2016/1
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Recent Submissions
- ItemPoint Spread Functions in Identification of Astronomical Objects from Poisson Noised Image(Společnost pro radioelektronické inženýrství, 2016-04) Mojzis, Frantisek; Kukal, Jaromir; Svihlik, JanThis article deals with modeling of astronomical objects, which is one of the most fundamental topics in astronomical science. Introduction part is focused on problem description and used methods. Point Spread Function Modeling part deals with description of basic models used in astronomical photometry and further on introduction of more sophisticated models such as combinations of interference, turbulence, focusing, etc. This paper also contains a~way of objective function definition based on the knowledge of Poisson distributed noise, which is included in astronomical data. The proposed methods are further applied to real astronomical data.
- ItemExperimental Verification of Inertial Navigation with MEMS for Forensic Investigation of Vehicle Collision(Společnost pro radioelektronické inženýrství, 2016-04) Tadic, Srdjan; Vukajlovic, Milan B.This paper studies whether low-grade inertial sensors can be adequate source of data for the accident characterization and the estimation of vehicle trajectory near crash. Paper presents outcomes of an experiment carried out in accredited safety performance assessment facility in which full-size passenger car was crashed and the recordings of different types of motion sensors were compared to investigate practical level of accuracy of consumer grade sensors versus reference equipment and cameras. Inertial navigation system was developed by combining motion sensors of different dynamic ranges to acquire and process vehicle crash data. Vehicle position was reconstructed in three-dimensional space using strap-down inertial mechanization. Difference between the computed trajectory and the ground-truth position acquired by cameras was on decimeter level within short time window of 750 ms. Experiment findings suggest that inertial sensors of this grade, despite significant stochastic variations and imperfections, can be valuable for estimation of velocity vector change, crash severity, direction of impact force, and for estimation of vehicle trajectory in crash proximity.
- ItemThe Distributed Convergence Classifier Using the Finite Difference(Společnost pro radioelektronické inženýrství, 2016-04) Kenyeres, Martin; Kenyeres, Jozef; Skorpil, VladislavThe paper presents a novel distributed classifier of the convergence, which allows to detect the convergence/the divergence of a distributed converging algorithm. Since this classifier is supposed to be primarily applied in wireless sensor networks, its proposal makes provision for the character of these networks. The classifier is based on the mechanism of comparison of the forward finite differences from two consequent iterations. The convergence/the divergence is classifiable only in terms of the changes of the inner states of a particular node and therefore, no message redundancy is required for its proper functionality.
- ItemAnalysis of Residue Probability Density Function and Comparator Offset Error in Pipelined ADCs(Společnost pro radioelektronické inženýrství, 2016-04) Fatemi-Behbahani, Esmaeil; Farshidi, Ebrahim; Ansari-Asl, KarimThis paper presents a new approach to analyze the convergence of residue probability density function (pdf) in pipelined ADCs. Compared to the previous approaches, in the proposed approach, in addition to the analysis of residue pdfs for different input densities, the analysis of the sub-ADC comparator offsets impact on output pdf is possible. Using Fourier analysis, it will be shown that the residue density converges to uniformity. In the half-bit redundant structure, residue pdf concentrates in the center half of the stage full-scale range and 6 dB of extra resolution can be gained. Also, the share of each stage in this resolution improvement is investigated. Examining the sub-ADC threshold offsets impact on residue pdfs, it is observed that with respect to the impact on converter additional resolution, the final stages offset errors are more significant than the first stages offsets.
- ItemRobust Tensor Analysis with Non-Greedy L1-Norm Maximization(Společnost pro radioelektronické inženýrství, 2016-04) Zhao, Limei; Jia, Weimin; Wang, Rong; Yu, QiangThe L1-norm based tensor analysis (TPCA-L1) is recently proposed for dimensionality reduction and feature extraction. However, a greedy strategy was utilized for solving the L1-norm maximization problem, which makes it prone to being stuck in local solutions. In this paper, we propose a robust TPCA with non-greedy L1-norm maximization (TPCA-L1 non-greedy), in which all projection directions are optimized simultaneously. Experiments on several face databases demonstrate the effectiveness of the proposed method.