A Refinement of some Overrelaxation Algorithms for Solving a System of Linear Equations

In this paper we propose a refinement of some successive overrelaxation methods based on the reverse Gauss–Seidel method for solving a system of linear equations Ax = b by the decomposition A = Tm − Em − Fm, where Tm is a banded matrix of bandwidth 2m + 1. We study the convergence of the methods and give software implementation of algorithms in Mathematica package with numerical examples. ACM Computing Classification System (1998): G.1.3.

A Comparative Analysis of Predictive Learning Algorithms on High-Dimensional Microarray Cancer Data

This research evaluates pattern recognition techniques on a subclass of big data where the dimensionality of the input space (p) is much larger than the number of observations (n). Specifically, we evaluate massive gene expression microarray cancer data where the ratio κ is less than one. We explore the statistical and computational challenges inherent in these high dimensional low sample size (HDLSS) problems and present statistical machine learning methods used to tackle and circumvent these difficulties. Regularization and kernel algorithms were explored in this research using seven datasets where κ < 1. These techniques require special attention to tuning necessitating several extensions of cross-validation to be investigated to support better predictive performance. While no single algorithm was universally the best predictor, the regularization technique produced lower test errors in five of the seven datasets studied.

Generalized Priority Systems. Analytical Results and Numerical Algorithms

A class of priority systems with non-zero switching times, referred as generalized priority systems, is considered. Analytical results regarding the distribution of busy periods, queue lengths and various auxiliary characteristics are presented. These results can be viewed as generalizations of the Kendall functional equation and the Pollaczek-Khintchin transform equation, respectively. Numerical algorithms for systems’ busy periods and traffic coefficients are developed. ACM Computing Classification System (1998): 60K25.

A fenntartható fejlődés elvei és azok érvényre jutása a banki gyakorlatban II. rész (The principles of sustainable development and their realization in the banking practice II. part)

Az utóbbi évtizedben a környezettudatos gazdálkodás szerepének felértékelődése új tevékenységi területet nyitott meg a bankok előtt, amit nemzetközi szóhasználattal élve green banking-nek neveznek. A fenntartható fejlődést támogató projektek banki finanszírozása számos lehetőséget nyit a pénzügyi intézmények számára – hírnév, új ügyfélbázis, kockázatcsökkentés, jövedelmezőség növelése –, amennyiben a bankok a fenntarthatóság elveit megfelelően integrálják működési stratégiájukba. A cikksorozat második részében a szerzők a bankok fenntartható működését támogató külső és belső motiváló tényezőit veszik vizsgálat alá, amelyet már nemzetközi elvek és megállapodások is szabályoznak és támogatnak. Kiemelt fontosságot tulajdonítanak a környezettudatos gazdálkodás banki stratégiába történő integrálásának, melynek alapjait teremthetik meg a cikkben vizsgált nemzetközi irányelvek. ________ In the last decade, a lot of publications came to light, dealing with the social and environmental responsibility of financial institutions. Banks are expected to improve their competitive advantage by demonstrating economic development, while promoting environment care and social responsibility. This sustainable finance means to manage environmental and social risks in corporate lending and project financing. Green financial institutions have developed a wide range of innovations that can support sustainable development. This article highlights a few external and internal factor that support this aim, and which are regulated by international principles. However the authors’ survey shows that in the relevant literature there are a lot of ways to define sustainability and its achievement. They suggest that all of them can be interpreted associated with green banking operation. The goal of this paper is to identify the role of financial institutions in achieving sustainable development, and stress its strategic importance. The authors also attempt to show, why and how can this sector become the promoter of sustainability.

