2D and 3D Polar Plume Analysis from the Three Vantage Positions of STEREO/EUVI A, B, and SOHO/EIT

Polar plumes are seen as elongated objects starting at the solar polar regions. Here, we analyze these objects from a sequence of images taken simultaneously by the three spacecraft telescopes STEREO/EUVI A and B, and SOHO/EIT. We establish a method capable of automatically identifying plumes in solar EUV images close to the limb at 1.01 - 1.39 R in order to study their temporal evolution. This plume-identification method is based on a multiscale Hough-wavelet analysis. Then two methods to determined their 3D localization and structure are discussed: First, tomography using the filtered back-projection and including the differential rotation of the Sun and, secondly, conventional stereoscopic triangulation. We show that tomography and stereoscopy are complementary to study polar plumes. We also show that this systematic 2D identification and the proposed methods of 3D reconstruction are well suited, on one hand, to identify plumes individually and on the other hand, to analyze the distribution of plumes and inter-plume regions. Finally, the results are discussed focusing on the plume position with their cross-section area.Comment: 22 pages, 10 figures, Solar Physics articl

Improved Stack-Slide Searches for Gravitational-Wave Pulsars

We formulate and optimize a computational search strategy for detecting gravitational waves from isolated, previously-unknown neutron stars (that is, neutron stars with unknown sky positions, spin frequencies, and spin-down parameters). It is well known that fully coherent searches over the relevant parameter-space volumes are not computationally feasible, and so more computationally efficient methods are called for. The first step in this direction was taken by Brady & Creighton (2000), who proposed and optimized a two-stage, stack-slide search algorithm. We generalize and otherwise improve upon the Brady-Creighton scheme in several ways. Like Brady & Creighton, we consider a stack-slide scheme, but here with an arbitrary number of semi-coherent stages and with a coherent follow-up stage at the end. We find that searches with three semi-coherent stages are significantly more efficient than two-stage searches (requiring about 2-5 times less computational power for the same sensitivity) and are only slightly less efficient than searches with four or more stages. We calculate the signal-to-noise ratio required for detection, as a function of computing power and neutron star spin-down-age, using our optimized searches.Comment: 19 pages, 7 figures, RevTeX

Automatic visual inspection of placement of bare dies in multichip modules

Multichip Modules are gaining lot of popularity in today\u27s IC technology, as they are good solutions for high density packaging. This thesis presents a method for checking the placement of bare dies on a common substrate of an MCM. This testing is done using Automatic Visual Inspection (AVI), which is better and more reliable, compared to manual inspection. Comparison is the basis in this thesis to detect faults in an MCM. The MCM to be tested is compared with a known good ideal MCM using image processing techniques. The mismatches, if any, between these two images, i.e. image of an MCM which is being tested and image of known good reference MCM, are evaluated to find the exact location and nature of the fault. This AVI is implemented completely in software using C language. Test cases and their results are presented

Calculating Staircase Slope from a Single Image

Realistic modeling of a 3D environment has grown in popularity due to the increasing realm of practical applications. Whether for practical navigation purposes, entertainment value, or architectural standardization, the ability to determine the dimensions of a room is becoming more and more important. One of the trickier, but critical, features within any multistory environment is the staircase. Staircases are difficult to model because of their uneven surface and various depth aspects. Coupling this need is a variety of ways to reach this goal. Unfortunately, many such methods rely upon specialized sensory equipment, multiple calibrated cameras, or other such impractical setups. Here, we propose a simpler approach. This paper outlines a method for extracting the slope dimensions of a staircase using a single monocular image. By relying on only a single image, we negate the need for extraneous accessories and glean as much information from common pictures. We do not hope to achieve the high level of accuracy seen from laser scanning methods but seek to produce a viable result that can both be helpful for current applications and serve as a building block that contributes to later development. When constructing our pipeline, we take into account several options. Each step can be achieved with different techniques which we evaluate and compare on either a qualitative or quantitative level. This leads to our final result which can accurately determine the slope of a staircase with an error rate of 31.1%. With a small amount of previous knowledge or preprocessing, this drops down to an average of 18.7% Overall, we deem this an acceptable and optimal result given the limited information and processing resources which the program was allowed to utilize

