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For biomedical imaging in mini-scale, the inverse problem of electrical impedance tomography (EIT) is severely ill-conditioned due to the number of electrodes is very limited. In this paper, a novel difference imaging algorithm for 2D-EIT using measurement estimation of virtual electrodes is proposed. The proposed reconstruction algorithm breaks though the limitation of micro EIT sensor’s structure and tackles the problem of low spatial resolution in mini-scale by introducing virtual electrodes. The inverse problem of EIT is decomposed into two separately tasks: estimation potentials of virtual electrodes with proper priors, determination of the conductivity distribution using both virtual and real electrodes. It is formulated as a possibility model using virtual electrodes’ potentials as latent variables. Real electrodes’ potentials are regarded as the observable variables. Conductivity distribution is predicted by a maximum likelihood of the possibility model by using EM algorithm. The proposed algorithm is verified by both simulation and experiment. In experiment, medaka fish embryo which is about 1mm in diameter is reconstructed. Comparing with Tikhonov and NOSER regularization method, average ICC is improved 11.54% from 0.7620 to 0.8499 in simulation and improved 5.79% from 0.6978 to 0.7382 in experiment. The algorithm not only performs well in conductivity reconstruction, but also in shape reconstruction. Average position error is decreased 47.57%, average shape error is decreased 24.44% in experiment. It is very suitable for image reconstruction in mini-scale. [ABSTRACT FROM AUTHOR]

This article proposes a new discrete-time predictive sliding mode control (DPSMC) for a parallel micropositioning piezostage to improve the motion accuracy in the presence of cross-coupling hysteresis nonlinearities and input constraints. Unlike the traditional linear discrete-time sliding mode control (DSMC), the proposed DPSMC is chattering free and has a faster convergence rate thanks to the design of a nonlinear discrete-time fast integral terminal sliding mode surface. Moreover, by combining with the receding horizon optimization, the sliding mode state is predicted to follow the expected trajectory of a predefined continuous sliding mode reaching law, which also allows the proposed controller to explicitly deal with constraints. The stability of the closed-loop system is analyzed under the model disturbances and constraints, and proves that the proposed DPSMC can offer a smaller quasi-sliding mode bandwidth than the traditional DSMC. The effectiveness of the proposed controller is validated by a series of numerical simulations and experiments. Results demonstrate the advantages of proposed DPSMC over the traditional DSMC method. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Driven-measurement pattern affects image reconstruction quality of Electrical Impedance Tomography (EIT). In this paper, optimal pattern selection approach based on machine learning is proposed. It converts the problem of driven-measurement pattern optimization to pattern recognition. It is simple, effective and fast. The selected optimal pattern is used for image reconstruction, which is aiming at an improvement. Support Vector Machines (SVM) is used to select the optimal pattern. Firstly, pattern candidates are generated. The optimal pattern is considered to be selected from them. Secondly, optimal pattern selection approach is introduced. SVM with estimated conductivity distribution as features is used for pattern recognition. Thirdly, the proposed approach is verified by both simulation and experiment. Average imaging correlation coefficient (ICC) of the reconstructed images for simulation models is improved 7.6163% from 0.7891 to 0.8492. In experiment, average ICC is improved 2.9439% from 0.7541 to 0.7763. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

For high control performance of cable-driven manipulators, we design a new adaptive time-delay control (ATDC) using enhanced nonsingular fast terminal sliding mode (NFTSM). The proposed ATDC uses time-delay estimation (TDE) to acquire the lumped dynamics in a simple way and founds a practical model-free structure. Then, a new enhanced NFTSM surface is developed to ensure fast convergence and high control accuracy. To acquire good comprehensive performance under lumped uncertainties, in this article we propose a novel adaptive algorithm for the control gain, which can regulate itself based on the control errors timely and accurately. Benefitting from the TDE and the proposed enhanced NFTSM surface and adaptive control gain, our proposed ATDC is model-free, fast response, and accurate. Theoretical analysis concerning system stability, and control precision and convergence speed are given based on Lyapunov theory. Finally, the advantages of our ATDC over existing methods are verified with comparative experiments. [ABSTRACT FROM AUTHOR]

