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dain

Depth-Aware Video Frame Interpolation (CVPR 2019)

daisho-usb3.0-fpga

SuperSpeed USB 3.0 FPGA platform

daisho-usb3ip

Test of the USB3 IP Core from Daisho on a Xilinx device

dal

Code for the paper "DAL: A Deep Depth-aware Long-term Tracker" (ICPR2020)

dali

A GPU-accelerated library containing highly optimized building blocks and an execution engine for data processing to accelerate deep learning training and inference applications.

damage_detection

Road damage detection and evasion in ROS

damping-dolphin

🐬 A utility to visualise and generate parameter sets for use in the damping model.

dana-accelerator-fpga

Dynamically Allocated Neural Network Accelerator for the RISC-V Rocket Microprocessor in Chisel

dap405-hsusb

USB High-Speed CMSIS-DAP debugger based on STM32F405

dap405-stm32f446

dap_project

Multiple DOA estimation & delay-and-sum beamforming

dapcmdr

J-LINK Commander like tool for JLink and DAPLink, adding HardFault diagnosis and SVD-based peripheral register access.

daplink

daplink-1

CMSIS-DAP (DAPLink) porting to Nuvoton M480

daplink-brochure

:closed_book: DAPLink设计与应用（基础篇）

dapprog

Offline SWD Programmer for Cortex-M Core MCU

daqnet-ice40

darklighter

Code and demo videos of DarkLighter---a low-light enhancer towards facilitating UAV tracking in the dark.

darknet8-badge

darkriscv

opensouce RISC-V implemented from scratch in one night!

darpa_modules

dart

Code to compute and analyse displacement arrays of rendered tractions

dart-1

FPGA design automation for real-time, dynamic partially reconfigurable application

dart_ips

a library of DART-enabled hw IPs

darwin

Darwin: A co-processor for long read alignment

darwin-wga-array-coprocessor

dashdap

STM32 DAP Debugger

dask

Parallel computing with task scheduling

dasr

[CVPR 2021] Unsupervised Degradation Representation Learning for Blind Super-Resolution

data-driven-robust-tracking-control-of-underactuated-mechanical-systems-using-identified-flat-output

This paper presents a data-driven control design framework to achieve robust tracking control without exploiting mathematical model of nonlinear underactuated mechanical systems (UMS). The method leverages the differential flatness property of linearized systems and online estimation and compensation of disturbances by active disturbance rejection control (ADRC). The differentially flat output is derived directly from measured data with unknown dynamics and parameters of UMS by the flat output identification (FOID) algorithm. A reduced nominal model of UMS is proposed to simplify the process of finding flat output and trajectory planning. Technique of sparse regression is applied to identify the relationships between flat output and system states, which reduces the order of the well-known extended state observer (ESO) and thereby make the ESO more effective for both trajectory planning and tracking in terms …