A new computational approach for edge-preserving image decomposition

Multimedia Tools and Applications - Decomposition of an image into its cartoon part and texture part has been an interesting area of research. It is an important pre-processing step in many...     

A new approach for designing fault-tolerant WDM networks

In recent years, path protection has emerged as a widely accepted technique for designing survivable WDM networks. This approach is attractive, since it is able to provide bandwidth guarantees in the presence of link failures. However, it requires allocating resources for backup lightpaths, which remain idle under normal fault-free conditions. In this paper, we introduce a new approach for designing fault-tolerant WDM networks, based on the concept of survivable routing. Survivable routing of a logical topology ensures that the lightpaths are routed in such a way that a single link failure does not disconnect the network. When a topology is generated using our approach, it is guaranteed to have a survivable routing. We further ensure that the logical topology is able to handle the entire traffic demand after any single link failure. We first present an ILP that optimally designs a survivable logical topology, and then propose a heuristic for larger networks. Experimental results demonstrate that this new approach is able to provide guaranteed bandwidth, and is much more efficient in terms of resource utilization, compared to both dedicated and shared path protection.     

A new distributed management mechanism for ASI based networks

Advanced Switching Interconnect (ASI) is a high-speed serial interconnect embodied in the Dolphin Express family of interconnect products. In order to support high availability, the ASI specification established a management infrastructure, which is in charge of maintaining network operation after the occurrence of a topological change. When such a change occurs, the management mechanism discovers the new topology, calculates a set of valid routing paths, and distributes them to endpoints within the fabric. Several implementations for such a management mechanism have been proposed that use a centralized approach. These solutions can have negative effects with respect to network service availability. With the aim of eliminating these potential negative effects, this paper proposes a distributed solution for the computation of new paths. The distributed solution is evaluated for management entities with different performance capabilities, and for a range of traffic patterns and load levels. Our results show that the new distributed solution significantly reduces the change assimilation time and the negative impact on the network service when it is compared to a centralized solution.     

A new approach to artificial neural networks

A novel approach to artificial neural networks is presented. The philosophy of this approach is based on two aspects: the design of task-specific networks, and a new neuron model with multiple synapses. The synapses' connective strengths are modified through selective and cumulative processes conducted by axo-axonic connections from a feedforward circuit. This new concept was applied to the position control of a planar two-link manipulator exhibiting excellent results on learning capability and generalization when compared with a conventional feedforward network. In the present paper, the example shows only a network developed from a neuronal reflexive circuit with some useful artifices, nevertheless without the intention of covering all possibilities devised.     

Laplacian pyramid networks: A new approach for multispectral pansharpening

Pansharpening is about fusing a high spatial resolution panchromatic image with a simultaneously acquired multispectral image with lower spatial resolution. In this paper, we propose a Laplacian pyramid pansharpening network architecture for accurately fusing a high spatial resolution panchromatic image and a low spatial resolution multispectral image, aiming at getting a higher spatial resolution multispectral image. The proposed architecture considers three aspects. First, we use the Laplacian pyramid method whose blur kernels are designed according to the sensors’ modulation transfer functions to separate the images into multiple scales for fully exploiting the crucial spatial information at different spatial scales. Second, we develop a fusion convolutional neural network (FCNN) for each scale, combining them to form the final multi-scale network architecture. Specifically, we use recursive layers for the FCNN to share parameters across and within pyramid levels, thus significantly reducing the network parameters. Third, a total loss consisting of multiple across-scale loss functions is employed for training, yielding higher accuracy. Extensive experimental results based on quantitative and qualitative assessments exploiting benchmarking datasets demonstrate that the proposed architecture outperforms state-of-the-art pansharpening methods. Code is available at https://github.com/ChengJin-git/LPPN.     

A hybrid computational approach to derive new ground-motion prediction equations

A novel hybrid method coupling genetic programming and orthogonal least squares, called GP/OLS, was employed to derive new ground-motion prediction equations (GMPEs). The principal ground-motion parameters formulated were peak ground acceleration (PGA), peak ground velocity (PGV) and peak ground displacement (PGD). The proposed GMPEs relate PGA, PGV and PGD to different seismic parameters including earthquake magnitude, earthquake source to site distance, average shear-wave velocity, and faulting mechanisms. The equations were established based on an extensive database of strong ground-motion recordings released by Pacific Earthquake Engineering Research Center (PEER). For more validity verification, the developed equations were employed to predict the ground-motion parameters of the Iranian plateau earthquakes. A sensitivity analysis was carried out to determine the contributions of the parameters affecting PGA, PGV and PGD. The sensitivity of the models to the variations of the influencing parameters was further evaluated through a parametric analysis. The obtained GMPEs are effectively capable of estimating the site ground-motion parameters. The equations provide a prediction performance better than or comparable with the attenuation relationships found in the literature. The derived GMPEs are remarkably simple and straightforward and can reliably be used for the pre-design purposes.     

