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An effective boundary element numerical algorithm combining the wavelet transform and generalized minimum residual method for the solution of the electric field problem in substations is proposed. The realistic numerical results are presented to demonstrate the validity and effectiveness of this algorithm for the achievement of more convergence ratio and computational efficiency on the premise of reliable accuracy; the proposed algorithm can be employed to perform the calculation of electric field in substations for the analysis of electromagnetic compatibility to improve the stable operation of power grid. [ABSTRACT FROM AUTHOR]

We compare the performance of three algorithms for production planning with workload-dependent lead times. These include a clearing function model using two different methods for estimating the clearing functions, and two iterative algorithms that combine linear programming and simulation models. Our experimental comparison uses a simulation model of a re-entrant bottleneck system built with attributes of a real-world semiconductor fabrication environment. We vary the bottleneck utilization, demand patterns, the mean time to failure, and the mean time to repair. Results indicate that the clearing function model performs better than the iterative algorithms on the scaled-down system considered, giving less variable production plans and higher profit values. [ABSTRACT FROM PUBLISHER]

Quantitative methods such as model-based predictive control are known to facilitate the design of strategies to manipulate biological systems. This study develops a sparse-grid-based adaptive model predictive control (MPC) strategy to direct HL60 cellular differentiation. Sparse-grid sampling and interpolation support a computationally efficient adaptive MPC scheme in which multiple data-consistent regions of the model parameter space are identified and used to calculate a control compromise. The algorithm is evaluated in silico with structural model mismatch. Simulations demonstrate how the multiscenario control strategy more effectively manages the mismatch compared to a single scenario approach. Furthermore, the controller is evaluated in vitro to differentiate HL60 cells in both normal and perturbed environments. The controller-derived input sequence successfully achieves and sustains the specified target level of granulocytes when implemented in the laboratory. The results and analysis given here imply that adoption of this experiment planning technique to direct cell differentiation within more complex tissue engineered constructs will require the use of a reasonably accurate mathematical model and an extension of this algorithm to multiobjective controller design. [ABSTRACT FROM PUBLISHER]

While fuzzy c-means is a popular soft-clustering method, its effectiveness is largely limited to spherical clusters. By applying kernel tricks, the kernel fuzzy c-means algorithm attempts to address this problem by mapping data with nonlinear relationships to appropriate feature spaces. Kernel combination, or selection, is crucial for effective kernel clustering. Unfortunately, for most applications, it is uneasy to find the right combination. We propose a multiple kernel fuzzy c-means (MKFC) algorithm that extends the fuzzy c-means algorithm with a multiple kernel-learning setting. By incorporating multiple kernels and automatically adjusting the kernel weights, MKFC is more immune to ineffective kernels and irrelevant features. This makes the choice of kernels less crucial. In addition, we show multiple kernel k-means to be a special case of MKFC. Experiments on both synthetic and real-world data demonstrate the effectiveness of the proposed MKFC algorithm. [ABSTRACT FROM PUBLISHER]

We present a two-stage stochastic programming model for committing reserves in systems with large amounts of wind power. We describe wind power generation in terms of a representative set of appropriately weighted scenarios, and we present a dual decomposition algorithm for solving the resulting stochastic program. We test our scenario generation methodology on a model of California consisting of 122 generators, and we show that the stochastic programming unit commitment policy outperforms common reserve rules. [ABSTRACT FROM PUBLISHER]

We propose a new shape-based inversion algorithm to identify an anomaly embedded in an inhomogeneous layered geometry. We apply our approach to microwave breast imaging where the geometry consists of several inhomogeneous layers and the potential tumor is embedded in the innermost layer. In addition to the tumor identification, we estimate the irregular transition layer between the breast inner layers. Our inversion algorithm is based on a low-dimensional parametric form of the relevant geometries, and uses multiple-frequency multi-source data to estimate the unknowns. Several numerical examples are provided to evaluate the effectiveness of our approach and demonstrate its robustness both to the initial choice of parameter values and uncertainty in the complex dielectric properties of the media even with the complex geometry and the simplified inverse model. [ABSTRACT FROM AUTHOR]

