Description: This review surveys micromachined gyroscope structure and circuitry technology. The principle of micromachined gyroscopes is first introduced. Then, different kinds of MEMS gyroscope structures, materials and fabrication technologies are illustrated. Micromachined gyroscopes are mainly categorized into micromachined vibrating gyroscopes (MVGs), piezoelectric vibrating gyroscopes (PVGs), surface acoustic wave (SAW) gyroscopes, bulk acoustic wave (BAW) gyroscopes, micromachined electrostatically suspended gyroscopes (MESGs), magnetically suspended gyroscopes (MSGs), micro fiber optic gyroscopes (MFOGs), micro fluid gyroscopes (MFGs), micro atom gyroscopes (MAGs), and special micromachined gyroscopes. Next, the control electronics of micromachined gyroscopes are analyzed. The control circuits are categorized into typical circuitry and special circuitry technologies. The typical circuitry technologies include typical analog circuitry and digital circuitry, while the special circuitry consists of sigma delta, mode matching, temperature/quadrature compensation and novel special technologies. Finally, the characteristics of various typical gyroscopes and their development tendency are discussed and investigated in detail.

Description: This paper presents a pioneering study on numerical modeling of load bearing characteristics of the jacket foundation pile for offshore wind turbines on the west coast of Taiwan. Because Taiwan is located in an earthquake prone area, there is significant interest in improving the prediction of the behavior of wind turbine jacket foundations subjected to seismic loading. Investigation of the bearing capacity of the jacket foundation pile for the offshore wind farm using effective stress analysis, with consideration of pore pressure generation and soil/liquid coupled analysis, was conducted. A new procedure to evaluate the design of offshore wind turbine foundation piles in the sand and clay inter-layered soil was also proposed. Static and dynamic analyses of bearing capacity of the jacket foundation pile were conducted. Results obtained demonstrate that the design process for the jacket foundation pile proposed in this study can properly reflect the interaction behavior of the foundation and the soil. In addition, the pore pressure generation model can be used to simulate soil liquefaction. The proposed method is also very useful in the evaluation of the design capabilities of offshore wind turbine jacket foundations.

Description: Around the estuary of the Zhuo-Shui River in Taiwan, the waters recede during the winter, causing an increase in bare land area and exposing a large amount of fine earth and sand particles that were deposited on the riverbed. Observations at the site revealed that when northeastern monsoons blow over bare land without vegetation or water cover, the fine particles are readily lifted by the wind, forming river dust, which greatly endangers the health of nearby residents. Therefore, determining which factors affect river dust and constructing a model to predict river dust concentration are extremely important in the research and development of a prototype warning system for areas at risk of river dust emissions. In this study, the region around the estuary of the Zhuo-Shui River (from the Zi-Qiang Bridge to the Xi-Bin Bridge) was selected as the research area. Data from a nearby air quality monitoring station were used to screen for days with river dust episodes. The relationships between PM10 concentration and meteorological factors or bare land area were analyzed at different temporal scales to explore the factors that affect river dust emissions. Study results showed that no single factor alone had adequate power to explain daily average or daily maximum PM10 concentration. Stepwise regression analysis of multiple factors showed that the model could not effectively predict daily average PM10 concentration, but daily maximum PM10 concentration could be predicted by a combination of wind velocity, temperature, and bare land area; the coefficient of determination for this model was 0.67. It was inferred that river dust episodes are caused by the combined effect of multiple factors. In addition, research data also showed a time lag effect between meteorological factors and hourly PM10 concentration. This characteristic was applied to the construction of a prediction model, and can be used in an early warning system for local residents.

Description: This paper aims to develop a multisensor data fusion technology-based smart home system by integrating wearable intelligent technology, artificial intelligence, and sensor fusion technology. We have developed the following three systems to create an intelligent smart home environment: (1) a wearable motion sensing device to be placed on residents’ wrists and its corresponding 3D gesture recognition algorithm to implement a convenient automated household appliance control system; (2) a wearable motion sensing device mounted on a resident’s feet and its indoor positioning algorithm to realize an effective indoor pedestrian navigation system for smart energy management; (3) a multisensor circuit module and an intelligent fire detection and alarm algorithm to realize a home safety and fire detection system. In addition, an intelligent monitoring interface is developed to provide in real-time information about the smart home system, such as environmental temperatures, CO concentrations, communicative environmental alarms, household appliance status, human motion signals, and the results of gesture recognition and indoor positioning. Furthermore, an experimental testbed for validating the effectiveness and feasibility of the smart home system was built and verified experimentally. The results showed that the 3D gesture recognition algorithm could achieve recognition rates for automated household appliance control of 92.0%, 94.8%, 95.3%, and 87.7% by the 2-fold cross-validation, 5-fold cross-validation, 10-fold cross-validation, and leave-one-subject-out cross-validation strategies. For indoor positioning and smart energy management, the distance accuracy and positioning accuracy were around 0.22% and 3.36% of the total traveled distance in the indoor environment. For home safety and fire detection, the classification rate achieved 98.81% accuracy for determining the conditions of the indoor living environment.