A new self-diagnosing approach based on petri nets and correlation graphs for fault management in wireless sensor networks

In most fault-detection algorithms in wireless sensor networks (WSNs), each sensor compares its data with the data of neighboring nodes. The majority of comparative methods will not work properly if more than half of the neighboring nodes of a sensor are faulty. Moreover, such comparative methods are unable to detect common mode failures (CMFs). Hence, having noticed the deficiencies of the existing comparative methods and as a reaction against such problems, we introduced a novel self-diagnosing approach to reduce the effect of neighboring node’s data in determining the status of nodes so that a sensor’s status will be determined independently of any comparisons. A sensor will be deemed to be fault-free if its components and the inner links between the components are flawless. In this paper, the behaviors of the components of a sensor are independently analyzed by means of the proposed model based on Petri nets and the links of the sensor’s components are investigated by means of the correlation graph. In addition, the authors extended and generalized the proposed method to all the nodes of a network and evaluated their operation. Simulation results showed that the modeling implemented by the HPSim tool can cover both permanent and transient faults accurately. Moreover, using the correlation graph and Pearson correlation coefficient helped us to gain confidence in the correctness of the inner links between the components of a sensor. Evaluation of the results indicated that the statistical results from the QI Macros tool were substantially similar to those from HPSim and Matlab tools. Furthermore, simulations results demonstrated that the detection accuracy and false alarm rate of the proposed method is acceptable even when the fault probability of each sensor is generally to be high. As a result, using these mechanisms leads to the development of the self-diagnosing capability in the sensors of a WSN.     

A framework for project management

Various technologies have been developed to try to realize the ideal of a fully integrated project support environment, including: centralized repositories, complex configuration management systems, process modelling and workflow, cooperating objects. The different approaches have led to a number of partial solutions and it has often proved difficult to integrate two or more of them to give more complete lifecycle coverage or a more complete view. This paper sets out some requirements for support environments for the management of software engineering projects by considering the major elements that constitute such a project and the necessary interactions between those elements.     

IT-based approach for effective management of project changes: A change management system (CMS)

In a perfect world, changes will be confined to the planning stages. However, late changes often occur during construction, and frequently cause serious disruption to the project. The need to make changes in a construction project is a matter of practical reality. Even the most thoughtfully planned project may necessitate changes due to various factors. The fundamental idea of any variation management system in a building project is to anticipate, recognize, evaluate, resolve, control, document, and learn from past variations in ways that support the overall viability of the project. Learning from past variations is imperative because the professionals can then improve and apply their experience in the future. Primarily, the study proposes six principles of change management. Based on these principles, a theoretical model for change management system (CMS) is developed. The theoretical model consists of six fundamental stages linked to two main components, i.e., a knowledge-base and a controls selection shell for making more informed decisions for effective management of variations. This paper argues that the information technology can be effectively used for providing an excellent opportunity for the professionals to learn from similar past projects and to better control project variations. Finally, the study briefly presents a knowledge-based decision support system (KBDSS) for the management of variations in educational building projects in Singapore. The KBDSS consists of two main components, i.e., a knowledge-base and a controls selection shell for selecting appropriate controls. The KBDSS is able to assist project managers by providing accurate and timely information for decision making, and a user-friendly system for analyzing and selecting the controls for variation orders for educational buildings. The CMS will enable the project team to take advantage of beneficial variations when the opportunity arises without an inordinate fear of the negative impacts. By having a systematic way to manage variations, the efficiency of project work and the likelihood of project success should increase. The study would assist building professionals in developing an effective variation management system. The system would be helpful for them to take proactive measures for reducing variation orders. Furthermore, with further generic enhancement and modification, the KBDSS will also be useful for the management of variations in other types of building projects, thus helping to raise the overall level of productivity in the construction industry. Hence, the system developed and the findings from this study would also be valuable for all building professionals in general.     