An implementation of the inverse vector Jiles-Atherton model for the solution of non-linear hysteretic finite element problems is presented. The implementation applies the fixed point method with differential reluctivity values obtained from the Jiles-Atherton model. Differential reluctivities are usually computed using numerical differentiation, which is ill-posed and amplifies small perturbations causing large sudden increases or decreases of differential reluctivity values, which may cause numerical problems. A rule based algorithm for conditioning differential reluctivity values is presented. Unwanted perturbations on the computed differential reluctivity values are eliminated or reduced with the aim to guarantee convergence. Details of the algorithm are presented together with an evaluation of the algorithm by a numerical example. The algorithm is shown to guarantee convergence, although the rate of convergence depends on the choice of algorithm parameters. [ABSTRACT FROM AUTHOR]

In reinforcement learning, large state and action spaces make the estimation of value functions impractical, so a value function is often represented as a linear combination of basis functions whose linear coefficients constitute parameters to be estimated. However, preparing basis functions requires a certain amount of prior knowledge and is, in general, a difficult task. To overcome this difficulty, an adaptive basis function construction technique has been proposed by Keller recently, but it requires excessive computational cost. We propose an efficient approach to this difficulty, in which the problem of approximating the value function is decomposed into a number of subproblems, each of which can be solved with small computational cost. Computer experiments show that the CPU time needed by our method is much smaller than that by the existing method. [ABSTRACT FROM AUTHOR]

The integral equation (IE) method is commonly utilized to model time-harmonic electromagnetic (EM) problems. One of the greatest challenges in its applications arises in the solution of the resulting ill-conditioned matrix equation. We introduce a new domain decomposition method (DDM) for the IE solution of EM wave scattering from non-penetrable objects. The proposed method is a non-overlapping/non-conformal DDM and it provides a computationally efficient and effective preconditioner for the IE matrix equations. Moreover, the proposed approach is very suitable for dealing with multi-scale electromagnetic problems since each sub-domain has its own characteristics length and will be meshed independently. Furthermore, for each sub-domain, we are free to choose the most effective IE sub-domain solver based on its local geometrical features and electromagnetic characteristics. Additionally, the multilevel fast multi-pole algorithm (MLFMA) is utilized to accelerate the computations of couplings between sub-domains. Numerical results demonstrate that the proposed method yields rapid convergence in the outer Krylov iterative solution process. Finally, simulations of several large-scale examples testify to the effectiveness and robustness of the proposed IE based DDM. [ABSTRACT FROM AUTHOR]

Patient studies based on diffusion tensor images (DTI) require spatial correspondence between subjects. We propose to obtain the correspondence from white matter tracts, by introducing a new method for nonrigid matching of white matter fiber tracts in DTI. The method boils down to point set registration that involves simultaneously clustering and matching of the data points. The tracts are implicitly warped to a common frame of reference to avoid the potential bias toward one of the datasets. The algorithm gradually refines from global to local registration, which is implemented through deterministic annealing. Special care was taken to incorporate the spatial relation between fiber points and the uncertainty in principal diffusion orientation. As a result, the computed clusters are oriented along the fiber tracts and discriminate between adjacent but distinct fiber tracts. This is validated on synthetic and clinical data. The root-mean-squared distance with respect to expert-annotated landmarks is low (3 mm). In contrast to a state-of-the-art nonrigid registration technique, the proposed method is more robust to residual misalignments in terms of measured fractional anisotropy values. [ABSTRACT FROM PUBLISHER]

The convolutional perfectly-matched-layer (CPML) absorbing boundary condition is fully capable of handling near-field wave absorption that usually combines near-grazing wave incidence with wave evanescence. The appropriate choice of the various CPML parameters to realize this potential for any given simulation problem is a challenging task that is typically achieved through exhaustive and time-consuming searches that involve large numbers of full-scale simulations. The presented work here uses a previously developed predictive system of equations that accurately determines numerical reflections off the PML interface and embeds it into a global optimization routine that reliably computes the required optimum CPML parameters. This predictive system of equations has also been extended and validated for the M24 and FV24 integral-based high-order FDTD algorithms. With this approach, the task of selecting optimum CPML parameters that would usually take several days of intense computations can now be accomplished within a few minutes on an average personal computer. [ABSTRACT FROM AUTHOR]

