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Since the 1970s, LDMOS transistors have been used in a variety of applications because of their versatility and monolithic integration with CMOS logic. Despite the advantages, interface defects (N IT ) generation due to Hot Carrier Degradation (HCD) has been a persistent reliability challenge for LDMOS transistors. Unfortunately, neither classical charge pumping nor single-pulse charge pumping techniques can be used to locate/quantify N IT in a source-body-tied LDMOS configuration. Here we: i) identify the multiple hotspots of HCD in an LDMOS using experimentally validated TCAD simulations; ii) introduce and implement a TCAD-enabled novel charge pumping technique to probe region-specific interface states in a source-body-tied (SBT) LDMOS; and iii) develop a unified multi-hotspot HCD model to interpret the degradation kinetics in power transistors. The analysis provides deep insights into the HCD in an LDMOS and the generalized charge pumping technique can be used to map interface states in a variety of transistors with non-traditional doping and contact configurations.

One of the more representative tasks to detect large-scale network behavior data is to use support vector machine (SVM) to extract the characteristics of network data to establish Intrusion detection model. However, the traditional "l 2 -norm" SVM can fit the model to some extent, it is prone to over fitting. In this paper, the sparse support vector machine (S-SVM) algorithm obtained by "l 1 -norm" is used to extract and classify the network behavior data set based on the sparseness of the network behavior data set. Column and constraint generation algorithm is introduced to solve the S-SVM model, and the one-to-one method is used to expand it into a multi-classification sparse support vector machine. Finally, the KDD (knowledge discovery and data mining) CUP 1999 data set was used for experiments. By comparing with the SGD (stochastic gradient descent) and Barrier algorithm, the efficiency and accuracy of this multi-classification S-SVM algorithm in NBAD (network behavior anomaly detection) were verified.

Although the CMOS-compatible Laterally Diffused MOSFET (LDMOS) is widely used in various applications as a versatile and efficient power electronic device, its hot carrier degradation (HCD) remains a persistent and important design challenge. None of the classical HCD models apply, because the geometric and doping complexities of the channel and drift regions create multiple hotspots with bias-dependent hot carrier injection into the oxide. To address these challenges, here we: 1) propose a novel geometrical partition of the LDMOS and represent each part by a TCAD-calibrated and experimentally validated tandem-FET compact model; 2) use the new compact model to propose an ‘ I − V spectroscopy’ methodology to deconvolve mobility and threshold degradation in the channel and the drift regions; 3) separate the degradation in the two regions by postprocessing measured I-V curves; 4) demonstrate that ΔV th determined by classical techniques, e.g., constant current (CC) or maximum transconductance (Gmmax), are contaminated by mobility degradation and must be corrected by the proposed technique for accurate lifetime projection.

The purpose of the introduction of IMS Technology into the power exchange network is to meet the development of information operations, so that the exchange platform can carry more functions of services and services. The grid also deployed the integration of unified communication, mobile office, video conference and other multimedia services while introducing IMS Technology, which added great pressure to the IMS service provision process. On this demand, this paper proposes an IMS service distribution and control technology based on the edge computing arrangement ability. It can marginalize the business functions and service processes, reduce the repeated interaction between SBC and the core network. It can lightweight IMS service registration, startup, conversation, deletion and other service protocols, and improve the quality of IMS service with the help of the edge computing service information proxy function And efficiency.

Traditional robotic walkers have primarily focused on safety and navigation. In this paper, we challenge the previous work on walkers by implementing a deep learning module developed with the goal of using a robot to provide mobility assistance to the elders. Through the data collected from multiple sensors, we are capable of leveraging the maneuverability of robotic walkers under different scenarios and gait requirements. This capability is achieved by CNN (Convolutional Neural Networks) and LSTM (Long Short-Term Memory) architectures. Thus, the system can provide personalized assistance to the elders performing the locomotion activities indoors accurately. Furthermore, the robot learns the optimal behavior based on the interactions with the environment in a supervised learning approach. To validate our system, we evaluated the system with some users who provided qualitative comments about the comfort degree of using the robot.