A computational approach for corner and vertex detection

Corners and vertexes are strong and useful features in computer vision for scene analysis, stereo matching, and motion analysis. Here, we deal with the development of a computational approach to these important features. We consider first a corner model and study analytically its behavior once it has been smoothed using the well-known Gaussian filter. This allows us to clarify the behavior of some well-knowncornerness measure based approaches used to detect these points of interest. Most of these classical approaches appear to detect points that do not correspond to the exact position of the corner. A new scale-space based approach that combines useful properties from the Laplacian and Beaudet's measure (Beaudet 1978) is then proposed in order to correct and detect exactly the corner position. An extension of this approach is then developed to solve the problem of trihedral vertex characterization and detection. In particular, it is shown that a trihedral vertex has two elliptic maxima on extremal contrast surfaces if the contrast is sufficient, and this allows us to classify trihedral vertexes in 2 classes: vertex, and vertex as corner. The corner-detection approach developed is applied to accurately detect trihedral vertexes using an additional test in order to make a distinction between trihedral vertexes and corners. Many experiments have been carried out using noisy synthetic data and real images containing corners and vertexes. Most of the promising results obtained are used to illustrate the experimental section of this paper.     

A new approach for automated parts recognition using time series analysisand neural networks

This paper presents a new approach for automated parts recognition. It is based on the use of the signature and autocorrelation functions for feature extraction and a neural network for the analysis of recognition. The signature represents the shapes of boundaries detected in digitized binary images of the parts. The autocorrelation coefficients computed from the signature are invariant to transformations such as scaling, translation and rotation of the parts. These unique extracted features are fed to the neural network. A multilayer perceptron with two hidden layers, along with a backpropagation learning algorithm, is used as a pattern classifier. In addition, the position information of the part for a robot with a vision system is described to permit grasping and pick-up. Experimental results indicate that the proposed approach is appropriate for the accurate and fast recognition and inspection of parts in automated manufacturing systems.     

A new synthesis approach for feedback neural networks based on theperceptron training algorithm

In this paper, a new synthesis approach is developed for associative memories based on the perceptron training algorithm. The design (synthesis) problem of feedback neural networks for associative memories is formulated as a set of linear inequalities such that the use of perceptron training is evident. The perceptron training in the synthesis algorithms is guaranteed to converge for the design of neural networks without any constraints on the connection matrix. For neural networks with constraints on the diagonal elements of the connection matrix, results concerning the properties of such networks and concerning the existence of such a network design are established. For neural networks with sparsity and/or symmetry constraints on the connection matrix, design algorithms are presented. Applications of the present synthesis approach to the design of associative memories realized by means of other feedback neural network models are studied. To demonstrate the applicability of the present results and to compare the present synthesis approach with existing design methods, specific examples are considered.     

A new approach to model virtual channels in interconnection networks

Dealing with virtual channels has always been a critical issue in developing analytical performance models for interconnection networks. Almost all previous studies relied on a method proposed by Dally to capture the effect of virtual channels multiplexing in the performance of interconnection networks. This paper presents a new method to model the effect of virtual channel multiplexing in high-speed wormhole-switched interconnection networks. Dally's method loses its accuracy as the traffic load increases due to blocking nature of wormhole-switched networks. Our new method is based on a finite capacity queue, M/G/1/V and comparing to Dally's method achieves a higher degree of accuracy under low, moderate and high traffic loads. Furthermore, its simplicity eases its employment under different network conditions and setup. The presented model is validated by means of an event driven simulator and a detailed comparison with Dally's method is presented.     

A new computational method for Volterra-Fredholm integral equations

The main purpose of this article is to demonstrate the use of the Adomian decomposition method for mixed nonlinear Volterra-Fredholm integral equations. A bound is also given for the Adomian decomposition series. Finally, numerical examples are presented to illustrate the implementation and accuracy of the decomposition method.     

A computational approach to synthesizing guards for hybrid systems

We propose a technique for synthesizing switching guards for hybrid systems to satisfy a given state-based safety constraint. Using techniques from sum of squares (SOS) optimization, we design guards defined by semialgebraic sets that trigger mode switches, and we guarantee that the synthesized switching policy does not allow Zeno executions. We demonstrate our approach on an example of switched affine systems and on an application to traffic ramp metering.     

A computational approach to edge detection

This paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution. We define detection and localization criteria for a class of edges, and present mathematical forms for these criteria as functionals on the operator impulse response. A third criterion is then added to ensure that the detector has only one response to a single edge. We use the criteria in numerical optimization to derive detectors for several common image features, including step edges. On specializing the analysis to step edges, we find that there is a natural uncertainty principle between detection and localization performance, which are the two main goals. With this principle we derive a single operator shape which is optimal at any scale. The optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a Gaussian-smoothed image. We extend this simple detector using operators of several widths to cope with different signal-to-noise ratios in the image. We present a general method, called feature synthesis, for the fine-to-coarse integration of information from operators at different scales. Finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge.