Objective Function Evaluations Research Articles

A multi-scale image watermarking based on integer wavelet transform and singular value decomposition

Image watermarking technique is one of effective solutions to protect copyright, and it is applied to a variety of information security application domains. It needs to meet four requirements of imperceptibility, robustness, capacity and security. A multi-scale and secure image watermarking method is proposed in this work, which is based on the Integer Wavelet Transform (IWT) and Singular Value Decomposition (SVD). Four IWT sub-bands are firstly obtained after 1-level IWT on the host image, and the corresponding singular diagonal matrices of four sub-bands can be obtained using SVD. Then, each singular diagonal matrix is divided into four non-overlapping sections in terms of the size of embedding watermark. Particularly, the size of upper left part is same as the size of watermark. The watermark can be directly embedded into four upper left parts afterwards by multiplying different scaling factors to complete the final watermarking operation. Especially, a novel optimized authentication mechanism is designed to resolve the false positive problem, which exists in the SVD-based watermarking algorithms. In addition, three-dimensional optimal mapping algorithm is proposed to search the optimal scaling factors through a novel objective evaluation function, and it can significantly improve the imperceptibility and robustness. The experimental test and comparison analysis illustrate that the proposed watermark scheme demonstrates a high imperceptibility with peak signal to noise ratio values of 45 dB and strong robustness with average normalized correlation values of 0.92.

Интеллектуальная программная система оптимизации адаптивного управления процессами бизнес-планирования

The article describes the functionality developed by the authors of an intelligent software system for optimizing adaptive control of business planning processes in the face of uncertainty. The results are based on a new method for optimizing adaptive project management using network economic and mathematical modeling. Based on this method, a methodology has been developed for solving the problem of optimizing adaptive control of business planning processes, which in the proposed intelligent software decision support system uses a block containing an adaptive control optimization model. As the objective function (evaluation functional) in the method used, the value of the length of the time period for the execution of the business plan, which needs to be minimized, is considered. The method used allows you to create a class of acceptable strategies for adaptive control of the implementation process for the business plan in question. Within the framework of this class of strategies, an optimal adaptive control strategy for the implementation of business planning processes is formed, the optimal time for its implementation and the optimal schedule for implementing the business plan as a whole, and the corresponding optimal adaptive control strategies are calculated. Application of the proposed new method in an intelligent software system allows for feedback and optimal time for the implementation of the business project as a whole. The developed intelligent system is designed to automate the modeling of business planning processes and optimize adaptive decision-making control during their implementation on the basis of network economic and mathematical modeling, as well as methods and tools for developing intelligent soft systems. The created system takes into account the existing specific technical and economic conditions and information support. The results obtained in this work can serve as the basis for creating intelligent instrumental systems for supporting managerial decision-making in the implementation of business planning processes in the face of information uncertainty and risks.

A generalizable hybrid search framework for optimizing expensive design problems using surrogate models

ABSTRACT Experimental optimization of physical and biological processes is a difficult task. To address this, sequential surrogate models combined with search algorithms have been employed to solve nonlinear high-dimensional design problems with expensive objective function evaluations. In this article, a hybrid surrogate framework was built to learn the optimal parameters of a diverse set of simulated design problems meant to represent real-world physical and biological processes in both dimensionality and nonlinearity. The framework uses a hybrid radial basis function/genetic algorithm with dynamic coordinate search response, utilizing the strengths of both algorithms. The new hybrid method performs at least as well as its constituent algorithms in 19 of 20 high-dimensional test functions, making it a very practical surrogate framework for a wide variety of optimization design problems. Experiments also show that the hybrid framework can be improved even more when optimizing processes with simulated noise.

