Current Research Projects
Applicant: Michael Coote
Title: Functionalized Cellulose Filtration of DEHP Found in Intravenous Medication Administration Sets
Period of Support: May 1, 2022 – April 30, 2023
Abstract: DEHP (diethylhexyl phthalate) is a plasticizer in the phthalate class of compounds which have been linked to a host of deleterious physical affects through endocrine disruption. While regulation has been implemented in many areas (e.g. children’s toys), DEHP is still found in intravenous administration sets used for medication administration. The proposed investigation seeks to develop material for use in a disposable, inline filtration system to selectively mitigate DEHP found in intravenous administration tubing. Cellulose will be functionalized to mimic DEHP functional groups (Figure 1) providing selective interactions with DEHP leached into solution and acting as an inexpensive adjunct method to reduce/ remove DEHP until its complete removal from IV administration sets is legislated or it is removed voluntarily by manufacturers. Anticipated outcomes of the project will be the fabrication of a successful, inexpensive and easily synthesized solid phase material capable of reducing/removing DEHP found in medical contexts while student researchers learn contemporary synthetic and analytical laboratory techniques such as cellulose functionalization, compound characterization, spectroscopy, scientific calculations, report writing and record keeping, and scholarly review and communications.
Applicant: Mohammad Khan
Title: Exploring an application of Hall effect Sensor for Tactile and Non-tactile use
Period of Support: May 1, 2022 – April 30, 2023
Abstract: Electronic circuitry for tactile application is widely used now a days. A hybrid application, both tactile and nontactile, can offer a new option. The Hall effect sensor can potentially be used for tactile and non-tactile purpose. Under the influence of a directional magnetic field and electric fields, the electrons or holes are accumulated on one side of a semiconductor based on its type and opposite charge on the other side. Based on the charges, a voltage is developed. The developed voltage is dependent of the fields applied. The study will focus on generating a pattern of voltages for a given sensor with varying magnetic field. Initially, for the study purpose, Arduino uno will be utilized to process the sensed data. The mapping of the sensed voltage can indicate a way to apply setup for the intended purpose.
Applicant: Gary Lange
Title: Research in the Study of the Shaping of Development from Perturbations in Environmental Factors and Endocrine System Disruption in Three Projects
Period of Support: May 1, 2022 – April 30, 2023
Abstract: This project consists of three related but distinct foci that student(s) will work on. All three projects experimentally assess the impact of environmentally relevant pollutants on the development, growth, morphology and behavior of organisms. In this work, one strand will focus on these effects related to work with rats, but will consist of two distinct parts, exploration of prenatal effects and exploration of perinatal effects of endocrine disrupting compounds. In the other strand, focus will be on environmental and endocrine disrupting effects related to work with fruit flies. Both animal models (rats and fruit flies) are well established, frequently utilized models for biological research and their findings are translational to other organisms, including humans. In the rat model research, exogenous exposure to a mixture of environmentally relevant chemical pollutants in early prenatal and/or perinatal development will occur and exposed pups will be assessed for impacts on growth, development and behavior through to adulthood. In the fly model research, the dual effects of ambiguous gravity and endocrine disruption will be examined from the egg stage of development through to adulthood to assess for impacts on growth, development and behavior of the fly. research attempts to further refine our understanding on how environmental pressures that a developing organism face will impact their growth, behavior, and ultimately the differentiation of their brain. Embryological stages of development are the periods at which an organism is most susceptible to effects of environmental pressures that will shape body morphology, including brain organization and function. Mentoring of a student(s) in this work will be especially helpful for students who wish to pursue post-baccalaureate opportunities such as biological research in graduate school, or advanced study in medical schools, veterinary schools, and other biologically oriented professional schools.
Applicant: A K M Monayem H. Mazumder
Tile: Performance Enhancement of Two-Stage EHD Gas Pump in a Rectangular Channel
Period of Support: May 1, 2022 – April 30, 2023
Abstract: Fluid flow driven by a two-stage electrohydrodynamic (EHD) gas pump will be critically examined by experiments and numerical simulations. The flow will be induced by pump with 8 emitting electrodes in two-stage charged at a combination of three different operating voltages (20 kV, 24 kV, and 28 kV). A numerical model will be developed based on the experimental study. The three-dimensional governing equations for the electric and flow fields will solve using the finite volume method. The EHD-induced flow will calculate first, and its results will be compared with the experimental data to validate the computational code. The numerical results enable vivid flow visualizations inside the channel, providing a great understanding of the development of the induced flow. The two-stage EHD gas pump, which can be produced and sustained air flows with a maximum volume flow rate will be considered more efficient when it is operated with uneven applied voltages. Students will perform a literature review for EHD techniques to gain complete research experience. They will involve the experimental setup design needed to perform the experiments. In that way, students will gain hands-on experience.
