搜索资源列表
vthreads_20100131
- 在XMOS多核MCU上实现一个硬件线程上跑软件多线程-The realization of a hardware thread running software on XMOS multicore multithreaded MCU
1.6678
- 该程序用于6678多核加载,适用于开发板。-The program for 6678 multi-core load applied to the development board.
code
- 利用多核学习对多种特征进行融合实现人的动作的识别。-Learn to use a variety of features multicore fusion achieve recognition of human movement.
gmkl
- 这是一般的多核学习,学习到的每一种特征用不同的核表示并用和或积的形式组合在一起,实现行为识别。-This is a general multicore learning, learning to use each feature represents a different nuclear and combined or integrated with, and form, to achieve behavior recognition.
multiprocessor_tutorial_final_v1
- 多核处理器系统整个源代码,可以在DE2开发板上运行,请大侠多多指点,-Multi-core processor systems throughout the source code can be run in the DE2 board, heroes lot of guidance, thank you
multicore_framework
- 通过CCSv5 开发环境,以及TI TMS320C66X 多核DSP,工程实现了多核框架的编写(基于sys-bios),同时调用clock模块周期触发功能函数,使得不同核之间进行数据的流水串行处理。-Through CCSv5 development environment, as well as TI TMS320C66X multicore DSP, the project can carry out a framework demo for the preparation of multi
hpc
- 基于OpenCV和OpenMP的多核处理图像边缘检测算法——Sobel的实现。 没有可视化界面。 需要先配置OpenCV和OpenMP。 理论依据:利用OpenMP我们可以实现多核并行处理边缘检测。根据Sobel原理,可以完全明确的是:每一个像素点的梯度计算都不依赖于其他的像素点!这就是实现多核并行处理边缘检测的关键。利用这个关键的特性,我们可以让多个核同时去计算多个像素点的梯度值,进而提高Sobel边缘检测算法的性能。 -The implementation of OpenCV
multiCore_Training
- TMS320C6678多核dsp培训文档-multiCore_Training of TMS320C6678
test
- 物理运算引擎是一个可伸缩的多平台游戏物理解决方案支持范围广泛的设备,从智能手机到高端多核cpu。而物理运算引擎SDK设计主要是为游戏开发者,也被研究人员,教育工作者,和模拟应用程序的开发人员需要实时性能和健壮的行为。特性包括离散和连续碰撞检测、形状和raycasting清洁工,解决刚体动力学、流体、和粒子,以及车辆和角色控制器。-PhysX is a scalable multi-platform game physics solution supporting a wide range of
6678
- 多核DSP操作系统关键技术研究与实现_吴灏 串行RapidIO互连系统的设计与实现_张强 知网论文 多核编程6678-Multicore DSP operating system key technology research and implementation _ Wu Hao Design and Implementation of Serial RapidIO interconnect system _ Zhang Qiang HowNet paper- Multicor
guss
- 【高斯核函数改进 参考价值1】LBF活动轮廓模型的改进.pdf【高斯核函数的改进 参考价值5】单核和多核相关向量机的比较研究.pdf-[Gaussian kernel improved reference value 1] .pdf LBF improved active contour models comparative study [.pdf Gaussian kernel improvements reference 5] single and multi-core related ve
ipp-samples
- 英特尔的ipp程序demo,可进行多核编程,运行效率大幅提升。-Intel' s ipp program demo, can be multi-core programming, significantly increasing operating efficiency.
intel_mutilcore_smoke-source-r1.2
- intel 基于多核优化游戏引擎示例代码。根据cpu核心数创建多个线程,并行处理AI 物理、特效 渲染等,提高帧率和流畅度。由于pudn文件大小限制,删除了资源文件,只留下源代码。-intel multicore optimized game engine example is based on code. Creating multiple threads based on the number of cpu core, parallel processing AI physics, effe
BPOPenMP
- BP算法的多核并行研究,输入输出txt文件内容保密,不能上传,大家根据代码自己加数据很简单。环境vs2010,技术openmp-BP algorithm of multi-core parallel study, input and output TXT file content confidential, can t upload, according to the code and data is very simple. Openmp vs2010 environment, technol
K1_STK_v1.1
- Keystone1 架构的DSP软件开发包,经过实际测试适用于 TI C6678 EVM板 / C6670 EVM板 / C6657 EVM板。 Keystone1 软件开发包(STK) IPC基本例程;C66X指令测试;SRIO例程;Memory测试例程;6670VCP2测试例程;I2C例程;SPI例程;EMIF例程;DualC6457 SRIO通信例程;Keystone_Timer例程;keystone_UART例程;keystone_Navigator例程;EMAC多核boot例子;C
C678test
- C6678 多核 测试代码,实际工程测试代码,具有强的可应用性,有rapdio测试代码-C6678 multi core test code, the actual engineering test code, with strong applicability, there are rapdio test code
MKAD
- 多核的异常检测(MKAD)算法用于一组文件的异常检测。它引入多喝到一个单优化函数,并使用一类支持向量机(OCSVM)框架进行实现-The Multiple Kernel Anomaly Detection (MKAD) algorithm is designed for anomaly detection over a set of files. It combines multiple kernels into a single optimization function using the
SimpleMKL
- 很经典的一个多核学习程序SimpleMKL,包括对应的文章和demo程序,可以很容易学习和上手MKL的原理和应用!-A classic learning program multicore SimpleMKL, including the corresponding articles and demo program that makes it easy to learn and to use the principles and applications of MKL!
GMKL-matlabcode
- 比较经典的一个多核学习MKL代码(GMKL Generalized MKL),里面包含其对应的文章,很容易学习和调试!-Learn more classic a multicore MKL Code (GMKL- Generalized MKL), which contains the corresponding article, it is easy to learn and debug!
demo_SGMKL
- 多核学习Sparse GMKL的代码,采用L1+L2范数约束,代码很详尽!-Multicore learning Sparse GMKL code, using L1+L2 norm constraint, the code is very detailed!