An entropy-based financial decision support system (e-FDSS) for project analysis in construction SMEs

Uncertainty contributes a major part in the accuracy of a decision-making process while its inconsistency is always difficult to be solved by existing decision-making tools. Entropy has been proved to be useful to evaluate the inconsistency of uncertainty among different respondents. The study demonstrates an entropy-based financial decision support system called e-FDSS. This integrated system provides decision support to evaluate attributes (funding options and multiple risks) available in projects. Fuzzy logic theory is included in the system to deal with the qualitative aspect of these options and risks. An adaptive genetic algorithm (AGA) is also employed to solve the decision algorithm in the system in order to provide optimal and consistent rates to these attributes. Seven simplified and parallel projects from a Hong Kong construction small and medium enterprise (SME) were assessed to evaluate the system. The result shows that the system calculates risk adjusted discount rates (RADR) of projects in an objective way. These rates discount project cash flow impartially. Inconsistency of uncertainty is also successfully evaluated by the use of the entropy method. Finally, the system identifies the favourable funding options that are managed by a scheme called SME Loan Guarantee Scheme (SGS). Based on these results, resource allocation could then be optimized and the best time to start a new project could also be identified throughout the overall project life cycle.

The parallel programming of voluntary and reflexive saccades

A novel two-step paradigm was used to investigate the parallel programming of consecutive, stimulus-elicited ('reflexive') and endogenous ('voluntary') saccades. The mean latency of voluntary saccades, made following the first reflexive saccades in two-step conditions, was significantly reduced compared to that of voluntary saccades made in the single-step control trials. The latency of the first reflexive saccades was modulated by the requirement to make a second saccade: first saccade latency increased when a second voluntary saccade was required in the opposite direction to the first saccade, and decreased when a second saccade was required in the same direction as the first reflexive saccade. A second experiment confirmed the basic effect and also showed that a second reflexive saccade may be programmed in parallel with a first voluntary saccade. The results support the view that voluntary and reflexive saccades can be programmed in parallel on a common motor map. (c) 2006 Elsevier Ltd. All rights reserved.

Variable step LMS algorithm using the accumulated instantaneous error concept

The convergence speed of the standard Least Mean Square adaptive array may be degraded in mobile communication environments. Different conventional variable step size LMS algorithms were proposed to enhance the convergence speed while maintaining low steady state error. In this paper, a new variable step LMS algorithm, using the accumulated instantaneous error concept is proposed. In the proposed algorithm, the accumulated instantaneous error is used to update the step size parameter of standard LMS is varied. Simulation results show that the proposed algorithm is simpler and yields better performance than conventional variable step LMS.

Parallel and distributed computing with Java

The Java language first came to public attention in 1995. Within a year, it was being speculated that Java may be a good language for parallel and distributed computing. Its core features, including being objected oriented and platform independence, as well as having built-in network support and threads, has encouraged this view. Today, Java is being used in almost every type of computer-based system, ranging from sensor networks to high performance computing platforms, and from enterprise applications through to complex research-based.simulations. In this paper the key features that make Java a good language for parallel and distributed computing are first discussed. Two Java-based middleware systems, namely MPJ Express, an MPI-like Java messaging system, and Tycho, a wide-area asynchronous messaging framework with an integrated virtual registry are then discussed. The paper concludes by highlighting the advantages of using Java as middleware to support distributed applications.

Unsupervised genetic algorithm deployed for intrusion detection

This paper represents the first step in an on-going work for designing an unsupervised method based on genetic algorithm for intrusion detection. Its main role in a broader system is to notify of an unusual traffic and in that way provide the possibility of detecting unknown attacks. Most of the machine-learning techniques deployed for intrusion detection are supervised as these techniques are generally more accurate, but this implies the need of labeling the data for training and testing which is time-consuming and error-prone. Hence, our goal is to devise an anomaly detector which would be unsupervised, but at the same time robust and accurate. Genetic algorithms are robust and able to avoid getting stuck in local optima, unlike the rest of clustering techniques. The model is verified on KDD99 benchmark dataset, generating a solution competitive with the solutions of the state-of-the-art which demonstrates high possibilities of the proposed method.

A new algorithm for OFDM joint data detection and phase noise cancellation

This paper proposes a new iterative algorithm for OFDM joint data detection and phase noise (PHN) cancellation based on minimum mean square prediction error. We particularly highlight the problem of "overfitting" such that the iterative approach may converge to a trivial solution. Although it is essential for this joint approach, the overfitting problem was relatively less studied in existing algorithms. In this paper, specifically, we apply a hard decision procedure at every iterative step to overcome the overfitting. Moreover, compared with existing algorithms, a more accurate Pade approximation is used to represent the phase noise, and finally a more robust and compact fast process based on Givens rotation is proposed to reduce the complexity to a practical level. Numerical simulations are also given to verify the proposed algorithm.

A sparse kernel density estimation algorithm using forward constrained regression

Using the classical Parzen window (PW) estimate as the target function, the sparse kernel density estimator is constructed in a forward constrained regression manner. The leave-one-out (LOO) test score is used for kernel selection. The jackknife parameter estimator subject to positivity constraint check is used for the parameter estimation of a single parameter at each forward step. As such the proposed approach is simple to implement and the associated computational cost is very low. An illustrative example is employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with comparable accuracy to that of the classical Parzen window estimate.

