi, S., Arif, S. M., & Lie, T. T. (2021, October). Optimized Power Dispatch for Solar-Storage System and Electric Vehicles with Multiple Buildings in Bilateral Contracts. In 2021 IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe) (pp. 1-5). IEEE.
8. Ahsan, S. M., Khan, H. A., Hussain, A., Tariq, S., & Zaffar, N. A. (2021). Harmonic Analysis of Grid-Connected Solar PV Systems with Nonlinear Household Loads in Low-Voltage Distribution Networks. Sustainability, 13(7), 3709.
9. Siraj, K., Ahsan, S. M., & Khan, H. A. (2021, June). Techno-economic Evaluation of Residential DC Power System for Multiple Distribution Voltages. In 2021 IEEE 48th Photovoltaic Specialists Conference (PVSC) (pp. 0858-0862). IEEE. 
10. S. M. Ahsan, and H. A. Khan, " LV Harmonic Analysis of Single-Phase Rooftop Solar PV Systems with Non-Linear Loads," (Accepted in IEEE Green Tech, USA, 2022)
11. S. M. Ahsan, H. A. Khan, N.-u. Hassan, and S. Ayyadi," Optimized Power Dispatch for Smart Building(s) and Electric Vehicles with V2X Operation," (Submitted in Energy Reports)

The Syed Babar Ali School of Science and Engineering (SBASSE) at LUMS, initiated in 2008, is the first private research school for science and engineering in Pakistan with a vision to carry out world-class, multidisciplinary education and research. Modelled on some of the leading universities of the world, it aims to be a paradigm shift for science and engineering education in the country. The Electrical Engineering (EE) programme at SBASSE is designed to enable students to be well-prepared to contribute to the rapidly changing and expanding needs of technology. The EE curriculum provides students with a strong foundation due to an emphasis on basic sciences and the development of excellent engineering skills through carefully planned core and elective courses. EE students also learn through a combination of design and lab work. The EE curriculum covers the essential breadth and depth needed for contemporary professional practice. Four important concentration areas have been identified: Communications, Signals and Systems Electronics and Embedded Systems Devices, Optics and Electromagnetics Modern Power and Energy Systems In the final year of their undergraduate studies, students are involved in a year-long capstone project to demonstrate their practical knowledge. This handbook consists of a brief description of the final year projects for the 2021–2022 session. This effort has been coordinated by Dr. Nauman Zafar Butt, with the assistance of Mr. Usama Bin Qasim and Mr. Affan Anwar.

Advancements in smart grid technologies have resulted in increased reliability of service for consumers. Although, faults and outages resulting in blackouts are inevitable, service is restored back to normal. Service restoration is a decision making and action actuating process (either executed centrally or distributively) in which the system operator executes a set of actions to minimize the impact of an outage on customers while maintaining stability and inter-operability of the grid.

In the literature, two main methodologies have been adopted to solve the load restoration problem. One approach considers it as a classical deterministic optimization problem with an objective to maximize (critical) load pickup incorporating several constraints. The other considers it as a multi-agent system (MAS) and restores the loads based on heuristics and agent behavioural modelling. Lack of standardization has limited the scope of communication technologies in smart grids, especially in the service restoration process. However, with the increasing installations of smart switches, tie-lines and sectionizers as well the deployment of new Distributed Energy Resources (DERs) an obvious but unexplored research area is examining the role of communication layer in realizing cyber-physical distribution systems. Agents can interact socially and cooperate to exchange sensory information and actions to restore loads over a communication layer. Also, there are new opportunities to consider Synchronous Generators or Back-up Generators (BUGs) as a source of providing black-start capability to grid-tied non-dispatchable inverters. In this way, the utility of BUGs has increased to harness power from non-dispatchable inverters rather than merely providing backup to local loads. The system operating conditions do not remain constant during the restoration period. This work aims to develop a cyber-physical framework for load restoration with intentional islanding (i.e Mirco-Grid Formation) and consideration of uncertainties in demand and renewable resources.

After a major blackout, the fault areas are isolated from the upstream network, where customers in the upstream are continued to be served from the main grid. Those in the downstream can either be served from main grid (after tie-line or sectionizer switching) or through several Micro-Grids (MGs-intentional islands). The challenge involved is ascertaining the span of the MGs based on the available generation and load within the MG, whilst keeping the grid stable, harnessing power from non-dispatchable DGs, determining the effect of delays in switching actions on stability and uncertainties associated with demand and generation.

