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Throughout the world, breast cancer is common cancer in women that contribute to high death amongst women. The early diagnosis and corresponding treatment can increase the possibilities of survival. In contrast, the challenging task is to detect the mass early in mammographic images, which is difficult due to noise and contrast. Mammography is the most capable technique used by radiologists frequently, which helps detect abnormal mass at an early stage; it is one of the methodologies to identify breast cancer. Here, a system is used to detect the tumor, with a modification in the grow cut algorithm. A new method is suggested based on segmentation by changing the modified grow cut algorithm by improving the region of interest. A modified grow cut algorithm has changed seed selection in mammogram images from manual into the semiautomatic way and has worked on non-defined borders. The earlier algorithm grows the region of interest only for the neighbor of current pixels and, consequently, a neighbor. Still, a change is made in the methodology for growing the region of interest within the class and between a neighbor cell's neighbors. This will ensure to get a more effective segmented area for abnormal mass than the previous method. The proposed technique is evaluated with the help of the mini M.I.A.S. database by considering circumscribed lesions, speculated lesions. Through result analysis, it is clear that the proposed technique gives better results for speculated, circumscribed lesions based on a comparison of ground truth images and segmented results.

Breast cancer is the most recurrent cancer in the United States after skin cancer. Early detection of masses in mammograms will help drop the death rate. This paper provides a hybrid approach based on a multiobjective evolutionary algorithm (MOEA) and cuckoo search. Using cuckoo search for decomposing problem into a single objective (single nest) for each Pareto optimal solution. The proposed method CS-MOEA/DE is evaluated using MIAS and DDSM datasets. A novel hybrid approach consists of nature-inspired cuckoo search and multiobjective optimization with Differential evolution, which is unique and includes detection of masses in a mammogram. The proposed work is evaluated based on 110 (50 + 60) images; the overall accuracy found for the proposed hybrid method is 96.74%. The experimental outcome shows that our proposed method provides better results than other state-of-the-art methods like the Otsu method, Kapur's Entropy, Cuckoo Search-based modified BHE.

Studies have shown that new generation of millennials have limited to no knowledge about managing their finances. This lack of awareness has created a need for financial literacy which is not only an essential employ-ability skill but also, a paramount life skill. Not only the younger generation but many individuals already in the corporate field are at their wit’s end when it comes to planning their finances and making correct financial decisions. This is where awareness in wealth management comes in. Wealth management is an investment advisory service. It also combines financial services to address the needs of individuals. It is more than just investment advice; it encompasses all parts of a person's financial life. The users can find all the information of different investments rather than integrating all the information from different places. They can generate a plan themselves or with the help of artificial intelligence and machine learning principles, manage their own and their family's current and future needs.

The purpose of this work is to develop a robust technique for image inpainting to restore small cracks as well as large regions those include the regions developed by object removal. The image to be restored is transformed into sub-bands by the use of DWT (Discrete Wavelet Transform). These sub-bands are reconstructed back to spatial domain to obtain the subspaces images that are at the same scale as the original image to be restored but having different resolutions. These subspace images are then subjected individually to ‘prioritized exemplar approach’ to fill-in different structures and textures simultaneously. We also optimize the patch size to cope up different sizes of textures, structures and varying resolution of the subspace images. These restored subspace images are superposed to obtain the final restored image. A number of images with changing complexion are used to estimate the effectiveness of the algorithm. The results shows visually plausible background where from the object is removed in variety of images with different structures and textures. The RMSE (Root Mean Squared Error) and PSNR (Peak Signal to Noise Ratio) measures are used to quantify the improvement over visual quality of the restoration.

A new Fuzzy Hypersphere Neural Network (FHNN) classifier is proposed in the framework of Radial Basis Function Neural Network (RBFNN) for which two class specific fuzzy clustering algorithms are suggested for the creation of fuzzy set hyperspheres (FSHs) in the hidden layer and the traditional least mean square (LMS) algorithm used to determine the weights between hidden to output layer is avoided by assigning the appropriate binary weights during training. The FHNN classifier utilizes FSH as pattern cluster and classes are represented by union of FSHs. The proposed fuzzy clustering algorithms are developed on the basis of inter-class and intra-class fuzzy membership metrics with maximum coverage of data points to create the FSHs in the hidden layer of FHNN. The weights between hidden to output layer of FHNN are adapted simultaneously during the creation of FSHs in the hidden layer. Unlike RBFNN and other similar models, the learning is: fast, independent of tuning parameters, formulates precise centroids and widths or radii of the FSHs, leading to 100% training accuracy on any training set. The learning rule 1 proposed for the FHNN is insusceptible to the order of data presentation and always results in identical clusters showing good data visualization, while the learning rule 2 eradicates the overlap between inter-class FSHs. The performance of FHNN is compared with the RBFNNs using twelve benchmark data sets. The empirical findings demonstrate that the FHNN is highly efficient classifier.

