The modulation of EEG variability between internally- and externally-driven cognitive states varies with maturation and task performance

PubMed Central

Willatt, Stephanie E.; Cortese, Filomeno; Protzner, Andrea B.

2017-01-01

Increasing evidence suggests that brain signal variability is an important measure of brain function reflecting information processing capacity and functional integrity. In this study, we examined how maturation from childhood to adulthood affects the magnitude and spatial extent of state-to-state transitions in brain signal variability, and how this relates to cognitive performance. We looked at variability changes between resting-state and task (a symbol-matching task with three levels of difficulty), and within trial (fixation, post-stimulus, and post-response). We calculated variability with multiscale entropy (MSE), and additionally examined spectral power density (SPD) from electroencephalography (EEG) in children aged 8–14, and in adults aged 18–33. Our results suggest that maturation is characterized by increased local information processing (higher MSE at fine temporal scales) and decreased long-range interactions with other neural populations (lower MSE at coarse temporal scales). Children show MSE changes that are similar in magnitude, but greater in spatial extent when transitioning between internally- and externally-driven brain states. Additionally, we found that in children, greater changes in task difficulty were associated with greater magnitude of modulation in MSE. Our results suggest that the interplay between maturational and state-to-state changes in brain signal variability manifest across different spatial and temporal scales, and influence information processing capacity in the brain. PMID:28750035

Modeling aging effects on two-choice tasks: response signal and response time data.

PubMed

Ratcliff, Roger

2008-12-01

In the response signal paradigm, a test stimulus is presented, and then at one of a number of experimenter-determined times, a signal to respond is presented. Response signal, standard response time (RT), and accuracy data were collected from 19 college-age and 19 60- to 75-year-old participants in a numerosity discrimination task. The data were fit with 2 versions of the diffusion model. Response signal data were modeled by assuming a mixture of processes, those that have terminated before the signal and those that have not terminated; in the latter case, decisions are based on either partial information or guessing. The effects of aging on performance in the regular RT task were explained the same way in the models, with a 70- to 100-ms increase in the nondecision component of processing, more conservative decision criteria, and more variability across trials in drift and the nondecision component of processing, but little difference in drift rate (evidence). In the response signal task, the primary reason for a slower rise in the response signal functions for older participants was variability in the nondecision component of processing. Overall, the results were consistent with earlier fits of the diffusion model to the standard RT task for college-age participants and to the data from aging studies using this task in the standard RT procedure. Copyright (c) 2009 APA, all rights reserved.

Modeling Aging Effects on Two-Choice Tasks: Response Signal and Response Time Data

PubMed Central

Ratcliff, Roger

2009-01-01

In the response signal paradigm, a test stimulus is presented, and then at one of a number of experimenter-determined times, a signal to respond is presented. Response signal, standard response time (RT), and accuracy data were collected from 19 college-age and 19 60- to 75-year-old participants in a numerosity discrimination task. The data were fit with 2 versions of the diffusion model. Response signal data were modeled by assuming a mixture of processes, those that have terminated before the signal and those that have not terminated; in the latter case, decisions are based on either partial information or guessing. The effects of aging on performance in the regular RT task were explained the same way in the models, with a 70- to 100-ms increase in the nondecision component of processing, more conservative decision criteria, and more variability across trials in drift and the nondecision component of processing, but little difference in drift rate (evidence). In the response signal task, the primary reason for a slower rise in the response signal functions for older participants was variability in the nondecision component of processing. Overall, the results were consistent with earlier fits of the diffusion model to the standard RT task for college-age participants and to the data from aging studies using this task in the standard RT procedure. PMID:19140659

Beat to beat variability in cardiovascular variables: noise or music?

NASA Technical Reports Server (NTRS)

Appel, M. L.; Berger, R. D.; Saul, J. P.; Smith, J. M.; Cohen, R. J.

1989-01-01

Cardiovascular variables such as heart rate, arterial blood pressure, stroke volume and the shape of electrocardiographic complexes all fluctuate on a beat to beat basis. These fluctuations have traditionally been ignored or, at best, treated as noise to be averaged out. The variability in cardiovascular signals reflects the homeodynamic interplay between perturbations to cardiovascular function and the dynamic response of the cardiovascular regulatory systems. Modern signal processing techniques provide a means of analyzing beat to beat fluctuations in cardiovascular signals, so as to permit a quantitative, noninvasive or minimally invasive method of assessing closed loop hemodynamic regulation and cardiac electrical stability. This method promises to provide a new approach to the clinical diagnosis and management of alterations in cardiovascular regulation and stability.

Timeseries Signal Processing for Enhancing Mobile Surveys: Learning from Field Studies

NASA Astrophysics Data System (ADS)

Risk, D. A.; Lavoie, M.; Marshall, A. D.; Baillie, J.; Atherton, E. E.; Laybolt, W. D.

2015-12-01

Vehicle-based surveys using laser and other analyzers are now commonplace in research and industry. In many cases when these studies target biologically-relevant gases like methane and carbon dioxide, the minimum detection limits are often coarse (ppm) relative to the analyzer's capabilities (ppb), because of the inherent variability in the ambient background concentrations across the landscape that creates noise and uncertainty. This variation arises from localized biological sinks and sources, but also atmospheric turbulence, air pooling, and other factors. Computational processing routines are widely used in many fields to increase resolution of a target signal in temporally dense data, and offer promise for enhancing mobile surveying techniques. Signal processing routines can both help identify anomalies at very low levels, or can be used inversely to remove localized industrially-emitted anomalies from ecological data. This presentation integrates learnings from various studies in which simple signal processing routines were used successfully to isolate different temporally-varying components of 1 Hz timeseries measured with laser- and UV fluorescence-based analyzers. As illustrative datasets, we present results from industrial fugitive emission studies from across Canada's western provinces and other locations, and also an ecological study that aimed to model near-surface concentration variability across different biomes within eastern Canada. In these cases, signal processing algorithms contributed significantly to the clarity of both industrial, and ecological processes. In some instances, signal processing was too computationally intensive for real-time in-vehicle processing, but we identified workarounds for analyzer-embedded software that contributed to an improvement in real-time resolution of small anomalies. Signal processing is a natural accompaniment to these datasets, and many avenues are open to researchers who wish to enhance existing, and future datasets.

Rolling element bearing defect diagnosis under variable speed operation through angle synchronous averaging of wavelet de-noised estimate

NASA Astrophysics Data System (ADS)

Mishra, C.; Samantaray, A. K.; Chakraborty, G.

2016-05-01

Rolling element bearings are widely used in rotating machines and their faults can lead to excessive vibration levels and/or complete seizure of the machine. Under special operating conditions such as non-uniform or low speed shaft rotation, the available fault diagnosis methods cannot be applied for bearing fault diagnosis with full confidence. Fault symptoms in such operating conditions cannot be easily extracted through usual measurement and signal processing techniques. A typical example is a bearing in heavy rolling mill with variable load and disturbance from other sources. In extremely slow speed operation, variation in speed due to speed controller transients or external disturbances (e.g., varying load) can be relatively high. To account for speed variation, instantaneous angular position instead of time is used as the base variable of signals for signal processing purposes. Even with time synchronous averaging (TSA) and well-established methods like envelope order analysis, rolling element faults in rolling element bearings cannot be easily identified during such operating conditions. In this article we propose to use order tracking on the envelope of the wavelet de-noised estimate of the short-duration angle synchronous averaged signal to diagnose faults in rolling element bearing operating under the stated special conditions. The proposed four-stage sequential signal processing method eliminates uncorrelated content, avoids signal smearing and exposes only the fault frequencies and its harmonics in the spectrum. We use experimental data1

A Quantitative Relationship between Signal Detection in Attention and Approach/Avoidance Behavior

PubMed Central

Viswanathan, Vijay; Sheppard, John P.; Kim, Byoung W.; Plantz, Christopher L.; Ying, Hao; Lee, Myung J.; Raman, Kalyan; Mulhern, Frank J.; Block, Martin P.; Calder, Bobby; Lee, Sang; Mortensen, Dale T.; Blood, Anne J.; Breiter, Hans C.

2017-01-01

This study examines how the domains of reward and attention, which are often studied as independent processes, in fact interact at a systems level. We operationalize divided attention with a continuous performance task and variables from signal detection theory (SDT), and reward/aversion with a keypress task measuring approach/avoidance in the framework of relative preference theory (RPT). Independent experiments with the same subjects showed a significant association between one SDT and two RPT variables, visualized as a three-dimensional structure. Holding one of these three variables constant, further showed a significant relationship between a loss aversion-like metric from the approach/avoidance task, and the response bias observed during the divided attention task. These results indicate that a more liberal response bias under signal detection (i.e., a higher tolerance for noise, resulting in a greater proportion of false alarms) is associated with higher “loss aversion.” Furthermore, our functional model suggests a mechanism for processing constraints with divided attention and reward/aversion. Together, our results argue for a systematic relationship between divided attention and reward/aversion processing in humans. PMID:28270776

A Quantitative Relationship between Signal Detection in Attention and Approach/Avoidance Behavior.

PubMed

Viswanathan, Vijay; Sheppard, John P; Kim, Byoung W; Plantz, Christopher L; Ying, Hao; Lee, Myung J; Raman, Kalyan; Mulhern, Frank J; Block, Martin P; Calder, Bobby; Lee, Sang; Mortensen, Dale T; Blood, Anne J; Breiter, Hans C

2017-01-01

This study examines how the domains of reward and attention, which are often studied as independent processes, in fact interact at a systems level. We operationalize divided attention with a continuous performance task and variables from signal detection theory (SDT), and reward/aversion with a keypress task measuring approach/avoidance in the framework of relative preference theory (RPT). Independent experiments with the same subjects showed a significant association between one SDT and two RPT variables, visualized as a three-dimensional structure. Holding one of these three variables constant, further showed a significant relationship between a loss aversion-like metric from the approach/avoidance task, and the response bias observed during the divided attention task. These results indicate that a more liberal response bias under signal detection (i.e., a higher tolerance for noise, resulting in a greater proportion of false alarms) is associated with higher "loss aversion." Furthermore, our functional model suggests a mechanism for processing constraints with divided attention and reward/aversion. Together, our results argue for a systematic relationship between divided attention and reward/aversion processing in humans.

Measurement fidelity of heart rate variability signal processing: The devil is in the details

PubMed Central

Jarrin, Denise C.; McGrath, Jennifer J.; Giovanniello, Sabrina; Poirier, Paul; Lambert, Marie

2017-01-01

Heart rate variability (HRV) is a particularly valuable quantitative marker of the flexibility and balance of the autonomic nervous system. Significant advances in software programs to automatically derive HRV have led to its extensive use in psychophysiological research. However, there is a lack of systematic comparisons across software programs used to derive HRV indices. Further, researchers report meager details on important signal processing decisions making synthesis across studies challenging. The aim of the present study was to evaluate the measurement fidelity of time- and frequency-domain HRV indices derived from three predominant signal processing software programs commonly used in clinical and research settings. Triplicate ECG recordings were derived from 20 participants using identical data acquisition hardware. Among the time-domain indices, there was strong to excellent correspondence (ICCavg =0.93) for SDNN, SDANN, SDNNi, rMSSD, and pNN50. The frequency-domain indices yielded excellent correspondence (ICCavg =0.91) for LF, HF, and LF/HF ratio, except for VLF which exhibited poor correspondence (ICCavg =0.19). Stringent user-decisions and technical specifications for nuanced HRV processing details are essential to ensure measurement fidelity across signal processing software programs. PMID:22820268

Variable frequency microwave furnace system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bible, D.W.; Lauf, R.J.

1994-06-14

A variable frequency microwave furnace system designed to allow modulation of the frequency of the microwaves introduced into a furnace cavity for testing or other selected applications. The variable frequency microwave furnace system includes a microwave signal generator or microwave voltage-controlled oscillator for generating a low-power microwave signal for input to the microwave furnace. A first amplifier may be provided to amplify the magnitude of the signal output from the microwave signal generator or the microwave voltage-controlled oscillator. A second amplifier is provided for processing the signal output by the first amplifier. The second amplifier outputs the microwave signal inputmore » to the furnace cavity. In the preferred embodiment, the second amplifier is a traveling-wave tube (TWT). A power supply is provided for operation of the second amplifier. A directional coupler is provided for detecting the direction of a signal and further directing the signal depending on the detected direction. A first power meter is provided for measuring the power delivered to the microwave furnace. A second power meter detects the magnitude of reflected power. Reflected power is dissipated in the reflected power load. 5 figs.« less

Variable frequency microwave furnace system

DOEpatents

Bible, D.W.; Lauf, R.J.

1994-06-14

A variable frequency microwave furnace system designed to allow modulation of the frequency of the microwaves introduced into a furnace cavity for testing or other selected applications. The variable frequency microwave furnace system includes a microwave signal generator or microwave voltage-controlled oscillator for generating a low-power microwave signal for input to the microwave furnace. A first amplifier may be provided to amplify the magnitude of the signal output from the microwave signal generator or the microwave voltage-controlled oscillator. A second amplifier is provided for processing the signal output by the first amplifier. The second amplifier outputs the microwave signal input to the furnace cavity. In the preferred embodiment, the second amplifier is a traveling-wave tube (TWT). A power supply is provided for operation of the second amplifier. A directional coupler is provided for detecting the direction of a signal and further directing the signal depending on the detected direction. A first power meter is provided for measuring the power delivered to the microwave furnace. A second power meter detects the magnitude of reflected power. Reflected power is dissipated in the reflected power load. 5 figs.

Variable threshold method for ECG R-peak detection.

PubMed

Kew, Hsein-Ping; Jeong, Do-Un

2011-10-01

In this paper, a wearable belt-type ECG electrode worn around the chest by measuring the real-time ECG is produced in order to minimize the inconvenient in wearing. ECG signal is detected using a potential instrument system. The measured ECG signal is transmits via an ultra low power consumption wireless data communications unit to personal computer using Zigbee-compatible wireless sensor node. ECG signals carry a lot of clinical information for a cardiologist especially the R-peak detection in ECG. R-peak detection generally uses the threshold value which is fixed. There will be errors in peak detection when the baseline changes due to motion artifacts and signal size changes. Preprocessing process which includes differentiation process and Hilbert transform is used as signal preprocessing algorithm. Thereafter, variable threshold method is used to detect the R-peak which is more accurate and efficient than fixed threshold value method. R-peak detection using MIT-BIH databases and Long Term Real-Time ECG is performed in this research in order to evaluate the performance analysis.

Nonparametric Signal Extraction and Measurement Error in the Analysis of Electroencephalographic Activity During Sleep

PubMed Central

Crainiceanu, Ciprian M.; Caffo, Brian S.; Di, Chong-Zhi; Punjabi, Naresh M.

2009-01-01

We introduce methods for signal and associated variability estimation based on hierarchical nonparametric smoothing with application to the Sleep Heart Health Study (SHHS). SHHS is the largest electroencephalographic (EEG) collection of sleep-related data, which contains, at each visit, two quasi-continuous EEG signals for each subject. The signal features extracted from EEG data are then used in second level analyses to investigate the relation between health, behavioral, or biometric outcomes and sleep. Using subject specific signals estimated with known variability in a second level regression becomes a nonstandard measurement error problem. We propose and implement methods that take into account cross-sectional and longitudinal measurement error. The research presented here forms the basis for EEG signal processing for the SHHS. PMID:20057925

Attention-Deficit/Hyperactivity Disorder and Behavioral Inhibition: A Meta-Analytic Review of the Stop-Signal Paradigm

ERIC Educational Resources Information Center

Alderson, R. Matt; Rapport, Mark D.; Kofler, Michael J.

2007-01-01

Deficient behavioral inhibition (BI) processes are considered a core feature of attention deficit/hyperactivity disorder (ADHD). This meta-analytic review is the first to examine the potential influence of a wide range of subject and task variable moderator effects on BI processes--assessed by the stop-signal paradigm--in children with ADHD…

Optical Signal Processing: Poisson Image Restoration and Shearing Interferometry

NASA Technical Reports Server (NTRS)

Hong, Yie-Ming

1973-01-01

Optical signal processing can be performed in either digital or analog systems. Digital computers and coherent optical systems are discussed as they are used in optical signal processing. Topics include: image restoration; phase-object visualization; image contrast reversal; optical computation; image multiplexing; and fabrication of spatial filters. Digital optical data processing deals with restoration of images degraded by signal-dependent noise. When the input data of an image restoration system are the numbers of photoelectrons received from various areas of a photosensitive surface, the data are Poisson distributed with mean values proportional to the illuminance of the incoherently radiating object and background light. Optical signal processing using coherent optical systems is also discussed. Following a brief review of the pertinent details of Ronchi's diffraction grating interferometer, moire effect, carrier-frequency photography, and achromatic holography, two new shearing interferometers based on them are presented. Both interferometers can produce variable shear.

Testing the Race Model Inequality in Redundant Stimuli with Variable Onset Asynchrony

ERIC Educational Resources Information Center

Gondan, Matthias

2009-01-01

In speeded response tasks with redundant signals, parallel processing of the signals is tested by the race model inequality. This inequality states that given a race of two signals, the cumulative distribution of response times for redundant stimuli never exceeds the sum of the cumulative distributions of response times for the single-modality…

Variable frequency microwave furnace system

DOEpatents

Bible, Don W.; Lauf, Robert J.

1994-01-01

A variable frequency microwave furnace system (10) designed to allow modulation of the frequency of the microwaves introduced into a furnace cavity (34) for testing or other selected applications. The variable frequency microwave furnace system (10) includes a microwave signal generator (12) or microwave voltage-controlled oscillator (14) for generating a low-power microwave signal for input to the microwave furnace. A first amplifier (18) may be provided to amplify the magnitude of the signal output from the microwave signal generator (12) or the microwave voltage-controlled oscillator (14). A second amplifier (20) is provided for processing the signal output by the first amplifier (18). The second amplifier (20) outputs the microwave signal input to the furnace cavity (34). In the preferred embodiment, the second amplifier (20) is a traveling-wave tube (TWT). A power supply (22) is provided for operation of the second amplifier (20). A directional coupler (24) is provided for detecting the direction of a signal and further directing the signal depending on the detected direction. A first power meter (30) is provided for measuring the power delivered to the microwave furnace (32). A second power meter (26) detects the magnitude of reflected power. Reflected power is dissipated in the reflected power load (28).

Designer cell signal processing circuits for biotechnology

PubMed Central

Bradley, Robert W.; Wang, Baojun

2015-01-01

Microorganisms are able to respond effectively to diverse signals from their environment and internal metabolism owing to their inherent sophisticated information processing capacity. A central aim of synthetic biology is to control and reprogramme the signal processing pathways within living cells so as to realise repurposed, beneficial applications ranging from disease diagnosis and environmental sensing to chemical bioproduction. To date most examples of synthetic biological signal processing have been built based on digital information flow, though analogue computing is being developed to cope with more complex operations and larger sets of variables. Great progress has been made in expanding the categories of characterised biological components that can be used for cellular signal manipulation, thereby allowing synthetic biologists to more rationally programme increasingly complex behaviours into living cells. Here we present a current overview of the components and strategies that exist for designer cell signal processing and decision making, discuss how these have been implemented in prototype systems for therapeutic, environmental, and industrial biotechnological applications, and examine emerging challenges in this promising field. PMID:25579192

Signal complexity and modular organization of the courtship behaviours of two sibling species of wolf spiders (Araneae: Lycosidae).

PubMed

Chiarle, Alberto; Isaia, Marco

2013-07-01

In this study, we compare the courtship behaviours of Pardosa proxima and P. vlijmi, two species of wolf spiders up to now regarded as "ethospecies", by means of motion analysis methodologies. In particular, we investigate the features of the signals, aiming at understanding the evolution of the courtship and its role in species delimitation and speciation processes. In our model, we highlight a modular structure of the behaviours and the presence of recurring units and phases. According to other similar cases concerning animal communication, we observed one highly variable and one stereotyped phase for both species. The stereotyped phase is here regarded as a signal related to species identity or an honest signal linked directly to the quality of the signaler. On the contrary, the variable phase aims to facilitate signal detection and assessment by the female reducing choice costs or errors. Variable phases include cues arisen from Fisherian runaway selection, female sensory exploitation and remaining of past selections. Copyright © 2013 Elsevier B.V. All rights reserved.

A Preliminary Investigation of the Reinforcement Function of Signal Detections in Simulated Baggage Screening: Further Support for the Vigilance Reinforcement Hypothesis

ERIC Educational Resources Information Center

Hogan, Lindsey C.; Bell, Matthew; Olson, Ryan

2009-01-01

The vigilance reinforcement hypothesis (VRH) asserts that errors in signal detection tasks are partially explained by operant reinforcement and extinction processes. VRH predictions were tested with a computerized baggage screening task. Our experiment evaluated the effects of signal schedule (extinction vs. variable interval 6 min) and visual…

A Soft Sensor for Bioprocess Control Based on Sequential Filtering of Metabolic Heat Signals

PubMed Central

Paulsson, Dan; Gustavsson, Robert; Mandenius, Carl-Fredrik

2014-01-01

Soft sensors are the combination of robust on-line sensor signals with mathematical models for deriving additional process information. Here, we apply this principle to a microbial recombinant protein production process in a bioreactor by exploiting bio-calorimetric methodology. Temperature sensor signals from the cooling system of the bioreactor were used for estimating the metabolic heat of the microbial culture and from that the specific growth rate and active biomass concentration were derived. By applying sequential digital signal filtering, the soft sensor was made more robust for industrial practice with cultures generating low metabolic heat in environments with high noise level. The estimated specific growth rate signal obtained from the three stage sequential filter allowed controlled feeding of substrate during the fed-batch phase of the production process. The biomass and growth rate estimates from the soft sensor were also compared with an alternative sensor probe and a capacitance on-line sensor, for the same variables. The comparison showed similar or better sensitivity and lower variability for the metabolic heat soft sensor suggesting that using permanent temperature sensors of a bioreactor is a realistic and inexpensive alternative for monitoring and control. However, both alternatives are easy to implement in a soft sensor, alone or in parallel. PMID:25264951

A soft sensor for bioprocess control based on sequential filtering of metabolic heat signals.