We propose a new navigation method for an omnidirectional mobile robot to maneuver in a complex and populated environment. In an indoor populated environment, for example, robot has to react to such circumstances and achieve both physical and psychological navigational safety. From social researches, human motion is a hybrid system composed of holonomic and non-holonomic movements. Based on this concept, we develop a socially-aware navigation method for an omnidirectional mobile robot to achieve natural, humanlike movement. By using laser range finder and camera as sensors, robot detects geometric features and human behavior information. From the geometric features robot can construct an environment model and extract the information about distances and directions of the obstacles. Whereas with the heading and orientation information from camera, robot can model individual humans successfully. While acquiring these models as context, a suitable navigation behavior would be executed. To interact with surroundings, we develop the extended Social Force Model(ESFM) to describe the interactive force, referred to as social force, with human and environment. As a whole, the contributions of our work are twofold one being that we propose a dynamic grouping model based on human behavior using learning-based method and another being that we develop an extended Social Force Model for the system based on which a successful navigation strategy can be realized.

Maintenance efficiency is the key to modern high-tech warfare. As the basis of condition-based maintenance, PHM(prognostics and health management) technology greatly improves maintenance efficiency by enabling the system with prediction power. To provide theoretical foundation for new equipment maintenance reform, the impact of PHM technology on maintenance support mode was analyzed from the member-level and the regional-level on aspects of maintenance support system, maintenance support resources, maintenance analysis and decision-making process. It is hoped that the results of the proposed study may contribute to the current reform of support modes for aircraft maintenance.

Renewables and Battery Electric Vehicles (BEVs) may potentially reduce environmental pollutions and traditional energy consumptions. However, existing literature does not study them holistically, combining technology with economy. In this paper, we first examine complimentary nature of these two technologies in a New York State college. Then, we establish an econometric model to study impacts on BEV demands due to renewables and five other socioeconomic factors, using 2010–2016 panel data from eleven countries. After multi-linear regressions, we observe that renewables impact BEV demands positively; i.e., one percent increment in renewables would yield 2% increment in BEV demands per 100 thousand people. High gasoline prices may lead to high BEV demands. The number of chargers impacts BEV demands positively. Population density does impact BEV demands positively, as well as education levels. The elasticity for GDP per capita is larger than one. Finally, we make recommendations to decision makers to synergistically promote renewables and BEVs. Planners should properly select locations encompassing renewables, chargers, parking facilities, highway rest areas, shopping centers and community activity centers.

This pilot study presented a capsule architecture-based classification model named WBCaps to automatically recognize the five types of white blood cells (WBC) from normal peripheral blood smears. The entire WBCs identification workflow including two sections were also reported here. The first section automatically located the WBCs from the whole slice of blood smears through a three-step image segmentation method including color normalization, color deconvolution and cell extraction. In the second section, the extracted cells were fed into WBCaps, which was a network cascaded by traditional convolutional layer, primary capsule layer and representation capsule layer, to generate the type prediction. We employed 3-fold cross validation to validate the proposed WBCs identification method on a small clinical dataset and yielded precision 0.99, recall 0.99 and F1-score 0.99. The result outperformed that of ResNet (precision 0.97, recall 0.97, F1-score 0.97) and VGG (precision 0.97, recall 0.97, F1-score 0.98). The proposed model could be a promising clinical tool for hematology analyzer to facilitate the cytological and morphological examination.

Front- and backend self-heating have become an important barrier to the sustained increase in processor speed. Indeed, the severity of backend Joule heating with shrinking BEOL features has emerged as a crucial issue for the ICs reliability. In this work, we introduce an analytical transient Joule heating $\Delta T_{\mathrm{J}}(t))$ model to calculate the self-heating of any wire located at any interconnect level within an IC. Specifically, we (i) propose a closed-form analytical formula to describe $\Delta T_{\mathrm{J}}(t)$ of an interconnect; (ii) use this formula to develop a SPICE-compatible compact model to predict the temperature distribution for any given circuits/systems; (iii) validate the model by experiments as well as finite element method (FEM) simulation; (iv) compare and validate $\Delta T_{\mathrm{J}}$ of different interconnect materials (e.g., Cu, Co, and Ru) as well as effectiveness of various cooling strategies (e.g., dummy vias, and larger wires), and finally (v) predict reliability of interconnects under various operating conditions. Our work demonstrates that Co can replace Cu for the lower BEOL levels (e.g., M1-M3) due to its superior reliability. However, excessive self-heating in Co makes Cu still a preferred option for the higher BEOL levels (M5-M10).

