Random numbers are used in a wide variety of applications from simulation and encryption to gambling and clinical trials. A good quality random number generator is an asset for applications like encryption, randomized designs and network and information security. Various mathematical models have been developed in the past to improve the quality of random numbers. It can be construed that in general to obtain random numbers of excellent quality, a complex mathematical model has to be used which can be a performance bottleneck. In this work, we propose a novel technique to implement a True Random Number Generator (TRNG) using sources of uncertainty found within Graphics Processing Units (GPUs) together with histogram equalization to obtain maximum entropy. We evaluate the random numbers generated by our approach using four tests. First, we measure the correlation values between two sequences of random numbers, second, we measure the entropy values, third, we use watermarking, an application used in network security and finally we use Monte Carlo analysis for pi-value calculation. Based on these quality measurements, our method has achieved better results than popular random number generators compared in this work. Furthermore, this approach is a massively scalable solution ideal for high performance computing implementations.

Development of an accurate image segmentation algorithm can be the most demanding part of a computer vision system. There is not a panacean method that can work with several different types of images and a segmentation approach is usually designed for solving a specific problem. In this work, histogram specification is proposed in order to help the segmentation step in what was found to be a robust way regardless of the segmentation approach used. Semiautomatic algorithms for histogram specification are discussed. Examples using different segmentation techniques are shown.

In recent years, the use of radar technology has been proposed in a wide range of subsurface imaging applications. Traditionally, linear scan trajectories are used to acquire data in most subsurface radar applications. However, novel applications, such as breast microwave imaging and wood inspection, require the use of nonlinear scan trajectories in order to adjust to the geometry of the scanned area. This paper proposes a novel reconstruction algorithm for subsurface radar data acquired along cylindrical scan trajectories. The spectrum of the collected data is processed in order to locate the spatial origin of the target reflections and remove the spreading of the target reflections which results from the different signal travel times along the scan trajectory. The proposed algorithm was successfully tested using experimental data collected from phantoms that mimic high contrast subsurface radar scenarios, yielding promising results. Practical considerations such as spatial resolution and sampling constraints are discussed and illustrated as well.

Canada is one of the major exporters of wheat in the world. The quality of these exports is well known and factors such as lack of insect infestation are very important. The use of thermal images for subsequent analysis of temperatures profiles for grain classification and insect detection is a method under investigation. This paper presents an approach for automatic image segmentation of the wheat kernels based on the combined use of wavelet analysis and pulse coupled neural networks. It is shown that using wavelets as a preprocessing technique yields a consistent accurate segmentation in terms of the iteration number in which the network yields reliable edges of the wheat kernels. Subsequent analysis of these segmentations can determine internal qualities such as infestations.

An all-focused image of a micromechanical structure can be extracted from several microscopic images with different focal planes. Spreading of the out-of-focus regions over the background, which shows the out-of-focus object bigger than its actual size, causes errors when a sharpness function is applied. The focused area with low contrast is also one of the difficulties faced in order to easily distinguish them from the out-of-focus regions. We propose a method to estimate the focused section of each image and create a final focused image by concatenating these areas. We let the size of the tested areas vary in order to obtain a more accurate result. The method finds focused pixels among the images using a voting method and pastes them to the final image. This approach does not rely on any information of the microscope. No a priori knowledge of an optical system is needed.

Traditional synthetic aperture radar (SAR) image processing involves a two-dimensional Fourier transform that may produce significant high intensity sidelobes. These artifacts have the potential of obscuring low intensity scatterers in the image. Spatially variant sidelobe apodization is a technique that reduces sidelobe levels in a Fourier image while maintaining the image resolution that would be obtained using the rectangular window. In this paper, the use of the Kaiser (1974) window for sidelobe apodization is studied. It is shown that the use of this window outperforms the use of the cosine-on-pedestal window. Albeit, the method proposed here is not easily implemented, high resolution and low sidelobe levels are obtained. The method was tested and results are shown when using this new sidelobe apodization technique on an actual SAR image.

Article Free Access Share on Symbolic computation of the index of quasilinear differential-algebraic equations Author: G. Thomas LMC/IMAG, 46 avenue F. Viallet, 38031 Grenoble CEDEX, France LMC/IMAG, 46 avenue F. Viallet, 38031 Grenoble CEDEX, FranceView Profile Authors Info & Claims ISSAC '96: Proceedings of the 1996 international symposium on Symbolic and algebraic computationOctober 1996Pages 196–203https://doi.org/10.1145/236869.237074Published:01 October 1996Publication History 2citation215DownloadsMetricsTotal Citations2Total Downloads215Last 12 Months21Last 6 weeks6 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF