Abstract:Study results in the last decades show that amount and quality of physical exercises, then the active participation, and now the cognitive involvement of patient in rehabilitation training are crucial to enhance recovery outcome of motor dysfunction patients after stroke. Rehabilitation robots mainly have been developed along this direction to satisfy requirements of recovery therapy, or focused on one or more of the above three points. Therefore, rehabilitation robot based on neuro-machine interaction has been proposed for the paralyzed limb training of post-stroke patient, which utilizes motor related EEG, UCSDI (Ultrasound Current Source Density Imaging), EMG for the robot control and feeds back the multi-sensory interaction information such as visual, auditory, force, haptic sensation to the patient simultaneously. This neuro-controlled and perceptual rehabilitation robot will bring great benefits to post-stroke patients. In order to develop such a kind of rehabilitation robot, some key technologies, such as noninvasive precise measurement and decoding of neural signals, realistic sensation feedback, coordinated control for both the rehabilitation robot and the patient, need to be solved. In this paper, some fundamental problems in developing and optimizing such a kind of rehabilitation robot based on neuro-machine interaction are proposed and discussed.

Abstract:Electroencephalographic (EEG)-based emotion recognition has received increasing attention in the field of human-computer interaction (HCI) recently, there however remains a number of challenges in building a generalized emotion recognition model, one of which includes the difficulty of an EEG-based emotion classifier trained on a specific task to handle other tasks. Little attention has been paid to this issue. The current study is to determine the feasibility of coping with this challenge using feature selection. 12 healthy volunteers were emotionally elicited when conducting picture induced and video induced tasks. Firstly, support vector machine (SVM) classifier was examined under within-task conditions (trained and tested on the same task) and cross-task conditions (trained on one task and tested on another task) for picture induced and video induced tasks. The within-task classification performed fairly well (classification accuracy: 51.6% for picture task and 94.4% for video task). Cross-task classification, however, deteriorated to low levels (around 44%). Trained and tested with the most robust feature subset selected by SVM-recursive feature elimination (RFE), the performance of cross-task classifier was significantly improved to above 68%. These results suggest that cross-task emotion recognition is feasible with proper methods and bring EEG-based emotion recognition models closer to being able to discriminate emotion states for any tasks.

Abstract:Straw incineration monitor is a key part of international environmental governance. In the paper, the combination of MODIS, MUX and TLC remote sensors is used to monitor straw burning fire points accurately. MODIS remote sensor has the characteristics of high temporal resolution and thermal infrared band, which can be used to judge the regional thermal abnormal variation and preliminary extract the suspicious thermal abnormal points. Combining with GIS information, the preliminary position of MODIS thermal abnormal points can be acquired. The MUX and TLC sensors of ZY-3 satellite in the preliminary position area can be pretreated, which includes radiometric calibration, atmospheric correction, geometric precision correction, ortho-rectification, etc. Through analyzing the physical properties and spectral information in the straw incineration area, the interpretation features of the straw incineration area will be determined. Then the high geographical resolution fusion image with two meters resolution can be interpreted, and the information of fire-point in high geographical resolution remote sensor can be extracted. Combining with the Google earth map to compare interpretation images in different time range of this area, and using ArcGIS platform to accurately position the confirmed fire point, the final position of the fire can be determined. Correspondingly, the combination of remote sensing sensors with high, medium and low resolution can be used to monitor the straw incineration point in county area. In experimental area, there are twenty-three straw burning fire points are found. The experimental results show that, this method can realize precise monitoring of straw incineration point in county area. However, straw incineration point monitoring in real time still need to be further investigated.

Abstract:Regularization methods were combined with line-of-sight tunable diode laser absorption spectroscopy (TDLAS) to measure nonuniform temperature distribution. Relying on measurements of 12 absorption transitions of water vapor from 1300 nm to 1350 nm, the temperature probability distribution of nonuniform temperature distribution, for which a parabolic temperature profile is selected as an example in this paper, was retrieved by making the use of regularization methods. To examine the effectiveness of regularization methods, truncated singular value decomposition (TSVD), Tikhonov regularization and a revised Tikhonov regularization method were implemented to retrieve the temperature probability distribution. The results derived by using the three regularization methods were compared with that by using constrained linear least-square fitting. The results show that regularization methods not only generate closer temperature probability distributions to the original, but also are less sensitive to measurement noise. Particularly, the revised Tikhonov regularization method generate solutions in better agreement with the original ones than those obtained by using TSVD and Tikhonov regularization methods. The results obtained in this work can enrich the temperature distribution information, which is expected to play a more important role in combustion diagnosis.

Abstract:In this paper, the design and experimental results for a novel high-stability sounding electrostatic field micro sensor are presented. By means of hermetic chip sealing, digital weak signal demodulation unit, and probe sensor structure design, harsh environmental adaptation problems such as low temperature, high humidity, low air pressure, waterfall are solved. The sensor has a high resolution of 14 V/m, a wide measurement range of ±100 kV/m, and is proved to have superior stability and performance in sounding electric field experiments than traditional sensors under different kinds of weather.

Abstract:Resonant temperature sensors have drawn considerable attention for their advantages such as high sensitivity, digitized signal output and high precision. This paper presents a new type of resonant temperature sensor, which uses capacitive micromachined ultrasonic transducer (CMUT) as the sensing element. A lumped electro-mechanical-thermal model was established to show its working principle for temperature measurement. The theoretical model explicitly explains the thermally induced changes in the resonant frequency of the CMUT. Then, the finite element method was used to further investigate the sensing performance. The numerical results agree well with the established analytical model qualitatively. The numerical results show that the resonant frequency varies linearly with the temperature over the range of 20 ℃ to 140℃ at the first four vibrating modes. However, the first order vibrating mode shows a higher sensitivity than the other three higher modes. When working at the first order vibrating mode, the temperature coefficient of the resonance frequency (TCf) can reach as high as -1114.3 ppm/℃ at a bias voltage equal to 90% of the collapse voltage of the MCUT. The corresponding nonlinear error was as low as 1.18%. It is discovered that the sensing sensitivity is dependent on the applied bias voltages. A higher sensitivity can be achieved by increasing the bias voltages.