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Sample records for optimal signal selection

  1. Improving Pertuzumab production by gene optimization and proper signal peptide selection.

    PubMed

    Ramezani, Amin; Mahmoudi Maymand, Elham; Yazdanpanah-Samani, Mahsa; Hosseini, Ahmad; Toghraie, Fatemeh Sadat; Ghaderi, Abbas

    2017-07-01

    Using proper signal peptide and codon optimization are important factors that must be considered when designing the vector to increase protein expression in Chinese Hamster Ovary (CHO) cells. The aim of the present study is to investigate how to enhance Pertuzumab production through heavy and light chain coding gene optimization and proper signal peptide selection. First, CHO-K1 cells were transiently transfected with whole-antibody-gene-optimized, variable-regions-optimized and non-optimized constructs and then we employed five different signal peptides to improve the secretion efficiency of Pertuzumab. Compared to the native antibody gene, a 3.8 fold increase in Pertuzumab production rate was achieved with the whole heavy and light chain sequence optimization. Although an overall two fold increase in monoclonal antibody production was achieved by human albumin signal peptide compared to the control signal peptide, this overproduction was not statistically significant. Selected signal peptides had no effect on the binding of Pertuzumab to the ErbB2 antigen. The combined data indicate that human albumin signal peptide along with whole antibody sequence optimization can be used to improve Pertuzumab production rates. This sequence was used to produce Pertuzumab producing CHO-K1 stably transfected cells. This result is useful for producing Pertuzumab as a biosimilar drug. Copyright © 2017 Elsevier Inc. All rights reserved.

  2. Compression of biomedical signals with mother wavelet optimization and best-basis wavelet packet selection.

    PubMed

    Brechet, Laurent; Lucas, Marie-Françoise; Doncarli, Christian; Farina, Dario

    2007-12-01

    We propose a novel scheme for signal compression based on the discrete wavelet packet transform (DWPT) decompositon. The mother wavelet and the basis of wavelet packets were optimized and the wavelet coefficients were encoded with a modified version of the embedded zerotree algorithm. This signal dependant compression scheme was designed by a two-step process. The first (internal optimization) was the best basis selection that was performed for a given mother wavelet. For this purpose, three additive cost functions were applied and compared. The second (external optimization) was the selection of the mother wavelet based on the minimal distortion of the decoded signal given a fixed compression ratio. The mother wavelet was parameterized in the multiresolution analysis framework by the scaling filter, which is sufficient to define the entire decomposition in the orthogonal case. The method was tested on two sets of ten electromyographic (EMG) and ten electrocardiographic (ECG) signals that were compressed with compression ratios in the range of 50%-90%. For 90% compression ratio of EMG (ECG) signals, the percent residual difference after compression decreased from (mean +/- SD) 48.6 +/- 9.9% (21.5 +/- 8.4%) with discrete wavelet transform (DWT) using the wavelet leading to poorest performance to 28.4 +/- 3.0% (6.7 +/- 1.9%) with DWPT, with optimal basis selection and wavelet optimization. In conclusion, best basis selection and optimization of the mother wavelet through parameterization led to substantial improvement of performance in signal compression with respect to DWT and randon selection of the mother wavelet. The method provides an adaptive approach for optimal signal representation for compression and can thus be applied to any type of biomedical signal.

  3. Selective Optimization

    DTIC Science & Technology

    2015-07-06

    optimization solvers, they typically exhibit extremely poor performance . We develop a variety of effective model and algorithm enhancement techniques...commercial optimization solvers, they typically exhibit extremely poor performance . We develop a variety of effective model and algorithm enhancement ...class of problems, and developed strengthened formulations and algorithmic techniques which perform significantly better than standard MIP

  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.

  6. Optimal temporal patterns for dynamical cellular signaling

    NASA Astrophysics Data System (ADS)

    Hasegawa, Yoshihiko

    2016-11-01

    Cells use temporal dynamical patterns to transmit information via signaling pathways. As optimality with respect to the environment plays a fundamental role in biological systems, organisms have evolved optimal ways to transmit information. Here, we use optimal control theory to obtain the dynamical signal patterns for the optimal transmission of information, in terms of efficiency (low energy) and reliability (low uncertainty). Adopting an activation-deactivation decoding network, we reproduce several dynamical patterns found in actual signals, such as steep, gradual, and overshooting dynamics. Notably, when minimizing the energy of the input signal, the optimal signals exhibit overshooting, which is a biphasic pattern with transient and steady phases; this pattern is prevalent in actual dynamical patterns. We also identify conditions in which these three patterns (steep, gradual, and overshooting) confer advantages. Our study shows that cellular signal transduction is governed by the principle of minimizing free energy dissipation and uncertainty; these constraints serve as selective pressures when designing dynamical signaling patterns.

  7. 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.

  8. A Theoretical and Empirical Integrated Method to Select the Optimal Combined Signals for Geometry-Free and Geometry-Based Three-Carrier Ambiguity Resolution

    PubMed Central

    Zhao, Dongsheng; Roberts, Gethin Wyn; Lau, Lawrence; Hancock, Craig M.; Bai, Ruibin

    2016-01-01

    Twelve GPS Block IIF satellites, out of the current constellation, can transmit on three-frequency signals (L1, L2, L5). Taking advantages of these signals, Three-Carrier Ambiguity Resolution (TCAR) is expected to bring much benefit for ambiguity resolution. One of the research areas is to find the optimal combined signals for a better ambiguity resolution in geometry-free (GF) and geometry-based (GB) mode. However, the existing researches select the signals through either pure theoretical analysis or testing with simulated data, which might be biased as the real observation condition could be different from theoretical prediction or simulation. In this paper, we propose a theoretical and empirical integrated method, which first selects the possible optimal combined signals in theory and then refines these signals with real triple-frequency GPS data, observed at eleven baselines of different lengths. An interpolation technique is also adopted in order to show changes of the AR performance with the increase in baseline length. The results show that the AR success rate can be improved by 3% in GF mode and 8% in GB mode at certain intervals of the baseline length. Therefore, the TCAR can perform better by adopting the combined signals proposed in this paper when the baseline meets the length condition. PMID:27854324

  9. A Theoretical and Empirical Integrated Method to Select the Optimal Combined Signals for Geometry-Free and Geometry-Based Three-Carrier Ambiguity Resolution.

    PubMed

    Zhao, Dongsheng; Roberts, Gethin Wyn; Lau, Lawrence; Hancock, Craig M; Bai, Ruibin

    2016-11-16

    Twelve GPS Block IIF satellites, out of the current constellation, can transmit on three-frequency signals (L1, L2, L5). Taking advantages of these signals, Three-Carrier Ambiguity Resolution (TCAR) is expected to bring much benefit for ambiguity resolution. One of the research areas is to find the optimal combined signals for a better ambiguity resolution in geometry-free (GF) and geometry-based (GB) mode. However, the existing researches select the signals through either pure theoretical analysis or testing with simulated data, which might be biased as the real observation condition could be different from theoretical prediction or simulation. In this paper, we propose a theoretical and empirical integrated method, which first selects the possible optimal combined signals in theory and then refines these signals with real triple-frequency GPS data, observed at eleven baselines of different lengths. An interpolation technique is also adopted in order to show changes of the AR performance with the increase in baseline length. The results show that the AR success rate can be improved by 3% in GF mode and 8% in GB mode at certain intervals of the baseline length. Therefore, the TCAR can perform better by adopting the combined signals proposed in this paper when the baseline meets the length condition.

  10. Selective channel combination of MRI signal phase.

    PubMed

    Vegh, Viktor; O'Brien, Kieran; Barth, Markus; Reutens, David C

    2016-11-01

    Signal magnitude can robustly be combined using the sum-of-squares approach. Methods have been developed to combine complex images. However, techniques based only on signal phase have not been developed and evaluated. We performed simulations to demonstrate the effect of noise on coil combination. 32-channel 7 Tesla human gradient echo MRI brain data were collected. We combined phase images based on phase noise leading to spatially selective and coil selective combination of phase images. We compared our selective combination approach to optimal noise distribution and adaptive combination methods. We found that selective combination of signal phases leads to improved phase signal-to-noise ratio. Furthermore, a phase shift can be present in combined phase images introduced by the method used to combine multiple channel phases. Mapping of signal phase from ultra-high field MRI data undoubtedly provides a wealth of information about the ageing brain and the effects of neurodegenerative disorders. Measurement of signal phase is essential in frequency shift mapping and in quantitative susceptibility mapping. The method used to combine signal phase should be informed by an understanding of the noise distribution in signal phase at the individual channel level. Magn Reson Med 76:1469-1477, 2016. © 2015 International Society for Magnetic Resonance in Medicine. © 2015 International Society for Magnetic Resonance in Medicine.

  11. 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.

  12. Selective disruption of the AKAP signaling complexes.

    PubMed

    Kennedy, Eileen J; Scott, John D

    2015-01-01

    Synthesis of the second messenger cAMP activates a variety of signaling pathways critical for all facets of intracellular regulation. Protein kinase A (PKA) is the major cAMP-responsive effector. Where and when this enzyme is activated has profound implications on the cellular role of PKA. A-Kinase Anchoring Proteins (AKAPs) play a critical role in this process by orchestrating spatial and temporal aspects of PKA action. A popular means of evaluating the impact of these anchored signaling events is to biochemically interfere with the PKA-AKAP interface. Hence, peptide disruptors of PKA anchoring are valuable tools in the investigation of local PKA action. This article outlines the development of PKA isoform-selective disruptor peptides, documents the optimization of cell-soluble peptide derivatives, and introduces alternative cell-based approaches that interrogate other aspects of the PKA-AKAP interface.

  13. 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.

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

    SciTech Connect

    Doerr, Christian

    2006-06-23

    The work presented in this thesis is mainly focused on the application in a Δms measurement. Chapter 1 starts with a general theoretical introduction on the unitarity triangle with a focus on the impact of a Δms 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 Δms (Δmd) 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 B0 → Dπ, D → Kππ and then applied to the kinematically very similar decay Bs → Dsπ, Ds→ Φπ, Φ → KK. Chapter 7 uses events selected by the neural network selection as input to an unbinned maximum likelihood fit and extracts the B0 lifetime and Δmd. In addition, an amplitude scan and an unbinned maximum likelihood fit of Δms is performed, applying the neural network selection developed for the decay channel Bs → Dsπ, Ds → Φπ, Φ → KK. Finally chapter 8 summarizes and gives an outlook.

  15. 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.

  16. 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

  17. Data selection in EEG signals classification.

    PubMed

    Wang, Shuaifang; Li, Yan; Wen, Peng; Lai, David

    2016-03-01

    The alcoholism can be detected by analyzing electroencephalogram (EEG) signals. However, analyzing multi-channel EEG signals is a challenging task, which often requires complicated calculations and long execution time. This paper proposes three data selection methods to extract representative data from the EEG signals of alcoholics. The methods are the principal component analysis based on graph entropy (PCA-GE), the channel selection based on graph entropy (GE) difference, and the mathematic combinations channel selection, respectively. For comparison purposes, the selected data from the three methods are then classified by three classifiers: the J48 decision tree, the K-nearest neighbor and the Kstar, separately. The experimental results show that the proposed methods are successful in selecting data without compromising the classification accuracy in discriminating the EEG signals from alcoholics and non-alcoholics. Among them, the proposed PCA-GE method uses only 29.69% of the whole data and 29.5% of the computation time but achieves a 94.5% classification accuracy. The channel selection method based on the GE difference also gains a 91.67% classification accuracy by using only 29.69% of the full size of the original data. Using as little data as possible without sacrificing the final classification accuracy is useful for online EEG analysis and classification application design.

  18. 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

  19. 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

  20. Self-extinction through optimizing selection.

    PubMed

    Parvinen, Kalle; Dieckmann, Ulf

    2013-09-21

    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. Copyright © 2013 Elsevier Ltd. All rights reserved.

  1. Optimal Hamiltonian Simulation by Quantum Signal Processing

    NASA Astrophysics Data System (ADS)

    Low, Guang Hao; Chuang, Isaac L.

    2017-01-01

    The physics of quantum mechanics is the inspiration for, and underlies, quantum computation. As such, one expects physical intuition to be highly influential in the understanding and design of many quantum algorithms, particularly simulation of physical systems. Surprisingly, this has been challenging, with current Hamiltonian simulation algorithms remaining abstract and often the result of sophisticated but unintuitive constructions. We contend that physical intuition can lead to optimal simulation methods by showing that a focus on simple single-qubit rotations elegantly furnishes an optimal algorithm for Hamiltonian simulation, a universal problem that encapsulates all the power of quantum computation. Specifically, we show that the query complexity of implementing time evolution by a d -sparse Hamiltonian H ^ for time-interval t with error ɛ is O [t d ∥H ^ ∥max+log (1 /ɛ ) /log log (1 /ɛ ) ] , which matches lower bounds in all parameters. This connection is made through general three-step "quantum signal processing" methodology, comprised of (i) transducing eigenvalues of H ^ into a single ancilla qubit, (ii) transforming these eigenvalues through an optimal-length sequence of single-qubit rotations, and (iii) projecting this ancilla with near unity success probability.

  2. Optimel: Software for selecting the optimal method

    NASA Astrophysics Data System (ADS)

    Popova, Olga; Popov, Boris; Romanov, Dmitry; Evseeva, Marina

    Optimel: software for selecting the optimal method automates the process of selecting a solution method from the optimization methods domain. Optimel features practical novelty. It saves time and money when conducting exploratory studies if its objective is to select the most appropriate method for solving an optimization problem. Optimel features theoretical novelty because for obtaining the domain a new method of knowledge structuring was used. In the Optimel domain, extended quantity of methods and their properties are used, which allows identifying the level of scientific studies, enhancing the user's expertise level, expand the prospects the user faces and opening up new research objectives. Optimel can be used both in scientific research institutes and in educational institutions.

  3. 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

  4. Selective signalling by cuttlefish to predators.

    PubMed

    Langridge, Keri V; Broom, Mark; Osorio, Daniel

    2007-12-18

    Rather than simply escaping, prey animals often attempt to deter an attack by signalling to an approaching predator, but this is a risky strategy if it allows time for the predator to draw closer (especially when the signal is a bluff). Because prey are vulnerable to multiple predators, the hunting techniques of which vary widely, it could well be beneficial for a prey animal to discriminate predators and to signal only to those that are likely to be deterred. Higher vertebrates make alarm calls that can identify the type of predator to the signaller's conspecifics, and a recent study shows that squirrels direct an infrared deterrent signal specifically at infrared-sensitive pit-vipers and not at other snakes. We show here that naïve juvenile cuttlefish (Sepia officinalis L.) use a visual signal selectively during encounters with different predatory species. We analysed sequences of defensive behaviours produced by cuttlefish, to control for effects of relative threat level (or 'response urgency'). This showed that a high contrast 'eyespot' signal, known as the deimatic display, was used before flight against visually oriented teleost fish, but not crabs and dogfish, which are chemosensory predators.

  5. 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

  6. Optimizing Clinical Research Participant Selection with Informatics.

    PubMed

    Weng, Chunhua

    2015-11-01

    Clinical research participants are often not reflective of 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.

  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.

  8. Occluded object imaging via optimal camera selection

    NASA Astrophysics Data System (ADS)

    Yang, Tao; Zhang, Yanning; Tong, Xiaomin; Ma, Wenguang; Yu, Rui

    2013-12-01

    High performance occluded object imaging in cluttered scenes is a significant challenging task for many computer vision applications. Recently the camera array synthetic aperture imaging is proved to be an effective way to seeing object through occlusion. However, the imaging quality of occluded object is often significantly decreased by the shadows of the foreground occluder. Although some works have been presented to label the foreground occluder via object segmentation or 3D reconstruction, these methods will fail in the case of complicated occluder and severe occlusion. In this paper, we present a novel optimal camera selection algorithm to solve the above problem. The main characteristics of this algorithm include: (1) Instead of synthetic aperture imaging, we formulate the occluded object imaging problem as an optimal camera selection and mosaicking problem. To the best of our knowledge, our proposed method is the first one for occluded object mosaicing. (2) A greedy optimization framework is presented to propagate the visibility information among various depth focus planes. (3) A multiple label energy minimization formulation is designed in each plane to select the optimal camera. The energy is estimated in the synthetic aperture image volume and integrates the multi-view intensity consistency, previous visibility property and camera view smoothness, which is minimized via Graph cuts. We compare our method with the state-of-the-art synthetic aperture imaging algorithms, and extensive experimental results with qualitative and quantitative analysis demonstrate the effectiveness and superiority of our approach.

  9. 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.

  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 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.

  11. Optimal signal processing for continuous qubit readout

    NASA Astrophysics Data System (ADS)

    Ng, Shilin; Tsang, Mankei

    2014-08-01

    The measurement of a quantum two-level system, or a qubit in modern terminology, often involves an electromagnetic field that interacts with the qubit, before the field is measured continuously and the qubit state is inferred from the noisy field measurement. During the measurement, the qubit may undergo spontaneous transitions, further obscuring the initial qubit state from the observer. Taking advantage of some well-known techniques in stochastic detection theory, here we propose a signal processing protocol that can infer the initial qubit state optimally from the measurement in the presence of noise and qubit dynamics. Assuming continuous quantum-nondemolition measurements with Gaussian or Poissonian noise and a classical Markov model for the qubit, we derive analytic solutions to the protocol in some special cases of interest using Itō calculus. Our method is applicable to multihypothesis testing for robust qubit readout and relevant to experiments on qubits in superconducting microwave circuits, trapped ions, nitrogen-vacancy centers in diamond, semiconductor quantum dots, or phosphorus donors in silicon.

  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. Active Learning With Optimal Instance Subset Selection.

    PubMed

    Fu, Yifan; Zhu, Xingquan; Elmagarmid, A K

    2013-04-01

    Active learning (AL) traditionally relies on some instance-based utility measures (such as uncertainty) to assess individual instances and label the ones with the maximum values for training. In this paper, we argue that such approaches cannot produce good labeling subsets mainly because instances are evaluated independently without considering their interactions, and individuals with maximal ability do not necessarily form an optimal instance subset for learning. Alternatively, we propose to achieve AL with optimal subset selection (ALOSS), where the key is to find an instance subset with a maximum utility value. To achieve the goal, ALOSS simultaneously considers the following: 1) the importance of individual instances and 2) the disparity between instances, to build an instance-correlation matrix. As a result, AL is transformed to a semidefinite programming problem to select a k-instance subset with a maximum utility value. Experimental results demonstrate that ALOSS outperforms state-of-the-art approaches for AL.

  14. Optimization methods for activities selection problems

    NASA Astrophysics Data System (ADS)

    Mahad, Nor Faradilah; Alias, Suriana; Yaakop, Siti Zulaika; Arshad, Norul Amanina Mohd; Mazni, Elis Sofia

    2017-08-01

    Co-curriculum activities must be joined by every student in Malaysia and these activities bring a lot of benefits to the students. By joining these activities, the students can learn about the time management and they can developing many useful skills. This project focuses on the selection of co-curriculum activities in secondary school using the optimization methods which are the Analytic Hierarchy Process (AHP) and Zero-One Goal Programming (ZOGP). A secondary school in Negeri Sembilan, Malaysia was chosen as a case study. A set of questionnaires were distributed randomly to calculate the weighted for each activity based on the 3 chosen criteria which are soft skills, interesting activities and performances. The weighted was calculated by using AHP and the results showed that the most important criteria is soft skills. Then, the ZOGP model will be analyzed by using LINGO Software version 15.0. There are two priorities to be considered. The first priority which is to minimize the budget for the activities is achieved since the total budget can be reduced by RM233.00. Therefore, the total budget to implement the selected activities is RM11,195.00. The second priority which is to select the co-curriculum activities is also achieved. The results showed that 9 out of 15 activities were selected. Thus, it can concluded that AHP and ZOGP approach can be used as the optimization methods for activities selection problem.

  15. Optimized periocular template selection for human recognition.

    PubMed

    Bakshi, Sambit; Sa, Pankaj K; Majhi, Banshidhar

    2013-01-01

    A novel approach for selecting a rectangular template around periocular region optimally potential for human recognition is proposed. A comparatively larger template of periocular image than the optimal one can be slightly more potent for recognition, but the larger template heavily slows down the biometric system by making feature extraction computationally intensive and increasing the database size. A smaller template, on the contrary, cannot yield desirable recognition though the smaller template performs faster due to low computation for feature extraction. These two contradictory objectives (namely, (a) to minimize the size of periocular template and (b) to maximize the recognition through the template) are aimed to be optimized through the proposed research. This paper proposes four different approaches for dynamic optimal template selection from periocular region. The proposed methods are tested on publicly available unconstrained UBIRISv2 and FERET databases and satisfactory results have been achieved. Thus obtained template can be used for recognition of individuals in an organization and can be generalized to recognize every citizen of a nation.

  16. 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.

  17. Optimization of solar-selective paint coatings

    NASA Astrophysics Data System (ADS)

    McChesney, M. A.; Zimmer, P. B.; Lin, R. J. H.

    1982-06-01

    The objective was the development of low-cost, high-performance, solar-selective paint coatings for solar flat-plate collector (FPC) use and passive thermal wall application. Thickness-sensitive selective paint coating development was intended to demonstrate large scale producibility. Thickness-insensitive selective paint (TISP) coating development was intended to develop and optimize the coating for passive solar systems and FPC applications. Low-cost, high-performance TSSP coatings and processes were developed to demonstrate large-scale producibility and meet all program goals. Dip, spray, roll, laminating and gravure processes were investigated and used to produce final samples. High-speed gravure coating was selected as the most promising process for solar foil fabrication. Development and optimization of TISP coatings was not completely successful. A variation in reflective metal pigment was suspected of being the primary problem, although other variables may have contributed. Consistent repeating of optical properties of these coatings achieved on the previous program was not achieved.

  18. 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.

  19. Selected Isotopes for Optimized Fuel Assembly Tags

    SciTech Connect

    Gerlach, David C.; Mitchell, Mark R.; Reid, Bruce D.; Gesh, Christopher J.; Hurley, David E.

    2008-10-01

    In support of our ongoing signatures project we present information on 3 isotopes selected for possible application in optimized tags that could be applied to fuel assemblies to provide an objective measure of burnup. 1. Important factors for an optimized tag are compatibility with the reactor environment (corrosion resistance), low radioactive activation, at least 2 stable isotopes, moderate neutron absorption cross-section, which gives significant changes in isotope ratios over typical fuel assembly irradiation levels, and ease of measurement in the SIMS machine 2. From the candidate isotopes presented in the 3rd FY 08 Quarterly Report, the most promising appear to be Titanium, Hafnium, and Platinum. The other candidate isotopes (Iron, Tungsten, exhibited inadequate corrosion resistance and/or had neutron capture cross-sections either too high or too low for the burnup range of interest.

  20. 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.

  1. 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

  2. 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.

  3. A nonparametric stochastic optimizer for TDMA-based neuronal signaling.

    PubMed

    Suzuki, Junichi; Phan, Dũng H; Budiman, Harry

    2014-09-01

    This paper considers neurons as a physical communication medium for intrabody networks of nano/micro-scale machines and formulates a noisy multiobjective optimization problem for a Time Division Multiple Access (TDMA) communication protocol atop the physical layer. The problem is to find the Pareto-optimal TDMA configurations that maximize communication performance (e.g., latency) by multiplexing a given neuronal network to parallelize signal transmissions while maximizing communication robustness (i.e., unlikeliness of signal interference) against noise in neuronal signaling. Using a nonparametric significance test, the proposed stochastic optimizer is designed to statistically determine the superior-inferior relationship between given two solution candidates and seek the optimal trade-offs among communication performance and robustness objectives. Simulation results show that the proposed optimizer efficiently obtains quality TDMA configurations in noisy environments and outperforms existing noise-aware stochastic optimizers.

  4. 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.

  5. 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.

  6. Optimal test selection for prediction uncertainty reduction

    DOE PAGES

    Mullins, Joshua; Mahadevan, Sankaran; Urbina, Angel

    2016-12-02

    Economic factors and experimental limitations often lead to sparse and/or imprecise data used for the calibration and validation of computational models. This paper addresses resource allocation for calibration and validation experiments, in order to maximize their effectiveness within given resource constraints. When observation data are used for model calibration, the quality of the inferred parameter descriptions is directly affected by the quality and quantity of the data. This paper characterizes parameter uncertainty within a probabilistic framework, which enables the uncertainty to be systematically reduced with additional data. The validation assessment is also uncertain in the presence of sparse and imprecisemore » data; therefore, this paper proposes an approach for quantifying the resulting validation uncertainty. Since calibration and validation uncertainty affect the prediction of interest, the proposed framework explores the decision of cost versus importance of data in terms of the impact on the prediction uncertainty. Often, calibration and validation tests may be performed for different input scenarios, and this paper shows how the calibration and validation results from different conditions may be integrated into the prediction. Then, a constrained discrete optimization formulation that selects the number of tests of each type (calibration or validation at given input conditions) is proposed. Furthermore, the proposed test selection methodology is demonstrated on a microelectromechanical system (MEMS) example.« less

  7. Optimal test selection for prediction uncertainty reduction

    SciTech Connect

    Mullins, Joshua; Mahadevan, Sankaran; Urbina, Angel

    2016-12-02

    Economic factors and experimental limitations often lead to sparse and/or imprecise data used for the calibration and validation of computational models. This paper addresses resource allocation for calibration and validation experiments, in order to maximize their effectiveness within given resource constraints. When observation data are used for model calibration, the quality of the inferred parameter descriptions is directly affected by the quality and quantity of the data. This paper characterizes parameter uncertainty within a probabilistic framework, which enables the uncertainty to be systematically reduced with additional data. The validation assessment is also uncertain in the presence of sparse and imprecise data; therefore, this paper proposes an approach for quantifying the resulting validation uncertainty. Since calibration and validation uncertainty affect the prediction of interest, the proposed framework explores the decision of cost versus importance of data in terms of the impact on the prediction uncertainty. Often, calibration and validation tests may be performed for different input scenarios, and this paper shows how the calibration and validation results from different conditions may be integrated into the prediction. Then, a constrained discrete optimization formulation that selects the number of tests of each type (calibration or validation at given input conditions) is proposed. Furthermore, the proposed test selection methodology is demonstrated on a microelectromechanical system (MEMS) example.

  8. Selective perborate signaling by deprotection of fluorescein and resorufin acetates.

    PubMed

    Choi, Myung Gil; Cha, Sunyoung; Park, Ji Eun; Lee, Haekyung; Jeon, Hye Lim; Chang, Suk-Kyu

    2010-04-02

    The acetate derivatives of fluorescein and resorufin exhibited a prominent turn-on type signaling behavior toward BO(3)(-) ions over other common anions. Signaling is based on the selective deprotection of acetate groups by perborate, which resulted in significant chromogenic and fluorogenic signaling. Compound 1 also exhibited a pronounced perborate selectivity over other commonly used oxidants in 90% aqueous acetonitrile solution.

  9. Practical Receiver for Optimal Discrimination of Binary Coherent Signals

    NASA Astrophysics Data System (ADS)

    Sych, Denis; Leuchs, Gerd

    2016-11-01

    We address the long-standing problem of discriminating coherent states with the minimum error rate. We show an optimum receiver for coherent states which admits a relatively simple implementation with current technologies. The receiver is based on multichannel splitting of the signal, followed by feed-forward signal displacement and photon-counting detection. We develop an optimal control strategy for a finite signal split and show convergence of the error rate to the Helstrom bound.

  10. Optimal selection of biochars for remediating metals ...

    EPA Pesticide Factsheets

    Approximately 500,000 abandoned mines across the U.S. pose a considerable, pervasive risk to human health and the environment due to possible exposure to the residuals of heavy metal extraction. Historically, a variety of chemical and biological methods have been used to reduce the bioavailability of the metals at mine sites. Biochar with its potential to complex and immobilize heavy metals, is an emerging alternative for reducing bioavailability. Furthermore, biochar has been reported to improve soil conditions for plant growth and can be used for promoting the establishment of a soil-stabilizing native plant community to reduce offsite movement of metal-laden waste materials. Because biochar properties depend upon feedstock selection, pyrolysis production conditions, and activation procedures used, they can be designed to meet specific remediation needs. As a result biochar with specific properties can be produced to correspond to specific soil remediation situations. However, techniques are needed to optimally match biochar characteristics with metals contaminated soils to effectively reduce metal bioavailability. Here we present experimental results used to develop a generalized method for evaluating the ability of biochar to reduce metals in mine spoil soil from an abandoned Cu and Zn mine. Thirty-eight biochars were produced from approximately 20 different feedstocks and produced via slow pyrolysis or gasification, and were allowed to react with a f

  11. 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.

  12. 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.

  13. 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.

  14. Selectivity in neurotrophin signaling: theme and variations.

    PubMed

    Segal, Rosalind A

    2003-01-01

    Neurotrophins are a family of growth factors critical for the development and functioning of the nervous system. Although originally identified as neuronal survival factors, neurotrophins elicit many biological effects, ranging from proliferation to synaptic modulation to axonal pathfinding. Recent data indicate that the nature of the signaling cascades activated by neurotrophins, and the biological responses that ensue, are specified not only by the ligand itself but also by the temporal pattern and spatial location of stimulation. Studies on neurotrophin signaling have revealed variations in the Ras/MAP kinase, PI3 kinase, and phospholipase C pathways, which transmit spatial and temporal information. The anatomy of neurons makes them particularly appropriate for studying how the location and tempo of stimulation determine the signal cascades that are activated by receptor tyrosine kinases such as the Trk receptors. These signaling variations may represent a general mechanism eliciting specificity in growth factor responses.

  15. 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.

  16. MIMO-OFDM signal optimization for SAR imaging radar

    NASA Astrophysics Data System (ADS)

    Baudais, J.-Y.; Méric, S.; Riché, V.; Pottier, É.

    2016-12-01

    This paper investigates the optimization of the coded orthogonal frequency division multiplexing (OFDM) transmitted signal in a synthetic aperture radar (SAR) context. We propose to design OFDM signals to achieve range ambiguity mitigation. Indeed, range ambiguities are well known to be a limitation for SAR systems which operates with pulsed transmitted signal. The ambiguous reflected signal corresponding to one pulse is then detected when the radar has already transmitted the next pulse. In this paper, we demonstrate that the range ambiguity mitigation is possible by using orthogonal transmitted wave as OFDM pulses. The coded OFDM signal is optimized through genetic optimization procedures based on radar image quality parameters. Moreover, we propose to design a multiple-input multiple-output (MIMO) configuration to enhance the noise robustness of a radar system and this configuration is mainly efficient in the case of using orthogonal waves as OFDM pulses. The results we obtain show that OFDM signals outperform conventional radar chirps for range ambiguity suppression and for robustness enhancement in 2 ×2 MIMO configuration.

  17. 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.

  18. A detection method of signal frequency based on optimization theory

    NASA Astrophysics Data System (ADS)

    Nie, Chunyan; Shi, Yaowu; Wang, Zhuwen; Guo, Bin

    2006-11-01

    The sensitive characteristic to initial value of chaos system and the immunity to noise sufficiently demonstrate the superiority in weak signal detection. In this paper Duffing equation is used as system detection model, on the basis of optimization theory, a most optimization searching method, which takes the variance of output X as the detected value is present. The basic principle and the theoretical algorithm about detecting the weak signal with this method are proposed. At the same time, the simulation experiments and the result analysis are given. The results indicated this method is rapidly, simple, convenient and the accuracy is high, which is a novel detecting frequency method. If this method were applied in signal processing field or other application field, it would have practical significance.

  19. 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.

  20. 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.

  1. Optimal wavelength selection for noncontact reflection photoplethysmography

    NASA Astrophysics Data System (ADS)

    Corral Martinez, Luis F.; Paez, Gonzalo; Strojnik, Marija

    2011-08-01

    In this work, we obtain backscattered signals from human forehead for wavelengths from 380 to 980 nm. The results reveal bands with strong pulsatile signals that carry useful information. We describe those bands as the most suitable wavelengths in the visible and NIR regions from which heart and respiratory rate parameters can be derived using long distance non-contact reflection photoplethysmography analysis. The latter results show the feasibility of a novel technique for remotely detection of vital signs in humans. This technique, which may include morphological analysis or maps of tissue oxygenation, is a further step to real non-invasive remote monitoring of patients.

  2. Water Quality Optimization through Selective Withdrawal.

    DTIC Science & Technology

    1983-03-01

    river. 16. Kaplan noted that Staha and Himmelblau compared the COMET al- gorithm to three nonlinear programming codes for 25 test problems. The...Mathematics, Vol 9. Staha, R. L. and Himmelblau , D. M. 1972. "Constrained Optimization Via Moving Exterior Truncations," presented at the Society for

  3. 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.

  4. 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

  5. Optimization of a crossing system using mate selection.

    PubMed

    Li, Yongjun; van der Werf, Julius H J; Kinghorn, Brian P

    2006-01-01

    A simple model based on one single identified quantitative trait locus (QTL) in a two-way crossing system was used to demonstrate the power of mate selection algorithms as a natural means of opportunistic line development for optimization of crossbreeding programs over multiple generations. Mate selection automatically invokes divergent selection in two parental lines for an over-dominant QTL and increased frequency of the favorable allele toward fixation in the sire-line for a fully-dominant QTL. It was concluded that an optimal strategy of line development could be found by mate selection algorithms for a given set of parameters such as genetic model of QTL, breeding objective and initial frequency of the favorable allele in the base populations, etc. The same framework could be used in other scenarios, such as programs involving crossing to exploit breed effects and heterosis. In contrast to classical index selection, this approach to mate selection can optimize long-term responses.

  6. Optimization of a crossing system using mate selection

    PubMed Central

    Li, Yongjun; Werf, Julius HJ van der; Kinghorn, Brian P

    2006-01-01

    A simple model based on one single identified quantitative trait locus (QTL) in a two-way crossing system was used to demonstrate the power of mate selection algorithms as a natural means of opportunistic line development for optimization of crossbreeding programs over multiple generations. Mate selection automatically invokes divergent selection in two parental lines for an over-dominant QTL and increased frequency of the favorable allele toward fixation in the sire-line for a fully-dominant QTL. It was concluded that an optimal strategy of line development could be found by mate selection algorithms for a given set of parameters such as genetic model of QTL, breeding objective and initial frequency of the favorable allele in the base populations, etc. The same framework could be used in other scenarios, such as programs involving crossing to exploit breed effects and heterosis. In contrast to classical index selection, this approach to mate selection can optimize long-term responses. PMID:16492372

  7. Optimal Selection of Army Military Construction Projects

    DTIC Science & Technology

    2002-06-01

    The second column defines the categories for each project. For example, a pier (category: waterfront restoration) receives ten points even though...world information system project selection (from a set of 28) for the Dubai Medical Center in the State of Dubai in the United Arab Emirates. After

  8. Multidimensional optimization of signal space distance parameters in WLAN positioning.

    PubMed

    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.

  9. 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

  10. Producer breeding objectives and optimal sire selection.

    PubMed

    Tozer, P R; Stokes, J R

    2002-12-01

    Information from an online survey of dairy producers was used to determine how important producers perceived three different objectives in the breeding problem. The objectives were: maximizing expected net merit of the progeny, minimizing the expected progeny inbreeding coefficient, and minimizing semen expenditure. Producers were asked to rank the three objectives and then to weight the importance of each objective relative to the others. This information was then used to determine weights to be used in a multiple-objective integer program designed to select individual mates for a herd of 76 Jersey cows with known genetic background and cow net merit. The results of the multiple-objective models show that rank and relative importance of producer objectives can affect the portfolio of sires selected. Producers whose primary objective was to maximize expected net merit had a range of average expected progeny net merit of $306 to $310, but the level of expected progeny inbreeding was from 6.99 to 10.45%, with a semen cost per conception of $35 to $41. For producers who selected minimizing progeny inbreeding as the primary goal in their breeding programs, the range of inbreeding was from 6.11 to 6.60%, with lower net merit range of $274 to $301 and semen expenditure of $30 to $37 per conception. One producer selected minimizing semen cost as the primary objective. For that producer's portfolio, the semen cost was $27 per conception and net merit was $288, with a progeny inbreeding coefficient of 10.68%. The results of this research suggest that producer information and goals have a substantial impact on the portfolio of sires selected by that producer to attain these goals.

  11. 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.

  12. 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.

  13. Selective robust optimization: A new intensity-modulated proton therapy optimization strategy

    SciTech Connect

    Li, Yupeng; Niemela, Perttu; Siljamaki, Sami; Vanderstraeten, Reynald; Liao, Li; Jiang, Shengpeng; Li, Heng; Poenisch, Falk; Zhu, X. Ronald; Sahoo, Narayan; Gillin, Michael; Zhang, Xiaodong

    2015-08-15

    Purpose: To develop a new robust optimization strategy for intensity-modulated proton therapy as an important step in translating robust proton treatment planning from research to clinical applications. Methods: In selective robust optimization, a worst-case-based robust optimization algorithm is extended, and terms of the objective function are selectively computed from either the worst-case dose or the nominal dose. Two lung cancer cases and one head and neck cancer case were used to demonstrate the practical significance of the proposed robust planning strategy. The lung cancer cases had minimal tumor motion less than 5 mm, and, for the demonstration of the methodology, are assumed to be static. Results: Selective robust optimization achieved robust clinical target volume (CTV) coverage and at the same time increased nominal planning target volume coverage to 95.8%, compared to the 84.6% coverage achieved with CTV-based robust optimization in one of the lung cases. In the other lung case, the maximum dose in selective robust optimization was lowered from a dose of 131.3% in the CTV-based robust optimization to 113.6%. Selective robust optimization provided robust CTV coverage in the head and neck case, and at the same time improved controls over isodose distribution so that clinical requirements may be readily met. Conclusions: Selective robust optimization may provide the flexibility and capability necessary for meeting various clinical requirements in addition to achieving the required plan robustness in practical proton treatment planning settings.

  14. 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.

  15. 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.

  16. 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.

  17. Defect Profile Estimation from Magnetic Flux Leakage Signal via Efficient Managing Particle Swarm Optimization

    PubMed Central

    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

  18. Selecting optimal partitioning schemes for phylogenomic datasets.

    PubMed

    Lanfear, Robert; Calcott, Brett; Kainer, David; Mayer, Christoph; Stamatakis, Alexandros

    2014-04-17

    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. 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. 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. 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.

  19. 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.

  20. 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.

  1. Selection of Structures with Grid Optimization, in Multiagent Data Warehouse

    NASA Astrophysics Data System (ADS)

    Gorawski, Marcin; Bańkowski, Sławomir; Gorawski, Michał

    The query optimization problem in data base and data warehouse management systems is quite similar. Changes to Joins sequences, projections and selections, usage of indexes, and aggregations are all decided during the analysis of an execution schedule. The main goal of these changes is to decrease the query response time. The optimization operation is often dedicated to a single node. This paper proposes optimization to grid or cluster data warehouses / databases. Tests were conducted in a multi-agent environment, and the optimization focused not only on a single node but on the whole system as well. A new idea is proposed here with multi-criteria optimization that is based on user-given parameters. Depending on query time, result admissible errors, and the level of system usage, task results were obtained along with grid optimization.

  2. 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.

  3. Optimal ROS Signaling Is Critical for Nuclear Reprogramming.

    PubMed

    Zhou, Gang; Meng, Shu; Li, Yanhui; Ghebre, Yohannes T; Cooke, John P

    2016-05-03

    Efficient nuclear reprogramming of somatic cells to pluripotency requires activation of innate immunity. Because innate immune activation triggers reactive oxygen species (ROS) signaling, we sought to determine whether there was a role of ROS signaling in nuclear reprogramming. We examined ROS production during the reprogramming of doxycycline (dox)-inducible mouse embryonic fibroblasts (MEFs) carrying the Yamanaka factors (Oct4, Sox2, Klf4, and c-Myc [OSKM]) into induced pluripotent stem cells (iPSCs). ROS generation was substantially increased with the onset of reprogramming. Depletion of ROS via antioxidants or Nox inhibitors substantially decreased reprogramming efficiency. Similarly, both knockdown and knockout of p22(phox)-a critical subunit of the Nox (1-4) complex-decreased reprogramming efficiency. However, excessive ROS generation using genetic and pharmacological approaches also impaired reprogramming. Overall, our data indicate that ROS signaling is activated early with nuclear reprogramming, and optimal levels of ROS signaling are essential to induce pluripotency. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

  4. A Tightly Regulated Genetic Selection System with Signaling-Active Alleles of Phytochrome B1[OPEN

    PubMed Central

    2017-01-01

    Selectable markers derived from plant genes circumvent the potential risk of antibiotic/herbicide-resistance gene transfer into neighboring plant species, endophytic bacteria, and mycorrhizal fungi. Toward this goal, we have engineered and validated signaling-active alleles of phytochrome B (eYHB) as plant-derived selection marker genes in the model plant Arabidopsis (Arabidopsis thaliana). By probing the relationship of construct size and induction conditions to optimal phenotypic selection, we show that eYHB-based alleles are robust substitutes for antibiotic/herbicide-dependent marker genes as well as surprisingly sensitive reporters of off-target transgene expression. PMID:27881727

  5. Approaches to Assess Functional Selectivity in GPCRs: Evaluating G Protein Signaling in an Endogenous Environment

    PubMed Central

    Bohn, Laura M.; Zhou, Lei; Ho, Jo-Hao

    2016-01-01

    Ligand-directed signaling, biased agonism, and functional selectivity are terms that describe the propensity of a ligand to drive signaling toward one GPCR pathway over another. Most of the early examples demonstrated to date examine the divergence between GPCR signaling to G protein coupling and βarrestin2 recruitment. As biased agonists begin to become available based on cell-based screening criteria, a need arises to determine if G protein signaling biases will be maintained in the endogenous setting, wherein receptors are functioning to control relevant biological responses. This report presents our method and offers tips for evaluating G protein signaling in endogenous tissues. Predominately, brain tissues are discussed here; optimization points that can be applied to any tissues are highlighted. PMID:26260601

  6. Fast Simulation and Optimization Tool to Explore Selective Neural Stimulation

    PubMed Central

    Dali, Mélissa; Rossel, Olivier; Guiraud, David

    2016-01-01

    In functional electrical stimulation, selective stimulation of axons is desirable to activate a specific target, in particular muscular function. This implies to simulate a fascicule without activating neighboring ones i.e. to be spatially selective. Spatial selectivity is achieved by the use of multicontact cuff electrodes over which the stimulation current is distributed. Because of the large number of parameters involved, numerical simulations provide a way to find and optimize electrode configuration. The present work offers a computation effective scheme and associated tool chain capable of simulating electrode-nerve interface and find the best spread of current to achieve spatial selectivity. PMID:27990231

  7. Fast Simulation and Optimization Tool to Explore Selective Neural Stimulation.

    PubMed

    Dali, Mélissa; Rossel, Olivier; Guiraud, David

    2016-06-13

    In functional electrical stimulation, selective stimulation of axons is desirable to activate a specific target, in particular muscular function. This implies to simulate a fascicule without activating neighboring ones i.e. to be spatially selective. Spatial selectivity is achieved by the use of multicontact cuff electrodes over which the stimulation current is distributed. Because of the large number of parameters involved, numerical simulations provide a way to find and optimize electrode configuration. The present work offers a computation effective scheme and associated tool chain capable of simulating electrode-nerve interface and find the best spread of current to achieve spatial selectivity.

  8. Discrete Biogeography Based Optimization for Feature Selection in Molecular Signatures.

    PubMed

    Liu, Bo; Tian, Meihong; Zhang, Chunhua; Li, Xiangtao

    2015-04-01

    Biomarker discovery from high-dimensional data is a complex task in the development of efficient cancer diagnoses and classification. However, these data are usually redundant and noisy, and only a subset of them present distinct profiles for different classes of samples. Thus, selecting high discriminative genes from gene expression data has become increasingly interesting in the field of bioinformatics. In this paper, a discrete biogeography based optimization is proposed to select the good subset of informative gene relevant to the classification. In the proposed algorithm, firstly, the fisher-markov selector is used to choose fixed number of gene data. Secondly, to make biogeography based optimization suitable for the feature selection problem; discrete migration model and discrete mutation model are proposed to balance the exploration and exploitation ability. Then, discrete biogeography based optimization, as we called DBBO, is proposed by integrating discrete migration model and discrete mutation model. Finally, the DBBO method is used for feature selection, and three classifiers are used as the classifier with the 10 fold cross-validation method. In order to show the effective and efficiency of the algorithm, the proposed algorithm is tested on four breast cancer dataset benchmarks. Comparison with genetic algorithm, particle swarm optimization, differential evolution algorithm and hybrid biogeography based optimization, experimental results demonstrate that the proposed method is better or at least comparable with previous method from literature when considering the quality of the solutions obtained.