The paper addresses the problem of rain streak removal from videos. While, Rain streak removal from scene is important but a lot of research in this area, robust and real time algorithms is unavailable in the market. Difficulties in the rain streak removal algorithm arises due to less visibility, less illumination, and availability of moving camera and objects. The challenge that plagues rain streak recovery algorithm is detecting rain streaks and replacing them with original values to recover the scene. In this paper, we discuss the use of photometric and chromatic properties for rain detection. Updated Gaussian Mixture Model (Updated GMM) has detected moving objects. This rain streak removal algorithm is used to detect rain streaks from videos and replace it with estimated values, which is equivalent to original value. The spatial and temporal properties are used to replace rain streaks with its original values.

Image analysis and restoration has been carried out by texture synthesis for large regions and inpainting algorithms for small cracks in images. A new approach that allow simultaneous fill-in of different structures and textures is being proposed in present study. A combination of structure inpainting and patch-based texture synthesis with patch size optimization is carried out (termed as ‘optimized patch based inpainting’) for filling and updating the target region that shows additional advantages over earlier approaches. The algorithm here uses the patch based inpainting having isophote driven patch-based texture synthesis at core. In present algorithm, ones the user selects the target regions, the algorithm automatically search and fills-in these regions with best matching information surrounding it. Patch-based filling is found to improve execution speed and robustness significantly over pixel-based fill. The fill-in is done in such a way that structure information arriving at the region boundaries is propagated inside. A number of examples on real and complex images are considered to demonstrate the effectiveness of the algorithm.

Offline handwritten signatures is a convincing evidence form of biometrics for verification. However, the verification of offline handwritten signatures is challenging task because of the variations in handwritten signatures. To address this difficulty, this paper proposes a new approach to represent the shape. In this newly proposed approach, the signature pixels are represented by: (1) Gaussian Weighting Based Tangent Angle, to represent the curve angle at the reference pixel; (2) a new shape descriptor, i.e. cylindrical shape context is proposed for a detailed and accurate description of the curve at corresponding pixel. Experimental results show that desired pixel matching results are obtained by using cylindrical shape context which automatically increases the accuracy of verification of offline handwritten signatures. The shape dissimilarity measures are computed and given to the Support Vector Machine with Radial Basis Function (RBF) kernel for classification of signature. The results obtained using GPDS synthetic signature database, UTSig persian offline signature database, and MCYT-75 offline signature database shows the effectiveness of proposed cylindrical shape context.

Optical character recognition (OCR) deals with the recognition of printed or handwritten characters. India being the multilingual country, and possessing the historical information in some of the old languages being practiced in India since ancient time, it is obvious that important information is still to be discovered from these ancient available documents. In this paper, we have devised a method which will identify the language of the script under observation. Character recognition itself is a challenging problem because of the variation in the font and size of the characters. In this paper, a scheme is developed for complete OCR for Marathi and Pali languages. The proposed system successfully segments out the lines and words of the Marathi and Pali documents. The proposed system is evaluated on ten Marathi and ten Pali documents comprised of 552 text lines and 6430 words. We obtained the promising results on the line segmentation with an accuracy of 99.25% and 98.6% and for word segmentation 97.6% and 96.5%, respectively, on Marathi and Pali language documents. Using K-NN classifier, the most frequently used words in Marathi and Pali documents are identified.

Underwater images are prone to contrast loss, limited visibility, and undesirable color cast. For underwater computer vision and pattern recognition algorithms, these images need to be pre-processed. We have addressed a novel solution to this problem by proposing fully automated underwater image dehazing using multimodal DWT fusion. Inputs for the combinational image fusion scheme are derived from Singular Value Decomposition (SVD) and Discrete Wavelet Transform (DWT) for contrast enhancement in HSV color space and color constancy using Shades of Gray algorithm respectively. To appraise the work conducted, the visual and quantitative analysis is performed. The restored images demonstrate improved contrast and effective enhancement in overall image quality and visibility. The proposed algorithm performs on par with the recent underwater dehazing techniques.

Offline handwritten character recognition is the process of recognizing given characters from the large set of characters. OCR system mainly focuses on the recognition of printed or handwritten characters of a scanned image. The proposed system extracts features that are based only on gradient of image which is helpful in exact recognition of characters. A technique to recognize handwritten Devanagari characters using combination of quadratic and SVM classifiers is presented in this paper. Features used are directional features that are strength, angle and histogram of gradient (SOG, AOG, HOG). Using a Gaussian filter, the strength and the angle features are down sampled to obtain a feature vector of 392 dimensions. These features are finally concatenated with HOG feature. Applying these to the combination of quadratic and SVM classifiers to obtain maximum accuracy of 95.81% using 3 fold cross validation.

