Decomposition Architecture Research Articles

An automated approach for noise identification to assist software architecture recovery techniques

Software systems’ concrete architecture often drifts from the intended architecture throughout their evolution. Program comprehension activities, like software architecture recovery, become very demanding, especially for large and complex systems due to the existence of noise, which is created by omnipresent and utility classes that obscure the system structure. Omnipresent classes represent crosscutting concerns, utilities or elementary domain concepts. The identification and filtering of noise is a necessary preprocessing step before attempting program comprehension techniques, especially for undocumented systems. In this paper, we propose an automated methodology for noise identification. Our methodology is based on the notion that noisy classes are widely used in a system, directly or indirectly. We combine classes’ usage significance with their participation in the system’s subgraphs, in order to identify the classes that are persistently used. Usage significance is measured according to Component Rank, a well-established metric in the literature, which ranks software artifacts according to their usage significance. The experimental results show that the proposed methodology successfully captures classes that produce noise and improves the results of existing algorithms for software systems’ architectural decomposition.

Processing algorithms for three‐dimensional data compression of ultrasonic radio frequency signals

Ultrasonic systems are widely used in imaging applications for non-destructive evaluation, quality assurance and medical diagnosis. These applications require large volumes of data to be processed, stored and/or transmitted in real-time. Therefore it is essential to compress the acquired ultrasonic radio frequency (RF) signal without inadvertently degrading desirable signal features. In this paper, two algorithms for ultrasonic signal compression are analysed based on: sub-band elimination using discrete wavelet transform; and decimation/interpolation using time-shift property of Fourier transform. Both algorithms offer high signal reconstruction quality with a peak signal-to-noise ratio (PSNR) between 36 to 39 dB for minimum 80% compression. The computational loads and signal reconstruction quality are examined in order to determine the best compression method in terms of the choice of DWT kernel, sub-band decomposition architecture and computational efficiency. Furthermore, for compressing a large amount of volumetric information, three-dimensional (3D) compression algorithms are designed by utilising the temporal and spatial correlation properties of the ultrasonic RF signals. The performance analysis indicates that the 3D compression algorithm presented in this paper offers an overall 3D compression ratio of 95% with a minimum PSNR of 27 dB.

Distributed Particle Swarm Optimization Based on Primal-Dual Decomposition Architectures

Distributed Particle Swarm Optimization Based on Primal-Dual Decomposition Architectures

Differential geometry tools for multidisciplinary design optimization, part II: application to QSD

Having previously developed a differential geometry framework for analyzing and conceptualizing Multidisciplinary Design Optimization (MDO) problems and methods, we now apply that framework to consider the Quasi-Separable Decomposition (QSD) architecture. Based on our theoretical investigations, we predict that QSD will fail to return feasible designs for MDO problems. In the same vein, we analyze the Individual Discipline Feasible (IDF) architecture, predict that IDF will converge to feasible designs, and propose a modified version of QSD which we believe will also output feasible design points. To test these predictions, we run all three architectures on a well-known analytical MDO problem. Our predictions regarding feasibility prove to be accurate: QSD does not return any feasible points, whereas all of the final design points from IDF and the modified QSD are feasible. Now that convergence to feasibility has been established, the next step is to investigate the optimization performance of various QSD modifications.

MIMO 검출기에 적용 가능한 저 복잡도 복합 QR 분해 구조

본 논문에서는 MIMO 검출기를 위한 저 복잡도 QR 분해 구조를 제시한다. 제안된 접근 방식에서는, QRD 하드웨어의 연산 복잡도를 감소시키기 위해 다양한 코딕 기반 QRD 알고리즘들이 효율적으로 조합된다. 다양한 QRD 알고리즘들에 대한 연산 복잡도 분석에 기초하여, QRD 과정의 매 단계마다 저 복잡도 접근 방식이 선택된다. 제안된 QRD 구조는 어떤 임의의 차원을 갖는 채널 매트릭스에도 적용 될 수 있고, 매트릭스 차원의 증가에 따라 연산 복잡도 감소도 늘어난다. 제안하는 QR 분해 하드웨어는 삼성 <TEX>$0.13{\mu}m$</TEX> 공정을 사용하여 구현되었다. 실험결과, <TEX>$4{\times}4$</TEX> 행렬의 QR 분해에 대한 제안 구조는 기존의 Householder 코딕 기반의 구조에 비해 47%의 QAR(QRD Rate/Gate count) 향상과 28%의 전력을 절감을 이뤄낼 수 있었다. This paper presents a low complexity QR decomposition (QRD) architecture for MIMO detector. In the proposed approach, various CORDIC-based QRD algorithms are efficiently combined together to reduce the computational complexity of the QRD hardware. Based on the computational complexity analysis on various QRD algorithms, a low complexity approach is selected at each stage of QRD process. The proposed QRD architecture can be applied to any arbitrary dimension of channel matrix, and the complexity reduction grows with the increasing matrix dimension. Our QR decomposition hardware was implemented using Samsung <TEX>$0.13{\mu}m$</TEX> technology. The numerical results show that the proposed architecture achieves 47% increase in the QAR (QRD Rate/Gate count) with 28.1% power savings over the conventional Householder CORDIC-based architecture for the <TEX>$4{\times}4$</TEX> matrix decomposition.