  9. Optimal noise-aided signal transmission through populations of neurons

    NASA Astrophysics Data System (ADS)

    Hoch, Thomas; Wenning, Gregor; Obermayer, Klaus

    2003-07-01

    Metabolic considerations and neurophysiological measurements indicate that biological neural systems prefer information transmission via many parallel low intensity channels, compared to few high intensity ones [S. B. Laughlin et al., Nature Neurosci. 1, 36 (1998)]. Furthermore, cortical neurons are exposed to a considerable amount of synaptic background activity, which increases the neurons’ conductance and leads to a fluctuating membrane potential that, on average, is close to the threshold [A. Destexhe and D. Paré, J. Neurophysiol. 81, 1531 (1999)]. Recent studies have shown that noise can improve the transmission of subthreshold signals in populations of neurons, e.g., if their response is pooled. In general, the optimal noise level depends on the stimulus distribution and on the number of neurons in the population. In this contribution we show that for a large enough number of neurons the latter dependency becomes weak, such that the optimal noise level becomes almost independent of the number of neurons in the population. First we investigate a binary threshold model of neurons. We derive an analytic expression for the optimal noise level at each single neuron, which—for a large enough population size—depends only on quantities that are locally available to a single neuron. Using numerical simulations, we then verify the weak dependence of the optimal noise level on population size in a more realistic framework using leaky integrate-and-fire as well as Hodgkin-Huxley type model neurons. Next we construct a cost function, where quality of information transmission is traded against its metabolic costs. Again we find that—for subthreshold signals—there is an optimal noise level which maximizes this cost. This noise level, however, is almost independent of the number of neurons, even for small population sizes, as numerical simulations using the Hodgkin-Huxley model show. Since the dependence of the optimal noise level on population size is weak for

  10. 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

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

    PubMed

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

    2015-08-01

    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.

  12. Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders.

    PubMed

    Subasi, Abdulhamit

    2013-06-01

    Support vector machine (SVM) is an extensively used machine learning method with many biomedical signal classification applications. In this study, a novel PSO-SVM model has been proposed that hybridized the particle swarm optimization (PSO) and SVM to improve the EMG signal classification accuracy. This optimization mechanism involves kernel parameter setting in the SVM training procedure, which significantly influences the classification accuracy. The experiments were conducted on the basis of EMG signal to classify into normal, neurogenic or myopathic. In the proposed method the EMG signals were decomposed into the frequency sub-bands using discrete wavelet transform (DWT) and a set of statistical features were extracted from these sub-bands to represent the distribution of wavelet coefficients. The obtained results obviously validate the superiority of the SVM method compared to conventional machine learning methods, and suggest that further significant enhancements in terms of classification accuracy can be achieved by the proposed PSO-SVM classification system. The PSO-SVM yielded an overall accuracy of 97.41% on 1200 EMG signals selected from 27 subject records against 96.75%, 95.17% and 94.08% for the SVM, the k-NN and the RBF classifiers, respectively. PSO-SVM is developed as an efficient tool so that various SVMs can be used conveniently as the core of PSO-SVM for diagnosis of neuromuscular disorders.

  13. Managing the Public Sector Research and Development Portfolio Selection Process: A Case Study of Quantitative Selection and Optimization

    DTIC Science & Technology

    2016-09-01

    PUBLIC SECTOR RESEARCH & DEVELOPMENT PORTFOLIO SELECTION PROCESS: A CASE STUDY OF QUANTITATIVE SELECTION AND OPTIMIZATION by Jason A. Schwartz...PUBLIC SECTOR RESEARCH & DEVELOPMENT PORTFOLIO SELECTION PROCESS: A CASE STUDY OF QUANTITATIVE SELECTION AND OPTIMIZATION 5. FUNDING NUMBERS 6...describing how public sector organizations can implement a research and development (R&D) portfolio optimization strategy to maximize the cost

  14. Low-power slice selective imaging of broad signals

    NASA Astrophysics Data System (ADS)

    Yang, Weiqi; Lee, Jae-Seung; Kharkov, Boris; Ilott, Andrew J.; Jerschow, Alexej

    2016-11-01

    One of the major challenges in using magnetic resonance imaging (MRI) to study immobile samples, such as solid materials or rigid tissues like bone or ligaments, is that the images appear dark due to these samples' short-lived signals. Although it is well known that narrowband signals can be excited in inhomogeneously-broadened lines, it is less well known that similar effects can be observed in dipolar-broadened systems. These long-lived signals have not been used much, mainly because their description frequently does not match intuition. While 3D imaging with these signals has previously been reported, here we focus on the demonstration of faster, 2D slice-selective imaging. The faster imaging provides more flexibility for visualizing these rigid objects. We also focus on the frequently-encountered regime wherein the maximum power achievable for rf pulses is significantly weaker than the linewidth. This regime is typically encountered in clinical MRI scans or large volume setups. When compared to UTE and conventional slice-selective spin echo methods, this technique provides better representations of the sample considered here (an eraser sample), and higher signal-to-noise ratios than spin-echo techniques in both the high and low power regimes.

  15. Low-power slice selective imaging of broad signals.

    PubMed

    Yang, Weiqi; Lee, Jae-Seung; Kharkov, Boris; Ilott, Andrew J; Jerschow, Alexej

    2016-11-01

    One of the major challenges in using magnetic resonance imaging (MRI) to study immobile samples, such as solid materials or rigid tissues like bone or ligaments, is that the images appear dark due to these samples' short-lived signals. Although it is well known that narrowband signals can be excited in inhomogeneously-broadened lines, it is less well known that similar effects can be observed in dipolar-broadened systems. These long-lived signals have not been used much, mainly because their description frequently does not match intuition. While 3D imaging with these signals has previously been reported, here we focus on the demonstration of faster, 2D slice-selective imaging. The faster imaging provides more flexibility for visualizing these rigid objects. We also focus on the frequently-encountered regime wherein the maximum power achievable for rf pulses is significantly weaker than the linewidth. This regime is typically encountered in clinical MRI scans or large volume setups. When compared to UTE and conventional slice-selective spin echo methods, this technique provides better representations of the sample considered here (an eraser sample), and higher signal-to-noise ratios than spin-echo techniques in both the high and low power regimes.

  16. Training set optimization under population structure in genomic selection

    USDA-ARS?s Scientific Manuscript database

    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...

  17. 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…

  18. 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)

  19. Convergent Evolution of Sodium Ion Selectivity in Metazoan Neuronal Signaling

    PubMed Central

    Gur Barzilai, Maya; Reitzel, Adam M.; Kraus, Johanna E.M.; Gordon, Dalia; Technau, Ulrich; Gurevitz, Michael; Moran, Yehu

    2012-01-01

    Summary Ion selectivity of metazoan voltage-gated Na+ channels is critical for neuronal signaling and has long been attributed to a ring of four conserved amino acids that constitute the ion selectivity filter (SF) at the channel pore. Yet, in addition to channels with a preference for Ca2+ ions, the expression and characterization of Na+ channel homologs from the sea anemone Nematostella vectensis, a member of the early-branching metazoan phylum Cnidaria, revealed a sodium-selective channel bearing a noncanonical SF. Mutagenesis and physiological assays suggest that pore elements additional to the SF determine the preference for Na+ in this channel. Phylogenetic analysis assigns the Nematostella Na+-selective channel to a channel group unique to Cnidaria, which diverged >540 million years ago from Ca2+-conducting Na+ channel homologs. The identification of Cnidarian Na+-selective ion channels distinct from the channels of bilaterian animals indicates that selectivity for Na+ in neuronal signaling emerged independently in these two animal lineages. PMID:22854023

  20. [Optimal selection method of technologies of medical wastes treatment].

    PubMed

    Zhou, Feng; Liu, Yong; Guo, Huai-cheng; Wang, Li-jing

    2006-06-01

    This paper investigate the medical wastes (MW) definition, production, characteristics and technical requirements, which is decisive for properly selecting methods for medical wastes treatment (MWT). Base on this, the advantages/disadvantages and adaptation of various treatment options are qualitatively analyzed and broadly compared. Then, four kinds of technologies, namely the thermal treatment, autoclaving, chemical disinfection, and microwave disinfection, are primarily chosen. Moreover, a hierarchy decision-making model considering the disposal status, economic level, policies and international turns is further set up. According to it, 4 proposed methods are effectively assessed. The result indicates that thermal treatment technology is the optimal choice for medical wastes treatment in Hangzhou city. Besides, the optimal selection method for medical wastes treatment is synthetically presented, which is suggested as a strong support for choosing optimal technology, and will contribute a lot to related research as well.

  1. Optimal selection of nodes to propagate influence on networks

    NASA Astrophysics Data System (ADS)

    Sun, Yifan

    2016-11-01

    How to optimize the spreading process on networks has been a hot issue in complex networks, marketing, epidemiology, finance, etc. In this paper, we investigate a problem of optimizing locally the spreading: identifying a fixed number of nodes as seeds which would maximize the propagation of influence to their direct neighbors. All the nodes except the selected seeds are assumed not to spread their influence to their neighbors. This problem can be mapped onto a spin glass model with a fixed magnetization. We provide a message-passing algorithm based on replica symmetrical mean-field theory in statistical physics, which can find the nearly optimal set of seeds. Extensive numerical results on computer-generated random networks and real-world networks demonstrate that this algorithm has a better performance than several other optimization algorithms.

  2. Natural selection on social signals: signal efficacy and the evolution of chameleon display coloration.

    PubMed

    Stuart-Fox, Devi; Moussalli, Adnan; Whiting, Martin J

    2007-12-01

    Whether general patterns of signal evolution can be explained by selection for signal efficacy (detectability) has yet to be established. To establish the importance of signal efficacy requires evidence that both signals and their detectability to receivers have evolved in response to habitat shifts in a predictable fashion. Here, we test whether habitat structure has predictable effects on the evolution of male and female display coloration in 21 lineages of African dwarf chameleon (Bradypodion), based on a phylogenetic comparative analysis. We used quantitative measures of display coloration and estimated signal detectability as the contrast of those colors among body regions or against the background vegetation as perceived by the chameleon visual system. Both male and female display colors varied predictably with different aspects of habitat structure. In several (but not all) instances, habitat-associated shifts in display coloration resulted in habitat-associated variation in detectability. While males exhibit a remarkable variety of colors and patterns, female display coloration is highly conserved, consisting in all populations of contrasting dark and light elements. This color pattern may maximize detectability across all habitat types, potentially explaining female conservatism. Overall, our results support the view that selection for signal efficacy plays an important role in the evolution of animal signals.

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

    PubMed

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

    2016-03-14

    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

    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.

  5. 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

  6. Efficient and scalable Pareto optimization by evolutionary local selection algorithms.

    PubMed

    Menczer, F; Degeratu, M; Street, W N

    2000-01-01

    Local selection is a simple selection scheme in evolutionary computation. Individual fitnesses are accumulated over time and compared to a fixed threshold, rather than to each other, to decide who gets to reproduce. Local selection, coupled with fitness functions stemming from the consumption of finite shared environmental resources, maintains diversity in a way similar to fitness sharing. However, it is more efficient than fitness sharing and lends itself to parallel implementations for distributed tasks. While local selection is not prone to premature convergence, it applies minimal selection pressure to the population. Local selection is, therefore, particularly suited to Pareto optimization or problem classes where diverse solutions must be covered. This paper introduces ELSA, an evolutionary algorithm employing local selection and outlines three experiments in which ELSA is applied to multiobjective problems: a multimodal graph search problem, and two Pareto optimization problems. In all these experiments, ELSA significantly outperforms other well-known evolutionary algorithms. The paper also discusses scalability, parameter dependence, and the potential distributed applications of the algorithm.

  7. 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

  8. Simple signaling games of sexual selection (Grafen's revisited).

    PubMed

    Bernhard, Pierre; Hamelin, Frédéric M

    2014-12-01

    We investigate several versions of a simple game of sexual selection, to explore the role of secondary sexual characters (the "handicap paradox") with the tools of signaling theory. Our models admit closed form solutions. They are very much inspired by Grafen's (J Theor Biol 144:517-546, 1990a; J Theor Biol 144:473-516, 1990b) seminal companion papers. By merging and simplifying his two approaches, we identify a not so minor artifact in the seminal study. We propose an alternative model to start with Grafen's sexual selection theory, with several similarities with Getty (Anim Behav 56:127-130, 1998).

  9. Optimal Signal Processing in Small Stochastic Biochemical Networks

    PubMed Central

    Ziv, Etay; Nemenman, Ilya; Wiggins, Chris H.

    2007-01-01

    We quantify the influence of the topology of a transcriptional regulatory network on its ability to process environmental signals. By posing the problem in terms of information theory, we do this without specifying the function performed by the network. Specifically, we study the maximum mutual information between the input (chemical) signal and the output (genetic) response attainable by the network in the context of an analytic model of particle number fluctuations. We perform this analysis for all biochemical circuits, including various feedback loops, that can be built out of 3 chemical species, each under the control of one regulator. We find that a generic network, constrained to low molecule numbers and reasonable response times, can transduce more information than a simple binary switch and, in fact, manages to achieve close to the optimal information transmission fidelity. These high-information solutions are robust to tenfold changes in most of the networks' biochemical parameters; moreover they are easier to achieve in networks containing cycles with an odd number of negative regulators (overall negative feedback) due to their decreased molecular noise (a result which we derive analytically). Finally, we demonstrate that a single circuit can support multiple high-information solutions. These findings suggest a potential resolution of the “cross-talk” phenomenon as well as the previously unexplained observation that transcription factors that undergo proteolysis are more likely to be auto-repressive. PMID:17957259

  10. Constrained nonlinear optimization approaches to color-signal separation.

    PubMed

    Chang, P R; Hsieh, T H

    1995-01-01

    Separating a color signal into illumination and surface reflectance components is a fundamental issue in color reproduction and constancy. This can be carried out by minimizing the error in the least squares (LS) fit of the product of the illumination and the surface spectral reflectance to the actual color signal. When taking in account the physical realizability constraints on the surface reflectance and illumination, the feasible solutions to the nonlinear LS problem should satisfy a number of linear inequalities. Four distinct novel optimization algorithms are presented to employ these constraints to minimize the nonlinear LS fitting error. The first approach, which is based on Ritter's superlinear convergent method (Luengerger, 1980), provides a computationally superior algorithm to find the minimum solution to the nonlinear LS error problem subject to linear inequality constraints. Unfortunately, this gradient-like algorithm may sometimes be trapped at a local minimum or become unstable when the parameters involved in the algorithm are not tuned properly. The remaining three methods are based on the stable and promising global minimizer called simulated annealing. The annealing algorithm can always find the global minimum solution with probability one, but its convergence is slow. To tackle this, a cost-effective variable-separable formulation based on the concept of Golub and Pereyra (1973) is adopted to reduce the nonlinear LS problem to be a small-scale nonlinear LS problem. The computational efficiency can be further improved when the original Boltzman generating distribution of the classical annealing is replaced by the Cauchy distribution.

  11. Signal filtering algorithm for depth-selective diffuse optical topography

    NASA Astrophysics Data System (ADS)

    Fujii, M.; Nakayama, K.

    2009-03-01

    A compact filtered backprojection algorithm that suppresses the undesirable effects of skin circulation for near-infrared diffuse optical topography is proposed. Our approach centers around a depth-selective filtering algorithm that uses an inverse problem technique and extracts target signals from observation data contaminated by noise from a shallow region. The filtering algorithm is reduced to a compact matrix and is therefore easily incorporated into a real-time system. To demonstrate the validity of this method, we developed a demonstration prototype for depth-selective diffuse optical topography and performed both computer simulations and phantom experiments. The results show that the proposed method significantly suppresses the noise from the shallow region with a minimal degradation of the target signal.

  12. Optimizing SNR for indoor visible light communication via selecting communicating LEDs

    NASA Astrophysics Data System (ADS)

    Wang, Lang; Wang, Chunyue; Chi, Xuefen; Zhao, Linlin; Dong, Xiaoli

    2017-03-01

    In this paper, we investigate the layout of LED to optimize SNR by selecting communicating LEDs (C-LEDs) in indoor visible light communication (VLC) system. Due to the inter-symbol interference (ISI) caused by the different arrival time of different optical rays, the SNR for any user is not optimal if a simple layout is adopted. It is interesting to investigate the LEDs layout for achieving optimal SNR in indoor VLC system. For a single user, LED signal is divided into the positive and negative components, they provide the power of desired signal and the power of ISI respectively. We introduce the concept of valid ratio (VR) which refers to the value of positive component over the negative component. Then we propose a VR threshold-based LED selection scheme which chooses C-LEDs by their VRs. For downlink broadcast VLC with multiple users, the SNRs of all users are different in a layout of C-LEDs. It is difficult to find a proper layout of C-LEDs to guarantee the BER of all users. To solve this problem, we propose an evolutionary algorithm (EA)-based scheme to optimize the SNR. The simulation results show that it is an effective method to improve SNR by selecting C-LEDs.

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

    PubMed Central

    2015-01-01

    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

  14. 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%).

  15. Optimized LOWESS normalization parameter selection for DNA microarray data

    PubMed Central

    Berger, John A; Hautaniemi, Sampsa; Järvinen, Anna-Kaarina; Edgren, Henrik; Mitra, Sanjit K; Astola, Jaakko

    2004-01-01

    Background Microarray data normalization is an important step for obtaining data that are reliable and usable for subsequent analysis. One of the most commonly utilized normalization techniques is the locally weighted scatterplot smoothing (LOWESS) algorithm. However, a much overlooked concern with the LOWESS normalization strategy deals with choosing the appropriate parameters. Parameters are usually chosen arbitrarily, which may reduce the efficiency of the normalization and result in non-optimally normalized data. Thus, there is a need to explore LOWESS parameter selection in greater detail. Results and discussion In this work, we discuss how to choose parameters for the LOWESS method. Moreover, we present an optimization approach for obtaining the fraction of data points utilized in the local regression and analyze results for local print-tip normalization. The optimization procedure determines the bandwidth parameter for the local regression by minimizing a cost function that represents the mean-squared difference between the LOWESS estimates and the normalization reference level. We demonstrate the utility of the systematic parameter selection using two publicly available data sets. The first data set consists of three self versus self hybridizations, which allow for a quantitative study of the optimization method. The second data set contains a collection of DNA microarray data from a breast cancer study utilizing four breast cancer cell lines. Our results show that different parameter choices for the bandwidth window yield dramatically different calibration results in both studies. Conclusions Results derived from the self versus self experiment indicate that the proposed optimization approach is a plausible solution for estimating the LOWESS parameters, while results from the breast cancer experiment show that the optimization procedure is readily applicable to real-life microarray data normalization. In summary, the systematic approach to obtain critical

  16. Relaxed Bi - Quadratic Optimization For Joint Filter Signal Design In Signal Dependent Space Time Adaptive Processing (STAP) (Preprint)

    DTIC Science & Technology

    2016-11-16

    AFRL-RY-WP-TP-2016-0197 RELAXED BI-QUADRATIC OPTIMIZATION FOR JOINT FILTER-SIGNAL DESIGN IN SIGNAL-DEPENDENT SPACE -TIME ADAPTIVE PROCESSING...SIGNAL DESIGN IN SIGNAL-DEPENDENT SPACE -TIME ADAPTIVE PROCESSING (STAP) (PREPRINT) 5a. CONTRACT NUMBER In-house 5b. GRANT NUMBER 5c. PROGRAM...processing, space -time adaptive processing (STAP) 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT: SAR 8. NUMBER OF PAGES 82 19a

  17. Optimization of the selective frequency damping parameters using model reduction

    NASA Astrophysics Data System (ADS)

    Cunha, Guilherme; Passaggia, Pierre-Yves; Lazareff, Marc

    2015-09-01

    In the present work, an optimization methodology to compute the best control parameters, χ and Δ, for the selective frequency damping method is presented. The optimization does not suppose any a priori knowledge of the flow physics, neither of the underlying numerical methods, and is especially suited for simulations requiring large quantity of grid elements and processors. It allows for obtaining an optimal convergence rate to a steady state of the damped Navier-Stokes system. This is achieved using the Dynamic Mode Decomposition, which is a snapshot-based method, to estimate the eigenvalues associated with global unstable dynamics. Validations test cases are presented for the numerical configurations of a laminar flow past a 2D cylinder, a separated boundary-layer over a shallow bump, and a 3D turbulent stratified-Poiseuille flow.

  18. Temporally selective processing of communication signals by auditory midbrain neurons

    PubMed Central

    Christensen-Dalsgaard, Jakob; Kelley, Darcy B.

    2011-01-01

    Perception of the temporal structure of acoustic signals contributes critically to vocal signaling. In the aquatic clawed frog Xenopus laevis, calls differ primarily in the temporal parameter of click rate, which conveys sexual identity and reproductive state. We show here that an ensemble of auditory neurons in the laminar nucleus of the torus semicircularis (TS) of X. laevis specializes in encoding vocalization click rates. We recorded single TS units while pure tones, natural calls, and synthetic clicks were presented directly to the tympanum via a vibration-stimulation probe. Synthesized click rates ranged from 4 to 50 Hz, the rate at which the clicks begin to overlap. Frequency selectivity and temporal processing were characterized using response-intensity curves, temporal-discharge patterns, and autocorrelations of reduplicated responses to click trains. Characteristic frequencies ranged from 140 to 3,250 Hz, with minimum thresholds of −90 dB re 1 mm/s at 500 Hz and −76 dB at 1,100 Hz near the dominant frequency of female clicks. Unlike units in the auditory nerve and dorsal medullary nucleus, most toral units respond selectively to the behaviorally relevant temporal feature of the rate of clicks in calls. The majority of neurons (85%) were selective for click rates, and this selectivity remained unchanged over sound levels 10 to 20 dB above threshold. Selective neurons give phasic, tonic, or adapting responses to tone bursts and click trains. Some algorithms that could compute temporally selective receptive fields are described. PMID:21289132

  19. 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.

  20. Parameter Optimization for Selected Correlation Analysis of Intracranial Pathophysiology

    PubMed Central

    Faltermeier, Rupert; Proescholdt, Martin A.; Bele, Sylvia; Brawanski, Alexander

    2015-01-01

    Recently we proposed a mathematical tool set, called selected correlation analysis, that reliably detects positive and negative correlations between arterial blood pressure (ABP) and intracranial pressure (ICP). Such correlations are associated with severe impairment of the cerebral autoregulation and intracranial compliance, as predicted by a mathematical model. The time resolved selected correlation analysis is based on a windowing technique combined with Fourier-based coherence calculations and therefore depends on several parameters. For real time application of this method at an ICU it is inevitable to adjust this mathematical tool for high sensitivity and distinct reliability. In this study, we will introduce a method to optimize the parameters of the selected correlation analysis by correlating an index, called selected correlation positive (SCP), with the outcome of the patients represented by the Glasgow Outcome Scale (GOS). For that purpose, the data of twenty-five patients were used to calculate the SCP value for each patient and multitude of feasible parameter sets of the selected correlation analysis. It could be shown that an optimized set of parameters is able to improve the sensitivity of the method by a factor greater than four in comparison to our first analyses. PMID:26693250

  1. Adjustable Bearing System with Selectively Optimized Installational Clearances

    DTIC Science & Technology

    1997-06-30

    ÄÖQ¥ÄL1HE mssx^. Navy Case No. 78,325 PATENTS ADJUSTABLE BEARING SYSTEM WITH SELECTIVELY OPTIMIZED INSTALLATIONAL CLEARANCES BACKGROUND OF THE... clearance 7 conditions. 8 9 10 .. small range of clearances within which to accommodate various operational conditions. Thus, a 12 very tight... clearance is extremely difficult to achieve for certain installations or conditions such as 13 quiet submarine control surface operation and

  2. Multi-Objective Particle Swarm Optimization Approach for Cost-Based Feature Selection in Classification.

    PubMed

    Zhang, Yong; Gong, Dun-Wei; Cheng, Jian

    2017-01-01

    Feature selection is an important data-preprocessing technique in classification problems such as bioinformatics and signal processing. Generally, there are some situations where a user is interested in not only maximizing the classification performance but also minimizing the cost that may be associated with features. This kind of problem is called cost-based feature selection. However, most existing feature selection approaches treat this task as a single-objective optimization problem. This paper presents the first study of multi-objective particle swarm optimization (PSO) for cost-based feature selection problems. The task of this paper is to generate a Pareto front of nondominated solutions, that is, feature subsets, to meet different requirements of decision-makers in real-world applications. In order to enhance the search capability of the proposed algorithm, a probability-based encoding technology and an effective hybrid operator, together with the ideas of the crowding distance, the external archive, and the Pareto domination relationship, are applied to PSO. The proposed PSO-based multi-objective feature selection algorithm is compared with several multi-objective feature selection algorithms on five benchmark datasets. Experimental results show that the proposed algorithm can automatically evolve a set of nondominated solutions, and it is a highly competitive feature selection method for solving cost-based feature selection problems.

  3. 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

  4. Magnetic MIMO Signal Processing and Optimization for Wireless Power Transfer

    NASA Astrophysics Data System (ADS)

    Yang, Gang; Moghadam, Mohammad R. Vedady; Zhang, Rui

    2017-06-01

    In magnetic resonant coupling (MRC) enabled multiple-input multiple-output (MIMO) wireless power transfer (WPT) systems, multiple transmitters (TXs) each with one single coil are used to enhance the efficiency of simultaneous power transfer to multiple single-coil receivers (RXs) by constructively combining their induced magnetic fields at the RXs, a technique termed "magnetic beamforming". In this paper, we study the optimal magnetic beamforming design in a multi-user MIMO MRC-WPT system. We introduce the multi-user power region that constitutes all the achievable power tuples for all RXs, subject to the given total power constraint over all TXs as well as their individual peak voltage and current constraints. We characterize each boundary point of the power region by maximizing the sum-power deliverable to all RXs subject to their minimum harvested power constraints. For the special case without the TX peak voltage and current constraints, we derive the optimal TX current allocation for the single-RX setup in closed-form as well as that for the multi-RX setup. In general, the problem is a non-convex quadratically constrained quadratic programming (QCQP), which is difficult to solve. For the case of one single RX, we show that the semidefinite relaxation (SDR) of the problem is tight. For the general case with multiple RXs, based on SDR we obtain two approximate solutions by applying time-sharing and randomization, respectively. Moreover, for practical implementation of magnetic beamforming, we propose a novel signal processing method to estimate the magnetic MIMO channel due to the mutual inductances between TXs and RXs. Numerical results show that our proposed magnetic channel estimation and adaptive beamforming schemes are practically effective, and can significantly improve the power transfer efficiency and multi-user performance trade-off in MIMO MRC-WPT systems.

  5. Themis controls thymocyte selection through regulation of T cell receptor-mediated signaling

    PubMed Central

    Fu, Guo; Vallée, Sébastien; Rybakin, Vasily; McGuire, Marielena V.; Ampudia, Jeanette; Brockmeyer, Claudia; Salek, Mogjiborahman; Fallen, Paul R.; Hoerter, John A.H.; Munshi, Anil; Huang, Yina H.; Hu, Jianfang; Fox, Howard S.; Sauer, Karsten; Acuto, Oreste; Gascoigne, Nicholas R.J.

    2009-01-01

    Themis (Thymocyte expressed molecule involved in selection), a member of a family of proteins with unknown functions, is highly conserved among vertebrates. Here we found that Themis is expressed in high amounts in thymocytes between the pre-T cell receptor (TCR) and positive selection checkpoints, and in low amounts in mature T cells. Themis-deficient thymocytes exhibit defective positive selection, which results in reduced numbers of mature thymocytes. Negative selection is also impaired in Themis-deficient mice. A higher percentage of Themis-deficient T cells exhibit CD4+CD25+Foxp3+ regulatory and CD62LloCD44hi memory phenotypes than in wild-type mice. Supporting a role for Themis in TCR signaling, this protein is phosphorylated quickly after TCR stimulation, and is needed for optimal TCR-driven Ca2+ mobilization and Erk activation. PMID:19597499

  6. Feature selection for optimized skin tumor recognition using genetic algorithms.

    PubMed

    Handels, H; Ross, T; Kreusch, J; Wolff, H H; Pöppl, S J

    1999-07-01

    In this paper, a new approach to computer supported diagnosis of skin tumors in dermatology is presented. High resolution skin surface profiles are analyzed to recognize malignant melanomas and nevocytic nevi (moles), automatically. In the first step, several types of features are extracted by 2D image analysis methods characterizing the structure of skin surface profiles: texture features based on cooccurrence matrices, Fourier features and fractal features. Then, feature selection algorithms are applied to determine suitable feature subsets for the recognition process. Feature selection is described as an optimization problem and several approaches including heuristic strategies, greedy and genetic algorithms are compared. As quality measure for feature subsets, the classification rate of the nearest neighbor classifier computed with the leaving-one-out method is used. Genetic algorithms show the best results. Finally, neural networks with error back-propagation as learning paradigm are trained using the selected feature sets. Different network topologies, learning parameters and pruning algorithms are investigated to optimize the classification performance of the neural classifiers. With the optimized recognition system a classification performance of 97.7% is achieved.

  7. 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.

  8. Dopamine signaling tunes spatial pattern selectivity in C. elegans

    PubMed Central

    Han, Bicheng; Dong, Yongming; Zhang, Lin; Liu, Yan; Rabinowitch, Ithai; Bai, Jihong

    2017-01-01

    Animals with complex brains can discriminate the spatial arrangement of physical features in the environment. It is unknown whether such sensitivity to spatial patterns can be accomplished in simpler nervous systems that lack long-range sensory modalities such as vision and hearing. Here we show that the nematode Caenorhabditis elegans can discriminate spatial patterns in its surroundings, despite having a nervous system of only 302 neurons. This spatial pattern selectivity requires touch-dependent dopamine signaling, including the mechanosensory TRP-4 channel in dopaminergic neurons and the D2-like dopamine receptor DOP-3. We find that spatial pattern selectivity varies significantly among C. elegans wild isolates. Electrophysiological recordings show that natural variations in TRP-4 reduce the mechanosensitivity of dopaminergic neurons. Polymorphic substitutions in either TRP-4 or DOP-3 alter the selectivity of spatial patterns. Together, these results demonstrate an ancestral role for dopamine signaling in tuning spatial pattern preferences in a simple nervous system. DOI: http://dx.doi.org/10.7554/eLife.22896.001 PMID:28349862

  9. Maximizing MR signal for 2D UTE slice selection in the presence of rapid transverse relaxation.

    PubMed

    Carl, Michael; Chiang, Jing-Tzyh Alan; Du, Jiang

    2014-10-01

    Ultrashort TE (UTE) sequences allow direct visualization of tissues with very short T2 relaxation times, such as tendons, ligaments, menisci, and cortical bone. In this work, theoretical calculations, simulations, and phantom studies, as well as in vivo imaging were performed to maximize signal-to-noise ratio (SNR) for slice selective RF excitation for 2D UTE sequences. The theoretical calculations and simulations were based on the Bloch equations, which lead to analytic expressions for the optimal RF pulse duration and amplitude to maximize magnetic resonance signal in the presence of rapid transverse relaxation. In steady state, it was found that the maximum signal amplitude was not obtained at the classical Ernst angle, but at an either lower or higher flip angle, depending on whether the RF pulse duration or amplitude was varied, respectively.

  10. Optimization of topical gels with betamethasone dipropionate: selection of gel forming and optimal cosolvent system.

    PubMed

    Băiţan, Mariana; Lionte, Mihaela; Moisuc, Lăcrămioara; Gafiţanu, Eliza

    2011-01-01

    The purpose of these studies was to develop a 0.05% betamethasone gel characterized by physical-chemical stability and good release properties. The preliminary studies were designed to select the gel-forming agents and the excipients compatible with betamethasone dipropionate. In order to formulate a clear gel without particles of drug substances in suspension, a solvent system for the drug substance was selected. The content of drug substance released, the rheological and in vitro release tests were the tools used for the optimal formulation selection. A stable carbomer gel was obtained by solubilization of betamethasone dipropionate in a vehicle composed by 40% PEG 400, 10% ethanol and 5% Transcutol.

  11. Selection of optimal measures of growth and reproduction for the sublethal Leptocheirus plumulosus sediment bioassay

    SciTech Connect

    Gray, B.R.; Wright, R.B.; Duke, B.M.; Farrar, J.D.; Emery, V.L. Jr.; Brandon, D.L.; Moore, D.W.

    1998-11-01

    This article describes the selection process used to identify optimal measures of growth and reproduction for the proposed 28-d sublethal sediment bioassay with the estuarine amphipod Leptocheirus plumulosus. The authors used four criteria (relevance of each measure to its respective endpoint, signal-to-noise ratio, redundancy relative to other measures of the same endpoint, and cost) to evaluate nine growth and seven reproductive measures. Optimal endpoint measures were identified as those receiving relatively high scores for all or most criteria. Measures of growth scored similarly on all criteria, except for cost. The cost of the pooled (female plus male) growth measures was substantially lower than the cost of the female and male growth measures because the latter required more labor (by approx. 25 min per replicate). Pooled dry weight was identified as the optimal growth measure over pooled length because the latter required additional labor and nonstandard software and equipment. Embryo and neonate measures of reproduction exhibited wide differences in labor costs but yielded similar scores for other criteria. In contrast, brooding measures of reproduction scored relatively low on endpoint relevance, signal-to-noise ratio, and redundancy criteria. The authors recommend neonates/survivor as the optimal measure of L. plumulosus reproduction because it exhibited high endpoint relevance and signal-to-noise ratios, was redundant to other reproductive measures, and required minimal time.

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

    PubMed

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

    2015-09-08

    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.

  13. A new approach to the optimal target selection problem

    NASA Astrophysics Data System (ADS)

    Elson, E. C.; Bassett, B. A.; van der Heyden, K.; Vilakazi, Z. Z.

    2007-03-01

    Context: This paper addresses a common problem in astronomy and cosmology: to optimally select a subset of targets from a larger catalog. A specific example is the selection of targets from an imaging survey for multi-object spectrographic follow-up. Aims: We present a new heuristic optimisation algorithm, HYBRID, for this purpose and undertake detailed studies of its performance. Methods: HYBRID combines elements of the simulated annealing, MCMC and particle-swarm methods and is particularly successful in cases where the survey landscape has multiple curvature or clustering scales. Results: HYBRID consistently outperforms the other methods, especially in high-dimensionality spaces with many extrema. This means many fewer simulations must be run to reach a given performance confidence level and implies very significant advantages in solving complex or computationally expensive optimisation problems. Conclusions: .HYBRID outperforms both MCMC and SA in all cases including optimisation of high dimensional continuous surfaces indicating that HYBRID is useful far beyond the specific problem of optimal target selection. Future work will apply HYBRID to target selection for the new 10 m Southern African Large Telescope in South Africa.

  14. 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.

  15. An R-D optimized transcoding resilient motion vector selection

    NASA Astrophysics Data System (ADS)

    Aminlou, Alireza; Semsarzadeh, Mehdi; Fatemi, Omid

    2014-12-01

    Selection of motion vector (MV) has a significant impact on the quality of an encoded, and particularly a transcoded video, in terms of rate-distortion (R-D) performance. The conventional motion estimation process, in most existing video encoders, ignores the rate of residuals by utilizing rate and distortion of motion compensation step. This approach implies that the selected MV depends on the quantization parameter. Hence, the same MV that has been selected for high bit rate compression may not be suitable for low bit rate ones when transcoding the video with motion information reuse technique, resulting in R-D performance degradation. In this paper, we propose an R-D optimized motion selection criterion that takes into account the effect of residual rate in MV selection process. Based on the proposed criterion, a new two-piece Lagrange multiplier selection is introduced for motion estimation process. Analytical evaluations indicate that our proposed scheme results in MVs that are less sensitive to changes in bit rate or quantization parameter. As a result, MVs in the encoded bitstream may be used even after the encoded sequence has been transcoded to a lower bit rate one using re-quantization. Simulation results indicate that the proposed technique improves the quality performance of coding and transcoding without any computational overhead.

  16. Bayesian analysis. II. Signal detection and model selection

    NASA Astrophysics Data System (ADS)

    Bretthorst, G. Larry

    In the preceding. paper, Bayesian analysis was applied to the parameter estimation problem, given quadrature NMR data. Here Bayesian analysis is extended to the problem of selecting the model which is most probable in view of the data and all the prior information. In addition to the analytic calculation, two examples are given. The first example demonstrates how to use Bayesian probability theory to detect small signals in noise. The second example uses Bayesian probability theory to compute the probability of the number of decaying exponentials in simulated T1 data. The Bayesian answer to this question is essentially a microcosm of the scientific method and a quantitative statement of Ockham's razor: theorize about possible models, compare these to experiment, and select the simplest model that "best" fits the data.

  17. 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.

  18. RNA polymerase II kinetics in polo polyadenylation signal selection

    PubMed Central

    Pinto, Pedro A B; Henriques, Telmo; Freitas, Marta O; Martins, Torcato; Domingues, Rita G; Wyrzykowska, Paulina S; Coelho, Paula A; Carmo, Alexandre M; Sunkel, Claudio E; Proudfoot, Nicholas J; Moreira, Alexandra

    2011-01-01

    Regulated alternative polyadenylation is an important feature of gene expression, but how gene transcription rate affects this process remains to be investigated. polo is a cell-cycle gene that uses two poly(A) signals in the 3′ untranslated region (UTR) to produce alternative messenger RNAs that differ in their 3′UTR length. Using a mutant Drosophila strain that has a lower transcriptional elongation rate, we show that transcription kinetics can determine alternative poly(A) site selection. The physiological consequences of incorrect polo poly(A) site choice are of vital importance; transgenic flies lacking the distal poly(A) signal cannot produce the longer transcript and die at the pupa stage due to a failure in the proliferation of the precursor cells of the abdomen, the histoblasts. This is due to the low translation efficiency of the shorter transcript produced by proximal poly(A) site usage. Our results show that correct polo poly(A) site selection functions to provide the correct levels of protein expression necessary for histoblast proliferation, and that the kinetics of RNA polymerase II have an important role in the mechanism of alternative polyadenylation. PMID:21602789

  19. [Structural Life Science towards the Regulation of Selective GPCR Signaling].

    PubMed

    Kobayashi, Takuya

    2016-01-01

    G protein-coupled receptors (GPCRs) are the largest family of receptors in the human genome. They are involved in many diseases and also the target of approximately 30% of all modern medicinal drugs. GPCRs respond to a broad spectrum of chemical entities, ranging from photons, protons, and calcium ions to small organic molecules (including odorants and neurotransmitters), peptides, and glycoproteins. Many GPCRs are members of closely related subfamilies that respond to the same hormone or neurotransmitter. However, they have different physiologic functions based on the cells in which they are expressed and the different signaling pathways that they exploit (e.g., coupling through heterotrimeric G-proteins such as Gs, Gi, and Gq, as well as β-arrestins). Antibody fragments including Fab and Fv can effectively stabilize and crystallize membrane proteins. However, using the mouse hybridoma technology it has been difficult to develop monoclonal antibodies that can recognize conformational epitopes of native GPCRs. We have recently succeeded in developing antibodies against native GPCRs using this technology in combination with our improved immunization and screening methods. In this symposium review, I present a successful example of prostaglandin E2 receptor (one of the GPCRs) crystallization using antibody fragments. To avoid several adverse effects of current therapeutics, it is essential to understand the molecular mechanism of GPCR signaling in a monomeric, dimeric, or oligomeric state. Also, we are interested in selectively regulating GPCR signaling via functional antibodies developed using our methods and/or the designed small organic molecules depending on the GPCR structure.

  20. Optimal control of mode-selective femtochemistry in multidimensional systems

    SciTech Connect

    Mitric, Roland; Bonacic-Koutecky, Vlasta

    2007-09-15

    We present a strategy for optimal control of the ground-state dynamics in multidimensional systems based on a combination of the semiclassical Wigner distribution approach with direct quantum chemical molecular dynamics (MD) 'on the fly'. This allows one to treat all degrees of freedom without the need for precalculation of global potential energy surfaces. We demonstrate the scope of our theoretical procedure on two prototype systems representing rigid symmetrical molecules (Na{sub 3}F) and flexible biomolecules with low-frequency modes (glycine). We show that the ground-state isomerization process can be selectively driven by ultrashort laser pulses with different shapes which are characteristic of the prototype systems. Thus, our method opens perspectives for control of the functionality of biomolecules. Moreover, assignment of the underlying processes to pulse shapes based on MD allows one to use optimal control as a tool for analysis.

  1. Compressed Sensing of Multichannel EEG Signals: The Simultaneous Cosparsity and Low-Rank Optimization.

    PubMed

    Liu, Yipeng; De Vos, Maarten; Van Huffel, Sabine

    2015-08-01

    This paper deals with the problems that some EEG signals have no good sparse representation and single-channel processing is not computationally efficient in compressed sensing of multichannel EEG signals. An optimization model with L0 norm and Schatten-0 norm is proposed to enforce cosparsity and low-rank structures in the reconstructed multichannel EEG signals. Both convex relaxation and global consensus optimization with alternating direction method of multipliers are used to compute the optimization model. The performance of multichannel EEG signal reconstruction is improved in term of both accuracy and computational complexity. The proposed method is a better candidate than previous sparse signal recovery methods for compressed sensing of EEG signals. The proposed method enables successful compressed sensing of EEG signals even when the signals have no good sparse representation. Using compressed sensing would much reduce the power consumption of wireless EEG system.

  2. Designing Pareto-optimal selection systems: formalizing the decisions required for selection system development.

    PubMed

    De Corte, Wilfried; Sackett, Paul R; Lievens, Filip

    2011-09-01

    The article presents an analytic method for designing Pareto-optimal selection systems where the applicants belong to a mixture of candidate populations. The method is useful in both applied and research settings. In an applied context, the present method is the first to assist the selection practitioner when deciding on 6 major selection design issues: (1) the predictor subset, (2) the selection rule, (3) the selection staging, (4) the predictor sequencing, (5) the predictor weighting, and (6) the stage retention decision issue. From a research perspective, the method offers a unique opportunity for studying the impact and relative importance of different strategies for reducing adverse impact. PsycINFO Database Record (c) 2011 APA, all rights reserved

  3. 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.

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

    PubMed

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

    2005-07-01

    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. © 2005 American Association of Physicists in Medicine.

  5. Plastic scintillation dosimetry: optimal selection of scintillating fibers and scintillators.

    PubMed

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

    2005-07-01

    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.

  6. Key elements in optimizing catalyst selections for resid FCC units

    SciTech Connect

    Yanik, S.J.; O`Connor, P.

    1995-09-01

    Achieving the optimum activity and yield structure from a commercial Resid FCC Unit (RFCC) is essential to maximizing profitability in today`s modern refinery. Proper catalyst selection is a key element in this optimization. This paper is written to provide FCC Process Engineers with an understanding of some basic elements of RFCC operation. The necessity of using realistic evaluation methods to assure proper RFCC catalyst selection is explained. The differences between Activity limited and Delta Coke limited RFCC operations are elucidated and the related catalyst performance requirements are discussed. The effect of the catalyst to oil ratio on conversion and on catalyst site utilization and poisoning plays a key role in the transition of an RFCC unit from Catalyst Activity limited regime to a Cat-to-Oil limited regime. For the Activity limited operation the catalyst resistance to poisons with the appropriate feedstock will be the most important selection criteria. For the Delta Coke limited operation, a reduction of the commercial delta coke of the catalyst will be crucial. The types of commercial delta coke are discussed and methods for their evaluation are suggested. In both cases the use of realistic catalyst evaluation methods and feedstock will be essential in order to arrive at the correct catalyst selection. Finally, commercial data comparisons illustrate the improvements in product value that can be achieved when the proper catalyst is chosen.

  7. 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'.

  8. 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%.

  9. Brachytherapy for clinically localized prostate cancer: optimal patient selection.

    PubMed

    Kollmeier, Marisa A; Zelefsky, Michael J

    2011-10-01

    The objective of this review is to present an overview of each modality and delineate how to best select patients who are optimal candidates for these treatment approaches. Prostate brachytherapy as a curative modality for clinically localized prostate cancer has become increasingly utilized over the past decade; 25% of all early cancers are now treated this way in the United States (1). The popularity of this treatment strategy lies in the highly conformal nature of radiation dose, low morbidity, patient convenience, and high efficacy rates. Prostate brachytherapy can be delivered by either a permanent interstitial radioactive seed implantation (low dose rate [LDR]) or a temporary interstitial insertion of iridium-192 (Ir192) afterloading catheters. The objective of both of these techniques is to deliver a high dose of radiation to the prostate gland while exposing normal surrounding tissues to minimal radiation dose. Brachytherapy techniques are ideal to achieve this goal given the close proximity of the radiation source to tumor and sharp fall off of the radiation dose cloud proximate to the source. Brachytherapy provides a powerful means of delivering dose escalation above and beyond that achievable with intensity-modulated external beam radiotherapy alone. Careful selection of appropriate patients for these therapies, however, is critical for optimizing both disease-related outcomes and treatment-related toxicity.

  10. 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.

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

    DOE PAGES

    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

  12. [System parameters selection and optimization of tunable diode laser absorption spectroscopy].

    PubMed

    Gao, Nan; Du, Zhen-Hui; Tang, Mia; Yang, Jie-Wen; Yang, Chun-Mei; Wang, Yan

    2010-12-01

    The system performance of tunable diode laser absorption spectroscopy (TDLAS) is affected by the modulation parameters such as modulation index, modulation frequency, scanning amplitude and scanning frequency. There is a lack of definite parameters selection basis in practical measurement. Aiming at this problem, the influence of modulation parameters on second harmonic signals was observed by experiment based on a certain theory in the present paper, and the basis and method of modulation parameters optimization for various system functions and demands were summarized by analyzing the signal characteristic including amplitude, signal to noise ratio, symmetry and peak width. For the system of concentration or temperature detection the amplitude and signal to noise ratio will be taken into prior consideration which require optimum modulation index, lower modulation frequency and lower scanning frequency. In condition of pressure detection deduced by lineshape the signal symmetry and peak width are more important to ascertain the modulation parameters according to practical demands. Scanning amplitude will be adjusted to obtain complete signal waveforms, then scanning frequency can be adjusted according to system speed and accuracy requirement. The result of the experiment provided a definite basis for conforming the working state of such system.