Optimizing the radiation pattern of smart antennas using genetic algorithms

This work is directed towards optimizing the radiation pattern of smart antennas using genetic algorithms. The structure of the smart antennas based on Space Division Multiple Access (SDMA) is proposed. It is composed of adaptive antennas, each of which has adjustable weight elements for amplitudes and phases of signals. The corresponding radiation pattern formula available for the utilization of numerical optimization techniques is deduced. Genetic algorithms are applied to search the best phase-amplitude weights or phase-only weights with which the optimal radiation pattern can be achieved. ^ One highlight of this work is the proposed optimal radiation pattern concept and its implementation by genetic algorithms. The results show that genetic algorithms are effective for the true Signal-Interference-Ratio (SIR) design of smart antennas. This means that not only nulls can be put in the directions of the interfering signals but also simultaneously main lobes can be formed in the directions of the desired signals. The optimal radiation pattern of a smart antenna possessing SDMA ability has been achieved. ^ The second highlight is on the weight search by genetic algorithms for the optimal radiation pattern design of antennas having more than one interfering signal. The regular criterion for determining which chromosome should be kept for the next step iteration is modified so as to improve the performance of the genetic algorithm iteration. The results show that the modified criterion can speed up and guarantee the iteration to be convergent. ^ In addition, the comparison between phase-amplitude perturbations and phase-only perturbations for the radiation pattern design of smart antennas are carried out. The effects of parameters used by the genetic algorithm on the optimal radiation pattern design are investigated. Valuable results are obtained. ^

Optimization of adaptive traffic signal control with transit signal priority at isolated intersections using parallel genetic algorithms

Optimization of adaptive traffic signal timing is one of the most complex problems in traffic control systems. This dissertation presents a new method that applies the parallel genetic algorithm (PGA) to optimize adaptive traffic signal control in the presence of transit signal priority (TSP). The method can optimize the phase plan, cycle length, and green splits at isolated intersections with consideration for the performance of both the transit and the general vehicles. Unlike the simple genetic algorithm (GA), PGA can provide better and faster solutions needed for real-time optimization of adaptive traffic signal control. ^ An important component in the proposed method involves the development of a microscopic delay estimation model that was designed specifically to optimize adaptive traffic signal with TSP. Macroscopic delay models such as the Highway Capacity Manual (HCM) delay model are unable to accurately consider the effect of phase combination and phase sequence in delay calculations. In addition, because the number of phases and the phase sequence of adaptive traffic signal may vary from cycle to cycle, the phase splits cannot be optimized when the phase sequence is also a decision variable. A "flex-phase" concept was introduced in the proposed microscopic delay estimation model to overcome these limitations. ^ The performance of PGA was first evaluated against the simple GA. The results show that PGA achieved both faster convergence and lower delay for both under- or over-saturated traffic conditions. A VISSIM simulation testbed was then developed to evaluate the performance of the proposed PGA-based adaptive traffic signal control with TSP. The simulation results show that the PGA-based optimizer for adaptive TSP outperformed the fully actuated NEMA control in all test cases. The results also show that the PGA-based optimizer was able to produce TSP timing plans that benefit the transit vehicles while minimizing the impact of TSP on the general vehicles. The VISSIM testbed developed in this research provides a powerful tool to design and evaluate different TSP strategies under both actuated and adaptive signal control. ^

Signal processing and information fusing algorithms for the synthesis of an alternative electromyogram/eye gaze tracking computer cursor control system

This research pursued the conceptualization and real-time verification of a system that allows a computer user to control the cursor of a computer interface without using his/her hands. The target user groups for this system are individuals who are unable to use their hands due to spinal dysfunction or other afflictions, and individuals who must use their hands for higher priority tasks while still requiring interaction with a computer. ^ The system receives two forms of input from the user: Electromyogram (EMG) signals from muscles in the face and point-of-gaze coordinates produced by an Eye Gaze Tracking (EGT) system. In order to produce reliable cursor control from the two forms of user input, the development of this EMG/EGT system addressed three key requirements: an algorithm was created to accurately translate EMG signals due to facial movements into cursor actions, a separate algorithm was created that recognized an eye gaze fixation and provided an estimate of the associated eye gaze position, and an information fusion protocol was devised to efficiently integrate the outputs of these algorithms. ^ Experiments were conducted to compare the performance of EMG/EGT cursor control to EGT-only control and mouse control. These experiments took the form of two different types of point-and-click trials. The data produced by these experiments were evaluated using statistical analysis, Fitts' Law analysis and target re-entry (TRE) analysis. ^ The experimental results revealed that though EMG/EGT control was slower than EGT-only and mouse control, it provided effective hands-free control of the cursor without a spatial accuracy limitation, and it also facilitated a reliable click operation. This combination of qualities is not possessed by either EGT-only or mouse control, making EMG/EGT cursor control a unique and practical alternative for a user's cursor control needs. ^