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Proposal Defence Committee

1. Dr Tariq Jadoon (Supervisor)
2. Prof Nauman Zaffar
3. Dr Naveed ul Hassan
4. Dr Hassan Abbas
5. Dr Hassan Jalil
6. Dr Raheel Zaffar

The motivation of the research is to provide a framework for a cyber-physical distribution systems with high penetration of DER. The research is conducted over a IEEE-123 node test feeder and results are presented to demonstrate the efficacy of the framework over real-world power distribution networks.

M. Majid Butt is a senior research specialist at Nokia Bell Labs, France, and an adjunct Research Professor at Trinity College Dublin, Dublin, Ireland. Prior to that, he has held various positions at the University of Glasgow, U.K., Trinity College Dublin, Ireland, and Fraunhofer HHI, Germany. His current research interests include communication techniques for wireless networks with a focus on radio resource allocation, scheduling algorithms, energy efficiency, and machine learning for RAN. He has authored more than 75 peer-reviewed conference and journal articles, 4 book chapters and filed over 30 patents in these areas. He frequently gives invited and technical tutorial talks on various topics in IEEE conferences including, ICC, Globecom, VTC, etc. Dr. Butt is a recipient of the Marie Curie Alain Bensoussan Post-Doctoral Fellowship from the European Research Consortium for Informatics and Mathematics. He has been an Associate editor for IEEE Communication Magazine, IEEE Open Journal of the Communication Society and IEEE Open Journal of Vehicular Technology. He is a senior member of IEEE and IEEE COMSOC Distinguished speaker for the class of 2022-2023.

 Dr. Ahmed Qureshi is an associate professor at the University of Alberta, Edmonton, Canada, leading the Additive Design and Manufacturing Systems (ADaMS) Laboratory at the Mechanical Engineering department. He has a bachelor's degree in Mechanical Engineering from UET Lahore, Pakistan, a Master's in Design and Industrialization, and Doctorate in Mechanical Engineering from Arts Et Metiers ParisTech, France. Following the doctorate, Dr. Qureshi worked as a research associate at the University of Luxembourg and a lecturer at the Newcastle University UK and its Singapore campus. Dr. Qureshi's research interests are in advanced design and manufacturing, focusing mainly on additive manufacturing systems engineering, design for X, and quality and qualification in manufacturing processes. His recent research projects include systems engineering projects for plasma transferred arc AM system for AM of Nickel-Tungsten Carbide parts, development of novel 4D ferromagnetic polymer-metal composite material jetting systems, and multi-degree of freedom robotic wire arc additive manufacturing systems for supportless printing. Dr. Qureshi also has extensive experience in quality assurance and control of mechanical and geometric properties of 3D printing/Additively manufactured parts. Dr. Qureshi's lab also innovates in digital design and manufacturing chains. This includes developing algorithms and software for additive manufacturing, reverse engineering, and computer-aided path planning.

Abdul Rehman Aslam is a PhD candidate in Electrical Engineering department. He has completed his MSc degree in Computer Engineering from the Lahore University of Management Sciences (LUMS), Lahore, Pakistan, in 2012 and BSc degree in Computer Engineering from the University of Engineering and Technology Taxila, Pakistan in 2008.

His research interests include embedded systems, digital design, on-chip implementation of machine learning/ deep learning algorithms, and low power bio-medical processors with special focus on neurological disorders. During his PhD, he has published multiple high impact journals, book chapters and presented in top international conferences and PhD forums. He is also the recipient of the Syed Babar Ali Research Award (SBARA), LUMS, Pakistan (2020–2022), Commonwealth split side Ph.D. scholarship for University of Glasgow, Scotland, U.K. (2021–2022), and IEEE Circuits and Systems Society Predoctoral research Award (2021–2022).

