Science.gov

Sample records for optimal signal selection

  1. Feature selection and classifier parameters estimation for EEG signals peak detection using particle swarm optimization.

    PubMed

    Adam, Asrul; Shapiai, Mohd Ibrahim; Tumari, Mohd Zaidi Mohd; Mohamad, Mohd Saberi; Mubin, Marizan

    2014-01-01

    Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model. PMID:25243236

  2. Feature Selection and Classifier Parameters Estimation for EEG Signals Peak Detection Using Particle Swarm Optimization

    PubMed Central

    Adam, Asrul; Mohd Tumari, Mohd Zaidi; Mohamad, Mohd Saberi

    2014-01-01

    Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model. PMID:25243236

  3. An ECG signal compressor based on the selection of optimal threshold levels of discrete wavelet transform coefficients.

    PubMed

    Al-Ajlouni, A F; Abo-Zahhad, M; Ahmed, S M; Schilling, R J

    2008-01-01

    Compression of electrocardiography (ECG) is necessary for efficient storage and transmission of the digitized ECG signals. Discrete wavelet transform (DWT) has recently emerged as a powerful technique for ECG signal compression due to its multi-resolution signal decomposition and locality properties. This paper presents an ECG compressor based on the selection of optimum threshold levels of DWT coefficients in different subbands that achieve maximum data volume reduction while preserving the significant signal morphology features upon reconstruction. First, the ECG is wavelet transformed into m subbands and the wavelet coefficients of each subband are thresholded using an optimal threshold level. Thresholding removes excessively small features and replaces them with zeroes. The threshold levels are defined for each signal so that the bit rate is minimized for a target distortion or, alternatively, the distortion is minimized for a target compression ratio. After thresholding, the resulting significant wavelet coefficients are coded using multi embedded zero tree (MEZW) coding technique. In order to assess the performance of the proposed compressor, records from the MIT-BIH Arrhythmia Database were compressed at different distortion levels, measured by the percentage rms difference (PRD), and compression ratios (CR). The method achieves good CR values with excellent reconstruction quality that compares favourably with various classical and state-of-the-art ECG compressors. Finally, it should be noted that the proposed method is flexible in controlling the quality of the reconstructed signals and the volume of the compressed signals by establishing a target PRD and a target CR a priori, respectively. PMID:19005960

  4. Particle Swarm Optimization Based Feature Enhancement and Feature Selection for Improved Emotion Recognition in Speech and Glottal Signals

    PubMed Central

    Muthusamy, Hariharan; Polat, Kemal; Yaacob, Sazali

    2015-01-01

    In the recent years, many research works have been published using speech related features for speech emotion recognition, however, recent studies show that there is a strong correlation between emotional states and glottal features. In this work, Mel-frequency cepstralcoefficients (MFCCs), linear predictive cepstral coefficients (LPCCs), perceptual linear predictive (PLP) features, gammatone filter outputs, timbral texture features, stationary wavelet transform based timbral texture features and relative wavelet packet energy and entropy features were extracted from the emotional speech (ES) signals and its glottal waveforms(GW). Particle swarm optimization based clustering (PSOC) and wrapper based particle swarm optimization (WPSO) were proposed to enhance the discerning ability of the features and to select the discriminating features respectively. Three different emotional speech databases were utilized to gauge the proposed method. Extreme learning machine (ELM) was employed to classify the different types of emotions. Different experiments were conducted and the results show that the proposed method significantly improves the speech emotion recognition performance compared to previous works published in the literature. PMID:25799141

  5. Particle swarm optimization based feature enhancement and feature selection for improved emotion recognition in speech and glottal signals.

    PubMed

    Muthusamy, Hariharan; Polat, Kemal; Yaacob, Sazali

    2015-01-01

    In the recent years, many research works have been published using speech related features for speech emotion recognition, however, recent studies show that there is a strong correlation between emotional states and glottal features. In this work, Mel-frequency cepstralcoefficients (MFCCs), linear predictive cepstral coefficients (LPCCs), perceptual linear predictive (PLP) features, gammatone filter outputs, timbral texture features, stationary wavelet transform based timbral texture features and relative wavelet packet energy and entropy features were extracted from the emotional speech (ES) signals and its glottal waveforms(GW). Particle swarm optimization based clustering (PSOC) and wrapper based particle swarm optimization (WPSO) were proposed to enhance the discerning ability of the features and to select the discriminating features respectively. Three different emotional speech databases were utilized to gauge the proposed method. Extreme learning machine (ELM) was employed to classify the different types of emotions. Different experiments were conducted and the results show that the proposed method significantly improves the speech emotion recognition performance compared to previous works published in the literature. PMID:25799141

  6. Optimal Prediction by Cellular Signaling Networks

    NASA Astrophysics Data System (ADS)

    Becker, Nils B.; Mugler, Andrew; ten Wolde, Pieter Rein

    2015-12-01

    Living cells can enhance their fitness by anticipating environmental change. We study how accurately linear signaling networks in cells can predict future signals. We find that maximal predictive power results from a combination of input-noise suppression, linear extrapolation, and selective readout of correlated past signal values. Single-layer networks generate exponential response kernels, which suffice to predict Markovian signals optimally. Multilayer networks allow oscillatory kernels that can optimally predict non-Markovian signals. At low noise, these kernels exploit the signal derivative for extrapolation, while at high noise, they capitalize on signal values in the past that are strongly correlated with the future signal. We show how the common motifs of negative feedback and incoherent feed-forward can implement these optimal response functions. Simulations reveal that E. coli can reliably predict concentration changes for chemotaxis, and that the integration time of its response kernel arises from a trade-off between rapid response and noise suppression.

  7. A signal invariant wavelet function selection algorithm.

    PubMed

    Garg, Girisha

    2016-04-01

    This paper addresses the problem of mother wavelet selection for wavelet signal processing in feature extraction and pattern recognition. The problem is formulated as an optimization criterion, where a wavelet library is defined using a set of parameters to find the best mother wavelet function. For estimating the fitness function, adopted to evaluate the performance of the wavelet function, analysis of variance is used. Genetic algorithm is exploited to optimize the determination of the best mother wavelet function. For experimental evaluation, solutions for best mother wavelet selection are evaluated on various biomedical signal classification problems, where the solutions of the proposed algorithm are assessed and compared with manual hit-and-trial methods. The results show that the solutions of automated mother wavelet selection algorithm are consistent with the manual selection of wavelet functions. The algorithm is found to be invariant to the type of signals used for classification. PMID:26253283

  8. Selecting the optimal anti-aliasing filter for multichannel biosignal acquisition intended for inter-signal phase shift analysis.

    PubMed

    Keresnyei, Róbert; Megyeri, Péter; Zidarics, Zoltán; Hejjel, László

    2015-01-01

    The availability of microcomputer-based portable devices facilitates the high-volume multichannel biosignal acquisition and the analysis of their instantaneous oscillations and inter-signal temporal correlations. These new, non-invasively obtained parameters can have considerable prognostic or diagnostic roles. The present study investigates the inherent signal delay of the obligatory anti-aliasing filters. One cycle of each of the 8 electrocardiogram (ECG) and 4 photoplethysmogram signals from healthy volunteers or artificially synthesised series were passed through 100-80-60-40-20 Hz 2-4-6-8th order Bessel and Butterworth filters digitally synthesized by bilinear transformation, that resulted in a negligible error in signal delay compared to the mathematical model of the impulse- and step responses of the filters. The investigated filters have as diverse a signal delay as 2-46 ms depending on the filter parameters and the signal slew rate, which is difficult to predict in biological systems and thus difficult to compensate for. Its magnitude can be comparable to the examined phase shifts, deteriorating the accuracy of the measurement. As a conclusion, identical or very similar anti-aliasing filters with lower orders and higher corner frequencies, oversampling, and digital low pass filtering are recommended for biosignal acquisition intended for inter-signal phase shift analysis. PMID:25514627

  9. Methods to optimize selective hyperthermia

    NASA Astrophysics Data System (ADS)

    Cowan, Thomas M.; Bailey, Christopher A.; Liu, Hong; Chen, Wei R.

    2003-07-01

    Laser immunotherapy, a novel therapy for breast cancer, utilizes selective photothermal interaction to raise the temperature of tumor tissue above the cell damage threshold. Photothermal interaction is achieved with intratumoral injection of a laser absorbing dye followed by non-invasive laser irradiation. When tumor heating is used in combination with immunoadjuvant to stimulate an immune response, anti-tumor immunity can be achieved. In our study, gelatin phantom simulations were used to optimize therapy parameters such as laser power, laser beam radius, and dye concentration to achieve maximum heating of target tissue with the minimum heating of non-targeted tissue. An 805-nm diode laser and indocyanine green (ICG) were used to achieve selective photothermal interactions in a gelatin phantom. Spherical gelatin phantoms containing ICG were used to simulate the absorption-enhanced target tumors, which were embedded inside gelatin without ICG to simulate surrounding non-targeted tissue. Different laser powers and dye concentrations were used to treat the gelatin phantoms. The temperature distributions in the phantoms were measured, and the data were used to determine the optimal parameters used in selective hyperthermia (laser power and dye concentration for this case). The method involves an optimization coefficient, which is proportional to the difference between temperatures measured in targeted and non-targeted gel. The coefficient is also normalized by the difference between the most heated region of the target gel and the least heated region. A positive optimization coefficient signifies a greater temperature increase in targeted gelatin when compared to non-targeted gelatin, and therefore, greater selectivity. Comparisons were made between the optimization coefficients for varying laser powers in order to demonstrate the effectinvess of this method in finding an optimal parameter set. Our experimental results support the proposed use of an optimization

  10. Optimal Distinctiveness Signals Membership Trust.

    PubMed

    Leonardelli, Geoffrey J; Loyd, Denise Lewin

    2016-07-01

    According to optimal distinctiveness theory, sufficiently small minority groups are associated with greater membership trust, even among members otherwise unknown, because the groups are seen as optimally distinctive. This article elaborates on the prediction's motivational and cognitive processes and tests whether sufficiently small minorities (defined by relative size; for example, 20%) are associated with greater membership trust relative to mere minorities (45%), and whether such trust is a function of optimal distinctiveness. Two experiments, examining observers' perceptions of minority and majority groups and using minimal groups and (in Experiment 2) a trust game, revealed greater membership trust in minorities than majorities. In Experiment 2, participants also preferred joining minorities over more powerful majorities. Both effects occurred only when minorities were 20% rather than 45%. In both studies, perceptions of optimal distinctiveness mediated effects. Discussion focuses on the value of relative size and optimal distinctiveness, and when membership trust manifests. PMID:27140657

  11. Optimal investment in social signals.

    PubMed

    Dessalles, Jean-Louis

    2014-06-01

    This study is an attempt to determine how much individuals should invest in social communication, depending on the type of relationships they may form. Two simple models of social relationships are considered. In both models, individuals emit costly signals to advertise their "quality" as potential friends. Relationships are asymmetrical or symmetrical. In the asymmetrical condition (first model), we observe that low-quality individuals are discouraged from signaling. In the symmetrical condition (second model), all individuals invest in communication. In both models, high-quality individuals (elite) do not compete and signal uniformly. The level of this uniform signal and the size of the "elite" turn out to be controlled by the accuracy of signals. The two models may be relevant to several aspects of animal and human social communication. PMID:24495174

  12. Optimal selection of wavelet-packet-based features using genetic algorithm in pathological assessment of patients' speech signal with unilateral vocal fold paralysis.

    PubMed

    Behroozmand, Roozbeh; Almasganj, Farshad

    2007-04-01

    Unilateral vocal fold paralysis (UVFP) is one of the most severe types of neurogenic laryngeal disorder in which the patients, due to their vocal cords malfunction, are confronted by some serious problems. As the effect of such pathologies would be significantly evident in the reduced quality and feature variation of dysphonic voices, this study is designed to scrutinize the piecewise variation of some specific types of these features, known as energy and entropy, all over the frequency range of pathological speech signals. In order to do so, the wavelet-packet coefficients, in five consecutive levels of decomposition, are used to extract the energy and entropy measures at different spectral sub-bands. As the decomposition procedure leads to a set of high-dimensional feature vectors, genetic algorithm is invoked to search for a group of optimal sub-band indexes for which the extracted features result in the highest recognition rate for pathological and normal subjects' classification. The results of our simulations, using support vector machine classifier, show that the highest recognition rate, for both optimized energy and entropy measures, is achieved at the fifth level of wavelet-packet decomposition. It is also found that entropy feature, with the highest recognition rate of 100% vs. 93.62% for energy, is more prominent in discriminating patients with UVFP from normal subjects. Therefore, entropy feature, in comparison with energy, demonstrates a more efficient description of such pathological voices and provides us a valuable tool for clinical diagnosis of unilateral laryngeal paralysis. PMID:17034780

  13. Optimization of the signal selection of exclusively reconstructed decays of B0 and B/s mesons at CDF-II

    SciTech Connect

    Doerr, Christian; /Karlsruhe U., EKP

    2006-06-01

    The work presented in this thesis is mainly focused on the application in a {Delta}m{sub s} measurement. Chapter 1 starts with a general theoretical introduction on the unitarity triangle with a focus on the impact of a {Delta}m{sub s} measurement. Chapter 2 then describes the experimental setup, consisting of the Tevatron collider and the CDF II detector, that was used to collect the data. In chapter 3 the concept of parameter estimation using binned and unbinned maximum likelihood fits is laid out. In addition an introduction to the NeuroBayes{reg_sign} neural network package is given. Chapter 4 outlines the analysis steps walking the path from the trigger level selection to fully reconstructed B mesons candidates. In chapter 5 the concepts and formulas that form the ingredients to an unbinned maximum likelihood fit of {Delta}m{sub s} ({Delta}m{sub d}) from a sample of reconstructed B mesons are discussed. Chapter 6 then introduces the novel method of using neural networks to achieve an improved signal selection. First the method is developed, tested and validated using the decay B{sup 0} {yields} D{pi}, D {yields} K{pi}{pi} and then applied to the kinematically very similar decay B{sub s} {yields} D{sub s}{pi}, D{sub s} {yields} {phi}{pi}, {phi} {yields} KK. Chapter 7 uses events selected by the neural network selection as input to an unbinned maximum likelihood fit and extracts the B{sup 0} lifetime and {Delta}m{sub d}. In addition, an amplitude scan and an unbinned maximum likelihood fit of {Delta}m{sub s} is performed, applying the neural network selection developed for the decay channel B{sub s} {yields} D{sub s}{pi}, D{sub s} {yields} {phi}{pi}, {phi} {yields} KK. Finally chapter 8 summarizes and gives an outlook.

  14. Optimizing calcium selective fluorimetric nanospheres.

    PubMed

    Kisiel, Anna; Kłucińska, Katarzyna; Gniadek, Marianna; Maksymiuk, Krzysztof; Michalska, Agata

    2015-11-01

    Recently it was shown that optical nanosensors based on alternating polymers e.g. poly(maleic anhydride-alt-1-octadecene) were characterized by a linear dependence of emission intensity on logarithm of concentration over a few of orders of magnitude range. In this work we focus on the material used to prepare calcium selective nanosensors. It is shown that alternating polymer nanosensors offer competitive performance in the absence of calcium ionophore, due to interaction of the nanospheres building blocks with analyte ions. The emission increase corresponds to increase of calcium ions contents in the sample within the range from 10(-4) to 10(-1) M. Further improvement in sensitivity (from 10(-6) to 10(-1) M) and selectivity can be achieved by incorporating calcium ionophore in the nanospheres. The optimal results were obtained for core-shell nanospheres, where the core was prepared from poly(styrene-co-maleic anhydride) and the outer layer from poly(maleic anhydride-alt-1-octadecene). Thus obtained chemosensors were showing linear dependence of emission on logarithm of calcium ions concentration within the range from 10(-7) to 10(-1) M. PMID:26452839

  15. Global and Local Optimization Algorithms for Optimal Signal Set Design

    PubMed Central

    Kearsley, Anthony J.

    2001-01-01

    The problem of choosing an optimal signal set for non-Gaussian detection was reduced to a smooth inequality constrained mini-max nonlinear programming problem by Gockenbach and Kearsley. Here we consider the application of several optimization algorithms, both global and local, to this problem. The most promising results are obtained when special-purpose sequential quadratic programming (SQP) algorithms are embedded into stochastic global algorithms.

  16. Optimal BLS: Optimizing transit-signal detection for Keplerian dynamics

    NASA Astrophysics Data System (ADS)

    Ofir, Aviv

    2015-08-01

    Transit surveys, both ground- and space-based, have already accumulated a large number of light curves that span several years. We optimize the search for transit signals for both detection and computational efficiencies by assuming that the searched systems can be described by Keplerian, and propagating the effects of different system parameters to the detection parameters. Importnantly, we mainly consider the information content of the transit signal and not any specific algorithm - and use BLS (Kovács, Zucker, & Mazeh 2002) just as a specific example.We show that the frequency information content of the light curve is primarily determined by the duty cycle of the transit signal, and thus the optimal frequency sampling is found to be cubic and not linear. Further optimization is achieved by considering duty-cycle dependent binning of the phased light curve. By using the (standard) BLS, one is either fairly insensitive to long-period planets or less sensitive to short-period planets and computationally slower by a significant factor of ~330 (for a 3 yr long dataset). We also show how the physical system parameters, such as the host star's size and mass, directly affect transit detection. This understanding can then be used to optimize the search for every star individually.By considering Keplerian dynamics explicitly rather than implicitly one can optimally search the transit signal parameter space. The presented Optimal BLS enhances the detectability of both very short and very long period planets, while allowing such searches to be done with much reduced resources and time. The Matlab/Octave source code for Optimal BLS is made available.

  17. A novel signal compression method based on optimal ensemble empirical mode decomposition for bearing vibration signals

    NASA Astrophysics Data System (ADS)

    Guo, Wei; Tse, Peter W.

    2013-01-01

    Today, remote machine condition monitoring is popular due to the continuous advancement in wireless communication. Bearing is the most frequently and easily failed component in many rotating machines. To accurately identify the type of bearing fault, large amounts of vibration data need to be collected. However, the volume of transmitted data cannot be too high because the bandwidth of wireless communication is limited. To solve this problem, the data are usually compressed before transmitting to a remote maintenance center. This paper proposes a novel signal compression method that can substantially reduce the amount of data that need to be transmitted without sacrificing the accuracy of fault identification. The proposed signal compression method is based on ensemble empirical mode decomposition (EEMD), which is an effective method for adaptively decomposing the vibration signal into different bands of signal components, termed intrinsic mode functions (IMFs). An optimization method was designed to automatically select appropriate EEMD parameters for the analyzed signal, and in particular to select the appropriate level of the added white noise in the EEMD method. An index termed the relative root-mean-square error was used to evaluate the decomposition performances under different noise levels to find the optimal level. After applying the optimal EEMD method to a vibration signal, the IMF relating to the bearing fault can be extracted from the original vibration signal. Compressing this signal component obtains a much smaller proportion of data samples to be retained for transmission and further reconstruction. The proposed compression method were also compared with the popular wavelet compression method. Experimental results demonstrate that the optimization of EEMD parameters can automatically find appropriate EEMD parameters for the analyzed signals, and the IMF-based compression method provides a higher compression ratio, while retaining the bearing defect

  18. Selected optimal shuttle entry computations

    NASA Technical Reports Server (NTRS)

    Sullivan, H. C.

    1974-01-01

    Parameterization and the Davidon-Fletcher-Powell method are used to study the characteristics of optimal shuttle entry trajectories. Two problems of thermal protective system weight minimization are considered: roll modulation and roll plus an angle-of-attack modulation. Both problems are targeted for the edges of the entry footprint. Results consistent with constraints on loads and control bounds are particularly well-behaved and strongly support 'energy' approximation results obtained for the case of symmetric flight by Kelley and Sullivan (1973). Furthermore, results indicate that optimal shuttle entry trajectories should be easy to duplicate and to analyze by using simple techniques.

  19. Personnel selection as a signaling game.

    PubMed

    Bangerter, Adrian; Roulin, Nicolas; König, Cornelius J

    2012-07-01

    Personnel selection involves exchanges of information between job market actors (applicants and organizations). These actors do not have an incentive to exchange accurate information about their ability and commitment to the employment relationship unless it is to their advantage. This state of affairs explains numerous phenomena in personnel selection (e.g., faking). Signaling theory describes a mechanism by which parties with partly conflicting interests (and thus an incentive for deception) can nevertheless exchange accurate information. We apply signaling theory to personnel selection, distinguishing between adaptive relationships between applicants and organizations, among applicants, and among organizations. In each case, repeated adaptations and counteradaptations between actors can lead to situations of equilibrium or escalation (arms races). We show that viewing personnel selection as a network of adaptive relationships among job market actors enables an understanding of both classic and underexplored micro- and macro-level selection phenomena and their dynamic interactions. PMID:22040263

  20. Pattern Selection by Dynamical Biochemical Signals

    PubMed Central

    Palau-Ortin, David; Formosa-Jordan, Pau; Sancho, José M.; Ibañes, Marta

    2015-01-01

    The development of multicellular organisms involves cells to decide their fate upon the action of biochemical signals. This decision is often spatiotemporally coordinated such that a spatial pattern arises. The dynamics that drive pattern formation usually involve genetic nonlinear interactions and positive feedback loops. These complex dynamics may enable multiple stable patterns for the same conditions. Under these circumstances, pattern formation in a developing tissue involves a selection process: why is a certain pattern formed and not another stable one? Herein we computationally address this issue in the context of the Notch signaling pathway. We characterize a dynamical mechanism for developmental selection of a specific pattern through spatiotemporal changes of the control parameters of the dynamics, in contrast to commonly studied situations in which initial conditions and noise determine which pattern is selected among multiple stable ones. This mechanism can be understood as a path along the parameter space driven by a sequence of biochemical signals. We characterize the selection process for three different scenarios of this dynamical mechanism that can take place during development: the signal either 1) acts in all the cells at the same time, 2) acts only within a cluster of cells, or 3) propagates along the tissue. We found that key elements for pattern selection are the destabilization of the initial pattern, the subsequent exploration of other patterns determined by the spatiotemporal symmetry of the parameter changes, and the speeds of the path compared to the timescales of the pattern formation process itself. Each scenario enables the selection of different types of patterns and creates these elements in distinct ways, resulting in different features. Our approach extends the concept of selection involved in cellular decision-making, usually applied to cell-autonomous decisions, to systems that collectively make decisions through cell

  1. Optimizing secondary tailgate support selection

    SciTech Connect

    Harwood, C.; Karmis, M.; Haycocks, C.; Luo, J.

    1996-12-01

    A model was developed to facilitate secondary tailgate support selection based on analysis of over 100 case studies, compiled from two different surveys of operating longwall coal mines in the United States. The ALPS (Analysis of Longwall Pillar Stability) program was used to determine adequacy of pillar design for the successful longwall case histories. A relationship was developed between the secondary support density necessary to maintain a stable tailgate entry during mining and the CMRR (Coal Mine Roof Rating). This relationship defines the lower bound of secondary support density currently used in longwall mines. The model used only successful tailgate case history data, with adequate ALPS SF according to the CMRR for each case. This model facilitates mine design by predicting secondary support density required for a tailgate entry depending on the ALPS SF and CMRR, which can result in significant economic benefits.

  2. Self-extinction through optimizing selection

    PubMed Central

    Parvinen, Kalle; Dieckmann, Ulf

    2013-01-01

    Evolutionary suicide is a process in which selection drives a viable population to extinction. So far, such selection-driven self-extinction has been demonstrated in models with frequency-dependent selection. This is not surprising, since frequency-dependent selection can disconnect individual-level and population-level interests through environmental feedback. Hence it can lead to situations akin to the tragedy of the commons, with adaptations that serve the selfish interests of individuals ultimately ruining a population. For frequency-dependent selection to play such a role, it must not be optimizing. Together, all published studies of evolutionary suicide have created the impression that evolutionary suicide is not possible with optimizing selection. Here we disprove this misconception by presenting and analyzing an example in which optimizing selection causes self-extinction. We then take this line of argument one step further by showing, in a further example, that selection-driven self-extinction can occur even under frequency-independent selection. PMID:23583808

  3. Feature Selection via Modified Gravitational Optimization Algorithm

    NASA Astrophysics Data System (ADS)

    Nabizadeh, Nooshin; John, Nigel

    2015-03-01

    Feature selection is the process of selecting a subset of relevant and most informative features, which efficiently represents the input data. We proposed a feature selection algorithm based on n-dimensional gravitational optimization algorithm (NGOA), which is based on the principle of gravitational fields. The objective function of optimization algorithm is a non-linear function of variables, which are called masses and defined based on extracted features. The forces between the masses as well as their new locations are calculated using the value of the objective function and the values of masses. We extracted variety of features applying different wavelet transforms and statistical methods on FLAIR and T1-weighted MR brain images. There are two classes of normal and abnormal tissues. Extracted features are divided into groups of five features. The best feature is selected in each group using N-dimensional gravitational optimization algorithm and support vector machine classifier. Then the selected features from each group make several groups of five features again and so on till desired number of features is selected. The advantage of NGOA algorithm is that the possibility of being drawn into a local optimal solution is very low. The experimental results show that our method outperforms some standard feature selection algorithms on both real-data and simulated brain tumor data.

  4. Optimizing Site Selection for HEDS

    NASA Astrophysics Data System (ADS)

    Marshall, J. R.

    1999-01-01

    MSP 2001 will be conducting environmental assessment for the Human exploration and Development of Space (HEDS) Program in order to safeguard future human exploration of the planet, in addition to geological studies being addressed by the APEX payload. In particular, the MECA experiment (see other abstracts, this volume), will address chemical toxicity of the soil, the presence of adhesive or abrasive soil dust components, and the geoelectrical-triboelectrical character of the surface environment. The attempt will be to quantify hazards to humans and machinery structures deriving from compounds that poison, corrode, abrade, invade (lungs or machinery), contaminate, or electrically interfere with the human presence. The DART experiment, will also address the size and electrical nature of airborne dust. Photo-imaging of the local scene with RAC and Pancam will be able to assess dust raising events such as local thermal vorticity-driven dust devils. The need to introduce discussion of HEDS landing site requirements stems from potential conflict, but also potential synergism with other '01 site requirements. In-situ Resource Utilization (ISRU) mission components desire as much solar radiation as possible, with some very limited amount of dust available; the planetary-astrobiology mission component desires sufficient rock abundance without inhibiting rover activities (and an interesting geological niche if available), the radiation component may again have special requirements, as will the engineers concerned with mission safety and mission longevity. The '01 mission affords an excellent opportunity to emphasize HEDS landing site requirements, given the constraint that both recent missions (Pathfinder, Mars '98) and future missions (MSP '03 & '05) have had or will have strong geological science drivers in the site selection process. What type of landing site best facilitates investigation of the physical, chemical, and behavioral properties of soil and dust? There are

  5. Overlay mark optimization using the KTD signal simulation system

    NASA Astrophysics Data System (ADS)

    Marchelli, Anat; Gutjahr, Karsten; Kubis, Michael; Sparka, Christian; Ghinovker, Mark; Navarra, Alessandra; Widmann, Amir

    2009-03-01

    As the overlay performance and accuracy requirements become tighter, the impact of process parameters on the target signal becomes more significant. Traditionally, in order to choose the optimum overlay target, several candidates are placed in the kerf area. The candidate targets are tested under different process conditions, before the target to be used in mass production is selected. The varieties of targets are left on the mass production mask and although they will not be used for overlay measurements they still consume kerf real estate. To improve the efficiency of the process we are proposing the KTD (KLA-Tencor Target Designer). It is an easy to use system that enables the user to select the optimum target based on advanced signal simulation. Implementing the KTD in production is expected to save 30% of kerf real estate due to more efficient target design process as well as reduced engineering time. In this work we demonstrate the capability of the KTD to simulate the Archer signal in the context of advanced DRAM processes. For several stacks we are comparing simulated target signals with the Archer100 signals. We demonstrate the robustness feature in the KTD application that enables the user to test the target sensitivity to process changes. The results indicate the benefit of using KTD in the target optimization process.

  6. Feature Selection via Chaotic Antlion Optimization

    PubMed Central

    Zawbaa, Hossam M.; Emary, E.; Grosan, Crina

    2016-01-01

    Background Selecting a subset of relevant properties from a large set of features that describe a dataset is a challenging machine learning task. In biology, for instance, the advances in the available technologies enable the generation of a very large number of biomarkers that describe the data. Choosing the more informative markers along with performing a high-accuracy classification over the data can be a daunting task, particularly if the data are high dimensional. An often adopted approach is to formulate the feature selection problem as a biobjective optimization problem, with the aim of maximizing the performance of the data analysis model (the quality of the data training fitting) while minimizing the number of features used. Results We propose an optimization approach for the feature selection problem that considers a “chaotic” version of the antlion optimizer method, a nature-inspired algorithm that mimics the hunting mechanism of antlions in nature. The balance between exploration of the search space and exploitation of the best solutions is a challenge in multi-objective optimization. The exploration/exploitation rate is controlled by the parameter I that limits the random walk range of the ants/prey. This variable is increased iteratively in a quasi-linear manner to decrease the exploration rate as the optimization progresses. The quasi-linear decrease in the variable I may lead to immature convergence in some cases and trapping in local minima in other cases. The chaotic system proposed here attempts to improve the tradeoff between exploration and exploitation. The methodology is evaluated using different chaotic maps on a number of feature selection datasets. To ensure generality, we used ten biological datasets, but we also used other types of data from various sources. The results are compared with the particle swarm optimizer and with genetic algorithm variants for feature selection using a set of quality metrics. PMID:26963715

  7. Classification of Motor Imagery EEG Signals with Support Vector Machines and Particle Swarm Optimization.

    PubMed

    Ma, Yuliang; Ding, Xiaohui; She, Qingshan; Luo, Zhizeng; Potter, Thomas; Zhang, Yingchun

    2016-01-01

    Support vector machines are powerful tools used to solve the small sample and nonlinear classification problems, but their ultimate classification performance depends heavily upon the selection of appropriate kernel and penalty parameters. In this study, we propose using a particle swarm optimization algorithm to optimize the selection of both the kernel and penalty parameters in order to improve the classification performance of support vector machines. The performance of the optimized classifier was evaluated with motor imagery EEG signals in terms of both classification and prediction. Results show that the optimized classifier can significantly improve the classification accuracy of motor imagery EEG signals. PMID:27313656

  8. Classification of Motor Imagery EEG Signals with Support Vector Machines and Particle Swarm Optimization

    PubMed Central

    Ma, Yuliang; Ding, Xiaohui; She, Qingshan; Luo, Zhizeng; Potter, Thomas; Zhang, Yingchun

    2016-01-01

    Support vector machines are powerful tools used to solve the small sample and nonlinear classification problems, but their ultimate classification performance depends heavily upon the selection of appropriate kernel and penalty parameters. In this study, we propose using a particle swarm optimization algorithm to optimize the selection of both the kernel and penalty parameters in order to improve the classification performance of support vector machines. The performance of the optimized classifier was evaluated with motor imagery EEG signals in terms of both classification and prediction. Results show that the optimized classifier can significantly improve the classification accuracy of motor imagery EEG signals. PMID:27313656

  9. Optimizing Clinical Research Participant Selection with Informatics

    PubMed Central

    Weng, Chunhua

    2015-01-01

    Clinical research participants are often not reflective of the real-world patients due to overly restrictive eligibility criteria. Meanwhile, unselected participants introduce confounding factors and reduce research efficiency. Biomedical Informatics, especially Big Data increasingly made available from electronic health records, offers promising aids to optimize research participant selection through data-driven transparency. PMID:26549161

  10. Optimal Signal Processing of Frequency-Stepped CW Radar Data

    NASA Technical Reports Server (NTRS)

    Ybarra, Gary A.; Wu, Shawkang M.; Bilbro, Griff L.; Ardalan, Sasan H.; Hearn, Chase P.; Neece, Robert T.

    1995-01-01

    An optimal signal processing algorithm is derived for estimating the time delay and amplitude of each scatterer reflection using a frequency-stepped CW system. The channel is assumed to be composed of abrupt changes in the reflection coefficient profile. The optimization technique is intended to maximize the target range resolution achievable from any set of frequency-stepped CW radar measurements made in such an environment. The algorithm is composed of an iterative two-step procedure. First, the amplitudes of the echoes are optimized by solving an overdetermined least squares set of equations. Then, a nonlinear objective function is scanned in an organized fashion to find its global minimum. The result is a set of echo strengths and time delay estimates. Although this paper addresses the specific problem of resolving the time delay between the two echoes, the derivation is general in the number of echoes. Performance of the optimization approach is illustrated using measured data obtained from an HP-851O network analyzer. It is demonstrated that the optimization approach offers a significant resolution enhancement over the standard processing approach that employs an IFFT. Degradation in the performance of the algorithm due to suboptimal model order selection and the effects of additive white Gaussion noise are addressed.

  11. Optimal Signal Processing of Frequency-Stepped CW Radar Data

    NASA Technical Reports Server (NTRS)

    Ybarra, Gary A.; Wu, Shawkang M.; Bilbro, Griff L.; Ardalan, Sasan H.; Hearn, Chase P.; Neece, Robert T.

    1995-01-01

    An optimal signal processing algorithm is derived for estimating the time delay and amplitude of each scatterer reflection using a frequency-stepped CW system. The channel is assumed to be composed of abrupt changes in the reflection coefficient profile. The optimization technique is intended to maximize the target range resolution achievable from any set of frequency-stepped CW radar measurements made in such an environment. The algorithm is composed of an iterative two-step procedure. First, the amplitudes of the echoes are optimized by solving an overdetermined least squares set of equations. Then, a nonlinear objective function is scanned in an organized fashion to find its global minimum. The result is a set of echo strengths and time delay estimates. Although this paper addresses the specific problem of resolving the time delay between the first two echoes, the derivation is general in the number of echoes. Performance of the optimization approach is illustrated using measured data obtained from an HP-X510 network analyzer. It is demonstrated that the optimization approach offers a significant resolution enhancement over the standard processing approach that employs an IFFT. Degradation in the performance of the algorithm due to suboptimal model order selection and the effects of additive white Gaussion noise are addressed.

  12. Optimization of Pulse Shape Discrimination of PROSPECT Liquid Scintillator Signals

    NASA Astrophysics Data System (ADS)

    Han, Ke; Prospect Collaboration

    2015-04-01

    PROSPECT, A Precision Oscillation and Spectrum Experiment, will use a segmented Li-6 doped liquid scintillator detector for precision measurement of the reactor anti-neutrino spectrum at the High Flux Isotope Reactor at Oak Ridge National Laboratory. PROSPECT also searches for very short baseline neutrino oscillation, an indication of the existence of eV-scale sterile neutrinos. Pulse shape analysis of the prompt anti-neutino signal and delayed neutron capture on Li-6 signal will greatly suppress background sources such as fast neutrons and accidental coincidence of gammas. In this talk, I will discuss different pulse shape parameters used in PROSPECT prototype detectors and multivariate optimization of event selection cuts based on those parameters.

