OTIS-MOT: an efficient interconnection network for parallel processing

Mesh of trees (MOT) is well known for its small diameter, high bisection width, simple decomposability and area universality. On the other hand, OTIS (Optical Transpose Interconnection System) provides an efficient optoelectronic model for massively parallel processing system. In this paper, we present OTIS-MOT as a competent candidate for a two-tier architecture that can take the advantages of both the OTIS and the MOT. We show that an n⁴_-n^{4}_{-} processor OTIS-MOT has diameter 8log n ^∗+1 (The base of the logarithm is assumed to be 2 throughout this paper.) and fault diameter 8log n+2 under single node failure. We establish other topological properties such as bisection width, multiple paths and the modularity. We show that many communication as well as application algorithms can run on this network in comparable time or even faster than other similar tree-based two-tier architectures. The communication algorithms including row/column-group broadcast and one-to-all broadcast are shown to require O(log n) time, multicast in O(n ²log n) time and the bit-reverse permutation in O(n) time. Many parallel algorithms for various problems such as finding polynomial zeros, sales forecasting, matrix-vector multiplication and the DFT computation are proposed to map in O(log n) time. Sorting and prefix computation are also shown to run in O(log n) time.     

A framework for development and evaluation of a dynamic subchannel allocation scheme in an OFDMA system

This paper presents a framework for allocating radio resources to the Access Points (APs) introducing an Access Point Controller (APC). Radio resources can be either time slots or subchannels. The APC assigns subchannels to the APs using a dynamic subchannel allocation scheme. The developed framework evaluates the dynamic subchannel allocation scheme for a downlink multicellular Orthogonal Frequency Division Multiple Access (OFDMA) system. In the considered system, each AP and the associated Mobile Terminals (MTs) are not operating on a frequency channel with fixed bandwidth, rather the channel bandwidth for each AP is dynamically adapted according to the traffic load. The subchannels assignment procedure is based on quality estimations due to the interference measurements and the current traffic load. The traffic load estimation is realized with the measurement of the utilization of the assigned radio resources. The reuse partitioning for the radio resources is done by estimating mutual Signal to Interference Ratio (SIR) of the APs. The developed dynamic subchannel allocation ensures Quality of Service (QoS), better traffic adaptability, and higher spectrum efficiency with less computational complexity.

Computationally efficient solutions for tracking people with a mobile robot: an experimental evaluation of Bayesian filters

Modern service robots will soon become an essential part of modern society. As they have to move and act in human environments, it is essential for them to be provided with a fast and reliable tracking system that localizes people in the neighborhood. It is therefore important to select the most appropriate filter to estimate the position of these persons. This paper presents three efficient implementations of multisensor-human tracking based on different Bayesian estimators: Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Sampling Importance Resampling (SIR) particle filter. The system implemented on a mobile robot is explained, introducing the methods used to detect and estimate the position of multiple people. Then, the solutions based on the three filters are discussed in detail. Several real experiments are conducted to evaluate their performance, which is compared in terms of accuracy, robustness and execution time of the estimation. The results show that a solution based on the UKF can perform as good as particle filters and can be often a better choice when computational efficiency is a key issue.     

Pool-based anonymous communication framework for high-performance computing

We propose and analyze in details the revised model of XPROB, an infinite family of pool-based anonymous communication systems that can be used in various applications including high performance computing environments. XPROB overcomes the limitations of APROB Channel that only resists a global delaying adversary (GDA). Each instance of XPROB uses a pool mix as its core component to provide resistance against a global active adversary (GAA), a stronger yet more practical opponent than a GDA. For XPROB, a GAA can drop messages from users but cannot break the anonymity of the senders of messages. Analysis and experimental evaluations show that each instance of XPROB provides greater anonymity than APROB Channel for the same traffic load and user behaviors (rate and number of messages sent). In XPROB, any message can be delivered with high probability within a few rounds after its arrival into the system; thus, an opponent cannot be certain when a message will be delivered. Furthermore, users can choose their own preference balance between anonymity and delay. Through the evaluation, we prove that XPROB can provide anonymity for users in high-performance computing environments.     

Analysis of an efficient rule-based motion planning system for simulating human crowds

This paper proposes a rule-based motion planning system for agent-based crowd simulation, consisting of sets of rules for both collision avoidance and collision response. In order to avoid an oncoming collision, a set of rules for velocity sampling and evaluation is proposed, which aims to choose a velocity with an expected time to collision larger than a predefined threshold. In order to improve the efficiency over existing methods, the sampling procedure terminates upon finding an appropriate velocity. Moreover, the proposed motion planning system does not guarantee a collision-free movement. In case of collision, another set of rules is also defined to direct the agent to make a corresponding response. The experiment results show that the proposed approach can be applied in different scenarios, while making the simulation execution efficient.     

