Using sky-classification to improve the short-term prediction of irradiance with sky images and convolutional neural networks

Clouds moving in front or away from the sun are the leading cause of irradiance variability. These variations have a repercussion on the electricity production of photovoltaic systems. Predicting such changes is essential for proper control of these systems and for maintaining grid stability. Images from the sky have proven to help with short...

Finding disentangled representations using VAE

This study presents a comparison of different VariationalAutoencoder(VAE) models to see which VAE models arebetter at finding disentangled representations. Specificallytheir ability to encode biological processes into distinct la-tent dimensions. The biological processes that will be lookedat are the cell cycle and differentiation state. The...

The effect of deep learning reconstruction on abdominal CT densitometry and image quality: a systematic review and meta-analysis

Objective: To determine the difference in CT values and image quality of abdominal CT images reconstructed by filtered back-projection (FBP), hybrid iterative reconstruction (IR), and deep learning reconstruction (DLR). Methods: PubMed and Embase were systematically searched for articles regarding CT densitometry in the abdomen and the image...

Predicting the infuence of Urban vacant lots on neighborhood property values

Vacant lots are municipally-owned land parcels which were acquired post-abandonment or due to tax foreclosures. With time, failure to sell or find alternate uses for vacant lots results in them causing adverse effects on the health and safety of residents, and cost the city both directly and indirectly. Although existing research has tried to...

Multi-frame deep learning models for action detection in surveillance videos

Visual surveillance technologies are increasingly being used to monitor public spaces. These technologies process the recordings of surveillance cameras. Such recordings contain depictions of human actions such as "running", "waving", and "aggression". In the field of computer vision, automated detection of human actions in videos is known as...

Harmonic Surface Networks

We present a new approach for deep learning on surfaces, combining geometric convolutional networks with rotationally equivariant networks. Existing work either learns rotationally invariant filters, or learns filters in the tangent plane without correctly relating orientations between different tangent planes (orientation ambiguity). We propose...

Convolutional Neural Network Architectures for Signals Supported on Graphs

Two architectures that generalize convolutional neural networks (CNNs) for the processing of signals supported on graphs are introduced. We start with the selection graph neural network (GNN), which replaces linear time invariant filters with linear shift invariant graph filters to generate convolutional features and reinterprets pooling as a...

CNN architectures for GRAPH data

In this ongoing work, we describe several architectures that generalize convolutional neural networks (CNNs) to process signals supported on graphs. The general idea of the replace time invariant filters with graph filters to generate convolutional features and to replace pooling with sampling schemes for graph signals. The different...

Efficient real-time camera based estimation of heart rate and its variability

Remote photo-plethysmography (rPPG) uses a remotely placed camera to estimating a person's heart rate (HR). Similar to how heart rate can provide useful information about a person's vital signs, insights about the underlying physio/psychological conditions can be obtained from heart rate variability (HRV). HRV is a measure of the fine...