Intube two-phase flow probabilities based on capacitance signal clustering

To study the objectivity in flow pattern mapping of horizontal two-phase flow in macroscale tubes, a capacitance sensor is developed for use with refrigerants. Sensor signals are gathered with R410A in an 8mm I.D. smooth tube at a saturation temperature of 15°C in the mass velocity range of 200 to 500kg/m²s and vapour quality range from 0 to 1 in steps of 0.025. A visual classification based on high speed camera images is made for comparison reasons. A statistical analysis of the sensor signals shows that the average and the variance are suitable for flow regime classification into slug flow, intermittent flow and annular flow by using a the fuzzy c-means clustering algorithm. This soft clustering algorithm perfectly predicts the slug/intermittent flow transition compared to our visual observations. The intermittent/annular flow transition is found at higher vapour qualities, but with the same trend compared to our observations and the prediction of [Barbieri et al., 2008, Flow patterns in convective boiling of refrigerant R-134a in smooth tubes of several diameters, 5th European Thermal-Sciences Conference, The Netherlands]. The intermittent/annular flow transition is very gradual. A probability approach can therefore better describe such a transition. The membership grades of the cluster algorithm can be interpreted as flow probabilities. These probabilities are further compared to time fraction functions of [Jassim et al., 2008, Prediction of refrigerant void fraction in horizontal tubes using probabilistic flow regime maps

Adaptive smoothness constraint image multilevel fuzzy enhancement algorithm

For the problems of poor enhancement effect and long time consuming of the traditional algorithm, an adaptive smoothness constraint image multilevel fuzzy enhancement algorithm based on secondary color-to-grayscale conversion is proposed. By using fuzzy set theory and generalized fuzzy set theory, a new linear generalized fuzzy operator transformation is carried out to obtain a new linear generalized fuzzy operator. By using linear generalized membership transformation and inverse transformation, secondary color-to-grayscale conversion of adaptive smoothness constraint image is performed. Combined with generalized fuzzy operator, the region contrast fuzzy enhancement of adaptive smoothness constraint image is realized, and image multilevel fuzzy enhancement is realized. Experimental results show that the fuzzy degree of the image is reduced by the improved algorithm, and the clarity of the adaptive smoothness constraint image is improved effectively. The time consuming is short, and it has some advantages

Application of Adaptive Filters in Processing of Solar Corona Images

Fotografování sluneční koróny patří mezi nejobtížnější úlohy astrofotografie a zároveň je jednou z klíčových metod pro studium koróny. Tato práce přináší ucelený souhrn metod pro pozorování sluneční koróny pomocí snímků. Práce obsahuje nutnou matematickou teorii, postup pro zpracování snímků a souhrn adaptivních filtrů pro vizualizaci koronálních struktur v digitálních obrazech. Dále přináší návrh nových metod určených především pro obrazy s vyšším obsahem šumu, než je běžné u obrazů bílé koróny pořízených během úplných zatmění Slunce, např. pro obrazy pořízené pomocí úzkopásmových filtrů. Fourier normalizing-radial-graded filter, který byl navržen v rámci této práce, je založen na aproximaci hodnot pixelů a jejich variability pomocí trigonometrických polynomů s využitím dalších vlastností obrazu.Solar corona photography counts among the most complicated tasks in astrophotography. It also plays a key role for research of the solar corona. This thesis brings an a complete overview of methods for imaging the solar corona. The thesis contains necessary methematical background, the sequence of steps for image processing, an overview of adaptive filters used for visualization of corona structures in digital images, and new methods are proposed, especially for images which contain more noise than it is typical for images of the white corona taken during total solar eclipses, e.g. images taken with narrow-band filters. The Fourier normalizing-radial-graded filter method that I proposed during my PhD study are based on approximation of pixel values and their variability with trigonometric polynomials using other properties of the image.