A Categorization Model of Requirements Based on Max‐Neef's Model of Human Needs

ABSTRACTRequirements categorization is an inherent part of the requirements engineering activity. Conventional approaches use a designer perspective (requirements organized according to design needs or attributes), a contractual perspective (requirements organized according to procurement or acquisition needs), or a combination of both. Such models present several inconveniences that result in limitation of system affordability: facilitate the generation of overlapping requirements, of design‐dependent requirements, and of a mix of requirements applicable to different levels of the architecture decomposition or to different products. The present research proposes a Need‐based Categorization (NbC) model that is system‐centric: Requirements are organized around the system. Inspired by Max‐Neef's model of human needs, the proposed model supports requirement elicitation by defining only what the system does, how well, where, and what it uses to accomplish it. The model facilitates the identification of constraints that limit the solution tradespace without supporting the satisfaction of new needs, of overlapping requirements, and of requirements that are not applicable to the system. Finally, the proposed model defines requirements in subsets that are associated with value to stakeholders, thus reflecting the actual dependency nature of requirements at a given level of an architecture decomposition, which promotes holistic decisions instead of local optimizations.

Architectural Decomposition of Video Decoders by Meansof an Intermediate Data Stream Format

The microprocessor industry trend towards many-core architectures introduced the necessity of devising appropriately scalable applications. While implementing software based video decoding, the main challenges are the optimized partitioning of decoder operations, efficient tracking of dependencies and resource synchronization for multiple parallel units. The same applies for hardware implementations of video decoders where monolithic approaches anticipate scalability of the design and reusability of already implemented core components.In this paper, we propose an intermediate data stream format (Meta Format Stream) which is suited for architectural decomposition of video decoding by replacing the conventional monolithic decoder architecture design with a pipelined structure. The Meta Format is forward-oriented and self contained and multistandard capable, so that processing of Meta Streams is independent of the originating bit stream. Our approach does not require special coding settings and is applicable to accelerated decoding of any standards-compliant bit stream. A H.264/AVC multiprocessing proposal is presented as a case study for the potential our our concept. The case study combines coarse grained frame-level parallel decoding of the bit stream with fine-grained macroblock level parallelism in the image processing stage.The proposed H.264 decoder achieved speedup factors of up to 7.6 on an 8 core machine with 2-way SMT. We are reporting actual decoding speeds of up to 150 frames per second in 2160p-resolution.

RECURSIVE QR DECOMPOSITION ARCHITECTURE FOR MIMO-OFDM DETECTION SYSTEMS

This paper presents a modified implementation of QR decomposition for multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) detection based on the Givens rotation method. The QR decomposition hardware is constructed using the coordinate rotation digital computer (CORDIC) algorithm operating with fewer gate counts and lower power consumption than do triangular systolic array (TSA) structures. Accurate signal transmission is essential to wireless communication systems. Thus, a more effective data detection algorithm and precise channel estimation method play vital roles in MIMO systems. Implementing data detection with QR decomposition helps reduce the complexity of MIMO-OFDM detection. Implementation results reveal that the proposed recursive QR decomposition (RQRD) architecture has lower clock latency than do TSA structures, and has a smaller hardware area than do Gram–Schmidt structures.

Cytotoxic effects of ZnO hierarchical architectures on RSC96 Schwann cells

The alteration in intracellular Zn2+ homeostasis is attributed to the generation of intracellular reactive oxygen species, which subsequently results in oxidative damage of organelles and cell apoptosis. In this work, the neurotoxic effects of ZnO hierarchical architectures (nanoparticles and microspheres, the prism-like and flower-like structures) were evaluated through the 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide assay using RSC96 Schwann cells as the model. Cell apoptosis and cell cycle were detected using flow cytometry. The concentration of Zn2+ in the culture media was monitored using atomic absorption spectrometry. The results show that ZnO nanoparticles and microspheres displayed significant cytotoxic effects on RSC96 Schwann cells in dose- and time-dependent manners, whereas no or low cytotoxic effect was observed when the cells were treated with the prism-like and flower-like ZnO. A remarkable cell apoptosis and G2/M cell cycle arrest were observed when RSC96 Schwann cells were exposed to ZnO nanoparticles and microspheres at a dose of 80 μg/mL for 12 h. The time-dependent increase of Zn2+ concentration in the culture media suggests that the cytotoxic effects were associated with the decomposition of ZnO hierarchical architecture and the subsequent release of Zn2+. These results provide new insights into the cytotoxic effects of complex ZnO architectures, which could be prominently dominated by nanoscale building blocks.

