Decomposition Architecture Research Articles

BIM for built cultural heritage: the case of the Baptistery of San Giovanni in Florence.

Whenever an operator approaches the three-dimensional modeling of an architectural object, he always must compose together a series of simple elements. This logic of additive and hierarchical composition finds an excellent application in BIM processes for built heritage, where the operator is called upon not only to compose the architectural elements together but also to break down the architecture by investigating its parts and mutual links. The main difference between the BIM model for new buildings and the existing one lies above all in the concept of the ideal model. In fact, while for the new building the model proceeds from a project and gradually develops with it, in the historical building the only starting point is the current state of the building, told through the surveys and documentation. It is necessary to model an as is, that starts from historical and bibliographic research in the first place, and which is supported by the survey and diagnostic analyses that account for the structure beyond the skin of the building, investigated by the numerical models. Once the model has been created, it can always be implemented with new information coming from further analysis and represent not only the current state of conservation of the artefact but also consider the different evolutions over time. In this context, the proposed research describes the BIM modeling process applied to the Baptistery of San Giovanni in Florence, trying to clarify the terminologies used in this application and defining an application methodology by stages, based on the decomposition and reconstruction of historical architecture.

Design structure matrix‐based modularization approach for complex systems with multiple design constraints

AbstractDesigning a complex system generally requires its decomposition into smaller modular constituents for the ease of design, integration, operation, and future upgrades. Typically, system decomposition analysis is conducted using an abstract system model during the initial system design stage. The design structure matrix (DSM) has been a popular tool for abstract system modeling. In the DSM, systems are modeled and decomposed into modular configurations using published clustering algorithms. In this paper, we present a novel approach for system architecture decomposition and modularization based on the DSM, which can incorporate multiple design constraints into clustering algorithms. Two clustering algorithms are modified to accommodate the system design constraints. The modified algorithms are used to decompose a static inverter (SIV) of an electrical train into modular configurations that include various design constraints. Finally, the results and analysis of the SIV modularization using new modified clustering algorithms are presented.

Informative Models for Architectural Heritage

BIM (Building Information Modeling) processes are the most effective way to know existing architectural structures, integrating the most advanced potentials of 3D modeling and the structured storage of heterogeneous information. Many HBIM (Heritage Building Information Modeling) applications lead to the systematization of survey data, even though a univocal working method is not yet clearly defined. This research considers the decomposition of architecture, based on structured criteria, and its reconstruction, through ideal models, as the main moments of the HBIM process. This hypothesis is verified through a procedure that links the survey 3D data with the characteristics of the ideal HBIM model, which allows a continuous comparison between the project model and as-built. The research provides for the setting up of a general methodology that, according to a growing approach to the complexity of the analyzed buildings, compares the process followed on two architectural structures. The study analyzes some important HBIM issues: The relationship between the semantic modeling and the surfaces’ continuity of architectural heritage; the relationship between the elements standardization, geometric irregularities, and material heterogeneity; the reliability of the built models; and the evaluation of the gap between an ideal model and the objective accuracy of surveying.

Clustering Information Distance by Nested-Lattice Compression

Clustering of mixed dataset is one of the key issues in complex system architecture decomposition and many other pattern recognition applications. Recent development of machine learning techniques, especially deep neural network models significantly boost clustering performance, whereas theoretical analysis explaining why it works and how to further improve the performance is limited in literature. From the information theoretical point of view, information distance based on Kolmogorov complexity captures non-feature similarities and works as an absolute information measure. Thus, it is valuable to model the clustering process of mixed discrete and continuous datasets with this generalized similarity measure, and investigate the practical implementation of the information distance, compression distance, to make it work within the machine learning-enabled clustering schemes. In this paper, a generative adversarial network (GAN)-based deep clustering scheme is modified to compare the similarities of the original data in order to reduce the deviation of projected features from real data characteristics. The compression distance is extended to be computable for continuous data with the assistance of the Wyner-Ziv setup, where theoretical limit is given via asymptotically optimal nested lattices. Flatness-based analysis is carried out with distance upper bound derived for non-vanishing flatness factor. Practical distance calculation is derived to incorporate with neural-network-based clustering. Simulation via practical lattice codes illustrates the consistency of the design and shows its potential effectiveness in clustering applications.