A parameterized shared-memory scheme for parameterized metaheuristics

This paper presents a parameterized shared-memory scheme for parameterized metaheuristics. The use of a parameterized metaheuristic facilitates experimentation with different metaheuristics and hybridation/combinations to adapt them to the particular problem we are working with. Due to the large number of experiments necessary for the metaheuristic selection and tuning, parallelism should be used to reduce the execution time. To obtain parallel versions of the metaheuristics and to adapt them to the characteristics of the parallel system, a unified parameterized shared-memory scheme is developed. Given a particular computational system and fixed parameters for the sequential metaheuristic, the appropriate selection of parameters in the unified parallel scheme eases the development of parallel efficient metaheuristics.     

TRLE—an efficient data compression scheme for image composition of volume rendering on distributed memory multicomputers

Data compression is a well-known method to improve the image composition time of parallel volume rendering on distributed memory multicomputers. In this paper, we propose an efficient data compression scheme, the template run-length encoding (TRLE) scheme, for image composition. Given an image with 2n×2n pixels, in the TRLE scheme, the image is treated as n×n blocks and each block has 2×2 pixels. Since a pixel can be a blank or non-blank pixel, there 16 templates in a block. To compress an image, the TRLE scheme encodes an image block by block similar to the run-length encoding scheme. However, the TRLE scheme can filter out or use small space to encode blocks whose four pixels are blank pixels, that is, the TRLE scheme can encode a partial image according to the shape of non-blank pixels. To evaluate the performance of the TRLE scheme, we compare the proposed scheme with the BR, the RLE, and the BRLC schemes. Since a data compression scheme needs to cooperate with some data communication schemes, in the implementation, the binary-swap, the parallel-pipelined, and the rotate-tiling data communication schemes are used. By combining the four data compression schemes with the three data communication schemes, we have twelve image composition methods. These twelve methods are implemented on an IBM SP2 parallel machine. Four volume datasets are used as test samples. The data computation time and the data communication time are measured. The experimental results show that the TRLE data compression scheme with the rotate-tiling data communication scheme outperforms other eleven image composition methods for all test samples.

A fault detection scheme for linear discrete-time systems with an integrated online performance evaluation

This paper is concerned with the design of the fault detection systems, into which a residual generation, evaluation and threshold are integrated, for linear discrete time-varying processes over a finite horizon. In the proposed design scheme, the residual generation is realised in the context of H_∞ fault estimation with a prescribed attenuation level. This attenuation level is minimised by using the Krein-space linear estimation theory and, subsequently, an H_∞ fault estimator with the minimum attenuation level is designed in terms of the solution to a set of Riccati-like recursions. For the residual evaluation and decision making purpose, the false alarm rate and fault detection rate indicators are introduced in the norm-based framework, which is integrated into the decision making procedure. For the online computations of the false alarm rate and fault detection rate indicators, further estimates delivered by the H_∞ fault estimator are applied without additional (online) computations. By means of checking the change in the false alarm rate and fault detection rate indicators, a decision is then made. In this way, the fault detection performance can be significantly improved. Finally, one application example is exploited to demonstrate the application of the proposed integrated fault detection and performance evaluation schemes.     

HUC-Prune: an efficient candidate pruning technique to mine high utility patterns

Traditional frequent pattern mining methods consider an equal profit/weight for all items and only binary occurrences (0/1) of the items in transactions. High utility pattern mining becomes a very important research issue in data mining by considering the non-binary frequency values of items in transactions and different profit values for each item. However, most of the existing high utility pattern mining algorithms suffer in the level-wise candidate generation-and-test problem and generate too many candidate patterns. Moreover, they need several database scans which are directly dependent on the maximum candidate length. In this paper, we present a novel tree-based candidate pruning technique, called HUC-Prune (High Utility Candidates Prune), to solve these problems. Our technique uses a novel tree structure, called HUC-tree (High Utility Candidates tree), to capture important utility information of the candidate patterns. HUC-Prune avoids the level-wise candidate generation process by adopting a pattern growth approach. In contrast to the existing algorithms, its number of database scans is completely independent of the maximum candidate length. Extensive experimental results show that our algorithm is very efficient for high utility pattern mining and it outperforms the existing algorithms.     