  13. Optimal probe selection in diagnostic search

    NASA Technical Reports Server (NTRS)

    Bhandari, Inderpal S.; Simon, Herbert A.; Siewiorek, Daniel P.

    1990-01-01

    Probe selection (PS) in machine diagnosis is viewed as a collection of models that apply under specific conditions. This makes it possible for three polynomial-time optimal algorithms to be developed for simplified PS models that allow different probes to have different costs. The work is compared with the research of Simon and Kadane (1975), who developed a collection of models for optimal problem-solving search. The relationship between these models and the three newly developed algorithms for PS is explored. Two of the algorithms are unlike the ones discussed by Simon and Kadane. The third cannot be related to the problem-solving models.

  14. Optimal selection theory for superconcurrency. Technical document

    SciTech Connect

    Freund, R.F.

    1989-10-01

    This paper describes a mathematical programming approach to finding an optimal, heterogeneous suite of processors to solve supercomputing problems. This technique, called superconcurrency, works best when the computational requirements are diverse and significant portions of the code are not tightly-coupled. It is also dependent on new methods of benchmarking and code profiling, as well as eventual use of AI techniques for intelligent management of the selected superconcurrent suite.

  15. Optimal remediation policy selection under general conditions

    SciTech Connect

    Wang, M.; Zheng, C.

    1997-09-01

    A new simulation-optimization model has been developed for the optimal design of ground-water remediation systems under a variety of field conditions. The model couples genetic algorithm (GA), a global search technique inspired by biological evolution, with MODFLOW and MT3D, two commonly used ground-water flow and solute transport codes. The model allows for multiple management periods in which optimal pumping/injection rates vary with time to reflect the changes in the flow and transport conditions during the remediation process. The objective function of the model incorporates multiple cost terms including the drilling cost, the installation cost, and the costs to extract and treat the contaminated ground water. The simulation-optimization model is first applied to a typical two-dimensional pump-and-treat example with one and three management periods to demonstrate the effectiveness and robustness of the new model. The model is then applied to a large-scale three-dimensional field problem to determine the minimum pumping needed to contain an existing contaminant plume. The optimal solution as determined in this study is compared with a previous solution based on trial-and-error selection.

  16. Optimizing signal and image processing applications using Intel libraries

    NASA Astrophysics Data System (ADS)

    Landré, Jérôme; Truchetet, Frédéric

    2007-01-01

    This paper presents optimized signal and image processing libraries from Intel Corporation. Intel Performance Primitives (IPP) is a low-level signal and image processing library developed by Intel Corporation to optimize code on Intel processors. Open Computer Vision library (OpenCV) is a high-level library dedicated to computer vision tasks. This article describes the use of both libraries to build flexible and efficient signal and image processing applications.

  17. Optimal Sensor Selection for Health Monitoring Systems

    NASA Technical Reports Server (NTRS)

    Santi, L. Michael; Sowers, T. Shane; Aguilar, Robert B.

    2005-01-01

    Sensor data are the basis for performance and health assessment of most complex systems. Careful selection and implementation of sensors is critical to enable high fidelity system health assessment. A model-based procedure that systematically selects an optimal sensor suite for overall health assessment of a designated host system is described. This procedure, termed the Systematic Sensor Selection Strategy (S4), was developed at NASA John H. Glenn Research Center in order to enhance design phase planning and preparations for in-space propulsion health management systems (HMS). Information and capabilities required to utilize the S4 approach in support of design phase development of robust health diagnostics are outlined. A merit metric that quantifies diagnostic performance and overall risk reduction potential of individual sensor suites is introduced. The conceptual foundation for this merit metric is presented and the algorithmic organization of the S4 optimization process is described. Representative results from S4 analyses of a boost stage rocket engine previously under development as part of NASA's Next Generation Launch Technology (NGLT) program are presented.

  18. Separator profile selection for optimal battery performance

    NASA Astrophysics Data System (ADS)

    Whear, J. Kevin

    Battery performance, depending on the application, is normally defined by power delivery, electrical capacity, cycling regime and life in service. In order to meet the various performance goals, the Battery Design Engineer can vary things such as grid alloys, paste formulations, number of plates and methods of construction. Another design option available to optimize the battery performance is the separator profile. The goal of this paper is to demonstrate how separator profile selection can be utilized to optimize battery performance and manufacturing efficiencies. Also time will be given to explore novel separator profiles which may bring even greater benefits in the future. All major lead-acid application will be considered including automotive, motive power and stationary.

  19. Optimal Portfolio Selection Under Concave Price Impact

    SciTech Connect

    Ma Jin; Song Qingshuo; Xu Jing; Zhang Jianfeng

    2013-06-15

    In this paper we study an optimal portfolio selection problem under instantaneous price impact. Based on some empirical analysis in the literature, we model such impact as a concave function of the trading size when the trading size is small. The price impact can be thought of as either a liquidity cost or a transaction cost, but the concavity nature of the cost leads to some fundamental difference from those in the existing literature. We show that the problem can be reduced to an impulse control problem, but without fixed cost, and that the value function is a viscosity solution to a special type of Quasi-Variational Inequality (QVI). We also prove directly (without using the solution to the QVI) that the optimal strategy exists and more importantly, despite the absence of a fixed cost, it is still in a 'piecewise constant' form, reflecting a more practical perspective.

  20. Intelligent Signal Processing for Detection System Optimization

    SciTech Connect

    Fu, C Y; Petrich, L I; Daley, P F; Burnham, A K

    2004-06-18

    A wavelet-neural network signal processing method has demonstrated approximately tenfold improvement in the detection limit of various nitrogen and phosphorus compounds over traditional signal-processing methods in analyzing the output of a thermionic detector attached to the output of a gas chromatograph. A blind test was conducted to validate the lower detection limit. All fourteen of the compound spikes were detected when above the estimated threshold, including all three within a factor of two above. In addition, two of six were detected at levels 1/2 the concentration of the nominal threshold. We would have had another two correct hits if we had allowed human intervention to examine the processed data. One apparent false positive in five nulls was traced to a solvent impurity, whose presence was identified by running a solvent aliquot evaporated to 1% residual volume, while the other four nulls were properly classified. We view this signal processing method as broadly applicable in analytical chemistry, and we advocate that advanced signal processing methods be applied as directly as possible to the raw detector output so that less discriminating preprocessing and post-processing does not throw away valuable signal.

  1. Intelligent Signal Processing for Detection System Optimization

    SciTech Connect

    Fu, C Y; Petrich, L I; Daley, P F; Burnham, A K

    2004-12-05

    A wavelet-neural network signal processing method has demonstrated approximately tenfold improvement over traditional signal-processing methods for the detection limit of various nitrogen and phosphorus compounds from the output of a thermionic detector attached to a gas chromatograph. A blind test was conducted to validate the lower detection limit. All fourteen of the compound spikes were detected when above the estimated threshold, including all three within a factor of two above the threshold. In addition, two of six spikes were detected at levels of 1/2 the concentration of the nominal threshold. Another two of the six would have been detected correctly if we had allowed human intervention to examine the processed data. One apparent false positive in five nulls was traced to a solvent impurity, whose presence was subsequently identified by analyzing a solvent aliquot evaporated to 1% residual volume, while the other four nulls were properly classified. We view this signal processing method as broadly applicable in analytical chemistry, and we advocate that advanced signal processing methods should be applied as directly as possible to the raw detector output so that less discriminating preprocessing and post-processing does not throw away valuable signal.

  2. Optimal Correlation Filters for Images with Signal-Dependent Noise

    NASA Technical Reports Server (NTRS)

    Downie, John D.; Walkup, John F.

    1994-01-01

    We address the design of optimal correlation filters for pattern detection and recognition in the presence of signal-dependent image noise sources. The particular examples considered are film-grain noise and speckle. Two basic approaches are investigated: (1) deriving the optimal matched filters for the signal-dependent noise models and comparing their performances with those derived for traditional signal-independent noise models and (2) first nonlinearly transforming the signal-dependent noise to signal-independent noise followed by the use of a classical filter matched to the transformed signal. We present both theoretical and computer simulation results that demonstrate the generally superior performance of the second approach in terms of the correlation peak signal-to-noise ratio.

  3. Selectively-informed particle swarm optimization.

    PubMed

    Gao, Yang; Du, Wenbo; Yan, Gang

    2015-01-01

    Particle swarm optimization (PSO) is a nature-inspired algorithm that has shown outstanding performance in solving many realistic problems. In the original PSO and most of its variants all particles are treated equally, overlooking the impact of structural heterogeneity on individual behavior. Here we employ complex networks to represent the population structure of swarms and propose a selectively-informed PSO (SIPSO), in which the particles choose different learning strategies based on their connections: a densely-connected hub particle gets full information from all of its neighbors while a non-hub particle with few connections can only follow a single yet best-performed neighbor. Extensive numerical experiments on widely-used benchmark functions show that our SIPSO algorithm remarkably outperforms the PSO and its existing variants in success rate, solution quality, and convergence speed. We also explore the evolution process from a microscopic point of view, leading to the discovery of different roles that the particles play in optimization. The hub particles guide the optimization process towards correct directions while the non-hub particles maintain the necessary population diversity, resulting in the optimum overall performance of SIPSO. These findings deepen our understanding of swarm intelligence and may shed light on the underlying mechanism of information exchange in natural swarm and flocking behaviors. PMID:25787315

  4. Selectively-informed particle swarm optimization

    PubMed Central

    Gao, Yang; Du, Wenbo; Yan, Gang

    2015-01-01

    Particle swarm optimization (PSO) is a nature-inspired algorithm that has shown outstanding performance in solving many realistic problems. In the original PSO and most of its variants all particles are treated equally, overlooking the impact of structural heterogeneity on individual behavior. Here we employ complex networks to represent the population structure of swarms and propose a selectively-informed PSO (SIPSO), in which the particles choose different learning strategies based on their connections: a densely-connected hub particle gets full information from all of its neighbors while a non-hub particle with few connections can only follow a single yet best-performed neighbor. Extensive numerical experiments on widely-used benchmark functions show that our SIPSO algorithm remarkably outperforms the PSO and its existing variants in success rate, solution quality, and convergence speed. We also explore the evolution process from a microscopic point of view, leading to the discovery of different roles that the particles play in optimization. The hub particles guide the optimization process towards correct directions while the non-hub particles maintain the necessary population diversity, resulting in the optimum overall performance of SIPSO. These findings deepen our understanding of swarm intelligence and may shed light on the underlying mechanism of information exchange in natural swarm and flocking behaviors. PMID:25787315

  5. Selectively-informed particle swarm optimization

    NASA Astrophysics Data System (ADS)

    Gao, Yang; Du, Wenbo; Yan, Gang

    2015-03-01

    Particle swarm optimization (PSO) is a nature-inspired algorithm that has shown outstanding performance in solving many realistic problems. In the original PSO and most of its variants all particles are treated equally, overlooking the impact of structural heterogeneity on individual behavior. Here we employ complex networks to represent the population structure of swarms and propose a selectively-informed PSO (SIPSO), in which the particles choose different learning strategies based on their connections: a densely-connected hub particle gets full information from all of its neighbors while a non-hub particle with few connections can only follow a single yet best-performed neighbor. Extensive numerical experiments on widely-used benchmark functions show that our SIPSO algorithm remarkably outperforms the PSO and its existing variants in success rate, solution quality, and convergence speed. We also explore the evolution process from a microscopic point of view, leading to the discovery of different roles that the particles play in optimization. The hub particles guide the optimization process towards correct directions while the non-hub particles maintain the necessary population diversity, resulting in the optimum overall performance of SIPSO. These findings deepen our understanding of swarm intelligence and may shed light on the underlying mechanism of information exchange in natural swarm and flocking behaviors.

  6. Optimal signal recovery for pulsed balanced detection

    NASA Astrophysics Data System (ADS)

    de Icaza Astiz, Yannick A.; Lucivero, Vito Giovanni; León-Montiel, R. de J.; Mitchell, Morgan W.

    2014-09-01

    We demonstrate a tool for filtering technical and electronic noises from pulses of light, especially relevant for signal processing methods in quantum optics experiments as a means to achieve the shot-noise level and reduce strong technical noise by means of a pattern function. We provide the theory of this pattern-function filtering based on balance detection. Moreover, we implement an experimental demonstration where 10 dB of technical noise is filtered after balance detection. Such filter can readily be used for probing magnetic atomic ensembles in environments with strong technical noise.

  7. Optimal and adaptive methods of processing hydroacoustic signals (review)

    NASA Astrophysics Data System (ADS)

    Malyshkin, G. S.; Sidel'nikov, G. B.

    2014-09-01

    Different methods of optimal and adaptive processing of hydroacoustic signals for multipath propagation and scattering are considered. Advantages and drawbacks of the classical adaptive (Capon, MUSIC, and Johnson) algorithms and "fast" projection algorithms are analyzed for the case of multipath propagation and scattering of strong signals. The classical optimal approaches to detecting multipath signals are presented. A mechanism of controlled normalization of strong signals is proposed to automatically detect weak signals. The results of simulating the operation of different detection algorithms for a linear equidistant array under multipath propagation and scattering are presented. An automatic detector is analyzed, which is based on classical or fast projection algorithms, which estimates the background proceeding from median filtering or the method of bilateral spatial contrast.

  8. Intramolecular conformational changes optimize protein kinase C signaling.

    PubMed

    Antal, Corina E; Violin, Jonathan D; Kunkel, Maya T; Skovsø, Søs; Newton, Alexandra C

    2014-04-24

    Optimal tuning of enzyme signaling is critical for cellular homeostasis. We use fluorescence resonance energy transfer reporters in live cells to follow conformational transitions that tune the affinity of a multidomain signal transducer, protein kinase C (PKC), for optimal response to second messengers. This enzyme comprises two diacylglycerol sensors, the C1A and C1B domains, that have a sufficiently high intrinsic affinity for ligand so that the enzyme would be in a ligand-engaged, active state if not for mechanisms that mask its domains. We show that both diacylglycerol sensors are exposed in newly synthesized PKC and that conformational transitions following priming phosphorylations mask the domains so that the lower affinity sensor, the C1B domain, is the primary diacylglycerol binder. The conformational rearrangements of PKC serve as a paradigm for how multimodule transducers optimize their dynamic range of signaling. PMID:24631122

  9. MaNGA: Target selection and Optimization

    NASA Astrophysics Data System (ADS)

    Wake, David

    2016-01-01

    The 6-year SDSS-IV MaNGA survey will measure spatially resolved spectroscopy for 10,000 nearby galaxies using the Sloan 2.5m telescope and the BOSS spectrographs with a new fiber arrangement consisting of 17 individually deployable IFUs. We present the simultaneous design of the target selection and IFU size distribution to optimally meet our targeting requirements. The requirements for the main samples were to use simple cuts in redshift and magnitude to produce an approximately flat number density of targets as a function of stellar mass, ranging from 1x109 to 1x1011 M⊙, and radial coverage to either 1.5 (Primary sample) or 2.5 (Secondary sample) effective radii, while maximizing S/N and spatial resolution. In addition we constructed a "Color-Enhanced" sample where we required 25% of the targets to have an approximately flat number density in the color and mass plane. We show how these requirements are met using simple absolute magnitude (and color) dependent redshift cuts applied to an extended version of the NASA Sloan Atlas (NSA), how this determines the distribution of IFU sizes and the resulting properties of the MaNGA sample.

  10. MaNGA: Target selection and Optimization

    NASA Astrophysics Data System (ADS)

    Wake, David

    2015-01-01

    The 6-year SDSS-IV MaNGA survey will measure spatially resolved spectroscopy for 10,000 nearby galaxies using the Sloan 2.5m telescope and the BOSS spectrographs with a new fiber arrangement consisting of 17 individually deployable IFUs. We present the simultaneous design of the target selection and IFU size distribution to optimally meet our targeting requirements. The requirements for the main samples were to use simple cuts in redshift and magnitude to produce an approximately flat number density of targets as a function of stellar mass, ranging from 1x109 to 1x1011 M⊙, and radial coverage to either 1.5 (Primary sample) or 2.5 (Secondary sample) effective radii, while maximizing S/N and spatial resolution. In addition we constructed a 'Color-Enhanced' sample where we required 25% of the targets to have an approximately flat number density in the color and mass plane. We show how these requirements are met using simple absolute magnitude (and color) dependent redshift cuts applied to an extended version of the NASA Sloan Atlas (NSA), how this determines the distribution of IFU sizes and the resulting properties of the MaNGA sample.

  11. Optical clock signal distribution and packaging optimization

    NASA Astrophysics Data System (ADS)

    Wu, Linghui

    Polymer-based waveguides for optoelectronic interconnects and packagings were fabricated by a fabrication process that is compatible with the Si CMOS packaging process. An optoelectronic interconnection layer (OIL) for the high-speed massive clock signal distribution for the Cray T-90 supercomputer board employing optical multimode channel waveguides in conjunction with surface-normal waveguide grating couplers and a 1-to-2 3 dB splitter was constructed. Equalized optical paths were realized using an optical H-tree structure having 48 optical fanouts. This device could be increased to 64 without introducing any additional complications. A 1-to-48 fanout H-tree structure using Ultradel 9000D series polyimide was fabricated. The propagation loss and splitting loss have been measured as 0.21 dB/cm and 0.4 dB/splitter at 850 nm. The power budget was discussed, and the H-tree waveguide fully satisfies the power budget requirement. A tapered waveguide coupler was employed to match the mode profile between the single-mode fiber and the multimode channel waveguides of the OIL. A thermo-optical based multimode switch was designed, fabricated, and tested. The finite difference method was used to simulate the thermal distribution in the polymer waveguide. Both stable and transient conditions have been calculated. The thermo-optical switch was fabricated and tested. The switching speed of 1 ms was experimentally confirmed, fitting well with the simulation results. Thermo-optic switching for randomly polarized light at wavelengths of 850 nm was experimental confirmed, as was a stable attenuation of 25 dB. The details of tapered waveguide fabrication were investigated. Compression-molded 3-D tapered waveguides were demonstrated for the first time. Not only the vertical depth variation but also the linear dimensions of the molded waveguides were well beyond the limits of what any other conventional waveguide fabrication method is capable of providing. Molded waveguides with

  12. Radar antenna pointing for optimized signal to noise ratio.

    SciTech Connect

    Doerry, Armin Walter; Marquette, Brandeis

    2013-01-01

    The Signal-to-Noise Ratio (SNR) of a radar echo signal will vary across a range swath, due to spherical wavefront spreading, atmospheric attenuation, and antenna beam illumination. The antenna beam illumination will depend on antenna pointing. Calculations of geometry are complicated by the curved earth, and atmospheric refraction. This report investigates optimizing antenna pointing to maximize the minimum SNR across the range swath.

  13. Immunology (1955-1975): the natural selection theory, the two signal hypothesis and positive repertoire selection.

    PubMed

    Forsdyke, Donald R

    2012-01-01

    Observations suggesting the existence of natural antibody prior to exposure of an organism to the corresponding antigen, led to the natural selection theory of antibody formation of Jerne in 1955, and to the two signal hypothesis of Forsdyke in 1968. Aspects of these were not only first discoveries but also foundational discoveries in that they influenced contemporaries in a manner that, from our present vantage point, appears to have been constructive. Jerne's later hypothesis (1971, European Journal of Immunology 1: 1-9), that antibody-like receptors on lymphocytes were selected over evolutionary time for reactivity with the major histocompatibility complex (MHC) antigens of the species, was a first, but it was incorrect, and was foundational only to the extent that it emphasized the need to explain the Simonsen phenomenon. Although easily construed as derivative of Jerne (1971), the affinity/avidity model of Forsdyke (1975, Journal of Theoretical Biology 52: 187-198), which predicted that cell-surface components, including MHC antigens, would restrict antigen-reactivity by somatically shaping lymphocyte repertoires, was actually an extension of the two signal hypothesis. While presenting a mechanism for the positive selection of lymphocyte repertoires, and explaining the Simonsen phenomenon, the affinity/avidity model was not foundational in that it had to be independently rediscovered. For science to advance optimally we must seek to close temporal gaps so that first discoveries are also foundational. Listening to young scientists may be part of the solution. PMID:21336661

  14. Predicting resistance by selection of signaling pathways

    PubMed Central

    Rosell, Rafael; Molina, Miguel Angel; Viteri, Santiago

    2014-01-01

    Epidermal growth factor receptor (EGFR) mutations occur in 17% of non-small-cell lung cancer (NSCLC) patients with notable response to single agent therapy but with low complete remission rate and, eventually, disease progression. Priming BIM, a pro-apoptotic signaling BH3-only protein, induces sensitivity to erlotinib in EGFR-mutant cell lines. Synthetic lethal approaches and preemptive therapies based on the initial expression of BIM may significantly improve the treatment outcome. EGFR mutations result in transient pro-death imbalance of survival and apoptotic signaling in response to EGFR inhibition. SHP2 is essential to the balance between ERK and the phosphoinositide-3-kinase (PI3K)/AKT and signal transducer activator of transcription (STAT) activity, while mTOR can be an additional marker for patients with high BIM expression. Furthermore, stromal hepatocyte growth factor (HGF) confers EGFR tyrosine kinase inhibitor (TKI) resistance and induces interreceptor crosstalk with integrin-b4, Eph2, CUB domain-containing protein-1 (CDCP1), AXL and JAK1. Only by understanding better, and in more depth, complex cancer molecular biology will we have the information that will help us to design strategies to augment efficacy of EGFR TKIs and offer our patients the best, most correct therapeutic option. PMID:25806289

  15. On optimization of integration properties of biphase coded signals

    NASA Astrophysics Data System (ADS)

    Qiu, Wanzhi; Xiang, Jingcheng

    Within the context of the requirements for agile waveforms with a large compression ratio in biphase coded radars and on the basis of the characteristics of interpulse integration processing of radar signals, the study proposes two sequence optimization criteria which are suitable for radar processing patterns: interpulse waveform agility - pulse compression - FFT, and MTI - pulse compression - noncoherent integration. Applications of these criteria to optimizing sequences of length 127 are carried out. The output peak ratio of mainlobe to sidelobe (RMS) is improved considerably without a weighting network, while the autocorrelation and cross correlation profles of the sequences are very satisfactory. The RMS of coherent integration and noncoherent integration of eight sequences are 34.12 and 28.1 dB, respectively, when the return signals have zero Doppler shift. These values are about 12 and 6 dB higher than the RMS of single signals before integration.

  16. Optimal sampling and quantization of synthetic aperture radar signals

    NASA Technical Reports Server (NTRS)

    Wu, C.

    1978-01-01

    Some theoretical and experimental results on optimal sampling and quantization of synthetic aperture radar (SAR) signals are presented. It includes a description of a derived theoretical relationship between the pixel signal to noise ratio of processed SAR images and the number of quantization bits per sampled signal, assuming homogeneous extended targets. With this relationship known, a solution may be realized for the problem of optimal allocation of a fixed data bit-volume (for specified surface area and resolution criterion) between the number of samples and the number of bits per sample. The results indicate that to achieve the best possible image quality for a fixed bit rate and a given resolution criterion, one should quantize individual samples coarsely and thereby maximize the number of multiple looks. The theoretical results are then compared with simulation results obtained by processing aircraft SAR data.

  17. On the application of optimal wavelet filter banks for ECG signal classification

    NASA Astrophysics Data System (ADS)

    Hadjiloucas, S.; Jannah, N.; Hwang, F.; Galvão, R. K. H.

    2014-03-01

    This paper discusses ECG signal classification after parametrizing the ECG waveforms in the wavelet domain. Signal decomposition using perfect reconstruction quadrature mirror filter banks can provide a very parsimonious representation of ECG signals. In the current work, the filter parameters are adjusted by a numerical optimization algorithm in order to minimize a cost function associated to the filter cut-off sharpness. The goal consists of achieving a better compromise between frequency selectivity and time resolution at each decomposition level than standard orthogonal filter banks such as those of the Daubechies and Coiflet families. Our aim is to optimally decompose the signals in the wavelet domain so that they can be subsequently used as inputs for training to a neural network classifier.

  18. Vertical optimization procedure for an integrated micropower signal preprocessor

    NASA Astrophysics Data System (ADS)

    Harrison, T. R.

    1980-06-01

    The need for low power operation is characteristic of all electronic measurement systems that transmit physiological data across the intact skin. Custom integrated circuits and hybrid microcircuit assembly techniques leave battery volume (or power drain) as the limiting factor for implanted-system lifetime and animal model size. First generation totally implantable Doppler blood flowmeters, telemeter Doppler-shifted signals having a 40 kHz bandwidth. External electronics are then used to convert this information into a flow signal with a maximum bandwidth of approximately 100 Hz. A vertically optimized system circuit device procedure utilizes a digital data link for telemetry to reduce flowmeter transmitter power consumption. In this CW Doppler ultrasonic flowmeter, a potential 400:1 reduction in FM transmitter power is possible through additional signal preprocessing in the implanted package. A 10:1 savings in transmitter power drain is realized by a novel preprocessor developed to encode and telemeter pulsed digital data rather than Doppler signals.

  19. Multidimensional Optimization of Signal Space Distance Parameters in WLAN Positioning

    PubMed Central

    Brković, Milenko; Simić, Mirjana

    2014-01-01

    Accurate indoor localization of mobile users is one of the challenging problems of the last decade. Besides delivering high speed Internet, Wireless Local Area Network (WLAN) can be used as an effective indoor positioning system, being competitive both in terms of accuracy and cost. Among the localization algorithms, nearest neighbor fingerprinting algorithms based on Received Signal Strength (RSS) parameter have been extensively studied as an inexpensive solution for delivering indoor Location Based Services (LBS). In this paper, we propose the optimization of the signal space distance parameters in order to improve precision of WLAN indoor positioning, based on nearest neighbor fingerprinting algorithms. Experiments in a real WLAN environment indicate that proposed optimization leads to substantial improvements of the localization accuracy. Our approach is conceptually simple, is easy to implement, and does not require any additional hardware. PMID:24757443

  20. Optimized source selection for intracavitary low dose rate brachytherapy

    SciTech Connect

    Nurushev, T.; Kim, Jinkoo

    2005-05-01

    A procedure has been developed for automating optimal selection of sources from an available inventory for the low dose rate brachytherapy, as a replacement for the conventional trial-and-error approach. The method of optimized constrained ratios was applied for clinical source selection for intracavitary Cs-137 implants using Varian BRACHYVISION software as initial interface. However, this method can be easily extended to another system with isodose scaling and shaping capabilities. Our procedure provides optimal source selection results independent of the user experience and in a short amount of time. This method also generates statistics on frequently requested ideal source strengths aiding in ordering of clinically relevant sources.

  1. Optimal Input Signal Design for Data-Centric Estimation Methods

    PubMed Central

    Deshpande, Sunil; Rivera, Daniel E.

    2013-01-01

    Data-centric estimation methods such as Model-on-Demand and Direct Weight Optimization form attractive techniques for estimating unknown functions from noisy data. These methods rely on generating a local function approximation from a database of regressors at the current operating point with the process repeated at each new operating point. This paper examines the design of optimal input signals formulated to produce informative data to be used by local modeling procedures. The proposed method specifically addresses the distribution of the regressor vectors. The design is examined for a linear time-invariant system under amplitude constraints on the input. The resulting optimization problem is solved using semidefinite relaxation methods. Numerical examples show the benefits in comparison to a classical PRBS input design. PMID:24317042

  2. Optimal Input Signal Design for Data-Centric Estimation Methods.

    PubMed

    Deshpande, Sunil; Rivera, Daniel E

    2013-01-01

    Data-centric estimation methods such as Model-on-Demand and Direct Weight Optimization form attractive techniques for estimating unknown functions from noisy data. These methods rely on generating a local function approximation from a database of regressors at the current operating point with the process repeated at each new operating point. This paper examines the design of optimal input signals formulated to produce informative data to be used by local modeling procedures. The proposed method specifically addresses the distribution of the regressor vectors. The design is examined for a linear time-invariant system under amplitude constraints on the input. The resulting optimization problem is solved using semidefinite relaxation methods. Numerical examples show the benefits in comparison to a classical PRBS input design. PMID:24317042

  3. Optimizing Site Selection in Urban Areas in Northern Switzerland

    NASA Astrophysics Data System (ADS)

    Plenkers, K.; Kraft, T.; Bethmann, F.; Husen, S.; Schnellmann, M.

    2012-04-01

    There is a need to observe weak seismic events (M<2) in areas close to potential nuclear-waste repositories or nuclear power plants, in order to analyze the underlying seismo-tectonic processes and estimate their seismic hazard. We are therefore densifying the existing Swiss Digital Seismic Network in northern Switzerland by additional 20 stations. The new network that will be in operation by the end of 2012, aims at observing seismicity in northern Switzerland with a completeness of M_c=1.0 and a location error < 0.5 km in epicenter and < 2 km in focal depth. Monitoring of weak seismic events in this region is challenging, because the area of interest is densely populated and geology is dominated by the Swiss molasse basin. A optimal network-design and a thoughtful choice for station-sites is, therefore, mandatory. To help with decision making we developed a step-wise approach to find the optimum network configuration. Our approach is based on standard network optimization techniques regarding the localization error. As a new feature, our approach uses an ambient noise model to compute expected signal-to-noise ratios for a given site. The ambient noise model uses information on land use and major infrastructures such as highways and train lines. We ran a series of network optimizations with increasing number of stations until the requirements regarding localization error and magnitude of completeness are reached. The resulting network geometry serves as input for the site selection. Site selection is done by using a newly developed multi-step assessment-scheme that takes into account local noise level, geology, infrastructure, and costs necessary to realize the station. The assessment scheme is weighting the different parameters and the most promising sites are identified. In a first step, all potential sites are classified based on information from topographic maps and site inspection. In a second step, local noise conditions are measured at selected sites. We

  4. On Optimal Input Design and Model Selection for Communication Channels

    SciTech Connect

    Li, Yanyan; Djouadi, Seddik M; Olama, Mohammed M

    2013-01-01

    In this paper, the optimal model (structure) selection and input design which minimize the worst case identification error for communication systems are provided. The problem is formulated using metric complexity theory in a Hilbert space setting. It is pointed out that model selection and input design can be handled independently. Kolmogorov n-width is used to characterize the representation error introduced by model selection, while Gel fand and Time n-widths are used to represent the inherent error introduced by input design. After the model is selected, an optimal input which minimizes the worst case identification error is shown to exist. In particular, it is proven that the optimal model for reducing the representation error is a Finite Impulse Response (FIR) model, and the optimal input is an impulse at the start of the observation interval. FIR models are widely popular in communication systems, such as, in Orthogonal Frequency Division Multiplexing (OFDM) systems.

  5. Optimized tuner selection for engine performance estimation

    NASA Technical Reports Server (NTRS)

    Simon, Donald L. (Inventor); Garg, Sanjay (Inventor)

    2013-01-01

    A methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented. This technique specifically addresses the underdetermined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multi-variable iterative search routine which seeks to minimize the theoretical mean-squared estimation error. Theoretical Kalman filter estimation error bias and variance values are derived at steady-state operating conditions, and the tuner selection routine is applied to minimize these values. The new methodology yields an improvement in on-line engine performance estimation accuracy.

  6. Defect profile estimation from magnetic flux leakage signal via efficient managing particle swarm optimization.

    PubMed

    Han, Wenhua; Xu, Jun; Wang, Ping; Tian, Guiyun

    2014-01-01

    In this paper, efficient managing particle swarm optimization (EMPSO) for high dimension problem is proposed to estimate defect profile from magnetic flux leakage (MFL) signal. In the proposed EMPSO, in order to strengthen exchange of information among particles, particle pair model was built. For more efficient searching when facing different landscapes of problems, velocity updating scheme including three velocity updating models was also proposed. In addition, for more chances to search optimum solution out, automatic particle selection for re-initialization was implemented. The optimization results of six benchmark functions show EMPSO performs well when optimizing 100-D problems. The defect simulation results demonstrate that the inversing technique based on EMPSO outperforms the one based on self-learning particle swarm optimizer (SLPSO), and the estimated profiles are still close to the desired profiles with the presence of low noise in MFL signal. The results estimated from real MFL signal by EMPSO-based inversing technique also indicate that the algorithm is capable of providing an accurate solution of the defect profile with real signal. Both the simulation results and experiment results show the computing time of the EMPSO-based inversing technique is reduced by 20%-30% than that of the SLPSO-based inversing technique. PMID:24926693

  7. A review of channel selection algorithms for EEG signal processing

    NASA Astrophysics Data System (ADS)

    Alotaiby, Turky; El-Samie, Fathi E. Abd; Alshebeili, Saleh A.; Ahmad, Ishtiaq

    2015-12-01

    Digital processing of electroencephalography (EEG) signals has now been popularly used in a wide variety of applications such as seizure detection/prediction, motor imagery classification, mental task classification, emotion classification, sleep state classification, and drug effects diagnosis. With the large number of EEG channels acquired, it has become apparent that efficient channel selection algorithms are needed with varying importance from one application to another. The main purpose of the channel selection process is threefold: (i) to reduce the computational complexity of any processing task performed on EEG signals by selecting the relevant channels and hence extracting the features of major importance, (ii) to reduce the amount of overfitting that may arise due to the utilization of unnecessary channels, for the purpose of improving the performance, and (iii) to reduce the setup time in some applications. Signal processing tools such as time-domain analysis, power spectral estimation, and wavelet transform have been used for feature extraction and hence for channel selection in most of channel selection algorithms. In addition, different evaluation approaches such as filtering, wrapper, embedded, hybrid, and human-based techniques have been widely used for the evaluation of the selected subset of channels. In this paper, we survey the recent developments in the field of EEG channel selection methods along with their applications and classify these methods according to the evaluation approach.

  8. Optimization of ultrasonic transducers for selective guided wave actuation

    NASA Astrophysics Data System (ADS)

    Miszczynski, Mateusz; Packo, Pawel; Zbyrad, Paulina; Stepinski, Tadeusz; Uhl, Tadeusz; Lis, Jerzy; Wiatr, Kazimierz

    2016-04-01

    The application of guided waves using surface-bonded piezoceramic transducers for nondestructive testing (NDT) and Structural Health Monitoring (SHM) have shown great potential. However, due to difficulty in identification of individual wave modes resulting from their dispersive and multi-modal nature, selective mode excitement methods are highly desired. The presented work focuses on an optimization-based approach to design of a piezoelectric transducer for selective guided waves generation. The concept of the presented framework involves a Finite Element Method (FEM) model in the optimization process. The material of the transducer is optimized in topological sense with the aim of tuning piezoelectric properties for actuation of specific guided wave modes.

