The Kalman filter is an efficient recursive filter that estimates the state of a linear dynamic system from a series of noisy measurements. It is used in a wide range of engineering applications from radar to computer vision, and is an important topic in control theory and control systems engineering. Together with the linear-quadratic regulator (LQR), the Kalman filter solves the linear-quadratic-Gaussian control problem (LQG). The Kalman filter, the linear-quadratic regulator and the linear-quadratic-Gaussian controller are solutions to what probably are the most fundamental problems in control theory.
Consider a BPSK and a QPSK system for the following two cases: 1) The probability that the symbol 1 is sent and the probability that the symbol 0 is sent are all the same. 2) The probability that the symbol 1 is sent is two times than the probability that the symbol 0 is sent. Assume that the noise is Gaussian distributed with mean=0 and 2 = 1.
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.
Implements mixture of binary (logistic) PCAs where pixels are modeled using Bernoulli distributions instead of Gaussian. The images do not need to be aligned.
This is an analog signal communication simulator, usign frequency modulation. It is designed in MATLAB-Simulink. The communications channel beetween the transmitter and the reciever is supposed to be affected by additive white Gaussian noise.
壓縮包中有5篇論文，分別為《Data-driven analysis of variables and dependencies in continuous optimization problems and EDAs》這是一篇博士論文，較為詳細(xì)的介紹了各種EDA算法；《Anisotropic adaptive variance scaling for Gaussian estimation of distribution algorithm》《Enhancing Gaussian Estimation of Distribution Algorithm by Exploiting Evolution Direction with Archive》《Niching an Archive-based Gaussian Estimation of Distribution Algorithm via Adaptive Clustering》《Supplementary material for Enhancing Gaussian Estimation of Distribution Algorithm by Exploiting Evolution Direction with Archive》《基于一般二階混合矩的高斯分布估計算法》介紹了一些基于EDA的創(chuàng)新算法。
Without conceding a blemish in the first edition, I think I had best come clean
and admit that I embarked on a second edition largely to adopt a more geometric
approach to the detection of signals in white Gaussian noise. Equally rigorous, yet
more intuitive, this approach is not only student-friendly, but also extends more
easily to the detection problem with random parameters and to the radar problem
Homogeneous Partitioning of the Surveillance Volume discusses the
implementation of the first of three sequentially complementary approaches for
increasing the probability of target detection within at least some of the cells of
the surveillance volume for a spatially nonGaussian or Gaussian “noise”
environment that is temporally Gaussian. This approach, identified in the Preface
as Approach A, partitions the surveillance volume into homogeneous contiguous
subdivisions.
