Text extraction and enhancement of binary images using cellular automata

Text characters embedded in images represent a rich source of information for content-based indexing and retrieval applications. However, these text characters are difficult to be detected and recognized due to their various sizes, grayscale values, and complex backgrounds. Existing methods cannot handle well those texts with different contrast or embedded in a complex image background. In this paper, a set of sequential algorithms for text extraction and enhancement of image using cellular automata are proposed. The image enhancement includes gray level, contrast manipulation, edge detection, and filtering. First, it applies edge detection and uses a threshold to filter out for low-contrast text and simplify complex background of high-contrast text from binary image. The proposed algorithm is simple and easy to use and requires only a sample texture binary image as an input. It generates textures with perceived quality, better than those proposed by earlier published techniques. The performance of our method is demonstrated by presenting experimental results for a set of text based binary images. The quality of thresholding is assessed using the precision and recall analysis of the resultant text in the binary image.     

Non-uniform illumination in concentrating solar cells

After a gap of more than two decades, Concentrator Photovoltaics (CPV) technology is once again under spotlight for making use of the best available solar cell technologies and improving the overall performance. CPV finds its use in a number of applications ranging from building integration to huge power generation units. Although the principles of solar concentration are well understood, many practical design, operation, control issues require further understanding and research. A particular issue for CPV technology is the non-uniformity of the incident flux which tends to cause hot spots, current mismatch and reduce the overall efficiency of the system. Understanding of this effect requires further research, and shall help to employ the most successful means of using solar concentrators. This study reviews the causes and effects of the non-uniformity in the CPV systems. It highlights the importance of this issue in solar cell design and reviews the methods for the solar cell characterization under non-uniform flux conditions. Finally, it puts forward a few methods of improving the CPV performance by reducing the non-uniformity effect on the concentrator solar cells.     

Destruction of percolative network using green synthesized gold nanoparticles: Formation of high dielectric material

Gold nanoparticles (AuNPs) of about 5 nm in diameter were biosynthesized at room temperature (300 K). The PVA/2.5 wt% KH₂PO₄ or KDP composite film and PVA/2.5 wt% KDP/AuNPs nanocomposite films with different concentrations of AuNPs were prepared. Interestingly, addition of 0.05 wt% of AuNPs to the PVA/2.5 wt% KDP percolative composite film destroys percolative behavior of this composite film. Furthermore, the PVA/2.5 wt% KDP/0.05 wt% AuNPs nanocomposite film exhibited high room temperature dielectric permittivity (ε′ ∼ 590 at 1 kHz). The behavior of AC conductivity (σ_ac) of the nanocomposite films indicated correlated barrier hopping type of conduction mechanism. The Cole–Davidson dielectric response becomes evident as the interfacial polarization process acquires a more symmetric form, tending to Debye relaxation. High value of ε′ promises direct application in capacitors. Moreover, the novel feature of destroying the percolative behavior by AuNPs may be applied even in other systems.     

Long‐Range Nanoparticle Surface‐Energy‐Transfer Ruler for Monitoring Photothermal Therapy Response

A recent gold nanotechnology‐driven approach opens up a new possibility for the destruction of cancer cells through photothermal therapy. Ultimately, photothermal therapy may enter into clinical therapy and, as a result, there is an urgent need for techniques to monitor the tumor response to therapy. Driven by this need, a nanoparticle surface‐energy‐transfer (NSET) approach to monitor the photothermal therapy process by measuring a simple fluorescence intensity change is reported. The fluorescence intensity change is due to the light‐controlled photothermal release of single‐stranded DNA/RNA via dehybridization during the therapy process. Time‐dependent results show that just by measuring the fluorescence intensity change, the photothermal therapy response during the therapy process can be monitored. The possible mechanism and operating principle of the NSET assay are discussed. Ultimately, this NSET assay could have enormous potential applications in rapid, on‐site monitoring of the photothermal therapy process, which is critical to providing effective treatment of cancer and multidrug‐resistant bacterial infections.     

Synthesis and characterization of niobium-doped potassium tetragonal tungsten bronzes, KxNbyW1?yO3

Needle-shaped crystals of sizes up to 5 μm × 5 μm × 40 μm of nominal composition K _x Nb _y W_1−y O₃ were synthesized by solid state method at 800 °C using appropriate amount of WO₃, WO₂, Nb₂O₅, and K₂WO₄. The samples were characterized with XRD, SEM, microprobe analysis, optical spectroscopy, and Raman spectroscopy. The XRD patterns of the samples show single phase of tetragonal tungsten bronze (TTB) type (P4/mbm, No. 127) up to y = 0.07. Structure refinements reveal an increase in cell parameter with increasing nominal niobium content within the TTB phase. The elemental compositions of the crystals determined by electron microprobe analysis also show an increase in Nb content with increasing y. With increasing Nb content the reflectivity minimum in the near infrared spectral range shifts towards lower wavenumber indicating the effect of decreasing carrier concentration. Pyrochlore type phase (KNbWO₆) is obtained as a second phase when nominal composition y > 0.07.     

Estimating degradation model parameters using neighborhood pattern distributions: an optimization approach

Noise models are crucial for designing image restoration algorithms, generating synthetic training data, and predicting algorithm performance. There are two related but distinct estimation scenarios. The first is model calibration, where it is assumed that the input ideal bitmap and the output of the degradation process are both known. The second is the general estimation problem, where only the image from the output of the degradation process is given. While researchers have addressed the problem of calibration of models, issues with the general estimation problems have not been addressed in the literature. In this paper, we describe a parameter estimation algorithm for a morphological, binary, page-level image degradation model. The inputs to the estimation algorithm are 1) the degraded image and 2) information regarding the font type (italic, bold, serif, sans serif). We simulate degraded images using our model and search for the optimal parameter by looking for a parameter value for which the local neighborhood pattern distributions in the simulated image and the given degraded image are most similar. The parameter space is searched using a direct search optimization algorithm. We use the p-value of the Kolmogorov-Smirnov test as the measure of similarity between the two neighborhood pattern distributions. We show results of our algorithm on degraded document images.     

Intelligent system based on local linear wavelet neural network and recursive least square approach for breast cancer classification

A new learning technique for local linear wavelet neural network (LLWNN) is presented in this paper. The difference of the network with conventional wavelet neural network (WNN) is that the connection weights between the hidden layer and output layer of conventional WNN are replaced by a local linear model. A hybrid training algorithm of Error Back propagation and Recursive Least Square (RLS) is introduced for training the parameters of LLWNN. The variance and centers of LLWNN are updated using back propagation and weights are updated using Recursive Least Square (RLS). Results on extracted breast cancer data from University of Wisconsin Hospital Madison show that the proposed approach is very robust, effective and gives better classification.     

Mass classification method in mammograms using correlated association rule mining

In this paper, we present an efficient computer-aided mass classification method in digitized mammograms using Association rule mining, which performs benign–malignant classification on region of interest that contains mass. One of the major mammographic characteristics for mass classification is texture. Association rule mining (ARM) exploits this important factor to classify the mass into benign or malignant. The statistical textural features used in characterizing the masses are mean, standard deviation, entropy, skewness, kurtosis and uniformity. The main aim of the method is to increase the effectiveness and efficiency of the classification process in an objective manner to reduce the numbers of false-positive of malignancies. Correlated association rule mining was proposed for classifying the marked regions into benign and malignant and 98.6% sensitivity and 97.4% specificity is achieved that is very much promising compare to the radiologist’s sensitivity 75%.