Abstract:The current commercial prostheses are lack of effective sensory feedback mechanism. Indirect stimulation methods such as mechanical stimulation were applied to achieve sensory feedback in some work, but they cannot evoke “natural” sensations. Some researchers evoked somatic sensations by intra-neural stimulation, which is invasive and with high restriction on electrode materials. In comparison, transcutaneous electrical stimulation (TNES) is easier to be implemented. Unfortunately, there is still no effective strategy to autonomous control electrical stimulation parameters. In this paper, we conducted a preliminary research on the relationship between electrical stimulation parameters and evoked somatic sensation based on Electroencephalogram (EEG) analysis. The experimental results proved that the phantom sensation of fingers can be evoked by TNES. In addition, the relationship between the position of stimulation and the position of evoked sensation be analyzed according to the EEG distribution. And it can be determined whether the stimulation intensity is enough to evoke obvious phantom sensation, by analyzing its curve characteristics of event-related potential (ERP). This work can be helpful to automatically control electrical stimulation parameters for evoking phantom sensations in the future.

Abstract:In order to optimize the video transmission in wireless network, a complete system is designed by combining dense small-cell network, software defined networking and scalable video coding technology. Through the cooperation between base stations in dense small-cell network, the radio spectrum utilization is improved, and the differential service is provided for different users by bitrate adaptation. This paper aims at maximizing the quality of user experience by making joint decision of user video layer selection and wireless resource allocation. By employing Lyapunov optimization method, the original problem is transformed into two independent subproblems that can be solved separately. A low complexity algorithm only relying on the current observation is presented. The experimental results verify that the proposed algorithm can perform well in dynamic environment and achieve a higher user experience.

Abstract:In this paper, the electromagnetic effect of parallel conductors under alternating magnetic fields is investigated. The basic electromagnetic model and the equivalent distributed parameter network model in the alternating magnetic fields of parallel conductors are studied and established. The mechanism of electromagnetic effect and electric current of parallel conductors under the excitation of alternating magnetic fields is analyzed. The results of relevant analysis have been validated. The current behavior model well explains the current high frequency oscillation of parallel double conductors.

Abstract:Structured light 3D reconstruction technology has been widely used in industrial detection. Nowadays, the more miniaturized and higher-precision industrial detection technology is becoming more important. The telecentric lens has many excellent characteristics, such as small perspective error, small lens distortion, less imaging distortion, and large depth of field, which attracts more attention. In this paper, the traditional lens is used to replace the camera lens in the traditional structured light system with the telecentric lens. Through the improvement of the traditional two-step calibration method, the affine model of the camera is calibrated, and then the calibrated camera is used to calibrate the parameters of the projector. The experimental results show that the telecentric structured light system can achieve high-precision and high-resolution 3D reconstruction in a small field of vision, and has an increased depth of field. It can be used for high-precision 3D detection of semiconductor devices and micro-components.

Abstract:With the rapid application of smart electric meters, a large amount of power consumption data is generated, which challenges the data storage and communication. Meanwhile, due to the extensive production properties and business modes, the power consumption states of factories are complicated, which makes data analysis difficult. In this paper, a new neural networks-based power consumption state recognition method is proposed. The main efforts include three aspects: the pre-processing of power data, the modeling of neural network to automatically recognize the power consumption states of factories and the performance testing of the proposed method. The experimental results demonstrate the rationality and effectiveness of our method. The correct recognition of power consumption states of factories is helpful to the power supply company to arrange the sensible power supply strategy and efficient power utilization plan.

Abstract:Oil and gas pipelines have a wide range of layout, long span, and that makes the daily maintenance work very laborious. To improve efficiency of maintenance, an oil and gas pipeline supervision system based on unmanned aerial vehicle (UAV) aerial photography is proposed and realized in this work. The system is based on brower/server architecture, and adopts the geographic information system and cloud platform technologies, which can realize trajectory planning, mission management, unmanned aerial vehicle flight data, flight trajectory, aerial video real-time display, fault early warning and playback of historical events etc. With the implementation of the proposed system, the petrochemical maintainers can understand real-time condition of the pipelines and response to the damage or disaster more efficiently.

Abstract:This paper proposed a solution to realize the virtual proximity sensor (P-Sensor) based on usual touch panel. The principle of the distance detection based on capacitance touch panel is introduced firstly. And the hardware system is developed according to the baseband and power management units. Using the UML (unified modeling language) sequence diagram, the control channel framework for the virtual P-Sensor is realized based on server-client system. With the distance detection results, the screen can be turn on or off automatically to save power consumption.

Abstract:To reduce the occurrence of traffic accidents caused by driver distraction, a behavior detection method based on histogram of oriented gradient (HOG) and support vector machine (SVM) was proposed in this paper. In the algorithm, interesting region of driver was detected first from the video images. Then the image was enhanced, smoothed and normalized. The histogram of oriented gradient was used to extract the feature of the target image. The cross-validation method was used to optimize the SVM parameters, and then used for the classification of driver behaviors. In the experiments, the proposed method was compared with classical SVM and the local binary patterns feature based SVM algorithms. The results show that, the proposed method can obtain better classification accuracy.