Bayesian Augmentation of Deep Learning to Improve Video Classification

Traditional automated video classification methods lack measures of uncertainty, meaning the network is unable to identify those cases in which its predictions are made with significant uncertainty. This leads to misclassification, as the traditional network classifies each observation with same amount of certainty, no matter what the observation is. Bayesian neural networks are a remedy to this issue by leveraging Bayesian inference to construct uncertainty measures for each prediction. Because exact Bayesian inference is typically intractable due to the large number of parameters in a neural network, Bayesian inference is approximated by utilizing dropout in a convolutional neural network. This research compared a traditional video classification neural network to its Bayesian equivalent based on performance and capabilities. The Bayesian network achieves higher accuracy than a comparable non-Bayesian video network and it further provides uncertainty measures for each classification

Implementation of a sinusoidal current drive for a brushless three phase motor using a common sense resistor for rotor position feedback

Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.Includes bibliographical references (p. 79).by Gregory Emil Swize.M.Eng

Bayesian Augmentation of CNN-LSTM for Video Classification with Uncertainty Measures

The success of Department of Defense (DoD) missions rely heavily on intelligence, surveillance, and reconnaissance (ISR) capabilities, which supply information about the activities and resources of an enemy or adversary. To secure this information, satellites and unmanned aircraft systems collect video data to be classified by either humans or machine learning networks. Traditional automated video classification methods lack measures of uncertainty, meaning the network is unable to identify those cases in which it predictions are made with significant uncertainty. This leads to misclassification, as the traditional network classifies each observation with same amount of certainty, no matter what the observation is. Bayesian neural networks offer a remedy to this issue by leveraging Bayesian inference to construct uncertainty measures for each prediction. Because exact Bayesian inference is typically intractable due to the large number of parameters in a neural network, Bayesian inference is approximated by utilizing dropout in a convolutional neural network

The Efficacy of Wristband Activity Trackers during Vigorous Exercise

Patients and clinicians rely on activity trackers to monitor heart rate, calorie expenditure, and steps during training and interventions. However, the efficacy of activity trackers during vigorous exercise, is not widely studied. PURPOSE: The objective of this study was to compare the effectiveness of activity trackers during vigorous activity. METHODS: Nineteen participants completed twenty minutes of vigorous intensity exercise by running or incline walking on a treadmill. Measurement devices worn during the testing period included two wristband activity trackers (Garmin (G) Forerunner 735xt™ and Fitbit (F) Surge™) and industry standard devices: a pedometer(P), Polar™ HR Chest Strap and Cosmed (C) Quark CPET face mask. RESULTS: No significant difference was found among the devices or industry standard for step count (STPG = 3096.56+/-380.05; STPF = 3072.72+/-353.26; STPP = 3052.44+/-408.52). No significant difference was found between the two trackers and the industry standard for energy expenditure (KCALG = 249.19+/-61.06; KCALF = 211.88+/-34.43; KCALC = 234.07+/-64.24). However, there was a significant difference between the two trackers for this same variable. At multiple times throughout the testing period, a significant difference was noted between the activity trackers and industry standard for heart rate. All testing significance was set at pCONCLUSION: This study sought to examine the efficacy of personal activity trackers as compared to industry standards during vigorous exercise. Both devices proved accurate in measuring steps and energy expenditure but proved inconsistent when monitoring heart rate

Predictors of Recurrent Ingestion of Gastrointestinal Foreign Bodies

BACKGROUND: Gastrointestinal foreign bodies are commonly encountered; however, little knowledge exists as to the causes of foreign body ingestions and why they occur repeatedly in some patients