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Assessment of the quality of local estimates of data sparsity is central for various adaptive algorithms in signal processing. Empirical bounds for the estimation performance of a frequently used measure of sparsity, namely the Gini–Simpson index are derived. Confidence bounds are derived for an unbiased estimator of this measure, with exponential convergence to the true (unknown) sparsity value, as the number of samples increases. The analysis is distribution‐free, as no parametric or distributional assumptions are made for the available data. [ABSTRACT FROM AUTHOR]

This paper presents a new method for degraded-document binarization, inspired by the attributes of the Human Visual System (HVS). It can deal with various types of degradations, such as uneven illumination, shadows, low contrast, smears, and heavy noise densities. The proposed algorithm combines the characteristics of the OFF center-surround cells of the HVS with the classic Otsu binarization technique. Cells of two different scales are combined, increasing the efficiency of the algorithm and reducing the extracted noise in the final output. A new response function, which regulates the output of the cell according to the local contrast and the local lighting conditions is also introduced. The Otsu technique is used to binarize the outputs of the OFF center-surround cells. Quantitative experiments performed on a set of various computer-generated degradations, such as noise, shadow, and low contrast demonstrate the superior performance of the proposed method against six other well-established techniques. Qualitative and OCR comparisons also confirm these results. [ABSTRACT FROM AUTHOR]

Abstract: The identification of damages produced by severe earthquakes on constructions is important for several reasons such as public safety, economical recourses management, infrastructure and urban planning. After the manifestation of an earthquake, engineers have to evaluate the safety of existing structures and decide the actions to be taken. In this study two techniques are proposed for automatic damage classification in buildings. The inherent information contained in accelerograms is described by 20 seismic parameters. Two classification models of earthquake damages based on artificial neural networks and neuro-fuzzy systems were designed. Furthermore, they were tested for their effectiveness to classify structural, architectural, mechanical–electrical-plumbing and contents damages. The proposed systems were trained and tested with three reinforced concrete frame structures. Results show correct classification rates up to 98%. According to these classification rates these techniques are proven a suitable tool for classification of earthquake damages in structures. [Copyright &y& Elsevier]

The compression efficiency of canonical Huffman coding is drastically affected by the size of the compression window, the choice of which is typically done heuristically analytic bounds for the worst‐case compression efficiency of canonical Huffman coding are introduced, as a function of the size of the compression window. The derived bounds offer fine‐grained control over the compression latency‐efficiency trade‐off. [ABSTRACT FROM AUTHOR]

A new algorithm for fast contrast modification of standard dynamic range (SDR) images (8 bits/channel) is presented. Its thrust is to enhance the contrast in the under-/over-exposed regions of SDR images, caused by the low dynamic range of the capturing device. It is motivated by the attributes of the shunting centre–surround cells of the human visual system. The main advantage of the proposed algorithm is its O(N) complexity which results in very fast execution, even when executed on a conventional personal computer (0.2 s/frame for a 640×480 pixel resolution on a 3 GHz Pentium 4). Thus, it moderately increases the computational burden if it is used as a pre-processing stage for other image processing algorithms. The proposed method is compared with other established algorithms, which can enhance the contrast in the under-/over-exposed regions of SDR images: the multi-scale Retinex with colour rendition, the McCann Retinex (McCann99), the rational mapping function and the automatic colour equalisation. The results obtained by this comparison indicate that the proposed algorithm exhibits at least comparable results in contrast modification tasks to the other algorithms, in significantly reduced execution times. [ABSTRACT FROM AUTHOR]

Chebyshev moments were recently proposed to address problems of moments of continuous orthogonal basis. They are moments of a discrete basis and are orthogonal in the domain of the image coordinate space, a feature which presents a number of advantages over moments of continuous orthogonal basis. No effort has been devoted yet to the efficiency of their calculation. In this paper, we present a hardware architecture which enables real-time processing of binary and grayscale images of up to 4 Mpixels. This architecture is realized in a field-programmable gate array of typical clock frequency of 100 MHz. [ABSTRACT FROM AUTHOR]

A new efficient technique for the classification of signals, in the form of earthquake-induced ground-acceleration time histories, according to the damage that they cause in buildings, is presented for the first time. A training set of real seismic accelerograms with well-known damage effects is utilised and fuzzy representations of prototype signals are extracted. These prototypes are selected with respect to the architectural and structural damage caused by the seismic-acceleration time histories. The classification of the unknown accelerograms takes place through a fuzzy comparison with the prototypes and each is classified to the most similar prototype. Real, seismic time–acceleration records were used for testing the algorithm and the high percentage of the correctly recognised signals prove the effectiveness of the algorithm. Correct classification rates of up to 84% are achieved. [ABSTRACT FROM AUTHOR]