Evaluation of Different Objective Functions Used in the SUFI-2 Calibration Process of SWAT-CUP on Water Balance Analysis: A Case Study of the Pursat River Basin, Cambodia

Many calibration techniques have been developed for the Soil and Water Assessment Tool (SWAT). Among them, the SWAT calibration and uncertainty program (SWAT-CUP) with sequential uncertainty fitting 2 (SUFI-2) algorithm is widely used and several objective functions have been implemented in its calibration process. In this study, eight different objective functions were used in a calibration of stream flow of the Pursat River Basin of Cambodia, a tropical monsoon and forested watershed, to examine their influences on the calibration results, parameter optimizations, and water resources estimations. As results, many objective functions performed better than satisfactory in calibrating the SWAT model. However, different objective functions defined different fitted values and sensitivity rank of the calibrated parameters, except Nash–Sutcliffe efficiency (NSE) and ratio of standard deviation of observations to root mean square error (RSR) which are equivalent and produced quite identical simulation results including parameter sensitivity and fitted parameter values, leading to the same water balance components and water yields estimations. As they generated reasonable fitted parameter values, either NSE or RSR gave better estimation results of annual average water yield and other water balance components such as annual average evapotranspiration, groundwater flow, surface runoff, and lateral flow according to the characteristics of the river basin and the results and data of previous studies. Moreover, either of them was also better in calibrating base flow, falling limb, and overall the entire flow phases of the hydrograph in this area.

Development, Validation, and Application of an Optimization Scheme for Impellers of Centrifugal Fans Using Computational Fluid Dynamics-Trained Metamodels

Abstract A quick method for the design of efficiency-optimal centrifugal fan impellers is presented. It is based on an evolutionary optimization algorithm that identifies the optimal geometrical parameters for a given aerodynamic objective function. The range of the geometrical parameters considered allows covering aerodynamic design points appropriate for the complete class of centrifugal fans. The quickness of the method stems from evaluating the objective function using metamodels. In total, four metamodels, based on local model networks (LMN) and multi-layer perceptrons (MLP), were trained and eventually aggregated to reduce the variance (stochastic) error. The training data consist of approximately 4000 characteristic curves obtained from automated numerical steady-state Reynolds-averaged Navier–Stokes (RANS) flow simulations. The computational domain as well as the number of grid nodes and their distribution in the domain were optimized in a pre-study. For verification, a grid independence study was carried out. In addition, two criteria were defined to detect aerodynamic operating points associated with non-physical performance predictions. Finally, validation was secured with experimental data from three exemplary impeller designs. The proposed optimization scheme requires a costly initial one-time computational fluid dynamics (CFD) effort, but then allows a quick design of centrifugal fan impellers for arbitrary design points. The search for an optimal centrifugal impeller requires less than 1 min on a standard personal computer, while allowing up to 105 objective function evaluations for one search. Moreover, predicted performance curves that always come along with each design were found to be very reliable in comparison with experiments.

Initial experience in direct graft perfusion assessment following orthotopic liver transplant

Objective: classical methods of determining arterial blood supply of the graft following orthotopic liver transplantation (OLT) reflect the presence of blood flow in the trunk and large branches of the A. hepatica, without the characteristic of completeness of blood filling of peripheral sections, which is very important for objective evaluation of function. The aim of this study is to establish the diagnostic value of a direct perfusion study (IFlow) of the graft. Materials and methods. From 1998 to 2019, 245 OLTs were conducted. From 2015 to 2019, arterial changes were detected in 24 (23%) patients after 104 OLTs. A perfusion study was performed in 9 patients with suspected arterial graft failure. According to the IFlow study, liver hypoperfusion due to stenosis and/or splenic steal syndrome was detected in 8 cases and became an indication for therapeutic intervention. Results. Hepatic stenting and/or splenic artery embolization was performed to improve arterial blood supply to the liver. Endovascular procedures performed restored the perfusion index from 0.24 (0.01–0.89) to 0.61 (0.35–0.98). Conclusion. Absence of ultrasound and multispiral computed tomography signs of arterial complications does not rule out the need for perfusion angiography. Perfusion angiography allows to objectify the angiography data and perform corrective intervention in good time.

