類神經電路驗證

With the rapid advancement of artificial intelligenceIoT, many solutions have been successfully implemented. However, for very large biomedical image computations, such as MRI’s, the deep learning/ training will be a lot more time consuming.

div style="text-align: justify"Constructing a functional connectomeits 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 imagingaccurate stimulation in living animal brains. This system allows us to establish functional connectomeneural 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 systemwill apply them in the brains of larger species such as mice. We expect that our technologies will be able to greatly enhance our knowledge of the brain operation principles. Our 3D all-optical physiology (AOP) platform incorporates single-photon point stimulationtwo-photon high-speed volumetric recordings (Optics Letters 2019, "Editors pick"). We have demonstrated its effectiveness in studying the anterior visual pathway of fruit flies (iScience2019). In comparison, contemporary high-speed AOP platforms are limited to single-depthdiscrete multi-plane recordings that are not suitable for studying functional connections. Our high-resolution computational model is constructed based on the combination of static connectomeAOP data,is much more realistic than the existing models. Our work aids establishing in-vivo 3D functional connectomescomputational models of the brains, thus provides insight into the mechanisms of brain functions./div

With the rapid advancement of artificial intelligence and IoT, many solutions have been successfully implemented. However, for very large biomedical image computations, such as MRI’s, the deep learning/ training will be a lot more time consuming. At the moment, GPU’s are considered the baby of the AI world, Google has also developed a TPU to expedite AI procedures. Both GPU and TPU make the process swift. Prof. Darsen Lu’s Research Team has built an open simulation platform called (simNemo) to enable design exploration by academic and industry users in Taiwan on both the AI platform and AI application fronts.

Constructing a functional connectomeits 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 imagingaccurate stimulation in living animal brains. This system allows us to establish functional connectomeneural 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 systemwill apply them in the brains of larger species such as mice. Our technologies will greatly enhance knowledge of brain operation.

With the rapid advancement of artificial intelligence and IoT, many solutions have been successfully implemented. However, for very large biomedical image computations, such as MRI’s, the deep learning/ training will be a lot more time consuming. At the moment, GPU’s are considered the baby of the AI world, Google has also developed a TPU to expedite AI procedures. Both GPU and TPU make the process swift. Prof. Darsen Lu’s Research Team has built an open simulation platform called (simNemo) to enable design exploration by academic and industry users in Taiwan on both the AI platform and AI application fronts.

1. The modular design of a electric breaker is proposed. A prototype of a solid-state circuit with an input voltage of 500 V and adjustable rated current including 50 A, 100 A, 150 A and 200 A. 2. Achieved zero-current cutoff within 110 µs using the characteristics of IGBTs to detect the current value. 3. A12 bits analog/digital converter is used to achieve a higher accuracy current detection effect when compared with the conventional analog circuit. It can also detect the fault current rising rate and correspond to different protection delay times according to the different fault current rising rate, so that the components in the solid-state breaker can withstand a lower fault current.

With the rapid advancement of artificial intelligenceIoT, many solutions have been successfully implemented. However, for very large biomedical image computations, such as MRI’s, the deep learning/ training will be a lot more time consuming.

The SPC process technology developed by our team has a patented cloth surface treatment and multiple layers of conductive circuit layers (Layout) without etching and transfer printing processes. It can not only be continuously automated production (piece to piece), but also Environmental protection, in line with the green trend of cleaner production.

If you experience symptoms such as difficulty hearing, frequent headaches, loss of balance, deteriorating hearing, etc., it might be wise to check if you have acoustic neuroma. Because symptoms are similar to other common ear issues, it isn’t always detected. Vestibular schwannoma is a benign tumor with a slow growth rate, however as it grows and affects surrounding nerves it may have side effects that could possibly be fatal if not addressed.

The DNN models of this technology include a LithoNet, an OPCNet, and a layout novelty detection network. LithoNet is a learning-based pre-simulation model for layout-to-SEM contour prediction, and OPCnet is a dual network of LithoNet for photomask optimization. Integrated with a well-trained LithoNet, our layout novelty detection network, consisting of a self-attention guided LithoNet and an autoencoder, can check if there are layout patterns easily resulting in local distortions in contours of metal lines based on multi-modal (global-local) feature fusion.

Similar to the critical point tracking technology of human vision, the split attention and spatial grouping enhancement module, combining the multi-path grouping architecture and spatial attention technology, can accurately extract important information from the image and improve the network performance. Moreover, adopting neural architecture search technology to automatically search for the most suitable network architecture based on current modules and hardware devices can balance diagnosis speed and high accuracy.

We have developed a movement assistive system using EEG-controlled functional electric stimulation system. Users’ movement intentions are recognized by brain computer interface, implemented by deep learning network, and the detected users’ intentions are then translated into commands to trigger a functional electric stimulation (FES) device to activate users’ particular movements, such as grasping, hand raising, holding a cup, etc. In our study, we have designed our own wireless dry-electrode EEG system and our own FES system.

NTU Medical Genie Precision Health Platform: The product is mainly composed of wearable devices, IoT environmental sensors, deep learning, personal health app and case management platform. It can collect and monitor user's lifestyle and environment automatically, and predict the possibility of emergency to assist medical staff in making decisions. In addition, we opened source the project to solve most clinical studies that require lots of time to build data collection tools and processes.

