Effect of wall surface wettability on collective behavior of hydrogen microbubbles rising along a wall

This paper presents an experimental study of the influence of wall surface wettability on the behavior of hydrogen microbubbles rising along a nearly vertical wall. Multiple optical diagnostics, including particle tracking velocimetry, have been employed for the study. The microbubble behavior observed along three different kinds of wall surfaces (hydrophobic, hydrophilic, and super-hydrophilic) was characterized by the microbubble-wall attachment, bubble size distribution, bubble coalescence, and microbubble layer formation. Microbubbles rising along the wall with poor wettability soon attach to the wall and grow to millimeters in size as a result of bubble coalescence. Such millimeter-sized bubbles detach from the wall because of their increased buoyancy, and eventually enhance transverse diffusion of microbubbles, which is known as the sweep-out effect. In contrast, in the case of very good wettability, almost no microbubbles attach to the wall and smoothly form a thin microbubble layer in the wall proximity. The observed phenomena contradict our intuitive expectation of the effect of surface wettability on gas bubbles, and hence may be regarded as a feature of microbubbles that distinguishes them from large bubbles

Laser-camera composite sensing for road detection and tracing

An important feature in most urban roads and similar environments, such as in theme parks, campus sites, industrial estates, science parks, and the like, is the existence of pavements or curbs on either side de?ning the road boundaries. These curbs, which are mostly parallel to the road, can be harnessed to extract useful features of the road for implementing autonomous navigation or driver assistance systems. However, vision-alone methods for extraction of such curbs or road edge features with accurate depth information is a formidable task, as the curb is not conspicuous in the vision image and also requires the use of stereo images. Further, bad lighting, adverse weather conditions, nonlinear lens aberrations, or lens glare due to sun and other bright light sources can severely impair the road image quality and thus the operation of vision-alone methods. In this paper an alternative and novel approach involving the fusion of 2D laser range and monochrome vision image data is proposed to improve the robustness and reliability. Experimental results are presented to demonstrate the viability and effectiveness of the proposed methodology and its robustness to different road configurations and shadows

High speed shadowgraphy for the study of liquid drops

The book contains invited lectures and selected contributions presented at the Enzo Levi and XVII Annual Meeting of the Fluid Dynamic Division of the Mexican Physical Society in 2011.This was work was sponsored by EPSRC grant number RG5560

Lane detection system for day vision using altera DE2

The active safety systems used in automotive field are largely exploiting lane detection technique for warning the vehicle drivers to correct any unintended road departure and to reach fully autonomous vehicles. Due to its ability, to be programmed, to perform complex mathematical functions and its characterization of high speed processing, Field Programmable Gate Array (FPGA) could cope with the requirement of lane detection implementation and application. In the present work, lane detection is implemented using FPGA for day vision. This necessitates utilization of image processing techniques like filtering, edge detection and thresholding. The lane detection is performed by firstly capturing the image from a video camera and converted to gray scale. Then, a noise filtering process for gray image is performed using Gaussian and average filter. Methods from first and second order edge detection techniques have been selected for the purpose of lane edge detection. The effect of manually changing the threshold level on image enhancement has been examined. The results showed that raising threshold level would better enhance the image. The type of FPGA device used in the present work is Altera DE2. Firstly, the version DE2 Cyclone II start with (11xxxxxx-xxxx) together with Genx camera has been used. This camera supports both formats NTSC and PAL, while the above version of FPGA backups only NTSC format. The software of lane detection is designed and coded using Verilog language

Principal Component Analysis based Image Fusion Routine with Application to Stamping Split Detection

This dissertation presents a novel thermal and visible image fusion system with application in online automotive stamping split detection. The thermal vision system scans temperature maps of high reflective steel panels to locate abnormal temperature readings indicative of high local wrinkling pressure that causes metal splitting. The visible vision system offsets the blurring effect of thermal vision system caused by heat diffusion across the surface through conduction and heat losses to the surroundings through convection. The fusion of thermal and visible images combines two separate physical channels and provides more informative result image than the original ones. Principal Component Analysis (PCA) is employed for image fusion to transform original image to its eigenspace. By retaining the principal components with influencing eigenvalues, PCA keeps the key features in the original image and reduces noise level. Then a pixel level image fusion algorithm is developed to fuse images from the thermal and visible channels, enhance the result image from low level and increase the signal to noise ratio. Finally, an automatic split detection algorithm is designed and implemented to perform online objective automotive stamping split detection. The integrated PCA based image fusion system for stamping split detection is developed and tested on an automotive press line. It is also assessed by online thermal and visible acquisitions and illustrates performance and success. Different splits with variant shape, size and amount are detected under actual operating conditions