PubMed

Paulsson, Dan; Gustavsson, Robert; Mandenius, Carl-Fredrik

2014-09-26

Soft sensors are the combination of robust on-line sensor signals with mathematical models for deriving additional process information. Here, we apply this principle to a microbial recombinant protein production process in a bioreactor by exploiting bio-calorimetric methodology. Temperature sensor signals from the cooling system of the bioreactor were used for estimating the metabolic heat of the microbial culture and from that the specific growth rate and active biomass concentration were derived. By applying sequential digital signal filtering, the soft sensor was made more robust for industrial practice with cultures generating low metabolic heat in environments with high noise level. The estimated specific growth rate signal obtained from the three stage sequential filter allowed controlled feeding of substrate during the fed-batch phase of the production process. The biomass and growth rate estimates from the soft sensor were also compared with an alternative sensor probe and a capacitance on-line sensor, for the same variables. The comparison showed similar or better sensitivity and lower variability for the metabolic heat soft sensor suggesting that using permanent temperature sensors of a bioreactor is a realistic and inexpensive alternative for monitoring and control. However, both alternatives are easy to implement in a soft sensor, alone or in parallel.

Packet communications in satellites with multiple-beam antennas and signal processing

NASA Technical Reports Server (NTRS)

Davies, R.; Chethik, F.; Penick, M.

1980-01-01

A communication satellite with a multiple-beam antenna and onboard signal processing is considered for use in a 'message-switched' data relay system. The signal processor may incorporate demodulation, routing, storage, and remodulation of the data. A system user model is established and key functional elements for the signal processing are identified. With the throughput and delay requirements as the controlled variables, the hardware complexity, operational discipline, occupied bandwidth, and overall user end-to-end cost are estimated for (1) random-access packet switching; and (2) reservation-access packet switching. Other aspects of this network (eg, the adaptability to channel switched traffic requirements) are examined. For the given requirements and constraints, the reservation system appears to be the most attractive protocol.

Multivariate Analysis of Ladle Vibration

NASA Astrophysics Data System (ADS)

Yenus, Jaefer; Brooks, Geoffrey; Dunn, Michelle

2016-08-01

The homogeneity of composition and uniformity of temperature of the steel melt before it is transferred to the tundish are crucial in making high-quality steel product. The homogenization process is performed by stirring the melt using inert gas in ladles. Continuous monitoring of this process is important to make sure the action of stirring is constant throughout the ladle. Currently, the stirring process is monitored by process operators who largely rely on visual and acoustic phenomena from the ladle. However, due to lack of measurable signals, the accuracy and suitability of this manual monitoring are problematic. The actual flow of argon gas to the ladle may not be same as the flow gage reading due to leakage along the gas line components. As a result, the actual degree of stirring may not be correctly known. Various researchers have used one-dimensional vibration, and sound and image signals measured from the ladle to predict the degree of stirring inside. They developed online sensors which are indeed to monitor the online stirring phenomena. In this investigation, triaxial vibration signals have been measured from a cold water model which is a model of an industrial ladle. Three flow rate ranges and varying bath heights were used to collect vibration signals. The Fast Fourier Transform was applied to the dataset before it has been analyzed using principal component analysis (PCA) and partial least squares (PLS). PCA was used to unveil the structure in the experimental data. PLS was mainly applied to predict the stirring from the vibration response. It was found that for each flow rate range considered in this study, the informative signals reside in different frequency ranges. The first latent variables in these frequency ranges explain more than 95 pct of the variation in the stirring process for the entire single layer and the double layer data collected from the cold model. PLS analysis in these identified frequency ranges demonstrated that the latent variables of the response and predictor variables are highly correlated. The predicted variable has shown linear relationship with the stirring energy and bath recirculation speed. This outcome can improve the predictability of the mixing status in ladle metallurgy and make the online control of the process easier. Industrial testing of this input will follow.

Device and method for skull-melting depth measurement

DOEpatents

Lauf, R.J.; Heestand, R.L.

1993-02-09

A method of skull-melting comprises the steps of: (a) providing a vessel adapted for a skull-melting process, the vessel having an interior, an underside, and an orifice connecting the interior and the underside; (b) disposing a waveguide in the orifice so that the waveguide protrudes sufficiently into the interior to interact with the skull-melting process; (c) providing a signal energy transducer in signal communication with the waveguide; (d) introducing into the vessel a molten working material; (e) carrying out the skull-melting process so that a solidified skull of the working material is formed, the skull and the vessel having an interface therebetween, the skull becoming fused to the waveguide so the signal energy can be transmitted through the waveguide and the skull without interference from the interface; (f) activating the signal energy transducer so that a signal is propagated through the waveguide; and, (g) controlling at least one variable of the skull-melting process utilizing feedback information derived from the propagated signal energy.

Device and method for skull-melting depth measurement

DOEpatents

Lauf, Robert J.; Heestand, Richard L.

1993-01-01

A method of skull-melting comprises the steps of: a. providing a vessel adapted for a skull-melting process, the vessel having an interior, an underside, and an orifice in connecting the interior and the underside; b. disposing a waveguide in the orifice so that the waveguide protrudes sufficiently into the interior to interact with the skull-melting process; c. providing a signal energy transducer in signal communication with the waveguide; d. introducing into the vessel a molten working material; e. carrying out the skull-melting process so that a solidified skull of the working material is formed, the skull and the vessel having an interface therebetween, the skull becoming fused to the waveguide so the signal energy can be transmitted through the waveguide and the skull without interference from the interface; f. activating the signal energy transducer so that a signal is propagated through the waveguide; and, g. controlling at least one variable of the skull-melting process utilizing feedback information derived from the propagated signal energy.

Digital Photon Correlation Data Processing Techniques

DTIC Science & Technology

1976-07-01

velocimeter signals. During the conduct of the contract a complementary theoretical effort with the NASA Langley Research Center was in progress ( NASI -13140...6.3.2 Variability Error In an earlier very brief contract with NASA Langley ( NASI -13140) a simplified variability error analysis was performed

Low Computational Signal Acquisition for GNSS Receivers Using a Resampling Strategy and Variable Circular Correlation Time

PubMed Central

Zhang, Yeqing; Wang, Meiling; Li, Yafeng

2018-01-01

For the objective of essentially decreasing computational complexity and time consumption of signal acquisition, this paper explores a resampling strategy and variable circular correlation time strategy specific to broadband multi-frequency GNSS receivers. In broadband GNSS receivers, the resampling strategy is established to work on conventional acquisition algorithms by resampling the main lobe of received broadband signals with a much lower frequency. Variable circular correlation time is designed to adapt to different signal strength conditions and thereby increase the operation flexibility of GNSS signal acquisition. The acquisition threshold is defined as the ratio of the highest and second highest correlation results in the search space of carrier frequency and code phase. Moreover, computational complexity of signal acquisition is formulated by amounts of multiplication and summation operations in the acquisition process. Comparative experiments and performance analysis are conducted on four sets of real GPS L2C signals with different sampling frequencies. The results indicate that the resampling strategy can effectively decrease computation and time cost by nearly 90–94% with just slight loss of acquisition sensitivity. With circular correlation time varying from 10 ms to 20 ms, the time cost of signal acquisition has increased by about 2.7–5.6% per millisecond, with most satellites acquired successfully. PMID:29495301

Low Computational Signal Acquisition for GNSS Receivers Using a Resampling Strategy and Variable Circular Correlation Time.

PubMed

Zhang, Yeqing; Wang, Meiling; Li, Yafeng

2018-02-24

For the objective of essentially decreasing computational complexity and time consumption of signal acquisition, this paper explores a resampling strategy and variable circular correlation time strategy specific to broadband multi-frequency GNSS receivers. In broadband GNSS receivers, the resampling strategy is established to work on conventional acquisition algorithms by resampling the main lobe of received broadband signals with a much lower frequency. Variable circular correlation time is designed to adapt to different signal strength conditions and thereby increase the operation flexibility of GNSS signal acquisition. The acquisition threshold is defined as the ratio of the highest and second highest correlation results in the search space of carrier frequency and code phase. Moreover, computational complexity of signal acquisition is formulated by amounts of multiplication and summation operations in the acquisition process. Comparative experiments and performance analysis are conducted on four sets of real GPS L2C signals with different sampling frequencies. The results indicate that the resampling strategy can effectively decrease computation and time cost by nearly 90-94% with just slight loss of acquisition sensitivity. With circular correlation time varying from 10 ms to 20 ms, the time cost of signal acquisition has increased by about 2.7-5.6% per millisecond, with most satellites acquired successfully.

Fetal Electrocardiogram Extraction and Analysis Using Adaptive Noise Cancellation and Wavelet Transformation Techniques.

PubMed

Sutha, P; Jayanthi, V E

2017-12-08

Birth defect-related demise is mainly due to congenital heart defects. In the earlier stage of pregnancy, fetus problem can be identified by finding information about the fetus to avoid stillbirths. The gold standard used to monitor the health status of the fetus is by Cardiotachography(CTG), cannot be used for long durations and continuous monitoring. There is a need for continuous and long duration monitoring of fetal ECG signals to study the progressive health status of the fetus using portable devices. The non-invasive method of electrocardiogram recording is one of the best method used to diagnose fetal cardiac problem rather than the invasive methods.The monitoring of the fECG requires development of a miniaturized hardware and a efficient signal processing algorithms to extract the fECG embedded in the mother ECG. The paper discusses a prototype hardware developed to monitor and record the raw mother ECG signal containing the fECG and a signal processing algorithm to extract the fetal Electro Cardiogram signal. We have proposed two methods of signal processing, first is based on the Least Mean Square (LMS) Adaptive Noise Cancellation technique and the other method is based on the Wavelet Transformation technique. A prototype hardware was designed and developed to acquire the raw ECG signal containing the mother and fetal ECG and the signal processing techniques were used to eliminate the noises and extract the fetal ECG and the fetal Heart Rate Variability was studied. Both the methods were evaluated with the signal acquired from a fetal ECG simulator, from the Physionet database and that acquired from the subject. Both the methods are evaluated by finding heart rate and its variability, amplitude spectrum and mean value of extracted fetal ECG. Also the accuracy, sensitivity and positive predictive value are also determined for fetal QRS detection technique. In this paper adaptive filtering technique uses Sign-sign LMS algorithm and wavelet techniques with Daubechies wavelet, employed along with de noising techniques for the extraction of fetal Electrocardiogram.Both the methods are having good sensitivity and accuracy. In adaptive method the sensitivity is 96.83, accuracy 89.87, wavelet sensitivity is 95.97 and accuracy is 88.5. Additionally, time domain parameters from the plot of heart rate variability of mother and fetus are analyzed.

Variability and change of sea level and its components in the Indo-Pacific region during the altimetry era

NASA Astrophysics Data System (ADS)

Wu, Quran; Zhang, Xuebin; Church, John A.; Hu, Jianyu

2017-03-01

Previous studies have shown that regional sea level exhibits interannual and decadal variations associated with the modes of climate variability. A better understanding of those low-frequency sea level variations benefits the detection and attribution of climate change signals. Nonetheless, the contributions of thermosteric, halosteric, and mass sea level components to sea level variability and trend patterns remain unclear. By focusing on signals associated with dominant climate modes in the Indo-Pacific region, we estimate the interannual and decadal fingerprints and trend of each sea level component utilizing a multivariate linear regression of two adjoint-based ocean reanalyses. Sea level interannual, decadal, and trend patterns primarily come from thermosteric sea level (TSSL). Halosteric sea level (HSSL) is of regional importance in the Pacific Ocean on decadal time scale and dominates sea level trends in the northeast subtropical Pacific. The compensation between TSSL and HSSL is identified in their decadal variability and trends. The interannual and decadal variability of temperature generally peak at subsurface around 100 m but that of salinity tend to be surface-intensified. Decadal temperature and salinity signals extend deeper into the ocean in some regions than their interannual equivalents. Mass sea level (MassSL) is critical for the interannual and decadal variability of sea level over shelf seas. Inconsistencies exist in MassSL trend patterns among various estimates. This study highlights regions where multiple processes work together to control sea level variability and change. Further work is required to better understand the interaction of different processes in those regions.

Design of signal reception and processing system of embedded ultrasonic endoscope

NASA Astrophysics Data System (ADS)

Li, Ming; Yu, Feng; Zhang, Ruiqiang; Li, Yan; Chen, Xiaodong; Yu, Daoyin

2009-11-01

Embedded Ultrasonic Endoscope, based on embedded microprocessor and embedded real-time operating system, sends a micro ultrasonic probe into coelom through the biopsy channel of the Electronic Endoscope to get the fault histology features of digestive organs by rotary scanning, and acquires the pictures of the alimentary canal mucosal surface. At the same time, ultrasonic signals are processed by signal reception and processing system, forming images of the full histology of the digestive organs. Signal Reception and Processing System is an important component of Embedded Ultrasonic Endoscope. However, the traditional design, using multi-level amplifiers and special digital processing circuits to implement signal reception and processing, is no longer satisfying the standards of high-performance, miniaturization and low power requirements that embedded system requires, and as a result of the high noise that multi-level amplifier brought, the extraction of small signal becomes hard. Therefore, this paper presents a method of signal reception and processing based on double variable gain amplifier and FPGA, increasing the flexibility and dynamic range of the Signal Reception and Processing System, improving system noise level, and reducing power consumption. Finally, we set up the embedded experiment system, using a transducer with the center frequency of 8MHz to scan membrane samples, and display the image of ultrasonic echo reflected by each layer of membrane, with a frame rate of 5Hz, verifying the correctness of the system.

Identifying the fingerprints of the anthropogenic component of land use/land cover changes on regional climate of the USA high plains

NASA Astrophysics Data System (ADS)

Mutiibwa, D.; Irmak, S.

2011-12-01

The majority of recent climate change studies have largely focused on detection and attribution of anthropogenic forcings of greenhouse gases, aerosols, stratospheric and tropospheric ozone. However, there is growing evidence that land cover/land use (LULC) change can significantly impact atmospheric processes from local to regional weather and climate variability. Human activities such as conversion of natural ecosystem to croplands and urban-centers, deforestation and afforestation impact biophysical properties of the land surfaces including albedo, energy balance, moisture-holding capacity of soil, and surface roughness. Alterations in these properties affect the heat and moisture exchanges between the land surface and atmospheric boundary layer, and ultimately impact the climate system. The challenge is to demonstrate that LULC changes produce a signal that can be discerned from natural climate noise. In this study, we attempt to detect the signature of anthropogenic forcing of LULC change on climate on regional scale. The signal projector investigated for detecting the signature of LULC changes on regional climate of the High Plains of the USA is the Normalized Difference Vegetation Index (NDVI). NDVI is an indicator that captures short and long-term geographical distribution of vegetation surfaces. The study develops an enhanced signal processing procedure to maximize the signal to noise ratio by introducing a pre-filtering technique of ARMA processes on the investigated climate and signal variables, before applying the optimal fingerprinting technique to detect the signals of LULC changes on observed climate, temperature, in the High Plains. The intent is to filter out as much noise as possible while still retaining the essential features of the signal by making use of the known characteristics of the noise and the anticipated signal. The study discusses the approach of identifying and suppressing the autocorrelation in optimal fingerprint analysis by applying linear transformation of ARMA processes to the analysis variables. With the assumption that natural climate variability is a near stationary process, the pre-filters are developed to generate stationary residuals. The High Plains region although impacted by droughts over the last three decades has had an increase in agricultural lands, both irrigated and non-irrigated. The study shows that for the most part of the High Plains region there is significant influence of evaporative cooling on regional climate during the summer months. As the vegetation coverage increases coupled with increased in irrigation application, the regional daytime surface energy in summer is increasingly redistributed into latent heat flux which increases the effect of evaporative cooling on summer temperatures. We included the anthropogenic forcing of CO2 on regional climate with the main purpose of surpassing the radiative heating effect of greenhouse gases from natural climate noise, to enhance the LULC signal-to-noise ratio. The warming signal due to greenhouse gas forcing is observed to be weakest in the central part of the High Plains. The results showed that the CO2 signal in the region was weak or is being surpassed by the evaporative cooling effect.

Evaluation of a laser scanning sensor for variable-rate tree sprayer development

USDA-ARS?s Scientific Manuscript database

Accurate canopy measurement capabilities are prerequisites to automate variable-rate sprayers. A 270° radial range laser scanning sensor was tested for its scanning accuracy to detect tree canopy profiles. Signals from the laser sensor and a ground speed sensor were processed with an embedded comput...

Cellular Decision Making by Non-Integrative Processing of TLR Inputs.

PubMed

Kellogg, Ryan A; Tian, Chengzhe; Etzrodt, Martin; Tay, Savaş

2017-04-04

Cells receive a multitude of signals from the environment, but how they process simultaneous signaling inputs is not well understood. Response to infection, for example, involves parallel activation of multiple Toll-like receptors (TLRs) that converge on the nuclear factor κB (NF-κB) pathway. Although we increasingly understand inflammatory responses for isolated signals, it is not clear how cells process multiple signals that co-occur in physiological settings. We therefore examined a bacterial infection scenario involving co-stimulation of TLR4 and TLR2. Independent stimulation of these receptors induced distinct NF-κB dynamic profiles, although surprisingly, under co-stimulation, single cells continued to show ligand-specific dynamic responses characteristic of TLR2 or TLR4 signaling rather than a mixed response, comprising a cellular decision that we term "non-integrative" processing. Iterating modeling and microfluidic experiments revealed that non-integrative processing occurred through interaction of switch-like NF-κB activation, receptor-specific processing timescales, cell-to-cell variability, and TLR cross-tolerance mediated by multilayer negative feedback. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

Temperature variability is integrated by a spatially embedded decision-making center to break dormancy in Arabidopsis seeds.

PubMed

Topham, Alexander T; Taylor, Rachel E; Yan, Dawei; Nambara, Eiji; Johnston, Iain G; Bassel, George W

2017-06-20

Plants perceive and integrate information from the environment to time critical transitions in their life cycle. Some mechanisms underlying this quantitative signal processing have been described, whereas others await discovery. Seeds have evolved a mechanism to integrate environmental information by regulating the abundance of the antagonistically acting hormones abscisic acid (ABA) and gibberellin (GA). Here, we show that hormone metabolic interactions and their feedbacks are sufficient to create a bistable developmental fate switch in Arabidopsis seeds. A digital single-cell atlas mapping the distribution of hormone metabolic and response components revealed their enrichment within the embryonic radicle, identifying the presence of a decision-making center within dormant seeds. The responses to both GA and ABA were found to occur within distinct cell types, suggesting cross-talk occurs at the level of hormone transport between these signaling centers. We describe theoretically, and demonstrate experimentally, that this spatial separation within the decision-making center is required to process variable temperature inputs from the environment to promote the breaking of dormancy. In contrast to other noise-filtering systems, including human neurons, the functional role of this spatial embedding is to leverage variability in temperature to transduce a fate-switching signal within this biological system. Fluctuating inputs therefore act as an instructive signal for seeds, enhancing the accuracy with which plants are established in ecosystems, and distributed computation within the radicle underlies this signal integration mechanism.

The value of crossdating to retain high-frequency variability, climate signals, and extreme events in environmental proxies.

PubMed

Black, Bryan A; Griffin, Daniel; van der Sleen, Peter; Wanamaker, Alan D; Speer, James H; Frank, David C; Stahle, David W; Pederson, Neil; Copenheaver, Carolyn A; Trouet, Valerie; Griffin, Shelly; Gillanders, Bronwyn M

2016-07-01

High-resolution biogenic and geologic proxies in which one increment or layer is formed per year are crucial to describing natural ranges of environmental variability in Earth's physical and biological systems. However, dating controls are necessary to ensure temporal precision and accuracy; simple counts cannot ensure that all layers are placed correctly in time. Originally developed for tree-ring data, crossdating is the only such procedure that ensures all increments have been assigned the correct calendar year of formation. Here, we use growth-increment data from two tree species, two marine bivalve species, and a marine fish species to illustrate sensitivity of environmental signals to modest dating error rates. When falsely added or missed increments are induced at one and five percent rates, errors propagate back through time and eliminate high-frequency variability, climate signals, and evidence of extreme events while incorrectly dating and distorting major disturbances or other low-frequency processes. Our consecutive Monte Carlo experiments show that inaccuracies begin to accumulate in as little as two decades and can remove all but decadal-scale processes after as little as two centuries. Real-world scenarios may have even greater consequence in the absence of crossdating. Given this sensitivity to signal loss, the fundamental tenets of crossdating must be applied to fully resolve environmental signals, a point we underscore as the frontiers of growth-increment analysis continue to expand into tropical, freshwater, and marine environments. © 2016 John Wiley & Sons Ltd.

Temperature variability is integrated by a spatially embedded decision-making center to break dormancy in Arabidopsis seeds

PubMed Central

Topham, Alexander T.; Taylor, Rachel E.; Yan, Dawei; Nambara, Eiji; Johnston, Iain G.

2017-01-01

Plants perceive and integrate information from the environment to time critical transitions in their life cycle. Some mechanisms underlying this quantitative signal processing have been described, whereas others await discovery. Seeds have evolved a mechanism to integrate environmental information by regulating the abundance of the antagonistically acting hormones abscisic acid (ABA) and gibberellin (GA). Here, we show that hormone metabolic interactions and their feedbacks are sufficient to create a bistable developmental fate switch in Arabidopsis seeds. A digital single-cell atlas mapping the distribution of hormone metabolic and response components revealed their enrichment within the embryonic radicle, identifying the presence of a decision-making center within dormant seeds. The responses to both GA and ABA were found to occur within distinct cell types, suggesting cross-talk occurs at the level of hormone transport between these signaling centers. We describe theoretically, and demonstrate experimentally, that this spatial separation within the decision-making center is required to process variable temperature inputs from the environment to promote the breaking of dormancy. In contrast to other noise-filtering systems, including human neurons, the functional role of this spatial embedding is to leverage variability in temperature to transduce a fate-switching signal within this biological system. Fluctuating inputs therefore act as an instructive signal for seeds, enhancing the accuracy with which plants are established in ecosystems, and distributed computation within the radicle underlies this signal integration mechanism. PMID:28584126

Interpolation algorithm for asynchronous ADC-data

NASA Astrophysics Data System (ADS)

Bramburger, Stefan; Zinke, Benny; Killat, Dirk

2017-09-01

This paper presents a modified interpolation algorithm for signals with variable data rate from asynchronous ADCs. The Adaptive weights Conjugate gradient Toeplitz matrix (ACT) algorithm is extended to operate with a continuous data stream. An additional preprocessing of data with constant and linear sections and a weighted overlap of step-by-step into spectral domain transformed signals improve the reconstruction of the asycnhronous ADC signal. The interpolation method can be used if asynchronous ADC data is fed into synchronous digital signal processing.