Synthesis of FSMs Based on Architectural Decomposition with Joined Multiple Encoding

Synthesis of FSMs Based on Architectural Decomposition with Joined Multiple EncodingThe method of synthesis of the logic circuit of finite state machine (FSM) with Mealy's outputs is proposed in this paper. Proposed method is based on the innovate encoding of microinstructions split into subsets. Code of microinstruction is represented as a part of current state code and code of microinstruction inside of current subset. It leads to realization of FSM as s double-level structure. It leads to diminishing of number of variables required for encoding of microinstructions. Such approach permits to decrease the number of required outputs of combinational part of FSM.

Mastering crosscutting architectural decisions with aspects

SUMMARYWhen reflecting upon driving system requirements such as security and availability, software architects often face decisions that have a broadly scoped impact on the software architecture. These decisions are the core of the architecting process because they typically have implications intertwined in a multitude of architectural elements and across multiple views. Without a modular representation and management of those crucial choices, architects cannot properly communicate, assess and reason about their crosscutting effects. The result is a number of architectural breakdowns, such as misinformed architectural evaluation, time‐consuming trade‐off analysis and unmanageable traceability. This paper presents an architectural documentation approach in which aspects are exploited as a natural way to capture widely‐scoped design decisions in a modular fashion. The approach consists of a simple high‐level notation to describe crosscutting decisions, and a supplementary language that allows architects to formally define how such architectural decisions affect the final architectural decomposition according to different views. On the basis of two case studies, we have systematically assessed to what extent our approach: (i) supports the description of heterogeneous forms of crosscutting architecture decisions, (ii) improves the support for architecture modularity analysis, and (iii) enhances upstream and downstream traceability of crosscutting architectural decisions. Copyright © 2012 John Wiley &amp; Sons, Ltd.

FPGA-Based High-Performance and Scalable Block LU Decomposition Architecture

Decomposition of a matrix into lower and upper triangular matrices (LU decomposition) is a vital part of many scientific and engineering applications, and the block LU decomposition algorithm is an approach well suited to parallel hardware implementation. This paper presents an approach to speed up implementation of the block LU decomposition algorithm using FPGA hardware. Unlike most previous approaches reported in the literature, the approach does not assume the matrix can be stored entirely on chip. The memory accesses are studied for various FPGA configurations, and a schedule of operations for scaling well is shown. The design has been synthesized for FPGA targets and can be easily retargeted. The design outperforms previous hardware implementations, as well as tuned software implementations including the ATLAS and MKL libraries on workstations.

Optimizing decomposition of software architecture for local recovery

The increasing size and complexity of software systems has led to an amplified number of potential failures and as such makes it harder to ensure software reliability. Since it is usually hard to prevent all the failures, fault tolerance techniques have become more important. An essential element of fault tolerance is the recovery from failures. Local recovery is an effective approach whereby only the erroneous parts of the system are recovered while the other parts remain available. For achieving local recovery, the architecture needs to be decomposed into separate units that can be recovered in isolation. Usually, there are many different alternative ways to decompose the system into recoverable units. It appears that each of these decomposition alternatives performs differently with respect to availability and performance metrics. We propose a systematic approach dedicated to optimizing the decomposition of software architecture for local recovery. The approach provides systematic guidelines to depict the design space of the possible decomposition alternatives, to reduce the design space with respect to domain and stakeholder constraints and to balance the feasible alternatives with respect to availability and performance. The approach is supported by an integrated set of tools and illustrated for the open-source MPlayer software.

High-order FIR filter design on FPGA using MA distributed algorithm

Concerning the problems of too much resource consumption and too low processing speed,a new high-order FIR filter targeted Field Programmable Gate Array(FPGA) was proposed.Firstly,polyphase decomposition architecture and pipeline technology were adopted to decompose high-order FIR filter into low-order ones,and then the proposed MA distributed algorithm architecture was used to implement the decomposed filters in the method.A series of serial and parallel FIR filters which order from 8 to 256 were implemented by ISE10.1 targeted Xilinx Xc2vp30-7ff896 FPGA device.The experimental results show that the proposed method effectively reduces the system resource consumption and improves the timing performance of the system.

