scholarly journals Integrating UAV Technology in an Ecological Monitoring System for Community Wildlife Management Areas in Tanzania

2019 ◽  
Vol 11 (21) ◽  
pp. 6116 ◽  
Author(s):  
Lazaro J. Mangewa ◽  
Patrick A. Ndakidemi ◽  
Linus K. Munishi
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Wildlife Management ◽  
Wildlife Conservation ◽  
Ecological Monitoring ◽  
Monitoring Systems ◽  
Data Generation ◽  
Time Data ◽  
Heterogeneous Habitats ◽  
Wildlife Management Areas

Unmanned aerial vehicles (UAV) have recently emerged as a new remote sensing aerial platform, and they are seemingly advancing real-time data generation. Nonetheless, considerable uncertainties remain in the extent to which wildlife managers can integrate UAVs into ecological monitoring systems for wildlife and their habitats. In this review, we discuss the recent progress and gaps in UAV use in wildlife conservation and management. The review notes that there is scanty information on UAV use in ecological monitoring of medium-to-large mammals found in groups in heterogeneous habitats. We also explore the need and extent to which the technology can be integrated into ecological monitoring systems for mammals in heterogeneous habitats and in topographically-challenging community wildlife-management areas, as a complementary platform to the traditional techniques. Based on its ability to provide high-resolution images in real-time, further experiments on its wider use in the ecological monitoring of wildlife on a spatiotemporal scale are important. The experimentation outputs will make the UAV a very reliable remote sensing platform that addresses the challenges facing conventional techniques.

scholarly journals Politics in African Wildlife Conservation: Wildlife Management Areas in Tanzania

2017 ◽  
Vol 3 (1) ◽  
pp. 62-65
Author(s):  
Juan José Del Valle Coello
Keyword(s):  
Wildlife Management ◽  
Wildlife Conservation ◽  
Conservation Strategies ◽  
Community Based ◽  
Community Based Conservation ◽  
Adoption And Implementation ◽  
The Social ◽  
Promotional Materials ◽  
Degree Of Control ◽  
Wildlife Management Areas

Starting in the 1980’s, an increasing number of international actors have advocated for a change in wildlife and resource conservation strategies, arguing for practices allowing for greater local management in a model known as “community-based conservation.” Focusing on Tanzania, a country known for its expansive wildlife and game reserves, this investigation examines the adoption and implementation of legislation allowing for locally-administered Wildlife Management Areas (WMA’s). This paper first documents the processes motivating the introduction of WMA legislation in Tanzania, then details the legislation’s contents themselves and attempts to evaluate the social and political results as best it can, using a combination of sources including previously conducted research, promotional materials, and NGO publications.Major aspects of legislation include the following: villages themselves choose to enter into WMA agreements with investors; investors collect the revenue and deliver it to the federal government, which in turn distributes it to villages and wildlife conservation programs; and village residents themselves determine how to allocate the revenue they receive. Results have been mixed; while many villages have benefitted from income received from participation in wildlife management, there have also been instances of coercion into participating, disputes between villages regarding WMA practices, and there has been a general lack of transparency in income collection and distribution. Furthermore, it is unclear to what extent recent legislation has actually given a greater degree of control to local government.

scholarly journals Integrating real-time data analysis into automatic tracking of social insects

2021 ◽  
Vol 8 (3) ◽  
Author(s):  
Alessio Sclocco ◽  
Shirlyn Jia Yun Ong ◽  
Sai Yan Pyay Aung ◽  
Serafino Teseo
Keyword(s):  
Data Analysis ◽  
Real Time ◽  
Recognition Algorithm ◽  
Video Tracking ◽  
Human Observer ◽  
Data Generation ◽  
Time Data ◽  
Real Time Data ◽  
Low Efficiency ◽  
Matrix Codes

