sEMG Sensor Using Polypyrrole-Coated Nonwoven Fabric Sheet for Practical Control of Prosthetic Hand

One of the greatest challenges of using a myoelectric prosthetic hand in daily life is to conveniently measure stable myoelectric signals. This study proposes a novel surface electromyography (sEMG) sensor using polypyrrole-coated nonwoven fabric sheet as electrodes (PPy electrodes) to allow people with disabilities to control prosthetic limbs. The PPy electrodes are sewn on an elastic band to guarantee close contact with the skin and thus reduce the contact electrical impedance between the electrodes and the skin. The sensor is highly customizable to fit the size and the shape of the stump so that people with disabilities can attach the sensor by themselves. The performance of the proposed sensor was investigated experimentally by comparing measurements of Ag/AgCl electrodes with electrolytic gel and the sEMG from the same muscle fibers. The high correlation coefficient (0.87) between the two types of sensors suggests the effectiveness of the proposed sensor. Another experiment of sEMG pattern recognition to control myoelectric prosthetic hands showed that the PPy electrodes are as effective as Ag/AgCl electrodes for measuring sEMG signals for practical myoelectric control. We also investigated the relation between the myoelectric signals\u27 signal-to-noise ratio and the source impedances by simultaneously measuring the source impedances and the myoelectric signals with a switching circuit. The results showed that differences in both the norm and the phase of the source impedance greatly affect the common mode noise in the signal

Single-crystal silver nanowires: Preparation and Surface-enhanced Raman Scattering (SERS) property

Ordered Ag nanowire arrays with high aspect ratio and high density self-supporting Ag nanowire patterns were successfully prepared using potentiostatic electrodeposition within the confined nanochannels of a commercial porous anodic aluminium oxide (AAO) template. X-ray diffraction and selected area electron diffraction analysis show that the as-synthesized samples have preferred (220) orientation. Transmission electron microscopy and scanning electron microscopy investigation reveal that large-area and ordered Ag nanowire arrays with smooth surface and uniform diameter were synthesized. Surface-enhanced Raman Scattering (SERS) spectra show that the Ag nanowire arrays as substrates have high SERS activity.Comment: 5 pages, 4 figure

Fuzzy Neural Network-Based Interacting Multiple Model for Multi-Node Target Tracking Algorithm

An interacting multiple model for multi-node target tracking algorithm was proposed based on a fuzzy neural network (FNN) to solve the multi-node target tracking problem of wireless sensor networks (WSNs). Measured error variance was adaptively adjusted during the multiple model interacting output stage using the difference between the theoretical and estimated values of the measured error covariance matrix. The FNN fusion system was established during multi-node fusion to integrate with the target state estimated data from different nodes and consequently obtain network target state estimation. The feasibility of the algorithm was verified based on a network of nine detection nodes. Experimental results indicated that the proposed algorithm could trace the maneuvering target effectively under sensor failure and unknown system measurement errors. The proposed algorithm exhibited great practicability in the multi-node target tracking of WSNs

On the Image Reconstruction of Capacitively Coupled Electrical Resistance Tomography (CCERT) with Entropy Priors

Regularization with priors is an effective approach to solve the ill-posed inverse problem of electrical tomography. Entropy priors have been proven to be promising in radiation tomography but have received less attention in the literature of electrical tomography. This work aims to investigate the image reconstruction of capacitively coupled electrical resistance tomography (CCERT) with entropy priors. Four types of entropy priors are introduced, including the image entropy, the projection entropy, the image-projection joint entropy, and the cross-entropy between the measurement projection and the forward projection. Correspondingly, objective functions with the four entropy priors are developed, where the first three are implemented under the maximum entropy strategy and the last one is implemented under the minimum cross-entropy strategy. Linear back-projection is adopted to obtain the initial image. The steepest descent method is utilized to optimize the objective function and obtain the final image. Experimental results show that the four entropy priors are effective in regularization of the ill-posed inverse problem of CCERT to obtain a reasonable solution. Compared with the initial image obtained by linear back projection, all the four entropy priors make sense in improving the image quality. Results also indicate that cross-entropy has the best performance among the four entropy priors in the image reconstruction of CCERT

