Combined with monitors in markets, stores,transportation stations, AI image recognition technology is used to detect crowdspeople without masksbroadcast warnings,assist the government in epidemic prevention-related work.

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.

Assisted by in-house AI deep compression toolchain (ezLabel, ezModel, ezQUANT, ezHybrid-M), the proposed technology supports automatic AI model designoptimization with the integrated performance of 120x model size reduction70x power reduction in 2D CNN model,develops a world-first 1/2/4/8-bit CNN model realized by the developed high efficiency Hybrid fixed point CNN NPU (Hybrid-NPU), which has been verified in Xilinx ZCU102 FPGAachieves the performance up to 2.5 TOPS(8-b)/ 20TOPS(1-b)@28nm technology running at 550MHz4TOPS/W energy efficiency.

This project uses AIoT technology to face the occurrence issue of incorrect preｓｃｒｉｐｔion drugs of the drug dispensing phase in hospital dispensing Room. The proposed learning-object-detection based deep technology AI mechanism can automatically identifyverify the correctness of the drugs in the drug bag, thereby significantly improving the accuracy of drug dispensing in Hospital.

"Tool replacementtool life diagnosis" in smart manufacturing is a key link between the quality of workpiecethe efficiency of the manufacturing process, so it is an indicator of the operating efficiency of machine tools. This technology combines on-site monitoring data, laboratory analysisdeep learning to provide best tool selectionprocessing parameter combinations for

This research team develops unmanned 3D intelligent marine aquaculture technology, using omni AIoT aquaculture technology, especially strengthening the integration of gyroplane, ROV, unmanned shipcentral control room,using long-distance intelligent control to make employees shift from traditional field work to basecentral control room. Due to the huge cross domain system of omni AIoT, it modularizes it, At the same time, edge computing technology is developed to facilitate commercial promotion.

This system help doctors to distinguish age-related macular degeneration (ARMD)polypoidal choroidal vasculopathy (PCV) by color fundus photography. We have collected large image dataset from Taipei Veterans General Hospitalother hospitals to establishvalidate CNN model, which had been tested by external image datasetmodified to improve the accuracy of the AI model.

AI model is consisted of detection, data pre-processingclassification ensemble model. We applied YOLO 3 to detect vertebral body from radiography,used data pre-processing to reduce image noisyenhance contrast, integrated ResNet34, DenseNet121, DenseNet201 into AI mode. Our result reveals the ensemble model has better performance in accuracy, sensitivityspecificity.

An automated agricultural machinean intelligent crop production decision-making system are developed for greenhouse asparagus growth monitoring. Automationenergy-saving machinery technologiesInternet of Things/information communication techniques are adopted to achieve the goal of "saving laboreffort"improve "management technologies". The developed machinesystem can greatly improve the qualityquantity of domestic asparagus production,achieve the dual goals of stabilizing the outputenhancing quality of harvested asparagus.

With the rise in computing power, deep-learning based computer-aided diagnosis systems have gained interest in the research community. Our system process the images to assist doctors to determine whether the patients have nodules in lungs. Meanwhile, we utilized the Feature Pyramid Network to extend the receptive field on the convolutional kernel, which improved the performance on the nodule detection with various locations in CXR. The semi-supervised learning mechanism also achieves the way of soft-annotation to reduce human effort in medical image annotation.

Induced pluripotent stem cells are a promising new technology in translation medicine,AI technology is an automaticpowerful platform that can help identify cell images with unique characteristics, predictprovide information about cell types, differentiation stages, cell quality,normal Or disease-specific feature information. The combination of this induced pluripotent stem cell differentiation platformAI-based artificial intelligence image analysis technology will have great benefits for future cell product applications.

Based on our long-term accumulated healthy brain image database, we used meta-analysismultivariate analytical algorithms to extract the cognitive biological characteristics of individual subjects. By using machine learning / artificial intelligence algorithms, these features were further calculated for capturing individual cognition according to related brain networks. This biological index could be a potential marker for evaluating individual brain health, cognitive functions,risk for neurodegenerative diseases.

"The primary goal of this project is to establish a complete hospital-based liver cancer database, profiles for data feature extraction,develop different cancer, prediction models. A Medical AI program to predict the poster treatment (including operationradiofrequency ablation) recurrence of liver cancer will be established. The program system will assist doctors in the medical decision, identify high-risk patients,adjust clinical follow-up programs."

Our team construct an AIoT smart aquaculture management system. The management system mainly consists of: (1) Image Behavior MonitoringAnalysis Subsystem (2) Smart Feeding Subsystem (3) IOT Subsystem including underwater sensors, ROV,Drone (4) Cloud Subsystem (5) Big Data Analysis Subsystem.

This Artificial Intelligence model interprets 12-lead electrocardiograms of adults to diagnose heart failure accuratelyefficiently. It is a fabulous heart failure screening tool that can detect more asymptomatic patients. Our goal is to help patients get early treatmentprevent disease deterioration.

Ted-ICU AI Platform: (1)Provide a single view of patients＇ EMRsvital signs (2)Support remote ICU monitoring (3)Online labeling tool & Expandable AI algorithms repository (4)Disease-specific prediction models (5)Standardized EMR templates

In agriculture, the team has developed a “Chinese Medicinal Functional Compound Prediction A.I. System” that can predict compounds production during cultivation period. Moreover, in application, we have implemented a smart agricultural platform-AgriTalk to link the “Functional Compound Prediction System”“Smart Fertilization System” to provide a comprehensive management in farm production.