Internet Protocol Television (IPTV) controller realization with Very Large Scale Integration (VLSI) modules

Internet Protocol Television (IPTV) is a system where a digital television service is delivered by using Internet Protocol over a network infrastructure. There is considerable confusion and concern about the IPTV, since two different technologies have to be mended together to provide the end customers with some thing better than the conventional television. In this research, functional architecture of the IPTV system was investigated. Very Large Scale Integration based system for streaming server controller were designed and different ways of hosting a web server which can be used to send the control signals to the streaming server controller were studied. The web server accepts inputs from the keyboard and FPGA board switches and depending on the preset configuration the server will open a selected web page and also sends the control signals to the streaming server controller. It was observed that the applications run faster on PowerPC since it is embedded into the FPGA. Commercial market and Global deployment of IPTV were discussed.

Genetic algorithms and investment strategies: A global perspective

The profitability of momentum portfolios in the equity markets is derived from the continuation of stock returns over medium time horizons. The empirical evidence of momentum, however, is significantly different across markets around the world. The purpose of this dissertation is to: (1) help global investors determine the optimal selection and holding periods for momentum portfolios, (2) evaluate the profitability of the optimized momentum portfolios in different time periods and market states, (3) assess the investment strategy profits after considering transaction costs, and (4) interpret momentum returns within the framework of prior studies on investors’ behavior. Improving on the traditional practice of selecting arbitrary selection and holding periods, a genetic algorithm (GA) is employed. The GA performs a thorough and structured search to capture the return continuations and reversals patterns of momentum portfolios. Three portfolio formation methods are used: price momentum, earnings momentum, and earnings and price momentum and a non-linear optimization procedure (GA). The focus is on common equity of the U.S. and a select number of countries, including Australia, France, Germany, Japan, the Netherlands, Sweden, Switzerland and the United Kingdom. The findings suggest that the evolutionary algorithm increases the annualized profits of the U.S. momentum portfolios. However, the difference in mean returns is statistically significant only in certain cases. In addition, after considering transaction costs, both price and earnings and price momentum portfolios do not appear to generate abnormal returns. Positive risk-adjusted returns net of trading costs are documented solely during “up” markets for a portfolio long in prior winners only. The results on the international momentum effects indicate that the GA improves the momentum returns by 2 to 5% on an annual basis. In addition, the relation between momentum returns and exchange rate appreciation/depreciation is examined. The currency appreciation does not appear to influence significantly momentum profits. Further, the influence of the market state on momentum returns is not uniform across the countries considered. The implications of the above findings are discussed with a focus on the practical aspects of momentum investing, both in the U.S. and globally.

Algorithms for scheduling parallel batch processing machines with non-identical job ready times

