Design of a user anonymous password authentication scheme without smart card

Recently, Jiang et al. and He et al. independently found security problems in Chen et al.'s remote user authentication scheme for non‐tamper‐proof storage devices like Universal Serial Bus stick and proposed improvements. Nonetheless, we detect that the schemes proposed by Jiang et al. and He et al. overlook a user's privacy. We also observe that Jiang et al.'s scheme is vulnerable to insider attack and denial of service attacks and lacks forward secrecy. We point out that the password changing facility in He et al.'s scheme is equivalent to undergoing registration, whereas in Jiang et al.'s scheme, it is unsuitable. Moreover, the login phase of both the schemes is incapable to prevent the use of wrong password leading to the computation of an unworkable login request. Therefore, we design a new scheme with user anonymity to surmount the identified weaknesses. Without adding much in communication/computational cost, our scheme provides more security characteristics and keeps the merits of the original schemes. As compared with its predecessor schemes, the proposed scheme stands out as a more apt user authentication method for common storage devices. We have also presented a formal proof of security of the proposed scheme based on the logic proposed by Burrows, Abadi and Needham (BAN logic). Copyright © 2014 John Wiley & Sons, Ltd.     

Characterization and selection of suitable grades of lactose as functional fillers for capsule filling: part 1

The purpose of this work is to characterize thermal, physical and mechanical properties of different grades of lactose and better understand the relationships between these properties and capsule filling performance. Eight grades of commercially available lactose were evaluated: Pharmatose 110?M, 125?M, 150?M, 200?M, 350?M (α-lactose monohydrate), AL (anhydrous lactose containing ～80% β-AL), DCL11 (spray dried α-lactose monohydrate containing ～15% amorphous lactose) and DCL15 (granulated α-lactose monohydrate containing ～12% β-AL). In this study, different lactose grades were characterized by thermal, solid state, physical and mechanical properties and later evaluated using principal component analysis (PCA) to assess the inter-relationships among some of these properties. The lactose grades were characterized by differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), X-ray diffraction (XRD), moisture sorption/desorption isotherms, particle size distribution; the flow was characterized by Carr Index (CI), critical orifice diameter (COD) and angle of friction. Plug mechanical strength was estimated from its diametric crushing strength. The first and second principal components (PC) captured 47.6% and 27.4% of variation in the physical and mechanical property data, respectively. The PCA plot grouped together 110?M, AL, DCL11 and DCL15 on the one side of plot which possessed superior properties for capsule formulation and these grades were selected for future formulation development studies (part II of this work).     

Systematic skin segmentation: merging spatial and non-spatial data

Skin detection is used in applications ranging from face detection, tracking of body parts, hand gesture analysis, to retrieval and blocking objectionable content. We present a systematic approach for robust skin segmentation using graph cuts. The skin segmentation process starts by exploiting the local skin information of detected faces. The detected faces are used as foreground seeds for calculating the foreground weights of the graph. If local skin information is not available, we opt for the universal seed. To increase the robustness, the decision tree based classifier is used to augment the universal seed weights when no local information is available in the image. With this setup, we achieve robust skin segmentation, outperforming off-line trained classifiers. The setup also provides a generic skin detection system, using positive training data only. With face detection, we take advantage of the contextual information present in the scene. With the weight augmentation, we provide a setup for merging spatial and non-spatial data. Experiments on two datasets with annotated pixel-level ground truth show that the systematic skin segmentation approach outperforms other approaches and provides robust skin detection.     

Automated detection of exudates and macula for grading of diabetic macular edema

Medical systems based on state of the art image processing and pattern recognition techniques are very common now a day. These systems are of prime interest to provide basic health care facilities to patients and support to doctors. Diabetic macular edema is one of the retinal abnormalities in which diabetic patient suffers from severe vision loss due to affected macula. It affects the central vision of the person and causes total blindness in severe cases. In this article, we propose an intelligent system for detection and grading of macular edema to assist the ophthalmologists in early and automated detection of the disease. The proposed system consists of a novel method for accurate detection of macula using a detailed feature set and Gaussian mixtures model based classifier. We also present a new hybrid classifier as an ensemble of Gaussian mixture model and support vector machine for improved exudate detection even in the presence of other bright lesions which eventually leads to reliable classification of input retinal image in different stages of macular edema. The statistical analysis and comparative evaluation of proposed system with existing methods are performed on publicly available standard retinal image databases. The proposed system has achieved average value of 97.3%, 95.9% and 96.8% for sensitivity, specificity and accuracy respectively on both databases.