Post-Processing of InSAR Deformation Time Series Using Clustering-Based Pattern Identification

Multi-temporal synthetic aperture radar interferometry(MT-InSAR)is a standard technique for mapping clustering and wide-scale deformation.A linear model is often used in phase unwrapping to overcome the underdetermination.It’s difficult to identify different types of nonlinear deformation.However,the interpretation of nonlinear deformation is very important in monitoring potential risk.This paper introduces a comprehensive approach for identifying and interpreting different types of deformation within InSAR datasets,integrating initial clustering and classification simplification.Initial classification is performed using the K-means clustering method to cluster the collected InSAR deformation time-series data.Then we use F test and Anderson-Darling test(AD test)to simplify the clusters after initial classification.This technique distinctly discerns the changing trends of deformation signals,thereby providing robust support for interpreting potential deformation scenarios within observed InSAR regions.

Improvement of Attributed Scattering Center Extraction by Using SAR Super-Resolution Preprocessing

Synthetic aperture radar(SAR)is able to acquire high-resolution method using the active microwave imaging method.SAR images are widely used in target recognition,classification,and surface analysis,with extracted features.Attribute scattering center(ASC)is able to describe the image features for these tasks.However,sidelobe effects reduce the accuracy and reliability of the estimated ASC model parameters.This paper incorporates the SAR super-resolution into the ASC extraction to improve its performance.Both filter bank and subspace methods are demonstrated for preprocessing to supress the sidelobe.Based on the preprocessed data,a reinforcement based ASC method is used to get the parameters.The experimental results show that the super-resolution method can reduce noise and suppress sidelobe effect,which improve accuracy of the estimated ASC model parameters.

Magnetized Micropillar-Enabled Wearable Sensors for Touchless and Intelligent Information Communication

The wearable sensors have recently attracted considerable attentions as communication interfaces through the information perception,decoding,and conveying process.However,it is still challenging to obtain a sensor that can convert detectable signals into multiple outputs for convenient,e cient,cryptic,and high-capacity information transmission.Herein,we present a capacitive sensor of magnetic field based on a tilted flexible micromagnet array(t-FMA)as the proposed interaction interface.With the bidirectional bending capability of t-FMA actuated by magnetic torque,the sensor can recognize both the magnitude and orientation of magnetic field in real time with non-overlapping capacitance signals.The optimized sensor exhibits the high sensitivity of over 1.3 T-1 and detection limit down to 1 mT with excellent durability.As a proof of concept,the sensor has been successfully demonstrated for convenient,e cient,and programmable interaction systems,e.g.,touchless Morse code and Braille communication.The distinguishable recognition of the magnetic field orientation and magnitude further enables the sensor unit as a high-capacity transmitter for cryptic information interaction(e.g.,encoded ID recognition)and multi-control instruction outputting.We believe that the proposed magnetic field sensor can open up a potential avenue for future applications including information communication,virtual reality device,and interactive robotics.

Fiber-junction design for directional bending sensors

Flexible sensors in wearable electronics have become increasingly multifunctional due to the development of materials synthesis and structure design.In particular,structural design can not only add capabilities to sensors fabricated from existing available and normal materials,but also offer opportunities for the fabrication of sensors with certain desired functions.Here,we designed a series of fiber-junction structure models,in which two fibers were simply hooked to each other to form a junction on a flexible printed circuit,for fabrication of directional bending sensors.The value and direction of bending angle are related to the change in electronic signal by a theoretical expression,allowing us to employ a simple and practicable method to use available conductive fiber materials to fabricate high-sensitivity,high-resolution and directional bending sensors.In addition,these models are generally applicable,which have broad combination with different conductive fiber,and corresponding bending sensors all possess capability of directional identification.Furthermore,the capability of identifying directional bending was demonstrated by human motion monitoring such as joint bending and muscle contraction.