  9. Optimizing of selective laser sintering method

    SciTech Connect

    Guo Suiyan

    1996-12-31

    In a SLS process, a computer-controlled laser scanner moves laser beam spot on flat powder bed and the laser beam heat the powder to cause sintering in the specific area. A series of such flat planes is linked together to construct a 3D object. SLS is a complex process which involves many process parameters. The laser beam properties, such as laser beam profile, intensity, and wave length, as well as its scanning speed and scanning path, are very important parameters. Laser properties, powder properties and sintering environment work together in a SLS process to determine whether SLS is successful. The objective of SLS is to make a part which has the same size as the CAD data. The accuracy of the final part from SLS is affected by a lot of parameters as mentioned above. How to control these parameters is a key to produce an acceptable final part. Laser parameters, powder material properties and processing environment can all affect the quality of SLS part. A lot of effort has been made in parametric analysis, material properties and processing environment for SLS by other researchers. The focus of this paper is to optimize laser parameters and scanning path to improve quality of SLS part and the processing speed. A scanning method is discussed to improve the quality and speed together.

  10. Signal-to-noise-optimal scaling of heterogenous population codes.

    PubMed

    Leibold, Christian

    2013-01-01

    Similarity measures for neuronal population responses that are based on scalar products can be little informative if the neurons have different firing statistics. Based on signal-to-noise optimality, this paper derives positive weighting factors for the individual neurons' response rates in a heterogeneous neuronal population. The weights only depend on empirical statistics. If firing follows Poisson statistics, the weights can be interpreted as mutual information per spike. The scaling is shown to improve linear separability and clustering as compared to unscaled inputs. PMID:23984844

  11. Power optimization in wearable biomedical systems: a signal processing perspective

    NASA Astrophysics Data System (ADS)

    Ghasemzadeh, Hassan

    2012-10-01

    Wearable monitoring systems have caught considerable attention recently due to their potential in many domains including smart health and well-being. These new biomedical monitoring systems aim to provide continuous patient monitoring and proactive care options. Realization of this vision requires research that addresses a number of challenges, in particular, regarding limited resources that the wearable sensor networks offer. This paper presents an overview of different strategies for prolonging system lifetimes through power optimization in such systems. Particular emphasis is given to enhancing processing and communication architectures with respect to the signal processing requirements of the system.

  12. Digital logic optimization using selection operators

    NASA Technical Reports Server (NTRS)

    Whitaker, Sterling R. (Inventor); Miles, Lowell H. (Inventor); Cameron, Eric G. (Inventor); Gambles, Jody W. (Inventor)

    2004-01-01

    According to the invention, a digital design method for manipulating a digital circuit netlist is disclosed. In one step, a first netlist is loaded. The first netlist is comprised of first basic cells that are comprised of first kernel cells. The first netlist is manipulated to create a second netlist. The second netlist is comprised of second basic cells that are comprised of second kernel cells. A percentage of the first and second kernel cells are selection circuits. There is less chip area consumed in the second basic cells than in the first basic cells. The second netlist is stored. In various embodiments, the percentage could be 2% or more, 5% or more, 10% or more, 20% or more, 30% or more, or 40% or more.

  13. Optimized Selective Coatings for Solar Collectors

    NASA Technical Reports Server (NTRS)

    Mcdonald, G.; Curtis, H. B.

    1967-01-01

    The spectral reflectance properties of black nickel electroplated over stainless steel and of black copper produced by oxidation of copper sheet were measured for various plating times of black nickel and for various lengths of time of oxidation of the copper sheet, and compared to black chrome over nickel and to converted zinc. It was determined that there was an optimum time for both plating of black nickel and for the oxidation of copper black. At this time the solar selective properties show high absorptance in the solar spectrum and low emittance in the infrared. The conditions are compared for production of optimum optical properties for black nickel, black copper, black chrome, and two black zinc conversions which at the same conditions had absorptances of 0.84, 0.90, 0.95, 0.84, and 0.92, respectively, and emittances of 0.18, 0.08, 0.09, 0.10, and 0.08, respectively.

  14. Efficient Simulation Budget Allocation for Selecting an Optimal Subset

    NASA Technical Reports Server (NTRS)

    Chen, Chun-Hung; He, Donghai; Fu, Michael; Lee, Loo Hay

    2008-01-01

    We consider a class of the subset selection problem in ranking and selection. The objective is to identify the top m out of k designs based on simulated output. Traditional procedures are conservative and inefficient. Using the optimal computing budget allocation framework, we formulate the problem as that of maximizing the probability of correc tly selecting all of the top-m designs subject to a constraint on the total number of samples available. For an approximation of this corre ct selection probability, we derive an asymptotically optimal allocat ion and propose an easy-to-implement heuristic sequential allocation procedure. Numerical experiments indicate that the resulting allocatio ns are superior to other methods in the literature that we tested, and the relative efficiency increases for larger problems. In addition, preliminary numerical results indicate that the proposed new procedur e has the potential to enhance computational efficiency for simulation optimization.

  15. Opposing selection and environmental variation modify optimal timing of breeding.

    PubMed

    Tarwater, Corey E; Beissinger, Steven R

    2013-09-17

    Studies of evolution in wild populations often find that the heritable phenotypic traits of individuals producing the most offspring do not increase proportionally in the population. This paradox may arise when phenotypic traits influence both fecundity and viability and when there is a tradeoff between these fitness components, leading to opposing selection. Such tradeoffs are the foundation of life history theory, but they are rarely investigated in selection studies. Timing of breeding is a classic example of a heritable trait under directional selection that does not result in an evolutionary response. Using a 22-y study of a tropical parrot, we show that opposing viability and fecundity selection on the timing of breeding is common and affects optimal breeding date, defined by maximization of fitness. After accounting for sampling error, the directions of viability (positive) and fecundity (negative) selection were consistent, but the magnitude of selection fluctuated among years. Environmental conditions (rainfall and breeding density) primarily and breeding experience secondarily modified selection, shifting optimal timing among individuals and years. In contrast to other studies, viability selection was as strong as fecundity selection, late-born juveniles had greater survival than early-born juveniles, and breeding later in the year increased fitness under opposing selection. Our findings provide support for life history tradeoffs influencing selection on phenotypic traits, highlight the need to unify selection and life history theory, and illustrate the importance of monitoring survival as well as reproduction for understanding phenological responses to climate change. PMID:24003118

  16. Selecting optimal partitioning schemes for phylogenomic datasets

    PubMed Central

    2014-01-01

    Background Partitioning involves estimating independent models of molecular evolution for different subsets of sites in a sequence alignment, and has been shown to improve phylogenetic inference. Current methods for estimating best-fit partitioning schemes, however, are only computationally feasible with datasets of fewer than 100 loci. This is a problem because datasets with thousands of loci are increasingly common in phylogenetics. Methods We develop two novel methods for estimating best-fit partitioning schemes on large phylogenomic datasets: strict and relaxed hierarchical clustering. These methods use information from the underlying data to cluster together similar subsets of sites in an alignment, and build on clustering approaches that have been proposed elsewhere. Results We compare the performance of our methods to each other, and to existing methods for selecting partitioning schemes. We demonstrate that while strict hierarchical clustering has the best computational efficiency on very large datasets, relaxed hierarchical clustering provides scalable efficiency and returns dramatically better partitioning schemes as assessed by common criteria such as AICc and BIC scores. Conclusions These two methods provide the best current approaches to inferring partitioning schemes for very large datasets. We provide free open-source implementations of the methods in the PartitionFinder software. We hope that the use of these methods will help to improve the inferences made from large phylogenomic datasets. PMID:24742000

  17. A Novel Variable Selection Method Based on a Partial KL Information Measure and Its Application to Channel Selection for Bioelectric Signal Classification

    NASA Astrophysics Data System (ADS)

    Shibanoki, Taro; Shima, Keisuke; Tsuji, Toshio; Takaki, Takeshi; Otsuka, Akira; Chin, Takaaki

    This paper proposes a novel variable selection method based on the KL information measure, and applies it to optimal channel selection for bioelectric signal classification. Generally, the accuracy of classifcation for bioelectric signals is greatly influenced by measuring positions of the signals as well as individual physical abilities of a user. Therefore, it is effective for classification to select optimal positions for each user in advance. In the proposed method, the probability density functions (pdfs) of measured data are estimated through learning of a multidimensional probabilistic neural network (PNN) based on the KL information theory. Then, a partial KL information measure is newly defined to evaluate contribution of each dimension in the data. The effective dimensions can be selected eliminating ineffective ones based on the partial KL information in a one-by-one manner. In the experiments, the proposed method was applied to EMG electrode selection with six subjects (including an amputee), and the effective channels were selected from all channels attached to each subject's forearm. Experimental results showed that the number of channels was reduced with 36.1±12.5 [%], and the average classification rate using selected channels by the proposed method was 98.99±1.31 [%]. These results indicated that the proposed method is capable to select effective channels (optimal or semi-optimal) for accurate classification.

  18. Novel genomic signals of recent selection in an Ethiopian population

    PubMed Central

    Tekola-Ayele, Fasil; Adeyemo, Adebowale; Chen, Guanjie; Hailu, Elena; Aseffa, Abraham; Davey, Gail; Newport, Melanie J; Rotimi, Charles N

    2015-01-01

    The recent feasibility of genome-wide studies of adaptation in human populations has provided novel insights into biological pathways that have been affected by adaptive pressures. However, only a few African populations have been investigated using these genome-wide approaches. Here, we performed a genome-wide analysis for evidence of recent positive selection in a sample of 120 individuals of Wolaita ethnicity belonging to Omotic-speaking people who have inhabited the mid- and high-land areas of southern Ethiopia for millennia. Using the 11 HapMap populations as the comparison group, we found Wolaita-specific signals of recent positive selection in several human leukocyte antigen (HLA) loci. Notably, the selected loci overlapped with HLA regions that we previously reported to be associated with podoconiosis–a geochemical lymphedema of the lower legs common in the Wolaita area. We found selection signals in PPARA, a gene involved in energy metabolism during prolonged food deficiency. This finding is consistent with the dietary use of enset, a crop with high-carbohydrate and low-fat and -protein contents domesticated in Ethiopia subsequent to food deprivation 10 000 years ago, and with metabolic adaptation to high-altitude hypoxia. We observed novel selection signals in CDKAL1 and NEGR1, well-known diabetes and obesity susceptibility genes. Finally, the SLC24A5 gene locus known to be associated with skin pigmentation was in the top selection signals in the Wolaita, and the alleles of single-nucleotide polymorphisms rs1426654 and rs1834640 (SLC24A5) associated with light skin pigmentation in Eurasian populations were of high frequency (47.9%) in this Omotic-speaking indigenous Ethiopian population. PMID:25370040

  19. Spatial and Functional Selectivity of Peripheral Nerve Signal Recording With the Transversal Intrafascicular Multichannel Electrode (TIME).

    PubMed

    Badia, Jordi; Raspopovic, Stanisa; Carpaneto, Jacopo; Micera, Silvestro; Navarro, Xavier

    2016-01-01

    The selection of suitable peripheral nerve electrodes for biomedical applications implies a trade-off between invasiveness and selectivity. The optimal design should provide the highest selectivity for targeting a large number of nerve fascicles with the least invasiveness and potential damage to the nerve. The transverse intrafascicular multichannel electrode (TIME), transversally inserted in the peripheral nerve, has been shown to be useful for the selective activation of subsets of axons, both at inter- and intra-fascicular levels, in the small sciatic nerve of the rat. In this study we assessed the capabilities of TIME for the selective recording of neural activity, considering the topographical selectivity and the distinction of neural signals corresponding to different sensory types. Topographical recording selectivity was proved by the differential recording of CNAPs from different subsets of nerve fibers, such as those innervating toes 2 and 4 of the hindpaw of the rat. Neural signals elicited by sensory stimuli applied to the rat paw were successfully recorded. Signal processing allowed distinguishing three different types of sensory stimuli such as tactile, proprioceptive and nociceptive ones with high performance. These findings further support the suitability of TIMEs for neuroprosthetic applications, by exploiting the transversal topographical structure of the peripheral nerves. PMID:26087496

  20. Optimal Selection of Parameters for Nonuniform Embedding of Chaotic Time Series Using Ant Colony Optimization.

    PubMed

    Shen, Meie; Chen, Wei-Neng; Zhang, Jun; Chung, Henry Shu-Hung; Kaynak, Okyay

    2013-04-01

    The optimal selection of parameters for time-delay embedding is crucial to the analysis and the forecasting of chaotic time series. Although various parameter selection techniques have been developed for conventional uniform embedding methods, the study of parameter selection for nonuniform embedding is progressed at a slow pace. In nonuniform embedding, which enables different dimensions to have different time delays, the selection of time delays for different dimensions presents a difficult optimization problem with combinatorial explosion. To solve this problem efficiently, this paper proposes an ant colony optimization (ACO) approach. Taking advantage of the characteristic of incremental solution construction of the ACO, the proposed ACO for nonuniform embedding (ACO-NE) divides the solution construction procedure into two phases, i.e., selection of embedding dimension and selection of time delays. In this way, both the embedding dimension and the time delays can be optimized, along with the search process of the algorithm. To accelerate search speed, we extract useful information from the original time series to define heuristics to guide the search direction of ants. Three geometry- or model-based criteria are used to test the performance of the algorithm. The optimal embeddings found by the algorithm are also applied in time-series forecasting. Experimental results show that the ACO-NE is able to yield good embedding solutions from both the viewpoints of optimization performance and prediction accuracy. PMID:23144038

  1. Optimal neural network architecture selection: effects on computer-aided detection of mammographic microcalcifications

    NASA Astrophysics Data System (ADS)

    Gurcan, Metin N.; Chan, Heang-Ping; Sahiner, Berkman; Hadjiiski, Lubomir M.; Petrick, Nicholas; Helvie, Mark A.

    2002-05-01

    We evaluated the effectiveness of an optimal convolution neural network (CNN) architecture selected by simulated annealing for improving the performance of a computer-aided diagnosis (CAD) system designed for the detection of microcalcification clusters on digitized mammograms. The performances of the CAD programs with manually and optimally selected CNNs were compared using an independent test set. This set included 472 mammograms and contained 253 biopsy-proven malignant clusters. Free-response receiver operating characteristic (FROC) analysis was used for evaluation of the detection accuracy. At a false positive (FP) rate of 0.7 per image, the film-based sensitivity was 84.6% with the optimized CNN, in comparison with 77.2% with the manually selected CNN. If clusters having images in both craniocaudal and mediolateral oblique views were analyzed together and a cluster was considered to be detected when it was detected in one or both views, at 0.7 FPs/image, the sensitivity was 93.3% with the optimized CNN and 87.0% with the manually selected CNN. This study indicates that classification of true positive and FP signals is an important step of the CAD program and that the detection accuracy of the program can be considerably improved by optimizing this step with an automated optimization algorithm.

  2. From Cellular Attractor Selection to Adaptive Signal Control for Traffic Networks

    PubMed Central

    Tian, Daxin; Zhou, Jianshan; Sheng, Zhengguo; Wang, Yunpeng; Ma, Jianming

    2016-01-01

    The management of varying traffic flows essentially depends on signal controls at intersections. However, design an optimal control that considers the dynamic nature of a traffic network and coordinates all intersections simultaneously in a centralized manner is computationally challenging. Inspired by the stable gene expressions of Escherichia coli in response to environmental changes, we explore the robustness and adaptability performance of signalized intersections by incorporating a biological mechanism in their control policies, specifically, the evolution of each intersection is induced by the dynamics governing an adaptive attractor selection in cells. We employ a mathematical model to capture such biological attractor selection and derive a generic, adaptive and distributed control algorithm which is capable of dynamically adapting signal operations for the entire dynamical traffic network. We show that the proposed scheme based on attractor selection can not only promote the balance of traffic loads on each link of the network but also allows the global network to accommodate dynamical traffic demands. Our work demonstrates the potential of bio-inspired intelligence emerging from cells and provides a deep understanding of adaptive attractor selection-based control formation that is useful to support the designs of adaptive optimization and control in other domains. PMID:26972968

  3. From Cellular Attractor Selection to Adaptive Signal Control for Traffic Networks

    NASA Astrophysics Data System (ADS)

    Tian, Daxin; Zhou, Jianshan; Sheng, Zhengguo; Wang, Yunpeng; Ma, Jianming

    2016-03-01

    The management of varying traffic flows essentially depends on signal controls at intersections. However, design an optimal control that considers the dynamic nature of a traffic network and coordinates all intersections simultaneously in a centralized manner is computationally challenging. Inspired by the stable gene expressions of Escherichia coli in response to environmental changes, we explore the robustness and adaptability performance of signalized intersections by incorporating a biological mechanism in their control policies, specifically, the evolution of each intersection is induced by the dynamics governing an adaptive attractor selection in cells. We employ a mathematical model to capture such biological attractor selection and derive a generic, adaptive and distributed control algorithm which is capable of dynamically adapting signal operations for the entire dynamical traffic network. We show that the proposed scheme based on attractor selection can not only promote the balance of traffic loads on each link of the network but also allows the global network to accommodate dynamical traffic demands. Our work demonstrates the potential of bio-inspired intelligence emerging from cells and provides a deep understanding of adaptive attractor selection-based control formation that is useful to support the designs of adaptive optimization and control in other domains.

  4. From Cellular Attractor Selection to Adaptive Signal Control for Traffic Networks.

    PubMed

    Tian, Daxin; Zhou, Jianshan; Sheng, Zhengguo; Wang, Yunpeng; Ma, Jianming

    2016-01-01

    The management of varying traffic flows essentially depends on signal controls at intersections. However, design an optimal control that considers the dynamic nature of a traffic network and coordinates all intersections simultaneously in a centralized manner is computationally challenging. Inspired by the stable gene expressions of Escherichia coli in response to environmental changes, we explore the robustness and adaptability performance of signalized intersections by incorporating a biological mechanism in their control policies, specifically, the evolution of each intersection is induced by the dynamics governing an adaptive attractor selection in cells. We employ a mathematical model to capture such biological attractor selection and derive a generic, adaptive and distributed control algorithm which is capable of dynamically adapting signal operations for the entire dynamical traffic network. We show that the proposed scheme based on attractor selection can not only promote the balance of traffic loads on each link of the network but also allows the global network to accommodate dynamical traffic demands. Our work demonstrates the potential of bio-inspired intelligence emerging from cells and provides a deep understanding of adaptive attractor selection-based control formation that is useful to support the designs of adaptive optimization and control in other domains. PMID:26972968

  5. Training set optimization under population structure in genomic selection

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The optimization of the training set (TRS) in genomic selection (GS) has received much interest in both animal and plant breeding, because it is critical to the accuracy of the prediction models. In this study, five different TRS sampling algorithms, stratified sampling, mean of the Coefficient of D...

  6. Optimal Financial Aid Policies for a Selective University.

    ERIC Educational Resources Information Center

    Ehrenberg, Ronald G.; Sherman, Daniel R.

    1984-01-01

    This paper provides a model of optimal financial aid policies for a selective university. The model implies that the financial aid package to be offered to each category of admitted applicants depends on the elasticity of the fraction who accept offers of admission with respect to the financial aid package offered them. (Author/SSH)

  7. Training set optimization under population structure in genomic selection

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The optimization of the training set (TRS) in genomic selection has received much interest in both animal and plant breeding, because it is critical to the accuracy of the prediction models. In this study, five different TRS sampling algorithms, stratified sampling, mean of the coefficient of determ...

  8. Multidimensional Adaptive Testing with Optimal Design Criteria for Item Selection

    ERIC Educational Resources Information Center

    Mulder, Joris; van der Linden, Wim J.

    2009-01-01

    Several criteria from the optimal design literature are examined for use with item selection in multidimensional adaptive testing. In particular, it is examined what criteria are appropriate for adaptive testing in which all abilities are intentional, some should be considered as a nuisance, or the interest is in the testing of a composite of the…

  9. Collimator Width Optimization in X-Ray Luminescent Computed Tomography (XLCT) with Selective Excitation Scheme

    PubMed Central

    Mishra, S.; Kappiyoor, R.

    2015-01-01

    X-ray luminescent computed tomography (XLCT) is a promising new functional imaging modality based on computed tomography (CT). This imaging technique uses X-ray excitable nanophosphors to illuminate objects of interest in the visible spectrum. Though there are several validations of the underlying technology, none of them have addressed the issues of performance optimality for a given design of the imaging system. This study addresses the issue of obtaining best image quality through optimizing collimator width to balance the signal to noise ratio (SNR) and resolution. The results can be generalized as to any XLCT system employing a selective excitation scheme. PMID:25642356

  10. Strategy Developed for Selecting Optimal Sensors for Monitoring Engine Health

    NASA Technical Reports Server (NTRS)

    2004-01-01

    Sensor indications during rocket engine operation are the primary means of assessing engine performance and health. Effective selection and location of sensors in the operating engine environment enables accurate real-time condition monitoring and rapid engine controller response to mitigate critical fault conditions. These capabilities are crucial to ensure crew safety and mission success. Effective sensor selection also facilitates postflight condition assessment, which contributes to efficient engine maintenance and reduced operating costs. Under the Next Generation Launch Technology program, the NASA Glenn Research Center, in partnership with Rocketdyne Propulsion and Power, has developed a model-based procedure for systematically selecting an optimal sensor suite for assessing rocket engine system health. This optimization process is termed the systematic sensor selection strategy. Engine health management (EHM) systems generally employ multiple diagnostic procedures including data validation, anomaly detection, fault-isolation, and information fusion. The effectiveness of each diagnostic component is affected by the quality, availability, and compatibility of sensor data. Therefore systematic sensor selection is an enabling technology for EHM. Information in three categories is required by the systematic sensor selection strategy. The first category consists of targeted engine fault information; including the description and estimated risk-reduction factor for each identified fault. Risk-reduction factors are used to define and rank the potential merit of timely fault diagnoses. The second category is composed of candidate sensor information; including type, location, and estimated variance in normal operation. The final category includes the definition of fault scenarios characteristic of each targeted engine fault. These scenarios are defined in terms of engine model hardware parameters. Values of these parameters define engine simulations that generate

  11. The importance of female choice, male-male competition, and signal transmission as causes of selection on male mating signals.

    PubMed

    Sullivan-Beckers, Laura; Cocroft, Reginald B

    2010-11-01

    Selection on advertisement signals arises from interacting sources including female choice, male-male competition, and the communication channel (i.e., the signaling environment). To identify the contribution of individual sources of selection, we used previously quantified relationships between signal traits and each putative source to predict relationships between signal variation and fitness in Enchenopa binotata treehoppers (Hemiptera: Membracidae). We then measured phenotypic selection on signals and compared predicted and realized relationships between signal traits and mating success. We recorded male signals, then measured lifetime mating success at two population densities in a realistic environment in which sources of selection could interact. We identified which sources best predicted the relationship between signal variation and mating success using a multiple regression approach. All signal traits were under selection in at least one of the two breeding seasons measured, and in some cases selection was variable between years. Female preference was the strongest source of selection shaping male signals. The E. binotata species complex is a model of ecological speciation initiated by host shifts. Signal and preference divergence contribute to behavioral isolation within the complex, and the finding that female mate preferences drive signal evolution suggests that speciation in this group results from both ecological divergence and sexual selection. PMID:20624180

  12. Optimizing Ligand Efficiency of Selective Androgen Receptor Modulators (SARMs).

    PubMed

    Handlon, Anthony L; Schaller, Lee T; Leesnitzer, Lisa M; Merrihew, Raymond V; Poole, Chuck; Ulrich, John C; Wilson, Joseph W; Cadilla, Rodolfo; Turnbull, Philip

    2016-01-14

    A series of selective androgen receptor modulators (SARMs) containing the 1-(trifluoromethyl)benzyl alcohol core have been optimized for androgen receptor (AR) potency and drug-like properties. We have taken advantage of the lipophilic ligand efficiency (LLE) parameter as a guide to interpret the effect of structural changes on AR activity. Over the course of optimization efforts the LLE increased over 3 log units leading to a SARM 43 with nanomolar potency, good aqueous kinetic solubility (>700 μM), and high oral bioavailability in rats (83%). PMID:26819671

  13. Optimization of Signal Decomposition Matched Filtering (SDMF) for Improved Detection of Copy-Number Variations.

    PubMed

    Stamoulis, Catherine; Betensky, Rebecca A

    2016-01-01

    We aim to improve the performance of the previously proposed signal decomposition matched filtering (SDMF) method [26] for the detection of copy-number variations (CNV) in the human genome. Through simulations, we show that the modified SDMF is robust even at high noise levels and outperforms the original SDMF method, which indirectly depends on CNV frequency. Simulations are also used to develop a systematic approach for selecting relevant parameter thresholds in order to optimize sensitivity, specificity and computational efficiency. We apply the modified method to array CGH data from normal samples in the cancer genome atlas (TCGA) and compare detected CNVs to those estimated using circular binary segmentation (CBS) [19], a hidden Markov model (HMM)-based approach [11] and a subset of CNVs in the Database of Genomic Variants. We show that a substantial number of previously identified CNVs are detected by the optimized SDMF, which also outperforms the other two methods. PMID:27295643

  14. Ant Colony Optimization Based Feature Selection Method for QEEG Data Classification

    PubMed Central

    Ozekes, Serhat; Gultekin, Selahattin; Tarhan, Nevzat

    2014-01-01

    Objective Many applications such as biomedical signals require selecting a subset of the input features in order to represent the whole set of features. A feature selection algorithm has recently been proposed as a new approach for feature subset selection. Methods Feature selection process using ant colony optimization (ACO) for 6 channel pre-treatment electroencephalogram (EEG) data from theta and delta frequency bands is combined with back propagation neural network (BPNN) classification method for 147 major depressive disorder (MDD) subjects. Results BPNN classified R subjects with 91.83% overall accuracy and 95.55% subjects detection sensitivity. Area under ROC curve (AUC) value after feature selection increased from 0.8531 to 0.911. The features selected by the optimization algorithm were Fp1, Fp2, F7, F8, F3 for theta frequency band and eliminated 7 features from 12 to 5 feature subset. Conclusion ACO feature selection algorithm improves the classification accuracy of BPNN. Using other feature selection algorithms or classifiers to compare the performance for each approach is important to underline the validity and versatility of the designed combination. PMID:25110496

  15. Improved Clonal Selection Algorithm Combined with Ant Colony Optimization

    NASA Astrophysics Data System (ADS)

    Gao, Shangce; Wang, Wei; Dai, Hongwei; Li, Fangjia; Tang, Zheng

    Both the clonal selection algorithm (CSA) and the ant colony optimization (ACO) are inspired by natural phenomena and are effective tools for solving complex problems. CSA can exploit and explore the solution space parallely and effectively. However, it can not use enough environment feedback information and thus has to do a large redundancy repeat during search. On the other hand, ACO is based on the concept of indirect cooperative foraging process via secreting pheromones. Its positive feedback ability is nice but its convergence speed is slow because of the little initial pheromones. In this paper, we propose a pheromone-linker to combine these two algorithms. The proposed hybrid clonal selection and ant colony optimization (CSA-ACO) reasonably utilizes the superiorities of both algorithms and also overcomes their inherent disadvantages. Simulation results based on the traveling salesman problems have demonstrated the merit of the proposed algorithm over some traditional techniques.

  16. Monte Carlo optimization for site selection of new chemical plants.

    PubMed

    Cai, Tianxing; Wang, Sujing; Xu, Qiang

    2015-11-01

    Geographic distribution of chemical manufacturing sites has significant impact on the business sustainability of industrial development and regional environmental sustainability as well. The common site selection rules have included the evaluation of the air quality impact of a newly constructed chemical manufacturing site to surrounding communities. In order to achieve this target, the simultaneous consideration should cover the regional background air-quality information, the emissions of new manufacturing site, and statistical pattern of local meteorological conditions. According to the above information, the risk assessment can be conducted for the potential air-quality impacts from candidate locations of a new chemical manufacturing site, and thus the optimization of the final site selection can be achieved by minimizing its air-quality impacts. This paper has provided a systematic methodology for the above purpose. There are total two stages of modeling and optimization work: i) Monte Carlo simulation for the purpose to identify background pollutant concentration based on currently existing emission sources and regional statistical meteorological conditions; and ii) multi-objective (simultaneous minimization of both peak pollutant concentration and standard deviation of pollutant concentration spatial distribution at air-quality concern regions) Monte Carlo optimization for optimal location selection of new chemical manufacturing sites according to their design data of potential emission. This study can be helpful to both determination of the potential air-quality impact for geographic distribution of multiple chemical plants with respect to regional statistical meteorological conditions, and the identification of an optimal site for each new chemical manufacturing site with the minimal environment impact to surrounding communities. The efficacy of the developed methodology has been demonstrated through the case studies. PMID:26283263

  17. Using Differential Evolution to Optimize Learning from Signals and Enhance Network Security

    SciTech Connect

    Harmer, Paul K; Temple, Michael A; Buckner, Mark A; Farquhar, Ethan

    2011-01-01

    Computer and communication network attacks are commonly orchestrated through Wireless Access Points (WAPs). This paper summarizes proof-of-concept research activity aimed at developing a physical layer Radio Frequency (RF) air monitoring capability to limit unauthorizedWAP access and mprove network security. This is done using Differential Evolution (DE) to optimize the performance of a Learning from Signals (LFS) classifier implemented with RF Distinct Native Attribute (RF-DNA) fingerprints. Performance of the resultant DE-optimized LFS classifier is demonstrated using 802.11a WiFi devices under the most challenging conditions of intra-manufacturer classification, i.e., using emissions of like-model devices that only differ in serial number. Using identical classifier input features, performance of the DE-optimized LFS classifier is assessed relative to a Multiple Discriminant Analysis / Maximum Likelihood (MDA/ML) classifier that has been used for previous demonstrations. The comparative assessment is made using both Time Domain (TD) and Spectral Domain (SD) fingerprint features. For all combinations of classifier type, feature type, and signal-to-noise ratio considered, results show that the DEoptimized LFS classifier with TD features is uperior and provides up to 20% improvement in classification accuracy with proper selection of DE parameters.

  18. Selection of optimal threshold to construct recurrence plot for structural operational vibration measurements

    NASA Astrophysics Data System (ADS)

    Yang, Dong; Ren, Wei-Xin; Hu, Yi-Ding; Li, Dan

    2015-08-01

    The structural health monitoring (SHM) involves the sampled operational vibration measurements over time so that the structural features can be extracted accordingly. The recurrence plot (RP) and corresponding recurrence quantification analysis (RQA) have become a useful tool in various fields due to its efficiency. The threshold selection is one of key issues to make sure that the constructed recurrence plot contains enough recurrence points. Different signals have in nature different threshold values. This paper is aiming at presenting an approach to determine the optimal threshold for the operational vibration measurements of civil engineering structures. The surrogate technique and Taguchi loss function are proposed to generate reliable data and to achieve the optimal discrimination power point where the threshold is optimum. The impact of selecting recurrence thresholds on different signals is discussed. It is demonstrated that the proposed method to identify the optimal threshold is applicable to the operational vibration measurements. The proposed method provides a way to find the optimal threshold for the best RP construction of structural vibration measurements under operational conditions.

  19. Hyperopt: a Python library for model selection and hyperparameter optimization

    NASA Astrophysics Data System (ADS)

    Bergstra, James; Komer, Brent; Eliasmith, Chris; Yamins, Dan; Cox, David D.

    2015-01-01

    Sequential model-based optimization (also known as Bayesian optimization) is one of the most efficient methods (per function evaluation) of function minimization. This efficiency makes it appropriate for optimizing the hyperparameters of machine learning algorithms that are slow to train. The Hyperopt library provides algorithms and parallelization infrastructure for performing hyperparameter optimization (model selection) in Python. This paper presents an introductory tutorial on the usage of the Hyperopt library, including the description of search spaces, minimization (in serial and parallel), and the analysis of the results collected in the course of minimization. This paper also gives an overview of Hyperopt-Sklearn, a software project that provides automatic algorithm configuration of the Scikit-learn machine learning library. Following Auto-Weka, we take the view that the choice of classifier and even the choice of preprocessing module can be taken together to represent a single large hyperparameter optimization problem. We use Hyperopt to define a search space that encompasses many standard components (e.g. SVM, RF, KNN, PCA, TFIDF) and common patterns of composing them together. We demonstrate, using search algorithms in Hyperopt and standard benchmarking data sets (MNIST, 20-newsgroups, convex shapes), that searching this space is practical and effective. In particular, we improve on best-known scores for the model space for both MNIST and convex shapes. The paper closes with some discussion of ongoing and future work.