  13. Optimal sinusoidal modelling of gear mesh vibration signals for gear diagnosis and prognosis

    NASA Astrophysics Data System (ADS)

    Man, Zhihong; Wang, Wenyi; Khoo, Suiyang; Yin, Juliang

    2012-11-01

    In this paper, the synchronous signal average of gear mesh vibration signals is modelled with the multiple modulated sinusoidal representations. The signal model parameters are optimised against the measured signal averages by using the batch learning of the least squares technique. With the optimal signal model, all components of a gear mesh vibration signal, including the amplitude modulations, the phase modulations and the impulse vibration component induced by gear tooth cracking, are identified and analysed with insight of the gear tooth crack development and propagation. In particular, the energy distribution of the impulse vibration signal, extracted from the optimal signal model, provides sufficient information for monitoring and diagnosing the evolution of the tooth cracking process, leading to the prognosis of gear tooth cracking. The new methodologies for gear mesh signal modelling and the diagnosis of the gear tooth fault development and propagation are validated with a set of rig test data, which has shown excellent performance.

  14. Genomic and chromatin signals underlying transcription start-site selection.

    PubMed

    Valen, Eivind; Sandelin, Albin

    2011-11-01

    A central question in cellular biology is how the cell regulates transcription and discerns when and where to initiate it. Locating transcription start sites (TSSs), the signals that specify them, and ultimately elucidating the mechanisms of regulated initiation has therefore been a recurrent theme. In recent years substantial progress has been made towards this goal, spurred by the possibility of applying genome-wide, sequencing-based analysis. We now have a large collection of high-resolution datasets identifying locations of TSSs, protein-DNA interactions, and chromatin features over whole genomes; the field is now faced with the daunting challenge of translating these descriptive maps into quantitative and predictive models describing the underlying biology. We review here the genomic and chromatin features that underlie TSS selection and usage, focusing on the differences between the major classes of core promoters. Copyright © 2011 Elsevier Ltd. All rights reserved.

  15. Real-Time Traffic Signal Control for Optimization of Traffic Jam Probability

    NASA Astrophysics Data System (ADS)

    Cui, Cheng-You; Shin, Ji-Sun; Miyazaki, Michio; Lee, Hee-Hyol

    Real-time traffic signal control is an integral part of urban traffic control system. It can control traffic signals online according to variation of traffic flow. In this paper, we propose a new method for the real-time traffic signal control system. The system uses a Cellular Automaton model and a Bayesian Network model to predict probabilistic distributions of standing vehicles, and uses a Particle Swarm Optimization method to calculate optimal traffic signals. A simulation based on real traffic data was carried out to show the effectiveness of the proposed real-time traffic signal control system CAPSOBN using a micro traffic simulator.

  16. Selecting Optimal Peptides for Targeted Proteomic Experiments in Human Plasma Using in vitro Synthesized Proteins as Analytical Standards

    PubMed Central

    Bollinger, James G.; Stergachis, Andrew B.; Johnson, Richard S.; Egertson, Jarrett D.; MacCoss, Michael J.

    2017-01-01

    Summary In targeted proteomics, the development of robust methodologies is dependent upon the selection of a set of optimal peptides for each protein-of-interest. Unfortunately, predicting which peptides and respective product ion transitions provide the greatest signal-to-noise ratio in a particular assay matrix is complicated. Using in vitro synthesized proteins as analytical standards, we report here an empirically driven method for the selection of said peptides in a human plasma assay matrix. PMID:26867746

  17. Selecting Optimal Peptides for Targeted Proteomic Experiments in Human Plasma Using In Vitro Synthesized Proteins as Analytical Standards.

    PubMed

    Bollinger, James G; Stergachis, Andrew B; Johnson, Richard S; Egertson, Jarrett D; MacCoss, Michael J

    2016-01-01

    In targeted proteomics, the development of robust methodologies is dependent upon the selection of a set of optimal peptides for each protein-of-interest. Unfortunately, predicting which peptides and respective product ion transitions provide the greatest signal-to-noise ratio in a particular assay matrix is complicated. Using in vitro synthesized proteins as analytical standards, we report here an empirically driven method for the selection of said peptides in a human plasma assay matrix.

  18. Discovery, Optimization, and Characterization of Novel D2 Dopamine Receptor Selective Antagonists

    PubMed Central

    2015-01-01

    The D2 dopamine receptor (D2 DAR) is one of the most validated drug targets for neuropsychiatric and endocrine disorders. However, clinically approved drugs targeting D2 DAR display poor selectivity between the D2 and other receptors, especially the D3 DAR. This lack of selectivity may lead to undesirable side effects. Here we describe the chemical and pharmacological characterization of a novel D2 DAR antagonist series with excellent D2 versus D1, D3, D4, and D5 receptor selectivity. The final probe 65 was obtained through a quantitative high-throughput screening campaign, followed by medicinal chemistry optimization, to yield a selective molecule with good in vitro physical properties, metabolic stability, and in vivo pharmacokinetics. The optimized molecule may be a useful in vivo probe for studying D2 DAR signal modulation and could also serve as a lead compound for the development of D2 DAR-selective druglike molecules for the treatment of multiple neuropsychiatric and endocrine disorders. PMID:24666157

  19. Ant colony optimization with selective evaluation for feature selection in character recognition

    NASA Astrophysics Data System (ADS)

    Oh, Il-Seok; Lee, Jin-Seon

    2010-01-01

    This paper analyzes the size characteristics of character recognition domain with the aim of developing a feature selection algorithm adequate for the domain. Based on the results, we further analyze the timing requirements of three popular feature selection algorithms, greedy algorithm, genetic algorithm, and ant colony optimization. For a rigorous timing analysis, we adopt the concept of atomic operation. We propose a novel scheme called selective evaluation to improve convergence of ACO. The scheme cut down the computational load by excluding the evaluation of unnecessary or less promising candidate solutions. The scheme is realizable in ACO due to the valuable information, pheromone trail which helps identify those solutions. Experimental results showed that the ACO with selective evaluation was promising both in timing requirement and recognition performance.

  20. 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

  1. Extended Salecker-Wigner formula for optimal accuracy in reading a clock via a massive signal particle

    SciTech Connect

    Kudaka, Shoju; Matsumoto, Shuichi

    2007-07-15

    In order to acquire an extended Salecker-Wigner formula from which to derive the optimal accuracy in reading a clock with a massive particle as the signal, von Neumann's classical measurement is employed, by which simultaneously both position and momentum of the signal particle can be measured approximately. By an appropriate selection of wave function for the initial state of the composite system (a clock and a signal particle), the formula is derived accurately. Valid ranges of the running time of a clock with a given optimal accuracy are also given. The extended formula means that contrary to the Salecker-Wigner formula there exists the possibility of a higher accuracy of time measurement, even if the mass of the clock is very small.

  2. Optimal experiment design for model selection in biochemical networks

    PubMed Central

    2014-01-01

    Background Mathematical modeling is often used to formalize hypotheses on how a biochemical network operates by discriminating between competing models. Bayesian model selection offers a way to determine the amount of evidence that data provides to support one model over the other while favoring simple models. In practice, the amount of experimental data is often insufficient to make a clear distinction between competing models. Often one would like to perform a new experiment which would discriminate between competing hypotheses. Results We developed a novel method to perform Optimal Experiment Design to predict which experiments would most effectively allow model selection. A Bayesian approach is applied to infer model parameter distributions. These distributions are sampled and used to simulate from multivariate predictive densities. The method is based on a k-Nearest Neighbor estimate of the Jensen Shannon divergence between the multivariate predictive densities of competing models. Conclusions We show that the method successfully uses predictive differences to enable model selection by applying it to several test cases. Because the design criterion is based on predictive distributions, which can be computed for a wide range of model quantities, the approach is very flexible. The method reveals specific combinations of experiments which improve discriminability even in cases where data is scarce. The proposed approach can be used in conjunction with existing Bayesian methodologies where (approximate) posteriors have been determined, making use of relations that exist within the inferred posteriors. PMID:24555498

  3. Portfolio optimization for seed selection in diverse weather scenarios

    PubMed Central

    Brdar, Sanja; Panić, Marko; Šašić, Isidora; Despotović, Danica; Knežević, Milivoje; Crnojević, Vladimir

    2017-01-01

    The aim of this work was to develop a method for selection of optimal soybean varieties for the American Midwest using data analytics. We extracted the knowledge about 174 varieties from the dataset, which contained information about weather, soil, yield and regional statistical parameters. Next, we predicted the yield of each variety in each of 6,490 observed subregions of the Midwest. Furthermore, yield was predicted for all the possible weather scenarios approximated by 15 historical weather instances contained in the dataset. Using predicted yields and covariance between varieties through different weather scenarios, we performed portfolio optimisation. In this way, for each subregion, we obtained a selection of varieties, that proved superior to others in terms of the amount and stability of yield. According to the rules of Syngenta Crop Challenge, for which this research was conducted, we aggregated the results across all subregions and selected up to five soybean varieties that should be distributed across the network of seed retailers. The work presented in this paper was the winning solution for Syngenta Crop Challenge 2017. PMID:28863173

  4. Portfolio optimization for seed selection in diverse weather scenarios.

    PubMed

    Marko, Oskar; Brdar, Sanja; Panić, Marko; Šašić, Isidora; Despotović, Danica; Knežević, Milivoje; Crnojević, Vladimir

    2017-01-01

    The aim of this work was to develop a method for selection of optimal soybean varieties for the American Midwest using data analytics. We extracted the knowledge about 174 varieties from the dataset, which contained information about weather, soil, yield and regional statistical parameters. Next, we predicted the yield of each variety in each of 6,490 observed subregions of the Midwest. Furthermore, yield was predicted for all the possible weather scenarios approximated by 15 historical weather instances contained in the dataset. Using predicted yields and covariance between varieties through different weather scenarios, we performed portfolio optimisation. In this way, for each subregion, we obtained a selection of varieties, that proved superior to others in terms of the amount and stability of yield. According to the rules of Syngenta Crop Challenge, for which this research was conducted, we aggregated the results across all subregions and selected up to five soybean varieties that should be distributed across the network of seed retailers. The work presented in this paper was the winning solution for Syngenta Crop Challenge 2017.

  5. Optimization of Type Ia Supernovae Selection, Photometric Typing, and Cosmology Constraints

    NASA Astrophysics Data System (ADS)

    Gjergo, Eda; Duggan, Jefferson; Cunningham, John; Kuhlmann, Steve; Biswas, Rahul; Kovacs, Eve

    2012-03-01

    We present results of an optimization study of selection criteria and photometric identification of Type Ia supernovae. The optimization study is the first to include detailed constraints on cosmology, including a time-dependent component of accelerated expansion. The study is performed on a simulated sample of Type Ia and core collapse supernovae from the Dark Energy Survey. In the next decade the number of detected Type Ia supernovae will increase dramatically (Bernstein et al. 2011, Abel et al. 2009), surpassing the resources available for spectroscopic confirmation of each supernova. This has produced an increased interest in the photometric identification of Type Ia supernovae. In order to improve the constraints on the accelerated expansion of the universe, discovered with Type Ia supernovae in the 1990's (Ries et al. 1998, Perlmutter et al. 1999), photometric typing of SN must be very robust. In this study we compare the template-based PSNID algorithm (Sako et al. 2010), with two Type Ia models MLCS2k2 (Riess et al. 2009) and SALT2 (Guy et al. 2007). We allow the pre-selection cuts, based on signal-to-noise ratios, to vary for each model. The optimal model plus pre-selection cuts is determined from the best cosmology constraint.

  6. Optimal band selection for dimensionality reduction of hyperspectral imagery

    NASA Technical Reports Server (NTRS)

    Stearns, Stephen D.; Wilson, Bruce E.; Peterson, James R.

    1993-01-01

    Hyperspectral images have many bands requiring significant computational power for machine interpretation. During image pre-processing, regions of interest that warrant full examination need to be identified quickly. One technique for speeding up the processing is to use only a small subset of bands to determine the 'interesting' regions. The problem addressed here is how to determine the fewest bands required to achieve a specified performance goal for pixel classification. The band selection problem has been addressed previously Chen et al., Ghassemian et al., Henderson et al., and Kim et al.. Some popular techniques for reducing the dimensionality of a feature space, such as principal components analysis, reduce dimensionality by computing new features that are linear combinations of the original features. However, such approaches require measuring and processing all the available bands before the dimensionality is reduced. Our approach, adapted from previous multidimensional signal analysis research, is simpler and achieves dimensionality reduction by selecting bands. Feature selection algorithms are used to determine which combination of bands has the lowest probability of pixel misclassification. Two elements required by this approach are a choice of objective function and a choice of search strategy.

  7. 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.

  8. Influenza B vaccine lineage selection--an optimized trivalent vaccine.

    PubMed

    Mosterín Höpping, Ana; Fonville, Judith M; Russell, Colin A; James, Sarah; Smith, Derek J

    2016-03-18

    Epidemics of seasonal influenza viruses cause considerable morbidity and mortality each year. Various types and subtypes of influenza circulate in humans and evolve continuously such that individuals at risk of serious complications need to be vaccinated annually to keep protection up to date with circulating viruses. The influenza vaccine in most parts of the world is a trivalent vaccine, including an antigenically representative virus of recently circulating influenza A/H3N2, A/H1N1, and influenza B viruses. However, since the 1970s influenza B has split into two antigenically distinct lineages, only one of which is represented in the annual trivalent vaccine at any time. We describe a lineage selection strategy that optimizes protection against influenza B using the standard trivalent vaccine as a potentially cost effective alternative to quadrivalent vaccines.

  9. Selection of an optimal treatment method for acute periodontitis disease.

    PubMed

    Aliev, Rafik A; Aliyev, B F; Gardashova, Latafat A; Huseynov, Oleg H

    2012-04-01

    The present paper is devoted to selection of an optimal treatment method for acute periodontitis by using fuzzy Choquet integral-based approach. We consider application of different treatment methods depending on development stages and symptoms of the disease. The effectiveness of application of different treatment methods in each stage of the disease is linguistically evaluated by a dentist. The stages of the disease are also linguistically described by a dentist. Dentist's linguistic evaluations are represented by fuzzy sets. The total effectiveness of the each considered treatment method is calculated by using fuzzy Choquet integral with fuzzy number-valued integrand and fuzzy number-valued fuzzy measure. The most effective treatment method is determined by using fuzzy ranking method.

  10. Adaptive Signal Detection for the Optimal Communications Receiver,

    DTIC Science & Technology

    1983-06-01

    atmospheric noise are considered. Since the liklihood ratio test nn which thp thpnrv ic: h~czaa i - DD ,*A 3 1473 EDITION OF INOV GS IS OBSOLESTE...transmitter and receiver at opposite ends of an additive noise channel can be improved (1) by increasing the ratio of signal power to noise power , (2...by changing the form of the signal while holding power constant, or (3) by designing better noise immunity into the receiver. This publication

  11. Signal convergence in fruits: a result of selection by frugivores?

    PubMed

    Lomáscolo, S B; Schaefer, H M

    2010-03-01

    The Dispersal Syndrome hypothesis remains contentious, stating that apparently nonrandom associations of fruit characteristics result from selection by seed dispersers. We examine a key assumption under this hypothesis, i.e. that fruit traits can be used as reliable signals by frugivores. We first test this assumption by looking at whether fruit colour allows birds and primates to distinguish between fruits commonly dispersed by birds or primates. Second, we test whether the colours of fruits dispersed by primates are more contrasting to primates than the colours of bird-dispersed fruits, expected if fruit colour is an adaptation to facilitate the detection by seed dispersers. Third, we test whether fruit colour has converged in unrelated plant species dispersed by similar frugivores. We use vision models based on peak sensitivities of birds' and primates' cone cells. We base our analyses on the visual systems of two types of birds (violet and ultraviolet based) and three types of primates (trichromatic primates from the Old and the New Worlds, and a dichromatic New World monkey). Using a Discriminant Function Analysis, we find that all frugivore groups can reliably discriminate between bird- and primate-dispersed fruits. Fruit colour can be a reliable signal to different seed dispersers. However, the colours of primate-dispersed fruits are less contrasting to primates than those of bird-dispersed fruits. Fruit colour convergence in unrelated plants is independent of phylogeny and can be better explained by disperser type, which supports the hypothesis that frugivores are important in fruit evolution. We discuss adaptive and nonadaptive hypotheses that can potentially explain the pattern we found.

  12. Spatial filter and feature selection optimization based on EA for multi-channel EEG.

    PubMed

    Wang, Yubo; Mohanarangam, Krithikaa; Mallipeddi, Rammohan; Veluvolu, K C

    2015-01-01

    The EEG signals employed for BCI systems are generally band-limited. The band-limited multiple Fourier linear combiner (BMFLC) with Kalman filter was developed to obtain amplitude estimates of the EEG signal in a pre-fixed frequency band in real-time. However, the high-dimensionality of the feature vector caused by the application of BMFLC to multi-channel EEG based BCI deteriorates the performance of the classifier. In this work, we apply evolutionary algorithm (EA) to tackle this problem. The real-valued EA encodes both the spatial filter and the feature selection into its solution and optimizes it with respect to the classification error. Three BMFLC based BCI configurations are proposed. Our results show that the BMFLC-KF with covariance matrix adaptation evolution strategy (CMAES) has the best overall performance.

  13. 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.

  14. Optimized bioregenerative space diet selection with crew choice.

    PubMed

    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.

  15. Optimization of killer assays for yeast selection protocols.

    PubMed

    Lopes, C A; Sangorrín, M P

    2010-01-01

    A new optimized semiquantitative yeast killer assay is reported for the first time. The killer activity of 36 yeast isolates belonging to three species, namely, Metschnikowia pulcherrima, Wickerhamomyces anomala and Torulaspora delbrueckii, was tested with a view to potentially using these yeasts as biocontrol agents against the wine spoilage species Pichia guilliermondii and Pichia membranifaciens. The effectiveness of the classical streak-based (qualitative method) and the new semiquantitative techniques was compared. The percentage of yeasts showing killer activity was found to be higher by the semiquantitative technique (60%) than by the qualitative method (45%). In all cases, the addition of 1% NaCl into the medium allowed a better observation of the killer phenomenon. Important differences were observed in the killer capacity of different isolates belonging to a same killer species. The broadest spectrum of action was detected in isolates of W. anomala NPCC 1023 and 1025, and M. pulcherrima NPCC 1009 and 1013. We also brought experimental evidence supporting the importance of the adequate selection of the sensitive isolate to be used in killer evaluation. The new semiquantitative method proposed in this work enables to visualize the relationship between the number of yeasts tested and the growth of the inhibition halo (specific productivity). Hence, this experimental approach could become an interesting tool to be taken into account for killer yeast selection protocols.

  16. 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.

  17. On the design of optimal input signals in system identification

    NASA Technical Reports Server (NTRS)

    Lopez-Toledo, A. A.; Athans, M.

    1974-01-01

    The problem of designing optimal inputs in the identification of multi-input multi-output linear systems with unknown time-varying parameters is considered using a Bayesian approach. A sensitivity index gives a measure of performance for the closed-loop system inputs. The computation of the optimal closed-loop mappings is shown to be a nontrivial exercise in stochastic control with no analytic solution, but optimal open-loop and affine laws yield much more tractable problems. For time-invariant systems, the sensitivity index considered is shown to be equivalent to the trace of the (strictly positive definite) information matrix associated with the system. Numerical examples are given. A Kalman filter is used to estimate the parameters. A necessary condition for the Kalman filter not to diverge when applying linear feedback is also given.

  18. Optimal Methods for Classification of Digitally Modulated Signals

    DTIC Science & Technology

    2013-03-01

    was developed for classifying MPSK and QAM . Future work will refine these two proposed concepts. Finally, this research explored MIMO codes design...24 3.4.3 QAM Case...The focus will be mainly in types such as QAM , MPSK and BPSK Spread Spectrum. The algorithms must have medium complexity and provide some optimal

  19. 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.

  20. When is an optimization not an optimization? Evaluation of clinical implications of information content (signal-to-noise ratio) in optimization of cardiac resynchronization therapy, and how to measure and maximize it.

    PubMed

    Pabari, Punam A; Willson, Keith; Stegemann, Berthold; van Geldorp, Irene E; Kyriacou, Andreas; Moraldo, Michela; Mayet, Jamil; Hughes, Alun D; Francis, Darrel P

    2011-05-01

    Impact of variability in the measured parameter is rarely considered in designing clinical protocols for optimization of atrioventricular (AV) or interventricular (VV) delay of cardiac resynchronization therapy (CRT). In this article, we approach this question quantitatively using mathematical simulation in which the true optimum is known and examine practical implications using some real measurements. We calculated the performance of any optimization process that selects the pacing setting which maximizes an underlying signal, such as flow or pressure, in the presence of overlying random variability (noise). If signal and noise are of equal size, for a 5-choice optimization (60, 100, 140, 180, 220 ms), replicate AV delay optima are rarely identical but rather scattered with a standard deviation of 45 ms. This scatter was overwhelmingly determined (ρ = -0.975, P < 0.001) by Information Content, [Formula: see text], an expression of signal-to-noise ratio. Averaging multiple replicates improves information content. In real clinical data, at resting, heart rate information content is often only 0.2-0.3; elevated pacing rates can raise information content above 0.5. Low information content (e.g. <0.5) causes gross overestimation of optimization-induced increment in VTI, high false-positive appearance of change in optimum between visits and very wide confidence intervals of individual patient optimum. AV and VV optimization by selecting the setting showing maximum cardiac function can only be accurate if information content is high. Simple steps to reduce noise such as averaging multiple replicates, or to increase signal such as increasing heart rate, can improve information content, and therefore viability, of any optimization process.

  1. Chromogenic and fluorogenic signaling of sulfite by selective deprotection of resorufin levulinate.

    PubMed

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

    2010-12-17

    A new sulfite-selective probe system based on resorufin was investigated. Levulinate of resorufin exhibited a prominent chromogenic and turn-on type fluorogenic signaling toward sulfite ions in aqueous media based on the selective deprotection of the levulinate group. The sulfite-selective signaling was possible in the presence of commonly encountered anions.

  2. 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…

  3. 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…

  4. 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

  5. An optimized image analysis algorithm for detecting nuclear signals in digital whole slides for histopathology.

    PubMed

    Paulik, Róbert; Micsik, Tamás; Kiszler, Gábor; Kaszál, Péter; Székely, János; Paulik, Norbert; Várhalmi, Eszter; Prémusz, Viktória; Krenács, Tibor; Molnár, Béla

    2017-06-01

    Nuclear estrogen receptor (ER), progesterone receptor (PR) and Ki-67 protein positive tumor cell fractions are semiquantitatively assessed in breast cancer for prognostic and predictive purposes. These biomarkers are usually revealed using immunoperoxidase methods resulting in diverse signal intensity and frequent inhomogeneity in tumor cell nuclei, which are routinely scored and interpreted by a pathologist during conventional light-microscopic examination. In the last decade digital pathology-based whole slide scanning and image analysis algorithms have shown tremendous development to support pathologists in this diagnostic process, which can directly influence patient selection for targeted- and chemotherapy. We have developed an image analysis algorithm optimized for whole slide quantification of nuclear immunostaining signals of ER, PR, and Ki-67 proteins in breast cancers. In this study, we tested the consistency and reliability of this system both in a series of brightfield and DAPI stained fluorescent samples. Our method allows the separation of overlapping cells and signals, reliable detection of vesicular nuclei and background compensation, especially in FISH stained slides. Detection accuracy and the processing speeds were validated in routinely immunostained breast cancer samples of varying reaction intensities and image qualities. Our technique supported automated nuclear signal detection with excellent efficacy: Precision Rate/Positive Predictive Value was 90.23 ± 4.29%, while Recall Rate/Sensitivity was 88.23 ± 4.84%. These factors and average counting speed of our algorithm were compared with two other open source applications (QuPath and CellProfiler) and resulted in 6-7% higher Recall Rate, while 4- to 30-fold higher processing speed. In conclusion, our image analysis algorithm can reliably detect and count nuclear signals in digital whole slides or any selected large areas i.e. hot spots, thus can support pathologists in assessing

  6. Genetic Algorithms Evolve Optimized Transforms for Signal Processing Applications

    DTIC Science & Technology

    2005-04-01

    Systems and Filter Banks , Prentice-Hall. Venkatachalam, V. and J. Aravena 1998. Detecting Periodic Behavior in Nonstationary Signals, IEEE-SP...Square Adaptive Filters , John Wiley. Holland, J. 1975. Adaptation in Natural and Artificial Systems , University of Michigan Press. Jones, E., P...Nonlinear Sciences, ISBN 0-620-23629-9. Odegard, J., R. Gopinath, and C. Burrus 1994. Design of Linear Phase Cosine Modulated Filter Banks for

  7. Coupling Condition In A Hololens - Optical Fiber System : Output Signal Optimization

    NASA Astrophysics Data System (ADS)

    Calvo, M. L.; De Pedraza, L.

    1988-04-01

    Based upon the scalar diffraction theory we have derived a very simple condition to control the optimization in the coupling phenomenon in a holocoupler - optical fiber system. A systematic numerical procedure allows a scanning simulation at the output plane of the system. The influence of the physical optimization parameters can be easily obtained giving an interesting criterium for the optimization of the output signal in a suitable experimental set up.

  8. 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.

  9. Selective optimization with compensation (SOC) competencies in depression.

    PubMed

    Weiland, Marcus; Dammermann, Claudia; Stoppe, Gabriela

    2011-09-01

    The metamodel of selective optimization with compensation (SOC) aims to integrate scientific knowledge about the nature of development and aging with a focus on successful adaptation. For the first time the present study examines how SOC competencies and depressive symptoms are associated. In particular, potential state or trait effects of SOC competencies are considered. Fifty-three patients (31 women and 22 men), aged 21 to 73 years, suffering from depression, were interviewed twice during inpatient treatment, first on admission to hospital and later during remission or on discharge, to assess the severity of depression and differences in the SOC competencies using standardized scales. For comparison purpose, data from a population based survey in Germany were used. The SOC scores in the first interview were significantly lower than those of the comparison collective (p<0.0001), but in remission there was no significant difference left. Younger and older patients showed no significant difference in their SOC competencies, neither regarding the severity of depressive symptoms on admission to the hospital, nor during remission. These findings support the hypothesis that the SOC ability is dynamic and mood dependent (state effect). Otherwise, there is no hint of life-long reduced SOC competencies or a trait effect which would be associated with an increased vulnerability to the development of a depressive disorder. Regarding the high prevalence of depression especially in the elderly and physically ill patients, (gerontological) studies on SOC competencies should take depression into account. Copyright © 2011 Elsevier B.V. All rights reserved.

  10. 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)

  11. Uncovering noisy social signals: Using optimization methods from experimental physics to study social phenomena.

    PubMed

    Kaptein, Maurits; van Emden, Robin; Iannuzzi, Davide

    2017-01-01

    Due to the ubiquitous presence of treatment heterogeneity, measurement error, and contextual confounders, numerous social phenomena are hard to study. Precise control of treatment variables and possible confounders is often key to the success of studies in the social sciences, yet often proves out of the realm of control of the experimenter. To amend this situation we propose a novel approach coined "lock-in feedback" which is based on a method that is routinely used in high-precision physics experiments to extract small signals out of a noisy environment. Here, we adapt the method to noisy social signals in multiple dimensions and evaluate it by studying an inherently noisy topic: the perception of (subjective) beauty. We show that the lock-in feedback approach allows one to select optimal treatment levels despite the presence of considerable noise. Furthermore, through the introduction of an external contextual shock we demonstrate that we can find relationships between noisy variables that were hitherto unknown. We therefore argue that lock-in methods may provide a valuable addition to the social scientist's experimental toolbox and we explicitly discuss a number of future applications.

  12. Uncovering noisy social signals: Using optimization methods from experimental physics to study social phenomena

    PubMed Central

    2017-01-01

    Due to the ubiquitous presence of treatment heterogeneity, measurement error, and contextual confounders, numerous social phenomena are hard to study. Precise control of treatment variables and possible confounders is often key to the success of studies in the social sciences, yet often proves out of the realm of control of the experimenter. To amend this situation we propose a novel approach coined “lock-in feedback” which is based on a method that is routinely used in high-precision physics experiments to extract small signals out of a noisy environment. Here, we adapt the method to noisy social signals in multiple dimensions and evaluate it by studying an inherently noisy topic: the perception of (subjective) beauty. We show that the lock-in feedback approach allows one to select optimal treatment levels despite the presence of considerable noise. Furthermore, through the introduction of an external contextual shock we demonstrate that we can find relationships between noisy variables that were hitherto unknown. We therefore argue that lock-in methods may provide a valuable addition to the social scientist’s experimental toolbox and we explicitly discuss a number of future applications. PMID:28306728

  13. [Optimized protocols for interphase FISH analysis of imprints and sections using split signal probes].

    PubMed

    Pelluard-Nehme, F; Dupont, T; Turmo, M; Merlio, J-P; Belaud-Rotureau, M-A

    2007-03-01

    Fluorescent in situ hybridization (FISH) analysis is a molecular technique allowing the detection of recurrent translocations in cancer. Several hybridization protocols were assayed in order to evaluate their performances for interphase FISH analysis of histological sections and imprints using split probes. Adult and foetal lymphoid tissues were selected. Touch imprints of fresh (EF) or frozen (EC) tissues, sections (CF) and isolated nuclei (NI) of formol-fixed paraffin-embedded tissues were performed. The cut-off values of the IGH, IGlambda, BCL-2, BCL-6, CCND1 and MYC DNA FISH split signal probes were calculated for adult reactive lymph nodes on the different histological preparations (EC, CF, CC, NI) and on several tissues for the IGH and BCL-6 probes. In reactive lymph nodes, the cut-off values of the probes were between 3 and 13% and found independent of the preparation type. Conversely, slight but significant variations of the cut-off level were observed when different foetal control tissues were assayed with the same probe set. Finally, this study provided optimized-protocols for FISH analysis of either fresh/frozen imprints or formalin-fixed paraffin-embedded sections using split signal DNA probes.

  14. 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

    ... 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 the... and productive scientific enterprise that utilizes biological select agents and toxins (BSAT)...

  15. Influence of multi-valued diagnostic signals on optimal sensor placement

    NASA Astrophysics Data System (ADS)

    Rostek, Kornel

    2017-01-01

    In this paper the comparison of results of optimal sensor placement with the use of binary and multi-valued diagnostic signals is given. The exoneration assumption was introduced and its effects were discussed. The influence of multi-valued diagnostic signals on several fault isolability metrics were discussed. The optimal sensor placement problem under budgetary constraints is formulated. A branch-and-bound algorithm solving this problem is described. It is used in context of three tanks system with 25 possible diagnostic signals.

  16. Feature selection using angle modulated simulated Kalman filter for peak classification of EEG signals.

    PubMed

    Adam, Asrul; Ibrahim, Zuwairie; Mokhtar, Norrima; Shapiai, Mohd Ibrahim; Mubin, Marizan; Saad, Ismail

    2016-01-01

    In the existing electroencephalogram (EEG) signals peak classification research, the existing models, such as Dumpala, Acir, Liu, and Dingle peak models, employ different set of features. However, all these models may not be able to offer good performance for various applications and it is found to be problem dependent. Therefore, the objective of this study is to combine all the associated features from the existing models before selecting the best combination of features. A new optimization algorithm, namely as angle modulated simulated Kalman filter (AMSKF) will be employed as feature selector. Also, the neural network random weight method is utilized in the proposed AMSKF technique as a classifier. In the conducted experiment, 11,781 samples of peak candidate are employed in this study for the validation purpose. The samples are collected from three different peak event-related EEG signals of 30 healthy subjects; (1) single eye blink, (2) double eye blink, and (3) eye movement signals. The experimental results have shown that the proposed AMSKF feature selector is able to find the best combination of features and performs at par with the existing related studies of epileptic EEG events classification.

  17. Optimal Estimation of the Average Areal Rainfall and Optimal Selection of Rain Gauge Locations

    NASA Astrophysics Data System (ADS)

    Bastin, G.; Lorent, B.; Duqué, C.; Gevers, M.

    1984-04-01

    We propose a simple procedure for the real-time estimation of the average rainfall over a catchment area. The rainfall is modeled as a two-dimensional random field. The average areal rainfall is computed by a linear unbiased minimum variance estimation method (kriging) which requires knowledge of the variogram of the random field. We propose a time-varying estimator for the variogram which takes into account the influences of both the seasonal variations and the rainfall intensity. Our average areal rainfall estimator has been implemented in practice. We illustrate its application to real data in two river basins in Belgium. Finally, it is shown how the method can be used for the optimal selection of the rain gauge locations in a basin.

  18. 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.

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

    PubMed Central

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

    2016-01-01

    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

  20. 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; Ferroni, Valerio; hide

    2016-01-01

    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 characterisation 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.

  1. 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.

  2. Analysis Methodology for Optimal Selection of Ground Station Site in Space Missions

    NASA Astrophysics Data System (ADS)

    Nieves-Chinchilla, J.; Farjas, M.; Martínez, R.

    2013-12-01

    Optimization of ground station sites is especially important in complex missions that include several small satellites (clusters or constellations) such as the QB50 project, where one ground station would be able to track several spatial vehicles, even simultaneously. In this regard the design of the communication system has to carefully take into account the ground station site and relevant signal phenomena, depending on the frequency band. To propose the optimal location of the ground station, these aspects become even more relevant to establish a trusted communication link due to the ground segment site in urban areas and/or selection of low orbits for the space segment. In addition, updated cartography with high resolution data of the location and its surroundings help to develop recommendations in the design of its location for spatial vehicles tracking and hence to improve effectiveness. The objectives of this analysis methodology are: completion of cartographic information, modelling the obstacles that hinder communication between the ground and space segment and representation in the generated 3D scene of the degree of impairment in the signal/noise of the phenomena that interferes with communication. The integration of new technologies of geographic data capture, such as 3D Laser Scan, determine that increased optimization of the antenna elevation mask, in its AOS and LOS azimuths along the horizon visible, maximizes visibility time with spatial vehicles. Furthermore, from the three-dimensional cloud of points captured, specific information is selected and, using 3D modeling techniques, the 3D scene of the antenna location site and surroundings is generated. The resulting 3D model evidences nearby obstacles related to the cartographic conditions such as mountain formations and buildings, and any additional obstacles that interfere with the operational quality of the antenna (other antennas and electronic devices that emit or receive in the same bandwidth

  3. The optimization of diffraction structures based on the principle selection of the main criterion

    NASA Astrophysics Data System (ADS)

    Kravets, O.; Beletskaja, S.; Lvovich, Ya; Lvovich, I.; Choporov, O.; Preobrazhenskiy, A.

    2017-02-01

    The possibilities of optimizing the characteristics of diffractive structures are analysed. A functional block diagram of a subsystem of diffractive structure optimization is shown. Next, a description of the method for the multicriterion optimization of diffractive structures is given. We then consider an algorithm for selecting the main criterion in the process of optimization. The algorithm efficiency is confirmed by an example of optimization of the diffractive structure.

  4. 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.

  5. Near-optimal signal demodulation method with a special quasi-Gaussian weight function

    NASA Astrophysics Data System (ADS)

    Kalinin, A. V.

    2006-12-01

    We consider a GSM cellular communication system using the enhanced general packet radio service (EGPRS) for high-speed information exchange. Transmission is performed by signals with eight-phase-shift keying (8-PSK) modulation with a special quasi-Gaussian weight function. To optimize demodulation of such signals in the presence of Rayleigh fadings, we propose a method using partial measurement of metrics, preliminary estimation of symbols, and decision feedback. We present the results of numerical simulation for the case of a channel with Rayleigh fadings of signals. High efficiency of the proposed method for demodulation of 8-PSK modulated signals is confirmed.

  6. Depth resolution improvement of streak tube imaging lidar using optimal signal width

    NASA Astrophysics Data System (ADS)

    Ye, Guangchao; Fan, Rongwei; Lu, Wei; Dong, Zhiwei; Li, Xudong; He, Ping; Chen, Deying

    2016-10-01

    Streak tube imaging lidar (STIL) is an active imaging system that has a high depth resolution with the use of a pulsed laser transmitter and streak tube receiver to produce three-dimensional (3-D) range images. This work investigates the optimal signal width of the lidar system, which is helpful to improve the depth resolution based on the centroid algorithm. Theoretical analysis indicates that the signal width has a significant effect on the depth resolution and the optimal signal width can be determined for a given STIL system, which is verified by both the simulation and experimental results. An indoor experiment with a planar target was carried out to validate the relation that the range error decreases first and then increases with the signal width, resulting in an optimal signal width of 8.6 pixels. Finer 3-D range images of a cartoon model were acquired by using the optimal signal width and a minimum range error of 5.5 mm was achieved in a daylight environment.

  7. Optimal secretion of alkali-tolerant xylanase in Bacillus subtilis by signal peptide screening.

    PubMed

    Zhang, Weiwei; Yang, Mingming; Yang, Yuedong; Zhan, Jian; Zhou, Yaoqi; Zhao, Xin

    2016-10-01

    Xylanases are industrially important enzymes for xylan digestion. We experimentally screened over 114 Sec and 24 Tat pathway signal peptides, with two different promoters, for optimal production of an alkaline active xylanase (XynBYG) from Bacillus pumilus BYG in a Bacillus subtilis host. Though both promoters yielded highly consistent secretion levels (0.97 Pearson correlation coefficient), the Sec pathway was found to be more efficient than the Tat pathway for XynBYG secretion. Furthermore, the optimal signal peptide (phoB) for XynBYG secretion was found to be different from the optimal peptides for cutinase and esterase reported in previous studies. A partial least squares regression analysis further identified several statistically important variables: helical properties, amino acid composition bias, and the discrimination score in Signal P. These variables explain the observed 23 % variance in the secretion yield of XynBYG by the different signal peptides. The results also suggest that the helical propensity of a signal peptide plays a significant role in the beta-rich xylanase, but not in the helix-rich cutinase, suggesting a coupling of the conformations between the signal peptide and its cargo protein for optimal secretion.

  8. Classification of epileptic EEG signals based on simple random sampling and sequential feature selection.

    PubMed

    Ghayab, Hadi Ratham Al; Li, Yan; Abdulla, Shahab; Diykh, Mohammed; Wan, Xiangkui

    2016-06-01

    Electroencephalogram (EEG) signals are used broadly in the medical fields. The main applications of EEG signals are the diagnosis and treatment of diseases such as epilepsy, Alzheimer, sleep problems and so on. This paper presents a new method which extracts and selects features from multi-channel EEG signals. This research focuses on three main points. Firstly, simple random sampling (SRS) technique is used to extract features from the time domain of EEG signals. Secondly, the sequential feature selection (SFS) algorithm is applied to select the key features and to reduce the dimensionality of the data. Finally, the selected features are forwarded to a least square support vector machine (LS_SVM) classifier to classify the EEG signals. The LS_SVM classifier classified the features which are extracted and selected from the SRS and the SFS. The experimental results show that the method achieves 99.90, 99.80 and 100 % for classification accuracy, sensitivity and specificity, respectively.

  9. FPGA based hardware optimized implementation of signal processing system for LFM pulsed radar

    NASA Astrophysics Data System (ADS)

    Azim, Noor ul; Jun, Wang

    2016-11-01

    Signal processing is one of the main parts of any radar system. Different signal processing algorithms are used to extract information about different parameters like range, speed, direction etc, of a target in the field of radar communication. This paper presents LFM (Linear Frequency Modulation) pulsed radar signal processing algorithms which are used to improve target detection, range resolution and to estimate the speed of a target. Firstly, these algorithms are simulated in MATLAB to verify the concept and theory. After the conceptual verification in MATLAB, the simulation is converted into implementation on hardware using Xilinx FPGA. Chosen FPGA is Xilinx Virtex-6 (XC6LVX75T). For hardware implementation pipeline optimization is adopted and also other factors are considered for resources optimization in the process of implementation. Focusing algorithms in this work for improving target detection, range resolution and speed estimation are hardware optimized fast convolution processing based pulse compression and pulse Doppler processing.

  10. Enhanced recombinant factor VII expression in Chinese hamster ovary cells by optimizing signal peptides and fed-batch medium.

    PubMed

    Peng, Lin; Yu, Xiao; Li, Chengyuan; Cai, Yanfei; Chen, Yun; He, Yang; Yang, Jianfeng; Jin, Jian; Li, Huazhong

    2016-04-01

    Signal peptides play an important role in directing and efficiently transporting secretory proteins to their proper locations in the endoplasmic reticulum of mammalian cells. The aim of this study was to enhance the expression of recombinant coagulation factor VII (rFVII) in CHO cells by optimizing the signal peptides and type of fed-batch culture medium used. Five sub-clones (O2, I3, H3, G2 and M3) with different signal peptide were selected by western blot (WB) analysis and used for suspension culture. We compared rFVII expression levels of 5 sub-clones and found that the highest rFVII expression level was obtained with the IgK signal peptide instead of Ori, the native signal peptide of rFVII. The high protein expression of rFVII with signal peptide IgK was mirrored by a high transcription level during suspension culture. After analyzing culture and feed media, the combination of M4 and F4 media yielded the highest rFVII expression of 20 mg/L during a 10-day suspension culture. After analyzing cell density and cell cycle, CHO cells feeding by F4 had a similar percentage of cells in G0/G1 and a higher cell density compared to F2 and F3. This may be the reason for high rFVII expression in M4+F4. In summary, rFVII expression was successfully enhanced by optimizing the signal peptide and fed-batch medium used in CHO suspension culture. Our data may be used to improve the production of other therapeutic proteins in fed-batch culture.

  11. Sexually selected UV signals in the tropical ornate jumping spider, Cosmophasis umbratica may incur costs from predation.

    PubMed

    Bulbert, Matthew W; O'Hanlon, James C; Zappettini, Shane; Zhang, Shichang; Li, Daiqin

    2015-02-01

    Sexually selected ornaments and signals are costly to maintain if they are maladaptive in nonreproductive contexts. The jumping spider Cosmophasis umbratica exhibits distinct sexual dichromatism with males displaying elaborate UV body markings that signal male quality. Female C. umbratica respond favorably to UV-reflecting males and ignore males that have their UV masked. However, Portia labiata, a UV-sensitive spider-eating specialist and a natural predator of C. umbratica, is known to use UV reflectance as a cue when hunting prey. We investigated the cost of these UV signals in C. umbratica in terms of their predation risk. Under experimental conditions, three choice scenarios were presented to P. labiata individuals. Choices by P. labiata were made between male C. umbratica with and without the UV signal; a UV-reflecting male and non-UV-reflecting female; and a UV-masked male and female. The presence and absence of UV signals was manipulated using an optical filter. Portia labiata exhibited a strong bias toward UV+ individuals. These results suggest the sexually selected trait of UV reflectance increases the visibility of males to UV-sensitive predators. The extent of this male-specific UV signal then is potentially moderated by predation pressure. Interestingly though, P. labiata still preferred males to females irrespective of whether UV reflectance was present or not. This suggests P. labiata can switch cues when conditions to detect UV reflectance are not optimal.

  12. 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

    2017-04-01

    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.

  13. 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.

  14. Photoacoustic signal simulation and detection optimization based on laser-scanning optical-resolution photoacoustic microscopy

    NASA Astrophysics Data System (ADS)

    Li, Lin; Du, Yi; Zhao, Qingliang; Li, Qian; Chai, Xinyu; Zhou, Chuanqing

    2014-11-01

    Laser-scanning optical-resolution photoacoustic microscopy (LSOR-PAM) has a high application potential in ophthalmology and other clinical fields because of its high resolution and imaging speed. The stationary unfocused ultrasonic transducer of this system decides the efficiency and field of view (FOV) of photoacoustic signal detection, but the refraction and attenuation of laser generated photoacoustic signal in different tissue mediums will cause signal strength and direction distribution uneven. In this study, we simulated the photoacoustic signal propagation and detection in compound medium models with different tissue parameters using k-space method based on LSOR-PAM imaging principle. The results show a distance related signal strength attenuation and FOV changes related to transducer angle. Our study provides a method for photoacoustic signal detection optimization for different complex tissue structure with LSOR-PAM.

  15. Photoacoustic correlation signal-to-noise ratio enhancement by coherent averaging and optical waveform optimization.

    PubMed

    Telenkov, Sergey A; Alwi, Rudolf; Mandelis, Andreas

    2013-10-01

    Photoacoustic (PA) imaging of biological tissues using laser diodes instead of conventional Q-switched pulsed systems provides an attractive alternative for biomedical applications. However, the relatively low energy of laser diodes operating in the pulsed regime, results in generation of very weak acoustic waves, and low signal-to-noise ratio (SNR) of the detected signals. This problem can be addressed if optical excitation is modulated using custom waveforms and correlation processing is employed to increase SNR through signal compression. This work investigates the effect of the parameters of the modulation waveform on the resulting correlation signal and offers a practical means for optimizing PA signal detection. The advantage of coherent signal averaging is demonstrated using theoretical analysis and a numerical model of PA generation. It was shown that an additional 5-10 dB of SNR can be gained through waveform engineering by adjusting the parameters and profile of optical modulation waveforms.

  16. 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…

  17. 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…

  18. Discovery and optimization of indazoles as potent and selective interleukin-2 inducible T cell kinase (ITK) inhibitors.

    PubMed

    Pastor, Richard M; Burch, Jason D; Magnuson, Steven; Ortwine, Daniel F; Chen, Yuan; De La Torre, Kelly; Ding, Xiao; Eigenbrot, Charles; Johnson, Adam; Liimatta, Marya; Liu, Yichin; Shia, Steven; Wang, Xiaolu; Wu, Lawren C; Pei, Zhonghua

    2014-06-01

    There is evidence that small molecule inhibitors of the non-receptor tyrosine kinase ITK, a component of the T-cell receptor signaling cascade, could represent a novel asthma therapeutic class. Moreover, given the expected chronic dosing regimen of any asthma treatment, highly selective as well as potent inhibitors would be strongly preferred in any potential therapeutic. Here we report hit-to-lead optimization of a series of indazoles that demonstrate sub-nanomolar inhibitory potency against ITK with strong cellular activity and good kinase selectivity. We also elucidate the binding mode of these inhibitors by solving the X-ray crystal structures of the complexes.

