Computer Engineering Commons^™

Renovating The Ipmu Via Internet Of Things For Pollutant Emission Estimations In Poultry Facilities, Joshua Dotto

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

The emissions of ammonia (NH₃), particulate matter (PM_2.5), and carbon dioxide (CO₂) are major concerns in poultry facilities. They can pose environmental concerns and nuances. Robust and affordable measurement systems are needed to accurately measure in-barn concentrations and quantify the emissions.

The Intelligent Portable Monitoring Unit (iPMU or PMU3) developed in 2016 was reconstructed into PMU4 to include upgraded NH₃ and PM_2.5 sensors and wireless connectivity for a low-cost, robust, and accurate air quality monitoring device with contactless data transfer using the concept of Internet of Things (IoT). In addition, a user-friendly …

Sequence-Based Bioinformatics Approaches To Predict Virus-Host Relationships In Archaea And Eukaryotes, Yingshan Li

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Viral metagenomics is independent of lab culturing and capable of investigating viromes of virtually any given environmental niches. While numerous sequences of viral genomes have been assembled from metagenomic studies over the past years, the natural hosts for the majority of these viral contigs have not been determined. Different computational approaches have been developed to predict hosts of bacteria phages. Nevertheless, little progress has been made in the virus-host prediction, especially for viruses that infect eukaryotes and archaea. In this study, by analyzing all documented viruses with known eukaryotic and archaeal hosts, we assessed the predictive power of four computational …

A Pipeline To Generate Deep Learning Surrogates Of Genome-Scale Metabolic Models, Achilles Rasquinha

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Genome-Scale Metabolic Models (GEMMs) are powerful reconstructions of biological systems that help metabolic engineers understand and predict growth conditions subjected to various environmental factors around the cellular metabolism of an organism in observation, purely in silico. Applications of metabolic engineering range from perturbation analysis and drug-target discovery to predicting growth rates of biotechnologically important metabolites and reaction objectives within dierent single-cell and multi-cellular organism types. GEMMs use mathematical frameworks for quantitative estimations of flux distributions within metabolic networks. The reasons behind why an organism activates, stuns, or fluctuates between alternative pathways for growth and survival, however, remain relatively unknown. GEMMs …

Tau Kinetics In Alzheimer's Disease, Daniel B. Hier, Sima Azizi, Matthew S. Thimgan, Donald C. Wunsch

Chemistry Faculty Research & Creative Works

The Cytoskeletal Protein Tau is Implicated in the Pathogenesis of Alzheimer's Disease Which is Characterized by Intra-Neuronal Neurofibrillary Tangles Containing Abnormally Phosphorylated Insoluble Tau. Levels of Soluble Tau Are Elevated in the Brain, the CSF, and the Plasma of Patients with Alzheimer's Disease. to Better Understand the Causes of These Elevated Levels of Tau, We Propose a Three-Compartment Kinetic Model (Brain, CSF, and Plasma). the Model Assumes that the Synthesis of Tau Follows Zero-Order Kinetics (Uncorrelated with Compartmental Tau Levels) and that the Release, Absorption, and Clearance of Tau is Governed by First-Order Kinetics (Linearly Related to Compartmental Tau Levels). …

Quality Evaluation Of Agricultural And Food Products By Using Image Processing And Soft Computing Paradigm, Narendra Vg

Technical Collection

My research interests revolve around the problem of quality evaluation of Agricultural and Food Products by using Image Processing and Soft Computing Paradigm. Much of my recent work focuses on develop a framework for quality evaluation of Edible Nuts using Computer Vision and Soft Computing Techniques. Also, my interest in developing a framework for defects recognition and classification of Fruits and Vegetables using deep learning methods. My research has also explored many problems related to Blockchain Technology while considering the supply chain management of Agricultural and Food products in between with formers, retailers, and consumers.

1. http://doi.org/10.1109/DELCON54057.2022.9752836
2. http://doi.org/10.1007/978-3-031-07012-9_56
3. http://doi.org/10.1007/978-981-15-8603-3_30
4. http://doi.org/10.1007/978-981-15-8603-3_29
5. http://doi.org/10.1007/978-981-15-8603-3_29

Seabem: An Artificial Intelligence Powered Web Application To Predict Cover Crop Biomass, Aime Christian Tuyishime, Andrea Basche

