Adaptive threshold triggering of GPS for long-term tracking in WSN

Long-term tracking is an expanding field with applications in logistics, ecology and wearable computing. The main challenge for longevity of tracking applications is the high energy consumption of GPS, which has been addressed by using low power sensors to trigger GPS activation upon detecting events of interest. While triggering can reduce power consumption, static thresholds can underperform in the longterm as context changes. This paper presents an auto-covariance based triggering algorithm that adapts trigger thresholds based on the incoming data and is effective with limited prior knowledge. We test the algorithm on empirical data from flying foxes and show that it outperforms static thresholding and existing adaptive algorithms from the literature

Obstacle avoidance with LGMD neuron: towards a neuromorphic UAV implementation

We present a neuromorphic adaptation of a spiking neural network model of the locust Lobula Giant Movement Detector (LGMD), which detects objects increasing in size in the field of vision (looming) and can be used to facilitate obstacle avoidance in robotic applications. Our model is constrained by the parameters of a mixed signal analog-digital neuromorphic device, developed by our group, and is driven by the output of a neuromorphic vision sensor. We demonstrate the performance of the model and how it may be used for obstacle avoidance on an unmanned areal vehicle (UAV)

Hybrid ensemble learning for triggering of GPS in long-term tracking applications

Long-term tracking is an expanding field with applications in logistics, ecology and wearable computing. The main challenge for longevity of tracking applications is the high energy consumption of GPS, which has been addressed by using low power sensors to trigger GPS activation upon detecting events of interest. While triggering can reduce power consumption, static thresholds can under-perform in the long-term as context changes. This paper presents a comparison between a dynamic adaptive threshold algorithm and off-line machine learning techniques. We test the algorithms on empirical data from flying foxes to show that off-line machine learning techniques improve the hit rate when compared to the dynamic adaptive threshold algorithm. We then combine the models into an on/off-line hybrid ensemble learning model to improve both hit rate and false alarm rate when compared to the dynamic adaptive threshold algorithm. The hybrid model also has lower false alarm rate and precision when compared to the stand alone machine learning algorithms. We also test the off-line machine learning techniques on unknown data to show that the hit and false alarm rates vary from node to node. This indicates that more consistent performance might be found through the development of on-line machine learning algorithms

REACT-R and unity integration

This paper presents REACT-R which is a modification of an existing cognitive architecture, ACT-R, to incorporate robot embodiment. Robot embodiment is facilitated by situating the cognitive architecture within the robot and allowing it to interact with the environment through its actuators and sensors. The REACT-R module offers flexibility by using a UDP connection to integrate with the Robot Operating System (ROS) allowing REACT-R to be used in simulations or on physical models. We have successfully integrated REACT-R with an interactive 3D Game Engine to autonomously control a simulated quadrotor flying vehicle. The cognitive model also contains varying levels of autonomy, providing a human pilot with the option of controlling different aspects or levels of robot operation

SCENH-rapport cultuurhistorie 3

Het plangebied Kerksteeg 6 is gelegen binnen het beschermd stadsgezicht van Medemblik. Het gebied bezit een hoge archeologische waarde en een zeer hoge historisch geografische waarde, aangeduid op de Cultuurhistorische Waardenkaart Noord-Holland (CHW-kaart). Het aangrenzende terrein van de Bonifatiuskerk heeft de status van beschermd archeologisch monument