感測器

Our IoT of food allergen detection system has high sensitivityspecificity by magnetic nano-beads with advanced material coating. This advanced material successful inhibit biofouling in biosensor system. Other than this, Our system combine nanotechnology, biotechnology, IoTAI model. Device is small any easily carry to anywhere.

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.

A CMOS image sensor is developed for the national space organization (NSPO) 3rd generation high-resolution remote sensing satellite,the main achievement is its capability to satisfy the demanded ground resolution of 0.5 m. This CMOS image sensor with a new time delay integration circuit is implemented using Back-Side Illumination CIS 0.13 μm technology. The anti-radiation capability of the chip is also boosted to extend the lifetime of the chip. Measurement systemdata analysis programs are developed to provide performance parameters of the proposed CMOS image sensor.

The TSRI biomedicalepidemic prevention sensor chip platform uses multi-functional integrated design, with advanced sensorchip design technologies integrating with AI systems, to achieve the goal of diversified epidemic prevention. The platform is open for R&D teams from industryuniversities to develop genetic engineering modification of specific virus typesestablish DNA identification technology, as well as to accelerate development cycle for emerging virus detectorsshorten the virus detection time.

Here, we introduce an electricity-free centrifuge platform based on a manual centrifuge. The provided centrifugal force is sufficient to produce a plasma purity of 99 separated in as little as 2−3 min. We then performed an immunoassay on a paper devicethe results were observed by a portable reader. As a result, the detection limit of the C-Reactive Protein is 1 ng/mL, with a total turnaround time of 7 min.

The objective of this project is to develop a portablewireless urine detection systemplatform for prevention of cardiovascular disease. The main idea is to develop a system-on-chip, a microelectrodemicrochannel chip to detect biomarkers concentrations in urine. And then, it will be wirelessly transmitted to a smart application platform to evaluate user’s cardiovascular status.

In this study, the embedded LiDARAI recognition of smart headlight for autonomous driving is demonstrated. At driving process, the vehicle can provide artificial intelligence to identify the type, distancespeed of the object to be measured based on the fusion data of the LiDAR point cloudoptical image of the environment,then feedback to the headlight control system. Finally, this smart headlight would perform the suitable lighting mode during the daynightfit the automotive lamp safety regulationsstandards (ECE R112SAE J3069).

Although masks protect us from COVID-19, they cover a substantial portion of the face,eliminates important facial expressions during communications. EmoMask is a human-AI collaborative mask that mediates these interactions in mask-wearing environments through emotion-aware sensorsAI algorithms, which works with humans to create facial expressions. The EmoMask system identifiesdifferentiates expressions using TinyML-based AI algorithm,conducts real-time classificationsregressions to create matching facial expressions during a communication while faces are covered.

This technique combines more than one AI models to precisely predict current under noisy data from current sensoroptimizes speed rate based on this AI forecasting model. At the same time, it considers the practical requirementslimitations of workpiece surface roughness, tool machine current load, etc. This technique provides decision supports for optimizing parameters of CNC milling machine to keep high efficiencyenhance energy conservation.

Through patented wearable sensor fusion technology, human 3D motions can be captured in real-time which allow 7-DOF robotic armdexterous robotic hand to perform anthropomorphic motiongrasping action. Integrated with eye-in-hand visual module, the adaptive gripper can intelligently decide optimal grasping strategy to ensure success grasping of arbitrary shape of objects in smart manufacturing applications.

The goal of this project is to study "low-temperature magnesium hydrogen storage materials and energy storage applications". Mg hydrogen storage materials with a hydrogen storage capacity of 5.0 wt% is developed,and their dehydrogenation rate at 250℃ will be significantly enhanced using a forcible pump. The Mg hydrogen storage powders are inserted into a tank for cyclic hydrogenation-dehydrogenation tests. The H2 gas desorbed from the tank is supplied to a high-temperature proton exchange membrane fuel cell (HT-PEMFC,160℃) for power generation.

Surface-enhanced Raman spectroscopy (SERS) is a useful analytical technique for detecting extremely small amounts of molecules. Herein, we designed a paper-based quasi-three-dimensional SERS substrate (P-SERS) that can provide potential to improve Raman analyses for food safety, pesticide poisoning, precision medicine, drug abuse and DNA/RNA testing. The sensitive, low-cost, flexible and disposable SERS substrate could be easily fabricated by physical deposition of gold nanoparticles array onto a filter paper. In this case, we are able to create non-continuous Au islands on the fiber surfaces, where the gaps between AuNPs can dramatically generate the high electric field to enhance Raman signal of target molecules.

This technology aims to automatically extractdefine features from a data-driven perspective through a series of data engineering, machine learningensemble learning technologies via the big data collected from equipment sensorsquality characteristic measurement results. For products that have no quality measurement, a virtual metrology resultpredicted confidence score are provided as a decision-making basis for process control.

This successful technology is the digital interventions on depression patients based on (1) heart rate variability bio-feedbacks (HRVBs) measured by a novel flexible sensor patch attached on patient’s wrist(2) an APP. An innovative flexible low-power PPG patch has been designed which is capable of providing weeks of high-quality PPG signals which are essential towards accurate estimation of HRV for favorable efficacy of designed digital interventions. Also, An AI model correlating the HRVsand PHQ-9/HDRS scores of depression patients will be built based on collected data.

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