A Low-Latency and Area-Efficient Gram–Schmidt-Based QRD Architecture for MIMO Receiver

Despite a low algorithmic complexity, Gram–Schmidt (GS) method has not been widely employed in the dedicated hardware architecture of matrix decomposition due to its expensive square-root and division operations. This paper presents a low-latency and area-efficient QR decomposition (QRD) architecture based on the modified GS method. The low complexity architecture is enabled by efficiently substituting the square roots and divisions with coordinate rotation digital computer (CORDIC) operations. In the proposed architecture, when implementing the division by the results of square root, the key design point is that the rotation directions of CORDIC are shared between vectoring and rotation modes using the orthogonality of the CORDIC rotation matrix. As a result, the division operation can be performed with the assistance of square root, leading to the hardware cost reduction. The overhead of scaling factor compensation has also been reduced with pre-scaling. The proposed low complexity scheme can be implemented in the semipipelined and iterative architectures for high throughput and small area applications, respectively. Hardware implementation results with 65-nm CMOS process show that the proposed semipipelined architecture achieves 17% and 141% improvement of normalized hardware efficiency in QRD and projection operations, respectively, compared with the conventional approaches.

Study on the performance improvement of urban rail transit system

The performance improvement is closely related to the energy-saving operation of urban rail transit system and the improvement of service quality, which involves the optimization of infrastructures, the application of advanced train control technology, the highly efficient transportation organization, and the recycle and utilization of regenerative energy, etc. With the energy consumption as an entry, we first discuss the performance of urban rail transit system, determinate the corresponding performance evaluation indexes, and establish the top-down decomposition architecture of global performance. Then, we analyze the performance emergence mechanism of urban rail transit system, and build the difference equations of calculating train behavior in the constant power and natural characteristic regions and the mathematical model of train behavior optimization. The simulations are used to analyze the influence of train light-weighting, train control, and the load ratio on the energy efficiency of train operation, which can help to improve the system performance. Finally, we discuss the recycle and utilization of regenerative energy generated from train braking, the energy-saving operation of the environment control systems, and the optimization of train stop scheme, etc. The research shows that the potential of improving the performance of urban rail transit system is enormous, and deserves more attention.

Analyses of EEG Oscillatory Activities during Slow and Fast Repetitive Movements using Holo-Hilbert Spectral Analysis.

Neural oscillatory activities existing in multiple fre-quency bands usually represent different levels of neurophysiolog-ical meanings, from micro-scale to macro-scale organizations. In this study, we adopted Holo-Hilbert spectral analysis (HHSA) to study the amplitude-modulated (AM) and frequency-modulated (FM) components in sensorimotor Mu rhythm, induced by slow- and fast-rate repetitive movements. The HHSA-based approach is a two-layer empirical mode decomposition (EMD) architecture, which firstly decomposes the EEG signal into a series of frequency-modulated intrinsic mode functions (IMF) and then decomposes each frequency-modulated IMF into a set of amplitude-modulated IMFs. With the HHSA, the FM and AM components were incor-porated with their instantaneous power to achieve full-informa-tional spectral analysis. We observed that the instantaneous power induced by slow-rate movements was significantly higher than that induced by fast-rate movements (p < 0.01, Wilcoxon signed rank test). The alpha-band AM frequencies induced by slow-rate movements were higher than those induced by fast-rate move-ments, while no statistical difference was found in beta-band AM frequencies. In addition, to study the functional coupling between the primary sensorimotor area and other brain regions, spectral coherence was applied and statistical difference was found in frontal area in slow-rate versus fast-rate movements. The discrep-ancy between slow- and fast-rate movements might be owing to the change of motor functional modes from default mode network (DMN) to automatic timing with the increase of movement rates. The use of HHSA for oscillatory activity analysis can be an effi-cient tool to provide informative interaction among different fre-quency bands.

Automatic hand phantom map generation and detection using decomposition support vector machines

