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Lyles College of Engineering

Research

Runtime Reconfigurable Cognitive Spectral Processor (R2CSP)
Lead Researcher: Dr. Aaron Stillmaker

The project focuses on the development and fabrication of a fine-grained many-core processing runtime reconfigurable spectral processor (R2CSP) for radio frequency (RF) spectral sensing leveraging the KiloCore architecture and physical design developed by  Dr. Stillmaker. Dr. Stillmaker and his team at Fresno State are responsible for the physical design of the R2CSP chip. The plan is to fabricate several test chips in TSMC’s 16 nm technology. This multi-institutional project is led by Dr. David Zhang from SRI in Princeton, NJ,  with Dr. Bevan Baas as a Co-PI from UC Davis.  The research has received $13 million for the two and a half-year program scheduled to end in November 2025.   
                   PROWESS Processor   Example of 40GHz spectrum running high throughput on R2CSP processing
                            (a)                                                                                                (b)

Figure 1.  (a) PROWESS System architecture using R2CSP chips packaged as a multi-chiplet device tested in Phase 2. (b) Example of 40GHz spectrum running high throughput on R2CSP processing 200 MSample/s sub-bands with policy-based tasks designed to switch quickly among different data streams and redeploy core resources to important signal processing by reconfiguration.


Evaluation of Power Gate Implementation on Fine-Grained Many-Core Processor Arrays
Lead Researcher: Dr. Aaron Stillmaker

With larger, more complex digital designs, power gates become particularly necessary. This project will explore the usage of power gates in large digital system to help identify optimal usage.  Power Gating is an effective method to reduce subthreshold leakage and is straightforward to implement in large systems, but the implementation becomes complex when implemented in a fine-grained methodology.  The project will explore design of power gates, placement of power gates, and algorithms for power gate usage.   

                                                                   Diagram showing the flow of current from a power gate to an individual gate

Figure 2. This is a diagram showing the flow of current from a power gate to an individual gate that needs to be powered.  Power gates are usually PMOS devices that are controlled with “sleep” signals to turn the gates on and off based on some algorithm.


Area and Energy Efficient Full Duplex Transceiver System for Wireless Network on Chip
Lead Researcher: Dr. Soumyasanta Laha 

Co-time co-frequency full-duplex wireless communication alleviates the issue of inefficient use of bandwidth in the existing co-time half-duplex communication. In the full-duplex mode the transmission and reception of the signal takes place simultaneously in the same frequency band. The primary challenge in designing such a full-duplex wireless device is self-interference. The transmit signal which is locally generated in the transceiver and thus has a very high-power level interferes with the low power received signal for being in the same frequency band. The received signal is thus submerged in the ‘self-interfered noise’ and cannot be recovered. Several techniques have been suggested over the last decade to reduce the self-interfered signal to the level where it can be neglected, thus eliminating self-interference. The current project aims to bring the self-interference cancellation to 60 dB or more, which is sufficient  for on-chip wireless communication such as wireless network on chips (WiNoCs). With regards to WiNoC, this is important because, WiNoCs require high data rates (10 Gbps and beyond) to support today’s high performance multi-core computer architecture and require simultaneous data transfer to support real time applications. The expected success of this research will motivate system level study with advanced sub-16 nm RF FinFET technology at 60 GHz and sub-THz frequencies to evaluate and compare the performance of existing and novel WiNoC architectures. Furthermore, the validation of the full duplex transceiver system for WiNoC applications will expand research insights for full duplex capability of other on-chip communications such as between wireless enabled chiplets or a wireless neural accelerator architecture.    

The current project is a novel co-time, co-frequency full duplex transceiver system (See Fig. 1) for wireless network on chip (WiNoC) applications in a cost-effective RF CMOS technology. 
                            . Block Diagram of the proposed Full Duplex Wireless Transceiver      Fig. 1. Block Diagram of the proposed Full Duplex Wireless Transceiver.


Design and Testing Effectiveness of an AI based non-invasive glucose monitoring Device
Lead Researcher: Dr. Soumyasanta Laha

The worldwide prevalence of diabetes was 422 million in 2021. Hypoglycemia is a common occurrence among insulin- dependent diabetics. Hypoglycemia unawareness results from reduced sympathetic adrenal response and patients have difficulty recognizing hypoglycemia events which puts them at high risk of adverse health events. These adverse health events include cardiovascular ischemia, dementia, falls, and even deaths. Hypoglycemia-related events resulted in 100,000 visits to emergency rooms and 30,000 hospital admissions between 2007 and 2011 in the United States. On the other hand, poorly managed hyperglycemia can also lead to adverse health events such as

cerebrovascular accidents, cardiovascular events, and peripheral vascular disease. Prompt identification of rapid fluctuations in blood sugar is the key to successful management of diabetes-related emergencies.

