In this paper, a signature verification method based on texture features involving off-line signatures written in two different Indian scripts is proposed. Both Local Binary Patterns (LBP) and Uniform Local Binary Patterns (ULBP), as powerful texture feature extraction techniques, are used for characterizing off-line signatures. The Nearest Neighbour (NN) technique is considered as the similarity metric for signature verification in the proposed method. To evaluate the proposed verification approach, a large Bangla and Hindi off-line signature dataset (BHSig260) comprising 6240 (260×24) genuine signatures and 7800 (260×30) skilled forgeries was introduced and further used for experimentation. We further used the GPDS-100 signature dataset for a comparison. The experiments were conducted, and the verification accuracies were separately computed for the LBP and ULBP texture features. There were no remarkable changes in the results obtained applying the LBP and ULBP features for verification when the BHSig260 and GPDS-100 signature datasets were used for experimentation.

A hexagonal‐shaped fractal antenna with triangular slot for super wideband (SWB) application is presented. The proposed antenna has total size of 20 × 33.4 × 1.57 mm3.The proposed antenna is designed of iterations of triangular slot on a hexagonal metallic patch with tapered microstrip line feed. A combination of semicircular and rectangular shaped partial ground plane is used to achieve a super wide bandwidth from 3 to 35 GHz with bandwidth ratio of 11.6:1. The antenna design, simulation and optimization are performed using IE3D Full Wave Electromagnetic Simulator. The proposed antenna is compact and has stable radiation pattern. The simulation and measured results are compared to demonstrate the performance of proposed antenna. © 2015 Wiley Periodicals, Inc. Microwave Opt Technol Lett 57:1659–1662, 2015

In this paper an efficient off-line signature verification method based on an interval symbolic representation and a fuzzy similarity measure is proposed. In the feature extraction step, a set of Local Binary Pattern (LBP) based features is computed from both the signature image and its under-sampled bitmap. Interval-valued symbolic data is then created for each feature in every signature class. As a result, a signature model composed of a set of interval values (corresponding to the number of features) is obtained for each individual's handwritten signature class. A novel fuzzy similarity measure is further proposed to compute the similarity between a test sample signature and the corresponding interval-valued symbolic model for the verification of the test sample. To evaluate the proposed verification approach, a benchmark offline English signature dataset (GPDS-300) and a large dataset (BHSig260) composed of Bangla and Hindi off-line signatures were used. A comparison of our results with some recent signature verification methods available in the literature was provided in terms of average error rate and we noted that the proposed method always outperforms when the number of training samples is eight or more.

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