CSET Presentations for Research Month

Department of Electrical and Computer Engineering and Technology  

 Date: Wed. April 26, 12:00pm

Title: Integration of multiple energy sources to the smart grid           

 Renewable energy such as wind and solar energy has been widely used during the past decade because of its environmentally friendly. The integration of these distributed energy sources to the grid is challenge due to their intrinsic characteristics such as the intermittence. Power converters are playing an important role in applications such as battery chargers, electric vehicles and especially in smart grid. This talk will cover the research of integration of different energy sources to the next generation grid.

Contact Person: Jianwu Zeng

Zoom link: https://minnstate.zoom.us/j/2420589122

 

 Date: April 22, 12:00 PM

Title: Carbon Nanomaterial – Protein Interactions: A Molecular Dynamics Study.

Carbon-based nanomaterial such as carbon nanotubes (CNT), graphene (Gr), graphene quantum dots (GQD) possesses excellent electrical properties as well as selective binding affinity to biomolecules. Adsorption of biomolecules such as proteins and DNA on such nanomaterials can manipulate their electrical property and can find application in nano-bio sensors. One of the direct approaches to study the adsorption of biomolecules on the nanomaterial surface is by molecular dynamics computations. Molecular dynamic computation uses high performance computing and can explain the structural changes in proteins or DNAs during the adsorption process. This presentation will give an insight into 3D graphical visualization of bio-nano interface at atomic level.

Contact Person: Bhushan Dharmadhikari

 Zoom: https://minnstate.zoom.us/j/94908813885

 

Date: April 28, 12:00 PM

Title: Graphene Oxide as a Catalytic Agent in Polymer Electrolyte Membrane Fuel Cell

Platinum (Pt) is commonly used as a catalyst in fuel cell production. Platinum being an expensive metal, makes commercialization of fuel cells uneconomical. Many research groups are working on finding the alternative catalytic material. Graphene and carbon-based nanomaterials have potential to replace Pt as catalyst in polymer electrolyte membrane fuel cell (PEMFC) due to their properties such as broad electrochemically active surface area, fast carrier mobility, high conductivity, and ability to stabilize and increase the durability of fuel cells. Proton conducting Polymer Electrolyte Membranes (PEMs) are used in PEMFCs to allow protons (H+) to pass through the electrodes. An effective PEM should be able to facilitate ion transfer in fuel cells. Nafion is one of the most widely used electrolytes in PEMs due to its high ionic conductivity and highly resistance to chemical reactions such as corrosion. However, water retention in Nafion membrane give rise to fouling effect, which reduces the ion transfer and performance of PEMFC. The amount of water retained also causes the polymer to swell, therefore, decreasing the efficiency of fuel cells. To overcome this drawback, hydrophobic Polycaprolactone (PCL) is being considered a potential electrolyte in this study. The objective of our research is to use Graphene Oxide as catalyst in electrodes of PEMFC and find alternative for Nafion membrane by using electrospun PCL nanofibers instead. The use of carbon-based nanomaterials has delivered commendable performance making commercialization of fuel cell products possible in near future.

 Shristi Silwal, Masters student in Electrical Engineering

Zoom Link: https://minnstate.zoom.us/j/94416949632

Meeting ID: 944 1694 9632

Passcode: 111111

 

Date: April 29, 12:00 PM

Title : Model based design and verification of PLC programs via Simulink Design

Programmable Logic Controllers (PLCs) have been widely applied in safety-critical industrial processes. Automated verification of PLC programs is a challenging task for control system engineers. A method of model-based verification of Simulink design models for verifying PLCs programs is proposed. In this work, PLC programs coded in the Structured Text (ST) language are assumed to be automatically generated from Simulink models using the tool Simulink PLC Coder from Mathworks. We utilize Simulink diagrams as system design models. Simulink is a powerful design tool for developing complex event-driven applications. To formally verify the functional properties of the design models, a verifying model compiler called Gene-auto is applied to automatically translate Simulink models to C code. The properties to be checked are also translated as C assertions, which are inserted into the translated C code. Then, the generated C code instrumented with assertions is formally verified with a bounded model checking tool for C program called CBMC. The approach is experimentally assessed on a water control system case study. Compared with the previous approach of translating a PLC program to a timed automata and verifying by the use of a model-checking tool, our approach is significantly more scalable to verify non-timing related functional properties.    

