This paper discusses the theoretical approximation and attempted measurement of the quantum <br/>force produced by material interactions though the use of a tuning fork-based atomic force microscopy <br/>device. This device was built and orientated specifically for the measurement of the Casimir force as a <br/>function of separation distance using a piezo actuator for approaching and a micro tuning fork for the <br/>force measurement. This project proceeds with an experimental measurement of the ambient Casmir force <br/>through the use of a tuning fork-based AFM to determine its viability in measuring the magnitude of the <br/>force interaction between an interface material and the tuning fork probe. The ambient measurements <br/>taken during the device’s development displayed results consistent with theoretical approximations, while<br/>demonstrating the capability to perform high-precision force measurements. The experimental results<br/>concluded in a successful development of a device which has the potential to measure forces of <br/>magnitude 10−6 to 10−9 at nanometric gaps. To conclude, a path to material analysis using an approach <br/>stage, alternative methods of testing, and potential future experiments are speculated upon.

Human activity recognition is the task of identifying a person’s movement from sensors in a wearable device, such as a smartphone, smartwatch, or a medical-grade device. A great method for this task is machine learning, which is the study of algorithms that learn and improve on their own with the help of massive amounts of useful data. These classification models can accurately classify activities with the time-series data from accelerometers and gyroscopes. A significant way to improve the accuracy of these machine learning models is preprocessing the data, essentially augmenting data to make the identification of each activity, or class, easier for the model. <br/>On this topic, this paper explains the design of SigNorm, a new web application which lets users conveniently transform time-series data and view the effects of those transformations in a code-free, browser-based user interface. The second and final section explains my take on a human activity recognition problem, which involves comparing a preprocessed dataset to an un-augmented one, and comparing the differences in accuracy using a one-dimensional convolutional neural network to make classifications.

This paper investigates near-field thermal radiation as the primary source of heat transfer between two parallel surfaces. This radiation takes place extremely close to the heated surfaces in study so the experimental set-up to be used will be done at the nanometer scale. The primary theory being investigated is that near-field radiation generates greater heat flux that conventional radiation governed by Planck’s law with maximum for blackbodies. Working with a phase shift material such as VO2 enables a switch-like effect to occur where the total amount of heat flux fluctuates as VO2 transitions from a metal to an insulator. In this paper, the theoretical heat flux and near-field radiation effect are modeled for a set-up of VO2 and SiO2 layers separated by different vacuum gaps. In addition, a physical experimental set-up is validated for future near-field radiation experiments.