  19. An artificial system for selecting the optimal surgical team.

    PubMed

    Saberi, Nahid; Mahvash, Mohsen; Zenati, Marco

    2015-01-01

    We introduce an intelligent system to optimize a team composition based on the team's historical outcomes and apply this system to compose a surgical team. The system relies on a record of the procedures performed in the past. The optimal team composition is the one with the lowest probability of unfavorable outcome. We use the theory of probability and the inclusion exclusion principle to model the probability of team outcome for a given composition. A probability value is assigned to each person of database and the probability of a team composition is calculated from them. The model allows to determine the probability of all possible team compositions even if there is no recoded procedure for some team compositions. From an analytical perspective, assembling an optimal team is equivalent to minimizing the overlap of team members who have a recurring tendency to be involved with procedures of unfavorable results. A conceptual example shows the accuracy of the proposed system on obtaining the optimal team.

  20. Symbol Detection for Faster-Than-Nyquist Signaling by Sum-of-Absolute-Values Optimization

    NASA Astrophysics Data System (ADS)

    Sasahara, Hampei; Hayashi, Kazunori; Nagahara, Masaaki

    2016-12-01

    In this letter, we propose a new symbol detection method for faster-than-Nyquist signaling (FTNS) systems. Based on frame theory, we formulate a symbol detection problem as a under-determined linear equation on a finite set. The problem is reformulated as a sum-of-absolute-values (SOAV) optimization that can be efficiently solved by the fast iterative shrinkage thresholding algorithm (FISTA). The proximity operator for the convex optimization is derived analytically. Simulation results are given to show that the proposed method can successfully detect symbols in faster-than-Nyquist signaling systems and has lower complexity in terms of computation time.

  1. 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

  2. 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.

  3. 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.

  4. Learning to sense sparse signals: simultaneous sensing matrix and sparsifying dictionary optimization.

    PubMed

    Duarte-Carvajalino, Julio Martin; Sapiro, Guillermo

    2009-07-01

    Sparse signal representation, analysis, and sensing have received a lot of attention in recent years from the signal processing, optimization, and learning communities. On one hand, learning overcomplete dictionaries that facilitate a sparse representation of the data as a liner combination of a few atoms from such dictionary leads to state-of-the-art results in image and video restoration and classification. On the other hand, the framework of compressed sensing (CS) has shown that sparse signals can be recovered from far less samples than those required by the classical Shannon-Nyquist Theorem. The samples used in CS correspond to linear projections obtained by a sensing projection matrix. It has been shown that, for example, a nonadaptive random sampling matrix satisfies the fundamental theoretical requirements of CS, enjoying the additional benefit of universality. On the other hand, a projection sensing matrix that is optimally designed for a certain class of signals can further improve the reconstruction accuracy or further reduce the necessary number of samples. In this paper, we introduce a framework for the joint design and optimization, from a set of training images, of the nonparametric dictionary and the sensing matrix. We show that this joint optimization outperforms both the use of random sensing matrices and those matrices that are optimized independently of the learning of the dictionary. Particular cases of the proposed framework include the optimization of the sensing matrix for a given dictionary as well as the optimization of the dictionary for a predefined sensing environment. The presentation of the framework and its efficient numerical optimization is complemented with numerous examples on classical image datasets.

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

    PubMed

    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.

  6. Optimization of processing parameters for the analysis and detection of embolic signals.

    PubMed

    Aydin, N; Markus, H S

    2000-09-01

    The fast Fourier transform (FFT), which is employed by all commercially available ultrasonic systems, provides a time-frequency representation of Doppler ultrasonic signals obtained from blood flow. The FFT assumes that the signal is stationary within the analysis window. However, the presence of short duration embolic signals invalidates this assumption. For optimal detection of embolic signals if FFT is used for signal processing, it is important that the FFT parameters such as window size, window type, and required overlap ratio should be optimized. The effect of varying window type, window size and window overlap ratio were investigated for both simulated embolic signals, and recorded from patients with carotid artery stenosis. An optimal compromise is the use of a Hamming or Hanning window with a FFT size of 64 (8.9 ms) or 128 (17.9 ms). A high overlap ratio should also be employed in order not to miss embolic events occurring at the edges of analysis windows. The degree of overlap required will depend on the FFT size. The minimum overlap should be 65% for a 64-point window and 80% for a 128-point window.

  7. 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…

  8. 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…

  9. Neural network classifier with analytic translation and scaling capabilities for optimal signal viewing

    SciTech Connect

    Vilim, R.B.; Wegerich, S.W.

    1995-12-31

    A neural network originally proposed by Szu for performing pattern recognition has been modified for use in a noisy manufacturing environment. Signals from the factory floor are frequently affine transformed and, as a consequence, a signal may not be properly aligned with respect to the input node that corresponds to the signal leading edge or with respect to the number of nodes representing the time varying part. Rater than translate and scale the presented signal, an operation which because of noise can be prone to numerical error since the signal is not smoothly varying, the network in this paper has the capability to analytically translate and scale its internal representation of the signal so that it overlays the presented signal. A response surface in the neighborhood of the stored reference signal is built during, training, and covers the range of translate and scale parameter values expected. A genetic algorithm is used to search over this hilly terrain to find the optimal values of these parameters so that the reference signal overlays the presented signal. The procedure is repeated over all hypothesized pattern classes with the best fit identifying the class.

  10. 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…

  11. Eliminating Scope and Selection Restrictions in Compiler Optimization

    DTIC Science & Technology

    2006-09-01

    C o d e S a m p le s O pt i l ev el (R ) O pt i l ev el (W ) If- co nv . (R ) If- co nv . (W ) Ld - S t (R ) Ld - S ...exploration performance” of each such subset is determined, as follows: Let R( s , c ) be the runtime of a code sample s when optimized using an...optimization configuration c . Then the exploration value of a set of configurations C on a set of code samples S is given by the

  12. Signal enhancement and suppression during visual-spatial selective attention.

    PubMed

    Couperus, J W; Mangun, G R

    2010-11-04

    Selective attention involves the relative enhancement of relevant versus irrelevant stimuli. However, whether this relative enhancement involves primarily enhancement of attended stimuli, or suppression of irrelevant stimuli, remains controversial. Moreover, if both enhancement and suppression are involved, whether they result from a single mechanism or separate mechanisms during attentional control or selection is not known. In two experiments using a spatial cuing paradigm with task-relevant targets and irrelevant distractors, target, and distractor processing was examined as a function of distractor expectancy. Additionally, in the second study the interaction of perceptual load and distractor expectancy was explored. In both experiments, distractors were either validly cued (70%) or invalidly cued (30%) in order to examine the effects of distractor expectancy on attentional control as well as target and distractor processing. The effects of distractor expectancy were assessed using event-related potentials recorded during the cue-to-target period (preparatory attention) and in response to the task-relevant target stimuli (selective stimulus processing). Analyses of distractor-present displays (anticipated versus unanticipated), showed modulations in brain activity during both the preparatory period and during target processing. The pattern of brain responses suggest both facilitation of attended targets and suppression of unattended distractors. These findings provide evidence for a two-process model of visual-spatial selective attention, where one mechanism (facilitation) influences relevant stimuli and another (suppression) acts to filter distracting stimuli.

  13. 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.

  14. 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.

  15. 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.

  16. 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

  17. 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.

  18. 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…

  19. 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…

  20. A Regression Design Approach to Optimal and Robust Spacing Selection.

    DTIC Science & Technology

    1981-07-01

    release and sale; its distribution is unlimited Acceso For NTIS GRA&I DEPARTMENT OF STATISTICS DTIC TAB Unannounced Southern Methodist University F...such as the Cauchy where A is a constant multiple of the identity. In fact, for the Cauchy distribution asymptotically optimal spacing sequences for

  1. A Fully Automated Trial Selection Method for Optimization of Motor Imagery Based Brain-Computer Interface.

    PubMed

    Zhou, Bangyan; Wu, Xiaopei; Lv, Zhao; Zhang, Lei; Guo, Xiaojin

    2016-01-01

    Independent component analysis (ICA) as a promising spatial filtering method can separate motor-related independent components (MRICs) from the multichannel electroencephalogram (EEG) signals. However, the unpredictable burst interferences may significantly degrade the performance of ICA-based brain-computer interface (BCI) system. In this study, we proposed a new algorithm frame to address this issue by combining the single-trial-based ICA filter with zero-training classifier. We developed a two-round data selection method to identify automatically the badly corrupted EEG trials in the training set. The "high quality" training trials were utilized to optimize the ICA filter. In addition, we proposed an accuracy-matrix method to locate the artifact data segments within a single trial and investigated which types of artifacts can influence the performance of the ICA-based MIBCIs. Twenty-six EEG datasets of three-class motor imagery were used to validate the proposed methods, and the classification accuracies were compared with that obtained by frequently used common spatial pattern (CSP) spatial filtering algorithm. The experimental results demonstrated that the proposed optimizing strategy could effectively improve the stability, practicality and classification performance of ICA-based MIBCI. The study revealed that rational use of ICA method may be crucial in building a practical ICA-based MIBCI system.

  2. Optimized digital backward propagation for phase modulated signals in mixed-optical fiber transmission link.

    PubMed

    Asif, Rameez; Lin, Chien-Yu; Holtmannspoetter, M; Schmauss, Bernhard

    2010-10-25

    The parametric optimization of Digital Backward Propagation (DBP) algorithm for mitigating fiber transmission impairments is proposed and numerically demonstrated for phase modulated signals in mixed-optical fiber transmission link. The optimization of parameters i.e. dispersion (D) and non-linear coefficient (γ) offer improved eye-opening (EO). We investigate the optimization of iterative and non-iterative symmetric split-step Fourier method (S-SSFM) for solving the inverse non-linear Schrödinger equation (NLSE). Optimized DBP algorithm, with step-size equal to fiber module length i.e. one calculation step per fiber span for obtaining higher computational efficiency, is implemented at the receiver as a digital signal processing (DSP) module. The system performance is evaluated by EO-improvement for diverse in-line compensation schemes. Using computationally efficient non-iterative symmetric split-step Fourier method (NIS-SSFM) upto 3.6 dB referenced EO-improvement can be obtained at 6 dBm signal launch power by optimizing and modifying DBP algorithm parameters, based on the characterization of the individual fiber types, in mixed-optical fiber transmission link.

  3. 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.

  4. Optimal effector functions in human natural killer cells rely upon autocrine bone morphogenetic protein signaling.

    PubMed

    Robson, Neil C; Hidalgo, Laura; McAlpine, Tristan; Wei, Heng; Martínez, Víctor G; Entrena, Ana; Melen, Gustavo J; MacDonald, Andrew S; Phythian-Adams, Alexander; Sacedón, Rosa; Maraskovsky, Eugene; Cebon, Jonathan; Ramírez, Manuel; Vicente, Angeles; Varas, Alberto

    2014-09-15

    Natural killer (NK) cells are critical for innate tumor immunity due to their specialized ability to recognize and kill neoplastically transformed cells. However, NK cells require a specific set of cytokine-mediated signals to achieve optimal effector function. Th1-associated cytokines promote effector functions that are inhibited by the prototypic Th2 cytokine IL4 and the TGFβ superfamily members TGFβ1 and activin-A. Interestingly, the largest subgroup of the TGFβ superfamily are the bone morphogenetic proteins (BMP), but the effects of BMP signaling on NK cell effector functions have not been evaluated. Here, we demonstrate that blood-circulating NK cells express type I and II BMP receptors, BMP-2 and BMP-6 ligands, and phosphorylated isoforms of Smad-1/-5/-8, which mediate BMP family member signaling. In opposition to the inhibitory effects of TGFβ1 or activin-A, autocrine BMP signaling was supportive to NK cell function. Mechanistic investigations in cytokine and TLR-L-activated NK cells revealed that BMP signaling optimized IFNγ and global cytokine and chemokine production, phenotypic activation and proliferation, and autologous dendritic cell activation and target cytotoxicity. Collectively, our findings identify a novel auto-activatory pathway that is essential for optimal NK cell effector function, one that might be therapeutically manipulated to help eradicate tumors. Cancer Res; 74(18); 5019-31. ©2014 AACR.

  5. Optimal effector functions in human natural killer cells rely upon autocrine bone morphogenetic protein signaling

    PubMed Central

    Mc Alpine, Tristan; Wei, Heng; Martínez, Víctor G.; Entrena, Ana; Melen, Gustavo J; MacDonald, Andrew S.; Phythian-Adams, Alexander; Sacedón, Rosa; Maraskovsky, Eugene; Cebon, Jonathan; Ramírez, Manuel

    2014-01-01

    Natural killer (NK) cells are critical for innate tumor immunity due to their specialized ability to recognize and kill neoplastically transformed cells. However, NK cells require a specific set of cytokine-mediated signals to achieve optimal effector function. Th1-associated cytokines promote effector functions which are inhibited by the prototypic Th-2 cytokine IL-4 and the TGF-β superfamily members TGF-β1 and activin-A. Interestingly, the largest subgroup of the TGF-β superfamily are the bone morphogenetic proteins (BMP), but the effects of BMP signaling to NK cell effector functions have not been evaluated. Here we demonstrate that blood-circulating NK cells express type I and II BMP receptors, BMP-2 and BMP-6 ligands, and phosphorylated isoforms of Smad-1/-5/-8 which mediate BMP family member signaling. In opposition to the inhibitory effects of TGF-β1 or activin-A, autocrine BMP signaling was supportive to NK cell function. Mechanistic investigations in cytokine and TLR-L activated NK cells revealed that BMP signaling optimized IFN-γ and global cytokine and chemokine production; phenotypic activation and proliferation; autologous DC activation and target cytotoxicity. Collectively, our findings identify a novel auto-activatory pathway that is essential for optimal NK cell effector function, one which might be therapeutically manipulated to help eradicate tumors. PMID:25038228

  6. Optimizing drilling performance using a selected drilling fluid

    SciTech Connect

    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.

  7. 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.

  8. Phase modulation signals optimization automatically for suppression of stimulated Brillouin scattering

    NASA Astrophysics Data System (ADS)

    Jiang, Min; Ran, Yang; Su, Rongtao; Zhou, Pu

    2016-11-01

    Phase modulation of the signal laser into multiple laser-lines is one of the common methods to suppress the stimulated Brillouin scattering (SBS) effect in high power narrow linewidth fiber amplifiers. In order to achieve optimal effect, the multiple laser-lines should have equal amplitudes. In this paper, the phase modulation signal we employed is the sum of a finite number of sinusoidal signals with different initial phases and different weights. The stochastic parallel gradient descent (SPGD) algorithm is used to search for the optimal initial phases and weights. Through numerical simulation, we obtain homogeneous symmetrical spectra with 11, 19 and 29 lines whose mean square errors of power density are less than 3%.

  9. The analysis of ground penetrating radar signal based on generalized S transform with parameters optimization

    NASA Astrophysics Data System (ADS)

    Xue, Wei; Zhu, Jichao; Rong, Xia; Huang, Yujin; Yang, Yue; Yu, Yunyun

    2017-05-01

    Ground penetrating radar (GPR) is widely used for subsurface detection due to the nondestructive characteristics. GPR signal is non-stationary because of complex medium environment, and time-frequency analysis is the powerful tool for the research of GPR signal. In this paper, a new generalized S transform with parameters optimization is proposed to analyze the GPR signal. In the proposed scheme, first a flexible window function replaces the fixed window function of S transform, then the criterion of time-frequency concentration is used to optimize the parameters of the window function, the aim is to improve the time-frequency resolution and applicability of S transform. The experimental results for synthetic data and practical GPR data show the proposed scheme can enhance the energy concentration in time-frequency domain effectively and provide better layer recognition and target detection performance.

  10. 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.

  11. 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.

  12. Optimized angle selection for radial sampled NMR experiments

    NASA Astrophysics Data System (ADS)

    Gledhill, John M.; Joshua Wand, A.

    2008-12-01

    Sparse sampling offers tremendous potential for overcoming the time limitations imposed by traditional Cartesian sampling of indirectly detected dimensions of multidimensional NMR data. Unfortunately, several otherwise appealing implementations are accompanied by spectral artifacts that have the potential to contaminate the spectrum with false peak intensity. In radial sampling of linked time evolution periods, the artifacts are easily identified and removed from the spectrum if a sufficient set of radial sampling angles is employed. Robust implementation of the radial sampling approach therefore requires optimization of the set of radial sampling angles collected. Here we describe several methods for such optimization. The approaches described take advantage of various aspects of the general simultaneous multidimensional Fourier transform in the analysis of multidimensional NMR data. Radially sampled data are primarily contaminated by ridges extending from authentic peaks. Numerical methods are described that definitively identify artifactual intensity and the optimal set of sampling angles necessary to eliminate it under a variety of scenarios. The algorithms are tested with both simulated and experimentally obtained triple resonance data.

  13. A parallel optimization method for product configuration and supplier selection based on interval

    NASA Astrophysics Data System (ADS)

    Zheng, Jian; Zhang, Meng; Li, Guoxi

    2017-06-01

    In the process of design and manufacturing, product configuration is an important way of product development, and supplier selection is an essential component of supply chain management. To reduce the risk of procurement and maximize the profits of enterprises, this study proposes to combine the product configuration and supplier selection, and express the multiple uncertainties as interval numbers. An integrated optimization model of interval product configuration and supplier selection was established, and NSGA-II was put forward to locate the Pareto-optimal solutions to the interval multiobjective optimization model.

  14. 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.

  15. 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.

  16. Selection of magnetorheological brake types via optimal design considering maximum torque and constrained volume

    NASA Astrophysics Data System (ADS)

    Nguyen, Q. H.; Choi, S. B.

    2012-01-01

    This research focuses on optimal design of different types of magnetorheological brakes (MRBs), from which an optimal selection of MRB types is identified. In the optimization, common types of MRB such as disc-type, drum-type, hybrid-types, and T-shaped type are considered. The optimization problem is to find the optimal value of significant geometric dimensions of the MRB that can produce a maximum braking torque. The MRB is constrained in a cylindrical volume of a specific radius and length. After a brief description of the configuration of MRB types, the braking torques of the MRBs are derived based on the Herschel-Bulkley model of the MR fluid. The optimal design of MRBs constrained in a specific cylindrical volume is then analysed. The objective of the optimization is to maximize the braking torque while the torque ratio (the ratio of maximum braking torque and the zero-field friction torque) is constrained to be greater than a certain value. A finite element analysis integrated with an optimization tool is employed to obtain optimal solutions of the MRBs. Optimal solutions of MRBs constrained in different volumes are obtained based on the proposed optimization procedure. From the results, discussions on the optimal selection of MRB types depending on constrained volumes are given.

  17. Optimizing the yield and selectivity of high purity nanoparticle clusters

    NASA Astrophysics Data System (ADS)

    Pease, Leonard F.

    2011-05-01

    Here we investigate the parameters that govern the yield and selectivity of small clusters composed of nanoparticles using a Monte Carlo simulation that accounts for spatial and dimensional distributions in droplet and nanoparticle density and size. Clustering nanoparticles presents a powerful paradigm with which to access properties not otherwise available using individual molecules, individual nanoparticles or bulk materials. However, the governing parameters that precisely tune the yield and selectivity of clusters fabricated via an electrospray droplet evaporation method followed by purification with differential mobility analysis (DMA) remain poorly understood. We find that the product of the electrospray droplet mean diameter to the third power and nanoparticle concentration governs the yield of individual clusters, while the ratio of the nanoparticle standard deviation to the mean diameter governs the selectivity. The resulting, easily accessible correlations may be used to minimize undesirable clustering, such as protein aggregation in the biopharmaceutical industry, and maximize the yield of a particular type of cluster for nanotechnology and energy applications.

  18. Optimization of selection for growth in Menz sheep while minimizing inbreeding depression in fitness traits.

    PubMed

    Gizaw, Solomon; Getachew, Tesfaye; Haile, Aynalem; Rischkowsky, Barbara; Sölkner, Johann; Tibbo, Markos

    2013-06-19

    The genetic trends in fitness (inbreeding, fertility and survival) of a closed nucleus flock of Menz sheep under selection during ten years for increased body weight were investigated to evaluate the consequences of selection for body weight on fitness. A mate selection tool was used to optimize in retrospect the actual selection and matings conducted over the project period to assess if the observed genetic gains in body weight could have been achieved with a reduced level of inbreeding. In the actual selection, the genetic trends for yearling weight, fertility of ewes and survival of lambs were 0.81 kg, -0.00026% and 0.016% per generation. The average inbreeding coefficient remained zero for the first few generations and then tended to increase over generations. The genetic gains achieved with the optimized retrospective selection and matings were highly comparable with the observed values, the correlation between the average breeding values of lambs born from the actual and optimized matings over the years being 0.99. However, the level of inbreeding with the optimized mate selections remained zero until late in the years of selection. Our results suggest that an optimal selection strategy that considers both genetic merits and coancestry of mates should be adopted to sustain the Menz sheep breeding program.

  19. 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.

  20. 49 CFR 236.13 - Spring switch; selection of signal control circuits through circuit controller.

    Code of Federal Regulations, 2013 CFR

    2013-10-01

    ... circuits through circuit controller. 236.13 Section 236.13 Transportation Other Regulations Relating to...; selection of signal control circuits through circuit controller. The control circuits of signals governing... circuit controller, or through the contacts of relay repeating the position of such circuit controller...

  1. 49 CFR 236.13 - Spring switch; selection of signal control circuits through circuit controller.

    Code of Federal Regulations, 2012 CFR

    2012-10-01

    ... circuits through circuit controller. 236.13 Section 236.13 Transportation Other Regulations Relating to...; selection of signal control circuits through circuit controller. The control circuits of signals governing... circuit controller, or through the contacts of relay repeating the position of such circuit controller...

  2. 49 CFR 236.13 - Spring switch; selection of signal control circuits through circuit controller.

    Code of Federal Regulations, 2014 CFR

    2014-10-01

    ... circuits through circuit controller. 236.13 Section 236.13 Transportation Other Regulations Relating to...; selection of signal control circuits through circuit controller. The control circuits of signals governing... circuit controller, or through the contacts of relay repeating the position of such circuit controller...

  3. 49 CFR 236.13 - Spring switch; selection of signal control circuits through circuit controller.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... circuits through circuit controller. 236.13 Section 236.13 Transportation Other Regulations Relating to...; selection of signal control circuits through circuit controller. The control circuits of signals governing... circuit controller, or through the contacts of relay repeating the position of such circuit controller...

  4. 49 CFR 236.13 - Spring switch; selection of signal control circuits through circuit controller.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... circuits through circuit controller. 236.13 Section 236.13 Transportation Other Regulations Relating to...; selection of signal control circuits through circuit controller. The control circuits of signals governing... circuit controller, or through the contacts of relay repeating the position of such circuit controller...

  5. A scenario optimization model for dynamic reserve site selection

    Treesearch

    Stephanie A. Snyder; Robert G. Haight; Charles S. ReVelle

    2004-01-01

    Conservation planners are called upon to make choices and trade-offs about the preservation of natural areas for the protection of species in the face of development pressures. We addressed the problem of selecting sites for protection over time with the objective of maximizing species representation, with uncertainty about future site development, and with periodic...

  6. Optimal parametrization of electrodynamical battery model using model selection criteria

    NASA Astrophysics Data System (ADS)

    Suárez-García, Andrés; Alfonsín, Víctor; Urréjola, Santiago; Sánchez, Ángel

    2015-07-01

    This paper describes the mathematical parametrization of an electrodynamical battery model using different model selection criteria. A good modeling technique is needed by the battery management units in order to increase battery lifetime. The elements of battery models can be mathematically parametrized to enhance their implementation in simulation environments. In this work, the best mathematical parametrizations are selected using three model selection criteria: the coefficient of determination (R2), the Akaike Information Criterion (AIC) and the Bayes Information Criterion (BIC). The R2 criterion only takes into account the error of the mathematical parametrizations, whereas AIC and BIC consider complexity. A commercial 40 Ah lithium iron phosphate (LiFePO4) battery is modeled and then simulated for contrasting. The OpenModelica open-source modeling and simulation environment is used for doing the battery simulations. The mean percent error of the simulations is 0.0985% for the models parametrized with R2 , 0.2300% for the AIC ones, and 0.3756% for the BIC ones. As expected, the R2 selected the most precise, complex and slowest mathematical parametrizations. The AIC criterion chose parametrizations with similar accuracy, but simpler and faster than the R2 ones.

  7. L-O-S-T: Logging Optimization Selection Technique

    Treesearch

    Jerry L. Koger; Dennis B. Webster

    1984-01-01

    L-O-S-T is a FORTRAN computer program developed to systematically quantify, analyze, and improve user selected harvesting methods. Harvesting times and costs are computed for road construction, landing construction, system move between landings, skidding, and trucking. A linear programming formulation utilizing the relationships among marginal analysis, isoquants, and...

  8. On some Methods for Constructing Optimal Subset Selection Procedures

    DTIC Science & Technology

    1976-09-01

    Reproduction in whole or in part is permitted for any purpose of the United States Government. cc-I U On Some Methods for Constructing Optimal...Reproduction in whole or in part is permitted for any purpose of the United States Government. 2/ LL-mma 1. A Family of’ distributions P, has SIP in...if" E ,,(X) , Eep(X) for,9 - _3 - - 𔃽 - al I non-decre:asin integralle function ip(x) and all (3 < It is ea;v to generalize Alam’s result in [1] as

  9. Ensembles of signal transduction models using Pareto Optimal Ensemble Techniques (POETs).

    PubMed

    Song, Sang Ok; Chakrabarti, Anirikh; Varner, Jeffrey D

    2010-07-01

    Mathematical modeling of complex gene expression programs is an emerging tool for understanding disease mechanisms. However, identification of large models sometimes requires training using qualitative, conflicting or even contradictory data sets. One strategy to address this challenge is to estimate experimentally constrained model ensembles using multiobjective optimization. In this study, we used Pareto Optimal Ensemble Techniques (POETs) to identify a family of proof-of-concept signal transduction models. POETs integrate Simulated Annealing (SA) with Pareto optimality to identify models near the optimal tradeoff surface between competing training objectives. We modeled a prototypical-signaling network using mass-action kinetics within an ordinary differential equation (ODE) framework (64 ODEs in total). The true model was used to generate synthetic immunoblots from which the POET algorithm identified the 117 unknown model parameters. POET generated an ensemble of signaling models, which collectively exhibited population-like behavior. For example, scaled gene expression levels were approximately normally distributed over the ensemble following the addition of extracellular ligand. Also, the ensemble recovered robust and fragile features of the true model, despite significant parameter uncertainty. Taken together, these results suggest that experimentally constrained model ensembles could capture qualitatively important network features without exact parameter information.

  10. 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

  11. Optimization of the Design of Pre-Signal System Using Improved Cellular Automaton

    PubMed Central

    Li, Yan; Li, Ke; Tao, Siran; Chen, Kuanmin

    2014-01-01

    The pre-signal system can improve the efficiency of intersection approach under rational design. One of the main obstacles in optimizing the design of pre-signal system is that driving behaviors in the sorting area cannot be well evaluated. The NaSch model was modified by considering slow probability, turning-deceleration rules, and lane changing rules. It was calibrated with field observed data to explore the interactions among design parameters. The simulation results of the proposed model indicate that the length of sorting area, traffic demand, signal timing, and lane allocation are the most important influence factors. The recommendations of these design parameters are demonstrated. The findings of this paper can be foundations for the design of pre-signal system and show promising improvement in traffic mobility. PMID:25435871

  12. Optimization of the design of pre-signal system using improved cellular automaton.

    PubMed

    Li, Yan; Li, Ke; Tao, Siran; Wan, Xia; Chen, Kuanmin

    2014-01-01

    The pre-signal system can improve the efficiency of intersection approach under rational design. One of the main obstacles in optimizing the design of pre-signal system is that driving behaviors in the sorting area cannot be well evaluated. The NaSch model was modified by considering slow probability, turning-deceleration rules, and lane changing rules. It was calibrated with field observed data to explore the interactions among design parameters. The simulation results of the proposed model indicate that the length of sorting area, traffic demand, signal timing, and lane allocation are the most important influence factors. The recommendations of these design parameters are demonstrated. The findings of this paper can be foundations for the design of pre-signal system and show promising improvement in traffic mobility.

  13. 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.

  14. Selection of optimal sensors for predicting performance of polymer electrolyte membrane fuel cell

    NASA Astrophysics Data System (ADS)

    Mao, Lei; Jackson, Lisa

    2016-10-01

    In this paper, sensor selection algorithms are investigated based on a sensitivity analysis, and the capability of optimal sensors in predicting PEM fuel cell performance is also studied using test data. The fuel cell model is developed for generating the sensitivity matrix relating sensor measurements and fuel cell health parameters. From the sensitivity matrix, two sensor selection approaches, including the largest gap method, and exhaustive brute force searching technique, are applied to find the optimal sensors providing reliable predictions. Based on the results, a sensor selection approach considering both sensor sensitivity and noise resistance is proposed to find the optimal sensor set with minimum size. Furthermore, the performance of the optimal sensor set is studied to predict fuel cell performance using test data from a PEM fuel cell system. Results demonstrate that with optimal sensors, the performance of PEM fuel cell can be predicted with good quality.

  15. 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.

  16. 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

  17. Optimization of neural network architecture for classification of radar jamming FM signals

    NASA Astrophysics Data System (ADS)

    Soto, Alberto; Mendoza, Ariadna; Flores, Benjamin C.

    2017-05-01

    The purpose of this study is to investigate several artificial Neural Network (NN) architectures in order to design a cognitive radar system capable of optimally distinguishing linear Frequency-Modulated (FM) signals from bandlimited Additive White Gaussian Noise (AWGN). The goal is to create a theoretical framework to determine an optimal NN architecture to achieve a Probability of Detection (PD) of 95% or higher and a Probability of False Alarm (PFA) of 1.5% or lower at 5 dB Signal to Noise Ratio (SNR). Literature research reveals that the frequency-domain power spectral densities characterize a signal more efficiently than its time-domain counterparts. Therefore, the input data is preprocessed by calculating the magnitude square of the Discrete Fourier Transform of the digitally sampled bandlimited AWGN and linear FM signals to populate a matrix containing N number of samples and M number of spectra. This matrix is used as input for the NN, and the spectra are divided as follows: 70% for training, 15% for validation, and 15% for testing. The study begins by experimentally deducing the optimal number of hidden neurons (1-40 neurons), then the optimal number of hidden layers (1-5 layers), and lastly, the most efficient learning algorithm. The training algorithms examined are: Resilient Backpropagation, Scaled Conjugate Gradient, Conjugate Gradient with Powell/Beale Restarts, Polak-Ribiére Conjugate Gradient, and Variable Learning Rate Backpropagation. We determine that an architecture with ten hidden neurons (or higher), one hidden layer, and a Scaled Conjugate Gradient for training algorithm encapsulates an optimal architecture for our application.

  18. Selection of Reserves for Woodland Caribou Using an Optimization Approach

    PubMed Central

    Schneider, Richard R.; Hauer, Grant; Dawe, Kimberly; Adamowicz, Wiktor; Boutin, Stan

    2012-01-01

    Habitat protection has been identified as an important strategy for the conservation of woodland caribou (Rangifer tarandus). However, because of the economic opportunity costs associated with protection it is unlikely that all caribou ranges can be protected in their entirety. We used an optimization approach to identify reserve designs for caribou in Alberta, Canada, across a range of potential protection targets. Our designs minimized costs as well as three demographic risk factors: current industrial footprint, presence of white-tailed deer (Odocoileus virginianus), and climate change. We found that, using optimization, 60% of current caribou range can be protected (including 17% in existing parks) while maintaining access to over 98% of the value of resources on public lands. The trade-off between minimizing cost and minimizing demographic risk factors was minimal because the spatial distributions of cost and risk were similar. The prospects for protection are much reduced if protection is directed towards the herds that are most at risk of near-term extirpation. PMID:22363702

  19. Selection of reserves for woodland caribou using an optimization approach.

    PubMed

    Schneider, Richard R; Hauer, Grant; Dawe, Kimberly; Adamowicz, Wiktor; Boutin, Stan

    2012-01-01

    Habitat protection has been identified as an important strategy for the conservation of woodland caribou (Rangifer tarandus). However, because of the economic opportunity costs associated with protection it is unlikely that all caribou ranges can be protected in their entirety. We used an optimization approach to identify reserve designs for caribou in Alberta, Canada, across a range of potential protection targets. Our designs minimized costs as well as three demographic risk factors: current industrial footprint, presence of white-tailed deer (Odocoileus virginianus), and climate change. We found that, using optimization, 60% of current caribou range can be protected (including 17% in existing parks) while maintaining access to over 98% of the value of resources on public lands. The trade-off between minimizing cost and minimizing demographic risk factors was minimal because the spatial distributions of cost and risk were similar. The prospects for protection are much reduced if protection is directed towards the herds that are most at risk of near-term extirpation.

  20. Dynamic optimization of open-loop input signals for ramp-up current profiles in tokamak plasmas

    NASA Astrophysics Data System (ADS)

    Ren, Zhigang; Xu, Chao; Lin, Qun; Loxton, Ryan; Teo, Kok Lay

    2016-03-01

    Establishing a good current spatial profile in tokamak fusion reactors is crucial to effective steady-state operation. The evolution of the current spatial profile is related to the evolution of the poloidal magnetic flux, which can be modeled in the normalized cylindrical coordinates using a parabolic partial differential equation (PDE) called the magnetic diffusion equation. In this paper, we consider the dynamic optimization problem of attaining the best possible current spatial profile during the ramp-up phase of the tokamak. We first use the Galerkin method to obtain a finite-dimensional ordinary differential equation (ODE) model based on the original magnetic diffusion PDE. Then, we combine the control parameterization method with a novel time-scaling transformation to obtain an approximate optimal parameter selection problem, which can be solved using gradient-based optimization techniques such as sequential quadratic programming (SQP). This control parameterization approach involves approximating the tokamak input signals by piecewise-linear functions whose slopes and break-points are decision variables to be optimized. We show that the gradient of the objective function with respect to the decision variables can be computed by solving an auxiliary dynamic system governing the state sensitivity matrix. Finally, we conclude the paper with simulation results for an example problem based on experimental data from the DIII-D tokamak in San Diego, California.

  1. Optimal selection of Orbital Replacement Unit on-orbit spares - A Space Station system availability model

    NASA Technical Reports Server (NTRS)

    Schwaab, Douglas G.

    1991-01-01

    A mathematical programing model is presented to optimize the selection of Orbital Replacement Unit on-orbit spares for the Space Station. The model maximizes system availability under the constraints of logistics resupply-cargo weight and volume allocations.

  2. 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.

  3. Application’s Method of Quadratic Programming for Optimization of Portfolio Selection

    NASA Astrophysics Data System (ADS)

    Kawamoto, Shigeru; Takamoto, Masanori; Kobayashi, Yasuhiro

    Investors or fund-managers face with optimization of portfolio selection, which means that determine the kind and the quantity of investment among several brands. We have developed a method to obtain optimal stock’s portfolio more rapidly from twice to three times than conventional method with efficient universal optimization. The method is characterized by quadratic matrix of utility function and constrained matrices divided into several sub-matrices by focusing on structure of these matrices.

  4. 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.

  5. 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.

  6. Genetic algorithm for the optimization of features and neural networks in ECG signals classification

    PubMed Central

    Li, Hongqiang; Yuan, Danyang; Ma, Xiangdong; Cui, Dianyin; Cao, Lu

    2017-01-01

    Feature extraction and classification of electrocardiogram (ECG) signals are necessary for the automatic diagnosis of cardiac diseases. In this study, a novel method based on genetic algorithm-back propagation neural network (GA-BPNN) for classifying ECG signals with feature extraction using wavelet packet decomposition (WPD) is proposed. WPD combined with the statistical method is utilized to extract the effective features of ECG signals. The statistical features of the wavelet packet coefficients are calculated as the feature sets. GA is employed to decrease the dimensions of the feature sets and to optimize the weights and biases of the back propagation neural network (BPNN). Thereafter, the optimized BPNN classifier is applied to classify six types of ECG signals. In addition, an experimental platform is constructed for ECG signal acquisition to supply the ECG data for verifying the effectiveness of the proposed method. The GA-BPNN method with the MIT-BIH arrhythmia database achieved a dimension reduction of nearly 50% and produced good classification results with an accuracy of 97.78%. The experimental results based on the established acquisition platform indicated that the GA-BPNN method achieved a high classification accuracy of 99.33% and could be efficiently applied in the automatic identification of cardiac arrhythmias. PMID:28139677

  7. Genetic algorithm for the optimization of features and neural networks in ECG signals classification

    NASA Astrophysics Data System (ADS)

    Li, Hongqiang; Yuan, Danyang; Ma, Xiangdong; Cui, Dianyin; Cao, Lu

    2017-01-01

    Feature extraction and classification of electrocardiogram (ECG) signals are necessary for the automatic diagnosis of cardiac diseases. In this study, a novel method based on genetic algorithm-back propagation neural network (GA-BPNN) for classifying ECG signals with feature extraction using wavelet packet decomposition (WPD) is proposed. WPD combined with the statistical method is utilized to extract the effective features of ECG signals. The statistical features of the wavelet packet coefficients are calculated as the feature sets. GA is employed to decrease the dimensions of the feature sets and to optimize the weights and biases of the back propagation neural network (BPNN). Thereafter, the optimized BPNN classifier is applied to classify six types of ECG signals. In addition, an experimental platform is constructed for ECG signal acquisition to supply the ECG data for verifying the effectiveness of the proposed method. The GA-BPNN method with the MIT-BIH arrhythmia database achieved a dimension reduction of nearly 50% and produced good classification results with an accuracy of 97.78%. The experimental results based on the established acquisition platform indicated that the GA-BPNN method achieved a high classification accuracy of 99.33% and could be efficiently applied in the automatic identification of cardiac arrhythmias.

  8. Optimizing the sequence of diameter distributions and selection harvests for uneven-aged stand management

    Treesearch

    Robert G. Haight; J. Douglas Brodie; Darius M. Adams

    1985-01-01

    The determination of an optimal sequence of diameter distributions and selection harvests for uneven-aged stand management is formulated as a discrete-time optimal-control problem with bounded control variables and free-terminal point. An efficient programming technique utilizing gradients provides solutions that are stable and interpretable on the basis of economic...

  9. 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.

  10. 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.

  11. About the use of vector optimization for company's contractors selection

    NASA Astrophysics Data System (ADS)

    Medvedeva, M. A.; Medvedev, M. A.

    2017-07-01

    For effective functioning of an enterprise it is necessary to make a right choice of partners: suppliers of raw material, buyers of finished products, and others with which the company interacts in the course of their business. However, the presence on the market of big amount of enterprises makes the choice of the most appropriate among them very difficult and requires the ability to objectively assess of the possible partners, based on multilateral analysis of their activities. This analysis can be carried out based on the solution of multiobjective problem of mathematical programming by using the methods of vector optimization. The present work addresses the theoretical foundations of such approach and also describes an algorithm realizing proposed method on practical example.

  12. Optimized signal detection and analysis methods for in vivo photoacoustic flow cytometry

    NASA Astrophysics Data System (ADS)

    Wang, Qiyan; Zhou, Quanyu; Yang, Ping; Wang, Xiaoling; Niu, Zhenyu; Suo, Yuanzhen; He, Hao; Gao, Wenyuan; Tang, Shuo; Wei, Xunbin

    2017-02-01

    Melanoma is known as a malignant tumor of melanocytes, which usually appear in the blood circulation at the metastasis stage of cancer. Thus the detection of circulating melanoma cells is useful for early diagnosis and therapy of cancer. Here we have developed an in vivo photoacoustic flow cytometry (PAFC) based on the photoacoustic effect to detect melanoma cells. However, the raw signals we obtain from the target cells contain noises such as environmental sonic noises and electronic noises. Therefore we apply correlation comparison and feature separation methods to the detection and verification of the in vivo signals. Due to similar shape and structure of cells, the photoacoustic signals usually have similar vibration mode. By analyzing the correlations and the signal features in time domain and frequency domain, we are able to provide a method for separating photoacoustic signals generated by target cells from background noises. The method introduced here has proved to optimize the signal acquisition and signal processing, which can improve the detection accuracy in PAFC.

  13. 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

  14. 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

  15. A Cathodic "Signal-off" Photoelectrochemical Aptasensor for Ultrasensitive and Selective Detection of Oxytetracycline.

    PubMed

    Yan, Kai; Liu, Yong; Yang, Yaohua; Zhang, Jingdong

    2015-12-15

    A novel cathodic "signal-off" strategy was proposed for photoelectrochemical (PEC) aptasensing of oxytetracycline (OTC). The PEC sensor was constructed by employing a p-type semiconductor BiOI doped with graphene (G) as photoactive species and OTC-binding aptamer as a recognition element. The morphological structure and crystalline phases of obtained BiOI-G nanocomposites were characterized by scanning electron microscopy (SEM) and X-ray diffraction (XRD). The UV-visible absorption spectroscopic analysis indicated that doping of BiOI with graphene improved the absorption of materials in the visible light region. Moreover, graphene could facilitate the electron transfer of BiOI modified electrode. As a result, the cathodic photocurrent response of BiOI under visible light irradiation was significantly promoted when a suitable amount of graphene was doped. When amine-functionalized OTC-binding aptamer was immobilized on the BiOI-G modified electrode, a cathodic PEC aptasensor was fabricated, which exhibited a declined photocurrent response to OTC. Under the optimized conditions, the photocurrent response of aptamer/BiOI-G/FTO was linearly proportional to the concentration of OTC ranging from 4.0 to 150 nM, with a detection limit (3S/N) of 0.9 nM. This novel PEC sensing strategy demonstrated an ultrasensitive method for OTC detection with high selectivity and good stability.

  16. 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

  17. Optimizing purebred selection for crossbred performance using QTL with different degrees of dominance

    PubMed Central

    Dekkers, Jack CM; Chakraborty, Reena

    2004-01-01

    A method was developed to optimize simultaneous selection for a quantitative trait with a known QTL within a male and a female line to maximize crossbred performance from a two-way cross. Strategies to maximize cumulative discounted response in crossbred performance over ten generations were derived by optimizing weights in an index of a QTL and phenotype. Strategies were compared to selection on purebred phenotype. Extra responses were limited for QTL with additive and partial dominance effects, but substantial for QTL with over-dominance, for which optimal QTL selection resulted in differential selection in male and female lines to increase the frequency of heterozygotes and polygenic responses. For over-dominant QTL, maximization of crossbred performance one generation at a time resulted in similar responses as optimization across all generations and simultaneous optimal selection in a male and female line resulted in greater response than optimal selection within a single line without crossbreeding. Results show that strategic use of information on over-dominant QTL can enhance crossbred performance without crossbred testing. PMID:15107268

  18. 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

  19. 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

  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.

    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

  1. 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

  2. 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.

  3. 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.

  4. 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.

  5. Analysis of signal to noise enhancement using a highly selective modulation tracking filter

    NASA Technical Reports Server (NTRS)

    Haden, C. R.; Alworth, C. W.

    1972-01-01

    Experiments are reported which utilize photodielectric effects in semiconductor loaded superconducting resonant circuits for suppressing noise in RF communication systems. The superconducting tunable cavity acts as a narrow band tracking filter for detecting conventional RF signals. Analytical techniques were developed which lead to prediction of signal-to-noise improvements. Progress is reported in optimization of the experimental variables. These include improved Q, new semiconductors, improved optics, and simplification of the electronics. Information bearing signals were passed through the system, and noise was introduced into the computer model.

  6. Particle swarm optimizer for weighting factor selection in intensity-modulated radiation therapy optimization algorithms.

    PubMed

    Yang, Jie; Zhang, Pengcheng; Zhang, Liyuan; Shu, Huazhong; Li, Baosheng; Gui, Zhiguo

    2017-01-01

    In inverse treatment planning of intensity-modulated radiation therapy (IMRT), the objective function is typically the sum of the weighted sub-scores, where the weights indicate the importance of the sub-scores. To obtain a high-quality treatment plan, the planner manually adjusts the objective weights using a trial-and-error procedure until an acceptable plan is reached. In this work, a new particle swarm optimization (PSO) method which can adjust the weighting factors automatically was investigated to overcome the requirement of manual adjustment, thereby reducing the workload of the human planner and contributing to the development of a fully automated planning process. The proposed optimization method consists of three steps. (i) First, a swarm of weighting factors (i.e., particles) is initialized randomly in the search space, where each particle corresponds to a global objective function. (ii) Then, a plan optimization solver is employed to obtain the optimal solution for each particle, and the values of the evaluation functions used to determine the particle's location and the population global location for the PSO are calculated based on these results. (iii) Next, the weighting factors are updated based on the particle's location and the population global location. Step (ii) is performed alternately with step (iii) until the termination condition is reached. In this method, the evaluation function is a combination of several key points on the dose volume histograms. Furthermore, a perturbation strategy - the crossover and mutation operator hybrid approach - is employed to enhance the population diversity, and two arguments are applied to the evaluation function to improve the flexibility of the algorithm. In this study, the proposed method was used to develop IMRT treatment plans involving five unequally spaced 6MV photon beams for 10 prostate cancer cases. The proposed optimization algorithm yielded high-quality plans for all of the cases, without human

  7. Optimizing drug exposure to minimize selection of antibiotic resistance.

    PubMed

    Olofsson, Sara K; Cars, Otto

    2007-09-01

    The worldwide increase in antibiotic resistance is a concern for public health. The fact that the choice of dose and treatment duration can affect the selection of antibiotic-resistant mutants is becoming more evident, and an increased number of studies have used pharmacodynamic models to describe the drug exposure and pharmacodynamic breakpoints needed to minimize and predict the development of resistance. However, there remains a lack of sufficient data, and future work is needed to fully characterize these target drug concentrations. More knowledge is also needed of drug pharmacodynamics versus bacteria with different resistance mutations and susceptibility levels. The dosing regimens should exhibit high efficacy not only against susceptible wild-type bacteria but, preferably, also against mutated bacteria that may exist in low numbers in "susceptible" populations. Thus, to prolong the life span of existing and new antibiotics, it is important that dosing regimens be carefully selected on the basis of pharmacokinetic and pharmacodynamic properties that prevent emergence of preexisting and newly formed mutants.

