Some limnological features of the northern shore areas of Volcano Island, Lake Taal

Abstract only.Physico-chemical and biological features of the northern shore areas of Volcano Island, Lake Taal observed at monthly intervals from four stations during the period 1994 and 1996 indicate varied microhabitats inhabited by a variety of plants and animals. Physico-chemical characteristics of surface waters were: temperature, 28-35°C; dissolved oxygen content, 3.5-6.2 ppm; pH, 7.5-8.9; salinity, 0-24 ppt; and conductivity, 1.6-4.3 S cm-1. Substratum types were mainly sandy with pebbles or rocks or sandy-muddy. Characteristic submerged plants were the eelgrass Vallisneria gigantea and filamentous green algae. In the eelgrass region, atyid shrimps, mostly Ciridina gracilirostris, commonly occur. Snails such as Melanoides costellaris and Terebia granifera were the most abundant benthic animals collected. Other invertebrates identified from core samples were Corbicula manilensis, annelids, crustaceans and chironomid larvae

The lamplighter group L(h)n

This paper discusses a finite group associated to a machine called a lamplighter, having n lamps with h settings arranged in a circle around a single lighter. The properties of the group are discussed. Criteria for determining whether group elements are conjugates, commutators or in the center are established

Design of a Breach Detection System for Social Distancing

The pandemic caused by the 2019 novel coronavirus introduced essential health protocols for everyone\u27s safety. One of which is maintaining a social distance of at least 1 meter as per the guideline set by World Health Organization (WHO). Currently, most spaces were designed prior to the implementation of the social/physical distancing protocol. This project aims to design and develop a detection system utilizing closed-circuit television cameras, to identify spaces where there is a possible breach in the social distancing protocol. The system will generate discrete data to be queried for tabulation, and analysis. The system will also generate a breach map, which indicates the area in the CCTV footage where increasing breaches occur and are marked in increasing color intensity. The system utilized the YOLO V3 object detection algorithm in identifying an object to be human. The system utilized perspective transformation and Euclidean distance estimation in approximating distance for the social distancing protocol. In summary, the human detection accuracy of the system is ≃ 91%, processing at a rate of 30 frames per second in real-time

Multiple Edge Computing Devices with Computer Vision for Social Distancing

Coronavirus disease, widely known as COVID-19 is an infectious disease caused by the SARS-CoV-2 virus. Once infected, a person can spread the virus through their nose or mouth in small particles when they cough, sneeze, speak, or breathe. According to the World Health Organization (WHO), one way to be protected from the risk of virus infection is to stay at least 1 meter apart from others while wearing a properly filtered mask. The study aims to design and develop a multiple edge computing system with computer vision capabilities to monitor the adherence of social distancing in multiple locations and in real time. An edge computing device uses a camera to process a stream of images. Graphical Processing Unit (GPU) was utilized for faster inference processing to detect people. The person\u27s location will undergo transformation to get a 2D perspective. Then, a distance calculation algorithm will be imposed to each pair of persons detected to detect breach of social distancing protocol. For every breach detected, location coordinates will be sent to the host database for visualization and monitoring. The use of multiple edge computing devices for computer vision application was compared to the IP camera system in monitoring multiple locations. It is found that utilization of multiple edge computing devices has significant advantages in terms of power consumption, data acquisition, image processing and inference, and setup cost

Design of a Breach Detection System for Social Distancing

The pandemic caused by the 2019 novel coronavirus introduced essential health protocols for everyone\u27s safety. One of which is maintaining a social distance of at least 1 meter as per the guideline set by World Health Organization (WHO). Currently, most spaces were designed prior to the implementation of the social/physical distancing protocol. This project aims to design and develop a detection system utilizing closed-circuit television cameras, to identify spaces where there is a possible breach in the social distancing protocol. The system will generate discrete data to be queried for tabulation, and analysis. The system will also generate a breach map, which indicates the area in the CCTV footage where increasing breaches occur and are marked in increasing color intensity. The system utilized the YOLO V3 object detection algorithm in identifying an object to be human. The system utilized perspective transformation and Euclidean distance estimation in approximating distance for the social distancing protocol. In summary, the human detection accuracy of the system is ≃ 91%, processing at a rate of 30 frames per second in real-time