Minnesota State University, Mankato
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Design and Implementation of a Low-Cost Raspberry Pi and AI-Based Intrusion Detection System for Surveillance
Get PDFAs security concerns continue to rise, there is a growing demand for affordable and intelligent surveillance solutions to ensure safety in homes, businesses, and other environments. Many individuals are embracing AI-driven technologies such as Closed-Circuit Television (CCTV), smart doorbells, and automated security systems to protect their properties. This project presents a design and implementation of a cost-effective AI-powered intrusion detection system utilizing Raspberry Pi 5 for home surveillance, with adaptability for broader applications. The system integrates a camera module and an LCD screen running on a Linux-based platform, with Python, and OpenCV as key software components. It employs dlib’s deep learning-based face recognition model to detect and authenticate individuals by cross-referencing live detected faces with a stored database of known persons. Additionally, this research compares the performance of dlib-based face recognition with the YOLO model, evaluating accuracy, speed, and computational efficiency to compare the two for real-time intrusion detection
Spatial Vulnerability Towards Flooding and Mitigation of Three Coastal Cities: Charleston, South Carolina | Passaic City, New Jersey| Virginia Beach, Virginia
Get PDFThis meta-analysis investigates the impact of flooding on three coastal communities along the Atlantic Coast: Charleston, South Carolina; Passaic City, New Jersey; and Virginia Beach, Virginia. One goal of this paper is to determine the adequacy or inadequacy in the flood mitigation responses of these communities so that future actions can be taken to improve flood mitigation measures. Another goal is to consider whether the cities experience environmental injustice in terms of flooding mitigation. This paper draws from governmental documents, news reports, local experiences, updated storm management systems, and scholarly articles. The methodology used in this meta-analysis is as follows: Review the rules and regulations of the community with an eye on flood mitigation. Identify the flood risks and residential areas that are impacted within the community. Identify the community’s spatial vulnerability. Identify the actions that the community is taking for protection. Compare the actions of the community to global flood response. The findings of this meta-analysis are that around the world, certain measures are taken to combat flood vulnerabilities in at-risk areas. These measures could be explored to help further mitigate flooding and environmental injustice within the cities in this case study
Dr. Martin Luther King Jr. Display
Get PDFBibliography and photographs of a display of government documents from Minnesota State University, Mankato.https://cornerstone.lib.mnsu.edu/lib-services-govdoc-display-holidays/1019/thumbnail.jp
Contents: Volume 1
No full texthttps://cornerstone.lib.mnsu.edu/maps-mn-river-surveys-volume1/1001/thumbnail.jp
Plate 41: Rum River Sheet 4
No full texthttps://cornerstone.lib.mnsu.edu/maps-mn-river-surveys-volume1/1042/thumbnail.jp
Plate 47: St. Louis River Sheet 4
No full texthttps://cornerstone.lib.mnsu.edu/maps-mn-river-surveys-volume1/1048/thumbnail.jp
Plate 05: Big Fork River Sheet 2
No full texthttps://cornerstone.lib.mnsu.edu/maps-mn-river-surveys-volume2/1004/thumbnail.jp
Plate 09: Big Fork River Sheet 6
No full texthttps://cornerstone.lib.mnsu.edu/maps-mn-river-surveys-volume2/1008/thumbnail.jp
Plate 36: Little Fork River Sheet 5
No full texthttps://cornerstone.lib.mnsu.edu/maps-mn-river-surveys-volume2/1034/thumbnail.jp
Plate 44: Ottertail River Sheet 5
No full texthttps://cornerstone.lib.mnsu.edu/maps-mn-river-surveys-volume2/1041/thumbnail.jp