  20. State-Selective Excitation of Quantum Systems via Geometrical Optimization.

    PubMed

    Chang, Bo Y; Shin, Seokmin; Sola, Ignacio R

    2015-09-01

    We lay out the foundations of a general method of quantum control via geometrical optimization. We apply the method to state-selective population transfer using ultrashort transform-limited pulses between manifolds of levels that may represent, e.g., state-selective transitions in molecules. Assuming that certain states can be prepared, we develop three implementations: (i) preoptimization, which implies engineering the initial state within the ground manifold or electronic state before the pulse is applied; (ii) postoptimization, which implies engineering the final state within the excited manifold or target electronic state, after the pulse; and (iii) double-time optimization, which uses both types of time-ordered manipulations. We apply the schemes to two important dynamical problems: To prepare arbitrary vibrational superposition states on the target electronic state and to select weakly coupled vibrational states. Whereas full population inversion between the electronic states only requires control at initial time in all of the ground vibrational levels, only very specific superposition states can be prepared with high fidelity by either pre- or postoptimization mechanisms. Full state-selective population inversion requires manipulating the vibrational coherences in the ground electronic state before the optical pulse is applied and in the excited electronic state afterward, but not during all times. PMID:26575896

  1. PDZ Domain Binding Selectivity Is Optimized Across the Mouse Proteome

    PubMed Central

    Stiffler, Michael A.; Chen, Jiunn R.; Grantcharova, Viara P.; Lei, Ying; Fuchs, Daniel; Allen, John E.; Zaslavskaia, Lioudmila A.; MacBeath, Gavin

    2009-01-01

    PDZ domains have long been thought to cluster into discrete functional classes defined by their peptide-binding preferences. We used protein microarrays and quantitative fluorescence polarization to characterize the binding selectivity of 157 mouse PDZ domains with respect to 217 genome-encoded peptides. We then trained a multidomain selectivity model to predict PDZ domain–peptide interactions across the mouse proteome with an accuracy that exceeds many large-scale, experimental investigations of protein-protein interactions. Contrary to the current paradigm, PDZ domains do not fall into discrete classes; instead, they are evenly distributed throughout selectivity space, which suggests that they have been optimized across the proteome to minimize cross-reactivity. We predict that focusing on families of interaction domains, which facilitates the integration of experimentation and modeling, will play an increasingly important role in future investigations of protein function. PMID:17641200

  2. Signal peptide optimization tool for the secretion of recombinant protein from Saccharomyces cerevisiae.

    PubMed

    Mori, Akihiro; Hara, Shoichi; Sugahara, Tomohiro; Kojima, Takaaki; Iwasaki, Yugo; Kawarasaki, Yasuaki; Sahara, Takehiko; Ohgiya, Satoru; Nakano, Hideo

    2015-11-01

    The secretion efficiency of foreign proteins in recombinant microbes is strongly dependent on the combination of the signal peptides (SPs) used and the target proteins; therefore, identifying the optimal SP sequence for each target protein is a crucial step in maximizing the efficiency of protein secretion in both prokaryotes and eukaryotes. In this study, we developed a novel method, named the SP optimization tool (SPOT), for the generation and rapid screening of a library of SP-target gene fusion constructs to identify the optimal SP for maximizing target protein secretion. In contrast to libraries generated in previous studies, SPOT fusion constructs are generated without adding the intervening sequences associated with restriction enzyme digestion sites. Therefore, no extra amino acids are inserted at the N-terminus of the target protein that might affect its function or conformational stability. As a model system, β-galactosidase (LacA) from Aspergillus oryzae was used as a target protein for secretion from Saccharomyces cerevisiae. In total, 60 SPs were selected from S. cerevisiae secretory proteins and utilized to generate the SP library. While many of the SP-LacA fusions were not secreted, several of the SPs, AGA2, CRH1, PLB1, and MF(alpha)1, were found to enhance LacA secretion compared to the WT sequence. Our results indicate that SPOT is a valuable method for optimizing the bioproduction of any target protein, and could be adapted to many host strains. PMID:25912446

  3. Optimal precursor ion selection for LC-MALDI MS/MS

    PubMed Central

    2013-01-01

    Background Liquid chromatography mass spectrometry (LC-MS) maps in shotgun proteomics are often too complex to select every detected peptide signal for fragmentation by tandem mass spectrometry (MS/MS). Standard methods for precursor ion selection, commonly based on data dependent acquisition, select highly abundant peptide signals in each spectrum. However, these approaches produce redundant information and are biased towards high-abundance proteins. Results We present two algorithms for inclusion list creation that formulate precursor ion selection as an optimization problem. Given an LC-MS map, the first approach maximizes the number of selected precursors given constraints such as a limited number of acquisitions per RT fraction. Second, we introduce a protein sequence-based inclusion list that can be used to monitor proteins of interest. Given only the protein sequences, we create an inclusion list that optimally covers the whole protein set. Additionally, we propose an iterative precursor ion selection that aims at reducing the redundancy obtained with data dependent LC-MS/MS. We overcome the risk of erroneous assignments by including methods for retention time and proteotypicity predictions. We show that our method identifies a set of proteins requiring fewer precursors than standard approaches. Thus, it is well suited for precursor ion selection in experiments with limited sample amount or analysis time. Conclusions We present three approaches to precursor ion selection with LC-MALDI MS/MS. Using a well-defined protein standard and a complex human cell lysate, we demonstrate that our methods outperform standard approaches. Our algorithms are implemented as part of OpenMS and are available under http://www.openms.de. PMID:23418672

  4. Field of view selection for optimal airborne imaging sensor performance

    NASA Astrophysics Data System (ADS)

    Goss, Tristan M.; Barnard, P. Werner; Fildis, Halidun; Erbudak, Mustafa; Senger, Tolga; Alpman, Mehmet E.

    2014-05-01

    The choice of the Field of View (FOV) of imaging sensors used in airborne targeting applications has major impact on the overall performance of the system. Conducting a market survey from published data on sensors used in stabilized airborne targeting systems shows a trend of ever narrowing FOVs housed in smaller and lighter volumes. This approach promotes the ever increasing geometric resolution provided by narrower FOVs, while it seemingly ignores the influences the FOV selection has on the sensor's sensitivity, the effects of diffraction, the influences of sight line jitter and collectively the overall system performance. This paper presents a trade-off methodology to select the optimal FOV for an imaging sensor that is limited in aperture diameter by mechanical constraints (such as space/volume available and window size) by balancing the influences FOV has on sensitivity and resolution and thereby optimizing the system's performance. The methodology may be applied to staring array based imaging sensors across all wavebands from visible/day cameras through to long wave infrared thermal imagers. Some examples of sensor analysis applying the trade-off methodology are given that highlights the performance advantages that can be gained by maximizing the aperture diameters and choosing the optimal FOV for an imaging sensor used in airborne targeting applications.

  5. Some useful upper bounds for the selection of optimal profiles

    NASA Astrophysics Data System (ADS)

    Daripa, Prabir

    2012-08-01

    In enhanced oil recovery by chemical flooding within tertiary oil recovery, it is often necessary to choose optimal viscous profiles of the injected displacing fluids that reduce growth rates of hydrodynamic instabilities the most thereby substantially reducing the well-known fingering problem and improving oil recovery. Within the three-layer Hele-Shaw model, we show in this paper that selection of the optimal monotonic viscous profile of the middle-layer fluid based on well known theoretical upper bound formula [P. Daripa, G. Pasa, A simple derivation of an upper bound in the presence of a viscosity gradient in three-layer Hele-Shaw flows, Journal of Statistical Mechanics (2006) 11. http://dx.doi.org/10.1088/1742-5468/2006/01/P01014] agrees very well with that based on the computation of maximum growth rate of instabilities from solving the linearized stability problem. Thus, this paper proposes a very simple, fast method for selection of the optimal monotonic viscous profiles of the displacing fluids in multi-layer flows.

  6. Implementing stationary-phase optimized selectivity in supercritical fluid chromatography.

    PubMed

    Delahaye, Sander; Lynen, Frédéric

    2014-12-16

    The performance of stationary-phase optimized selectivity liquid chromatography (SOS-LC) for improved separation of complex mixtures has been demonstrated before. A dedicated kit containing column segments of different lengths and packed with different stationary phases is commercially available together with algorithms capable of predicting and ranking isocratic and gradient separations over vast amounts of possible column combinations. Implementation in chromatographic separations involving compressible fluids, as is the case in supercritical fluid chromatography, had thus far not been attempted. The challenge of this approach is the dependency of solute retention with the mobile-phase density, complicating linear extrapolation of retention over longer or shorter columns segments, as is the case in conventional SOS-LC. In this study, the possibilities of performing stationary-phase optimized selectivity supercritical fluid chromatography (SOS-SFC) are demonstrated with typical low density mobile phases (94% CO2). The procedure is optimized with the commercially available column kit and with the classical isocratic SOS-LC algorithm. SOS-SFC appears possible without any density correction, although optimal correspondence between prediction and experiment is obtained when isopycnic conditions are maintained. As also the influence of the segment order appears significantly less relevant than expected, the use of the approach in SFC appears as promising as is the case in HPLC. Next to the classical use of SOS for faster baseline separation of all solutes in a mixture, the benefits of the approach for predicting as wide as possible separation windows around to-be-purified solutes in semipreparative SFC are illustrated, leading to significant production rate improvements in (semi)preparative SFC. PMID:25393519

  7. Plastic scintillation dosimetry: Optimal selection of scintillating fibers and scintillators

    SciTech Connect

    Archambault, Louis; Arsenault, Jean; Gingras, Luc; Sam Beddar, A.; Roy, Rene; Beaulieu, Luc

    2005-07-15

    Scintillation dosimetry is a promising avenue for evaluating dose patterns delivered by intensity-modulated radiation therapy plans or for the small fields involved in stereotactic radiosurgery. However, the increase in signal has been the goal for many authors. In this paper, a comparison is made between plastic scintillating fibers and plastic scintillator. The collection of scintillation light was measured experimentally for four commercial models of scintillating fibers (BCF-12, BCF-60, SCSF-78, SCSF-3HF) and two models of plastic scintillators (BC-400, BC-408). The emission spectra of all six scintillators were obtained by using an optical spectrum analyzer and they were compared with theoretical behavior. For scintillation in the blue region, the signal intensity of a singly clad scintillating fiber (BCF-12) was 120% of that of the plastic scintillator (BC-400). For the multiclad fiber (SCSF-78), the signal reached 144% of that of the plastic scintillator. The intensity of the green scintillating fibers was lower than that of the plastic scintillator: 47% for the singly clad fiber (BCF-60) and 77% for the multiclad fiber (SCSF-3HF). The collected light was studied as a function of the scintillator length and radius for a cylindrical probe. We found that symmetric detectors with nearly the same spatial resolution in each direction (2 mm in diameter by 3 mm in length) could be made with a signal equivalent to those of the more commonly used asymmetric scintillators. With augmentation of the signal-to-noise ratio in consideration, this paper presents a series of comparisons that should provide insight into selection of a scintillator type and volume for development of a medical dosimeter.

  8. Membrane association of the CD3ε signaling domain is required for optimal T cell development and function1

    PubMed Central

    Bettini, Matthew L.; Guy, Clifford; Dash, Pradyot; Vignali, Kate M.; Hamm, David E.; Dobbins, Jessica; Gagnon, Etienne; Thomas, Paul G.; Wucherpfennig, Kai W.; Vignali, Dario A.A.

    2014-01-01

    The T cell receptor (TCR):CD3 complex transduces signals that are critical for optimal T cell development and adaptive immunity. In resting T cells, the CD3ε cytoplasmic tail associates with the plasma membrane via a proximal basic-rich stretch (BRS). Here we show that mice lacking a functional CD3ε-BRS exhibited substantial reductions in thymic cellularity and limited CD4−CD8− double negative-3 (DN3) to DN4 thymocyte transition, due to enhanced DN4 TCR signaling resulting in increased cell death and TCR downregulation in all subsequent populations. Furthermore, positive, but not negative, T cell selection was affected in mice lacking a functional CD3ε-BRS, which led to limited peripheral T cell function and substantially reduced responsiveness to influenza infection. Collectively, these results indicate membrane association of the CD3ε signaling domain is required for optimal thymocyte development and peripheral T cell function. PMID:24899501

  9. Tests of variable-band multilayers designed for investigating optimal signal-to-noise vs artifact signal ratios in Dual-Energy Digital Subtraction Angiography (DDSA) imaging systems

    SciTech Connect

    Boyers, D.; Ho, A.; Li, Q.; Piestrup, M.; Rice, M.; Tatchyn, R.

    1993-08-01

    In recent work, various design techniques were applied to investigate the feasibility of controlling the bandwidth and bandshape profiles of tungsten/boron-carbon (W/B{sub 4}C) and tungsten/silicon (W/Si) multilayers for optimizing their performance in synchrotron radiation based angiographical imaging systems at 33 keV. Varied parameters included alternative spacing geometries, material thickness ratios, and numbers of layer pairs. Planar optics with nominal design reflectivities of 30%--94% and bandwidths ranging from 0.6%--10% were designed at the Stanford Radiation Laboratory, fabricated by the Ovonic Synthetic Materials Company, and characterized on Beam Line 4-3 at the Stanford Synchrotron Radiation Laboratory, in this paper we report selected results of these tests and review the possible use of the multilayers for determining optimal signal to noise vs. artifact signal ratios in practical Dual-Energy Digital Subtraction Angiography systems.

  10. Optimal Selection of Threshold Value 'r' for Refined Multiscale Entropy.

    PubMed

    Marwaha, Puneeta; Sunkaria, Ramesh Kumar

    2015-12-01

    Refined multiscale entropy (RMSE) technique was introduced to evaluate complexity of a time series over multiple scale factors 't'. Here threshold value 'r' is updated as 0.15 times SD of filtered scaled time series. The use of fixed threshold value 'r' in RMSE sometimes assigns very close resembling entropy values to certain time series at certain temporal scale factors and is unable to distinguish different time series optimally. The present study aims to evaluate RMSE technique by varying threshold value 'r' from 0.05 to 0.25 times SD of filtered scaled time series and finding optimal 'r' values for each scale factor at which different time series can be distinguished more effectively. The proposed RMSE was used to evaluate over HRV time series of normal sinus rhythm subjects, patients suffering from sudden cardiac death, congestive heart failure, healthy adult male, healthy adult female and mid-aged female groups as well as over synthetic simulated database for different datalengths 'N' of 3000, 3500 and 4000. The proposed RMSE results in improved discrimination among different time series. To enhance the computational capability, empirical mathematical equations have been formulated for optimal selection of threshold values 'r' as a function of SD of filtered scaled time series and datalength 'N' for each scale factor 't'. PMID:26577486

  11. Optimizing Hammermill Performance Through Screen Selection and Hammer Design

    SciTech Connect

    Neal A. Yancey; Tyler L. Westover; Christopher T. Wright

    2013-01-01

    Background: Mechanical preprocessing, which includes particle size reduction and mechanical separation, is one of the primary operations in the feedstock supply system for a lignocellulosic biorefinery. It is the means by which raw biomass from the field or forest is mechanically transformed into an on-spec feedstock with characteristics better suited for the fuel conversion process. Results: This work provides a general overview of the objectives and methodologies of mechanical preprocessing and then presents experimental results illustrating (1) improved size reduction via optimization of hammer mill configuration, (2) improved size reduction via pneumatic-assisted hammer milling, and (3) improved control of particle size and particle size distribution through proper selection of grinder process parameters. Conclusion: Optimal grinder configuration for maximal process throughput and efficiency is strongly dependent on feedstock type and properties, such moisture content. Tests conducted using a HG200 hammer grinder indicate that increasing the tip speed, optimizing hammer geometry, and adding pneumatic assist can increase grinder throughput as much as 400%.

  12. Optimal subinterval selection approach for power system transient stability simulation

    DOE PAGESBeta

    Kim, Soobae; Overbye, Thomas J.

    2015-10-21

    Power system transient stability analysis requires an appropriate integration time step to avoid numerical instability as well as to reduce computational demands. For fast system dynamics, which vary more rapidly than what the time step covers, a fraction of the time step, called a subinterval, is used. However, the optimal value of this subinterval is not easily determined because the analysis of the system dynamics might be required. This selection is usually made from engineering experiences, and perhaps trial and error. This paper proposes an optimal subinterval selection approach for power system transient stability analysis, which is based on modalmore » analysis using a single machine infinite bus (SMIB) system. Fast system dynamics are identified with the modal analysis and the SMIB system is used focusing on fast local modes. An appropriate subinterval time step from the proposed approach can reduce computational burden and achieve accurate simulation responses as well. As a result, the performance of the proposed method is demonstrated with the GSO 37-bus system.« less

  13. Optimal subinterval selection approach for power system transient stability simulation

    SciTech Connect

    Kim, Soobae; Overbye, Thomas J.

    2015-10-21

    Power system transient stability analysis requires an appropriate integration time step to avoid numerical instability as well as to reduce computational demands. For fast system dynamics, which vary more rapidly than what the time step covers, a fraction of the time step, called a subinterval, is used. However, the optimal value of this subinterval is not easily determined because the analysis of the system dynamics might be required. This selection is usually made from engineering experiences, and perhaps trial and error. This paper proposes an optimal subinterval selection approach for power system transient stability analysis, which is based on modal analysis using a single machine infinite bus (SMIB) system. Fast system dynamics are identified with the modal analysis and the SMIB system is used focusing on fast local modes. An appropriate subinterval time step from the proposed approach can reduce computational burden and achieve accurate simulation responses as well. As a result, the performance of the proposed method is demonstrated with the GSO 37-bus system.

  14. Batch-Form Solutions to Optimal Input Signal Recovery in the Presence of Noises

    NASA Astrophysics Data System (ADS)

    Lin, Ping; Phan, Minh Q.; Ketcham, Stephen A.

    2013-12-01

    This paper studies the problem of optimally recovering the input signals to a linear time-invariant system in the presence of input and measurement noises. The emphasis is on batch-form solutions which are suitable for short-duration large-domain signal propagation applications. The system, the input and measurement noise covariances, the noise-corrupted output signals are assumed known, and we seek to recover the input signals that enter the system prior to being corrupted by input noise. The input signal recovery is optimal in the sense that the optimal Kalman filter residual is correctly recovered from the given information. Various solution techniques are considered and a weighted least-squares solution is found to be the simplest and most practical in short-duration signal recovery applications.

  15. Simultaneous seismic random noise attenuation and signal preservation by optimal spatiotemporal TFPF

    NASA Astrophysics Data System (ADS)

    Lin, Hongbo; Li, Yue; Ma, Haitao; Xu, Liping

    2016-05-01

    The time-frequency peak filtering (TFPF) algorithm has been successfully applied to seismic random noise attenuation. However, the time-frequency peak filtering with fixed-type spatiotemporal filtering trajectories fails to preserve reflected signals in seismic events which have complex geometric structure. An optimal spatiotemporal TFPF (OST-TFPF) is proposed here combining the Shapiro-Francia (S-F) statistic to reduce random noise and preserve seismic signals simultaneously. In the novel algorithm, the S-F statistic is first calculated for seismic data to detect seismic events based on the fact that the non-Gaussian seismic signals lead to smaller values of the S-F statistic comparing to seismic random noise which is general Gaussian. Then, optimal spatiotemporal filtering trajectory can be constructed based on the S-F statistic to coincide with the shape of each event. Finally, the optimal spatiotemporal TFPF de-noises seismic data along the optimal trajectories. Since the resampled signals along the trajectories matching the geometric structures of seismic events become more linear compared to signals in time, the OST-TFPF gives better signal estimation while attenuating random noise. Synthetic and field data examples demonstrate that the optimal spatiotemporal TFPF is effective in the denoising and signal-preserving of the seismic data with low signal-to-noise ratio. Moreover, the OST-TFPF also obtains good performance in preservation of seismic event with complex geometric structure.

  16. Optimal quantum state estimation with use of the no-signaling principle

    SciTech Connect

    Han, Yeong-Deok; Bae, Joonwoo; Wang Xiangbin; Hwang, Won-Young

    2010-12-15

    A simple derivation of the optimal state estimation of a quantum bit was obtained by using the no-signaling principle. In particular, the no-signaling principle determines a unique form of the guessing probability independent of figures of merit, such as the fidelity or information gain. This proves that the optimal estimation for a quantum bit can be achieved by the same measurement for almost all figures of merit.

  17. Multiobjective Optimization for Model Selection in Kernel Methods in Regression

    PubMed Central

    You, Di; Benitez-Quiroz, C. Fabian; Martinez, Aleix M.

    2016-01-01

    Regression plays a major role in many scientific and engineering problems. The goal of regression is to learn the unknown underlying function from a set of sample vectors with known outcomes. In recent years, kernel methods in regression have facilitated the estimation of nonlinear functions. However, two major (interconnected) problems remain open. The first problem is given by the bias-vs-variance trade-off. If the model used to estimate the underlying function is too flexible (i.e., high model complexity), the variance will be very large. If the model is fixed (i.e., low complexity), the bias will be large. The second problem is to define an approach for selecting the appropriate parameters of the kernel function. To address these two problems, this paper derives a new smoothing kernel criterion, which measures the roughness of the estimated function as a measure of model complexity. Then, we use multiobjective optimization to derive a criterion for selecting the parameters of that kernel. The goal of this criterion is to find a trade-off between the bias and the variance of the learned function. That is, the goal is to increase the model fit while keeping the model complexity in check. We provide extensive experimental evaluations using a variety of problems in machine learning, pattern recognition and computer vision. The results demonstrate that the proposed approach yields smaller estimation errors as compared to methods in the state of the art. PMID:25291740

  18. Multiobjective optimization for model selection in kernel methods in regression.

    PubMed

    You, Di; Benitez-Quiroz, Carlos Fabian; Martinez, Aleix M

    2014-10-01

    Regression plays a major role in many scientific and engineering problems. The goal of regression is to learn the unknown underlying function from a set of sample vectors with known outcomes. In recent years, kernel methods in regression have facilitated the estimation of nonlinear functions. However, two major (interconnected) problems remain open. The first problem is given by the bias-versus-variance tradeoff. If the model used to estimate the underlying function is too flexible (i.e., high model complexity), the variance will be very large. If the model is fixed (i.e., low complexity), the bias will be large. The second problem is to define an approach for selecting the appropriate parameters of the kernel function. To address these two problems, this paper derives a new smoothing kernel criterion, which measures the roughness of the estimated function as a measure of model complexity. Then, we use multiobjective optimization to derive a criterion for selecting the parameters of that kernel. The goal of this criterion is to find a tradeoff between the bias and the variance of the learned function. That is, the goal is to increase the model fit while keeping the model complexity in check. We provide extensive experimental evaluations using a variety of problems in machine learning, pattern recognition, and computer vision. The results demonstrate that the proposed approach yields smaller estimation errors as compared with methods in the state of the art. PMID:25291740

  19. Optimized bioregenerative space diet selection with crew choice

    NASA Technical Reports Server (NTRS)

    Vicens, Carrie; Wang, Carolyn; Olabi, Ammar; Jackson, Peter; Hunter, Jean

    2003-01-01

    Previous studies on optimization of crew diets have not accounted for choice. A diet selection model with crew choice was developed. Scenario analyses were conducted to assess the feasibility and cost of certain crew preferences, such as preferences for numerous-desserts, high-salt, and high-acceptability foods. For comparison purposes, a no-choice and a random-choice scenario were considered. The model was found to be feasible in terms of food variety and overall costs. The numerous-desserts, high-acceptability, and random-choice scenarios all resulted in feasible solutions costing between 13.2 and 17.3 kg ESM/person-day. Only the high-sodium scenario yielded an infeasible solution. This occurred when the foods highest in salt content were selected for the crew-choice portion of the diet. This infeasibility can be avoided by limiting the total sodium content in the crew-choice portion of the diet. Cost savings were found by reducing food variety in scenarios where the preference bias strongly affected nutritional content.

  20. Considerations on the Optimal and Efficient Processing of Information-Bearing Signals

    ERIC Educational Resources Information Center

    Harms, Herbert Andrew

    2013-01-01

    Noise is a fundamental hurdle that impedes the processing of information-bearing signals, specifically the extraction of salient information. Processing that is both optimal and efficient is desired; optimality ensures the extracted information has the highest fidelity allowed by the noise, while efficiency ensures limited resource usage. Optimal…

  1. Induction motor fault diagnosis based on the k-NN and optimal feature selection

    NASA Astrophysics Data System (ADS)

    Nguyen, Ngoc-Tu; Lee, Hong-Hee

    2010-09-01

    The k-nearest neighbour (k-NN) rule is applied to diagnose the conditions of induction motors. The features are extracted from the time vibration signals while the optimal features are selected by a genetic algorithm based on a distance criterion. A weight value is assigned to each feature to help select the best quality features. To improve the classification performance of the k-NN rule, each of the k neighbours are evaluated by a weight factor based on the distance to the test pattern. The proposed k-NN is compared to the conventional k-NN and support vector machine classification to verify the performance of an induction motor fault diagnosis.

  2. Socially selected ornaments influence hormone titers of signalers and receivers.

    PubMed

    Tibbetts, Elizabeth A; Crocker, Katherine; Huang, Zachary Y

    2016-07-26

    Decades of behavioral endocrinology research have shown that hormones and behavior have a bidirectional relationship; hormones both influence and respond to social behavior. In contrast, hormones are often thought to have a unidirectional relationship with ornaments. Hormones influence ornament development, but little empirical work has tested how ornaments influence hormones throughout life. Here, we experimentally alter a visual signal of fighting ability in Polistes dominulus paper wasps and measure the behavioral and hormonal consequences of signal alteration in signalers and receivers. We find wasps that signal inaccurately high fighting ability receive more aggression than controls and receiving aggression reduces juvenile hormone (JH) titers. As a result, immediately after contests, inaccurate signalers have lower JH titers than controls. Ornaments also directly influence rival JH titers. Three hours after contests, wasps who interacted with rivals signaling high fighting ability have higher JH titers than wasps who interacted with rivals signaling low fighting ability. Therefore, ornaments influence hormone titers of both signalers and receivers. We demonstrate that relationships between hormones and ornaments are flexible and bidirectional rather than static and unidirectional. Dynamic relationships among ornaments, behavior, and physiology may be an important, but overlooked factor in the evolution of honest communication. PMID:27402762

  3. New indicator for optimal preprocessing and wavelength selection of near-infrared spectra.

    PubMed

    Skibsted, E T S; Boelens, H F M; Westerhuis, J A; Witte, D T; Smilde, A K

    2004-03-01

    Preprocessing of near-infrared spectra to remove unwanted, i.e., non-related spectral variation and selection of informative wavelengths is considered to be a crucial step prior to the construction of a quantitative calibration model. The standard methodology when comparing various preprocessing techniques and selecting different wavelengths is to compare prediction statistics computed with an independent set of data not used to make the actual calibration model. When the errors of reference value are large, no such values are available at all, or only a limited number of samples are available, other methods exist to evaluate the preprocessing method and wavelength selection. In this work we present a new indicator (SE) that only requires blank sample spectra, i.e., spectra of samples that are mixtures of the interfering constituents (everything except the analyte), a pure analyte spectrum, or alternatively, a sample spectrum where the analyte is present. The indicator is based on computing the net analyte signal of the analyte and the total error, i.e., instrumental noise and bias. By comparing the indicator values when different preprocessing techniques and wavelength selections are applied to the spectra, the optimal preprocessing technique and the optimal wavelength selection can be determined without knowledge of reference values, i.e., it minimizes the non-related spectral variation. The SE indicator is compared to two other indicators that also use net analyte signal computations. To demonstrate the feasibility of the SE indicator, two near-infrared spectral data sets from the pharmaceutical industry were used, i.e., diffuse reflectance spectra of powder samples and transmission spectra of tablets. Especially in pharmaceutical spectroscopic applications, it is expected beforehand that the non-related spectral variation is rather large and it is important to remove it. The indicator gave excellent results with respect to wavelength selection and optimal

  4. Signal Scaling Improves the Signal-to-Noise Ratio of Measurements with Segmented 2D-Selective Radiofrequency Excitations

    PubMed Central

    Finsterbusch, Jürgen; Busch, Martin G.; Larson, Peder E. Z.

    2016-01-01

    Purpose Segmented 2D-selective radiofrequency excitations can be used to acquire irregularly shaped target regions, e.g., in single-voxel MR spectroscopy, without involving excessive radiofrequency pulse durations. However, segments covering only outer k-space regions nominally use reduced B1 amplitudes (i.e., smaller flip angles) and yield lower signal contributions, which decreases the efficiency of the measurement. The purpose of this study was to show that applying the full flip angle for all segments and scaling down the acquired signal appropriately (signal scaling) retains the desired signal amplitude but reduces the noise level accordingly and, thus, increases the signal-to-noise ratio. Methods The principles and improvements of signal scaling were demonstrated with MR imaging and spectroscopy experiments at 3 T for a single-line segmentation of a blipped-planar trajectory. Results The observed signal-to-noise ration gain depended on the 2D-selective radiofrequency excitation’s resolution, field-of-excitation, and its excitation profile and was between 40 and 500% for typical acquisition parameters. Conclusion Signal scaling can further improve the performance of measurements with segmented 2D-selective radiofrequency excitations, e.g., for MR spectroscopy of anatomically defined voxels. PMID:23440633

  5. Dynamic nuclear polarization and optimal control spatial-selective 13C MRI and MRS

    NASA Astrophysics Data System (ADS)

    Vinding, Mads S.; Laustsen, Christoffer; Maximov, Ivan I.; Søgaard, Lise Vejby; Ardenkjær-Larsen, Jan H.; Nielsen, Niels Chr.

    2013-02-01

    Aimed at 13C metabolic magnetic resonance imaging (MRI) and spectroscopy (MRS) applications, we demonstrate that dynamic nuclear polarization (DNP) may be combined with optimal control 2D spatial selection to simultaneously obtain high sensitivity and well-defined spatial restriction. This is achieved through the development of spatial-selective single-shot spiral-readout MRI and MRS experiments combined with dynamic nuclear polarization hyperpolarized [1-13C]pyruvate on a 4.7 T pre-clinical MR scanner. The method stands out from related techniques by facilitating anatomic shaped region-of-interest (ROI) single metabolite signals available for higher image resolution or single-peak spectra. The 2D spatial-selective rf pulses were designed using a novel Krotov-based optimal control approach capable of iteratively fast providing successful pulse sequences in the absence of qualified initial guesses. The technique may be important for early detection of abnormal metabolism, monitoring disease progression, and drug research.

  6. Near optimal energy selective x-ray imaging system performance with simple detectors

    SciTech Connect

    Alvarez, Robert E.

    2010-02-15

    Purpose: This article describes a method to achieve near optimal performance with low energy resolution detectors. Tapiovaara and Wagner [Phys. Med. Biol. 30, 519-529 (1985)] showed that an energy selective x-ray system using a broad spectrum source can produce images with a larger signal to noise ratio (SNR) than conventional systems using energy integrating or photon counting detectors. They showed that there is an upper limit to the SNR and that it can be achieved by measuring full spectrum information and then using an optimal energy dependent weighting. Methods: A performance measure is derived by applying statistical detection theory to an abstract vector space of the line integrals of the basis set coefficients of the two function approximation to the x-ray attenuation coefficient. The approach produces optimal results that utilize all the available energy dependent data. The method can be used with any energy selective detector and is applied not only to detectors using pulse height analysis (PHA) but also to a detector that simultaneously measures the total photon number and integrated energy, as discussed by Roessl et al. [Med. Phys. 34, 959-966 (2007)]. A generalization of this detector that improves the performance is introduced. A method is described to compute images with the optimal SNR using projections in a ''whitened'' vector space transformed so the noise is uncorrelated and has unit variance in both coordinates. Material canceled images with optimal SNR can also be computed by projections in this space. Results: The performance measure is validated by showing that it provides the Tapiovaara-Wagner optimal results for a detector with full energy information and also a conventional detector. The performance with different types of detectors is compared to the ideal SNR as a function of x-ray tube voltage and subject thickness. A detector that combines two bin PHA with a simultaneous measurement of integrated photon energy provides near ideal

  7. Optimal Design of Calibration Signals in Space Borne Gravitational Wave Detectors

    NASA Technical Reports Server (NTRS)

    Nofrarias, Miquel; Karnesis, Nikolaos; Gibert, Ferran; Armano, Michele; Audley, Heather; Danzmann, Karsten; Diepholz, Ingo; Dolesi, Rita; Ferraioli, Luigi; Thorpe, James I.

    2014-01-01

    Future space borne gravitational wave detectors will require a precise definition of calibration signals to ensure the achievement of their design sensitivity. The careful design of the test signals plays a key role in the correct understanding and characterization of these instruments. In that sense, methods achieving optimal experiment designs must be considered as complementary to the parameter estimation methods being used to determine the parameters describing the system. The relevance of experiment design is particularly significant for the LISA Pathfinder mission, which will spend most of its operation time performing experiments to characterize key technologies for future space borne gravitational wave observatories. Here we propose a framework to derive the optimal signals in terms of minimum parameter uncertainty to be injected to these instruments during its calibration phase. We compare our results with an alternative numerical algorithm which achieves an optimal input signal by iteratively improving an initial guess. We show agreement of both approaches when applied to the LISA Pathfinder case.

  8. Electrosensory optimization to conspecific phasic signals for mating.

    PubMed

    Tricas, T C; Michael, S W; Sisneros, J A

    1995-12-29

    Ampullary electroreceptor systems in fishes and aquatic amphibians are known to function in prey localization by the movement of the animal through a weak dc field produced by their prey. The round stingray produces an electric field with a complex geometry that is modulated rhythmically by movements of the spiracles and gill slits during ventilation. This weak stimulus is used in the field by reproductively active male stingrays to locate mates, and also by female rays to locate buried consexuals. Electrosensory primary afferent neurons are most sensitive to stimuli that vary sinusoidally at the same frequency as the natural respiratory movements. The match between primary afferent frequency sensitivity and the ventilatory phasic signals produced by conspecifics indicates that the electrosensory system serves an important biological function in the social behavior of elasmobranchs. PMID:8787848

  9. Making the optimal decision in selecting protective clothing

    SciTech Connect

    Price, J. Mark

    2007-07-01

    Protective Clothing plays a major role in the decommissioning and operation of nuclear facilities. Literally thousands of employee dress-outs occur over the life of a decommissioning project and during outages at operational plants. In order to make the optimal decision on which type of protective clothing is best suited for the decommissioning or maintenance and repair work on radioactive systems, a number of interrelating factors must be considered, including - Protection; - Personnel Contamination; - Cost; - Radwaste; - Comfort; - Convenience; - Logistics/Rad Material Considerations; - Reject Rate of Laundered Clothing; - Durability; - Security; - Personnel Safety including Heat Stress; - Disposition of Gloves and Booties. In addition, over the last several years there has been a trend of nuclear power plants either running trials or switching to Single Use Protective Clothing (SUPC) from traditional protective clothing. In some cases, after trial usage of SUPC, plants have chosen not to switch. In other cases after switching to SUPC for a period of time, some plants have chosen to switch back to laundering. Based on these observations, this paper reviews the 'real' drivers, issues, and interrelating factors regarding the selection and use of protective clothing throughout the nuclear industry. (authors)

  10. Applications of Optimal Building Energy System Selection and Operation

    SciTech Connect

    Marnay, Chris; Stadler, Michael; Siddiqui, Afzal; DeForest, Nicholas; Donadee, Jon; Bhattacharya, Prajesh; Lai, Judy

    2011-04-01

    Berkeley Lab has been developing the Distributed Energy Resources Customer Adoption Model (DER-CAM) for several years. Given load curves for energy services requirements in a building microgrid (u grid), fuel costs and other economic inputs, and a menu of available technologies, DER-CAM finds the optimum equipment fleet and its optimum operating schedule using a mixed integer linear programming approach. This capability is being applied using a software as a service (SaaS) model. Optimisation problems are set up on a Berkeley Lab server and clients can execute their jobs as needed, typically daily. The evolution of this approach is demonstrated by description of three ongoing projects. The first is a public access web site focused on solar photovoltaic generation and battery viability at large commercial and industrial customer sites. The second is a building CO2 emissions reduction operations problem for a University of California, Davis student dining hall for which potential investments are also considered. And the third, is both a battery selection problem and a rolling operating schedule problem for a large County Jail. Together these examples show that optimization of building u grid design and operation can be effectively achieved using SaaS.

  11. Optimizing Electromagnetically Induced Transparency Signals with Laguerre-Gaussian Beams

    NASA Astrophysics Data System (ADS)

    Holtfrerich, Matthew; Akin, Tom; Krzyzewski, Sean; Marino, Alberto; Abraham, Eric

    2016-05-01

    We have performed electromagnetically induced transparency in ultracold Rubidium atoms using a Laguerre-Gaussian laser mode as the control beam. Laguerre-Gaussian modes are characterized by a ring type transverse intensity profile and carry intrinsic orbital angular momentum. This angular momentum carried by the control beam can be utilized in optical computing applications which is unavailable to the more common Gaussian laser field. Specifically, we use a Laguerre-Gaussian control beam with a Gaussian probe to show that the linewidth of the transmission spectrum can be narrowed when compared to a Gaussian control beam that has the same peak intensity. We present data extending this work to compare control fields in both the Gaussian and Laguerre-Gaussian modes with constant total power. We have made efforts to find the optical overlap that best minimizes the transmission linewidth while also maintaining signal contrast. This was done by changing the waist size of the control beam with respect to the probe. The best results were obtained when the waist of a Laguerre-Gaussian control beam is equal to the waist of the Gaussian probe resulting in narrow linewidth features.

