Efficient Mapping Scheme Research Articles

Fisher Information-Based Optimization of Mapped Fourier Grid Methods

The mapped Fourier grid method (mapped-FGM) is a simple and efficient discrete variable representation (DVR) numerical technique for solving atomic radial Schrödinger differential equations. It is set up on equidistant grid points, and the mapping, a suitable coordinate transformation to the radial variable, deals with the potential energy peculiarities that are incompatible with constant step grids. For a given constrained number of grid points, classical phase space and semiclassical arguments help in selecting the mapping function and the maximum radial extension, while the energy does not generally exhibit a variational extremization trend. In this work, optimal computational parameters and mapping quality are alternatively assessed using the extremization of (coordinate and momentum) Fisher information. A benchmark system (hydrogen atom) is employed, where energy eigenvalues and Fisher information are traced in a standard convergence procedure. High-precision energy eigenvalues exhibit a correlation with the extrema of Fisher information measures. Highly efficient mapping schemes (sometimes classically counterintuitive) also stand out with these measures. Same trends are evidenced in the solution of Dalgarno–Lewis equations, i.e., inhomogeneous counterparts of the radial Schrödinger equation occurring in perturbation theory. A detailed analysis of the results, implications on more complex single valence electron Hamiltonians, and future extensions are also included.

An Efficient Mapping Scheme on Neural Networks for Linear Massive MIMO Detection

For massive multiple-input multiple-output (MIMO) communication systems, simple linear detectors such as zero forcing (ZF) and minimum mean square error (MMSE) can achieve near-optimal detection performance with reduced computational complexity. However, such linear detectors always involve complicated matrix inversion, which will suffer from high computational overhead in the practical implementation. Due to the massive parallel-processing and efficient hardware-implementation nature, the neural network has become a promising approach to signal processing for the future wireless communications. In this paper, we first propose an efficient neural network to calculate the pseudo-inverses for any type of matrices based on the improved Newton's method, termed as the PINN. Through detailed analysis and derivation, the linear massive MIMO detectors are mapped on PINNs, which can take full advantage of the research achievements of neural networks in both algorithms and hardwares. Furthermore, an improved limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) quasi-Newton method is studied as the learning algorithm of PINNs to achieve a better performance/complexity trade-off. Simulation results finally validate the efficiency of the proposed scheme.

COMPARISON OF PERFORMANCES BETWEEN SC-FDMA AND OFDMA SYSTEMS UNDER DIFFERENT SUBCARRIER MAPPING SCHEMES

To improve the uplink performance of wireless network for long term evolution (LTE) single carrier frequency division multiple access (SC-FDMA) and orthogonal frequency division multiple access (OFDMA) are used. Two major concerns of these SC-FDMA and OFDMA are high peak-to-average power ratio (PAPR) and efficient subcarrier mapping scheme. This paper presents a performance comparison of SC-FDMA and OFDMA under different subcarrier mapping schemes which includes localized FDMA (LFDMA), distributed FDMA (DFDMA) and interleaved FDMA (IFDMA). Quadrature amplitude modulation (QAM) with different constellation sizes is used. The symbol error rate (SER) in SC-FDMA is minimized using appropriate mapping scheme. The PAPR is less in SC-FDMA compared to OFDMA. SC-FDMA with IFDMA shows better performance for the PAPR reduction compared to the OFDMA. Compared to OFDMA, using SC-FDMA with 8-QAM and IFDMA, PAPR is reduced by 78%, using SC-FDMA with 16-QAM and IFDMA, PAPR is reduced by 80%, using SC-FDMA with 64-QAM and IFDMA, PAPR is reduced by 68%.