Real-time Defogging of Single Image of IoTs-based Surveillance Video Based on MAP

Due to the atmospheric scattering phenomenon in fog weather, the current monitoring video image defogging method cannot estimate the fog density of the image. This paper proposes a real-time defogging algorithm for single images of IoTs surveillance video based on maximum a posteriori (MAP). Under the condition of single image sequence, the posterior probability of the high-resolution single image is set to the maximum, which improves the MAP design super-resolution image reconstruction. This paper introduces fuzzy classification to calculate atmospheric light intensity, and obtains a single image of IoTs surveillance video by the atmospheric dissipation function. The improved algorithm has the largest signal-to-noise ratio after defogging, and the maximum value is as high as 40.99 dB. The average time for defogging of 7 experimental surveillance video images is only 2.22 s, and the real-time performance is better. It can be concluded that the proposed algorithm has excellent defogging performance and strong applicability

Design of automatic vision-based inspection system for solder joint segmentation

Purpose: Computer vision has been widely used in the inspection of electronic components. This paper proposes a computer vision system for the automatic detection, localisation, and segmentation of solder joints on Printed Circuit Boards (PCBs) under different illumination conditions. Design/methodology/approach: An illumination normalization approach is applied to an image, which can effectively and efficiently eliminate the effect of uneven illumination while keeping the properties of the processed image the same as in the corresponding image under normal lighting conditions. Consequently special lighting and instrumental setup can be reduced in order to detect solder joints. These normalised images are insensitive to illumination variations and are used for the subsequent solder joint detection stages. In the segmentation approach, the PCB image is transformed from an RGB color space to a YIQ color space for the effective detection of solder joints from the background. Findings: The segmentation results show that the proposed approach improves the performance significantly for images under varying illumination conditions. Research limitations/implications: This paper proposes a front-end system for the automatic detection, localisation, and segmentation of solder joint defects. Further research is required to complete the full system including the classification of solder joint defects. Practical implications: The methodology presented in this paper can be an effective method to reduce cost and improve quality in production of PCBs in the manufacturing industry. Originality/value: This research proposes the automatic location, identification and segmentation of solder joints under different illumination conditions

Radiographic contrast-enhancement masks in digital radiography

Radiographic film/screen (F/S) images have a narrow latitude or dynamic range. The film’s ability to record and view all the anatomy within the x-ray field is limited by this narrow dynamic range. The advent of digital radiographic means of storing and displaying radiographic images has improved the ability to record and visualise all of the anatomy. The problem still exists in digital radiography (DR) when radiographic examinations of certain anatomical regions are undertaken. In this work, the value of anatomically shaped radiographic contrast-enhancement masks (RCMs) in improving image contrast and reducing the dynamic range of images in DR was examined. Radiographic contrast-enhancement masks are digital masks that alter the radiographic contrast in DR images. The shape of these masks can be altered by the user. Anatomically shaped RCMs have been modelled on tissue compensation filters (TCFs) commonly used in F/S radiographic examinations. The prime purpose of a TCF is to reduce the dynamic range of photons reaching the image receptor and hence improve radiographic contrast in the resultant image. RCMs affect the dynamic range of the image rather than the energy source of the image, that of the x-ray photons. The research consisted of three distinct phases. The first phase was to examine physical TCFs and their effects on F/S radiographic images. Physical TCFs are used in radiographic F/S examinations to attenuate the x-ray beam to compensate for varying patient tissue thicknesses and/or densities. The effect of the TCF is to reduce resultant radiographic optical density variations in the image, allowing the viewer to observe a range of densities within the image which would otherwise not be visualised. Physical TCFs are commonly aluminium- or lead-based materials that attenuate the x-ray beam. A TCF has varying physical thickness to differentially attenuate the iii beam and is shaped for specific anatomical situations. During this project, various commonly used physical TCFs were examined. Measurements of size and thickness were made. Characteristics of linear attenuation coefficients and half-value thicknesses were delineated for various TCF materials and at various energies. The second phase of the research was to model the physical TCFs in a digital environment and apply the RCMs to DR images. The digital RCMs were created with similar characteristics to mimic the shapes to the physical TCFs. The RCM characteristics can be adjusted by the viewer of the image to suit the anatomy being imaged. Anatomically shaped RCMs were designed to assist in overcoming a limitation when viewing digital radiographic images, that of the dynamic range of the image. Anatomically shaped RCMs differ from other means of controlling the dynamic range of a digital radiographic image. It has been shown that RCMs can reduce the range of optical densities within images with a large dynamic range, to facilitate visualisation of all anatomy within the image. Physical TCFs are used within a specific range of radiographic F/S examinations. Digital radiographic images from this range of examinations were collected from various clinical radiological centres. Anatomically shaped RCMs were applied to the images to improve radiographic contrast of the images. The third phase of the research was to ascertain the benefits of the use of RCMs. Various other methods are currently in use to reduce the dynamic range of digital radiographic images. It is generally accepted that these methods also introduce noise into the image and hence reduce image quality. Quantitative comparisons of noise within the image were undertaken. The anatomically shaped RCMs introduced less noise than current methods designed to reduce the dynamic range of digital radiographic images. It was shown that RCM methods do not affect image quality. Radiographers make subjective assessment of digital radiographic image quality as part of their professional practice. To assess the subjective quality of images enhanced with anatomically shaped RCMs, a survey of radiographers and other iv qualified people was undertaken to ascertain any improvement in RCM-modified images compared to the original images. Participants were provided with eight pairs of image to compare. Questions were asked in the survey as to which image had the better range of optical densities; in which image the anatomy was easiest to visualise; which image had the simplest contrast and density manipulation for optimal visualisation; and which image had the overall highest image quality. Responses from 123 participants were received and analysed. The statistical analysis showed a higher preference by radiographers for the digital radiographic images in which the RCMs had been applied. Comparisons were made between anatomical regions and between patient-related factors of size, age and whether pathology was present in the image or not. The conclusion was drawn that digital RCMs correctly applied to digital radiographic images decrease the dynamic range of the image, allowing the entire anatomy to be visualised in one image. Radiographic contrast in the image can be maximised whilst maintaining image quality. Using RCMs in some digital radiographic examinations, radiographers will be able to present optimised images to referring clinicians. It is envisaged that correctly applied RCMs in certain radiographic examinations will enhance radiographic image quality and possibly lead to improved diagnosis from these images