Continuous glucose monitoring (CGM) systems greatly improve self or parental management by identifying particularly abnormal variations in the blood sugar and sending appropriate alerts to patient’s phone or tablet have been helpful to identify fluctuations in the blood glucose and can alert the patient thereby avoidance of severe outcomes related to hypo or hyperglycemia. Current versions of CGM are minimally invasive where a metal is inserted into the skin and serves as a sensor. It emits blood sugar oxidizing enzyme. The interaction of this enzyme with blood sugar molecules results in the formation of hydrogen peroxides amongst other compounds. The reaction also results in the generation of a current. The charge on this current is measured and it corresponds to the appropriate amount of blood sugar in the interstitial cells.

The project aims to explore the possibility of CGM using a non-invasive device at the highest extent possible to enhance diabetic care integrating the two well established detections methodologies: optical & electromagnetic. Specifically, a non-invasive CGM prototype wireless system on chip (See Fig. 1) device as an application specific integrated circuit (ASIC) integrating two detection methodologies will be implemented using a robust AI/ML algorithm. 
The AI/ML based non-invasive glucose monitoring device as a wireless system on chip prototype. 

 

 

Fig 1. The AI/ML based non-invasive glucose monitoring device as a wireless system on chip prototype.




The ML algorithms bring unprecedented accuracy and efficiency to the data analytic results that have been traditionally relied on statistical and mathematical models. In this study, the two detection methods of glucose monitoring are to be compared against a commercial CGM device to train the ML model. The adaptive ML algorithm will be particularly investigated to compensate for the absence of blood or other bodily fluids in the non-invasive detection. The adaptive learning is a technique that is applied in AI model development which provides a real-time instant result as a previous data point is incorporated to reconfigure the model.


Prototype Shirt for Electrocardiography and Electromyography Analysis
Lead Researcher: Dr. Nan Wang

The use of textile-integrated sensors and systems has produced great interest in the last years. Textiles are excellent interfaces for bio-signal sensing. They are flexible, stretchable and conform to the body. As they are used daily and at all times, they are an interesting solution for ubiquitous, continuous health monitoring. Breathing rate and movement monitoring have been proposed and tested using extension sensors based on knitted textiles made with textile conductive yarns, as well as using specially made rubber coatings doped with carbon fibers, and conductive polymers. The sensing techniques are evaluated for use in the development of a shirt integrating ECG/EMG measurement, moisture detection and breathing movement detection for use in applications such as monitoring of individuals in risk environments (firefighters, workers in specific industries, etc.), sports, health monitoring for the elderly, continuous electronic health records, or other. 
                                                     Prototype shirtProtoype shirt 2
                                                          Graph


Bulldog Mote- Low Power Sensor Node and Design Methodologies for Wireless Sensor Networks
Lead Researcher: Dr. Nan Wang

The major goals of the project are the design and implementation of the following components: (1) efficient low-power methodologies implemented throughout all WSN design layers from application to the physical layer, (2) a new WSN sensor node, the Bulldog Mote, created using various low power methodologies, and (3) energy harvesting technologies for sensor node architecture.
                                                                         electronic piece of equipment
              Graph depicting LED and buttons          Graph depicting voltage


Extended-Gate Field Effect Transistor (EGFET)-Based sensor
Lead Researcher: Dr. Zoulikha Mouffak

This research is focused on the use of a EGFET sensor as a pH sensor or specific chemical detector. We are exploring this setup to target biomedical applications.  When the MOSFET is connected to a sensitive electrode through the chemical solution we want to analyze, the sensitive electrode works as an “extended gate”. Modification of the solution concentration has resulted in changes in the drain current when running I-V characteristics.  We developed a ITO/PET EGFET pH sensor using  ITO (Indium Tin Oxide)/PET (Polyethylene Terephthalate) sensing electrode as the extended gate part of an EGFET obtained from a combination of FETs from the CD4007 chip. The device was tested by immersing the ITO/PET electrode in several chemical solutions of acidic and basic nature, including hydrogen peroxide, acetic acid, sulfuric acid, and ammonium hydroxide, at different concentrations. Using a Tektronix 4200A sourcemeter, we plotted the current–voltage (I–V) characteristics for the different chemical solutions, and we established a correlation to the pH changes. Results from the plotted I–V characteristics show a great dependance of the drain current (ID) on solution concentration (Fig. 3-b).  The  pH of the used solutions  established a relationship between the drain current and the pH value. The results showed a  consistent decrease in the current with an increase in the pH value, although with different rates depending on the solution. The device showed high voltage sensitivity at 0.23 V per pH unit when tested in sulfuric acid. These results were recently published in Sensors. https://doi.org/10.3390/s23208350

               Proposed EGFET schematic diagram  (a)           characteristics for sulfuric acid aqueous solutions (b)   

