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Memory-Centric X-ray Computational Tomography(CT is an iterative reconstruction technique that minimizes redundant computations with higher memory accesses. MemXCT implements a sparse matrix-vector multiplication(SpMV with multi-stage buffering and two-level pseudo-Hilbert ordering for optimization. Motivated by the need to validate conclusions from previous work, we reproduce the numerical results, the algorithms performance and the scaling behavior of the algorithms as the number of MPI processes increases on Azure.

The fundamental unit of each evolutionary algorithm is the individual. Each individual represents a potential solution to the problem at hand. Despite the importance of individual solution for multi-objective algorithms’ performance the majority of the existing implementations select a simplistic approach by assuming identical behavior for all candidate solutions of a population. However, from the biological analogue we know that individuals do not react similarly to the same stimulus. This is called character and it is lacking from existing implementations. In this paper, we emulate the corresponding human social analogue by generating individuals that exhibit different behavior when are subject to the same stimulus. The implementation of different behaviors is facilitated through a novel mutation operator. The experimental results favor the proposed approach when compared with other state-of-the-art algorithms for a number of test instances.

The design of an analogue IC layout is a time-consuming and manual process. Despite several studies in the sector, some geometric restrictions have resulted in disadvantages in the process of automated analogue IC layout design. As a result, analogue design has a performance lag when compared to manual design. This prevents the deployment of a large range of automated tools. With the recent technical developments, this challenge is resolved using machine learning techniques. This study investigates performance-driven placement in the VLSI IC design process, as well as analogue IC performance prediction by utilizing various machine learning approaches. Further, several amplifier designs are simulated. From the simulation results, it is evident that, when compared to the manual layout, an improved performance is obtained by using the proposed approach.

Aim: The main aim of the study proposed is to perform higher classification of fake political news by implementing fake news detectors using machine learning classifiers by comparing their performance. Materials and Methods: By considering two groups such as Decision Tree algorithm and Naive Bayes algorithm. The algorithms have been implemented and tested over a dataset which consists of 44,000 records. Through the programming experiment which is performed using N=10 iterations on each algorithm to identify various scales of fake news and true news classification. Result: After performing the experiment the mean accuracy of 99.6990 by using Decision Tree algorithm and the accuracy of 95.3870 by using Naive Bayes algorithm for fake political news in. There is a statistical significant difference in accuracy for two algorithms is p

[EN] Power converters are electronic devices widely applied in industry, and in recent years, for renewable energy electronic systems, they can regulate voltage levels and actuate as interfaces, however, to do so, is needed a controller. Proportional-Integral-Derivative (PID) are applied to power converters comparing output voltage versus a reference voltage to reduce and anticipate error. Using PID controllers may be complicated since must be previously tuned prior to their use. Many methods for PID controllers tunning have been proposed, from classical to metaheuristic approaches. Between the metaheuristic approaches, bio-inspired algorithms are a feasible solution; Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) are often used; however, they need many initial parameters to be specified, this can lead to local solutions, and not necessarily the global optimum. In recent years, new generation metaheuristic algorithms with fewer initial parameters had been proposed. The Grey Wolf Optimizer (GWO) algorithm is based on wolves¿ herds chasing habits. In this work, a comparison between PID controllers tunning using GWO, PSO, and GA algorithms for a Boost Converter is made. The converter is modeled by state-space equations, and then the optimization of the related PID controller is made using MATLAB/Simulink software. The algorithm¿s performance is evaluated using the Root Mean Squared Error (RMSE). Results show that the proposed GWO algorithm is a feasible solution for the PID controller tunning problem for power converters since its overall performance is better than the obtained by the PSO and GA., The authors wish to thank the Institute of Energy Engineering of the Polytechnic University of Valencia, Spain, and the Department of Water and Energy Studies of the University of Guadalajara, Mexico, for all their support and collaboration.

