Common electrical hazards that occur at home are usually associated with the usage of faulty home appliances and electrical distribution. Circuit breakers are normally used to protect overloading at residential homes. We envisage that by monitoring continuously the usage characteristics of electrical power at every power point in real time could be the best method for protection. Presently, wireless sensor network (WSN) has been used in home monitoring to optimally manage the ‘well-being’ of power distribution in home in order to minimize hazards caused by electrical faults. This technique incidentally helps controlling the tariff by maintaining the power usage optimally. It has also been found that not much study has yet been made towards the use of WSN for monitoring electrical power and the consequent hazards with a view to ensure the welfare of residents at home. Based on this idea, we have developed a real time WSN based Electrical Hazard monitoring system that also uses android mobile application environment. The android based smart home monitoring, christened as ABASH system the details of which we present in this paper, detects the threshold violation in electrical parameters and produces an appropriate alert signal for home owners to enable them to take proper remedial actions depending on the situation, much before the circuit breaker operates. The proposed system has been validated specifically for the Brunei Electric standard as a case study. The details are provided in the paper.

The selection of suitable material plays fundamental and vital role in product development as each material possesses individual characteristics that contribute many aspects to suit the particular application. A wrong selection of materials favours huge cost contribution as well as product failure. Hence among the various available materials the selection of a particular becomes difficult. A better methodology is more and more needed for the selection of material. Multi Attribute Decision Making (MADM) methods based on mathematical models provide a ranking to the different alternatives, there by the decision making become easier. This paper presents the selection of Magnesium alloy material to use in automotive wheel applications using MADM methods. The influence of weighting factors has also been discussed.

Independent Component Analysis is used for the automation and detection of brain artifacts. The Independent Component Analysis (ICA) here is used for the segmentation of artifact peaks in the signal. Then the Discrete Wavelet Transform is applied for multi-level transfer of signal data until the reception of significant result. We have extended our search and applied the Double Density Algorithm for the multi-level transfer. The results obtained were analyzed from the data set of EEG signals taken with a outsource reference. Since the method is parameter free implementations in clinical settings are imaginable.