CONFIGURABLE 2k/4k/8k FFT-IFFT CORE FOR DVB-T AND DVB-H

Modulation technique uses a modifier module IFFT signal data from frequency domain to time domain. While at the demodulation part, FFT module is used to change the return signal from the output of the IFFT and converted them from the time domain into the frequency domain. FFT�IFFT modules are made to support 2k/4k/8k FFT and IFFT algorithms. FFT�IFFT 2k/4k/8k Core are built using the radix 2, radix 4 and radix 8. Core is designed to be able to receive data continuously, without buffer (temporary data container). The FFT�IFFT 2k/4k/8k module designs started with the functional description in model. Then the design of hardware architecture is made based on functional design in model. Then the architecture design will be used in making model bit precision. Furthermore the model bit precision design is used as a foundation in designing RTL. The result of FFT�IFFT modules meet the standard specified by the DVB consortium, with a maximum test frequency of FFT�IFFT 2k/4k/8k Core is 69.36 MHz using FPGA STRATIX II EP2S60-F1020C3 that surpass the requirements in the standard DVB�T/DVB�H (40 MHz). In addition, the module has a high throughput with the average of 39.82 M sym /

Penalized Clustering of Large Scale Functional Data with Multiple Covariates

In this article, we propose a penalized clustering method for large scale data with multiple covariates through a functional data approach. In the proposed method, responses and covariates are linked together through nonparametric multivariate functions (fixed effects), which have great flexibility in modeling a variety of function features, such as jump points, branching, and periodicity. Functional ANOVA is employed to further decompose multivariate functions in a reproducing kernel Hilbert space and provide associated notions of main effect and interaction. Parsimonious random effects are used to capture various correlation structures. The mixed-effect models are nested under a general mixture model, in which the heterogeneity of functional data is characterized. We propose a penalized Henderson's likelihood approach for model-fitting and design a rejection-controlled EM algorithm for the estimation. Our method selects smoothing parameters through generalized cross-validation. Furthermore, the Bayesian confidence intervals are used to measure the clustering uncertainty. Simulation studies and real-data examples are presented to investigate the empirical performance of the proposed method. Open-source code is available in the R package MFDA

Functional dependencies for XML : axiomatisation and normal form in the presence of frequencies and identifiers : a thesis presented in partial fulfilment of the requirements for the degree of Master of Sciences in Information Sciences at Massey University, Palmerston North, New Zealand

XML has gained popularity as a markup language for publishing and exchanging data on the web. Nowadays, there are also ongoing interests in using XML for representing and actually storing data. In particular, much effort has been directed towards turning XML into a real data model by improving the semantics that can be expressed about XML documents. Various works have addressed how to define different classes of integrity constraints and the development of a normalisation theory for XML. One area which received little to no attention from the research community up to five years ago is the study of functional dependencies in the context of XML [37]. Since then, there has been increasingly more research investigating functional dependencies in XML. Nevertheless, a comprehensive dependency theory and normalisation theory for XML have yet to emerge. Functional dependencies are an integral part of database theory in the relational data model (RDM). In particular, functional dependencies have been vital in the investigation of how to design "good" relational database schemas which avoid or minimise problems relating to data redundancy and data inconsistency. Since the same problems can be shown to exist in poorly designed XML schemas 1 , there is a need to investigate how these problems can be eliminated in the context of XML. We believe that the study of an analogy to relational functional dependencies in the context of XML is equally significant towards designing "good" XML schemas. [FROM INTRODUCTION

Automated analysis of quantitative image data using isomorphic functional mixed models, with application to proteomics data

Image data are increasingly encountered and are of growing importance in many areas of science. Much of these data are quantitative image data, which are characterized by intensities that represent some measurement of interest in the scanned images. The data typically consist of multiple images on the same domain and the goal of the research is to combine the quantitative information across images to make inference about populations or interventions. In this paper we present a unified analysis framework for the analysis of quantitative image data using a Bayesian functional mixed model approach. This framework is flexible enough to handle complex, irregular images with many local features, and can model the simultaneous effects of multiple factors on the image intensities and account for the correlation between images induced by the design. We introduce a general isomorphic modeling approach to fitting the functional mixed model, of which the wavelet-based functional mixed model is one special case. With suitable modeling choices, this approach leads to efficient calculations and can result in flexible modeling and adaptive smoothing of the salient features in the data. The proposed method has the following advantages: it can be run automatically, it produces inferential plots indicating which regions of the image are associated with each factor, it simultaneously considers the practical and statistical significance of findings, and it controls the false discovery rate.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS407 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Rdesign: A data dictionary with relational database design capabilities in Ada

Data Dictionary is defined to be the set of all data attributes, which describe data objects in terms of their intrinsic attributes, such as name, type, size, format and definition. It is recognized as the data base for the Information Resource Management, to facilitate understanding and communication about the relationship between systems applications and systems data usage and to help assist in achieving data independence by permitting systems applications to access data knowledge of the location or storage characteristics of the data in the system. A research and development effort to use Ada has produced a data dictionary with data base design capabilities. This project supports data specification and analysis and offers a choice of the relational, network, and hierarchical model for logical data based design. It provides a highly integrated set of analysis and design transformation tools which range from templates for data element definition, spreadsheet for defining functional dependencies, normalization, to logical design generator

Advanced single permanent magnet axipolar ironless stator ac motor for electric passenger vehicles

A program was conducted to design and develop an advanced-concept motor specifically created for propulsion of electric vehicles with increased range, reduced energy consumption, and reduced life-cycle costs in comparison with conventional systems. The motor developed is a brushless, dc, rare-earth cobalt, permanent magnet, axial air gap inductor machine that uses an ironless stator. Air cooling is inherent provided by the centrifugal-fan action of the rotor poles. An extensive design phase was conducted, which included analysis of the system performance versus the SAE J227a(D) driving cycle. A proof-of-principle model was developed and tested, and a functional model was developed and tested. Full generator-level testing was conducted on the functional model, recording electromagnetic, thermal, aerodynamic, and acoustic noise data. The machine demonstrated 20.3 kW output at 1466 rad/s and 160 dc. The novel ironless stator demonstated the capability to continuously operate at peak current. The projected system performance based on the use of a transistor inverter is 23.6 kW output power at 1466 rad/s and 83.3 percent efficiency. Design areas of concern regarding electric vehicle applications include the inherently high windage loss and rotor inertia

Fixture knowledge model development and implementation based on a functional design approach

The development of a knowledge model applied to fixture design is a complex task. The main purpose of such model is the development of a knowledge-based application to assist fixture designers. It comprises a detailed specification of the types and structures of data involved in the execution of the inference process needed to create a fixture solution for machining a raw part. A development method together with a knowledge model for automating fixture design is proposed. The development was divided into three parts: Design Process Model, definition of Top-level functional functions and Product Knowledge Model. Adopting a functional design approach, the fixture design solution was created in two levels: functional and detailed. The functional level is based on fixture functional elements and the detailed one is based on fixture commercial elements. The definitions and concepts used in the application are specified in several Units of Knowledge (UoK) that comprises the Fixture Knowledge Model. Common Knowledge Analysis and Design Structuring (CommonKADS), Methodology and software tools Oriented to KBE Applications (MOKA), Integrated DEFinition for Function Modelling (IDEF0) and Unified Modelling Language (UML) are the methodologies and techniques used in the proposed method. Finally, a prototype KBE application for fixture design was developed