Peter Tawdross

Local Parameters Particle Swarm Optimization

Recently the particle swarm optimization (PSO) has been used in many engineering applications, which operate in dynamic environment and has proved its competitiveness over genetic algorithmin many natural number approaches. In the state of the art, it is assumed that all the particles have the same parameters, while in the real world; each individual has its own character, which means each particle has different parameters. In this paper, we study the feasibility and the behavior of local parameters for each particle in the PSO, and control the parameters by a simple algorithm. More advanced control algorithm can be applied to improve the search. Adjusting our PSO for different applications is easier as the swarm parameters are adjusted automatically for each particle. However, this modification of PSO can be applied for any type of PSO to improve it. As an example, we apply it to the hierarchical particle swarm optimization (HPSO). The results are obtained in static and dynamic environments. Local approach with a naive controller overcomes the other approaches in most of the cases.

Intrinsic Evolution of Predictable Behavior Evolvable Hardware in Dynamic Environment

Sensor electronics performance is susceptible to static and dynamic deviations. Even laser trimming still can¿t deal with all the deviations. Recently, analog reconfigurable electronics offers a solution to compensate these effects. The state of the art uses genetic algorithm (GA) to find an arbitrary topology to fulfill the given specifications, which can cause hardware with unpredictable behavior. In case of any environmental change, the state of the art starts the evolution from scratch. Considering the robustness of the reconfiguration approach, we used the particle swarm optimization (PSO) [13] as an alternative to GA for reconfiguration of programmable sensor electronics. In this paper, we extend our work to investigate the PSO methods for dynamic environment in which the hardware can track the environmental change without starting from scratch. We run the algorithm on a real hardware (intrinsic evolution). Our hardware was designed in such a way that its performance is predictable by employing standard circuit topologies.

Mixtrinsic Multi-Objective Reconfiguration of Evolvable Sensor Electronics

Recently, evolvable hardware has been developed with the objective to deal with sensor electronics problems such as static and dynamic deviations. However the industrial specifications and requirements are not considered in the hardware-learning loop during the intrinsic optimization. Indeed, it minimizes the error between the required output and the real output generated by a given test signal. In our work, we optimize the standard specifications of the hardware to obtain predictable behavior hardware. However, some of the industrial specifications need expensive equipments and some others are time consuming. In this paper we introduce a new approach, which simulates a set of the specifications that is hard to be measured due to the cost/time requirements, e.g., A0, Phi, etc. On the other hand, the set of specifications that is more sensitive to the instance deviations is measured intrinsically, e.g., offset, CMR, etc. We employ the programmable operational amplifier from L. Lakshmanan et al., (2005) as a case study. Our approach succeeds to optimize the amplifier to meet the industrial specifications at low cost setup.

Dynamic Reconfiguration Algorithm for Field Programmable Analog Scalable Device Array (FPADA) with Fixed Topology

The sensor electronics and mixed signal processing are sensitive to the system changes by aging, temperature distribution inside the chip, or additional factors of the external environment. These factors can’t effectively be considered in the design phase. For some hardware trimming is done to increase the accuracy of the circuits at a certain temperature, which increase the cost but still can’t deal with all the mentioned problems. In this paper, we present a soft computing dynamic reconfiguration algorithm for FPADA dynamically reconfigurable hardware (DRHW) for compensating the temperature distribution, aging, and other environment influences dynamically without changing the main topology of the device, where the topology is done by human or by the synthesis tools. The main advantage of our algorithm is that it keeps the hardware structure, and changes only the dimensions of the model to evolve it with the new environment in order to have a reliable DRHW with a predictable performing. Evolving this hardware is done by evolving the dimensions of parameters of the device by the standard measurement techniques. The objective of the evolution is to optimize the set of all the device parameters. In our experimental work, we developed a working environment for DRHW, and demonstrated it with an operational amplifier for compensating the effect of temperature distribution inside the amplifier, this amplifier is evolved for optimizing selection of relevant amplifier parameters.

Towards Generic On-the-Fly Reconfigurable Sensor Electronics for Embedded System- First Measurement Results of Reconfigurable Folded

The adaptation and robust sensing capabilities of living organisms remain envy to engineers. Several research efforts have been started to mimic these capabilities and exploit them in technical devices, and systems. Embedded systems for sensor applications, comprise of irreplaceable analog and mixed signal components. The considered electronics and sensors themselves are prone to numerous static and dynamic influences and mismatches. Precise design methodology, trimming/calibration is mandatory to restore the functionality. Recent block level granular approaches using field programmable analog array and the more recent approaches from evolutionary electronics providing transistor level granularity using field programmable transistor array offers considerable extensions. In our work, we started on a new medium level granular approach called field programmable medium granular mixed signal array (FPMA) providing basic building blocks of heterogeneous array of active and passive devices to build established circuit structure which are adaptive, fault-tolerant, bio-inspired, and dynamically reconfigurable i.e., trimmable. FPMA also supports rapid prototyping. Our design objective is to create cells of clear compatibility to that of industrial standards having predictable behavior and maintaining quality along with the incorporation of design knowledge. In this paper, measurement results of our first dynamic reconfigurable operational amplifier in an extrinsic fashion are presented. Specific generic configurable cells under the control of optimization techniques are considered. The aspired embedded system architecture will be illustrated and finally the summary of results will be furnished

Towards Organic Sensing System-First Measurement Result of Self-x Sensor Signal Amplifier

The adaptation and robust sensing capabilities of living organisms remains envy to engineers. Numerous research efforts have been started to mimic these capabilities of living beings such as self-configuration, self-diagnosis and self-healing etc., and exploit them in technical devices, systems, and appliances. Ubiquitous embedded systems comprises of indispensable analog and mixed-signal component for sensor and actuator interfacing. The regarded sensor electronics are vulnerable to numerous static and dynamic influences, mismatches and to substrate noise. Specific generic cells for sensor amplifiers and analog/mixed-signal arrays are regarded in our work, which can be configured and changed/repaired under the control of an optimisation technique based on swarm intelligence. The approach targets on yielding fast, flexible, adaptive HW/SW platform supporting rapid prototyping and maintaining quality. The first chip design of a reconfigurable sensor signal amplifier has been tested by extrinsically generated PSO based configurations. The embedded system architecture and the measured results will be presented and an outlook on intrinsic system design will be given.