Wireless sensor networks are collections of autonomous devices with computational, sensing and wireless communication capabilities. Research in this area has been growing in the past few years given the wide range of applications that can benefit from such a technology. In this paper, the development of a highly modular and miniaturized wireless platform for sensor networks is described. The system incorporates a radio transceiver (in the 2.4 GHz ISM Band) with embedded protocol software to minimize power consumption and maximize data throughput. Additional input capability for sensor and actuator integration can be incorporated seamlessly due to the modular nature of the system. The total system is packaged in a modular 25 mm cubed form factor. A smaller, (10 mm cubed), prototype is currently under development. Ongoing development of highly miniaturized nodes is discussed.

This work contains the results of an initial study into the FPGA implementation of a spiking neural network. This work was undertaken as a task in a project that aims to design and develop a new kind of tangible collaborative autonomous agent. The project intends to exploit/investigate methods for engineering emergent collective behaviour in large societies of actual miniature agents that can learn and evolve. Such multi-agent systems could be used to detect and collectively repair faults in a variety of applications where it is difficult for humans to gain access, such as fluidic environments found in critical components of material/industrial systems. The initial achievement of implementation of a spiking neural network on a FPGA hardware platform and results of a robotic wall following task are discussed by comparison with software driven robots and simulations.

In this demonstration, a new sensor platform named DSYS25 is presented. The platform has a unique hardware design and runs a customized version of the TinyOS operating system. Transceiver hardware and packaging distinguish the D-Systems platform from other available designs.

The paper describes research that has been carried out in "extrovert-Gadgets", a research project funded in the context of EU IST/FET proactive initiative "Disappearing Computer". It presents a set of architectures for the composition of ubiquitous computing applications. The proposed architectures are part of GAS (Gadgetware Architectural Style), a generic architectural style, which can be used to describe everyday environments populated with computational artifacts. The overall innovation of the GAS approach lies in viewing the process where people configure and use complex collections of interacting eGadgets, as having much in common with the process where system builders design software systems out of components. This approach regards the everyday environment as being populated with tens even hundreds of artifacts, which people (who are always in control) associate in ad-hoc and dynamic ways.

Parametric, model based, spectral estimation techniques can offer increased frequency resolution over conventional short-term fast Fourier transform methods, overcoming limitations caused by the windowing of sampled, time domain, input data. However, parametric techniques are significantly more computationally demanding than the Fourier based methods and require a wider range of arithmetic functionality; for example, operations such as division and square-root are often necessary. These arithmetic processes exhibit communication bottleneck and their hardware implementation can be inefficient when used in conjunction with multipliers. A programmable, bit-serial, multiplier/divider, which overcomes the bottleneck problems by using a data interleaving scheme, is introduced. This interleaved processor is used to show how the parametric modified covariance spectral estimator can be efficiently routed on a field programmable gate array for real-time applications.