Hybrid photonic signal processing

NASA Astrophysics Data System (ADS)

Ghauri, Farzan Naseer

This thesis proposes research of novel hybrid photonic signal processing systems in the areas of optical communications, test and measurement, RF signal processing and extreme environment optical sensors. It will be shown that use of innovative hybrid techniques allows design of photonic signal processing systems with superior performance parameters and enhanced capabilities. These applications can be divided into domains of analog-digital hybrid signal processing applications and free-space---fiber-coupled hybrid optical sensors. The analog-digital hybrid signal processing applications include a high-performance analog-digital hybrid MEMS variable optical attenuator that can simultaneously provide high dynamic range as well as high resolution attenuation controls; an analog-digital hybrid MEMS beam profiler that allows high-power watt-level laser beam profiling and also provides both submicron-level high resolution and wide area profiling coverage; and all optical transversal RF filters that operate on the principle of broadband optical spectral control using MEMS and/or Acousto-Optic tunable Filters (AOTF) devices which can provide continuous, digital or hybrid signal time delay and weight selection. The hybrid optical sensors presented in the thesis are extreme environment pressure sensors and dual temperature-pressure sensors. The sensors employ hybrid free-space and fiber-coupled techniques for remotely monitoring a system under simultaneous extremely high temperatures and pressures.

Towards the understanding of network information processing in biology

NASA Astrophysics Data System (ADS)

Singh, Vijay

Living organisms perform incredibly well in detecting a signal present in the environment. This information processing is achieved near optimally and quite reliably, even though the sources of signals are highly variable and complex. The work in the last few decades has given us a fair understanding of how individual signal processing units like neurons and cell receptors process signals, but the principles of collective information processing on biological networks are far from clear. Information processing in biological networks, like the brain, metabolic circuits, cellular-signaling circuits, etc., involves complex interactions among a large number of units (neurons, receptors). The combinatorially large number of states such a system can exist in makes it impossible to study these systems from the first principles, starting from the interactions between the basic units. The principles of collective information processing on such complex networks can be identified using coarse graining approaches. This could provide insights into the organization and function of complex biological networks. Here I study models of biological networks using continuum dynamics, renormalization, maximum likelihood estimation and information theory. Such coarse graining approaches identify features that are essential for certain processes performed by underlying biological networks. We find that long-range connections in the brain allow for global scale feature detection in a signal. These also suppress the noise and remove any gaps present in the signal. Hierarchical organization with long-range connections leads to large-scale connectivity at low synapse numbers. Time delays can be utilized to separate a mixture of signals with temporal scales. Our observations indicate that the rules in multivariate signal processing are quite different from traditional single unit signal processing.

A fuzzy decision tree for fault classification.

PubMed

Zio, Enrico; Baraldi, Piero; Popescu, Irina C

2008-02-01

In plant accident management, the control room operators are required to identify the causes of the accident, based on the different patterns of evolution of the monitored process variables thereby developing. This task is often quite challenging, given the large number of process parameters monitored and the intense emotional states under which it is performed. To aid the operators, various techniques of fault classification have been engineered. An important requirement for their practical application is the physical interpretability of the relationships among the process variables underpinning the fault classification. In this view, the present work propounds a fuzzy approach to fault classification, which relies on fuzzy if-then rules inferred from the clustering of available preclassified signal data, which are then organized in a logical and transparent decision tree structure. The advantages offered by the proposed approach are precisely that a transparent fault classification model is mined out of the signal data and that the underlying physical relationships among the process variables are easily interpretable as linguistic if-then rules that can be explicitly visualized in the decision tree structure. The approach is applied to a case study regarding the classification of simulated faults in the feedwater system of a boiling water reactor.

Brain signal variability as a window into the bidirectionality between music and language processing: moving from a linear to a nonlinear model

PubMed Central

Hutka, Stefanie; Bidelman, Gavin M.; Moreno, Sylvain

2013-01-01

There is convincing empirical evidence for bidirectional transfer between music and language, such that experience in either domain can improve mental processes required by the other. This music-language relationship has been studied using linear models (e.g., comparing mean neural activity) that conceptualize brain activity as a static entity. The linear approach limits how we can understand the brain’s processing of music and language because the brain is a nonlinear system. Furthermore, there is evidence that the networks supporting music and language processing interact in a nonlinear manner. We therefore posit that the neural processing and transfer between the domains of language and music are best viewed through the lens of a nonlinear framework. Nonlinear analysis of neurophysiological activity may yield new insight into the commonalities, differences, and bidirectionality between these two cognitive domains not measurable in the local output of a cortical patch. We thus propose a novel application of brain signal variability (BSV) analysis, based on mutual information and signal entropy, to better understand the bidirectionality of music-to-language transfer in the context of a nonlinear framework. This approach will extend current methods by offering a nuanced, network-level understanding of the brain complexity involved in music-language transfer. PMID:24454295

Brain signal variability as a window into the bidirectionality between music and language processing: moving from a linear to a nonlinear model.

PubMed

Hutka, Stefanie; Bidelman, Gavin M; Moreno, Sylvain

2013-12-30

There is convincing empirical evidence for bidirectional transfer between music and language, such that experience in either domain can improve mental processes required by the other. This music-language relationship has been studied using linear models (e.g., comparing mean neural activity) that conceptualize brain activity as a static entity. The linear approach limits how we can understand the brain's processing of music and language because the brain is a nonlinear system. Furthermore, there is evidence that the networks supporting music and language processing interact in a nonlinear manner. We therefore posit that the neural processing and transfer between the domains of language and music are best viewed through the lens of a nonlinear framework. Nonlinear analysis of neurophysiological activity may yield new insight into the commonalities, differences, and bidirectionality between these two cognitive domains not measurable in the local output of a cortical patch. We thus propose a novel application of brain signal variability (BSV) analysis, based on mutual information and signal entropy, to better understand the bidirectionality of music-to-language transfer in the context of a nonlinear framework. This approach will extend current methods by offering a nuanced, network-level understanding of the brain complexity involved in music-language transfer.

Neurons in Dorsal Anterior Cingulate Cortex Signal Postdecisional Variables in a Foraging Task

PubMed Central

Hayden, Benjamin Y.

2014-01-01

The dorsal anterior cingulate cortex (dACC) is a key hub of the brain's executive control system. Although a great deal is known about its role in outcome monitoring and behavioral adjustment, whether and how it contributes to the decision process remain unclear. Some theories suggest that dACC neurons track decision variables (e.g., option values) that feed into choice processes and is thus “predecisional.” Other theories suggest that dACC activity patterns differ qualitatively depending on the choice that is made and is thus “postdecisional.” To compare these hypotheses, we examined responses of 124 dACC neurons in a simple foraging task in which monkeys accepted or rejected offers of delayed rewards. In this task, options that vary in benefit (reward size) and cost (delay) appear for 1 s; accepting the option provides the cued reward after the cued delay. To get at dACC neurons' contributions to decisions, we focused on responses around the time of choice, several seconds before the reward and the end of the trial. We found that dACC neurons signal the foregone value of the rejected option, a postdecisional variable. Neurons also signal the profitability (that is, the relative value) of the offer, but even these signals are qualitatively different on accept and reject decisions, meaning that they are also postdecisional. These results suggest that dACC can be placed late in the decision process and also support models that give it a regulatory role in decision, rather than serving as a site of comparison. PMID:24403162

Time-division multiplexer uses digital gates

NASA Technical Reports Server (NTRS)

Myers, C. E.; Vreeland, A. E.

1977-01-01

Device eliminates errors caused by analog gates in multiplexing a large number of channels at high frequency. System was designed for use in aerospace work to multiplex signals for monitoring such variables as fuel consumption, pressure, temperature, strain, and stress. Circuit may be useful in monitoring variables in process control and medicine as well.

Interannual to multidecadal climate forcings on groundwater resources of the U.S. West Coast

USGS Publications Warehouse

Velasco, Elzie M.; Gurdak, Jason J.; Dickinson, Jesse; Ferré, T.P.A.; Corona, Claudia

2017-01-01

Study regionThe U.S. West Coast, including the Pacific Northwest and California Coastal Basins aquifer systems.Study focusGroundwater response to interannual to multidecadal climate variability has important implications for security within the water–energy–food nexus. Here we use Singular Spectrum Analysis to quantify the teleconnections between AMO, PDO, ENSO, and PNA and precipitation and groundwater level fluctuations. The computer program DAMP was used to provide insight on the influence of soil texture, depth to water, and mean and period of a surface infiltration flux on the damping of climate signals in the vadose zone.New hydrological insights for the regionWe find that PDO, ENSO, and PNA have significant influence on precipitation and groundwater fluctuations across a north-south gradient of the West Coast, but the lower frequency climate modes (PDO) have a greater influence on hydrologic patterns than higher frequency climate modes (ENSO and PNA). Low frequency signals tend to be preserved better in groundwater fluctuations than high frequency signals, which is a function of the degree of damping of surface variable fluxes related to soil texture, depth to water, mean and period of the infiltration flux. The teleconnection patterns that exist in surface hydrologic processes are not necessarily the same as those preserved in subsurface processes, which are affected by damping of some climate variability signals within infiltrating water.

Tunable single-to-dual channel wavelength conversion in an ultra-wideband SC-PPLN.

PubMed

Ahlawat, Meenu; Bostani, Ameneh; Tehranchi, Amirhossein; Kashyap, Raman

2013-11-18

We experimentally demonstrate tunable dual channel broadcasting of a signal over the C-band for wavelength division multiplexed (WDM) optical networks. This is based on cascaded χ(2) nonlinear mixing processes in a specially engineered, 20-mm-long step-chirped periodically poled lithium niobate with a broad 28-nm second harmonic (SH) bandwidth in the 1.55-μm spectral range. A 10-GHz picosecond mode-locked laser was used as a signal along with a CW pump to generate two pulsed idlers, which are simultaneously tuned across the C-band by detuning of the pump wavelength within the broad SH bandwidth. Variable-input, variable-output scheme of tuned idlers is successfully achieved by tuning the signal wavelength. Pump or signal wavelength tuning of ~10 nm results in the idlers spreading across 30 nm in the C-band.

Time-Frequency Signal Representations Using Interpolations in Joint-Variable Domains

DTIC Science & Technology

2016-06-14

distribution kernels,” IEEE Trans. Signal Process., vol. 42, no. 5, pp. 1156–1165, May 1994. [25] G. S. Cunningham and W. J. Williams , “Kernel...interpolated data. For comparison, we include sparse reconstruction and WVD and Choi– Williams distribution (CWD) [23], which are directly applied to...Prentice-Hall, 1995. [23] H. I. Choi and W. J. Williams , “Improved time-frequency representa- tion of multicomponent signals using exponential kernels

Neural correlates of word production stages delineated by parametric modulation of psycholinguistic variables.

PubMed

Wilson, Stephen M; Isenberg, Anna Lisette; Hickok, Gregory

2009-11-01

Word production is a complex multistage process linking conceptual representations, lexical entries, phonological forms and articulation. Previous studies have revealed a network of predominantly left-lateralized brain regions supporting this process, but many details regarding the precise functions of different nodes in this network remain unclear. To better delineate the functions of regions involved in word production, we used event-related functional magnetic resonance imaging (fMRI) to identify brain areas where blood oxygen level-dependent (BOLD) responses to overt picture naming were modulated by three psycholinguistic variables: concept familiarity, word frequency, and word length, and one behavioral variable: reaction time. Each of these variables has been suggested by prior studies to be associated with different aspects of word production. Processing of less familiar concepts was associated with greater BOLD responses in bilateral occipitotemporal regions, reflecting visual processing and conceptual preparation. Lower frequency words produced greater BOLD signal in left inferior temporal cortex and the left temporoparietal junction, suggesting involvement of these regions in lexical selection and retrieval and encoding of phonological codes. Word length was positively correlated with signal intensity in Heschl's gyrus bilaterally, extending into the mid-superior temporal gyrus (STG) and sulcus (STS) in the left hemisphere. The left mid-STS site was also modulated by reaction time, suggesting a role in the storage of lexical phonological codes.

Stability of Intercellular Exchange of Biochemical Substances Affected by Variability of Environmental Parameters

NASA Astrophysics Data System (ADS)

Mihailović, Dragutin T.; Budinčević, Mirko; Balaž, Igor; Mihailović, Anja

Communication between cells is realized by exchange of biochemical substances. Due to internal organization of living systems and variability of external parameters, the exchange is heavily influenced by perturbations of various parameters at almost all stages of the process. Since communication is one of essential processes for functioning of living systems it is of interest to investigate conditions for its stability. Using previously developed simplified model of bacterial communication in a form of coupled difference logistic equations we investigate stability of exchange of signaling molecules under variability of internal and external parameters.

Stochastic modeling of neurobiological time series: Power, coherence, Granger causality, and separation of evoked responses from ongoing activity

NASA Astrophysics Data System (ADS)

Chen, Yonghong; Bressler, Steven L.; Knuth, Kevin H.; Truccolo, Wilson A.; Ding, Mingzhou

2006-06-01

In this article we consider the stochastic modeling of neurobiological time series from cognitive experiments. Our starting point is the variable-signal-plus-ongoing-activity model. From this model a differentially variable component analysis strategy is developed from a Bayesian perspective to estimate event-related signals on a single trial basis. After subtracting out the event-related signal from recorded single trial time series, the residual ongoing activity is treated as a piecewise stationary stochastic process and analyzed by an adaptive multivariate autoregressive modeling strategy which yields power, coherence, and Granger causality spectra. Results from applying these methods to local field potential recordings from monkeys performing cognitive tasks are presented.

Development of a variable structure-based fault detection and diagnosis strategy applied to an electromechanical system

NASA Astrophysics Data System (ADS)

Gadsden, S. Andrew; Kirubarajan, T.

2017-05-01

Signal processing techniques are prevalent in a wide range of fields: control, target tracking, telecommunications, robotics, fault detection and diagnosis, and even stock market analysis, to name a few. Although first introduced in the 1950s, the most popular method used for signal processing and state estimation remains the Kalman filter (KF). The KF offers an optimal solution to the estimation problem under strict assumptions. Since this time, a number of other estimation strategies and filters were introduced to overcome robustness issues, such as the smooth variable structure filter (SVSF). In this paper, properties of the SVSF are explored in an effort to detect and diagnosis faults in an electromechanical system. The results are compared with the KF method, and future work is discussed.

Micro-electro-mechanical systems (MEMS) and agile lensing-based modules for communications, sensing and signal processing

NASA Astrophysics Data System (ADS)

Reza, Syed Azer

This dissertation proposes the use of the emerging Micro-Electro-Mechanical Systems (MEMS) and agile lensing optical device technologies to design novel and powerful signal conditioning and sensing modules for advanced applications in optical communications, physical parameter sensing and RF/optical signal processing. For example, these new module designs have experimentally demonstrated exceptional features such as stable loss broadband operations and high > 60 dB optical dynamic range signal filtering capabilities. The first part of the dissertation describes the design and demonstration of digital MEMS-based signal processing modules for communication systems and sensor networks using the TI DLP (Digital Light Processing) technology. Examples of such modules include optical power splitters, narrowband and broadband variable fiber optical attenuators, spectral shapers and filters. Compared to prior works, these all-digital designs have advantages of repeatability, accuracy, and reliability that are essential for advanced communications and sensor applications. The next part of the dissertation proposes, analyzes and demonstrates the use of analog opto-fluidic agile lensing technology for sensor networks and test and measurement systems. Novel optical module designs for distance sensing, liquid level sensing, three-dimensional object shape sensing and variable photonic delay lines are presented and experimentally demonstrated. Compared to prior art module designs, the proposed analog-mode modules have exceptional performances, particularly for extreme environments (e.g., caustic liquids) where the free-space agile beam-based sensor provide remote non-contact access for physical sensing operations. The dissertation also presents novel modules involving hybrid analog-digital photonic designs that make use of the different optical device technologies to deliver the best features of both analog and digital optical device operations and controls. Digital controls are achieved through the use of the digital MEMS technology and analog controls are realized by employing opto-fluidic agile lensing technology and acousto-optic technology. For example, variable fiber-optic attenuators and spectral filters are proposed using the hybrid design. Compared to prior art module designs, these hybrid designs provide a higher module dynamic range and increased resolution that are critical in various advanced system applications. In summary, the dissertation shows the added power of hybrid optical designs using both the digital and analog photonic signal processing versus just all-digital or all-analog module designs.

Natural and management influences on freshwater inflows and salinity in the San Francisco Estuary at monthly to interannual scales

USGS Publications Warehouse

Knowles, Noah

2002-01-01

Understanding the processes controlling the physics, chemistry, and biology of the San Francisco Estuary and their relation to climate variability is complicated by the combined influence on freshwater inflows of natural variability and upstream management. To distinguish these influences, alterations of estuarine inflow due to major reservoirs and freshwater pumping in the watershed were inferred from available data. Effects on salinity were estimated by using reconstructed estuarine inflows corresponding to differing levels of impairment to drive a numerical salinity model. Both natural and management inflow and salinity signals show strong interannual variability. Management effects raise salinities during the wet season, with maximum influence in spring. While year‐to‐year variations in all signals are very large, natural interannual variability can greatly exceed the range of management effects on salinity in the estuary.

Maximally reliable Markov chains under energy constraints.

PubMed

Escola, Sean; Eisele, Michael; Miller, Kenneth; Paninski, Liam

2009-07-01

Signal-to-noise ratios in physical systems can be significantly degraded if the outputs of the systems are highly variable. Biological processes for which highly stereotyped signal generations are necessary features appear to have reduced their signal variabilities by employing multiple processing steps. To better understand why this multistep cascade structure might be desirable, we prove that the reliability of a signal generated by a multistate system with no memory (i.e., a Markov chain) is maximal if and only if the system topology is such that the process steps irreversibly through each state, with transition rates chosen such that an equal fraction of the total signal is generated in each state. Furthermore, our result indicates that by increasing the number of states, it is possible to arbitrarily increase the reliability of the system. In a physical system, however, an energy cost is associated with maintaining irreversible transitions, and this cost increases with the number of such transitions (i.e., the number of states). Thus, an infinite-length chain, which would be perfectly reliable, is infeasible. To model the effects of energy demands on the maximally reliable solution, we numerically optimize the topology under two distinct energy functions that penalize either irreversible transitions or incommunicability between states, respectively. In both cases, the solutions are essentially irreversible linear chains, but with upper bounds on the number of states set by the amount of available energy. We therefore conclude that a physical system for which signal reliability is important should employ a linear architecture, with the number of states (and thus the reliability) determined by the intrinsic energy constraints of the system.

A sequential method for spline approximation with variable knots. [recursive piecewise polynomial signal processing

NASA Technical Reports Server (NTRS)

Mier Muth, A. M.; Willsky, A. S.

1978-01-01

In this paper we describe a method for approximating a waveform by a spline. The method is quite efficient, as the data are processed sequentially. The basis of the approach is to view the approximation problem as a question of estimation of a polynomial in noise, with the possibility of abrupt changes in the highest derivative. This allows us to bring several powerful statistical signal processing tools into play. We also present some initial results on the application of our technique to the processing of electrocardiograms, where the knot locations themselves may be some of the most important pieces of diagnostic information.

Apparatus and method for microwave processing of materials

DOEpatents

Johnson, A.C.; Lauf, R.J.; Bible, D.W.; Markunas, R.J.

1996-05-28

Disclosed is a variable frequency microwave heating apparatus designed to allow modulation of the frequency of the microwaves introduced into a furnace cavity for testing or other selected applications. The variable frequency heating apparatus is used in the method of the present invention to monitor the resonant processing frequency within the furnace cavity depending upon the material, including the state thereof, from which the workpiece is fabricated. The variable frequency microwave heating apparatus includes a microwave signal generator and a high-power microwave amplifier or a microwave voltage-controlled oscillator. A power supply is provided for operation of the high-power microwave oscillator or microwave amplifier. A directional coupler is provided for detecting the direction and amplitude of signals incident upon and reflected from the microwave cavity. A first power meter is provided for measuring the power delivered to the microwave furnace. A second power meter detects the magnitude of reflected power. Reflected power is dissipated in the reflected power load. 10 figs.

Research on photodiode detector-based spatial transient light detection and processing system

NASA Astrophysics Data System (ADS)

Liu, Meiying; Wang, Hu; Liu, Yang; Zhao, Hui; Nan, Meng

2016-10-01

In order to realize real-time signal identification and processing of spatial transient light, the features and the energy of the captured target light signal are first described and quantitatively calculated. Considering that the transient light signal has random occurrence, a short duration and an evident beginning and ending, a photodiode detector based spatial transient light detection and processing system is proposed and designed in this paper. This system has a large field of view and is used to realize non-imaging energy detection of random, transient and weak point target under complex background of spatial environment. Weak signal extraction under strong background is difficult. In this paper, considering that the background signal changes slowly and the target signal changes quickly, filter is adopted for signal's background subtraction. A variable speed sampling is realized by the way of sampling data points with a gradually increased interval. The two dilemmas that real-time processing of large amount of data and power consumption required by the large amount of data needed to be stored are solved. The test results with self-made simulative signal demonstrate the effectiveness of the design scheme. The practical system could be operated reliably. The detection and processing of the target signal under the strong sunlight background was realized. The results indicate that the system can realize real-time detection of target signal's characteristic waveform and monitor the system working parameters. The prototype design could be used in a variety of engineering applications.

