1.Low-Power Processing-in-sensor CMOS Image Sensor 2.Implementation of Low-PowerLow-Latency Deep Learning Chip based on Neuromorphic Intelligence (collaborated with MOST 108-2622-8-007-009) 3.Development of Neuromorphic Chip based on The Fruit Fly VisualSpatial Sensory Systems 4.Hardware-Software Co-Design for Neuromorphic AI Chips

Dynamic vision sensors have been investigated to report motion-only images for moving object recognition. Less but essential information helps post-process recognition algorithm reduces computationimproves accuracy. Implement low powerlow latency deep Learning chip based on neuromorphic Intelligence. The neuromorphic obstacle detection algorithm integrates visualproprioceptive signals. The algorithm is characterized by its efficiencylow power consumption. We possess the next-generation in-memory computing AI chipsnext-generation UAV key softwarehardware technology

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.

This technology used special process to enhance the functional ingredients, accelerate the processenhance the safety of black garlic. The bioactive test had confirmed the effect of improving gastrointestinal functionhepatoprotective effect. In the future, it is expected that this technologies will be applied to related industries.

As we continue to face the rapid increase in confirmed Coronavirus cases around the world, we created an AI-based pneumonia detection platform for COVID-19. The system is able to automatically detect high-risk patients with pneumonia that will then send information to doctors. With that information, the doctors are then able to make follow-up decisions and provide a treatment plan after the diagnosis. In specific, doctors from the Department of Medical Imaging provided us thousands of positive and negative chest x-rays for pneumonia as a training set. Our system has already been tested with and adopted by doctors at the NCKU Hospital. The system achieved 95% accuracy to detect the pneumonia symptom, based on 1400 test images.

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.

In the machining, the cutting between the toolthe material produces chips. Cutting parameterstool wear can affect the colorshape of the chips. According to the observer＇s experience, the cutting conditions for chip formation can be known. It means that using cutting chips to decide wear of cutting tools is theoretically a feasible method.

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 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.

Two parts in this integrated creative invention: deep processing for frozen aquatic meal productshigh-valued utilization for byproducts. The deep processing technology for fish meal is the commodities without thawingwashing process while cooking. The technology also can remove fishy smells. Byproducts are full of rich nutrition but usually were used as animal feeding food, sold by low prices,evenly discarded. This technology is developed to extract nutrition factors in fish bone/scales by using an autoclave system with a series of heatingpressure procedures.

This research develops an innovative method for patterning arbitrarycomplicated 3D microstructures on metallic roller surfaces. This roller mold manufacturing system consists of digital light processing, arrayed microlensesoptical fibers,precision servo-controlled motion technologies. Based on this arrayed UV beam pen system, seamless roller molds are fabricated for roller imprintin

"Four automatic rumor detection models based on artificial intelligencenatural language processing. Rumor Detection on Twitter Using Multiloss Hierarchical BiLSTM with an Attenuation Factor Exploiting Microblog Conversation Structures to Detect Rumors Birds of a Feather Rumor Together? Exploring HomogeneityConversation Structure in Social Media for Rumor Detection Meet The Truth: Leverage Objective FactsSubjective Views for Interpretable Rumor Detection Experimental results show that these four models have greatly improved the accuracy of rumor detection, surpassing other SOTA models."

We invented a non-destructive terahertz (THz) deep-learning computed tomography system based on time-domain spectroscopy. In the method, THz time-domain signals are profiled. Multiple features are retrieved from those profiles by a trained modeltransformed to the spatial domain to reconstruct a cross-sectional tomographic image. We have also invented a 3D THz tomographic system based on multi-scale spatio-spectral feature fusion in a multi-scale manner. We believe our work will stimulate further applicable research of THz tomographic imaging with advanced computer vision techniques.

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.

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 is used for advanced driver assistance systems (ADAS)automatic driving. At present, our project will use a more sensitive CMOS single photon detector array to reduce the number of photons required for detection to less than ten, in order to break through the cost barriers of applications of LiDARestablish low-cost, high-performance LiDAR modules.

"1. Use PE Cluster to make the calculation reconfigurable,can be modularized into 36X PEs, supporting up to 2160PEs 2. It can be configured as an independent IP with high computing throughputlow power consumption. The computing performance can reach 864 GOPS@200MHz when 2160PEs are employed."

We propose a novel AI-based few-shot self-supervised learning method for automatic optical inspection image quality assessmentcomponent detection based on only few training images. Our method iteratively learns the feature representations of the components by using self-similarity of these components. With the large number of self-learned representations, the appearance variations of each component are then effectively learned in the AI model for component detectionmeasurement. The computation complexity of our method is significantly lower than that of deep learning methods.

We propose a semantic-guided framework (FashionOn+) that generates image-based virtual try-on results with arbitrary poses. FashionOn+ contains three stages: (I) conducts the semantic segmentation to have the prior knowledge of body parts for rendering the corresponding texture in stage (II). (III) refines two salient regions, i.e., faceclothes, to generate high-quality results. With the novel architecture, we win first place in the Multi-pose Virtual Try-on Challenge in CVPR, 2020. Further, we tackle the low-resolution limitation (256x192)achieve high-resolution results (640x480).

This project develops a high-precision maskless lithography system consists of digital light processing, microlens / spatial filters arrays,precision servo-controlled motion technologies. Based on this maskless UV lithographic system, we are able to achieve UV patterning of arbitrarycomplicated 2D/3D microstructures for advanced IC substratepackaging.

Here we present a portable microfluidic device that can perform immunoassay for infectious disease diagnosis or detect heavy metal ions in water samples. The device is composed of two major components: nanomaterials modified paper-based detection platform and semi-automated flow manipulator. A reusable 3D-printed device is used to hold a disposable detection pad that modified with functional nanomaterials for water quality monitoring or antibodies for disease diagnosis. Besides, the use of spring-containing 3D-printed syringes manipulates a large-volume sample without manual actuation. Meanwhile, by controlling the flow rate via the linked flow regulator at the syringe outlet, solutions can react stably with the paper platform, which provides detection and pre-concentration effects.

The proposed technologies apply deep learning methodsbig clinical data for lung cancer decision support. The system consists of: 1) CT radiogenomicspatho-genomics for automatic cancer detectionprediction of EGFR mutation 2) demographics to predict survival 3) genomics to predict cancer recurrencemetastasis,4) drug response inference for the best target therapy option. The modules are constructed on an AI-based Clinical Decision Support System (CDSS-SDM). The system is expected to provide physicianspatients with personalized medication recommendations.

Searchlight is composed to high brightness redgreen LEDs. The message of Morse code is transmitted through LED colorlengths of ”on/off” time in human-machine interface. CCD image sensor integrated with LEDs receives light signal of LED from another ship,then identify the real message through the image recognition software.

"The IHC staining intensity grading algorithm combines a series of complementary processes of deep learningimage processing in order to overcome the difficulties in cell boundary segmentationgrade definition,provides a cell-based IHC staining intensity gradingquantification capability. The proposed method serves as a useful assistive tool for physicians in performing accurate staining quantification in tissue microscopic images. In addition, it analyzes panoramic pathological images in order to determine the factors most strongly associated with likely disease recurrence."

We developed a wearable ultrasound monitoring device to monitor the collapse of tongue roots of sleep respiratory patients throughout the night,completed clinical trials in the sleep center. The experimental results show that this device can effectively monitor OSA. The tongue structure changes at the timeintegrates ultrasound information into the commercial PSG system. It is expected