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Autonomous vehicle test at a limited field or even on the public road has its limitations. Simulation is essential to increase the test coverage. Taiwan Semiconductor Research Institute(TSRI) develop an autonomous vehicle test system based on CarMaker, which is a simulation tool from IPG Automotive. 13 testing scenarios and 6 testing routes corresponding to the physical test at Shalun testing field are built and included in this autonomous vehicle test system. An AEB(Autonomous Emergency Braking) function will be demonstrated to show how this autonomous vehicle test system works.

(1) Image recognition3D positioning of the pest: Modularize the system for testing on the orchard. (2) High efficiency laser pest control scanning system: It was developed to be suitable for windlessbreezy environments. (3) Enhance the economic tracked vehicle for hillside field: Self-developed power designsteering control based on dynamic model,(4) Field UGV control platform: Based on the preciseaccurate positioning in field, when robot operates on the reference path which planned by map, it can do path trackingdynamically obstacle avoiding in real-time.

”AI2 Robot City” is a game-based learning kit for primary and secondary school students, which combines AI image-recognition teaching tool of MIT App Inventor and the computational thinking board game named as "Robot City." Through this learning kit, users will learn to make smart cars, create image-recognition models, write and perform mobile application. Users will learn to write a program to recognize the personal cards in the board game, and furthermore to control the smart cars with blue-tooth and compete in the computational thinking board game.

This system provides multiple functions for digital slides, allowing users to complete all tasks without switching between different software systems. The functions include, Project management, Image management, Annotation, Zoon In / Zoom Out view, Navigation view, Category shortcuts, Authority management,Annotation overview.

The sweet pepper cultivationplant protection robot developed includes three parts: the intelligent image identificationpositioning system of sweet pepper diseasesinsect pests is composed of YOLO4 deep learning modelimage 3D vision integration. It can perform disease detectionspatial positioning,guide the robotic arm spraying device to accurately spray the diseased. The automatic walking system can use GPS to plan the movement path of the plant protection robot in the field, search for plants infected with pestsdiseases,perform the tasks of pest control.

This technology aims to develop a set of UAV observationmonitoring technology for the atmospheric boundary layer. The Key technologies include mission-oriented integration technology, ultra-high vertical flight capability, long-distance data wireless transmission, real-time data displaycorrection, automatic take-off, data analysis technology,data upload to the cloud in 4G. This technology has participated in international experimentshas entered the stage of international promotion.

This project integrated with industrial foresight technologies, including UAV, artificial intelligenceimage recognition, to collect real-time images, apply algorithm in evaluation, link the technology of IOT (Internet of Things)environment sensing,use unmanned vehicle to conduct controlling work.

Constructing a functional connectome and its computational model is a crucial step toward understanding the mechanisms of brain functions. To achieve this goal, we developed two correlated technologies: (1) An all-optical physiology (AOP) that is capable of millisecond volumetric imaging and accurate stimulation in living animal brains. This system allows us to establish functional connectome and neural coding with a single-cell resolution. (2) A cellular-level spiking neural circuit simulation system that is capable of tuning itself based on the input data from the AOP system. We have demonstrated our technologies in the Drosophila late visual system and will apply them in the brains of larger species such as mice. Our technologies will greatly enhance knowledge of brain operation.

By integration of professions including phytopathology, pesticides analysis, aquaticlivestock management, microbial fermentation, epidemiology,artificial intelligence, our interdisciplinary team jointly have developed microbial products for multi-industry applications including crop health care, circular agriculture, livestockaquaculture. The intelligent monitoring modelsmanagement technology for evaluating the efficacy of microbial products are implemented to provide key time pointdecision support for microbial products application,in turns to enhance the application efficacy. The developed microbial agentstechnology would greatly reduce the use of chemical pesticidesantibioticsenhance agricultural output valuemarket competitiveness.

Our technique is a liver tumor detection method using machine learning which is the most popular AI technique in these years. the high-magnification image provides more details, but the view is smaller in contrast, a low-magnification image has the larger view but less details. Thus, our method combines the two magnificationsyields more accurate tumor detection results.

This breaking-through technology aims at designingconstructing a compliant prone-type ring-scanning inspection platform incorporated with a simultaneous multi-frequency driving NIR light source to complete a diffuse optical imaging system. The realized system has two features superior to other counterpart imaging equipments: (i) compliant flexible optical channels to collect optical information,(ii) simultaneous multiple frequency driving light source. It brings the benefits to improve the accuracy of reconstructed imagesshorten examination time for test subjects.

“MeDA OXR: Real-time Hazard Recognition System,” which can automatically screeninterpret the medical images in real-time, is designed for emergency rooms to assist physicians in diagnosis, reducing medical risks,improving overall efficiency. Combining portable X-RayAI algorithms, the system performs real-timeaccurate preliminary screeningdiagnoses of diseases, such as pneumothorax, pneumonia,tuberculosis. It can also locate the nasogastric tube, endotracheal tube,central venous catheter, while misplacement of that is sent to the physicians when detected.

This demonstration presents an active-matrix, 1920x1080, 370 nm UV MicroLED display. There are 2,073,600 pixels fabricated on this display which is smaller than a dollar coin,its diagonal has an extremely high pixel density of 3200 pixels per inch (ppi). The size of each pixel is 5 μm in diameter,the pixel pitch is 8 μm. The key technology of this achievement lies in the fabrication of pixels with good characteristicshigh uniformity,the successful development of flip-chip technology combining MicroLEDdriving integrated circuit pixels. This result can display imagesvideos.

PCA-BM includes two models: “Automatic BMs Segmentation AI Model”" Distant Brain Failure Prediction Model". The former one uses C2FNAS (coarse to fine network architecture search) to detect the location, size,number of brain metastases. The latter uses radiomics to extract numerous radiographic featuresemploys machine learning methods such as XGBoost to establish a prognostic model of brain metastases. PCA-BM provides more precision treatment decisions for patientsimproves personalizedaccurate overall stereotactic radiosurgery planning.

"1.Background: The deterioration of lithium batteries leads to safetycost issues. Ultrasonic detection has the advantages of cost, size,immediacy. 2.Detection: Under the control environment, the ultrasonic signal is actuated on each part of the battery,the reflectedtransmitted wave is collected. 3.Analysis: It has methods such as spectrum, image,DL to evaluate the SoH, the distribution of degradation types,the location of faulty cells in the battery module. 4.Theory: Use the acoustic properties of the battery with software simulation to verify the measured data."