  8. Optimizing the Selectivity of Surface-Adsorbing Multivalent Polymers

    PubMed Central

    2014-01-01

    Multivalent polymers are macromolecules containing multiple chemical moieties designed to bind to complementary moieties on a target; for example, a protein with multiple ligands that have affinity for receptors on a cell surface. Though the individual ligand–receptor bonds are often weak, the combinatorial entropy associated with the different possible ligand–receptor pairs leads to a binding transition that can be very sharp with respect to control parameters, such as temperature or surface receptor concentration. We use mean-field self-consistent field theory to study the binding selectivity of multivalent polymers to receptor-coated surfaces. Polymers that have their ligands clustered into a contiguous domain, either located at the chain end or chain midsection, exhibit cooperative surface adsorption and superselectivity when the polymer concentration is low. On the other hand, when the ligands are uniformly spaced along the chain backbone, selectivity is substantially reduced due to the lack of binding cooperativity and due to crowding of the surface by the inert polymer segments in the chain backbone. PMID:25400296

  9. Optimization of a quantitative signal detection algorithm for spontaneous reports of adverse events post immunization.

    PubMed

    Van Holle, Lionel; Bauchau, Vincent

    2013-05-01

    To optimize the efficiency of signal detection by maximizing the proportion of true positive (TP) signals among signals detected by a disproportionality algorithm. We compared 176 different combinations of stratification factors, sex (S), age (A), region (R) and year of report (Y), and cut-off values of a Multi-Item Gamma Poisson Schrinker (MGPS) algorithm. Spontaneous adverse event reports of eight vaccines from the GlaxoSmithKline Biologicals safety database were used. Defining events listed in the Product Information as proxy of true safety signals, we compared each algorithm performance in terms of positive predictive value (PPV). For each vaccine, each algorithm was ranked according to PPV. Median rank and overall PPV were computed across vaccines. For a standard cut-off of 2, the optimal stratification factors differed by vaccine and led to a set of algorithms with a median rank of 34.5 (PPV = 0.22; 34 TP). Keeping the original SARY stratification led to optimal cut-offs that differed by vaccine and a set of algorithms with a median rank of 1.75 (PPV = 0.20; 142 TP). The optimal combination of cut-off and stratification led to different algorithms by vaccine with a median rank of 1 (PPV = 0.19; 139 TP). The best unique algorithm parameterization across vaccines was 0.8-SARY (cut-off-stratification), with a median rank of 3 (PPV = 0.20; 195 TP). The original 2-SARY was one of the worst algorithms, with a median rank of 150.75 (PPV = 0.13; 8 TP). Within the scope of this study, a unique MGPS algorithm across vaccines with the original full stratification but a lowered cut-off provided major performance improvement. Copyright © 2012 John Wiley & Sons, Ltd.

  10. 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

  11. Risk based treatment selection and optimization of contaminated site remediation

    SciTech Connect

    Heitzer, A.; Scholz, R.W.

    1995-12-31

    During the past few years numerous remediation technologies for the cleanup of contaminated sites have been developed. Because of the associated uncertainties concerning treatment reliability it is important to develop strategies to characterize their risks to achieve the cleanup requirements. For this purpose it is necessary to integrate existing knowledge on treatment efficacy and efficiency into the planning process for the management of contaminated sites. Based on field-scale experience data for the remediation of soils contaminated with petroleum hydrocarbons, two treatment technologies, biological land treatment and phyisco-chemical soil washing, were analyzed with respect to their general performance risks to achieve given cleanup standards. For a specific contamination scenario, efficient application ranges were identified using the method of linear optimization in combination with sensitivity analysis. Various constraints including cleanup standards, available financial budget, amount of contamination and others were taken into account. While land treatment was found to be most efficient at higher cleanup standards and less contaminated soils, soil washing exhibited better efficiency at lower cleanup standards and higher contaminated soils. These results compare favorably with practical experiences and indicate the utility of this approach to support decision making and planning processes for the general management of contaminated sites. In addition, the method allows for the simultaneous integration of various aspects such as risk based characteristics of treatment technologies, cleanup standards and more general ecological and economical remedial action objectives.

  12. Stationary phase optimized selectivity liquid chromatography: Basic possibilities of serially connected columns using the "PRISMA" principle.

    PubMed

    Nyiredy, Sz; Szucs, Zoltán; Szepesy, L

    2007-07-20

    A new procedure (stationary phase optimized selectivity liquid chromatography: SOS-LC) is described for the optimization of the HPLC stationary phase, using serially connected columns and the principle of the "PRISMA" model. The retention factors (k) of the analytes were determined on three different stationary phases. By use of these data the k values were predicted applying theoretically combined stationary phases. These predictions resulted in numerous intermediate theoretical separations from among which only the optimal one was assembled and tested. The overall selectivity of this separation was better than that of any individual base stationary phase. SOS-LC is independent of the mechanism and the scale of separation.

  13. 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.

  14. Optimal filtering of gear signals for early damage detection based on the spectral kurtosis

    NASA Astrophysics Data System (ADS)

    Combet, F.; Gelman, L.

    2009-04-01

    In this paper, we propose a methodology for the enhancement of small transients in gear vibration signals in order to detect local tooth faults, such as pitting, at an early stage of damage. We propose to apply the optimal denoising (Wiener) filter based on the spectral kurtosis (SK). The originality is to estimate and apply this filter to the gear residual signal, as classically obtained after removing the mesh harmonics from the time synchronous average (TSA). This presents several advantages over the direct estimation from the raw vibration signal: improved signal/noise ratio, reduced interferences from other stages of the gearbox and easier detection of excited structural resonance(s) within the range of the mesh harmonic components. From the SK-based filtered residual signal, called SK-residual, we define the local power as the smoothed squared envelope, which reflects both the energy and the degree of non-stationarity of the fault-induced transients. The methodology is then applied to an industrial case and shows the possibility of detection of relatively small tooth surface pitting (less than 10%) in a two-stage helical reduction gearbox. The adjustment of the resolution for the SK estimation appears to be optimal when the length of the analysis window is approximately matched with the mesh period of the gear. The proposed approach is also compared to an inverse filtering (blind deconvolution) approach. However, the latter turns out to be more unstable and sensitive to noise and shows a lower degree of separation, quantified by the Fisher criterion, between the estimated diagnostic features in the pitted and unpitted cases. Thus, the proposed optimal filtering methodology based on the SK appears to be well adapted for the early detection of local tooth damage in gears.

  15. 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

  16. Sexually selected UV signals in the tropical ornate jumping spider, Cosmophasis umbratica may incur costs from predation

    PubMed Central

    Bulbert, Matthew W; O'Hanlon, James C; Zappettini, Shane; Zhang, Shichang; Li, Daiqin

    2015-01-01

    Sexually selected ornaments and signals are costly to maintain if they are maladaptive in nonreproductive contexts. The jumping spider Cosmophasis umbratica exhibits distinct sexual dichromatism with males displaying elaborate UV body markings that signal male quality. Female C. umbratica respond favorably to UV-reflecting males and ignore males that have their UV masked. However, Portia labiata, a UV-sensitive spider-eating specialist and a natural predator of C. umbratica, is known to use UV reflectance as a cue when hunting prey. We investigated the cost of these UV signals in C. umbratica in terms of their predation risk. Under experimental conditions, three choice scenarios were presented to P. labiata individuals. Choices by P. labiata were made between male C. umbratica with and without the UV signal; a UV-reflecting male and non-UV-reflecting female; and a UV-masked male and female. The presence and absence of UV signals was manipulated using an optical filter. Portia labiata exhibited a strong bias toward UV+ individuals. These results suggest the sexually selected trait of UV reflectance increases the visibility of males to UV-sensitive predators. The extent of this male-specific UV signal then is potentially moderated by predation pressure. Interestingly though, P. labiata still preferred males to females irrespective of whether UV reflectance was present or not. This suggests P. labiata can switch cues when conditions to detect UV reflectance are not optimal. PMID:25750717

  17. 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

  18. 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…

  19. 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…

  20. 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.

  1. Automatic parameter optimization in epsilon-filter for acoustical signal processing utilizing correlation coefficient.

    PubMed

    Abe, Tomomi; Hashimoto, Shuji; Matsumoto, Mitsuharu

    2010-02-01

    epsilon-filter can reduce most kinds of noise from a single-channel noisy signal while preserving signals that vary drastically such as speech signals. It can reduce not only stationary noise but also nonstationary noise. However, it has some parameters whose values are set empirically. So far, there have been few studies to evaluate the appropriateness of the parameter settings for epsilon-filter. This paper employs the correlation coefficient of the filter output and the difference between the filter input and output as the evaluation function of the parameter setting. This paper also describes the algorithm to set the optimal parameter value of epsilon-filter automatically. To evaluate the adequateness of the obtained parameter, the mean absolute error is calculated. The experimental results show that the adequate parameter in epsilon-filter can be obtained automatically by using the proposed method.

  2. Optimal design of optical reference signals by use of a genetic algorithm

    NASA Astrophysics Data System (ADS)

    Saez-Landete, José; Salcedo-Sanz, Sancho; Rosa-Zurera, Manuel; Alonso, José; Bernabeu, Eusebio

    2005-10-01

    A new technique for the generation of optical reference signals with optimal properties is presented. In grating measurement systems a reference signal is needed to achieve an absolute measurement of the position. The optical signal is the autocorrelation of two codes with binary transmittance. For a long time, the design of this type of code has required great computational effort, which limits the size of the code to ˜30 elements. Recently, the application of the dividing rectangles (DIRECT) algorithm has allowed the automatic design of codes up to 100 elements. Because of the binary nature of the problem and the parallel processing of the genetic algorithms, these algorithms are efficient tools for obtaining codes with particular autocorrelation properties. We design optimum zero reference codes with arbitrary length by means of a genetic algorithm enhanced with a restricted search operator.

  3. Signal to noise ratio of energy selective x-ray photon counting systems with pileup.

    PubMed

    Alvarez, Robert E

    2014-11-01

    To derive fundamental limits on the effect of pulse pileup and quantum noise in photon counting detectors on the signal to noise ratio (SNR) and noise variance of energy selective x-ray imaging systems. An idealized model of the response of counting detectors to pulse pileup is used. The model assumes a nonparalyzable response and delta function pulse shape. The model is used to derive analytical formulas for the noise and energy spectrum of the recorded photons with pulse pileup. These formulas are first verified with a Monte Carlo simulation. They are then used with a method introduced in a previous paper [R. E. Alvarez, "Near optimal energy selective x-ray imaging system performance with simple detectors," Med. Phys. 37, 822-841 (2010)] to compare the signal to noise ratio with pileup to the ideal SNR with perfect energy resolution. Detectors studied include photon counting detectors with pulse height analysis (PHA), detectors that simultaneously measure the number of photons and the integrated energy (NQ detector), and conventional energy integrating and photon counting detectors. The increase in the A-vector variance with dead time is also computed and compared to the Monte Carlo results. A formula for the covariance of the NQ detector is developed. The validity of the constant covariance approximation to the Cramèr-Rao lower bound (CRLB) for larger counts is tested. The SNR becomes smaller than the conventional energy integrating detector (Q) SNR for 0.52, 0.65, and 0.78 expected number photons per dead time for counting (N), two, and four bin PHA detectors, respectively. The NQ detector SNR is always larger than the N and Q SNR but only marginally so for larger dead times. Its noise variance increases by a factor of approximately 3 and 5 for the A1 and A2 components as the dead time parameter increases from 0 to 0.8 photons per dead time. With four bin PHA data, the increase in variance is approximately 2 and 4 times. The constant covariance approximation

  4. 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.

  5. On the complexity of discrete feature selection for optimal classification.

    PubMed

    Peña, Jose M; Nilsson, Roland

    2010-08-01

    Consider a classification problem involving only discrete features that are represented as random variables with some prescribed discrete sample space. In this paper, we study the complexity of two feature selection problems. The first problem consists in finding a feature subset of a given size k that has minimal Bayes risk. We show that for any increasing ordering of the Bayes risks of the feature subsets (consistent with an obvious monotonicity constraint), there exists a probability distribution that exhibits that ordering. This implies that solving the first problem requires an exhaustive search over the feature subsets of size k. The second problem consists of finding the minimal feature subset that has minimal Bayes risk. In the light of the complexity of the first problem, one may think that solving the second problem requires an exhaustive search over all of the feature subsets. We show that, under mild assumptions, this is not true. We also study the practical implications of our solutions to the second problem.

  6. Storage of human biospecimens: selection of the optimal storage temperature.

    PubMed

    Hubel, Allison; Spindler, Ralf; Skubitz, Amy P N

    2014-06-01

    Millions of biological samples are currently kept at low tempertures in cryobanks/biorepositories for long-term storage. The quality of the biospecimen when thawed, however, is not only determined by processing of the biospecimen but the storage conditions as well. The overall objective of this article is to describe the scientific basis for selecting a storage temperature for a biospecimen based on current scientific understanding. To that end, this article reviews some physical basics of the temperature, nucleation, and ice crystal growth present in biological samples stored at low temperatures (-20°C to -196°C), and our current understanding of the role of temperature on the activity of degradative molecules present in biospecimens. The scientific literature relevant to the stability of specific biomarkers in human fluid, cell, and tissue biospecimens is also summarized for the range of temperatures between -20°C to -196°C. These studies demonstrate the importance of storage temperature on the stability of critical biomarkers for fluid, cell, and tissue biospecimens.

  7. 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.

  8. Applying optimal model selection in principal stratification for causal inference.

    PubMed

    Odondi, Lang'o; McNamee, Roseanne

    2013-05-20

    Noncompliance to treatment allocation is a key source of complication for causal inference. Efficacy estimation is likely to be compounded by the presence of noncompliance in both treatment arms of clinical trials where the intention-to-treat estimate provides a biased estimator for the true causal estimate even under homogeneous treatment effects assumption. Principal stratification method has been developed to address such posttreatment complications. The present work extends a principal stratification method that adjusts for noncompliance in two-treatment arms trials by developing model selection for covariates predicting compliance to treatment in each arm. We apply the method to analyse data from the Esprit study, which was conducted to ascertain whether unopposed oestrogen (hormone replacement therapy) reduced the risk of further cardiac events in postmenopausal women who survive a first myocardial infarction. We adjust for noncompliance in both treatment arms under a Bayesian framework to produce causal risk ratio estimates for each principal stratum. For mild values of a sensitivity parameter and using separate predictors of compliance in each arm, principal stratification results suggested that compliance with hormone replacement therapy only would reduce the risk for death and myocardial reinfarction by about 47% and 25%, respectively, whereas compliance with either treatment would reduce the risk for death by 13% and reinfarction by 60% among the most compliant. However, the results were sensitive to the user-defined sensitivity parameter.

  9. EEG channel selection using particle swarm optimization for the classification of auditory event-related potentials.

    PubMed

    Gonzalez, Alejandro; Nambu, Isao; Hokari, Haruhide; Wada, Yasuhiro

    2014-01-01

    Brain-machine interfaces (BMI) rely on the accurate classification of event-related potentials (ERPs) and their performance greatly depends on the appropriate selection of classifier parameters and features from dense-array electroencephalography (EEG) signals. Moreover, in order to achieve a portable and more compact BMI for practical applications, it is also desirable to use a system capable of accurate classification using information from as few EEG channels as possible. In the present work, we propose a method for classifying P300 ERPs using a combination of Fisher Discriminant Analysis (FDA) and a multiobjective hybrid real-binary Particle Swarm Optimization (MHPSO) algorithm. Specifically, the algorithm searches for the set of EEG channels and classifier parameters that simultaneously maximize the classification accuracy and minimize the number of used channels. The performance of the method is assessed through offline analyses on datasets of auditory ERPs from sound discrimination experiments. The proposed method achieved a higher classification accuracy than that achieved by traditional methods while also using fewer channels. It was also found that the number of channels used for classification can be significantly reduced without greatly compromising the classification accuracy.

  10. EEG Channel Selection Using Particle Swarm Optimization for the Classification of Auditory Event-Related Potentials

    PubMed Central

    Hokari, Haruhide

    2014-01-01

    Brain-machine interfaces (BMI) rely on the accurate classification of event-related potentials (ERPs) and their performance greatly depends on the appropriate selection of classifier parameters and features from dense-array electroencephalography (EEG) signals. Moreover, in order to achieve a portable and more compact BMI for practical applications, it is also desirable to use a system capable of accurate classification using information from as few EEG channels as possible. In the present work, we propose a method for classifying P300 ERPs using a combination of Fisher Discriminant Analysis (FDA) and a multiobjective hybrid real-binary Particle Swarm Optimization (MHPSO) algorithm. Specifically, the algorithm searches for the set of EEG channels and classifier parameters that simultaneously maximize the classification accuracy and minimize the number of used channels. The performance of the method is assessed through offline analyses on datasets of auditory ERPs from sound discrimination experiments. The proposed method achieved a higher classification accuracy than that achieved by traditional methods while also using fewer channels. It was also found that the number of channels used for classification can be significantly reduced without greatly compromising the classification accuracy. PMID:24982944

  11. 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.

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

    PubMed Central

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

    2017-01-01

    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. PMID:27008349

  13. 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.

  14. Visualization of multi-property landscapes for compound selection and optimization

    NASA Astrophysics Data System (ADS)

    de la Vega de León, Antonio; Kayastha, Shilva; Dimova, Dilyana; Schultz, Thomas; Bajorath, Jürgen

    2015-08-01

    Compound optimization generally requires considering multiple properties in concert and reaching a balance between them. Computationally, this process can be supported by multi-objective optimization methods that produce numerical solutions to an optimization task. Since a variety of comparable multi-property solutions are usually obtained further prioritization is required. However, the underlying multi-dimensional property spaces are typically complex and difficult to rationalize. Herein, an approach is introduced to visualize multi-property landscapes by adapting the concepts of star and parallel coordinates from computer graphics. The visualization method is designed to complement multi-objective compound optimization. We show that visualization makes it possible to further distinguish between numerically equivalent optimization solutions and helps to select drug-like compounds from multi-dimensional property spaces. The methodology is intuitive, applicable to a wide range of chemical optimization problems, and made freely available to the scientific community.

  15. Visualization of multi-property landscapes for compound selection and optimization.

    PubMed

    de la Vega de León, Antonio; Kayastha, Shilva; Dimova, Dilyana; Schultz, Thomas; Bajorath, Jürgen

    2015-08-01

    Compound optimization generally requires considering multiple properties in concert and reaching a balance between them. Computationally, this process can be supported by multi-objective optimization methods that produce numerical solutions to an optimization task. Since a variety of comparable multi-property solutions are usually obtained further prioritization is required. However, the underlying multi-dimensional property spaces are typically complex and difficult to rationalize. Herein, an approach is introduced to visualize multi-property landscapes by adapting the concepts of star and parallel coordinates from computer graphics. The visualization method is designed to complement multi-objective compound optimization. We show that visualization makes it possible to further distinguish between numerically equivalent optimization solutions and helps to select drug-like compounds from multi-dimensional property spaces. The methodology is intuitive, applicable to a wide range of chemical optimization problems, and made freely available to the scientific community.

  16. 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.

  17. 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.

  18. A particle swarm optimization algorithm for beam angle selection in intensity-modulated radiotherapy planning.

    PubMed

    Li, Yongjie; Yao, Dezhong; Yao, Jonathan; Chen, Wufan

    2005-08-07

    Automatic beam angle selection is an important but challenging problem for intensity-modulated radiation therapy (IMRT) planning. Though many efforts have been made, it is still not very satisfactory in clinical IMRT practice because of overextensive computation of the inverse problem. In this paper, a new technique named BASPSO (Beam Angle Selection with a Particle Swarm Optimization algorithm) is presented to improve the efficiency of the beam angle optimization problem. Originally developed as a tool for simulating social behaviour, the particle swarm optimization (PSO) algorithm is a relatively new population-based evolutionary optimization technique first introduced by Kennedy and Eberhart in 1995. In the proposed BASPSO, the beam angles are optimized using PSO by treating each beam configuration as a particle (individual), and the beam intensity maps for each beam configuration are optimized using the conjugate gradient (CG) algorithm. These two optimization processes are implemented iteratively. The performance of each individual is evaluated by a fitness value calculated with a physical objective function. A population of these individuals is evolved by cooperation and competition among the individuals themselves through generations. The optimization results of a simulated case with known optimal beam angles and two clinical cases (a prostate case and a head-and-neck case) show that PSO is valid and efficient and can speed up the beam angle optimization process. Furthermore, the performance comparisons based on the preliminary results indicate that, as a whole, the PSO-based algorithm seems to outperform, or at least compete with, the GA-based algorithm in computation time and robustness. In conclusion, the reported work suggested that the introduced PSO algorithm could act as a new promising solution to the beam angle optimization problem and potentially other optimization problems in IMRT, though further studies need to be investigated.

  19. Coupling between protein level selection and codon usage optimization in the evolution of bacteria and archaea.

    PubMed

    Ran, Wenqi; Kristensen, David M; Koonin, Eugene V

    2014-03-25

    The relationship between the selection affecting codon usage and selection on protein sequences of orthologous genes in diverse groups of bacteria and archaea was examined by using the Alignable Tight Genome Clusters database of prokaryote genomes. The codon usage bias is generally low, with 57.5% of the gene-specific optimal codon frequencies (Fopt) being below 0.55. This apparent weak selection on codon usage contrasts with the strong purifying selection on amino acid sequences, with 65.8% of the gene-specific dN/dS ratios being below 0.1. For most of the genomes compared, a limited but statistically significant negative correlation between Fopt and dN/dS was observed, which is indicative of a link between selection on protein sequence and selection on codon usage. The strength of the coupling between the protein level selection and codon usage bias showed a strong positive correlation with the genomic GC content. Combined with previous observations on the selection for GC-rich codons in bacteria and archaea with GC-rich genomes, these findings suggest that selection for translational fine-tuning could be an important factor in microbial evolution that drives the evolution of genome GC content away from mutational equilibrium. This type of selection is particularly pronounced in slowly evolving, "high-status" genes. A significantly stronger link between the two aspects of selection is observed in free-living bacteria than in parasitic bacteria and in genes encoding metabolic enzymes and transporters than in informational genes. These differences might reflect the special importance of translational fine-tuning for the adaptability of gene expression to environmental changes. The results of this work establish the coupling between protein level selection and selection for translational optimization as a distinct and potentially important factor in microbial evolution. IMPORTANCE Selection affects the evolution of microbial genomes at many levels, including both

  20. The influence of the type of the test signal on the result of numerical optimization of regulators

    NASA Astrophysics Data System (ADS)

    Zhmud, V. A.; Dimitrov, L. V.

    2017-01-01

    This paper studies the effect of the choice of the test signal on the result of optimization of the regulator by the example of PID and PI2D structures. It is shown that if the quality response to ramp signals is necessary, optimization of the system only by the simple signals is ineffective. In this case, the resulting system is characterized by excessively large overshoot when the step is supplied to the system input. The paper proposes a method for designing an effective system consisting in numerical optimization for two parallel operating control systems. Its effectiveness has been proved by simulation.

  1. Constraint-selected and search-optimized families of Daubechies wavelet filters computable by spectral factorization

    NASA Astrophysics Data System (ADS)

    Taswell, Carl

    2000-09-01

    A unifying algorithm has been developed to systematize the collection of compact Daubechies wavelets computable by spectral factorization of a symmetric positive polynomial. This collection comprises all classes of real and complex orthogonal and biorthogonal wavelet filters with maximal flatness for their minimal length. The main algorithm incorporates spectral factorization of the Daubechies product filter into analysis and synthesis filters. The spectral factors are found for search-optimized families by examining a desired criterion over combinatorial subsets of roots indexed by binary codes, and for constraint-selected families by imposing sufficient constraints on the roots without any optimizing search for an extremal property. Daubechies wavelet filter families have been systematized to include those constraint-selected by the principle of separably disjoint roots, and those search-optimized for time-domain regularity, frequency-domain selectivity, time-frequency uncertainty, and phase nonlinearity. The latter criterion permits construction of the least and most asymmetric and least and most symmetric real and complex orthogonal filters. Biorthogonal symmetric spline and balanced-length filters with linear phase are also computable by these methods. This systematized collection has been developed in the context of a general framework enabling evaluation of the equivalence of constraint-selected and search-optimized families with respect to the filter coefficients and roots and their characteristics. Some of the constraint-selected families have been demonstrated to be equivalent to some of the search-optimized families, thereby obviating the necessity for any search in their computation.

  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. (c) 2010 Optical Society of America.

  3. 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.

  4. 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

  5. Dopaminergic reward signals selectively decrease fMRI activity in primate visual cortex

    PubMed Central

    Arsenault, J.T.; Nelissen, K.; Jarraya, B.; Vanduffel, W.

    2013-01-01

    Summary Stimulus-reward coupling without attention can induce highly specific perceptual learning effects, suggesting that rewards trigger selective plasticity within visual cortex. Additionally, dopamine-releasing events - temporally-surrounding stimulus-reward associations - selectively enhance memory. These forms of plasticity may be evoked by selective modulation of stimulus representations during dopamine-inducing events. However, it remains to be shown whether dopaminergic signals can selectively modulate visual cortical activity. We measured fMRI activity in monkey visual cortex during reward-only trials apart from intermixed cue-reward trials. Rewards without visual stimulation selectively decreased fMRI activity within the cue representations that had been paired with rewards during other trials. Behavioral tests indicated that these same uncued reward trials strengthened cue-reward associations. Furthermore, such spatially-specific activity modulations depended on prediction error, as shown by manipulations of reward magnitude, cue-reward probability, cue-reward familiarity, and dopamine signaling. This cue-selective negative reward signal offers a mechanism for selectively gating sensory cortical plasticity. PMID:23522051

  6. 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

  7. 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

  8. Visuospatial reorienting signals in the human temporo-parietal junction are independent of response selection.

    PubMed

    Astafiev, Serguei V; Shulman, Gordon L; Corbetta, Maurizio

    2006-01-01

    This study contrasts visuospatial reorienting and response selection signals in the right temporo-parietal junction (TPJ) with functional magnetic resonance imaging. The overall goal was to investigate whether spatial orienting signals and motor signals interacted or were independent in TPJ. The right TPJ showed a greater response to targets at in-validly rather than validly cued locations, but no significant modulation from the effector used to respond. We suggest that TPJ may work as a modality-independent 'circuit breaker' for the dorsal fronto-parietal attention system, directing attention to salient events and enabling a variety of responses to those events.

  9. Mode-selective wavelength conversion of OFDM-QPSK signals in a multimode silicon waveguide.

    PubMed

    Qiu, Ying; Li, Xiang; Luo, Ming; Chen, Daigao; Wang, Jiamin; Xu, Jing; Yang, Qi; Yu, Shaohua

    2017-02-20

    We experimentally demonstrate on-chip mode-selective wavelength conversions based on the degenerate four-wave mixing (FWM) nonlinear effect in a few-mode silicon waveguide. A multimode waveguide with tapered directional coupler based mode (de)multiplexers is designed and fabricated. Using signals with advanced modulation formats all-optical wavelength conversions of 102.6-Gb/s OFDM-QPSK signals are verified. Experimental results show that only small optical signal-to-noise ratio (OSNR) penalties are observed after wavelength conversion of both modes, which are less than 2 dB for OFDM-QPSK at 7% forward error correction (FEC) threshold.

  10. 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

  11. 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.

  12. Enhanced Refocusing of Fat Signals using Optimized Multi-pulse Echo Sequences

    PubMed Central

    Stokes, Ashley M.; Feng, Yesu; Mitropoulos, Tanya; Warren, Warren S.

    2012-01-01

    Endogenous magnetic resonance contrast based on the localized composition of fat in vivo can provide functional information. We found that the unequal pulse timings of the Uhrig’s Dynamical Decoupling (UDD) multipulse echo sequences significantly alter the signal intensity compared to conventional, equal-spaced Carr-Purcell-Meiboom-Gill (CPMG) sequences. The signal increases and decreases depending on the tissue and sequence parameters, as well as on the interpulse spacings; particularly strong differences were observed in fatty tissues, which have a highly structured morphology and a wide range of chemical shifts and J-couplings. We found that the predominant mechanism for fat refocusing under multipulse echo sequences is the chemical structure, with stimulated echoes playing a pivotal role. As a result, specialized pulse sequences can be designed to optimize refocusing of the fat chemical shifts and J-couplings, where the degree of refocusing can be tailored to specific types of fats. To determine the optimal time delays, we simulated various UDD and CPMG pulse sequence timings, and these results are compared to experimental results obtained on excised and in vivo fatty tissue. Applications to intermolecular multiple-quantum coherence (iMQC) imaging, where the improved echo refocusing translates directly into signal enhancements, are presented as well. PMID:22627966

  13. Optimizing seismic monitoring for landslides by using on-site artificially generated signals

    NASA Astrophysics Data System (ADS)

    Yfantis, Georgios; Martinez Carvajal, Hernan Eduardo; Pytharouli, Stella; Lunn, Rebecca

    2013-04-01

    We develop a methodology for the optimization of seismic arrays used for landslide monitoring. We design an experimental field set-up that generates signals caused by soil friction, such as those during a landslide. The set-up allows for controlling the normal stress, moisture content and the size of the slippage interface area. The optimization is based on the frequency and energy content analysis of the emitted artificial landslide signals and how local geology affects them. This can significantly improve the detection threshold of the monitoring system, the design of which, to-date, is mainly based on speculations for the site conditions or expensive borehole logs and its optimization on time-consuming trial and error procedures. We use a concrete cylinder, 0.5m high and 0.65m wide filled with high porous tropical clay excavated from the experimental site. The cylinder is placed on a 4m long, 2m wide clay strip, free from any surficial vegetation. As the cylinder is moved horizontally along the corridor, soil friction generates signals. By varying the load applied by the material within the cylinder we simulate slippage at different depths. Five different normal stress levels between 11.9kPa and 22.5kPa, corresponding to depths of 0.7 and 1.4m respectively, are simulated. The load applied on the slippage surface is the only variable, thus allowing the investigation of the normal stress effect on the emitted signals. The experiment is completed within 3 hours, under the same weather conditions. Therefore, no changes in the clay, i.e. moisture content, take place. We minimize the ambient noise by performing the experiment during the night. For the monitoring of the generated seismic signals we use 12 short period 3-component seismometers with natural frequency of 2Hz. Eight sensors are deployed as a linear array (spacing between them is 1 to 2m with the first and last sensors being 2m and 15m away from the strip, respectively) perpendicular to the direction of

  14. 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.

  15. An Enhanced Grey Wolf Optimization Based Feature Selection Wrapped Kernel Extreme Learning Machine for Medical Diagnosis.

    PubMed

    Li, Qiang; Chen, Huiling; Huang, Hui; Zhao, Xuehua; Cai, ZhenNao; Tong, Changfei; Liu, Wenbin; Tian, Xin

    2017-01-01

    In this study, a new predictive framework is proposed by integrating an improved grey wolf optimization (IGWO) and kernel extreme learning machine (KELM), termed as IGWO-KELM, for medical diagnosis. The proposed IGWO feature selection approach is used for the purpose of finding the optimal feature subset for medical data. In the proposed approach, genetic algorithm (GA) was firstly adopted to generate the diversified initial positions, and then grey wolf optimization (GWO) was used to update the current positions of population in the discrete searching space, thus getting the optimal feature subset for the better classification purpose based on KELM. The proposed approach is compared against the original GA and GWO on the two common disease diagnosis problems in terms of a set of performance metrics, including classification accuracy, sensitivity, specificity, precision, G-mean, F-measure, and the size of selected features. The simulation results have proven the superiority of the proposed method over the other two competitive counterparts.

  16. 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

  17. 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.

  18. Rational optimization of tolC as a powerful dual selectable marker for genome engineering

    PubMed Central

    Gregg, Christopher J.; Lajoie, Marc J.; Napolitano, Michael G.; Mosberg, Joshua A.; Goodman, Daniel B.; Aach, John; Isaacs, Farren J.; Church, George M.

    2014-01-01

    Selection has been invaluable for genetic manipulation, although counter-selection has historically exhibited limited robustness and convenience. TolC, an outer membrane pore involved in transmembrane transport in E. coli, has been implemented as a selectable/counter-selectable marker, but counter-selection escape frequency using colicin E1 precludes using tolC for inefficient genetic manipulations and/or with large libraries. Here, we leveraged unbiased deep sequencing of 96 independent lineages exhibiting counter-selection escape to identify loss-of-function mutations, which offered mechanistic insight and guided strain engineering to reduce counter-selection escape frequency by ∼40-fold. We fundamentally improved the tolC counter-selection by supplementing a second agent, vancomycin, which reduces counter-selection escape by 425-fold, compared colicin E1 alone. Combining these improvements in a mismatch repair proficient strain reduced counter-selection escape frequency by 1.3E6-fold in total, making tolC counter-selection as effective as most selectable markers, and adding a valuable tool to the genome editing toolbox. These improvements permitted us to perform stable and continuous rounds of selection/counter-selection using tolC, enabling replacement of 10 alleles without requiring genotypic screening for the first time. Finally, we combined these advances to create an optimized E. coli strain for genome engineering that is ∼10-fold more efficient at achieving allelic diversity than previous best practices. PMID:24452804

  19. Regulation of cardiomyocyte signaling by RGS proteins: differential selectivity towards G proteins and susceptibility to regulation.

    PubMed

    Hao, Jianming; Michalek, Christina; Zhang, Wei; Zhu, Ming; Xu, Xiaomei; Mende, Ulrike

    2006-07-01

    Many signals that regulate cardiomyocyte growth, differentiation and function are mediated via heterotrimeric G proteins, which are under the control of RGS proteins (Regulators of G protein Signaling). Several RGS proteins are expressed in the heart, but so far little is known about their function and regulation. Using adenoviral gene transfer, we conducted the first comprehensive analysis of the capacity and selectivity of the major cardiac RGS proteins (RGS2-RGS5) to regulate central G protein-mediated signaling pathways in adult ventricular myocytes (AVM). All four RGS proteins potently inhibited Gq/11-mediated phospholipase C beta stimulation and cell growth (assessed in neonatal myocytes). Importantly, RGS2 selectively inhibited Gq/11 signaling, whereas RGS3, RGS4 and RGS5 had the capacity to regulate both Gq/11 and Gi/o signaling (carbachol-induced cAMP inhibition). Gs signaling was unaffected, and, contrary to reports in other cell lines, RGS2-RGS5 did not appear to regulate adenylate cyclase directly in AVM. Since RGS proteins can be highly regulated in their expression by many different stimuli, we also tested the hypothesis that RGS expression is subject to G protein-mediated regulation in AVM and determined the specificity with which enhanced G protein signaling alters endogenous RGS expression in AVM. RGS2 mRNA and protein were markedly but transiently up-regulated by enhanced Gq/11 signaling (alpha1-adrenergic stimulation or Galphaq* overexpression), possibly by a negative feedback mechanism. In contrast, the other negative regulators of Gq/11 signaling (RGS3-RGS5) were unchanged. Endogenous RGS2 (but not RGS3-RGS5) expression was also up-regulated in cells with enhanced AC signaling (beta-adrenergic or forskolin stimulation). Taken together, these findings suggest diverse roles of RGS proteins in regulating myocyte signaling. RGS2 emerged as the only selective and highly regulated inhibitor of Gq/11 signaling that could potentially become a promising

  20. Optimization of meander line radiators for frequency selective surfaces by using genetic algorithm

    NASA Astrophysics Data System (ADS)

    Bucuci, Stefania C.; Dumitrascu, Ana; Danisor, Alin; Berescu, Serban; Tamas, Razvan D.

    2015-02-01

    In this paper we propose the use of frequency selective surfaces based on meander line radiators, as targets for monitoring slow displacements with synthetic aperture radars. The optimization of the radiators is performed by using genetic algorithms on only two parameters i.e., gain and size. As an example, we have optimized a single meander antenna, resonating in the X-band, at 9.65 GHz.

  1. 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.

  2. 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.

  3. 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.

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

    PubMed

    Twomey, Deirdre M; Kelly, Simon P; O'Connell, Redmond G

    2016-07-13

    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. 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 on the human brain has

  5. 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

  6. Optimizing the signal-to-noise ratio for X-ray photon correlation spectroscopy.

    PubMed

    Falus, P; Lurio, L B; Mochrie, S G J

    2006-05-01

    An analysis is presented of how to optimize the experimental beamline configuration for achieving the best possible signal-to-noise ratio (SNR) in X-ray photon correlation spectroscopy experiments using area detectors. It is shown that there exists an optimum detector distance; namely, the highest SNR is achieved by matching the angular pixel size with the angular source size. Binning several pixels together can increase the SNR by permitting to match the shape of a detector pixel to the shape of the source. It is also shown that collimating slits several times wider than the effective transverse coherence length are optimal; further, it is demonstrated that the energy dependence of the SNR is dictated by the energy dependence of detector efficiency and source brilliance. Ultimately the effects of focusing and low longitudinal coherence are discussed.

  7. Optimal experimental design in an epidermal growth factor receptor signalling and down-regulation model.

    PubMed

    Casey, F P; Baird, D; Feng, Q; Gutenkunst, R N; Waterfall, J J; Myers, C R; Brown, K S; Cerione, R A; Sethna, J P

    2007-05-01

    We apply the methods of optimal experimental design to a differential equation model for epidermal growth factor receptor signalling, trafficking and down-regulation. The model incorporates the role of a recently discovered protein complex made up of the E3 ubiquitin ligase, Cbl, the guanine exchange factor (GEF), Cool-1 (beta -Pix) and the Rho family G protein Cdc42. The complex has been suggested to be important in disrupting receptor down-regulation. We demonstrate that the model interactions can accurately reproduce the experimental observations, that they can be used to make predictions with accompanying uncertainties, and that we can apply ideas of optimal experimental design to suggest new experiments that reduce the uncertainty on unmeasurable components of the system.

  8. Perceptually optimized gain function for cochlear implant signal-to-noise ratio based noise reduction.

    PubMed

    Mauger, Stefan J; Dawson, Pam W; Hersbach, Adam A

    2012-01-01

    Noise reduction in cochlear implants has achieved significant speech perception improvements through spectral subtraction and signal-to-noise ratio based noise reduction techniques. Current methods use gain functions derived through mathematical optimization or motivated by normal listening psychoacoustic experiments. Although these gain functions have been able to improve speech perception, recent studies have indicated that they are not optimal for cochlear implant noise reduction. This study systematically investigates cochlear implant recipients' speech perception and listening preference of noise reduction with a range of gain functions. Results suggest an advantageous gain function and show that gain functions currently used for noise reduction are not optimal for cochlear implant recipients. Using the cochlear implant optimised gain function, a 27% improvement over the current advanced combination encoder (ACE) stimulation strategy in speech weighted noise and a 7% improvement over current noise reduction strategies were observed in babble noise conditions. The optimized gain function was also most preferred by cochlear implant recipients. The CI specific gain function derived from this study can be easily incorporated into existing noise reduction strategies, to further improve listening performance for CI recipients in challenging environments.

  9. Effect of Collision Energy Optimization on the Measurement of Peptides by Selected Reaction Monitoring (SRM) Mass Spectrometry

    PubMed Central

    MacLean, Brendan; Tomazela, Daniela M.; Abbatiello, Susan E.; Zhang, Shucha; Whiteaker, Jeffrey R.; Paulovich, Amanda G.; Carr, Steven A.; MacCoss, Michael J.

    2010-01-01

    Proteomics experiments based on Selected Reaction Monitoring (SRM, also referred to as Multiple Reaction Monitoring or MRM) are being used to target large numbers of protein candidates in complex mixtures. At present, instrument parameters are often optimized for each peptide, a time and resource intensive process. Large SRM experiments are greatly facilitated by having the ability to predict MS instrument parameters that work well with the broad diversity of peptides they target. For this reason, we investigated the impact of using simple linear equations to predict the collision energy (CE) on peptide signal intensity and compared it with the empirical optimization of the CE for each peptide and transition individually. Using optimized linear equations, the difference between predicted and empirically derived CE values was found to be an average gain of only 7.8% of total peak area. We also found that existing commonly used linear equations fall short of their potential, and should be recalculated for each charge state and when introducing new instrument platforms. We provide a fully automated pipeline for calculating these equations and individually optimizing CE of each transition on SRM instruments from Agilent, Applied Biosystems, Thermo-Scientific and Waters in the open source Skyline software tool (http://proteome.gs.washington.edu/software/skyline). PMID:21090646

  10. Photorefractive two-beam coupling optimal thresholding filter for additive signal-dependent noise reduction

    NASA Astrophysics Data System (ADS)

    Fu, Jack; Khoury, Jehad; Cronin-Golomb, Mark; Woods, Charles L.

    1995-01-01

    Computer simulations of photorefractive thresholding filters for the reduction of artifact or dust noise demonstrate an increase in signal-to-noise ratio (SNR) of 70% to 95%, respectively, of that provided by the Wiener filter for inputs with a SNR of approximately 3. These simple, nearly optimal filters use a spectral thresholding profile that is proportional to the envelope of the noise spectrum. Alternative nonlinear filters with either 1/ nu or constant thresholding profiles increase the SNR almost as much as the noise-envelope thresholding filter.

  11. Optimal selection on water-supply pipe of building based on analytic hierarchy process

    NASA Astrophysics Data System (ADS)

    Wei, Tianyun; Chen, Guiqing

    2017-04-01

    The main problem of pipes used in water-supply system was analyzed, and the commonly used pipe and their main features were introduced in this paper. The principles that the selection on water-supply pipes should follow were pointed out. Analytic Hierarchy Process (AHP) using 9 scaling was applied to optimize water-supply pipes quantitatively. The optimal water-supply pipes were determined according to the sorting result of comprehensive evaluation index. It could provide the reference to select the reasonable water-supply pipes for the engineers.

  12. 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.

  13. selectSNP – An R package for selecting SNPs optimal for genetic evaluation

    USDA-ARS?s Scientific Manuscript database

    There has been a huge increase in the number of SNPs in the public repositories. This has made it a challenge to design low and medium density SNP panels, which requires careful selection of available SNPs considering many criteria, such as map position, allelic frequency, possible biological functi...

  14. Network dynamics for optimal compressive-sensing input-signal recovery.

    PubMed

    Barranca, Victor J; Kovačič, Gregor; Zhou, Douglas; Cai, David

    2014-10-01

    By using compressive sensing (CS) theory, a broad class of static signals can be reconstructed through a sequence of very few measurements in the framework of a linear system. For networks with nonlinear and time-evolving dynamics, is it similarly possible to recover an unknown input signal from only a small number of network output measurements? We address this question for pulse-coupled networks and investigate the network dynamics necessary for successful input signal recovery. Determining the specific network characteristics that correspond to a minimal input reconstruction error, we are able to achieve high-quality signal reconstructions with few measurements of network output. Using various measures to characterize dynamical properties of network output, we determine that networks with highly variable and aperiodic output can successfully encode network input information with high fidelity and achieve the most accurate CS input reconstructions. For time-varying inputs, we also find that high-quality reconstructions are achievable by measuring network output over a relatively short time window. Even when network inputs change with time, the same optimal choice of network characteristics and corresponding dynamics apply as in the case of static inputs.

  15. Network dynamics for optimal compressive-sensing input-signal recovery

    NASA Astrophysics Data System (ADS)

    Barranca, Victor J.; Kovačič, Gregor; Zhou, Douglas; Cai, David

    2014-10-01

    By using compressive sensing (CS) theory, a broad class of static signals can be reconstructed through a sequence of very few measurements in the framework of a linear system. For networks with nonlinear and time-evolving dynamics, is it similarly possible to recover an unknown input signal from only a small number of network output measurements? We address this question for pulse-coupled networks and investigate the network dynamics necessary for successful input signal recovery. Determining the specific network characteristics that correspond to a minimal input reconstruction error, we are able to achieve high-quality signal reconstructions with few measurements of network output. Using various measures to characterize dynamical properties of network output, we determine that networks with highly variable and aperiodic output can successfully encode network input information with high fidelity and achieve the most accurate CS input reconstructions. For time-varying inputs, we also find that high-quality reconstructions are achievable by measuring network output over a relatively short time window. Even when network inputs change with time, the same optimal choice of network characteristics and corresponding dynamics apply as in the case of static inputs.

  16. 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.

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

    PubMed

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

    2012-02-06

    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.

  18. Genome-wide association study reveals sex-specific selection signals against autosomal nucleotide variants.

    PubMed

    Ryu, Dongchan; Ryu, Jihye; Lee, Chaeyoung

    2016-05-01

    A genome-wide association study (GWAS) was conducted to examine genetic associations of common autosomal nucleotide variants with sex in a Korean population with 4183 males and 4659 females. Nine genetic association signals were identified in four intragenic and five intergenic regions (P<5 × 10(-8)). Further analysis with an independent data set confirmed two intragenic association signals in the genes encoding protein phosphatase 1, regulatory subunit 12B (PPP1R12B, intron 12, rs1819043) and dynein, axonemal, heavy chain 11 (DNAH11, intron 61, rs10255013), which are directly involved in the reproductive system. This study revealed autosomal genetic variants associated with sex ratio by GWAS for the first time. This implies that genetic variants in proximity to the association signals may influence sex-specific selection and contribute to sex ratio variation. Further studies are required to reveal the mechanisms underlying sex-specific selection.

  19. Combining predictors to achieve optimal trade-offs between selection quality and adverse impact.

    PubMed

    De Corte, Wilfried; Lievens, Filip; Sackett, Paul R

    2007-09-01

    The authors propose a procedure to determine (a) predictor composites that result in a Pareto-optimal trade-off between the often competing goals in personnel selection of quality and adverse impact and (b) the relative importance of the quality and impact objectives that correspond to each of these trade-offs. They also investigated whether the obtained Pareto-optimal composites continue to perform well under variability of the selection parameters that characterize the intended selection decision. The results of this investigation indicate that this is indeed the case. The authors suggest that the procedure be used as one of a number of potential strategies for addressing the quality-adverse impact problem in settings where estimates of the selection parameters (e.g., validity estimates, predictor intercorrelations, subgroup mean differences on the predictors and criteria) are available from either a local validation study or meta-analytic research. (c) 2007 APA.

  20. Selective waste collection optimization in Romania and its impact to urban climate

    NASA Astrophysics Data System (ADS)

    Mihai, Šercǎianu; Iacoboaea, Cristina; Petrescu, Florian; Aldea, Mihaela; Luca, Oana; Gaman, Florian; Parlow, Eberhard

    2016-08-01

    According to European Directives, transposed in national legislation, the Member States should organize separate collection systems at least for paper, metal, plastic, and glass until 2015. In Romania, since 2011 only 12% of municipal collected waste was recovered, the rest being stored in landfills, although storage is considered the last option in the waste hierarchy. At the same time there was selectively collected only 4% of the municipal waste. Surveys have shown that the Romanian people do not have selective collection bins close to their residencies. The article aims to analyze the current situation in Romania in the field of waste collection and management and to make a proposal for selective collection containers layout, using geographic information systems tools, for a case study in Romania. Route optimization is used based on remote sensing technologies and network analyst protocols. Optimizing selective collection system the greenhouse gases, particles and dust emissions can be reduced.

  1. Feature Selection and Parameters Optimization of SVM Using Particle Swarm Optimization for Fault Classification in Power Distribution Systems.

    PubMed

    Cho, Ming-Yuan; Hoang, Thi Thom

    2017-01-01

    Fast and accurate fault classification is essential to power system operations. In this paper, in order to classify electrical faults in radial distribution systems, a particle swarm optimization (PSO) based support vector machine (SVM) classifier has been proposed. The proposed PSO based SVM classifier is able to select appropriate input features and optimize SVM parameters to increase classification accuracy. Further, a time-domain reflectometry (TDR) method with a pseudorandom binary sequence (PRBS) stimulus has been used to generate a dataset for purposes of classification. The proposed technique has been tested on a typical radial distribution network to identify ten different types of faults considering 12 given input features generated by using Simulink software and MATLAB Toolbox. The success rate of the SVM classifier is over 97%, which demonstrates the effectiveness and high efficiency of the developed method.

  2. Feature Selection and Parameters Optimization of SVM Using Particle Swarm Optimization for Fault Classification in Power Distribution Systems

    PubMed Central

    2017-01-01

    Fast and accurate fault classification is essential to power system operations. In this paper, in order to classify electrical faults in radial distribution systems, a particle swarm optimization (PSO) based support vector machine (SVM) classifier has been proposed. The proposed PSO based SVM classifier is able to select appropriate input features and optimize SVM parameters to increase classification accuracy. Further, a time-domain reflectometry (TDR) method with a pseudorandom binary sequence (PRBS) stimulus has been used to generate a dataset for purposes of classification. The proposed technique has been tested on a typical radial distribution network to identify ten different types of faults considering 12 given input features generated by using Simulink software and MATLAB Toolbox. The success rate of the SVM classifier is over 97%, which demonstrates the effectiveness and high efficiency of the developed method. PMID:28781591

  3. Genome-wide signals of positive selection in strongylocentrotid sea urchins.

    PubMed

    Kober, Kord M; Pogson, Grant H

    2017-07-21

    Comparative genomics studies investigating the signals of positive selection among groups of closely related species are still rare and limited in taxonomic breadth. Such studies show great promise in advancing our knowledge about the proportion and the identity of genes experiencing diversifying selection. However, methodological challenges have led to high levels of false positives in past studies. Here, we use the well-annotated genome of the purple sea urchin, Strongylocentrotus purpuratus, as a reference to investigate the signals of positive selection at 6520 single-copy orthologs from nine sea urchin species belonging to the family Strongylocentrotidae paying careful attention to minimizing false positives. We identified 1008 (15.5%) candidate positive selection genes (PSGs). Tests for positive selection along the nine terminal branches of the phylogeny identified 824 genes that showed lineage-specific adaptive diversification (1.67% of branch-sites tests performed). Positively selected codons were not enriched at exon borders or near regions containing missing data, suggesting a limited contribution of false positives caused by alignment or annotation errors. Alignments were validated at 10 loci with re-sequencing using Sanger methods. No differences were observed in the rates of synonymous substitution (d S), GC content, and codon bias between the candidate PSGs and those not showing positive selection. However, the candidate PSGs had 68% higher rates of nonsynonymous substitution (d N) and 33% lower levels of heterozygosity, consistent with selective sweeps and opposite to that expected by a relaxation of selective constraint. Although positive selection was identified at reproductive proteins and innate immunity genes, the strongest signals of adaptive diversification were observed at extracellular matrix proteins, cell adhesion molecules, membrane receptors, and ion channels. Many candidate PSGs have been widely implicated as targets of pathogen binding

  4. Optimized selective lactate excitation with a refocused multiple-quantum filter

    NASA Astrophysics Data System (ADS)

    Holbach, Mirjam; Lambert, Jörg; Johst, Sören; Ladd, Mark E.; Suter, Dieter

    2015-06-01

    Selective detection of lactate signals in in vivo MR spectroscopy with spectral editing techniques is necessary in situations where strong lipid or signals from other molecules overlap the desired lactate resonance in the spectrum. Several pulse sequences have been proposed for this task. The double-quantum filter SSel-MQC provides very good lipid and water signal suppression in a single scan. As a major drawback, it suffers from significant signal loss due to incomplete refocussing in situations where long evolution periods are required. Here we present a refocused version of the SSel-MQC technique that uses only one additional refocussing pulse and regains the full refocused lactate signal at the end of the sequence.

  5. The emotion recognition system based on autoregressive model and sequential forward feature selection of electroencephalogram signals.

    PubMed

    Hatamikia, Sepideh; Maghooli, Keivan; Nasrabadi, Ali Motie

    2014-07-01

    Electroencephalogram (EEG) is one of the useful biological signals to distinguish different brain diseases and mental states. In recent years, detecting different emotional states from biological signals has been merged more attention by researchers and several feature extraction methods and classifiers are suggested to recognize emotions from EEG signals. In this research, we introduce an emotion recognition system using autoregressive (AR) model, sequential forward feature selection (SFS) and K-nearest neighbor (KNN) classifier using EEG signals during emotional audio-visual inductions. The main purpose of this paper is to investigate the performance of AR features in the classification of emotional states. To achieve this goal, a distinguished AR method (Burg's method) based on Levinson-Durbin's recursive algorithm is used and AR coefficients are extracted as feature vectors. In the next step, two different feature selection methods based on SFS algorithm and Davies-Bouldin index are used in order to decrease the complexity of computing and redundancy of features; then, three different classifiers include KNN, quadratic discriminant analysis and linear discriminant analysis are used to discriminate two and three different classes of valence and arousal levels. The proposed method is evaluated with EEG signals of available database for emotion analysis using physiological signals, which are recorded from 32 participants during 40 1 min audio visual inductions. According to the results, AR features are efficient to recognize emotional states from EEG signals, and KNN performs better than two other classifiers in discriminating of both two and three valence/arousal classes. The results also show that SFS method improves accuracies by almost 10-15% as compared to Davies-Bouldin based feature selection. The best accuracies are %72.33 and %74.20 for two classes of valence and arousal and %61.10 and %65.16 for three classes, respectively.

  6. Signals of selection in outlier loci in a widely dispersing species across an environmental mosaic.

    PubMed

    Pespeni, Melissa H; Palumbi, Stephen R

    2013-07-01

    Local adaptation reflects a balance between natural selection and gene flow and is classically thought to require the retention of locally adapted alleles. However, organisms with high dispersal potential across a spatially or temporally heterogeneous landscape pose an interesting challenge to this view requiring local selection every generation or when environmental conditions change to generate adaptation in adults. Here, we test for geographical and sequence-based signals of selection in five putatively adaptive and two putatively neutral genes identified in a previous genome scan of the highly dispersing purple sea urchin, Strongylocentrotus purpuratus. Comparing six populations spanning the species' wide latitudinal range from Canada to Baja California, Mexico, we find positive tests for selection in the putative adaptive genes and not in the putative neutral genes. Specifically, we find an excess of low-frequency and nonsynonymous polymorphisms in two transcription factors and a transporter protein, and an excess of common amino acid polymorphisms in the two transcription factors, suggestive of spatially balancing selection. We test for a genetic correlation with temperature, a dominant environmental variable in this coastal ecosystem. We find mild clines and a stronger association of genetic variation with temperature than latitude in four of the five putative adaptive loci and a signal of local adaptation in the Southern California Bight. Overall, patterns of genetic variation match predictions based on spatially or temporally balancing selection in a heterogeneous landscape and illustrate the value of geographical and coalescent tests on candidate loci identified in a genome-wide scan for selection.

  7. Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization.

    PubMed

    Cui, Huanqing; Shu, Minglei; Song, Min; Wang, Yinglong

    2017-03-01

    Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors' memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm.

  8. Direct-aperture optimization applied to selection of beam orientations in intensity-modulated radiation therapy

    NASA Astrophysics Data System (ADS)

    Bedford, J. L.; Webb, S.

    2007-01-01

    Direct-aperture optimization (DAO) was applied to iterative beam-orientation selection in intensity-modulated radiation therapy (IMRT), so as to ensure a realistic segmental treatment plan at each iteration. Nested optimization engines dealt separately with gantry angles, couch angles, collimator angles, segment shapes, segment weights and wedge angles. Each optimization engine performed a random search with successively narrowing step sizes. For optimization of segment shapes, the filtered backprojection (FBP) method was first used to determine desired fluence, the fluence map was segmented, and then constrained direct-aperture optimization was used thereafter. Segment shapes were fully optimized when a beam angle was perturbed, and minimally re-optimized otherwise. The algorithm was compared with a previously reported method using FBP alone at each orientation iteration. An example case consisting of a cylindrical phantom with a hemi-annular planning target volume (PTV) showed that for three-field plans, the method performed better than when using FBP alone, but for five or more fields, neither method provided much benefit over equally spaced beams. For a prostate case, improved bladder sparing was achieved through the use of the new algorithm. A plan for partial scalp treatment showed slightly improved PTV coverage and lower irradiated volume of brain with the new method compared to FBP alone. It is concluded that, although the method is computationally intensive and not suitable for searching large unconstrained regions of beam space, it can be used effectively in conjunction with prior class solutions to provide individually optimized IMRT treatment plans.

  9. Direct-aperture optimization applied to selection of beam orientations in intensity-modulated radiation therapy.

    PubMed

    Bedford, J L; Webb, S

    2007-01-21

    Direct-aperture optimization (DAO) was applied to iterative beam-orientation selection in intensity-modulated radiation therapy (IMRT), so as to ensure a realistic segmental treatment plan at each iteration. Nested optimization engines dealt separately with gantry angles, couch angles, collimator angles, segment shapes, segment weights and wedge angles. Each optimization engine performed a random search with successively narrowing step sizes. For optimization of segment shapes, the filtered backprojection (FBP) method was first used to determine desired fluence, the fluence map was segmented, and then constrained direct-aperture optimization was used thereafter. Segment shapes were fully optimized when a beam angle was perturbed, and minimally re-optimized otherwise. The algorithm was compared with a previously reported method using FBP alone at each orientation iteration. An example case consisting of a cylindrical phantom with a hemi-annular planning target volume (PTV) showed that for three-field plans, the method performed better than when using FBP alone, but for five or more fields, neither method provided much benefit over equally spaced beams. For a prostate case, improved bladder sparing was achieved through the use of the new algorithm. A plan for partial scalp treatment showed slightly improved PTV coverage and lower irradiated volume of brain with the new method compared to FBP alone. It is concluded that, although the method is computationally intensive and not suitable for searching large unconstrained regions of beam space, it can be used effectively in conjunction with prior class solutions to provide individually optimized IMRT treatment plans.

  10. Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization

    PubMed Central

    Cui, Huanqing; Shu, Minglei; Song, Min; Wang, Yinglong

    2017-01-01

    Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors’ memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm. PMID:28257060

  11. Optimal Wavelength Selection on Hyperspectral Data with Fused Lasso for Biomass Estimation of Tropical Rain Forest

    NASA Astrophysics Data System (ADS)

    Takayama, T.; Iwasaki, A.

    2016-06-01

    Above-ground biomass prediction of tropical rain forest using remote sensing data is of paramount importance to continuous large-area forest monitoring. Hyperspectral data can provide rich spectral information for the biomass prediction; however, the prediction accuracy is affected by a small-sample-size problem, which widely exists as overfitting in using high dimensional data where the number of training samples is smaller than the dimensionality of the samples due to limitation of require time, cost, and human resources for field surveys. A common approach to addressing this problem is reducing the dimensionality of dataset. Also, acquired hyperspectral data usually have low signal-to-noise ratio due to a narrow bandwidth and local or global shifts of peaks due to instrumental instability or small differences in considering practical measurement conditions. In this work, we propose a methodology based on fused lasso regression that select optimal bands for the biomass prediction model with encouraging sparsity and grouping, which solves the small-sample-size problem by the dimensionality reduction from the sparsity and the noise and peak shift problem by the grouping. The prediction model provided higher accuracy with root-mean-square error (RMSE) of 66.16 t/ha in the cross-validation than other methods; multiple linear analysis, partial least squares regression, and lasso regression. Furthermore, fusion of spectral and spatial information derived from texture index increased the prediction accuracy with RMSE of 62.62 t/ha. This analysis proves efficiency of fused lasso and image texture in biomass estimation of tropical forests.

  12. Optimal band selection for high dimensional remote sensing data using genetic algorithm

    NASA Astrophysics Data System (ADS)

    Zhang, Xianfeng; Sun, Quan; Li, Jonathan

    2009-06-01

    A 'fused' method may not be suitable for reducing the dimensionality of data and a band/feature selection method needs to be used for selecting an optimal subset of original data bands. This study examined the efficiency of GA in band selection for remote sensing classification. A GA-based algorithm for band selection was designed deliberately in which a Bhattacharyya distance index that indicates separability between classes of interest is used as fitness function. A binary string chromosome is designed in which each gene location has a value of 1 representing a feature being included or 0 representing a band being not included. The algorithm was implemented in MATLAB programming environment, and a band selection task for lithologic classification in the Chocolate Mountain area (California) was used to test the proposed algorithm. The proposed feature selection algorithm can be useful in multi-source remote sensing data preprocessing, especially in hyperspectral dimensionality reduction.

  13. A computerized traffic control algorithm to determine optimal traffic signal settings. Ph.D. Thesis - Toledo Univ.

    NASA Technical Reports Server (NTRS)

    Seldner, K.

    1977-01-01

    An algorithm was developed to optimally control the traffic signals at each intersection using a discrete time traffic model applicable to heavy or peak traffic. Off line optimization procedures were applied to compute the cycle splits required to minimize the lengths of the vehicle queues and delay at each intersection. The method was applied to an extensive traffic network in Toledo, Ohio. Results obtained with the derived optimal settings are compared with the control settings presently in use.

  14. 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

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

    PubMed

    Zhao, Yu-Xiang; Chou, Chien-Hsing

    2016-06-14

    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.

  16. Adaptive feature selection using v-shaped binary particle swarm optimization

    PubMed Central

    Dong, Hongbin; Zhou, Xiurong

    2017-01-01

    Feature selection is an important preprocessing method in machine learning and data mining. This process can be used not only to reduce the amount of data to be analyzed but also to build models with stronger interpretability based on fewer features. Traditional feature selection methods evaluate the dependency and redundancy of features separately, which leads to a lack of measurement of their combined effect. Moreover, a greedy search considers only the optimization of the current round and thus cannot be a global search. To evaluate the combined effect of different subsets in the entire feature space, an adaptive feature selection method based on V-shaped binary particle swarm optimization is proposed. In this method, the fitness function is constructed using the correlation information entropy. Feature subsets are regarded as individuals in a population, and the feature space is searched using V-shaped binary particle swarm optimization. The above procedure overcomes the hard constraint on the number of features, enables the combined evaluation of each subset as a whole, and improves the search ability of conventional binary particle swarm optimization. The proposed algorithm is an adaptive method with respect to the number of feature subsets. The experimental results show the advantages of optimizing the feature subsets using the V-shaped transfer function and confirm the effectiveness and efficiency of the feature subsets obtained under different classifiers. PMID:28358850

  17. Multiobjective binary biogeography based optimization for feature selection using gene expression data.

    PubMed

    Li, Xiangtao; Yin, Minghao

    2013-12-01

    Gene expression data play an important role in the development of efficient cancer diagnoses and classification. However, gene expression data are usually redundant and noisy, and only a subset of them present distinct profiles for different classes of samples. Thus, selecting high discriminative genes from gene expression data has become increasingly interesting in the field of bioinformatics. In this paper, a multi-objective biogeography based optimization method is proposed to select the small subset of informative gene relevant to the classification. In the proposed algorithm, firstly, the Fisher-Markov selector is used to choose the 60 top gene expression data. Secondly, to make biogeography based optimization suitable for the discrete problem, binary biogeography based optimization, as called BBBO, is proposed based on a binary migration model and a binary mutation model. Then, multi-objective binary biogeography based optimization, as we called MOBBBO, is proposed by integrating the non-dominated sorting method and the crowding distance method into the BBBO framework. Finally, the MOBBBO method is used for gene selection, and support vector machine is used as the classifier with the leave-one-out cross-validation method (LOOCV). In order to show the effective and efficiency of the algorithm, the proposed algorithm is tested on ten gene expression dataset benchmarks. Experimental results demonstrate that the proposed method is better or at least comparable with previous particle swarm optimization (PSO) algorithm and support vector machine (SVM) from literature when considering the quality of the solutions obtained.

  18. Quantum-behaved particle swarm optimization: analysis of individual particle behavior and parameter selection.

    PubMed

    Sun, Jun; Fang, Wei; Wu, Xiaojun; Palade, Vasile; Xu, Wenbo

    2012-01-01

    Quantum-behaved particle swarm optimization (QPSO), motivated by concepts from quantum mechanics and particle swarm optimization (PSO), is a probabilistic optimization algorithm belonging to the bare-bones PSO family. Although it has been shown to perform well in finding the optimal solutions for many optimization problems, there has so far been little analysis on how it works in detail. This paper presents a comprehensive analysis of the QPSO algorithm. In the theoretical analysis, we analyze the behavior of a single particle in QPSO in terms of probability measure. Since the particle's behavior is influenced by the contraction-expansion (CE) coefficient, which is the most important parameter of the algorithm, the goal of the theoretical analysis is to find out the upper bound of the CE coefficient, within which the value of the CE coefficient selected can guarantee the convergence or boundedness of the particle's position. In the experimental analysis, the theoretical results are first validated by stochastic simulations for the particle's behavior. Then, based on the derived upper bound of the CE coefficient, we perform empirical studies on a suite of well-known benchmark functions to show how to control and select the value of the CE coefficient, in order to obtain generally good algorithmic performance in real world applications. Finally, a further performance comparison between QPSO and other variants of PSO on the benchmarks is made to show the efficiency of the QPSO algorithm with the proposed parameter control and selection methods.

  19. EMD self-adaptive selecting relevant modes algorithm for FBG spectrum signal

    NASA Astrophysics Data System (ADS)

    Chen, Yong; Wu, Chun-ting; Liu, Huan-lin

    2017-07-01

    Noise may reduce the demodulation accuracy of fiber Bragg grating (FBG) sensing signal so as to affect the quality of sensing detection. Thus, the recovery of a signal from observed noisy data is necessary. In this paper, a precise self-adaptive algorithm of selecting relevant modes is proposed to remove the noise of signal. Empirical mode decomposition (EMD) is first used to decompose a signal into a set of modes. The pseudo modes cancellation is introduced to identify and eliminate false modes, and then the Mutual Information (MI) of partial modes is calculated. MI is used to estimate the critical point of high and low frequency components. Simulation results show that the proposed algorithm estimates the critical point more accurately than the traditional algorithms for FBG spectral signal. While, compared to the similar algorithms, the signal noise ratio of the signal can be improved more than 10 dB after processing by the proposed algorithm, and correlation coefficient can be increased by 0.5, so it demonstrates better de-noising effect.

  20. Selective killing of cancer cells by Ashwagandha leaf extract and its component Withanone involves ROS signaling.

    PubMed

    Widodo, Nashi; Priyandoko, Didik; Shah, Navjot; Wadhwa, Renu; Kaul, Sunil C

    2010-10-21

    Ashwagandha is a popular Ayurvedic herb used in Indian traditional home medicine. It has been assigned a variety of health-promoting effects of which the mechanisms remain unknown. We previously reported the selective killing of cancer cells by leaf extract of Ashwagandha (i-Extract) and its purified component Withanone. In the present study, we investigated its mechanism by loss-of-function screening (abrogation of i-Extract induced cancer cell killing) of the cellular targets and gene pathways. Randomized ribozyme library was introduced into cancer cells prior to the treatment with i-Extract. Ribozymes were recovered from cells that survived the i-Extract treatment. Gene targets of the selected ribozymes (as predicted by database search) were analyzed by bioinformatics and pathway analyses. The targets were validated for their role in i-Extract induced selective killing of cancer cells by biochemical and molecular assays. Fifteen gene-targets were identified and were investigated for their role in specific cancer cell killing activity of i-Extract and its two major components (Withaferin A and Withanone) by undertaking the shRNA-mediated gene silencing approach. Bioinformatics on the selected gene-targets revealed the involvement of p53, apoptosis and insulin/IGF signaling pathways linked to the ROS signaling. We examined the involvement of ROS-signaling components (ROS levels, DNA damage, mitochondrial structure and membrane potential) and demonstrate that the selective killing of cancer cells is mediated by induction of oxidative stress. Ashwagandha leaf extract and Withanone cause selective killing of cancer cells by induction of ROS-signaling and hence are potential reagents that could be recruited for ROS-mediated cancer chemotherapy.

  1. Selective Killing of Cancer Cells by Ashwagandha Leaf Extract and Its Component Withanone Involves ROS Signaling

    PubMed Central

    Widodo, Nashi; Priyandoko, Didik; Shah, Navjot; Wadhwa, Renu; Kaul, Sunil C.

    2010-01-01

    Background and Purpose Ashwagandha is a popular Ayurvedic herb used in Indian traditional home medicine. It has been assigned a variety of health-promoting effects of which the mechanisms remain unknown. We previously reported the selective killing of cancer cells by leaf extract of Ashwagandha (i-Extract) and its purified component Withanone. In the present study, we investigated its mechanism by loss-of-function screening (abrogation of i-Extract induced cancer cell killing) of the cellular targets and gene pathways. Methodology/Principal Findings Randomized ribozyme library was introduced into cancer cells prior to the treatment with i-Extract. Ribozymes were recovered from cells that survived the i-Extract treatment. Gene targets of the selected ribozymes (as predicted by database search) were analyzed by bioinformatics and pathway analyses. The targets were validated for their role in i-Extract induced selective killing of cancer cells by biochemical and molecular assays. Fifteen gene-targets were identified and were investigated for their role in specific cancer cell killing activity of i-Extract and its two major components (Withaferin A and Withanone) by undertaking the shRNA-mediated gene silencing approach. Bioinformatics on the selected gene-targets revealed the involvement of p53, apoptosis and insulin/IGF signaling pathways linked to the ROS signaling. We examined the involvement of ROS-signaling components (ROS levels, DNA damage, mitochondrial structure and membrane potential) and demonstrate that the selective killing of cancer cells is mediated by induction of oxidative stress. Conclusion Ashwagandha leaf extract and Withanone cause selective killing of cancer cells by induction of ROS-signaling and hence are potential reagents that could be recruited for ROS-mediated cancer chemotherapy. PMID:20975835

  2. A new and fast image feature selection method for developing an optimal mammographic mass detection scheme

    PubMed Central

    Tan, Maxine; Pu, Jiantao; Zheng, Bin

    2014-01-01

    Purpose: Selecting optimal features from a large image feature pool remains a major challenge in developing computer-aided detection (CAD) schemes of medical images. The objective of this study is to investigate a new approach to significantly improve efficacy of image feature selection and classifier optimization in developing a CAD scheme of mammographic masses. Methods: An image dataset including 1600 regions of interest (ROIs) in which 800 are positive (depicting malignant masses) and 800 are negative (depicting CAD-generated false positive regions) was used in this study. After segmentation of each suspicious lesion by a multilayer topographic region growth algorithm, 271 features were computed in different feature categories including shape, texture, contrast, isodensity, spiculation, local topological features, as well as the features related to the presence and location of fat and calcifications. Besides computing features from the original images, the authors also computed new texture features from the dilated lesion segments. In order to select optimal features from this initial feature pool and build a highly performing classifier, the authors examined and compared four feature selection methods to optimize an artificial neural network (ANN) based classifier, namely: (1) Phased Searching with NEAT in a Time-Scaled Framework, (2) A sequential floating forward selection (SFFS) method, (3) A genetic algorithm (GA), and (4) A sequential forward selection (SFS) method. Performances of the four approaches were assessed using a tenfold cross validation method. Results: Among these four methods, SFFS has highest efficacy, which takes 3%–5% of computational time as compared to GA approach, and yields the highest performance level with the area under a receiver operating characteristic curve (AUC) = 0.864 ± 0.034. The results also demonstrated that except using GA, including the new texture features computed from the dilated mass segments improved the AUC

  3. A new and fast image feature selection method for developing an optimal mammographic mass detection scheme.

    PubMed

    Tan, Maxine; Pu, Jiantao; Zheng, Bin

    2014-08-01

    Selecting optimal features from a large image feature pool remains a major challenge in developing computer-aided detection (CAD) schemes of medical images. The objective of this study is to investigate a new approach to significantly improve efficacy of image feature selection and classifier optimization in developing a CAD scheme of mammographic masses. An image dataset including 1600 regions of interest (ROIs) in which 800 are positive (depicting malignant masses) and 800 are negative (depicting CAD-generated false positive regions) was used in this study. After segmentation of each suspicious lesion by a multilayer topographic region growth algorithm, 271 features were computed in different feature categories including shape, texture, contrast, isodensity, spiculation, local topological features, as well as the features related to the presence and location of fat and calcifications. Besides computing features from the original images, the authors also computed new texture features from the dilated lesion segments. In order to select optimal features from this initial feature pool and build a highly performing classifier, the authors examined and compared four feature selection methods to optimize an artificial neural network (ANN) based classifier, namely: (1) Phased Searching with NEAT in a Time-Scaled Framework, (2) A sequential floating forward selection (SFFS) method, (3) A genetic algorithm (GA), and (4) A sequential forward selection (SFS) method. Performances of the four approaches were assessed using a tenfold cross validation method. Among these four methods, SFFS has highest efficacy, which takes 3%-5% of computational time as compared to GA approach, and yields the highest performance level with the area under a receiver operating characteristic curve (AUC) = 0.864 ± 0.034. The results also demonstrated that except using GA, including the new texture features computed from the dilated mass segments improved the AUC results of the ANNs optimized

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

    PubMed Central

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

    2013-01-01

    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

  5. Optimization of highly selective 2,4-diaminopyrimidine-5-carboxamide inhibitors of Sky kinase.

    PubMed

    Powell, Noel A; Hoffman, Jennifer K; Ciske, Fred L; Kohrt, Jeffrey T; Baxi, Sangita M; Peng, Yun-Wen; Zhong, Min; Catana, Cornel; Ohren, Jeff; Perrin, Lisa A; Edmunds, Jeremy J

    2013-02-15

    Optimization of the ADME properties of a series of 2,4-diaminopyrimidine-5-carboxamide inhibitors of Sky kinase resulted in the identification of highly selective compounds with properties suitable for use as in vitro and in vivo tools to probe the effects of Sky inhibition. Copyright © 2012 Elsevier Ltd. All rights reserved.

  6. Self-Regulatory Strategies in Daily Life: Selection, Optimization, and Compensation and Everyday Memory Problems

    ERIC Educational Resources Information Center

    Robinson, Stephanie A.; Rickenbach, Elizabeth H.; Lachman, Margie E.

    2016-01-01

    The effective use of self-regulatory strategies, such as selection, optimization, and compensation (SOC) requires resources. However, it is theorized that SOC use is most advantageous for those experiencing losses and diminishing resources. The present study explored this seeming paradox within the context of limitations or constraints due to…

  7. Self-Regulatory Strategies in Daily Life: Selection, Optimization, and Compensation and Everyday Memory Problems

    ERIC Educational Resources Information Center

    Robinson, Stephanie A.; Rickenbach, Elizabeth H.; Lachman, Margie E.

    2016-01-01

    The effective use of self-regulatory strategies, such as selection, optimization, and compensation (SOC) requires resources. However, it is theorized that SOC use is most advantageous for those experiencing losses and diminishing resources. The present study explored this seeming paradox within the context of limitations or constraints due to…

  8. Selection, Optimization, and Compensation: An Action-Related Approach to Work and Partnership.

    ERIC Educational Resources Information Center

    Wiese, Bettina S.; Baltes, Paul B.; Freund, Alexandra M.

    2000-01-01

    Data from German professionals (n=206) were used to test selective optimization with compensation (SOC)--goal setting in career and partnership domains and use of means to achieve goals. A positive relationship was found between SOC behaviors and successful life management; it was more predictive for the partnership domain. (Contains 82…

  9. Subjective Career Success and Emotional Well-Being: Longitudinal Predictive Power of Selection, Optimization, and Compensation.

    ERIC Educational Resources Information Center

    Wiese, Bettina S.; Freund, Alexandra M.; Baltes, Paul B.

    2002-01-01

    A 3-year study of 82 young professionals found that work-related well-being was predicted by selection (commitment to personal goals), optimization (application of goal-related skills), and compensation (maintaining goals in the face of loss). The degree of compensation predicted emotional well-being and job satisfaction 3 years later. (Contains…

  10. Optimal cytoreduction with neutral argon plasma energy in selected patients with ovarian and primitive peritoneal cancer.

    PubMed

    Renaud, Marie Claude; Sebastianelli, Alexandra

    2013-01-01

    Epithelial ovarian cancer (EOC) is a deadly disease for which optimal cytoreduction to microscopic disease has shown the best correlation with survival. Electrically neutral argon plasma technology is a novel surgical tool to allow aggressive cytoreduction in selected patients with EOC, primary peritoneal cancer, and tubal cancer. We conducted a prospective feasibility study of the use of neutral argon plasma technology to complete cytoreductive surgery in order to assess its ability to obtain optimal cytoreduction. Six patients had their surgery completed with the neutral argon plasma device. None of the patients would have had optimal surgery unless the device had been available. All patients had cytoreduction to less than 5 mm to 10 mm without additional morbidity. One patient had complete cytoreduction, and two had residual disease of less than 2 mm. Electrically neutral plasma argon technology is a useful technology to maximize cytoreduction and to reduce tumour burden in selected cases of EOC.

  11. Selective Segmentation for Global Optimization of Depth Estimation in Complex Scenes

    PubMed Central

    Liu, Sheng; Jin, Haiqiang; Mao, Xiaojun; Zhai, Binbin; Zhan, Ye; Feng, Xiaofei

    2013-01-01

    This paper proposes a segmentation-based global optimization method for depth estimation. Firstly, for obtaining accurate matching cost, the original local stereo matching approach based on self-adapting matching window is integrated with two matching cost optimization strategies aiming at handling both borders and occlusion regions. Secondly, we employ a comprehensive smooth term to satisfy diverse smoothness request in real scene. Thirdly, a selective segmentation term is used for enforcing the plane trend constraints selectively on the corresponding segments to further improve the accuracy of depth results from object level. Experiments on the Middlebury image pairs show that the proposed global optimization approach is considerably competitive with other state-of-the-art matching approaches. PMID:23766717

  12. [Strategy of probe selection for studying mRNAs that participate in receptor-mediated apoptosis signaling].

    PubMed

    Solntsev, L A; Starikova, V D; Sakharnov, N A; Knyazev, D I; Utkin, O V

    2015-01-01

    Death receptors (DRs) and the participants of DR-mediated signaling are characterized by a large number of mRNA isoforms generated by alternative splicing. Due to their high labor intensity and high cost, conventional methods (RT-PCR and RT-PCR in real time) are ineffective when the simultaneous detection of a plurality of mRNA isoforms is needed. In this regard, the use of DNA biochips is has prospective applications in analyzing the expression of many genes simultaneously. In this paper, we suggest an optimal strategy of probes selection aimed at detecting the maximum number of mRNA splice variants generated by major participants of DR-signaling. The objects of the study were 185 genes that form 1134 mRNA isoforms. As a result, a biochip design was developed that enables the detection of 499 mRNA isoforms (44% of total mRNA splice variants). The proposed strategy combines a high degree of modularity, the use of modern high-performance computers, and broad opportunities for setting up the selection criteria in accordance with the objectives of the study.

  13. Optimization of noise in non-integrated instrumentation amplifier for the amplification of very low electrophysiological [corrected] signals. Case of electro cardio graphic signals (ECG).

    PubMed

    Ngounou, Guy Merlin; Kom, Martin

    2014-12-01

    In this paper we present an instrumentation amplifier with discrete elements and optimized noise for the amplification of very low signals. In amplifying signals of very weak amplitude, the noise can completely absorb these signals if the used amplifier does not present the optimal guarantee to minimize the noise. Based on related research and re-viewing of recent patents Journal of Medical Systems, 30:205-209, 2006, we suggest an approach of noise reduction in amplification much more thoroughly than re-viewing of recent patents and we deduce from it the general criteria necessary and essential to achieve this optimization. The comparison of these criteria with the provisions adopted in practice leads to the inadequacy of conventional amplifiers for effective noise reduction. The amplifier we propose is an instrumentation amplifier with active negative feedback and optimized noise for the amplification of signals with very low amplitude. The application of this method in the case of electro cardio graphic signals (ECG) provides simulation results fully in line with forecasts.

  14. Optimization of polarizer azimuth in improving signal-to-noise ratio in Kerr microscopy.

    PubMed

    Wang, X; Lian, J; Xu, X J; Li, X; Li, P; Li, M M; Wang, Y; Liu, Y X

    2016-03-01

    The magneto optical Kerr effect (MOKE) is a widely used technique in magnetic domain imaging for its high surface sensitivity and external magnetic compatibility. Optimization of Kerr microscopy will improve the detecting sensitivity and provide high-quality domain images. In this work, we provide a method to optimize the polarizer azimuth in improving the signal-to-noise ratio (S/N) in longitudinal Kerr microscopy with the generalized magneto optical ellipsometry. Detailed analysis of the MOKE signal and the noise components are provided to study the optimum polarizer and analyzer azimuth combinations. Results show that, for a fixed polarizer angle 1°, the laser intensity noise and the shot noise, which vary with the input laser power, have a similar amplitude and decline with the analyzer azimuth increasing. When the analyzer is set at the extinction place, the Johnson noise plays a dominate role in the total noise. Then, the S/N values are calculated to find the optimum polarizer and analyzer azimuth. Results show that the optimum polarizer and analyzer azimuth combination for Permalloy is (18.35°, 68.35°) under an incident angle of 45°. After that, the S/N of 200 nm Permalloy at different analyzer angles with the polarizer azimuth set at 18.35° is measured to verify the validity of the simulation results. At last, the S/N at different incident angles is calculated. Results show that the optimum incident angle of 200 nm Permalloy film to improve the S/N is 70.35° under the polarizer and analyzer angles set at the optimal combinations (18.35°, 68.35°).

  15. Targeted loss of SHP1 in murine thymocytes dampens TCR signaling late in selection.

    PubMed

    Martinez, Ryan J; Morris, Anna B; Neeld, Dennis K; Evavold, Brian D

    2016-09-01

    SHP1 is a tyrosine phosphatase critical to proximal regulation of TCR signaling. Here, analysis of CD4-Cre SHP1(fl/fl) conditional knockout thymocytes using CD53, TCRβ, CD69, CD4, and CD8α expression demonstrates the importance of SHP1 in the survival of post selection (CD53(+) ), single-positive thymocytes. Using Ca(2+) flux to assess the intensity of TCR signaling demonstrated that SHP1 dampens the signal strength of these same mature, postselection thymocytes. Consistent with its dampening effect, TCR signal strength was also probed functionally using peptides that can mediate selection of the OT-I TCR, to reveal increased negative selection mediated by lower-affinity ligand in the absence of SHP1. Our data show that SHP1 is required for the survival of mature thymocytes and the generation of the functional T-cell repertoire, as its absence leads to a reduction in the numbers of CD4(+) and CD8(+) naïve T cells in the peripheral lymphoid compartments.

  16. Optimization of a Dibenzodiazepine Hit to a Potent and Selective Allosteric PAK1 Inhibitor

    PubMed Central

    2015-01-01

    The discovery of inhibitors targeting novel allosteric kinase sites is very challenging. Such compounds, however, once identified could offer exquisite levels of selectivity across the kinome. Herein we report our structure-based optimization strategy of a dibenzodiazepine hit 1, discovered in a fragment-based screen, yielding highly potent and selective inhibitors of PAK1 such as 2 and 3. Compound 2 was cocrystallized with PAK1 to confirm binding to an allosteric site and to reveal novel key interactions. Compound 3 modulated PAK1 at the cellular level and due to its selectivity enabled valuable research to interrogate biological functions of the PAK1 kinase. PMID:26191365

  17. Selective TRIF-Dependent Signaling by a Synthetic Toll-Like Receptor 4 Agonist

    PubMed Central

    Bowen, William S.; Minns, Laurie A.; Johnson, David A.; Mitchell, Thomas C.; Hutton, Melinda M.; Evans, Jay T.

    2013-01-01

    In response to ligand binding to the Toll-like receptor 4 (TLR4) and myeloid differentiation-2 (MD-2) receptor complex, two major signaling pathways are activated that involve different adaptor proteins. One pathway depends on myeloid differentiation marker 88 (MyD88), which elicits proinflammatory responses, whereas the other depends on Toll–IL-1 receptor (TIR) domain–containing adaptor inducing interferon-β (TRIF), which elicits type I interferon production. Here, we showed that the TLR4 agonist and vaccine adjuvant CRX-547, a member of the aminoalkyl glucosaminide 4-phosphate (AGP) class of synthetic lipid A mimetics, displayed TRIF-selective signaling in human cells, which was dependent on a minor structural modification to the carboxyl bioisostere corresponding to the 1-phosphate group on most lipid A types. CRX-547 stimulated little or no activation of MyD88-dependent signaling molecules or cytokines, whereas its ability to activate the TRIF-dependent pathway was similar to that of a structurally related inflammatory AGP and of lipopolysaccharide from Salmonella minnesota. This TRIF-selective signaling response resulted in the production of substantially less of the proinflammatory mediators that are associated with MyD88 signaling, thereby potentially reducing toxicity and improving the therapeutic index of this synthetic TLR4 agonist and vaccine adjuvant. PMID:22337809

  18. Signal to noise ratio of energy selective x-ray photon counting systems with pileup

    PubMed Central

    Alvarez, Robert E.

    2014-01-01

    Purpose: To derive fundamental limits on the effect of pulse pileup and quantum noise in photon counting detectors on the signal to noise ratio (SNR) and noise variance of energy selective x-ray imaging systems. Methods: An idealized model of the response of counting detectors to pulse pileup is used. The model assumes a nonparalyzable response and delta function pulse shape. The model is used to derive analytical formulas for the noise and energy spectrum of the recorded photons with pulse pileup. These formulas are first verified with a Monte Carlo simulation. They are then used with a method introduced in a previous paper [R. E. Alvarez, “Near optimal energy selective x-ray imaging system performance with simple detectors,” Med. Phys. 37, 822–841 (2010)] to compare the signal to noise ratio with pileup to the ideal SNR with perfect energy resolution. Detectors studied include photon counting detectors with pulse height analysis (PHA), detectors that simultaneously measure the number of photons and the integrated energy (NQ detector), and conventional energy integrating and photon counting detectors. The increase in the A-vector variance with dead time is also computed and compared to the Monte Carlo results. A formula for the covariance of the NQ detector is developed. The validity of the constant covariance approximation to the Cramèr–Rao lower bound (CRLB) for larger counts is tested. Results: The SNR becomes smaller than the conventional energy integrating detector (Q) SNR for 0.52, 0.65, and 0.78 expected number photons per dead time for counting (N), two, and four bin PHA detectors, respectively. The NQ detector SNR is always larger than the N and Q SNR but only marginally so for larger dead times. Its noise variance increases by a factor of approximately 3 and 5 for the A1 and A2 components as the dead time parameter increases from 0 to 0.8 photons per dead time. With four bin PHA data, the increase in variance is approximately 2 and 4 times. The

  19. Exploring the optimal performances of irreversible single resonance energy selective electron refrigerators

    NASA Astrophysics Data System (ADS)

    Zhou, Junle; Chen, Lingen; Ding, Zemin; Sun, Fengrui

    2016-05-01

    Applying finite-time thermodynamics (FTT) and electronic transport theory, the optimal performances of irreversible single resonance energy selective electron (ESE) refrigerator are analyzed. The effects of heat leakage between two electron reservoirs on optimal performances are discussed. The influences of system operating parameters on cooling load, coefficient of performance (COP), figure of merit and ecological function are demonstrated using numerical examples. Comparative performance analyses among different objective functions show that performance characteristics at maximum ecological function and maximum figure of merit are of great practical significance. Combining the two optimization objectives of maximum ecological function and maximum figure of merit together, more specific optimal ranges of cooling load and COP are obtained. The results can provide some advices to the design of practical electronic machine systems.

  20. Cutting Edge: CD3 ITAM Diversity Is Required for Optimal TCR Signaling and Thymocyte Development.

    PubMed

    Bettini, Matthew L; Chou, Po-Chein; Guy, Clifford S; Lee, Thomas; Vignali, Kate M; Vignali, Dario A A

    2017-09-01

    For the αβ or γδTCR chains to integrate extracellular stimuli into the appropriate intracellular cellular response, they must use the 10 ITAMs found within the CD3 subunits (CD3γε, CD3δε, and ζζ) of the TCR signaling complex. However, it remains unclear whether each specific ITAM sequence of the individual subunit (γεδζ) is required for thymocyte development or whether any particular CD3 ITAM motif is sufficient. In this article, we show that mice utilizing a single ITAM sequence (γ, ε, δ, ζa, ζb, or ζc) at each of the 10 ITAM locations exhibit a substantial reduction in thymic cellularity and limited CD4(-)CD8(-) (double-negative) to CD4(+)CD8(+) (double-positive) maturation because of low TCR expression and signaling. Together, the data suggest that ITAM sequence diversity is required for optimal TCR signal transduction and subsequent T cell maturation. Copyright © 2017 by The American Association of Immunologists, Inc.

  1. Sexual dimorphism and directional sexual selection on aposematic signals in a poison frog

    PubMed Central

    Maan, Martine E.; Cummings, Molly E.