         
                          function of the measured pH of 4 solutions    (c)            function of the measured pH of 4 solutions (d)

Fig. 1. Proposed EGFET schematic diagram (a), Sulfuric acid I–V characteristics for different concent- rations for applied gate voltage VGS = 5 V (b), ID –VGS characteristics for sulfuric acid aqueous solutions with concentrations of 10%, 1%, 0.1%, and 0.01% and corresponding pH values 0.8, 1.70, 2.59, and 3.57 (c), and Drain current at VGS = 5 V and VDS = 4 V as a function of the measured pH of 4 solutions: sulfuric acid, acetic acid, hydrogen peroxide, and ammonium hydroxide (d). 
                    EGFET I-V characterization using a porous silicon interdigitated capacitor as the extended gate, measurement setup          family of I-V characteristics obtained from solutions of different concentrations
Fig. 2. EGFET I-V characterization using a porous silicon interdigitated capacitor as the extended gate, measurement setup (left), and family of I-V characteristics obtained from solutions of different concentrations (right).


Fabrication of Porous silicon (PSi) films for sensing devices
Lead Researcher: Dr. Zoulikha Mouffak

We investigate the fabrication of porous silicon using <100> silicon wafers with different resistivities and varying anodization currents.  Porous silicon is made using electrochemical etching. During the etch process, the anode is connected to the metallized backside of the silicon sample, while the cathode is connected to a platinum wire that we submerge in an HF acid solution (Fig. 1). The applied constant current used and the resistivity of the wafer define the pores size and quality, while the duration of the recipe defines the thickness of the film. We analyze the surface morphology using scanning electron microscopy (SEM) and photoluminescence tests (Figure 2). PSi films grown on higher resistivity wafers show more luminescence and porosity.
(a) Lab-made Electrochemical etch cell filled with HF and used for the fabrication of porous silicon (b) The etch cell terminals are connected to a Keithley 2400 for constant current supply  (c) The recipe is run through Labview for a set time and restrictions on voltage

Fig. 1. Lab-made Electrochemical etch cell filled with HF and used for the fabrication of porous silicon (a). The etch cell terminals are connected to a Keithley 2400 for constant current supply (b. The recipe is run through Labview for a set time and restrictions on voltage (c).


A digital twin-based condition monitoring method for power converters in agricultural applications
Lead Researcher: Dr. Woonki Na

In this research. a digital twin-based condition monitoring method is proposed for power converters in agricultural applications. The digital twin is a virtual replica of the physical power converter and is able to update itself continuously as seen in Fig. 1. The model is demonstrated or tested using various types of power converters, including AC/DC, DC/DC, and DC/AC converters. The proposed digital twin system is capable of updating itself continuously, which means it can reflect real-time changes or conditions of the physical power converter, allowing for monitoring, analysis, and potential optimization.
                                                             Digital Twin Concept for Power Converters

Fig. 1  Digital Twin Concept for Power Converters

Also, an artificial neural network (ANN) algorithm[1] is being used as part of the project. 

The goal of applying the ANN algorithm is to minimize the difference between the output waveforms generated by the digital twin including fault diagnosis. These are the electrical waveforms produced by the power converter, which can include voltage and current waveforms. The digital twin is attempting to replicate these waveforms as accurately as possible. Thresholds are used as criteria to determine whether the digital twin's output is sufficiently close to the physical power converter's output. If the difference between the two waveforms falls within these thresholds, it's considered acceptable or successful.  ANN would be used for optimization as well. It learns from the data generated by the digital twin and the physical converter to adjust its parameters and minimize the waveform differences.


Computer Arithmetic and Cryptographic Hardware
Lead Researcher: Dr. Hayssam El-Razouk

This research is focused on novel algorithms and hardware architectures for symmetric and asymmetric cryptosystems. Completed and current projects include efficient hardware for polynomial ring and finite field operations targeting post-quantum ciphers, Elliptic Curve Digital Signatures, Elliptic Curve Key Exchange, Advanced Encryption Standard (AES), Welch-Gong (WG) Stream Ciphers, and SNOW 3G/5G stream cipher. The research has also focused on developing designs for efficient hardware of fixed-point multipliers. Completed and current projects include low-area and low-power approximate fixed-point multipliers for machine learning and image processing applications.


Side-Channel Leaks
Lead Researcher: Dr. Hayssam El-Razouk

This research is focused on analyzing side-channel leaks in state-of-the-art cryptosystems like the Advanced Encryption Standard (AES), Elliptic Curve Key Exchange/ Digital Signatures, and Post-Quantum ciphers. Investigating novel countermeasures to improve the side-channel leak resistance of such cryptosystems. Completed and current projects include Differential Power Analysis (DPA) and countermeasures for AES, Elliptic Curve Key Exchange, and post-quantum ciphers.