Contact Person: Nannan He

Zoom link: https://minnstate.zoom.us/j/2213580776

 

Department of Integrated Engineering 

Friday, April 16, Noon

Title: Developing Your Skills as a Reviewer to Build your Research Skills 

Audience: Faculty and graduate students who participate in a peer review process or plan to, across a wide range of disciplines

A one-hour interactive workshop examining reviews of journal submissions and discussing processes and best practices for giving high-quality feedback in the peer-review process. Peer review is a component of service but can also be a significant component of professional development and can contribute to scholarly activity. Participants will be able to identify effective feedback and leave with guiding questions to strengthen the reviews they create. Workshop facilitators are the editor of the Journal of Engineering Education, a former NSF program officer, and two faculty members who have participated in a reviewer training program run by JEE. 

Speakers: Becky Bates, Lisa Benson, Rob Sleezer, Catherine Spence 

Contact: Becky Bates (bates@mnsu.edu)

 

 Friday, April 23, 12:00 pm 

Title: Seeking External Funding

Audience: Faculty and graduate students interested in developing proposals for external funding, especially in STEM fields or fields funded by the National Science Foundation

Description: A one-hour interactive overview of resources and advice for seeking external funding. The focus will be on submissions to the National Science Foundation. Presenters are a former NSF program officer and a senior faculty member who has received funding for workshops that support the development of NSF proposals. 

  • Speakers: Becky Bates, Jennifer Karlin

Contact: Becky Bates (bates@mnsu.edu)

 

Wednesday, April 7, 4:00 pm

Title: What Initiates Evidence-Based Reasoning? Situations that Prompt Students to Support their Design Ideas and Decisions

Audience: Those interested in Pre-college education and/or STEM education; Pre-college teachers, administrators, and curriculum developers, faculty teaching STEM education students

Description: Dr. Emilie A. Siverling will present the findings of a qualitative study that explored fifth through eighth-grade students’ use of evidence-based reasoning (EBR) during engineering design-based STEM integration activities. This study offers implications for teachers and curriculum developers about how to explicitly integrate scaffolds for EBR into design-based curricula, as well as what situations teachers can look for to observe student-directed use of EBR. The full journal article corresponding to the presentation will be published in the April 2021 issue of the Journal of Engineering Education.

Speaker: Emilie A. Siverling, PhD

Contact: Emilie A. Siverling, PhD (emilie.siverling@mnsu.edu)

Website: https://cset.mnsu.edu/departments/integrated-engineering/faculty-and-staff/emilie-siverling/

 

Department of Mathematics and Statistics

Date: April 21, 2021, 9:00 AM-3:00PM

Title: Graduate Students Thesis/APP Project Presentations

This will be a collection of talks presented by graduate students who are planning to graduate in Spring/Summer 2021

Contact person: Ruijun Zhao

Website information – links  https://cset.mnsu.edu/departments/mathematics-and-statistics/news--events/

Zoom link will be available from the link above.

 

Department of Physics and Astronomy

Date & Time: April 20, 2021, 3:00PM

Title: The Physics of Nature’s Music 

Spring is almost here. We can feel it in the air, and with anticipation we wait to hear one of the most beautiful sounds that nature provides, birdsong. We certainly enjoy hearing their songs, but how are birds able to produce such complex vocalizations? Is birdsong innate or learned behavior? 

At first sight these questions seem only for a biologist to address. In this talk, I will show you what physics and mathematical modeling have to offer to the understanding of biological systems, in particular to the birdsong field. I will present some of my past, present and (hopefully) future contributions to this paradigmatic model system for complex learned behavior.

Contact person: Dr. Jorge Mendez

Intended audience: General undergraduate/graduate/faculty audience.

How to attend: In person at TRC 122 or join Zoom Meeting
https://minnstate.zoom.us/j/99093786562
Meeting ID: 990 9378 6562
Passcode: 247461

 

Date & Time: April 20, 2021, 3:30PM

Title: Laser-induced thermal effects in meteorites

Meteorites can provide valuable clues about planet formation since they are considered some of the most primitive surviving materials of our solar system. This information can be obtained through their physical properties which can be characterized using microscopy and spectroscopy techniques. In particular, Raman spectroscopy has been used extensively on meteoritic samples since it is a nondestructive tool that provides information about their structure and mineralogical composition. However, the power of the laser excitation source used in this technique can alter the properties of the samples due to thermal effects.