A mixed selective edge-based smoothed PFEM with second-order cone programming for geotechnical large deformation analysis

Traditional particle finite element method (PFEM) with a six-node triangular (T6) element using an iteration algorithm often suffers from high computational costs and non-convergence problems. This paper proposes a novel dynamic implicit optimisation based smoothed PFEM for solving geotechnical large deformation problems. The governing equations are treated as an equivalent min–max optimisation that is recast as a second-order cone programming (SOCP) problem. Owing to the particularity of the min–max optimisation problem, which needs to simultaneously interpolate displacement and stress field, a novel mixed constant-stress selective edge-based smoothed strain element is proposed in which strain and stress are constant in the smoothing domain. The two-level mesh repartitioning scheme is implemented into the proposed method to overcome volumetric locking. Next, a mass lumping scheme using nodal integration is developed to improve computational efficiency. An efficient and simple variable mapping scheme is finally proposed and implemented into PFEM to tackle large deformation problems. Several classical statics and dynamics examples are used to validate the proposed method. All results demonstrate that the proposed method only using the lower-order element offers high accuracy, high efficiency, mesh insensitivity and is free of volumetric locking for geotechnical problems under both small and large deformation.

A Massive Image Recognition Algorithm Based on Attribute Modelling and Knowledge Acquisition

In this paper, an in-depth study and analysis of attribute modelling and knowledge acquisition of massive images are conducted using image recognition. For the complexity of association relationships between attributes of incomplete data, a single-output subnetwork modelling method for incomplete data is proposed to build a neural network model with each missing attribute as output alone and other attributes as input in turn, and the network structure can deeply portray the association relationships between each attribute and other attributes. To address the problem of incomplete model inputs due to the presence of missing values, we propose to treat and describe the missing values as system-level variables and realize the alternate update of network parameters and dynamic filling of missing values through iterative learning among subnets. The method can effectively utilize the information of all the present attribute values in incomplete data, and the obtained subnetwork population model is a fit to the attribute association relationships implied by all the present attribute values in incomplete data. The strengths and weaknesses of existing image semantic modelling algorithms are analysed. To reduce the workload of manually labelling data, this paper proposes the use of a streaming learning algorithm to automatically pass image-level semantic labels to pixel regions of an image, where the algorithm does not need to rely on external detectors and a priori knowledge of the dataset. Then, an efficient deep neural network mapping algorithm is designed and implemented for the microprocessing architecture and software programming framework of this edge processor, and a layout scheme is proposed to place the input feature maps outside the kernel DDR and the reordered convolutional kernel matrices inside the kernel storage body and to design corresponding efficient vectorization algorithms for the multidimensional matrix convolution computation, multidimensional pooling computation, local linear normalization, etc., which exist in the deep convolutional neural network model. The efficient vectorized mapping scheme is designed for the multidimensional matrix convolution computation, multidimensional pooling computation, local linear normalization, etc. in the deep convolutional neural network model so that the utilization of MAC components in the core loop can reach 100%.

Improving the support for XML dynamic updates using a hybridization labeling scheme (ORD-GAP)

Background : As the standard for the exchange of data over the World Wide Web, it is important to ensure that the eXtensible Markup Language (XML) database is capable of supporting not only efficient query processing but also capable of enduring frequent data update operations over the dynamic changes of Web content. Most of the existing XML annotation is based on a labeling scheme to identify each hierarchical position of the XML nodes. This computation is costly as any updates will cause the whole XML tree to be re-labelled. This impact can be observed on large datasets. Therefore, a robust labeling scheme that avoids re-labeling is crucial. Method: Here, we present ORD-GAP (named after Order Gap), a robust and persistent XML labeling scheme that supports dynamic updates. ORD-GAP assigns unique identifiers with gaps in-between XML nodes, which could easily identify the level, Parent-Child (P-C), Ancestor-Descendant (A-D) and sibling relationship. ORD-GAP adopts the OrdPath labeling scheme for any future insertion. Results: We demonstrate that ORD-GAP is robust enough for dynamic updates, and have implemented it in three use cases: (i) left-most, (ii) in-between and (iii) right-most insertion. Experimental evaluations on DBLP dataset demonstrated that ORD-GAP outperformed existing approaches such as ORDPath and ME Labeling concerning database storage size, data loading time and query retrieval. On average, ORD-GAP has the best storing and query retrieval time. Conclusion: The main contributions of this paper are: (i) A robust labeling scheme named ORD-GAP that assigns certain gap between each node to support future insertion, and (ii) An efficient mapping scheme, which built upon ORD-GAP labeling scheme to transform XML into RDB effectively.