Zernike moments have been proven to be very powerful image descriptors. However, their computational complexity makes them unsuitable for real-time applications. In this paper, a mathematical relationship between geometric and Zernike moments is extracted. In this way, the computation of geometric moments is adequate to derive Zernike moments. Since geometric moments can be efficiently implemented in hardware and their calculation can be performed in real-time, we propose here a new real-time hardware architecture for the computation of Zernike moments. This method outperforms existing software approaches, especially for large images, allowing real-time processing of images up to 4 Mpixels. [ABSTRACT FROM AUTHOR]

Due to the fact that image libraries have largely become overcrowded, the need of improved methods for retrieving color images from such libraries has increased immensely. Color-based Image Retrieval (CBIR) is a collection of techniques for retrieving images on the basis of color. An efficient tool, which is widely used in CBIR, is image color histograms. In this paper we present a comparison among the methods used to compare global and local histograms in the color spaces of RGB, HSV, L*a*b* and LCH. Moreover, the computational burden of all the systems carried out was compared and discussed both in terms of CPU time and number of computations. One of the key results from the experiments with global histograms was that the main computational burden was produced by the color space transforms rather than the comparative techniques. All the comparisons of the featured methods were performed through simulations under the environment of the MathWorks' Matlab package. [ABSTRACT FROM AUTHOR]

Abstract: Geometric moments are used in numerous image analysis tasks. While several approaches have been proposed, real-time computation is still inefficient. In this paper, a mathematical formulation of the relationship of the all-pole filter output to the geometric moments is discussed. Using this formulation, architectures for implementing in hardware geometric moments of 1, 2 or 3D objects are proposed. Four implementations are programmed in field programmable logic devices (FPGA) devices with typical clock frequencies of 40MHz. The speed of these devices is adequate for real-time applications, and they can be used to extract local as well as global geometric moments. [Copyright &y& Elsevier]

The emergence of multimedia, as well as the availability of large image and video archives and the development of information highways have recently attracted research efforts in providing tools for effective retrieval of data based on their contents. Content-based image retrieval mainly refers to the search for an image database to retrieve an image with the required characteristics. So far, major breakthrough in image retrieval has not been achieved. Furthermore, there is no single universal approach to the content-based retrieval problems, even in a restricted domain application.

In this paper, a hardware implementation of a fuzzy processor suitable for morphological color image processing applications is presented for the first time. From the hardware point of view, only a small number of algorithms for hardware implementation of soft gray-scale morphological filters have been reported in the literature, since research in mathematical morphology focuses mainly on possible extensions of the standard definitions (e.g., color and fuzzy mathematical morphology). The proposed digital hardware structure is based on a sequence of pipeline stages, and parallel processing is used in order to minimize computational times. It is capable of performing the basic morphological operations of standard and soft erosion/dilation for color images of 24-bit resolution. For the computation of morphological operations, a 3 × 3-pixel image neighborhood and the corresponding structuring element are used. However, the system can be easily expanded to accommodate windows of larger sizes. The architecture of the processor is generic; the units that perform the fuzzy inference can be utilized for other fuzzy applications. It was designed, compiled, and simulated using the MAX+PLUS II Programmable Logic Development System by Altera Corporation. The fuzzy processor exhibits a level of inference performance of 601 KFLIPS with 54 rules, and can be used for real-time applications where the need for short processing times is of the utmost importance. The selection of a latest technology computer system (Pentium 4/3 GHz with SSE-2) can speed up image processing applications, but the time required still cannot be compared to the corresponding time using this hardware structure. [ABSTRACT FROM AUTHOR]

A new architecture of the binary cellular automata (CA), called programmable CA (PCA), is presented for the first time in this letter. The basic properties of the PCA are also studied. The proposed architecture is simple and fast and aims at providing efficient solutions to a variety of CA applications. [ABSTRACT FROM AUTHOR]

This paper presents the design and implementation, on a single VLSI chip, of a new high speed device performing histogram generation of video rate images. This high speed VLSI chip is designed to work at 13 MHz and will accommodate histograms of images of up to 682 pixels per horizontal line. The die size dimensions for the chip are 6.27 mm x 5.41 mm = 33.95 mm[sup 2]. The chip is intended to be used as a part of machine vision systems in industrial pattern recognition, where the need for real-time processing is crucial, through threshold selection and acceleration of image processing algorithms based on histograms. [ABSTRACT FROM AUTHOR]