Description: Electroencephalogram-based emotion recognition (EEG-ER) has received increasing attention in the fields of health care, affective computing, and brain-computer interface (BCI). However, satisfactory ER performance within a bi-dimensional and non-discrete emotional space using single-trial EEG data remains a challenging task. To address this issue, we propose a three-layer scheme for single-trial EEG-ER. In the first layer, a set of spectral powers of different EEG frequency bands are extracted from multi-channel single-trial EEG signals. In the second layer, the kernel Fisher’s discriminant analysis method is applied to further extract features with better discrimination ability from the EEG spectral powers. The feature vector produced by layer 2 is called a kernel Fisher’s emotion pattern (KFEP), and is sent into layer 3 for further classification where the proposed imbalanced quasiconformal kernel support vector machine (IQK-SVM) serves as the emotion classifier. The outputs of the three layer EEG-ER system include labels of emotional valence and arousal. Furthermore, to collect effective training and testing datasets for the current EEG-ER system, we also use an emotion-induction paradigm in which a set of pictures selected from the International Affective Picture System (IAPS) are employed as emotion induction stimuli. The performance of the proposed three-layer solution is compared with that of other EEG spectral power-based features and emotion classifiers. Results on 10 healthy participants indicate that the proposed KFEP feature performs better than other spectral power features, and IQK-SVM outperforms traditional SVM in terms of the EEG-ER accuracy. Our findings also show that the proposed EEG-ER scheme achieves the highest classification accuracies of valence (82.68%) and arousal (84.79%) among all testing methods.

Description: Energies, Vol. 11, Pages 1743: A Dual-Voltage-Vector Model-Free Predictive Current Controller for Synchronous Reluctance Motor Drive Systems Energies doi: 10.3390/en11071743 Authors: Cheng-Kai Lin Jen-te Yu Hao-Qun Huang Jyun-Ting Wang Hsing-Cheng Yu Yen-Shin Lai For current control in power conversion and motor drive systems, there exist three classic methods in the literature and they are the hysteresis current control (HCC), the sine pulse-width modulation (SPWM), and the space vector pulse width modulation (SVPWM). HCC is easy to implement, but has relatively large current harmonic distortion as the disadvantage. On the other hand, the SPWM and SVPWM use modulation technique, commonly together with at least one proportional-integral (PI) regulator to reduce load current ripples, and hence demanding more computation time. This paper aims to improve the performance of a recently proposed new current control method—the single-voltage-vector model predictive current control (SVV-MPCC), for synchronous reluctance motor (SynRMs) drives. To that end, a dual-voltage-vector model-free predictive current control (DVV-MFPCC) for SynRMs is proposed. Unlike the SVV-MPCC that applies only a single voltage vector per sampling period, the proposed DVV-MFPCC is capable of providing two successive segmentary current predictions in the next sampling period through all possible combinations from any two candidate switching states increasing the number of applicable switching modes from seven to nineteen and reducing the prediction error effectively. Moreover, the new control does not utilize any parameters of the SynRM nor its mathematical model. The performance is effectively enhanced compared to that of SVV-MPCC. The working principle of the DVV-MFPCC will be detailed in this paper. Finally, the SVV-MPCC, the single-voltage-vector model-free predictive current control (SVV-MFPCC), the dual-voltage-vector model predictive current control (DVV-MPCC), and the DVV-MFPCC are realized to control the stator currents of SynRM through a 32-bit microcontroller TMS320F28377S. Experimental results are provided to validate the new method and verify that the DVV-MFPCC performs better than do the SVV-MPCC, the SVV-MFPCC, and the DVV-MPCC.