Improving the support for XML dynamic updates using a hybridization labeling scheme (ORD-GAP).

Background: As the standard for the exchange of data over the World Wide Web, it is important to ensure that the eXtensible Markup Language (XML) database is capable of supporting not only efficient query processing but also capable of enduring frequent data update operations over the dynamic changes of Web content. Most of the existing XML annotation is based on a labeling scheme to identify each hierarchical position of the XML nodes. This computation is costly as any updates will cause the whole XML tree to be re-labelled. This impact can be observed on large datasets. Therefore, a robust labeling scheme that avoids re-labeling is crucial. Method: Here, we present ORD-GAP (named after Order Gap), a robust and persistent XML labeling scheme that supports dynamic updates. ORD-GAP assigns unique identifiers with gaps in-between XML nodes, which could easily identify the level, Parent-Child (P-C), Ancestor-Descendant (A-D) and sibling relationship. ORD-GAP adopts the OrdPath labeling scheme for any future insertion. Results: We demonstrate that ORD-GAP is robust enough for dynamic updates, and have implemented it in three use cases: (i) left-most, (ii) in-between and (iii) right-most insertion. Experimental evaluations on DBLP dataset demonstrated that ORD-GAP outperformed existing approaches such as ORDPath and ME Labeling concerning database storage size, data loading time and query retrieval. On average, ORD-GAP has the best storing and query retrieval time. Conclusion: The main contributions of this paper are: (i) A robust labeling scheme named ORD-GAP that assigns certain gap between each node to support future insertion, and (ii) An efficient mapping scheme, which built upon ORD-GAP labeling scheme to transform XML into RDB effectively.

ITrust-A Trustworthy and Efficient Mapping Scheme in Elliptic Curve Cryptography.

Recently, many platforms have outsourced tasks to numerous smartphone devices known as Mobile Crowd-sourcing System (MCS). The data is collected and transferred to the platform for further analysis and processing. These data needs to maintain confidentiality while moving from smartphones to the platform. Moreover, the limitations of computation resources in smartphones need to be addressed to balance the confidentiality of the data and the capabilities of the devices. For this reason, elliptic curve cryptography (ECC) is accepted, widespread, and suitable for use in limited resources environments such as smartphone devices. ECC reduces energy consumption and maximizes devices’ efficiency by using small crypto keys with the same strength of the required cryptography of other cryptosystems. Thus, ECC is the preferred approach for many environments, including the MCS, Internet of Things (IoT) and wireless sensor networks (WSNs). Many implementations of ECC increase the process of encryption and/or increase the space overhead by, for instance, incorrectly mapping points to EC with extra padding bits. Moreover, the wrong mapping method used in ECC results in increasing the computation efforts. This study provides comprehensive details about the mapping techniques used in the ECC mapping phase, and presents performance results about widely used elliptic curves. In addition, it suggests an optimal enhanced mapping method and size of padding bit to secure communications that guarantee the successful mapping of points to EC and reduce the size of padding bits.

A New Single-Image Super-Resolution Using Efficient Feature Fusion and Patch Similarity in Non-Euclidean Space

Efficient trade-off between the reconstruction qualities and the processing time of any single-image super-resolution reconstruction (SISRR) approach is critically influenced by two major aspects. These aspects are (i) appropriate representation of image patch in feature space and (ii) effective searching of candidate patches from the pool of training patches or learned dictionary. This paper proposes a neighbor embedding-based SISRR method. Novelties of our work include integration of (i) efficient feature mapping scheme which fuses multiple correlated features naturally, (ii) faster searching of candidate patches by measuring the patch correlation in non-Euclidean space and (iii) adaptive selection of neighborhood size using patch characteristic. Correlation among features is modeled via global covariance matrix, and the fusion process enables to preserve sufficient structural, spatial correlation among patches. Distance functions based on notion of generalized eigenvalue are used for measuring patch similarity which support faster searching of candidate patches. Performance analysis of the suggested method is compared with some of the competent state-of-the-art methodologies. From the simulated result analysis, proposed work is found to be outperforming in terms of sharpened image details with diminished effect of artifacts at a reasonable computational burden.

