ZigBee is the latest wireless technology which provides three lowest solutions, low data rate, low power consumption and low cost. One of the ultimate goals of ZigBee is to provide a energy saving solution. Low power consumption and power management are important characteristics of ZigBee. To explore the concept of energy saving, an integrated ZigBee automation system (ZAS) for lighting automation and power management is investigated. In this paper, the details of system design for ZAS are described including the system overview, deployment as well as the power management algorithms.

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.

A fully integrated dual-band (868/915MHz and 2.4GHz) low-noise amplifier is designed using 0.18m RFCMOS technology for ZigBee development. In both bands, achieved gains are better than 15dB and the resulting noise figures are better than 2.0dB. The input and the output reflections are measured to be better than -10dB 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 1.2V supply voltage with 10mA current dissipation.Department of Electronic and Information Engineerin

The application of localization in healthcare system is a crucial topic which helps to locate the position of patent or the elderly in case urgency happens. From this aspect, a wireless technology is adopted to provide an efficient localization monitoring system for patients or the elderly in indoor area. The location of patients can be obtained through the developed algorithm. Fuzzy C-Means clustering (FCM) is one of the applicable techniques to locate the position of patients. However, low accuracy of FCM is the main problem. For this reason, the revised FCM localization algorithm, Calibrated Fuzzy C-Means Clustering Algorithm (C-FCM) is proposed in this investigation based on received signal strength (RSS) in wearable device. The proposed algorithm is evaluated through experiment and it has a percentage improvement of 14% compared with FCM.

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.

Smart transportation is an application of intelligent system on transportation domain, expected to bring the society environmental and economic advantages. By combining with IoT techniques, the concept is being enhanced and raised to a system level. Numerous data are able to collect and effective analysis technique is needed. Here in this paper, we proposed a framework of employing IoT technique to construct a free time navigation system. The system aims at providing a real-time quantification of traffic conditions and suggests optimal route based on the information retrieved. The system can be basically separated into two major components: (i) the traffic condition estimation module and the (ii) real-time routing algorithm. In the first component, traffic conditions of roads will be estimated based the information collected from sensors installed on vehicles. Based on these location and speed information, the traffic condition can be quantified using a weighted kernel density estimation (WKDE) function. This function is a function of time and provides a real time insight of the overall traffic condition. By combining this information and the topological structure of the road network, a more accurate time consumption on each road can be estimated and hence enable a better routing.