Plug-in hybrid electric vehicles are a midterm solution to reduce the transportation sector's dependency on oil. However, if implemented in a large scale without control, peak load increases significantly and the grid may be overloaded. Two algorithms to address this problem are proposed and analyzed. Both are based on a forecast of future electricity prices and use dynamic programming to find the economically optimal solution for the vehicle owner. The first optimizes the charging time and energy flows. It reduces daily electricity cost substantially without increasing battery degradation. The latter also takes into account vehicle to grid support as a means of generating additional profits by participating in ancillary service markets. Constraints caused by vehicle utilization as well as technical limitations are taken into account. An analysis, based on data of the California independent system operator, indicates that smart charge timing reduces daily electricity costs for driving from $0.43 to $0.2. Provision of regulating power substantially improves plug-in hybrid electric vehicle economics and the daily profits amount to $1.71, including the cost of driving. [ABSTRACT FROM PUBLISHER]

The occurrence of so-called sticking points in a lift is pervasive in weight training practice. Biomechanically complex exercises often exhibit multi-modal variation of effective force exerted against the load as a function of the elevation and velocity of the load. This results in a variety of possible loci for the occurrence of sticking points and makes the problem of designing the optimal training strategy to overcome them challenging. In this article a case founded on theoretical grounds is made against a purely empirical method. It is argued that the nature of the problem considered and the wide range of variables involved limit the generality of conclusions which can be drawn from experimental studies alone. Instead an alternative is described, whereby a recently proposed mathematical model of neuromuscular adaptation is employed in a series of computer simulations. These are used to examine quantitatively the effects of differently targeted partial range of motion (ROM) training approaches. Counter-intuitively and in contrast to common training practices, the key novel insight inferred from the obtained results is that in some cases the most effective approach for improving performance in an exercise with a sticking point at a particular point in the ROM is to improve force production capability at a different and possibly remote position in the lift. In the context of the employed model, this result is explained by changes in the neuromuscular and biomechanical environment for force production. [ABSTRACT FROM AUTHOR]

A new method named fast directional multilevel algorithm is proposed for analyzing wave scattering. Similar to a fast multipole method, an oct-tree structure is used in the new method. Unlike the conventional MLFMA, the kernel is evaluated by a new method, instead of approximating the Green's function by multipole expansion. The fast directional multilevel algorithm is first used to solve the wave scattering problem via combined field integral equation (CFIE) using Rao-Wilton-Glisson (RWG) basis functions. The low rank representations are extended to apply in the low frequency regime which is proved efficient and stable. The numerical results demonstrate that the computational complexity of this new multilevel algorithm can achieve the complexity of O(N\log N). This algorithm is robust and performs well on other oscillatory kernels because of its not depending on the explicit form of kernels. [ABSTRACT FROM PUBLISHER]

We address a p-shift finite impulse response (FIR) unbiased estimator (UE) for linear discrete time-varying filtering (p=0), p-step prediction (p>0), and p-lag smoothing (p<0) in state space with no requirements for initial conditions and zero mean noise. A solution is found in a batch form and represented in a computationally efficient iterative Kalman-like one. It is shown that the Kalman-like FIR UE is able to outperform the Kalman filter if the noise covariances and initial conditions are not known exactly, noise is not white, and both the system and measurement noise components need to be filtered out. Otherwise, the errors are similar. Extensive numerical studies of the FIR UE are provided in Gaussian and non-Gaussian environments with outliers and temporary uncertainties. [ABSTRACT FROM PUBLISHER]

The dyadic Green's function (DGF) method is an attractive approach for the calculation of electromagnetic scattering in spherically symmetric layered geometry. We investigate a recent formulation and reveal a problem that is associated with the nonconverging series that are employed for the calculation of the DGF in unbounded media. To overcome this deficiency, we introduce a new formulation that gives the desired solution analytically. The new technique combines operations on scalar Green's function with a rotation of the coordinate system. Our formulation also accelerates the algorithm to evaluate the DGF in spherically multilayered media. We present examples where we compare the conventional formulation with our new one. [ABSTRACT FROM AUTHOR]