In this presentation, I will talk about the laser-induced thermal effects we have observed in a meteoritic sample by analyzing the Raman spectra parameters of the minerals found as a function of the laser excitation power and correlating them with changes that can occur on the topography of the irradiated regions using optical microscopy.

Contact person: Speaker: Mohamed Zakariya (physics major). Advisor: Dr. Analía “Yanil” Dall’Asén.

Intended audience: General undergraduate/graduate/faculty audience.

How to attend: In person at TRC 122 or join Zoom Meeting
https://minnstate.zoom.us/j/99093786562
Meeting ID: 990 9378 6562
Passcode: 247461

 

Department of Computer Information Science

Date & Time: April 15, 8:00 - 8:30 am

Meeting Link: https://minnstate.zoom.us/j/92213974371?pwd=MDhmbHZ6QXRobWNsVGdLbGZnampNUT09&from=addon

Passcode: 428369

Title: What does the twitter sentiments say about the COVID-19 Vaccine?

This topic will be presented by Ilma Sherif, a CIS graduate student, working on her thesis under supervision of Dr. Naseef Mansoor (CIS faculty). This will be presented in the CADSCOM conference.

The coronavirus disease (COVID-19) pandemic led to substantial public discussion. Understanding these discussions can help institutions and individuals navigate through this pandemic. In this paper, we analyze and investigate the twitter sentiments toward COVID-19 vaccine. Starting from a publicly available twitter dataset on COVID-19 vaccine from Kaggle, we create a unified dataset containing data about public sentiments, sentiment scores, and COVID-19 cases for various U.S. states. To generate a sentiment scores from the tweets, we have applied a Valence Aware Dictionary and sEntiment Reasoner (VADER) sentiment analyzer. These scores were then classified to positive, negative, and neutral sentiment classes using a simple threshold-based classifier. From our analysis, we observe that in our dataset around 41.93% of the tweets are positive, 17.64% tweets are negative, and 40.42% tweets are neutral. We also analyzed the data based on geographic locations of the tweets to answer the following questions - 1) Is there any relationship between the number of tweets and the number of COVID-19 cases? 2) Is there any shift in the public sentiment after the approval of the vaccine? Our analysis shows high correlation between the number of tweets and the number ofCOVID-19 cases as well as a decrease in negative sentiment after the approval of the vaccine.

 

Date & Time: April 15, 9:00 - 9:30 am

Meeting Link: https://minnstate.zoom.us/j/96411426671?pwd=UkhwSFNNQWUwdW1vWjZ4QVhrYzVsZz09&from=addon

Passcode: 089322

Title: Xtreme-NoC: Extreme Gradient Boosting based latency model for Network-on-Chip Architectures

This topic will be presented by Ilma Sherif, a CIS graduate student, working on her thesis under supervision of Dr. Naseef Mansoor (CIS faculty)

Due to the heterogeneous integration of the cores, execution of diverse applications on a many processor chip, application mapping strategies, and many other factors, the design of Network-on-Chip (NoC) plays a crucial role to ensuring optimum performance of these systems. Design of an optimal NoC architecture poses a performance optimization problem with constraints on power, and area. Determination of these optimal network configurations is carried out by guided (genetic algorithm) or unguided (grid search) algorithms to explore the NoC design space. At each step of this design space exploration, a network configuration is simulated for performance, area, and power for a wide range of applications. A system level modeling is required to conduct these simulations to accurately captures the timing behavior, energy profile, and area requirements of the network. Based on the accuracy of the network model, network configuration, and application running on the system, these simulations can be extremely slow.  An alternative, to such network simulation is to use analytical network models utilizing classical queuing theory and treat each input channel in the NoC router as an M/M/1, M/G/1/N, or G/G/1 queue. Such analytical models provide good estimation of network performance like latency only under certain assumptions i.e.: a Poisson process for the network traffic with an exponential packet service time, and an exponential distribution for packet length. Unfortunately, these assumptions are not guaranteed for real application-based traffic patterns, and the accuracy of the analytical models are disputable. Hence, an accurate NoC performance model with accelerated runtime is required to ameliorate the slow design space exploration process of NoC architectures. To accelerate the design space exploration, in this work, we propose Xtreme-NoC, an extreme gradient boosting based NoC latency model that can predict the accuracy of NoC architectures with 98.1% accuracy. To show the effectiveness of the proposed model, we compare our model with other regression models and show that our model can predict the latency with higher accuracy. 