A Comparative Study of Infill Sampling Criteria for Computationally Expensive Constrained Optimization Problems

Engineering optimization problems often involve computationally expensive black-box simulations of underlying physical phenomena. This paper compares the performance of four constrained optimization algorithms relying on a Gaussian process model and an infill sampling criterion under the framework of Bayesian optimization. The four infill sampling criteria include expected feasible improvement (EFI), constrained expected improvement (CEI), stepwise uncertainty reduction (SUR), and augmented Lagrangian (AL). Numerical tests were rigorously performed on a benchmark set consisting of nine constrained optimization problems with features commonly found in engineering, as well as a constrained structural engineering design optimization problem. Based upon several measures including statistical analysis, our results suggest that, overall, the EFI and CEI algorithms are significantly more efficient and robust than the other two methods, in the sense of providing the most improvement within a very limited number of objective and constraint function evaluations, and also in the number of trials for which a feasible solution could be located.

Contemporary Analysis of Olfactory Dysfunction in Mild to Moderate Covid 19 Patients in A Tertiary Health Care Centre.

Introduction: The World Health Organization declared COVID-19 a pandemic on March 11, 2020. The virus that causes COVID-19 was designated as severe acute respiratory syndrome corona virus 2 (SARS-CoV-2). Several studies have reported chemosensory dysfunction, such as anosmia and ageusia, as common findings in COVID-19 positive patients. To date, qualitative olfactory testing has been performed only in a very few cohort studies on COVID-19 patients. However, objective testing is necessary to verify or determine the true magnitude of their deficits. Moreover, the proportion of COVID-19 patients exhibiting true olfactory disturbances is unknown. Aim of the Study: To determine the true prevalence of olfactory dysfunction in COVID-19 patients by objective assessment in mild to moderate symptomatic patients. Materials & Methodology: This was a prospective cross-sectional analytical study. All patients who were COVID-19 positive and having mild to moderate symptoms and not admitted in ICU formed part of the study group. Objective evaluation of smell function was done. Results: Self-reported smell dysfunction was present in 26.9% patients (n=62) and taste dysfunction was seen in 10.9% (n=25) of patients. On quantitative assessment of smell dysfunction, it was noted that 41.3% (n=95) of patients had some form of smell dysfunction out of which 70.5% patients (n=67) had hyposmia and 29.5% patients (n=28) had anosmia. Conclusion: Incidence was found to be more by objective assessment when compared to self-reported symptoms.

The Feasibility of Longitudinal Upper Extremity Motor Function Assessment Using EEG

Motor function assessment is crucial in quantifying motor recovery following stroke. In the rehabilitation field, motor function is usually assessed using questionnaire-based assessments, which are not completely objective and require prior training for the examiners. Some research groups have reported that electroencephalography (EEG) data have the potential to be a good indicator of motor function. However, those motor function scores based on EEG data were not evaluated in a longitudinal paradigm. The ability of the motor function scores from EEG data to track the motor function changes in long-term clinical applications is still unclear. In order to investigate the feasibility of using EEG to score motor function in a longitudinal paradigm, a convolutional neural network (CNN) EEG model and a residual neural network (ResNet) EEG model were previously generated to translate EEG data into motor function scores. To validate applications in monitoring rehabilitation following stroke, the pre-established models were evaluated using an initial small sample of individuals in an active 14-week rehabilitation program. Longitudinal performances of CNN and ResNet were evaluated through comparison with standard Fugl–Meyer Assessment (FMA) scores of upper extremity collected in the assessment sessions. The results showed good accuracy and robustness with both proposed networks (average difference: 1.22 points for CNN, 1.03 points for ResNet), providing preliminary evidence for the proposed method in objective evaluation of motor function of upper extremity in long-term clinical applications.