Progress in the development of high-sensitivity magnetic-field measurements has stimulated interest in understanding the magnetic noise of conductive materials, especially of magnetic shields based on high-permeability materials and/or high-conductivity materials. For example, SQUIDs and atomic magnetometers have been used in many experiments with mu-metal shields, and additionally SQUID systems frequently have radio frequency shielding based on thin conductive materials. Typical existing approaches to modeling noise only work with simple shield and sensor geometries while common experimental setups today consist of multiple sensor systems with complex shield geometries. With complex sensor arrays used in, for example, MEG and Ultra Low Field MRI studies, knowledge of the noise correlation between sensors is as important as knowledge of the noise itself. This is crucial for incorporating efficient noise cancelation schemes for the system. We developed an approach that allows us to calculate the Johnson noise for arbitrary shaped shields and multiple sensor systems. The approach is efficient enough to be able to run on a single PC system and return results on a minute scale. With a multiple sensor system our approach calculates not only the noise for each sensor but also the noise correlation matrix between sensors. Here we will show how the algorithm can be implemented. [ABSTRACT FROM AUTHOR]

Affine projection (AP) algorithms have been proposed in recent years for use in active noise control systems. This is due to their potential high convergence speed along with their robustness and moderate computational cost. However, these algorithms can exhibit an excessive computational cost for high projection orders (just when higher convergence speed is achieved). Thus, computationally efficient versions of these algorithms have been proposed. For the particular case of the AP algorithms applied to active noise control, the use of the conventional filtered-x structure instead of the commonly used modified filtered-x method can be understood as an efficient strategy, since it needs fewer operations to update the adaptive filter coefficients. However, the use of this structure implies different algorithm behavior for the following two reasons: the signals needed in the coefficient updates do not correspond exactly to the AP algorithm and this structure introduces a delay between the update of the adaptive filter coefficients and its effect on the noise signal. In practice, this dual effect mainly affects convergence of the algorithms in the transient regime. This correspondence presents a mathematical model so that the transient behavior of the conventional filtered-x AP algorithm can be predicted from the reference signal statistics and algorithm parameters. [ABSTRACT FROM PUBLISHER]

The reliable detection of water and ice over road surfaces is an important issue in improving the traffic safety and in reducing the costs for maintenance, particularly during winter. A low-cost capacitive sensor for the estimation of road conditions is studied. A simulation model was developed to investigate the capacitance of the sensor when air, water, or ice is covering its surface and to assess the effect of the variation of an environmental temperature or of the thickness of water or ice. An algorithm for the estimation of the state of the sensor (dry, wet, or icy) was developed based on the results of the simulations, which indicated that the time derivative of the estimated capacitance provided optimal information. The accuracy and reliability of the estimates provided by the sensor were assessed in laboratory experiments, placing more sensors in a climatic chamber and investigating the estimated state of the sensors and the timing of the identification of wet-icy or icy-wet transitions. Reliable estimates were obtained by all the sensors, with a dispersion of the transition times on the order of a few minutes. The sensor was also investigated in the field. Two sensors (one of which was bituminized) were embedded in a road pavement to monitor continuously the road surface condition for a month. Both sensors provided indications in line with the environmental conditions, identifying properly the icy condition and indicating the wet state of the road during both rain and fog. Thus, the sensor is suggested as a feasible tool for monitoring the road conditions to support information systems improving the security and efficient maintenance of roads during winter. [ABSTRACT FROM AUTHOR]

A nonlinear discrete-time neural observer for discrete-time unknown nonlinear systems in presence of external disturbances and parameter uncertainties is presented. It is based on a discrete-time recurrent high-order neural network trained with an extended Kalman-filter based algorithm. This brief includes the stability proof based on the Lyapunov approach. The applicability of the proposed scheme is illustrated by real-time implementation for a three phase induction motor. [ABSTRACT FROM AUTHOR]