Automatic video tracking has become a standard tool for investigating the social behaviour of insects. The recent integration of computer vision in tracking technologies will probably lead to fully automated behavioural pattern classification within the next few years. However, many current systems rely on offline data analysis and use computationally expensive techniques to track pre-recorded videos. To address this gap, we developed BACH (Behaviour Analysis maCHine), a software that performs video tracking of insect groups in real time. BACH uses object recognition via convolutional neural networks and identifies individually tagged insects via an existing matrix code recognition algorithm. We compared the tracking performances of BACH and a human observer (HO) across a series of short videos of ants moving in a two-dimensional arena. We found that BACH detected ant shapes only slightly worse than the HO. However, its matrix code-mediated identification of individual ants only attained human-comparable levels when ants moved relatively slowly, and fell when ants walked relatively fast. This happened because BACH had a relatively low efficiency in detecting matrix codes in blurry images of ants walking at high speeds. BACH needs to undergo hardware and software adjustments to overcome its present limits. Nevertheless, our study emphasizes the possibility of, and the need for, further integrating real-time data analysis into the study of animal behaviour. This will accelerate data generation, visualization and sharing, opening possibilities for conducting fully remote collaborative experiments.

scholarly journals A traffic model aware of real time data

2016 ◽  
Vol 26 (03) ◽  
pp. 445-467 ◽  
Author(s):  
Rinaldo M. Colombo ◽  
Francesca Marcellini
Keyword(s):  
Real Time ◽  
Traffic Monitoring ◽  
Traffic Model ◽  
Monitoring Systems ◽  
Well Posedness ◽  
Time Data ◽  
Numerical Integrations ◽  
Real Time Data ◽  
Ill Posed ◽  
Gps Devices

Nowadays, traffic monitoring systems have access to real time data, e.g. through GPS devices. We propose a new traffic model able to take into account these data and, hence, able to describe the effects of unpredictable accidents. The well-posedness of this model is proved and numerical integrations show qualitative features of the resulting solutions. As a further motivation for the use of real time data, we show that the inverse problem for the Lighthill–Whitham and Richards (LWR) model is ill-posed.

Edge Computing and IoT Technologies for Medical Applications

2021 ◽  
pp. 270-287
Author(s):  
Kumar R. ◽  
Ayshwarya B. ◽  
Muruganantham A. ◽  
Velmurugan R.
Keyword(s):  
Real Time ◽  
Continuous Glucose Monitoring ◽  
Health Monitoring ◽  
System Architecture ◽  
Glucose Monitoring ◽  
Monitoring Systems ◽  
Time Data ◽  
Health Monitoring Systems ◽  
Sugar Levels ◽  
High Level

Dynamic observation of blood sugar levels is essential for patients diagnosed with diabetes mellitus in order to control the glycaemia. Inevitably, they must accomplish a capillary test three times per day and laboratory test once or twice per month. These regular methods make patients uncomfortable because patients have to prick their finger every time in order to measure the glucose concentration. Modern health monitoring systems rely on IoT. However, the number of advanced IoT-based continuous glucose monitoring systems is small and has several limitations. Here the authors study feasibility of invasive and continuous glucose monitoring system utilizing IoT-based approach. They designed an IoT-based system architecture from a sensor device to a back-end system for presenting real-time data in various forms to end-users. The results show that the system is able to achieve continuous glucose monitoring remotely in real time, and a high level of energy efficiency can be achieved by applying the nRF compound, power management, and energy harvesting unit altogether in the sensor units.

scholarly journals Energy Efficient and Real-Time Remote Sensing in AI-Powered Drone

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Bongjae Kim ◽  
Jinman Jung ◽  
Hong Min ◽  
Junyoung Heo
Keyword(s):  
Remote Sensing ◽  
Energy Consumption ◽  
Data Analysis ◽  
Real Time ◽  
Energy Efficient ◽  
Urban Areas ◽  
Computation Offloading ◽  
Time Constraints ◽  
Time Data ◽  
Real Time Data