The design and implementation, using custom ICs, of a new real-time vector pipeline colour space transformer, which performs the transformation of the RGB colour coordinates to chromaticity coordinates is presented. Its minimum rate of operation is 66.5 MIPS and it can handle high resolution colour images of up to 682 pizels per horizontal line. The high speed of operation was achieved by pipelining the data in a vector fashion. The device is intended to be used as the front end of colour machine vision systems in industrial pattern recognition, where the need for short processing times is crucial. [ABSTRACT FROM AUTHOR]

Assessment of the quality of local estimates of data sparsity is central for various adaptive algorithms in signal processing. Empirical bounds for the estimation performance of a frequently used measure of sparsity, namely the Gini-Simpson index are derived. Confidence bounds are derived for an unbiased estimator of this measure, with exponential convergence to the true (unknown) sparsity value, as the number of samples increases. The analysis is distribution-free, as no parametric or distributional assumptions are made for the available data. [ABSTRACT FROM AUTHOR]

In this work, a novel approach in post-earthquake structural damage estimation is investigated. The approach is formulated as a problem of both damage approximation and localization. The inter-story drift ratio and the global damage index of Park/Ang (DIG,PA) are the estimated damage indicators for each floor of the structure. Artificial neural networks (ANNs), random forests (RFs), support vector machines (SVMs) with linear and radial basis function (RBF) kernels and adaptive neuro-fuzzy inference systems (ANFISs) are tested to predict the seismic damage state of each floor of an 8-storey reinforced concrete (r/c) building subjected to 155 natural and artificially generated seismic accelerograms. The damage potential of the accelerograms is described by three seismic parameters extracted from the response of the structure. The set of seismic accelerograms is defined by combining two outlier detection techniques, isolation forests and Z-score, while the set of seismic parameters is confirmed by minimum redundancy maximum relevance (mRMR) feature selection algorithm. Optimization methods are used to fine-tune the performance of all networks. Results indicate RFs and ANNs among the models with optimal performances, reaching average correct classification rates of up to 96.87% and 91.87% with RFs, and 96.25% and 90.12% with ANNs, for DIG,PA and ISDR, respectively. [ABSTRACT FROM AUTHOR]

Abstract: Creating collision-free trajectories for mobile robots, known as the path planning problem, is considered to be one of the basic problems in robotics. In case of multiple robotic systems, the complexity of such systems increases proportionally with the number of robots, due to the fact that all robots must act as one unit to complete one composite task, such as retaining a specific formation. The proposed path planner employs a combination of Cellular Automata (CA) and Ant Colony Optimization (ACO) techniques in order to create collision-free trajectories for every robot of a team while their formation is kept immutable. The method reacts with obstacle distribution changes and therefore can be used in dynamical or unknown environments, without the need of a priori knowledge of the space. The team is divided into subgroups and all the desired pathways are created with the combined use of a CA path planner and an ACO algorithm. In case of lack of pheromones, paths are created using the CA path planner. Compared to other methods, the proposed method can create accurate collision-free paths in real time with low complexity while the implemented system is completely autonomous. A simulation environment was created to test the effectiveness of the applied CA rules and ACO principles. Moreover, the proposed method was implemented in a system using a real world simulation environment, called Webots. The CA and ACO combined algorithm was applied to a team of multiple simulated robots without the interference of a central control. Simulation and experimental results indicate that accurate collision free paths could be created with low complexity, confirming the robustness of the method. [ABSTRACT FROM AUTHOR]

Abstract: Cellular automata (CA) are a powerful technique for modelling otherwise intractably complex systems. On the other hand, earthquake can be defined as a spatially extended dissipative dynamic system that naturally evolves into a critical state with no characteristic time or length scales. In this paper, a two-dimensional CA model capable of reproducing some prominent features of earthquake data is presented. The proposed model with continuous states and discrete time, comprises cell-charges and aims at simulating earthquake activity with the usage of potentials. Several measurements have been carried out at different critical states, leading to different paths to criticality, for various cascade (earthquake) sizes, various cell activities and different neighbourhood sizes. Most notably, the produced simulation results emulate the Gutenberg–Richter (GR) scaling law, in both quantitative and qualitative way. Furthermore, the CA model has been implemented with a user-friendly interface and the user can change several of its parameters, in order to study various hypotheses concerning the aforementioned earthquake activity features. [Copyright &y& Elsevier]