This technique uses Tainan 11 (TN11), which occupies about 70 of the rice planting area in Taiwan. Building the early watering warning system of crops by combining the physiological indicators of rice with environmental dataartificial intelligence technology. The system can analyzepredict the impending water shortage for rice in advanceissue drought warning immediately. Therefore, decision makers can give the timelyappropriate amount for rice irrigationimprove the efficiency of using waterreducing the cost of farmersfarming losses.

The Air/Ground cooperation for optimal rice harvesting model is established to provide a visual harvesting decision service on a cloud platform. Drones and mobile devices are employing to estimate grain moisture and forecast the variation of harvest moisture content (HMC) in the coming days by huge amounts of imagery data, deep learning algorithms, and weather forecasts. This model can benefit in several aspects, such as setting an accurate and comprehensive optimal harvest schedule, reducing the cost of agricultural apparatus and barn ovens, ensuring the rice quality, and maximizing farmers' benefits. The potential value of the model practice could be more than a billion in Taiwan.

This project will establish a National biobank consortium of Taiwan through the fund support from the government. The National Health Research Institutes will establish a Central Office for the collaboration works, which includes collecting the number of samples of the human biological database in each biobank that agreed to join the Consortium. All Alliance need to follow the same SOP for the collection processthe quality of the biosample. Adequateconsistent clinical data will also be established under the Information Security Management Treaty. Since the contents of the biobanks from different institute are quite different, we will be able to quickly establish a largecomprehensive biobank network.In line with the need of biotechnologypharmaceutical industries.

The HeaortaNet is developed by the TW-CVAI team. The HeaortaNet is a deep learning model trained by 70,000 axial images from 200 patients with verified annotations of the pericardiumaorta. It shortens the time for data processing from 60 minutes, by manual segmentation of both pericardiumaorta, to 0.4 seconds. The segmentation accuracy, as assessed by dice similarity coefficient, is 94.8 for the pericardium,91.6 for the aorta. The imaging-based Cardiovascular Risk Prediction module was constructed by analyzing data from the National Health Insurance Databank.

"The Crowdsourcing UAV Platform for AI Agricultural Survey(AIAS) provides automatic mosaicing serviceis equipped with four breakthrough technologies, including (1)massive images gridㄒding, (2)parallel computing technology, (3)UAV task specification standardization,(4)UAV task matchmaking. AIAS platform is committed to creating aerial UBER crowdsourcing services in various aspects, such as (1)crop distribution surveys, (2)large-scale lodging surveys, (3)agricultural insurance investigation, (4)surveys on illegal use of farmland,(5)surveys on the subsidy of fallow."

In the face of the COVID-19 pandemic, many restaurants have been closed for maintaining anti-epidemic measures. NCHC combined multiple AI technologiesdeveloped an automated service experience to provide a way for people to still enjoy the services received at restaurants while maintaining social distancing. To achieve this, we＇ve incorporated technologies including the following: NLP in AI, Cloud-Based Smart Point Cloud Processing (CSPCP), method for face searching in images, intelligent video analysisretrieval,artificial intelligence of things (AIoT).

The invention focuses on the early fry culture, including algae, live-food organism, water quality control, fine image detection,automated intelligent culture technology. By connecting environmental parameter sensing equipment, fine image observation equipment, automatic feedback/delivering equipment,central intelligent learning/processing platform, an intelligent production system of aquatic fry can be completed,the big data on production parameters can be collected, optimizing breeding strategiesefficiency,increasing the breeding profitability more than 10 times.

In the context of FinTech, we present Financial Graph Attention Networks (FinGAT) to recommend high-profitable stocks in terms of return ratio using time series of stock pricessector info. Our FinGAT can learn the long-short-term price tendency,model the latent interactionsinfluence between stockssectors without any hand-crafted effort. Experiments conducted on Taiwan Stock, S&P 500,NASDAQ stock markets exhibit remarkable accuracy of FinGAT, comparing to state-of-the-arts (by 11, 14,12 performance improvement). The tech is published in IEEE TKDE 2021.

This system uses the innovative AIoT application to target annoying scalp maintenance problemsdevelops an intelligent scalp detectionmanagement system. The core of the technology is to use deep learning-based object detection models. Tens of thousands of microscopic image data sets that are labeledtrained for scalp symptoms. As a result, the scalp image recognition module is develop, such as dandruff, hair loss, oil, and inflammation, etc. Hence this system can provide scalp detection, and maintenance effectiveness tracking functions lead scalp maintenance services to a new level of intelligent management.

In Taiwan, there are almost 96 million people look for medical treatment due to diabetes. The project successfully used the infrared light measurement system to observe the differences between the bilateral feet,collocating with the Chaos Synchronization to measure the differences between different signals, to decrease the probability of the attendance as the ultimate objective .

This technology develops an intelligent surgical pressure detection system, including a pressure sensing element embedded in a surgical cotton pad, a signal reading device,a big data analysis database. The measurement data can be transferred to the AI big data database. Using AI technology, the surgeon＇s force can be recognized in real timethe calculation result can be immediately returned to the system APP. Let the surgeon who performs the operation get the most real-time information, which can avoid organ damage during the operation,effectively protect the safety of patientsthe rights of physicians.

Super fast terapixel server allows multiple users to viewanalyze gigapixelterapixel images with mobile devices, such as iPhones, iPads, Android mobiles,laptops at the same time. Users could annotateanalyze the data using built-in tools such as 3d effect, color deconvolution, color quantification, nuclei/object detection, lengtharea measurement,quantitative reporting system.

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.