Remote sensing using drones has the advantage of being able to quickly monitor large areas such as rivers, oceans, mountains, and urban areas. In the case of applications dealing with large sensing data, it is not possible to send data from a drone to the server online, so it must be copied to the server offline after the end of the flight. However, online transmission is essential for applications that require real-time data analysis. The existing computation offloading scheme enables online transmission by processing large amounts of data in a drone and transferring it to the server, but without consideration for real-time constraints. We propose a novel computation offloading scheme which considers real-time constraints while minimizing the energy consumption of drones. Experimental results showed that the proposed scheme satisfied real-time constraints compared to the existing computation offloading scheme. Furthermore, the proposed technique showed that real-time constraints were satisfied even in situations where delays occurred on the server due to the processing of requests from multiple drones.

scholarly journals A Real-Time Tree Crown Detection Approach for Large-Scale Remote Sensing Images on FPGAs

2019 ◽  
Vol 11 (9) ◽  
pp. 1025 ◽  
Author(s):  
Weijia Li ◽  
Conghui He ◽  
Haohuan Fu ◽  
Juepeng Zheng ◽  
Runmin Dong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Large Scale ◽  
Satellite Image ◽  
Detection Accuracy ◽  
Tree Crown ◽  
Remote Sensing Images ◽  
Time Data ◽  
Original Algorithm ◽  
Detection Approach

The on-board real-time tree crown detection from high-resolution remote sensing images is beneficial for avoiding the delay between data acquisition and processing, reducing the quantity of data transmission from the satellite to the ground, monitoring the growing condition of individual trees, and discovering the damage of trees as early as possible, etc. Existing high performance platform based tree crown detection studies either focus on processing images in a small size or suffer from high power consumption or slow processing speed. In this paper, we propose the first FPGA-based real-time tree crown detection approach for large-scale satellite images. A pipelined-friendly and resource-economic tree crown detection algorithm (PF-TCD) is designed through reconstructing and modifying the workflow of the original algorithm into three computational kernels on FPGAs. Compared with the well-optimized software implementation of the original algorithm on an Intel 12-core CPU, our proposed PF-TCD obtains the speedup of 18.75 times for a satellite image with a size of 12,188 × 12,576 pixels without reducing the detection accuracy. The image processing time for the large-scale remote sensing image is only 0.33 s, which satisfies the requirements of the on-board real-time data processing on satellites.

scholarly journals Portability of semantic and spatial–temporal machine learning methods to analyse social media for near-real-time disaster monitoring

2021 ◽  
Author(s):  
Clemens Havas ◽  
Bernd Resch
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Social Media ◽  
Emergency Management ◽  
Natural Disasters ◽  
Real Time ◽  
Monitoring Systems ◽  
Management Service ◽  
Machine Learning Methods ◽  
Spatiotemporal Information

AbstractUp-to-date information about an emergency is crucial for effective disaster management. However, severe restrictions impede the creation of spatiotemporal information by current remote sensing-based monitoring systems, especially at the beginning of a disaster. Multiple publications have shown promising results in complementing monitoring systems through spatiotemporal information extracted from social media data. However, various monitoring system criteria, such as near-real-time capabilities or applicability for different disaster types and use cases, have not yet been addressed. This paper presents an improved version of a recently proposed methodology to identify disaster-impacted areas (hot spots and cold spots) by combining semantic and geospatial machine learning methods. The process of identifying impacted areas is automated using semi-supervised topic models for various kinds of natural disasters. We validated the portability of our approach through experiments with multiple natural disasters and disaster types with differing characteristics, whereby one use case served to prove the near-real-time capability of our approach. We demonstrated the validity of the produced information by comparing the results with official authority datasets provided by the United States Geological Survey and the National Hurricane Centre. The validation shows that our approach produces reliable results that match the official authority datasets. Furthermore, the analysis result values are shown and compared to the outputs of the remote sensing-based Copernicus Emergency Management Service. The information derived from different sources can thus be considered to reliably detect disaster-impacted areas that were not detected by the Copernicus Emergency Management Service, particularly in densely populated cities.

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