Large Families of “Grey” Arrays with Perfect Auto-Correlation and Optimal Cross-Correlation

2017 ◽  
pp. 46-56 ◽  
Author(s):  
Imants Svalbe ◽  
Matthew Ceko ◽  
Andrew Tirkel
Keyword(s):  
Cross Correlation ◽  
Auto Correlation ◽  
Large Families

Large Families of “Grey” Arrays with Perfect Auto-correlation and Optimal Cross-Correlation

2018 ◽  
Vol 61 (2) ◽  
pp. 237-248
Author(s):  
Matthew Ceko ◽  
Imants Svalbe ◽  
Timothy Petersen ◽  
Andrew Tirkel
Keyword(s):  
Cross Correlation ◽  
Auto Correlation ◽  
Large Families

scholarly journals New Orthogonal Small Set Kasami Code Sequence

2015 ◽  
Vol 14 (1) ◽  
Author(s):  
I Nyoman Pramaita ◽  
I G.A.G.K. Diafari ◽  
DNKP Negara ◽  
Agus Dharma
Keyword(s):  
Correlation Function ◽  
Cross Correlation ◽  
Code Sequence ◽  
Cross Correlation Function ◽  
Auto Correlation ◽  
M Sequence ◽  
Auto Correlation Function ◽  
Small Set

In this paper, the authors propose the design of a new orthogonal small set Kasami code sequence generated using combination of non-orthogonal m-sequence and small set Kasami code sequence. The authors demonstrate that the proposed code sequence has comparable auto-correlation function (ACF), cross- correlation function (CCF), peak cross-correlation values with that of the existing orthogonal small set Kasami code sequence. Though the proposed code sequence has less code sequence sets than that of the existing orthogonal small set Kasami code sequence, the proposed code sequence possesses one more numbers of members in each code sequence set. The members of the same code set of the proposed code sequence are orthogonal to each other.

scholarly journals Distributed and Communication-Efficient Spatial Auto-Correlation Subsurface Imaging in Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2427 ◽  
Author(s):  
Maria Valero ◽  
Fangyu Li ◽  
Jose Clemente ◽  
Wenzhan Song
Keyword(s):  
Sensor Networks ◽  
Ambient Noise ◽  
Cross Correlation ◽  
Seismic Velocity ◽  
Computational Cost ◽  
Seismic Network ◽  
Communication Process ◽  
Velocity Variation ◽  
Auto Correlation ◽  
Subsurface Monitoring

A wireless seismic network can be effectively used as a tool for subsurface monitoring and imaging. By recording and analyzing ambient noise, a seismic network can image underground infrastructures and provide velocity variation information of the subsurface that can help to detect anomalies. By studying the variation in the noise cross-correlation function of the noise, it is possible to determine the subsurface seismic velocity and image underground infrastructures. Ambient noise imaging can be done in a decentralized fashion using Distributed Spatial Auto-Correlation (dSPAC). In dSPAC over sensor networks, the cross-correlation is the most intensive communication process since nodes need to communicate their data with neighbor nodes. In this paper, a new communication-reduced method for cross-correlation is presented to meet bandwidth and cost of communication constraints in networks while ambient noise imaging is performed using dSPAC method. By applying the proposed communication-reduced method, we show that energy and computational cost of the nodes is also preserved.

scholarly journals A Path Loss and Shadowing Model for Multilink Vehicle-to-Vehicle Channels in Urban Intersections

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4433 ◽  
Author(s):  
Mikael Nilsson ◽  
Carl Gustafson ◽  
Taimoor Abbas ◽  
Fredrik Tufvesson
Keyword(s):  
Large Scale ◽  
Cross Correlation ◽  
Path Loss ◽  
Loss Model ◽  
Correlation Distance ◽  
Path Loss Model ◽  
V2v Communication ◽  
Vehicle To Vehicle ◽  
Auto Correlation ◽  
The Cross

The non line-of-sight (NLOS) scenario in urban intersections is critical in terms of traffic safety—a scenario where Vehicle-to-Vehicle (V2V) communication really can make a difference by enabling communication and detection of vehicles around building corners. A few NLOS V2V channel models exist in the literature but they all have some form of limitation, and therefore further research is need. In this paper, we present an alternative NLOS path loss model based on analysis from measured V2V communication channels at 5.9 GHz between six vehicles in two urban intersections. We analyze the auto-correlation of the large scale fading process and the influence of the path loss model on this. In cases where a proper model for the path loss and the antenna pattern is included, the de-correlation distance for the auto-correlation is as low as 2–4 m, and the cross-correlation for the large scale fading between different links can be neglected. Otherwise, the de-correlation distance has to be much longer and the cross-correlation between the different communication links needs to be considered separately, causing the computational complexity to be unnecessarily large. With these findings, we stress that vehicular ad-hoc network (VANET) simulations should be based on the current geometry, i.e., a proper path loss model should be applied depending on whether the V2V communication is blocked or not by other vehicles or buildings.