The penetration ability of the Green wavelength in multispectral Light Detection and Ranging (LiDAR) pulses into water bodies makes it a valuable tool in DTM generation for land and certainly bathymetry. Titan is the first active multispectral LiDAR that extends information for 3D point clouds from a single sensor. It is provided by Teledyne Optech is an example of the multispectral LiDAR system. It has three different wavelengths mid-infrared (MIR), near-infrared (NIR), and green (1.550, 1.064, and 0.532 µm) respectively. This research aims to compare the performance of two ground-filtering algorithms to obtain digital terrain models in the forest and coastal areas. The tested area is located at the Southwest tip of Tobermory (Ontario, Canada). Two filtering algorithms are used; Adaptive Triangulated Irregular Network (ATIN) and Elevation Threshold with an Expand Window (ETEW). The two filtering algorithms performance were evaluated using the cloud to cloud (C2C) method provided by cloud compare software. The results show that ATIN gives results better than ETEW in the three different wavelengths of multispectral LiDAR data. إن قدرة اختراق الطول الموجي الأخضر في نبضات الضوء متعدد الأطياف والمدى (الليدار) في الأجسام المائية تجعله أداة قيمة في توليد نموذج تضاريس رقمي للأرض وبالتأکيد قياس الأعماق. "تيتان" هو أول ليدار نشط متعدد الأطياف يقدم معلومات عن السحب ثلاثية الأبعاد من مستشعر واحد. يتم توفيره بواسطة شرکه تيليدين اوبتک وهو مثال لنظام الليدار متعدد الأطياف. لديه ثلاثة أطوال موجية مختلفة وهي الأشعة تحت الحمراء ، والأشعة تحت الحمراء القريبة ، والأخضر (1.550 و 1.064 و 0.532 مايکرو متر) على التوالي. يهدف هذا البحث إلى مقارنة أداء خوارزميتي تصفية الأرض للحصول على نماذج التضاريس الرقمية في الغابات والمناطق الساحلية. تقع المنطقة المختبرة في الطرف الجنوبي الغربي من توبرموري (أونتاريو ، کندا). يتم استخدام خوارزميتي تصفية وهما شبکة غير منتظمة متکيفة مثلثة وعتبة ارتفاع مع نافذة موسعة. تم تقييم أداء خوارزميتي التصفية باستخدام طريقة السحابة إلى السحابة المقدمة من برنامج مقارنة السحابة. وقد أظهرت النتائج أن مرشح الشبکه الغير منتظمة المتکيفه المثلثه يعطي نتائج أفضل من عتبه الارتفاع مع النافذة الموسعة في الأطوال الموجية الثلاثة المختلفة لبيانات الليدار متعددة الأطياف.

There are problems that are difficult to solve through mathematical programming or by classical methods. These problems are called hard problems due to their high complexity or high dimension. On the other hand, mataheuristics intends to seek a better solution to a problem. The Improvement Harmony Search algorithm is proposed under modification of the bandwidth parameter increasing the quality of the exploitation of the solutions. That is why within the state of the art, are mentioned several versions of harmonic search. The state of the art is supports the fact that the algorithm belongs to the category of those who make modifications to its parameters. This research demonstrates the ability of ImHS to solve a problem of high complexity focused on solving four-bar mechanism designs, whose solutions imply high dimension and which are also classified as hard problems. The two problems that are solved in this investigation, are problems very attacked within the state of the art by various metaheuristics. A comparison is then made against previous solutions with traditional metaheuristics and other versions of harmony search algorithm. Finally, the effectiveness of performance is demonstrated, where proposed algorithm it exceeded five metaheuristic algorithms and five harmony search versions. An optimum is provided in an easy and useful way, the parametric statistics are improved and the number of feasible solutions is exceeded in NP-hard problems as in the case of problems with four-bar mechanisms.

A guided wave-based structural health monitoring (GW-SHM) system aims at determining the integrity of a wide variety of plate-like structures such as aircraft fuselages, pipes, and fuel tanks. It is often based on a sparse grid of piezoelectric transducers for exciting and sensing GWs that under certain conditions interact with damage while propagating. In recent years, various defect imaging algorithms have been proposed for processing GWs signals and, particularly, for computing an image representing the integrity of the studied structure. The performance of the GW-SHM system highly depends on a signal processing methodology. This paper compares defect localization accuracy of the three state-of-art defect imaging algorithms (delay-and-sum, minimum variance, and excitelet) applied to an extensive simulated database of GWs propagation and GWs-defect interaction in aluminum plate under varying temperature and transducers degradation. This study is conducted in order to provide statistical inferences, essential for SHM system performance demonstration.