  12. Genome-wide signals of positive selection in human evolution

    PubMed Central

    Enard, David; Messer, Philipp W.; Petrov, Dmitri A.

    2014-01-01

    The role of positive selection in human evolution remains controversial. On the one hand, scans for positive selection have identified hundreds of candidate loci, and the genome-wide patterns of polymorphism show signatures consistent with frequent positive selection. On the other hand, recent studies have argued that many of the candidate loci are false positives and that most genome-wide signatures of adaptation are in fact due to reduction of neutral diversity by linked deleterious mutations, known as background selection. Here we analyze human polymorphism data from the 1000 Genomes Project and detect signatures of positive selection once we correct for the effects of background selection. We show that levels of neutral polymorphism are lower near amino acid substitutions, with the strongest reduction observed specifically near functionally consequential amino acid substitutions. Furthermore, amino acid substitutions are associated with signatures of recent adaptation that should not be generated by background selection, such as unusually long and frequent haplotypes and specific distortions in the site frequency spectrum. We use forward simulations to argue that the observed signatures require a high rate of strongly adaptive substitutions near amino acid changes. We further demonstrate that the observed signatures of positive selection correlate better with the presence of regulatory sequences, as predicted by the ENCODE Project Consortium, than with the positions of amino acid substitutions. Our results suggest that adaptation was frequent in human evolution and provide support for the hypothesis of King and Wilson that adaptive divergence is primarily driven by regulatory changes. PMID:24619126

  13. Sexual selection accelerates signal evolution during speciation in birds

    PubMed Central

    Seddon, Nathalie; Botero, Carlos A.; Tobias, Joseph A.; Dunn, Peter O.; MacGregor, Hannah E. A.; Rubenstein, Dustin R.; Uy, J. Albert C.; Weir, Jason T.; Whittingham, Linda A.; Safran, Rebecca J.

    2013-01-01

    Sexual selection is proposed to be an important driver of diversification in animal systems, yet previous tests of this hypothesis have produced mixed results and the mechanisms involved remain unclear. Here, we use a novel phylogenetic approach to assess the influence of sexual selection on patterns of evolutionary change during 84 recent speciation events across 23 passerine bird families. We show that elevated levels of sexual selection are associated with more rapid phenotypic divergence between related lineages, and that this effect is restricted to male plumage traits proposed to function in mate choice and species recognition. Conversely, we found no evidence that sexual selection promoted divergence in female plumage traits, or in male traits related to foraging and locomotion. These results provide strong evidence that female choice and male–male competition are dominant mechanisms driving divergence during speciation in birds, potentially linking sexual selection to the accelerated evolution of pre-mating reproductive isolation. PMID:23864596

  14. Hybrid Feature Selection for Myoelectric Signal Classification Using MICA

    NASA Astrophysics Data System (ADS)

    Naik, Ganesh R.; Kumar, Dinesh K.

    2010-03-01

    This paper presents a novel method to enhance the performance of Independent Component Analysis (ICA) of myoelectric signal by decomposing the signal into components originating from different muscles. First, we use Multi run ICA (MICA) algorithm to separate the muscle activities. Pattern classification of the separated signal is performed in the second step with a back propagation neural network. The focus of this work is to establish a simple, yet robust system that can be used to identify subtle complex hand actions and gestures for control of prosthesis and other computer assisted devices. Testing was conducted using several single shot experiments conducted with five subjects. The results indicate that the system is able to classify four different wrist actions with near 100% accuracy.

  15. Wild Western Lowland Gorillas Signal Selectively Using Odor

    PubMed Central

    Klailova, Michelle; Lee, Phyllis C.

    2014-01-01

    Mammals communicate socially through visual, auditory and chemical signals. The chemical sense is the oldest sense and is shared by all organisms including bacteria. Despite mounting evidence for social chemo-signaling in humans, the extent to which it modulates behavior is debated and can benefit from comparative models of closely related hominoids. The use of odor cues in wild ape social communication has been only rarely explored. Apart from one study on wild chimpanzee sniffing, our understanding is limited to anecdotes. We present the first study of wild gorilla chemo-communication and the first analysis of olfactory signaling in relation to arousal levels and odor strength in wild apes. If gorilla scent is used as a signaling mechanism instead of only a sign of arousal or stress, odor emission should be context specific and capable of variation as a function of the relationships between the emitter and perceiver(s). Measured through a human pungency scale, we determined the factors that predicted extreme levels of silverback odor for one wild western lowland gorilla (Gorilla gorilla gorilla) group silverback. Extreme silverback odor was predicted by the presence and intensity of inter-unit interactions, silverback anger, distress and long-calling auditory rates, and the absence of close proximity between the silverback and mother of the youngest infant. Odor strength also varied according to the focal silverback's strategic responses during high intensity inter-unit interactions. Silverbacks appear to use odor as a modifiable form of communication; where odor acts as a highly flexible, context dependent signaling mechanism to group members and extra-group units. The importance of olfaction to ape social communication may be especially pertinent in Central African forests where limited visibility may necessitate increased reliance on other senses. PMID:25006973

  16. Wild western lowland gorillas signal selectively using odor.

    PubMed

    Klailova, Michelle; Lee, Phyllis C

    2014-01-01

    Mammals communicate socially through visual, auditory and chemical signals. The chemical sense is the oldest sense and is shared by all organisms including bacteria. Despite mounting evidence for social chemo-signaling in humans, the extent to which it modulates behavior is debated and can benefit from comparative models of closely related hominoids. The use of odor cues in wild ape social communication has been only rarely explored. Apart from one study on wild chimpanzee sniffing, our understanding is limited to anecdotes. We present the first study of wild gorilla chemo-communication and the first analysis of olfactory signaling in relation to arousal levels and odor strength in wild apes. If gorilla scent is used as a signaling mechanism instead of only a sign of arousal or stress, odor emission should be context specific and capable of variation as a function of the relationships between the emitter and perceiver(s). Measured through a human pungency scale, we determined the factors that predicted extreme levels of silverback odor for one wild western lowland gorilla (Gorilla gorilla gorilla) group silverback. Extreme silverback odor was predicted by the presence and intensity of inter-unit interactions, silverback anger, distress and long-calling auditory rates, and the absence of close proximity between the silverback and mother of the youngest infant. Odor strength also varied according to the focal silverback's strategic responses during high intensity inter-unit interactions. Silverbacks appear to use odor as a modifiable form of communication; where odor acts as a highly flexible, context dependent signaling mechanism to group members and extra-group units. The importance of olfaction to ape social communication may be especially pertinent in Central African forests where limited visibility may necessitate increased reliance on other senses. PMID:25006973

  17. Ultra-fast fluence optimization for beam angle selection algorithms

    NASA Astrophysics Data System (ADS)

    Bangert, M.; Ziegenhein, P.; Oelfke, U.

    2014-03-01

    Beam angle selection (BAS) including fluence optimization (FO) is among the most extensive computational tasks in radiotherapy. Precomputed dose influence data (DID) of all considered beam orientations (up to 100 GB for complex cases) has to be handled in the main memory and repeated FOs are required for different beam ensembles. In this paper, the authors describe concepts accelerating FO for BAS algorithms using off-the-shelf multiprocessor workstations. The FO runtime is not dominated by the arithmetic load of the CPUs but by the transportation of DID from the RAM to the CPUs. On multiprocessor workstations, however, the speed of data transportation from the main memory to the CPUs is non-uniform across the RAM; every CPU has a dedicated memory location (node) with minimum access time. We apply a thread node binding strategy to ensure that CPUs only access DID from their preferred node. Ideal load balancing for arbitrary beam ensembles is guaranteed by distributing the DID of every candidate beam equally to all nodes. Furthermore we use a custom sorting scheme of the DID to minimize the overall data transportation. The framework is implemented on an AMD Opteron workstation. One FO iteration comprising dose, objective function, and gradient calculation takes between 0.010 s (9 beams, skull, 0.23 GB DID) and 0.070 s (9 beams, abdomen, 1.50 GB DID). Our overall FO time is < 1 s for small cases, larger cases take ~ 4 s. BAS runs including FOs for 1000 different beam ensembles take ~ 15-70 min, depending on the treatment site. This enables an efficient clinical evaluation of different BAS algorithms.

  18. A Survey of Stochastic Simulation and Optimization Methods in Signal Processing

    NASA Astrophysics Data System (ADS)

    Pereyra, Marcelo; Schniter, Philip; Chouzenoux, Emilie; Pesquet, Jean-Christophe; Tourneret, Jean-Yves; Hero, Alfred O.; McLaughlin, Steve

    2016-03-01

    Modern signal processing (SP) methods rely very heavily on probability and statistics to solve challenging SP problems. SP methods are now expected to deal with ever more complex models, requiring ever more sophisticated computational inference techniques. This has driven the development of statistical SP methods based on stochastic simulation and optimization. Stochastic simulation and optimization algorithms are computationally intensive tools for performing statistical inference in models that are analytically intractable and beyond the scope of deterministic inference methods. They have been recently successfully applied to many difficult problems involving complex statistical models and sophisticated (often Bayesian) statistical inference techniques. This survey paper offers an introduction to stochastic simulation and optimization methods in signal and image processing. The paper addresses a variety of high-dimensional Markov chain Monte Carlo (MCMC) methods as well as deterministic surrogate methods, such as variational Bayes, the Bethe approach, belief and expectation propagation and approximate message passing algorithms. It also discusses a range of optimization methods that have been adopted to solve stochastic problems, as well as stochastic methods for deterministic optimization. Subsequently, areas of overlap between simulation and optimization, in particular optimization-within-MCMC and MCMC-driven optimization are discussed.

  19. A quadratic weight selection algorithm. [for optimal flight control

    NASA Technical Reports Server (NTRS)

    Broussard, J. R.

    1981-01-01

    A new numerical algorithm is presented which determines a positive semi-definite state weighting matrix in the linear-quadratic optimal control design problem. The algorithm chooses the weighting matrix by placing closed-loop eigenvalues and eigenvectors near desired locations using optimal feedback gains. A simplified flight control design example is used to illustrate the algorithms capabilities.

  20. Bicoid Signal Extraction with a Selection of Parametric and Nonparametric Signal Processing Techniques

    PubMed Central

    Ghodsi, Zara; Silva, Emmanuel Sirimal; Hassani, Hossein

    2015-01-01

    The maternal segmentation coordinate gene bicoid plays a significant role during Drosophila embryogenesis. The gradient of Bicoid, the protein encoded by this gene, determines most aspects of head and thorax development. This paper seeks to explore the applicability of a variety of signal processing techniques at extracting bicoid expression signal, and whether these methods can outperform the current model. We evaluate the use of six different powerful and widely-used models representing both parametric and nonparametric signal processing techniques to determine the most efficient method for signal extraction in bicoid. The results are evaluated using both real and simulated data. Our findings show that the Singular Spectrum Analysis technique proposed in this paper outperforms the synthesis diffusion degradation model for filtering the noisy protein profile of bicoid whilst the exponential smoothing technique was found to be the next best alternative followed by the autoregressive integrated moving average. PMID:26197438

  1. Bicoid signal extraction with a selection of parametric and nonparametric signal processing techniques.

    PubMed

    Ghodsi, Zara; Silva, Emmanuel Sirimal; Hassani, Hossein

    2015-06-01

    The maternal segmentation coordinate gene bicoid plays a significant role during Drosophila embryogenesis. The gradient of Bicoid, the protein encoded by this gene, determines most aspects of head and thorax development. This paper seeks to explore the applicability of a variety of signal processing techniques at extracting bicoid expression signal, and whether these methods can outperform the current model. We evaluate the use of six different powerful and widely-used models representing both parametric and nonparametric signal processing techniques to determine the most efficient method for signal extraction in bicoid. The results are evaluated using both real and simulated data. Our findings show that the Singular Spectrum Analysis technique proposed in this paper outperforms the synthesis diffusion degradation model for filtering the noisy protein profile of bicoid whilst the exponential smoothing technique was found to be the next best alternative followed by the autoregressive integrated moving average. PMID:26197438

  2. Stimulus design for model selection and validation in cell signaling.

    PubMed

    Apgar, Joshua F; Toettcher, Jared E; Endy, Drew; White, Forest M; Tidor, Bruce

    2008-02-01

    Mechanism-based chemical kinetic models are increasingly being used to describe biological signaling. Such models serve to encapsulate current understanding of pathways and to enable insight into complex biological processes. One challenge in model development is that, with limited experimental data, multiple models can be consistent with known mechanisms and existing data. Here, we address the problem of model ambiguity by providing a method for designing dynamic stimuli that, in stimulus-response experiments, distinguish among parameterized models with different topologies, i.e., reaction mechanisms, in which only some of the species can be measured. We develop the approach by presenting two formulations of a model-based controller that is used to design the dynamic stimulus. In both formulations, an input signal is designed for each candidate model and parameterization so as to drive the model outputs through a target trajectory. The quality of a model is then assessed by the ability of the corresponding controller, informed by that model, to drive the experimental system. We evaluated our method on models of antibody-ligand binding, mitogen-activated protein kinase (MAPK) phosphorylation and de-phosphorylation, and larger models of the epidermal growth factor receptor (EGFR) pathway. For each of these systems, the controller informed by the correct model is the most successful at designing a stimulus to produce the desired behavior. Using these stimuli we were able to distinguish between models with subtle mechanistic differences or where input and outputs were multiple reactions removed from the model differences. An advantage of this method of model discrimination is that it does not require novel reagents, or altered measurement techniques; the only change to the experiment is the time course of stimulation. Taken together, these results provide a strong basis for using designed input stimuli as a tool for the development of cell signaling models. PMID

  3. Comparison of Genetic Algorithm, Particle Swarm Optimization and Biogeography-based Optimization for Feature Selection to Classify Clusters of Microcalcifications

    NASA Astrophysics Data System (ADS)

    Khehra, Baljit Singh; Pharwaha, Amar Partap Singh

    2016-06-01

    Ductal carcinoma in situ (DCIS) is one type of breast cancer. Clusters of microcalcifications (MCCs) are symptoms of DCIS that are recognized by mammography. Selection of robust features vector is the process of selecting an optimal subset of features from a large number of available features in a given problem domain after the feature extraction and before any classification scheme. Feature selection reduces the feature space that improves the performance of classifier and decreases the computational burden imposed by using many features on classifier. Selection of an optimal subset of features from a large number of available features in a given problem domain is a difficult search problem. For n features, the total numbers of possible subsets of features are 2n. Thus, selection of an optimal subset of features problem belongs to the category of NP-hard problems. In this paper, an attempt is made to find the optimal subset of MCCs features from all possible subsets of features using genetic algorithm (GA), particle swarm optimization (PSO) and biogeography-based optimization (BBO). For simulation, a total of 380 benign and malignant MCCs samples have been selected from mammogram images of DDSM database. A total of 50 features extracted from benign and malignant MCCs samples are used in this study. In these algorithms, fitness function is correct classification rate of classifier. Support vector machine is used as a classifier. From experimental results, it is also observed that the performance of PSO-based and BBO-based algorithms to select an optimal subset of features for classifying MCCs as benign or malignant is better as compared to GA-based algorithm.

  4. Intelligent feature selection techniques for pattern classification of Lamb wave signals

    SciTech Connect

    Hinders, Mark K.; Miller, Corey A.

    2014-02-18

    Lamb wave interaction with flaws is a complex, three-dimensional phenomenon, which often frustrates signal interpretation schemes based on mode arrival time shifts predicted by dispersion curves. As the flaw severity increases, scattering and mode conversion effects will often dominate the time-domain signals, obscuring available information about flaws because multiple modes may arrive on top of each other. Even for idealized flaw geometries the scattering and mode conversion behavior of Lamb waves is very complex. Here, multi-mode Lamb waves in a metal plate are propagated across a rectangular flat-bottom hole in a sequence of pitch-catch measurements corresponding to the double crosshole tomography geometry. The flaw is sequentially deepened, with the Lamb wave measurements repeated at each flaw depth. Lamb wave tomography reconstructions are used to identify which waveforms have interacted with the flaw and thereby carry information about its depth. Multiple features are extracted from each of the Lamb wave signals using wavelets, which are then fed to statistical pattern classification algorithms that identify flaw severity. In order to achieve the highest classification accuracy, an optimal feature space is required but it’s never known a priori which features are going to be best. For structural health monitoring we make use of the fact that physical flaws, such as corrosion, will only increase over time. This allows us to identify feature vectors which are topologically well-behaved by requiring that sequential classes “line up” in feature vector space. An intelligent feature selection routine is illustrated that identifies favorable class distributions in multi-dimensional feature spaces using computational homology theory. Betti numbers and formal classification accuracies are calculated for each feature space subset to establish a correlation between the topology of the class distribution and the corresponding classification accuracy.

  5. Intelligent feature selection techniques for pattern classification of Lamb wave signals

    NASA Astrophysics Data System (ADS)

    Hinders, Mark K.; Miller, Corey A.

    2014-02-01

    Lamb wave interaction with flaws is a complex, three-dimensional phenomenon, which often frustrates signal interpretation schemes based on mode arrival time shifts predicted by dispersion curves. As the flaw severity increases, scattering and mode conversion effects will often dominate the time-domain signals, obscuring available information about flaws because multiple modes may arrive on top of each other. Even for idealized flaw geometries the scattering and mode conversion behavior of Lamb waves is very complex. Here, multi-mode Lamb waves in a metal plate are propagated across a rectangular flat-bottom hole in a sequence of pitch-catch measurements corresponding to the double crosshole tomography geometry. The flaw is sequentially deepened, with the Lamb wave measurements repeated at each flaw depth. Lamb wave tomography reconstructions are used to identify which waveforms have interacted with the flaw and thereby carry information about its depth. Multiple features are extracted from each of the Lamb wave signals using wavelets, which are then fed to statistical pattern classification algorithms that identify flaw severity. In order to achieve the highest classification accuracy, an optimal feature space is required but it's never known a priori which features are going to be best. For structural health monitoring we make use of the fact that physical flaws, such as corrosion, will only increase over time. This allows us to identify feature vectors which are topologically well-behaved by requiring that sequential classes "line up" in feature vector space. An intelligent feature selection routine is illustrated that identifies favorable class distributions in multi-dimensional feature spaces using computational homology theory. Betti numbers and formal classification accuracies are calculated for each feature space subset to establish a correlation between the topology of the class distribution and the corresponding classification accuracy.

  6. 75 FR 39437 - Optimizing the Security of Biological Select Agents and Toxins in the United States

    Federal Register 2010, 2011, 2012, 2013, 2014

    2010-07-08

    ..., 2010. [FR Doc. 2010-16864 Filed 7-7-10; 11:15 am] Billing code 3195-W0-P ... Executive Order 13546--Optimizing the Security of Biological Select Agents and Toxins in the United States... July 2, 2010 Optimizing the Security of Biological Select Agents and Toxins in the United States By...

  7. To Eat or Not to Eat: An Easy Simulation of Optimal Diet Selection in the Classroom

    ERIC Educational Resources Information Center

    Ray, Darrell L.

    2010-01-01

    Optimal diet selection, a component of optimal foraging theory, suggests that animals should select a diet that either maximizes energy or nutrient consumption per unit time or minimizes the foraging time needed to attain required energy or nutrients. In this exercise, students simulate the behavior of foragers that either show no foraging…

  8. Resonance Raman enhancement optimization in the visible range by selecting different excitation wavelengths

    NASA Astrophysics Data System (ADS)

    Wang, Zhong; Li, Yuee

    2015-09-01

    Resonance enhancement of Raman spectroscopy (RS) has been used to significantly improve the sensitivity and selectivity of detection for specific components in complicated environments. Resonance RS gives more insight into the biochemical structure and reactivity. In this field, selecting a proper excitation wavelength to achieve optimal resonance enhancement is vital for the study of an individual chemical/biological ingredient with a particular absorption characteristic. Raman spectra of three azo derivatives with absorption spectra in the visible range are studied under the same experimental conditions at 488, 532, and 633 nm excitations. Universal laws in the visible range have been concluded by analyzing resonance Raman (RR) spectra of samples. The long wavelength edge of the absorption spectrum is a better choice for intense enhancement and the integrity of a Raman signal. The obtained results are valuable for applying RR for the selective detection of biochemical constituents whose electronic transitions take place at energies corresponding to the visible spectra, which is much friendlier to biologial samples compared to ultraviolet.

  9. Energetic optimization of ion conduction rate by the K+ selectivity filter

    NASA Astrophysics Data System (ADS)

    Morais-Cabral, João H.; Zhou, Yufeng; MacKinnon, Roderick

    2001-11-01

    The K+ selectivity filter catalyses the dehydration, transfer and rehydration of a K+ ion in about ten nanoseconds. This physical process is central to the production of electrical signals in biology. Here we show how nearly diffusion-limited rates are achieved, by analysing ion conduction and the corresponding crystallographic ion distribution in the selectivity filter of the KcsA K+ channel. Measurements with K+ and its slightly larger analogue, Rb+, lead us to conclude that the selectivity filter usually contains two K+ ions separated by one water molecule. The two ions move in a concerted fashion between two configurations, K+-water-K+-water (1,3 configuration) and water-K+-water-K+ (2,4 configuration), until a third ion enters, displacing the ion on the opposite side of the queue. For K+, the energy difference between the 1,3 and 2,4 configurations is close to zero, the condition of maximum conduction rate. The energetic balance between these configurations is a clear example of evolutionary optimization of protein function.

  10. Methodology and method and apparatus for signaling with capacity optimized constellations

    NASA Technical Reports Server (NTRS)

    Barsoum, Maged F. (Inventor); Jones, Christopher R. (Inventor)

    2011-01-01

    Communication systems having transmitter, includes a coder configured to receive user bits and output encoded bits at an expanded output encoded bit rate, a mapper configured to map encoded bits to symbols in a symbol constellation, a modulator configured to generate a signal for transmission via the communication channel using symbols generated by the mapper. In addition, the receiver includes a demodulator configured to demodulate the received signal via the communication channel, a demapper configured to estimate likelihoods from the demodulated signal, a decoder that is configured to estimate decoded bits from the likelihoods generated by the demapper. Furthermore, the symbol constellation is a capacity optimized geometrically spaced symbol constellation that provides a given capacity at a reduced signal-to-noise ratio compared to a signal constellation that maximizes d.sub.min.

  11. Polyhedral Interpolation for Optimal Reaction Control System Jet Selection

    NASA Technical Reports Server (NTRS)

    Gefert, Leon P.; Wright, Theodore

    2014-01-01

    An efficient algorithm is described for interpolating optimal values for spacecraft Reaction Control System jet firing duty cycles. The algorithm uses the symmetrical geometry of the optimal solution to reduce the number of calculations and data storage requirements to a level that enables implementation on the small real time flight control systems used in spacecraft. The process minimizes acceleration direction errors, maximizes control authority, and minimizes fuel consumption.

  12. Age-Related Differences in Goals: Testing Predictions from Selection, Optimization, and Compensation Theory and Socioemotional Selectivity Theory

    ERIC Educational Resources Information Center

    Penningroth, Suzanna L.; Scott, Walter D.

    2012-01-01

    Two prominent theories of lifespan development, socioemotional selectivity theory and selection, optimization, and compensation theory, make similar predictions for differences in the goal representations of younger and older adults. Our purpose was to test whether the goals of younger and older adults differed in ways predicted by these two…

  13. Multicomponent floral signals elicit selective foraging in bumblebees

    NASA Astrophysics Data System (ADS)

    Gegear, Robert J.

    2005-06-01

    Flower constancy, or the tendency of individual pollinators to visit sequentially a single flower type even when other equally rewarding types are available, has important implications for animal-pollinated plants. Yet, the proximal reason for the behaviour still remains poorly understood. Here I show that bumblebees visiting equally rewarding flowers that differ in size and odour are more flower constant and less efficient (visited fewer flowers per minute) than bees visiting flowers that differ in size only and odour only. These results are consistent with the view that flower constancy in pollinators is related to their inability to perceive, process or recall multicomponent floral signals. I discuss these findings in the context of pollinator behavioural mechanisms and the evolution of floral diversity.

  14. Time and frequency constrained sonar signal design for optimal detection of elastic objects.

    PubMed

    Hamschin, Brandon; Loughlin, Patrick J

    2013-04-01

    In this paper, the task of model-based transmit signal design for optimizing detection is considered. Building on past work that designs the spectral magnitude for optimizing detection, two methods for synthesizing minimum duration signals with this spectral magnitude are developed. The methods are applied to the design of signals that are optimal for detecting elastic objects in the presence of additive noise and self-noise. Elastic objects are modeled as linear time-invariant systems with known impulse responses, while additive noise (e.g., ocean noise or receiver noise) and acoustic self-noise (e.g., reverberation or clutter) are modeled as stationary Gaussian random processes with known power spectral densities. The first approach finds the waveform that preserves the optimal spectral magnitude while achieving the minimum temporal duration. The second approach yields a finite-length time-domain sequence by maximizing temporal energy concentration, subject to the constraint that the spectral magnitude is close (in a least-squares sense) to the optimal spectral magnitude. The two approaches are then connected analytically, showing the former is a limiting case of the latter. Simulation examples that illustrate the theory are accompanied by discussions that address practical applicability and how one might satisfy the need for target and environmental models in the real-world. PMID:23556586

  15. Symmetry breaking in optimal timing of traffic signals on an idealized two-way street

    NASA Astrophysics Data System (ADS)

    Panaggio, Mark J.; Ottino-Löffler, Bertand J.; Hu, Peiguang; Abrams, Daniel M.

    2013-09-01

    Simple physical models based on fluid mechanics have long been used to understand the flow of vehicular traffic on freeways; analytically tractable models of flow on an urban grid, however, have not been as extensively explored. In an ideal world, traffic signals would be timed such that consecutive lights turned green just as vehicles arrived, eliminating the need to stop at each block. Unfortunately, this “green-wave” scenario is generally unworkable due to frustration imposed by competing demands of traffic moving in different directions. Until now this has typically been resolved by numerical simulation and optimization. Here, we develop a theory for the flow in an idealized system consisting of a long two-way road with periodic intersections. We show that optimal signal timing can be understood analytically and that there are counterintuitive asymmetric solutions to this signal coordination problem. We further explore how these theoretical solutions degrade as traffic conditions vary and automotive density increases.

  16. Optimization of parameters affecting signal intensity in an LTQ-orbitrap in negative ion mode: A design of experiments approach.

    PubMed

    Lemonakis, Nikolaos; Skaltsounis, Alexios-Leandros; Tsarbopoulos, Anthony; Gikas, Evagelos

    2016-01-15

    A multistage optimization of all the parameters affecting detection/response in an LTQ-orbitrap analyzer was performed, using a design of experiments methodology. The signal intensity, a critical issue for mass analysis, was investigated and the optimization process was completed in three successive steps, taking into account the three main regions of an orbitrap, the ion generation, the ion transmission and the ion detection regions. Oleuropein and hydroxytyrosol were selected as the model compounds. Overall, applying this methodology the sensitivity was increased more than 24%, the resolution more than 6.5%, whereas the elapsed scan time was reduced nearly to its half. A high-resolution LTQ Orbitrap Discovery mass spectrometer was used for the determination of the analytes of interest. Thus, oleuropein and hydroxytyrosol were infused via the instruments syringe pump and they were analyzed employing electrospray ionization (ESI) in the negative high-resolution full-scan ion mode. The parameters of the three main regions of the LTQ-orbitrap were independently optimized in terms of maximum sensitivity. In this context, factorial design, response surface model and Plackett-Burman experiments were performed and analysis of variance was carried out to evaluate the validity of the statistical model and to determine the most significant parameters for signal intensity. The optimum MS conditions for each analyte were summarized and the method optimum condition was achieved by maximizing the desirability function. Our observation showed good agreement between the predicted optimum response and the responses collected at the predicted optimum conditions. PMID:26592625

  17. Response Selection and Response Execution in Task Switching: Evidence from a Go-Signal Paradigm

    ERIC Educational Resources Information Center

    Philipp, Andrea M.; Jolicoeur, Pierre; Falkenstein, Michael; Koch, Iring

    2007-01-01

    The present study used a go/no-go signal delay (GSD) to explore the role of response-related processes in task switching. A go/no-go signal was presented at either 100 ms or 1,500 ms after the stimulus. Participants were encouraged to use the GSD for response selection and preparation. The data indicate that the opportunity to select and prepare a…

  18. Optimization of a digital lock-in algorithm with a square-wave reference for frequency-divided multi-channel sensor signal detection

    NASA Astrophysics Data System (ADS)

    Zhang, Shengzhao; Li, Gang; Lin, Ling; Zhao, Jing

    2016-08-01

    A digital lock-in detection technique is commonly used to measure the amplitude and phase of a selected frequency signal. A technique that uses a square wave as the reference signal has an advantage over the one using a sinusoidal wave due to its easier implementation and higher computational efficiency. However, demodulating multiple-frequency composite signals using square wave reference may result in interference between channels. To avoid interference between channels and reduce the computational complexity, we modify the calculations and determine the optimal parameter settings of the low-pass filter and carrier frequency, as detailed in this paper. The results of our analysis show that when the length of the average filter and carrier frequencies are properly set, the interference between the channels is removed. This optimization produces the digital lock-in detection suitable for measuring multi-channel sensor signals.

  19. Optimization of a digital lock-in algorithm with a square-wave reference for frequency-divided multi-channel sensor signal detection.

    PubMed

    Zhang, Shengzhao; Li, Gang; Lin, Ling; Zhao, Jing

    2016-08-01

    A digital lock-in detection technique is commonly used to measure the amplitude and phase of a selected frequency signal. A technique that uses a square wave as the reference signal has an advantage over the one using a sinusoidal wave due to its easier implementation and higher computational efficiency. However, demodulating multiple-frequency composite signals using square wave reference may result in interference between channels. To avoid interference between channels and reduce the computational complexity, we modify the calculations and determine the optimal parameter settings of the low-pass filter and carrier frequency, as detailed in this paper. The results of our analysis show that when the length of the average filter and carrier frequencies are properly set, the interference between the channels is removed. This optimization produces the digital lock-in detection suitable for measuring multi-channel sensor signals. PMID:27587155

  20. Optimal Bandwidth Selection in Observed-Score Kernel Equating

    ERIC Educational Resources Information Center

    Häggström, Jenny; Wiberg, Marie

    2014-01-01

    The selection of bandwidth in kernel equating is important because it has a direct impact on the equated test scores. The aim of this article is to examine the use of double smoothing when selecting bandwidths in kernel equating and to compare double smoothing with the commonly used penalty method. This comparison was made using both an equivalent…

  1. Identifying or measuring selected substances or toxins in a subject using resonant raman signals

    NASA Technical Reports Server (NTRS)

    Lambert, James L. (Inventor); Borchert, Mark S. (Inventor)

    2005-01-01

    Methods and systems of the present invention identify the presence of and/or the concentration of a selected analyte in a subject by: (a) illuminating a selected region of the eye of a subject with an optical excitation beam, wherein the excitation beam wavelength is selected to generate a resonant Raman spectrum of the selected analyte with a signal strength that is at least 100 times greater than Raman spectrums generated by non-resonant wavelengths and/or relative to signals of normal constituents present in the selected region of the eye; (b) detecting a resonant Raman spectrum corresponding to the selected illuminated region of the eye; and (c) identifying the presence, absence and/or the concentration of the selected analyte in the subject based on said detecting step. The apparatus may also be configured to be able to obtain biometric data of the eye to identify (confirm the identity of) the subject.

  2. Signal Transduction and Molecular Targets of Selected Flavonoids

    PubMed Central

    Bode, Ann M.

    2013-01-01

    Abstract Significance: Diet exerts a major influence on the risk for developing cancer and heart disease. Food factors such as flavonoids are alleged to protect cells from premature aging and disease by shielding DNA, proteins, and lipids from oxidative damage. Recent Advances: Our work has focused on clarifying the effects of dietary components on cancer cell proliferation and tumor growth, discovering mechanisms to explain the effects, and identifying the specific molecular targets of these compounds. Our strategy for identifying specific molecular targets of phytochemicals involves the use of supercomputer technology combined with protein crystallography, molecular biology, and experimental laboratory verification. Critical Issues: One of the greatest challenges for scientists is to reduce the accumulation of distortion and half truths reported in the popular media regarding the health benefits of certain foods or food supplements. The use of these is not new, but interest has increased dramatically because of perceived health benefits that are presumably acquired without unpleasant side effects. Flavonoids are touted to exert many beneficial effects in vitro. However, whether they can produce these effects in vivo is disputed. Future Directions: The World Health Organization indicates that one third of all cancer deaths are preventable and that diet is closely linked to prevention. Based on this idea and epidemiological findings, attention has centered on dietary phytochemicals as an effective intervention in cancer development. However, an unequivocal link between diet and cancer has not been established. Thus, identifying cancer preventive dietary agents with specific molecular targets is essential to move forward toward successful cancer prevention. Antioxid. Redox Signal. 19, 163–180. PMID:23458437

  3. Transferability of optimally-selected climate models in the quantification of climate change impacts on hydrology

    NASA Astrophysics Data System (ADS)

    Chen, Jie; Brissette, François P.; Lucas-Picher, Philippe

    2016-02-01

    Given the ever increasing number of climate change simulations being carried out, it has become impractical to use all of them to cover the uncertainty of climate change impacts. Various methods have been proposed to optimally select subsets of a large ensemble of climate simulations for impact studies. However, the behaviour of optimally-selected subsets of climate simulations for climate change impacts is unknown, since the transfer process from climate projections to the impact study world is usually highly non-linear. Consequently, this study investigates the transferability of optimally-selected subsets of climate simulations in the case of hydrological impacts. Two different methods were used for the optimal selection of subsets of climate scenarios, and both were found to be capable of adequately representing the spread of selected climate model variables contained in the original large ensemble. However, in both cases, the optimal subsets had limited transferability to hydrological impacts. To capture a similar variability in the impact model world, many more simulations have to be used than those that are needed to simply cover variability from the climate model variables' perspective. Overall, both optimal subset selection methods were better than random selection when small subsets were selected from a large ensemble for impact studies. However, as the number of selected simulations increased, random selection often performed better than the two optimal methods. To ensure adequate uncertainty coverage, the results of this study imply that selecting as many climate change simulations as possible is the best avenue. Where this was not possible, the two optimal methods were found to perform adequately.

  4. Optimal design and selection of magneto-rheological brake types based on braking torque and mass

    NASA Astrophysics Data System (ADS)

    Nguyen, Q. H.; Lang, V. T.; Choi, S. B.