Blind identification of underdetermined mixtures can be addressed efficiently by using the second ChAracteristic Function (CAF) of the observations. Our contribution is twofold. First, we propose the use of a Levenberg-Marquardt algorithm, herein called LEMACAF, as an alternative to an Alternating Least Squares algorithm known as ALESCAF, which has been used recently in the case of real mixtures of real sources. Second, we extend the CAF approach to the case of complex sources for which the previous algorithms are not suitable. We show that the complex case involves an appropriate tensor stowage, which is linked to a particular tensor decomposition. An extension of the LEMACAF algorithm, called LEMACAF\BBC then proposed to blindly estimate the mixing matrix by exploiting this tensor decomposition. In our simulation results, we first provide performance comparisons between third- and fourth-order versions of ALESCAF and LEMACAF in various situations involving BPSK sources. Then, a performance study of LEMACAF\BBC is carried out considering 4-QAM sources. These results show that the proposed algorithm provides satisfying estimations especially in the case of a large underdeterminacy level. [ABSTRACT FROM AUTHOR]

Automated detection of lesions in retinal images is a crucial step towards efficient early detection, or screening, of large at-risk populations. In particular, the detection of microaneurysms, usually the first sign of diabetic retinopathy (DR), and the detection of drusen, the hallmark of age-related macular degeneration (AMD), are of primary importance. In spite of substantial progress made, detection algorithms still produce 1) false positives—target lesions are mixed up with other normal or abnormal structures in the eye, and 2) false negatives—the large variability in the appearance of the lesions causes a subset of these target lesions to be missed. We propose a general framework for detecting and characterizing target lesions almost instantaneously. This framework relies on a feature space automatically derived from a set of reference image samples representing target lesions, including atypical target lesions, and those eye structures that are similar looking but are not target lesions. The reference image samples are obtained either from an expert- or a data-driven approach. Factor analysis is used to derive the filters generating this feature space from reference samples. Previously unseen image samples are then classified in this feature space. We tested this approach by training it to detect microaneurysms. On a set of images from 2739 patients including 67 with referable DR, DR detection area under the receiver-operating characteristic curve (AUC) was comparable (AUC=0.927) to our previously published red lesion detection algorithm (AUC=0.929). We also tested the approach on the detection of AMD, by training it to differentiate drusen from Stargardt's disease lesions, and achieved an AUC=0.850 on a set of 300 manually detected drusen and 300 manually detected flecks. The entire image processing sequence takes less than a second on a standard PC compared to minutes in our previous approach, allowing instantaneous detection. Free-response receiver-operating characteristic analysis showed the superiority of this approach over a framework where false positives and the atypical lesions are not explicitly modeled. A greater performance was achieved by the expert-driven approach for DR detection, where the designer had sound expert knowledge. However, for both problems, a comparable performance was obtained for both expert- and data-driven approaches. This indicates that annotation of a limited number of lesions suffices for building a detection system for any type of lesion in retinal images, if no expert-knowledge is available. We are studying whether the optimal filter framework also generalizes to the detection of any structure in other domains. [ABSTRACT FROM AUTHOR]

A new ray-tracing method in 3-D heterogeneous isotropic media is proposed based on the bilinear travel-time interpolation and wavefront group marching method (GMM). In this method, an irregular cell discretization scheme is used to accurately describe arbitrarily undulant interfaces in a model. The ray tracing is carried out by forward–backward processing. In the forward step, the travel time in an irregular cell is expressed in terms of the bilinear interpolation of the known travel times on the cell's surfaces. Then, the wavefront is evolved from the source to the whole computational domain by using the newly developed travel-time solver and the fast wavefront expansion GMM. In the backward step, each ray path is traced from the receiver by finding the intersection points of potential ray propagation vectors with the surfaces of the relevant cells. The same travel-time solver is used to compute the candidate intersection points on all surfaces of each relevant cell, and the point with the minimum travel time is selected as a ray point from which the similar step is continued until the sources are found. Several numerical experiments are presented to demonstrate that the new algorithm is accurate, efficient, and robust. [ABSTRACT FROM AUTHOR]