Service Robot can tell stories to various audience who need a robot to accompany, such as children, students, and old people. It is desired that robot can discuss the story with the users after it just told a story. Discussion can be achieved with dialogue template; however, it is costly to edit the templates manually. In this paper we report how we build a system that adopts the information extraction technology to synthesize dialogue template in AIML, so that a Chatbot can chat about a new story with a human user.

With the rising trends of dialog system, interactions with questions become a vital human-machine interface in realizing intentions and generating adequate feedback, thereby establishing question generation as an important and interesting research topic. In the education domain, question generation is also useful for reducing time and effort for teachers, as well as parents. Extensive research has been conducted on question generation recently but little focused on question generation in Chinese. In addition, previous papers examining question generation in Chinese, have been poorly reviewed due to the clumsy handling of sentence complexities of Chinese. In this article, we focus on developing a question generation technique based on children’s tales. For the training and testing dataset, approximately one hundred articles from the website, 兒童 故事(http://www.yfes.tn.edu.tw/yfesvi/story.htm) were used. To achieve better quality of the questions automatically generated, approaches and methods were carefully chosen in this system. The results show that questions generated from our system are mostly acceptable, which reflects the main objective of our system.

Stacked intermetal dielectrics grown by a Plasma Enhanced Chemical Vapor Deposition (PECVD) technique are widely used as a capacitive voltage divider to integrate low and high power ICs. The voltage-divider must sustain multi-kV operation for years in harsh (hot and humid) environment. Therefore, a fundamental understanding of the degradation mechanisms of the dielectric is an essential prerequisite for its safe operation. While the reliability of PECVD oxides has been extensively studied, the reliability of stacked oxides, with numerous chemically and mechanically polished (CMP) interfaces, is not fully understood. In fact, the dielectric reliability would differ dramatically if the stack behaves as a single thick capacitor vs. if CMP-damaged interfaces render the stack into a set of capacitors connected in series. In this paper, we use a wide range of the stacked intermetal dielectric (= 1∼20 μτη) to study their Time-dependent dielectric breakdown (TDDB) degradation mechanism. Our results demonstrate that the stacked dielectric do behave as a single unit, but unlike conventional TDDB in submicron gate oxide, the TDDB of stacked dielectrics is determined by impact ionization and charge trapping. We explored the degradation mechanism in detail through experiments and simulation; the results are embedded in an acceleration model that can be used to predict TDDB lifetime at arbitrary operating conditions.

The objective of the study is to generate the product hierarchical categories in e-commerce, particularly for e-commerce giants such as Taobao or Jingdong. For e-commerce websites the amount of products is huge, and a hierarchical structure is necessary for consumers to browse them. We find that there are two problems in the current websites: firstly, the hierarchy is shallow; there are often too many products in the same category, it is hard for a consumer browse them. Secondly, the hierarchy is constructed manually, when new products come, it is hard to update the hierarchy. Based on the product description analysis, it is possible to solve the problems. In this study, we will use the deep learning word embedding technology and clustering algorithm to construct a deeper product hierarchy automatically. The results will help the customers to choose products with a more clear structure and also help the e-commerce company to save the maintaining effort on the product hierarchy.

Split manufacturing can mitigate security vulnerabilities at untrusted foundries by exposing only partial designs. Even so, attackers can make educated guess according to design conventions and thereby recover entire chip designs. In this work, a routing perturbation-based defense method is proposed such that such attacks become very difficult while wirelength/timing overhead is restricted to be very small. Experimental results on benchmark circuits confirm the effectiveness of the proposed techniques. The new techniques also significantly outperform the latest previous work.