    2015-06-01

    In developing magnetorheological brakes (MRBs), it is well known that the braking torque and the mass of the MRBs are important factors that should be considered in the product’s design. This research focuses on the optimal design of different types of MRBs, from which we identify an optimal selection of MRB types, considering braking torque and mass. In the optimization, common types of MRBs such as disc-type, drum-type, hybrid-type, and T-shape types are considered. The optimization problem is to find an optimal MRB structure that can produce the required braking torque while minimizing its mass. After a brief description of the configuration of the MRBs, the MRBs’ braking torque is derived based on the Herschel-Bulkley rheological model of the magnetorheological fluid. Then, the optimal designs of the MRBs are analyzed. The optimization objective is to minimize the mass of the brake while the braking torque is constrained to be greater than a required value. In addition, the power consumption of the MRBs is also considered as a reference parameter in the optimization. A finite element analysis integrated with an optimization tool is used to obtain optimal solutions for the MRBs. Optimal solutions of MRBs with different required braking torque values are obtained based on the proposed optimization procedure. From the results, we discuss the optimal selection of MRB types, considering braking torque and mass.

  5. Optimization of Swine Breeding Programs Using Genomic Selection with ZPLAN.

    PubMed

    Lopez, B M; Kang, H S; Kim, T H; Viterbo, V S; Kim, H S; Na, C S; Seo, K S

    2016-05-01

    The objective of this study was to evaluate the present conventional selection program of a swine nucleus farm and compare it with a new selection strategy employing genomic enhanced breeding value (GEBV) as the selection criteria. The ZPLAN+ software was employed to calculate and compare the genetic gain, total cost, return and profit of each selection strategy. The first strategy reflected the current conventional breeding program, which was a progeny test system (CS). The second strategy was a selection scheme based strictly on genomic information (GS1). The third scenario was the same as GS1, but the selection by GEBV was further supplemented by the performance test (GS2). The last scenario was a mixture of genomic information and progeny tests (GS3). The results showed that the accuracy of the selection index of young boars of GS1 was 26% higher than that of CS. On the other hand, both GS2 and GS3 gave 31% higher accuracy than CS for young boars. The annual monetary genetic gain of GS1, GS2 and GS3 was 10%, 12%, and 11% higher, respectively, than that of CS. As expected, the discounted costs of genomic selection strategies were higher than those of CS. The costs of GS1, GS2 and GS3 were 35%, 73%, and 89% higher than those of CS, respectively, assuming a genotyping cost of $120. As a result, the discounted profit per animal of GS1 and GS2 was 8% and 2% higher, respectively, than that of CS while GS3 was 6% lower. Comparison among genomic breeding scenarios revealed that GS1 was more profitable than GS2 and GS3. The genomic selection schemes, especially GS1 and GS2, were clearly superior to the conventional scheme in terms of monetary genetic gain and profit. PMID:26954222

  6. Optimization of Swine Breeding Programs Using Genomic Selection with ZPLAN+

    PubMed Central

    Lopez, B. M.; Kang, H. S.; Kim, T. H.; Viterbo, V. S.; Kim, H. S.; Na, C. S.; Seo, K. S.

    2016-01-01

    The objective of this study was to evaluate the present conventional selection program of a swine nucleus farm and compare it with a new selection strategy employing genomic enhanced breeding value (GEBV) as the selection criteria. The ZPLAN+ software was employed to calculate and compare the genetic gain, total cost, return and profit of each selection strategy. The first strategy reflected the current conventional breeding program, which was a progeny test system (CS). The second strategy was a selection scheme based strictly on genomic information (GS1). The third scenario was the same as GS1, but the selection by GEBV was further supplemented by the performance test (GS2). The last scenario was a mixture of genomic information and progeny tests (GS3). The results showed that the accuracy of the selection index of young boars of GS1 was 26% higher than that of CS. On the other hand, both GS2 and GS3 gave 31% higher accuracy than CS for young boars. The annual monetary genetic gain of GS1, GS2 and GS3 was 10%, 12%, and 11% higher, respectively, than that of CS. As expected, the discounted costs of genomic selection strategies were higher than those of CS. The costs of GS1, GS2 and GS3 were 35%, 73%, and 89% higher than those of CS, respectively, assuming a genotyping cost of $120. As a result, the discounted profit per animal of GS1 and GS2 was 8% and 2% higher, respectively, than that of CS while GS3 was 6% lower. Comparison among genomic breeding scenarios revealed that GS1 was more profitable than GS2 and GS3. The genomic selection schemes, especially GS1 and GS2, were clearly superior to the conventional scheme in terms of monetary genetic gain and profit. PMID:26954222

  7. Self-Selection, Optimal Income Taxation, and Redistribution

    ERIC Educational Resources Information Center

    Amegashie, J. Atsu

    2009-01-01

    The author makes a pedagogical contribution to optimal income taxation. Using a very simple model adapted from George A. Akerlof (1978), he demonstrates a key result in the approach to public economics and welfare economics pioneered by Nobel laureate James Mirrlees. He shows how incomplete information, in addition to the need to preserve…

  8. Methodology and Method and Apparatus for Signaling with Capacity Optimized Constellations

    NASA Technical Reports Server (NTRS)

    Barsoum, Maged F. (Inventor); Jones, Christopher R. (Inventor)

    2016-01-01

    Design Methodology and Method and Apparatus for Signaling with Capacity Optimized Constellation Abstract Communication systems are described that use geometrically PSK shaped constellations that have increased capacity compared to conventional PSK constellations operating within a similar SNR band. The geometrically shaped PSK constellation is optimized based upon parallel decoding capacity. In many embodiments, a capacity optimized geometrically shaped constellation can be used to replace a conventional constellation as part of a firmware upgrade to transmitters and receivers within a communication system. In a number of embodiments, the geometrically shaped constellation is optimized for an Additive White Gaussian Noise channel or a fading channel. In numerous embodiments, the communication uses adaptive rate encoding and the location of points within the geometrically shaped constellation changes as the code rate changes.

  9. Optimizing promoters and secretory signal sequences for producing ethanol from inulin by recombinant Saccharomyces cerevisiae carrying Kluyveromyces marxianus inulinase.

    PubMed

    Hong, Soo-Jeong; Kim, Hyo Jin; Kim, Jin-Woo; Lee, Dae-Hee; Seo, Jin-Ho

    2015-02-01

    Inulin is a polyfructan that is abundant in plants such as Jerusalem artichoke, chicory and dahlia. Inulinase can easily hydrolyze inulin to fructose, which is consumed by microorganisms. Generally, Saccharomyces cerevisiae, an industrial workhorse strain for bioethanol production, is known for not having inulinase activity. The inulinase gene from Kluyveromyces marxianus (KmINU), with the ability of converting inulin to fructose, was introduced into S. cerevisiae D452-2. The inulinase gene was fused to three different types of promoter (GPD, PGK1, truncated HXT7) and secretory signal sequence (KmINU, MFα1, SUC2) to generate nine expression cassettes. The inulin fermentation performance of the nine transformants containing different promoter and signal sequence combinations for inulinase production were compared to select an optimized expression system for efficient inulin fermentation. Among the nine inulinase-producing transformants, the S. cerevisiae carrying the PGK1 promoter and MFα1 signal sequence (S. cerevisiae D452-2/p426PM) showed not only the highest specific KmINU activity, but also the best inulin fermentation capability. Finally, a batch fermentation of the selected S. cerevisiae D452-2/p426PM in a bioreactor with 188.2 g/L inulin was performed to produce 80.2 g/L ethanol with 0.43 g ethanol/g inulin of ethanol yield and 1.22 g/L h of ethanol productivity. PMID:25142154

  10. Optimal coherent control of coherent anti-Stokes Raman scattering: Signal enhancement and background elimination

    NASA Astrophysics Data System (ADS)

    Gao, Fang; Shuang, Feng; Shi, Junhui; Rabitz, Herschel; Wang, Haifeng; Cheng, Ji-Xin

    2012-04-01

    The ability to enhance resonant signals and eliminate the non-resonant background is analyzed for coherent anti-Stokes Raman scattering (CARS). The analysis is done at a specific frequency as well as for broadband excitation using femtosecond pulse-shaping techniques. An appropriate objective functional is employed to balance resonant signal enhancement against non-resonant background suppression. Optimal enhancement of the signal and minimization of the background can be achieved by shaping the probe pulse alone while keeping the pump and Stokes pulses unshaped. In some cases analytical forms for the probe pulse can be found, and numerical simulations are carried out for other circumstances. It is found that a good approximate optimal solution for resonant signal enhancement in two-pulse CARS is a superposition of linear and arctangent-type phases for the pump. The well-known probe delay method is shown to be a quasi-optimal scheme for broadband background suppression. The results should provide a basis to improve the performance of CARS spectroscopy and microscopy.

  11. Optimizing drilling performance using a selected drilling fluid

    DOEpatents

    Judzis, Arnis; Black, Alan D.; Green, Sidney J.; Robertson, Homer A.; Bland, Ronald G.; Curry, David Alexander; Ledgerwood, III, Leroy W.

    2011-04-19

    To improve drilling performance, a drilling fluid is selected based on one or more criteria and to have at least one target characteristic. Drilling equipment is used to drill a wellbore, and the selected drilling fluid is provided into the wellbore during drilling with the drilling equipment. The at least one target characteristic of the drilling fluid includes an ability of the drilling fluid to penetrate into formation cuttings during drilling to weaken the formation cuttings.

  12. Optimizing selection with several constraints in poultry breeding.

    PubMed

    Chapuis, H; Pincent, C; Colleau, J J

    2016-02-01

    Poultry breeding schemes permanently face the need to control the evolution of coancestry and some critical traits, while selecting for a main breeding objective. The main aims of this article are first to present an efficient selection algorithm adapted to this situation and then to measure how the severity of constraints impacted on the degree of loss for the main trait, compared to BLUP selection on the main trait, without any constraint. Broiler dam and sire line schemes were mimicked by simulation over 10 generations and selection was carried out on the main trait under constraints for coancestry and for another trait, antagonistic with the main trait. The selection algorithm was a special simulated annealing (adaptative simulated annealing (ASA)). It was found to be rapid and able to meet constraints very accurately. A constraint on the second trait was found to induce an impact similar to or even greater than the impact of the constraint on coancestry. The family structure of selected poultry populations made it easy to control the evolution of coancestry at a reasonable cost but was not as useful for reducing the cost of controlling evolution of the antagonistic traits. Multiple constraints impacted almost additively on the genetic gain for the main trait. Adding constraints for several traits would therefore be justified in real life breeding schemes, possibly after evaluating their impact through simulated annealing. PMID:26220593

  13. Selection of Mother Wavelet Functions for Multi-Channel EEG Signal Analysis during a Working Memory Task

    PubMed Central

    Al-Qazzaz, Noor Kamal; Hamid Bin Mohd Ali, Sawal; Ahmad, Siti Anom; Islam, Mohd Shabiul; Escudero, Javier

    2015-01-01

    We performed a comparative study to select the efficient mother wavelet (MWT) basis functions that optimally represent the signal characteristics of the electrical activity of the human brain during a working memory (WM) task recorded through electro-encephalography (EEG). Nineteen EEG electrodes were placed on the scalp following the 10–20 system. These electrodes were then grouped into five recording regions corresponding to the scalp area of the cerebral cortex. Sixty-second WM task data were recorded from ten control subjects. Forty-five MWT basis functions from orthogonal families were investigated. These functions included Daubechies (db1–db20), Symlets (sym1–sym20), and Coiflets (coif1–coif5). Using ANOVA, we determined the MWT basis functions with the most significant differences in the ability of the five scalp regions to maximize their cross-correlation with the EEG signals. The best results were obtained using “sym9” across the five scalp regions. Therefore, the most compatible MWT with the EEG signals should be selected to achieve wavelet denoising, decomposition, reconstruction, and sub-band feature extraction. This study provides a reference of the selection of efficient MWT basis functions. PMID:26593918

  14. 49 CFR 236.13 - Spring switch; selection of signal control circuits through circuit controller.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... 49 Transportation 4 2011-10-01 2011-10-01 false Spring switch; selection of signal control... SYSTEMS, DEVICES, AND APPLIANCES Rules and Instructions: All Systems General § 236.13 Spring switch... facing movements over a main track spring switch shall be selected through the contacts of a...

  15. 49 CFR 236.13 - Spring switch; selection of signal control circuits through circuit controller.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... 49 Transportation 4 2010-10-01 2010-10-01 false Spring switch; selection of signal control... SYSTEMS, DEVICES, AND APPLIANCES Rules and Instructions: All Systems General § 236.13 Spring switch... facing movements over a main track spring switch shall be selected through the contacts of a...

  16. 49 CFR 236.13 - Spring switch; selection of signal control circuits through circuit controller.

    Code of Federal Regulations, 2012 CFR

    2012-10-01

    ... 49 Transportation 4 2012-10-01 2012-10-01 false Spring switch; selection of signal control... SYSTEMS, DEVICES, AND APPLIANCES Rules and Instructions: All Systems General § 236.13 Spring switch... facing movements over a main track spring switch shall be selected through the contacts of a...

  17. 49 CFR 236.13 - Spring switch; selection of signal control circuits through circuit controller.

    Code of Federal Regulations, 2013 CFR

    2013-10-01

    ... 49 Transportation 4 2013-10-01 2013-10-01 false Spring switch; selection of signal control... SYSTEMS, DEVICES, AND APPLIANCES Rules and Instructions: All Systems General § 236.13 Spring switch... facing movements over a main track spring switch shall be selected through the contacts of a...

  18. 49 CFR 236.13 - Spring switch; selection of signal control circuits through circuit controller.

    Code of Federal Regulations, 2014 CFR

    2014-10-01

    ... 49 Transportation 4 2014-10-01 2014-10-01 false Spring switch; selection of signal control... SYSTEMS, DEVICES, AND APPLIANCES Rules and Instructions: All Systems General § 236.13 Spring switch... facing movements over a main track spring switch shall be selected through the contacts of a...

  19. A signal integration model of thymic selection and natural regulatory T cell commitment.

    PubMed

    Khailaie, Sahamoddin; Robert, Philippe A; Toker, Aras; Huehn, Jochen; Meyer-Hermann, Michael

    2014-12-15

    The extent of TCR self-reactivity is the basis for selection of a functional and self-tolerant T cell repertoire and is quantified by repeated engagement of TCRs with a diverse pool of self-peptides complexed with self-MHC molecules. The strength of a TCR signal depends on the binding properties of a TCR to the peptide and the MHC, but it is not clear how the specificity to both components drives fate decisions. In this study, we propose a TCR signal-integration model of thymic selection that describes how thymocytes decide among distinct fates, not only based on a single TCR-ligand interaction, but taking into account the TCR stimulation history. These fates are separated based on sustained accumulated signals for positive selection and transient peak signals for negative selection. This spans up the cells into a two-dimensional space where they are either neglected, positively selected, negatively selected, or selected as natural regulatory T cells (nTregs). We show that the dynamics of the integrated signal can serve as a successful basis for extracting specificity of thymocytes to MHC and detecting the existence of cognate self-peptide-MHC. It allows to select a self-MHC-biased and self-peptide-tolerant T cell repertoire. Furthermore, nTregs in the model are enriched with MHC-specific TCRs. This allows nTregs to be more sensitive to activation and more cross-reactive than conventional T cells. This study provides a mechanistic model showing that time integration of TCR-mediated signals, as opposed to single-cell interaction events, is needed to gain a full view on the properties emerging from thymic selection. PMID:25392533

  20. Adaptive Optimal Kernel Smooth-Windowed Wigner-Ville Distribution for Digital Communication Signal

    NASA Astrophysics Data System (ADS)

    Tan, Jo Lynn; Sha'ameri, Ahmad Zuribin

    2009-12-01

    Time-frequency distributions (TFDs) are powerful tools to represent the energy content of time-varying signal in both time and frequency domains simultaneously but they suffer from interference due to cross-terms. Various methods have been described to remove these cross-terms and they are typically signal-dependent. Thus, there is no single TFD with a fixed window or kernel that can produce accurate time-frequency representation (TFR) for all types of signals. In this paper, a globally adaptive optimal kernel smooth-windowed Wigner-Ville distribution (AOK-SWWVD) is designed for digital modulation signals such as ASK, FSK, and M-ary FSK, where its separable kernel is determined automatically from the input signal, without prior knowledge of the signal. This optimum kernel is capable of removing the cross-terms and maintaining accurate time-frequency representation at SNR as low as 0 dB. It is shown that this system is comparable to the system with prior knowledge of the signal.

  1. An Optimization-Driven Analysis Pipeline to Uncover Biomarkers and Signaling Paths: Cervix Cancer

    PubMed Central

    Lorenzo, Enery; Camacho-Caceres, Katia; Ropelewski, Alexander J.; Rosas, Juan; Ortiz-Mojer, Michael; Perez-Marty, Lynn; Irizarry, Juan; Gonzalez, Valerie; Rodríguez, Jesús A.; Cabrera-Rios, Mauricio; Isaza, Clara

    2015-01-01

    Establishing how a series of potentially important genes might relate to each other is relevant to understand the origin and evolution of illnesses, such as cancer. High-throughput biological experiments have played a critical role in providing information in this regard. A special challenge, however, is that of trying to conciliate information from separate microarray experiments to build a potential genetic signaling path. This work proposes a two-step analysis pipeline, based on optimization, to approach meta-analysis aiming to build a proxy for a genetic signaling path. PMID:26388997

  2. Optimal search-based gene subset selection for gene array cancer classification.

    PubMed

    Li, Jiexun; Su, Hua; Chen, Hsinchun; Futscher, Bernard W

    2007-07-01

    High dimensionality has been a major problem for gene array-based cancer classification. It is critical to identify marker genes for cancer diagnoses. We developed a framework of gene selection methods based on previous studies. This paper focuses on optimal search-based subset selection methods because they evaluate the group performance of genes and help to pinpoint global optimal set of marker genes. Notably, this paper is the first to introduce tabu search (TS) to gene selection from high-dimensional gene array data. Our comparative study of gene selection methods demonstrated the effectiveness of optimal search-based gene subset selection to identify cancer marker genes. TS was shown to be a promising tool for gene subset selection. PMID:17674622

  3. Optimization of Metamaterial Selective Emitters for Use in Thermophotovoltaic Applications

    NASA Astrophysics Data System (ADS)

    Pfiester, Nicole A.

    The increasing costs of fossil fuels, both financial and environmental, has motivated many to look into sustainable energy sources. Thermophotovoltaics (TPVs), specialized photovoltaic cells focused on the infrared range, offer an opportunity to achieve both primary energy capture, similar to traditional photovoltaics, as well as secondary energy capture in the form of waste heat. However, to become a feasible energy source, TPV systems must become more efficient. One way to do this is through the development of selective emitters tailored to the bandgap of the TPV diode in question. This thesis proposes the use of metamaterial emitters as an engineerable, highly selective emitter that can withstand the temperatures required to collect waste heat. Metamaterial devices made of platinum and a dielectric such as alumina or silicon nitride were initially designed and tested as perfect absorbers. High temperature robustness testing demonstrates the device's ability to withstand the rigors of operating as a selective emitter.

  4. Optimization of Heavy Chain and Light Chain Signal Peptides for High Level Expression of Therapeutic Antibodies in CHO Cells

    PubMed Central

    Haryadi, Ryan; Ho, Steven; Kok, Yee Jiun; Pu, Helen X.; Zheng, Lu; Pereira, Natasha A.; Li, Bin; Bi, Xuezhi; Goh, Lin-Tang; Yang, Yuansheng; Song, Zhiwei

    2015-01-01

    Translocation of a nascent protein from the cytosol into the ER mediated by its signal peptide is a critical step in protein secretion. The aim of this work was to develop a platform technology to optimize the signal peptides for high level production of therapeutic antibodies in CHO cells. A database of signal peptides from a large number of human immunoglobulin (Ig) heavy chain (HC) and kappa light chain (LC) was generated. Most of the HC signal peptides contain 19 amino acids which can be divided into three domains and the LC signal peptides contain 22 amino acids. The signal peptides were then clustered according to sequence similarity. Based on the clustering, 8 HC and 2 LC signal peptides were analyzed for their impacts on the production of 5-top selling antibody therapeutics, namely, Herceptin, Avastin, Remicade, Rituxan, and Humira. The best HC and LC signal peptides for producing these 5 antibodies were identified. The optimized signal peptides for Rituxan is 2-fold better compared to its native signal peptides which are available in the public database. Substitution of a single amino acid in the optimized HC signal peptide for Avastin reduced its production significantly. Mass spectrometry analyses revealed that all optimized signal peptides are accurately removed in the mature antibodies. The results presented in this report are particularly important for the production of these 5 antibodies as biosimilar drugs. They also have the potential to be the best signal peptides for the production of new antibodies in CHO cells. PMID:25706993

  5. Optimization of heavy chain and light chain signal peptides for high level expression of therapeutic antibodies in CHO cells.

    PubMed

    Haryadi, Ryan; Ho, Steven; Kok, Yee Jiun; Pu, Helen X; Zheng, Lu; Pereira, Natasha A; Li, Bin; Bi, Xuezhi; Goh, Lin-Tang; Yang, Yuansheng; Song, Zhiwei

    2015-01-01

    Translocation of a nascent protein from the cytosol into the ER mediated by its signal peptide is a critical step in protein secretion. The aim of this work was to develop a platform technology to optimize the signal peptides for high level production of therapeutic antibodies in CHO cells. A database of signal peptides from a large number of human immunoglobulin (Ig) heavy chain (HC) and kappa light chain (LC) was generated. Most of the HC signal peptides contain 19 amino acids which can be divided into three domains and the LC signal peptides contain 22 amino acids. The signal peptides were then clustered according to sequence similarity. Based on the clustering, 8 HC and 2 LC signal peptides were analyzed for their impacts on the production of 5-top selling antibody therapeutics, namely, Herceptin, Avastin, Remicade, Rituxan, and Humira. The best HC and LC signal peptides for producing these 5 antibodies were identified. The optimized signal peptides for Rituxan is 2-fold better compared to its native signal peptides which are available in the public database. Substitution of a single amino acid in the optimized HC signal peptide for Avastin reduced its production significantly. Mass spectrometry analyses revealed that all optimized signal peptides are accurately removed in the mature antibodies. The results presented in this report are particularly important for the production of these 5 antibodies as biosimilar drugs. They also have the potential to be the best signal peptides for the production of new antibodies in CHO cells. PMID:25706993

  6. Optimizing visual comfort for stereoscopic 3D display based on color-plus-depth signals.

    PubMed

    Shao, Feng; Jiang, Qiuping; Fu, Randi; Yu, Mei; Jiang, Gangyi

    2016-05-30

    Visual comfort is a long-facing problem in stereoscopic 3D (S3D) display. In this paper, targeting to produce S3D content based on color-plus-depth signals, a general framework for depth mapping to optimize visual comfort for S3D display is proposed. The main motivation of this work is to remap the depth range of color-plus-depth signals to a new depth range that is suitable to comfortable S3D display. Towards this end, we first remap the depth range globally based on the adjusted zero disparity plane, and then present a two-stage global and local depth optimization solution to solve the visual comfort problem. The remapped depth map is used to generate the S3D output. We demonstrate the power of our approach on perceptually uncomfortable and comfortable stereoscopic images. PMID:27410090

  7. Wavelet transform based on the optimal wavelet pairs for tunable diode laser absorption spectroscopy signal processing.

    PubMed

    Li, Jingsong; Yu, Benli; Fischer, Horst

    2015-04-01

    This paper presents a novel methodology-based discrete wavelet transform (DWT) and the choice of the optimal wavelet pairs to adaptively process tunable diode laser absorption spectroscopy (TDLAS) spectra for quantitative analysis, such as molecular spectroscopy and trace gas detection. The proposed methodology aims to construct an optimal calibration model for a TDLAS spectrum, regardless of its background structural characteristics, thus facilitating the application of TDLAS as a powerful tool for analytical chemistry. The performance of the proposed method is verified using analysis of both synthetic and observed signals, characterized with different noise levels and baseline drift. In terms of fitting precision and signal-to-noise ratio, both have been improved significantly using the proposed method. PMID:25741689

  8. Selection of Optimal Auxiliary Soil Nutrient Variables for Cokriging Interpolation

    PubMed Central

    Song, Genxin; Zhang, Jing; Wang, Ke

    2014-01-01

    In order to explore the selection of the best auxiliary variables (BAVs) when using the Cokriging method for soil attribute interpolation, this paper investigated the selection of BAVs from terrain parameters, soil trace elements, and soil nutrient attributes when applying Cokriging interpolation to soil nutrients (organic matter, total N, available P, and available K). In total, 670 soil samples were collected in Fuyang, and the nutrient and trace element attributes of the soil samples were determined. Based on the spatial autocorrelation of soil attributes, the Digital Elevation Model (DEM) data for Fuyang was combined to explore the coordinate relationship among terrain parameters, trace elements, and soil nutrient attributes. Variables with a high correlation to soil nutrient attributes were selected as BAVs for Cokriging interpolation of soil nutrients, and variables with poor correlation were selected as poor auxiliary variables (PAVs). The results of Cokriging interpolations using BAVs and PAVs were then compared. The results indicated that Cokriging interpolation with BAVs yielded more accurate results than Cokriging interpolation with PAVs (the mean absolute error of BAV interpolation results for organic matter, total N, available P, and available K were 0.020, 0.002, 7.616, and 12.4702, respectively, and the mean absolute error of PAV interpolation results were 0.052, 0.037, 15.619, and 0.037, respectively). The results indicated that Cokriging interpolation with BAVs can significantly improve the accuracy of Cokriging interpolation for soil nutrient attributes. This study provides meaningful guidance and reference for the selection of auxiliary parameters for the application of Cokriging interpolation to soil nutrient attributes. PMID:24927129

  9. Selecting radiotherapy dose distributions by means of constrained optimization problems.

    PubMed

    Alfonso, J C L; Buttazzo, G; García-Archilla, B; Herrero, M A; Núñez, L

    2014-05-01

    The main steps in planning radiotherapy consist in selecting for any patient diagnosed with a solid tumor (i) a prescribed radiation dose on the tumor, (ii) bounds on the radiation side effects on nearby organs at risk and (iii) a fractionation scheme specifying the number and frequency of therapeutic sessions during treatment. The goal of any radiotherapy treatment is to deliver on the tumor a radiation dose as close as possible to that selected in (i), while at the same time conforming to the constraints prescribed in (ii). To this day, considerable uncertainties remain concerning the best manner in which such issues should be addressed. In particular, the choice of a prescription radiation dose is mostly based on clinical experience accumulated on the particular type of tumor considered, without any direct reference to quantitative radiobiological assessment. Interestingly, mathematical models for the effect of radiation on biological matter have existed for quite some time, and are widely acknowledged by clinicians. However, the difficulty to obtain accurate in vivo measurements of the radiobiological parameters involved has severely restricted their direct application in current clinical practice.In this work, we first propose a mathematical model to select radiation dose distributions as solutions (minimizers) of suitable variational problems, under the assumption that key radiobiological parameters for tumors and organs at risk involved are known. Second, by analyzing the dependence of such solutions on the parameters involved, we then discuss the manner in which the use of those minimizers can improve current decision-making processes to select clinical dosimetries when (as is generally the case) only partial information on model radiosensitivity parameters is available. A comparison of the proposed radiation dose distributions with those actually delivered in a number of clinical cases strongly suggests that solutions of our mathematical model can be

  10. Selection of optimal auxiliary soil nutrient variables for Cokriging interpolation.

    PubMed

    Song, Genxin; Zhang, Jing; Wang, Ke

    2014-01-01

    In order to explore the selection of the best auxiliary variables (BAVs) when using the Cokriging method for soil attribute interpolation, this paper investigated the selection of BAVs from terrain parameters, soil trace elements, and soil nutrient attributes when applying Cokriging interpolation to soil nutrients (organic matter, total N, available P, and available K). In total, 670 soil samples were collected in Fuyang, and the nutrient and trace element attributes of the soil samples were determined. Based on the spatial autocorrelation of soil attributes, the Digital Elevation Model (DEM) data for Fuyang was combined to explore the coordinate relationship among terrain parameters, trace elements, and soil nutrient attributes. Variables with a high correlation to soil nutrient attributes were selected as BAVs for Cokriging interpolation of soil nutrients, and variables with poor correlation were selected as poor auxiliary variables (PAVs). The results of Cokriging interpolations using BAVs and PAVs were then compared. The results indicated that Cokriging interpolation with BAVs yielded more accurate results than Cokriging interpolation with PAVs (the mean absolute error of BAV interpolation results for organic matter, total N, available P, and available K were 0.020, 0.002, 7.616, and 12.4702, respectively, and the mean absolute error of PAV interpolation results were 0.052, 0.037, 15.619, and 0.037, respectively). The results indicated that Cokriging interpolation with BAVs can significantly improve the accuracy of Cokriging interpolation for soil nutrient attributes. This study provides meaningful guidance and reference for the selection of auxiliary parameters for the application of Cokriging interpolation to soil nutrient attributes. PMID:24927129

  11. Detection capability of a pulsed Ground Penetrating Radar utilizing an oscilloscope and Radargram Fusion Approach for optimal signal quality

    NASA Astrophysics Data System (ADS)

    Seyfried, Daniel; Schoebel, Joerg

    2015-07-01

    In scientific research pulsed radars often employ a digital oscilloscope as sampling unit. The sensitivity of an oscilloscope is determined in general by means of the number of digits of its analog-to-digital converter and the selected full scale vertical setting, i.e., the maximal voltage range displayed. Furthermore oversampling or averaging of the input signal may increase the effective number of digits, hence the sensitivity. Especially for Ground Penetrating Radar applications high sensitivity of the radar system is demanded since reflection amplitudes of buried objects are strongly attenuated in ground. Hence, in order to achieve high detection capability this parameter is one of the most crucial ones. In this paper we analyze the detection capability of our pulsed radar system utilizing a Rohde & Schwarz RTO 1024 oscilloscope as sampling unit for Ground Penetrating Radar applications, such as detection of pipes and cables in the ground. Also effects of averaging and low-noise amplification of the received signal prior to sampling are investigated by means of an appropriate laboratory setup. To underline our findings we then present real-world radar measurements performed on our GPR test site, where we have buried pipes and cables of different types and materials in different depths. The results illustrate the requirement for proper choice of the settings of the oscilloscope for optimal data recording. However, as we show, displaying both strong signal contributions due to e.g., antenna cross-talk and direct ground bounce reflection as well as weak reflections from objects buried deeper in ground requires opposing trends for the oscilloscope's settings. We therefore present our Radargram Fusion Approach. By means of this approach multiple radargrams recorded in parallel, each with an individual optimized setting for a certain type of contribution, can be fused in an appropriate way in order to finally achieve a single radargram which displays all

  12. Improvement of the energy resolution via an optimized digital signal processing in GERDA Phase I

    NASA Astrophysics Data System (ADS)

    Agostini, M.; Allardt, M.; Bakalyarov, A. M.; Balata, M.; Barabanov, I.; Barros, N.; Baudis, L.; Bauer, C.; Becerici-Schmidt, N.; Bellotti, E.; Belogurov, S.; Belyaev, S. T.; Benato, G.; Bettini, A.; Bezrukov, L.; Bode, T.; Borowicz, D.; Brudanin, V.; Brugnera, R.; Budjáš, D.; Caldwell, A.; Cattadori, C.; Chernogorov, A.; D'Andrea, V.; Demidova, E. V.; Vacri, A. di; Domula, A.; Doroshkevich, E.; Egorov, V.; Falkenstein, R.; Fedorova, O.; Freund, K.; Frodyma, N.; Gangapshev, A.; Garfagnini, A.; Grabmayr, P.; Gurentsov, V.; Gusev, K.; Hegai, A.; Heisel, M.; Hemmer, S.; Heusser, G.; Hofmann, W.; Hult, M.; Inzhechik, L. V.; Janicskó Csáthy, J.; Jochum, J.; Junker, M.; Kazalov, V.; Kihm, T.; Kirpichnikov, I. V.; Kirsch, A.; Klimenko, A.; Knöpfle, K. T.; Kochetov, O.; Kornoukhov, V. N.; Kuzminov, V. V.; Laubenstein, ********************M.; Lazzaro, A.; Lebedev, V. I.; Lehnert, B.; Liao, H. Y.; Lindner, M.; Lippi, I.; Lubashevskiy, A.; Lubsandorzhiev, B.; Lutter, G.; Macolino, C.; Majorovits, B.; Maneschg, W.; Medinaceli, E.; Misiaszek, M.; Moseev, P.; Nemchenok, I.; Palioselitis, D.; Panas, K.; Pandola, L.; Pelczar, K.; Pullia, A.; Riboldi, S.; Rumyantseva, N.; Sada, C.; Salathe, M.; Schmitt, C.; Schneider, B.; Schönert, S.; Schreiner, J.; Schütz, A.-K.; Schulz, O.; Schwingenheuer, B.; Selivanenko, O.; Shirchenko, M.; Simgen, H.; Smolnikov, A.; Stanco, L.; Stepaniuk, M.; Ur, C. A.; Vanhoefer, L.; Vasenko, A. A.; Veresnikova, A.; von Sturm, K.; Wagner, V.; Walter, M.; Wegmann, A.; Wester, T.; Wilsenach, H.; Wojcik, M.; Yanovich, E.; Zavarise, P.; Zhitnikov, I.; Zhukov, S. V.; Zinatulina, D.; Zuber, K.; Zuzel, G.

    2015-06-01

    An optimized digital shaping filter has been developed for the Gerda experiment which searches for neutrinoless double beta decay in Ge. The Gerda Phase I energy calibration data have been reprocessed and an average improvement of 0.3 keV in energy resolution (FWHM) corresponding to 10 % at the value for decay in Ge is obtained. This is possible thanks to the enhanced low-frequency noise rejection of this Zero Area Cusp (ZAC) signal shaping filter.

  13. Complex multivariate sexual selection on male acoustic signaling in a wild population of Teleogryllus commodus.

    PubMed

    Bentsen, Caroline L; Hunt, John; Jennions, Michael D; Brooks, Robert

    2006-04-01

    Mate choice may impose both linear (i.e., directional) and nonlinear (i.e., quadratic and correlational) sexual selection on advertisement traits. Traditionally, mate recognition and sensory tuning have been thought to impose stabilizing (i.e., negative quadratic) sexual selection, whereas adaptive mate choice effects directional selection. It has been suggested that adaptive choice may exert positive quadratic and/or correlational sexual selection. Earlier, we showed that five structural components of the advertisement call of male field crickets (Teleogryllus commodus) were under multivariate stabilizing selection under laboratory conditions. Here we experimentally estimate selection on these five traits plus a measure of calling activity (the number of repeats in a looped bout of calling) in the field. There was general support for multivariate stabilizing selection on call structure, and calling activity was under strong positive directional selection, as predicted for a signal of genetic quality. There was, however, also appreciable correlational selection, suggesting an interaction between male call structure and calling effort. Interestingly, selection for short interbout durations of silence favored longer intercall durations in the field, in contrast to results from continuous looped call playback in the laboratory. We discuss the general importance of nonlinear selection in the honest signaling of genetic quality. PMID:16670989

  14. Optimizing selection of decentralized stormwater management strategies in urbanized regions

    NASA Astrophysics Data System (ADS)

    Yu, Z.; Montalto, F.