Honors Theses

SEABEM, the Stacked Ensemble Algorithms Biomass Estimator Model, is a web application with a stacked ensemble of Machine Learning (ML) algorithms running on the backend to predict cover crop biomass for locations in Sub-Saharan. The SEABEM model was developed using a previously developed database of crop growth and yield that included site characteristics such as latitude, longitude, soil texture (sand, silt, and clay percentages), temperature, and precipitation. The goal of SEABEM is to provide global farmers, mainly small-scale African farmers, the knowledge they need before practicing and benefiting from cover crops while avoiding the expensive and time-consuming operations that come …

A Machine Learning Framework For Identifying Molecular Biomarkers From Transcriptomic Cancer Data, Md Abdullah Al Mamun

FIU Electronic Theses and Dissertations

Cancer is a complex molecular process due to abnormal changes in the genome, such as mutation and copy number variation, and epigenetic aberrations such as dysregulations of long non-coding RNA (lncRNA). These abnormal changes are reflected in transcriptome by turning oncogenes on and tumor suppressor genes off, which are considered cancer biomarkers.

However, transcriptomic data is high dimensional, and finding the best subset of genes (features) related to causing cancer is computationally challenging and expensive. Thus, developing a feature selection framework to discover molecular biomarkers for cancer is critical.

Traditional approaches for biomarker discovery calculate the fold change for each …

A Kinetic Model For Blood Biomarker Levels After Mild Traumatic Brain Injury, Sima Azizi, Daniel B. Hier, Blaine Allen, Tayo Obafemi-Ajayi, Gayla R. Olbricht, Matthew S. Thimgan, Donald C. Wunsch

Mathematics and Statistics Faculty Research & Creative Works

Traumatic brain injury (TBI) imposes a significant economic and social burden. The diagnosis and prognosis of mild TBI, also called concussion, is challenging. Concussions are common among contact sport athletes. After a blow to the head, it is often difficult to determine who has had a concussion, who should be withheld from play, if a concussed athlete is ready to return to the field, and which concussed athlete will develop a post-concussion syndrome. Biomarkers can be detected in the cerebrospinal fluid and blood after traumatic brain injury and their levels may have prognostic value. Despite significant investigation, questions remain as …

Body And Tail Coordination In The Bluespot Salamander (Ambystoma Laterale) During Limb Regeneration, Cassandra M. Donatelli, Keegan Lutek, Keshav Gupta, Emily M. Standen

Engineering Faculty Articles and Research

Animals are incredibly good at adapting to changes in their environment, a trait envied by most roboticists. Many animals use different gaits to seamlessly transition between land and water and move through non-uniform terrains. In addition to adjusting to changes in their environment, animals can adjust their locomotion to deal with missing or regenerating limbs. Salamanders are an amphibious group of animals that can regenerate limbs, tails, and even parts of the spinal cord in some species. After the loss of a limb, the salamander successfully adjusts to constantly changing morphology as it regenerates the missing part. This quality is …

Predicting Vasovagal Responses: A Model-Based And Machine Learning Approach, Theodore Raphan, Sergei B. Yakushi

Publications and Research

Vasovagal syncope (VVS) or neurogenically induced fainting has resulted in falls, fractures, and death. Methods to deal with VVS are to use implanted pacemakers or beta blockers. These are often ineffective because the underlying changes in the cardiovascular system that lead to the syncope are incompletely understood and diagnosis of frequent occurrences of VVS is still based on history and a tilt test, in which subjects are passively tilted from a supine position to 20◦ from the spatial vertical (to a 70◦ position) on the tilt table and maintained in that orientation for 10–15 min. Recently, is has been shown …

A Bibliometric Analysis Of Plant Disease Classification With Artificial Intelligence Based On Scopus And Wos, Shivali Amit Wagle, Harikrishnan R

Library Philosophy and Practice (e-journal)

The maneuver of Artificial Intelligence (AI) techniques in the field of agriculture help in the classification of diseases. Early prediction of the disease benefits in taking relevant management steps. This is an important step towards controlling the disease growth that will yield good quality products to fulfill the global food demand. The main objective of this paper is to study the extent of research work done in this area of plant disease classification. The paper discusses the bibliometric analysis of plant disease classification with AI in Scopus and Web of Science core collection (WOS) database in analyzing the research by …

Bibliometric Review On Image Based Plant Phenotyping, Shrikrishna Ulhas Kolhar, Jayant Jagtap

Library Philosophy and Practice (e-journal)