GOPS: efficient RBF surrogate global optimization algorithm with high dimensions and many parallel processors including application to multimodal water quality PDE model calibration

This paper describes a new parallel global surrogate-based algorithm Global Optimization in Parallel with Surrogate (GOPS) for the minimization of continuous black-box objective functions that might have multiple local minima, are expensive to compute, and have no derivative information available. The task of picking P new evaluation points for P processors in each iteration is addressed by sampling around multiple center points at which the objective function has been previously evaluated. The GOPS algorithm improves on earlier algorithms by (a) new center points are selected based on bivariate non-dominated sorting of previously evaluated points with additional constraints to ensure the objective value is below a target percentile and (b) as iterations increase, the number of centers decreases, and the number of evaluation points per center increases. These strategies and the hyperparameters controlling them significantly improve GOPS’s parallel performance on high dimensional problems in comparison to other global optimization algorithms, especially with a larger number of processors. GOPS is tested with up to 128 processors in parallel on 14 synthetic black-box optimization benchmarking test problems (in 10, 21, and 40 dimensions) and one 21-dimensional parameter estimation problem for an expensive real-world nonlinear lake water quality model with partial differential equations that takes 22 min for each objective function evaluation. GOPS numerically significantly outperforms (especially on high dimensional problems and with larger numbers of processors) the earlier algorithms SOP and PSD-MADS-VNS (and these two algorithms have outperformed other algorithms in prior publications).

Application of artificial intelligence and evolutionary algorithms in simulation-based optimal design of a piezoelectric energy harvester

This paper tackles the problem of finding the optimal design parameters for a piezoelectric energy harvester. A new simulation-based optimization procedure is proposed with the goal of acquiring the optimal geometric and circuit design parameters that leads to higher energy harvesting efficiency and also enhances the obtained electrical power. The basis of the optimization platform is a numerical model of the energy harvesting system operating during electrical transient of charging an external storage capacitor. The model consists of a cantilever beam partially coated with piezoelectric patches, a non-linear interfacing and conditioning circuit, and a storage device. The numerical model simulates a complete energy harvesting scenario from piezoelectric transduction, to power enhancement and conditioning through interfacing circuit and energy storage. Two different case studies are considered for beams under harmonic tip-force, and harmonic base-excitation. Since performing multiple simulations in order to evaluate the objective function is computationally expensive and imposes time and space (memory) complexities, a more efficient Neural Network (NN) model is first trained based on a set of training data obtained from the numerical model. Performance and accuracy of the NN training is studied using available statistical methods. Second, a Genetic Algorithm (GA) optimization performs a block-box optimization procedure, using the trained Neural Network model for objective function evaluation. Finally, a thorough analysis of the optimal design parameters obtained from the optimization process is provided.

A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean

This study develops a novel metaheuristic algorithm that is motivated by the behavior of jellyfish in the ocean and is called artificial Jellyfish Search (JS) optimizer. The simulation of the search behavior of jellyfish involves their following the ocean current, their motions inside a jellyfish swarm (active motions and passive motions), a time control mechanism for switching among these movements, and their convergences into jellyfish bloom. JS optimizer is tested using a comprehensive set of mathematical benchmark functions and applied to a series of structural engineering problems. Fifty small/average-scale and twenty-five large-scale functions involving various dimensions were used to validate JS optimizer, which was compared with ten well-known metaheuristic algorithms. JS optimizer was found to outperform those algorithms in solving mathematical benchmark functions. The JS algorithm was then used to solve structural optimization problems, including 25-bar tower design, 52-bar tower design and 582-bar tower design problems. In those cases, JS not only performed best but also required the fewest evaluations of objective functions. Therefore, JS is potentially an excellent metaheuristic algorithm for solving optimization problems.

