In this paper, we propose a glove-based Arabic sign language recognition system using a novel technique for sequential data classification. We compile a sensor-based dataset of 40 sentences using an 80-word lexicon. In the dataset, hand movements are captured using two DG5-VHand data gloves. Data labeling is performed using a camera to synchronize hand movements with their corresponding sign language words. Low-complexity preprocessing and feature extraction techniques are applied to capture and emphasize the temporal dependence of the data. Subsequently, a Modified k-Nearest Neighbor (MKNN) approach is used for classification. The proposed MKNN makes use of the context of feature vectors for the purpose of accurate classification. The proposed solution achieved a sentence recognition rate of 98.9%. The results are compared against an existing vision-based approach that uses the same set of sentences. The proposed solution is superior in terms of classification rates while eliminating restrictions of vision-based systems.

As the smart grid emerges from concept to implementation, power utilities start to optimize power consumed by Heating, Ventilation and Air-Conditioning Systems (HVACs). Engineers started to design controllers that better manage the power consumption of the HVAC systems. HVACs consume more than 50% of generated power in many countries that have hot and/or cold weather. Such huge power loads may exceed the generated power. Due to this large power demands, some utilities have to resort to rationalize the available electricity to its consumers randomly. This paper presents a fuzzy logic based system that monitors and controls residential HVACs units, where demand exceeds the supplied electrical power. The proposed system reads the rooms temperature and turns ON/OFF the units alternatively to maintain the cooling/heating based on the available supplied power; i.e. shedding the load without compromising the residents comfort. The proposed fuzzy logic rule-based system was designed, simulated and tested.

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