    2009-01-01

    It is commonly assumed that natural selection imposed by predators is the prevailing force driving the evolution of aposematic traits. Here, we demonstrate that aposematic signals are shaped by sexual selection as well. We evaluated sexual selection for coloration brightness in populations of the poison frog Oophaga [Dendrobates] pumilio in Panama's Bocas del Toro archipelago. We assessed female preferences for brighter males by manipulating the perceived brightness of spectrally matched males in two-way choice experiments. We found strong female preferences for bright males in two island populations and weaker or ambiguous preferences in females from mainland populations. Spectral reflectance measurements, coupled with an O. pumilio-specific visual processing model, showed that O. pumilio coloration was significantly brighter in island than in mainland morphs. In one of the island populations (Isla Solarte), males were significantly more brightly colored than females. Taken together, these results provide evidence for directional sexual selection on aposematic coloration and document sexual dimorphism in vertebrate warning coloration. Although aposematic signals have long been upheld as exemplars of natural selection, our results show that sexual selection should not be ignored in studies of aposematic evolution. PMID:19858491

  2. EUROmediCAT signal detection: an evaluation of selected congenital anomaly‐medication associations

    PubMed Central

    Given, Joanne E.; Loane, Maria; Luteijn, Johannes M.; Morris, Joan K.; de Jong van den Berg, Lolkje T.W.; Garne, Ester; Addor, Marie‐Claude; Barisic, Ingeborg; de Walle, Hermien; Gatt, Miriam; Klungsoyr, Kari; Khoshnood, Babak; Latos‐Bielenska, Anna; Nelen, Vera; Neville, Amanda J.; O'Mahony, Mary; Pierini, Anna; Tucker, David; Wiesel, Awi

    2016-01-01

    Aims To evaluate congenital anomaly (CA)‐medication exposure associations produced by the new EUROmediCAT signal detection system and determine which require further investigation. Methods Data from 15 EUROCAT registries (1995–2011) with medication exposures at the chemical substance (5th level of Anatomic Therapeutic Chemical classification) and chemical subgroup (4th level) were analysed using a 50% false detection rate. After excluding antiepileptics, antidiabetics, antiasthmatics and SSRIs/psycholeptics already under investigation, 27 associations were evaluated. If evidence for a signal persisted after data validation, a literature review was conducted for prior evidence of human teratogenicity. Results Thirteen out of 27 CA‐medication exposure signals, based on 389 exposed cases, passed data validation. There was some prior evidence in the literature to support six signals (gastroschisis and levonorgestrel/ethinylestradiol (OR 4.10, 95% CI 1.70–8.53; congenital heart disease/pulmonary valve stenosis and nucleoside/tide reverse transcriptase inhibitors (OR 5.01, 95% CI 1.99–14.20/OR 28.20, 95% CI 4.63–122.24); complete absence of a limb and pregnen (4) derivatives (OR 6.60, 95% CI 1.70–22.93); hypospadias and pregnadien derivatives (OR 1.40, 95% CI 1.10–1.76); hypospadias and synthetic ovulation stimulants (OR 1.89, 95% CI 1.28–2.70). Antipropulsives produced a signal for syndactyly while the literature revealed a signal for hypospadias. There was no prior evidence to support the remaining six signals involving the ordinary salt combinations, propulsives, bulk‐forming laxatives, hydrazinophthalazine derivatives, gonadotropin releasing hormone analogues and selective serotonin agonists. Conclusion Signals which strengthened prior evidence should be prioritized for further investigation, and independent evidence sought to confirm the remaining signals. Some chance associations are expected and confounding by indication is possible. PMID

  3. Low Emissions and Delay Optimization for an Isolated Signalized Intersection Based on Vehicular Trajectories

    PubMed Central

    2015-01-01

    A traditional traffic signal control system is established based on vehicular delay, queue length, saturation and other indicators. However, due to the increasing severity of urban environmental pollution issues and the development of a resource-saving and environmentally friendly social philosophy, the development of low-carbon and energy-efficient urban transport is required. This paper first defines vehicular trajectories and the calculation of vehicular emissions based on VSP. Next, a regression analysis method is used to quantify the relationship between vehicular emissions and delay, and a traffic signal control model is established to reduce emissions and delay using the enumeration method combined with saturation constraints. Finally, one typical intersection of Changchun is selected to verify the model proposed in this paper; its performance efficiency is also compared using simulations in VISSIM. The results of this study show that the proposed model can significantly reduce vehicle delay and traffic emissions simultaneously. PMID:26720095

  4. Low Emissions and Delay Optimization for an Isolated Signalized Intersection Based on Vehicular Trajectories.

    PubMed

    Lin, Ciyun; Gong, Bowen; Qu, Xin

    2015-01-01

    A traditional traffic signal control system is established based on vehicular delay, queue length, saturation and other indicators. However, due to the increasing severity of urban environmental pollution issues and the development of a resource-saving and environmentally friendly social philosophy, the development of low-carbon and energy-efficient urban transport is required. This paper first defines vehicular trajectories and the calculation of vehicular emissions based on VSP. Next, a regression analysis method is used to quantify the relationship between vehicular emissions and delay, and a traffic signal control model is established to reduce emissions and delay using the enumeration method combined with saturation constraints. Finally, one typical intersection of Changchun is selected to verify the model proposed in this paper; its performance efficiency is also compared using simulations in VISSIM. The results of this study show that the proposed model can significantly reduce vehicle delay and traffic emissions simultaneously.

  5. Selection of Thermal Worst-Case Orbits via Modified Efficient Global Optimization

    NASA Technical Reports Server (NTRS)

    Moeller, Timothy M.; Wilhite, Alan W.; Liles, Kaitlin A.

    2014-01-01

    Efficient Global Optimization (EGO) was used to select orbits with worst-case hot and cold thermal environments for the Stratospheric Aerosol and Gas Experiment (SAGE) III. The SAGE III system thermal model changed substantially since the previous selection of worst-case orbits (which did not use the EGO method), so the selections were revised to ensure the worst cases are being captured. The EGO method consists of first conducting an initial set of parametric runs, generated with a space-filling Design of Experiments (DoE) method, then fitting a surrogate model to the data and searching for points of maximum Expected Improvement (EI) to conduct additional runs. The general EGO method was modified by using a multi-start optimizer to identify multiple new test points at each iteration. This modification facilitates parallel computing and decreases the burden of user interaction when the optimizer code is not integrated with the model. Thermal worst-case orbits for SAGE III were successfully identified and shown by direct comparison to be more severe than those identified in the previous selection. The EGO method is a useful tool for this application and can result in computational savings if the initial Design of Experiments (DoE) is selected appropriately.

  6. SHAPE Selection (SHAPES) enrich for RNA structure signal in SHAPE sequencing-based probing data.

    PubMed

    Poulsen, Line Dahl; Kielpinski, Lukasz Jan; Salama, Sofie R; Krogh, Anders; Vinther, Jeppe

    2015-05-01

    Selective 2' Hydroxyl Acylation analyzed by Primer Extension (SHAPE) is an accurate method for probing of RNA secondary structure. In existing SHAPE methods, the SHAPE probing signal is normalized to a no-reagent control to correct for the background caused by premature termination of the reverse transcriptase. Here, we introduce a SHAPE Selection (SHAPES) reagent, N-propanone isatoic anhydride (NPIA), which retains the ability of SHAPE reagents to accurately probe RNA structure, but also allows covalent coupling between the SHAPES reagent and a biotin molecule. We demonstrate that SHAPES-based selection of cDNA-RNA hybrids on streptavidin beads effectively removes the large majority of background signal present in SHAPE probing data and that sequencing-based SHAPES data contain the same amount of RNA structure data as regular sequencing-based SHAPE data obtained through normalization to a no-reagent control. Moreover, the selection efficiently enriches for probed RNAs, suggesting that the SHAPES strategy will be useful for applications with high-background and low-probing signal such as in vivo RNA structure probing. © 2015 Poulsen et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society.

  7. Signal design and perception in Hypocnemis antbirds: evidence for convergent evolution via social selection.

    PubMed

    Tobias, Joseph A; Seddon, Nathalie

    2009-12-01

    Natural selection is known to produce convergent phenotypes through mimicry or ecological adaptation. It has also been proposed that social selection--i.e., selection exerted by social competition--may drive convergent evolution in signals mediating interspecific communication, yet this idea remains controversial. Here, we use color spectrophotometry, acoustic analyses, and playback experiments to assess the hypothesis of adaptive signal convergence in two competing nonsister taxa, Hypocnemis peruviana and H. subflava (Aves: Thamnophilidae). We show that the structure of territorial songs in males overlaps in sympatry, with some evidence of convergent character displacement. Conversely, nonterritorial vocal and visual signals in males are strikingly diagnostic, in line with 6.8% divergence in mtDNA sequences. The same pattern of variation applies to females. Finally, we show that songs in both sexes elicit strong territorial responses within and between species, whereas songs of a third, allopatric and more closely related species (H. striata) are structurally divergent and elicit weaker responses. Taken together, our results provide compelling evidence that social selection can act across species boundaries to drive convergent or parallel evolution in taxa competing for space and resources.

  8. Selecting danger signals: dissociable roles of nucleus accumbens shell and core glutamate in predictive fear learning.

    PubMed

    Li, Susan S Y; McNally, Gavan P

    2015-06-01

    Conditioned stimuli (CSs) vary in their reliability as predictors of danger. Animals must therefore select among CSs those that are appropriate to enter into an association with the aversive unconditioned stimulus (US). The actions of prediction error instruct this stimulus selection so that when prediction error is large, attention to the CS is maintained and learning occurs but when prediction is small attention to the CS is withdrawn and learning is prevented. Here we studied the role of glutamate acting at rat nucleus accumbens shell (AcbSh) and core (AcbC) α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors in this selection of danger signals. Using associative blocking and unblocking designs in rats, we show that antagonizing AcbSh AMPA receptors via infusions of 2,3-dihydroxy-6-nitro-7-sulphamoyl-benzo[f]quinoxaline-2,3-dione (NBQX; 0.5 μg) prevents the unblocking of fear learning, whereas antagonizing AcbC AMPA receptors via infusions of NBQX (0.5 μg) prevents both the blocking and unblocking of fear learning. These results identify dissociable but complementary roles for AcbSh and AcbC glutamate acting at AMPA receptors in selecting danger signals: AcbSh AMPA receptors upregulate attention and learning to CSs that signal surprising USs, whereas AcbC AMPA receptors encode the predicted outcome of each trial.

  9. SHAPE Selection (SHAPES) enrich for RNA structure signal in SHAPE sequencing-based probing data

    PubMed Central

    Poulsen, Line Dahl; Kielpinski, Lukasz Jan; Salama, Sofie R.; Krogh, Anders; Vinther, Jeppe

    2015-01-01

    Selective 2′ Hydroxyl Acylation analyzed by Primer Extension (SHAPE) is an accurate method for probing of RNA secondary structure. In existing SHAPE methods, the SHAPE probing signal is normalized to a no-reagent control to correct for the background caused by premature termination of the reverse transcriptase. Here, we introduce a SHAPE Selection (SHAPES) reagent, N-propanone isatoic anhydride (NPIA), which retains the ability of SHAPE reagents to accurately probe RNA structure, but also allows covalent coupling between the SHAPES reagent and a biotin molecule. We demonstrate that SHAPES-based selection of cDNA–RNA hybrids on streptavidin beads effectively removes the large majority of background signal present in SHAPE probing data and that sequencing-based SHAPES data contain the same amount of RNA structure data as regular sequencing-based SHAPE data obtained through normalization to a no-reagent control. Moreover, the selection efficiently enriches for probed RNAs, suggesting that the SHAPES strategy will be useful for applications with high-background and low-probing signal such as in vivo RNA structure probing. PMID:25805860

  10. A diagnostic signal selection scheme for planetary gearbox vibration monitoring under non-stationary operational conditions

    NASA Astrophysics Data System (ADS)

    Feng, Ke; Wang, KeSheng; Zhang, Mian; Ni, Qing; Zuo, Ming J.

    2017-03-01

    The planetary gearbox, due to its unique mechanical structures, is an important rotating machine for transmission systems. Its engineering applications are often in non-stationary operational conditions, such as helicopters, wind energy systems, etc. The unique physical structures and working conditions make the vibrations measured from planetary gearboxes exhibit a complex time-varying modulation and therefore yield complicated spectral structures. As a result, traditional signal processing methods, such as Fourier analysis, and the selection of characteristic fault frequencies for diagnosis face serious challenges. To overcome this drawback, this paper proposes a signal selection scheme for fault-emphasized diagnostics based upon two order tracking techniques. The basic procedures for the proposed scheme are as follows. (1) Computed order tracking is applied to reveal the order contents and identify the order(s) of interest. (2) Vold-Kalman filter order tracking is used to extract the order(s) of interest—these filtered order(s) constitute the so-called selected vibrations. (3) Time domain statistic indicators are applied to the selected vibrations for faulty information-emphasized diagnostics. The proposed scheme is explained and demonstrated in a signal simulation model and experimental studies and the method proves to be effective for planetary gearbox fault diagnosis.

  11. A general method to select representative models for decision making and optimization under uncertainty

    NASA Astrophysics Data System (ADS)

    Shirangi, Mehrdad G.; Durlofsky, Louis J.

    2016-11-01

    The optimization of subsurface flow processes under geological uncertainty technically requires flow simulation to be performed over a large set of geological realizations for each function evaluation at every iteration of the optimizer. Because flow simulation over many permeability realizations (only permeability is considered to be uncertain in this study) may entail excessive computation, simulations are often performed for only a subset of 'representative' realizations. It is however challenging to identify a representative subset that provides flow statistics in close agreement with those from the full set, especially when the decision parameters (e.g., time-varying well pressures, well locations) are unknown a priori, as they are in optimization problems. In this work, we introduce a general framework, based on clustering, for selecting a representative subset of realizations for use in simulations involving 'new' sets of decision parameters. Prior to clustering, each realization is represented by a low-dimensional feature vector that contains a combination of permeability-based and flow-based quantities. Calculation of flow-based features requires the specification of a (base) flow problem and simulation over the full set of realizations. Permeability information is captured concisely through use of principal component analysis. By computing the difference between the flow response for the subset and the full set, we quantify the performance of various realization-selection methods. The impact of different weightings for flow and permeability information in the cluster-based selection procedure is assessed for a range of examples involving different types of decision parameters. These decision parameters are generated either randomly, in a manner that is consistent with the solutions proposed in global stochastic optimization procedures such as GA and PSO, or through perturbation around a base case, consistent with the solutions considered in pattern search

  12. A Novel Method of Failure Sample Selection for Electrical Systems Using Ant Colony Optimization

    PubMed Central

    Tian, Shulin; Yang, Chenglin; Liu, Cheng

    2016-01-01

    The influence of failure propagation is ignored in failure sample selection based on traditional testability demonstration experiment method. Traditional failure sample selection generally causes the omission of some failures during the selection and this phenomenon could lead to some fearful risks of usage because these failures will lead to serious propagation failures. This paper proposes a new failure sample selection method to solve the problem. First, the method uses a directed graph and ant colony optimization (ACO) to obtain a subsequent failure propagation set (SFPS) based on failure propagation model and then we propose a new failure sample selection method on the basis of the number of SFPS. Compared with traditional sampling plan, this method is able to improve the coverage of testing failure samples, increase the capacity of diagnosis, and decrease the risk of using. PMID:27738424

  13. Optimization of adiponectin-derived peptides for inhibition of cancer cell growth and signaling.

    PubMed

    Otvos, Laszlo; Kovalszky, Ilona; Olah, Julia; Coroniti, Roberta; Knappe, Daniel; Nollmann, Friederike I; Hoffmann, Ralf; Wade, John D; Lovas, Sandor; Surmacz, Eva

    2015-05-01

    Adiponectin, an adipose tissue-excreted adipokine plays protective roles in metabolic and cardiovascular diseases and exerts anti-cancer activities, partially by interfering with leptin-induced signaling. Previously we identified the active site in the adiponectin protein, and generated both a nanomolar monomeric agonist of the adiponectin receptor (10-mer ADP355) and an antagonist (8-mer ADP400) to modulate various adiponectin receptor-mediated cellular functions. As physiologically circulating adiponectin forms multimeric complexes, we also generated an agonist dimer with improved biodistribution and in vitro efficacy. In the current report, we attempted to optimize the monomeric agonist structure. Neither extension of the peptide up to 14-mer analogs nor reinstallation of native residues in permissible positions enhanced significantly the activity profile. The only substitutions that resulted in 5-10-fold improved agonistic activity were the replacement of turn-forming Gly4 and Tyr7 residues with Pro and Hyp, respectively, yielding the more active native β-sheet structure. All peptides retained good stability in human serum exhibiting half-lives >2 h. The cellular efficacy and stability rankings among the peptides followed expected structure-activity relationship trends. To investigate whether simultaneous activation of adiponectin pathways and inhibition of leptin-induced signals can result in cytostatic and anti-oncogenic signal transduction processes, we developed a chimera of the leptin receptor antagonist peptide Allo-aca (placed to the N-terminus) and ADP355 (at the C-terminus). The in vitro anti-tumor activity and intracellular signaling of the chimera were dominated by the more active Allo-aca component. The ADP355 part, however, reversed unfavorable in vivo metabolic effects of the leptin receptor antagonist.

  14. Implementation and optimization of ultrasound signal processing algorithms on mobile GPU

    NASA Astrophysics Data System (ADS)

    Kong, Woo Kyu; Lee, Wooyoul; Kim, Kyu Cheol; Yoo, Yangmo; Song, Tai-Kyong

    2014-03-01

    A general-purpose graphics processing unit (GPGPU) has been used for improving computing power in medical ultrasound imaging systems. Recently, a mobile GPU becomes powerful to deal with 3D games and videos at high frame rates on Full HD or HD resolution displays. This paper proposes the method to implement ultrasound signal processing on a mobile GPU available in the high-end smartphone (Galaxy S4, Samsung Electronics, Seoul, Korea) with programmable shaders on the OpenGL ES 2.0 platform. To maximize the performance of the mobile GPU, the optimization of shader design and load sharing between vertex and fragment shader was performed. The beamformed data were captured from a tissue mimicking phantom (Model 539 Multipurpose Phantom, ATS Laboratories, Inc., Bridgeport, CT, USA) by using a commercial ultrasound imaging system equipped with a research package (Ultrasonix Touch, Ultrasonix, Richmond, BC, Canada). The real-time performance is evaluated by frame rates while varying the range of signal processing blocks. The implementation method of ultrasound signal processing on OpenGL ES 2.0 was verified by analyzing PSNR with MATLAB gold standard that has the same signal path. CNR was also analyzed to verify the method. From the evaluations, the proposed mobile GPU-based processing method has no significant difference with the processing using MATLAB (i.e., PSNR<52.51 dB). The comparable results of CNR were obtained from both processing methods (i.e., 11.31). From the mobile GPU implementation, the frame rates of 57.6 Hz were achieved. The total execution time was 17.4 ms that was faster than the acquisition time (i.e., 34.4 ms). These results indicate that the mobile GPU-based processing method can support real-time ultrasound B-mode processing on the smartphone.

  15. The effect of genomic information on optimal contribution selection in livestock breeding programs.

    PubMed

    Clark, Samuel A; Kinghorn, Brian P; Hickey, John M; van der Werf, Julius H J

    2013-10-30

    Long-term benefits in animal breeding programs require that increases in genetic merit be balanced with the need to maintain diversity (lost due to inbreeding). This can be achieved by using optimal contribution selection. The availability of high-density DNA marker information enables the incorporation of genomic data into optimal contribution selection but this raises the question about how this information affects the balance between genetic merit and diversity. The effect of using genomic information in optimal contribution selection was examined based on simulated and real data on dairy bulls. We compared the genetic merit of selected animals at various levels of co-ancestry restrictions when using estimated breeding values based on parent average, genomic or progeny test information. Furthermore, we estimated the proportion of variation in estimated breeding values that is due to within-family differences. Optimal selection on genomic estimated breeding values increased genetic gain. Genetic merit was further increased using genomic rather than pedigree-based measures of co-ancestry under an inbreeding restriction policy. Using genomic instead of pedigree relationships to restrict inbreeding had a significant effect only when the population consisted of many large full-sib families; with a half-sib family structure, no difference was observed. In real data from dairy bulls, optimal contribution selection based on genomic estimated breeding values allowed for additional improvements in genetic merit at low to moderate inbreeding levels. Genomic estimated breeding values were more accurate and showed more within-family variation than parent average breeding values; for genomic estimated breeding values, 30 to 40% of the variation was due to within-family differences. Finally, there was no difference between constraining inbreeding via pedigree or genomic relationships in the real data. The use of genomic estimated breeding values increased genetic gain in

  16. Auto-OBSD: Automatic parameter selection for reliable Oscillatory Behavior-based Signal Decomposition with an application to bearing fault signature extraction

    NASA Astrophysics Data System (ADS)

    Huang, Huan; Baddour, Natalie; Liang, Ming

    2017-03-01

    Bearing signals are often contaminated by in-band interferences and random noise. Oscillatory Behavior-based Signal Decomposition (OBSD) is a new technique which decomposes a signal according to its oscillatory behavior, rather than frequency or scale. Due to the low oscillatory transients of bearing fault-induced signals, the OBSD can be used to effectively extract bearing fault signatures from a blurred signal. However, the quality of the result highly relies on the selection of method-related parameters. Such parameters are often subjectively selected and a systematic approach has not been reported in the literature. As such, this paper proposes a systematic approach to automatic selection of OBSD parameters for reliable extraction of bearing fault signatures. The OBSD utilizes the idea of Morphological Component Analysis (MCA) that optimally projects the original signal to low oscillatory wavelets and high oscillatory wavelets established via the Tunable Q-factor Wavelet Transform (TQWT). In this paper, the effects of the selection of each parameter on the performance of the OBSD for bearing fault signature extraction are investigated. It is found that some method-related parameters can be fixed at certain values due to the nature of bearing fault-induced impulses. To adaptively tune the remaining parameters, index-guided parameter selection algorithms are proposed. A Convergence Index (CI) is proposed and a CI-guided self-tuning algorithm is developed to tune the convergence-related parameters, namely, penalty factor and number of iterations. Furthermore, a Smoothness Index (SI) is employed to measure the effectiveness of the extracted low oscillatory component (i.e. bearing fault signature). It is shown that a minimum SI implies an optimal result with respect to the adjustment of relevant parameters. Thus, two SI-guided automatic parameter selection algorithms are also developed to specify two other parameters, i.e., Q-factor of high-oscillatory wavelets and

  17. An optimized method for the estimation of the respiratory rate from electrocardiographic signals: implications for estimating minute ventilation

    PubMed Central

    Weiss, Eric H.; Sayadi, Omid; Ramaswamy, Priya; Merchant, Faisal M.; Sajja, Naveen; Foley, Lori; Laferriere, Shawna

    2014-01-01

    It is well-known that respiratory activity influences electrocardiographic (ECG) morphology. In this article we present a new algorithm for the extraction of respiratory rate from either intracardiac or body surface electrograms. The algorithm optimizes selection of ECG leads for respiratory analysis, as validated in a swine model. The algorithm estimates the respiratory rate from any two ECG leads by finding the power spectral peak of the derived ratio of the estimated root-mean-squared amplitude of the QRS complexes on a beat-by-beat basis across a 32-beat window and automatically selects the lead combination with the highest power spectral signal-to-noise ratio. In 12 mechanically ventilated swine, we collected intracardiac electrograms from catheters in the right ventricle, coronary sinus, left ventricle, and epicardial surface, as well as body surface electrograms, while the ventilation rate was varied between 7 and 13 breaths/min at tidal volumes of 500 and 750 ml. We found excellent agreement between the estimated and true respiratory rate for right ventricular (R2 = 0.97), coronary sinus (R2 = 0.96), left ventricular (R2 = 0.96), and epicardial (R2 = 0.97) intracardiac leads referenced to surface lead ECGII. When applied to intracardiac right ventricular-coronary sinus bipolar leads, the algorithm exhibited an accuracy of 99.1% (R2 = 0.97). When applied to 12-lead body surface ECGs collected in 4 swine, the algorithm exhibited an accuracy of 100% (R2 = 0.93). In conclusion, the proposed algorithm provides an accurate estimation of the respiratory rate using either intracardiac or body surface signals without the need for additional hardware. PMID:24858847

  18. An optimized method for the estimation of the respiratory rate from electrocardiographic signals: implications for estimating minute ventilation.

    PubMed

    Weiss, Eric H; Sayadi, Omid; Ramaswamy, Priya; Merchant, Faisal M; Sajja, Naveen; Foley, Lori; Laferriere, Shawna; Armoundas, Antonis A

    2014-08-01

    It is well-known that respiratory activity influences electrocardiographic (ECG) morphology. In this article we present a new algorithm for the extraction of respiratory rate from either intracardiac or body surface electrograms. The algorithm optimizes selection of ECG leads for respiratory analysis, as validated in a swine model. The algorithm estimates the respiratory rate from any two ECG leads by finding the power spectral peak of the derived ratio of the estimated root-mean-squared amplitude of the QRS complexes on a beat-by-beat basis across a 32-beat window and automatically selects the lead combination with the highest power spectral signal-to-noise ratio. In 12 mechanically ventilated swine, we collected intracardiac electrograms from catheters in the right ventricle, coronary sinus, left ventricle, and epicardial surface, as well as body surface electrograms, while the ventilation rate was varied between 7 and 13 breaths/min at tidal volumes of 500 and 750 ml. We found excellent agreement between the estimated and true respiratory rate for right ventricular (R(2) = 0.97), coronary sinus (R(2) = 0.96), left ventricular (R(2) = 0.96), and epicardial (R(2) = 0.97) intracardiac leads referenced to surface lead ECGII. When applied to intracardiac right ventricular-coronary sinus bipolar leads, the algorithm exhibited an accuracy of 99.1% (R(2) = 0.97). When applied to 12-lead body surface ECGs collected in 4 swine, the algorithm exhibited an accuracy of 100% (R(2) = 0.93). In conclusion, the proposed algorithm provides an accurate estimation of the respiratory rate using either intracardiac or body surface signals without the need for additional hardware.

  19. A feasibility study: Selection of a personalized radiotherapy fractionation schedule using spatiotemporal optimization

    SciTech Connect

    Kim, Minsun Stewart, Robert D.; Phillips, Mark H.

    2015-11-15

    Purpose: To investigate the impact of using spatiotemporal optimization, i.e., intensity-modulated spatial optimization followed by fractionation schedule optimization, to select the patient-specific fractionation schedule that maximizes the tumor biologically equivalent dose (BED) under dose constraints for multiple organs-at-risk (OARs). Methods: Spatiotemporal optimization was applied to a variety of lung tumors in a phantom geometry using a range of tumor sizes and locations. The optimal fractionation schedule for a patient using the linear-quadratic cell survival model depends on the tumor and OAR sensitivity to fraction size (α/β), the effective tumor doubling time (T{sub d}), and the size and location of tumor target relative to one or more OARs (dose distribution). The authors used a spatiotemporal optimization method to identify the optimal number of fractions N that maximizes the 3D tumor BED distribution for 16 lung phantom cases. The selection of the optimal fractionation schedule used equivalent (30-fraction) OAR constraints for the heart (D{sub mean} ≤ 45 Gy), lungs (D{sub mean} ≤ 20 Gy), cord (D{sub max} ≤ 45 Gy), esophagus (D{sub max} ≤ 63 Gy), and unspecified tissues (D{sub 05} ≤ 60 Gy). To assess plan quality, the authors compared the minimum, mean, maximum, and D{sub 95} of tumor BED, as well as the equivalent uniform dose (EUD) for optimized plans to conventional intensity-modulated radiation therapy plans prescribing 60 Gy in 30 fractions. A sensitivity analysis was performed to assess the effects of T{sub d} (3–100 days), tumor lag-time (T{sub k} = 0–10 days), and the size of tumors on optimal fractionation schedule. Results: Using an α/β ratio of 10 Gy, the average values of tumor max, min, mean BED, and D{sub 95} were up to 19%, 21%, 20%, and 19% larger than those from conventional prescription, depending on T{sub d} and T{sub k} used. Tumor EUD was up to 17% larger than the conventional prescription. For fast proliferating

  20. A feasibility study: Selection of a personalized radiotherapy fractionation schedule using spatiotemporal optimization.

    PubMed

    Kim, Minsun; Stewart, Robert D; Phillips, Mark H

    2015-11-01

    To investigate the impact of using spatiotemporal optimization, i.e., intensity-modulated spatial optimization followed by fractionation schedule optimization, to select the patient-specific fractionation schedule that maximizes the tumor biologically equivalent dose (BED) under dose constraints for multiple organs-at-risk (OARs). Spatiotemporal optimization was applied to a variety of lung tumors in a phantom geometry using a range of tumor sizes and locations. The optimal fractionation schedule for a patient using the linear-quadratic cell survival model depends on the tumor and OAR sensitivity to fraction size (α/β), the effective tumor doubling time (Td), and the size and location of tumor target relative to one or more OARs (dose distribution). The authors used a spatiotemporal optimization method to identify the optimal number of fractions N that maximizes the 3D tumor BED distribution for 16 lung phantom cases. The selection of the optimal fractionation schedule used equivalent (30-fraction) OAR constraints for the heart (Dmean≤45 Gy), lungs (Dmean≤20 Gy), cord (Dmax≤45 Gy), esophagus (Dmax≤63 Gy), and unspecified tissues (D05≤60 Gy). To assess plan quality, the authors compared the minimum, mean, maximum, and D95 of tumor BED, as well as the equivalent uniform dose (EUD) for optimized plans to conventional intensity-modulated radiation therapy plans prescribing 60 Gy in 30 fractions. A sensitivity analysis was performed to assess the effects of Td (3-100 days), tumor lag-time (Tk=0-10 days), and the size of tumors on optimal fractionation schedule. Using an α/β ratio of 10 Gy, the average values of tumor max, min, mean BED, and D95 were up to 19%, 21%, 20%, and 19% larger than those from conventional prescription, depending on Td and Tk used. Tumor EUD was up to 17% larger than the conventional prescription. For fast proliferating tumors with Td less than 10 days, there was no significant increase in tumor BED but the treatment course could be

  1. General equations for optimal selection of diagnostic image acquisition parameters in clinical X-ray imaging.

    PubMed

    Zheng, Xiaoming

    2017-08-18

    The purpose of this work was to examine the effects of relationship functions between diagnostic image quality and radiation dose on the governing equations for image acquisition parameter variations in X-ray imaging. Various equations were derived for the optimal selection of peak kilovoltage (kVp) and exposure parameter (milliAmpere second, mAs) in computed tomography (CT), computed radiography (CR), and direct digital radiography. Logistic, logarithmic, and linear functions were employed to establish the relationship between radiation dose and diagnostic image quality. The radiation dose to the patient, as a function of image acquisition parameters (kVp, mAs) and patient size (d), was used in radiation dose and image quality optimization. Both logistic and logarithmic functions resulted in the same governing equation for optimal selection of image acquisition parameters using a dose efficiency index. For image quality as a linear function of radiation dose, the same governing equation was derived from the linear relationship. The general equations should be used in guiding clinical X-ray imaging through optimal selection of image acquisition parameters. The radiation dose to the patient could be reduced from current levels in medical X-ray imaging.

  2. A novel variable selection approach that iteratively optimizes variable space using weighted binary matrix sampling.

    PubMed

    Deng, Bai-chuan; Yun, Yong-huan; Liang, Yi-zeng; Yi, Lun-zhao

    2014-10-07

    In this study, a new optimization algorithm called the Variable Iterative Space Shrinkage Approach (VISSA) that is based on the idea of model population analysis (MPA) is proposed for variable selection. Unlike most of the existing optimization methods for variable selection, VISSA statistically evaluates the performance of variable space in each step of optimization. Weighted binary matrix sampling (WBMS) is proposed to generate sub-models that span the variable subspace. Two rules are highlighted during the optimization procedure. First, the variable space shrinks in each step. Second, the new variable space outperforms the previous one. The second rule, which is rarely satisfied in most of the existing methods, is the core of the VISSA strategy. Compared with some promising variable selection methods such as competitive adaptive reweighted sampling (CARS), Monte Carlo uninformative variable elimination (MCUVE) and iteratively retaining informative variables (IRIV), VISSA showed better prediction ability for the calibration of NIR data. In addition, VISSA is user-friendly; only a few insensitive parameters are needed, and the program terminates automatically without any additional conditions. The Matlab codes for implementing VISSA are freely available on the website: https://sourceforge.net/projects/multivariateanalysis/files/VISSA/.

  3. Isotope selective photoionization of NaK by optimal control: theory and experiment.

    PubMed

    Schäfer-Bung, Boris; Bonacić-Koutecký, Vlasta; Sauer, Franziska; Weber, Stefan M; Wöste, Ludger; Lindinger, Albrecht

    2006-12-07

    We present a joint theoretical and experimental study of the maximization of the isotopomer ratio (23)Na(39)K(23)Na(41)K using tailored phase-only as well as amplitude and phase modulated femtosecond laser fields obtained in the framework of optimal control theory and closed loop learning (CLL) technique. A good agreement between theoretically and experimentally optimized pulse shapes is achieved which allows to assign the optimized processes directly to the pulse shapes obtained by the experimental isotopomer selective CLL approach. By analyzing the dynamics induced by the optimized pulses we show that the mechanism involving the dephasing of the wave packets between the isotopomers (23)Na (39)K and (23)Na (41)K on the first excited state is responsible for high isotope selective ionization. Amplitude and phase modulated pulses, moreover, allow to establish the connection between the spectral components of the pulse and corresponding occupied vibronic states. It will be also shown that the leading features of the theoretically shaped pulses are independent from the initial conditions. Since the underlying processes can be assigned to the individual features of the shaped pulses, we show that optimal control can be used as a tool for analysis.

  4. Temporal cAMP Signaling Selectivity by Natural and Synthetic MC4R Agonists

    PubMed Central

    Molden, Brent M.; Cooney, Kimberly A.; West, Kirk; Van Der Ploeg, Lex H. T.

    2015-01-01

    The melanocortin-4 receptor (MC4R) is a G protein-coupled receptor expressed in the brain, where it controls energy balance through pathways including α-melanocyte-stimulating hormone (α-MSH)-dependent signaling. We have reported that the MC4R can exist in an active conformation that signals constitutively by increasing cAMP levels in the absence of receptor desensitization. We asked whether synthetic MC4R agonists differ in their ability to increase intracellular cAMP over time in Neuro2A cells expressing endogenous MC4R and exogenous, epitope-tagged hemagglutinin-MC4R-green fluorescent protein. By analyzing intracellular cAMP in a temporally resolved Förster resonance energy transfer assay, we show that withdrawal of α-MSH leads to a quick reversal of cAMP induction. By contrast, the synthetic agonist melanotan II (MTII) induces a cAMP signal that persists for at least 1 hour after removal of MTII from the medium and cannot be antagonized by agouti related protein. Similarly, in mHypoE-42 immortalized hypothalamic neurons, MTII, but not α-MSH, induced persistent AMP kinase signal, which occurs downstream of increased cAMP. By using a fluorescence recovery after photobleaching assay, it appears that the receptor exposed to MTII continues to signal after being internalized. Similar to MTII, the synthetic MC4R agonists, THIQ and BIM-22511, but not LY2112688, induced prolonged cAMP signaling after agonist withdrawal. However, agonist-exposed MC4R desensitized to the same extent, regardless of the ligand used and regardless of differences in receptor intracellular retention kinetics. In conclusion, α-MSH and LY2112688, when compared with MTII, THIQ, and BIM-22511, vary in the duration of the acute cAMP response, showing distinct temporal signaling selectivity, possibly linked to specific cell compartments from which cAMP signals may originate. PMID:26418335

  5. Temporal cAMP Signaling Selectivity by Natural and Synthetic MC4R Agonists.

    PubMed

    Molden, Brent M; Cooney, Kimberly A; West, Kirk; Van Der Ploeg, Lex H T; Baldini, Giulia

    2015-11-01

    The melanocortin-4 receptor (MC4R) is a G protein-coupled receptor expressed in the brain, where it controls energy balance through pathways including α-melanocyte-stimulating hormone (α-MSH)-dependent signaling. We have reported that the MC4R can exist in an active conformation that signals constitutively by increasing cAMP levels in the absence of receptor desensitization. We asked whether synthetic MC4R agonists differ in their ability to increase intracellular cAMP over time in Neuro2A cells expressing endogenous MC4R and exogenous, epitope-tagged hemagglutinin-MC4R-green fluorescent protein. By analyzing intracellular cAMP in a temporally resolved Förster resonance energy transfer assay, we show that withdrawal of α-MSH leads to a quick reversal of cAMP induction. By contrast, the synthetic agonist melanotan II (MTII) induces a cAMP signal that persists for at least 1 hour after removal of MTII from the medium and cannot be antagonized by agouti related protein. Similarly, in mHypoE-42 immortalized hypothalamic neurons, MTII, but not α-MSH, induced persistent AMP kinase signal, which occurs downstream of increased cAMP. By using a fluorescence recovery after photobleaching assay, it appears that the receptor exposed to MTII continues to signal after being internalized. Similar to MTII, the synthetic MC4R agonists, THIQ and BIM-22511, but not LY2112688, induced prolonged cAMP signaling after agonist withdrawal. However, agonist-exposed MC4R desensitized to the same extent, regardless of the ligand used and regardless of differences in receptor intracellular retention kinetics. In conclusion, α-MSH and LY2112688, when compared with MTII, THIQ, and BIM-22511, vary in the duration of the acute cAMP response, showing distinct temporal signaling selectivity, possibly linked to specific cell compartments from which cAMP signals may originate.

  6. German criteria for selection of hearing protectors in the interest of good signal audibility.

    PubMed

    Liedtke, Martin

    2009-01-01

    The German transport and personal protective equipment (PPE) technical committees of the German Social Accident Insurance have laid down criteria, which have since become established, for hearing protectors to be used in railway systems and road traffic in Germany: only hearing protectors which do not significantly impair the audibility of auditory warning signals may be used. In addition, the Institute for Occupational Safety and Health of the German Social Accident Insurance (BGIA) has proposed a simple criterion for the selection of hearing protectors for workplaces outside railway systems and road traffic which perform well with regard to signal audibility (general), speech intelligibility, and perception of informative operating sound (AIP). This criterion is based upon the research carried out in the field of signal audibility in railway systems and road traffic and upon an additional study. It has been established by the German PPE technical committee and is presented here.

  7. Noradrenergic control of associative synaptic plasticity by selective modulation of instructive signals

    PubMed Central

    Carey, Megan R.; Regehr, Wade G.

    2010-01-01

    Synapses throughout the brain are modified through associative mechanisms in which one input provides an instructive signal for changes in the strength of a second co-activated input. In cerebellar Purkinje cells, climbing fiber synapses provide an instructive signal for plasticity at parallel fiber synapses. Here we show that noradrenaline activates α2-adrenergic receptors to control short-term and long-term associative plasticity of parallel fiber synapses. This regulation of plasticity does not reflect a conventional direct modulation of the postsynaptic Purkinje cell or presynaptic parallel fibers. Instead, noradrenaline reduces associative plasticity by selectively decreasing the probability of release at the climbing fiber synapse, which in turn decreases climbing fiber-evoked dendritic calcium signals. These findings raise the possibility that targeted presynaptic modulation of instructive synapses could provide a general mechanism for dynamic context-dependent modulation of associative plasticity. PMID:19376071

  8. Space debris selection and optimal guidance for removal in the SSO with low-thrust propulsion

    NASA Astrophysics Data System (ADS)

    Olympio, J. T.; Frouvelle, N.

    2014-06-01

    The current paper deals with the mission design of a generic active space debris removal spacecraft. Considered space debris are all on sun-synchronous orbits. A perturbed Lambert's problem, modelling the transfer between two space 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 the optimal space debris sequence and mission strategy. Low-thrust optimisation is then performed to turn bi-impulse transfers into optimal low-thrust transfers, and refine the selected scenarios.

  9. An integrated sandstone acidizing fluid selection and simulation to optimize treatment design

    SciTech Connect

    Sumotarto, U.; Hill, A.D.; Sepehrnoori, K.

    1995-12-31

    An optimized design of a matrix treatment involves fluid selection and acidizing simulations to predict the outcome of the treatment. Many matrix acidizing treatment designs and fluid selections have been successfully accomplished by utilizing expert system technology. However, none of these present a complete and optimized result (i.e., by utilizing the output of the expert system to predict the acidizing outcome using an acidizing numerical simulator). In the meantime, several acidizing computer simulation studies have been conducted separately. This paper presents a study which integrates the treatment design, particularly the fluid selection process, and acidizing simulation for sandstone formations. Required parameters for sandstone acidizing such as acid type, concentration, volume, and injection rate/pressure are first selected using an expert system. The output from the expert system is further used for the input to an acidizing numerical simulator (UTACID). A new sandstone acidizing reaction model, appropriate for a high-temperature environment, and anisotropic medium have been implemented into UTACID to enhance the performance of the simulator. The expert system and the simulator have been integrated to provide an optimization tool for sandstone acidizing treatment design and simulation.

  10. Efficient and Optimal Attitude Determination Using Recursive Global Positioning System Signal Operations

    NASA Technical Reports Server (NTRS)

    Crassidis, John L.; Lightsey, E. Glenn; Markley, F. Landis

    1998-01-01

    In this paper, a new and efficient algorithm is developed for attitude determination from Global Positioning System signals. The new algorithm is derived from a generalized nonlinear predictive filter for nonlinear systems. This uses a one time-step ahead approach to propagate a simple kinematics model for attitude determination. The advantages of the new algorithm over previously developed methods include: it provides optimal attitudes even for coplanar baseline configurations; it guarantees convergence even for poor initial conditions; it is a non-iterative algorithm; and it is computationally efficient. These advantages clearly make the new algorithm well suited to on-board applications. The performance of the new algorithm is tested on a dynamic hardware simulator. Results indicate that the new algorithm accurately estimates the attitude of a moving vehicle, and provides robust attitude estimates even when other methods, such as a linearized least-squares approach, fail due to poor initial starting conditions.

  11. Optimal Signal Filtration in Optical Sensors with Natural Squeezing of Vacuum Noises

    NASA Technical Reports Server (NTRS)

    Gusev, A. V.; Kulagin, V. V.

    1996-01-01

    The structure of optimal receiver is discussed for optical sensor measuring a small displacement of probe mass. Due to nonlinear interaction of the field and the mirror, a reflected wave is in squeezed state (natural squeezing), two quadratures of which are correlated and therefore one can increase signal-to-noise ratio and overcome the SQL. A measurement procedure realizing such correlation processing of two quadratures is clarified. The required combination of quadratures can be produced via mixing of pump field reflected from the mirror with local oscillator phase modulated field in duel-detector homodyne scheme. Such measurement procedure could be useful not only for resonant bar gravitational detector but for laser longbase interferometric detectors as well.

  12. Signal Analysis Algorithms for Optimized Fitting of Nonresonant Laser Induced Thermal Acoustics Damped Sinusoids

    NASA Technical Reports Server (NTRS)

    Balla, R. Jeffrey; Miller, Corey A.

    2008-01-01

    This study seeks a numerical algorithm which optimizes frequency precision for the damped sinusoids generated by the nonresonant LITA technique. It compares computed frequencies, frequency errors, and fit errors obtained using five primary signal analysis methods. Using variations on different algorithms within each primary method, results from 73 fits are presented. Best results are obtained using an AutoRegressive method. Compared to previous results using Prony s method, single shot waveform frequencies are reduced approx.0.4% and frequency errors are reduced by a factor of approx.20 at 303K to approx. 0.1%. We explore the advantages of high waveform sample rates and potential for measurements in low density gases.

  13. General spatial phase-shifting interferometry by optimizing the signal retrieving function.

    PubMed

    Wang, Yi; Qiu, Xiang; Xiong, Jiaxiang; Li, Bingbo; Zhong, Liyun; Liu, Shengde; Zhou, Yunfei; Tian, Jindong; Lu, Xiaoxu

    2017-04-03

    A general spatial phase-shifting (GSPS) interferometry method is proposed to achieve phase retrieval from one-frame spatial carrier frequency interferogram. By optimizing the internal signal retrieving function of the spatial phase-shifting (SPS) method, the accuracy, anti-noise ability and speed of phase retrieval can be significantly improved, meanwhile the corresponding local calculation property is reserved. Especially, in the case that the ratio of the spatial carrier to the phase variation rate are small, the proposed method reveals obvious advantage in the accuracy improvement relative to the conventional SPS methods, so the more details of measured sample can be effectively reserved through introducing smaller spatial carrier frequency, and this will facilitate its application in interference microscopy. The principle analysis, numerical simulation and experimental result are employed to verify the performance of the proposed GSPS method.

  14. Optimization of high-resolution continuous flow analysis for transient climate signals in ice cores.

    PubMed

    Bigler, Matthias; Svensson, Anders; Kettner, Ernesto; Vallelonga, Paul; Nielsen, Maibritt E; Steffensen, Jørgen Peder

    2011-05-15

    Over the past two decades, continuous flow analysis (CFA) systems have been refined and widely used to measure aerosol constituents in polar and alpine ice cores in very high-depth resolution. Here we present a newly designed system consisting of sodium, ammonium, dust particles, and electrolytic meltwater conductivity detection modules. The system is optimized for high-resolution determination of transient signals in thin layers of deep polar ice cores. Based on standard measurements and by comparing sections of early Holocene and glacial ice from Greenland, we find that the new system features a depth resolution in the ice of a few millimeters which is considerably better than other CFA systems. Thus, the new system can resolve ice strata down to 10 mm thickness and has the potential of identifying annual layers in both Greenland and Antarctic ice cores throughout the last glacial cycle.

  15. The plant physical features selected by wildcats as signal posts: an economic approach to fecal marking

    NASA Astrophysics Data System (ADS)

    Piñeiro, Ana; Barja, Isabel

    2012-10-01

    The chemical signals of solitary and territorial felid species are essential for their intraspecific communication. We studied the selection of plant substrates during the fecal marking behavior of the European wildcat Felis silvestris from September 2008 to June 2009 in a protected area in Northwest Spain. The aim of the study was to examine the selection of plants as signal posts with respect to their physical characteristics. We hypothesized that wildcats deposit their fecal marks on plants with physical characteristics (e.g., size, species, and visual conspicuousness) that enhance the olfactory and visual effectiveness of the signal. Our results indicate that diameter, plant group, visual conspicuousness, and interaction between the diameter and plant group influence the decision of wildcats to deposit their fecal marks on plants. The wildcats chose plants with greater diameters and greater visual conspicuousness as scent-marking posts. Moreover, the wildcats chose woody and herbaceous plants, and certain plant species were marked more frequently than expected at random. Indeed, our results indicate that the fecal marks were not randomly distributed on the plants: the wildcats chose to place their marks on plants with certain physical characteristics that maximized the detectability of the signal by intruders and potential mates, thus facilitating the spatial distribution of the species.

