Genetic algorithms (GAs) have been introduced into site layout planning as reported in a number of studies. In these studies, the objective functions were defined so as to employ the GAs in searching for the optimal site layout. However, few studies have been carried out to investigate the actual closeness of relationships between site facilities; it is these relationships that ultimately govern the site layout. This study has determined that the underlying factors of site layout planning for medium-size projects include work flow, personnel flow, safety and environment, and personal preferences. By finding the weightings on these factors and the corresponding closeness indices between each facility, a closeness relationship has been deduced. Two contemporary mathematical approaches -fuzzy logic theory and an entropy measure -were adopted in finding these results in order to minimize the uncertainty and vagueness of the collected data and improve the quality of the information. GAs were then applied to searching for the optimal site layout in a medium-size government project using the GeneHunter software. The objective function involved minimizing the total travel distance. An optimal layout was obtained within a short time. This reveals that the application of GA to site layout planning is highly promising and efficient.

The widely adoption of Electric Vehicle (EV) has been identified as a major challenge for future development of smart grids. The ever increasing electric vehicle charging further increases the energy demand. This paper reports the development of an Advanced Metering Infrastructure (AMI) as an effective tool to reshape the load profile of EV charging by adopting appropriate demand side management strategy. This paper presents a total solution for EV charging service platform (EVAMI) based on power line and internet communication. It must be stressed that the development of Third Party Customer Service Platform in this investigation facilitates a single bill to be issued to EV owners. Hence, EV owners understand their energy usage and thus may perform energy saving activity efficiently. Experiment and evaluation of the proposed system show that the throughput achieved is about 5 Mbps at 10 ms end to end delay in Power line Communication. By introducing two dimensional dynamic pricing and charging schedule, the proposed EVAMI successfully reduces 36% peak consumption and increases the “off peak” consumption by 54%. Therefore the EVAMI does not only reduce the peak consumption but also relocates the energy demand effectively

A fully integrated dual-band (868/915 MHz and 2.4 GHz) low-noise amplifier is designed using 0.18 mm RFCMOS technology for ZigBee development. In both bands, achieved gains are better than 15 dB and the resulting noise figures are better than 2.0 dB. The input and the output reflections are measured to be better than 210 dB in both bands. By tuning varactors in input and output LC tanks, frequency drifts due to unexpected parasitics and process variations are easily compensated. The amplifier works at 1.2 V supply voltage with 10 mA current dissipation.Introduction: The development of IEEE 802.15.4 ZigBee, operating in the 868/915 MHz (low band) and 2.4 GHz (high band) ISM bands, has been studied extensively recently as a result of its wide range of applications. One of the mostly adopted early approaches to achieve dual-band for other applications was to implement two receiver chains and use a switch to select one of the frequencies [1]. Such an approach generally degrades the noise figure (NF) because of the insertion loss of the switch. The second approach is to select one set of tuning networks by using a switch [2]. The drawbacks are the signal loss due to the switches in the signal path as well as the area hungry topology for the two sets of tuning networks. A third approach is to tune the input and the output to different frequencies by using complex multiband filters. However multiband filters are difficult to implement on chip [3].The present development is based on a multiband theory [4] to cater for dual-band characteristics. In the present ZigBee investigation, the receiver receives two signal bands simultaneously without using switches, hence resulting in lower loss. Our former work on 0.35 mm simulation [5] for RFCMOS showed that the dual-band topology shown in Fig. 1 is feasible and the analysis in [5] is used in the present work and hence is not repeated here. In the present 0.18 mm RFCMOS development, a frequency calibration method is also developed and realised by using varactors to compensate the frequency drifts due to the random parasitics and process variation.

Each year, some 30 percent of global deaths are caused by cardiovascular diseases. This figure is worsening due to both the increasing elderly population and severe shortages of medical personnel. The development of a cardiovascular diseases classifier (CDC) for auto-diagnosis will help address solve the problem. Former CDCs did not achieve quick evaluation of cardiovascular diseases. In this letter, a new CDC to achieve speedy detection is investigated. This investigation incorporates the analytic hierarchy process (AHP)-based multiple criteria decision analysis (MCDA) to develop feature vectors using a Support Vector Machine. The MCDA facilitates the efficient assignment of appropriate weightings to potential patients, thus scaling down the number of features. Since the new CDC will only adopt the most meaningful features for discrimination between healthy persons versus cardiovascular disease patients, a speedy detection of cardiovascular diseases has been successfully implemented.

A high fuel efficiency management scheme for plug-in hybrid electric vehicles (PHEVs) has been developed. In order to achieve fuel consumption reduction, an adaptive genetic algorithm scheme has been designed to adaptively manage the energy resource usage. The objective function of the genetic algorithm is implemented by designing a fuzzy logic controller which closely monitors and resembles the driving conditions and environment of PHEVs, thus trading off between petrol versus electricity for optimal driving efficiency. Comparison between calculated results and publicized data shows that the achieved efficiency of the fuzzified genetic algorithm is better by 10% than existing schemes. The developed scheme, if fully adopted, would help reduce over 600 tons of CO2 emissions worldwide every day.