A Decentralized Eigenvalue Computation Method for Spectrum Sensing Based on Average Consensus

Frequenz ◽  
2016 ◽  
Vol 70 (7-8) ◽  
Author(s):  
Jafar Mohammadi ◽  
Steffen Limmer ◽  
Sławomir Stańczak
Keyword(s):  
Spectrum Sensing ◽  
Computation Method ◽  
Power Method ◽  
Inference Problem ◽  
Eigenvalue Computation ◽  
Average Consensus ◽  
Computation Algorithm ◽  
Eigenvalue Estimation

AbstractThis paper considers eigenvalue estimation for the decentralized inference problem for spectrum sensing. We propose a decentralized eigenvalue computation algorithm based on the power method, which is referred to as

Distributed Cooperative Spectrum Sensing Algorithm Based on Average Consensus

2015 ◽  
Vol 713-715 ◽  
pp. 1090-1093
Author(s):  
Yong Xiu Feng ◽  
Ai Qin Bao ◽  
Deng Yin Zhang
Keyword(s):  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Signal To Noise Ratio ◽  
False Alarm Probability ◽  
Consensus Algorithm ◽  
Noise Effect ◽  
Distributed Spectrum Sensing ◽  
Average Consensus ◽  
Deviation Criterion ◽  
Interaction Channel

The existing distributed spectrum sensing algorithms usually assume that the information in interaction channel is totally correct and did not consider noise effect. To solve these problems, a new distributed cooperative spectrum sensing scheme based on average consensus is investigated in this paper. Based on minimum mean square deviation criterion, we design an iterative matrix suitable for consensus algorithm with considering the noise of interaction channel. Simulation results show that the proposed method achieves better detection performance under noise effect of interaction channel and outperforms conventional scheme by 11% at-5dB signal to noise ratio (SNR) and 0.1 false alarm probability.

Object Movement Computation from Two Images

2015 ◽  
Vol 752-753 ◽  
pp. 1085-1089 ◽  
Author(s):  
Ji Hun Park ◽  
Sung Hun Park
Keyword(s):  
Likelihood Estimation ◽  
Input Image ◽  
External Parameter ◽  
Computation Method ◽  
Feature Points ◽  
Rigid Object ◽  
Object Movement ◽  
Stationary Objects ◽  
Computation Algorithm ◽  
Parameter Values

This paper presents a new object movement computation method using ray vectors generated from two cameras. We compute camera's internal and external parameters of the input images using computed values from partially overlapping input image frames which has the same corresponding fixed feature points. This is achieved by computing fixed points in the environment, camera distortion values and internal and external parameters from stationary objects. Ray vectors cast from each camera to feature points keep camera external parameter values. Using computed camera external parameters, a tracked object's rigid object movement is estimated using maximum likelihood estimation by setting projected intersection points between ray vectors as a part of objective function. Our method is demonstrated and the results are compared to our another movement computation algorithm.

scholarly journals Uniqueness of the Level Two Bayesian Network Representing a Probability Distribution

2011 ◽  
Vol 2011 ◽  
pp. 1-13 ◽  
Author(s):  
Linda Smail
Keyword(s):  
Artificial Intelligence ◽  
Probability Distribution ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Probabilistic Models ◽  
Probability Distributions ◽  
Random Variables ◽  
Inference Problem ◽  
Computation Algorithm

Bayesian Networks are graphic probabilistic models through which we can acquire, capitalize on, and exploit knowledge. they are becoming an important tool for research and applications in artificial intelligence and many other fields in the last decade. This paper presents Bayesian networks and discusses the inference problem in such models. It proposes a statement of the problem and the proposed method to compute probability distributions. It also uses D-separation for simplifying the computation of probabilities in Bayesian networks. Given a Bayesian network over a family of random variables, this paper presents a result on the computation of the probability distribution of a subset of using separately a computation algorithm and D-separation properties. It also shows the uniqueness of the obtained result.

An Effective Spectrum Sensing Method Based on Maximum Eigenvector

2014 ◽  
Vol 556-562 ◽  
pp. 4522-4525
Author(s):  
Rui Yan Du ◽  
Fu Lai Liu ◽  
Ya Ping Wu
Keyword(s):  
Spectrum Sensing ◽  
Detection Probability ◽  
Fundamental Problem ◽  
Signal To Noise Ratio ◽  
Power Method ◽  
Smoothing Technique ◽  
Signal To Noise ◽  
Radio System ◽  
Temporal Smoothing ◽  
Spectrum Holes

Spectrum sensing is a fundamental problem for cognitive radio system as it allows secondary user (SU) to find spectrum holes for opportunistic reuse. This paper presents a new spectrum sensing method based on the data stacking technique (temporal smoothing technique) and power method. The “maximum eigenvector” is used to detect the spectrum holes. Compared with the previous works, the proposed approach can provide better performance, such as higher detection probability in the lower signal-to-noise-ratio (SNR) scenario, etc.

Sign in / Sign up

Export Citation Format

Share Document