XML-REG: Transforming XML Into Relational Using Hybrid-Based Mapping Approach

eXtensible Markup Language (XML) is one of the most used standards for information sharing between applications and devices, both on the internet and local network. However, relational database (RDB) has been used by many enterprises as their data management system and will require an amount of cost to change the system completely, if they are to change to XML technology solely. Thus, a mapping scheme is required to provide seamless integration on bridging XML technologies and RDBs. In this paper, an efficient model-based mapping scheme named XML-REG is proposed. The XML document will first be read and parsed into the parser, namely Streaming API for XML (StAX) parser. Then, each node will then be assigned with unique identification label to show the exact position of nodes in the document. Subsequently, by employing the proposed algorithm, data will then be transformed into tables in the RDB storage. As the result, two tables, namely (i) value table to store information carried by text node of the document, and (ii) path table to store the hierarchy structure of the document will be created. Experimental evaluations demonstrated that XML-REG outperformed some existing approaches, such as Mini-XML, XAncestor, XMap and XRecursive in terms of data storage size, mapping time and query retrieval time. In addition, the scalability test has also been conducted to show the capability of these approaches in supporting huge datasets, by scaling the DBLP dataset by times 5, times 10 and times 15. The results showed that XML-REG has the closest to linear graph compared to other existing approaches. On average, XML-REG showed the best performance in terms of query retrieval time and database storage size.

Shadow Detection in Single RGB Images Using a Context Preserver Convolutional Neural Network Trained by Multiple Adversarial Examples.

Automatic identification of shadow regions in an image is a basic and yet very important task in many computer vision applications such as object detection, target tracking, and visual data analysis. Although shadow detection is a well-studied topic, current methods for identification of shadow are not as accurate as required. In this work, we propose a deep-learning method for shadow detection at a pixel-level that is suitable for single RGB images. The proposed CNN-based method benefits from a novel architecture through which global and local shadow attributes are identified using a new and efficient mapping scheme in the skip connection. It extracts and preserves shadow context in multiple layers and utilizes them gradually in multiple blocks to generate final shadow masks. The training phase of the network is simple and can be directly and easily adapted for other image segmentation tasks. The performance of the proposed system is evaluated on three publicly available datasets sbudataset,stcgan,ucf, where it outperforms the state-of-the-art Balanced Error Rates (BER) by 3%, 6.2%, and 11.4%.

Fuzzy Method and Neural Network Model Parallel Implementation of Multi-Layer Neural Network Based on Cloud Computing for Real Time Data Transmission in Large Offshore Platform

Abstract With the rapid development of electronic technology, network technology and cloud computing technology, the current data is increasing in the way of mass, has entered the era of big data. Based on cloud computing clusters, this paper proposes a novel method of parallel implementation of multilayered neural networks based on Map-Reduce. Namely in order to meet the requirements of big data processing, this paper presents an efficient mapping scheme for a fully connected multi-layered neural network, which is trained by using error back propagation (BP) algorithm based on Map-Reduce on cloud computing clusters (MRBP). The batch-training (or epoch-training) regimes are used by effective segmentation of samples on the clusters, and are adopted in the separated training method, weight summary to achieve convergence by iterating. For a parallel BP algorithm on the clusters and a serial BP algorithm on uniprocessor, the required time for implementing the algorithms is derived. The performance parameters, such as speed-up, optimal number and minimum of data nodes are evaluated for the parallel BP algorithm on the clusters. Experiment results demonstrate that the proposed parallel BP algorithm in this paper has better speed-up, faster convergence rate, less iterations than that of the existed algorithms.