BackgroundThere is a need for providing sensory feedback for myoelectric prosthesis users. Providing tactile feedback can improve object manipulation abilities, enhance the perceptual embodiment of myoelectric prostheses and help reduce phantom limb pain. Many amputees have referred sensation from their missing hand on their residual limbs (phantom maps). This skin area can serve as a target for providing amputees with non-invasive tactile sensory feedback. One of the challenges of providing sensory feedback on the phantom map is to define the accurate boundary of each phantom digit because the phantom map distribution varies from person to person.MethodsIn this paper, automatic phantom map detection methods based on four decomposition support vector machine algorithms and three sampling methods are proposed, complemented by fuzzy logic and active learning strategies. The algorithms and methods are tested on two databases: the first one includes 400 generated phantom maps, whereby the phantom map generation algorithm was based on our observation of the phantom maps to ensure smooth phantom digit edges, variety, and representativeness. The second database includes five reported phantom map images and transformations thereof. The accuracy and training/ classification time of each algorithm using a dense stimulation array (with 100 times 100 actuators) and two coarse stimulation arrays (with 3 times 5 and 4 times 6 actuators) are presented and compared.ResultsBoth generated and reported phantom map images share the same trends. Majority-pooling sampling effectively increases the training size, albeit introducing some noise, and thus produces the smallest error rates among the three proposed sampling methods. For different decomposition architectures, one-vs-one reduces unclassified regions and in general has higher classification accuracy than the other architectures. By introducing fuzzy logic to bias the penalty parameter, the influence of pooling-induced noise is reduced. Moreover, active learning with different strategies was also tested and shown to improve the accuracy by introducing more representative training samples. Overall, dense arrays employing one-vs-one fuzzy support vector machines with majority-pooling sampling have the smallest average absolute error rate (8.78% for generated phantom maps and 11.5% for reported and transformed phantom map images). The detection accuracy of coarse arrays was found to be significantly lower than for dense array.ConclusionsThe results demonstrate the effectiveness of support vector machines using a dense array in detecting refined phantom map shapes, whereas coarse arrays are unsuitable for this task. We therefore propose a two-step approach, using first a non-wearable dense array to detect an accurate phantom map shape, then to apply a wearable coarse stimulation array customized according to the detection results. The proposed methodology can be used as a tool for helping haptic feedback designers and for tracking the evolvement of phantom maps.

Entropy‐based system assessment metric for determining architecture's robustness to different stakeholder perspectives

AbstractIn this study, a new system assessment metric is proposed to evaluate the robustness of system architectures to different stakeholder perspectives and decomposition preferences. Typically, the decomposition of a system architecture into a modular configuration is based on the function‐based perspective of the design teams, which can be different from the perspectives of other stakeholders such as the manufacturing teams and field service organization. In this paper, a system architecture is decomposed into different modular configurations on the basis of the preferred perspectives of various stakeholders. The generated configurations are then compared to function‐based modular configurations in terms of the diffusion of the original function‐based module components into modules generated from different perspectives. The degree of diffusion is measured using a newly proposed entropy based metric referred to as the module diffusion index. The proposed metric provides stakeholders with a quantifiable measure of the robustness of a particular system architecture with respect to the preferred decomposition strategy of different stakeholders. The proposed metric can be used to assess different system architectures in terms of their robustness to perspective‐based decompositions. A case study is conducted wherein different mechanical clock architectures are assessed in terms of the robustness to different perspective‐based decompositions to demonstrate the effectiveness of the proposed metric.

When do product architectures mirror organisational architectures? The combined role of product complexity and the rate of technological change

ABSTRACTIn the modularity literature, an architectural decomposition and ‘mirroring’ between task boundaries, knowledge boundaries, and firm boundaries has been suggested as a way to enhance managerial efficiency and as a source of potential strategic advantage. Despite its intuitive appeal, empirical support for ‘mirroring’ is significant but mixed. In this paper, we utilise an industrial economics and knowledge-based perspective to hypothesise how the combined effects of product architecture type, product complexity and the rate of product component change may influence task, knowledge and firm boundaries and hence be associated with either phases of mirroring or non-mirroring (‘misting’). We suggest that whether mirroring or misting is an efficient strategic choice is influenced by the characteristics of both the product architecture and the rate of technological change at the product component level, and changes across time as products evolve. Our framework helps to reconcile existing mixed evidence and provides the foundation for further empirical research.

The Tension Matrix and the Concept of Elemental Decomposition: Improving Identification of Conflicting Requirements

Conventional approaches to system design use requirements as boundary conditions against which the design activity occurs. Decisions at a given level of the architecture decomposition can result in a flowing down of conflicting requirements, which are easy to fulfill in isolation but extremely difficult when dealt with simultaneously. Designing against such sets of requirements considerably limits system affordability. Conventional approaches to identifying these conflicts are either time efficient, yet ineffective, or effective, but time consuming. This paper proposes a novel method that sits between those extremes. It enables quick identification of conflicts, while still maintaining a good level of effectiveness. The proposed method, which is called the tension matrix, is built on three pillars: 1) heuristics to identify conflicting requirements, aimed at reducing completeness uncertainty; 2) targeted modeling; and 3) elemental decomposition. The effectiveness of the method was validated with a case study, on which a combination of retrospective assessment with a prospective analysis was employed. The results confirmed that the proposed tension matrix and the concept of elemental decomposition provide higher levels of effectiveness in identifying conflicting requirements, before initiating architectural or design activities, than conventional approaches.

