Abstract

In 2012, Das et al. proposed a new password-based user authentication scheme in hierarchical wireless sensor networks [Journal of Network and Computer Applications 35(5) (2012) 1646-1656]. The proposed scheme achieves better security and efficiency as compared to those for other existing password-based user authentication schemes proposed in the literature. This scheme supports to change dynamically the user’s password locally at any time without contacting the base station or gateway node. This scheme also supports dynamic node addition after the initial deployment of nodes in the existing sensor network. In this paper, we simulate this proposed scheme for formal security verification using the widely-accepted Automated Validation of Internet Security Protocols nd Applications (AVISPA) tool. AVISPA tool ensures that whether a protocol is insecure against possible passive and active attacks, including the replay and man-in-the-middle attacks. Using the AVISPA model checkers, we show that Das et al.’s scheme is secure against possible assive and active attacks.

Abstract

Friendsourcing games aim to collect useful information about individuals by targeting their socially connected groups or friends. The current method of eliciting information is to pose direct questions to friends and expect a truthful response in return. However, such an approach not only becomes monotonous after some time but also suffers from problems like social awkwardness and reticence and thus, affecting the accuracy of the generated responses. In this paper, we present Power of Friends, a novel approach to friendsourcing games, which involves identifying the unanimous opinion of all the friends about a question related to an individual. We tested our proposed approach with seven different groups and were able to generate 47 facts about members of the selected groups. Participants particularly enjoyed the guesswork and interactive elements of the game. Based on the feedback obtained from the study, we propose four strategies for designing similar friendsourcing games.

Abstract

Here the prototype of an intelligent flash light is presented which helps for outdoor navigation by projecting a directional arrow that guides a user from one point to another. The torchlight can be used for navigating in new campuses, can be used as a tool for search and rescue and also can be used for trekking and biking. By directly projecting the associated meta data like name of the place, distance to target and the heading information the torch eliminates the overhead involved in reading a map and comparing it with the surroundings to further decipher the map. It is an intelligent-ubiquitous flash light which over comes the limitation imposed by limited screen size of a GPS cellphone, a GPS watch or a GPS navigating device used in a car.

Abstract

Here we present the design of a highly dexterous 48 dof robotic hand. This high dexterity was possible because of the unique design of joints based on magnetic sliding and spherical spheres. The hand is tendon driven and is portable. We believe this is the robotic hand with the highest degree of freedom till date. Dexterity in robotic hands is an important parameter towards the complexity of the tasks they can do. It has been a design challenge to build an anthropomorphic robotic hand that can completely replicate the human hand in terms of its motion, torque and form factor. Here we have looked at the problem from a design perspective and achieved a hand with degrees of freedom more than the human hand in itself.

Abstract

Concerns of privacy, template security and efficiency ofbiometric authentication systems have received consider-able research attention in the recent past. Binary templaterepresentations have been shown to provide significant im-provements in efficiency without compromising accuracyfor a variety of biometric modalities including fingerprints,palmprints and iris. Most of the secure authentication pro-tocols work with generic feature representations or proposenew secure templates for specific modalities. In this work,we propose an authentication protocol that works with anybinary biometric representation that employs hamming dis-tance for comparison and convert it into a secure, privacypreserving protocol that provides template protection. Wealso provide the ability to use masks while computing thehamming distance. The protocol retains the accuracy of theunderlying representation as demonstrated by our experi-ments

Abstract

Sparse representation based image restoration tech-niques have shown to be successful in solving various inverse problems such as denoising, in painting, and super-resolution,etc. on natural images and videos. In this paper, we explore the use of sparse representation based methods specifically to restore the degraded document images. While natural images form a very small subset of all possible images admitting the possibility of sparse representation, document images are significantly more restricted and are expected to be ideally suited for such a representation. However, the binary nature of textual document images makes dictionary learning and coding techniques unsuitable to be applied directly. We leverage the fact that different characters possess similar strokes, curves, and edges, and learn a dictionary that gives sparse decomposition for patches. Experimental results show significant improvement in image quality and OCR performance on documents collected from a variety of sources such as magazines and books. This method is therefore, ideally suited for restoring highly degraded images in repositories such as digital libraries.

Abstract

Robust segmentation of ballistic strokes fromonline handwritten traces is critical in parameter estimationof stroke based models for applications such as recognition,synthesis, and writer identification. In this paper we propose anew method for segmenting ballistic strokes from online hand-writing. Traditional methods of ballistic stroke segmentationrely on detection of local minima of pen speed. Unfortunately,this approach is highly sensitive to noise, in sensing and inboth spatial and temporal dimensions. We decompose theproblem into two steps, where the spatial noise is filteredout in the first step. The ballistic stroke boundaries are thendetected at the local curvature maxima, which we show to beinvariant to temporal sampling noise. We also propose a bag-of-strokes representation based on ballistic stroke segmentationfor online character recognition that improves the state-of-the-art recognition accuracies on multiple datasets

Abstract

We present a novel method for reducing the effectsof ink-bleed in handwritten documents. We go beyond the existing works on ink bleed detection and removal. We consider each pixel in a document as a result of combination of foreground,ink-bleed and background. We carry of a decomposition of the document image into separate foreground ink, ink-bleed, and background layers. We propose an efficient MRF formulation to achieve this separation. Degradation model for the ink and paper is proposed. The ability to extract the contributions of the three components to each pixel allows us to recover finer details of the writing. Quantitative and qualitative results on a set of historic manuscripts as well as synthetically generated documents demonstrate the effectiveness of our approach

Abstract

We aim to learn local orientation field patterns in fingerprints and correct distorted field patterns in noisy fingerprint images. This is formulated as a learning problem and achieved using two continuous restricted Boltzmann machines. The learnt orientation fields are then used in conjunction with traditional Gabor based algorithms for fingerprint enhancement. Orientation fields extracted by gradient-based methods are local, and do not consider neighboring orientations. If some amount of noise is present in a fingerprint, then these methods perform poorly when enhancing the image, affecting fingerprint matching. This paper presents a method to correct the resulting noisy regions over patches of the fingerprint by training two continuous restricted Boltzmann machines. The continuous RBMs are trained with clean fingerprint images and applied to overlapping patches of the input fingerprint. Experimental results show that one can successfully restore patches of noisy fingerprint images.

Abstract

Binarization of text in natural scene images is a challenging task due to the variations in color, size, and font of the text and the results are often affected by complex back-grounds, different lighting conditions, shadows and reflections.A robust solution to this problem can significantly enhance the accuracy of scene text recognition algorithms leading to a variety of applications such as scene understanding, automatic localization and navigation, and image retrieval. In this paper,we propose a method to extract and binarize text from images that contains complex background. We use an Independent Component Analysis (ICA) based technique to map out the text region, which is inherently uniform in nature, while removing shadows, specularity and reflections, which are included in the background. The technique identifies the text regions from the components extracted by ICA using a global thresholding method to isolate the foreground text. We show the results of our algorithm on some of the most complex word images from the ICDAR 2003 Robust Word Recognition Dataset and compare with previously reported methods