Applicant: Rhett Mohler
Title: Using a UAV to Evaluate the Effectiveness of Herbicide Treatments on the Invasive Plant Species. Phragmites Australis on the Crow Island Game Area
Period of Support: June 22, 2022 – December 22, 2022
Abstract: Phragmites australis is a highly invasive plant species in wet and semi-wet environments, such as marshes and shorelines. If not treated properly, P. australis can quickly spread to form dense monocultures over large areas. Consequently, wetland managers are interested in tracking the spread of this plant in order to better prioritize their control efforts and evaluating the effectiveness of these control efforts (usually herbicide treatments). Imagery from Uncrewed Aerial Vehicles (UAVs) is an ideal tool for doing both. This study will compare UAV imagery that was gathered in summer 2021 to imagery from the summer of 2022 to assess the effectiveness of a herbicide treatment in fall of 2021. Imagery and ground-truth data will be gathered starting in July and continue through August and possibly late September. Each image will be processed to locate any P. australis, and the accuracy of this process will be calculated. This will produce maps both of areas where P. australis was effectively killed and where the herbicide treatments were ineffective or spots were missed. These maps will be shared with the Michigan DNR (MDNR) to help inform their treatment processes in the future. A student will be involved with every step of this process, from conducting background research, to gathering data in the field with a UAV, to processing the imagery in the laboratory, to disseminating the results of the research.
Applicant: Avishek Mukherjee
Title: Estimation of CSI using Positional Antenna Configurations in Indoor Wireless Networks
Period of Support: June 27, 2022 – April 30, 2023
Abstract: In this proposal, I propose CSIFinder, a class of signal processing algorithms to estimate the Channel State Information (CSI) in indoor wireless networks. In Wi-Fi networks, the CSI is a vector of complex numbers that can be used to indicate the quality of the wireless channel. The CSI can be measured between any transmitter and receiver antenna pair and is measured at the receiver and sent back to the transmitter to make decisions like rate and channel selection, among others. The CSI is measured for each receiver and transmitter antenna pair so for a 3x3 antenna system (i.e., the receiver has three antennas, and the sender has three antennas) the CSI typically exceeds 1500 bytes of data, which incurs a large overhead when sending it back to the transmitter. This proposal investigates the possibility of estimating the CSI in the space domain, which is predicting the CSI between a transmitter and receiver antenna pair by looking at the CSI of an adjacent antenna pair. In addition, CSIFinder also looks at the possibility of estimating the CSI for a single antenna pair positioned at various angles to determine the best angular positioning of the transmitting antenna on a router. If successful, this will solve a key problem facing wireless networks today. With wireless devices utilizing increasingly larger antenna arrays and occupying larger frequency spectrums, measuring the CSI between all transmitting and receiving pairs on all wireless channels can be time consuming. Approximating some of these measurements, will allow network access points to quickly decide on modulation parameters and achieve better service with higher speeds.
Applicant: Aos Mulahuwaish
Title: Deep Neural Networks for Bot Detection
Period of Support: May 1, 2022 – April 30, 2023
Abstract: Detecting bots, automated social media accounts governed by software but disguising as human users has strong implications. For example, bots have been used to sway political elections by distorting online discourse, manipulating the stock market, or pushing anti-vaccine conspiracy theories that caused health epidemics. Most techniques proposed to date detect bots at the account level by processing many social media posts and leveraging network structure, temporal dynamics, sentiment analysis, etc. In this proposal, we propose a deep neural network based on contextual long short-term memory (LSTM) architecture that exploits both content and metadata to detect bots at the tweet level: contextual features are extracted from user metadata and fed as an auxiliary input to LSTM deep nets processing the tweet text, we also propose a technique based on synthetic minority oversampling to generate a large labeled dataset suitable for deep nets training, from a minimal amount of labeled data (roughly 3,000 examples of sophisticated Twitter bots). This project ensures student participation in all phases of research in recent neural networks and machine learning algorithms and techniques for solving practical problems, from data collection, data analytics, and the implementation of the algorithms to writing manuscripts as a co-author at the end. Having the student engaged in state-of-the-art research would add a new dimension to his/her knowledge base and encourage him/her to pursue scholarly activities and higher studies in the future. Overall, research of this kind would promote SVSU’s undergraduate research activities.
Applicant: Aos Mulahuwaish
Title: A Fuzzy Logic Approach to Beaconing for Vehicular Ad Hoc Networks
Period of Support: May 1, 2022 – April 30, 2023
Abstract: Vehicular Ad Hoc Network (VANET) is an emerging technology field that allows vehicles to communicate together in the absence of fixed infrastructure. The basic premise of VANET is that a vehicle can detect other vehicles in the vicinity. This cognizance awareness of other vehicles can be achieved through beaconing. Shortly, many VANET applications will rely on beaconing to enhance information sharing. Further, the uneven distribution of vehicles, ranging from dense rush hour traffic to sparse late-night volumes, creates a pressing need for an adaptive beaconing rate control mechanism to compromise network load and precise awareness between vehicles. To this end, we propose an intelligent Adaptive Beaconing Rate (ABR) approach based on fuzzy logic to control beaconing frequency by considering traffic characteristics. The proposed ABR considers the percentage of vehicles traveling in the same direction and the status of vehicles as inputs of the fuzzy decision-making system to tune the beaconing rate according to the vehicular traffic characteristics. To achieve a fair comparison with fixed beaconing schemes, we will implement the ABR approach in JIST/SWANs simulation. This project ensures student participation in all phases of research in recent wireless networks algorithms and technology for solving practical problems, from developing and implementing the algorithms to writing manuscripts as a co-author at the end. Having the student engaged in state-of-the-art research would add a new dimension to his/her knowledge base and encourage him/her to pursue scholarly activities and higher studies in the future. Overall, research of this kind would promote SVSU’s undergraduate research activities.