This paper presents a novel feature extraction framework for content-based image retrieval (CBIR). Discrete wavelet transform (DWT) based Local tetra pattern (LTrP) is used to obtain the feature map from an input image. Decomposition of DWT up to single level and the features obtained from it would make the CBIR system very sensitive to noise. Therefore, decomposition up to three scales is used to remove noise. On each of the sub band LTrP is applied and 130 features are extracted. Further, Artificial Neural Network (ANN) is employed for index matching and image retrieval task which gives the classification accuracy of 97.9 % for Corel 1K database. We have compared our proposed feature extraction scheme with LTrP and other existing local descriptor. Results show that combination of DWT and Local Tetra Patterns (DWT + LTrP) extracts more robust features than LTrP alone. Also, the effect of different wavelet filters on the accuracy of the system has been analyzed. Proposed framework has been tested on Corel-1000 and Corel-10000 databases. We have used average retrieval rate, precision, and recall for performance measure of the CBIR system. Proposed method outperforms the retrieval rate from 75.9% using LTrP to 97.9% using proposed method on Corel 1K database. Improvement in performance measures as compared to the other existing methods evidence that, the proposed spatio-frequency local descriptor is more robust for image retrieval task.

A large number of articles have been devoted to the application of “texture synthesis” for large regions and “inpainting” algorithms for small cracks in an image. A new approach that allows the simultaneous filling in of different structures and textures is discussed in this present study. The combination of structure inpainting and patch-based texture synthesis carried out (termed as “patch-based inpainting”) for filling and updating the target region shows additional advantages over earlier approaches. The algorithm discussed here uses the patch-based inpainting with isophote-driven patch-based texture synthesis at the core. In this algorithm, once the user selects the regions to be restored, the algorithm automatically searches and fills in these regions with the best matching information surrounding them. We have assigned high priorities to the pixels on the boundary and the structure by computing data terms D(p), and the texture and corners are prioritized by computing the confidence C(p) of the pixel. We also regularized and weighted the confidence of the pixels, RC(p), to achieve a balance of the two. The patch search area near the pixel patch to be filled is bounded for algorithm speed improvement. Patch-based filling significantly improve execution speed compared with pixel-based filling. Filling in is done in such a way that the structure information arriving at the region boundaries is propagated inside. A number of examples on real and synthetic images are used to demonstrate the effectiveness of the algorithm. Robustness with respect to the shape of the selected target region is also demonstrated.

Recovering dehazed image from the single underwater image has always been a challenging task. Inspired from dark channel prior, we extend it further to dehaze the underwater images using a single color channel. The red color component undergoes maximum attenuation on account of the longest wavelength and dominates the dark channel prior in the underwater scenario. We developed the algorithm based on the red color channel, and obtained depth map using morphological operation. The proposed approach significantly restores the color, minimizes the effect of haze and improves the contrast. Experimental results based on qualitative and quantitative analysis exhibits that the proposed method performs efficiently as compared to existing dehazing algorithms.

Sensor Network (WSN) consists of a large population of sensor nodes capable of computation, communication and sensing. Limited storage, processing, and transmission power are inherent limitations of WSN. IEEE 802.15.4 is a new standard, uniquely designed for low rate Wireless Personal Area Network (LR-WPAN), developed for applications that demand low throughput. Main features of this standard are network flexibility, low cost, very low power consumption and low data rate in an ad-hoc self-organizing network. In this paper, performance analysis of IEEE 802.15.4 is performed using Qualnet 4.5. Star topology, multi-hop peer to peer network along with MANET routing protocols such as AODV, DSR and DYMO are used for analysis of Quality of Service (QoS) parameters like throughput, packet delivery ratio, average end-to-end delay, jitter, total energy consumption, and network scalability as the performance metrics. The comparative analysis of result show that AODV outperforms other two in star topology whereas DSR has slightly upper hand in multi-hop topology for varying traffic loads, and in beacon enabled mode.

Underwater images are prone to contrast loss, limited visibility, and undesirable color cast. For underwater computer vision and pattern recognition algorithms, these images need to be pre-processed. We have addressed a novel solution to this problem by proposing fully automated underwater image dehazing using multimodal DWT fusion. Inputs for the combinational image fusion scheme are derived from Singular Value Decomposition (SVD) and Discrete Wavelet Transform (DWT) for contrast enhancement in HSV color space and color constancy using Shades of Gray algorithm respectively. To appraise the work conducted, the visual and quantitative analysis is performed. The restored images demonstrate improved contrast and effective enhancement in overall image quality and visibility. The proposed algorithm performs on par with the recent underwater dehazing techniques.