A proportional Genetic Algorithm (${p}$ GA) is proposed to solve the inverse problem for the image reconstruction of static electrical impedance tomography (EIT). The ${p}$ GA obtains static EIT image with higher convergence speed and better reconstruction image quality by combining Genetic Algorithm (GA) with a ratio objective function. Although GA gets better performance for EIT inverse problem than any other traditional methods like Tikhonov regularization, it is high time complex and slow convergence. In this paper, a ratio objective function is proposed to solve the problem. Firstly, two kinds of methods: the Tikhonov regularization algorithm and the ${p}$ GA are used for image reconstruction in the simulation. The image quality of the two algorithms are compared. Secondly, the parameters such as population number and crossover mode of ${p}$ GA are optimized, initial values are set to improve the convergence speed of the algorithm, and the reconstructed image is processed to improve the quality of the image. Finally, experiments are conducted to verify the stability of the ${p}$ GA under certain noise conditions. In the experiment, different numbers and shapes of targets are placed in the sensor and an EIT system based on 34980A data acquisition system is used for data collection. The image reconstructed by ${p}$ GA shows that, the position and shape of the targets can accurately correspond to the real object. [ABSTRACT FROM AUTHOR]

Parallel mechanisms have been employed as architectures of high-precision vibration isolation systems. However, their performances in all degrees of freedom (DOFs) are nonidentical. The conventional solution to this problem is isotropic mechanism design, which is laborious and can hardly be achieved. This article proposes a novel concept; namely, isotropic control, to solve this problem. Dynamic equations of parallel mechanisms with base excitation are established and analyzed. An isotropic control framework is then synthesized in modal space. We derive an explicit relationship between modal control force and actuation force in joint space, enabling implementation of the isotropic controller. The multi-DOF system is transformed into multiidentical single-DOF systems. Under the framework of isotropic control, parallel mechanisms obtain an identical frequency response for all modes. An identical corner frequency, active damping, and rate of low-frequency transmissibility are achieved for all modes using a combining proportional, integral, and double integral compensator as a subcontroller. A 6-UPS parallel mechanism is presented as an example to demonstrate effectiveness of the new approach. [ABSTRACT FROM AUTHOR]

A wearable electrical impedance tomographic (wEIT) sensor with 8 electrodes is developed to realize gesture recognition with machine learning algorithms. To optimize the wEIT sensor, gesture recognition rates are compared by using a series of electrodes with different materials and shapes. To improve the gesture recognition rates, several Machine Learning algorithms are used to recognize three different gestures with the obtained voltage data. To clarify the gesture recognition mechanism, an electrical model of the electrode-skin contact impedance is established. Experimental results show that: rectangular copper electrodes realize the highest recognition rate; the existence of the electrode-skin contact impedance could improve the gesture recognition rate; Medium Gaussian SVM (Support Vector Machine) algorithm is the optimal algorithm with an average recognition rate of 95%. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The applications of robotics to the rehabilitation training of neuromuscular impairments have received increasing attention due to their promising prospects. The effectiveness of robot-assisted training directly depends on the control strategy applied in the therapy program. This paper presents an upper extremity exoskeleton for the functional recovery training of disabled patients. A minimal-intervention-based admittance control strategy is developed to induce the active participation of patients and maximize the use of recovered motor functions during training. The proposed control strategy can transit among three control modes, including human-conduct mode, robot-assist mode, and motion-restricted mode, based on the real-time position tracking errors of the end-effector. The human–robot interaction in different working areas can be modulated according to the motion intention of patient. Graphical guidance developed in Unity-3-D environment is introduced to provide visual training instructions. Furthermore, to improve training performance, the controller parameters should be adjusted in accordance with the hemiplegia degree of patients. For the patients with severe paralysis, robotic assistance should be increased to guarantee the accomplishment of training. For the patients recovering parts of motor functions, robotic assistance should be reduced to enhance the training intensity of effected limb and improve therapeutic effectiveness. The feasibility and effectiveness of the proposed control scheme are validated via training experiments with two healthy subjects and six stroke patients with different degrees of hemiplegia. [ABSTRACT FROM AUTHOR]