A fog-based security framework for intelligent traffic light control system

Multimedia Tools and Applications - The world is facing many problems including that of traffic congestion. To highlight the issue of traffic congestion worldwide specially in urban areas and to...     

Algebraic properties of cryptosystem PGM

In the late 1970s Magliveras invented a private-key cryptographic system calledPermutation Group Mappings (PGM). PGM is based on the prolific existence of certain kinds of factorization sets, calledlogarithmic signatures, for finite permutation groups. PGM is an endomorphic system with message space ℤ_|G| for a given finite permutation groupG. In this paper we prove several algebraic properties of PGM. We show that the set of PGM transformations ℐ _G is not closed under functional composition and hence not a group. This set is 2-transitive on ℤ_|G| if the underlying groupG is not hamiltonian and not abelian. Moreover, if the order ofG is not a power of 2, then the set of transformations contains an odd permutation. An important consequence of these results is that the group generated by the set of transformations is nearly always the symmetric group ℒ_|G|. Thus, allowing multiple encryption, any permutation of the message space is attainable. This property is one of the strongest security conditions that can be offered by a private-key encryption system. S. S. Magliveras was supported in part by NSF/NSA Grant Number MDA904-82-H0001, by U.S. West Communications, and by the Center for Communication and Information Science of the University of Nebraska.     

Characterization of acoustic signals due to surface discharges on H.V. glass insulators using wavelet radial basis function neural networks

A hybrid model incorporating wavelet and radial basis function neural network is presented which is used to detect, identify and characterize the acoustic signals due to surface discharge activity and hence differentiate abnormal operating conditions from the normal ones. The tests were carried out on cleaned and polluted high voltage glass insulators by using surface tracking and erosion test procedure of international electrotechnical commission 60587. A laboratory experiment was conducted by preparing the prototypes of the discharges. This study suggests a feature extraction and classification algorithm for surface discharge classification, which when combined together reduced the dimensionality of the feature space to a manageable dimension, by “marrying” the wavelet to radial basis function neural network very high levels of classification are achieved. Wavelet signal treatment toolbox is used to recover the surface discharge acoustic signals by eliminating the noisy portion and to reduce the dimension of the feature input vector. A radial basis function neural network classifier was used to classify the surface discharge and assess the suitability of this feature vector in classification. This learning method is proved to be effective by applying the wavelet radial basis function neural network in the classification of surface discharge fault data set. The test results show that the proposed approach is efficient and reliable.     

Investigations on the adhesion and interfacial properties of polyurethane foam/thermoplastic materials

The adhesion and interfacial properties of polyurethane (PU) foams with thermoplastic (TP) materials were investigated using different techniques. The adhesion performance of PU foam with TP materials was evaluated using the peel test method, and the adhesion durability was checked after different climate treatments. X‐ray photoelectron spectroscopy (XPS), atomic force microscopy (AFM), and contact angle measurements were used to study the surface and interface morphology of PU foam and TP material system. Three types of PU foam samples which differ in their composition and also five commercially available TP blends systems, based on poly(carbonate), poly(styrene‐co‐maleic anhydride), poly(acrylonitrile‐butadiene‐styrene), and silicone acrylate rubber have been used. The slow reacting foam shows the best adhesion properties with all the TP materials. The climate treatments strongly effected the PU foam adhesion durability with poly(carbonate) containing TP materials (70–80% loss in adhesion), but nearly no effect with poly(styrene‐co‐maleic anhydride). The samples with lowered adhesion could be separated by peeling without visible foam residues on the TP surface. AFM, XPS, and surface tension studies have shown that the surface properties of the TP material are still governed by the PU foam. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 104: 479–488, 2007     

Fuzzy triangulation signature for detection of change in human emotion from face video image sequence

The present article proposes a geometry-based fuzzy relational technique for capturing gradual change in human emotion over time available from relevant face image sequences. As associated features, we make use of fuzzy membership arising out of five triangle signatures such as - (i) Fuzzy Isosceles Triangle Signature (FIS), (ii) Fuzzy Right Triangle Signature (FRS), (iii) Fuzzy Right Isosceles Triangle Signature (FIRS), (iv) Fuzzy Equilateral Triangle Signature (FES), and (v) Other Fuzzy Triangles Signature (OFS) to achieve the task of appropriate classification of facial transition from neutrality to one among the six expressions viz. anger (AN), disgust (DI), fear (FE), happiness (HA), sadness (SA) and surprise (SU). The effectiveness of the Multilayer Perceptron (MLP) classifier is tested and validated through 10 fold cross-validation method on three benchmark image sequence datasets namely Extended Cohn-Kanade (CK+), M&M Initiative (MMI), and Multimedia Understanding Group (MUG). Experimental outcomes are found to have achieved accuracy to the tune of 98.47%, 93.56%, and 99.25% on CK+, MMI, and MUG respectively vindicating the effectiveness by exhibiting the superiority of our proposed technique in comparison to other state-of-the-art methods in this regard.