A NON-NESTED INFILLING STRATEGY FOR MULTIFIDELITY BASED EFFICIENT GLOBAL OPTIMIZATION

Efficient Global Optimization (EGO) has become a standard approach for the global optimization of complex systems with high computational costs. EGO uses a training set of objective function values computed at selected input points to construct a statistical surrogate model, with low evaluation cost, on which the optimization procedure is applied. The training set is sequentially enriched, selecting new points, according to a prescribed infilling strategy, in order to converge to the optimum of the original costly model. Multi-fidelity approaches combining evaluations of the quantity of interest at different fidelity levels have been recently introduced to reduce the computational cost of building a global surrogate model. However, the use of multi-fidelity approaches in the context of EGO is still a research topic. In this work, we propose a new effective infilling strategy for multi-fidelity EGO. Our infilling strategy has the particularity of relying on non-nested training sets, a characteristic that comes with several computational benefits. For the enrichment of the multi-fidelity training set, the strategy selects the next input point together with the fidelity level of the objective function evaluation. This characteristic is in contrast with previous nested approaches, which require estimation all lower fidelity levels and are more demanding to update the surrogate. The resulting EGO procedure achieves a significantly reduced computational cost, avoiding computations at useless fidelity levels whenever possible, but it is also more robust to low correlations between levels and noisy estimations. Analytical problems are used to test and illustrate the efficiency of the method. It is finally applied to the optimization of a fully nonlinear fluid-structure interaction system to demonstrate its feasibility on real large-scale problems, with fidelity levels mixing physical approximations in the constitutive models and discretization refinements.

P0655CIRCULATING 1,25-DIHYDROXYVITAMIN D AND ITS RATIO WITH 1-84PTH ARE PREDICTORS OF WORSENING RENAL FUNCTION IN CKD3 PATIENTS

Abstract Background and Aims The variable deterioration of patients with chronic kidney disease poses an omnipresent need for improved renal care management. At present, renal function in CKD patients is measured or estimated using the glomerular filtration rate (mGFR and eGFR). While mGFR affords the most objective and accurate evaluation of kidney function, it is not routinely used for repeated assessment due to its expense and difficulty to execute. Although eGFR provides the most diffuse approach for the monitoring of renal function, its commonality does not come without limitations: above 60mL/min*1.73m2, serum creatinine, and therefore eGFR variability are increasingly impacted by muscle metabolism/mass, age, gender, diet and fluid imbalances. eGFR can neither anticipate nor predict kidney damage. In CKD, deficiency of 1,25-dihydroxyvitamin D (1,25(OH)2D) develops during the progression of kidney failure, due to the decreased capability of renal proximal tubule cells to mediate the final hydroxylation step of 25-hydroxyvitamin D to 1,25(OH)2D. While not measured routinely in the clinic, new methods make the measurement of 1,25(OH)2D possible and reliable, and data on its performance as a biomarker are emerging. The aim of this study is to describe the ability of the LIAISON® XL 1,25 Dihydroxyvitamin D measurement to predict worsening renal function in conjunction with other relevant analytes. Method This monocentric study was observational, prospective and descriptive, with a longitudinal approach. 71 patients were assessed for mGFR determination by 99mTc-diethylenetriamine-pentaacetic acid at baseline. Twenty percent worsening of renal function, based on eGFR, provided the primary endpoint which was evaluated at 3 and 6 months. In addition, serum levels of 1,25(OH)2D, 25(OH)D, 1-84 PTH, intact FGF23, sclerostin, alkaline phosphatase (bone-specific and total) and calcium/phosphorus along with 24h urine levels of creatinine, phosphorus, calcium and sodium were measured. Endpoint analyses were performed by receiver operating characteristics, univariate, and multivariate logistic regressions. Results ROC analyses of 1,25(OH)2D and the 1,25(OH)2D/1-84PTH ratio for patients with early CKD (stage 3) showed very good performance in predicting worsening of renal function with AUCs of 0.916 (p<0.0001, criterion ≤29 pg/mL) and 0.791 (p<0.0005, criterion ≤0.844), respectively. Significance was lost for both markers’ outcome prediction with CKD4 subjects. Of interest, endpoint prediction by 1,25(OH)2D was also better in all patients <70 years old (AUC=0.883. p<0.0001). In univariate logistic regression analysis, 1,25(OH)2D is the single and strongest predictor of WRF (p=0.0029) corroborating the ROC analysis. In backward-elimination multi-variate regression, the 1,25(OH)2D/1-84PTH ratio and 1-84PTH are the final variables in the regression model (p=0.00013). Interestingly, while the ratio correlates well with mGFR (R=0.541), 1,25(OH)2D does not (R=0.116). Conclusion 1,25(OH)2D and the ratio of 1,25(OH)2D/1-84PTH are strong predictors of outcome in CKD3 patients, and thus these measurements may indicate improved clinical care for this important class of patients. Further research of these biomarkers is needed to expand the observations to larger numbers and more diverse populations.

