Mohammad A. U. Khan

LSec: lightweight security protocol for distributed wireless sensor network

Constraint specific wireless sensor networks need energy efficient and secure communication mechanisms. In this paper we propose Lightweight Security protocol (LSec) that fulfils both requirements. LSec provides authentication and authorization of sensor nodes with simple secure key exchange scheme. It also provides confidentiality of data and protection mechanism against intrusions and anomalies. LSec is memory efficient that requires 72 bytes of memory storage for keys. It only introduces 74.125 bytes of transmission and reception cost per connection.

On-Line Signature Verification by Exploiting Inter-Feature Dependencies

The traditional on-line signature verification process involves use of various dynamic features such as velocity, pressure, acceleration, angles, etc. The idea is to device a composite vector structure combining more than one feature where each feature is treated independently. Our proposed research work is an attempt to exploit the inter-feature dependencies by employing a higher dimensional vector approach. The strategy adopted here is to obtain pressure strokes with respect to various velocity bands. The strokes thus obtained are found to portray a reasonably accurate basis for discriminating genuine vs forgery class. The simulation results validate our assumptions and show improvements in the discriminating index

Rib suppression in frontal chest radiographs: A blind source separation approach

Chest radiographs play an important role in the diagnosis of lung cancer. Detection of pulmonary nodules in chest radiographs forms the basis of early detection. Due to its sparse bone structure and overlapping of the nodule with ribs and clavicles the nodule is difficult to detect in conventional chest radiographs. We present a technique based on independent component analysis (ICA) for the suppression of posterior ribs and clavicles which will enhance the visibility of the nodules and aid the radiologist in diagnosis.

Efficient hardware implementation strategy for local normalization of fingerprint images

Global techniques do not produce satisfying and definitive results for fingerprint image normalization due to the non-stationary nature of the image contents. Local normalization techniques are employed, which are a better alternative to deal with local image statistics. Conventional local normalization techniques involve pixelwise division by the local variance and thus have the potential to amplify unwanted noise structures, especially in low-activity background regions. To counter the background noise amplification, the research work presented here introduces a correction factor that, once multiplied with the output of the conventional normalization algorithm, will enhance only the feature region of the image while avoiding the background area entirely. In essence, its task is to provide the job of foreground segmentation. A modified local normalization has been proposed along with its efficient hardware structure. On the way to achieve real-time hardware implementation, certain important computationally efficient approximations are deployed. Test results show an improved speed for the hardware architecture while sustaining reasonable enhancement benchmarks.

Fingerprint image enhancement using Principal Component Analysis (PCA) filters

A new method based upon Principal Component Analysis (PCA) for fingerprint enhancement is proposed in this paper. PCA is a useful statistical technique that has found application in fields such as face recognition and image compression, and is a common technique for finding patterns in data of high dimension. In the proposed method image is first decomposed into directional images using decimation free Directional Filter bank DDFB. Then PCA is applied to these directional fingerprint image which gives the PCA filtered images. Which are basically directional images. Then these directional images are reconstructed into one image which is the enhanced one. Simulation results are included illustrating the capability of the proposed method.

Scale and rotation invariant recognition of cursive Pashto script using SIFT features

Cursive scripts such as Urdu, Pashto and Arabic contain large number of unique shapes called ligatures. Recognition of thousands of ligatures is challenging due to variations of various kinds including scaling, orientation, font style, spatial location/registration of ligatures and limited number of samples available for training. Accurate segmentation is a key challenge for analytic approaches, whereas holistic approaches suffer due to limitations of various feature representation schemes.

Efficient Hardware Implementation For Fingerprint Image Enhancement Using Anisotropic Gaussian Filter

A real-time image filtering technique is proposed which could result in faster implementation for fingerprint image enhancement. One major hurdle associated with fingerprint filtering techniques is the expensive nature of their hardware implementations. To circumvent this, a modified anisotropic Gaussian filter is efficiently adopted in hardware by decomposing the filter into two orthogonal Gaussians and an oriented line Gaussian. An architecture is developed for dynamically controlling the orientation of the line Gaussian filter. To further improve the performance of the filter, the input image is homogenized by a local image normalization. In the proposed structure, for a middle-range reconfigurable FPGA, both parallel compute-intensive and real-time demands were achieved. We manage to efficiently speed up the image-processing time and improve the resource utilization of the FPGA. Test results show an improved speed for its hardware architecture while maintaining reasonable enhancement benchmarks.

Average Entropy: A New Uncertainty Measure with Application to Image Segmentation

Various modifications have been suggested in the past to extend Shannon entropy to continuous random variables. This article investigates these modifications, and suggests a new entropy measure with the name of average entropy (AE). AE is more general than Shannon entropy in the sense that its definition encompasses both continuous as well as discrete domains. It is additive, positive and attains zero only when the distribution is uniform. The main characteristic of the suggested measure lies in its consistency behavior. Many properties of AE, including its relationship with Kullback–Leibler information measure, are studied. Precise theorems about the vanishing of the conditional AE for both continuous and discrete distributions are provided. Toward the end, the measure is tested for its effectiveness in image segmentation. [Received March 2014. Revised June 2015.]

A spatial domain scar removal strategy for fingerprint image enhancement

Fingerprints are the oldest and most widely used form of biometric identification. Many researchers have addressed the fingerprint classification problem and significant progress has been made in designing automatic fingerprint identification systems (AFIS) over the past two decades. However, some design factors such as lack of reliable minutia extraction algorithms, difficulty in quantitatively defining a reliable match between fingerprint images, poor image acquisition, low contrast images create bottlenecks in achieving the desired performance. Noticeable among them is the fact that digitally acquired fingerprint images are rarely of perfect quality to be used directly with AFIS; one important step is fingerprint enhancement. Conventional fingerprint enhancement methods, such as Gabor and anisotropic filters, do fill the holes and gaps in ridge lines but lack the necessary capability to tackle scar lines. For scar lines, an explicit filling process is proposed that is a mix of Fourier and spatial domain strategies. The proposed method is to make use of the Fourier domain directional field to trace an appropriate candidate for the scar pixels to be replaced with. The necessary components of the process are locating scars, estimating directional field, and the filling strategy. This process can act as front-end to the subsequent Gabor and anisotropic diffusion filtering. The simulation results for synthetic, as well as real fingerprints, show improved performance regarding better extraction of genuine minutia points. HighlightsWe Model a new proposed method to make use of the Fourier domain directional field to trace an appropriate candidate for the scar pixels to be replaced with.The necessary components of the process are locating scars, finding the directional field, and the filling strategy.The strategy relies on the fact that in these linear scars, the ridge/valley pattern is still intact across the scar region.Using this information, the scar boundary is filled with appropriate normal region pixels using the local orientation field.This process can act as front-end to the subsequent Gabor and anisotropic diffusion filtering.

Modular multiplication using the core function in the residue number system

Modular multiplication can be performed in the residue number system (RNS) using a type of Montgomery reduction. This paper presents an alternative in which RNS modular multiplication are performed by using the core function. All of the intermediate calculations use short wordlength operations within the RNS. This work contributes to the long wordlength modular multiplication operation