Ultra‑High Sensitivity Anisotropic Piezoelectric Sensors for Structural Health Monitoring and Robotic Perception

Monitoring minuscule mechanical signals,both in magnitude and direction,is imperative in many application scenarios,e.g.,structural health monitoring and robotic sensing systems.However,the piezoelectric sensor struggles to satisfy the requirements for directional recognition due to the limited piezoelectric coefficient matrix,and achieving sensitivity for detecting micrometer-scale deformations is also challenging.Herein,we develop a vector sensor composed of lead zirconate titanate-electronic grade glass fiber composite filaments with oriented arrangement,capable of detecting minute anisotropic deformations.The as-prepared vector sensor can identify the deformation directions even when subjected to an unprecedented nominal strain of 0.06%,thereby enabling its utility in accurately discerning the 5μm-height wrinkles in thin films and in monitoring human pulse waves.The ultra-high sensitivity is attributed to the formation of porous ferroelectret and the efficient load transfer efficiency of continuous lead zirconate titanate phase.Additionally,when integrated with machine learning techniques,the sensor’s capability to recognize multi-signals enables it to differentiate between 10 types of fine textures with 100%accuracy.The structural design in piezoelectric devices enables a more comprehensive perception of mechanical stimuli,offering a novel perspective for enhancing recognition accuracy.

People Recognition via Tongue Print Using Deep and Machine Learning

The tongue is a unique organ that is well protected inside the mouth and not affected by external factors;it is also difficult to forge.Several biometric systems are widely used for authentication and recognition,such as fingerprints,faces,iris,sound,and retina.Traditional biometrics represent a challenge and an obstacle as they can be falsified,duplicates can be made(e.g.,iris,face,fingers,and signature),or they are expensive and rarely used(e.g.,DNA).The increased security measures called for modern biometrics that is more secure,less expensive,and cannot be falsified.As a result,the goal of this paper is to create a system for distinguishing people based on their tongue prints.It will contribute to solving many forensic issues and increasing electronic security because it has features suitable for identification and biometrically distinguishing between people.In this paper,the tongue is located based on the fixed window size method.After tongue localization,feature extraction using the VGG-16 model,and a classification system that uses both transfer learning and machine learning as VGG-16,XGBoost,KNN,and random forest classifiers,extracted features are then trained for personal identification.The dataset consisted of 1085 tongue images of 138 people with a test ratio of 20%,and the results achieved an accuracy of 92%.The process of distinguishing people through tongue prints has proven to be effective and accurate.

Illumination Invariant Recognition of Three-Dimensional Texture in Color Images

In this paper, illumination-affine invariant methods are presented based onaffine moment normalization techniques, Zernike moments, and multiband correlation functions. Themethods are suitable for the illumination invariant recognition of 3D color texture. Complex valuedmoments (i.e., Zernike moments) and affine moment normalization are used in the derivation ofillumination affine invariants where the real valued affine moment invariants fail to provide affineinvariants that are independent of illumination changes. Three different moment normalizationmethods have been used, two of which are based on affine moment normalization technique and thethird is based on reducing the affine transformation to a Euclidian transform. It is shown that fora change of illumination and orientation, the affinely normalized Zernike moment matrices arerelated by a linear transform. Experimental results are obtained in two tests: the first is usedwith textures of outdoor scenes while the second is performed on the well-known CUReT texturedatabase. Both tests show high recognition efficiency of the proposed recognition methods.