Reliability-based design optimization of steel frames using direct design

This paper presents an effective method for reliability-based design optimization (RBDO) of steel frames by combining direct design using nonlinear inelastic analysis, an improved importance sampling technique for structural failure probability analysis, and an improved differential evolution (DE) algorithm for optimization. The nonlinear inelastic analysis using the beam-column approach is used to capture the second-order effects and the inelastic behavior of structures. An improved importance sampling technique based on nonlinear inelastic analysis of the structure is employed that significantly reduces the number of structural analyses required for calculating the structural failure probability. An improved DE algorithm, which can effectively eliminate the redundant structural analyses for objective function evaluation, is utilized. A six-story space frame is studied to demonstrate the computational efficiency of the proposed method.

How to catch a lion in the desert: on the solution of the coverage directed generation (CDG) problem

The testing and verification of a complex hardware or software system, such as modern integrated circuits (ICs) found in everything from smartphones to servers, can be a difficult process. One of the most difficult and time-consuming tasks a verification team faces is reaching coverage closure, or hitting all events in the coverage space. Coverage-directed-generation (CDG), or the automatic generation of tests that can hit hard-to-hit coverage events, and thus provide coverage closure, holds the potential to save verification teams significant simulation resources and time. In this paper, we propose a new approach to the CDG problem by formulating the CDG problem as a noisy derivative free optimization (DFO) problem. However, this formulation is complicated by the fact that derivatives of the objective function are unavailable, and the objective function evaluations are corrupted by noise. We solve this noisy optimization problem by utilizing techniques from direct optimization coupled with a robust noise estimator, and by leveraging techniques from inverse problems to estimate the gradient of the noisy objective function. We demonstrate the efficiency and reliability of this new approach through numerical experiments with an abstract model of part of IBM's NorthStar processor, a superscalar in-order processor designed for servers.

Optimal GENCO's bidding strategy in a power exchange facilitating combined power and emission trading using Intelligent Programmed Genetic Algorithm

Currently, for developing optimal bidding strategy considering carbon credit trading, Generating Company (GENCO) separately participates in electric power and emission market. Recently, researchers proposed a new scheme in which emission trading is facilitated within Power Exchange (PX). Developing optimal GENCO's bidding strategy considering carbon credit trading within PX is a complex single-objective optimization problem with generator's surplus as the main objective function. Obtaining its solution using a simple evolutionary optimization algorithm is difficult and time-consuming. Therefore, this paper presents an application of Intelligent Programmed Genetic Algorithm (IPGA) equipped with advanced deterministic diversity creating operator for solving optimal GENCO's bidding strategy problem with minimum number of objective function evaluations. The performance of IPGA is first tested on three different categories of standard test functions by comparing its simulation results with that obtained using binary-coded GA, simulated annealing, particle swarm optimization, biogeography-based optimization, teaching learning-based optimization, and differential evolution-based hybrid GA. A system comprising of six GENCOs and two buyers is used to validate the applicability of IPGA. IPGA along with other algorithms was used to obtain optimal bidding curve for 24 hours for GENCO GC-1. Simulation results clearly show much faster convergence for IPGA in terms of the number of objective function evaluations required to reach optima. Result also shows that GENCO GC-1 can obtain much higher GC-1 surplus, carbon credit limit, and social welfare by utilizing the obtained bidding strategy using IPGA as compared with the bidding strategy obtained by other algorithms.

