Hao Ran Chi

Interference-Mitigated ZigBee-Based Advanced Metering Infrastructure

An interference-mitigated ZigBee-based advanced metering infrastructure (AMI) solution, namely IMM2ZM, has been developed for high-traffics smart metering (SM). The IMM2ZM incorporates multiradios multichannels network architecture and features an interference mitigation design by using multiobjective optimization. To evaluate the performance of the network due to interference, the channel-swapping time (Tcs) has been investigated. Analysis shows that when the sensitivity (PRχ) is less than -12 dBm, Tcs increases tremendously. Evaluation shows that there are significant improvements in the performance of the application-layer transmission rate (σ) and the average delay (D). The improvement figures are σ > ~300% and D > 70% in a 10-floor building, σ > ~280 % and D > 65% in a 20-floor building, and σ > ~270% and D > 56% in a 30-floor building. Further analysis reveals that IMM2ZM results in typically less than 0.43 s delay for a 30-floor building under interference. This performance fulfills the latency requirement of less than 0.5 s for SMs in the USA (Magazine of Department of Energy Communications, USA, 2010). The IMM2ZM provides a high-traffics interference-mitigated ZigBee AMI solution.

A High Fuel Consumption Efficiency Management Scheme for PHEVs Using an Adaptive Genetic Algorithm

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.

An Accurate ECG-Based Transportation Safety Drowsiness Detection Scheme

Many traffic injuries and deaths are caused by the drowsiness of drivers during driving. Existing drowsiness detection schemes are not accurate due to various reasons. To resolve this problem, an accurate driver drowsiness classifier (DDC) has been developed using an electrocardiogram genetic algorithm-based support vector machine (ECG GA-SVM). In existing studies, a cross correlation kernel and a convolution kernel have both been applied for performing the classification. The DDC is designed by a Mercer kernel KDDC formed by commuting the cross correlation kernel Kxcorr,ij and the convolution kernel Kconv,ij. Kxcorr,ij, and captures the symmetric information among ECG signals from different classes, while Kconv,ij captures the antisymmetric information among ECG signals from the same class. The final KDDC (a precomputed kernel) is obtained by a genetic mutation using a multiobjective genetic algorithm. This renders an optimal KDDC that confidently serves as the full descriptor of the drowsiness. The performance of KDDC is compared with the most prevailing kernels. The obtained DDC yields an overall accuracy of 97.01%, sensitivity of 97.16%, and specificity of 96.86%. The analysis reveals that the accuracy of KDDC is better than those of both Kxcorr,ij and Kconv,ij by more than 11%, and typical kernels including linear, quadratic, third order polynomial, and Gaussian radial basis function by 17-63%, respectively. Comparing with related works using the image-based method and the biometric signal-based method, KDDC improves the accuracy by 48.4-87.2%. Testing results showed that KDDC has a less than 1% deviation from simulated results. Also, the average delay of DDC was bounded by 0.55 ms. This renders the real time implementation. Thus, the developed ECG GA-SVM provides an accurate and instantaneous warning to the drivers before they fall into sleep. As a result this ensures the public transport safety.

ZigBee based wireless sensor network in smart metering

A new network applies on high traffic ZigBee based wireless network. The new network incorporates multi-radio multi-channel technology and improves the efficiency of data transmission. The latency performance of the proposed network is analyzed by OPNET.

Electrocardiogram based classifier for driver drowsiness detection

Driver drowsiness may cause traffic injuries and death. In literature, various methods, for instance, image-based, vehicle-based, and biometric-signals-based, have been proposed for driver drowsiness detection. In this paper, a new approach using Electrocardiogram is discussed. Performance evaluation is carried out for the driver drowsiness classifier. The developed classifier yields overall accuracy, sensitivity, and specificity of 76.93%, 77.36%, and 76.5% respectively. Results have revealed that the performance of proposed classifier is better than traditional methods.

A Precise Drunk Driving Detection Using Weighted Kernel Based on Electrocardiogram

Globally, 1.2 million people die and 50 million people are injured annually due to traffic accidents. These traffic accidents cost

Traffic condition monitoring using weighted kernel density for intelligent transportation

500 billion dollars. Drunk drivers are found in 40% of the traffic crashes. Existing drunk driving detection (DDD) systems do not provide accurate detection and pre-warning concurrently. Electrocardiogram (ECG) is a proven biosignal that accurately and simultaneously reflects human’s biological status. In this letter, a classifier for DDD based on ECG is investigated in an attempt to reduce traffic accidents caused by drunk drivers. At this point, it appears that there is no known research or literature found on ECG classifier for DDD. To identify drunk syndromes, the ECG signals from drunk drivers are studied and analyzed. As such, a precise ECG-based DDD (ECG-DDD) using a weighted kernel is developed. From the measurements, 10 key features of ECG signals were identified. To incorporate the important features, the feature vectors are weighted in the customization of kernel functions. Four commonly adopted kernel functions are studied. Results reveal that weighted feature vectors improve the accuracy by 11% compared to the computation using the prime kernel. Evaluation shows that ECG-DDD improved the accuracy by 8% to 18% compared to prevailing methods.

An improved drowsiness detection scheme

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.

Energy-saving IAQ monitoring ZigBee network using VIKOR decision making method

Worldwide, more than 50 million people are injured in each year because of traffic accidents and their expenditure costs 1% to 3% of the worlds GDP. Drowsy driver is one of the leading causes of the traffic accidents. To reduce the accidents, drowsy driver detection (DDD) using penalized cross-correlation kernel (KPC) has been developed. The cross-correlation is to measure the similarity of ECG signals and this phenomenon is reflected in the resultant cross-correlation coefficients. The proposed work with KPC is regarded as a more accurate algorithm for driver drowsiness detection.

Detecting Parkinson's diseases via the characteristics of the intrinsic mode functions of filtered electromyograms

Indoor Air Quality (IAQ) is an urgent topic nowadays. It is concluded that 90% of humans life is spent indoor. However, it is commonly known that materials used in construction or furniture is often detected to release Volatile organic compounds (VOC) which affect IAQ significantly and lead to dizziness, respiratory irritation, fatigue, asthma and allergic airway disease and even cancer. As a result, IAQ monitoring system assists of improving IAQ, and wireless sensor network is an efficient method for building up the system network. In this paper, a new ZigBee network for IAQ monitoring system is designed. A Multi-criteria decision-making method VIKOR is used to figure out the best parameters of the MAC layer and CSMA/CA mechanism under this environment. The network designed can achieve 35% improvement of energy saving without affecting the latency and throughput performance compared with the commonly-used TOPSIS method.