Co skewness

This report presents the analysis of different modulation schemes D4.1 for VLC systems of the VIDAS project. Considering the final prototype design and application, the deliverable D4.1 was projected. The detail analysis of various modulation schemes are carried out and a robust technique based on direct sequence spread spectrum (DSSS) is followed. DSSS technique though necessitates use of high bandwidth while minimizing the effect of noise. Since the final application does not require very high data rate of transmission but robustness against the noise (external lights) becomes necessary. The analysis is followed by model development using Matlab/Simulink. The performance of both of these systems are compared and evaluated. Some of the simulation results are presented.

This paper presents a high-level clock distribution strategy for usage in a design-and-reuse environment. This strategy allows for controlled clock distribution across an arbitrary number of blocks through the usage of controlled delay lines. A new clock frequency multiplication structure optimised for this clock distribution strategy is finally proposed, since multifrequency clock support is highly desired. 1 This work has been sponsored partially by project Genclock, funded by Praxis XXI.

Many faculty members, as well as students, in the area of educational research methodology, sometimes have a need for generating data to use for simulation and computation purposes, demonstration of multivariate analysis techniques, or construction of student projects or assignments. As a great teaching tool, using simulated data helps us understand the intricacies of statistical concepts and techniques. The process of generating multivariate normal data is a nontrivial process and practical guides without dense mathematics are limited in the literature (Nissen and Saft, 2014). Hence, the purpose of this paper is to offer researchers a practical guide for and a quick access to generating multivariate random data with a given mean and variance-covariance structure. A detailed outline of simulating multivariate normal data with a given mean and variancecovariance matrix using Eigen (or spectral) and Cholesky decompositions is presented and implemented in statistical computing platform R version 3.4.4 (R Core Team, 2018).

Boron concentrations and isotopic compositions of atmospheric dust and dissolved depositions were monitored over a two-year period (2012-2013) in the forest ecosystem of Montiers (Northeastern France). This time series allows the determination of the boron atmospheric inputs to this forest ecosystem and contributes to refine our understanding of the sources and processes that control the boron atmospheric cycle. Mean annual dust and dissolved boron atmospheric depositions are comparable in size (13 g·ha·yrand 16 g·ha·yr, respectively), which however show significant intra- and interannual variations. Boron isotopes in dust differ from dissolved inputs, with an annual mean value of +1 ‰ and +18 ‰ for, respectively. The notable high boron contents (190-390 μg·g) of the dust samples are interpreted as resulting from localized spreading of boron-rich fertilizers, thus indicating a significant local impact of regional agricultural activities. Boron isotopes in dissolved depositions show ...

— In our thesis Virtualization technology to allocate data center resources dynamically based on application demands and support green computing by optimizing the number of servers in use. We introduce the concept of " skewness " to measure the unevenness in the multidimensional resource utilization of a server. By minimizing skewness, we can combine different types of workloads nicely and improve the overall utilization of server resources. Resource allocation process must be scalable both in the number of machines in the cloud and the number of sites that the cloud hosts. Green scheduling can determine which server to be in running state. Control a different set of technique is used to predict non-stationary workloads of the system. We develop a set of heuristics that prevent overload in the system effectively while saving energy used. Trace driven simulation and experiment results demonstrate that our algorithm achieves good performance.

This paper introduces a method for simulating multivariate samples that have exact means, covariances, skewness and kurtosis. We introduce a new class of rectangular orthogonal matrix which is fundamental to the methodology and we call these matrices L matrices. They may be deterministic, parametric or data specific in nature. The target moments determine the L matrix then infinitely many random samples with the exact same moments may be generated by multiplying the L matrix by arbitrary random orthogonal matrices. This methodology is thus termed "ROM simulation". Considering certain elementary types of random orthogonal matrices we demonstrate that they generate samples with different characteristics. ROM simulation has applications to many problems that are resolved using standard Monte Carlo methods. But no parametric assumptions are required (unless parametric L matrices are used) so there is no sampling error caused by the discrete approximation of a continuous distribution, which is a major source of error in standard Monte Carlo simulations. For illustration, we apply ROM simulation to determine the value-at-risk of a stock portfolio.