Parallel implementation of multilayered neural networks based on Map-Reduce on cloud computing clusters

To meet the requirements of big data processing, this paper presents an efficient mapping scheme for a fully connected multilayered neural network, which is trained by using back-propagation (BP) algorithm based on Map-Reduce of cloud computing clusters. The batch-training (or epoch-training) regimes are used by effective segmentation of samples on the clusters, and are adopted in the separated training method, weight summary to achieve convergence by iterating. For a parallel BP algorithm on the clusters and a serial BP algorithm on an uniprocessor, the required time for implementing the algorithms is derived. The performance parameters, such as speedup, optimal number and minimum of data nodes are evaluated for the parallel BP algorithm on the clusters. Experiment results demonstrate that the proposed parallel BP algorithm in this paper has better speedup, faster convergence rate, less iterations than that of the existed algorithms.

Multiprocessor Real-Time Systems with Shared Resources: Utilization Bound and Mapping

In real-time systems, both scheduling theory and resource access protocols have been studied extensively. However, there is very limited research on scheduling algorithms for real-time systems with shared resources, where the problem becomes more prominent with the emergence of multicore processors. In this paper, focusing on partitioned-EDF scheduling and MSRP resource access protocol, we study the utilization bound and efficient task mapping schemes for a set of periodic real-time tasks that access shared resources in multiprocessor/multicore systems. Specifically, with synchronization overhead being considered, we illustrate the schedulability anomaly for such systems. We develop the first synchronization-cognizant utilization bound and further analyze its non-monotonicity where the bound can decrease when more processors are deployed. Then, we show that finding the optimal mapping for tasks with shared resources is NP-hard. Based on a novel approach that iteratively tightens the synchronization overhead, we propose two efficient synchronization-cognizant task mapping algorithms (SC-TMA) with the goal of achieving better schedulability and balanced workload on deployed processors. Finally, the proposed SC-TMA schemes are evaluated through extensive simulations with synthetic tasks. The results show that, the schedulability ratio and (average) system load under SC-TMA are close to that of an INLP (Integer Non-Linear Programming) based solution for small task systems. When compared to the existing task mapping algorithms, SC-TMA obtain much better schedulability ratio and lower/balanced workload on all processors.

RLAM: A Dynamic and Efficient Reinforcement Learning-Based Adaptive Mapping Scheme in Mobile WiMAX Networks

WiMAX (Worldwide Interoperability for Microwave Access) constitutes a candidate networking technology towards the 4G vision realization. By adopting the Orthogonal Frequency Division Multiple Access (OFDMA) technique, the latest IEEE 802.16x amendments manage to provide QoS-aware access services with full mobility support. A number of interesting scheduling and mapping schemes have been proposed in research literature. However, they neglect a considerable asset of the OFDMA-based wireless systems: the dynamic adjustment of the downlink-to-uplink width ratio. In order to fully exploit the supported mobile WiMAX features, we design, develop, and evaluate a rigorous adaptive model, which inherits its main aspects from the reinforcement learning field. The model proposed endeavours to efficiently determine the downlink-to-uplinkwidth ratio, on a frame-by-frame basis, taking into account both the downlink and uplink traffic in the Base Station (BS). Extensive evaluation results indicate that the model proposed succeeds in providing quite accurate estimations, keeping the average error rate below 15% with respect to the optimal sub-frame configurations. Additionally, it presents improved performance compared to other learning methods (e.g., learning automata) and notable improvements compared to static schemes that maintain a fixed predefined ratio in terms of service ratio and resource utilization.

RLAM: A Dynamic and Efficient Reinforcement Learning-Based Adaptive Mapping Scheme in Mobile WiMAX Networks

WiMAX Worldwide Interoperability for Microwave Access constitutes a candidate networking technology towards the 4G vision realization. By adopting the Orthogonal Frequency Division Multiple Access OFDMA technique, the latest IEEE 802.16x amendments manage to provide QoS-aware access services with full mobility support. A number of interesting scheduling and mapping schemes have been proposed in research literature. However, they neglect a considerable asset of the OFDMA-based wireless systems: the dynamic adjustment of the downlink-to-uplink width ratio. In order to fully exploit the supported mobile WiMAX features, we design, develop, and evaluate a rigorous adaptive model, which inherits its main aspects from the reinforcement learning field. The model proposed endeavours to efficiently determine the downlink-to-uplink width ratio, on a frame-by-frame basis, taking into account both the downlink and uplink traffic in the Base Station BS. Extensive evaluation results indicate that the model proposed succeeds in providing quite accurate estimations, keeping the average error rate below 15% with respect to the optimal sub-frame configurations. Additionally, it presents improved performance compared to other learning methods e.g., learning automata and notable improvements compared to static schemes that maintain a fixed predefined ratio in terms of service ratio and resource utilization.