This paper presents a fully-integrated Ka-band 4RX/4TX phased-array transceiver IC. The four transceiver channels are synchronized by an I/Q standing wave oscillator (SWO). Their signal phases are shifted by hybrid analog IF and LO phase interpolators, and can be controlled digitally and independently. The SWO further composes an on-chip PLL, and is locked to a 1.09 GHz off-chip global reference to scale to a large array at board level. Each receiver channel achieves 66 dB maximum conversion gain and 4.4 dB minimum noise figure. Each transmitter channel achieves 18 dBm maximum output power. The chip is mounted on a high-frequency laminate, inside which embedded cables connect its RF pads to Vivaldi antennas also inside the same PCB. With the PCB, each channel achieves 360° phase shifting capability under a maximum calibrated 3.7° phase errors. The PCB module presents successful beam steering with 23° measured four-channel 3-dB beamwidth. This chip consumes a peak 5 W power at transmit mode, and is fabricated in TSMC 65nm CMOS technology with a 4 × 2.5 mm2 chip area.

In this paper we report how we improve our Chinese grammar error detection system. We focus on the recognition of word selection error, which is the hardest error type defined in 2015 NLP-TEA shared task CGED. Four major error types was defined in CGED shared task, including redundant word error, missing word error, word selection error and word disorder error. Based on the conditional random fields (CRF) model, our system trains a linear tagger that can be used to recognize the errors in learners' essays. In 2015 CGED shared task, we have found that our system can achieve high precision and accuracy. Our previous system used the features in the sentence only, however, it is not sufficient to recognize the word selection error. In this paper, we propose that by integrating the collocation feature into the CRF model, our system can improve the performance on the recognition of the selection errors.

According to statistics from the National Cancer Institute, an increasing rate of breast cancer in the US makes the treatment of cancers more important. Besides conventional treatments, chemotherapy (CT) and radiation therapy (RT), different techniques are also being used to increase the efficiency of CT and RT for breast cancer. Hyperthermia, also called thermotherapy, is an adjunctive therapy that can be paired with conventional treatments. This method exposes electromagnetic energy into the targeted tissue to raise the temperature up to 45 °C, which increases perfusion and drug delivery inside the cancerous cells. However, high input power, screening room requirements with long application time prevents hyperthermia from applying in all clinics. Therefore, mild hyperthermia is experimented to make the process more applicable for patients. In this study, the goal is to observe the effect of low input power on tissue temperature at different depths of the breast in shorter time compared to current systems. Tissue temperature can be increased by 2–3 °C using this method. This technique will be applied by using a flexible microwave (MW) antenna applicator, which consists of 9 microwave antennas integrated in a circular-shaped PDMS, that is designed and fabricated. So, it can be given a shape on the breast. The breast can also be heated homogeneously with the located multiple antennas on the applicator. The antennas on the applicator are powered through feeding circuit, then it is attached on the breast mimicking gel. After that, fiber optic temperature sensors are placed at the depth of the gels and just under the surface. Firstly, measurements are taken of the temperature increase at the depth of 1 cm with 1W power at 1.6 GHz without time limits. Then, gradually increased power up to 5W is applied at 1cm depth. Thereafter, the depth is increased and the measurements are repeated at 2.5cm and 4cm depths for all power levels again with a limited time to 10 minutes. We show that the proposed applicator is very efficient for mild-hyperthermia for chemo-thermotherapy of the breast.

Social network websites such as Facebook, Tweeter, and Plurk have become a useful marketing toolkit. Many companies find that it can provide new opportunities on mining customers' opinions and get better understanding of the customers. The aim of the paper is to analyze the sentiment of users' opinions from corporation-run social networks, the Facebook Fan Pages. The goal is to find the topics and the associated sentiment of the topics in a given Fan Page run by a single corporation. Sentiment analysis in previous works was often based on a sentiment dictionary; we follow the traditional approach with the help of some additional rules to improve the performance. Combining the result of topic extraction and sentiment analysis, we try to find the most interested events in the given Fan Page. The results can be used in advanced marketing for the corporation and to satisfy more users.

