No abstract

Field Programmable Gate Array devices (FPGAs) are used in many applications. FPGAs are subjected to faults especially in the space where they are subjected to radiation. Faults in FPGAs may be recoverable (transient) or non-recoverable (permanent). Recoverable faults can be resolved by reconfiguring the system, on the other hand non-recoverable faults, require the relocation of the logic to a non-faulty area. This paper proposes an adaptive fault classifier that can be used to differentiate fault types. The classification guides the system to use the suitable recovery strategy. Experimental results show the operation of the classifier and adaptation layers, the proposed classifier layer is smaller in area compared to previous work. The adaptation layer works satisfactory to cope with environment changes. Both of the layers work independently of the design on any other layer.

No abstract

No abstract

The rising number of home devices, as well as the differences in technology between them (smart and traditional), are the causes of the difficulty in balancing the use of energy in the home. Furthermore, increased demand for energy often leads to pressure on electricity networks and utilities, which leads to interruptions or disruptions of services caused by peak load. All these factors contribute to the adoption of energy management systems to control and reduce usage and automatically monitor the usage of home devices. When consumption is controlled, it is beneficial to customers and electricity companies when demand is reduced at peak times.This research will present a prototype for an energy management system to control and monitor the usage of devices in the home, and automatic control utilizing MATLAB simulation and implementation employing a Microcontroller to assess Demand Response.The suggested algorithm would manage consumption to reach the lowest usage possible to decrease the monthly bill, reduce peak load based on customer priority, and ensure that the consumption does not exceed the specified value. The algorithm's accuracy is demonstrated by several situations of consumption in the home, where the algorithm was able to lower consumption by up to 39.5% when compared to consumption without utilizing the suggested algorithm.