This research is motivated by a practical application observed at a printed circuit board (PCB) manufacturing facility. After assembly, the PCBs (or jobs) are tested in environmental stress screening (ESS) chambers (or batch processing machines) to detect early failures. Several PCBs can be simultaneously tested as long as the total size of all the PCBs in the batch does not violate the chamber capacity. PCBs from different production lines arrive dynamically to a queue in front of a set of identical ESS chambers, where they are grouped into batches for testing. Each line delivers PCBs that vary in size and require different testing (or processing) times. Once a batch is formed, its processing time is the longest processing time among the PCBs in the batch, and its ready time is given by the PCB arriving last to the batch. ESS chambers are expensive and a bottleneck. Consequently, its makespan has to be minimized. ^ A mixed-integer formulation is proposed for the problem under study and compared to a formulation recently published. The proposed formulation is better in terms of the number of decision variables, linear constraints and run time. A procedure to compute the lower bound is proposed. For sparse problems (i.e. when job ready times are dispersed widely), the lower bounds are close to optimum. ^ The problem under study is NP-hard. Consequently, five heuristics, two metaheuristics (i.e. simulated annealing (SA) and greedy randomized adaptive search procedure (GRASP)), and a decomposition approach (i.e. column generation) are proposed—especially to solve problem instances which require prohibitively long run times when a commercial solver is used. Extensive experimental study was conducted to evaluate the different solution approaches based on the solution quality and run time. ^ The decomposition approach improved the lower bounds (or linear relaxation solution) of the mixed-integer formulation. At least one of the proposed heuristic outperforms the Modified Delay heuristic from the literature. For sparse problems, almost all the heuristics report a solution close to optimum. GRASP outperforms SA at a higher computational cost. The proposed approaches are viable to implement as the run time is very short. ^

Efficient asynchronous scheduling algorithms for cost-effective buffered crossbar switches

Buffered crossbar switches have recently attracted considerable attention as the next generation of high speed interconnects. They are a special type of crossbar switches with an exclusive buffer at each crosspoint of the crossbar. They demonstrate unique advantages over traditional unbuffered crossbar switches, such as high throughput, low latency, and asynchronous packet scheduling. However, since crosspoint buffers are expensive on-chip memories, it is desired that each crosspoint has only a small buffer. This dissertation proposes a series of practical algorithms and techniques for efficient packet scheduling for buffered crossbar switches. To reduce the hardware cost of such switches and make them scalable, we considered partially buffered crossbars, whose crosspoint buffers can be of an arbitrarily small size. Firstly, we introduced a hybrid scheme called Packet-mode Asynchronous Scheduling Algorithm (PASA) to schedule best effort traffic. PASA combines the features of both distributed and centralized scheduling algorithms and can directly handle variable length packets without Segmentation And Reassembly (SAR). We showed by theoretical analysis that it achieves 100% throughput for any admissible traffic in a crossbar with a speedup of two. Moreover, outputs in PASA have a large probability to avoid the more time-consuming centralized scheduling process, and thus make fast scheduling decisions. Secondly, we proposed the Fair Asynchronous Segment Scheduling (FASS) algorithm to handle guaranteed performance traffic with explicit flow rates. FASS reduces the crosspoint buffer size by dividing packets into shorter segments before transmission. It also provides tight constant performance guarantees by emulating the ideal Generalized Processor Sharing (GPS) model. Furthermore, FASS requires no speedup for the crossbar, lowering the hardware cost and improving the switch capacity. Thirdly, we presented a bandwidth allocation scheme called Queue Length Proportional (QLP) to apply FASS to best effort traffic. QLP dynamically obtains a feasible bandwidth allocation matrix based on the queue length information, and thus assists the crossbar switch to be more work-conserving. The feasibility and stability of QLP were proved, no matter whether the traffic distribution is uniform or non-uniform. Hence, based on bandwidth allocation of QLP, FASS can also achieve 100% throughput for best effort traffic in a crossbar without speedup.

Modeling communication services for rapid realization

The availability and pervasiveness of new communication services, such as mobile networks and multimedia communication over digital networks, has resulted in strong demands for approaches to modeling and realizing customized communication systems. The stovepipe approach used to develop today's communication applications is no longer effective because it results in a lengthy and expensive development cycle. To address this need, the Communication Virtual Machine (CVM) technology has been developed by researchers at Florida International University. The CVM technology includes the Communication Modeling Language (CML) and the platform, CVM, to model and rapidly realize communication models. ^ In this dissertation, we investigate the basic communication primitives needed to capture and specify an end-user's requirements for communication-intensive applications, and how these specifications can be automatically realized. To identify the basic communication primitives, we perform a feature analysis on a set of communication-intensive scenarios from the healthcare domain. Based on the feature analysis, we define a new version of CML that includes the meta-model definition (abstract syntax and static semantics) and a partial behavior model (operational semantics). To validate our CML definition, we present a case study that shows how one of the scenarios from the healthcare domain is modeled and automatically realized. ^