The four-parameter sine wave (FPSW) is commonly used to characterize noise, signal to noise, and the effective number of bits in analog-to-digital converters. An algorithm based on separable nonlinear least squares fitting is presented. In this algorithm, the frequency is the sole iterative variable. Numerical tests show that if the cycle in the record (CiR) is greater than 1, then the convergence is faster and is less dependent on the CiR and SNR. When the CiR is less than one, the convergence is significantly more sensitive to the CiR, the SNR, and the initial phase. Additionally, the lower the frequency, the more difficult the convergence. [ABSTRACT FROM PUBLISHER]

The efficient solution of the bidomain equations is a fundamental tool in the field of cardiac electrophysiology. When choosing a finite element discretization of the coupled system, one has to deal with the solution of a large, highly sparse system of linear equations. The conjugate gradient algorithm, along with suitable preconditioning, is the natural choice in this scenario. In this study, we identify the optimal preconditioners with respect to both stimulus protocol and mesh geometry. The results are supported by a comprehensive study of the mesh-dependence properties of several preconditioning techniques found in the literature. Our results show that when only intracellular stimulus is considered, incomplete LU factorization remains a valid choice for current cardiac geometries. However, when extracellular shocks are delivered to tissue, preconditioners that take into account the structure of the system minimize execution time and ensure mesh-independent convergence. [ABSTRACT FROM AUTHOR]

Background HIV physicians have limited time for cognitive screening. Here we developed an extra-brief, clinically based tool for predicting HIV-associated neurocognitive impairment (HAND) in order to determine which HIV-positive individuals require a more comprehensive neurological/ neuropsychological (NP) assessment. Methods Ninety-seven HIV-positive individuals with advanced disease recruited in an HIV out-patient clinic received standard NP testing. A screening algorithm was developed using support vector machines, an optimized prediction procedure for classifying individuals into two groups (here NP-impaired and NP-normal) based on a set of predictors. Results The final algorithm utilized age, current CD4 cell count, past central nervous system HIV-related diseases and current treatment duration and required approximately 3 min to complete, with a good overall prediction accuracy of 78% (against the gold standard; NP-impairment status derived from standard NP testing) and a good specificity of 70%. Conclusion This noncognitive-based algorithm should prove useful to identify HIV-infected patients with advanced disease at high risk of HAND who require more formal assessment. We propose staged guidelines, using the algorithm, for improved HAND therapeutic management. Future larger, international studies are planned to test the predictive effect of nadir CD4 cell count, hepatitis C virus infection, gender, ethnicity and HIV viral clade. We recommend the use of this first version for HIV-infected Caucasian men with advanced disease. [ABSTRACT FROM AUTHOR]

A scheme for state estimation and control under information constraints is implemented for “LAAS Helicopter benchmark”. Parameter estimates of the “Helicopter” pitch dynamics model are obtained by means of the real-time identification algorithm. The hybrid continuous-discrete observation procedure for transmission of the measured data over the limited-band communication channel with adaptive tuning of the coder range parameter is proposed and used in the experiments for pitch motion control of the “Helicopter”. Experimental results for pitch motion control of the “Helicopter” are presented, showing efficiency of the proposed method. [ABSTRACT FROM PUBLISHER]

A novel mathematical model of the actin dynamics in living cells under steady-state conditions has been developed for fluorescence recovery after photobleaching (FRAP) experiments. As opposed to other FRAP fitting models, which use the average lifetime of actins in filaments and the actin turnover rate as fitting parameters, our model operates with unbiased actin association/dissociation rate constants and accounts for the filament length. The mathematical formalism is based on a system of stochastic differential equations. The derived equations were validated on synthetic theoretical data generated by a stochastic simulation algorithm adapted for the simulation of FRAP experiments. Consistent with experimental findings, the results of this work showed that (1) fluorescence recovery is a function of the average filament length, (2) the F-actin turnover and the FRAP are accelerated in the presence of actin nucleating proteins, (3) the FRAP curves may exhibit both a linear and non-linear behaviour depending on the parameters of actin polymerisation, and (4) our model resulted in more accurate parameter estimations of actin dynamics as compared with other FRAP fitting models. Additionally, we provide a computational tool that integrates the model and that can be used for interpretation of FRAP data on actin cytoskeleton. [ABSTRACT FROM AUTHOR]

