For any construction project, there exists a phase known as pre-construction analysis. This planning stage involves the definition of the project, the identification of potential issues, planning and scheduling, the scope, cost estimation, and analysis of needs for the job. By conducting a review of literature to identify key themes and best practices in pre-construction site analysis, as well as reviewing pre-construction analysis documents for GallopNYC case study to identify areas of concern, I was able to compare these case study findings against key themes from the literature findings to identify common patterns between the two.
The project’s primary purpose is to construct a machine learning and find a compressed model data using unsupervised learning, Principal Component Analysis, to predict the party of Legislators. In the last semester, Spring 2020, at Emerging Scholars Program(ESP), I completed a project, “Decision Tree Predicting the Party of Legislators,” to build a decision tree model to predict legislators’ parties’ based on their roll call votes. That time, the data we used the roll call votes, Office of Clerk U.S. House of Representatives Data Sets (Categorical values) collected in 2018 and 2019 given by the legislators. We analyzed the same 2018 and 2019 vote data, but in the numerical format using Principal Component Analysis (PCA) in this new project. This project shows that Principal Component Analysis (PCA) and Decision Tree have the same accuracy and identified legislators who frequently voted against their parties. All the data analyses are done using R and Excel.
Hello! I am Anny Baez Silfa, an upper sophomore in the Computer Engineering Technology Department. Technology has always caught my attention, as well as how a machine works, what it’s made of and how technology can be involved in medicine. This interest has led me to get involved in projects. Currently, I have been updating an ongoing project “Roboqueen” belonging to the same department. This project consists of a full-body interactive robotic mannequin made of cardboard slices and an aluminum frame. Last semester the cardboard hands were improved with 3D printed robotic Arms. Now, due to interest in helping people with disabilities, I decided to update the robotic hand and made a 3D Bionic Arm. With this update, it would simulate a prosthetic arm for future development.
MRI is a useful electromagnetic imaging modality offering good structural definition as well as molecular properties in healthy and diseased tissues. We propose to measure and model the sample signal and noise changes in fruits infused with radiology contrast nanoparticles. Iodinated and Gadolinium contrast media provide long range energy correlation and seems to alter Proton relaxation times near the infusion areas in fruits. We used Apples and sweet potatoes to perform this experiment. The process of this experiment was not hard, but the results were incredibly grateful. From our preliminary observations of Gadolinium and Iodine contrast agents, it is difficult to quantify the degree of dissociation of such agents. However, it is plausible that Iodine complexes interact with divalent or trivalent cations in the studied fruits stronger, than Gadolinium complex (Gadavist) that was tested as observed within a few minutes.
This project involves the interruption of protein dynamics and natural development of biological reactions, particularly in fresh, hatchable chicken eggs with a small amount of radiopaque compounds and subsequent imaging of diffusion and molecular interaction using “soft” x-rays in the mammographic energy range. Time series analysis of affected protein layers due to “toxic” interaction with Gadolinium and Iodine moieties in naturally occurring proteins in chicken egg yolk are observed at various resolutions and x-ray energy (kVp) and flux (mAs) conditions. Both of these groups of nanomaterials may offer new insights in nanomedicine where tissue transport is gaining importance.
Image compression is commonly used to reduce the file size of images without losing significant information of the original images. Images can be stored and transmitted more efficiently when their file sizes are smaller. In this project, we try to compress images using principal component analysis (PCA). The main idea of PCA is to reduce the dimensionality of the data by finding the directions of maximum variance in high dimensional data and project it into a smaller dimensional sub-space, while retaining the most information of the original data. This project contains two parts: (1) calculating the Principal Components (PCs) using the covariance matrix; (2) projecting the data on the PCA sub-space that is obtained from the calculated PCs. For our experiments, we write a program in R that does the task of compressing images.
For anyone who wants to easily understand, how Alzheimer’s Disease (AD) is observed through Diffusion Tensor Imaging in MRI, look no further. First, the abstract is the beginning section that will show the ADNI website has been utilized and what patterns have been observed. Second, the introduction defines what AD is and what happens in the brain when someone is enduring AD. Third, the Method shows what tools I used to obtain information for this poster. You will notice there are a couple of definitions in layman’s terms to help you understand DTI and it’s physics in clarity. Next, there are brain scans and some charts with simple captions to help you understand the results of DTI brain scans. Followed by key points of AD being observed in MRI and a recap of what each image represents in the discussion section. I’ve given acknowledgements to those who were not mentioned. Feel free to refer back to the references in the last section of this poster.
The creation of my app combines Augmented Reality with Artificial Intelligence, in particular Natural Language Processing, Computer Vision, and Image Recognition. The aim of this app is to enhance the ability of the visually impaired to navigate their way via super-imposed “waypoints” (like over-sized arrows), affording greater independence in their handling of day-to-day affairs. My initial studies with a Microsoft Hololens and Computer Vision/Voice Recognition show that objects can be identified in text form. My prototype demonstrates successful identification of everyday objects from 40-99%. Collected data suggests success in employing a virtual assistant to guide the user in their navigation to a destination. Next steps are taking this from the lab onto the street.
Have you ever thought about why it takes so long to recover after a disaster? There are several reasons why which include logistical issues, risk management, and community issues. The research that I performed by analyzing the issues of post disaster rebuild for the Labor Day Hurricane in 1935 in the Florida Upper Keys and comparing it to common issues after a post disaster. Through my research I hope to get better understanding of challenges faced after post disasters. This research topic is significant to me because I want people to become aware of problems after post disasters.
