Digital Commons Network^™

Video Error Concealment Using Convolutional Neural Network, Shashi Khanal

MSU Graduate Theses

Missing information in the video frames is estimated as close as possible to the actual data during video error concealment process. Blocks or slices of information in the video frames can be missing in the decoder due to various reasons like corrupt media drives, network congestion, etc. which reduces the quality of experience for the viewers. One approach to deal with missing information in the video decoder is to use error concealment techniques to fill the missing information. Until now many of these error concealment techniques were based on conventional methods such as block copy, motion vector prediction, and interpolation. …

Automating The Measurements Of Galaxy Redshifts And Ism Properties Using Cnn, Rohan Pattnaik

Theses

Studying the effects that the local environments of galaxies have on their interstellar medium (ISM) properties is crucial for understanding galaxy evolution and large scale structure of the universe. In order to do that we need precise measurements of ISM properties like Star Formation Rate (SFR), metallicity (Z), ionization parameter (U), gas pressure, and extinction. Accurate estimation of redshift and emission line fluxes from a galaxy's spectrum is the first step in measuring these ISM properties. Current techniques for these measurements still rely on time-consuming manual efforts or error-prone cross-correlation codes that are already struggling to process the vast quantities …

Deep Learning On Image Forensics And Anti-Forensics, Zhangyi Shen

Dissertations

Image forensics protect the authenticity and integrity of digital images. On the contrary, as the countermeasures of digital forensics, anti-forensics is applied to expose the vulnerability of forensics tools. Consequently, forensics researchers could develop forensics tools against possible new attacks. This dissertation investigation demonstrates two image forensics methods based on convolutional neural network (CNN) and two image anti-forensics methods based on generative adversarial network (GAN).

Detecting unsharp masking (USM) sharpened image is the first study in this dissertation. A CNN architecture comprises four convolutional layers and a classification module is proposed to discriminate sharpened images and unsharpened images. The results …

Land Cover Image Segmentation Based On Individual Class Binary Segmentation, Sathyanarayanan Somasunder

Theses

Remote sensing techniques have been developed over the past decades to acquire data without being in contact of the target object or data source. Their application on land-cover image segmentation has attracted significant attention in recent years. With the help of satellites, scientists and researchers can collect and store high resolution image data that can be further processed, segmented, and classified. However, these research results have not yet been synthesized to provide coherent guidance on the effect of variant land-cover segmentation processes. In this paper, we present a novel model that augments segmentation using smaller networks to segment individual classes. …

Assessing Convolutional Neural Network Animal Classification Models For Practical Applications In Wildlife Conservation, Julia Larson

Master's Theses

Convolution neural network models (CNNs) can successfully identify animal species in camera-trap images in simplified testing environments. CNN performance in more complex, realistic environments is understudied. Here the Wellington Camera Traps dataset was used to simulate a wildlife conservation project to detect invasive species at low population levels using camera-trap images and CNN models. Ten CNNs were developed and analyzed with seven testing datasets, simulating 13 possible project scenarios. Model performance was measured using standard computer science metrics, top-1, and top-5 accuracy, and two novel performance metrics developed for this research to directly reflect wildlife conservation goals, false alarm rate, …

Temporal Convolutional Neural Network For Intrusion Detection, Luis Javier Romo Jr.

Theses and Dissertations

Intrusion detection is an important endeavor for large organizations who are constantly targeted by malicious actors. The nature of network traffic data creates many challenges for researchers that want to create an accurate and efficient system for detecting attacks on networks. Many machine learning algorithms have been developed to take on this task. In this paper, we will review some of these techniques, some data sets used to test these techniques, and an experiment where we developed an intrusion detection system that uses a convolution neural network that can perform sequence modeling. This convolutional neural network outperformed a long-shorted term …

A Programmable Processing-In-Memory Architecture For Memory Intensive Applications, Mark Connolly

Theses

While both processing and memory architectures are rapidly improving in performance, memory architecture is lagging behind. As performance of processing architecture continues to eclipse that of memory, the memory architecture continues to become an increasingly unavoidable bottleneck in computer architecture. There are two drawbacks that are commonly associated with memory accesses: i) large delays causing the processor to remain idle waiting on data to become available and ii) the power consumption required to transfer the data. These performance issues are especially notable in research and enterprise computing applications such as deep learning models. Even when data for an application such …

Analyzing Emotion Regulation Using Multimodal Data, Geeta Madhav Gali

Theses

An emotion is a state of one's mind that derives from situations, mood, etc. Understanding people's emotions is a complex problem. People express their emotions using facial expressions or via voice modulations; also, they use emotion regulation strategies such as cognitive reappraisal and expressive suppression in certain instances. Emotion regulation is a method by which we alter our emotions by changing how we express and perceive different situations. Understanding these emotional regulations helps to interact better, communicate and provide care. In this research, we analyze, visualize the differences and distinguish the emotions of disgust and humor with and without emotion …

Unobtrusive Assessment Of Student Engagement Levels In Online Classroom Environment Using Emotion Analysis, Sasirekha Anbusegaran

Electronic Theses and Dissertations

Measuring student engagement has emerged as a significant factor in the process of learning and a good indicator of the knowledge retention capacity of the student. As synchronous online classes have become more prevalent in recent years, gauging a student's attention level is more critical in validating the progress of every student in an online classroom environment. This paper details the study on profiling the student attentiveness to different gradients of engagement level using multiple machine learning models. Results from the high accuracy model and the confidence score obtained from the cloud-based computer vision platform - Amazon Rekognition were then …