    2011-12-01

    A variety of decentralized stormwater options are available for implementation in urbanized regions. These strategies, which include bio-retention, porous pavement, green roof etc., vary in terms of cost, ability to reduce runoff, and site applicability. This paper explores the tradeoffs between different types of stormwater control meastures that could be applied in a typical urban study area. A nested optimization strategy first identifies the most cost-effective (e.g. runoff reduction / life cycle cost invested ) options for individual land parcel typologies, and then scales up the results with detailed attention paid to uncertainty in adoption rates, life cycle costs, and hydrologic performance. The study is performed with a custom built stochastic rainfall-runoff model (Monte Carlo techniques are used to quantify uncertainties associated with phased implementation of different strategies and different land parcel typologies under synthetic precipitation ensembles). The results are presented as a comparison of cost-effectiveness over the time span of 30 years, and state an optimized strategy on the cumulative cost-effectiveness over the period.

  15. Optimal design of calibration signals in space-borne gravitational wave detectors

    NASA Astrophysics Data System (ADS)

    Nofrarias, Miquel; Karnesis, Nikolaos; Gibert, Ferran; Armano, Michele; Audley, Heather; Danzmann, Karsten; Diepholz, Ingo; Dolesi, Rita; Ferraioli, Luigi; Ferroni, Valerio; Hewitson, Martin; Hueller, Mauro; Inchauspe, Henri; Jennrich, Oliver; Korsakova, Natalia; McNamara, Paul W.; Plagnol, Eric; Thorpe, James I.; Vetrugno, Daniele; Vitale, Stefano; Wass, Peter; Weber, William J.

    2016-05-01

    Future space-borne gravitational wave detectors will require a precise definition of calibration signals to ensure the achievement of their design sensitivity. The careful design of the test signals plays a key role in the correct understanding and characterization of these instruments. In that sense, methods achieving optimal experiment designs must be considered as complementary to the parameter estimation methods being used to determine the parameters describing the system. The relevance of experiment design is particularly significant for the LISA Pathfinder mission, which will spend most of its operation time performing experiments to characterize key technologies for future space-borne gravitational wave observatories. Here we propose a framework to derive the optimal signals—in terms of minimum parameter uncertainty—to be injected into these instruments during the calibration phase. We compare our results with an alternative numerical algorithm which achieves an optimal input signal by iteratively improving an initial guess. We show agreement of both approaches when applied to the LISA Pathfinder case.

  16. Optimization of gene sequences under constant mutational pressure and selection

    NASA Astrophysics Data System (ADS)

    Kowalczuk, M.; Gierlik, A.; Mackiewicz, P.; Cebrat, S.; Dudek, M. R.

    1999-12-01

    We have analyzed the influence of constant mutational pressure and selection on the nucleotide composition of DNA sequences of various size, which were represented by the genes of the Borrelia burgdorferi genome. With the help of MC simulations we have found that longer DNA sequences accumulate much less base substitutions per sequence length than short sequences. This leads us to the conclusion that the accuracy of replication may determine the size of genome.

  17. Sensor Selection and Optimization for Health Assessment of Aerospace Systems

    NASA Technical Reports Server (NTRS)

    Maul, William A.; Kopasakis, George; Santi, Louis M.; Sowers, Thomas S.; Chicatelli, Amy

    2008-01-01

    Aerospace systems are developed similarly to other large-scale systems through a series of reviews, where designs are modified as system requirements are refined. For space-based systems few are built and placed into service these research vehicles have limited historical experience to draw from and formidable reliability and safety requirements, due to the remote and severe environment of space. Aeronautical systems have similar reliability and safety requirements, and while these systems may have historical information to access, commercial and military systems require longevity under a range of operational conditions and applied loads. Historically, the design of aerospace systems, particularly the selection of sensors, is based on the requirements for control and performance rather than on health assessment needs. Furthermore, the safety and reliability requirements are met through sensor suite augmentation in an ad hoc, heuristic manner, rather than any systematic approach. A review of the current sensor selection practice within and outside of the aerospace community was conducted and a sensor selection architecture is proposed that will provide a justifiable, defendable sensor suite to address system health assessment requirements.

  18. Sensor Selection and Optimization for Health Assessment of Aerospace Systems

    NASA Technical Reports Server (NTRS)

    Maul, William A.; Kopasakis, George; Santi, Louis M.; Sowers, Thomas S.; Chicatelli, Amy

    2007-01-01

    Aerospace systems are developed similarly to other large-scale systems through a series of reviews, where designs are modified as system requirements are refined. For space-based systems few are built and placed into service. These research vehicles have limited historical experience to draw from and formidable reliability and safety requirements, due to the remote and severe environment of space. Aeronautical systems have similar reliability and safety requirements, and while these systems may have historical information to access, commercial and military systems require longevity under a range of operational conditions and applied loads. Historically, the design of aerospace systems, particularly the selection of sensors, is based on the requirements for control and performance rather than on health assessment needs. Furthermore, the safety and reliability requirements are met through sensor suite augmentation in an ad hoc, heuristic manner, rather than any systematic approach. A review of the current sensor selection practice within and outside of the aerospace community was conducted and a sensor selection architecture is proposed that will provide a justifiable, dependable sensor suite to address system health assessment requirements.

  19. Cortical membrane potential signature of optimal states for sensory signal detection

    PubMed Central

    McGinley, Matthew J.; David, Stephen V.; McCormick, David A.

    2015-01-01

    The neural correlates of optimal states for signal detection task performance are largely unknown. One hypothesis holds that optimal states exhibit tonically depolarized cortical neurons with enhanced spiking activity, such as occur during movement. We recorded membrane potentials of auditory cortical neurons in mice trained on a challenging tone-in-noise detection task while assessing arousal with simultaneous pupillometry and hippocampal recordings. Arousal measures accurately predicted multiple modes of membrane potential activity, including: rhythmic slow oscillations at low arousal, stable hyperpolarization at intermediate arousal, and depolarization during phasic or tonic periods of hyper-arousal. Walking always occurred during hyper-arousal. Optimal signal detection behavior and sound-evoked responses, at both sub-threshold and spiking levels, occurred at intermediate arousal when pre-decision membrane potentials were stably hyperpolarized. These results reveal a cortical physiological signature of the classically-observed inverted-U relationship between task performance and arousal, and that optimal detection exhibits enhanced sensory-evoked responses and reduced background synaptic activity. PMID:26074005

  20. Multi-Bandwidth Frequency Selective Surfaces for Near Infrared Filtering: Design and Optimization

    NASA Technical Reports Server (NTRS)

    Cwik, Tom; Fernandez, Salvador; Ksendzov, A.; LaBaw, Clayton C.; Maker, Paul D.; Muller, Richard E.

    1998-01-01

    Frequency selective surfaces are widely used in the microwave and millimeter wave regions of the spectrum for filtering signals. They are used in telecommunication systems for multi-frequency operation or in instrument detectors for spectroscopy. The frequency selective surface operation depends on a periodic array of elements resonating at prescribed wavelengths producing a filter response. The size of the elements is on the order of half the electrical wavelength, and the array period is typically less than a wavelength for efficient operation. When operating in the optical region, diffraction gratings are used for filtering. In this regime the period of the grating may be several wavelengths producing multiple orders of light in reflection or transmission. In regions between these bands (specifically in the infrared band) frequency selective filters consisting of patterned metal layers fabricated using electron beam lithography are beginning to be developed. The operation is completely analogous to surfaces made in the microwave and millimeter wave region except for the choice of materials used and the fabrication process. In addition, the lithography process allows an arbitrary distribution of patterns corresponding to resonances at various wavelengths to be produced. The design of sub-millimeter filters follows the design methods used in the microwave region. Exacting modal matching, integral equation or finite element methods can be used for design. A major difference though is the introduction of material parameters and thicknesses that may not be important in longer wavelength designs. This paper describes the design of multi- bandwidth filters operating in the 1-5 micrometer wavelength range. This work follows on a previous design. In this paper extensions based on further optimization and an examination of the specific shape of the element in the periodic cell will be reported. Results from the design, manufacture and test of linear wedge filters built

  1. Multi-Bandwidth Frequency Selective Surfaces for Near Infrared Filtering: Design and Optimization

    NASA Technical Reports Server (NTRS)

    Cwik, Tom; Fernandez, Salvador; Ksendzov, A.; LaBaw, Clayton C.; Maker, Paul D.; Muller, Richard E.

    1999-01-01

    Frequency selective surfaces are widely used in the microwave and millimeter wave regions of the spectrum for filtering signals. They are used in telecommunication systems for multi-frequency operation or in instrument detectors for spectroscopy. The frequency selective surface operation depends on a periodic array of elements resonating at prescribed wavelengths producing a filter response. The size of the elements is on the order of half the electrical wavelength, and the array period is typically less than a wavelength for efficient operation. When operating in the optical region, diffraction gratings are used for filtering. In this regime the period of the grating may be several wavelengths producing multiple orders of light in reflection or transmission. In regions between these bands (specifically in the infrared band) frequency selective filters consisting of patterned metal layers fabricated using electron beam lithography are beginning to be developed. The operation is completely analogous to surfaces made in the microwave and millimeter wave region except for the choice of materials used and the fabrication process. In addition, the lithography process allows an arbitrary distribution of patterns corresponding to resonances at various wavelengths to be produced. The design of sub-millimeter filters follows the design methods used in the microwave region. Exacting modal matching, integral equation or finite element methods can be used for design. A major difference though is the introduction of material parameters and thicknesses tha_ may not be important in longer wavelength designs. This paper describes the design of multi-bandwidth filters operating in the I-5 micrometer wavelength range. This work follows on previous design [1,2]. In this paper extensions based on further optimization and an examination of the specific shape of the element in the periodic cell will be reported. Results from the design, manufacture and test of linear wedge filters built

  2. Atomic library optimization for pulse ultrasonic sparse signal decomposition and reconstruction

    NASA Astrophysics Data System (ADS)

    Song, Shoupeng; Li, Yingxue; Dogandžić, Aleksandar

    2016-02-01

    Compressive sampling of pulse ultrasonic NDE signals could bring significant savings in the data acquisition process. Sparse representation of these signals using an atomic library is key to their interpretation and reconstruction from compressive samples. However, the obstacles to practical applicability of such representations are: large size of the atomic library and computational complexity of the sparse decomposition and reconstruction. To help solve these problems, we develop a method for optimizing the ranges of parameters of traditional Gabor-atom library to match a real pulse ultrasonic signal in terms of correlation. As a result of atomic-library optimization, the number of the atoms is greatly reduced. Numerical simulations compare the proposed approach with the traditional method. Simulation results show that both the time efficiency and signal reconstruction energy error are superior to the traditional one even with small-scale atomic library. The performance of the proposed method is also explored under different noise levels. Finally, we apply the proposed method to real pipeline ultrasonic testing data, and the results indicate that our reduced atomic library outperforms the traditional library.

  3. Confinement Sensing and Signal Optimization via Piezo1/PKA and Myosin II Pathways.

    PubMed

    Hung, Wei-Chien; Yang, Jessica R; Yankaskas, Christopher L; Wong, Bin Sheng; Wu, Pei-Hsun; Pardo-Pastor, Carlos; Serra, Selma A; Chiang, Meng-Jung; Gu, Zhizhan; Wirtz, Denis; Valverde, Miguel A; Yang, Joy T; Zhang, Jin; Konstantopoulos, Konstantinos

    2016-05-17

    Cells adopt distinct signaling pathways to optimize cell locomotion in different physical microenvironments. However, the underlying mechanism that enables cells to sense and respond to physical confinement is unknown. Using microfabricated devices and substrate-printing methods along with FRET-based biosensors, we report that, as cells transition from unconfined to confined spaces, intracellular Ca(2+) level is increased, leading to phosphodiesterase 1 (PDE1)-dependent suppression of PKA activity. This Ca(2+) elevation requires Piezo1, a stretch-activated cation channel. Moreover, differential regulation of PKA and cell stiffness in unconfined versus confined cells is abrogated by dual, but not individual, inhibition of Piezo1 and myosin II, indicating that these proteins can independently mediate confinement sensing. Signals activated by Piezo1 and myosin II in response to confinement both feed into a signaling circuit that optimizes cell motility. This study provides a mechanism by which confinement-induced signaling enables cells to sense and adapt to different physical microenvironments. PMID:27160899

  4. An approach to selecting the optimal sensing coil configuration structure for switched reluctance motor rotor position measurement

    NASA Astrophysics Data System (ADS)

    Cai, Jun; Deng, Zhiquan

    2015-02-01

    Accurate rotor position signal is highly required for controlling the switched reluctance motor (SRM). The use of galvanic isolated sensing coils can provide independent circuit for position estimation without affecting the SRM actuation. However, the cross-coupling between main winding and sensing coil, and the mutual coupling between adjacent phase sensing coils may affect the position estimation performance seriously. In this paper, three sensing coil configurations in a 12/8 structure SRM are analyzed and compared for selecting an optimal configuration that can effectively minimize the bad effects of the cross-coupling factors. The finite element analysis and experimental results are provided for verification.

  5. An approach to selecting the optimal sensing coil configuration structure for switched reluctance motor rotor position measurement.

    PubMed

    Cai, Jun; Deng, Zhiquan

    2015-02-01

    Accurate rotor position signal is highly required for controlling the switched reluctance motor (SRM). The use of galvanic isolated sensing coils can provide independent circuit for position estimation without affecting the SRM actuation. However, the cross-coupling between main winding and sensing coil, and the mutual coupling between adjacent phase sensing coils may affect the position estimation performance seriously. In this paper, three sensing coil configurations in a 12/8 structure SRM are analyzed and compared for selecting an optimal configuration that can effectively minimize the bad effects of the cross-coupling factors. The finite element analysis and experimental results are provided for verification. PMID:25725876

  6. A method to optimize selection on multiple identified quantitative trait loci

    PubMed Central

    Chakraborty, Reena; Moreau, Laurence; Dekkers, Jack CM

    2002-01-01

    A mathematical approach was developed to model and optimize selection on multiple known quantitative trait loci (QTL) and polygenic estimated breeding values in order to maximize a weighted sum of responses to selection over multiple generations. The model allows for linkage between QTL with multiple alleles and arbitrary genetic effects, including dominance, epistasis, and gametic imprinting. Gametic phase disequilibrium between the QTL and between the QTL and polygenes is modeled but polygenic variance is assumed constant. Breeding programs with discrete generations, differential selection of males and females and random mating of selected parents are modeled. Polygenic EBV obtained from best linear unbiased prediction models can be accommodated. The problem was formulated as a multiple-stage optimal control problem and an iterative approach was developed for its solution. The method can be used to develop and evaluate optimal strategies for selection on multiple QTL for a wide range of situations and genetic models. PMID:12081805

  7. Automated selection of appropriate pheromone representations in ant colony optimization.

    PubMed

    Montgomery, James; Randall, Marcus; Hendtlass, Tim

    2005-01-01

    Ant colony optimization (ACO) is a constructive metaheuristic that uses an analogue of ant trail pheromones to learn about good features of solutions. Critically, the pheromone representation for a particular problem is usually chosen intuitively rather than by following any systematic process. In some representations, distinct solutions appear multiple times, increasing the effective size of the search space and potentially misleading ants as to the true learned value of those solutions. In this article, we present a novel system for automatically generating appropriate pheromone representations, based on the characteristics of the problem model that ensures unique pheromone representation of solutions. This is the first stage in the development of a generalized ACO system that could be applied to a wide range of problems with little or no modification. However, the system we propose may be used in the development of any problem-specific ACO algorithm. PMID:16053571

  8. Selection of optimal composition-control parameters for friable materials

    SciTech Connect

    Pak, Yu.N.; Vdovkin, A.V.

    1988-05-01

    A method for composition analysis of coal and minerals is proposed which uses scattered gamma radiation and does away with preliminary sample preparation to ensure homogeneous particle density, surface area, and size. Reduction of the error induced by material heterogeneity has previously been achieved by rotation of the control object during analysis. A further refinement is proposed which addresses the necessity that the contribution of the radiation scattered from each individual surface to the total intensity be the same. This is achieved by providing a constant linear rate of travel for the irradiated spot through back-and-forth motion of the sensor. An analytical expression is given for the laws of motion for the sensor and test tube which provides for uniform irradiated area movement along a path analogous to the Archimedes spiral. The relationships obtained permit optimization of measurement parameters in analyzing friable materials which are not uniform in grain size.

  9. Robust breathing signal extraction from cone beam CT projections based on adaptive and global optimization techniques.

    PubMed

    Chao, Ming; Wei, Jie; Li, Tianfang; Yuan, Yading; Rosenzweig, Kenneth E; Lo, Yeh-Chi

    2016-04-21

    We present a study of extracting respiratory signals from cone beam computed tomography (CBCT) projections within the framework of the Amsterdam Shroud (AS) technique. Acquired prior to the radiotherapy treatment, CBCT projections were preprocessed for contrast enhancement by converting the original intensity images to attenuation images with which the AS image was created. An adaptive robust z-normalization filtering was applied to further augment the weak oscillating structures locally. From the enhanced AS image, the respiratory signal was extracted using a two-step optimization approach to effectively reveal the large-scale regularity of the breathing signals. CBCT projection images from five patients acquired with the Varian Onboard Imager on the Clinac iX System Linear Accelerator (Varian Medical Systems, Palo Alto, CA) were employed to assess the proposed technique. Stable breathing signals can be reliably extracted using the proposed algorithm. Reference waveforms obtained using an air bellows belt (Philips Medical Systems, Cleveland, OH) were exported and compared to those with the AS based signals. The average errors for the enrolled patients between the estimated breath per minute (bpm) and the reference waveform bpm can be as low as -0.07 with the standard deviation 1.58. The new algorithm outperformed the original AS technique for all patients by 8.5% to 30%. The impact of gantry rotation on the breathing signal was assessed with data acquired with a Quasar phantom (Modus Medical Devices Inc., London, Canada) and found to be minimal on the signal frequency. The new technique developed in this work will provide a practical solution to rendering markerless breathing signal using the CBCT projections for thoracic and abdominal patients. PMID:27008349

  10. Robust breathing signal extraction from cone beam CT projections based on adaptive and global optimization techniques

    NASA Astrophysics Data System (ADS)

    Chao, Ming; Wei, Jie; Li, Tianfang; Yuan, Yading; Rosenzweig, Kenneth E.; Lo, Yeh-Chi

    2016-04-01

    We present a study of extracting respiratory signals from cone beam computed tomography (CBCT) projections within the framework of the Amsterdam Shroud (AS) technique. Acquired prior to the radiotherapy treatment, CBCT projections were preprocessed for contrast enhancement by converting the original intensity images to attenuation images with which the AS image was created. An adaptive robust z-normalization filtering was applied to further augment the weak oscillating structures locally. From the enhanced AS image, the respiratory signal was extracted using a two-step optimization approach to effectively reveal the large-scale regularity of the breathing signals. CBCT projection images from five patients acquired with the Varian Onboard Imager on the Clinac iX System Linear Accelerator (Varian Medical Systems, Palo Alto, CA) were employed to assess the proposed technique. Stable breathing signals can be reliably extracted using the proposed algorithm. Reference waveforms obtained using an air bellows belt (Philips Medical Systems, Cleveland, OH) were exported and compared to those with the AS based signals. The average errors for the enrolled patients between the estimated breath per minute (bpm) and the reference waveform bpm can be as low as  -0.07 with the standard deviation 1.58. The new algorithm outperformed the original AS technique for all patients by 8.5% to 30%. The impact of gantry rotation on the breathing signal was assessed with data acquired with a Quasar phantom (Modus Medical Devices Inc., London, Canada) and found to be minimal on the signal frequency. The new technique developed in this work will provide a practical solution to rendering markerless breathing signal using the CBCT projections for thoracic and abdominal patients.

  11. A Survey on Optimal Signal Processing Techniques Applied to Improve the Performance of Mechanical Sensors in Automotive Applications

    PubMed Central

    Hernandez, Wilmar

    2007-01-01

    In this paper a survey on recent applications of optimal signal processing techniques to improve the performance of mechanical sensors is made. Here, a comparison between classical filters and optimal filters for automotive sensors is made, and the current state of the art of the application of robust and optimal control and signal processing techniques to the design of the intelligent (or smart) sensors that today's cars need is presented through several experimental results that show that the fusion of intelligent sensors and optimal signal processing techniques is the clear way to go. However, the switch between the traditional methods of designing automotive sensors and the new ones cannot be done overnight because there are some open research issues that have to be solved. This paper draws attention to one of the open research issues and tries to arouse researcher's interest in the fusion of intelligent sensors and optimal signal processing techniques.

  12. Key signaling pathways in the muscle-invasive bladder carcinoma: Clinical markers for disease modeling and optimized treatment.

    PubMed

    Kiselyov, Alex; Bunimovich-Mendrazitsky, Svetlana; Startsev, Vladimir

    2016-06-01

    In this review, we evaluate key molecular pathways and markers of muscle-invasive bladder cancer (MIBC). Overexpression and activation of EGFR, p63, and EMT genes are suggestive of basal MIBC subtype generally responsive to chemotherapy. Alterations in PPARγ, ERBB2/3, and FGFR3 gene products and their signaling along with deregulated p53, cytokeratins KRT5/6/14 in combination with the cellular proliferation (Ki-67), and cell cycle markers (p16) indicate the need for more radical treatment protocols. Similarly, the "bell-shape" dynamics of Shh expression levels may suggest aggressive MIBC. A panel of diverse biological markers may be suitable for simulation studies of MIBC and development of an optimized treatment protocol. We conducted a critical evaluation of PubMed/Medline and SciFinder databases related to MIBC covering the period 2009-2015. The free-text search was extended by adding the following keywords and phrases: bladder cancer, metastatic, muscle-invasive, basal, luminal, epithelial-to-mesenchymal transition, cancer stem cell, mutations, immune response, signaling, biological markers, molecular markers, mathematical models, simulation, epigenetics, transmembrane, transcription factor, kinase, predictor, prognosis. The resulting selection of ca 500 abstracts was further analyzed in order to select the latest publications relevant to MIBC molecular markers of immediate clinical significance. PMID:26547270

  13. Warning signals are under positive frequency-dependent selection in nature.

    PubMed

    Chouteau, Mathieu; Arias, Mónica; Joron, Mathieu

    2016-02-23

    Positive frequency-dependent selection (FDS) is a selection regime where the fitness of a phenotype increases with its frequency, and it is thought to underlie important adaptive strategies resting on signaling and communication. However, whether and how positive FDS truly operates in nature remains unknown, which hampers our understanding of signal diversity. Here, we test for positive FDS operating on the warning color patterns of chemically defended butterflies forming multiple coexisting mimicry assemblages in the Amazon. Using malleable prey models placed in localities showing differences in the relative frequencies of warningly colored prey, we demonstrate that the efficiency of a warning signal increases steadily with its local frequency in the natural community, up to a threshold where protection stabilizes. The shape of this relationship is consistent with the direct effect of the local abundance of each warning signal on the corresponding avoidance knowledge of the local predator community. This relationship, which differs from purifying selection acting on each mimetic pattern, indicates that predator knowledge, integrated over the entire community, is saturated only for the most common warning signals. In contrast, among the well-established warning signals present in local prey assemblages, most are incompletely known to local predators and enjoy incomplete protection. This incomplete predator knowledge should generate strong benefits to life history traits that enhance warning efficiency by increasing the effective frequency of prey visible to predators. Strategies such as gregariousness or niche convergence between comimics may therefore readily evolve through their effects on predator knowledge and warning efficiency. PMID:26858416

  14. Warning signals are under positive frequency-dependent selection in nature

    PubMed Central

    Chouteau, Mathieu; Arias, Mónica; Joron, Mathieu

    2016-01-01

    Positive frequency-dependent selection (FDS) is a selection regime where the fitness of a phenotype increases with its frequency, and it is thought to underlie important adaptive strategies resting on signaling and communication. However, whether and how positive FDS truly operates in nature remains unknown, which hampers our understanding of signal diversity. Here, we test for positive FDS operating on the warning color patterns of chemically defended butterflies forming multiple coexisting mimicry assemblages in the Amazon. Using malleable prey models placed in localities showing differences in the relative frequencies of warningly colored prey, we demonstrate that the efficiency of a warning signal increases steadily with its local frequency in the natural community, up to a threshold where protection stabilizes. The shape of this relationship is consistent with the direct effect of the local abundance of each warning signal on the corresponding avoidance knowledge of the local predator community. This relationship, which differs from purifying selection acting on each mimetic pattern, indicates that predator knowledge, integrated over the entire community, is saturated only for the most common warning signals. In contrast, among the well-established warning signals present in local prey assemblages, most are incompletely known to local predators and enjoy incomplete protection. This incomplete predator knowledge should generate strong benefits to life history traits that enhance warning efficiency by increasing the effective frequency of prey visible to predators. Strategies such as gregariousness or niche convergence between comimics may therefore readily evolve through their effects on predator knowledge and warning efficiency. PMID:26858416

  15. Selection for social signalling drives the evolution of chameleon colour change.

    PubMed

    Stuart-Fox, Devi; Moussalli, Adnan

    2008-01-01

    Rapid colour change is a remarkable natural phenomenon that has evolved in several vertebrate and invertebrate lineages. The two principal explanations for the evolution of this adaptive strategy are (1) natural selection for crypsis (camouflage) against a range of different backgrounds and (2) selection for conspicuous social signals that maximise detectability to conspecifics, yet minimise exposure to predators because they are only briefly displayed. Here we show that evolutionary shifts in capacity for colour change in southern African dwarf chameleons (Bradypodion spp.) are associated with increasingly conspicuous signals used in male contests and courtship. To the chameleon visual system, species showing the most dramatic colour change display social signals that contrast most against the environmental background and amongst adjacent body regions. We found no evidence for the crypsis hypothesis, a finding reinforced by visual models of how both chameleons and their avian predators perceive chameleon colour variation. Instead, our results suggest that selection for conspicuous social signals drives the evolution of colour change in this system, supporting the view that transitory display traits should be under strong selection for signal detectability. PMID:18232740

  16. Selection for Social Signalling Drives the Evolution of Chameleon Colour Change

    PubMed Central

    Stuart-Fox, Devi; Moussalli, Adnan

    2008-01-01

    Rapid colour change is a remarkable natural phenomenon that has evolved in several vertebrate and invertebrate lineages. The two principal explanations for the evolution of this adaptive strategy are (1) natural selection for crypsis (camouflage) against a range of different backgrounds and (2) selection for conspicuous social signals that maximise detectability to conspecifics, yet minimise exposure to predators because they are only briefly displayed. Here we show that evolutionary shifts in capacity for colour change in southern African dwarf chameleons (Bradypodion spp.) are associated with increasingly conspicuous signals used in male contests and courtship. To the chameleon visual system, species showing the most dramatic colour change display social signals that contrast most against the environmental background and amongst adjacent body regions. We found no evidence for the crypsis hypothesis, a finding reinforced by visual models of how both chameleons and their avian predators perceive chameleon colour variation. Instead, our results suggest that selection for conspicuous social signals drives the evolution of colour change in this system, supporting the view that transitory display traits should be under strong selection for signal detectability. PMID:18232740

  17. Determination of an Optimal Recruiting-Selection Strategy to Fill a Specified Quota of Satisfactory Personnel.

    ERIC Educational Resources Information Center

    Sands, William A.

    Managers of military and civilian personnel systems justifiably demand an estimate of the payoff in dollars and cents, which can be expected to result from the implementation of a proposed selection program. The Cost of Attaining Personnel Requirements (CAPER) Model provides an optimal recruiting-selection strategy for personnel decisions which…

  18. Variation in predator species abundance can cause variable selection pressure on warning signaling prey

    PubMed Central

    Valkonen, Janne K; Nokelainen, Ossi; Niskanen, Martti; Kilpimaa, Janne; Björklund, Mats; Mappes, Johanna

    2012-01-01

    Predation pressure is expected to drive visual warning signals to evolve toward conspicuousness. However, coloration of defended species varies tremendously and can at certain instances be considered as more camouflaged rather than conspicuous. Recent theoretical studies suggest that the variation in signal conspicuousness can be caused by variation (within or between species) in predators' willingness to attack defended prey or by the broadness of the predators' signal generalization. If some of the predator species are capable of coping with the secondary defenses of their prey, selection can favor reduced prey signal conspicuousness via reduced detectability or recognition. In this study, we combine data collected during three large-scale field experiments to assess whether variation in avian predator species (red kite, black kite, common buzzard, short-toed eagle, and booted eagle) affects the predation pressure on warningly and non-warningly colored artificial snakes. Predation pressure varied among locations and interestingly, if common buzzards were abundant, there were disadvantages to snakes possessing warning signaling. Our results indicate that predator community can have important consequences on the evolution of warning signals. Predators that ignore the warning signal and defense can be the key for the maintenance of variation in warning signal architecture and maintenance of inconspicuous signaling. PMID:22957197

  19. Variation in predator species abundance can cause variable selection pressure on warning signaling prey.

    PubMed

    Valkonen, Janne K; Nokelainen, Ossi; Niskanen, Martti; Kilpimaa, Janne; Björklund, Mats; Mappes, Johanna

    2012-08-01

    Predation pressure is expected to drive visual warning signals to evolve toward conspicuousness. However, coloration of defended species varies tremendously and can at certain instances be considered as more camouflaged rather than conspicuous. Recent theoretical studies suggest that the variation in signal conspicuousness can be caused by variation (within or between species) in predators' willingness to attack defended prey or by the broadness of the predators' signal generalization. If some of the predator species are capable of coping with the secondary defenses of their prey, selection can favor reduced prey signal conspicuousness via reduced detectability or recognition. In this study, we combine data collected during three large-scale field experiments to assess whether variation in avian predator species (red kite, black kite, common buzzard, short-toed eagle, and booted eagle) affects the predation pressure on warningly and non-warningly colored artificial snakes. Predation pressure varied among locations and interestingly, if common buzzards were abundant, there were disadvantages to snakes possessing warning signaling. Our results indicate that predator community can have important consequences on the evolution of warning signals. Predators that ignore the warning signal and defense can be the key for the maintenance of variation in warning signal architecture and maintenance of inconspicuous signaling. PMID:22957197

  20. SLOPE—ADAPTIVE VARIABLE SELECTION VIA CONVEX OPTIMIZATION

    PubMed Central

    Bogdan, Małgorzata; van den Berg, Ewout; Sabatti, Chiara; Su, Weijie; Candès, Emmanuel J.

    2015-01-01

    We introduce a new estimator for the vector of coefficients β in the linear model y = Xβ + z, where X has dimensions n × p with p possibly larger than n. SLOPE, short for Sorted L-One Penalized Estimation, is the solution to minb∈ℝp12‖y−Xb‖ℓ22+λ1|b|(1)+λ2|b|(2)+⋯+λp|b|(p),where λ1 ≥ λ2 ≥ … ≥ λp ≥ 0 and |b|(1)≥|b|(2)≥⋯≥|b|(p) are the decreasing absolute values of the entries of b. This is a convex program and we demonstrate a solution algorithm whose computational complexity is roughly comparable to that of classical ℓ1 procedures such as the Lasso. Here, the regularizer is a sorted ℓ1 norm, which penalizes the regression coefficients according to their rank: the higher the rank—that is, stronger the signal—the larger the penalty. This is similar to the Benjamini and Hochberg [J. Roy. Statist. Soc. Ser. B 57 (1995) 289–300] procedure (BH) which compares more significant p-values with more stringent thresholds. One notable choice of the sequence {λi} is given by the BH critical values λBH(i)=z(1−i⋅q/2p), where q ∈ (0, 1) and z(α) is the quantile of a standard normal distribution. SLOPE aims to provide finite sample guarantees on the selected model; of special interest is the false discovery rate (FDR), defined as the expected proportion of irrelevant regressors among all selected predictors. Under orthogonal designs, SLOPE with λBH provably controls FDR at level q. Moreover, it also appears to have appreciable inferential properties under more general designs X while having substantial power, as demonstrated in a series of experiments running on both simulated and real data. PMID:26709357

  1. A novel EMD selecting thresholding method based on multiple iteration for denoising LIDAR signal

    NASA Astrophysics Data System (ADS)

    Li, Meng; Jiang, Li-hui; Xiong, Xing-long

    2015-06-01

    Empirical mode decomposition (EMD) approach has been believed to be potentially useful for processing the nonlinear and non-stationary LIDAR signals. To shed further light on its performance, we proposed the EMD selecting thresholding method based on multiple iteration, which essentially acts as a development of EMD interval thresholding (EMD-IT), and randomly alters the samples of noisy parts of all the corrupted intrinsic mode functions to generate a better effect of iteration. Simulations on both synthetic signals and LIDAR signals from real world support this method.

  2. Experimental validation of an optimized signal processing method to handle non-linearity in swept-source optical coherence tomography.

    PubMed

    Vergnole, Sébastien; Lévesque, Daniel; Lamouche, Guy

    2010-05-10

    We evaluate various signal processing methods to handle the non-linearity in wavenumber space exhibited by most laser sources for swept-source optical coherence tomography. The following methods are compared for the same set of experimental data: non-uniform discrete Fourier transforms with Vandermonde matrix or with Lomb periodogram, resampling with linear interpolation or spline interpolation prior to fast-Fourier transform (FFT), and resampling with convolution prior to FFT. By selecting an optimized Kaiser-Bessel window to perform the convolution, we show that convolution followed by FFT is the most efficient method. It allows small fractional oversampling factor between 1 and 2, thus a minimal computational time, while retaining an excellent image quality. PMID:20588899

  3. Efficient Sampling Set Selection for Bandlimited Graph Signals Using Graph Spectral Proxies

    NASA Astrophysics Data System (ADS)

    Anis, Aamir; Gadde, Akshay; Ortega, Antonio

    2016-07-01

    We study the problem of selecting the best sampling set for bandlimited reconstruction of signals on graphs. A frequency domain representation for graph signals can be defined using the eigenvectors and eigenvalues of variation operators that take into account the underlying graph connectivity. Smoothly varying signals defined on the nodes are of particular interest in various applications, and tend to be approximately bandlimited in the frequency basis. Sampling theory for graph signals deals with the problem of choosing the best subset of nodes for reconstructing a bandlimited signal from its samples. Most approaches to this problem require a computation of the frequency basis (i.e., the eigenvectors of the variation operator), followed by a search procedure using the basis elements. This can be impractical, in terms of storage and time complexity, for real datasets involving very large graphs. We circumvent this issue in our formulation by introducing quantities called graph spectral proxies, defined using the powers of the variation operator, in order to approximate the spectral content of graph signals. This allows us to formulate a direct sampling set selection approach that does not require the computation and storage of the basis elements. We show that our approach also provides stable reconstruction when the samples are noisy or when the original signal is only approximately bandlimited. Furthermore, the proposed approach is valid for any choice of the variation operator, thereby covering a wide range of graphs and applications. We demonstrate its effectiveness through various numerical experiments.