A formally specified ontology management API as a registry for ubiquitous computing systems

Recently, several standards have emerged for ontology markup languages that can be used to formalize all kinds of knowledge. However, there are no widely accepted standards yet that define APIs to manage ontological data. Processing ontological information still suffers from the heterogeneity imposed by the plethora of available ontology management systems. Moreover, ubiquitous computing environments usually comprise software components written in a variety of different programming languages, which makes it particularly difficult to establish a common ontology management API with programming language agnostic semantics. We implemented an ontological Knowledge Base Server, which can expose the functionality of arbitrary off-the-shelf ontology management systems via a formally specified and well defined API. A case study was carried out in order to demonstrate the feasibility of our approach to use a formally specified ontology management API to implement a registry for ubiquitous computing systems.     

A new noise-compensated estimation scheme for multichannel autoregressive signals from noisy observations

In many engineering applications concerning the recovery of signals from noisy observations, a common approach consists in adopting autoregressive (AR) models. This paper is concerned with not only the estimation of multichannel autoregressive (MAR) model parameters but also the recovery of signals. A new noise compensated parameter estimation scheme is introduced in this paper. It contains an advanced least square vector (ALSV) algorithm which not only keeps the advantage of blindly estimating the MAR parameters and the variance-covariance matrix of observation noises, but also aims at ensuring the variance-covariance matrix to be symmetric in each iterative procedure. Moreover, the estimation of variance-covariance matrix of input noise is proposed, and then we form an optimal filtering to recover the signals. In the numerical simulations, the estimation performance of the ALSV estimation algorithm significantly outperforms that of other existed methods. Moreover, the optimal filtering based on the ALSV algorithm leads to more accurate recovery of the true signals.     

Learning multi-linear representations of distributions for efficient inference

We examine the class of multi-linear representations (MLR) for expressing probability distributions over discrete variables. Recently, MLR have been considered as intermediate representations that facilitate inference in distributions represented as graphical models. We show that MLR is an expressive representation of discrete distributions and can be used to concisely represent classes of distributions which have exponential size in other commonly used representations, while supporting probabilistic inference in time linear in the size of the representation. Our key contribution is presenting techniques for learning bounded-size distributions represented using MLR, which support efficient probabilistic inference. We demonstrate experimentally that the MLR representations we learn support accurate and very efficient inference.     

Philippine computing—an overview

This study evaluates the capacity of the Internet to enhance development in emerging regions through Sen's freedom perspective. The paper begins with a qualitative evaluation of the Internet's potential as a freedom enhancer through examples and literature study. It then presents a quantitative evaluation based on web access logs obtained from the AirJaldi network in rural India. We categorize the data based on Sen's freedoms to contribute an information and communication technology-freedom taxonomy and note the challenges in doing so. The usage logs indicate that indeed users may have experienced enhancement in all of Sen's freedom categories; yet our qualitative evaluation suggests there is much unexploited potential. We conclude that it is important to look at the Internet-based Information and Communication Technologies for Development (ICTD) projects through Sen's freedom lens and call for such projects to be evaluated based on these broad freedom goals rather than on focused development goals.     

Scalable and efficient key management for heterogeneous sensor networks

As typical wireless sensor networks (WSNs) have resource limitations, predistribution of secret keys is possibly the most practical approach for secure network communications. In this paper, we propose a key management scheme based on random key predistribution for heterogeneous wireless sensor networks (HSNs). As large-scale homogeneous networks suffer from high costs of communication, computation, and storage requirements, the HSNs are preferred because they provide better performance and security solutions for scalable applications in dynamic environments. We consider hierarchical HSN consisting of a small number high-end sensors and a large number of low-end sensors. To address storage overhead problem in the constraint sensor nodes, we incorporate a key generation process, where instead of generating a large pool of random keys, a key pool is represented by a small number of generation keys. For a given generation key and a publicly known seed value, a keyed-hash function generates a key chain; these key chains collectively make a key pool. As dynamic network topology is native to WSNs, the proposed scheme allows dynamic addition and removal of nodes. This paper also reports the implementation and the performance of the proposed scheme on Crossbow’s MicaZ motes running TinyOS. The results indicate that the proposed scheme can be applied efficiently in resource-constrained sensor networks. We evaluate the computation and storage costs of two keyed-hash algorithms for key chain generation, HMAC-SHA1 and HMAC-MD5.

