Essays about: "Computer intensive methods"
Showing result 1 - 5 of 18 essays containing the words Computer intensive methods.
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1. Automatic Semantic Segmentation of Indoor Datasets
University essay from Blekinge Tekniska Högskola/Institutionen för datavetenskapAbstract : Background: In recent years, computer vision has undergone significant advancements, revolutionizing fields such as robotics, augmented reality, and autonomoussystems. Key to this transformation is Simultaneous Localization and Mapping(SLAM), a fundamental technology that allows machines to navigate and interactintelligently with their surroundings. READ MORE
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2. Sim2Real: Generating synthetic images from industry CAD models with domain randomization
University essay from Uppsala universitet/Institutionen för informationsteknologiAbstract : Deep learning methods for computer vision applications require massive visual data for model training. Although it is possible to utilize public datasets such as ImageNet, MS COCO, and CIFAR-100, it becomes problematic when there is a need for more task-specific data when new training data collection typically is needed. READ MORE
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3. A General Purpose Near Data Processing Architecture Optimized for Data-intensive Applications
University essay from Lunds universitet/Institutionen för elektro- och informationsteknikAbstract : In recent years, as Internet of Things (IoT) and machine learning technologies have advanced, there has been increasing interest in the study of energy-efficient and flexible architectures for embedded systems. To bridge the performance gap between microprocessors and memory systems, Near-Data Processing (NDP) was introduced. READ MORE
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4. Application of Bootstrap in Approximate Bayesian Computation (ABC)
University essay from Uppsala universitet/Statistik, AI och data scienceAbstract : The ABC algorithm is a Bayesian method which simulates samples from the posterior distribution. In this thesis, the method is applied on both synthetic and observed data of a regression model. Under normal error distribution a conjugate prior and the likelihood function are used in the algorithm. READ MORE
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5. Failure Inference in Drilling Bits: : Leveraging YOLO Detection for Dominant Failure Analysis
University essay from Uppsala universitet/Institutionen för informationsteknologiAbstract : Detecting failures in tricone drill bits is crucial in the mining industry due to their potential consequences, including operational losses, safety hazards, and delays in drilling operations. Timely identification of failures allows for proactive maintenance and necessary measures to ensure smooth drilling processes and minimize associated risks. READ MORE