Enhancing thermal mixing in turbulent bubbly flow by adding salt

The presence of bubbles in a turbulent flow changes the flow drastically and enhances the mixing. Adding salt to the bubbly aqueous flow changes the bubble coalescence properties as compared to pure water. Here we provide direct experimental evidence that also the turbulent thermal energy spectra are strongly changed. Experiments were performed in the Twente Mass and Heat Transfer water tunnel,in which we can measure the thermal spectra in bubbly turbulence in salty water. We find that the mean bubble diameter decreases with increasing concentration of salt (NaCl), due to the inhibition of bubble coalescence. With increasing salinity, the transition frequency from the classical $-5/3$ scaling of the thermal energy spectrum to the bubble induced $-3$ scaling shifts to higher frequencies, thus enhancing the overall thermal energy. We relate this frequency shift to the smaller size of the bubbles for the salty bubbly flow. Finally we measure the heat transport in the bubbly flow, and show how it varies with changing void fraction and salinity: Increases in both result into increases in the number of extreme events.Comment: 18 pages, 10 figures, submitted to International Journal of Multiphase Flo

Lane Detection For Automatic Cars

The first stage in developing an autonomous car is the lane detection system. To help us identify lanes, we've borrowed a pair of ready-made models. As a rule, these two models are very time-consuming and expensive to compute. To lessen the burden on the computer, we developed a technique called the "row anchor based" approach. The computational burden is reduced, and the no-visual-clue issue is addressed by using this technique. It is exceedingly challenging to identify lanes when we are unable to see them clearly, as occurs in inclement weather, when water is on the lanes, or when the lanes are not designated. No-visual-clue is the term for this kind of issue. ResNet-18, which is used for pretrained models, has been utilized. Because of this, velocity will rise. ResNet-34 is another option, but it is too resource-intensive for this particular project. Road detection from one image is used to locate the road in a picture so it can be used as a district in the automation of the driving system within the vehicles for moving the vehicle on the correct road given a picture captured from a camera attached to a vehicle moving on a road, which road may or may not be level, have clearly described edges, or have some previous acknowledged patterns thereon. Here, we apply techniques for vanishing point identification, Hough Transformation Space, area of interest detection, edge detection, and canny edge detection for road recognition to locate the road inside the picture acquired by the vehicle. To train our model to recognize the road in the fresh image processed by the car, we typically use hundreds of images of roads from different locations

Automatic Segmentation of Optic Disc in Eye Fundus Images: A Survey

Optic disc detection and segmentation is one of the key elements for automatic retinal disease screening systems. The aim of this survey paper is to review, categorize and compare the optic disc detection algorithms and methodologies, giving a description of each of them, highlighting their key points and performance measures. Accordingly, this survey firstly overviews the anatomy of the eye fundus showing its main structural components along with their properties and functions. Consequently, the survey reviews the image enhancement techniques and also categorizes the image segmentation methodologies for the optic disc which include property-based methods, methods based on convergence of blood vessels, and model-based methods. The performance of segmentation algorithms is evaluated using a number of publicly available databases of retinal images via evaluation metrics which include accuracy and true positive rate (i.e. sensitivity). The survey, at the end, describes the different abnormalities occurring within the optic disc region

Detection, Quantification and Classification of Ripened Tomatoes: A Comparative Analysis of Image Processing and Machine Learning

In this paper, specifically for detection of ripe/unripe tomatoes with/without defects in the crop field, two distinct methods are described and compared. One is a machine learning approach, known as ‘Cascaded Object Detector’ and the other is a composition of traditional customized methods, individually known as ‘Colour Transformation’, ‘Colour Segmentation’ and ‘Circular Hough Transformation’. The (Viola Jones) Cascaded Object Detector generates ‘histogram of oriented gradient’ (HOG) features to detect tomatoes. For ripeness checking, the RGB mean is calculated with a set of rules. However, for traditional methods, color thresholding is applied to detect tomatoes either from a natural or solid background and RGB colour is adjusted to identify ripened tomatoes. In this work, Colour Segmentation is applied in the detection of tomatoes with defects, which has not previously been applied under machine learning techniques. The function modules of this algorithm are fed formatted images, captured by a camera mounted on a mobile robot. This robot was designed, built and operated in a tomato field to identify and quantify both green and ripened tomatoes as well as to detect damaged/blemished ones. This algorithm is shown to be optimally feasible for any micro-controller based miniature electronic devices in terms of its run time complexity of O(n3) for traditional method in best and average cases. Comparisons show that the accuracy of the machine learning method is 95%, better than that of the Colour Segmentation Method using MATLAB. This result is potentially significant for farmers in crop fields to identify the condition of tomatoes quickly