Sign-Select Lookahead CORDIC based High-Speed QR Decomposition Architecture for MIMO Receiver Applications

This paper presents a high-speed QR decomposition architecture for the multi-input-multi- output (MIMO) receiver based on Givens rotation. Under fast-varying channel, since the inverse matrix calculation has to be performed frequently in MIMO receiver, a high performance and low latency QR decomposition module is highly required. The proposed QR decomposition architecture is composed of Sign-Select Lookahead (SSL) coordinate rotation digital computer (CORDIC). In the SSL-CORDIC, the sign bits, which are computed ahead to select which direction to rotate, are used to select one of the last iteration results, therefore, the data dependencies on the previous iterations are efficiently removed. Our proposed QR decomposition module is implemented using TSMC 0.25 µm CMOS process. Experimental results show that the proposed QR architecture achieves 34.83% speed-up over the Compact CORDIC based architecture for the 4 × 4 matrix decomposition.

High precision slope deformation monitoring model based on the GPS/Pseudolites technology in open-pit mine

As the number and geometric intensity of visual satellites are susceptible to large slopes in open-pit mines, we propose integration of GPS/Pseudolites (PLs) positioning technology which can increase the number of visible satellites, strengthen the geometric intensity of satellites and provide a precision solution for slope deformation monitoring. However, the un-modeled systematic errors are still the main limiting factors for high precision baseline solution. In order to eliminate the un-modeled systematic error, the Empirical Mode Decomposition (EMD) theory is employed. The multi-scale decomposition and reconstruction architecture are defined here on the basis of the EMD theory and the systematic error mitigation model is demonstrated as well. A standard of the scale selection for the systematic error elimination is given in terms of the mean of the accumulated standardized modes. Thereafter, the scheme of the GPS/PLs baseline solution based on the EMD is suggested. The simulation and experiment results show that the precision factors (DOP) are reduced greatly when PLs is located suitably. The proposed scheme dramatically improves the reliability of ambiguity resolution and the precision of baseline vector after systematic error being eliminated, and provides an effective model for high precision slope deformation monitoring in open-pit mine.

Canonic Design of Parallel Cascades of Symmetric Two-Port Microring Networks

A method for obtaining the optimal canonic design of optical filters based on parallel-cascaded arrays of symmetric two-port microring networks is presented. The approach is based on the all-pass decomposition of the parallel-cascaded microring architecture, whereby the filter is transformed into an equivalent sum-difference all-pass microring circuit via a similarity transformation of the transfer matrix. The sum-difference transformation also helps reveal important properties of the parallel-cascaded microring architecture, such as the doubly complementary nature of its transfer functions, and the existence and uniqueness of the canonic form of parallel-cascaded arrays of symmetric microring networks.

FLORA: a framework for decomposing software architecture to introduce local recovery

AbstractThe decomposition of software architecture into modular units is usually driven by the required quality concerns. In this paper we focus on the impact of local recovery concern on the decomposition of the software system. For achieving local recovery, the system needs to be decomposed into separate units that can be recovered in isolation. However, it appears that this required decomposition for recovery is usually not aligned with the decomposition based on functional concerns. Moreover, introducing local recovery to a software system, while preserving the existing decomposition, is not trivial and requires substantial development and maintenance effort. To reduce this effort we propose a framework that supports the decomposition and implementation of software architecture for local recovery. The framework provides reusable abstractions for defining recoverable units and the necessary coordination and communication protocols for recovery. We discuss our experiences in the application and evaluation of the framework for introducing local recovery to the open‐source media player called MPlayer. Copyright © 2009 John Wiley &amp; Sons, Ltd.

Testing diagnostics of modern microprocessors with the use of functional models

A new approach for developing functional diagnostic tests of processors with parallelism of the level of computer code is represented. The approach is based on functional decomposition of the processor architecture and use of functional models. The approach is applied to developing the technique for testing mechanisms of storage and transmission of conveyor process data. Models and algorithms for generation of tests of these mechanisms are developed. The performance of diagnostic tests is approved on the model.

EMD-based GPS baseline solution and validation test

Abstract A GPS baseline solution model is presented, based on the Empirical Mode Decomposition (EMD), which has the advantage of eliminating the error effects outside the model. The EMD technique is a new signal processing method for non-linear time series, which decomposes a time series into a finite and often small number of Intrinsic Mode Functions (IMFs). The decomposition procedure is adaptive and data-driven which is suitable for non-linear data series analysis. A multi-scale decomposition and reconstruction architecture is defined on the basis of the EMD theory and the error mitigation model is demonstrated as well. A standard of the scale selection for the elimination of errors, outside the model, was given in terms of the mean of the accumulated standardized modes. Thereafter, the scheme of the GPS baseline solution based on the EMD is suggested. The float solution residuals of the Double-Difference (DD) observation equation are used to extract the errors outside the model applied to modify the GPS DD measurements. Then the float solution was given again and the fixed solution was obtained by a Lambda algorithm. Three schemes are designed to test the proposed model and the experimental results show that the proposed model dramatically improves the reliability of ambiguity resolution after the elimination of errors outside the model.