Committee Members:  Dr. Muhammad Awais Bin Altaf (LUMS), Dr. Muhammad Adeel Ahmad Pasha (LUMS), Dr Nadeem Ahmad Khan (LUMS), Dr. Hadi Heidari (University of Glasgow, UK)

List of Publications:

Journals

_{1.       A. R. Aslam, and M. A. B. Altaf, “A 8-Channel EEG based machine learning Emotion detection classification processor for Neurological Disorder”, IEEE Transactions on Biomedical Circuits and Systems (TBioCAS), vol. 14, no. 4, pp. 838-851, Aug. 2020.}

_{2.       A. R. Aslam, and M. A. B. Altaf, “A 10.13µJ/Classification 2-Channel Deep Neural Network Based SoC for Negative Emotion Outburst Detection of Autistic Children”, IEEE Transactions on Biomedical Circuits and Systems (TBioCAS), vol. 15, no. 5, pp. 1039-1052, Oct. 2021.}

_{3.       A. R. Aslam, N. Hafeez, H. Heidari and M. A. B. Altaf, “Channels and Feature Identification with Large Scale Feature Extraction for Emotions and ASD Classification”, Frontiers in Neuroscience (FRNS), 2022 [Under Review].}

Book Chapter

_{1.       A. R. Aslam, and M. A. B. Altaf, “Machine learning–based patient-specific processor for the early intervention in autistic children through emotion detection”, Neural Engineering Techniques for Autism Spectrum Disorder, Chapter 14, pp. 287-313, Jul. 2021.}

Conferences

_{1.       A. R. Aslam, N. Hafeez, H. Heidari and M. A. B. Altaf, "An 8.62 μ W Processor for Autism Spectrum Disorder Classification using Shallow Neural Network," in Proc. IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), June. 2021 pp. 1-4.}

_{2.       A. R. Aslam, T. Iqbal, M. Aftab, W. Saadeh and M. A. Bin Altaf, "A10.13uJ/classification 2-channel Deep Neural Network-based SoC for Emotion Detection of Autistic Children," in Proc. IEEE Custom Integrated Circuits Conference (CICC), March. 2020, pp. 1-4.}

_{3.       A. R. Aslam, and M. A. B. Altaf, An 8 Channel Patient-Specific Neuromorphic Processor for the Early Screening of Autistic Children through Emotion Detection, in Proc. IEEE International Symposium on Circuits and Systems (ISCAS), May. 2019, pp. 1-4.}

_{4.       M. Sheeraz, A. R. Aslam and M. A. Bin Altaf, "Multiphysiological Shallow Neural Network-Based Mental Stress Detection System for Wearable Environment, “ in Proc. IEEE International Symposium on Circuits and Systems (ISCAS), May. 2022 [Accepted]}

_{5.       M. Sheeraz, A. R. Aslam, H. Heidari and M. A. Bin Altaf, "A Closed-Loop Ear Wearable EEG Measurement Device with Realtime Electrode Skin Impedance Measurement,“ IEEE International New Circuits and Systems Conference (NEWCAS), June. 2022 [Under Review]}

_{6.       M. Sheeraz, A. R. Aslam, N. Hafeez, H. Heidari and M. A. Bin Altaf, "A Wearable High Blood Pressure Classification Processor Using Photoplethysmogram Signals through Power Spectral Density Features, “IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), June. 2022 [Under Review]}

_{7.       A. Hina, H. Nadeem, A. R. Aslam, and W. Saadeh, Live Demonstration: A Single LED PPG-Based Noninvasive Glucose Monitoring Prototype System, in Proc. IEEE International Symposium on Circuits and Systems (ISCAS), May 26-29, 2019.}

Asif Rehmat is the executive vice president at Powersoft19. He has more than 18 years of experience in multiple disciplines of engineering, project management and product development. Asif is working at Powersoft19 for last 14+ years. His expertise lies in bridging the technical, communication and cultural gaps in the global and offshore teams. He has been involved in the success of various safety critical projects from inception to delivery. He has managed firmware, hardware, enterprise application, mobile apps, and desktop application projects at Powersoft19. Asif holds the master's degree in Electrical Engineering and had been a faculty member of University of Engineering and Technology, Lahore.

Dr. Abubakr Muhammad is an associate professor and chair of electrical engineering, the founding director of the Center for Water Informatics & Technology (WIT), and the lead for NCRA National Agricultural Robotics Lab at LUMS. He received his Ph.D. in Electrical Engineering in 2005 from Georgia Institute of Technology, USA winning an institute-wide best Ph.D. Dissertation Award. He received postdoctoral fellowships at the University of Pennsylvania and McGill University. Since 2008, his group at LUMS has done applied research in robotics and automation with applications to water, agriculture, and the environment. He has held visiting positions at other top institutes including the University of Oxford, ICTP, and KAUST.  He serves on various advisory panels to government agencies and industry on the use of emerging digital technologies.