The Central Bureau of Statistics of Indonesia (BPS) classified the target households into three different categories which were very poor households (RSTM), poor households (RTM), and nearly-poor households (RTSM). BPS need some method that can accelerate the classification process to assist the performance of BPS in order to shorten the processing time. The data scale that used in the classification of poor households was ordinal. Generally, calculations of classification using ordinal asscales only can be found in the software WEKA Ordinal Class Classifier (OCC) that was one of the existing classification in WEKA. OCC could be resolve to attributes that are nominal, numerical, and ordinal. So in this research, OCC would be using to classify poor households. By comparing the algorithms performance there were several stages that need to be traversed. The first was the data collection stage, the second was the data processing stage and information by using preprocessing, the third was the analysis stage with tools WEKA. The fourth was a test stage by counting the value of accuracy, precision, and recall. The last stage was evaluation by comparing actual data with predictive data of the result of calculating system. From the classification process, it can be concluded that OCC has the highest accuracy, precision, and recall level which is 90% (3803) of training set and 10% (423) of testing set with accuracy of 90.5437%, precision 0.919, and recall 0.905.

The rice monitoring based on Sentinel-2 (SC-S2) has been developed for over nine months. It has been observed as the first and only system which generate rice growth stages maps in 10 m spatial resolution using machine learning in Indonesia. However, the SC-S2 use Support Vector Machine to separate the rice growth stages, which may have poor performances. The objective of this study is to investigate the performance of other classifiers to increase the performance of SC-S2. We used survey data from the field campaign in 2018 and synchronized with Sentinel-2 bands. The model dataset was trained using 61 machine learning algorithms to create 61 rice growth stages models. The models were applied to the Sentinel-2 image of part of Indramayu area. The accuracy, computational time and visual inspection score were collected, and the final score was calculated. The results are the highest final score is Shrinkage Discriminant Analysis, with overall accuracy 88.1% (pp

With the few numbers of cardiologists in Indonesia who not evenly distributed, especially in rural areas, there has been a lot of smart telehealth specifically developed for heart monitoring using ECG. Many techniques have been developed to improve the accuracy of this device by using datasets that are mostly imbalanced, more positive data than negative. This paper presents the comparison of negative to positive training dataset ratio on atrial fibrillation classification machine learning algorithms performance. An AliveCor ECG recording dataset is train with deep neural networks, support vector machine and logistic regression as classifier with three different ratios, 1:1, 1:5 to 1:All. Results show an increase in classifier performance along with the increasing number of negative data.

Çok Taşıyıcılı Kod BölmeliÇoklu Erişim (MC-CDMA), kablolu ve kablosuz haberleşme uygulamalarındakullanılan, yüksek hızlarda ve servis kaliteli veri iletişimi sağlayan birçoklu erişim tekniğidir. MC-CDMA çok kullanıcılı birteknoloji olduğu için sistemdeki kullanıcı sayısının artması; kullanıcı başınadüşen taşıyıcı sayısını azaltmakta, bunun sonucunda alıcı taraftan alınanverinin bit hata oranının (BHO) artmasına ve kullanıcı başına düşen verihızlarının azalmasına neden olmaktadır. Bu sorunu engellemek için alt taşıyıcıtahsis algoritmaları kullanılmaktadır. Bu çalışmada, MC-CDMA sistemindetoplam hızı artırmak ve verimliliği sağlamak için alt taşıyıcı tahsisalgoritmalarının performansı farklı kullanıcı sayıları için incelenmiştir., Multi Carrier Code Division Multiple Access is a multiple access techniquethat is used wireless and wireline applications and provides high rate and quality of service (Qos) data transmission. Increasing the number of users decreases the number of subcarrier peruser as result of this, increased bit error rate (BER) of received data anddecrease data rate per users in the system because MC-CDMA is a multiusertechnology. To overcome this problem, subcarrier allocation algorithms areused. In this work, performances of the subcarrier allocation algorithms areinvestigated to increase the total rate and fertilisation of the system fordifferent numbers of user.

