進階篩選

Technical category
    • 基於深度學習之視障人士的行走輔助系統

      FutureTech 基於深度學習之視障人士的行走輔助系統

      We provides a wearable device for the visually impaired to walk outdoors. By the deep learning network, the system can recognizeguide the visually impaired to walk on safe areas such as sidewalkscrosswalk. In addition, it can recognize the types of common obstaclesguide the visually impaired to avoid it in advance. Finally, we can convert the Google Maps route into easy-to-understand voice prompts instruction to guide the visually impaired to move in the right direction.
    • 低功耗高性能AI神經網路之設計、加速及佈署

      FutureTech 低功耗高性能AI神經網路之設計、加速及佈署

      "We will demonstrate the following three technical achievements of our joint project: 1. Deployment of HarDNet on GPU (power consumption: 200 Watts) 2. Deployment of HarDNet on FPGA (power consumption: several tens of Watts) [winning 2nd place in the FPGA track, LPCVC 2020] 3. Deployment of HarDNet on lightweight edge devices such as Raspberry Pi (power consumption: single-digit, 10 Watts) [winning 3rd place in the DSP track4th place in the CPU track, LPCVC 2020]"
    • 人工智慧於息肉狀脈絡膜血管病變之診斷與治療決策之應用

      AI & IOT Application FutureTech 人工智慧於息肉狀脈絡膜血管病變之診斷與治療決策之應用

      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.
    • 智慧醫院 ICD10 病歷分類自動編碼系統

      FutureTech 智慧醫院 ICD10 病歷分類自動編碼系統

      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 codeICD-10 disposal code, perform machine learning model trainingcode prediction. In addition, the combination codemedical order-related coding rules in practice are used to establish corresponding rules to optimize the accuracy of AI prediction.
    • 自我網路架構搜尋與注意力機制之肺部電腦斷層掃描結節輔助診斷系統

      FutureTech 自我網路架構搜尋與注意力機制之肺部電腦斷層掃描結節輔助診斷系統

      Similar to the critical point tracking technology of human vision, the split attentionspatial grouping enhancement module, combining the multi-path grouping architecturespatial attention technology, can accurately extract important information from the imageimprove the network performance. Moreover, adopting neural architecture search technology to automatically search for the most suitable network architecture based on current moduleshardware devices can balance diagnosis speedhigh accuracy.
    • 硬科技:人工智慧讓謠言無處可藏

      FutureTech 硬科技:人工智慧讓謠言無處可藏

      "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."
    • Occlusion resistant face detectionrecognition system

      AI & IOT Application Innotech Expo Occlusion resistant face detectionrecognition system

      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.
    • Intelligent Image RecognitionAnalysis System for Small-sized Insect Pest

      AI & IOT Application FutureTech Intelligent Image RecognitionAnalysis System for Small-sized Insect Pest

      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.
    • The Application of Intelligent Agricultural Control System on Orchard

      AI & IOT Application FutureTech The Application of Intelligent Agricultural Control System on Orchard

      This project integrated with industrial foresight technologies, including UAV, artificial intelligenceimage recognition, to collect real-time images, apply algorithm in evaluation, link the technology of IOT (Internet of Things)environment sensing,use unmanned vehicle to conduct controlling work.
    • Visualization of brain connectomics: all-optical volumetric imaging/stimulation and spiking neural circuit models

      FutureTech Visualization of brain connectomics: all-optical volumetric imaging/stimulation and spiking neural circuit models

      Constructing a functional connectome and its 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 imaging and accurate stimulation in living animal brains. This system allows us to establish functional connectome and neural 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 system and will apply them in the brains of larger species such as mice. Our technologies will greatly enhance knowledge of brain operation.
    • 串連電商及線下購物的新消費型態 - 高擬真虛擬試穿

      AI & IOT Application FutureTech 串連電商及線下購物的新消費型態 - 高擬真虛擬試穿

      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).
    • 應用於自駕模型賽車之深度強化式學習技術

      FutureTech 應用於自駕模型賽車之深度強化式學習技術

      We develop an image-based sim-to-real transfer technique for deep reinforcement learning. First, we train a teacher model to move along a near optimal path. We then use this model to teach a student model the correct actions along with randomization. The technique bridges the sim-to-real gap, improving the driving speedrobustness of the simulator-trained student model in the real world.
    • AI Liver Tumor Detector

