Self-powered piezoelectric player-interactive patch for guitar learning assistance

Device-assisted practice for instrument learning has been widely used by professional and amateur musicians to improve their learning efficiency.This study fabricates a novel self-powered and flexible player-interactive patch for guitar-learning assistance based on a piezoelectric T-ZnO/PVDF film.The system consists primarily of three parts:a flexible piezoelectric T-ZnO/PVDF film for pressure sensing,a signal processing module for analyzing the sensed signal,and light-emitting diode(LED)indicators for visualizing guitar performance.The sensing film can be conformably fixed on a guitar and can convert the mechanical energy generated by pressing a finger on a string into a piezoelectric signal without any external power supply.The output voltage of the film can act as a sensing signal for guitar performance,and both the response and recovery times are short.As fingers press on different strings,a series of piezoelectric signals are generated and transferred to the signal processing module,subsequently lighting up LEDs of different colors.The actions of the fingers during guitar playing are reflected by the corresponding LED indicators.The proposed system can help players adjust their posture and rhythm in real time,thus improving their playing technique.This study demonstrates the potential application of self-powered sensing systems in musical instrument learning assistance.

Microstructural image based convolutional neural networks for efficient prediction of full-field stress maps in short fiber polymer composites

The increased demand for superior materials has highlighted the need of investigating the mechanical properties of composites to achieve enhanced constitutive relationships.Fiber-reinforced polymer composites have emerged as an integral part of materials development with tailored mechanical properties.However,the complexity and heterogeneity of such composites make it considerably more challenging to have precise quantification of properties and attain an optimal design of structures through experimental and computational approaches.In order to avoid the complex,cumbersome,and labor-intensive experimental and numerical modeling approaches,a machine learning(ML)model is proposed here such that it takes the microstructural image as input with a different range of Young’s modulus of carbon fibers and neat epoxy,and obtains output as visualization of the stress component S11(principal stress in the x-direction).For obtaining the training data of the ML model,a short carbon fiberfilled specimen under quasi-static tension is modeled based on 2D Representative Area Element(RAE)using finite element analysis.The composite is inclusive of short carbon fibers with an aspect ratio of 7.5that are infilled in the epoxy systems at various random orientations and positions generated using the Simple Sequential Inhibition(SSI)process.The study reveals that the pix2pix deep learning Convolutional Neural Network(CNN)model is robust enough to predict the stress fields in the composite for a given arrangement of short fibers filled in epoxy over the specified range of Young’s modulus with high accuracy.The CNN model achieves a correlation score of about 0.999 and L2 norm of less than 0.005 for a majority of the samples in the design spectrum,indicating excellent prediction capability.In this paper,we have focused on the stage-wise chronological development of the CNN model with optimized performance for predicting the full-field stress maps of the fiber-reinforced composite specimens.The development of such a robust and efficient algorithm would significantly reduce the amount of time and cost required to study and design new composite materials through the elimination of numerical inputs by direct microstructural images.

Teaching Chinese sign language with a smartphone

Background There is a large group of deaf-mutes worldwide, and sign language is a major communication tool in this community. It is necessary for deaf-mutes to be able to communicate with others who are capable of hearing, and hearing people also need to understand sign language, which produces a great demand for sign language tuition. Even though there have already been a large number of books written about sign language, it is inefficient to learn sign language through reading alone, and the same can be said on watching videos. To solve this problem, we developed a smartphone-based interactive Chinese sign language teaching system that facilitates sign language learning. Methods The system provides a learner with some learning modes and captures the learner's actions using the front camera of the smartphone. At present, the system provides a vocabulary set with 1000 frequently used words, and the learner can evaluate his/her sign action by subjective or objective comparison. In the mode of word recognition, the users can play any word within the vocabulary, and the system will return the top three retrieved candidates;thus, it can remind the learners what the sign is. Results This system provides interactive learning to enable a user to efficiently learn sign language. The system adopts an algorithm based on point cloud recognition to evaluate a user's sign and costs about 700ms of inference time for each sample, which meets the real-time requirements. Conclusion This interactive learning system decreases the communication barriers between deaf-mutes and hearing people.

Mobile Assisted Chinese Learning as a Foreign Language:An Overview of Publications between 2007 and 2019

This paper presents a critical overview of studies on mobile assisted language learning(MALL)in teaching Chinese as a foreign language(CFL)during the period 2007−2019.In the review,keyword and reference searches were conducted to identify and select empirical studies during the review period.Thematic and frequency analyses were employed on the data.This identified methodological trends and research outcomes in the reviewed studies.As shown in the results,most of the reviewed studies used qualitative methods to examine the effect of mobile CFL learning on formal learning in higher education settings.These studies document the positive impact that mobile technology has on CFL learning.Their attention is primarily on the use of mobile learning in Chinese vocabulary acquisition,language skill development and mobile seamless learning.Suggestions are provided for further research to support continuous mobile assisted CFL teaching and learning.

COMPUTER ASSISTED INSTRUCTION AND ITS APPLICATION IN ENGLISH LEARNING

This paper briefly describes the development of computer assisted instruction(CAI) abroad and in China, lists the advantages of CAI and deals with its application in English learning. Some suggestions about how to make better use of CAI in ELT are also given.