According to statistics from the National Cancer Institute, an increasing rate of breast cancer in the US makes the treatment of cancers more important. Besides conventional treatments, chemotherapy (CT) and radiation therapy (RT), different techniques are also being used to increase the efficiency of CT and RT for breast cancer. Hyperthermia, also called thermotherapy, is an adjunctive therapy that can be paired with conventional treatments. This method exposes electromagnetic energy into the targeted tissue to raise the temperature up to 45 °C, which increases perfusion and drug delivery inside the cancerous cells. However, high input power and screening room requirements with long application time prevents hyperthermia from being applied in all clinics. Therefore, mild hyperthermia is experimented to make the process more applicable for patients. In this study, the goal is to observe the effect of low input power on tissue temperature at different depths of the breast with a shorter application time compared to current systems. Tissue temperature can be increased by 2–3 °C using this method. This technique will be applied by using a flexible microwave antenna applicator, which consists of 9 microwave antennas integrated in a circular-shaped PDMS, that is designed and fabricated. The breast can also be heated homogeneously with the located multiple antennas on the applicator. Six of the antennas are powered by an amplifier with a power divider, then it is attached on the breast mimicking gel. After that, fiber optic temperature sensors are placed at the depth of the gels and just under the surface. Measurements are taken of the temperature increase at the depth of 1 cm, with 1W power, at 450 Mhz without time limits while gradually increasing the power up to 5W. Thereafter, the depth is increased and the measurements are repeated at 2.5cm and 4cm depths for all power levels again with a limited time of 10 minutes. Design of the flexible microwave antenna applicator and test results will be presented.

The development of a reliable continuous glucose monitoring technology, which would lessen the complications associated with diabetes through optimal glycemic control, is a key to improving the quality of lives of patients living with the disease. In recent years, considerable progress has been made in developing implantable biosensors that can continually monitor glucose levels. These biosensors rely on the interstitial fluid within the dermis to measure the interstitial glucose (IG) levels. However, to be truly beneficial, the implanted sensor must be able to function properly for an extended period of time. The biosensors developed thus far can only remain functional up several weeks after their implantation in the body. Contributing factors for this loss of functionality include the degradation and fouling of the sensor, and the changes in the tissue surrounding the sensor such as fibrosis and inflammation. While researchers explore potential solutions to improve the current implantable biosensors, there is an urgent need to investigate alternative technologies. Unless a reliable technology for long-term glucose monitoring is developed, continuous glucose monitoring is expected to remain problematic, and patients will continue to face the life-threatening complications associated with poor glycemic control. In order to provide a potential solution to this problem, in this study, we propose a lab-on-chip antenna sensor for continuous monitoring of the glucose concentration in the interstitial fluid. Unlike biosensors that require direct contact with the interstitial fluid in order to trigger necessary chemical reactions to operate, the new sensor works on an electromagnetic energy propagation principle. Thus, it does not need direct interface with the interstitial fluid and can be fabricated using biocompatible materials allowing it to remain functional in the body for years.

This paper proposes a new approach to star image denoising, recognizing and centroiding for the airborne application, especially during the daytime. To extend attitude determination of aircraft to daytime, one prerequisite is to precisely obtain the centroid of the target star. To date, there has not been an adequate analytical model and experimental method to solve this problem effectively. Generally, three main problems of degraded images lie in the low signal-to-noise ratio, high intensity of the background and distorted star shape. To clearly validate the given method on star recognition, we focused on the static environment, without the severe distortion and motion blur. To demonstrate the proposed approach, an experiment was conducted with a camera and host computer. Specifically, it consisted of the star image acquisition, image processing and star recognition. Due to the denoising process, the noise component was eliminated effectively. Besides, by the connected region analysis and minimal area screening, the star pattern can be completely isolated. On top of that, the percentage of recognition amounted to 86.7% and the centroid precision was within 1/10 pixel. The experimental results demonstrate the potential of the proposed approach to the star recognition and extraction, during the daytime for airborne application.