Crystal growth conducted under microgravity conditions has had a profound impact on improving our understanding of melt crystal growth processes. Here, we present a brief history of microgravity crystal growth and discuss the development of appropriate models to interpret and optimize these growth experiments. The need for increased model realism and predictive capability demands new approaches for describing phenomena important at several disparate length scales. Of special importance is the ability to represent three-dimensional and transient continuum transport (flows, heat, and mass transfer), phase-change phenomena (thermodynamics and kinetics), and system design (such as furnace heat transfer during melt growth). An overview of mathematical models and numerical algorithms employed to represent such multiscale effects is presented. [ABSTRACT FROM AUTHOR]

A mathematical model is proposed for numerical studies of hybrid combustion waves in cylindrical and spherical vehicles. A code based on the previously developed algorithm is written, and a large series of numerical computations is performed. For different flow rates of the gas, various characteristics of the process are considered, such as the position of the stationary point, maximum temperature of the gas, gas velocity at the stationary point, width of the combustion zone, etc. Transitional regimes with a varied flow rate of the gas are modeled. [ABSTRACT FROM AUTHOR]

Background: At present, immune checkpoint inhibitors, such as pembrolizumab, are widely used in the therapy of advanced non-resectable melanoma, as they induce more durable responses than other available treatments. However, the overall response rate does not exceed 50% and, considering the high costs and low life expectancy of nonresponding patients, there is a need to select potential responders before therapy. Our aim was to develop a new personalization algorithm which could be beneficial in the clinical setting for predicting time to disease progression under pembrolizumab treatment.Methods: We developed a simple mathematical model for the interactions of an advanced melanoma tumor with both the immune system and the immunotherapy drug, pembrolizumab. We implemented the model in an algorithm which, in conjunction with clinical pretreatment data, enables prediction of the personal patient response to the drug. To develop the algorithm, we retrospectively collected clinical data of 54 patients with advanced melanoma, who had been treated by pembrolizumab, and correlated personal pretreatment measurements to the mathematical model parameters. Using the algorithm together with the longitudinal tumor burden of each patient, we identified the personal mathematical models, and simulated them to predict the patient's time to progression. We validated the prediction capacity of the algorithm by the Leave-One-Out cross-validation methodology.Results: Among the analyzed clinical parameters, the baseline tumor load, the Breslow tumor thickness, and the status of nodular melanoma were significantly correlated with the activation rate of CD8+ T cells and the net tumor growth rate. Using the measurements of these correlates to personalize the mathematical model, we predicted the time to progression of individual patients (Cohen's κ = 0.489). Comparison of the predicted and the clinical time to progression in patients progressing during the follow-up period showed moderate accuracy (R2 = 0.505).Conclusions: Our results show for the first time that a relatively simple mathematical mechanistic model, implemented in a personalization algorithm, can be personalized by clinical data, evaluated before immunotherapy onset. The algorithm, currently yielding moderately accurate predictions of individual patients' response to pembrolizumab, can be improved by training on a larger number of patients. Algorithm validation by an independent clinical dataset will enable its use as a tool for treatment personalization. [ABSTRACT FROM AUTHOR]

We present an efficient algorithm for simulating diffuse reflections of sound in a static scene. Our approach is built on recent advances in precomputed light transport techniques for visual rendering and uses them to develop an improved acoustic radiance transfer technique. We precompute a direct-to-indirect acoustic transfer operator for a scene, and use it to map direct sound incident on the surfaces of the scene to multibounce diffuse indirect sound, which is gathered at the listener to compute the final impulse response. Our algorithm decouples the transfer operator from the source position so we can efficiently update the acoustic response at the listener when the source moves. We highlight its performance on various benchmarks and observe significant speedups over prior methods based on acoustic radiance transfer. [ABSTRACT FROM PUBLISHER]