 

Date & Time: April 16, 12:30 - 1:00 pm

Meeting Link: https://minnstate.zoom.us/j/91248356331

Passcode: 102912

Title: What Can a Remote Access Hardware Trojan do to a Network-on-Chip

Dr. Naseef Mansoor (CIS faculty) will present this topic to CIS 602 research seminar class (taught by Dr. Mahbub Syed)

Interconnection networks such as Network-on-Chips(NoCs) for multi/many-core processors are critical infrastructure of the system as they enable data communication among the processing cores, caches, memory, and other peripherals.  Given the criticality of the interconnects,  the  system can be severely subverted if the interconnection is compromised.  The threat of Hardware Trojans (HTs) penetrating complex hardware systems such as multi/many-core processors are increasing due to the increasing presence of third-party players in a System-on-chip(SoC)  design.  Even by deploying naive HTs, an adversary can exploit the NoC backbone of the processor and get access to communication patterns in the system. In this paper, we discuss that one or more HTs embedded in the NoC of a multi/many-core processor is capable of leaking sensitive information regarding traffic patterns to an external malicious attacker, who, in turn, can analyze the HT payload data with advanced algorithms such as machine learning to infer the applications running on the processor or reverse engineer architectural Intellectual Property(IP)  of  the system.  Here,  we  entertain the idea of using routing obfuscation to achieve a desired trade-off between defense against HTs and performance penalties.  We also discuss the possibility of making this trade-off a tunable design parameter that can be adjusted at run-time based on external threat perception.

 

Date & Time: April 26, 12 pm (noon)

How to attend: Zoom link, https://minnstate.zoom.us/j/6253726036

Speaker: Von Korff, Benjamin 

Title: Covid19 Forecasting with FBProphet, and comparison to ARIMA, LSTM, W-ARIMA, W-LSTM 

Contact Person: Dr. Suboh Alkhushayni

The novel coronavirus (Covid19) has led to over 212,804 deaths in the United States by October 2020. With cases in the United States continuing to rapidly rise through the fall of 2020, the United States has been one of the hardest hit countries by the pandemic. Development of time series forecasting and mathematical modeling is crucial to making decisions to manage the pandemic. We compared a hybrid ARIMA-based, logistic-growth model, FBProphet, used to forecast the timing of the pandemic peak to 4 other modeling techniques: ARIMA, wavelet-ARIMA, LSTM, and wavelet-LSTM. 90-day forecasts for cases and deaths were created for each model. Wavelet transformation improved model performance compared to an untransformed model for ARIMA and LSTM for death forecasting, but untransformed ARIMA performed better than wavelet-ARIMA. The choice of wavelet family was extremely important to model performance, with db3 having the best performance. While the logistic-FBProphet approach was useful for providing a long-term forecast of the pandemic peak, it tended to under-predict cases and deaths due to the recent rapid spread of covid19 in the fall in the United States. ARIMA and wavelet-ARIMA showed the best performance of the models based on MSE for cases and deaths, respectively. Prior to this analysis, FBProphet has been understudied for forecasting covid19, especially in the United States and has typically not been compared to other forecasting methods when used. These results suggest that while FBProphet has a strength in forecasting long term peaks, shorter term forecasting models are critical due to the unpredictable nature of the pandemic, and applying models based on a single logistic growth curve has the potential to under-predict future cases.

 

Date & Time:

Speaker:

Title: The Application of Differing Machine Learning Algorithms and Their Related Performance in Detecting Skin Cancers and Melanomas 

Contact Person: Dr. Suboh Alkhushayni

https://minnstate.zoom.us/my/alkhushayni

Zoom ID: 625 372 6036 

 

Date & Time: April 26, 01:30 pm.