Long-reach passive optical networks are one of the proposed solutions to the merging of access and metro networks into a single system for lowering deployment costs. As propagation delays increase proportional to the network range, new designs of scheduling and bandwidth distribution schemes need to be considered to mitigate its effects on overall packet delay. This paper presents an interleaved polling scheme for Ethernet Passive Optical Networks based on the well-known IPACT algorithm with Limited Service granting. The proposed algorithm takes advantage of the idle time gaps caused by heterogeneous propagation delays and bursty traffic, and utilizes an enhanced Limited Service granting scheme based on the packet delay approximation of ONUs. Simulation results show that the proposed scheduling and bandwidth distribution schemes achieve lower packet delays in providing multimedia services in long-reach EPON systems when compared to IPACT.

Textual Entailment (TE) is a critical issue in natural language processing (NLP); many NLP applications can be benefited from the recognition of textual entailment (RTE). In this paper we report our observation on how to improve the Chinese textual entailment system and the experiment results on the NTCIR-10 RITE-2 dataset. To complement the traditional machine learning approach, which treat every input pair equally with the same features and the same process, our system classify different entailment cases and treat them separately. The experiment results show great improvement.

While extensive studies on various networks properties have been carried out, little has been done on large-scale online community networks. An online community network essentially is a group-centered online platform, that is much different from traditional social network. Based on the log data of a university online community, we investigate the statistical characteristics of the evolving network during the different observation period, and find that the degree distribution of the network follows a composite power laws form. In addition, the online community network shows the small world effect, well-interconnected modules, and the hallmark of “scale-free”. Further research indicates that the online community network is high robust to random removals, but fail to intentional attacks, especially to attacks based on the betweenness, that shows some nodes with high betweenness play a key role in maintaining the online community network communication capabilities. Our analysis aims to provide a platform for drawing a host of practical implications for information diffusion and promoting valuable communication, as well as for the management and design of online communities.

This paper reports the high light extraction efficiency will be obtained by considering the systematic design of two-dimensional sub-wavelength photonic crystals, which is created on the p-side of InGaN-based light emitting diodes.

In this paper, we describe how we improve our system for Chinese Textual Entailment Recognition by a monolingual machine translation system. Previously, our approach is based on the standard supervised learning classification. We integrate the result of monolingual machine translation system with the other available computational linguistic resources of Chinese language processing to build the system for the natural language processing application. We observed the training corpus and list all possible features. The features include surface text, semantic and syntactical information, such as POS tagging, synonym substitution, and dependency relation. The annotated data is used in training statistical models and build the classifier for the Binary-class Chinese textual Entailment Recognition task. The experiment result shows that the monolingual machine translation technology can improve the system performance in both 10-fold cross validation and open test.

This paper describes a new method of constructing a saliency map that can be used in object finding for a robot vision system. A saliency map shows the weight of each pixel in an image. The weights represent the probability of the location of a given object. Traditionally, a saliency map is constructed based on the analysis of the image color features of the RGB color space. We redefine the saliency map of an image on the HSL color space, which makes our method more robust under a wide range of luminance. Experimental results show that the range can cover most cases in everyday life. Thus, the system can successfully find objects under different conditions and various light sources.

Short term load forecasting (STLF) is an important process in electric power operation and control system. Support Vector Regression (SVR) is proved to be a successful application in STLF, and can get great accuracy and efficiency compared to other STLF models. However, when deal large scale sample size, SVR is poor on the performance. With the development of cloud computing, it is changing people's life in more and more areas. Windows Azure Platform is a cloud computing platform developed by Microsoft. It can easily scale up or down to get compute or storage resource according to requirements. Take into account the advantage and convenience, we propose a parallel SVR model based on Windows Azure Platform to solve the large scale dataset problem of SVR. This model is verified with ENUN standard dataset, the results shows that the model of SVR based on Windows Azure Platform has apparently improvement on efficiency than standard SVR model.