Description: Sensors, Vol. 18, Pages 983: An Experimental Study of a Micro-Projection Enabled Optical Terminal for Short-Range Bidirectional Multi-Wavelength Visible Light Communications Sensors doi: 10.3390/s18040983 Authors: Hsi-Hsir Chou Cheng-Yu Tsai Jhih-Shan Jiang A micro-projection enabled short-range communication (SRC) approach using red-, green- and blue-based light-emitting diodes (RGB-LEDs) has experimentally demonstrated recently that micro-projection and high-speed data transmission can be performed simultaneously. In this research, a reconfigurable design of a polarization modulated image system based on the use of a Liquid Crystal on Silicon based Spatial Light Modulator (LCoS-based SLM) serving as a portable optical terminal capable of micro-projection and bidirectional multi-wavelength communications is proposed and experimentally demonstrated. For the proof of concept, the system performance was evaluated through a bidirectional communication link at a transmission distance over 0.65 m. In order to make the proposed communication system architecture compatible with the data modulation format of future possible wireless communication system, baseband modulation scheme, i.e., Non-Return-to-Zero On-Off-Keying (NRZ_OOK), M-ary Phase Shift Keying (M-PSK) and M-ary Quadrature Amplitude Modulation (M-QAM) were used to investigate the system transmission performance. The experimental results shown that an acceptable BER (satisfying the limitation of Forward Error Correction, FEC standard) and crosstalk can all be achieved in the bidirectional multi-wavelength communication scenario.

Description: This paper presents a novel digital miniaturization method for a prototype silicon micro-gyroscope (SMG) with the symmetrical and decoupled structure. The schematic blocks of the overall system consist of high precision analog front-end interface, high-speed 18-bit analog to digital convertor, a high-performance core Field Programmable Gate Array (FPGA) chip and other peripherals such as high-speed serial ports for transmitting data. In drive mode, the closed-loop drive circuit are implemented by automatic gain control (AGC) loop and software phase-locked loop (SPLL) based on the Coordinated Rotation Digital Computer (CORDIC) algorithm. Meanwhile, the sense demodulation module based on varying step least mean square demodulation (LMSD) are addressed in detail. All kinds of algorithms are simulated by Simulink and DSPbuilder tools, which is in good agreement with the theoretical design. The experimental results have fully demonstrated the stability and flexibility of the system.

Description: This paper presents a novel micro dynamically tuned gyroscope (MDTG) with adjustable static capacitance. First, the principle of MDTG is theoretically analyzed. Next, some simulations under the optimized structure parameters are given as a reference for the mask design of the rotor wafer and electrode plates. As two key components, the process flows of the rotor wafer and electrode plates are described in detail. All the scanning electron microscopy (SEM) photos show that the fabrication process is effective and optimized. Then, an assembly model is designed for the static capacitance adjustable MDTG, whose static capacitance can be changed by rotating the lower electrode plate support and substituting gasket rings of different thicknesses. Thus, the scale factor is easily changeable. Afterwards, the digitalized closed-loop measurement circuit is simulated. The discrete correction and decoupling modules are designed to make the closed-loop stable and cross-coupling effect small. The dual axis closed-loop system bandwidths can reach more than 60 Hz and the dual axis scale factors are completely symmetrical. All the simulation results demonstrate the proposed fabrication of the MDTG can meet the application requirements. Finally, the paper presents the test results of static and dynamic capacitance values which are consistent with the simulation values.

Description: Driving safety has become a global topic of discussion with the recent development of the Smart Car concept. Many of the current car safety monitoring systems are based on image discrimination techniques, such as sensing the vehicle drifting from the main road, or changes in the driver’s facial expressions. However, these techniques are either too simplistic or have a low success rate as image processing is easily affected by external factors, such as weather and illumination. We developed a drowsiness detection mechanism based on an electroencephalogram (EEG) reading collected from the driver with an off-the-shelf mobile sensor. This sensor employs wireless transmission technology and is suitable for wear by the driver of a vehicle. The following classification techniques were incorporated: Artificial Neural Networks, Support Vector Machine, and k Nearest Neighbor. These classifiers were integrated with integration functions after a genetic algorithm was first used to adjust the weighting for each classifier in the integration function. In addition, since past studies have shown effects of music on a person’s state-of-mind, we propose a personalized music recommendation mechanism as a part of our system. Through the in-car stereo system, this music recommendation mechanism can help prevent a driver from becoming drowsy due to monotonous road conditions. Experimental results demonstrate the effectiveness of our proposed drowsiness detection method to determine a driver’s state of mind, and the music recommendation system is therefore able to reduce drowsiness.