In vivo tissue diagnosis for myocardial infarction using optical spectroscopy with novel spectral interpretation algorithms

In recent decades, the rapid development of optical spectroscopy for tissue diagnosis has been indicative of its high clinical value. The goal of this research is to prove the feasibility of using diffuse reflectance spectroscopy and fluorescence spectroscopy to assess myocardial infarction (MI) in vivo. The proposed optical technique was designed to be an intra-operative guidance tool that can provide useful information about the condition of an infarct for surgeons and researchers. ^ In order to gain insight into the pathophysiological characteristics of an infarct, two novel spectral analysis algorithms were developed to interpret diffuse reflectance spectra. The algorithms were developed based on the unique absorption properties of hemoglobin for the purpose of retrieving regional hemoglobin oxygenation saturation and concentration data in tissue from diffuse reflectance spectra. The algorithms were evaluated and validated using simulated data and actual experimental data. ^ Finally, the hypothesis of the study was validated using a rabbit model of MI. The mechanism by which the MI was induced was the ligation of a major coronary artery of the left ventricle. Three to four weeks after the MI was induced, the extent of myocardial tissue injury and the evolution of the wound healing process were investigated using the proposed spectroscopic methodology as well as histology. The correlations between spectral alterations and histopathological features of the MI were analyzed statistically. ^ The results of this PhD study demonstrate the applicability of the proposed optical methodology for assessing myocardial tissue damage induced by MI in vivo. The results of the spectral analysis suggest that connective tissue proliferation induced by MI significantly alter the characteristics of diffuse reflectance and fluorescence spectra. The magnitudes of the alterations could be quantitatively related to the severity and extensiveness of connective tissue proliferation.^

Automated detection of hematological abnormalities through classification of flow cytometric data patterns

Flow Cytometry analyzers have become trusted companions due to their ability to perform fast and accurate analyses of human blood. The aim of these analyses is to determine the possible existence of abnormalities in the blood that have been correlated with serious disease states, such as infectious mononucleosis, leukemia, and various cancers. Though these analyzers provide important feedback, it is always desired to improve the accuracy of the results. This is evidenced by the occurrences of misclassifications reported by some users of these devices. It is advantageous to provide a pattern interpretation framework that is able to provide better classification ability than is currently available. Toward this end, the purpose of this dissertation was to establish a feature extraction and pattern classification framework capable of providing improved accuracy for detecting specific hematological abnormalities in flow cytometric blood data. ^ This involved extracting a unique and powerful set of shift-invariant statistical features from the multi-dimensional flow cytometry data and then using these features as inputs to a pattern classification engine composed of an artificial neural network (ANN). The contribution of this method consisted of developing a descriptor matrix that can be used to reliably assess if a donor’s blood pattern exhibits a clinically abnormal level of variant lymphocytes, which are blood cells that are potentially indicative of disorders such as leukemia and infectious mononucleosis. ^ This study showed that the set of shift-and-rotation-invariant statistical features extracted from the eigensystem of the flow cytometric data pattern performs better than other commonly-used features in this type of disease detection, exhibiting an accuracy of 80.7%, a sensitivity of 72.3%, and a specificity of 89.2%. This performance represents a major improvement for this type of hematological classifier, which has historically been plagued by poor performance, with accuracies as low as 60% in some cases. This research ultimately shows that an improved feature space was developed that can deliver improved performance for the detection of variant lymphocytes in human blood, thus providing significant utility in the realm of suspect flagging algorithms for the detection of blood-related diseases.^