93 research outputs found
Sensing and Processing for Infrared Vision: Methods and Applications
No full textDear readers and fellow researchers, [...
Effect of high-energy shot peening on the corrosion behavior and chloride threshold concentration of AISI 316L stainless steel rebar in simulated concrete pore solution
No full textThe corrosion behavior and chloride threshold concentration of AISI 316L stainless steel reinforcement bar (rebar), subjected to high-energy shot peening (HESPed), are compared with its solution-annealed (SAed) counterpart in simulated concrete pore solution (CPS). X-ray diffraction (XRD) and transmission electron microscopy (TEM) revealed that HESP significantly reduces the surface grain size to the ultrafine/nanometric range and increases the density of various dislocation structures, including dislocation walls and cells. This augmented presence of grain boundaries and dislocations, acting as high diffusivity paths, contributed to the formation of a thicker surface film with higher Cr content and lower donor/acceptor density in the HESPed sample compared to the SAed. Moreover, corrosion behavior analyses in CPSs with NaCl concentrations ranging from 0.0 to 2.0 M demonstrated that HESPed samples exhibit broader passivation ranges, more positive corrosion potentials, and higher polarization resistance under equivalent chloride concentrations. Additionally, threshold chloride concentrations obtained from the potentiodynamic polarization tests show values between 0.5 and 1.0 M for the SAed and between 1.0 and 1.5 M for the HESPed samples. Based on diffusion equations, a physical model is proposed to elucidate the improved behavior resulting from the HESP treatment
Investigating Psychometric Properties of Wechsler Memory Scale-Third Edition for the Students of Tehran Universities
No full textThe major aim of the present research was investigating psychometric properties of Wechsler Memory Scale-Third Edition (WMS-III) for the students of Tehran Universities. Therefore, this scale was administrated to 266 (120 male, 144 female) students of two universities "Shahed" and "Tarbiat Moallem". The participants were selected through mulitistep cluster sampling. Reliability coefficient of the subtests ranged from 0.65 to 0.85, and for indexes ranged from 0.75 to 0.86. Then, the interscorer agreement of the subtests that needed clinical judgment (such as Logical memory I, II and Family Pictures I, II) was evaluated. The correlation coefficients among scorers was higher than 0/80.The correlation between WMS-III and WAIS-R Short Form was computed for the construct validity which was low. This indicates that WMS-III and WAIS-R short form assessed separate construct although they have significant relations. The intercorrelation between WMS-III subtests and indexes revealed high correlation between modality-specific subtests and indexes, whereas, it has low correlation with other subtests and indexes. Exploratory Factor Analysis (Principle Component Analysis with varimax rotation) was used in order to investigate Factor Structure of WMS-III. The findings obtained showed three factor structures (auditory memory, visual memory and working memory) explain %76/845 of the total variance. These results are similar to previous researches in case of number of factors, on the other hand, this data in subtests of auditory memory was different from the research literature
A false-alarm aware methodology to develop robust and efficient multi-scale infrared small target detection algorithm
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Fast, Accurate and Object Boundary-Aware Surface Normal Estimation from Depth Maps
No full textThis paper proposes a fast and accurate surface normal estimation method
which can be directly used on depth maps (organized point clouds). The surface
normal estimation process is formulated as a closed-form expression. In order
to reduce the effect of measurement noise, the averaging operation is utilized
in multi-direction manner. The multi-direction normal estimation process is
reformulated in the next step to be implemented efficiently. Finally, a simple
yet effective method is proposed to remove erroneous normal estimation at depth
discontinuities. The proposed method is compared to well-known surface normal
estimation algorithms. The results show that the proposed algorithm not only
outperforms the baseline algorithms in term of accuracy, but also is fast
enough to be used in real-time applications
Multiple Cylinder Extraction from Organized Point Clouds
No full textMost man-made objects are composed of a few basic geometric primitives (GPs) such as spheres, cylinders, planes, ellipsoids, or cones. Thus, the object recognition problem can be considered as one of geometric primitives extraction. Among the different geometric primitives, cylinders are the most frequently used GPs in real-world scenes. Therefore, cylinder detection and extraction are of great importance in 3D computer vision. Despite the rapid progress of cylinder detection algorithms, there are still two open problems in this area. First, a robust strategy is needed for the initial sample selection component of the cylinder extraction module. Second, detecting multiple cylinders simultaneously has not yet been investigated in depth. In this paper, a robust solution is provided to address these problems. The proposed solution is divided into three sub-modules. The first sub-module is a fast and accurate normal vector estimation algorithm from raw depth images. With the estimation method, a closed-form solution is provided for computing the normal vector at each point. The second sub-module benefits from the maximally stable extremal regions (MSER) feature detector to simultaneously detect cylinders present in the scene. Finally, the detected cylinders are extracted using the proposed cylinder extraction algorithm. Quantitative and qualitative results show that the proposed algorithm outperforms the baseline algorithms in each of the following areas: normal estimation, cylinder detection, and cylinder extraction
Fast and Robust Small Infrared Target Detection Using Absolute Directional Mean Difference Algorithm
No full textInfrared small target detection in an infrared search and track (IRST) system
is a challenging task. This situation becomes more complicated when high
gray-intensity structural backgrounds appear in the field of view (FoV) of the
infrared seeker. While the majority of the infrared small target detection
algorithms neglect directional information, in this paper, a directional
approach is presented to suppress structural backgrounds and develop a more
effective detection algorithm. To this end, a similar concept to the average
absolute gray difference (AAGD) is utilized to construct a novel directional
small target detection algorithm called absolute directional mean difference
(ADMD). Also, an efficient implementation procedure is presented for the
proposed algorithm. The proposed algorithm effectively enhances the target area
and eliminates background clutter. Simulation results on real infrared images
prove the significant effectiveness of the proposed algorithm.Comment: The Final version (Accepted in Signal Processing journal
New charge balancing method based on imbalanced biphasic current pulses for functional electrical stimulation
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