An on-line algorithm of uncertain time delay estimation in a continuous system

In this paper, we present an on-line estimation algorithm for an uncertain time delay in a continuous system based on the observational input-output data, subject to observational noise. The first order Pade approximation is used to approximate the time delay. At each time step, the algorithm combines the well known Kalman filter algorithm and the recursive instrumental variable least squares (RIVLS) algorithm in cascade form. The instrumental variable least squares algorithm is used in order to achieve the consistency of the delay parameter estimate, since an error-in-the-variable model is involved. An illustrative example is utilized to demonstrate the efficacy of the proposed approach.

A relative path attenuation approximation algorithm for DVB-T systems

In the United Kingdom and in fact throughout Europe, the chosen standard for digital terrestrial television is the European Telecommunications Standards Institute (ETSI) ETN 300 744 also known as Digital Video Broadcasting - Terrestrial (DVB-T). The modulation method under this standard was chosen to be Orthogonal Frequency Division Multiplex (0FD4 because of the apparent inherent capability for withstanding the effects of multipath. Within the DVB-T standard, the addition of pilot tones was included that can be used for many applications such as channel impulse response estimation or local oscillator phase and frequency offset estimation. This paper demonstrates a technique for an estimation of the relative path attenuation of a single multipath signal that can be used as a simple firmware update for a commercial set-top box. This technique can be used to help eliminate the effects of multipath(1).

Hierarchical composite regular parallel architecture

The design space of emerging heterogenous multi-core architectures with re-configurability element makes it feasible to design mixed fine-grained and coarse-grained parallel architectures. This paper presents a hierarchical composite array design which extends the curret design space of regular array design by combining a sequence of transformations. This technique is applied to derive a new design of a pipelined parallel regular array with different dataflow between phases of computation.

Investigation into the performance of an algorithm for deriving concise descriptions of optic flow fields for real-time control

This paper describes a region-based algorithm for deriving a concise description of a first order optical flow field. The algorithm described achieves performance improvements over existing algorithms without compromising the accuracy of the flow field values calculated. These improvements are brought about by not computing the entire flow field between two consecutive images, but by considering only the flow vectors of a selected subset of the images. The algorithm is presented in the context of a project to balance a bipedal robot using visual information.

Extending the friction cone algorithm for arbitrary polygon based haptic objects

Most haptic environments are based on single point interactions whereas in practice, object manipulation requires multiple contact points between the object, fingers, thumb and palm. The Friction Cone Algorithm was developed specifically to work well in a multi-finger haptic environment where object manipulation would occur. However, the Friction Cone Algorithm has two shortcomings when applied to polygon meshes: there is no means of transitioning polygon boundaries or feeling non-convex edges. In order to overcome these deficiencies, Face Directed Connection Graphs have been developed as well as a robust method for applying friction to non-convex edges. Both these extensions are described herein, as well as the implementation issues associated with them.

Force shading and bump mapping using the friction cone algorithm

Previous work has presented the friction cone algorithm, a generalised method to resolve forces on a simulated haptic object when two or more fingers are in contact. Two extensions to the friction cone algorithm are presented: force shading and bump mapping. Force shading removes the discontinuities that are present when transitioning from one face to the next, whilst bump mapping provides a mechanism for rendering haptic textures on polygonal surfaces. Both these extensions can be combined whilst still maintaining the friction cone algorithm's intrinsic ability to simulate arbitrarily complex friction models.

A novel two pass hexagonal search algorithm for motion estimation

This paper presents a novel two-pass algorithm constituted by Linear Hashtable Motion Estimation Algorithm (LHMEA) and Hexagonal Search (HEXBS). compensation. for block base motion On the basis of research from previous algorithms, especially an on-the-edge motion estimation algorithm called hexagonal search (HEXBS), we propose the LHMEA and the Two-Pass Algorithm (TPA). We introduce hashtable into video compression. In this paper we employ LHMEA for the first-pass search in all the Macroblocks (MB) in the picture. Motion Vectors (MV) are then generated from the first-pass and are used as predictors for second-pass HEXBS motion estimation, which only searches a small number of MBs. The evaluation of the algorithm considers the three important metrics being time, compression rate and PSNR. The performance of the algorithm is evaluated by using standard video sequences and the results are compared to current algorithms. Experimental results show that the proposed algorithm can offer the same compression rate as the Full Search. LHMEA with TPA has significant improvement on HEXBS and shows a direction for improving other fast motion estimation algorithms, for example Diamond Search.

Two-Pass Hexagonal algorithm with improved Hashtable structure for motion estimation

This paper presents an improved Two-Pass Hexagonal (TPA) algorithm constituted by Linear Hashtable Motion Estimation Algorithm (LHMEA) and Hexagonal Search (HEXBS) for motion estimation. In the TPA, Motion Vectors (MV) are generated from the first-pass LHMEA and are used as predictors for second-pass HEXBS motion estimation, which only searches a small number of Macroblocks (MBs). The hashtable structure of LHMEA is improved compared to the original TPA and LHMEA. The evaluation of the algorithm considers the three important metrics being processing time, compression rate and PSNR. The performance of the algorithm is evaluated by using standard video sequences and the results are compared to current algorithms.