Kiumi Akingbehin

Alternatives for short range low power wireless communications

The three leading contending standards for short-range low-power wireless communications (Bluetooth, Zigbee, Ultra-Wideband) are compared as to viability and practicality for implementation. The three standards all operate completely or partially in the largely unregulated ISM band with transmission ranges varying from a few meters to a maximum of around 15 meters. An ongoing case-study involves the development of a hybrid wireless automotive harness. The decision process leading to the choice of Bluetooth over the other two contenders is presented. Ongoing work with the wireless automotive harness includes a restructuring of the software to function as a layer under a controller area network (CAN). Computer models are also being developed to provide extensive studies of the performance, reliability, and security implications.

A hybrid architecture for programmable computing and evolutionary learning

Abstract The relation between the structure and the computational function of the brain must have a plastic quality in order for it to have developed through the Darwinian mechanism of variation and selection. It is likely that such structure-function plasticity plays a role in ontogenetic learning as well. Present-day digital computers, by contrast, are highly programmable. But this powerful property entails rigidly defined structure-function relations that are generally incompatible with self-organization through evolution. Hybrid systems could combine the advantages of both programmability and evolvability. The system described in this paper comprises a conventional machine that communicates with an evolution-amenable cellular automaton system. The latter learns to use its low-level parallelism through a “copy thy neighbor” evolutionary algorithm. We describe some simulation experiments with the system and consider how direct fabrication might be achieved using silicon as well as nonsilicon technologies.

On programming an adaptable network of molecular processing elements

To utilize the computational style and massive parallelism provided by molecular computing devices, conventional techniques which individually program molecules need to be augmented by techniques which collectively program a network of molecules. The author describes such an evolutionary programming technique. It consists of a training phase, in which the network is trained to recognize known patterns, and a performing phase, in which the network is presented with unknown patterns. A description is given of the results obtained when the technique is applied to a simulated network of computing devices. The computational tasks performed are those which have been traditionally difficult for conventional programming techniques.<<ETX>>

Bridging the gap between molecular electronics and biocomputing

Various biocomputing models for molecular electronic devices are examined. They are the feedforward connectionist network (artificial neural network), the enzymatic neuron, and a hybrid model that attempts to capture the programmability of silicon electronic devices and the adaptability of molecular electronic devices. Greater interaction between researchers in molecular electronics and in biocomputing is urged.<<ETX>>