A post quantum secure construction of an authentication protocol for satellite communication

Satellite's communication system is used to communicate under significant distance and circumstances where the other communication systems are not comfortable. Since all the data are exchanged over a public channel, so the security of the data is an essential component for the communicating parties. Both key exchange and authentication are two cryptographic tools to establish a secure communication between two parties. Currently, various kinds of authentication protocols are available to establish a secure network, but all of them depend on number–theoretical (discrete logarithm problem/factorization assumption) hard assumptions. Due to Shor's and Grover's computing algorithm number theoretic assumptions are breakable by quantum computers. Although Kumar and Garg have proposed a quantum attack-resistant protocol for satellite communication, it cannot resist stolen smart card attack. We have analyzed that how Kumar and Garg is vulnerable to the stolen smart card attack using differential power analysis attack described in He et al and Chen and Chen. We have also analyzed the modified version of signal leakage attack and sometimes called improved signal leakage attack on Kumar and Garg's protocol. We have tried to construct a secure and efficient authentication protocol for satellites communication that is secure against quantum computing. This is more efficient as it requires only three messages of exchange. This paper includes security proof and performance of the proposed authentication and key agreement protocol.     

Electrohydrodynamic‐Induced SERS Immunoassay for Extensive Multiplexed Biomarker Sensing

Cancer diagnosis and patient monitoring require sensitive and simultaneous measurement of multiple cancer biomarkers considering that single biomarker analysis present inadequate information on the underlying biological transformations. Thus, development of sensitive and selective assays for multiple biomarker detection might improve clinical diagnosis and expedite the treatment process. Herein, a microfluidic platform for the rapid, sensitive, and parallel detection of multiple cancer‐specific protein biomarkers from complex biological samples is presented. This approach utilizes alternating current electrohydrodynamic‐induced surface shear forces that provide exquisite control over fluid flow thereby enhancing target–sensor interactions and minimizing non‐specific binding. Further, the use of surface‐enhanced Raman scattering‐based spectral encoding with individual barcodes for different targets enables specific and simultaneous detection of captured protein biomarkers. Using this approach, the specific and sensitive detection of clinically relevant biomarkers including human epidermal growth factor receptor 2 (HER2); Mucin 1, cell surface associated (MUC1); epidermal growth factor receptor; and Mucin 16, cell surface associated (MUC16) at concentrations as low as 10 fg mL^?1 in patient serum is demonstrated. Successful target detection from patient samples further demonstrates the potential of this current approach for the clinical diagnosis, which envisages a clinical translation for a rapid and sensitive appraisal of clinical samples in cancer diagnostics.     

Electrochemical capacitance of nanostructured ruthenium-doped tin oxide Sn<Subscript>1–<Emphasis Type="Italic">x</Emphasis></Subscript>Ru<Subscript><Emphasis Type="Italic">x</Emphasis></Subscript>O<Subscript>2</Subscript> by the microemulsion method

Synthesis of nanostructured Ru-doped SnO₂ was successfully carried out using the reverse microemulsion method. The phase purity and the crystallite size were analyzed by XRD. The surface morphology and the microstructure of synthesized nanoparticles were analyzed by SEM and TEM. The vibration mode of nanoparticles was investigated using FTIR and Raman studies. The electrochemical behavior of the Ru-doped SnO₂ electrode was evaluated in a 0.1 mol/L Na₂SO₄ solution using cyclic voltammetry. The 5% Ru-doped SnO₂ electrode exhibited a high specific capacitance of 535.6 F/g at a scan rate 20 mV/s, possessing good conductivity as well as the electrocycling stability. The Ru-doped SnO₂ composite shows excellent electrochemical properties, suggesting that this composite is a promising material for supercapacitors.     

Crawl through Neighbors: A Simple Curve Reconstruction Algorithm

Given a planar point set sampled from an object boundary, the process of approximating the original shape is called curve reconstruction. In this paper, a novel non‐parametric curve reconstruction algorithm based on Delaunay triangulation has been proposed and it has been theoretically proved that the proposed method reconstructs the original curve under ε‐sampling. Starting from an initial Delaunay seed edge, the algorithm proceeds by finding an appropriate neighbouring point and adding an edge between them. Experimental results show that the proposed algorithm is capable of reconstructing curves with different features like sharp corners, outliers, multiple objects, objects with holes, etc. The proposed method also works for open curves. Based on a study by a few users, the paper also discusses an application of the proposed algorithm for reconstructing hand drawn skip stroke sketches, which will be useful in various sketch based interfaces.     

Adaptive simulation for system reliability analysis of large structures

This article proposes an efficient simulation-based methodology to estimate the system reliability of large structures. The proposed method uses a hybrid approach: first, a probabilistic enumeration technique is used to identify a significant system failure sequence. This provides an initial sampling domain for an adaptive importance sampling procedure. As further simulations are performed, information about other significant sequences is incorporated to refine the sampling domain and to estimate the failure probability of the system. The adaptive sampling overcomes the restrictive assumptions of analytical techniques, yet achieves the robustness and accuracy of basic Monte Carlo simulation in an efficient manner. In this article, the proposed method is implemented using the ANSYS finite element software, and is applied to the system reliability estimation of two redundant structural systems, a six-story building frame and a transmission tower. Both ductile and brittle failure modes are considered. The method is structured in a modular form such that it can be easily applied to different types of problems and commercial software, thus facilitating practical application.     