Plant phenotyping is a quantitative description of structural, physiological and temporal traits of plants resulting from interaction of plant genotypes with the environment. A rapid development is in progress in the field of image-based plant phenotyping. Plant phenotyping has wide range of applications in plant breeding research, plant growth prediction, biotic and abiotic stress analysis, crop management and early disease detection. The main motive is to provide detailed bibliometric review in order to know the available literature and current research trends in the area of plant phenotyping using plant images. The bibliometric analysis is primarily based on Scopus, web of …

Covid-19 And Biocybersecurity's Increasing Role On Defending Forward, Xavier Palmer, Lucas N. Potter, Saltuk Karahan

Electrical & Computer Engineering Faculty Publications

The evolving nature of warfare has been changing with cybersecurity and the use of advanced biotechnology in each aspect of the society is expanding and overlapping with the cyberworld. This intersection, which has been described as “biocybersecurity” (BCS), can become a major front of the 21st-century conflicts. There are three lines of BCS which make it a critical component of overall cybersecurity: (1) cyber operations within the area of BCS have life threatening consequences to a greater extent than other cyber operations, (2) the breach in health-related personal data is a significant tool for fatal attacks, and (3) health-related misinformation …

An Accurate Vegetation And Non-Vegetation Differentiation Approach Based On Land Cover Classification, Chiman Kwan, David Gribben, Bulent Ayhan, Jiang Li, Sergio Bernabe, Antonio Plaza

Electrical & Computer Engineering Faculty Publications

Accurate vegetation detection is important for many applications, such as crop yield estimation, landcover land use monitoring, urban growth monitoring, drought monitoring, etc. Popular conventional approaches to vegetation detection incorporate the normalized difference vegetation index (NDVI), which uses the red and near infrared (NIR) bands, and enhanced vegetation index (EVI), which uses red, NIR, and the blue bands. Although NDVI and EVI are efficient, their accuracies still have room for further improvement. In this paper, we propose a new approach to vegetation detection based on land cover classification. That is, we first perform an accurate classification of 15 or more …

The Law Of Black Mirror - Syllabus, Yafit Lev-Aretz, Nizan Packin

Open Educational Resources

Using episodes from the show Black Mirror as a study tool - a show that features tales that explore techno-paranoia - the course analyzes legal and policy considerations of futuristic or hypothetical case studies. The case studies tap into the collective unease about the modern world and bring up a variety of fascinating key philosophical, legal, and economic-based questions.

Machine Learning Approaches For Fracture Risk Assessment: A Comparative Analysis Of Genomic And Phenotypic Data In 5130 Older Men, Qing Wu, Fatma Nasoz, Jongyun Jung, Bibek Bhattarai, Mira V. Han

Public Health Faculty Publications

The study aims were to develop fracture prediction models by using machine learning approaches and genomic data, as well as to identify the best modeling approach for fracture prediction. The genomic data of Osteoporotic Fractures in Men, cohort Study (n = 5130), were analyzed. After a comprehensive genotype imputation, genetic risk score (GRS) was calculated from 1103 associated Single Nucleotide Polymorphisms for each participant. Data were normalized and split into a training set (80%) and a validation set (20%) for analysis. Random forest, gradient boosting, neural network, and logistic regression were used to develop prediction models for major osteoporotic fractures …

On-Site And External Energy Harvesting In Underground Wireless, Usman Raza, Abdul Salam

Faculty Publications

Energy efficiency is vital for uninterrupted long-term operation of wireless underground communication nodes in the field of decision agriculture. In this paper, energy harvesting and wireless power transfer techniques are discussed with applications in underground wireless communications (UWC). Various external wireless power transfer techniques are explored. Moreover, key energy harvesting technologies are presented that utilize available energy sources in the field such as vibration, solar, and wind. In this regard, the Electromagnetic(EM)- and Magnetic Induction(MI)-based approaches are explained. Furthermore, the vibration-based energy harvesting models are reviewed as well. These energy harvesting approaches lead to design of an efficient wireless underground …

Scalable Profiling And Visualization For Characterizing Microbiomes, Camilo Valdes

FIU Electronic Theses and Dissertations

Metagenomics is the study of the combined genetic material found in microbiome samples, and it serves as an instrument for studying microbial communities, their biodiversities, and the relationships to their host environments. Creating, interpreting, and understanding microbial community profiles produced from microbiome samples is a challenging task as it requires large computational resources along with innovative techniques to process and analyze datasets that can contain terabytes of information.