Teaching–learning-based metaheuristic scheme for modifying neural computing in appraising energy performance of building

Early assessment of the energy performance of buildings (EPB) is focused in this study. This task is carried out by predicting the cooling load (CL) in a residential building. To this end, due to the drawbacks of neural computing approaches (e.g., local minima), a novel metaheuristic technique, namely teaching–learning-based optimization (TLBO) is employed to modify a multi-layer perceptron neural network (MLPNN). The complexity of the proposed model is also optimized by a trial and error process. Evaluating the results revealed a high efficiency for this scheme. In this sense, the prediction error of the MLPNN was reduced by around 20%, and the correlation between the measured and forecasted CLs rose from 0.8875 to 0.9207. It was also deduced that the TLBO outperforms two benchmark optimizers of cuckoo optimization algorithm (COA) and league championship algorithm (LCA) in terms of both modeling accuracy and network complexity. Moreover, the TLBO-MLP emerged as the most time-effective hybrid as it required considerably lower computation time than COA-MLP and LCA-MLP. Regarding these advantages, the proposed model can be promisingly used for early assessment of EPB in practice.

     

Cloud computing in e-Science: research challenges and opportunities

Service-oriented architecture (SOA), workflow, the Semantic Web, and Grid computing are key enabling information technologies in the development of increasingly sophisticated e-Science infrastructures and application platforms. While the emergence of Cloud computing as a new computing paradigm has provided new directions and opportunities for e-Science infrastructure development, it also presents some challenges. Scientific research is increasingly finding that it is difficult to handle “big data” using traditional data processing techniques. Such challenges demonstrate the need for a comprehensive analysis on using the above-mentioned informatics techniques to develop appropriate e-Science infrastructure and platforms in the context of Cloud computing. This survey paper describes recent research advances in applying informatics techniques to facilitate scientific research particularly from the Cloud computing perspective. Our particular contributions include identifying associated research challenges and opportunities, presenting lessons learned, and describing our future vision for applying Cloud computing to e-Science. We believe our research findings can help indicate the future trend of e-Science, and can inform funding and research directions in how to more appropriately employ computing technologies in scientific research. We point out the open research issues hoping to spark new development and innovation in the e-Science field.     

A game-theoretic method of fair resource allocation for cloud computing services

As cloud-based services become more numerous and dynamic, resource provisioning becomes more and more challenging. A QoS constrained resource allocation problem is considered in this paper, in which service demanders intend to solve sophisticated parallel computing problem by requesting the usage of resources across a cloud-based network, and a cost of each computational service depends on the amount of computation. Game theory is used to solve the problem of resource allocation. A practical approximated solution with the following two steps is proposed. First, each participant solves its optimal problem independently, without consideration of the multiplexing of resource assignments. A Binary Integer Programming method is proposed to solve the independent optimization. Second, an evolutionary mechanism is designed, which changes multiplexed strategies of the initial optimal solutions of different participants with minimizing their efficiency losses. The algorithms in the evolutionary mechanism take both optimization and fairness into account. It is demonstrated that Nash equilibrium always exists if the resource allocation game has feasible solutions.     

Convergence of an implicit–explicit midpoint scheme for computational micromagnetics

Based on lowest-order finite elements in space, we consider the numerical integration of the Landau–Lifschitz–Gilbert equation (LLG). The dynamics of LLG is driven by the so-called effective field which usually consists of the exchange field, the external field, and lower-order contributions such as the stray field. The latter requires the solution of an additional partial differential equation in full space. Following Bartels and Prohl (2006), we employ the implicit midpoint rule to treat the exchange field. However, in order to treat the lower-order terms effectively, we combine the midpoint rule with an explicit Adams–Bashforth scheme. The resulting integrator is formally of second-order in time, and we prove unconditional convergence towards a weak solution of LLG. Numerical experiments underpin the theoretical findings.     

An efficient algorithm for learning to rank from preference graphs

In this paper, we introduce a framework for regularized least-squares (RLS) type of ranking cost functions and we propose three such cost functions. Further, we propose a kernel-based preference learning algorithm, which we call RankRLS, for minimizing these functions. It is shown that RankRLS has many computational advantages compared to the ranking algorithms that are based on minimizing other types of costs, such as the hinge cost. In particular, we present efficient algorithms for training, parameter selection, multiple output learning, cross-validation, and large-scale learning. Circumstances under which these computational benefits make RankRLS preferable to RankSVM are considered. We evaluate RankRLS on four different types of ranking tasks using RankSVM and the standard RLS regression as the baselines. RankRLS outperforms the standard RLS regression and its performance is very similar to that of RankSVM, while RankRLS has several computational benefits over RankSVM.