A new method to detect transitory signatures and local time/space variability structures in the climate system: the scale-dependent correlation analysis

NASA Astrophysics Data System (ADS)

Rodó, Xavier; Rodríguez-Arias, Miquel-Àngel

2006-10-01

The study of transitory signals and local variability structures in both/either time and space and their role as sources of climatic memory, is an important but often neglected topic in climate research despite its obvious importance and extensive coverage in the literature. Transitory signals arise either from non-linearities, in the climate system, transitory atmosphere-ocean couplings, and other processes in the climate system evolving after a critical threshold is crossed. These temporary interactions that, though intense, may not last long, can be responsible for a large amount of unexplained variability but are normally considered of limited relevance and often, discarded. With most of the current techniques at hand these typology of signatures are difficult to isolate because the low signal-to-noise ratio in midlatitudes, the limited recurrence of the transitory signals during a customary interval of data considered. Also, there is often a serious problem arising from the smoothing of local or transitory processes if statistical techniques are applied, that consider all the length of data available, rather than taking into account the size of the specific variability structure under investigation. Scale-dependent correlation (SDC) analysis is a new statistical method capable of highlighting the presence of transitory processes, these former being understood as temporary significant lag-dependent autocovariance in a single series, or covariance structures between two series. This approach, therefore, complements other approaches such as those resulting from the families of wavelet analysis, singular-spectrum analysis and recurrence plots. A main feature of SDC is its high-performance for short time series, its ability to characterize phase-relationships and thresholds in the bivariate domain. Ultimately, SDC helps tracking short-lagged relationships among processes that locally or temporarily couple and uncouple. The use of SDC is illustrated in the present paper by means of some synthetic time-series examples of increasing complexity, and it is compared with wavelet analysis in order to provide a well-known reference of its capabilities. A comparison between SDC and companion techniques is also addressed and results are exemplified for the specific case of some relevant El Niño-Southern Oscillation teleconnections.

Incorporating signal-dependent noise for hyperspectral target detection

NASA Astrophysics Data System (ADS)

Morman, Christopher J.; Meola, Joseph

2015-05-01

The majority of hyperspectral target detection algorithms are developed from statistical data models employing stationary background statistics or white Gaussian noise models. Stationary background models are inaccurate as a result of two separate physical processes. First, varying background classes often exist in the imagery that possess different clutter statistics. Many algorithms can account for this variability through the use of subspaces or clustering techniques. The second physical process, which is often ignored, is a signal-dependent sensor noise term. For photon counting sensors that are often used in hyperspectral imaging systems, sensor noise increases as the measured signal level increases as a result of Poisson random processes. This work investigates the impact of this sensor noise on target detection performance. A linear noise model is developed describing sensor noise variance as a linear function of signal level. The linear noise model is then incorporated for detection of targets using data collected at Wright Patterson Air Force Base.

Fault diagnosis of motor bearing with speed fluctuation via angular resampling of transient sound signals

NASA Astrophysics Data System (ADS)

Lu, Siliang; Wang, Xiaoxian; He, Qingbo; Liu, Fang; Liu, Yongbin

2016-12-01

Transient signal analysis (TSA) has been proven an effective tool for motor bearing fault diagnosis, but has yet to be applied in processing bearing fault signals with variable rotating speed. In this study, a new TSA-based angular resampling (TSAAR) method is proposed for fault diagnosis under speed fluctuation condition via sound signal analysis. By applying the TSAAR method, the frequency smearing phenomenon is eliminated and the fault characteristic frequency is exposed in the envelope spectrum for bearing fault recognition. The TSAAR method can accurately estimate the phase information of the fault-induced impulses using neither complicated time-frequency analysis techniques nor external speed sensors, and hence it provides a simple, flexible, and data-driven approach that realizes variable-speed motor bearing fault diagnosis. The effectiveness and efficiency of the proposed TSAAR method are verified through a series of simulated and experimental case studies.

VLSI implementation of a new LMS-based algorithm for noise removal in ECG signal

NASA Astrophysics Data System (ADS)

Satheeskumaran, S.; Sabrigiriraj, M.

2016-06-01

Least mean square (LMS)-based adaptive filters are widely deployed for removing artefacts in electrocardiogram (ECG) due to less number of computations. But they posses high mean square error (MSE) under noisy environment. The transform domain variable step-size LMS algorithm reduces the MSE at the cost of computational complexity. In this paper, a variable step-size delayed LMS adaptive filter is used to remove the artefacts from the ECG signal for improved feature extraction. The dedicated digital Signal processors provide fast processing, but they are not flexible. By using field programmable gate arrays, the pipelined architectures can be used to enhance the system performance. The pipelined architecture can enhance the operation efficiency of the adaptive filter and save the power consumption. This technique provides high signal-to-noise ratio and low MSE with reduced computational complexity; hence, it is a useful method for monitoring patients with heart-related problem.

Functional Neuronal Processing of Human Body Odors

PubMed Central

Lundström, Johan N.; Olsson, Mats J.

2013-01-01

Body odors carry informational cues of great importance for individuals across a wide range of species, and signals hidden within the body odor cocktail are known to regulate several key behaviors in animals. For a long time, the notion that humans may be among these species has been dismissed. We now know, however, that each human has a unique odor signature that carries information related to his or her genetic makeup, as well as information about personal environmental variables, such as diet and hygiene. Although a substantial number of studies have investigated the behavioral effects of body odors, only a handful have studied central processing. Recent studies have, however, demonstrated that the human brain responds to fear signals hidden within the body odor cocktail, is able to extract kin specific signals, and processes body odors differently than other perceptually similar odors. In this chapter, we provide an overview of the current knowledge of how the human brain processes body odors and the potential importance these signals have for us in everyday life. PMID:20831940

Using EEG/MEG Data of Cognitive Processes in Brain-Computer Interfaces

NASA Astrophysics Data System (ADS)

Gutiérrez, David

2008-08-01

Brain-computer interfaces (BCIs) aim at providing a non-muscular channel for sending commands to the external world using electroencephalographic (EEG) and, more recently, magnetoencephalographic (MEG) measurements of the brain function. Most of the current implementations of BCIs rely on EEG/MEG data of motor activities as such neural processes are well characterized, while the use of data related to cognitive activities has been neglected due to its intrinsic complexity. However, cognitive data usually has larger amplitude, lasts longer and, in some cases, cognitive brain signals are easier to control at will than motor signals. This paper briefy reviews the use of EEG/MEG data of cognitive processes in the implementation of BCIs. Specifically, this paper reviews some of the neuromechanisms, signal features, and processing methods involved. This paper also refers to some of the author's work in the area of detection and classifcation of cognitive signals for BCIs using variability enhancement, parametric modeling, and spatial fltering, as well as recent developments in BCI performance evaluation.

Using EEG/MEG Data of Cognitive Processes in Brain-Computer Interfaces

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gutierrez, David

2008-08-11

Brain-computer interfaces (BCIs) aim at providing a non-muscular channel for sending commands to the external world using electroencephalographic (EEG) and, more recently, magnetoencephalographic (MEG) measurements of the brain function. Most of the current implementations of BCIs rely on EEG/MEG data of motor activities as such neural processes are well characterized, while the use of data related to cognitive activities has been neglected due to its intrinsic complexity. However, cognitive data usually has larger amplitude, lasts longer and, in some cases, cognitive brain signals are easier to control at will than motor signals. This paper briefy reviews the use of EEG/MEGmore » data of cognitive processes in the implementation of BCIs. Specifically, this paper reviews some of the neuromechanisms, signal features, and processing methods involved. This paper also refers to some of the author's work in the area of detection and classifcation of cognitive signals for BCIs using variability enhancement, parametric modeling, and spatial fltering, as well as recent developments in BCI performance evaluation.« less

Processes of 30-90 days sea surface temperature variability in the northern Indian Ocean during boreal summer

NASA Astrophysics Data System (ADS)

Vialard, J.; Jayakumar, A.; Gnanaseelan, C.; Lengaigne, M.; Sengupta, D.; Goswami, B. N.

2012-05-01

During summer, the northern Indian Ocean exhibits significant atmospheric intraseasonal variability associated with active and break phases of the monsoon in the 30-90 days band. In this paper, we investigate mechanisms of the Sea Surface Temperature (SST) signature of this atmospheric variability, using a combination of observational datasets and Ocean General Circulation Model sensitivity experiments. In addition to the previously-reported intraseasonal SST signature in the Bay of Bengal, observations show clear SST signals in the Arabian Sea related to the active/break cycle of the monsoon. As the atmospheric intraseasonal oscillation moves northward, SST variations appear first at the southern tip of India (day 0), then in the Somali upwelling region (day 10), northern Bay of Bengal (day 19) and finally in the Oman upwelling region (day 23). The Bay of Bengal and Oman signals are most clearly associated with the monsoon active/break index, whereas the relationship with signals near Somali upwelling and the southern tip of India is weaker. In agreement with previous studies, we find that heat flux variations drive most of the intraseasonal SST variability in the Bay of Bengal, both in our model (regression coefficient, 0.9, against ~0.25 for wind stress) and in observations (0.8 regression coefficient); ~60% of the heat flux variation is due do shortwave radiation and ~40% due to latent heat flux. On the other hand, both observations and model results indicate a prominent role of dynamical oceanic processes in the Arabian Sea. Wind-stress variations force about 70-100% of SST intraseasonal variations in the Arabian Sea, through modulation of oceanic processes (entrainment, mixing, Ekman pumping, lateral advection). Our ~100 km resolution model suggests that internal oceanic variability (i.e. eddies) contributes substantially to intraseasonal variability at small-scale in the Somali upwelling region, but does not contribute to large-scale intraseasonal SST variability due to its small spatial scale and random phase relation to the active-break monsoon cycle. The effect of oceanic eddies; however, remains to be explored at a higher spatial resolution.

Ecological prediction with nonlinear multivariate time-frequency functional data models

USGS Publications Warehouse

Yang, Wen-Hsi; Wikle, Christopher K.; Holan, Scott H.; Wildhaber, Mark L.

2013-01-01

Time-frequency analysis has become a fundamental component of many scientific inquiries. Due to improvements in technology, the amount of high-frequency signals that are collected for ecological and other scientific processes is increasing at a dramatic rate. In order to facilitate the use of these data in ecological prediction, we introduce a class of nonlinear multivariate time-frequency functional models that can identify important features of each signal as well as the interaction of signals corresponding to the response variable of interest. Our methodology is of independent interest and utilizes stochastic search variable selection to improve model selection and performs model averaging to enhance prediction. We illustrate the effectiveness of our approach through simulation and by application to predicting spawning success of shovelnose sturgeon in the Lower Missouri River.

Effect of signal jitter on the spectrum of rotor impulsive noise

NASA Technical Reports Server (NTRS)

Brooks, Thomas F.

1987-01-01

The effect of randomness or jitter of the acoustic waveform on the spectrum of rotor impulsive noise is studied because of its importance for data interpretation. An acoustic waveform train is modelled representing rotor impulsive noise. The amplitude, shape, and period between occurrences of individual pulses are allowed to be randomized assuming normal probability distributions. Results, in terms of the standard deviations of the variable quantities, are given for the autospectrum as well as special processed spectra designed to separate harmonic and broadband rotor noise components. Consideration is given to the effect of accuracy in triggering or keying to a rotor one per revolution signal. An example is given showing the resultant spectral smearing at the high frequencies due to the pulse signal period variability.

Effect of signal jitter on the spectrum of rotor impulsive noise

NASA Technical Reports Server (NTRS)

Brooks, Thomas F.

1988-01-01

The effect of randomness or jitter of the acoustic waveform on the spectrum of rotor impulsive noise is studied because of its importance for data interpretation. An acoustic waveform train is modeled representing rotor impulsive noise. The amplitude, shape, and period between occurrences of individual pulses are allowed to be randomized assuming normal probability distributions. Results, in terms of the standard deviations of the variable quantities, are given for the autospectrum as well as special processed spectra designed to separate harmonic and broadband rotor noise components. Consideration is given to the effect of accuracy in triggering or keying to a rotor one per revolution signal. An example is given showing the resultant spectral smearing at the high frequencies due to the pulse signal period variability.

Measuring Radiofrequency and Microwave Radiation from Varying Signal Strengths

NASA Technical Reports Server (NTRS)

Davis, Bette; Gaul, W. C.

2007-01-01

This viewgraph presentation discusses the process of measuring radiofrequency and microwave radiation from various signal strengths. The topics include: 1) Limits and Guidelines; 2) Typical Variable Standard (IEEE) Frequency Dependent; 3) FCC Standard 47 CFR 1.1310; 4) Compliance Follows Unity Rule; 5) Multiple Sources Contribute; 6) Types of RF Signals; 7) Interfering Radiations; 8) Different Frequencies Different Powers; 9) Power Summing - Peak Power; 10) Contribution from Various Single Sources; 11) Total Power from Multiple Sources; 12) Are You Out of Compliance?; and 13) In Compliance.

Experimental demonstration of entanglement-assisted coding using a two-mode squeezed vacuum state

NASA Astrophysics Data System (ADS)

Mizuno, Jun; Wakui, Kentaro; Furusawa, Akira; Sasaki, Masahide

2005-01-01

We have experimentally realized the scheme initially proposed as quantum dense coding with continuous variables [

Intersubject Variability in Fearful Face Processing: The Link Between Behavior and Neural Activation

PubMed Central

Doty, Tracy J.; Japee, Shruti; Ingvar, Martin; Ungerleider, Leslie G.

2014-01-01

Stimuli that signal threat show considerable variability in the extent to which they enhance behavior, even among healthy individuals. However, the neural underpinning of this behavioral variability is not well understood. By manipulating expectation of threat in an fMRI study of fearful vs. neutral face categorization, we uncovered a network of areas underlying variability in threat processing in healthy adults. We explicitly altered expectation by presenting face images at three different expectation levels: 80%, 50%, and 20%. Subjects were instructed to report as fast and as accurately as possible whether the face was fearful (signaled threat) or not. An uninformative cue preceded each face by 4 seconds (s). By taking the difference between response times (RT) to fearful compared to neutral faces, we quantified an overall fear RT bias (i.e. faster to fearful than neutral faces) for each subject. This bias correlated positively with late trial fMRI activation (8 s after the face) during unexpected fearful face trials in bilateral ventromedial prefrontal cortex, the left subgenual cingulate cortex, and the right caudate nucleus and correlated negatively with early trial fMRI activation (4 s after the cue) during expected neutral face trials in bilateral dorsal striatum and the right ventral striatum. These results demonstrate that the variability in threat processing among healthy adults is reflected not only in behavior but also in the magnitude of activation in medial prefrontal and striatal regions that appear to encode affective value. PMID:24841078

Intersubject variability in fearful face processing: the link between behavior and neural activation.

PubMed

Doty, Tracy J; Japee, Shruti; Ingvar, Martin; Ungerleider, Leslie G

2014-12-01

Stimuli that signal threat show considerable variability in the extents to which they enhance behavior, even among healthy individuals. However, the neural underpinning of this behavioral variability is not well understood. By manipulating expectation of threat in an fMRI study of fearful versus neutral face categorization, we uncovered a network of areas underlying variability in threat processing in healthy adults. We explicitly altered expectations by presenting face images at three different expectation levels: 80 %, 50 %, and 20 %. Subjects were instructed to report as quickly and accurately as possible whether the face was fearful (signaled threat) or not. An uninformative cue preceded each face by 4 s. By taking the difference between reaction times (RTs) to fearful and neutral faces, we quantified an overall fear RT bias (i.e., faster to fearful than to neutral faces) for each subject. This bias correlated positively with late-trial fMRI activation (8 s after the face) during unexpected-fearful-face trials in bilateral ventromedial prefrontal cortex, the left subgenual cingulate cortex, and the right caudate nucleus, and correlated negatively with early-trial fMRI activation (4 s after the cue) during expected-neutral-face trials in bilateral dorsal striatum and the right ventral striatum. These results demonstrate that the variability in threat processing among healthy adults is reflected not only in behavior, but also in the magnitude of activation in medial prefrontal and striatal regions that appear to encode affective value.

A novel technique for fetal heart rate estimation from Doppler ultrasound signal

PubMed Central

2011-01-01

Background The currently used fetal monitoring instrumentation that is based on Doppler ultrasound technique provides the fetal heart rate (FHR) signal with limited accuracy. It is particularly noticeable as significant decrease of clinically important feature - the variability of FHR signal. The aim of our work was to develop a novel efficient technique for processing of the ultrasound signal, which could estimate the cardiac cycle duration with accuracy comparable to a direct electrocardiography. Methods We have proposed a new technique which provides the true beat-to-beat values of the FHR signal through multiple measurement of a given cardiac cycle in the ultrasound signal. The method consists in three steps: the dynamic adjustment of autocorrelation window, the adaptive autocorrelation peak detection and determination of beat-to-beat intervals. The estimated fetal heart rate values and calculated indices describing variability of FHR, were compared to the reference data obtained from the direct fetal electrocardiogram, as well as to another method for FHR estimation. Results The results revealed that our method increases the accuracy in comparison to currently used fetal monitoring instrumentation, and thus enables to calculate reliable parameters describing the variability of FHR. Relating these results to the other method for FHR estimation we showed that in our approach a much lower number of measured cardiac cycles was rejected as being invalid. Conclusions The proposed method for fetal heart rate determination on a beat-to-beat basis offers a high accuracy of the heart interval measurement enabling reliable quantitative assessment of the FHR variability, at the same time reducing the number of invalid cardiac cycle measurements. PMID:21999764

A simple solution for model comparison in bold imaging: the special case of reward prediction error and reward outcomes.

PubMed

Erdeniz, Burak; Rohe, Tim; Done, John; Seidler, Rachael D

2013-01-01

Conventional neuroimaging techniques provide information about condition-related changes of the BOLD (blood-oxygen-level dependent) signal, indicating only where and when the underlying cognitive processes occur. Recently, with the help of a new approach called "model-based" functional neuroimaging (fMRI), researchers are able to visualize changes in the internal variables of a time varying learning process, such as the reward prediction error or the predicted reward value of a conditional stimulus. However, despite being extremely beneficial to the imaging community in understanding the neural correlates of decision variables, a model-based approach to brain imaging data is also methodologically challenging due to the multicollinearity problem in statistical analysis. There are multiple sources of multicollinearity in functional neuroimaging including investigations of closely related variables and/or experimental designs that do not account for this. The source of multicollinearity discussed in this paper occurs due to correlation between different subjective variables that are calculated very close in time. Here, we review methodological approaches to analyzing such data by discussing the special case of separating the reward prediction error signal from reward outcomes.

The Importance of Freshwater to Spatial Variability of Aragonite Saturation State in the Gulf of Alaska

NASA Astrophysics Data System (ADS)

Siedlecki, Samantha A.; Pilcher, Darren J.; Hermann, Albert J.; Coyle, Ken; Mathis, Jeremy

2017-11-01

High-latitude and subpolar regions like the Gulf of Alaska (GOA) are more vulnerable than equatorial regions to rising carbon dioxide (CO2) levels, in part due to local processes that amplify the global signal. Recent field observations have shown that the shelf of the GOA is currently experiencing seasonal corrosive events (carbonate mineral saturation states Ω, Ω < 1), including suppressed Ω in response to ocean acidification as well as local processes like increased low-alkalinity glacial meltwater discharge. While the glacial discharge mainly influences the inner shelf, on the outer shelf, upwelling brings corrosive waters from the deep GOA. In this work, we develop a high-resolution model for carbon dynamics in the GOA, identify regions of high variability of Ω, and test the sensitivity of those regions to changes in the chemistry of glacial meltwater discharge. Results indicate the importance of this climatically sensitive and relatively unconstrained regional freshwater forcing for Ω variability in the nearshore. The increase was nearly linear at 0.002 Ω per 100 µmol/kg increase in alkalinity in the freshwater runoff. We find that the local winds, biological processes, and freshwater forcing all contribute to the spatial distribution of Ω and identify which of these three is highly correlated to the variability in Ω. Given that the timing and magnitude of these processes will likely change during the next few decades, it is critical to elucidate the effect of local processes on the background ocean acidification signal using robust models, such as the one described here.

Accurate derivation of heart rate variability signal for detection of sleep disordered breathing in children.

PubMed

Chatlapalli, S; Nazeran, H; Melarkod, V; Krishnam, R; Estrada, E; Pamula, Y; Cabrera, S

2004-01-01

The electrocardiogram (ECG) signal is used extensively as a low cost diagnostic tool to provide information concerning the heart's state of health. Accurate determination of the QRS complex, in particular, reliable detection of the R wave peak, is essential in computer based ECG analysis. ECG data from Physionet's Sleep-Apnea database were used to develop, test, and validate a robust heart rate variability (HRV) signal derivation algorithm. The HRV signal was derived from pre-processed ECG signals by developing an enhanced Hilbert transform (EHT) algorithm with built-in missing beat detection capability for reliable QRS detection. The performance of the EHT algorithm was then compared against that of a popular Hilbert transform-based (HT) QRS detection algorithm. Autoregressive (AR) modeling of the HRV power spectrum for both EHT- and HT-derived HRV signals was achieved and different parameters from their power spectra as well as approximate entropy were derived for comparison. Poincare plots were then used as a visualization tool to highlight the detection of the missing beats in the EHT method After validation of the EHT algorithm on ECG data from the Physionet, the algorithm was further tested and validated on a dataset obtained from children undergoing polysomnography for detection of sleep disordered breathing (SDB). Sensitive measures of accurate HRV signals were then derived to be used in detecting and diagnosing sleep disordered breathing in children. All signal processing algorithms were implemented in MATLAB. We present a description of the EHT algorithm and analyze pilot data for eight children undergoing nocturnal polysomnography. The pilot data demonstrated that the EHT method provides an accurate way of deriving the HRV signal and plays an important role in extraction of reliable measures to distinguish between periods of normal and sleep disordered breathing (SDB) in children.

Motivational salience signal in the basal forebrain is coupled with faster and more precise decision speed.

PubMed

Avila, Irene; Lin, Shih-Chieh

2014-03-01

The survival of animals depends critically on prioritizing responses to motivationally salient stimuli. While it is generally believed that motivational salience increases decision speed, the quantitative relationship between motivational salience and decision speed, measured by reaction time (RT), remains unclear. Here we show that the neural correlate of motivational salience in the basal forebrain (BF), defined independently of RT, is coupled with faster and also more precise decision speed. In rats performing a reward-biased simple RT task, motivational salience was encoded by BF bursting response that occurred before RT. We found that faster RTs were tightly coupled with stronger BF motivational salience signals. Furthermore, the fraction of RT variability reflecting the contribution of intrinsic noise in the decision-making process was actively suppressed in faster RT distributions with stronger BF motivational salience signals. Artificially augmenting the BF motivational salience signal via electrical stimulation led to faster and more precise RTs and supports a causal relationship. Together, these results not only describe for the first time, to our knowledge, the quantitative relationship between motivational salience and faster decision speed, they also reveal the quantitative coupling relationship between motivational salience and more precise RT. Our results further establish the existence of an early and previously unrecognized step in the decision-making process that determines both the RT speed and variability of the entire decision-making process and suggest that this novel decision step is dictated largely by the BF motivational salience signal. Finally, our study raises the hypothesis that the dysregulation of decision speed in conditions such as depression, schizophrenia, and cognitive aging may result from the functional impairment of the motivational salience signal encoded by the poorly understood noncholinergic BF neurons.