  16. The plant physical features selected by wildcats as signal posts: an economic approach to fecal marking.

    PubMed

    Piñeiro, Ana; Barja, Isabel

    2012-10-01

    The chemical signals of solitary and territorial felid species are essential for their intraspecific communication. We studied the selection of plant substrates during the fecal marking behavior of the European wildcat Felis silvestris from September 2008 to June 2009 in a protected area in Northwest Spain. The aim of the study was to examine the selection of plants as signal posts with respect to their physical characteristics. We hypothesized that wildcats deposit their fecal marks on plants with physical characteristics (e.g., size, species, and visual conspicuousness) that enhance the olfactory and visual effectiveness of the signal. Our results indicate that diameter, plant group, visual conspicuousness, and interaction between the diameter and plant group influence the decision of wildcats to deposit their fecal marks on plants. The wildcats chose plants with greater diameters and greater visual conspicuousness as scent-marking posts. Moreover, the wildcats chose woody and herbaceous plants, and certain plant species were marked more frequently than expected at random. Indeed, our results indicate that the fecal marks were not randomly distributed on the plants: the wildcats chose to place their marks on plants with certain physical characteristics that maximized the detectability of the signal by intruders and potential mates, thus facilitating the spatial distribution of the species.

  17. Application of RGS box proteins to evaluate G-protein selectivity in receptor-promoted signaling.

    PubMed

    Hains, Melinda D; Siderovski, David P; Harden, T Kendall

    2004-01-01

    Regulator of G-protein signaling (RGS) domains bind directly to GTP-bound Galpha subunits and accelerate their intrinsic GTPase activity by up to several thousandfold. The selectivity of RGS proteins for individual Galpha subunits has been illustrated. Thus, the expression of RGS proteins can be used to inhibit signaling pathways activated by specific G protein-coupled receptors (GPCRs). This article describes the use of specific RGS domain constructs to discriminate among G(i/o), Gq-and G(12/13)-mediated activation of phospholipase C (PLC) isozymes in COS-7 cells. Overexpression of the N terminus of GRK2 (amino acids 45-178) or p115 RhoGEF (amino acids 1-240) elicited selective inhibition of Galphaq- or Galpha(12/13)-mediated signaling to PLC activation, respectively. In contrast, RGS2 overexpression was found to inhibit PLC activation by both G(i/o)- and Gq-coupled GPCRs. RGS4 exhibited dramatic receptor selectivity in its inhibitory actions; of the G(i/o)- and Gq-coupled GPCRs tested (LPA1, LPA2, P2Y1, S1P3), only the Gq-coupled lysophosphatidic acid-activated LPA2 receptor was found to be inhibited by RGS4 overexpression.

  18. CBAP promotes thymocyte negative selection by facilitating T-cell receptor proximal signaling

    PubMed Central

    Ho, K-C; Chiang, Y-J; Lai, A C-Y; Liao, N-S; Chang, Y-J; Yang-Yen, H-F; Yen, J J-Y

    2014-01-01

    T-cell receptor (TCR)-transduced signaling is critical to thymocyte development at the CD4/CD8 double-positive stage, but the molecules involved in this process are not yet fully characterized. We previously demonstrated that GM-CSF/IL-3/IL-5 receptor common β-chain-associated protein (CBAP) modulates ZAP70-mediated T-cell migration and adhesion. On the basis of the high expression of CBAP during thymocyte development, we investigated the function of CBAP in thymocyte development using a CBAP knockout mouse. CBAP-deficient mice showed normal early thymocyte development and positive selection. In contrast, several negative selection models (including TCR transgene, superantigen staphylococcal enterotoxin B, and anti-CD3 antibody treatment) revealed an attenuation of TCR-induced thymocyte deletion in CBAP knockout mice. This phenotype correlated with a reduced accumulation of BIM upon TCR crosslinking in CBAP-deficient thymocytes. Loss of CBAP led to reduced TCR-induced phosphorylation of proteins involved in both proximal and distal signaling events, including ZAP70, LAT, PLCγ1, and JNK1/2. Moreover, TCR-induced association of LAT signalosome components was reduced in CBAP-deficient thymocytes. Our data demonstrate that CBAP is a novel component in the TCR signaling pathway and modulates thymocyte apoptosis during negative selection. PMID:25393474

  19. MAXBAND Version 3.1: Heuristic and optimal approach for setting the left turn phase sequences in signalized networks

    SciTech Connect

    Pillai, R.S.; Rathi, A.K.

    1995-02-01

    The main objective of synchronized signal timing is to keep traffic moving along arterials in platoons throughout the signal system by proper setting of left turn phase sequence at signals along the arterials/networks. The synchronization of traffic signals located along the urban/suburban arterials in metropolitan areas is perhaps one of the most cost-effective methods for improving traffic flow along these streets. MAXBAND Version 2.1 (formerly known as MAXBAND-86), a progression-based optimization model, is used for generating signal timing plan for urban networks. This model formulates the problem as a mixed integer linear program and uses Land and Powell branch and bound search to arrive at the optimal solution. The computation time of MAXBAND Version 2.1 tends to be excessive for realistic multiarterial network problems due to the exhaustive nature of the branch and bound search technique. Furthermore, the Land and Powell branch and bound code is known to be numerically unstable, which results in suboptimal solutions for network problems with a range on the cycle time variable. This report presents the development of a new version of MAXBAND called MAXBAND Version 3.1. This new version has a fast heuristic algorithm and a fast optimal algorithm for generating signal timing plan for arterials and networks. MAXBAND 3.1 can generate optimal/near-optimal solutions in fraction of the time needed to compute the optimal solution by Version 2.1. The heuristic algorithm in the new model is based on restricted search using branch and bound technique. The algorithm for generating the optimal solution is faster and more efficient than version 2.1 algorithm. Furthermore, the new version is numerically stable. The efficiency of the new model is demonstrated by numerical results for a set of test problems.

  20. Optimization of simultaneous multislice EPI for concurrent functional perfusion and BOLD signal measurements at 7T

    PubMed Central

    Poser, Benedikt A; Huber, Laurentius; Pfeuffer, Josef; Uludağ, Kâmil

    2016-01-01

    Purpose To overcome limitations of previous ultra‐high‐field arterial spin labeling (ASL) techniques concerning temporal resolution and brain coverage by utilizing the simultaneous multi‐slice (SMS) approach. Methods An optimized, flow‐alternating inversion recovery quantitative imaging of perfusion using a single subtraction II scheme was developed that tackles the challenges of 7 tesla (T) ASL. The implementation of tailored labeling radiofrequency pulses reduced the effect of transmit field ( B1+) inhomogeneities. The proposed approach utilizes an SMS echo‐planar imaging (EPI) readout to efficiently achieve large brain coverage. Results A pulsed ASL (PASL) technique with large brain coverage is described and optimized that can be applied at temporal resolutions below 2.5 s, similar to those achievable at 1.5 and 3T magnetic field strength. The influences of within‐ and through‐slice acceleration factors and reconstruction parameters on perfusion and blood‐oxygenation‐level‐dependent (BOLD)‐signal image and temporal signal‐to‐noise ratio (SNR) are presented. The proposed approach yielded twice the brain coverage as compared to conventional PASL at 7T, without notable loss in image quality. Conclusion The presented SMS EPI PASL at 7T overcomes current limitations in SNR, temporal resolution, and spatial coverage for functional perfusion and BOLD signal as well as baseline perfusion measurements. Magn Reson Med 78:121–129, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. PMID:27465273

  1. Optimizing the Intrinsic Signal-to-Noise Ratio of MRI Strip Detectors

    PubMed Central

    Kumar, Ananda; Bottomley, Paul A.

    2007-01-01

    An MRI detector is formed from a conducting strip separated by a dielectric substrate from a ground plane, and tuned to a quarter-wavelength. By distributing discrete tuning elements along the strip, the geometric design may be adjusted to optimize the signal-to-noise ratio (SNR) for a given application. Here a numerical electromagnetic (EM) method of moments (MoM) is applied to determine the length, width, substrate thickness, dielectric constant, and number of tuning elements that yield the best intrinsic SNR (ISNR) of the strip detector at 1.5 Tesla. The central question of how strip performance compares with that of a conventional optimized loop coil is also addressed. The numerical method is validated against the known ISNR performance of loop coils, and its ability to predict the tuning capacitances and performance of seven experimental strip detectors of varying length, width, substrate thickness, and dielectric constant. We find that strip detectors with low-dielectric constant, moderately thin-substrate, and length about 1.3 (±0.2) times the depth of interest perform best. The ISNR of strips is comparable to that of loops (i.e., higher close to the detector but lower at depth). The SNR improves with two inherently-decoupled strips, whose sensitivity profile is well-suited to parallel MRI. The findings are summarized as design “rules of thumb.” PMID:16724302

  2. Optimizing Estimates of Instantaneous Heart Rate from Pulse Wave Signals with the Synchrosqueezing Transform.

    PubMed

    Wu, Hau-Tieng; Lewis, Gregory F; Davila, Maria I; Daubechies, Ingrid; Porges, Stephen W

    2016-10-17

    With recent advances in sensor and computer technologies, the ability to monitor peripheral pulse activity is no longer limited to the laboratory and clinic. Now inexpensive sensors, which interface with smartphones or other computer-based devices, are expanding into the consumer market. When appropriate algorithms are applied, these new technologies enable ambulatory monitoring of dynamic physiological responses outside the clinic in a variety of applications including monitoring fatigue, health, workload, fitness, and rehabilitation. Several of these applications rely upon measures derived from peripheral pulse waves measured via contact or non-contact photoplethysmography (PPG). As technologies move from contact to non-contact PPG, there are new challenges. The technology necessary to estimate average heart rate over a few seconds from a noncontact PPG is available. However, a technology to precisely measure instantaneous heat rate (IHR) from non-contact sensors, on a beat-to-beat basis, is more challenging. The objective of this paper is to develop an algorithm with the ability to accurately monitor IHR from peripheral pulse waves, which provides an opportunity to measure the neural regulation of the heart from the beat-to-beat heart rate pattern (i.e., heart rate variability). The adaptive harmonic model is applied to model the contact or non-contact PPG signals, and a new methodology, the Synchrosqueezing Transform (SST), is applied to extract IHR. The body sway rhythm inherited in the non-contact PPG signal is modeled and handled by the notion of wave-shape function. The SST optimizes the extraction of IHR from the PPG signals and the technique functions well even during periods of poor signal to noise. We contrast the contact and non-contact indices of PPG derived heart rate with a criterion electrocardiogram (ECG). ECG and PPG signals were monitored in 21 healthy subjects performing tasks with different physical demands. The root mean square error of IHR

  3. Optimization of signal extraction and front-end design in a fast,multigap ionization chamber

    SciTech Connect

    Datte, P.S.; Manfredi, P.F.; Millaud, J.E.; Placidi, M.; Ratti,L.; Speziali, V.; Traversi, G.; Turner, W.C.

    2001-11-05

    This paper discusses the criteria that have been adopted tooptimize the signal processing in a shower detector to be employed as LHCbeam luminosity monitor. The original aspect ofthis instrument is itsablility to operate on a bunch-by-bunch basis. This means that it mustperform accurate charge measurements at a repetition rate of 40 MHz. Thedetector must withstand an integrated dose of 100 Grad, that is, two tothree orders of magnitude beyond those expected in the experiments. Tomeet the above requirements, an ionization chamber consisting of severalgaps of thickness 0.5 mm, filled with a gas that is expected to beradiation resistant, has been designed. Crucial in the development of thesystem is the signal processing, as the electronic noise may set thedominant limitation to the accuracy of the measurement. This is relatedto two aspects. One is the short time available for the chargemeasurement. The second one is the presence of a few meter cable betweenthe detector and the preamplifier, as this must be located out of theregion of highest radiation field. Therefore the optimization of thesignal-to-noise ratio requires that the best configuration of the chambergaps be determined under the constraint of the presence of a cable ofnon-negligible length between detector and preamplifier. The remoteplacement of the amplifying electronics will require that the front-endelectronics be radiation hard although to a lesser extent than thedetector.

  4. Optimal selection of individuals for repeated covariate measurements in follow-up studies.

    PubMed

    Reinikainen, Jaakko; Karvanen, Juha; Tolonen, Hanna

    2016-12-01

    Repeated covariate measurements bring important information on the time-varying risk factors in long epidemiological follow-up studies. However, due to budget limitations, it may be possible to carry out the repeated measurements only for a subset of the cohort. We study cost-efficient alternatives for the simple random sampling in the selection of the individuals to be remeasured. The proposed selection criteria are based on forms of the D-optimality. The selection methods are compared with the simulation studies and illustrated with the data from the East-West study carried out in Finland from 1959 to 1999. The results indicate that cost savings can be achieved if the selection is focused on the individuals with high expected risk of the event and, on the other hand, on those with extreme covariate values in the previous measurements. © The Author(s) 2014.

  5. Optoelectronic optimization of mode selective converter based on liquid crystal on silicon

    NASA Astrophysics Data System (ADS)

    Wang, Yongjiao; Liang, Lei; Yu, Dawei; Fu, Songnian

    2016-03-01

    We carry out comprehensive optoelectronic optimization of mode selective converter used for the mode division multiplexing, based on liquid crystal on silicon (LCOS) in binary mode. The conversion error of digital-to-analog (DAC) is investigated quantitatively for the purpose of driving the LCOS in the application of mode selective conversion. Results indicate the DAC must have a resolution of 8-bit, in order to achieve high mode extinction ratio (MER) of 28 dB. On the other hand, both the fast axis position error of half-wave-plate (HWP) and rotation angle error of Faraday rotator (FR) have negative influence on the performance of mode selective conversion. However, the commercial products provide enough angle error tolerance for the LCOS-based mode selective converter, taking both of insertion loss (IL) and MER into account.

  6. In Vitro Selection of Optimal DNA Substrates for Ligation by a Water-Soluble Carbodiimide

    NASA Technical Reports Server (NTRS)

    Harada, Kazuo; Orgel, Leslie E.

    1994-01-01

    We have used in vitro selection to investigate the sequence requirements for efficient template-directed ligation of oligonucleotides at 0 deg C using a water-soluble carbodiimide as condensing agent. We find that only 2 bp at each side of the ligation junction are needed. We also studied chemical ligation of substrate ensembles that we have previously selected as optimal by RNA ligase or by DNA ligase. As anticipated, we find that substrates selected with DNA ligase ligate efficiently with a chemical ligating agent, and vice versa. Substrates selected using RNA ligase are not ligated by the chemical condensing agent and vice versa. The implications of these results for prebiotic chemistry are discussed.

  7. In Vitro Selection of Optimal DNA Substrates for Ligation by a Water-Soluble Carbodiimide

    NASA Technical Reports Server (NTRS)

    Harada, Kazuo; Orgel, Leslie E.

    1994-01-01

    We have used in vitro selection to investigate the sequence requirements for efficient template-directed ligation of oligonucleotides at 0 deg C using a water-soluble carbodiimide as condensing agent. We find that only 2 bp at each side of the ligation junction are needed. We also studied chemical ligation of substrate ensembles that we have previously selected as optimal by RNA ligase or by DNA ligase. As anticipated, we find that substrates selected with DNA ligase ligate efficiently with a chemical ligating agent, and vice versa. Substrates selected using RNA ligase are not ligated by the chemical condensing agent and vice versa. The implications of these results for prebiotic chemistry are discussed.

  8. On the non-stationarity of financial time series: impact on optimal portfolio selection

    NASA Astrophysics Data System (ADS)

    Livan, Giacomo; Inoue, Jun-ichi; Scalas, Enrico

    2012-07-01

    We investigate the possible drawbacks of employing the standard Pearson estimator to measure correlation coefficients between financial stocks in the presence of non-stationary behavior, and we provide empirical evidence against the well-established common knowledge that using longer price time series provides better, more accurate, correlation estimates. Then, we investigate the possible consequences of instabilities in empirical correlation coefficient measurements on optimal portfolio selection. We rely on previously published works which provide a framework allowing us to take into account possible risk underestimations due to the non-optimality of the portfolio weights being used in order to distinguish such non-optimality effects from risk underestimations genuinely due to non-stationarities. We interpret such results in terms of instabilities in some spectral properties of portfolio correlation matrices.

  9. A Scheme to Optimize Flow Routing and Polling Switch Selection of Software Defined Networks.

    PubMed

    Chen, Huan; Li, Lemin; Ren, Jing; Wang, Yang; Zhao, Yangming; Wang, Xiong; Wang, Sheng; Xu, Shizhong

    2015-01-01

    This paper aims at minimizing the communication cost for collecting flow information in Software Defined Networks (SDN). Since flow-based information collecting method requires too much communication cost, and switch-based method proposed recently cannot benefit from controlling flow routing, jointly optimize flow routing and polling switch selection is proposed to reduce the communication cost. To this end, joint optimization problem is formulated as an Integer Linear Programming (ILP) model firstly. Since the ILP model is intractable in large size network, we also design an optimal algorithm for the multi-rooted tree topology and an efficient heuristic algorithm for general topology. According to extensive simulations, it is found that our method can save up to 55.76% communication cost compared with the state-of-the-art switch-based scheme.

  10. A Scheme to Optimize Flow Routing and Polling Switch Selection of Software Defined Networks

    PubMed Central

    Chen, Huan; Li, Lemin; Ren, Jing; Wang, Yang; Zhao, Yangming; Wang, Xiong; Wang, Sheng; Xu, Shizhong

    2015-01-01

    This paper aims at minimizing the communication cost for collecting flow information in Software Defined Networks (SDN). Since flow-based information collecting method requires too much communication cost, and switch-based method proposed recently cannot benefit from controlling flow routing, jointly optimize flow routing and polling switch selection is proposed to reduce the communication cost. To this end, joint optimization problem is formulated as an Integer Linear Programming (ILP) model firstly. Since the ILP model is intractable in large size network, we also design an optimal algorithm for the multi-rooted tree topology and an efficient heuristic algorithm for general topology. According to extensive simulations, it is found that our method can save up to 55.76% communication cost compared with the state-of-the-art switch-based scheme. PMID:26690571

  11. Top down and bottom up selection drives variations in frequency and form of a visual signal

    PubMed Central

    Yeh, Chien-Wei; Blamires, Sean J.; Liao, Chen-Pan; Tso, I.-Min

    2015-01-01

    The frequency and form of visual signals can be shaped by selection from predators, prey or both. When a signal simultaneously attracts predators and prey, selection may favour a strategy that minimizes risks while attracting prey. Accordingly, varying the frequency and form of the silken decorations added to their web may be a way that Argiope spiders minimize predation while attracting prey. Nonetheless, the role of extraneous factors renders the influences of top down and bottom up selection on decoration frequency and form variation difficult to discern. Here we used dummy spiders and decorations to simulate four possible strategies that the spider Argiope aemula may choose and measured the prey and predator attraction consequences for each in the field. The strategy of decorating at a high frequency with a variable form attracted the most prey, while that of decorating at a high frequency with a fixed form attracted the most predators. These results suggest that mitigating the cost of attracting predators while maintaining prey attraction drives the use of variation in decoration form by many Argiope spp. when decorating frequently. Our study highlights the importance of considering top-down and bottom up selection pressure when devising evolutionary ecology experiments. PMID:25828030

  12. Top down and bottom up selection drives variations in frequency and form of a visual signal.

    PubMed

    Yeh, Chien-Wei; Blamires, Sean J; Liao, Chen-Pan; Tso, I-Min

    2015-03-30

    The frequency and form of visual signals can be shaped by selection from predators, prey or both. When a signal simultaneously attracts predators and prey selection may favour a strategy that minimizes risks while attracting prey. Accordingly, varying the frequency and form of the silken decorations added to their web may be a way that Argiope spiders minimize predation while attracting prey. Nonetheless, the role of extraneous factors renders the influences of top down and bottom up selection on decoration frequency and form variation difficult to discern. Here we used dummy spiders and decorations to simulate four possible strategies that the spider Argiope aemula may choose and measured the prey and predator attraction consequences for each in the field. The strategy of decorating at a high frequency with a variable form attracted the most prey, while that of decorating at a high frequency with a fixed form attracted the most predators. These results suggest that mitigating the cost of attracting predators while maintaining prey attraction drives the use of variation in decoration form by many Argiope spp. when decorating frequently. Our study highlights the importance of considering top-down and bottom up selection pressure when devising evolutionary ecology experiments.

  13. A Heckman selection model for the safety analysis of signalized intersections

    PubMed Central

    Wong, S. C.; Zhu, Feng; Pei, Xin; Huang, Helai; Liu, Youjun

    2017-01-01

    Purpose The objective of this paper is to provide a new method for estimating crash rate and severity simultaneously. Methods This study explores a Heckman selection model of the crash rate and severity simultaneously at different levels and a two-step procedure is used to investigate the crash rate and severity levels. The first step uses a probit regression model to determine the sample selection process, and the second step develops a multiple regression model to simultaneously evaluate the crash rate and severity for slight injury/kill or serious injury (KSI), respectively. The model uses 555 observations from 262 signalized intersections in the Hong Kong metropolitan area, integrated with information on the traffic flow, geometric road design, road environment, traffic control and any crashes that occurred during two years. Results The results of the proposed two-step Heckman selection model illustrate the necessity of different crash rates for different crash severity levels. Conclusions A comparison with the existing approaches suggests that the Heckman selection model offers an efficient and convenient alternative method for evaluating the safety performance at signalized intersections. PMID:28732050

  14. Optimal Needle Grasp Selection for Automatic Execution of Suturing Tasks in Robotic Minimally Invasive Surgery

    PubMed Central

    Liu, Taoming; Çavuşoğlu, M. Cenk

    2015-01-01

    This paper presents algorithms for optimal selection of needle grasp, for autonomous robotic execution of the minimally invasive surgical suturing task. In order to minimize the tissue trauma during the suturing motion, the best practices of needle path planning that are used by surgeons are applied for autonomous robotic surgical suturing tasks. Once an optimal needle trajectory in a well-defined suturing scenario is chosen, another critical issue for suturing is the choice of needle grasp for the robotic system. Inappropriate needle grasp increases operating time requiring multiple re-grasps to complete the desired task. The proposed methods use manipulability, dexterity and torque metrics for needle grasp selection. A simulation demonstrates the proposed methods and recommends a variety of grasps. Then a realistic demonstration compares the performances of the manipulator using different grasps. PMID:26413382

  15. Optimal Needle Grasp Selection for Automatic Execution of Suturing Tasks in Robotic Minimally Invasive Surgery.

    PubMed

    Liu, Taoming; Çavuşoğlu, M Cenk

    2015-05-01

    This paper presents algorithms for optimal selection of needle grasp, for autonomous robotic execution of the minimally invasive surgical suturing task. In order to minimize the tissue trauma during the suturing motion, the best practices of needle path planning that are used by surgeons are applied for autonomous robotic surgical suturing tasks. Once an optimal needle trajectory in a well-defined suturing scenario is chosen, another critical issue for suturing is the choice of needle grasp for the robotic system. Inappropriate needle grasp increases operating time requiring multiple re-grasps to complete the desired task. The proposed methods use manipulability, dexterity and torque metrics for needle grasp selection. A simulation demonstrates the proposed methods and recommends a variety of grasps. Then a realistic demonstration compares the performances of the manipulator using different grasps.

  16. Techniques for optimal crop selection in a controlled ecological life support system

    NASA Technical Reports Server (NTRS)

    Mccormack, Ann; Finn, Cory; Dunsky, Betsy

    1993-01-01

    A Controlled Ecological Life Support System (CELSS) utilizes a plant's natural ability to regenerate air and water while being grown as a food source in a closed life support system. Current plant research is directed toward obtaining quantitative empirical data on the regenerative ability of each species of plant and the system volume and power requirements. Two techniques were adapted to optimize crop species selection while at the same time minimizing the system volume and power requirements. Each allows the level of life support supplied by the plants to be selected, as well as other system parameters. The first technique uses decision analysis in the form of a spreadsheet. The second method, which is used as a comparison with and validation of the first, utilizes standard design optimization techniques. Simple models of plant processes are used in the development of these methods.

  17. Techniques for optimal crop selection in a controlled ecological life support system

    NASA Technical Reports Server (NTRS)

    Mccormack, Ann; Finn, Cory; Dunsky, Betsy

    1992-01-01

    A Controlled Ecological Life Support System (CELSS) utilizes a plant's natural ability to regenerate air and water while being grown as a food source in a closed life support system. Current plant research is directed toward obtaining quantitative empirical data on the regenerative ability of each species of plant and the system volume and power requirements. Two techniques were adapted to optimize crop species selection while at the same time minimizing the system volume and power requirements. Each allows the level of life support supplied by the plants to be selected, as well as other system parameters. The first technique uses decision analysis in the form of a spreadsheet. The second method, which is used as a comparison with and validation of the first, utilizes standard design optimization techniques. Simple models of plant processes are used in the development of these methods.

  18. Imaging multicellular specimens with real-time optimized tiling light-sheet selective plane illumination microscopy

    PubMed Central

    Fu, Qinyi; Martin, Benjamin L.; Matus, David Q.; Gao, Liang

    2016-01-01

    Despite the progress made in selective plane illumination microscopy, high-resolution 3D live imaging of multicellular specimens remains challenging. Tiling light-sheet selective plane illumination microscopy (TLS-SPIM) with real-time light-sheet optimization was developed to respond to the challenge. It improves the 3D imaging ability of SPIM in resolving complex structures and optimizes SPIM live imaging performance by using a real-time adjustable tiling light sheet and creating a flexible compromise between spatial and temporal resolution. We demonstrate the 3D live imaging ability of TLS-SPIM by imaging cellular and subcellular behaviours in live C. elegans and zebrafish embryos, and show how TLS-SPIM can facilitate cell biology research in multicellular specimens by studying left-right symmetry breaking behaviour of C. elegans embryos. PMID:27004937

  19. Fusion of remote sensing images based on pyramid decomposition with Baldwinian Clonal Selection Optimization

    NASA Astrophysics Data System (ADS)

    Jin, Haiyan; Xing, Bei; Wang, Lei; Wang, Yanyan

    2015-11-01

    In this paper, we put forward a novel fusion method for remote sensing images based on the contrast pyramid (CP) using the Baldwinian Clonal Selection Algorithm (BCSA), referred to as CPBCSA. Compared with classical methods based on the transform domain, the method proposed in this paper adopts an improved heuristic evolutionary algorithm, wherein the clonal selection algorithm includes Baldwinian learning. In the process of image fusion, BCSA automatically adjusts the fusion coefficients of different sub-bands decomposed by CP according to the value of the fitness function. BCSA also adaptively controls the optimal search direction of the coefficients and accelerates the convergence rate of the algorithm. Finally, the fusion images are obtained via weighted integration of the optimal fusion coefficients and CP reconstruction. Our experiments show that the proposed method outperforms existing methods in terms of both visual effect and objective evaluation criteria, and the fused images are more suitable for human visual or machine perception.

  20. Maximal area and conformal welding heuristics for optimal slice selection in splenic volume estimation

    NASA Astrophysics Data System (ADS)

    Gutenko, Ievgeniia; Peng, Hao; Gu, Xianfeng; Barish, Mathew; Kaufman, Arie

    2016-03-01

    Accurate estimation of splenic volume is crucial for the determination of disease progression and response to treatment for diseases that result in enlargement of the spleen. However, there is no consensus with respect to the use of single or multiple one-dimensional, or volumetric measurement. Existing methods for human reviewers focus on measurement of cross diameters on a representative axial slice and craniocaudal length of the organ. We propose two heuristics for the selection of the optimal axial plane for splenic volume estimation: the maximal area axial measurement heuristic and the novel conformal welding shape-based heuristic. We evaluate these heuristics on time-variant data derived from both healthy and sick subjects and contrast them to established heuristics. Under certain conditions our heuristics are superior to standard practice volumetric estimation methods. We conclude by providing guidance on selecting the optimal heuristic for splenic volume estimation.

  1. Optimal Sensor Selection for Classifying a Set of Ginsengs Using Metal-Oxide Sensors.

    PubMed

    Miao, Jiacheng; Zhang, Tinglin; Wang, You; Li, Guang

    2015-07-03

    The sensor selection problem was investigated for the application of classification of a set of ginsengs using a metal-oxide sensor-based homemade electronic nose with linear discriminant analysis. Samples (315) were measured for nine kinds of ginsengs using 12 sensors. We investigated the classification performances of combinations of 12 sensors for the overall discrimination of combinations of nine ginsengs. The minimum numbers of sensors for discriminating each sample set to obtain an optimal classification performance were defined. The relation of the minimum numbers of sensors with number of samples in the sample set was revealed. The results showed that as the number of samples increased, the average minimum number of sensors increased, while the increment decreased gradually and the average optimal classification rate decreased gradually. Moreover, a new approach of sensor selection was proposed to estimate and compare the effective information capacity of each sensor.

  2. Optimal Sensor Selection for Classifying a Set of Ginsengs Using Metal-Oxide Sensors

    PubMed Central

    Miao, Jiacheng; Zhang, Tinglin; Wang, You; Li, Guang

    2015-01-01

    The sensor selection problem was investigated for the application of classification of a set of ginsengs using a metal-oxide sensor-based homemade electronic nose with linear discriminant analysis. Samples (315) were measured for nine kinds of ginsengs using 12 sensors. We investigated the classification performances of combinations of 12 sensors for the overall discrimination of combinations of nine ginsengs. The minimum numbers of sensors for discriminating each sample set to obtain an optimal classification performance were defined. The relation of the minimum numbers of sensors with number of samples in the sample set was revealed. The results showed that as the number of samples increased, the average minimum number of sensors increased, while the increment decreased gradually and the average optimal classification rate decreased gradually. Moreover, a new approach of sensor selection was proposed to estimate and compare the effective information capacity of each sensor. PMID:26151212

  3. An Approach to Feature Selection Based on Ant Colony Optimization and Rough Set

    NASA Astrophysics Data System (ADS)

    Wu, Junyun; Qiu, Taorong; Wang, Lu; Huang, Haiquan

    Feature selection plays an important role in many fields. This paper proposes a method for feature selection which combined the rough set method and ant colony optimization algorithm. The algorithm used the attribute dependence and the attribute importance as the inspiration factor which applied to the transfer rules. For further, the quality of classification based on rough set method and the length of the feature subset were used to build the pheromone update strategy. Through the test of data set, results show that the proposed method is feasible.

  4. Discovery of GSK2656157: An Optimized PERK Inhibitor Selected for Preclinical Development.

    PubMed

    Axten, Jeffrey M; Romeril, Stuart P; Shu, Arthur; Ralph, Jeffrey; Medina, Jesús R; Feng, Yanhong; Li, William Hoi Hong; Grant, Seth W; Heerding, Dirk A; Minthorn, Elisabeth; Mencken, Thomas; Gaul, Nathan; Goetz, Aaron; Stanley, Thomas; Hassell, Annie M; Gampe, Robert T; Atkins, Charity; Kumar, Rakesh

    2013-10-10

    We recently reported the discovery of GSK2606414 (1), a selective first in class inhibitor of protein kinase R (PKR)-like endoplasmic reticulum kinase (PERK), which inhibited PERK activation in cells and demonstrated tumor growth inhibition in a human tumor xenograft in mice. In continuation of our drug discovery program, we applied a strategy to decrease inhibitor lipophilicity as a means to improve physical properties and pharmacokinetics. This report describes our medicinal chemistry optimization culminating in the discovery of the PERK inhibitor GSK2656157 (6), which was selected for advancement to preclinical development.

  5. Selection, optimization, and compensation as strategies of life management: correction to Freund and Baltes (1998)

    PubMed

    Freund; Baltes

    1999-12-01

    Because of a scoring error, the data reported in Freund and Baltes (1998) were reanalyzed. Except for finding a lower positive manifold involving the 3 components of selection, optimization, and compensation (SOC), the outcome of this reanalysis supports the major findings previously reported: Old and very old participants of the Berlin Aging Study reporting SOC-related behaviors also reported higher levels of well-being and aging well. Corrected versions of Tables 3, 6, and 7 are presented.

  6. Ant-cuckoo colony optimization for feature selection in digital mammogram.

    PubMed

    Jona, J B; Nagaveni, N

    2014-01-15

    Digital mammogram is the only effective screening method to detect the breast cancer. Gray Level Co-occurrence Matrix (GLCM) textural features are extracted from the mammogram. All the features are not essential to detect the mammogram. Therefore identifying the relevant feature is the aim of this work. Feature selection improves the classification rate and accuracy of any classifier. In this study, a new hybrid metaheuristic named Ant-Cuckoo Colony Optimization a hybrid of Ant Colony Optimization (ACO) and Cuckoo Search (CS) is proposed for feature selection in Digital Mammogram. ACO is a good metaheuristic optimization technique but the drawback of this algorithm is that the ant will walk through the path where the pheromone density is high which makes the whole process slow hence CS is employed to carry out the local search of ACO. Support Vector Machine (SVM) classifier with Radial Basis Kernal Function (RBF) is done along with the ACO to classify the normal mammogram from the abnormal mammogram. Experiments are conducted in miniMIAS database. The performance of the new hybrid algorithm is compared with the ACO and PSO algorithm. The results show that the hybrid Ant-Cuckoo Colony Optimization algorithm is more accurate than the other techniques.

  7. An Enhanced Grey Wolf Optimization Based Feature Selection Wrapped Kernel Extreme Learning Machine for Medical Diagnosis

    PubMed Central

    Li, Qiang; Zhao, Xuehua; Cai, ZhenNao; Tong, Changfei; Liu, Wenbin; Tian, Xin

    2017-01-01

    In this study, a new predictive framework is proposed by integrating an improved grey wolf optimization (IGWO) and kernel extreme learning machine (KELM), termed as IGWO-KELM, for medical diagnosis. The proposed IGWO feature selection approach is used for the purpose of finding the optimal feature subset for medical data. In the proposed approach, genetic algorithm (GA) was firstly adopted to generate the diversified initial positions, and then grey wolf optimization (GWO) was used to update the current positions of population in the discrete searching space, thus getting the optimal feature subset for the better classification purpose based on KELM. The proposed approach is compared against the original GA and GWO on the two common disease diagnosis problems in terms of a set of performance metrics, including classification accuracy, sensitivity, specificity, precision, G-mean, F-measure, and the size of selected features. The simulation results have proven the superiority of the proposed method over the other two competitive counterparts. PMID:28246543

  8. Enhanced selectivity and search speed for method development using one-segment-per-component optimization strategies.

    PubMed

    Tyteca, Eva; Vanderlinden, Kim; Favier, Maxime; Clicq, David; Cabooter, Deirdre; Desmet, Gert

    2014-09-05

    Linear gradient programs are very frequently used in reversed phase liquid chromatography to enhance the selectivity compared to isocratic separations. Multi-linear gradient programs on the other hand are only scarcely used, despite their intrinsically larger separation power. Because the gradient-conformity of the latest generation of instruments has greatly improved, a renewed interest in more complex multi-segment gradient liquid chromatography can be expected in the future, raising the need for better performing gradient design algorithms. We explored the possibilities of a new type of multi-segment gradient optimization algorithm, the so-called "one-segment-per-group-of-components" optimization strategy. In this gradient design strategy, the slope is adjusted after the elution of each individual component of the sample, letting the retention properties of the different analytes auto-guide the course of the gradient profile. Applying this method experimentally to four randomly selected test samples, the separation time could on average be reduced with about 40% compared to the best single linear gradient. Moreover, the newly proposed approach performed equally well or better than the multi-segment optimization mode of a commercial software package. Carrying out an extensive in silico study, the experimentally observed advantage could also be generalized over a statistically significant amount of different 10 and 20 component samples. In addition, the newly proposed gradient optimization approach enables much faster searches than the traditional multi-step gradient design methods. Copyright © 2014 Elsevier B.V. All rights reserved.

  9. Successful aging at work: an applied study of selection, optimization, and compensation through impression management.

    PubMed

    Abraham, J D; Hansson, R O

    1995-03-01

    Although many abilities basic to human performance appear to decrease with age, research has shown that job performance does not generally show comparable declines. Baltes and Baltes (1990) have proposed a model of successful aging involving Selection, Optimization, and Compensation (SOC), that may help explain how individuals maintain important competencies despite age-related losses. In the present study, involving a total of 224 working adults ranging in age from 40 to 69 years, occupational measures of Selection, Optimization, and Compensation through impression management (Compensation-IM) were developed. The three measures were factorially distinct and reliable (Cronbach's alpha > .80). Moderated regression analyses indicated that: (1) the relationship between Selection and self-reported ability/performance maintenance increased with age (p < or = .05); and (2) the relationship between both Optimization and Compensation-IM and goal attainment (i.e., importance-weighted ability/performance maintenance) increased with age (p < or = .05). Results suggest that the SOC model of successful aging may be useful in explaining how older workers can maintain important job competencies. Correlational evidence also suggests, however, that characteristics of the job, workplace, and individual may mediate the initiation and effectiveness of SOC behaviors.

  10. Application of an Optimal Tuner Selection Approach for On-Board Self-Tuning Engine Models

    NASA Technical Reports Server (NTRS)

    Simon, Donald L.; Armstrong, Jeffrey B.; Garg, Sanjay

    2012-01-01

    An enhanced design methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented in this paper. It specific-ally addresses the under-determined estimation problem, in which there are more unknown parameters than available sensor measurements. This work builds upon an existing technique for systematically selecting 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. While the existing technique was optimized for open-loop engine operation at a fixed design point, in this paper an alternative formulation is presented that enables the technique to be optimized for an engine operating under closed-loop control throughout the flight envelope. The theoretical Kalman filter mean squared estimation error at a steady-state closed-loop operating point is derived, and the tuner selection approach applied to minimize this error is discussed. A technique for constructing a globally optimal tuning parameter vector, which enables full-envelope application of the technology, is also presented, along with design steps for adjusting the dynamic response of the Kalman filter state estimates. Results from the application of the technique to linear and nonlinear aircraft engine simulations are presented and compared to the conventional approach of tuner selection. The new methodology is shown to yield a significant improvement in on-line Kalman filter estimation accuracy.

  11. Optimal landing site selection based on safety index during planetary descent

    NASA Astrophysics Data System (ADS)

    Cui, Pingyuan; Ge, Dantong; Gao, Ai

    2017-03-01

    Landing safety is the prior concern in planetary exploration missions. With the development of precise landing technology, future missions require vehicles to land on places of great scientific interest which are usually surrounded by rocks and craters. In order to perform a safe landing, the vehicle should be capable of detecting hazards, estimating its fuel consumption as well as touchdown performance, and locating a safe spot to land. The landing site selection process can be treated as an optimization problem which, however, cannot be efficiently solved through traditional optimization methods due to its complexity. Hence, the paper proposes a synthetic landing area assessment criterion, safety index, as a solution of the problem, which selects the best landing site by assessing terrain safety, fuel consumption and touchdown performance during descent. The computation effort is cut down after reducing the selection scope and the optimal landing site is found through a quick one-dimensional search. A typical example based on the Mars Science Laboratory mission is simulated to demonstrate the capability of the method. It is proved that the proposed strategy manages to pick out a safe landing site for the mission effectively. The safety index can be applied in various planetary descent phases and provides reference for future mission designs.

  12. Binding Selectivity of Abaloparatide for PTH-Type-1-Receptor Conformations and Effects on Downstream Signaling.

    PubMed

    Hattersley, Gary; Dean, Thomas; Corbin, Braden A; Bahar, Hila; Gardella, Thomas J

    2016-01-01

    The PTH receptor type 1 (PTHR1) mediates the actions of two endogenous polypeptide ligands, PTH and PTHrP, and thereby plays key roles in bone biology. Based on its capacity to stimulate bone formation, the peptide fragment PTH (1-34) is currently in use as therapy for osteoporosis. Abaloparatide (ABL) is a novel synthetic analog of human PTHrP (1-34) that holds promise as a new osteoporosis therapy, as studies in animals suggest that it can stimulate bone formation with less of the accompanying bone resorption and hypercalcemic effects that can occur with PTH (1-34). Recent studies in vitro suggest that certain PTH or PTHrP ligand analogs can distinguish between two high-affinity PTHR1 conformations, R(0) and RG, and that efficient binding to R(0) results in prolonged signaling responses in cells and prolonged calcemic responses in animals, whereas selective binding to RG results in more transient responses. As intermittent PTH ligand action is known to favor the bone-formation response, whereas continuous ligand action favors the net bone-resorption/calcemic response, we hypothesized that ABL binds more selectively to the RG vs the R(0) PTHR1 conformation than does PTH (1-34), and thus induces more transient signaling responses in cells. We show that ABL indeed binds with greater selectivity to the RG conformation than does PTH (1-34), and as a result of this RG bias, ABL mediates more transient cAMP responses in PTHR1-expressing cells. The findings provide a plausible mechanism (ie, transient signaling via RG-selective binding) that can help account for the favorable anabolic effects that ABL has on bone.

  13. Binding Selectivity of Abaloparatide for PTH-Type-1-Receptor Conformations and Effects on Downstream Signaling

    PubMed Central

    Hattersley, Gary; Dean, Thomas; Corbin, Braden A.; Bahar, Hila

    2016-01-01

    The PTH receptor type 1 (PTHR1) mediates the actions of two endogenous polypeptide ligands, PTH and PTHrP, and thereby plays key roles in bone biology. Based on its capacity to stimulate bone formation, the peptide fragment PTH (1–34) is currently in use as therapy for osteoporosis. Abaloparatide (ABL) is a novel synthetic analog of human PTHrP (1–34) that holds promise as a new osteoporosis therapy, as studies in animals suggest that it can stimulate bone formation with less of the accompanying bone resorption and hypercalcemic effects that can occur with PTH (1–34). Recent studies in vitro suggest that certain PTH or PTHrP ligand analogs can distinguish between two high-affinity PTHR1 conformations, R0 and RG, and that efficient binding to R0 results in prolonged signaling responses in cells and prolonged calcemic responses in animals, whereas selective binding to RG results in more transient responses. As intermittent PTH ligand action is known to favor the bone-formation response, whereas continuous ligand action favors the net bone-resorption/calcemic response, we hypothesized that ABL binds more selectively to the RG vs the R0 PTHR1 conformation than does PTH (1–34), and thus induces more transient signaling responses in cells. We show that ABL indeed binds with greater selectivity to the RG conformation than does PTH (1–34), and as a result of this RG bias, ABL mediates more transient cAMP responses in PTHR1-expressing cells. The findings provide a plausible mechanism (ie, transient signaling via RG-selective binding) that can help account for the favorable anabolic effects that ABL has on bone. PMID:26562265

  14. New Signal Readout Principle for Solid-Contact Ion-Selective Electrodes.

    PubMed

    Vanamo, Ulriika; Hupa, Elisa; Yrjänä, Ville; Bobacka, Johan

    2016-04-19

    A novel approach to signal transduction concerning solid-contact ion-selective electrodes (SC-ISE) with a conducting polymer (CP) as the solid contact is investigated. The method presented here is based on constant potential coulometry, where the potential of the SC-ISE vs the reference electrode is kept constant using a potentiostat. The change in the potential at the interface between the ion-selective membrane (ISM) and the sample solution, due to the change in the activity of the primary ion, is compensated with a corresponding but opposite change in the potential of the CP solid contact. This enforced change in the potential of the solid contact results in a transient reducing/oxidizing current flow through the SC-ISE. By measuring and integrating the current needed to transfer the CP to a new state of equilibrium, the total cumulated charge that is linearly proportional to the change of the logarithm of the primary ion activity is obtained. In this work, different thicknesses of poly(3,4-ethylenedioxythiophene) (PEDOT) doped with poly(styrenesulfonate) (PSS) were used as solid contact. Also, coated wire electrodes (CWEs) were included in the study to show the general validity of the new approach. The ISM employed was selective for K(+) ions, and the selectivity of the membrane under implementation of the presented transduction mechanism was confirmed by measurements performed with a constant background concentration of Na(+) ions. A unique feature of this signal readout principle is that it allows amplification of the analytical signal by increasing the capacitance (film thickness) of the solid contact of the SC-ISE.

  15. Parameter-adjusted stochastic resonance system for the aperiodic echo chirp signal in optimal FrFT domain

    NASA Astrophysics Data System (ADS)

    Lin, Li-feng; Yu, Lei; Wang, Huiqi; Zhong, Suchuan

    2017-02-01

    In order to improve the system performance for moving target detection and localization, this paper presents a new aperiodic chirp signal and additive noise driving stochastic dynamical system, in which the internal frequency has the linear variation matching with the driving frequency. By using the fractional Fourier transform (FrFT) operator with the optimal order, the proposed time-domain dynamical system is transformed into the equivalent FrFT-domain system driven by the periodic signal and noise. Therefore, system performance is conveniently analyzed from the view of output signal-to-noise ratio (SNR) in optimal FrFT domain. Simulation results demonstrate that the output SNR, as a function of system parameter, shows the different generalized SR behaviors in the case of various internal parameters of driving chirp signal and external parameters of the moving target.

  16. Selecting the optimal healthcare centers with a modified P-median model: a visual analytic perspective.

    PubMed

    Jia, Tao; Tao, Hongbing; Qin, Kun; Wang, Yulong; Liu, Chengkun; Gao, Qili

    2014-10-22

    In a conventional P-median model, demanding points are likely assigned to the closest supplying facilities, but this method exhibits evident limitations in real cases. This paper proposed a modified P-median model in which exact and approximate strategies are used. The first strategy aims to enumerate all of the possible combinations of P facilities, and the second strategy adopts simulated annealing to allocate resources considering capacity constraint and spatial compactness constraint. These strategies allow us to choose optimal