      AI & IOT Application FutureTech AI Liver Tumor Detector

      Our technique is a liver tumor detection method using machine learning which is the most popular AI technique in these years. the high-magnification image provides more details, but the view is smaller in contrast, a low-magnification image has the larger view but less details. Thus, our method combines the two magnificationsyields more accurate tumor detection results.
    • Breakthroughsachievements of key technologies of six-axis force/torque sensor

      Smart machinerynovel materials FutureTech Breakthroughsachievements of key technologies of six-axis force/torque sensor

      Multi-axis force/torque sensors are key components of precision machinesrobot arms. Development of the multi-axis force/torque sensor is a popular research topic in Taiwan. This research developed design methods, calibration machinescalibration algorithms for multi-axis force/torque sensors. These efforts contribute the enhancement of precision of 0.20 (1.25 times than commercialize pro
    • 基於深度學習的光刻電路失真預測,光罩修正及新穎布局圖樣偵測的設計自動化技術

      FutureTech 基於深度學習的光刻電路失真預測,光罩修正及新穎布局圖樣偵測的設計自動化技術

      The DNN models of this technology include a LithoNet, an OPCNet,a layout novelty detection network. LithoNet is a learning-based pre-simulation model for layout-to-SEM contour prediction,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 LithoNetan 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.
    • Using 3-D Capsule Network for Nodule Detection in Lung CT Image

      AI & IOT Application FutureTech Using 3-D Capsule Network for Nodule Detection in Lung CT Image

      The computer-aided nodule detection system in CT image consists of the search sliding window, YOLOv2 architecture, 3-D CapsNet, skip connection,post-processing. First, the CT image is divided into numerous VOIs by sliding window. Second, a 3-D CapsNet based on YOLOv2 architectureskip connection is applied to the VOIs for classifying VOIs as nodulenot. Finally, the non-maximum suppression algorithm is performed to decide the final detection result.
    • 腦電波控制功能性電刺激活動輔具系統

      FutureTech 腦電波控制功能性電刺激活動輔具系統

      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,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 systemour own FES system.
    • 陣列感測光達之智慧三維感測影像處理系統

      FutureTech 陣列感測光達之智慧三維感測影像處理系統

      Artificial intelligence 3D sensing image processing system based on array sensing Lidar aims to construct 3D image with high-quality immersion for AR/VR. The developed 3D scene recording system is based on the color camerahigh-accuracy chaotic LiDAR. The chaotic Lidar with APD arrayTOF sensors supports millimeter-accuracyinterference-avoiding capability within 100 meter in both indooroutdoor environments. High-performance embedded CNN processor supports high-throughput, high energy-efficient,low DRAM bandwidth computations for various image AI applications.
    • 人工智慧輔助胰臟癌偵測工具-PANCREASaver

      FutureTech 人工智慧輔助胰臟癌偵測工具-PANCREASaver

      PANCREASaver contains a “PC automatic segmentation model” (image segmentation)a “PC analysis AI model” (image classification) that can read the DICOM format of postcontrast CT images directly for the automatic analysis process. After conducting prep-processing with image processing algorithms, C2FNAS is employed to illustrate the tumor position prior to the diagnosis conducted by CNN. The results can be provided to the physician for diagnostic reference so as to reduce early omissionsincrease the detection rate of pancreatic cancer.
    • Artificial Intelligent 3D Sensing Image Processing System for Array Sensing Lidar

      Smart machinerynovel materials FutureTech Artificial Intelligent 3D Sensing Image Processing System for Array Sensing Lidar

      High-accuracy 3D sensingAI image processing system for constructing high-quality immersion 3D image for AR/VR. Chaotic Lidar with APD arrayTOF sensors supports millimeter-accuracyinterference-avoiding capability. High-performance CNN processor supports high-performancelow DRAM bandwidth computations for various image AI applications.
    • Neuromorphic Intelligent Visual System for Low-Power Edge Devices

      AI & IOT Application FutureTech Neuromorphic Intelligent Visual System for Low-Power Edge Devices

      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
    • AI深度壓縮工具鏈及混合定點數CNN運算加速器

      FutureTech AI深度壓縮工具鏈及混合定點數CNN運算加速器

      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.
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