Applied Cryptography
Lead Researcher: Dr. Hayssam El-Razouk

This research is focused on investigating  novel applications for secure Internet of things (IoT) and embedded systems. Completed and current projects include innovative utilization of cryptography, Augmented reality (AR), and Bluetooth technologies for building new applications targeting user accountability and privacy of digital data.


Innovative Nano-material Synthesis: toward the development, manufacturing, and characterization of cutting-edge electro-active and photoactive nanomaterials and composites, ensuring their suitability for advanced manufacturing processes
Lead Researcher: Dr. Sankha Banerjee

Research Project Description: This project is focused on the comprehensive creation, production, and analysis of state-of-the-art electro-active and photoactive nanomaterials and composites, ensuring their appropriateness for advanced manufacturing processes. The objectives are (i) Fabrication/Synthesis Method Optimization: Develop and optimize fabrication and synthesis methods, including simulation-based techniques, for thin films and bulk active materials such as perovskite and MXene-based materials and composites. (ii) Mechanism Investigation: Explore the mechanisms influencing material properties by varying fabrication parameters and employing computational techniques. (iii) Microstructural Analysis: Investigate the effects of physical relationships between different phases in an electroactive material or composite and their impact on measured properties. (iv) Characterization and Testing: Conduct thorough characterization and testing of active nanomaterials utilizing electron microscopy and spectroscopic techniques. (v) Optimization and Tailoring: Optimize and tailor material properties and performance based on the parameters identified in the previous objectives and specific application requirements.

Sub-project Example: Wet lab fabrication of Perovskite/Wurtzite-Oxide (e.g. ZnO, BaTiO3) and 2D Materials-based (e.g. MXenes) flexible electro-active thin films with active polymer and co-polymer matrices (e.g. PVDF/PVDF-TrFE)” - The purpose of this project is to study the role of the dielectric and piezoelectric properties of graphene-based composite piezoelectric materials. Due to its unique electrical and mechanical properties such as it being stretchable up to 20% of its initial length and having high conductivity due to the unidirectional structure with the ballistic transport of electrons, the role of the 2D phase is critical in tailoring the electrical and mechanical properties of the multiphasic composites.  
                                                      Scanning electron micrographs of the cross-section and synthesized thin film surface of active nanocomposite thin films, ZnO nanostructures such as nanowires, nano-flowers, and nanoflakes

Fig. 1: Scanning electron micrographs of the cross-section and synthesized thin film surface of active nanocomposite thin films, ZnO nanostructures such as nanowires, nano-flowers, and nanoflakes. 


Smart Platforms for Health Monitoring of Devices and systems: The design and implementation of intelligent systems utilizing advanced active nanomaterial systems to monitor and assess the health of structures, particularly in advanced manufacturing processes
Lead Researcher: Dr. Sankha Banerjee

The objectives of this research area are centered around the development and implementation of agricultural health monitoring systems. The key goals include (i) Sensor Diversity for Agricultural Health Monitoring: Develop various types of sensors tailored for agricultural health monitoring systems, utilizing electro-active and photo-active perovskite/wurtzite and 2D structure-based materials. (ii) Optimization of Fabrication Techniques and Design Parameters: Modify fabrication techniques and design parameters of the materials to enhance their dynamic properties. (iii) Energy-Efficient Wireless Sensor Networks for Agriculture: Design energy-efficient wireless sensor networks specifically for agricultural health monitoring systems. (iv) Real-time Analysis and Machine Learning for sensor application feasibility: Perform real-time analysis of streaming data and develop machine learning models to forecast the health and progressive damage of agricultural structures under various conditions, including aging and extreme loads.

Sub-project Example: “Study of the Efficacy and economic feasibility of a perovskite-oxide based active-sensor Non-Invasive Glucose Monitoring System Using Infrared Light Intensity Correlations: Toward the Development of Measurement Metrics Using Data Analytics for monitoring plant health” The goals and objectives of this subproject are to verify the feasibility of non-invasive optical glucose monitoring using light intensity correlations based detection mechanism continuously in by adapting wearable form-factor using Flextronics and advanced signal processing techniques to assess and monitor plant health. Data collected at each glucose concentration are compared to establish a correlation between the glucose concentration and the refracted light voltage reading. After evaluating performance characteristics, via Mean Absolute Error (MAE), Mean Squared Error (MSE), and Coefficient of Determination (R2), a Decision Tree Regression model displayed the greatest overall performance values.
                                                                Non-invasive nutrient monitoring system including comprehensive machine learning process flow in Dr. Banerjee’s laboratory
 Figure 2.  Non-invasive nutrient monitoring system including comprehensive machine learning process flow in Dr. Banerjee’s laboratory [6, 7, 8].