Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Artificial Intelligence and Robotics (3)
- Computer Engineering (2)
- Computer and Systems Architecture (2)
- Digital Communications and Networking (2)
- Engineering (2)
-
- Other Computer Sciences (2)
- Applied Mathematics (1)
- Biomedical Engineering and Bioengineering (1)
- Business (1)
- Business Analytics (1)
- Civil Engineering (1)
- Civil and Environmental Engineering (1)
- Computational Engineering (1)
- Data Science (1)
- Data Storage Systems (1)
- Databases and Information Systems (1)
- Electrical and Computer Engineering (1)
- Graphics and Human Computer Interfaces (1)
- Numerical Analysis and Scientific Computing (1)
- OS and Networks (1)
- Other Applied Mathematics (1)
- Software Engineering (1)
- Statistical Models (1)
- Statistics and Probability (1)
- Systems and Communications (1)
- Systems and Integrative Engineering (1)
- Transportation Engineering (1)
- Institution
Articles 1 - 4 of 4
Full-Text Articles in Systems Architecture
Crash Detecting System Using Deep Learning, Yogesh Reddy Muddam
Crash Detecting System Using Deep Learning, Yogesh Reddy Muddam
Electronic Theses, Projects, and Dissertations
Accidents pose a significant risk to both individual and property safety, requiring effective detection and response systems. This work introduces an accident detection system using a convolutional neural network (CNN), which provides an impressive accuracy of 86.40%. Trained on diverse data sets of images and videos from various online sources, the model exhibits complex accident detection and classification and is known for its prowess in image classification and visualization.
CNN ensures better accident detection in various scenarios and road conditions. This example shows its adaptability to a real-world accident scenario and enhances its effectiveness in detecting early events. A key …
Deep Learning For Photovoltaic Characterization, Adrian Manuel De Luis Garcia
Deep Learning For Photovoltaic Characterization, Adrian Manuel De Luis Garcia
Graduate Theses and Dissertations
This thesis introduces a novel approach to Photovoltaic (PV) installation segmentation by proposing a new architecture to understand and identify PV modules from overhead imagery. Pivotal to this concept is the creation of a new Transformer-based network, S3Former, which focuses on small object characterization and modelling intra- and inter- object differentiation inside an image. Accurate mapping of PV installations is pivotal for understanding their adoption and guiding energy policy decisions. Drawing insights from current Deep Learning methodologies for image segmentation and building upon State-of-the-Art (SOTA) techniques in solar cell mapping, this work puts forth S3Former with the following enhancements: 1. …
Signal Processing And Data Analysis For Real-Time Intermodal Freight Classification Through A Multimodal Sensor System., Enrique J. Sanchez Headley
Signal Processing And Data Analysis For Real-Time Intermodal Freight Classification Through A Multimodal Sensor System., Enrique J. Sanchez Headley
Graduate Theses and Dissertations
Identifying freight patterns in transit is a common need among commercial and municipal entities. For example, the allocation of resources among Departments of Transportation is often predicated on an understanding of freight patterns along major highways. There exist multiple sensor systems to detect and count vehicles at areas of interest. Many of these sensors are limited in their ability to detect more specific features of vehicles in traffic or are unable to perform well in adverse weather conditions. Despite this limitation, to date there is little comparative analysis among Laser Imaging and Detection and Ranging (LIDAR) sensors for freight detection …
Edge-Cloud Iot Data Analytics: Intelligence At The Edge With Deep Learning, Ananda Mohon M. Ghosh
Edge-Cloud Iot Data Analytics: Intelligence At The Edge With Deep Learning, Ananda Mohon M. Ghosh
Electronic Thesis and Dissertation Repository
Rapid growth in numbers of connected devices, including sensors, mobile, wearable, and other Internet of Things (IoT) devices, is creating an explosion of data that are moving across the network. To carry out machine learning (ML), IoT data are typically transferred to the cloud or another centralized system for storage and processing; however, this causes latencies and increases network traffic. Edge computing has the potential to remedy those issues by moving computation closer to the network edge and data sources. On the other hand, edge computing is limited in terms of computational power and thus is not well suited for …