Faculty Research

My research focuses on low-power analog, mixed-signal, and RF integrated circuits, with a particular emphasis on improving design efficiency through partial automation. Below are some projects that I have been working on. These projects provide opportunities for students to gain hands-on experience in both fundamental circuit design and emerging applications in wireless communication, neuromorphic computing, and AI-driven design.

1. Analog Layout Automation – Developing SKILL-based APIs to accelerate the analog layout design. Prerequisite: ECE 540/L.
2. Area Efficient RF Integrated Circuits – Designing RF circuit blocks with minimal passive components. Example: inductorless low-noise amplifier. Prerequisites: ECE 540/L and ECE 642.
3. Low power Neuromorphic Circuits – Exploring biologically inspired computation through hardware implementations. Examples: Design of low power Spike-Timing Dependent Plasticity (STDP) synapse circuits and neuron circuits for spiking neural networks. Prerequisites: ECE 540/L.
4. AI-Assisted Digital Design – Applying AI techniques to accelerate the design process of digital integrated circuits. Prerequisite: ECE 540/L and ECE 442/L.

Dr. S. K. Ramesh

Dr. S.K Ramesh: S. K. Ramesh is a Professor of ECE and Director and PI of the Secure for Student Success (SfS2) program, (https://www.ecs.csun.edu/sfs2) supported with a multi-year Title V grant from the US Department of Education. Prof. Ramesh’s areas of interest are Optical Communications, Photonics and Analog IC design. He is a Life Fellow of IEEE and has served the profession in many roles including ABET President, IEEE-HKN President and on the Boards of IEEE, and ABET. For additional information please visit https://www.csun.edu/engineering-computer-science/ramesh.

The current research activity is focused on the development of novel Quantum-Wells (QW) based on AlGaN/GaN optically triggered heterojunction-field effect transistors (OPTOHEMTs) for high frequency (THz order) modulator with low noise, high gain and high spectral responsive multi-wavelength detectors for secured free space optical UV communication.

The GaN HEMT reliability is receiving increasing attention for worldwide due to numerous application as UV photodetector arrays with different wavelengths for air missile defense sensor for missile plume identification by using as OPTO-HEMTs as high speed solar blind photodetector (UV detection with amplification). Additionally UV OPTO-HEMTs have been identified biotechnological uses including military applications such as biosensors for detection of microbial pathogens, biological toxins, toxic chemicals, etc and covert UV systems for short range communication for battlefield communications. Other current research is based on development of electronic device AlGaN/GaN HFETs for high frequency and high power aided performances for high frequency power application for satellite communication and minimization of reverse recovery for EV applications.

My research projects are in the areas of RF/microwave circuit design and antenna engineering. Examples of student Masters projects under my supervision include: RF front-end receiver circuit design for a software defined radio, adaptive (smart) antenna array design with digital signal processing, metal 3D-printed broadband antennas, and reflectarray antennas for small satellites.

Dr. Md Sahabul Alam is an Assistant Professor in the Department of Electrical and Computer Engineering at California State University, Northridge (CSUN).

His current research interests include non-terrestrial communications—focusing particularly on High-Altitude Platform Station (HAPS)-based communications, the integration of terrestrial and non-terrestrial networks to provide ultra-agile wireless access architectures for 6G, machine-learning/artificial-intelligence applications in wireless communications and signal processing, smart-grid communications, optical wireless communications, reliable wireless communications in non-Gaussian noise environments, non-orthogonal multiple-access (NOMA), and massive multiple-input multiple-output (MIMO) for terrestrial and aerial networks. He has published more than 45 technical papers in peer-reviewed journals and international conferences in his areas of interest. To support experimental research on 5G, 6G, and Wi-Fi networks, Dr. Alam recently purchased licensed Wireless InSite software and Software-Defined Radios (SDRs).

For more information, please feel free to reach out to Dr. Alam directly at md-sahabul.alam@csun.edu.

Dr. Narimani’s research focuses on the security, operation, and optimization of modern power and cyber-physical systems. His work addresses challenges in cybersecurity for critical infrastructures and smart grid applications, including demand-side and demand charge management. He also explores power system operation and control, applies complex network theory to analyze large-scale interconnected systems, and develops advanced methods in convex optimization and mathematical programming to improve efficiency and resilience. A current focus of his research is assessing the impact of electric vehicle charging stations on power grids, with the goal of advancing sustainable, secure, and reliable energy systems.

Dr. Sedghisigarchi’s research focused on control, operation, and protection of modern power system networks. His research area focuses on Microgrid applications and control, Electric Vehicles (EV), Distributed Energy Resources modeling and control, Applications of AI in smart grids, Energy management and modern power system operation, control and protection.

In the field of Machine Learning (ML) and Artificial Intelligence (AI), the utilization of vest amounts of data, commonly referred to as big data, plays a pivotal role in enhancing the performance of ML and AI methods. Data can be viewed as a special type of signal, characterized by discretization, noise, distribution across diverse sources and locations, and extremely large scale. Dr. Cho addresses some of the most challenging problems in ML and AI from the perspectives of signal processing and optimization, leading the MOSAIC Lab, whose mission is to understanding the bigger picture by connecting pieces across ML, AI, signal processing, and optimization, and creating synergies among them. Current research at the MOSAIC Lab focuses on fundamental problems such as designing optimal distributed algorithms and network architectures for efficient federated learning, developing scalable algorithms for distributed control systems, and providing rigorous theoretical analyses.

My research involves design methodologies and architectures for digital signal processing, computer arithmetic, image processing techniques, embedded systems, and application specific processors. This includes implementing efficient techniques and algorithms to implement DSP functions on reconfigurable hardware, implementing complex logic functions, arithmetic operations, image processing algorithms, etc. for reconfigurable hardware. Much of my work focuses on reconfigurable SoC in which software/hardware co-design is necessary. I have used Xilinx SoCs and FPGAs as a hardware platform. The nature of my diverse experience and interests has led me to integrate all the techniques and use the available EDA tools to convert functions to FPGA and SoC friendly architectures.

My research interests consist of five interrelated tracks: the engineering of reconfigurable hardware as a common platform for DSP functions and systems, computer arithmetic, design for low power, embedded systems, and design optimization. These paths share much in terms, but I tend to identify individual projects as belonging exclusively to one path or the other. In the following I’ll try to elaborate on my research interests individually.