Supporting a multimode mobile device to seamlessly switch its connections between various wireless access networks, such as WiMAX, Wi-Fi, and LTE, is an important research issue in 4G communications. The IEEE 802.21 working group defines the Media Independent Handover (MIH) standard to provide cross layer control and the information of network environment and services to optimize the handoff process. This work designs and implements the MIH middleware in a Linux platform to provide MIH event services (MIH-ES), MIH command services (MIH-CS) and MIH information services (MIH-IS). Based on the MIH software, we develop a high quality VoIP system which integrates Stream Control Transmission Protocol (SCTP), two MIH-IS based user motion detection (UMD) services and an adaptive QoS playout algorithm (AQP). The MIH services, multi-homing capability and dynamic address configuration extension of SCTP are applied in the VoIP system to perform seamless handoffs. Moreover, AQP adjusts the playout buffer by using the retransmission of partial reliable SCTP (PR-SCTP) and the MIH-ES to reduce the packet loss rate and improve speech quality. The experiment results indicate that MIH can enhance the performance of the handoff process and the quality of wireless VoIP services.

A low-power wideband divide-by-odd-ratio ring-oscillator-based complementary-injection-locked frequency divider (CILFD) is proposed. The multiple-input complementary-injection scheme widens its locking range over 100% to sufficiently cover the PVT variations. The CILFD consumes dynamic power only with no additional power penalty for larger division ratios. The differential input and output are realized employing auxiliary cross-connected inverters. The CILFDs with division ratios of 3 and 7 are implemented in a 0.18 µm CMOS technology. The measurement results show a locking range from 1.4 to 4.4 GHz with an input incident power of −4 dBm. The power consumption while locked at 4.7 GHz is 0.9 mW from a single 1.8-V power supply.

A high voltage full-bridge resonant inverter is proposed for driving carbon nanotube field emission lamp (CNTL). First, we construct an equivalent model of CNTL to exhibit its electric characteristics. Moreover, the high frequency sinusoidal driving strategy is employed to eliminate the arcing phenomenon caused by the traditional DC driving. And then the PLL feedback controller is used to stabilize the lamp luminance. Complete design considerations and mathematical model are detailed. All the experimental results agree with the theoretical predictions.

The composite blades of wind turbines are made up of composite shell components. However, blades are often simplified as Eulerian beam in finite element analysis. In design both special and general purpose finite element codes are used worldwide. The verification of a special finite element code BModes vs. a popular general purpose finite element code ANSYS are performed via a uniform blade in both still and spinning cases in this paper. The test reveals that general finite element codes like ANSYS might overestimate the lag frequencies in spinning case. After verification of beam model, the approach is applied to complete the modal analysis of a practical wind turbine blade. The results are compared with a known shell model from literature. The comparison validates the simplified Eulerian beam model. Finally a guideline of usage for these two models is given.

Stock market forecasting has attracted a lot of research interests in previous literature. Traditionally, the autoregressive moving average (ARMA) model has been one of the most widely used linear models in time series forecasting. However, the ARMA model cannot easily capture the nonlinear patterns. And recent studies have shown that artificial neural networks (ANN) method achieved better performance than traditional statistical ones. ANN approaches have, however, suffered from difficulties with generalization, producing models that can overfit the data. Support vector machines (SVMs), a novel neural network technique, have been successfully applied in solving nonlinear regression estimation problems. Therefore, this investigation proposes a hybrid methodology that exploits the unique strength of the ARMA model and the SVMs model in the stock market forecasting problem in an attempt to provide a model with better explanatory power. Real data sets of stock market were used to examine the forecasting accuracy of the proposed model. The results of computational tests are very promising.

An electronic ballast, high reliability, stability and safety, must prepare the protection functions in order to avoid more critical hazards. In this paper, the high performance protections proposed mainly false ignition, open and short circuit protection actions have been completely designed and shown. Then, the protection circuit owns more benefits as small size, large cost saving and high efficiency. Moreover, this one can yet become a module structure as hybrid or integrated circuit. Now, the protection functions will be applied to and verified by the electronic ballast with the self-excited series resonant inverter system for fluorescent lamps.