Motivational Salience Signal in the Basal Forebrain Is Coupled with Faster and More Precise Decision Speed

PubMed Central

Avila, Irene; Lin, Shih-Chieh

2014-01-01

The survival of animals depends critically on prioritizing responses to motivationally salient stimuli. While it is generally believed that motivational salience increases decision speed, the quantitative relationship between motivational salience and decision speed, measured by reaction time (RT), remains unclear. Here we show that the neural correlate of motivational salience in the basal forebrain (BF), defined independently of RT, is coupled with faster and also more precise decision speed. In rats performing a reward-biased simple RT task, motivational salience was encoded by BF bursting response that occurred before RT. We found that faster RTs were tightly coupled with stronger BF motivational salience signals. Furthermore, the fraction of RT variability reflecting the contribution of intrinsic noise in the decision-making process was actively suppressed in faster RT distributions with stronger BF motivational salience signals. Artificially augmenting the BF motivational salience signal via electrical stimulation led to faster and more precise RTs and supports a causal relationship. Together, these results not only describe for the first time, to our knowledge, the quantitative relationship between motivational salience and faster decision speed, they also reveal the quantitative coupling relationship between motivational salience and more precise RT. Our results further establish the existence of an early and previously unrecognized step in the decision-making process that determines both the RT speed and variability of the entire decision-making process and suggest that this novel decision step is dictated largely by the BF motivational salience signal. Finally, our study raises the hypothesis that the dysregulation of decision speed in conditions such as depression, schizophrenia, and cognitive aging may result from the functional impairment of the motivational salience signal encoded by the poorly understood noncholinergic BF neurons. PMID:24642480

Multi-format all-optical processing based on a large-scale, hybridly integrated photonic circuit.

PubMed

Bougioukos, M; Kouloumentas, Ch; Spyropoulou, M; Giannoulis, G; Kalavrouziotis, D; Maziotis, A; Bakopoulos, P; Harmon, R; Rogers, D; Harrison, J; Poustie, A; Maxwell, G; Avramopoulos, H

2011-06-06

We investigate through numerical studies and experiments the performance of a large scale, silica-on-silicon photonic integrated circuit for multi-format regeneration and wavelength-conversion. The circuit encompasses a monolithically integrated array of four SOAs inside two parallel Mach-Zehnder structures, four delay interferometers and a large number of silica waveguides and couplers. Exploiting phase-incoherent techniques, the circuit is capable of processing OOK signals at variable bit rates, DPSK signals at 22 or 44 Gb/s and DQPSK signals at 44 Gbaud. Simulation studies reveal the wavelength-conversion potential of the circuit with enhanced regenerative capabilities for OOK and DPSK modulation formats and acceptable quality degradation for DQPSK format. Regeneration of 22 Gb/s OOK signals with amplified spontaneous emission (ASE) noise and DPSK data signals degraded with amplitude, phase and ASE noise is experimentally validated demonstrating a power penalty improvement up to 1.5 dB.

Sequential pattern formation governed by signaling gradients

NASA Astrophysics Data System (ADS)

Jörg, David J.; Oates, Andrew C.; Jülicher, Frank

2016-10-01

Rhythmic and sequential segmentation of the embryonic body plan is a vital developmental patterning process in all vertebrate species. However, a theoretical framework capturing the emergence of dynamic patterns of gene expression from the interplay of cell oscillations with tissue elongation and shortening and with signaling gradients, is still missing. Here we show that a set of coupled genetic oscillators in an elongating tissue that is regulated by diffusing and advected signaling molecules can account for segmentation as a self-organized patterning process. This system can form a finite number of segments and the dynamics of segmentation and the total number of segments formed depend strongly on kinetic parameters describing tissue elongation and signaling molecules. The model accounts for existing experimental perturbations to signaling gradients, and makes testable predictions about novel perturbations. The variety of different patterns formed in our model can account for the variability of segmentation between different animal species.

A digital signal processing system for coherent laser radar

NASA Technical Reports Server (NTRS)

Hampton, Diana M.; Jones, William D.; Rothermel, Jeffry

1991-01-01

A data processing system for use with continuous-wave lidar is described in terms of its configuration and performance during the second survey mission of NASA'a Global Backscatter Experiment. The system is designed to estimate a complete lidar spectrum in real time, record the data from two lidars, and monitor variables related to the lidar operating environment. The PC-based system includes a transient capture board, a digital-signal processing (DSP) board, and a low-speed data-acquisition board. Both unprocessed and processed lidar spectrum data are monitored in real time, and the results are compared to those of a previous non-DSP-based system. Because the DSP-based system is digital it is slower than the surface-acoustic-wave signal processor and collects 2500 spectra/s. However, the DSP-based system provides complete data sets at two wavelengths from the continuous-wave lidars.

Integration of ENSO Signal Power Through Hydrological Processes in the Little River Watershed

NASA Astrophysics Data System (ADS)

Keener, V. W.; Jones, J. W.; Bosch, D. D.; Cho, J.

2011-12-01

The relationship of the El-Nino/Southern Oscillation (ENSO) to hydrology is typically discussed in terms of the ability to separate significantly different hydrologic variable responses versus the anomaly that has taken place. Most of the work relating ENSO trends to proxy variables had been done on precipitation records until the mid 1990s, at which point increasing numbers of studies started to focus on ENSO relationships with streamflow as well as other environmental variables. The signals in streamflow are typically complex, representing the integration of both climatic, landscape, and anthropological responses that are able to strengthen the inherent ENSO signal in chaotic regional precipitation data. There is a need to identify climate non-stationarities related to ENSO and their links to watershed-scale outcomes. For risk-management in particular, inter-annual modes of climate variability and their seasonal expression are of interest. In this study, we analyze 36 years of historical monthly streamflow data from the Little River Watershed (LWR), a coastal plain ecosystem in Georgia, in conjunction with wavelet spectral analysis and modeling via the Soil & Water Assessment Tool (SWAT). Using both spectral and physical models allows us to identify the mechanism by which the ENSO signal power in surface and simulated groundwater flow is strengthened as compared to precipitation. The clear increase in the power of the inter-annual climate signal is demonstrated by shared patterns in water budget and exceedance curves, as well as in high ENSO related energy in the 95% significant wavelet spectra for each variable and the NINO 3.4 index. In the LRW, the power of the ENSO teleconnection is increased in both the observed and simulated stream flow through the mechanisms of groundwater flow and interflow, through confinement by a geological layer, the Hawthorn Formation. This non-intuitive relationship between ENSO signal strength and streamflow could prove to be helpful for making seasonal climate predictions in a geographic area with a weaker than desirable ENSO signal, as a predictive relationship could be found between streamflow or other proxy hydro-climatic variables.

Study of time reversibility/irreversibility of cardiovascular data: theoretical results and application to laser Doppler flowmetry and heart rate variability signals

NASA Astrophysics Data System (ADS)

Humeau-Heurtier, Anne; Mahé, Guillaume; Chapeau-Blondeau, François; Rousseau, David; Abraham, Pierre

2012-07-01

Time irreversibility can be qualitatively defined as the degree of a signal for temporal asymmetry. Recently, a time irreversibility characterization method based on entropies of positive and negative increments has been proposed for experimental signals and applied to heart rate variability (HRV) data (central cardiovascular system (CVS)). The results led to interesting information as a time asymmetry index was found different for young subjects and elderly people or heart disease patients. Nevertheless, similar analyses have not yet been conducted on laser Doppler flowmetry (LDF) signals (peripheral CVS). We first propose to further investigate the above-mentioned characterization method. Then, LDF signals, LDF signals reduced to samples acquired during ECG R peaks (LDF_RECG signals) and HRV recorded simultaneously in healthy subjects are processed. Entropies of positive and negative increments for LDF signals show a nonmonotonic pattern: oscillations—more or less pronounced, depending on subjects—are found with a period matching the one of cardiac activity. However, such oscillations are not found with LDF_RECG nor with HRV. Moreover, the asymmetry index for LDF is markedly different from the ones of LDF_RECG and HRV. The cardiac activity may therefore play a dominant role in the time irreversibility properties of LDF signals.

Investigating the neural bases for intra-subject cognitive efficiency changes using functional magnetic resonance imaging

PubMed Central

Rao, Neena K.; Motes, Michael A.; Rypma, Bart

2014-01-01

Several fMRI studies have examined brain regions mediating inter-subject variability in cognitive efficiency, but none have examined regions mediating intra-subject variability in efficiency. Thus, the present study was designed to identify brain regions involved in intra-subject variability in cognitive efficiency via participant-level correlations between trial-level reaction time (RT) and trial-level fMRI BOLD percent signal change on a processing speed task. On each trial, participants indicated whether a digit-symbol probe-pair was present or absent in an array of nine digit-symbol probe-pairs while fMRI data were collected. Deconvolution analyses, using RT time-series models (derived from the proportional scaling of an event-related hemodynamic response function model by trial-level RT), were used to evaluate relationships between trial-level RTs and BOLD percent signal change. Although task-related patterns of activation and deactivation were observed in regions including bilateral occipital, bilateral parietal, portions of the medial wall such as the precuneus, default mode network regions including anterior cingulate, posterior cingulate, bilateral temporal, right cerebellum, and right cuneus, RT-BOLD correlations were observed in a more circumscribed set of regions. Positive RT-BOLD correlations, where fast RTs were associated with lower BOLD percent signal change, were observed in regions including bilateral occipital, bilateral parietal, and the precuneus. RT-BOLD correlations were not observed in the default mode network indicating a smaller set of regions associated with intra-subject variability in cognitive efficiency. The results are discussed in terms of a distributed area of regions that mediate variability in the cognitive efficiency that might underlie processing speed differences between individuals. PMID:25374527

Investigating the neural bases for intra-subject cognitive efficiency changes using functional magnetic resonance imaging.

PubMed

Rao, Neena K; Motes, Michael A; Rypma, Bart

2014-01-01

Several fMRI studies have examined brain regions mediating inter-subject variability in cognitive efficiency, but none have examined regions mediating intra-subject variability in efficiency. Thus, the present study was designed to identify brain regions involved in intra-subject variability in cognitive efficiency via participant-level correlations between trial-level reaction time (RT) and trial-level fMRI BOLD percent signal change on a processing speed task. On each trial, participants indicated whether a digit-symbol probe-pair was present or absent in an array of nine digit-symbol probe-pairs while fMRI data were collected. Deconvolution analyses, using RT time-series models (derived from the proportional scaling of an event-related hemodynamic response function model by trial-level RT), were used to evaluate relationships between trial-level RTs and BOLD percent signal change. Although task-related patterns of activation and deactivation were observed in regions including bilateral occipital, bilateral parietal, portions of the medial wall such as the precuneus, default mode network regions including anterior cingulate, posterior cingulate, bilateral temporal, right cerebellum, and right cuneus, RT-BOLD correlations were observed in a more circumscribed set of regions. Positive RT-BOLD correlations, where fast RTs were associated with lower BOLD percent signal change, were observed in regions including bilateral occipital, bilateral parietal, and the precuneus. RT-BOLD correlations were not observed in the default mode network indicating a smaller set of regions associated with intra-subject variability in cognitive efficiency. The results are discussed in terms of a distributed area of regions that mediate variability in the cognitive efficiency that might underlie processing speed differences between individuals.

Motor-sensory confluence in tactile perception.

PubMed

Saig, Avraham; Gordon, Goren; Assa, Eldad; Arieli, Amos; Ahissar, Ehud

2012-10-03

Perception involves motor control of sensory organs. However, the dynamics underlying emergence of perception from motor-sensory interactions are not yet known. Two extreme possibilities are as follows: (1) motor and sensory signals interact within an open-loop scheme in which motor signals determine sensory sampling but are not affected by sensory processing and (2) motor and sensory signals are affected by each other within a closed-loop scheme. We studied the scheme of motor-sensory interactions in humans using a novel object localization task that enabled monitoring the relevant overt motor and sensory variables. We found that motor variables were dynamically controlled within each perceptual trial, such that they gradually converged to steady values. Training on this task resulted in improvement in perceptual acuity, which was achieved solely by changes in motor variables, without any change in the acuity of sensory readout. The within-trial dynamics is captured by a hierarchical closed-loop model in which lower loops actively maintain constant sensory coding, and higher loops maintain constant sensory update flow. These findings demonstrate interchangeability of motor and sensory variables in perception, motor convergence during perception, and a consistent hierarchical closed-loop perceptual model.

Spatiotemporal Dependency of Age-Related Changes in Brain Signal Variability

PubMed Central

McIntosh, A. R.; Vakorin, V.; Kovacevic, N.; Wang, H.; Diaconescu, A.; Protzner, A. B.

2014-01-01

Recent theoretical and empirical work has focused on the variability of network dynamics in maturation. Such variability seems to reflect the spontaneous formation and dissolution of different functional networks. We sought to extend these observations into healthy aging. Two different data sets, one EEG (total n = 48, ages 18–72) and one magnetoencephalography (n = 31, ages 20–75) were analyzed for such spatiotemporal dependency using multiscale entropy (MSE) from regional brain sources. In both data sets, the changes in MSE were timescale dependent, with higher entropy at fine scales and lower at more coarse scales with greater age. The signals were parsed further into local entropy, related to information processed within a regional source, and distributed entropy (information shared between two sources, i.e., functional connectivity). Local entropy increased for most regions, whereas the dominant change in distributed entropy was age-related reductions across hemispheres. These data further the understanding of changes in brain signal variability across the lifespan, suggesting an inverted U-shaped curve, but with an important qualifier. Unlike earlier in maturation, where the changes are more widespread, changes in adulthood show strong spatiotemporal dependence. PMID:23395850

A silicon avalanche photodiode detector circuit for Nd:YAG laser scattering

NASA Astrophysics Data System (ADS)

Hsieh, C.-L.; Haskovec, J.; Carlstrom, T. N.; Deboo, J. C.; Greenfield, C. M.; Snider, R. T.; Trost, P.

1990-06-01

A silicon avalanche photodiode with an internal gain of about 50 to 100 is used in a temperature controlled environment to measure the Nd:YAG laser Thomson scattered spectrum in the wavelength range from 700 to 1150 nm. A charge sensitive preamplifier was developed for minimizing the noise contribution from the detector electronics. Signal levels as low as 20 photoelectrons (S/N = 1) can be detected. Measurements show that both the signal and the variance of the signal vary linearly with the input light level over the range of interest, indicating Poisson statistics. The signal is processed using a 100 ns delay line and a differential amplifier which subtracts the low frequency background light component. The background signal is amplified with a computer controlled variable gain amplifier and is used for an estimate of the measurement error, calibration, and Z sub eff measurements of the plasma. The signal processing was analyzed using a theoretical model to aid the system design and establish the procedure for data error analysis.

Silicon avalanche photodiode detector circuit for Nd:YAG laser scattering

NASA Astrophysics Data System (ADS)

Hsieh, C. L.; Haskovec, J.; Carlstrom, T. N.; DeBoo, J. C.; Greenfield, C. M.; Snider, R. T.; Trost, P.

1990-10-01

A silicon avalanche photodiode with an internal gain of about 50 to 100 is used in a temperature-controlled environment to measure the Nd:YAG laser Thomson scattered spectrum in the wavelength range from 700 to 1150 nm. A charge-sensitive preamplifier has been developed for minimizing the noise contribution from the detector electronics. Signal levels as low as 20 photoelectrons (S/N=1) can be detected. Measurements show that both the signal and the variance of the signal vary linearly with the input light level over the range of interest, indicating Poisson statistics. The signal is processed using a 100 ns delay line and a differential amplifier which subtracts the low-frequency background light component. The background signal is amplified with a computer-controlled variable gain amplifier and is used for an estimate of the measurement error, calibration, and Zeff measurements of the plasma. The signal processing has been analyzed using a theoretical model to aid the system design and establish the procedure for data error analysis.

Back to Pupillometry: How Cortical Network State Fluctuations Tracked by Pupil Dynamics Could Explain Neural Signal Variability in Human Cognitive Neuroscience

PubMed Central

2017-01-01

Abstract The mammalian thalamocortical system generates intrinsic activity reflecting different states of excitability, arising from changes in the membrane potentials of underlying neuronal networks. Fluctuations between these states occur spontaneously, regularly, and frequently throughout awake periods and influence stimulus encoding, information processing, and neuronal and behavioral responses. Changes of pupil size have recently been identified as a reliable marker of underlying neuronal membrane potential and thus can encode associated network state changes in rodent cortex. This suggests that pupillometry, a ubiquitous measure of pupil dilation in cognitive neuroscience, could be used as an index for network state fluctuations also for human brain signals. Considering this variable may explain task-independent variance in neuronal and behavioral signals that were previously disregarded as noise. PMID:29379876

Apparatus and method for microwave processing of materials

DOEpatents

Johnson, Arvid C.; Lauf, Robert J.; Bible, Don W.; Markunas, Robert J.

1996-01-01

A variable frequency microwave heating apparatus (10) designed to allow modulation of the frequency of the microwaves introduced into a furnace cavity (34) for testing or other selected applications. The variable frequency heating apparatus (10) is used in the method of the present invention to monitor the resonant processing frequency within the furnace cavity (34) depending upon the material, including the state thereof, from which the workpiece (36) is fabricated. The variable frequency microwave heating apparatus (10) includes a microwave signal generator (12) and a high-power microwave amplifier (20) or a microwave voltage-controlled oscillator (14). A power supply (22) is provided for operation of the high-power microwave oscillator (14) or microwave amplifier (20). A directional coupler (24) is provided for detecting the direction and amplitude of signals incident upon and reflected from the microwave cavity (34). A first power meter (30) is provided for measuring the power delivered to the microwave furnace (32). A second power meter (26) detects the magnitude of reflected power. Reflected power is dissipated in the reflected power load (28).

Autogenic Deposits as A Potential Recorder of High-Frequency Signals: The Role of Autogenic Processes Revisited

NASA Astrophysics Data System (ADS)

Li, H.; Plink-Bjorklund, P.

2017-12-01

Studies (e.g., Jerolmack and Paola, 2010) have suggested that autogenic processes act as a filter for high-frequency environmental signals, and the underlying assumption is that autogenic processes can cause fluctuations in sediment and water discharge that modify or shred the signal. This assumption, however, fails to recognize that autogenic processes and their final products are dynamic and that they can respond to allogenic forcings. We compile a database containing published field studies, physical experiments, and numerical modeling works, and analyze the data under different boundary conditions. Our analyses suggest different conclusions. Autogenic processes are intrinsic to the sedimentary system, and they possess distinct patterns under steady boundary conditions. Upon changing boundary conditions, the autogenic patterns are also likely to change (depending on the magnitude of the change in the boundary conditions). Therefore, the pattern change provides us with the opportunity to restore the high-frequency signals that may not pass through the transfer zone. Here we present the theoretical basis for using autogenic deposits to infer high-frequency signals as well as modern and ancient field examples, physical experiments, and modeling works to illustrate the autogenic response to allogenic forcings. The field studies show the potential of using autogenic deposits to restore short-term climatic variability. The experiments demonstrate that autogenic processes in rivers are closely linked to sediment and water discharge. The modeling examples reveal the counteracting effects of some autogenic processes to form a self-organized pattern under a set of specific boundary conditions. We also highlight the limitations and challenges that need more research efforts to restore high-frequency signals. Some critical issues include the magnitude of the signals, the effect of the interference between different signals, and the incompleteness of the autogenic deposits.

Is Abdominal Fetal Electrocardiography an Alternative to Doppler Ultrasound for FHR Variability Evaluation?

PubMed Central

Jezewski, Janusz; Wrobel, Janusz; Matonia, Adam; Horoba, Krzysztof; Martinek, Radek; Kupka, Tomasz; Jezewski, Michal

2017-01-01

Great expectations are connected with application of indirect fetal electrocardiography (FECG), especially for home telemonitoring of pregnancy. Evaluation of fetal heart rate (FHR) variability, when determined from FECG, uses the same criteria as for FHR signal acquired classically—through ultrasound Doppler method (US). Therefore, the equivalence of those two methods has to be confirmed, both in terms of recognizing classical FHR patterns: baseline, accelerations/decelerations (A/D), long-term variability (LTV), as well as evaluating the FHR variability with beat-to-beat accuracy—short-term variability (STV). The research material consisted of recordings collected from 60 patients in physiological and complicated pregnancy. The FHR signals of at least 30 min duration were acquired dually, using two systems for fetal and maternal monitoring, based on US and FECG methods. Recordings were retrospectively divided into normal (41) and abnormal (19) fetal outcome. The complex process of data synchronization and validation was performed. Obtained low level of the signal loss (4.5% for US and 1.8% for FECG method) enabled to perform both direct comparison of FHR signals, as well as indirect one—by using clinically relevant parameters. Direct comparison showed that there is no measurement bias between the acquisition methods, whereas the mean absolute difference, important for both visual and computer-aided signal analysis, was equal to 1.2 bpm. Such low differences do not affect the visual assessment of the FHR signal. However, in the indirect comparison the inconsistencies of several percent were noted. This mainly affects the acceleration (7.8%) and particularly deceleration (54%) patterns. In the signals acquired using the electrocardiography the obtained STV and LTV indices have shown significant overestimation by 10 and 50% respectively. It also turned out, that ability of clinical parameters to distinguish between normal and abnormal groups do not depend on the acquisition method. The obtained results prove that the abdominal FECG, considered as an alternative to the ultrasound approach, does not change the interpretation of the FHR signal, which was confirmed during both visual assessment and automated analysis. PMID:28559852

Recollection is a continuous process: implications for dual-process theories of recognition memory.