On the Performance of a Capacitively Coupled Electrical Impedance Tomography Sensor with Different Configurations

Capacitively coupled electrical impedance tomography (CCEIT) is a new kind of electrical resistance tomography (ERT) which realizes contactless measurement by capacitive coupling and extends traditional resistance measurement to total impedance measurement. This work investigates the performance of a CCEIT sensor with three different configurations, including the unshielded configuration, the shielded configuration A (the CCEIT sensor with the external shield) and the shielded configuration B (the CCEIT sensor with both the external shield and the radial screens). The equivalent circuit models of the measurement electrode pair of the CCEIT sensor with different configurations were developed. Additionally, three CCEIT prototypes corresponding to the three configurations were developed. Both the simulation work and experiments were carried out to compare various aspects of the three CCEIT prototypes, including the sensitivity distribution, the impedance measurement and the practical imaging performance. Simulation results show that shielded configurations improve the overall average sensitivity of the sensitivity distributions. Shielded configuration A contributes to improve the uniformity of the sensitivity distributions, while shielded configuration B reduces the uniformity in most cases. Experimental results show that the shielded configurations have no significant influence on the imaging quality of the real part of impedance measurement, but do make sense in improving the imaging performance of the imaginary part and the amplitude of impedance measurement. However, configuration B (with radial screens) has no significant advantage over configuration A (without radial screens). This work provides an insight into how shielding measures influence the performance of the CCEIT sensor, in addition to playing an important role in shielding unwanted noise and disturbances. The research results can provide a useful reference for further development of CCEIT sensors

Application of the grey theory to dynamic analyses of the Baiquan Spring flow rate in Xinxiang

The Baiquan Spring in Xinxiang has many functions, such as water supply, agricultural irrigation, humanities, tourism and ecology. It is of great significance to study the dynamics of the spring flow rate and establish a dynamic prediction model for the spring water resources evaluation and protection. In order to further study the dynamic characteristics of the Baiquan Spring flow rate in Xinxiang and evaluate karst water resources in the spring area, based on the data of annually measured spring flow rate and annual average precipitation in the spring area from 1964 to 1978, the main influencing factors of the spring flow rate are determined by using the stepwise regression analysis, and a stepwise regression model is established, with remarkable regression effect. On the basis of the stepwise regression analysis, this paper establishes the GM(1, 2) model, NSGM(1, 2) model and GM(0, 2) model for the dynamic prediction of the spring flow rate. The results show that (1) from 1964 to 1978, the Baiquan spring flow rate was mainly controlled by the precipitation in the spring area, and the spring flow rate lagged behind the precipitation for one year, reflecting the dynamic characteristics of the spring water in the natural state. (2) The accuracy levels of the three grey models are the highest (excellent). (3) From 1964 to 1978, the measured discharge of the Baiquan spring ranged from 2.347 to 6.448 m3/s, with an average of 3.904 m3/s. The predicted values of the stepwise regression model range from 1.882 to 6.383 m3/s, with an average of 3.904 m3/s. The predicted value of the GM(1, 2) model varies between 2.327 and 6.448 m3/s, with an average of 3.939 m3/s. The predicted values of the NSGM(1, 2) model range from 2.133 to 6.448 m3/s, with an average of 3.927 m3/s. The predicted values of the GM(0, 2) model range from 1.787 to 6.448 m3/s, with an average of 3.907 m3/s. (4) The average relative errors of the stepwise regression model and the three grey models mentioned above are 7.794%, 7.292%, 7.122% and 7.797% respectively, all of which are less than 10%, indicating that they can be used for dynamic prediction of the spring water. Among them, the NSGM(1, 2) model has a higher accuracy and better fitting to the inflection point of the curve. (5) According to the spring flow rate from 1964 to 2030 predicted by the four models, the exploitation resources of the karst water in the Baiquan spring area should not exceed 1.69 m3/s from the angle of spring protection. The research results can not only provide scientific basis for spring flow dynamic prediction and spring area water resources evaluation, but also provide reference for the study of groundwater dynamics in similar areas