On the solution of linearly constrained optimization problems by means of barrier algorithms

Many practical problems require the solution of large-scale constrained optimization problems for which preserving feasibility is a key issue, and the evaluation of the objective function is very expensive. In these cases it is mandatory to start with a feasible approximation of the solution, the obtention of which should not require objective function evaluations. The necessity of solving this type of problems motivated us to revisit the classical barrier approach for nonlinear optimization, providing a careful implementation of a modern version of this method. This is the main objective of the present paper. For completeness, we provide global convergence results and comparative numerical experiments with one of the state-of-the-art interior-point solvers for continuous optimization.

Characteristic feature analysis of continuous optimization problems based on Variability Map of objective function for optimization algorithm configuration

AbstractAdvanced optimization algorithms with a variety of configurable parameters become increasingly difficult to apply effectively to solving optimization problems. Appropriate algorithm configuration becomes highly relevant, still remaining a computationally expensive operation. Development of machine learning methods allows to model and predict the efficiency of different solving strategies and algorithm configurations depending on properties of optimization problem to be solved. The paper suggests the Dependency Decomposition approach to reduce computational complexity of modeling the efficiency of optimization algorithm, also considering the amount of computational resources available for optimization problem solving. The approach requires development of explicit Exploratory Landscape Analysis methods to assess a variety of significant characteristic features of optimization problems. The results of feature assessment depend on the number of sample points analyzed and their location in the design space, on top of that some of methods require additional evaluations of objective function. The paper proposes new landscape analysis methods based on given points without the need of any additional objective function evaluations. An algorithm of building a so-called Full Variability Map is suggested based on informativeness criteria formulated for groups of sample points. The paper suggests Generalized Information Content method for analysis of Full Variability Map which allows to get accurate and stable estimations of objective function features. The Sectorization method of Variability Map analysis is proposed to assess characteristic features reflecting such properties of objective function that are critical for optimization algorithm efficiency. The resulting features are invariant to the scale of objective function gradients which positively affects the generalizing ability of problems classification algorithm. The procedure of the comparative study of effectiveness of landscape analysis algorithms is introduced. The results of computational experiments indicate reliability of applying the suggested landscape analysis methods to optimization problem characterization and classification.

Chromatic Pupil Campimetry Reveals Functional Defects in Exudative Age-Related Macular Degeneration with Differences Related to Disease Activity.

PurposeThe purpose of this study was to use chromatic pupil campimetry (CPC) for an objective evaluation of local retinal function in exudative age-related macular degeneration (AMD) and to assess disease activity.MethodsGaze-controlled CPC was performed in 19 subjects with optical coherence tomography–confirmed exudative AMD (75 ± 4 years; 11 women) and the results compared with those of an age-matched control group (n = 11; 72 ± 6 years; 8 women). Local retinal function was evaluated by measuring pupil responses to 3° red stimuli (60 cd/m2, 1 second) at 41 positions covering 30° of the central visual field on a dim blue background (test duration 6 minutes). Primary outcome parameters were relative maximal pupil constriction amplitude (% from baseline) and latency to constriction onset.ResultsPupil constriction amplitudes were significantly reduced in the macular region, and especially in the fovea in AMD (16% ± 4.7%; mean ± standard deviation), compared with the control group (24% ± 6%; P = 0.00036). Receiver operating characteristic values were 0.84 for the constriction amplitude in the fovea, and 0.9 for the steepness angle between periphery and center. Mean latency to constriction onset in the fovea in AMD was significantly longer (333 ± 53 ms; normals 273 ± 59 ms, P = 0.0072), and particularly in the active compared with the inactive status of exudative AMD (P = 0.01).ConclusionsCPC detected functional changes in exudative AMD with high sensitivity. Time dynamics of active exudative AMD differed from disease inactivity.Translational RelevanceWith the combination of short recording time, objectiveness of the measurement and gaze-correction for fixation problems, this method presents a suitable complement to the currently used clinical functional tests of the macula.