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The universal application of wearable strain sensors in various situations for human-activity monitoring is considerably limited by the contradiction between high sensitivity and broad working range. There still remains a huge challenge to design sensors featuring simultaneous broad working range and high sensitivity. Herein, a typical bilayer-conductive structure Ti3C2T x MXene/carbon nanotubes (CNTs)/thermoplastic polyurethane (TPU) composite film was developed by a simple and scalable vacuum filtration process utilizing a porous electrospun thermoplastic polyurethane (TPU) mat as a skeleton. The MXene/CNTs/TPU strain sensor is composed of two parts: a brittle densely stacked MXene upper lamella and a flexible MXene/CNT-decorated fibrous network lower layer. Benefiting from the synergetic effect of the two parts along with hydrogen-bonding interactions between the porous TPU fiber mat and MXene sheets, the MXene/CNTs/TPU strain sensor possesses both a broad working range (up to 330%) and high sensitivity (maximum gauge factor of 2911) as well as superb long-term durability (2600 cycles under the strain of 50%). Finally, the sensor can be successfully employed for human movement monitoring, from tiny facial expressions, respiration, and pulse beat to large-scale finger and elbow bending, demonstrating a promising and attractive application for wearable devices and human–machine interaction.

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In this paper, a novel adaptive robust online constructive fuzzy control (AR-OCFC) scheme, employing an online constructive fuzzy approximator (OCFA), to deal with tracking surface vehicles with uncertainties and unknown disturbances is proposed. Significant contributions of this paper are as follows: 1) unlike previous self-organizing fuzzy neural networks, the OCFA employs decoupled distance measure to dynamically allocate discriminable and sparse fuzzy sets in each dimension and is able to parsimoniously self-construct high interpretable T-S fuzzy rules; 2) an OCFA-based dominant adaptive controller (DAC) is designed by employing the improved projection-based adaptive laws derived from the Lyapunov synthesis which can guarantee reasonable fuzzy partitions; 3) closed-loop system stability and robustness are ensured by stable cancelation and decoupled adaptive compensation, respectively, thereby contributing to an auxiliary robust controller (ARC); and 4) global asymptotic closed-loop system can be guaranteed by AR-OCFC consisting of DAC and ARC and all signals are bounded. Simulation studies and comprehensive comparisons with state-of-the-arts fixed- and dynamic-structure adaptive control schemes demonstrate superior performance of the AR-OCFC in terms of tracking and approximation accuracy.

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The deep penetration of tablets in daily life has made them attractive targets for keystroke inference attacks that aim to infer a tablet user's typed inputs. This paper presents VISIBLE, a novel video-assisted keystroke inference framework to infer a tablet user's typed inputs from surreptitious video recordings of tablet backside motion. VISIBLE is built upon the observation that the keystrokes on different positions of the tablet's soft keyboard cause its backside to exhibit different motion patterns. VISIBLE uses complex steerable pyramid decomposition to detect and quantify the subtle motion patterns of the tablet backside induced by a user's keystrokes, differentiates different motion patterns using a multi-class Support Vector Machine, and refines the inference results using a dictionary and linguistic relationship. Extensive experiments demonstrate the high efficacy of VISIBLE for inferring single keys, words, and sentences. In contrast to previous keystroke inference attacks, VISIBLE does not require the attacker to visually see the tablet user's input process or install any malware on the tablet.

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