electrodes, whose capacitance changes under pressure due to the deformation of the dielectric layer. Although capacitive pressure sensors have some advantages including simple device structure and easy fabrication, they typically exhibit low sensitivity and also require more sophisticated readout circuits that can measure very small capacitance change (typically in the range of hundreds of femtofarad). Moreover, parasitic capacitance and crosstalk between the pixels could also lead to reduced sensitivity and spatial resolution. Piezoelectric materials such as polyvinylidene difluoride that can generate electrical charges from mechanical impact can also be used for pressure sensing. [19] However, such piezoelectric sensors are not suitable for measuring static pressure as they only respond to dynamic changes in pressure. Considering the above, resistive pressure sensors are more promising as they typically offer great sensitivity and only require very basic readout circuit that can measure resistance change. The resistive pressure sensors are typically made using thin films of conductors, such as nanocomposites [15] or nanowires, [16] whose electrical resistance changes under mechanical strain due to microscopic change in morphology or increase in distance between the conductive fillers. [21][22][23] For sensor fabrication, inkjet printing [24][25][26][27][28][29][30][31][32][33] has been widely used and the advantages are multifold. First, the printing process greatly simplifies the fabrication by completely eliminating the need for masks (used in photolithography), as well as high temperature or high vacuum processes commonly used in semiconductor microfabrication. Moreover, it is an additive and highly scalable process that can greatly reduce materials waste. For these reasons, the inkjet printing process could allow the sensors to be low-cost and potentially disposable. Many types of printed sensors including strain sensor, [29][30][31] temperature sensor, [24,32] and humidity sensor [27,28] have already been demonstrated.We have recently demonstrated the use of inkjet-printed silver nanoparticle (AgNP) pattern on an elastomer substrate as an ultrasensitive strain sensor. [31] Inspired by the capability of using printed AgNP thin film for strain sensing and its very high sensitivity, in this work, we demonstrate a printed resistive pressure sensor whose sensing mechanism is based on pressure-induced strain. The sensor consists of a conductive AgNP layer that is directly printed onto a polydimethylsiloxane (PDMS) substrate and subsequently encapsulated by Soft pressure sensors may find a wide range of applications in soft robotics, biomedical devices, and smart wearables. Here, an inkjet-printed resistive pressure sensor that offers high sensitivity and can be fabricated using a very simple process is reported. The device is composed of a conductive silver nanoparticle (AgNP) layer directly printed onto a polydimethylsiloxane substrate and encapsulated by a VHB tape. The pressure is measured through change in e...

Soft pressure sensors are one class of the essential devices for robotics and wearable device applications. Despite the tremendous progress, sensors that can reliably detect both positive and negative pressures have not yet been demonstrated. In this paper, a soft capacitive pressure sensor, made using a convenient and low-cost screen-printing process that can reliably detect both positive and negative pressures from −60 to 20 kPa, is reported. The sensor is made with an Ecoflex-0030 dielectric layer, conductive and stretchable poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (with ionic additives) electrodes, and polydimethylsiloxane encapsulation layers. Air gaps are designed and incorporated into the dielectric layer to significantly enhance the sample deformation and pressure response especially to negative pressure. The sensor exhibits repeatable response for thousands of cycles, even under bending or stretching conditions. Lastly, to demonstrate the practical application, a 12 × 12-pixel sensor array that can automatically measure both positive and negative pressure distributions has been reported under −20 and 10 kPa.

No abstract

In this paper, a new variational Bayesian adaptive cubature Kalman filter (VBACKF) is proposed for nonlinear state estimation. Although the conventional VBACKF performs better than cubature Kalman filtering (CKF) in solving nonlinear systems with time-varying measurement noise, its performance may degrade due to the uncertainty of the system model. To overcome this drawback, a multilayer feed-forward neural network (MFNN) is used to aid the conventional VBACKF, generalizing it to attain higher estimation accuracy and robustness. In the proposed neural-network-aided variational Bayesian adaptive cubature Kalman filter (NN-VBACKF), the MFNN is used to turn the state estimation of the VBACKF adaptively, and it is used for both state estimation and in the online training paradigm simultaneously. To evaluate the performance of the proposed method, it is compared with CKF and VBACKF via target tracking problems. The simulation results demonstrate that the estimation accuracy and robustness of the proposed method are better than those of the CKF and VBACKF.

No abstract