Electrical impedance tomography (EIT) is considered as a potential candidate for brain stroke imaging due to its compactness and potential use in bedside and emergency settings. The electrode-skin contact impedance and low conductivity of skull pose some practical challenges to EIT head imaging. This work studies the application of capacitively coupled electrical impedance tomography (CCEIT) in brain imaging for the first time. CCEIT is a new contactless EIT technique that uses voltage excitation without direct contact with the skin, as oppose to directly injecting current to the skin in EIT. Because the safety issue of a new technique should be strictly treated, simulation work based on a simplified head model was carried out to investigate the safety aspects of CCEIT. By comparing with standard EIT excited by a typical safe current level used in brain imaging, the safe excitation reference of CCEIT is obtained. This is done by comparing the maximum level of internal electrical field (internal current density) of EIT and that of CCEIT. Simulation results provide useful knowledge of excitation signal level of CCEIT and also show a critical comparison with traditional EIT. Practical experiments were carried out with a twelve-electrode CCEIT phantom, saline and carrot samples. Experimental results show the feasibility and potential of CCEIT for stroke imaging. In this work, the anomaly diameter resolution is 10 mm (1/18 of the phantom diameter), which indicates that small-volume stroke could be detected. This is achieved by a low excitation voltage of 1V, showing the possibility of even better performance when higher but yet safe level of excitation voltages is used.

This work proposes a new positioning method based on multiple ultrasonic sensors for the autonomous mobile robot. Unlike the conventional ultrasonic positioning methods, this new method can realize higher accuracy ultrasonic positioning without additional temperature information. Three ultrasonic sensors are used for positioning. A generalized measurement model is established for general sensor configuration. A simplified measurement model, which considers the computational complexity, is also established for linear/simplified sensor configuration. Three time-of-flight signals are obtained from the three ultrasonic sensors. The coordinates of the target are calculated by the ratios of time-of-flights. Positioning experiments were carried out to verify the feasibility and effectiveness of the proposed method. Experimental results show that the new ultrasonic positioning method is effective, both the two established models can implement positioning successfully, and the positioning accuracy is satisfactory. Compared with the conventional ultrasonic positioning method with the default ultrasonic velocity, the positioning accuracy is greatly improved by the proposed method. Compared with the ultrasonic positioning method with additional temperature compensation, the results obtained by the proposed method are comparable.

Based on linear back projection (LBP) algorithm, density-based spatial clustering of applications with noise (DBSCAN) algorithm and image fusion technique, a new image reconstruction method of capacitively coupled electrical impedance tomography (CCEIT) is proposed in this work. LBP is used to obtain the original image of the real part and the original image of the imaginary part. The gray level thresholds of the two original images are determined by DBSCAN clustering algorithm and the initial image of the real part and the initial image of the imaginary part are obtained by gray level threshold filtering, respectively. Finally, the two initial images are fused and the final image is obtained. Image reconstruction experiments are carried out with a 12-electrode CCEIT system prototype. The experimental results verify the effectiveness of the proposed image reconstruction method. Experimental results show that the quality of the final image is satisfactory. Compared with the conventional electrical tomography (ET) image reconstruction methods, the new proposed image reconstruction method needs less prior knowledge or manual intervention and realizes more effective usage of the impedance information. The research results also indicate that the DBSCAN clustering algorithm is an effective way to determine the gray level thresholds of the original images and the mean square error (MSE) is a reasonable criterion for image fusion. Meanwhile, LBP + DBSCAN is an effective algorithm for image reconstruction and image fusion technique is an effective way to utilize the whole impedance information of the conductive gas–liquid two-phase flow.

Combining the transmission “Z” mode and the reflection “V” mode, a new hybrid dual-path ultrasonic sensor is designed to make full use of the advantages of the two modes. With the hybrid sensor and a new model-based time-of-flight (TOF) measurement method, a new model-based hybrid ultrasonic gas flowmeter is developed. The model-based TOF measurement method, which includes a signal filtering part and a model fitting part, is proposed to implement accurate TOF measurement. The signal filtering is implemented by a Butterworth bandpass filter and the model fitting is implemented by a hybrid Gauss–Newton structured BFGS method. A new hybrid ultrasonic flowmeter prototype with the inner diameter of 100 mm was developed and dynamic experiments were carried out. Experimental results show that the development of the hybrid ultrasonic flowmeter is successful. With the flowmeter prototype, the maximum relative error and the repeatability error of gas flowrate measurement are 1.80% and 0.45%, respectively, for a common flowrate ranges from 30 to 500 m ³ /h.

Impedance imaging is an effective approach for non-intrusively reconstruct the distribution of dielectric parameters inside the region of interest. Most common form of impedance imaging is electrical impedance tomography (EIT), which requires direct contact with the medium via electrodes. In this work we present a novel impedance imaging using capacitive coupling which provides a contactless method, totally non-invasive and non-intrusive, by measuring the phase. There are less attentions in many prior works to the phase information of the voltage/current measurements. This work studies for the first time the capacitively coupled electrical phase spectroscopy for contactless dielectric parameter imaging. A 12-electrode capacitively coupled test phantom and a measurement system were used to obtain the phase measurements within a wideband frequency range from 200 kHz to 15 MHz. Background data with different conductivity levels were investigated in the experiments to show a broad application possibility. The forward modelling was implemented by simulation and the image reconstruction based on phase measurements was implemented with the total variation algorithm. The potentials, possibilities and challenges of such capacitively coupled dielectric spectroscopy tomography with phase data are discussed in this work.

A convolutional neural network (CNN)-based image reconstruction algorithm for two-phase material imaging is presented and verified with experimental data from a capacitively coupled electrical resistance tomography (CCERT) sensor. As a contactless version of electrical resistance tomography (ERT), CCERT has advantages such as no invasion, low cost, no radiation, and rapid response for two-phase material imaging. Besides that, CCERT avoids contact error of ERT by imaging from outside of the pipe. Forward modeling was implemented based on the practical circular array sensor, and the inverse image reconstruction was realized by a CNN-based supervised learning algorithm, as well as the well-known total variation (TV) regularization algorithm for comparison. The 2D, monochrome, 2500-pixel image was divided into 625 clusters, and each cluster was used individually to train its own CNN to solve the 16 classes classification problem. Inherent regularization for the assumption of binary materials enabled us to use a classification algorithm with CNN. The iterative TV regularization algorithm achieved a close state of the two-phase material reconstruction by its sparsity-based assumption. The supervised learning algorithm established the mathematical model that mapped the simulated resistance measurement to the pixel patterns of the clusters. The training process was carried out only using simulated measurement data, but simulated and experimental tests were both conducted to investigate the feasibility of applying a multi-layer CNN for CCERT imaging. The performance of the CNN algorithm on the simulated data is demonstrated, and the comparison between the results created by the TV-based algorithm and the proposed CNN algorithm with the real-world data is also provided.