PubMed

Mickes, Laura; Wais, Peter E; Wixted, John T

2009-04-01

Dual-process theory, which holds that recognition decisions can be based on recollection or familiarity, has long seemed incompatible with signal detection theory, which holds that recognition decisions are based on a singular, continuous memory-strength variable. Formal dual-process models typically regard familiarity as a continuous process (i.e., familiarity comes in degrees), but they construe recollection as a categorical process (i.e., recollection either occurs or does not occur). A continuous process is characterized by a graded relationship between confidence and accuracy, whereas a categorical process is characterized by a binary relationship such that high confidence is associated with high accuracy but all lower degrees of confidence are associated with chance accuracy. Using a source-memory procedure, we found that the relationship between confidence and source-recollection accuracy was graded. Because recollection, like familiarity, is a continuous process, dual-process theory is more compatible with signal detection theory than previously thought.

Reversed Priming Effects May Be Driven by Misperception Rather than Subliminal Processing.

PubMed

Sand, Anders

2016-01-01

A new paradigm for investigating whether a cognitive process is independent of perception was recently suggested. In the paradigm, primes are shown at an intermediate signal strength that leads to trial-to-trial and inter-individual variability in prime perception. Here, I used this paradigm and an objective measure of perception to assess the influence of prime identification responses on Stroop priming. I found that sensory states producing correct and incorrect prime identification responses were also associated with qualitatively different priming effects. Incorrect prime identification responses were associated with reversed priming effects but in contrast to previous studies, I interpret this to result from the (mis-)perception of primes rather than from a subliminal process. Furthermore, the intermediate signal strength also produced inter-individual variability in prime perception that strongly influenced priming effects: only participants who on average perceived the primes were Stroop primed. I discuss how this new paradigm, with a wide range of d' values, is more appropriate when regression analysis on inter-individual identification performance is used to investigate perception-dependent processing. The results of this study, in line with previous results, suggest that drawing conclusions about subliminal processes based on data averaged over individuals may be unwarranted.

Reversed Priming Effects May Be Driven by Misperception Rather than Subliminal Processing

PubMed Central

Sand, Anders

2016-01-01

A new paradigm for investigating whether a cognitive process is independent of perception was recently suggested. In the paradigm, primes are shown at an intermediate signal strength that leads to trial-to-trial and inter-individual variability in prime perception. Here, I used this paradigm and an objective measure of perception to assess the influence of prime identification responses on Stroop priming. I found that sensory states producing correct and incorrect prime identification responses were also associated with qualitatively different priming effects. Incorrect prime identification responses were associated with reversed priming effects but in contrast to previous studies, I interpret this to result from the (mis-)perception of primes rather than from a subliminal process. Furthermore, the intermediate signal strength also produced inter-individual variability in prime perception that strongly influenced priming effects: only participants who on average perceived the primes were Stroop primed. I discuss how this new paradigm, with a wide range of d′ values, is more appropriate when regression analysis on inter-individual identification performance is used to investigate perception-dependent processing. The results of this study, in line with previous results, suggest that drawing conclusions about subliminal processes based on data averaged over individuals may be unwarranted. PMID:26925016

Measuring temporal stability of positron emission tomography standardized uptake value bias using long-lived sources in a multicenter network.

PubMed

Byrd, Darrin; Christopfel, Rebecca; Arabasz, Grae; Catana, Ciprian; Karp, Joel; Lodge, Martin A; Laymon, Charles; Moros, Eduardo G; Budzevich, Mikalai; Nehmeh, Sadek; Scheuermann, Joshua; Sunderland, John; Zhang, Jun; Kinahan, Paul

2018-01-01

Positron emission tomography (PET) is a quantitative imaging modality, but the computation of standardized uptake values (SUVs) requires several instruments to be correctly calibrated. Variability in the calibration process may lead to unreliable quantitation. Sealed source kits containing traceable amounts of [Formula: see text] were used to measure signal stability for 19 PET scanners at nine hospitals in the National Cancer Institute's Quantitative Imaging Network. Repeated measurements of the sources were performed on PET scanners and in dose calibrators. The measured scanner and dose calibrator signal biases were used to compute the bias in SUVs at multiple time points for each site over a 14-month period. Estimation of absolute SUV accuracy was confounded by bias from the solid phantoms' physical properties. On average, the intrascanner coefficient of variation for SUV measurements was 3.5%. Over the entire length of the study, single-scanner SUV values varied over a range of 11%. Dose calibrator bias was not correlated with scanner bias. Calibration factors from the image metadata were nearly as variable as scanner signal, and were correlated with signal for many scanners. SUVs often showed low intrascanner variability between successive measurements but were also prone to shifts in apparent bias, possibly in part due to scanner recalibrations that are part of regular scanner quality control. Biases of key factors in the computation of SUVs were not correlated and their temporal variations did not cancel out of the computation. Long-lived sources and image metadata may provide a check on the recalibration process.

Wireless photoplethysmographic device for heart rate variability signal acquisition and analysis.

PubMed

Reyes, Ivan; Nazeran, Homer; Franco, Mario; Haltiwanger, Emily

2012-01-01

The photoplethysmographic (PPG) signal has the potential to aid in the acquisition and analysis of heart rate variability (HRV) signal: a non-invasive quantitative marker of the autonomic nervous system that could be used to assess cardiac health and other physiologic conditions. A low-power wireless PPG device was custom-developed to monitor, acquire and analyze the arterial pulse in the finger. The system consisted of an optical sensor to detect arterial pulse as variations in reflected light intensity, signal conditioning circuitry to process the reflected light signal, a microcontroller to control PPG signal acquisition, digitization and wireless transmission, a receiver to collect the transmitted digital data and convert them back to their analog representations. A personal computer was used to further process the captured PPG signals and display them. A MATLAB program was then developed to capture the PPG data, detect the RR peaks, perform spectral analysis of the PPG data, and extract the HRV signal. A user-friendly graphical user interface (GUI) was developed in LabView to display the PPG data and their spectra. The performance of each module (sensing unit, signal conditioning, wireless transmission/reception units, and graphical user interface) was assessed individually and the device was then tested as a whole. Consequently, PPG data were obtained from five healthy individuals to test the utility of the wireless system. The device was able to reliably acquire the PPG signals from the volunteers. To validate the accuracy of the MATLAB codes, RR peak information from each subject was fed into Kubios software as a text file. Kubios was able to generate a report sheet with the time domain and frequency domain parameters of the acquired data. These features were then compared against those calculated by MATLAB. The preliminary results demonstrate that the prototype wireless device could be used to perform HRV signal acquisition and analysis.

Remote Entanglement by Coherent Multiplication of Concurrent Quantum Signals

NASA Astrophysics Data System (ADS)

Roy, Ananda; Jiang, Liang; Stone, A. Douglas; Devoret, Michel

2015-10-01

Concurrent remote entanglement of distant, noninteracting quantum entities is a crucial function for quantum information processing. In contrast with the existing protocols which employ the addition of signals to generate entanglement between two remote qubits, the continuous variable protocol we present is based on the multiplication of signals. This protocol can be straightforwardly implemented by a novel Josephson junction mixing circuit. Our scheme would be able to generate provable entanglement even in the presence of practical imperfections: finite quantum efficiency of detectors and undesired photon loss in current state-of-the-art devices.

Functional neuronal processing of human body odors.

PubMed

Lundström, Johan N; Olsson, Mats J

2010-01-01

Body odors carry informational cues of great importance for individuals across a wide range of species, and signals hidden within the body odor cocktail are known to regulate several key behaviors in animals. For a long time, the notion that humans may be among these species has been dismissed. We now know, however, that each human has a unique odor signature that carries information related to his or her genetic makeup, as well as information about personal environmental variables, such as diet and hygiene. Although a substantial number of studies have investigated the behavioral effects of body odors, only a handful have studied central processing. Recent studies have, however, demonstrated that the human brain responds to fear signals hidden within the body odor cocktail, is able to extract kin specific signals, and processes body odors differently than other perceptually similar odors. In this chapter, we provide an overview of the current knowledge of how the human brain processes body odors and the potential importance these signals have for us in everyday life. Copyright © 2010 Elsevier Inc. All rights reserved.

Impacts of Model Bias on the Climate Change Signal and Effects of Weighted Ensembles of Regional Climate Model Simulations: A Case Study over Southern Québec, Canada

DOE PAGES

Eum, Hyung-Il; Gachon, Philippe; Laprise, René

2016-01-01

This study examined the impact of model biases on climate change signals for daily precipitation and for minimum and maximum temperatures. Through the use of multiple climate scenarios from 12 regional climate model simulations, the ensemble mean, and three synthetic simulations generated by a weighting procedure, we investigated intermodel seasonal climate change signals between current and future periods, for both median and extreme precipitation/temperature values. A significant dependence of seasonal climate change signals on the model biases over southern Québec in Canada was detected for temperatures, but not for precipitation. This suggests that the regional temperature change signal is affectedmore » by local processes. Seasonally, model bias affects future mean and extreme values in winter and summer. In addition, potentially large increases in future extremes of temperature and precipitation values were projected. For three synthetic scenarios, systematically less bias and a narrow range of mean change for all variables were projected compared to those of climate model simulations. In addition, synthetic scenarios were found to better capture the spatial variability of extreme cold temperatures than the ensemble mean scenario. Finally, these results indicate that the synthetic scenarios have greater potential to reduce the uncertainty of future climate projections and capture the spatial variability of extreme climate events.« less

Impacts of Model Bias on the Climate Change Signal and Effects of Weighted Ensembles of Regional Climate Model Simulations: A Case Study over Southern Québec, Canada

DOE Office of Scientific and Technical Information (OSTI.GOV)

Eum, Hyung-Il; Gachon, Philippe; Laprise, René

This study examined the impact of model biases on climate change signals for daily precipitation and for minimum and maximum temperatures. Through the use of multiple climate scenarios from 12 regional climate model simulations, the ensemble mean, and three synthetic simulations generated by a weighting procedure, we investigated intermodel seasonal climate change signals between current and future periods, for both median and extreme precipitation/temperature values. A significant dependence of seasonal climate change signals on the model biases over southern Québec in Canada was detected for temperatures, but not for precipitation. This suggests that the regional temperature change signal is affectedmore » by local processes. Seasonally, model bias affects future mean and extreme values in winter and summer. In addition, potentially large increases in future extremes of temperature and precipitation values were projected. For three synthetic scenarios, systematically less bias and a narrow range of mean change for all variables were projected compared to those of climate model simulations. In addition, synthetic scenarios were found to better capture the spatial variability of extreme cold temperatures than the ensemble mean scenario. Finally, these results indicate that the synthetic scenarios have greater potential to reduce the uncertainty of future climate projections and capture the spatial variability of extreme climate events.« less

The effect of signal variability on the histograms of anthropomorphic channel outputs: factors resulting in non-normally distributed data

NASA Astrophysics Data System (ADS)

Elshahaby, Fatma E. A.; Ghaly, Michael; Jha, Abhinav K.; Frey, Eric C.

2015-03-01

Model Observers are widely used in medical imaging for the optimization and evaluation of instrumentation, acquisition parameters and image reconstruction and processing methods. The channelized Hotelling observer (CHO) is a commonly used model observer in nuclear medicine and has seen increasing use in other modalities. An anthropmorphic CHO consists of a set of channels that model some aspects of the human visual system and the Hotelling Observer, which is the optimal linear discriminant. The optimality of the CHO is based on the assumption that the channel outputs for data with and without the signal present have a multivariate normal distribution with equal class covariance matrices. The channel outputs result from the dot product of channel templates with input images and are thus the sum of a large number of random variables. The central limit theorem is thus often used to justify the assumption that the channel outputs are normally distributed. In this work, we aim to examine this assumption for realistically simulated nuclear medicine images when various types of signal variability are present.

Mover Position Detection for PMTLM Based on Linear Hall Sensors through EKF Processing

PubMed Central

Yan, Leyang; Zhang, Hui; Ye, Peiqing

2017-01-01

Accurate mover position is vital for a permanent magnet tubular linear motor (PMTLM) control system. In this paper, two linear Hall sensors are utilized to detect the mover position. However, Hall sensor signals contain third-order harmonics, creating errors in mover position detection. To filter out the third-order harmonics, a signal processing method based on the extended Kalman filter (EKF) is presented. The limitation of conventional processing method is first analyzed, and then EKF is adopted to detect the mover position. In the EKF model, the amplitude of the fundamental component and the percentage of the harmonic component are taken as state variables, and they can be estimated based solely on the measured sensor signals. Then, the harmonic component can be calculated and eliminated. The proposed method has the advantages of faster convergence, better stability and higher accuracy. Finally, experimental results validate the effectiveness and superiority of the proposed method. PMID:28383505

Fault Detection and Diagnosis In Hall-Héroult Cells Based on Individual Anode Current Measurements Using Dynamic Kernel PCA

NASA Astrophysics Data System (ADS)

Yao, Yuchen; Bao, Jie; Skyllas-Kazacos, Maria; Welch, Barry J.; Akhmetov, Sergey

2018-04-01

Individual anode current signals in aluminum reduction cells provide localized cell conditions in the vicinity of each anode, which contain more information than the conventionally measured cell voltage and line current. One common use of this measurement is to identify process faults that can cause significant changes in the anode current signals. While this method is simple and direct, it ignores the interactions between anode currents and other important process variables. This paper presents an approach that applies multivariate statistical analysis techniques to individual anode currents and other process operating data, for the detection and diagnosis of local process abnormalities in aluminum reduction cells. Specifically, since the Hall-Héroult process is time-varying with its process variables dynamically and nonlinearly correlated, dynamic kernel principal component analysis with moving windows is used. The cell is discretized into a number of subsystems, with each subsystem representing one anode and cell conditions in its vicinity. The fault associated with each subsystem is identified based on multivariate statistical control charts. The results show that the proposed approach is able to not only effectively pinpoint the problematic areas in the cell, but also assess the effect of the fault on different parts of the cell.

Movement Complexity and Neuromechanical Factors Affect the Entropic Half-Life of Myoelectric Signals

PubMed Central

Hodson-Tole, Emma F.; Wakeling, James M.

2017-01-01

Appropriate neuromuscular functioning is essential for survival and features underpinning motor control are present in myoelectric signals recorded from skeletal muscles. One approach to quantify control processes related to function is to assess signal variability using measures such as Sample Entropy. Here we developed a theoretical framework to simulate the effect of variability in burst duration, activation duty cycle, and intensity on the Entropic Half-Life (EnHL) in myoelectric signals. EnHLs were predicted to be <40 ms, and to vary with fluctuations in myoelectric signal amplitude and activation duty cycle. Comparison with myoelectic data from rats walking and running at a range of speeds and inclines confirmed the range of EnHLs, however, the direction of EnHL change in response to altered locomotor demand was not correctly predicted. The discrepancy reflected different associations between the ratio of the standard deviation and mean signal intensity (Ist:It¯) and duty factor in simulated and physiological data, likely reflecting additional information in the signals from the physiological data (e.g., quiescent phase content; variation in action potential shapes). EnHL could have significant value as a novel marker of neuromuscular responses to alterations in perceived locomotor task complexity and intensity. PMID:28974932

Kepler AutoRegressive Planet Search: Motivation & Methodology

NASA Astrophysics Data System (ADS)

Caceres, Gabriel; Feigelson, Eric; Jogesh Babu, G.; Bahamonde, Natalia; Bertin, Karine; Christen, Alejandra; Curé, Michel; Meza, Cristian

2015-08-01

The Kepler AutoRegressive Planet Search (KARPS) project uses statistical methodology associated with autoregressive (AR) processes to model Kepler lightcurves in order to improve exoplanet transit detection in systems with high stellar variability. We also introduce a planet-search algorithm to detect transits in time-series residuals after application of the AR models. One of the main obstacles in detecting faint planetary transits is the intrinsic stellar variability of the host star. The variability displayed by many stars may have autoregressive properties, wherein later flux values are correlated with previous ones in some manner. Auto-Regressive Moving-Average (ARMA) models, Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH), and related models are flexible, phenomenological methods used with great success to model stochastic temporal behaviors in many fields of study, particularly econometrics. Powerful statistical methods are implemented in the public statistical software environment R and its many packages. Modeling involves maximum likelihood fitting, model selection, and residual analysis. These techniques provide a useful framework to model stellar variability and are used in KARPS with the objective of reducing stellar noise to enhance opportunities to find as-yet-undiscovered planets. Our analysis procedure consisting of three steps: pre-processing of the data to remove discontinuities, gaps and outliers; ARMA-type model selection and fitting; and transit signal search of the residuals using a new Transit Comb Filter (TCF) that replaces traditional box-finding algorithms. We apply the procedures to simulated Kepler-like time series with known stellar and planetary signals to evaluate the effectiveness of the KARPS procedures. The ARMA-type modeling is effective at reducing stellar noise, but also reduces and transforms the transit signal into ingress/egress spikes. A periodogram based on the TCF is constructed to concentrate the signal of these periodic spikes. When a periodic transit is found, the model is displayed on a standard period-folded averaged light curve. We also illustrate the efficient coding in R.

Enhancement of MS Signal Processing For Improved Cancer Biomarker Discovery

NASA Astrophysics Data System (ADS)

Si, Qian

Technological advances in proteomics have shown great potential in detecting cancer at the earliest stages. One way is to use the time of flight mass spectroscopy to identify biomarkers, or early disease indicators related to the cancer. Pattern analysis of time of flight mass spectra data from blood and tissue samples gives great hope for the identification of potential biomarkers among the complex mixture of biological and chemical samples for the early cancer detection. One of the keys issues is the pre-processing of raw mass spectra data. A lot of challenges need to be addressed: unknown noise character associated with the large volume of data, high variability in the mass spectroscopy measurements, and poorly understood signal background and so on. This dissertation focuses on developing statistical algorithms and creating data mining tools for computationally improved signal processing for mass spectrometry data. I have introduced an advanced accurate estimate of the noise model and a half-supervised method of mass spectrum data processing which requires little knowledge about the data.

Beeping and piping: characterization of two mechano-acoustic signals used by honey bees in swarming

NASA Astrophysics Data System (ADS)

Schlegel, Thomas; Visscher, P. Kirk; Seeley, Thomas D.

2012-12-01

Of the many signals used by honey bees during the process of swarming, two of them—the stop signal and the worker piping signal—are not easily distinguished for both are mechano-acoustic signals produced by scout bees who press their bodies against other bees while vibrating their wing muscles. To clarify the acoustic differences between these two signals, we recorded both signals from the same swarm and at the same time, and compared them in terms of signal duration, fundamental frequency, and frequency modulation. Stop signals and worker piping signals differ in all three variables: duration, 174 ± 64 vs. 602 ± 377 ms; fundamental frequency, 407 vs. 451 Hz; and frequency modulation, absent vs. present. While it remains unclear which differences the bees use to distinguish the two signals, it is clear that they do so for the signals have opposite effects. Stop signals cause inhibition of actively dancing scout bees whereas piping signals cause excitation of quietly resting non-scout bees.

Optimization of an angle-beam ultrasonic approach for characterization of impact damage in composites

NASA Astrophysics Data System (ADS)

Henry, Christine; Kramb, Victoria; Welter, John T.; Wertz, John N.; Lindgren, Eric A.; Aldrin, John C.; Zainey, David

2018-04-01

Advances in NDE method development are greatly improved through model-guided experimentation. In the case of ultrasonic inspections, models which provide insight into complex mode conversion processes and sound propagation paths are essential for understanding the experimental data and inverting the experimental data into relevant information. However, models must also be verified using experimental data obtained under well-documented and understood conditions. Ideally, researchers would utilize the model simulations and experimental approach to efficiently converge on the optimal solution. However, variability in experimental parameters introduce extraneous signals that are difficult to differentiate from the anticipated response. This paper discusses the results of an ultrasonic experiment designed to evaluate the effect of controllable variables on the anticipated signal, and the effect of unaccounted for experimental variables on the uncertainty in those results. Controlled experimental parameters include the transducer frequency, incidence beam angle and focal depth.

Exploiting Virtual Synchrony in Distributed Systems

DTIC Science & Technology

1987-02-01

for distributed systems yield the best performance relative to the level of synchronization guaranteed by the primitive . A pro- grammer could then... synchronization facility. Semaphores Replicated binary and general semaphores . Monitors Monitor lock, condition variables and signals. Deadlock detection...We describe applications of a new software abstraction called the virtually synchronous process group. Such a group consists of a set of processes

Process observation in fiber laser-based selective laser melting

NASA Astrophysics Data System (ADS)

Thombansen, Ulrich; Gatej, Alexander; Pereira, Milton

2015-01-01

The process observation in selective laser melting (SLM) focuses on observing the interaction point where the powder is processed. To provide process relevant information, signals have to be acquired that are resolved in both time and space. Especially in high-power SLM, where more than 1 kW of laser power is used, processing speeds of several meters per second are required for a high-quality processing results. Therefore, an implementation of a suitable process observation system has to acquire a large amount of spatially resolved data at low sampling speeds or it has to restrict the acquisition to a predefined area at a high sampling speed. In any case, it is vitally important to synchronously record the laser beam position and the acquired signal. This is a prerequisite that allows the recorded data become information. Today, most SLM systems employ f-theta lenses to focus the processing laser beam onto the powder bed. This report describes the drawbacks that result for process observation and suggests a variable retro-focus system which solves these issues. The beam quality of fiber lasers delivers the processing laser beam to the powder bed at relevant focus diameters, which is a key prerequisite for this solution to be viable. The optical train we present here couples the processing laser beam and the process observation coaxially, ensuring consistent alignment of interaction zone and observed area. With respect to signal processing, we have developed a solution that synchronously acquires signals from a pyrometer and the position of the laser beam by sampling the data with a field programmable gate array. The relevance of the acquired signals has been validated by the scanning of a sample filament. Experiments with grooved samples show a correlation between different powder thicknesses and the acquired signals at relevant processing parameters. This basic work takes a first step toward self-optimization of the manufacturing process in SLM. It enables the addition of cognitive functions to the manufacturing system to the extent that the system could track its own process. The results are based on analyzing and redesigning the optical train, in combination with a real-time signal acquisition system which provides a solution to certain technological barriers.