An Indo-Soviet experiment on anmhd generator test section at the Soviet U-02 facility

This paper summarizes the major results of the joint Indo-Soviet experiment for testing the Indianmhd generator channel section, designed and fabricated at the Bhabha Atomic Research Centre, Bombay, which was carried out at the U-02 facility in Moscow, USSR, in May 1980. The total test duration was 65 hours and included electrophysical tests and life tests under applied electric fields. The main purpose of the tests was to substantiate the physical concepts, computer codes, design features and special processing techniques involved in the development ofmhd generators for the Indian pilot plant at Tiruchirapalli. The experimental observations on the phenomena of heat transfer to the walls, gas dynamics in the channel, electrical characteristics of the generator and near-electrode processes including the analysis of arc spots correlate with the theoretical estimates based on present understanding of the physical processes occuring in similarmhd generators. The post-operational inspection of the channel section and extensive investigation of materials through microscopic analysis, chemical analysis and x-ray analysis are also reported in this paper. The joint test programme has clearly demonstrated the definite operating capability of the test section and has given sufficient information and encouragement for building better and improved channels for the future.     

Validation of models with multivariate output

This paper develops metrics for validating computational models with experimental data, considering uncertainties in both. A computational model may generate multiple response quantities and the validation experiment might yield corresponding measured values. Alternatively, a single response quantity may be predicted and observed at different spatial and temporal points. Model validation in such cases involves comparison of multiple correlated quantities. Multiple univariate comparisons may give conflicting inferences. Therefore, aggregate validation metrics are developed in this paper. Both classical and Bayesian hypothesis testing are investigated for this purpose, using multivariate analysis. Since, commonly used statistical significance tests are based on normality assumptions, appropriate transformations are investigated in the case of non-normal data. The methodology is implemented to validate an empirical model for energy dissipation in lap joints under dynamic loading.     

Understanding sources of user variability in computer-based data entry performance

The pervasive use of computers in work settings implies that an increased number of workers, with varying levels of skills and abilities, will be performing computerbased tasks. This study investigated the impact of age, cognitive abilities, and computer experience on the performance of a real world data entry task. One hundred and ten subjects, ranging in age from 20 - 75 years, performed the task for nine hours following task training. The results indicated that abilities such as visuo-spatial skills, motor skills and processing speed had a significant impact on performance as did age and prior computer experience. With respect to age, the older participants completed less work than the younger and middle-aged subjects. Age differences in psychomotor skills and processing speed appeared to be important factors underlying age effects. In fact, the data indicated that after controlling for differences in these abilities age was no longer a significant predictor of work output. Further, after controlling for differences in work output the older people made fewer errors than the younger people. Overall the data suggest that older people will be at a disadvantage in the performance of computer-based data entry work to the extent to which speed of responding is emphasized. However, if speed of responding is not a critical element of performance they will be able to achieve comparable levels of performance to that of younger people.     

Chitosan Schiff base as effective corrosion inhibitor for mild steel in acid medium

An environment friendly inhibitor, chitosan thiophene carboxaldehyde Schiff base, was synthesized by a condensation reaction of the carbonyl group of thiophene 2‐carboxaldehyde and free amino groups of chitosan. The chitosan Schiff base was characterized by UV spectroscopy, Fourier transform IR spectroscopy, elemental analysis and thermal analysis. The surface morphology of the Schiff base derivative was examined by SEM. Gravimetric and electrochemical techniques were used to explore the behaviour of the chitosan thiophene derivative as a corrosion inhibitor for mild steel in an acidic environment. The effects of inhibitor concentration, exposure time and temperature were investigated. The chitosan Schiff base showed a good inhibition performance of 92% inhibition efficiency at room temperature for 12 h of immersion in a weight loss experiment. The electrochemical results showed that the chitosan derivative acts as an effective mixed type inhibitor. The adsorption of the inhibitor followed the Temkin isotherm model. SEM and AFM techniques were used to characterize the protective layer formed on the mild steel substrate. © 2016 Society of Chemical Industry     

A support vector regression approach to detection in large-MIMO systems

We propose a support vector regression approach for symbol detection in large-MIMO systems employing spatial multiplexing. We explore the applicability of machine learning algorithms, in particular support vector machines, to address one of the recent research problem in communications.The machine learning capability is exploited to achieve fast detection in large dimension systems. The performance of the proposed method is compared with lattice reduction aided detection which is currently the popular choice and the improvement in terms of bit error rate is demonstrated. The sparse formulation of the problem matrix reduces the computational complexity and enables faster detection. The proposed detection algorithm is tailored to address both uncorrelated and correlated channel conditions as well.