The community profiles are critical because they provide information about what microorganisms are present in the sample, and in what proportions. This is particularly important as many human diseases and environmental disasters …

A New Ectotherm 3d Tracking And Behavior Analytics System Using A Depth-Based Approach With Color Validation, With Preliminary Data On Kihansi Spray Toad (Nectophrynoides Asperginis) Activity, Philip Bal, Damian Lyons, Avishai Shuter

Faculty Publications

The Kihansi spray toad (Nectophrynoides asperginis), classified as Extinct in the Wild by the IUCN, is being bred at the Wildlife Conservation Society’s (WCS) Bronx Zoo as part of an effort to successfully reintroduce the species into the wild. Thousands of toads live at the Bronx Zoo presenting an opportunity to learn more about their behaviors for the first time, at scale. It is impractical to perform manual observations for long periods of time. This paper reports on the development of a RGB-D tracking and analytics approach that allows researchers to accurately and efficiently gather information about the toads’ behavior. …

Internet Of Things In Agricultural Innovation And Security, Abdul Salam

Faculty Publications

The agricultural Internet of Things (Ag-IoT) paradigm has tremendous potential in transparent integration of underground soil sensing, farm machinery, and sensor-guided irrigation systems with the complex social network of growers, agronomists, crop consultants, and advisors. The aim of the IoT in agricultural innovation and security chapter is to present agricultural IoT research and paradigm to promote sustainable production of safe, healthy, and profitable crop and animal agricultural products. This chapter covers the IoT platform to test optimized management strategies, engage farmer and industry groups, and investigate new and traditional technology drivers that will enhance resilience of the farmers to the …

Internet Of Things In Water Management And Treatment, Abdul Salam

Faculty Publications

The goal of the water security IoT chapter is to present a comprehensive and integrated IoT based approach to environmental quality and monitoring by generating new knowledge and innovative approaches that focus on sustainable resource management. Mainly, this chapter focuses on IoT applications in wastewater and stormwater, and the human and environmental consequences of water contaminants and their treatment. The IoT applications using sensors for sewer and stormwater monitoring across networked landscapes, water quality assessment, treatment, and sustainable management are introduced. The studies of rate limitations in biophysical and geochemical processes that support the ecosystem services related to water quality …

Vegetation Detection Using Deep Learning And Conventional Methods, Bulent Ayhan, Chiman Kwan, Bence Budavari, Liyun Kwan, Yan Lu, Daniel Perez, Jiang Li, Dimitrios Skarlatos, Marinos Vlachos

Electrical & Computer Engineering Faculty Publications

Land cover classification with the focus on chlorophyll-rich vegetation detection plays an important role in urban growth monitoring and planning, autonomous navigation, drone mapping, biodiversity conservation, etc. Conventional approaches usually apply the normalized difference vegetation index (NDVI) for vegetation detection. In this paper, we investigate the performance of deep learning and conventional methods for vegetation detection. Two deep learning methods, DeepLabV3+ and our customized convolutional neural network (CNN) were evaluated with respect to their detection performance when training and testing datasets originated from different geographical sites with different image resolutions. A novel object-based vegetation detection approach, which utilizes NDVI, computer …

Subsurface Mimo: A Beamforming Design In Internet Of Underground Things For Digital Agriculture Applications, Abdul Salam

Faculty Publications

In underground (UG) multiple-input and multiple-output (MIMO), the transmit beamforming is used to focus energy in the desired direction. There are three different paths in the underground soil medium through which the waves propagates to reach at the receiver. When the UG receiver receives a desired data stream only from the desired path, then the UG MIMO channel becomes three path (lateral, direct, and reflected) interference channel. Accordingly, the capacity region of the UG MIMO three path interference channel and degrees of freedom (multiplexing gain of this MIMO channel requires careful modeling). Therefore, expressions are required for the degree of …

A Data-Driven Approach For Detecting Stress In Plants Using Hyperspectral Imagery, Suraj Gampa

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

A phenotype is an observable characteristic of an individual and is a function of its genotype and its growth environment. Individuals with different genotypes are impacted differently by exposure to the same environment. Therefore, phenotypes are often used to understand morphological and physiological changes in plants as a function of genotype and biotic and abiotic stress conditions. Phenotypes that measure the level of stress can help mitigate the adverse impacts on the growth cycle of the plant. Image-based plant phenotyping has the potential for early stress detection by means of computing responsive phenotypes in a non-intrusive manner. A large number …

Effective Plant Discrimination Based On The Combination Of Local Binary Pattern Operators And Multiclass Support Vector Machine Methods, Vi N T Le, Beniamin Apopei, Kamal Alameh

