Non Parametric Distributed Inference in Sensor Networks Using Box Particles Messages

This paper deals with the problem of inference in distributed systems where the probability model is stored in a distributed fashion. Graphical models provide powerful tools for modeling this kind of problems. Inspired by the box particle filter which combines interval analysis with particle filtering to solve temporal inference problems, this paper introduces a belief propagation-like message-passing algorithm that uses bounded error methods to solve the inference problem defined on an arbitrary graphical model. We show the theoretic derivation of the novel algorithm and we test its performance on the problem of calibration in wireless sensor networks. That is the positioning of a number of randomly deployed sensors, according to some reference defined by a set of anchor nodes for which the positions are known a priori. The new algorithm, while achieving a better or similar performance, offers impressive reduction of the information circulating in the network and the needed computation times

Controlling a remotely located Robot using Hand Gestures in real time: A DSP implementation

Telepresence is a necessity for present time as we can't reach everywhere and also it is useful in saving human life at dangerous places. A robot, which could be controlled from a distant location, can solve these problems. This could be via communication waves or networking methods. Also controlling should be in real time and smooth so that it can actuate on every minor signal in an effective way. This paper discusses a method to control a robot over the network from a distant location. The robot was controlled by hand gestures which were captured by the live camera. A DSP board TMS320DM642EVM was used to implement image pre-processing and fastening the whole system. PCA was used for gesture classification and robot actuation was done according to predefined procedures. Classification information was sent over the network in the experiment. This method is robust and could be used to control any kind of robot over distance

Nano-scale reservoir computing

This work describes preliminary steps towards nano-scale reservoir computing using quantum dots. Our research has focused on the development of an accumulator-based sensing system that reacts to changes in the environment, as well as the development of a software simulation. The investigated systems generate nonlinear responses to inputs that make them suitable for a physical implementation of a neural network. This development will enable miniaturisation of the neurons to the molecular level, leading to a range of applications including monitoring of changes in materials or structures. The system is based around the optical properties of quantum dots. The paper will report on experimental work on systems using Cadmium Selenide (CdSe) quantum dots and on the various methods to render the systems sensitive to pH, redox potential or specific ion concentration. Once the quantum dot-based systems are rendered sensitive to these triggers they can provide a distributed array that can monitor and transmit information on changes within the material.Comment: 8 pages, 9 figures, accepted for publication in Nano Communication Networks, http://www.journals.elsevier.com/nano-communication-networks/. An earlier version was presented at the 3rd IEEE International Workshop on Molecular and Nanoscale Communications (IEEE MoNaCom 2013