Osteochondromas are rare tumors of skull and commonest site of intracranial involvement is skull base. Complete excision is usually curative. We report a case of giant osteochondroma of the fronto-sylvian, which we believe is the largest reported intracranial osteochondroma until date.

Biometrics has always been an integral part of human identification and verification, with offline signature verification being a most crucial component of it. It is a challenging task as the signa...

Performance of medium length LDPC codes is directly proportional to error floor .In these codes, error floor can be mainly improved through elimination of short cycles. Hence, for effective elimination of short cycles, it is customary to use a girth conditioning in random construction process. Various accurate techniques, such as, density evolution and EXIT chart are developed for performance prediction of large block length, iteratively decoded LDPC codes. However, for medium block length, this method evolved with erroneous results. Even though, the medium block length LDPC codes have many application, short cycles degrades the performance and adds computational complexity in decoding process.

Energy consumption is a major challenge in wireless sensor network (WSN). Most of the routing algorithms focus on energy efficient paths, For the analysis of such algorithms at low cost and in less time; we believe that, simulation gives the better approximation. Therefore, in this paper, we are proposing a simulation model for WSN. Literature survey is done on energy-aware routing protocols, in which, it is found that, Minimum Total Transmission Power Routing (MTPR) and Minimum Battery Cost Routing (MBCR) Protocol, most comprehensively captures tradeoffs of energy efficiency and network lifetime respectively. Proposed simulation model is implemented using Qualnet 4.5 and applied to MTPR and MBCR to analyze its performance. Through this study, we lay a foundation for further research work on enhancements in extending the network lifetime of WSN. Experimental result shows that there is always a tradeoff between energy efficiency and network lifetime.

The use of computer vision based approach has started to bring the intelligence to many of the modern machineries. Such kind of high performance image processing systems can be efficiently built using Field Programmable Gate Arrays (FPGAs). This paper presents the design and implementation of FPGA based Computer Vision System for offset error computation of a new proposed registration mark pattern in 4-color web offset printing machines. The color printing quality of offset machine degrades due to a genuine problem of registration error caused by improper alignment of the prints from each process color section. This system can be used in an automated registration control system for web printing press that will control the position of each of CMYK cylinders depending on offset error calculated which will improve printing quality.

Arachnoid cysts are rare lesions occurring anywhere in the cerebrospinal axis. The sylvian fissure remains the most favoured site for their occurrence, followed by cerebellopontine angle, suprasellar, and quadrigeminal cisterns. Retroclival arachnoid cysts are very rare. We report a retroclival arachnoid cyst with bilateral cerebellopontine angle extensions with hemifacial spasm in a 26-year-old woman. The patient underwent surgery and her hemifacial spasm improved.

We investigated the area of the tidemark in osteoarthritic and normal cartilage by stereology as well as the number of vessels penetrating the tidemark. The area of the tidemark was significantly increased in osteoarthritis, as was the number of vessels penetrating the tidemark. These three-dimensional findings are new compared to previous two-dimensional findings where it has been discussed whether there were any differences between osteoarthritic cartilage and normal cartilage with respect to duplications of the tidemark and vascularisation. The present findings using stereology may erase the uncertainty of previous findings, and support the hypothesis that primary osteoarthritis is partly of vascular origin.

Lalor et al. (1991) have suggested the occurrence of sensitization to titanium after implant failure. We report a case of excessive and prolonged deposition of titanium and titanium alloy-debris after disassembly of a modular acetabular component in which no sensitization was found.

In a prospective clinical study of the outcome of alarm treatment in nocturnal enuretics, 60 children were included: 40 boys and 20 girls, mean age 8.2 years (range 5.1–14.4). All were treated with enuresis alarms and had 2 or more enuretic events during the initial 14 days of treatment. None had diurnal enuresis. In each child, the enuretic and voluntary voiding frequencies during the initial 14 and last 14 days of treatment were compared. We found that 43 children had a 75% reduction or more of the enuretic events. 28 children substituted the former enuretic events by sleep, 15 changed the enuresis by voluntary voidings. Only 17 children had no effect of the alarm treatment. No parameters were found to predict the outcome. In conclusion, the outcome of successful alarm treatment occurs in two distinct forms. Either the child is left asleep without wetting his bed; or the child wakes up spontaneously from sleep and goes to the bathroom.