Purpose: Inter- and intra-observer variability adversely affects interpretation of highly advantageous MRI data in breast cancer diagnosis, prognosis and therapeutic decisions. Machine learning (ML) might potentially provide support to the physicians for objective decision strategies. In this work, ML methods using BI-RADS features were tested for non-invasively determining molecular subtypes of breast cancer. Methods: In this IRB approved study, Out of 126 consecutive patients' retrospective data with written consent, 69 patients (mean±std age 48.24±11.62 and range [27-82]) with full histopathological and MRI data were selected. In surgical histopathological data ER+PR+HER2- was classified as luminal HER2(-), ER+PR+HER+ was classified as luminal HER2(+), ER-PR-HER2+ was classified as HER2(enriched) and ER-PR-HER2- was classified as triple negative. The cohort revealed 51 luminal HER2(-), 11 luminal HER2(+), 7 triple negative cases without any HER2(enriched) occurrence. DCE, DW, T2W MRI data were obtained on a 1.5T Magnetom Espree (Siemens, Erlangen, Germany) scanner which were subsequently interpreted in consensus by 2 radiologists of 15 and 10 years of experience. Age, mass and non-mass properties, non-enhancing BI-RADS findings, ADC values obtained from radiologist drawn ROIS and kinetic curve properties were fed to 22 standard ML algorithms provided in Matlab® (Mathworks, Natick, MA, USA) as predictors for 3 categories. The algorithms were run by cross validating on 50 folds whereby reported classifier accuracy was obtained from each of the observations when in held-out fold. Results: Out of 22 ML algorithms tested Support Vector Machine's Fine Gaussian variant, Ensemble Boosted Trees and Fine K-Nearest Neighbor resulted in 76.8%,71.0% and 53.6% accuracy with (100%, 18%, 0%), (92%, 9%, 14%) and (67%, 18%, 14%) true positive rates for predicting each histopathological category respectively. Furthermore, PCA based dimension reduction worsened the outcomes indicating high sensitivity to the feature set. Conclusion: ML algorithms demonstrated potential as a decision support system increasing assessment objectivity with an added non-invasiveness advantage. However, overall algorithm performance indicates further studies with larger cohorts and broader feature are necessary for refinements and improving ML methodology in breast cancer imaging. Citation Format: Ozcan A, Aksoy Ozcan U, Ekemen S, Ulus S, Oyan B. Testing machine learning algorithms' performance for non-invasively identifying breast cancer molecular subtypes using BI-RADS evaluations [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P5-02-03.

In multi-objective optimization problems, the optimization target is to obtain a set of non-dominated solutions. Comparing solution sets is crucial in evaluating the performances of different optimization algorithms. The use of performance indicators is common in comparing those sets and, subsequently, optimization algorithms. A good solution set must be close to the Pareto-optimal front, well-distributed, maximally extended and fully filled. Therefore, an effective performance indicator must encompass these features as a whole and must be Pareto dominance compliant. Unfortunately, some of the known indicators often fail to properly reflect the quality of a solution set or cost a lot to compute. This paper demonstrates that the Degree of Approximation (DOA) quality indicator, is a weakly Pareto compliant unary indicator that gives a good estimation of the match between the approximated front and the Pareto-optimal front. Moreover, DOA computation is easy and fast.

In practical multi-objective optimization problems, respective decision-makers might be interested in some optimal solutions that have objective values closer to their specified values. Guided multi-objective evolutionary algorithms (guided MOEAs) have been significantly used to guide their evolutionary search direction toward these optimal solutions using by decision makers. However, most guided MOEAs need to be iteratively and interactively evaluated and then guided by decision-makers through re-formulating or re-weighting objectives, and it might negatively affect the algorithms performance. In this paper, a novel guided MOEA that uses a dynamic polar-based region around a particular point in objective space is proposed. Based on the region, new selection operations are designed such that the algorithm can guide the evolutionary search toward optimal solutions that are close to the particular point in objective space without the iterative and interactive efforts. The proposed guided MOEA is tested on the multi-criteria decision-making problem of flexible logistics network design with different desired points. Experimental results show that the proposed guided MOEA outperforms two most effective guided and non-guided MOEAs, R-NSGA-II and NSGA-II.

Biometrics is measurable characteristics specific to an individual. Face detection has diverse applications especially as an identification solution which can meet the crying needs in security areas. While traditionally 2D images of faces have been used, 3D scans that contain both 3D data and registered color are becoming easier to acquire. Before 3D face images can be used to identify an individual, they require some form of initial alignment information, typically based on facial feature locations. We follow this by a discussion of the algorithms performance when constrained to frontal images and an analysis of its performance on a more complex dataset with significant head pose variation using 3D face data for detection provides a promising route to improved performance.

The purpose of this research is to find a set of relevant parameters for the emotion recognition. In this study we used the recordings from the emotion database SROL which is part of the project "Voiced Sounds of Romanian Language". The database was validated by human listeners. The recognition accuracy of the correct expressed emotion (neutral tone, joy, fury and sadness) for the entire database was 63.97%. We used for the classification of input data the Recurrent Fuzzy C-Means (FCM) and WKNN algorithms. We compared the cluster position with the statistical parameters extracted from vowels in order to establish the relevance of each parameter in the recognition of the emotions. For the extracted parameters for each vowel (mean, median and standard deviation of fundamental frequency - F0 and F1-F4 formants, jitter, and shimmer) the FCM algorithm gave satisfactory results in the phonemes recognition, but not to the emotions. For this reason we used WKNN algorithm in classification, which provided the errors around 20-30% comparing with FCM algorithm when the classification errors are around 40-50%.