Highly-Flexible and Optimized, Area: and Energy-Efficient Matrix Decomposition Accelerators based on Givens Algorithms and CORDIC Rotations

Matrix decomposition accelerators are attractive SoC components for many applications with a wide range of specifications. In this work, a new family of highly area- and energy-efficient modular matrix decomposition architectures based on the givens-algorithms and CORDIC rotations are elaborated. Accurate algebraic cost models enable for early cost estimation as well as for cross-level optimization over architecture, micro-architecture and circuit-level using a rich set of parameters. Quantitative results for an exemplary QRD application, implemented in 40-nm CMOS technology, are presented.

QR decomposition architecture using the iteration look‐ahead modified Gram–Schmidt algorithm

QR decomposition is extensively adopted in multiple-input–multiple-output orthogonal frequency-division multiplexing wireless communication systems, and is one of the performance bottlenecks in lots of high-performance wireless communication algorithms. To implement low processing latency QR decomposition with hardware, the authors propose a novel iterative look-ahead modified Gram–Schmidt (ILMGS) algorithm based on the traditional modified Gram–Schmidt (MGS) algorithm. They also design the corresponding triangular systolic array (TSA) architecture with the proposed ILMGS algorithm, which only needs n time slots for a n × n real matrix. For reducing the hardware overhead, they modify the TSA architecture into an iterative architecture. They also design a modified iterative architecture to further reduce the hardware overhead. The implementation results show that the normalised processing latency of the modified iterative architecture based on the proposed ILMGS algorithm is 1.36 times lower than the one based on the MGS algorithm. To the best of the authors’ knowledge, the designed architecture achieves the superior latency performance than the existing works.

Implementation of a Systems Engineering Approach to the Management of a Planetary Defense Team Project in an Intensive Space Studies Program Using IPPD

AbstractPlanetary Defense is an important and challenging topic, and long‐term mitigation of asteroid impacts have been discussed by governments and space agencies; however, short‐term solutions of comet impacts are rarely considered. 34 participants from 17 countries tackled this problem, a comet impact two years after detection, at the 2015‐Space Studies Program and produced the Roadmap for EArth Defense Initiatives (READI). The Integrated Product and Process Development (IPPD) methodology was used to identify the mission statement, define requirements and needs, and provide possible solutions. This paper concentrates on how the IPPD was applied as a systems approach to each phase of our project, where the whole system was Planetary Defense, and its architectural decomposition was divided into five key elements: detection and tracking, deflection techniques, global collaboration, outreach and education, and evacuation and recovery. The importance of applying human factor concepts throughout the project is addressed through challenges faced, solutions applied, and lessons learned. Verification and validation of selected options are presented, along with the use of various project management tools. Furthermore, we discuss our results and recommendations for Planetary Defense, as they are products of our analysis using the IPPD approach to systems engineering.

Efficient architectural structural element decomposition

A model that combines symmetries and free-form polylines for decomposing a point cloud into ASEs.Methods for detecting structural cues (dominant mirror symmetries and rotational symmetries, as well as residual free-form parts) in point clouds.A global energy formulation and optimization approach for partitioning a point cloud into meaningful structural components based on structural cues. Decomposing 3D building models into architectural elements is an essential step in understanding their 3D structure. Although we focus on landmark buildings, our approach generalizes to arbitrary 3D objects. We formulate the decomposition as a multi-label optimization that identifies individual elements of a landmark. This allows our system to cope with noisy, incomplete, outlier-contaminated 3D point clouds. We detect four types of structural cues, namely dominant mirror symmetries, rotational symmetries, shape primitives, and polylines capturing free-form shapes of the landmark not explained by symmetry. Our novel method combine these cues enables modeling the variability present in complex 3D models, and robustly decomposing them into architectural structural elements. Our proposed architectural decomposition facilitates significant 3D model compression and shape-specific modeling.

Availability analysis of software architecture decomposition alternatives for local recovery

We present an efficient and easy-to-use methodology to predict--at design time--the availability of systems that support local recovery. Our analysis techniques work at the architectural level, whe...

