Recent advances in multimodal sensing integration and decoupling strategies for tactile perception

Human skin perceives external environmental stimulus by the synergies between the subcutaneous tactile corpuscles.Soft electronics with multiple sensing capabilities by mimicking the function of human skin are of significance in health monitoring and artificial sensation.The last decade has witnessed unprecedented development and convergence between multimodal tactile sensing devices and soft bioelectronics.Despite these advances,traditional flexible electronics achieve multimodal tactile sensing for pressure,strain,temperature,and humidity by integrating monomodal sensing devices together.This strategy results in high energy consumption,limited integration,and complex manufacturing process.Various multimodal sensors and crosstalk-free sensing mechanisms have been proposed to bridge the gap between natural sensory system and artificial perceptual system.In this review,we provide a comprehensive summary of tactile sensing mechanism,integration design principles,signal-decoupling strategies,and current applications for multimodal tactile perception.Finally,we highlight the current challenges and present the future perspectives to promote the development of multimodal tactile perception.

Multimode sensing based on optical microcavities

Optical microcavities have the ability to confne photons in small mode volumes for long periods of time,greatly enhancing light-matter interactions,and have become one of the research hotspots in international academia.In recent years,sensing applications in complex environments have inspired the development of multimode optical microcavity sensors.These multimode sensors can be used not only for multi-parameter detection but also to improve measurement precision.In this review,we introduce multimode sensing methods based on optical microcavities and present an overview of the multimode single/multi-parameter optical microcavities sensors.Expected further research activities are also put forward.

Class-guided coupled dictionary learning for multispectral-hyperspectral remote sensing image collaborative classification

The fine classification of large-scale scenes is becoming more and more important in optical remote sensing applications.As two kinds of typical optical remote sensing data,multispectral images(MSIs)and hyperspectral images(HSIs)have complementary characteristics.The MSI has a large swath and short revisit period,but the number of bands is limited with low spectral resolution,leading to weak separability of between class spectra.Compared with MSI,HSI has hundreds of bands and each of them is narrow in bandwidth,which enable it to have the ability of fine classification,but too long in aspects of revisit period.To make efficient use of their combined advantages,multispectral-hyperspectral remote sensing image collaborative classification has become one of hot topics in remote sensing.To deal with the collaborative classification,most of current methods are unsupervised and only consider the HSI reconstruction as the objective.In this paper,a class-guided coupled dictionary learning method is proposed,which is obviously distinguished from the current methods.Specifically,the proposed method utilizes the labels of training samples to construct discriminative sparse representation coefficient error and classification error as regularization terms,so as to enforce the learned coupled dictionaries to be both representational and discriminative.The learned coupled dictionaries facilitate pixels from the same category have similar sparse represent coefficients,while pixels from different categories have different sparse represent coefficients.The experiments on three pairs of HSI and MSI have shown better classification performance.

A flexible and high temperature tolerant strain sensor of La0.7Sr0.3MnO3/Mica

Flexible sensors have been widely investigated due to their broad application prospects in various flexible electronics.However,most of the presently studied flexible sensors are only suitable for working at room temperature,and their applications at high or low temperatures are still a big challenge.In this work,we present a multimodal flexible sensor based on functional oxide La0.7Sr0.3MnO3(LSMO)thin film deposited on mica substrate.As a strain sensor,it shows excellent sensitivity to mechanical bending and high bending durability(up to 3600 cycles).Moreover,the LSMO/Mica sensor also shows a sensitive response to the magnetic field,implying its multimodal sensing ability.Most importantly,it can work in a wide temperature range from extreme low temperature down to 20K to high temperature up to 773 K.The flexible sensor based on the flexible LSMO/mica hetero-structure shows great potential applications for flexible electronics using at extreme temperature environment in the future.