Abstract: Content-based image retrieval (CBIR) is a collection of techniques for retrieving images on the basis of features, such as color, texture and shape. An efficient tool, which is widely used in CBIR, is that of color image histograms. The classic method of color histogram creation results in very large histograms with large variations between neighboring bins. Thus, small changes in the image might result in great changes in the histogram. Moreover, the fact that each color space consists of three components leads to 3-dimensional histograms. Manipulating and comparing 3D histograms is a complicated and computationally expensive procedure. The need, therefore, for reduction of the three dimensions to one could lead to efficient approaches. This procedure of projecting the 3D histogram onto one single-dimension histogram is called histogram linking. In this paper, a new fuzzy linking method of color histogram creation is proposed based on the L*a*b* color space and provides a histogram which contains only 10 bins. The histogram creation method in hand was assessed based on the performances achieved in retrieving similar images from a widely diverse image collection. The experimental results prove that the proposed method is less sensitive to various changes in the images (such as lighting variations, occlusions and noise) than other methods of histogram creation. [Copyright &y& Elsevier]

Non-linear filters are large family of filters used in signal and image processing. They have found numerous applications such as in digital image restoration, speech processing and coding, digital TV applications, etc. In this paper, two binary non-linear filters, the three-point median filter and the 2×2-pixel morphological opening filter are designed using nanoelectronic single-electron circuitry. In single-electron circuits bits of information are represented by the presence or absence of single electrons at conducting islands. The two nanoelectronic filters are simulated using a Monte Carlo technique and their correct and stable logical operation is confirmed. [Copyright &y& Elsevier]

This paper presents a new approach to the generalization of the concepts of grayscale morphology to color images. A new vector ordering scheme is proposed, infimum and supremum operators are defined, and the fundamental vector morphological operations are extracted. The basic properties of the presented vector morphology are described and its similarities to grayscale morphological operators are pointed out. The main advantages of the proposed methodology are that is vector preserving and provides improved results in many morphological applications. Furthermore, experimental results demonstrate the applicability of the proposed technique in a number of image processing and analysis problems, such as noise removal, edge detection and skeleton extraction. [Copyright &y& Elsevier]

A new technique suitable for reduction of the number of colors in a color image is presented in this article. It is based on the use of the image Principal Color Components (PCC), which consist of the image color components and additional image components extracted with the use of proper spatial features. The additional spatial features are used to enhance the quality of the final image. First, the principal colors of the image and the principal colors of each PCC are extracted. Three algorithms were developed and tested for this purpose. Using Kohonen self-organizing feature maps (SOFM) as classifiers, the principal color components of each PCC are obtained and a look-up table, containing the principal colors of the PCC, is constructed. The final colors are extracted from the look-up table entries through a SOFM by setting the number of output neurons equal to the number of the principal colors obtained for the original image. To speed up the entire algorithm and reduce memory requirements, a fractal scanning subsampling technique is employed. The method is independent of the color scheme; it is applicable to any type of color images and can be easily modified to accommodate any type of spatial features. Several experimental and comparative results exhibiting the performance of the proposed technique are presented. © 2002 Wiley Periodicals, Inc. Int J Imaging Syst Technol 12, 117–127, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.10019 [ABSTRACT FROM AUTHOR]

In this paper, an intelligent forecasting model, a recurrent neural network (RNN) with nonlinear autoregressive architecture, for daily and hourly solar radiation and wind speed prediction is proposed for the enhancement of the power management strategies (PMSs) of hybrid renewable energy systems (HYRES). The presented model (RNN) is applicable to an autonomous HYRES, where its estimations can be used by a central control unit in order to create in real time the proper PMSs for the efficient subsystems' utilization and overall process optimization. For this purpose, a flexible network-based design of the HYRES is used and, moreover, applied to a specific system located on Olvio, near Xanthi, Greece, as part of Systems Sunlight S.A. facilities. The simulation results indicated that RNN is capable of assimilating the given information and delivering some satisfactory future estimation achieving regression coefficient from 0.93 up to 0.99 that can be used to safely calculate the available green energy. Moreover, it has some sufficient for the specific problem computational power, as it can deliver the final results in just a few seconds. As a result, the RNN framework, trained with local meteorological data, successfully manages to enhance and optimize the PMS based on the provided solar radiation and wind speed prediction and make the specific HYRES suitable for use as a stand-alone remote energy plant. [ABSTRACT FROM AUTHOR]

