The system, “Occluded Resistant Face DetectionRecognition System”, contains different scale detectors for calculating face locations. Additive angular margin loss is added into the training phase for achieving high efficiencyaccuracy. The system can achieves 80 accuracy under the 50 face occluded.

The technology is an integrated solution incorporating the retrievable personality for multimodal emotion sensing for spoken services. This framework provides a real-world flexibility that enables the estimation of the target speaker emotion states without manual personalization.

The dialogue system is the main subsystem of the visually impaired navigation system, which provides destinations for the navigation system through multiple dialogues. We use the knowledge graph as the basis for reasoning. In terms of close-range navigation, deep learning technology is used to develop RGB camera detection depth algorithm, indoor semantic cutting algorithm, integrated detection depth estimationindoor semantic cutting in indoor obstacle avoidance, etc. The whole system uses the CellS software design framework to integrate distributed AIoT systems.

In order to solve the undiscovered problem that caused by the fall ofelders, we develop a deep learning based systemnamed it 「SkyEye」,which includes our own sensor 「AI Falling Image Sensor」, a cloud sever that storespushes information about fall eventsa mobile app that communicates with users.

The major cost of conventional RFID tags comes from a microchip. To reduce the start-up cost, we develop a chipless RFID system that eliminates the use of the chip. This research organize the real-world issues of chipless RFID into four subjects, including the design of chipless tags, chipless RFID readers, reader signal processing,wireless propagation channel. We successfully integrate the four topics, developing a calibration-free, orientation-insensitive,high-capacity chipless RFID system with multi-tag detection capability.

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 study built an automatic insect pest image identification system based on tiny Yolov3 deep learning model. By optimizing the tiny Yolov3 detection model, images of insect pests on scanned sticky paper can be automatically identified. The system achieves a testing accuracy of 0.93, 0.90 for whitefliesthrips respectively.

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.

Due to the fast development of 5G networks, it is critical to identify users service types to allocate resources intelligently. Our technology focuses on users mobility type identification by extracting practical features from users cellular information. We proposed a system architecturehave collected 700-hour data with 150 GB. By using our dataother datasets in the world, we show that our technology can achieve 95 accuracy,reduce 16 energy consumption compared to traditional methods.

We develop the new neural network architecture to improve execution speedaccuracy. The result shows that our AI detection system can run on a microcomputer in real-time, meanwhile its accuracy 90. We put that microcomputer on a dronereturn the result to smartphone App in real-time.

To resolve public safety and security issues, NCHC developed intelligent video analytics and retrieval system, which can integrate the attribute analysis and recognition of vehicles/pedestrians from CCTV surveillance cameras, utilize TWCC high-speed operation in developments such as AI image semantic analysis and video synopsis technique, to effectively and rapidly search for key targets(vehicles/pedestrians) and provide short clips of the targets’ moving path simultaneously, and as a result fulfill the quick search application service of technological surveillance and Investigation.

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.

This technology integrates 1000+ times of UAV imaging experiences with labeled rice lodging images for training. A rice lodging recognition model using deep learning reaches 90 accuracy. The recognition model can be deployed in a microcomputer mounted on UAVs to implement edge computing. While taking aerial images, the inference can be completedreveal lodging areadamage level in-time.

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.

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 pills detection system is built based on the Feature Pyramid NetworkConvolution Normal Network (CNN). Two-stage CNN architecture is used for pills localizationpill classification. The accuracyexecution time of the pill detection are 900.02s, respectively.

The overall system first extracts embedded speaker identity features using a neural network model, then the deep neural network speech enhancement takes the augmented features as the input to generate the enhanced spectra. With the additional embedded features, the speech enhancement system can be guided to generate the optimal output corresponding to the speaker identity.

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.

"1. Automatically generate Verilog code tools based on the hardware architecture of convolutional neural networks: 4 different hardware architectures (output stationary, weight stationary, Tree architecture, NVDLA) can be generated for the currently more commonly used DNN networks. 2. Visual performance index analysis tool: According to the selected DNN model,the choice of hardware architecture specifications, analyze the performance index."

(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.

Laser material recognition technologyintelligent color compensation lighting system, with AUV to realize the function of underwater rust detectionimage color restoration, real-time acquisition of rust information through spectral analysisdisplay of true rust color on the image, so that the algorithm can be accurate Determine the health of the cylinder. The vehicle will establish the health information of the wind turbine to help the inspector make the best maintenance strategymaintain the power generation efficiency of the wind turbine in the best condition.

During the pandemic, it is necessary to reduce contactwear masks, but identification is still required. The original technology includes the development of inertial measurement units beneath the walking surface, real-time detection of gait,accurate predictions of gait parameters with machine learning models. Most importantly, our technology can use a small number of samples (only one footstep data) for identifications. Non-contact, non-monitoring,rapid identification are going to be the future applications in smart buildings/homes.

”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.

1. CNN model architecture optimization techniques: Propose an Agilev3-L architecture, with a performance index of mAP@50:95FPS:FP16 up to 171.79 2. GOP-mode acceleration scheme for real time inference: employ GOP-modeTRACKING algorithms to enhance the processing rate. 3. Propose a rapid data labeling system HiTag.

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 project proposes a smart contact lens ideally capable of simultaneously observing the tear evaporation rate, the tear osmolarity,the ocular surface temperature. To solve the shortcoming of the current difficulty in quantifying DES, assist doctors in the diagnosis of dry eye conditions, the effectivenessselectivity of drugs, the treatment statusrecovery level.

The technology aims to develop an AI-based integrated system for emotional detections (anger, sadness, happiness,neutral)multimodal physiological signals (ECG, EEG,PPG),apply to patients with cardiovascular disease. Patients monitor their emotionalphysical statusadminister bio-neuro-feedback to improve their well-being, track disease progression,prevent adverse prognosis.

The technology combines specific visual recognitionelectrochemical quantitative detection in a single portable device, providing instantconvenient copper ion detection. The sensing sensitivity has reached the standards of Taiwanthe United States for heavy metal detection for food, human body,environments. In use, this portableuser-friendly interface device not only makes it simple to report back to the EPA database for water quality analysis, but also it can be utilized for real-time detection from youngsters to the elderly.

Integrating deep learning, 3D information analysis, hyperspectral analysis, computer vision analysiscombined the multi-source images to develop fruit quality monitoring techniques, including: planting area monitoring, position monitoring, yield monitoring, harvesting time prediction, fruit maturity measurement, fruit quality testing, to achieve the goal of enhancing industrial value.

Optical coherence tomography using an optical probe with a diameter of 0.9 mm, combined with the 14-18 gauge needle, is used for clinical needle puncture. Using the real-time image obtained from the tip of the needle in the tissue, the needle position can be identified. Combined with artificial intelligence can achieve objective, accurate and automatic identification of the tissue, which has been successfully verified in anesthesia and laparoscopic surgery.