Search Results (5)

Polarimetric color cameras are a forefront technology that simultaneously captures polarimetric and color information by analyzing polarization states across different color channels, commonly red, green, and blue. In general, each of these color channels can carry different polarization information. Therefore, measuring the polarization Stokes vector at several discrete wavelengths simultaneously and with the highest possible resolution is of interest in multiple research areas. However, when a commercial color polarization sensor is used under simultaneous narrowband RGB illumination mode, its channels cannot be assumed to represent independent wavelength channels. Spectral overlap of the color filters introduces color crosstalk between wavelength-dependent analyzer intensities, which may bias the reconstructed Stokes parameters if it is not corrected before polarimetric inversion. Several methods have been proposed in the literature to address the color crosstalk problem but they typically assume that the polarization state is identical for all wavelengths. This assumption does not generally hold for real samples, which exhibit wavelength-dependent depolarization, retardance, and dichroism. To the best of our knowledge, this is the first work presenting a method that addresses the color crosstalk problem without assuming that the polarization state is identical across all wavelengths. In addition, Fourier domain demosaicking techniques are applied to interpolate the data and reconstruct the images. The present study demonstrates how the proposed method leads to an accurate recovery of chromatic and polarimetric information on both synthetic and real-world datasets. To test our approach, narrowband light beams at three wavelengths (470, 554, 630 nm), with different spatial polarization and degree of linear polarization distributions, have been simulated and validated with simulated and experimental data. The results demonstrate the feasibility of the method for accurate three polarization channels measurements. Full article

Passive millimeter-wave (PMMW) imaging technology has become a highly promising technology that can protect privacy in human body security inspections. However, most existing methods rely on single-pixel and single-polarization processing mechanisms, which often lead to discrete false-alarm pixels or missed detections in practical applications. Although multi-polarization information can provide richer distinguishing features, the current methods typically depend on limited Stokes parameters or artificially designed polarization features, lacking a systematic framework to fully exploit the intrinsic potential of multi-polarization information. In this paper, we propose a novel multi-scale edge-preserving decomposition model, termed Gaussian and weighted average curvature filtering (GWACF), to hierarchically decompose a multi-polarization PMMW image into three structural layers: base structural (BS) layer, coarse structural (CS) layer, and fine structural (FS) layer. Furthermore, we also propose a fusion strategy in which a gradient-domain pulse-coupled neural network (PCNN) is employed to fuse the texture-rich CS and FS layers, while the energy attribute fusion method is applied to the BS layer where primary structure and background information play a dominant role. This method effectively leverages complementary polarimetric information without introducing artifacts or compromising edge sharpness. Experimental results demonstrate that the proposed method effectively enhances the brightness temperature (BT) contrast of concealed objects. Compared with existing mainstream methods, it exhibits notable advantages in both detection accuracy and robustness. Full article

An oil filter is a necessary and significant part of many manufacturing processes and equipment. Unlike the structural design and filter material selection, the particle movement in the filter during filtration is the fundamental factor influencing the filter’s performance, but this has not attracted enough attention. Due to the small size and large number of particles in the filter, it is difficult to monitor every particle’s movement. Therefore, this work used a hydraulic oil filter as a case study. Computational Fluid Dynamics (CFD) was coupled with the Discrete Phase Model (DPM) to investigate the particle motion in the filter. A filter boundary function was programmed to simulate the filter cartridge zone. The effects of inlet velocity and oil temperature/viscosity on the particle movement and filtration performance were studied. The results showed that a low-velocity zone existed and trapped some contaminant particles, particularly for particles with large Stokes numbers. The results also demonstrated that increased temperature induced an apparent reduction in filtering efficiency within the first 1.8 s from 0.61 to 0.49 when the temperature increased from 15 °C to 70 °C for 25 μm particles. Full article

We examine the time-dependent three-dimensional gas-particle flow in an intact wall-flow filter consisting of channels alternatively plugged at each end and a partially damaged filter in which the rear plugs are removed. Our focus is placed on highlighting the differences in the flow pattern and the deposition process between the two geometries. The Navier–Stokes equations are solved for the fluid flow coupled with a Brinkman/Forchheimmer model in order to simulate the flow in the porous walls and plugs. Discrete particle simulation is utilized to determine the nanoparticle trajectories. Using this scheme, we are able to characterize the main features of the flow fields developing in the intact and damaged filters with respect to the Reynolds number and identify those affecting the transport and deposition of particles that have three representative response times. We present fluid velocity iso-contours, which describe the flow regimes inside the channels, as well as in regions upstream and downstream of them. We provide evidence of local recirculating bubbles at the entrance of the channels and after their exit, whereas back-flow occurs in front of the rear plugs of the intact channels. We show that the flow leaves the channels as strong jets that may break up for certain flow parameters, leading to turbulence with features that depend on the presence of the rear plugs. The removal of the rear plugs affects the flow distribution, which, in turn influences the flow rates along the channels and through the walls. We describe the particle trajectories and the topology of deposited particles and show that particles follow closely the streamlines, which may cross the surface of permeable walls for both flow configurations. The distribution of deposited particles resembles the spatial variation of the through-wall flow rate, exhibiting two peak values at both ends of the intact filter channel, and one local maximum near the entrance of the damaged filter channel that is diminished at the exit. We also investigate in detail the particle deposition on the frontal face and indicate that particle accumulation at the edges of the entrance is favored for particles with low response times in flows with high fluid mass rates for both intact and damaged filters. Finally, we examine the filtration efficiency for the defective channels without rear plugs and show that fewer particles are captured as the Reynolds number is increased. A smaller reduction of the filtration efficiency is also predicted with increasing particle size. Full article

In this report, we present several results in the theory of $\alpha$ -models of turbulence with improved accuracy that have been developed in recent years. The $\alpha$ -models considered herein are the Leray- $\alpha$ model, the zeroth Approximate Deconvolution Model (ADM) turbulence model, the modified Leray- $\alpha$ and the Navier–Stokes- $\alpha$ model. For all of the models from above, the accuracy is limited to ${\alpha }^2$ in smooth flow regions. Better accuracy requires decreasing the filter radius $\alpha$ , which, in turn, requires a smaller mesh width that will lead in the end to a higher computational cost. Instead, one can use approximate deconvolution (without decreasing the mesh size) to attain better accuracy. Such deconvolution methods have been considered recently in many studies that show the efficiency of this approach. For smooth flows, periodic boundary conditions and van Cittert deconvolution operator of order N, the expected accuracy is ${\alpha }^{2N+2}$ . In a bounded domain, such results are valid only in case special conditions are satisfied. In more general conditions, the author has recently proved that, in the case of the ADM, the expected accuracy of the finite element method with Taylor–Hood elements and Crank–Nicolson time stepping method is $\Delta t^2+h^2+K^N{\alpha }^2$ , where the constant $K<1$ depends on the ratio $\alpha /h$ , which is assumed constant. In this study, we present the extension of the result to the rest of the models. Full article