With the development of science and technology, more and more kinds of electronic products come out, and the demand for charging is becoming more and more important. The compound annual growth rate of wireless charging exceeds 40％. For the demand for wireless charging equipment, the nitride developed by our team Gallium power transistors can achieve high-efficiency wireless charging through power levels ranging from 25 watts to 2500 watts and meet fast charging. It is one of the best applications for exhibiting the characteristics of gallium nitride transistors.

Use NLP techniques to realize the automatic coding of ICD10. According to the input of the patient’s age, gender, medical order, admissions, progress note, surgical records, discharge, ICD-10 diagnostic code and ICD-10 disposal code, perform machine learning model training and code prediction.

AI is applied to assist data cleaning and cross-database authority control of names and institutions in the bibliographic materials such as patents, papers and research projects.

We use DRL to train the models for personalized laddervisual learning based on Scaffolding Learning theory. It could improve the quality of our AI Go services. The services are running on an online AI-assisted board games learning platform for teaching, learningplaying Go. It will be the largest AI Go service with one billion annual AI service count.

National Cheng Kung University’s AI medical technology research results are gaining international recognition. Professor Akira Ishibashi of Hokkaido University, who specializes in the R&D of air purification systems for home sleep environments, has shown great interest in the smart sleep assessmentresearch conducted by Prof. Sheng-Fu Liang, Director of the NCKU Institute of Medical Informatics. The two parties signed a memorandum of cooperation at NCKU. Past cooperation on “T-CUSP” serves as a foundation for further research on sleep sensingimprovement with the goal of constructing a comprehensive sleep enhancement program.

Development of AI Platform for Smart Drone - Intelligent Flight: Due to its high mobility and the ability to fly in the sky, the drone has inspired more and more innovative applications/services in recent years. The goal of this project is to resolve the problem of blindly flying an unmanned aerial vehicle (UAV, which a drone in our case) when it is out of human sight or the range of wireless communication, and three major research and development directions will be considered in this project. Three artificial intelligence (AI) technologies, namely, smart sensing, smart control, and smart simulation, are applied in this project. Smart sensing - a flight system is developed, which can avoid the obstacles, complete a flight mission, and land safely. Smart control - an intelligence flight control system and a light-weighted somatosensory vest are developed. Smart simulation - a cost-effective training system and a 3D model simplification method are designed.

The technology uses cathodic arc deposition combined with electromagnet field control and optical emission spectroscopy (OES) for the deposition of new functional high entropy alloy (HEA) nitride coatings. The HEA nitride coatings possessed such high hardness and excellent hydrophobicity with low surface energy which are beneficial for high speed dry cutting. Finally, the coatings were deposited on the micro end mills for cutting test of printed circuit board. The HEA ACZN coated tools can possess high wear resistance and outperform the uncoated tools at least 4 times of tool lives.

Intelligent Portable Neurofeedback Training System: Highlights of our product: 1. High accurate EEG value (with rigorous hardware and software design and verification) 2. real-time feedback by computer screen or APP 3. can be used at home (easy to ware, easy to use)

This technology uses the ultrasonic non-destructive test technology to detect Li-ion battery packs which are commonly used in EVs. After the detection is completed, the ultrasonic signals are input into the AI diagnostic & evaluation model to obtain the status of each pouch cell in the battery pack.

A small organic molecule with multiple redox-active sites shows promise in the development of next-generation electric energy storage systems, especially for electric cars, green energy, and large-scale energy storage, when used as an active materials in a cathode in lithium-ion batteries (LIBs). Herein, an organic LIB with cathode material based on hexaazatriphenylene embedded quinone exhibits high capacity, high rate capability, and high cycling stability, which are far superior to that of commercial LIBs.

This project proposes a learning from demonstration (LfD) system that allows robots to be not only taught by human via demonstration but also adjusted by themselves.

This project proposes a learning from demonstration (LfD) system that allows robots to be not only taught by human via demonstration but also adjusted by themselves.

1.2 kV SiC Super Junction MOSFET: We have developed the first 1.2 kV SJ MOSFET in Taiwan in 2020. Its threshold voltage is 3.5 V, saturation current is about 12 A, and the specific on resistance is 8mΩ*cm2.

Nano-sized silicon reacts rapidly with most water solutions to form pure hydrogen. However, high cost and low availability are major issue. Our group has developed novel nano-sized silicon mass-production process, greatly improves its feasibility and cost for hydrogen generation. The in-time hydrogen generation by nano-size silicon, fuel-cell system and lithium ion battery system are all well integrated into one portable power system, which can satisfy general personnel electricity demand for some special conditions, such as battle field, emergency case and remote regions.

This project proposes a learning from demonstration (LfD) system that allows robots to be not only taught by human via demonstration but also adjusted by themselves.

This project integrates clinical physical and mental medical practice, medical engineering, information engineering and social welfare units, adopts cross-fields and uses AI technology, and builds an innovative artificial intelligence auxiliary evaluation and treatment system for dementia care based on cultural context. Establish an early intervention mechanism for early detection of dementia, and establish measures to improve cognitive function, emotional state, social and daily behavioral functions.

The technology combines specific visual recognition and electrochemical quantitative detection in a single portable device, providing instant and convenient copper ion detection. The sensing sensitivity has reached the standards of Taiwan and the United States for heavy metal detection for food, human body, and environments. In use, this portable and user-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.