Framework for extracting and solving combination puzzles

Selles töös uuritakse, kuidas arvuti nägemisega seotud algoritme on võimalik rakendada objektide tuvastuse probleemile. Täpsemalt, kas arvuti nägemist on võimalik kasutada päris maailma kombinatoorsete probleemide lahendamiseks. Idee kasutada arvuti rakendust probleemide lahendamiseks, tulenes tähelepanekust, et probleemide lahenduse protsessid on kõik enamasti algoritmid. Sellest võib järeldada, et arvutid sobivad algoritmiliste probleemide lahendamiseks paremini kui inimesed, kellel võib sama ülesande peale kuluda kordades kauem. Siiski ei vaatle arvutid probleeme samamoodi nagu inimesed ehk nad ei saa probleeme analüüsida. Niisiis selle töö panuseks saab olema erinevate arvuti nägemise algoritmide uurimine, mille eesmärgiks on päris maailma kombinatoorsete probleemide tõlgendamine abstraktseteks struktuurideks, mida arvuti on võimeline mõistma ning lahendama.Praegu on antud valdkonnas vähe materiali, mis annab hea võimaluse panustada sellesse valdkonda. Seda saavutatakse läbi empiirilise uurimise testide kogumiku kujul selleks, et veenduda millised lähenemised on kõige paremad. Nende eesmärkide saavutamiseks töötati läbi suur hulk arvuti nägemisega seotud materjale ning teooriat. Lisaks võeti ka arvesse reaalaja toimingute tähtsus, mida võib näha erinevate liikumisest struktuuri eraldavate algoritmide(SLAM, PTAM) õpingutest, mida hiljem edukalt kasutati navigatsiooni ja liitreaalsuse probleemide lahendamiseks. Siiski tuleb mainida, et neid algoritme ei kasutatud objektide omaduste tuvastamiseks.See töö uurib, kuidas saab erinevaid lähenemisi kasutada selleks, et aidata vähekogenud kasutajaid kombinatoorsete päris maailma probleemide lahendamisel. Lisaks tekib selle töö tulemusena võimalus tuvastada objektide liikumist (translatsioon, pöörlemine), mida saab kasutada koos virutaalse probleemi mudeliga, et parandada kasutaja kogemust.This thesis describes and investigates how computer vision algorithms and stereo vision algorithms may be applied to the problem of object detection. In particular, if computer vision can aid on puzzle solving. The idea to use computer application for puzzle solving came from the fact that all solution techniques are algorithms in the end. This fact leads to the conclusion that algorithms are well solved by machines, for instance, a machine requires milliseconds to compute the solution while a human can handle this in minutes or hours. Unfortunately, machines cannot see puzzles from human perspective thus cannot analyze them. Hence, the contribution of this thesis is to study different computer vision approaches from non-related solutions applied to the problem of translating the physical puzzle model into the abstract structure that can be understood and solved by a machine.Currently, there is a little written on this subject, therefore, there is a great chance to contribute. This is achieved through empirical research represented as a set of experiments in order to ensure which approaches are suitable. To accomplish these goals huge amount of computer vision theory has been studied. In addition, the relevance of real-time operations was taken into account. This was manifested through the Different real-time Structure from Motion algorithms (SLAM, PTAM) studies that were successfully applied for navigation or augmented reality problems; however, none of them for object characteristics extraction.This thesis examines how these different approaches can be applied to the given problem to help inexperienced users solve the combination puzzles. Moreover, it produces a side effect which is a possibility to track objects movement (rotation, translation) that can be used for manipulating a rendered game puzzle and increase interactivity and engagement of the user