This paper reviews the latest development and emerging technologies on the application of process tomography to multiphase flow measurement in industrial and biomedical fields. In the industrial applications, electrical resistance tomography and electrical capacitance tomography are popularly applied in multiphase flow measurement dependent on the electrical properties of the objects. In particular, applications to circulating fluidized bed, trickle-bed reactor and temperature measurement are discussed. In the biomedical applications, in most cases, measurement is conducted in a static condition. Two latest applications in flowing condition are reviewed, which are thrombus detection in blood flow and cell sensing with a microfluidic device. Finally, the challenges to process tomography for multiphase flow measurement on industrial and biomedical applications are addressed.

This article presents a new fractional-order normalized Bouc–Wen (BW) (FONBW) model to describe the asymmetric and rate-dependent hysteresis nonlinearity of piezoelectric actuators (PEAs). In view of the fact that the classical BW (CBW) model is only efficient for the symmetric and rate-independent hysteresis description, the FONBW model is devoted to characterizing the asymmetric and rate-dependent behaviors of the hysteresis in PEAs by adopting an $N$ th-order polynomial input function and two fractional operators, respectively. Different from the traditional modified BW models, the proposed FONBW model also eliminates the redundancy of parameters in the CBW model via the normalization processing. By this way, the developed FONBW model has a relatively simple mathematic expression with fewer parameters to simultaneously characterize the asymmetric and rate-dependent hysteresis behaviors of PEAs. Model parameters are identified by the self-adaptive differential evolution algorithm. To validate the effectiveness of the proposed model, a series of model verification and inverse-multiplicative-structure-based feedforward control experiments are carried out on a PEA system. Results show that the proposed model is superior to the CBW model and traditional modified BW model in modeling accuracy and hysteresis compensation.

Image reconstruction of Electrical Impedance Tomography (EIT) is a highly nonlinear ill-posed inverse problem, which is sensitive to the measurement noise and model errors. An improved Convolutional Neural Network (CNN) method is proposed for the EIT lung imaging. The proposed method is optimized based on the Visual Geometry Group (VGG) model, adding the batch normalization (BN) layer, ELU activation function, global average pooling (GAP) layer, and radial basis function (RBF) neural network. These optimizations help speed up network convergence, and improve reconstruction accuracy and robustness. Nearly 10 thousand EIT simulation models generated from chest CT images of 60 patients are used for the network training. The chest deformation, lung hyperdilation and atelectasis are randomly simulated during the model generation process. The proposed method after training is tested through a series of simulation data and experimental models. The reconstruction quality is quantitatively compared by calculating the root mean square error (RMSE) and image correlation coefficient (ICC). On average, the proposed method achieves 0.082 RMSE and 0.892 ICC through experimental results. The proposed method achieves high-resolution and robust shape reconstructions with multiphase conductivity for EIT lung imaging, especially in the presence of the measurement noise and interference. The proposed method is promising in providing quantitative images for potential clinical applications, such as human thorax imaging.

A portable electrical impedance tomography (EIT) system has been developed with Red Pitaya STEMlab for biomedical applications. The Red Pitaya STEMlab is a portable device to realize voltage generation and data acquisition for the EIT system. The EIT system includes a modified howland circuit as a voltage-controlled current source, a high-speed analogy multiplexer module, an 8-electrode array, and a personal computer. The generalized vector sampled pattern matching algorithm and the Tikhonov regularization algorithm are used to reconstruct the image generated by the EIT system. The reconstructed images by using Red Pitaya STEMlab are compared with a commercial impedance analyzer IM3570 within the frequencies of f = 100 KHz. The results show that the maximum difference of the image correlation between Red Pitaya STEMlab and IM3570 is 5.36%. Finally, the EIT system is used to image the conductivity of eggs during heating process. These results verified that the developed portable EIT system with Red Pitaya STEMlab could measure the biological tissue in a high accuracy at low cost.

A portable electrochemical impedance spectroscopy (EIS) system has been developed based on FPGA for bio-detection. FPGA is a programmable signal processing chip, which cooperates with DAC module to generate the sweep excitation signal, controls ADC module to perform dual channel acquisition of voltage of a peripheral circuit, performs FFT and parameter calculation and transmits the result to PC for data processing. Based on FPGA internal programming, the system greatly reduces the size and achieves a single multi-frequency measurement, which enhances integration and portability of the system. The measurements of the electrical equivalent circuit by using the EIS system is compared with a high-precision commercial ZX70A impedance analyzer in the range of limited frequency of 100 Hz - 500 kHz. The result shows that the maximum relative error of amplitude and the maximum absolute error of phase are 2.42% and 0.936°, respectively. Finally, the EIS system is used to measure electrical impedance change during egg heating. The result shows that the impedance amplitude of egg decreases linearly with the increase of heating time and electrical characteristics of raw eggs and heated eggs are significantly different. These results verified that the developed EIS system achieve portable high-precision measurement of biological tissue.

Electrical impedance tomography (EIT) is a noninvasive measurement technique that estimates the internal resistivity distribution based on the boundary voltage–current data measured from the surface of the conductor. If a thin stretchable soft material with a certain piezoresistive property is used as the electrical conductor, EIT has the ability to reconstruct the position where the resistivity changes due to the inside pressure contact, so that a large‐scale artificial sensitive skin is provided for robotics. First, the different conduction principles and material types of artificial sensitive skins are discussed, which is next followed by the different driving modes and image reconstruction techniques. Then, details on how EIT is used for robotic skin applications are described. Finally, the development trends and future potentials of EIT‐based robotic skins are expounded.