Research outputs 2014 to 2021

Accurate crop and weed discrimination plays a critical role in addressing the challenges of weed management in agriculture. The use of herbicides is currently the most common approach to weed control. However, herbicide resistant plants have long been recognised as a major concern due to the excessive use of herbicides. Effective weed detection techniques can reduce the cost of weed management and improve crop quality and yield. A computationally efficient and robust plant classification algorithm is developed and applied to the classification of three crops: Brassica napus (canola), Zea mays (maize/corn), and radish. The developed algorithm is based on the …

Microcontroller Based Granular Urea Application Attachment For Rice Transplanter, Md Towfiqur Rahman, Md Monjurul Alam, Md Mosharraf Hossain, Muhammad Rashed Al Mamun

Biological Systems Engineering: Papers and Publications

Transplanting and fertilizer application for rice production in Bangladesh are tedious, time consuming and laborious task, and mostly done manually. Mechanical transplanting of rice becoming popular in the country in recent years and few machines have been developed for granular urea deep placement, however, having some limitations. Placing granular urea precisely along with rice transplanting, an attempt was under taken to design and fabricate an electronic control granular urea applicator to be attach with a 4-row walk behind type rice transplanter. Fabrication of the electronic granular urea applicator was done in the workshop of the Department of Farm Power and …

A Dexterous, Glove-Based Teleoperable Low-Power Soft Robotic Arm For Delicate Deep-Sea Biological Exploration, Brennan T. Phillips, Kaitlyn P. Becker, Shunichi Kurumaya, Kevin C. Galloway, Griffin Whittredge, Daniel M. Vogt, Clark B. Teeple, Michelle H. Rosen, Vincent A. Pieribone, David F. Gruber, Robert J. Wood

Publications and Research

Modern marine biologists seeking to study or interact with deep-sea organisms are confronted with few options beyond industrial robotic arms, claws, and suction samplers. This limits biological interactions to a subset of “rugged” and mostly immotile fauna. As the deep sea is one of the most biologically diverse and least studied ecosystems on the planet, there is much room for innovation in facilitating delicate interactions with a multitude of organisms. The biodiversity and physiology of shallow marine systems, such as coral reefs, are common study targets due to the easier nature of access; SCUBA diving allows for in situ delicate …

Prototype Of A Fish Inspired Swimming Silk Robot, Cassandra M. Donatelli, Sarah A. Bradner, Juanita Mathews, Erin Sanders, Casey R. Culligan, David Kaplan, Eric D. Tytell

Engineering Faculty Articles and Research

Elongate fishes have evolved hundreds of times throughout the tree of life. They occupy many aquatic environments, from streams and ponds to the deepest parts of the ocean. Due to their long body and numerous vertebrae, they are also highly flexible animals, which makes them useful as bioinspiration for designs in the field of soft robotics. We present a biodegradable soft robot prototype, inspired by elongate fishes. The robot's body is primarily composed of a silk hydrogel with embedded fibers to mimic the structure of natural fish skin. When actuated at the front, the flexible gel prototype mimics the undulatory …

Optimizing The Use Of A Liquid Handling Robot To Conduct A High Throughput Forward Chemical Genetics Screen Of Arabidopsis Thaliana, B. Kirtley Amos, Victoria G. Pook, Seth Debolt

Horticulture Faculty Publications

Chemical genetics is increasingly being employed to decode traits in plants that may be recalcitrant to traditional genetics due to gene redundancy or lethality. However, the probability of a synthetic small molecule being bioactive is low; therefore, thousands of molecules must be tested in order to find those of interest. Liquid handling robotics systems are designed to handle large numbers of samples, increasing the speed with which a chemical library can be screened in addition to minimizing/standardizing error. To achieve a high-throughput forward chemical genetics screen of a library of 50,000 small molecules on Arabidopsis thaliana (Arabidopsis), protocols using a …

A Real-Time Wireless Sweat Rate Measurement System For Physical Activity Monitoring, Andrew Brueck, Tashfin Iftekhar, Alicja B. Stannard, Kumar Yelamarthi, Tolga Kaya

Exercise Science Faculty Publications

There has been significant research on the physiology of sweat in the past decade, with one of the main interests being the development of a real-time hydration monitor that utilizes sweat. The contents of sweat have been known for decades; sweat provides significant information on the physiological condition of the human body. However, it is important to know the sweat rate as well, as sweat rate alters the concentration of the sweat constituents, and ultimately affects the accuracy of hydration detection. Towards this goal, a calorimetric based flow-rate detection system was built and tested to determine sweat rate in real …