We address the Primary User (PU) detection (spectrum sensing) problem, relevant to cognitive radio, from a finite random matrix theoretical (RMT) perspective. Specifically, we employ recently-derived closed-form and exact expressions for the distribution of the standard condition number (SCN) of uncorrelated and semi-correlated random dual central Wishart matrices of finite sizes in the design Hypothesis-Testing algorithms to detect the presence of PU signals. In particular, two algorithms are designed, with basis on the SCN distribution in the absence (H_0) and in the presence (H_1) of PU signals, respectively. Due to an inherent property of the SCN's, the H_0 test requires no estimation of SNR or any other information on the PU signal, while the H_1 test requires SNR only. Further attractive advantages of the new techniques are: a) due to the accuracy of the finite SCN distributions, superior performance is achieved under a finite number of samples, compared to asymptotic RMT-based alternatives; b) since expressions to model the SCN statistics both in the absence and presence of PU signal are used, the statistics of the spectrum sensing problem in question is completely characterized; and c) as a consequence of a) and b), accurate and simple analytical expressions for the receiver operating characteristic (ROC) — both in terms of the probability of detection as a function of the probability of false alarm (P_D versus P_F) and in terms of the probability of acquisition as a function of the probability of miss detection (P_A versus P_M) — are yielded. It is also shown that the proposed finite RMT-based algorithms outperform all similar alternatives currently known in the literature, at a substantially lower complexity. In the process, several new results on the distributions of eigenvalues and SCNs of random Wishart Matrices are offered, including a closed-form of the Marchenko-Pastur's Cumulative Density Function (CDF) and extensions of the latter, as well as variations of asymptotic the distributions of extreme eigenvalues (Tracy-Widom) and their ratio (Tracy-Widom-Curtiss), which are simpler than those obtained with the "spiked population model". [ABSTRACT FROM AUTHOR]

We investigate the problem of source localization based on measuring time difference of signal arrivals (TDOA) from the source emitter. Taking into account the colored measurement noise, we adopt a min-max principle to develop two lower complexity semidefinite relaxation algorithms that can be reliably solved using semidefinite programming. The reduction of algorithm complexity is achieved through a simple, but effective method to select a reference node among participating measurement nodes such that only selective time differences of signal arrival are exploited. Our estimation methods are insensitive to the source locations and can be used either as the final location estimate or as the initial point for more traditional search algorithms. [ABSTRACT FROM PUBLISHER]

Variations in the RF chain of radio transmitters can be used as a signature to uniquely associate wireless devices with a given transmission. Previous approaches, which have varied from transient analysis to machine learning, do not provide verifiable accuracy, which is essential for admissibility of the methods in the court. Here we detail a first step toward a model-based approach, which uses statistical models of RF transmitter components that are amenable for analysis. Algorithms based on statistical signal processing methods are developed to exploit non-linearities of wireless transmitters for the purpose of user identification in wireless systems. The decision rules are derived and their performance is analyzed. In order to establish the viability of the proposed approach, the practical variations of transmitter chain components are analyzed based on simulations, measurements and manufacturers' specifications. Results show that the proposed identification methods can be effective, even for short data records and relatively low signal-to-noise ratios, when exploiting imperfections of commercially used RF transmitters. [ABSTRACT FROM AUTHOR]

Packet losses in the network have a considerable performance impact on transport-layer throughput. For reliable data transfer, lost packets require retransmissions and thus cause very long delays. This tail of the packet delay distribution causes performance problems. There are several approaches to trading off networking resources up-front to reduce long delays for some packets (e.g., forward error correction, network coding). We propose packet pacing as an alternative that changes traffic characteristics favorably by adding intentional delay in packet transmissions. This intentional delay counters the principle of best effort but can reduce the burstiness of traffic and improve overall network operation — in particular in network with small packet buffers. As a result, pacing improves transport-layer performance, providing a tradeoff example where small amounts of additional delay can significantly increase connection bandwidth. We present a Queue Length Based Pacing (QLBP) algorithm that paces network traffic using a single queue and that can be implemented with small computational and memory overhead. We present a detailed analysis on delay bounds and the quantitative impact of QLBP pacing on network traffic. Through simulation, we show how the proposed pacing technique can improve connection throughput in small-buffer networks. [ABSTRACT FROM PUBLISHER]