International Association of Engineers

Abstract Advancement of information and communication technology (ICT) has facilitated electronic communication among its users having dispersed geographical location. This concept of electronic communication may be implemented in multivariate service sectors to cater services to end user, i.e., Citizen. India being a developing nation have to afford huge establishments and manpower to deliver services regularly to its Citizen. Due to this situation, enormous amount of recurring expenditure is mounted over the financial structure of nation. Moreover, the services delivered through conventional mode takes sufficient time to reach the intended user, particularly, in case of distantly located user. To solve these issues, electronic mode of message communication may be adopted to provide electronic services in timely and budget-friendly manner. The only concern of this approach is security of sensitive information which is transmitted through public communication channel, i.e., Internet. To resolve this issue, hybrid cryptographic security protocols should be used to ensure privacy, integrity, non-repudiation and authentication (PINA) during its implementation in real-world scenario. Furthermore, to provide an user friendly system, a Citizen-centric single window interface have been modeled. As the primary objective of this paper, authors will extend it further to deliver cloud healthcare facilities to Citizen through the proposed single window interface, i.e., multipurpose electronic card (MEC). To simulate the real-world implementation, in this paper, authors have performed object-oriented modeling (OOM) of proposed cloud healthcare system.KeywordsCloud computingCloud healthcareObject-oriented modeling (OOM)

Hardware interfacing system for visual C++ plays an important role in co-design of the embedded computer system.It links the software part and the hardware part of the system.The design process supports software interface implementation and hardware interface synthesis.This paper shows the hardware and software interfaces can be implemented by using low cost laboratory apparatus in embedded computer system, which includes the primitive interface, the synchronous interface and the data communication protocol between the hardware and the software.

In this paper we analyse the role of some of the building blocks in SHA-256. We show that the disturbance correction strategy is applicable to the SHA-256 architecture and we prove that functions $\Sigma$, $\sigma$ are vital for the security of SHA-256 by showing that for a variant without them it is possible to find collisions with complexity 2 64 hash operations. As a step towards an analysis of the full function, we present the results of our experiments on Hamming weights of expanded messages for different variants of the message expansion and show that there exist low-weight expanded messages for XOR-linearised variants.

Concatenation is a method of building long codes out of shorter ones, it attempts to meet the problem of decoding complexity by breaking the required computation into manageable segments. Concatenated Continuous Phase Frequency Shift Keying (CPFSK) facilitates powerful error correction. CPFSK also has the advantage of being bandwidth efficient and compatible with non-linear amplifiers. Bandwidth efficient concatenated coded modulation schemes were designed for communication over Additive White Gaussian noise (AWGN), and Rayleigh fading channels. An analytical bounds on the performance of serial concatenated convolutional codes (SCCC), and parallel concatenated convolutionalcodes (PCCC), were derived as a base of comparison with the third category known as hybrid concatenated trellis codes scheme (HCTC). An upper bound to the average maximum-likelihood bit error probability of the three schemes were obtained. Design rules for the parallel, outer, and inner codes that maximize the interleaver's gain were discussed. Finally, a low complexity iterative decoding algorithm that yields a better performance is proposed.

Applied Time Series Analysis and Innovative Computing contains the applied time series analysis and innovative computing paradigms, with frontier application studies for the time series problems based on the recent works at the Oxford University Computing Laboratory, University of Oxford, the University of Hong Kong, and the Chinese University of Hong Kong. The monograph was drafted when the author was a post-doctoral fellow in Harvard School of Engineering and Applied Sciences, Harvard University. It provides a systematic introduction to the use of innovative computing paradigms as an investigative tool for applications in time series analysis. Applied Time Series Analysis and Innovative Computing offers the state of art of tremendous advances in applied time series analysis and innovative computing paradigms and also serves as an excellent reference work for researchers and graduate students working on applied time series analysis and innovative computing paradigms.

Abstract There are many reasons to analyze the time series data, for example, to understand the underlying generating mechanism better, to achieve optimal control of the system, or to obtain better forecasting of future values. Applied time series analysis consists of empirical models for analyzing time series in order to extract meaningful statistics and other properties of the time series data. Time series models have various forms and represent different stochastic processes. Time series analysis model is usually classified as either time domain model or frequency domain model. Time domain models include the auto-correlation and cross-correlation analysis. In a time domain model, mathematical functions are usually used to study the data with respect to time. The three broad classes for modeling the variations of time series process are the autoregressive models, the integrated models, and the moving average models. The autoregressive integrated moving average models are the general class of these models for forecasting a time series that can be stationarized by transformations such as differencing. In a frequency domain model, the analysis of mathematical functions or signals is conducted with respect to frequency rather than time. Mathematical models can be used to convert the time series data between the time and frequency domains. The parameters and features in the frequency domain can be used as inputs for the mathematical models like discrimination analysis and improved results can be obtained.KeywordsTime series analysisBasic characteristicsEstimation of correlationAutoregressionMathematical models

The largest and the deepest super-structure known today is the Shapley super-cluster. This is the sky area with the highest over-density of galaxy clusters and therefore also an ideal region to test the effects of a high density environment on galaxies and on clusters. We performed an X-ray survey of a wide region surrounding the Shapley super-structure. Additionally to previously known super-cluster X-ray members, we identified diffuse X-ray emission from 35 cluster candidates without previous X-ray detection. 21 of them were previously known, optically selected super-cluster members, while the other candidates had not been previously detected in any wavelength range. Optical follow-up observations revealed that at least four of these new candidates also have optical cluster counterparts. The super-cluster shows a slightly flattened and elongated morphology. Clusters outside the central dense core are preferentially located in four perpendicular filaments in a similar way to what is seen in simulations of Large Scale Structure. We measure the cluster number density in the region to be more than one order of magnitude higher than the mean density of rich Abell clusters previously observed at similar Galactic latitudes; this over-density, in the super-cluster outskirts, is mainly due to an excess of low X-ray luminous clusters (with respect to an average population), which leads us to think that the whole region is still accreting low luminosity, small objects from the outskirts. Pushing our total X-ray mass estimate to fainter clusters would drastically increase the total super-cluster mass measure, because of the presence of the rich X-ray low luminosity population.