Localizing wild chimpanzees with passive acoustics

Localizing wildlife contributes in multiple ways to species conservation. Data on animal locations can reveal elements of social behavior, habitat use, population dynamics, and be useful in calculating population density. Acoustic localization systems (ALS) are a non-invasive method widely used in the marine sciences but not well established and rarely employed for terrestrial species. We deployed an acoustic array in a mountainous environment with heterogeneous vegetation, comprised of four custom-built GPS synchronized acoustic sensors at about 500 m intervals in Issa Valley, western Tanzania, covering an area of nearly 2 km2. Our goal was to assess the precision and error of the estimated locations by conducting playback tests, but also by comparing the estimated locations of wild chimpanzee calls with their true locations obtained in parallel during follows of individual chimpanzees. We assessed the factors influencing localization error, such as wind speed and temperature, which fluctuate during the day and are known to affect sound transmission. We localized 282 playback sounds and found that the mean localization error was 27 ± 21.8 m. Localization was less prone to error and more precise during early mornings (6:30 h) compared to other periods. We further localized 22 wild chimpanzee loud calls within 52 m of the location of a researcher closely following the calling individuals. We demonstrate that acoustic localization is a powerful tool for chimpanzee monitoring, with multiple behavioral and conservation applications. Its applicability in studying social dynamics and revealing density estimation among many others, especially but not exclusively for loud calling species, provides an efficient way of monitoring populations and informing conservation plans to mediate species loss

Spatial Sensor Network Based Target Tracking By Classification

The wide use of sensor networks in the day to day communication in recent trends made tracking a significant feature in monitoring systems. The automated systems capable of detection and tracking of targets is a desirable application in many fields. Firstly, deploy a sensor network with appropriate space between sensors and then introduce targets into the network. As the sensors detect the targets, each sensor communicates with neighborhood sensor nodes and one of those sensors are elected as cell-head which will calculate the position of target from the data and transmit that to sink. This process is repeated iteratively to track the moving target. Feature extraction methods and classification techniques have been studied to classify targets by their type. For the challenging task of Multi-target tracking, the methods of sequential Bayesian filtering and Sequential Monte Carlo-Particle Hypothesis Density filters are sought. Accurate algorithms have been simulated for Localization and tracking of target using the data of sensor strengths which are collaboratively communicated among the sensors. Direction of moving target inside a cell was estimated. Algorithm for Hierarchical multi-hop communication model was established

GUARDIANS final report

Emergencies in industrial warehouses are a major concern for firefghters. The large dimensions together with the development of dense smoke that drastically reduces visibility, represent major challenges. The Guardians robot swarm is designed to assist fire fighters in searching a large warehouse. In this report we discuss the technology developed for a swarm of robots searching and assisting fire fighters. We explain the swarming algorithms which provide the functionality by which the robots react to and follow humans while no communication is required. Next we discuss the wireless communication system, which is a so-called mobile ad-hoc network. The communication network provides also one of the means to locate the robots and humans. Thus the robot swarm is able to locate itself and provide guidance information to the humans. Together with the re ghters we explored how the robot swarm should feed information back to the human fire fighter. We have designed and experimented with interfaces for presenting swarm based information to human beings