In the picture of the Internet of Things (IoT), smart human-machine interfaces (HMIs) play a significant role. Desired attributes for smart HMIs include multidimensional sensing, flexibility with stable responsiveness, and a long operational lifetime. To this end, in this article, we present a polyvinylidene fluoride (PVDF)/Au/polyethylene naphthalate (PEN) sandwiched architecture. Piezoelectric property of the PVDF is utilized for dynamic force sensing and touch-induced energy harvesting. A mutual capacitive based technique is employed for two-dimensional (2D) position recognition, hover distance measurement and curvature self-detection, as well as helping in compensating the static force detection shortage of the piezoelectric sensing layer. The presented HMI system achieved an overall force sensitivity at 0.07 N and capacitive resolution at 0.004 pF; and a force responsivity at 2.36 V/N in the pressure range of 1 N-30 N. In each touch event, the maximum capacitance change and the electric energy harvested in a stored capacitor are averaged at 0.016 pF and 0.038 nJ, respectively. Furthermore, interdigitated electrodes are implemented for self-detecting the device curvature. With pre-correlating of the force-voltage response under different bending degrees, a stable force detection response is realized when the shape of the device changes. The work presented in this article advances the field of human-machine interactivities.

In this article, a novel 3-D image reconstruction method was proposed for planar array ECT by combining depth estimation and sparse representation. The central depth of target in the region of interest is first estimated from an image initially reconstructed by the Tikhonov regularization. Second, sparse representation is employed to depict the permittivity distribution using the Gaussian radial basis functions defined at voxels over the plane in the estimated depth. Then, the 3-D image is reconstructed by solving a dedicated optimization problem using the Gauss–Newton method. The proposed method reduces the dimension of the unknowns, improves the condition number of the inverse problem, and enhances the image quality greatly. Numerical simulations and experiments were carried out to validate the proposed method. Results show that the proposed method can provide more accurate 3-D images of single or multiple inclusions at varying depths than conventional Tikhonov, total variation (TV), and Landweber methods while relieving the adverse effect of the nonuniform sensitivity of planar array ECT with varying depths on image reconstruction. The proposed method is of great potential in detecting and imaging subsurface abnormities.

Electrical capacitance tomography (ECT) is a technique to visualize the permittivity distribution inside a domain from the inter-electrode mutual capacitances on the domain boundary. The potential applications of ECT in nondestructive testing depend on accurate and stable image reconstruction. To improve image quality, this paper presents a reconstruction framework with the sparse representation of phase boundaries in two-phase distributions. By adopting sparsity-promoting basis, i.e. radial basis functions, the reconstruction problem is transformed into searching for the optimal phase boundaries and real permittivity values of inclusions by formulating an optimization problem accordingly. This can reduce the number of unknowns significantly and has a strong ability to accommodate to the topology changes of inclusion geometries, which can improve the reconstruction accuracy and robustness. Both simulation and phantom experiments are performed to investigate the reconstruction performance, especially regarding the anti-noise ability and robustness against choices of model parameters. Compared with typical conventional methods, our method can not only provide significantly improved image quality, but also accurately estimate the real permittivity values of inclusions. This would enable ECT to be applied in challenging nondestructive testing applications.