The design and development of synthetic aperture radar (SAR) system for a particular application often requires redesign of software and hardware to optimize the system performance. In addition, evaluations of the performance of existing autofocus and image formation algorithms are required for the SAR system designers to select a most suitable algorithm for a given image quality requirements. This is a time-consuming taskwithout a reconfigurable and comprehensive software package. Thus, a comprehensive SAR integrated simulator and processor software is needed to aid the system designers in optimizing all the system parameters and performance. This paper presents an integrated SAR simulator and processor (iSARSIMP) software package and the performance of three selected SAR autofocus algorithms has been evaluated as examples to demonstrate the usefulness of the iSARSIMP for SAR system designers. In the performance evaluation, simulated and actual SAR raw data were used for further analysis and comparison of the three selected autofocus algorithms.

We review recently developed decomposition algorithms for molecular dynamics and spin dynamics simulations of many-body systems. These methods are time reversible, symplectic, and the error in the total energy thus generated is bounded. In general, these techniques are accurate for much larger time steps than more standard integration methods. Illustrations of decomposition algorithms performance are shown for spin dynamics simulations of a Heisenberg ferromagnet.

In multi‐ and many‐objective optimization problems, the optimization target is to obtain a set of non‐dominated solutions close to the Pareto‐optimal front, well‐distributed, maximally extended and fully filled. Comparing solution sets is crucial in evaluating the performance of different optimization algorithms. The use of performance indicators is common in comparing those sets and, subsequently, optimization algorithms. Therefore, an effective performance indicator must encompass these features as a whole and, above all, it must be Pareto dominance compliant. Unfortunately, some of the known indicators often fail to properly reflect the quality of a solution set or cost a lot to compute. This paper demonstrates that the Degree of Approximation (DOA) quality indicator is a weakly Pareto compliant unary indicator that gives a good estimation of the match between the approximated front and the Pareto‐optimal front.

In this paper, an investigation of the influence of the population size on the Genetic Algorithm (GA) and Ant Colony Optimization (ACO) performance for a model parameter identification problem, is considered. The mathematical model of an E. coli fed-batch cultivation process is studied. The three model parameters—maximum specific growth rate (\(\mu _{max}\)), saturation constant (\(k_{S}\)) and yield coefficient (\(Y_{S/X}\)) are estimated using different population sizes. Population sizes between 5 and 200 chromosomes and 5 and 100 ants in the population are tested with constant number of generations. In order to obtain meaningful information about the influence of the population size a considerable number of independent runs of the GA are performed. The observed results show that the optimal population size is 100 chromosomes for GA and 70 ants for ACO for 200 generations. In this case accurate model parameters values are obtained in reasonable computational time. Further increase of the population size, above 100 chromosomes for GA and 70 ants for ACO, does not improve the solution accuracy. Moreover, the computational time is increased significantly.

Previous algorithms for the construction of belief networks structures from data are mainly based either on independence criteria or on scoring metrics. The aim of this paper is to present a hybrid methodology that is a combination of these two approaches, which benefits from characteristics of each one, and to develop two operative algorithms based on this methodology. Results of the evaluation of the algorithms on the well-known Alarm network are presented, as well as the algorithms performance issues and some open problems.

We consider the problem of how two heterogeneous robots can arrange to meet in an unknown environment from unknown starting locations: that is, the problem of arranging a robot rendezvous. We are interested, in particular, in allowing two robots to rendezvous so that they can collaboratively explore an unknown environment. Specifically, we address the problem of how a pair of exploring agents that cannot communicate with one another over long distances can meet if they start exploring at different unknown locations in an unknown environment. We propose several alternative algorithms that robots could use in attempting to rendezvous quickly while continuing to explore. These algorithms exemplify different classes of strategy whose relative suitability depends on characteristics of the problem definition. We consider the performance of our proposed algorithms analytically with respect to both expected- and worst-case behavior. We then examine their behavior under a wider set of conditions using both numerical analysis and also a simulation of multi-agent exploration and rendezvous. We examine the exploration speed, and show that a multi-robot system can explore an unknown environment faster than a single-agent system, even with the constraint of performing rendezvous to allow communication. We conclude with a demonstration of rendezvous implemented on a pair of actual robots.