Partially Coherent Constellation Design and Bit-Mapping with Coding for Correlated Fading Channels

The space-time (ST) constellation design problem and the related bit mapping schemes for multiple-input multipleoutput (MIMO) systems in spatially correlated Rayleigh fading channels with imperfect channel estimation at the receiver are considered. We assume that the channel estimation error covariance matrix, as well as transmit and receive correlation matrices are perfectly known both at the transmitter and at the receiver. We derive the cutoff rate (CR) and an upper bound (UB) on the average bit error probability (ABEP) expression for spatially correlated channels and propose them as the constellation design criteria subject to a given average transmit power constraint. Additionally, to use the resulting constellations together with forward error correction (FEC) codes requires efficient bit mapping schemes. Because these constellations lack geometrical symmetry in general, the Gray mapping is not always possible in the majority of the constellations obtained. Moreover, different mapping schemes may lead to highly different bit error rate (BER) performances. Thus, an efficient bit mapping scheme called the modified binary switching algorithm (MBSA) is proposed. It minimizes an upper bound on the ABEP (UB-ABEP) and is used to find the bit mapping schemes. Numerical examples show that the designed constellation and its optimized bit mapping together with turbo codes outperform the conventional constellations with respect to the frame error rate (FER).

A Distributed Workflow Management System with Case Study of Real-life Scientific Applications on Grids

Next-generation scientific applications feature complex workflows comprised of many computing modules with intricate inter-module dependencies. Supporting such scientific workflows in wide-area networks especially Grids and optimizing their performance are crucial to the success of collaborative scientific discovery. We develop a Scientific Workflow Automation and Management Platform (SWAMP), which enables scientists to conveniently assemble, execute, monitor, control, and steer computing workflows in distributed environments via a unified web-based user interface. The SWAMP architecture is built entirely on a seamless composition of web services: the functionalities of its own are provided and its interactions with other tools or systems are enabled through web services for easy access over standard Internet protocols while being independent of different platforms and programming languages. SWAMP also incorporates a class of efficient workflow mapping schemes to achieve optimal end-to-end performance based on rigorous performance modeling and algorithm design. The performance superiority of SWAMP over existing workflow mapping schemes is justified by extensive simulations, and the system efficacy is illustrated by large-scale experiments on real-life scientific workflows for climate modeling through effective system implementation, deployment, and testing on the Open Science Grid.

S-XML: An efficient mapping scheme to bridge XML and relational database

XML has recently emerged as the leading medium for data storage and data transfer over the World Wide Web due to its adaptable structure and flexibility in defining the tags. Many organizations had adopted XML as the principal facet in their online business applications. On the other hand, relational database is still widely used as the back-end database in most organizations. The diversity of these models need to be taken into account to ensure transparent and seamless integration. In this paper, we propose s-XML, an effective mapping scheme to bridge XML and relational database. Experimental results indicate that (1) s-XML is robust in terms of database storage and data loading; (2) s-XML processes query efficiently for complex chain and twig queries; and (3) s-XML is able to support large and skew-structured dataset as compared to relational DTD, Attribute and Edge approaches.

Online mapping of MPI-2 dynamic tasks to processes and threads

In recent years, distributed platforms became largely used on HPC, and most of these architectures have different levels of parallelism. Hence, one of the key design stages in parallel programming is task mapping which attempts to maximise processor utilisation and minimise communication cost. However, this depends on a programming environment with efficient mapping scheme. This paper presents a library to MPI-2 (libSpawn) that implements a scheme to map tasks between processes and threads in order to minimise communications and task creation costs. We evaluated the libSpawn with two dynamic MPI programs: Fibonacci and Mergesort. Our experiments demonstrate that the mapping scheme offers significant performance improvements.