Peginterferon-alpha ( Peg IFNa) frequently causes neutropenia, mainly due to bone marrow suppression. The aim of this study was to explore factors that are associated with infections during antiviral treatment. We analysed data from 275 chronic hepatitis C ( CHC) patients with compensated liver disease who underwent 318 courses of Peg IFNa and ribavirin. Neutropenia was defined as neutrophils <1000 cells/μL. Mean leucocytes count significantly decreased from baseline to treatment nadir (7081 ± 2182 vs 3293 ± 1331 cells/μL, P < 0.001), while neutropenia was observed in 32% during treatment. Thirty-one infections were observed. The incidence rate for infection was assessed at 1.46 infections per 100 person-months of therapy. The hazard rate for infection did not correlate with the neutrophils' nadir or the decrease in white blood cells. In multivariate Cox's regression analysis, cirrhosis was the only factor that was significantly associated with the occurrence of infection. Our data show that the development of bacterial infections during treatment with Peg IFNa and ribavirin in patients with compensated CHC is not associated with reduction or the nadir of white cells or neutrophil counts. Baseline cirrhosis is the only factor related with infection during treatment. The common practice of dose adjustment or discontinuation of interferon should be revised; careful assessment of liver damage before therapy and close monitoring during therapy are essential in all patients receiving interferon-based regimes, to minimize the detrimental consequences of infections. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an extensive simulation tool based on a Cellular Automata (CA) system that models fundamental seismic characteristics of a region. The CA-based dynamic model consists of cells-charges and it is used for the simulation of the earthquake process. The simulation tool has remarkably accelerated the response of the model by incorporating principles of the High Performance Computing (HPC). Extensive programming features of parallel computing have been applied, thus improving its processing effectiveness. The tool implements an enhanced (or hyper-) 2-dimensional version of the proposed CA model. Regional characteristics that depend on the seismic background of the area under study are assigned to the model with the application of a user-friendly software environment. The model is evaluated with real data that correspond to a circular region around Skyros Island, Greece, for different time periods, as for example one of 45years (1901–1945). The enhanced 2-dimensional version of the model incorporates all principal characteristics of the 2-dimensional one, also including groups of CA cells that interact with others, located to a considerable distance in an attempt to simulate long-range interaction. The advanced simulation tool has been thoroughly evaluated. Several measurements have been made for different critical states, as well as for various cascade (earthquake) sizes, cell activities and different neighbourhood sizes. Simulation results qualitatively approach the Gutenberg–Richter (GR) scaling law and reveal fundamental characteristics of the system. [Copyright &y& Elsevier]

Abstract: A two-dimensional (2-D) cellular automata (CA) dynamic system constituted of cells-charges has been proposed for the simulation of the earthquake process. In this paper, the study is focused on the optimal parameterisation of the model introducing the use of genetic algorithm (GA). The optimisation of the CA model parameterisation, by applying a standard GA, extends its ability to study various hypotheses concerning the seismicity of the region under consideration. The GA evolves an initially random population of candidate solutions of model parameters, such that in time appropriate solutions to emerge. The quality criterion is realised by taking into account the extent that the simulation results match the Gutenberg–Richter (GR) law derived from recorded data of the area under test. The simulation results presented here regard regions of Greece with different seismic and geophysical characteristics. The results found are in good quantitative and qualitative agreement with the GR scaling relations. [Copyright &y& Elsevier]

Seismicity is an extended geophysical characteristic of the Greek dominion. There are certain areas of high seismic activity, as well as, regions of low seismicity where strong earthquakes are rather rare events. Consequently, it is of great interest to present a methodology concerning the earthquake process in Greece even for areas considered to be of low seismicity. In this paper, a study of the earthquake activity of an area in Northeastern Greece, centred at Xanthi, Thrace, extending over a region of radius R =80km, during a certain time period is presented. A two-dimensional cellular automaton (CA) dynamic system consisting of cells representing charges is used for the simulation of the earthquake process. The model has been tested as well as calibrated using the recorded events of the above-mentioned region as initial conditions. The simulation results are found in good quantitative and qualitative agreement with the Gutenberg–Richter (GR) scaling relations. Finally, the CA model has a user-friendly interface and enables the user to change several of its parameters, in order to study various hypotheses concerning the seismicity of the region under consideration. [Copyright &y& Elsevier]