PROGRAMMABLE PARALLEL FBD SIGMA DELTA ADC RECONSTRUCTION STAGE DESIGN FOR SOFTWARE DEFINED RADIO RECEIVER

Today’s bottleneck of signal processing in multistandard software defined radio (SDR) receiver is the analog-to-digital converter (ADC). Therefore, the authors present in this paper the design and simulation results of a programmable parallel frequency band decomposition (FBD) architecture for ADC. The designed parallel architecture is composed of six parallel branches based on discrete-time (DT) 4th order sigma delta modulators using single-bit quantizers. Each branch processes a sub-bandwidth of the received signal. Only needed branches are selected according to the chosen standard. The parallel sigma delta modulators’ outputs are handled by a demodulation-based digital reconstruction stage in order to provide the FBD sigma delta-based ADC output signal. The digital reconstruction stage differs from one communication standard to another. In this paper, its design is discussed for the UMTS use case. The objective is to propose a digital reconstruction design with optimized complexity. In fact, the authors propose a comparative study between some configurations of demodulation, decimation and filtering processes. Technical choices and simulation results are discussed. For UMTS use case, the proposed FBD sigma delta-based ADC architecture ensures a computed signal-to-noise ratio (SNR) over 74 dB.

The Concept of Order of Conflict in Requirements Engineering

Conventional approaches to system design use requirements as boundary conditions against which the design activity occurs. Decisions at a given level of the architecture decomposition can result in the flowing down of conflicting requirements, which are easy to fulfill in isolation but extremely difficult when dealt with simultaneously. Designing against such sets of requirements considerably limits system affordability. Existing research on the evaluation of such conflicts primarily seek to determine the level of conflicts between pairs of requirements. We assert in this paper that these methods are incomplete and using traditional methodologies can result in missing significant conflicts between groups of requirements. We provide a mathematical proof for this assertion and present two case studies that support the mathematical proof. We present the concept of “order of conflict.” The objective of this paper is to prove why pairwise-based conflicting requirements identification and analysis methods based on pairwise comparisons are flawed.

Analysis of Cloud Network Management Using Resource Allocation and Task Scheduling Services

Network failure in cloud datacenter could result from inefficient resource allocation; scheduling and logical segmentation of physical machines (network constraints). This is highly undesirable in Distributed Cloud Computing Networks (DCCNs) running mission critical services. Such failure has been identified in the University of Nigeria datacenter network situated in the south eastern part of Nigeria. In this paper, the architectural decomposition of a proposed DCCN was carried out while exploring its functionalities for grid performance. Virtualization services such as resource allocation and task scheduling were employed in heterogeneous server clusters. The validation of the DCCN performance was carried out using trace files from Riverbed Modeller 17.5 in order to ascertain the influence of virtualization on server resource pool. The QoS metrics considered in the analysis are: the service delay time, resource availability, throughput and utilization. From the validation analysis of the DCCN, the following results were obtained: average throughput (bytes/Sec) for DCCN = 40.00%, DCell = 33.33% and BCube = 26.67%. Average resource availability response for DCCN = 38.46%, DCell = 33.33%, and BCube = 28.21%. DCCN density on resource utilization = 40% (when logically isolated) and 60% (when not logically isolated). From the results, it was concluded that using virtualization in a cloud DataCenter servers will result in enhanced server performance offering lower average wait time even with a higher request rate and longer duration of resource use (service availability). By evaluating these recursive architectural designs for network operations, enterprises ready for Spine and leaf model could further develop their network resource management schemes for optimal performance.

A Set of Heuristics to Support Early Identification of Conflicting Requirements

AbstractConventional approaches to system design use requirements as boundary conditions against which the design activity occurs. Decisions at a given level of the architecture decomposition can result in the flowing down of conflicting requirements, which are easy to fulfill in isolation but extremely difficult when dealt with simultaneously. Designing against such sets of requirements considerably limits system affordability. Identification of such conflicts is usually performed in industry by subject matter experts. Such evaluations are primarily driven by experience. As a result, effectiveness in identifying conflicting requirements is strongly dependent on the person making the assessment. We propose in this paper a set of heuristics that supports identification of conflicting requirements by providing direction and focus in the identification effort. The heuristics have been derived from a combination of literature review and experience from practitioners. In particular, a set of practitioners was interviewed orally and through a questionnaire in order to abstract and generalize their individual experiences.