How to attend: Zoom link, https://minnstate.zoom.us/j/6253726036

Speaker: Mohannad Rayani

Title: Sentimental Analysis of COVID-19 Vaccine Related Tweets

Contact Person: Mentor-Dr.Suboh Alkhushayni 

The year 2020 will be remembered in history for the widespread destruction caused by the COVID-19 pandemic. It is an infectious disease caused by a coronavirus called SARS-CoV-2 (Dai, et al., 2020). Started from a Wuhan city in China, the highly infected disease has spread across the world without discriminating against any geographical boundaries. Currently, nearly 990 thousand have lost their lives because of this deadly virus. Pharmaceutical companies around the world have started working on the vaccine with many of them being in their final trial stage. However, vaccines take many years before they are available to the general public. In the current scenario, it is expected that it will be available by the end of the year. There is no doubt that in the past vaccines have been proven to be life saviors, but in the case of COVID-19, people around the world are skeptical about its vaccine with the vaccine discussion being controversial. Twitter is an online platform where people share their thoughts in the form of tweets. In this study, we will be classifying people's views, sentiments, and emotions regarding the COVID-19 vaccine. This study will help clarify people's state of mind regarding the vaccine and their trust factor regarding various pharma companies. Our study and research will be focusing around the tweets related to the COVID-19 vaccine. Our study’s main motive is to construct a machine learning model that will analyze the sentiments within tweets related to COVID-19 vaccine. It will be beneficial for the authorities to look upon the details of why people are having negative or positive emotions to either work on their concerns or predict the response to the COVID-19 vaccine.

 

Date & Time: April 26, 2:00 pm.

How to attend: Zoom link, https://minnstate.zoom.us/j/6253726036

Speaker:  Benie Bebela

Title: Analyzing the trend and forecasting of Covid-19 outbreak using machine learning technique.

Contact Person: Mentor- Dr.Suboh Alkhushayni 

By “Analyzing the trend and forecasting of Covid-19 outbreak using machine learning technique.”, we are conducting a study which results will serve as a predictive weapon against the disease. These will help the healthcare system anticipate a probable increase or decrease in term of cases and most importantly know what the next move in the mitigation process of the virus should be.  The main goal is not to directly stop the virus because that is not an easy task at all but step by step breaking down its evolution, analyze certain factors such as deaths, recoveries and forecast what will happen next. As patriotic being, governments have asked us to conduct ourselves as responsible citizens by respecting the regulations and guidelines such as social distancing, wearing masks and many more. We have seen many schools, universities and other professional institutions close across the country and even the world to slow the propagation. Essential crowed places such as malls or supermarkets had a limited number of people per hour and had required masks and 6 feet social distancing. Important events such as Olympics, NBA season, graduations and many more had been either cancelled or suspended. Some cities like New York had been on complete shut down for weeks and people were in a sense forced to live in isolation for the good of everyone. All these regulations though they have shown themselves extremely helpful could not do more than slowing the spread of the virus which is fine but not enough to elaborate an accurate preventive and tackling strategy against the virus.

 

Date & Time: April 27, 1:00 - 1:30 pm

Meeting Link: https://minnstate.zoom.us/j/92930646270

Passcode: 493236

Title: Using Extreme Value Statistics to Assess Wildfire Risk in Colorado

Student: Christopher Maher (CIS student)

Faculty: Dr. John Burke (CIS faculty)

The year 2020 will be memorable for many reasons, but among other things it was possibly the worst year for wildfires in Colorado in the last 100 years. Colorado had several major fires, including three of the largest fires ever recorded, which burned over 600,000 acres, destroyed hundreds of structures, displaced thousands of people, cost residents millions of dollars and had the unfortunate loss of several lives (5280.com, 2020; cpr.org, 2020).  These losses were so large that they have called into question Colorado’s policies of fire suppression, where fires are put out by firefighters before all fuel has been consumed. It has even been suggested that these fires are evidence of ever larger fires in the future. 

This may be true, however, although such extreme events may have large social and economic consequences, they are not well characterized by such measures as mean or median. Extreme events are by definition upper-tail events and should be analyzed using Extreme Value Analysis. This research in progress will use the generalized extreme value (GEV) distribution and the generalized Pareto distribution (GPD) to analyze the possible frequency and size of wildfires in Colorado.