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- Big Data; Computer Vision; Convolutional Neural Networks; Object Detection; Edge Detection; Flood Label Detection; Floodwater Level Estimation; Flood Severity Estimation; Image Processing; (1)
- Deep Learning; CHO Bioprocessing; Transfer Learning; Big Data; LSTM; Feature Engineering (1)
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Articles 1 - 5 of 5
Full-Text Articles in Computer Engineering
Performance Modeling Of Inline Compression With Software Caching For Reducing The Memory Footprint In Pysdc, Sansriti Ranjan
Performance Modeling Of Inline Compression With Software Caching For Reducing The Memory Footprint In Pysdc, Sansriti Ranjan
All Theses
Modern HPC applications compute and analyze massive amounts of data. The data volume is growing faster than memory capabilities and storage improvements leading to performance bottlenecks. An example of this is pySDC, a framework for solving collocation problems iteratively using parallel-in-time methods. These methods require storing and exchanging 3D volume data for each parallel point in time. If a simulation consists of M parallel-in-time stages, where the full spatial problem has to be stored for the next iteration, the memory demand for a single state variable is M ×Nx ×Ny ×Nz per time-step. For an application simulation with many state …
Scalable Data-Driven Predictive Modeling And Analytics For Cho Process Development Optimization, Sarah Mbiki
Scalable Data-Driven Predictive Modeling And Analytics For Cho Process Development Optimization, Sarah Mbiki
All Dissertations
In 1982, the FDA approved the first recombinant therapeutic protein, and since then, the biopharmaceutical industry has continued to develop innovative and highly effective biological drugs for various illnesses1. These drugs are produced using host organisms that are modified to hold the genetic encoding of the targeted protein1. Of the many host organisms, Chinese hamster ovary (CHO) cells are often used due to capability to perform posttranslational modification (PTM): which allows human-like synthesis of proteins unlikely to invoke immunogenicity in humans 1,2.
Despite all the positive attributes, many challenges are associated with CHO cell cultures, …
Improving Intelligent Transportation Safety And Reliability Through Lowering Costs, Integrating Machine Learning, And Studying Model Sensitivity, Cavender Holt
All Theses
As intelligent transportation becomes increasingly prevalent in the domain of transportation, it is essential to understand the safety, reliability, and performance of these systems. We investigate two primary areas in the problem domain. The first area concerns increasing the feasibility and reducing the cost of deploying pedestrian detection systems to intersections in order to increase safety. By allowing pedestrian detection to be placed in intersections, the data can be better utilized to create systems to prevent accidents from occurring. By employing a dynamic compression scheme for pedestrian detection, we show the reduction of network bandwidth improved by 2.12× over the …
The Development Of Tigra: A Zero Latency Interface For Accelerator Communication In Risc-V Processors, Wesley Brad Green
The Development Of Tigra: A Zero Latency Interface For Accelerator Communication In Risc-V Processors, Wesley Brad Green
All Dissertations
Field programmable gate arrays (FPGA) give developers the ability to design application specific hardware by means of software, providing a method of accelerating algorithms with higher power efficiency when compared to CPU or GPU accelerated applications. FPGA accelerated applications tend to follow either a loosely coupled or tightly coupled design. Loosely coupled designs often use OpenCL to utilize the FPGA as an accelerator much like a GPU, which provides a simplifed design flow with the trade-off of increased overhead and latency due to bus communication. Tightly coupled designs modify an existing CPU to introduce instruction set extensions to provide a …
Application Of Image Processing And Convolutional Neural Networks For Flood Image Classification And Semantic Segmentation, Jaku Rabinder Rakshit Pally
Application Of Image Processing And Convolutional Neural Networks For Flood Image Classification And Semantic Segmentation, Jaku Rabinder Rakshit Pally
All Theses
Floods are among the most destructive natural hazards that affect millions of people across the world leading to severe loss of life and damage to property, critical infrastructure, and the environment. Deep learning algorithms are exceptionally valuable tools for collecting and analyzing the catastrophic readiness and countless actionable flood data. Convolutional neural networks (CNNs) are one form of deep learning algorithms widely used in computer vision which can be used to study flood images and assign learnable weights and biases to various objects in the image. Here, we leveraged and discussed how connected vision systems can be used to embed …