  4. The Role of Sexual Selection in the Evolution of Chemical Signals in Insects

    PubMed Central

    Steiger, Sandra; Stökl, Johannes

    2014-01-01

    Chemical communication is the most ancient and widespread form of communication. Yet we are only beginning to grasp the complexity of chemical signals and the role they play in sexual selection. Focusing on insects, we review here the recent progress in the field of olfactory-based sexual selection. We will show that there is mounting empirical evidence that sexual selection affects the evolution of chemical traits, but form and strength of selection differ between species. Studies indicate that some chemical signals are expressed in relation to an individual’s condition and depend, for example, on age, immunocompetence, fertility, body size or degree of inbreeding. Males or females might benefit by choosing based on those traits, gaining resources or “good genes”. Other chemical traits appear to reliably reflect an individual’s underlying genotype and are suitable to choose a mating partner that matches best the own genotype. PMID:26462692

  5. Dual signaling of hydrazine by selective deprotection of dichlorofluorescein and resorufin acetates.

    PubMed

    Choi, Myung Gil; Moon, Jung Ok; Bae, Jihee; Lee, Jung Woo; Chang, Suk-Kyu

    2013-05-14

    The highly selective chemosignaling behaviors for hydrazine by a reaction-based probe of dichlorofluorescein and resorufin acetates were investigated. Hydrazinolysis of latent dichlorofluorescein and resorufin acetate fluorochromes caused prominent chromogenic and fluorescent turn-on type signals. The probes selectively detected hydrazine in the presence of commonly encountered metal ions and anions as background. Dichlorofluorescein and resorufin acetates selectively detected hydrazine with detection limits of 9.0 × 10(-8) M and 8.2 × 10(-7) M, respectively. Furthermore, hydrazine was selectively detected over other closely related compounds, such as hydroxylamine, ethylenediamine, and ammonia. As a possible application of the acetate probes, hydrazine signaling in tap water was tested. PMID:23487180

  6. Selection for optimal crew performance - Relative impact of selection and training

    NASA Technical Reports Server (NTRS)

    Chidester, Thomas R.

    1987-01-01

    An empirical study supporting Helmreich's (1986) theoretical work on the distinct manner in which training and selection impact crew coordination is presented. Training is capable of changing attitudes, while selection screens for stable personality characteristics. Training appears least effective for leadership, an area strongly influenced by personality. Selection is least effective for influencing attitudes about personal vulnerability to stress, which appear to be trained in resource management programs. Because personality correlates with attitudes before and after training, it is felt that selection may be necessary even with a leadership-oriented training cirriculum.

  7. Genetic association of marbling score with intragenic nucleotide variants at selection signals of the bovine genome.

    PubMed

    Ryu, J; Lee, C

    2016-04-01

    Selection signals of Korean cattle might be attributed largely to artificial selection for meat quality. Rapidly increased intragenic markers of newly annotated genes in the bovine genome would help overcome limited findings of genetic markers associated with meat quality at the selection signals in a previous study. The present study examined genetic associations of marbling score (MS) with intragenic nucleotide variants at selection signals of Korean cattle. A total of 39 092 nucleotide variants of 407 Korean cattle were utilized in the association analysis. A total of 129 variants were selected within newly annotated genes in the bovine genome. Their genetic associations were analyzed using the mixed model with random polygenic effects based on identical-by-state genetic relationships among animals in order to control for spurious associations produced by population structure. Genetic associations of MS were found (P<3.88×10-4) with six intragenic nucleotide variants on bovine autosomes 3 (cache domain containing 1, CACHD1), 5 (like-glycosyltransferase, LARGE), 16 (cell division cycle 42 binding protein kinase alpha, CDC42BPA) and 21 (snurportin 1, SNUPN; protein tyrosine phosphatase, non-receptor type 9, PTPN9; chondroitin sulfate proteoglycan 4, CSPG4). In particular, the genetic associations with CDC42BPA and LARGE were confirmed using an independent data set of Korean cattle. The results implied that allele frequencies of functional variants and their proximity variants have been augmented by directional selection for greater MS and remain selection signals in the bovine genome. Further studies of fine mapping would be useful to incorporate favorable alleles in marker-assisted selection for MS of Korean cattle. PMID:26621608

  8. Model-free approach to optimal signal light timing for system-wide traffic control

    SciTech Connect

    Spall, J.C.; Chin, D.C.

    1994-12-31

    A long-standing problem in traffic engineering is to optimize the flow of vehicles through a given road network. Improving the timing of the traffic signals at intersections in the network is generally the most powerful and cost-effective means of achieving this goal. However, because of the many complex aspects of a traffic system-human behavioral considerations, vehicle flow interactions within the network, weather effects, traffic accidents, long-term (e.g., seasonal) variation, etc.-it has been notoriously difficult to determine the optimal signal light timing. This is especially the case on a system- wide (multiple intersection) basis. Much of this difficulty has stemmed from the need to build extremely complex open-loop models of the traffic dynamics as a component of the control strategy. This paper presents a fundamentally different approach for optimal light timing that eliminates the need for such an open-loop model. The approach is based on a neural network (or other function approximator) serving as the basis for the control law, with the weight estimation occurring in closed-loop mode via the simultaneous perturbation stochastic approximation (SPSA) algorithm. Since the SPSA algorithm requires only loss function measurements (no gradients of the loss function), there is no open-loop model required for the weight estimation. The approach is illustrated by simulation on a six-intersection network with moderate congestion and stochastic, nonlinear effects.

  9. Reduction of false arrhythmia alarms using signal selection and machine learning.

    PubMed

    Eerikäinen, Linda M; Vanschoren, Joaquin; Rooijakkers, Michael J; Vullings, Rik; Aarts, Ronald M

    2016-08-01

    In this paper, we propose an algorithm that classifies whether a generated cardiac arrhythmia alarm is true or false. The large number of false alarms in intensive care is a severe issue. The noise peaks caused by alarms can be high and in a noisy environment nurses can experience stress and fatigue. In addition, patient safety is compromised because reaction time of the caregivers to true alarms is reduced. The data for the algorithm development consisted of records of electrocardiogram (ECG), arterial blood pressure, and photoplethysmogram signals in which an alarm for either asystole, extreme bradycardia, extreme tachycardia, ventricular fibrillation or flutter, or ventricular tachycardia occurs. First, heart beats are extracted from every signal. Next, the algorithm selects the most reliable signal pair from the available signals by comparing how well the detected beats match between different signals based on [Formula: see text]-score and selecting the best match. From the selected signal pair, arrhythmia specific features, such as heart rate features and signal purity index are computed for the alarm classification. The classification is performed with five separate Random Forest models. In addition, information on the local noise level of the selected ECG lead is added to the classification. The algorithm was trained and evaluated with the PhysioNet/Computing in Cardiology Challenge 2015 data set. In the test set the overall true positive rates were 93 and 95% and true negative rates 80 and 83%, respectively for events with no information and events with information after the alarm. The overall challenge scores were 77.39 and 81.58. PMID:27454128

  10. Optimizing landfill site selection by using land classification maps.

    PubMed

    Eskandari, M; Homaee, M; Mahmoodi, S; Pazira, E; Van Genuchten, M Th

    2015-05-01

    Municipal solid waste disposal is a major environmental concern throughout the world. Proper landfill siting involves many environmental, economic, technical, and sociocultural challenges. In this study, a new quantitative method for landfill siting that reduces the number of evaluation criteria, simplifies siting procedures, and enhances the utility of available land evaluation maps was proposed. The method is demonstrated by selecting a suitable landfill site near the city of Marvdasht in Iran. The approach involves two separate stages. First, necessary criteria for preliminary landfill siting using four constraints and eight factors were obtained from a land classification map initially prepared for irrigation purposes. Thereafter, the criteria were standardized using a rating approach and then weighted to obtain a suitability map for landfill siting, with ratings in a 0-1 domain and divided into five suitability classes. Results were almost identical to those obtained with a more traditional environmental landfill siting approach. Because of far fewer evaluation criteria, the proposed weighting method was much easier to implement while producing a more convincing database for landfill siting. The classification map also considered land productivity. In the second stage, the six best alternative sites were evaluated for final landfill siting using four additional criteria. Sensitivity analyses were furthermore conducted to assess the stability of the obtained ranking. Results indicate that the method provides a precise siting procedure that should convince all pertinent stakeholders. PMID:25666474

  11. Selection of optimal muscle set for 16-channel standing neuroprosthesis

    PubMed Central

    Gartman, Steven J.; Audu, Musa L.; Kirsch, Robert F.; Triolo, Ronald J.

    2009-01-01

    The Case Western Reserve University/Department of Veterans Affairs 8-channel lower-limb neuroprosthesis can restore standing to selected individuals with paraplegia by application of functional electrical stimulation. The second generation of this system will include 16 channels of stimulation and a closed-loop control scheme to provide automatic postural corrections. This study used a musculoskeletal model of the legs and trunk to determine which muscles to target with the new system in order to maximize the range of postures that can be statically maintained, which should increase the system’s ability to provide adequate support to maintain standing when the user’s posture moves away from a neutral stance, either by an external disturbance or a volitional change in posture by the user. The results show that the prime muscle targets should be the medial gastrocnemius, tibialis anterior, vastus lateralis, semimembranosus, gluteus maximus, gluteus medius, adductor magnus, and erector spinae. This set of 16 muscles supports 42 percent of the standing postures that are attainable by the nondisabled model. Coactivation of the lateral gastrocnemius and peroneus longus with the medial gastrocnemius and of the peroneus tertius with the tibialis anterior increased the percentage of feasible postures to 71 percent. PMID:16847793

  12. Info-gap robustness of an input signal optimization algorithm for damage detection

    NASA Astrophysics Data System (ADS)

    Pasquali, M.; Stull, C. J.; Farrar, C. R.

    2015-01-01

    Info-Gap Decision Theory is adopted to assess the robustness of a technique aimed at identifying the optimal excitation signal to be used for active sensing approaches to damage detection. Here the term "active sensing" refers to procedures where a known input is applied to the structure to enhance the damage detection process. Given limited system response measurements and ever-present physical limits on the level of excitation, the ultimate goal of the mentioned technique is to improve the detectability of damage by increasing the difference between measured outputs of the undamaged and damaged systems. In particular, a two degree-of-freedom mass-spring-damper system characterized by the presence of a nonlinear stiffness is considered. Uncertainty is introduced to the system in the form of deviations of its parameters (mass, stiffness, damping ratio) from their nominal values. Variations in the performance of the mentioned technique are then evaluated both in terms of changes in the estimated difference between the responses of the damaged and undamaged systems and in terms of deviations of the identified optimal input signal from its nominal estimation. Finally, plots of the performances of the analyzed algorithm for different levels of uncertainty are obtained, enabling a clear evaluation of the risks connected with designing excitation signals for damage detection, when the parameters that dictate system behavior (e.g. stiffness, mass) are poorly characterized or improperly modeled.

  13. Fault Detection of Roller-Bearings Using Signal Processing and Optimization Algorithms

    PubMed Central

    Kwak, Dae-Ho; Lee, Dong-Han; Ahn, Jong-Hyo; Koh, Bong-Hwan

    2014-01-01

    This study presents a fault detection of roller bearings through signal processing and optimization techniques. After the occurrence of scratch-type defects on the inner race of bearings, variations of kurtosis values are investigated in terms of two different data processing techniques: minimum entropy deconvolution (MED), and the Teager-Kaiser Energy Operator (TKEO). MED and the TKEO are employed to qualitatively enhance the discrimination of defect-induced repeating peaks on bearing vibration data with measurement noise. Given the perspective of the execution sequence of MED and the TKEO, the study found that the kurtosis sensitivity towards a defect on bearings could be highly improved. Also, the vibration signal from both healthy and damaged bearings is decomposed into multiple intrinsic mode functions (IMFs), through empirical mode decomposition (EMD). The weight vectors of IMFs become design variables for a genetic algorithm (GA). The weights of each IMF can be optimized through the genetic algorithm, to enhance the sensitivity of kurtosis on damaged bearing signals. Experimental results show that the EMD-GA approach successfully improved the resolution of detectability between a roller bearing with defect, and an intact system. PMID:24368701

  14. Fault detection of roller-bearings using signal processing and optimization algorithms.

    PubMed

    Kwak, Dae-Ho; Lee, Dong-Han; Ahn, Jong-Hyo; Koh, Bong-Hwan

    2013-01-01

    This study presents a fault detection of roller bearings through signal processing and optimization techniques. After the occurrence of scratch-type defects on the inner race of bearings, variations of kurtosis values are investigated in terms of two different data processing techniques: minimum entropy deconvolution (MED), and the Teager-Kaiser Energy Operator (TKEO). MED and the TKEO are employed to qualitatively enhance the discrimination of defect-induced repeating peaks on bearing vibration data with measurement noise. Given the perspective of the execution sequence of MED and the TKEO, the study found that the kurtosis sensitivity towards a defect on bearings could be highly improved. Also, the vibration signal from both healthy and damaged bearings is decomposed into multiple intrinsic mode functions (IMFs), through empirical mode decomposition (EMD). The weight vectors of IMFs become design variables for a genetic algorithm (GA). The weights of each IMF can be optimized through the genetic algorithm, to enhance the sensitivity of kurtosis on damaged bearing signals. Experimental results show that the EMD-GA approach successfully improved the resolution of detectability between a roller bearing with defect, and an intact system. PMID:24368701

  15. Controlling chaos with weak periodic signals optimized by a genetic algorithm.

    PubMed

    Soong, C Y; Huang, W T; Lin, F P; Tzeng, P Y

    2004-01-01

    In the present study we develop a relatively novel and effective chaos control approach with a multimode periodic disturbance applied as a control signal and perform an in-depth analysis on this nonfeedback chaos control strategy. Different from previous chaos control schemes, the present method is of two characteristic features: (1) the parameters of the controlling signal are optimized by a genetic algorithm (GA) with the largest Lyapunov exponent used as an index of the stability, and (2) the optimization is justified by a fitness function defined with the target Lyapunov exponent and the controlling power. This novel method is then tested on the noted Rössler and Lorenz systems with and without the presence of noise. The results disclosed that, compared to the existing chaos control methods, the present GA-based control needs only significantly reduced signal power and a shorter transient stage to achieve the preset control goal. The switching control ability and the robustness of the proposed method for cases with sudden change in a system parameter and/or with the presence of noise environment are also demonstrated. PMID:15324156

  16. Signalling thresholds and negative B-cell selection in acute lymphoblastic leukaemia.

    PubMed

    Chen, Zhengshan; Shojaee, Seyedmehdi; Buchner, Maike; Geng, Huimin; Lee, Jae Woong; Klemm, Lars; Titz, Björn; Graeber, Thomas G; Park, Eugene; Tan, Ying Xim; Satterthwaite, Anne; Paietta, Elisabeth; Hunger, Stephen P; Willman, Cheryl L; Melnick, Ari; Loh, Mignon L; Jung, Jae U; Coligan, John E; Bolland, Silvia; Mak, Tak W; Limnander, Andre; Jumaa, Hassan; Reth, Michael; Weiss, Arthur; Lowell, Clifford A; Müschen, Markus

    2015-05-21

    B cells are selected for an intermediate level of B-cell antigen receptor (BCR) signalling strength: attenuation below minimum (for example, non-functional BCR) or hyperactivation above maximum (for example, self-reactive BCR) thresholds of signalling strength causes negative selection. In ∼25% of cases, acute lymphoblastic leukaemia (ALL) cells carry the oncogenic BCR-ABL1 tyrosine kinase (Philadelphia chromosome positive), which mimics constitutively active pre-BCR signalling. Current therapeutic approaches are largely focused on the development of more potent tyrosine kinase inhibitors to suppress oncogenic signalling below a minimum threshold for survival. We tested the hypothesis that targeted hyperactivation--above a maximum threshold--will engage a deletional checkpoint for removal of self-reactive B cells and selectively kill ALL cells. Here we find, by testing various components of proximal pre-BCR signalling in mouse BCR-ABL1 cells, that an incremental increase of Syk tyrosine kinase activity was required and sufficient to induce cell death. Hyperactive Syk was functionally equivalent to acute activation of a self-reactive BCR on ALL cells. Despite oncogenic transformation, this basic mechanism of negative selection was still functional in ALL cells. Unlike normal pre-B cells, patient-derived ALL cells express the inhibitory receptors PECAM1, CD300A and LAIR1 at high levels. Genetic studies revealed that Pecam1, Cd300a and Lair1 are critical to calibrate oncogenic signalling strength through recruitment of the inhibitory phosphatases Ptpn6 (ref. 7) and Inpp5d (ref. 8). Using a novel small-molecule inhibitor of INPP5D (also known as SHIP1), we demonstrated that pharmacological hyperactivation of SYK and engagement of negative B-cell selection represents a promising new strategy to overcome drug resistance in human ALL. PMID:25799995

  17. Abstract and Effector-Selective Decision Signals Exhibit Qualitatively Distinct Dynamics before Delayed Perceptual Reports

    PubMed Central

    Twomey, Deirdre M.; Kelly, Simon P.

    2016-01-01

    Electrophysiological research has isolated neural signatures of decision formation in a variety of brain regions. Studies in rodents and monkeys have focused primarily on effector-selective signals that translate the emerging decision into a specific motor plan, but, more recently, research on the human brain has identified an abstract signature of evidence accumulation that does not appear to play any direct role in action preparation. The functional dissociations between these distinct signal types have only begun to be characterized, and their dynamics during decisions with deferred actions with or without foreknowledge of stimulus-effector mapping, a commonly studied task scenario in single-unit and functional imaging investigations, have not been established. Here we traced the dynamics of distinct abstract and effector-selective decision signals in the form of the broad-band centro-parietal positivity (CPP) and limb-selective β-band (8–16 and 18–30 Hz) EEG activity, respectively, during delayed-reported motion direction decisions with and without foreknowledge of direction-response mapping. With foreknowledge, the CPP and β-band signals exhibited a similar gradual build-up following evidence onset, but whereas choice-predictive β-band activity persisted up until the delayed response, the CPP dropped toward baseline after peaking. Without foreknowledge, the CPP exhibited identical dynamics, whereas choice-selective β-band activity was eliminated. These findings highlight qualitative functional distinctions between effector-selective and abstract decision signals and are of relevance to the assumptions founding functional neuroimaging investigations of decision-making. SIGNIFICANCE STATEMENT Neural signatures of evidence accumulation have been isolated in numerous brain regions. Although animal neurophysiology has largely concentrated on effector-selective decision signals that translate the emerging decision into a specific motor plan, recent research

  18. Design and optimization of a multi-element piezoelectric transducer for mode-selective generation of guided waves

    NASA Astrophysics Data System (ADS)

    Yazdanpanah Moghadam, Peyman; Quaegebeur, Nicolas; Masson, Patrice

    2016-07-01

    A novel multi-element piezoelectric transducers (MEPT) is designed, optimized, machined and experimentally tested to improve structural health monitoring systems for mode-selective generation of guided waves (GW) in an isotropic structure. GW generation using typical piezoceramics makes the signal processing and consequently damage detection very complicated because at any driving frequency at least two fundamental symmetric (S 0) and antisymmetric (A 0) modes are generated. To prevent this, mode selective transducer design is proposed based on MEPT. A numerical method is first developed to extract the interfacial stress between a single piezoceramic element and a host structure and then used as the input of an analytical model to predict the GW propagation through the thickness of an isotropic plate. Two novel objective functions are proposed to optimize the interfacial shear stress for both suppressing unwanted mode(s) and maximizing the desired mode. Simplicity and low manufacturing cost are two main targets driving the design of the MEPT. A prototype MEPT is then manufactured using laser micro-machining. An experimental procedure is presented to validate the performances of the MEPT as a new solution for mode-selective GW generation. Experimental tests illustrate the high capability of the MEPT for mode-selective GW generation, as unwanted mode is suppressed by a factor up to 170 times compared with the results obtained with a single piezoceramic.

  19. Biomass selection for optimal anaerobic treatment of olive mill wastewater.

    PubMed

    Sabbah, I; Yazbak, A; Haj, J; Saliba, A; Basheer, S

    2005-01-01

    This research was conducted to identify the most efficient biomass out of five different types of biomass sources for anaerobic treatment of Olive Mill Wastewater (OMW). This study was first focused on examining the selected biomass in anaerobic batch systems with sodium acetate solutions (control study). Then, the different types of biomass were tested with raw OMW (water-diluted) and with pretreated OMW by coagulation-flocculation using Poly Aluminum Chloride (PACl) combined with hydrated lime (Ca(OH)2). Two types of biomass from wastewater treatment systems of a citrus juice producing company "PriGat" and from a citric acid manufacturing factory "Gadot", were found to be the most efficient sources of microorganisms to anaerobically treat both sodium acetate solution and OMW. Both types of biomass were examined under different concentration ranges (1-40 g l(-1)) of OMW in order to detect the maximal COD tolerance for the microorganisms. The results show that 70-85% of COD removal was reached using Gadot biomass after 8-10 days when the initial concentration of OMW was up to 5 g l(-1), while a similar removal efficiency was achieved using OMW of initial COD concentration of 10 g l(-1) in 2-4 days of contact time with the PriGat biomass. The physico-chemical pretreatment of OMW was found to enhance the anaerobic activity for the treatment of OMW with initial concentration of 20 g l(-1) using PriGat biomass. This finding is attributed to reducing the concentrations of polyphenols and other toxicants originally present in OMW upon the applied pretreatment process. PMID:15747599

  20. Dose selection for optimal treatment results and avoidance of complications.

    PubMed

    Nagano, Hisato; Nakayama, Satoshi; Shuto, Takashi; Asada, Hiroyuki; Inomori, Shigeo

    2009-01-01

    What is the optimal treatment for metastatic brain tumors (MBTs)? We present our experience with gamma knife (GK) treatments for patients with five or more MBTs. Our new formula for predicting patient survival time (ST), which was derived by combining tumor control probability (TCP) calculated by Colombo's formula and normal tissue complication probability (NTCP) estimated by Flickinger's integrated logistic formula, was also evaluated. ST=a*[(C-NTCP)*TCP]+b; a, b, C: const. Forty-one patients (23 male, 18 female) with more than five MBTs were treated between March 1992 and February 2000. The tumors originated in the lung in 15 cases, in the breast in 8. Four patients had previously undergone whole brain irradiation (WBI). Ten patients were given concomitant WBI. Thirteen patients had additional extracranial metastatic lesions. TCP and NTCP were calculated using Excel add-in software. Cox's proportional hazards model was used to evaluate correlations between certain variables and ST. The independent variables evaluated were patient factors (age in years and performance status), tumor factors (total volume and number of tumors in each patient), treatment factors (TCP, NTCP and marginal dose) and the values of (C-NTCP)*TCP. Total tumor number was 403 (median 7, range 5-56). The median total tumor volume was 9.8 cm3 (range 0.8-111.8 cm3). The marginal dose ranged from 8 to 22 Gy (median 16.0Gy), TCP from 0.0% to 83% (median 15%) and NTCP from 0.0% to 31% (median 6.0%). (0.39-NTCP)*TCP ranged from 0.0 to 0.21 (median 0.055). Follow-up was 0.2 to 26.2 months, with a median of 5.4 months. Multiple-sample tests revealed no differences in STs among patients with MBTs of different origins (p=0.50). The 50% STs of patients with MBTs originating from the breast, lung and other sites were 5.9, 7.8 and 3.5 months, respectively. Only TCP and (0.39-NTCP)*TCP were statistically significant covariates (p=0.014, 0.001, respectively), and the latter was a more important predictor of

  1. Separate visual signals for saccade initiation during target selection in the primate superior colliculus.

    PubMed

    White, Brian J; Munoz, Douglas P

    2011-02-01

    The primary function of the superior colliculus (SC) is to orient the visual system toward behaviorally relevant stimuli defined by features such as color. However, a longstanding view has held that visual activity in the SC arises exclusively from achromatic pathways. Recently, we reported evidence that the primate SC is highly sensitive to signals originating from chromatic pathways, but these signals are delayed relative to luminance signals (White et al., 2009). Here, we describe a functional consequence of this difference in visual arrival time on the processes leading to target selection and saccade initiation. Two rhesus monkeys performed a simple color-singleton selection task in which stimuli carried a chromatic component only (target and distractors were isoluminant with the background, but differed in chromaticity) or a combined chromatic-achromatic component (36% luminance contrast added equally to all stimuli). Although visual responses were delayed in the chromatic-only relative to the combined chromatic-achromatic condition, SC neurons discriminated the target from distractors at approximately the same time provided stimulus chromaticity was held constant. However, saccades were triggered sooner, and with more errors, with the chromatic-achromatic condition, suggesting that luminance signals associated with these stimuli increased the probability of triggering a saccade before the target color was adequately discriminated. These results suggest that separate mechanisms may independently influence the saccadic command in the SC, one linked to the arrival time of pertinent visual signals, and another linked to the output of the visual selection process. PMID:21289164

  2. Fluorescence signaling of Zr4+ by hydrogen peroxide assisted selective desulfurization of thioamide.

    PubMed

    Hwang, Jiyoung; Choi, Myung Gil; Eor, Suyoung; Chang, Suk-Kyu

    2012-02-01

    Thioamide derivative with a pyrene fluorophore was smoothly transformed to its corresponding amide by Zr(4+) ions in the presence of hydrogen peroxide. The transformation was evidenced by (1)H NMR spectroscopy and the signaling was completed within 10 min after sample preparation. Interference from Ag(+) and Hg(2+) ions in Zr(4+)-selective fluorescence signaling was readily suppressed with the use of Sn(2+) as a reducing additive. Discrimination of Zr(4+) from closely related hafnium, which is a frequent contaminant in commercial zirconium, was not possible. Prominent Zr(4+)-selective turn-on type fluorescence signaling was possible with a detection limit of 4.6 × 10(-6) M in an aqueous 99% ethanol solution. PMID:22260347

  3. Calibration optimization of laser-induced deflection signal for measuring absorptance of laser components.

    PubMed

    Zhang, Xiaorong; Li, Bincheng

    2015-03-10

    Different configurations of the laser-induced deflection (LID) technique have been developed recently to measure the absolute bulk and coating absorption of laser components directly. In order to obtain the absolute absorptance value of the surface or coating of a laser component, a reference sample with the same geometry and material as the test sample and with resist heating mounted on the surface of the reference sample was employed to calibrate LID signals. Due to the difference in the excitation approaches in measuring LID signals of the test and reference samples (laser beam irradiation versus surface resist heating), this calibration procedure may bring significant errors in the determination of the absorptance of the test sample. In this paper, theoretical models describing the temperature rise distributions within a test sample excited with flat-top beam irradiation and within a reference sample excited with surface resist heating are developed. Based on these temperature models and the finite-element analysis method, the LID signals used to determine the absorptance of the surface or coating of a laser component and the corresponding calibration error are analyzed. The computation results show that the calibration error depends largely on the probe beam position for normal or transverse LID signals and may be minimized by optimizing the probe beam geometry. PMID:25968359

  4. EEG data classification through signal spatial redistribution and optimized linear discriminants.

    PubMed

    Gutiérrez, David; Escalona-Vargas, Diana I

    2010-01-01

    This paper presents a preprocessing technique for improving the classification of electroencephalographic (EEG) data in brain-computer interfaces (BCI) for the case of realistic measuring conditions, such as low signal-to-noise ratio (SNR), reduced number of measuring electrodes, and reduced amount of data used to train the classifier. The proposed method is based on a linear minimum mean squared error (LMMSE) spatial filter specifically designed to improve the SNR of the signals before being classified. The design parameters of the spatial filter are obtained through an optimized version of Fisher's linear discriminant (FLD) whose area under the receiver operating characteristics (ROC) curve is maximized. The combination of the spatial filter and the optimized FLD increases the SNR and changes the spatial distribution of the measured signals. As a result, the signals can be more easily discriminated by means of a simple sign detector or threshold-based classifier. A series of experiments on simulated EEG data compare the performance of the proposed classification scheme to the performance of the Mahalanobis distance-based classifier, which is widely used in BCI systems. Numerical results show that the proposed preprocessing technique enhances the classifier's performance even for low SNR conditions and few measurements, while the Mahalanobis classifier is not reliable under such realistic operating conditions. Furthermore, real EEG data from a self-paced key typing experiment is used to demonstrate the applicability of the preprocessing technique. The proposed method has the potential of improving the efficiency of real-life BCI systems, as well as reducing the computational complexity associated with their implementation. PMID:19523710

  5. Estimation of Optimal Measurement Position of Human Forearm EMG Signal by Discriminant Analysis Based on Wilks' lambda

    NASA Astrophysics Data System (ADS)

    Kiso, Atsushi; Taniguchi, Yu; Seki, Hirokazu

    This paper describes the estimation of the optimal measurement position by discriminant analysis based on Wilks' lambda for myoelectric hand control. In previous studies, for motion discrimination, the myoelectric signals were measured at the same positions. However, the optimal measurement positions of the myoelectric signals for motion discrimination differ depending on the remaining muscles of amputees. Therefore, the purpose of this study is to estimate the optimal and fewer measurement positions for precise motion discrimination of a human forearm. This study proposes a method for estimating the optimal measurement positions by discriminant analysis based on Wilks' lambda, using the myoelectric signals measured at multiple positions. The results of some experiments on the myoelectric hand simulator show the effectiveness of the proposed optimal measurement position estimation method.

  6. Modeling Network Intrusion Detection System Using Feature Selection and Parameters Optimization

    NASA Astrophysics Data System (ADS)

    Kim, Dong Seong; Park, Jong Sou

    Previous approaches for modeling Intrusion Detection System (IDS) have been on twofold: improving detection model(s) in terms of (i) feature selection of audit data through wrapper and filter methods and (ii) parameters optimization of detection model design, based on classification, clustering algorithms, etc. In this paper, we present three approaches to model IDS in the context of feature selection and parameters optimization: First, we present Fusion of Genetic Algorithm (GA) and Support Vector Machines (SVM) (FuGAS), which employs combinations of GA and SVM through genetic operation and it is capable of building an optimal detection model with only selected important features and optimal parameters value. Second, we present Correlation-based Hybrid Feature Selection (CoHyFS), which utilizes a filter method in conjunction of GA for feature selection in order to reduce long training time. Third, we present Simultaneous Intrinsic Model Identification (SIMI), which adopts Random Forest (RF) and shows better intrusion detection rates and feature selection results, along with no additional computational overheads. We show the experimental results and analysis of three approaches on KDD 1999 intrusion detection datasets.

  7. Feature Selection Method Based on Neighborhood Relationships: Applications in EEG Signal Identification and Chinese Character Recognition.

    PubMed

    Zhao, Yu-Xiang; Chou, Chien-Hsing

    2016-01-01

    In this study, a new feature selection algorithm, the neighborhood-relationship feature selection (NRFS) algorithm, is proposed for identifying rat electroencephalogram signals and recognizing Chinese characters. In these two applications, dependent relationships exist among the feature vectors and their neighboring feature vectors. Therefore, the proposed NRFS algorithm was designed for solving this problem. By applying the NRFS algorithm, unselected feature vectors have a high priority of being added into the feature subset if the neighboring feature vectors have been selected. In addition, selected feature vectors have a high priority of being eliminated if the neighboring feature vectors are not selected. In the experiments conducted in this study, the NRFS algorithm was compared with two feature algorithms. The experimental results indicated that the NRFS algorithm can extract the crucial frequency bands for identifying rat vigilance states and identifying crucial character regions for recognizing Chinese characters. PMID:27314346

  8. Feature Selection Method Based on Neighborhood Relationships: Applications in EEG Signal Identification and Chinese Character Recognition

    PubMed Central

    Zhao, Yu-Xiang; Chou, Chien-Hsing

    2016-01-01

    In this study, a new feature selection algorithm, the neighborhood-relationship feature selection (NRFS) algorithm, is proposed for identifying rat electroencephalogram signals and recognizing Chinese characters. In these two applications, dependent relationships exist among the feature vectors and their neighboring feature vectors. Therefore, the proposed NRFS algorithm was designed for solving this problem. By applying the NRFS algorithm, unselected feature vectors have a high priority of being added into the feature subset if the neighboring feature vectors have been selected. In addition, selected feature vectors have a high priority of being eliminated if the neighboring feature vectors are not selected. In the experiments conducted in this study, the NRFS algorithm was compared with two feature algorithms. The experimental results indicated that the NRFS algorithm can extract the crucial frequency bands for identifying rat vigilance states and identifying crucial character regions for recognizing Chinese characters. PMID:27314346

  9. Debris Selection and Optimal Path Planning for Debris Removal on the SSO: Impulsive-Thrust Option

    NASA Astrophysics Data System (ADS)

    Olympio, J. T.; Frouvelle, N.

    2013-08-01

    The current paper deals with the mission design of a generic active space debris removal spacecraft. Considered debris are all on a sun-synchronous orbit. A perturbed Lambert's problem, modelling the transfer between two debris, is devised to take into account J2 perturbation, and to quickly evaluate mission scenarios. A robust approach, using techniques of global optimisation, is followed to find optimal debris sequence and mission strategy. Manoeuvres optimization is then performed to refine the selected trajectory scenarii.

  10. Stronger signal of recent selection for lactase persistence in Maasai than in Europeans.

    PubMed

    Schlebusch, Carina M; Sjödin, Per; Skoglund, Pontus; Jakobsson, Mattias

    2013-05-01

    Continued ability to digest lactose after weaning provides a possible selective advantage to individuals who have access to milk as a food source. The lactase persistence (LP) phenotype exists at varying frequencies in different populations and SNPs that modulate the regulation of the LCT gene have been identified in many of these populations. Very strong positive selection for LP has been illustrated for a single SNP (rs4988235) in northwestern European populations, which has become a textbook example of the effect of recent selective sweeps on genetic variation and linkage disequilibrium. In this study, we employed two different methods to detect signatures of positive selection in an East African pastoralist population in the HapMap collection, the Maasai from Kenya, and compared results with other HapMap populations. We found that signatures of recent selection coinciding with the LCT gene are the strongest across the genome in the Maasai population. Furthermore, the genome-wide signal of recent positive selection on haplotypic variation and population differentiation around the LCT gene is greater in the Maasai than in the CEU population (northwestern European descent), possibly due to stronger selection pressure, but it could also be an indication of more recent selection in Maasai compared with the Central European group or more efficient selection in the Maasai due to less genetic drift for their larger effective population size. This signal of recent selection is driven by a putative East African LP haplotype that is different from the haplotype that contributes to the LP phenotype in northwestern Europe. PMID:22948027