Binary Search Tree Implementation with extra features like Maximum Depth of a tree, Minimum element in the BST and Mirroring of BST.
標(biāo)簽： Implementation features Maximum Minimum
上傳時(shí)間： 2017-09-03
上傳用戶：秦莞爾w
Implementation of a queue using a Linklist.
標(biāo)簽： Implementation Linklist queue using
上傳時(shí)間： 2014-01-19
上傳用戶：2467478207
Josephus Problem implementation.
標(biāo)簽： implementation Josephus Problem
上傳時(shí)間： 2017-09-03
上傳用戶：lunshaomo
Implementation of a Doubly linked list.
標(biāo)簽： Implementation Doubly linked list
上傳時(shí)間： 2013-12-04
上傳用戶：一諾88
this class is an implementation of the kd-tree algorithm
標(biāo)簽： implementation algorithm kd-tree class
上傳時(shí)間： 2013-12-17
上傳用戶：笨小孩
This article describes a new efficient implementation of the Cooley-Tukey fast Fourier transform (FFT) algorithm using C++ template metaprogramming. Thank to the recursive nature of the FFT, the source code is more readable and faster than the classical implementation. The efficiency is proved by performance benchmarks on different platforms.
標(biāo)簽： implementation Cooley-Tukey describes efficient
上傳時(shí)間： 2013-12-23
上傳用戶：netwolf
the musiccollection struts 1 application i netbeans implementation (strut for dummies book source)
標(biāo)簽： musiccollection implementation application netbeans
上傳時(shí)間： 2017-09-08
上傳用戶：invtnewer
SiftGPU is an implementation of SIFT [1] for GPU. SiftGPU processes pixels parallely to build Gaussian pyramids and detect DoG Keypoints. Based on GPU list generation, SiftGPU then uses a GPU/CPU mixed method to efficiently build compact keypoint lists. Finally keypoints are processed parallely to get their orientations and descriptors.
標(biāo)簽： SiftGPU implementation processes parallely
上傳時(shí)間： 2013-11-27
上傳用戶：zhangjinzj
A version of a sparse bundle adjustment implementation with adjustable intrinsics and distortion parameters (either shared by all views or varying for each view)
標(biāo)簽： implementation adjustment adjustable distortion
上傳時(shí)間： 2017-09-09
上傳用戶：Andy123456
Implementation of GPU (Graphics Processing Unit) that rendered triangle based models. Our goal was to generate complex models with a movable camera. We wanted to be able to render complex images that consisted of hundreds to thousands of triangles. We wanted to apply interpolated shading on the objects, so that they appeared more smooth and realisitc, and to have a camera that orbitted around the object, which allowed us to look arond the object with a stationary light source. We chose to do this in hardware, because our initial implementation using running software on the NIOS II processor was too slow. Implementing parallelism in hardware is also easier to do than in software, which allows for more efficiency. We used Professor Land s floating point hardware, which allowed us to do calculations efficiency, which is essential to graphics.
標(biāo)簽： Implementation Processing Graphics rendered
上傳時(shí)間： 2014-11-22
上傳用戶：shawvi
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