     

A Study of Web Services Projects in the Financial Services Industry

This study examines the significance of technological, methodological, and business factors in contributing to the success of initial Web Services projects. Focusing on four case studies from the financial services sector, the authors' findings suggest that a strong focus on business factors is associated with successful Web Services strategies.     

Deep features-based speech emotion recognition for smart affective services

Emotion recognition from speech signals is an interesting research with several applications like smart healthcare, autonomous voice response systems, assessing situational seriousness by caller affective state analysis in emergency centers, and other smart affective services. In this paper, we present a study of speech emotion recognition based on the features extracted from spectrograms using a deep convolutional neural network (CNN) with rectangular kernels. Typically, CNNs have square shaped kernels and pooling operators at various layers, which are suited for 2D image data. However, in case of spectrograms, the information is encoded in a slightly different manner. Time is represented along the x-axis and y-axis shows frequency of the speech signal, whereas, the amplitude is indicated by the intensity value in the spectrogram at a particular position. To analyze speech through spectrograms, we propose rectangular kernels of varying shapes and sizes, along with max pooling in rectangular neighborhoods, to extract discriminative features. The proposed scheme effectively learns discriminative features from speech spectrograms and performs better than many state-of-the-art techniques when evaluated its performance on Emo-DB and Korean speech dataset.

     

Assessment of organic acid-rich bio-sap to generate electricity

The study has focused on electricity generation from organic acid-rich bio-substrate like star fruit (Averrhoa carambola). The sap of star fruit was selected as an electrolyte due to the presence of significant amounts of organic acids such as citric acid and ascorbic acid. To preserve the sap, 2% phenol by volume was used to reduce the growth of microorganisms, and the addition of phenol did not affect the initial pH. It was observed that due to an increase in the electrode surface area, reaction rate and current generation had been amplified. Internal resistance also decreased rapidly because of the large electrode surface area. Furthermore, internal resistance was the significant barrier in electricity generation, which was also successfully controlled by the baffle flow agitation system. Moreover, the baffle flow agitation system reduces the formation of dead zones and increases the total dissolved solids inside the electrochemical cell compartments during operation.     

An empirical study of software release notes

Release notes are an important source of information about a new software release. Such notes contain information regarding what is new, changed, and/or got fixed in a release. Despite the importance of release notes, they are rarely explored in the research literature. Little is known about the contained information, e.g., contents and structure, in release notes. To better understand the types of contained information in release notes, we manually analyzed 85 release notes across 15 different software systems. In our manual analysis, we identify six different types of information (e.g., caveats and addressed issues) that are contained in release notes. Addressed issues refer to new features, bugs, and improvements that were integrated in that particular release. We observe that most release notes list only a selected number of addressed issues (i.e., 6-26 % of all addressed issues in a release). We investigated nine different factors (e.g., issue priority and type) to better understand the likelihood of an issue being listed in release notes. The investigation is conducted on eight release notes of three software systems using four machine learning techniques. Results show that certain factors, e.g., issue type, have higher influence on the likelihood of an issue to be listed in release notes. We use machine learning techniques to automatically suggest the issues to be listed in release notes. Our results show that issues listed in all release notes can be automatically determined with an average precision of 84 % and an average recall of 90 %. To train and build the classification models, we also explored three scenarios: (a) having the user label some issues for a release and automatically suggest the remaining issues for that particular release, (b) using the previous release notes for the same software system, and (c) using prior releases for the current software system and the rest of the studied software systems. Our results show that the content of release notes vary between software systems and across the versions of the same software system. Nevertheless, automated techniques can provide reasonable support to the writers of such notes with little training data. Our study provides developers with empirically-supported advice about release notes instead of simply relying on adhoc advice from on-line inquiries.     

Orientation-Dependent Developments in Misorientation and Residual Stress in Rolled Aluminum: The Defining Role of Dislocation Interactions

Orientation-dependent developments in misorientation and residual stress, in rolled aluminum, were quantified experimentally and simulated numerically. The latter involved analysis using a crystal plasticity finite element model, accounting for anisotropies in slip system hardening but neglecting near-neighbor interactions, and discrete dislocation dynamics of the single crystals. Both were successful in capturing the experimental patterns of orientation dependence. Numerical simulations, without slip transfer across the neighboring grains, thus established the defining role of dislocation interactions in establishing orientation-sensitive microstructural evolution.