In this paper we propose a general spectral theory for tensors. Our proposed factorization decomposes a tensor into a product of orthogonal and scaling tensors. At the same time, our factorization yields an expansion of a tensor as a summation of outer products of lower order tensors . Our proposed factorization shows the relationship between the eigen-objects and the generalised

In this paper we propose a generalized spectral theory for tensors. Our proposed factoriza-tion decomposes a tensor into a product of orthogonal and diagonal tensors. At the same time, our factorization offers an expansion of a tensor as a summation of lower ...

In this paper we propose a general spectral theory for tensors. Our proposed factorization decomposes a tensor into a product of orthogonal and scaling tensors. At the same time, our factorization yields an expansion of a tensor as a summation of outer products of lower order tensors. Our proposed factorization shows the relationship between the eigen-objects and the generalised characteristic polynomials. Our framework is based on a consistent multilinear algebra which explains how to generalise the notion of matrix hermicity, matrix transpose, and most importantly the notion of orthogonality. Our proposed factorization for a tensor in terms of lower order tensors can be recursively applied so as to naturally induces a spectral hierarchy for tensors.

Di seguito si riportano le equazioni per la stima dell'evaporazione da evaporimetro di classe A r "Contributo alla stima dell'evaporazione dalle superfici liquide nell'Italia Centrale" Geologica Ro Per ottenere l'evaporazione da superfici liquide occorre moltiplicare l'evaporazione da evaporim generalmente compreso tra 0.6 e 0.8, ma che per laghi poco profondi può assumere valori mag Il calcolo dell'evaporazione da evaporimetro in questo foglio excel è stato eseguito utilizzando l che tiene conto della temperatura media mensile e degli indici di insolazione mensili di Thornth I coefficienti dell'eq. 1 utilizzati nel calcolo dell'evaporazione sono quelli ottenuti con i dati men Questi coefficienti posso ritenersi rappresentativi per l'Italia Centrale.

This paper proposes an analytical model to evaluate deskewing uncertainty, considering floorplanning and scalability issues. It can be a helpful tool in evaluating the potential gains of clock deskewing or finding the best deskewing topology for a given application, at an early design stage. Also, we show that all deskewing schemes trade static for dynamic clock uncertainty and thus, our model can be used to determine the maximum tolerable noise levels if a deskewing scheme is to be applied.

An algorithm to generate samples with approximate first-, second-, and third-order moments is presented extending
the Cholesky matrix decomposition to a Cholesky tensor decomposition of an arbitrary order. The tensor decomposition of the first-, second-, and third-order objective moments generates a non-linear system of equations. The algorithm solves these equations by numerical methods. The results show that the optimisation algorithm delivers samples with an approximate error of 0.1%–4% between the components of the objective and the sample moments. An application for sensitivity analysis of portfolio risk assessment with Value-at-Risk (VaR) is provided. A comparison with previous methods available in the literature suggests that methodology proposed reduces the error of the objective moments in the generated samples.

Cloud computing is the delivery of computing and storage capacity as a service to a
community of end users. The name “cloud computing” comes from the use of a cloud-shaped symbol as
an abstraction for the complex infrastructure it contains in system diagrams. Cloud computing entrusts
services with a user's software, data and computation over a network. End users access cloud-based
applications through a web browser or mobile application or a light-weight desktop while the business
software and user's data are stored on servers at a remote location. Proponents claim that cloud
computing environment allows enterprises to get their applications up and running faster, with
improved manageability and less maintenance, and enables IT industry to more rapidly adjust
resources to meet fluctuating and unpredictable business demand. In this paper, we present a system
that uses virtualization technology to allocate the data center resources dynamically based on the
application demands and support green computing by optimizing the number of servers in use. This
method multiplexes virtual to physical resources adaptively based on the changing demand. We use the
concept of skewness metric to combine virtual machines with different resource characteristics
appropriately so that the capacities of servers are well utilized.

In this paper we propose a general spectral theory for tensors. Our proposed factorization decomposes a tensor into a product of orthogonal and scaling tensors. At the same time, our factorization yields an expansion of a tensor as a summation of outer products of lower order tensors . Our proposed factorization shows the relationship between the eigen-objects and the generalised characteristic polynomials. Our framework is based on a consistent multilinear algebra which explains how to generalise the notion of matrix hermicity, matrix transpose, and most importantly the notion of orthogonality. Our proposed factorization for a tensor in terms of lower order tensors can be recursively applied so as to naturally induces a spectral hierarchy for tensors.