An In-Vehicle Vision-Based Driver's Drowsiness Detection System

[[abstract]]Many traffic accidents have been reported due to driver’s drowsiness/fatigue. Drowsiness degrades driving performance due to the declinations of visibility, situational awareness and decision-making capability. In this study, a vision-based drowsiness detection and warning system is presented, which attempts to bring to the attention of a driver to his/her own potential drowsiness. The information provided by the system can also be utilized by adaptive systems to manage noncritical operations, such as starting a ventilator, spreading fragrance, turning on a radio, and providing entertainment options. In high drowsiness situation, the system may initiate navigation aids and alert others to the drowsiness of the driver. The system estimates the fatigue level of a driver based on his/her facial images acquired by a video camera mounted in the front of the vehicle. There are five major steps involved in the system process: preprocessing, facial feature extraction, face tracking, parameter estimation, and reasoning. In the preprocessing step, the input image is sub-sampled for reducing the image size and in turn the processing time. A lighting compensation process is next applied to the reduced image in order to remove the influences of ambient illumination variations. Afterwards, for each image pixel a number of chrominance values are calculated, which are to be used in the next step for detecting facial features. There are four sub-steps constituting the feature extraction step: skin detection, face localization, eyes and mouth detection, and feature confirmation. To begin, the skin areas are located in the image based on the chrominance values of pixels calculated in the previous step and a predefined skin model. We next search for the face region within the largest skin area. However, the detected face is typically imperfect. Facial feature detection within the imperfect face region is unreliable. We actually look for facial features throughout the entire image. As to the face region, it will later be used to confirm the detected facial features. Once facial features are located, they are tracked over the video sequence until they are missed detecting in a video image. At this moment, the facial feature detection process is revoked again. Although facial feature detection is time consuming, facial feature tracking is fast and reliable. During facial feature tracking, parameters of facial expression, including percentage of eye closure over time, eye blinking frequency, durations of eye closure, gaze and mouth opening, as well as head orientation, are estimated. The estimated parameters are then utilized in the reasoning step to determine the driver’s drowsiness level. A fuzzy integral technique is employed, which integrates various types of parameter values to arrive at a decision about the drowsiness level of the driver. A number of video sequences of different drivers and illumination conditions have been tested. The results revealed that our system can work reasonably in daytime. We may extend the system in the future work to apply in nighttime. For this, infrared sensors should be included.