By Mirsad Hadžikadić, Samir Avdaković
This quantity spans quite a lot of technical disciplines and applied sciences, together with complicated structures, biomedical engineering, electric engineering, strength, telecommunications, mechanical engineering, civil engineering, and desktop technological know-how. The papers integrated during this quantity have been awarded on the foreign Symposium on leading edge and Interdisciplinary purposes of complicated applied sciences (IAT), held in Neum, Bosnia and Herzegovina on June 26 and 27, 2016. This hugely interdisciplinary quantity is dedicated to varied facets and kinds of structures. structures pondering is important for effectively development and realizing man-made, common, and social structures.
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Additional resources for Advanced Technologies, Systems, and Applications
He H, Starzyk JA (2006) A self-organizing learning array system for power quality classiﬁcation based on wavelet transform. IEEE Trans Power Delivery 21(1):286–295 7. Mallat S (1998) A wavelet tour of signal processing. Academic, San Diego, CA 8. Morales C, Ronquillo–Jarillo G, Campos–Enríquez JO (2009) Multi-scale analysis of well– logging data in petrophysical and stratigraphic correlation. Geoﬁsica 9. Avdakovic S, Omerhodzic I, Badnjevic A, Boskovic D (2015) Diagnosis of epilepsy from EEG signals using global wavelet power spectrum.
In Section V we refer to zero crossing man/min ratio, Xz/max/min, which indicates approximate length/width ratio. Zil truck dimensions can be found on line and compared. Figure 17 shows ideal rectangular target, with length L = 32, and width W = 16 feet, for 1-foot resolution as in MSTAR. Here zero crossings correspond to W, L and 45° X projection, so that we have: Target Signatures and Pose Estimation 49 Fig. 17 Synthetic signature statistics across 3 poses Table 6 Pose j sample autocorrelations (PjSS) Variable Description Xcor Xzcor Xecor Xnpix Signature autocorrelation function Signature autocorrelation zero crossing Signature autocorrelation error Number of pixels along X Xproj ¼ L sinð45Þ þ W cosð45Þ ¼ 35:6 6 Signature Statistics Across One Target The last column in Table 1 indicates ith Target Signature Auto Correlation (TiSAC) features for each of four targets across every pose, for both X(i, j) and Y(i, j).
These capture cross statistics for poses and can be used to discriminate poses. Figures 18 and 19 show Pearson and Spearman Fig. 18 Zil truck pose shifted correlations Fig. 19 Ideal rectangular pose shifted correlations Target Signatures and Pose Estimation 51 coefﬁcients, as well as correlation envelopes, as a function of the pose for Zil Truck and for our rectangular synthetic target. 7 Statistics Across Different Targets The ﬁnal step in our target signature analysis is to look at the cross target statistics and correlations.
Advanced Technologies, Systems, and Applications by Mirsad Hadžikadić, Samir Avdaković