Representation of ocean-atmosphere processes associated with extended monsoon episodes over South Asia in CFSv2

NASA Astrophysics Data System (ADS)

Mohan, T. S.; Annamalai, H.; Marx, Larry; Huang, Bohua; Kinter, James

2018-02-01

In the present study, we analyze 30-years output from free run solutions of CFSv2 coupled model to assess the model’s representation of extended (>7 days) active and break monsoon episodes over south Asia. Process based diagnostics is applied to the individual and composite events to identify precursor signals in both ocean and atmospheric variables. Our examination suggests that CFSv2, like most coupled models, depict systematic biases in variables important for ocean-atmosphere interactions. Nevertheless, model solutions capture many aspects of monsoon extended break and active episodes realistically, encouraging us to apply process-based diagnostics. Diagnostics reveal that sea surface temperature (SST) variations over the northern Bay of Bengal where the climatological mixed-layer is thin, lead the in-situ precipitation anomalies by about 8 (10) days during extended active (break) episodes, and the precipitation anomalies over central India by 10-14 days. Mixed-layer heat budget analysis indicates for a close correspondence between SST tendency and net surface heat flux (Q_net). MSE budgets indicate that horizontal moisture advection to be a coherent precursor signal ( 10 days) during both extended break (dry advection) and active (moist advection) events. The lead timings in these precursor signals in CFSv2 solutions will be of potential use to monitor and predict extended monsoon episodes. Diagnostics, however, also indicate that for about 1/3 of the identified extended break and active episodes, inconsistencies in budget terms suggest precursor signals could lead to false alarms. Apart from false alarms, compared to observations, CFSv2 systematically simulates a greater number of extended monsoon active episodes.

Model-Based Fault Diagnosis for Turboshaft Engines

NASA Technical Reports Server (NTRS)

Green, Michael D.; Duyar, Ahmet; Litt, Jonathan S.

1998-01-01

Tests are described which, when used to augment the existing periodic maintenance and pre-flight checks of T700 engines, can greatly improve the chances of uncovering a problem compared to the current practice. These test signals can be used to expose and differentiate between faults in various components by comparing the responses of particular engine variables to the expected. The responses can be processed on-line in a variety of ways which have been shown to reveal and identify faults. The combination of specific test signals and on-line processing methods provides an ad hoc approach to the isolation of faults which might not otherwise be detected during pre-flight checkout.

Decomposition of Near-Infrared Spectroscopy Signals Using Oblique Subspace Projections: Applications in Brain Hemodynamic Monitoring.

PubMed

Caicedo, Alexander; Varon, Carolina; Hunyadi, Borbala; Papademetriou, Maria; Tachtsidis, Ilias; Van Huffel, Sabine

2016-01-01

Clinical data is comprised by a large number of synchronously collected biomedical signals that are measured at different locations. Deciphering the interrelationships of these signals can yield important information about their dependence providing some useful clinical diagnostic data. For instance, by computing the coupling between Near-Infrared Spectroscopy signals (NIRS) and systemic variables the status of the hemodynamic regulation mechanisms can be assessed. In this paper we introduce an algorithm for the decomposition of NIRS signals into additive components. The algorithm, SIgnal DEcomposition base on Obliques Subspace Projections (SIDE-ObSP), assumes that the measured NIRS signal is a linear combination of the systemic measurements, following the linear regression model y = Ax + ϵ . SIDE-ObSP decomposes the output such that, each component in the decomposition represents the sole linear influence of one corresponding regressor variable. This decomposition scheme aims at providing a better understanding of the relation between NIRS and systemic variables, and to provide a framework for the clinical interpretation of regression algorithms, thereby, facilitating their introduction into clinical practice. SIDE-ObSP combines oblique subspace projections (ObSP) with the structure of a mean average system in order to define adequate signal subspaces. To guarantee smoothness in the estimated regression parameters, as observed in normal physiological processes, we impose a Tikhonov regularization using a matrix differential operator. We evaluate the performance of SIDE-ObSP by using a synthetic dataset, and present two case studies in the field of cerebral hemodynamics monitoring using NIRS. In addition, we compare the performance of this method with other system identification techniques. In the first case study data from 20 neonates during the first 3 days of life was used, here SIDE-ObSP decoupled the influence of changes in arterial oxygen saturation from the NIRS measurements, facilitating the use of NIRS as a surrogate measure for cerebral blood flow (CBF). The second case study used data from a 3-years old infant under Extra Corporeal Membrane Oxygenation (ECMO), here SIDE-ObSP decomposed cerebral/peripheral tissue oxygenation, as a sum of the partial contributions from different systemic variables, facilitating the comparison between the effects of each systemic variable on the cerebral/peripheral hemodynamics.

On the degree distribution of horizontal visibility graphs associated with Markov processes and dynamical systems: diagrammatic and variational approaches

NASA Astrophysics Data System (ADS)

Lacasa, Lucas

2014-09-01

Dynamical processes can be transformed into graphs through a family of mappings called visibility algorithms, enabling the possibility of (i) making empirical time series analysis and signal processing and (ii) characterizing classes of dynamical systems and stochastic processes using the tools of graph theory. Recent works show that the degree distribution of these graphs encapsulates much information on the signals' variability, and therefore constitutes a fundamental feature for statistical learning purposes. However, exact solutions for the degree distributions are only known in a few cases, such as for uncorrelated random processes. Here we analytically explore these distributions in a list of situations. We present a diagrammatic formalism which computes for all degrees their corresponding probability as a series expansion in a coupling constant which is the number of hidden variables. We offer a constructive solution for general Markovian stochastic processes and deterministic maps. As case tests we focus on Ornstein-Uhlenbeck processes, fully chaotic and quasiperiodic maps. Whereas only for certain degree probabilities can all diagrams be summed exactly, in the general case we show that the perturbation theory converges. In a second part, we make use of a variational technique to predict the complete degree distribution for special classes of Markovian dynamics with fast-decaying correlations. In every case we compare the theory with numerical experiments.

Temperature and Pressure Dependence of Signal Amplitudes for Electrostriction Laser-Induced Thermal Acoustics

NASA Technical Reports Server (NTRS)

Herring, Gregory C.

2015-01-01

The relative signal strength of electrostriction-only (no thermal grating) laser-induced thermal acoustics (LITA) in gas-phase air is reported as a function of temperature T and pressure P. Measurements were made in the free stream of a variable Mach number supersonic wind tunnel, where T and P are varied simultaneously as Mach number is varied. Using optical heterodyning, the measured signal amplitude (related to the optical reflectivity of the acoustic grating) was averaged for each of 11 flow conditions and compared to the expected theoretical dependence of a pure-electrostriction LITA process, where the signal is proportional to the square root of [P*P /( T*T*T)].

Time-Warp–Invariant Neuronal Processing

PubMed Central

Gütig, Robert; Sompolinsky, Haim

2009-01-01

Fluctuations in the temporal durations of sensory signals constitute a major source of variability within natural stimulus ensembles. The neuronal mechanisms through which sensory systems can stabilize perception against such fluctuations are largely unknown. An intriguing instantiation of such robustness occurs in human speech perception, which relies critically on temporal acoustic cues that are embedded in signals with highly variable duration. Across different instances of natural speech, auditory cues can undergo temporal warping that ranges from 2-fold compression to 2-fold dilation without significant perceptual impairment. Here, we report that time-warp–invariant neuronal processing can be subserved by the shunting action of synaptic conductances that automatically rescales the effective integration time of postsynaptic neurons. We propose a novel spike-based learning rule for synaptic conductances that adjusts the degree of synaptic shunting to the temporal processing requirements of a given task. Applying this general biophysical mechanism to the example of speech processing, we propose a neuronal network model for time-warp–invariant word discrimination and demonstrate its excellent performance on a standard benchmark speech-recognition task. Our results demonstrate the important functional role of synaptic conductances in spike-based neuronal information processing and learning. The biophysics of temporal integration at neuronal membranes can endow sensory pathways with powerful time-warp–invariant computational capabilities. PMID:19582146

Tacholess order-tracking approach for wind turbine gearbox fault detection

NASA Astrophysics Data System (ADS)

Wang, Yi; Xie, Yong; Xu, Guanghua; Zhang, Sicong; Hou, Chenggang

2017-09-01

Monitoring of wind turbines under variable-speed operating conditions has become an important issue in recent years. The gearbox of a wind turbine is the most important transmission unit; it generally exhibits complex vibration signatures due to random variations in operating conditions. Spectral analysis is one of the main approaches in vibration signal processing. However, spectral analysis is based on a stationary assumption and thus inapplicable to the fault diagnosis of wind turbines under variable-speed operating conditions. This constraint limits the application of spectral analysis to wind turbine diagnosis in industrial applications. Although order-tracking methods have been proposed for wind turbine fault detection in recent years, current methods are only applicable to cases in which the instantaneous shaft phase is available. For wind turbines with limited structural spaces, collecting phase signals with tachometers or encoders is difficult. In this study, a tacholess order-tracking method for wind turbines is proposed to overcome the limitations of traditional techniques. The proposed method extracts the instantaneous phase from the vibration signal, resamples the signal at equiangular increments, and calculates the order spectrum for wind turbine fault identification. The effectiveness of the proposed method is experimentally validated with the vibration signals of wind turbines.

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

Three-way principal component analysis of the volatile fraction by HS-SPME/GC of aceto balsamico tradizionale of modena.

PubMed

Cocchi, Marina; Durante, Caterina; Grandi, Margherita; Manzini, Daniela; Marchetti, Andrea

2008-01-15

The present research is aimed at monitoring the evolution of the volatile organic compounds of different samples of aceto balsamico tradizionale of modena (ABTM) during ageing. The flavouring compounds, headspace fraction, of the vinegars of four batterie were sampled by solid phase microextraction technique (SPME), and successively analysed by gas chromatography. Obtaining a data set characterized by different sources of variability such as, different producers, samples of different age and chromatographic profile. The gas chromatographic signals were processed by a three-way data analysis method (Tucker3), which allows an easy visualisation of the data by furnishing a distinct set of graphs for each source of variability. The obtained results indicate that the samples can be separated according to their age highlighting the chemical constituents, which play a major role for their differentiation. The present study represents an example of how the application of Tucker3 models, on gas chromatographic signals may help to follow the transformation processes of food products.

Combination of process and vibration data for improved condition monitoring of industrial systems working under variable operating conditions

NASA Astrophysics Data System (ADS)

Ruiz-Cárcel, C.; Jaramillo, V. H.; Mba, D.; Ottewill, J. R.; Cao, Y.

2016-01-01

The detection and diagnosis of faults in industrial processes is a very active field of research due to the reduction in maintenance costs achieved by the implementation of process monitoring algorithms such as Principal Component Analysis, Partial Least Squares or more recently Canonical Variate Analysis (CVA). Typically the condition of rotating machinery is monitored separately using vibration analysis or other specific techniques. Conventional vibration-based condition monitoring techniques are based on the tracking of key features observed in the measured signal. Typically steady-state loading conditions are required to ensure consistency between measurements. In this paper, a technique based on merging process and vibration data is proposed with the objective of improving the detection of mechanical faults in industrial systems working under variable operating conditions. The capabilities of CVA for detection and diagnosis of faults were tested using experimental data acquired from a compressor test rig where different process faults were introduced. Results suggest that the combination of process and vibration data can effectively improve the detectability of mechanical faults in systems working under variable operating conditions.

Spectral analysis of temporal non-stationary rainfall-runoff processes

NASA Astrophysics Data System (ADS)

Chang, Ching-Min; Yeh, Hund-Der

2018-04-01

This study treats the catchment as a block box system with considering the rainfall input and runoff output being a stochastic process. The temporal rainfall-runoff relationship at the catchment scale is described by a convolution integral on a continuous time scale. Using the Fourier-Stieltjes representation approach, a frequency domain solution to the convolution integral is developed to the spectral analysis of runoff processes generated by temporal non-stationary rainfall events. It is shown that the characteristic time scale of rainfall process increases the runoff discharge variability, while the catchment mean travel time constant plays the role in reducing the variability of runoff discharge. Similar to the behavior of groundwater aquifers, catchments act as a low-pass filter in the frequency domain for the rainfall input signal.

The generic MESSy submodel TENDENCY (v1.0) for process-based analyses in Earth system models

NASA Astrophysics Data System (ADS)

Eichinger, R.; Jöckel, P.

2014-07-01

The tendencies of prognostic variables in Earth system models are usually only accessible, e.g. for output, as a sum over all physical, dynamical and chemical processes at the end of one time integration step. Information about the contribution of individual processes to the total tendency is lost, if no special precautions are implemented. The knowledge on individual contributions, however, can be of importance to track down specific mechanisms in the model system. We present the new MESSy (Modular Earth Submodel System) infrastructure submodel TENDENCY and use it exemplarily within the EMAC (ECHAM/MESSy Atmospheric Chemistry) model to trace process-based tendencies of prognostic variables. The main idea is the outsourcing of the tendency accounting for the state variables from the process operators (submodels) to the TENDENCY submodel itself. In this way, a record of the tendencies of all process-prognostic variable pairs can be stored. The selection of these pairs can be specified by the user, tailor-made for the desired application, in order to minimise memory requirements. Moreover, a standard interface allows the access to the individual process tendencies by other submodels, e.g. for on-line diagnostics or for additional parameterisations, which depend on individual process tendencies. An optional closure test assures the correct treatment of tendency accounting in all submodels and thus serves to reduce the model's susceptibility. TENDENCY is independent of the time integration scheme and therefore the concept is applicable to other model systems as well. Test simulations with TENDENCY show an increase of computing time for the EMAC model (in a setup without atmospheric chemistry) of 1.8 ± 1% due to the additional subroutine calls when using TENDENCY. Exemplary results reveal the dissolving mechanisms of the stratospheric tape recorder signal in height over time. The separation of the tendency of the specific humidity into the respective processes (large-scale clouds, convective clouds, large-scale advection, vertical diffusion and methane oxidation) show that the upward propagating water vapour signal dissolves mainly because of the chemical and the advective contribution. The TENDENCY submodel is part of version 2.42 or later of MESSy.

The generic MESSy submodel TENDENCY (v1.0) for process-based analyses in Earth System Models

NASA Astrophysics Data System (ADS)

Eichinger, R.; Jöckel, P.

2014-04-01

The tendencies of prognostic variables in Earth System Models are usually only accessible, e.g., for output, as sum over all physical, dynamical and chemical processes at the end of one time integration step. Information about the contribution of individual processes to the total tendency is lost, if no special precautions are implemented. The knowledge on individual contributions, however, can be of importance to track down specific mechanisms in the model system. We present the new MESSy (Modular Earth Submodel System) infrastructure submodel TENDENCY and use it exemplarily within the EMAC (ECHAM/MESSy Atmospheric Chemistry) model to trace process-based tendencies of prognostic variables. The main idea is the outsourcing of the tendency accounting for the state variables from the process operators (submodels) to the TENDENCY submodel itself. In this way, a record of the tendencies of all process-prognostic variable pairs can be stored. The selection of these pairs can be specified by the user, tailor-made for the desired application, in order to minimise memory requirements. Moreover a standard interface allows the access to the individual process tendencies by other submodels, e.g., for on-line diagnostics or for additional parameterisations, which depend on individual process tendencies. An optional closure test assures the correct treatment of tendency accounting in all submodels and thus serves to reduce the models susceptibility. TENDENCY is independent of the time integration scheme and therefore applicable to other model systems as well. Test simulations with TENDENCY show an increase of computing time for the EMAC model (in a setup without atmospheric chemistry) of 1.8 ± 1% due to the additional subroutine calls when using TENDENCY. Exemplary results reveal the dissolving mechanisms of the stratospheric tape recorder signal in height over time. The separation of the tendency of the specific humidity into the respective processes (large-scale clouds, convective clouds, large-scale advection, vertical diffusion and methane-oxidation) show that the upward propagating water vapour signal dissolves mainly because of the chemical and the advective contribution. The TENDENCY submodel is part of version 2.42 or later of MESSy.

Processes Affecting Variability of Fluorescence Signals from Benthic Targets in Shallow Waters

DTIC Science & Technology

1997-09-30

processes in the Department of Chemistry at Brookhaven National Laboratory. The model organisms used are primarily cultured zooxanthellae obtained from...and closed (Fm) photosystem II reaction centers in the zooxanthellae isolated from the fire coral, Montipora. The short lifetime curve corresponds...individual zooxanthellae strains, is highly correlated Figure 2. The correlation between the average fluorescence lifetimes, calculated from a four

Correlated receptor transport processes buffer single-cell heterogeneity

PubMed Central

Kallenberger, Stefan M.; Unger, Anne L.; Legewie, Stefan; Lymperopoulos, Konstantinos; Eils, Roland

2017-01-01

Cells typically vary in their response to extracellular ligands. Receptor transport processes modulate ligand-receptor induced signal transduction and impact the variability in cellular responses. Here, we quantitatively characterized cellular variability in erythropoietin receptor (EpoR) trafficking at the single-cell level based on live-cell imaging and mathematical modeling. Using ensembles of single-cell mathematical models reduced parameter uncertainties and showed that rapid EpoR turnover, transport of internalized EpoR back to the plasma membrane, and degradation of Epo-EpoR complexes were essential for receptor trafficking. EpoR trafficking dynamics in adherent H838 lung cancer cells closely resembled the dynamics previously characterized by mathematical modeling in suspension cells, indicating that dynamic properties of the EpoR system are widely conserved. Receptor transport processes differed by one order of magnitude between individual cells. However, the concentration of activated Epo-EpoR complexes was less variable due to the correlated kinetics of opposing transport processes acting as a buffering system. PMID:28945754

Reaction Time Variability Associated with Reading Skills in Poor Readers with ADHD

PubMed Central

Tamm, Leanne; Epstein, Jeffery N.; Denton, Carolyn A.; Vaughn, Aaron J.; Peugh, James; Willcutt, Erik G.

2014-01-01

Objective Linkages between neuropsychological functioning (i.e., response inhibition, processing speed, reaction time variability) and word reading have been documented among children with Attention-Deficit/Hyperactivity Disorder (ADHD) and children with Reading Disorders. However, associations between neuropsychological functioning and other aspects of reading (i.e., fluency, comprehension) have not been well-documented among children with comorbid ADHD and Reading Disorder. Method Children with ADHD and poor word reading (i.e., ≤25th percentile) completed a stop signal task (SST) and tests of word reading, reading fluency, and reading comprehension. Multivariate multiple regression was conducted predicting the reading skills from SST variables [i.e., mean reaction time (MRT), reaction time standard deviation (SDRT), and stop signal reaction time (SSRT)]. Results SDRT predicted word reading, reading fluency, and reading comprehension. MRT and SSRT were not associated with any reading skill. After including word reading in models predicting reading fluency and reading comprehension, the effects of SDRT were minimized. Discussion Reaction time variability (i.e., SDRT) reflects impairments in information processing and failure to maintain executive control. The pattern of results from this study suggest SDRT exerts its effects on reading fluency and reading comprehension through its effect on word reading (i.e., decoding) and that this relation may be related to observed deficits in higher-level elements of reading. PMID:24528537

Dynamic control of a homogeneous charge compression ignition engine

DOEpatents

Duffy, Kevin P [Metamora, IL; Mehresh, Parag [Peoria, IL; Schuh, David [Peoria, IL; Kieser, Andrew J [Morton, IL; Hergart, Carl-Anders [Peoria, IL; Hardy, William L [Peoria, IL; Rodman, Anthony [Chillicothe, IL; Liechty, Michael P [Chillicothe, IL

2008-06-03

A homogenous charge compression ignition engine is operated by compressing a charge mixture of air, exhaust and fuel in a combustion chamber to an autoignition condition of the fuel. The engine may facilitate a transition from a first combination of speed and load to a second combination of speed and load by changing the charge mixture and compression ratio. This may be accomplished in a consecutive engine cycle by adjusting both a fuel injector control signal and a variable valve control signal away from a nominal variable valve control signal. Thereafter in one or more subsequent engine cycles, more sluggish adjustments are made to at least one of a geometric compression ratio control signal and an exhaust gas recirculation control signal to allow the variable valve control signal to be readjusted back toward its nominal variable valve control signal setting. By readjusting the variable valve control signal back toward its nominal setting, the engine will be ready for another transition to a new combination of engine speed and load.

Phospholipase C-β1 and β4 Contribute to Non-Genetic Cell-to-Cell Variability in Histamine-Induced Calcium Signals in HeLa Cells

PubMed Central

Ishida, Sachiko; Matsu-ura, Toru; Fukami, Kiyoko; Michikawa, Takayuki; Mikoshiba, Katsuhiko

2014-01-01

A uniform extracellular stimulus triggers cell-specific patterns of Ca2+ signals, even in genetically identical cell populations. However, the underlying mechanism that generates the cell-to-cell variability remains unknown. We monitored cytosolic inositol 1,4,5-trisphosphate (IP3) concentration changes using a fluorescent IP3 sensor in single HeLa cells showing different patterns of histamine-induced Ca2+ oscillations in terms of the time constant of Ca2+ spike amplitude decay and the Ca2+ oscillation frequency. HeLa cells stimulated with histamine exhibited a considerable variation in the temporal pattern of Ca2+ signals and we found that there were cell-specific IP3 dynamics depending on the patterns of Ca2+ signals. RT-PCR and western blot analyses showed that phospholipase C (PLC)-β1, -β3, -β4, -γ1, -δ3 and -ε were expressed at relatively high levels in HeLa cells. Small interfering RNA-mediated silencing of PLC isozymes revealed that PLC-β1 and PLC-β4 were specifically involved in the histamine-induced IP3 increases in HeLa cells. Modulation of IP3 dynamics by knockdown or overexpression of the isozymes PLC-β1 and PLC-β4 resulted in specific changes in the characteristics of Ca2+ oscillations, such as the time constant of the temporal changes in the Ca2+ spike amplitude and the Ca2+ oscillation frequency, within the range of the cell-to-cell variability found in wild-type cell populations. These findings indicate that the heterogeneity in the process of IP3 production, rather than IP3-induced Ca2+ release, can cause cell-to-cell variability in the patterns of Ca2+ signals and that PLC-β1 and PLC-β4 contribute to generate cell-specific Ca2+ signals evoked by G protein-coupled receptor stimulation. PMID:24475116

Interpretation of the Lempel-Ziv complexity measure in the context of biomedical signal analysis.

PubMed

Aboy, Mateo; Hornero, Roberto; Abásolo, Daniel; Alvarez, Daniel

2006-11-01

Lempel-Ziv complexity (LZ) and derived LZ algorithms have been extensively used to solve information theoretic problems such as coding and lossless data compression. In recent years, LZ has been widely used in biomedical applications to estimate the complexity of discrete-time signals. Despite its popularity as a complexity measure for biosignal analysis, the question of LZ interpretability and its relationship to other signal parameters and to other metrics has not been previously addressed. We have carried out an investigation aimed at gaining a better understanding of the LZ complexity itself, especially regarding its interpretability as a biomedical signal analysis technique. Our results indicate that LZ is particularly useful as a scalar metric to estimate the bandwidth of random processes and the harmonic variability in quasi-periodic signals.