The continuous development of pattern recognition approaches increases the need for evaluation tools to quantify algorithms performance and establish precise inter-algorithm comparison. So far, few performance evaluating metrics in pattern recognition algorithms are known in the literature, especially in remote sensing images. In this paper, four metrics are proposed for this purpose. The advantages and drawbacks of these metrics are first described, then some experimentation results are the presented in order to validate our contribution.

Clustering analysis includes a number of different algorithms and methods for grouping objects by their similar characteristics into categories. In recent years, considerable effort has been made to improve such algorithms performance. In this sense, this paper explores three different bio-inspired metaheuristics in the clustering problem: Genetic Algorithms (GAs), Ant Colony Optimization (ACO), and Artificial Immune Systems (AIS). This paper proposes some refinements to be applied to these metaheuristics in order to improve their performance in the data clustering problem. The performance of the proposed algorithms is compared on five different numeric UCI databases. The results show that GA, ACO and AIS based algorithms are able to efficiently and automatically forming natural groups from a pre-defined number of clusters.

Our goal is to characterize and to be able to predict the search cost, of some of the most important CSP algorithms and heuristics when solving CSP problems by obtaining a statistical model of the algorithm runtime based on inexpensively computed parameters obtained from the CSP problem specification and the associated constraints and nogoods graphs. Such a model will give us three important items concerning the studied CSP problems. First, the model provides a tool to predict the search cost of a given instance, allowing a portfolio of solvers to decide for the best algorithm before to proceed. Second, the models will give an insight about which are the main features that characterize the complexity of a RBCSP. Finally, another potential benefit of the model is pointing out which features are the algorithms most sensible to, thus helping to guess potential areas of improvement. Research partially supported by projects TIC2003-00950 and TIN2004-07933-C03-03 funded by the Ministerio de Educacion y Ciencia.

The ATLAS trigger system has been used for the online event selection for three years of LHC data-taking and is preparing for the next run. The trigger system consists of a hardware level-1 and a software high-level trigger (HLT) which is implemented in a region-of-interest based level-2 stage and a event filter operating after event building with offline-like software. During the past three years, the luminosity and pile-up (number of collisions per beam crossing) has increased significantly placing escalating demands on the rejection and timing performance. The HLT algorithms advanced during this period to maintain and even improve performance. Also discussed is the work towards the merging of the two HLT levels in to a single level HLT.

The condition for algebraic Bayesian networks (ABN) global logical-probabilistic inference algorithms performance is the absence of cycles in its secondary structure. The primary structure, on which an acyclic secondary structure can be synthesized is called an acyclic one. The goal of the work is to propose an algorithm to detect primary structure acyclicity based on estimates of the number of edges in its secondary structure without the direct construction of the secondary structure, and estimation of the algorithm complexity. The algorithm for detection ABN primary structure acyclicity based on number of minimal join graph edges estimating via brute force is formulated, its correctness is proven, its complexity is estimated, improvement in the speed of this algorithm is proposed, the improved algorithm correctness if proven and its performance time are estimated . Also the possibility for improving the algorithm performance speed through the use of algorithms for ABN tertiary polystructure elements synthesis is discussed.

Objective testing of centerline extraction accuracy in quantitative coronary angiography (QCA) algorithms is a very difficult task. Standard tools for this task are not yet available. The authors present a simulation tool that generates synthetic angiographic images of a single coronary artery with predetermined centerline and diameter function. This simulation tool was used creating a library of images for the objective comparison and evaluation of QCA algorithms. This technique also provides the means for understanding the relationship between the algorithms performance and limitations and the vessel's geometrical parameters. In this paper, two algorithms are evaluated and the results are presented.

The objective of this work was to develop a systematic method of combining multifrequency polarized SAR images. It is shown that the traditional methods of correlation, hard targets, and template matching fail to produce acceptable results. Hence, a new algorithm was developed and tested. The new approach combines the three traditional methods and an interpolation method. An example is shown that demonstrates the new algorithms performance. The results are summarized suggestions for future research are presented.

The paper deals with the performance analysis of three Monte Carlo algorithms for some models of computer architectures. To estimate the performance and the speedup of these algorithms, we introduce a special modification of the criterion for the time required to achieve a preset probable error and consider a serial (von Neumann) architecture, a pipeline architecture, and two MIMD (Multiple Instruction stream, Multiple Data stream) parallel architectures. An approach to constructing Monte Carlo vector algorithms to be efficiently run on pipeline computers has also been considered.