Autonomous Water Quality Monitoring and Water Surface Cleaning for Unmanned Surface Vehicle.

Water is one of the most precious resources. However, industrial development has made water pollution a critical problem today and thus water quality monitoring and surface cleaning are essential for water resource protection. In this study, we have used the sensor fusion technology as a basis to develop a multi-function unmanned surface vehicle (MF-USV) for obstacle avoidance, water-quality monitoring, and water surface cleaning. The MF-USV comprises a USV control unit, a locomotion module, a positioning module, an obstacle avoidance module, a water quality monitoring system, a water surface cleaning system, a communication module, a power module, and a remote human-machine interface. We equip the MF-USV with the following functions: (1) autonomous obstacle detection, avoidance, and navigation positioning, (2) water quality monitoring, sampling, and positioning, (3) water surface detection and cleaning, and (4) remote navigation control and real-time information display. The experimental results verified that when the floating garbage located in the visual angle ranged from -30° to 30° on the front of the MF-USV and the distances between the floating garbage and the MF-USV were 40 and 70 cm, the success rates of floating garbage detection are all 100%. When the distance between the floating garbage and the MF-USV was 130 cm and the floating garbage was located on the left side (15°~30°), left front side (0°~15°), front side (0°), right front side (0°~15°), and the right side (15°~30°), the success rates of the floating garbage collection were 70%, 92%, 95%, 95%, and 75%, respectively. Finally, the experimental results also verified that the applications of the MF-USV and relevant algorithms to obstacle avoidance, water quality monitoring, and water surface cleaning were effective.

Design and Implementation of a Smart Home System Using Multisensor Data Fusion Technology.

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.

A Portable Array-Type Optical Fiber Sensing Instrument for Real-Time Gas Detection.

A novel optical fiber array-type of sensing instrument with temperature compensation for real-time detection was developed to measure oxygen, carbon dioxide, and ammonia simultaneously. The proposed instrument is multi-sensing array integrated with real-time measurement module for portable applications. The sensing optical fibers were etched and polished before coating to increase sensitivities. The ammonia and temperature sensors were each composed of a dye-coated single-mode fiber with constructing a fiber Bragg grating and a long-period filter grating for detecting light intensity. Both carbon dioxide and oxygen sensing structures use multimode fibers where 1-hydroxy-3,6,8-pyrene trisulfonic acid trisodium salt is coated for carbon dioxide sensing and Tris(2,2'-bipyridyl) dichlororuthenium(II) hexahydrate and Tris(bipyridine)ruthenium(II) chloride are coated for oxygen sensing. Gas-induced fluorescent light intensity variation was applied to detect gas concentration. The portable gas sensing array was set up by integrating with photo-electronic measurement modules and a human-machine interface to detect gases in real time. The measured data have been processed using piecewise-linear method. The sensitivity of the oxygen sensor were 1.54%/V and 9.62%/V for concentrations less than 1.5% and for concentrations between 1.5% and 6%, respectively. The sensitivity of the carbon dioxide sensor were 8.33%/V and 9.62%/V for concentrations less than 2% and for concentrations between 2% and 5%, respectively. For the ammonia sensor, the sensitivity was 27.78%/V, while ammonia concentration was less than 2%.

Automatic microfluidic fluorescence-array measurement system for detecting organic phosphate.

In this study, an automatic microfluidic fluorescence-array measurement system is developed to detect the concentration of organic phosphate based on the luminol-hydrogen peroxide catalytic fluorescent mechanism. Not only sample quantity and cost can be reduced, but also detection time, accuracy and precision can be improved in the system. The system is composed of a CCD image module, a stepper motor with driver, a microfluidic fluorescence array, a background light elimination module, and a dynamic image-analyzed interface. The pesticides of chlorpyrifos and fenitrothion of organic phosphate are chosen as experimental samples. Only a 2.5 µ l quantity of sample is required to have a fast response time of 1.4 second. Experimental results show that the sensitivities of chlorpyrifos and fenitrothion are 1.88 V/ppm in the range of 0.166 ∼ 10 ppm with averaged error of 1.66% and 0.32 V/ppm in the range of 0.03 ∼ 10 ppm with averaged error of 1.68% respectively. The organophosphorus effective detection range of the developed system covers the legal prescription for pesticide residues.

The designs and applications of a scanning interface with electrical signal detection on the scalp for the severely disabled.

This study discussed a computer-aided program development that meets the requirements of people with physical disabilities. A number of control modes, such as electrode signal recorded on the scalp and blink control, were combined with the scanning human-machine interface to improve the external input/output device. Moreover, a novel and precise algorithm, which filters noise and reduces misrecognition of the system, was proposed. A convenient assistive device can assist people with physical disabilities to meet their requirements for independent living and communication with the outside. The traditional scanning keyboard is changed, and only the phonetic notations are typed instead of characters, thus the time of tone and function selection could be saved, and the typing time could be also reduced. Barrier-free computer assistive devices and interface for people with physical disabilities in typing or speech could allow them to use a scanning keyboard to select phonetic symbols instead of Chinese characters to express their thoughts. The human-machine interface controls can obtain more reliable results as 99.8% connection success rate and 95% typing success rate.

A novel method for concentration evaluation of reading behaviors with electrical activity recorded on the scalp.

In this paper, a concentration evaluation of reading behaviors with electrical signal detection on the head is presented. The electrode signal is extracted by brain-computer-interface (BCI) to monitor the user's degree of concentration, where the user is reminded by sound to concentrate, or teaching staffs are reminded to help users improve reading habits, in order to facilitate the user's ability to concentrate. The digital signal processing methods, such as the Kalman Filter, Fast Fourier Transform, the Hamming window, the average value of the total energy of a frame, correlation coefficient, and novel judgment algorithm are used to obtain the corresponding parameters of concentration evaluation. Users can correct their manner of reading with reminders. The repeated test results may be expected to lie with a probability of 95%. Such model training results in better learning effect.