AUTO- AND CROSS-CORRELATION OF PHASES OF THE WHOLE-SKY CMB AND FOREGROUND MAPS FROM THE 1-YEAR WMAP DATA

2006 ◽  
Vol 15 (08) ◽  
pp. 1283-1298 ◽  
Author(s):  
LUNG-YIH CHIANG ◽  
PAVEL D. NASELSKY
Keyword(s):  
Cross Correlation ◽  
Random Phase ◽  
Cosmological Parameters ◽  
Microwave Background ◽  
Return Mapping ◽  
Auto Correlation ◽  
Dimensional Uniformity ◽  
Cross Correlations ◽  
Wmap Data

The issue of non-Gaussianity is not only related to distinguishing the theories of the origin of primordial fluctuations, but also crucial for the determination of cosmological parameters in the framework of inflation paradigm. We present a method for testing non-Gaussianity on the whole-sky cosmic microwave background (CMB) anisotropies. This method is based on the Kuiper's statistic to probe the two-dimensional uniformity on a periodic mapping square associating phases: return mapping of phases of the derived CMB (similar to auto-correlation) and cross-correlations between phases of the derived CMB and foregrounds. Since phases reflect morphology, detection of cross-correlation of phases signifies the contamination of foreground signals in the derived CMB map. The advantage of this method is that one can cross-check the auto- and cross-correlation of phases of the derived maps and foregrounds, and mark off those multipoles in which the non-Gaussianity results from the foreground contaminations. We apply this statistic on the derived signals from the 1-year WMAP data. The auto-correlations of phases from the internal linear combination map show the significance above 95% C.L. against the random phase hypothesis on 17 spherical harmonic multipoles, among which some have pronounced cross-correlations with the foreground maps. We find that most of the non-Gaussianity found in the derived maps are from foreground contaminations. With this method we are better equipped to approach the issue of non-Gaussianity of primordial origin for the upcoming Planck mission.

scholarly journals Correlation Structures of PM2.5 Concentration Series in the Korean Peninsula

2019 ◽  
Vol 9 (24) ◽  
pp. 5441
Author(s):  
Gyuchang Lim ◽  
Seungsik Min
Keyword(s):  
Cross Correlation ◽  
Moving Average ◽  
Air Pollutant ◽  
Fluctuation Analysis ◽  
Auto Correlation ◽  
Cross Correlation Analysis ◽  
Correlation Structures ◽  
Spatio Temporal ◽  
Pm2.5 Concentration ◽  
Mass Concentrations

In this paper, the authors investigate the idiosyncratic features of auto- and cross-correlation structures of PM2.5 (particulate matter of diameter less than 2.5 μ m ) mass concentrations using DFA (detrended fluctuation analysis) methodologies. Since air pollutant mass concentrations are greatly affected by geographical, topographical, and meteorological conditions, their correlation structures can have non-universal properties. To this end, the authors firstly examine the spatio-temporal statistics of PM2.5 daily average concentrations collected from 18 monitoring stations in Korea, and then select five sites from those stations with overall lower and higher concentration levels in order to make up two groups, namely, G1 and G2, respectively. Firstly, to compare characteristic behaviors of the auto-correlation structures of the two groups, we performed DFA and MFDFA (multifractal DFA) analyses on both and then confirmed that the G2 group shows a clear crossover behavior in DFA and MFDFA analyses, while G1 shows no crossover. This finding implies that there are possibly two different scale-dependent underlying dynamics in G2. Furthermore, in order to confirm that different underlying dynamics govern G1 and G2, the authors conducted DCCA (detrended cross-correlation analysis) analysis on the same and different groups. As a result, in the same group, coupling behavior became more prominent between two series as the scale increased, while, in the different group, decoupling behavior was observed. This result also implies that different dynamics govern G1 and G2. Lastly, we presented a stochastic model, namely, ARFIMA (auto-regressive fractionally integrated moving average) with periodic trends, to reproduce behaviors of correlation structures from real PM2.5 concentration time series. Although those models succeeded in reproducing crossover behaviors in the auto-correlation structure, they yielded no valid results in decoupling behavior among heterogeneous groups.

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