Learning a common dictionary for subject-transfer decoding with resting calibration.

PubMed

Morioka, Hiroshi; Kanemura, Atsunori; Hirayama, Jun-ichiro; Shikauchi, Manabu; Ogawa, Takeshi; Ikeda, Shigeyuki; Kawanabe, Motoaki; Ishii, Shin

2015-05-01

Brain signals measured over a series of experiments have inherent variability because of different physical and mental conditions among multiple subjects and sessions. Such variability complicates the analysis of data from multiple subjects and sessions in a consistent way, and degrades the performance of subject-transfer decoding in a brain-machine interface (BMI). To accommodate the variability in brain signals, we propose 1) a method for extracting spatial bases (or a dictionary) shared by multiple subjects, by employing a signal-processing technique of dictionary learning modified to compensate for variations between subjects and sessions, and 2) an approach to subject-transfer decoding that uses the resting-state activity of a previously unseen target subject as calibration data for compensating for variations, eliminating the need for a standard calibration based on task sessions. Applying our methodology to a dataset of electroencephalography (EEG) recordings during a selective visual-spatial attention task from multiple subjects and sessions, where the variability compensation was essential for reducing the redundancy of the dictionary, we found that the extracted common brain activities were reasonable in the light of neuroscience knowledge. The applicability to subject-transfer decoding was confirmed by improved performance over existing decoding methods. These results suggest that analyzing multisubject brain activities on common bases by the proposed method enables information sharing across subjects with low-burden resting calibration, and is effective for practical use of BMI in variable environments. Copyright © 2015 Elsevier Inc. All rights reserved.

Anticipatory Reward Processing in Addicted Populations: A Focus on the Monetary Incentive Delay Task

PubMed Central

Balodis, Iris M.; Potenza, Marc N.

2014-01-01

Advances in brain imaging techniques have allowed neurobiological research to temporally analyze signals coding for the anticipation of rewards. In addicted populations, both hypo- and hyper-responsiveness of brain regions (e.g., ventral striatum) implicated in drug effects and reward system processing have been reported during anticipation of generalized reward. Here, we discuss the current state of knowledge of reward processing in addictive disorders from a widely used and validated task: the Monetary Incentive Delay Task (MIDT). The current paper constrains review to those studies applying the MIDT in addicted and at-risk adult populations, with a focus on anticipatory processing and striatal regions activated during task performance, as well as the relationship of these regions with individual difference (e.g., impulsivity) and treatment outcome variables. We further review drug influences in challenge studies as a means to examine acute influences on reward processing in abstinent, recreationally using and addicted populations. Here, we discuss that generalized reward processing in addicted and at-risk populations is often characterized by divergent anticipatory signaling in the ventral striatum. Although methodological/task variations may underlie some discrepant findings, anticipatory signaling in the ventral striatum may also be influenced by smoking status, drug metabolites and treatment status in addicted populations. Divergent results across abstinent, recreationally using and addicted populations demonstrate complexities in interpreting findings. Future studies will benefit from focusing on characterizing how impulsivity and other addiction-related features relate to anticipatory striatal signaling over time. Additionally, identifying how anticipatory signals recover/adjust following protracted abstinence will be important in understanding recovery processes. PMID:25481621

Bond graph modeling and experimental verification of a novel scheme for fault diagnosis of rolling element bearings in special operating conditions

NASA Astrophysics Data System (ADS)

Mishra, C.; Samantaray, A. K.; Chakraborty, G.

2016-09-01

Vibration analysis for diagnosis of faults in rolling element bearings is complicated when the rotor speed is variable or slow. In the former case, the time interval between the fault-induced impact responses in the vibration signal are non-uniform and the signal strength is variable. In the latter case, the fault-induced impact response strength is weak and generally gets buried in the noise, i.e. noise dominates the signal. This article proposes a diagnosis scheme based on a combination of a few signal processing techniques. The proposed scheme initially represents the vibration signal in terms of uniformly resampled angular position of the rotor shaft by using the interpolated instantaneous angular position measurements. Thereafter, intrinsic mode functions (IMFs) are generated through empirical mode decomposition (EMD) of resampled vibration signal which is followed by thresholding of IMFs and signal reconstruction to de-noise the signal and envelope order tracking to diagnose the faults. Data for validating the proposed diagnosis scheme are initially generated from a multi-body simulation model of rolling element bearing which is developed using bond graph approach. This bond graph model includes the ball and cage dynamics, localized fault geometry, contact mechanics, rotor unbalance, and friction and slip effects. The diagnosis scheme is finally validated with experiments performed with the help of a machine fault simulator (MFS) system. Some fault scenarios which could not be experimentally recreated are then generated through simulations and analyzed through the developed diagnosis scheme.

Noise facilitates transcriptional control under dynamic inputs.

PubMed

Kellogg, Ryan A; Tay, Savaş

2015-01-29

Cells must respond sensitively to time-varying inputs in complex signaling environments. To understand how signaling networks process dynamic inputs into gene expression outputs and the role of noise in cellular information processing, we studied the immune pathway NF-κB under periodic cytokine inputs using microfluidic single-cell measurements and stochastic modeling. We find that NF-κB dynamics in fibroblasts synchronize with oscillating TNF signal and become entrained, leading to significantly increased NF-κB oscillation amplitude and mRNA output compared to non-entrained response. Simulations show that intrinsic biochemical noise in individual cells improves NF-κB oscillation and entrainment, whereas cell-to-cell variability in NF-κB natural frequency creates population robustness, together enabling entrainment over a wider range of dynamic inputs. This wide range is confirmed by experiments where entrained cells were measured under all input periods. These results indicate that synergy between oscillation and noise allows cells to achieve efficient gene expression in dynamically changing signaling environments. Copyright © 2015 Elsevier Inc. All rights reserved.

Two-Stage Variable Sample-Rate Conversion System

NASA Technical Reports Server (NTRS)

Tkacenko, Andre

2009-01-01

A two-stage variable sample-rate conversion (SRC) system has been pro posed as part of a digital signal-processing system in a digital com munication radio receiver that utilizes a variety of data rates. The proposed system would be used as an interface between (1) an analog- todigital converter used in the front end of the receiver to sample an intermediatefrequency signal at a fixed input rate and (2) digita lly implemented tracking loops in subsequent stages that operate at v arious sample rates that are generally lower than the input sample r ate. This Two-Stage System would be capable of converting from an input sample rate to a desired lower output sample rate that could be var iable and not necessarily a rational fraction of the input rate.

Tribotronic Tuning Diode for Active Analog Signal Modulation.

PubMed

Zhou, Tao; Yang, Zhi Wei; Pang, Yaokun; Xu, Liang; Zhang, Chi; Wang, Zhong Lin

2017-01-24

Realizing active interaction with external environment/stimuli is a great challenge for current electronics. In this paper, a tribotronic tuning diode (TTD) is proposed by coupling a variable capacitance diode and a triboelectric nanogenerator in free-standing sliding mode. When the friction layer is sliding on the device surface for electrification, a reverse bias voltage is created and applied to the diode for tuning the junction capacitance. When the sliding distance increases from 0 to 25 mm, the capacitance of the TTD decreases from about 39 to 8 pF. The proposed TTD has been integrated into analog circuits and exhibited excellent performances in frequency modulation, phase shift, and filtering by sliding a finger. This work has demonstrated tunable diode and active analog signal modulation by tribotronics, which has great potential to replace ordinary variable capacitance diodes in various practical applications such as signal processing, electronic tuning circuits, precise tuning circuits, active sensor networks, electronic communications, remote controls, flexible electronics, etc.

Signal and noise extraction from analog memory elements for neuromorphic computing.

PubMed

Gong, N; Idé, T; Kim, S; Boybat, I; Sebastian, A; Narayanan, V; Ando, T

2018-05-29

Dense crossbar arrays of non-volatile memory (NVM) can potentially enable massively parallel and highly energy-efficient neuromorphic computing systems. The key requirements for the NVM elements are continuous (analog-like) conductance tuning capability and switching symmetry with acceptable noise levels. However, most NVM devices show non-linear and asymmetric switching behaviors. Such non-linear behaviors render separation of signal and noise extremely difficult with conventional characterization techniques. In this study, we establish a practical methodology based on Gaussian process regression to address this issue. The methodology is agnostic to switching mechanisms and applicable to various NVM devices. We show tradeoff between switching symmetry and signal-to-noise ratio for HfO 2 -based resistive random access memory. Then, we characterize 1000 phase-change memory devices based on Ge 2 Sb 2 Te 5 and separate total variability into device-to-device variability and inherent randomness from individual devices. These results highlight the usefulness of our methodology to realize ideal NVM devices for neuromorphic computing.

Nonlinear Estimation of Discrete-Time Signals Under Random Observation Delay

DOE Office of Scientific and Technical Information (OSTI.GOV)

Caballero-Aguila, R.; Jimenez-Lopez, J. D.; Hermoso-Carazo, A.

2008-11-06

This paper presents an approximation to the nonlinear least-squares estimation problem of discrete-time stochastic signals using nonlinear observations with additive white noise which can be randomly delayed by one sampling time. The observation delay is modelled by a sequence of independent Bernoulli random variables whose values, zero or one, indicate that the real observation arrives on time or it is delayed and, hence, the available measurement to estimate the signal is not up-to-date. Assuming that the state-space model generating the signal is unknown and only the covariance functions of the processes involved in the observation equation are ready for use,more » a filtering algorithm based on linear approximations of the real observations is proposed.« less

Microcontroller - Based System for Electrogastrography Monitoring Through Wireless Transmission

NASA Astrophysics Data System (ADS)

Haddab, S.; Laghrouche, M.

2009-01-01

Electrogastrography (EGG) is a non-invasive method for recording the electrical activity of the stomach. This paper presents a system designed for monitoring the EGG physiological variables of a patient outside the hospital environment. The signal acquisition is achieved by means of an ambulatory system carried by the patient and connected to him through skin electrodes. The acquired signal is transmitted via the Bluetooth to a mobile phone where the data are stored into the memory and then transferred via the GSM network to the processing and diagnostic unit in the hospital. EGG is usually contaminated by artefacts and other signals, which are sometimes difficult to remove. We have used a neural network method for motion artefacts removal and biological signal separation.

PAU/GNSS-R: Implementation, Performance and First Results of a Real-Time Delay-Doppler Map Reflectometer Using Global Navigation Satellite System Signals

PubMed Central

Marchan-Hernandez, Juan Fernando; Camps, Adriano; Rodriguez-Alvarez, Nereida; Bosch-Lluis, Xavier; Ramos-Perez, Isaac; Valencia, Enric

2008-01-01

Signals from Global Navigation Satellite Systems (GNSS) were originally conceived for position and speed determination, but they can be used as signals of opportunity as well. The reflection process over a given surface modifies the properties of the scattered signal, and therefore, by processing the reflected signal, relevant geophysical data regarding the surface under study (land, sea, ice…) can be retrieved. In essence, a GNSS-R receiver is a multi-channel GNSS receiver that computes the received power from a given satellite at a number of different delay and Doppler bins of the incoming signal. The first approaches to build such a receiver consisted of sampling and storing the scattered signal for later post-processing. However, a real-time approach to the problem is desirable to obtain immediately useful geophysical variables and reduce the amount of data. The use of FPGA technology makes this possible, while at the same time the system can be easily reconfigured. The signal tracking and processing constraints made necessary to fully design several new blocks. The uniqueness of the implemented system described in this work is the capability to compute in real-time Delay-Doppler maps (DDMs) either for four simultaneous satellites or just one, but with a larger number of bins. The first tests have been conducted from a cliff over the sea and demonstrate the successful performance of the instrument to compute DDMs in real-time from the measured reflected GNSS/R signals. The processing of these measurements shall yield quantitative relationships between the sea state (mainly driven by the surface wind and the swell) and the overall DDM shape. The ultimate goal is to use the DDM shape to correct the sea state influence on the L-band brightness temperature to improve the retrieval of the sea surface salinity (SSS). PMID:27879862

Efficacious insect and disease control with laser-guided air-assisted sprayer

USDA-ARS?s Scientific Manuscript database

Efficacy of a newly developed air-assisted variable-rate sprayer was investigated for the control of arthropod pests and plant diseases in six commercial fields. The sprayer was integrated with a high-speed laser scanning sensor, a custom-designed signal processing program, an automatic flow control...

Processes Affecting the Variability of Fluorescence Signals from Benthic Targets in Shallow Waters

DTIC Science & Technology

1999-09-30

isolated green fluorescent proteins from a variety of cnidarians and characterized their spectral signatures and biochemical characteristics. RESULTS...types of GFP-like fluorophores are present in Cnidarians . The first one had the only excitation band centered at ca. 495 nm; the second type had two

Proteomic Prediction of Breast Cancer Risk: A Cohort Study

DTIC Science & Technology

2007-03-01

Total 1728 1189 68.81 (c) Data processing. Data analysis was performed using in-house software (Du P , Angeletti RH. Automatic deconvolution of...isotope-resolved mass spectra using variable selection and quantized peptide mass distribution. Anal Chem., 78:3385-92, 2006; P Du, R Sudha, MB...control. Reportable Outcomes So far our publications have been on the development of algorithms for signal processing: 1. Du P , Angeletti RH

Vestibular blueprint in early vertebrates.

PubMed

Straka, Hans; Baker, Robert

2013-11-19

Central vestibular neurons form identifiable subgroups within the boundaries of classically outlined octavolateral nuclei in primitive vertebrates that are distinct from those processing lateral line, electrosensory, and auditory signals. Each vestibular subgroup exhibits a particular morpho-physiological property that receives origin-specific sensory inputs from semicircular canal and otolith organs. Behaviorally characterized phenotypes send discrete axonal projections to extraocular, spinal, and cerebellar targets including other ipsi- and contralateral vestibular nuclei. The anatomical locations of vestibuloocular and vestibulospinal neurons correlate with genetically defined hindbrain compartments that are well conserved throughout vertebrate evolution though some variability exists in fossil and extant vertebrate species. The different vestibular subgroups exhibit a robust sensorimotor signal processing complemented with a high degree of vestibular and visual adaptive plasticity.

Visible spectrum-based non-contact HRV and dPTT for stress detection

NASA Astrophysics Data System (ADS)

Kaur, Balvinder; Hutchinson, J. Andrew; Ikonomidou, Vasiliki N.

2017-05-01

Stress is a major health concern that not only compromises our quality of life, but also affects our physical health and well-being. Despite its importance, our ability to objectively detect and quantify it in a real-time, non-invasive manner is very limited. This capability would have a wide variety of medical, military, and security applications. We have developed a pipeline of image and signal processing algorithms to make such a system practical, which includes remote cardiac pulse detection based on visible spectrum videos and physiological stress detection based on the variability in the remotely detected cardiac signals. First, to determine a reliable cardiac pulse, principal component analysis (PCA) was applied for noise reduction and independent component analysis (ICA) was applied for source selection. To determine accurate cardiac timing for heart rate variability (HRV) analysis, a blind source separation method based least squares (LS) estimate was used to determine signal peaks that were closely related to R-peaks of the electrocardiogram (ECG) signal. A new metric, differential pulse transit time (dPTT), defined as the difference in arrival time of the remotely acquired cardiac signal at two separate distal locations, was derived. It was demonstrated that the remotely acquired metrics, HRV and dPTT, have potential for remote stress detection. The developed algorithms were tested against human subject data collected under two physiological conditions using the modified Trier Social Stress Test (TSST) and the Affective Stress Response Test (ASRT). This research provides evidence that the variability in remotely-acquired blood wave (BW) signals can be used for stress (high and mild) detection, and as a guide for further development of a real-time remote stress detection system based on remote HRV and dPTT.

UWB delay and multiply receiver

DOEpatents

Dallum, Gregory E.; Pratt, Garth C.; Haugen, Peter C.; Romero, Carlos E.

2013-09-10

An ultra-wideband (UWB) delay and multiply receiver is formed of a receive antenna; a variable gain attenuator connected to the receive antenna; a signal splitter connected to the variable gain attenuator; a multiplier having one input connected to an undelayed signal from the signal splitter and another input connected to a delayed signal from the signal splitter, the delay between the splitter signals being equal to the spacing between pulses from a transmitter whose pulses are being received by the receive antenna; a peak detection circuit connected to the output of the multiplier and connected to the variable gain attenuator to control the variable gain attenuator to maintain a constant amplitude output from the multiplier; and a digital output circuit connected to the output of the multiplier.

Multifractal Properties of Process Control Variables

NASA Astrophysics Data System (ADS)

Domański, Paweł D.

2017-06-01

Control system is an inevitable element of any industrial installation. Its quality affects overall process performance significantly. The assessment, whether control system needs any improvement or not, requires relevant and constructive measures. There are various methods, like time domain based, Minimum Variance, Gaussian and non-Gaussian statistical factors, fractal and entropy indexes. Majority of approaches use time series of control variables. They are able to cover many phenomena. But process complexities and human interventions cause effects that are hardly visible for standard measures. It is shown that the signals originating from industrial installations have multifractal properties and such an analysis may extend standard approach to further observations. The work is based on industrial and simulation data. The analysis delivers additional insight into the properties of control system and the process. It helps to discover internal dependencies and human factors, which are hardly detectable.

Contribution of LFP dynamics to single-neuron spiking variability in motor cortex during movement execution

PubMed Central

Rule, Michael E.; Vargas-Irwin, Carlos; Donoghue, John P.; Truccolo, Wilson

2015-01-01

Understanding the sources of variability in single-neuron spiking responses is an important open problem for the theory of neural coding. This variability is thought to result primarily from spontaneous collective dynamics in neuronal networks. Here, we investigate how well collective dynamics reflected in motor cortex local field potentials (LFPs) can account for spiking variability during motor behavior. Neural activity was recorded via microelectrode arrays implanted in ventral and dorsal premotor and primary motor cortices of non-human primates performing naturalistic 3-D reaching and grasping actions. Point process models were used to quantify how well LFP features accounted for spiking variability not explained by the measured 3-D reach and grasp kinematics. LFP features included the instantaneous magnitude, phase and analytic-signal components of narrow band-pass filtered (δ,θ,α,β) LFPs, and analytic signal and amplitude envelope features in higher-frequency bands. Multiband LFP features predicted single-neuron spiking (1ms resolution) with substantial accuracy as assessed via ROC analysis. Notably, however, models including both LFP and kinematics features displayed marginal improvement over kinematics-only models. Furthermore, the small predictive information added by LFP features to kinematic models was redundant to information available in fast-timescale (<100 ms) spiking history. Overall, information in multiband LFP features, although predictive of single-neuron spiking during movement execution, was redundant to information available in movement parameters and spiking history. Our findings suggest that, during movement execution, collective dynamics reflected in motor cortex LFPs primarily relate to sensorimotor processes directly controlling movement output, adding little explanatory power to variability not accounted by movement parameters. PMID:26157365

Processing data base information having nonwhite noise

DOEpatents

Gross, Kenneth C.; Morreale, Patricia

1995-01-01

A method and system for processing a set of data from an industrial process and/or a sensor. The method and system can include processing data from either real or calculated data related to an industrial process variable. One of the data sets can be an artificial signal data set generated by an autoregressive moving average technique. After obtaining two data sets associated with one physical variable, a difference function data set is obtained by determining the arithmetic difference between the two pairs of data sets over time. A frequency domain transformation is made of the difference function data set to obtain Fourier modes describing a composite function data set. A residual function data set is obtained by subtracting the composite function data set from the difference function data set and the residual function data set (free of nonwhite noise) is analyzed by a statistical probability ratio test to provide a validated data base.

NOLM-based all-optical 40 Gbit/s format conversion through sum-frequency generation (SFG) in a PPLN waveguide

NASA Astrophysics Data System (ADS)

Wang, Jian; Sun, Junqiang

2005-11-01

A novel all-optical format conversion scheme from NRZ to RZ based on sum-frequency generation (SFG) in a periodically poled LiNbO 3 (PPLN) waveguide is proposed, using a nonlinear optical loop mirror (NOLM). The conversion mechanism relies on the combination of attenuation and nonlinear phase shift induced on the clockwise signal field during the SFG process. The SFG between pump, and co- and counter- propagating signals in the PPLN waveguide are numerically studied, showing that counter-propagating SFG can be ignored when quasi-phase matching (QPM) for SFG during co-propagating interaction. The nonlinear phase shift induced on the clockwise signal field is analyzed in detail, showing that it is more effective to yield large values for nonlinear phase shift when appropriately phase mismatched for the SFG process. Two tuning schemes are proposed depend on whether the sum-frequency wavelength is variable or fixed. It is found that the latter has a rather wide 3dB signal conversion bandwidth approximately 154nm. Finally, the influence of reversible process of SFG is discussed and the optimum arrangement of pump and signal peak powers is theoretically demonstrated. The result shows that proper power arrangement, pump width, and waveguide length are necessary for achieving a good conversion effect.

The Relationship Between Intensity Coding and Binaural Sensitivity in Adults With Cochlear Implants.

PubMed

Todd, Ann E; Goupell, Matthew J; Litovsky, Ruth Y

Many bilateral cochlear implant users show sensitivity to binaural information when stimulation is provided using a pair of synchronized electrodes. However, there is large variability in binaural sensitivity between and within participants across stimulation sites in the cochlea. It was hypothesized that within-participant variability in binaural sensitivity is in part affected by limitations and characteristics of the auditory periphery which may be reflected by monaural hearing performance. The objective of this study was to examine the relationship between monaural and binaural hearing performance within participants with bilateral cochlear implants. Binaural measures included dichotic signal detection and interaural time difference discrimination thresholds. Diotic signal detection thresholds were also measured. Monaural measures included dynamic range and amplitude modulation detection. In addition, loudness growth was compared between ears. Measures were made at three stimulation sites per listener. Greater binaural sensitivity was found with larger dynamic ranges. Poorer interaural time difference discrimination was found with larger difference between comfortable levels of the two ears. In addition, poorer diotic signal detection thresholds were found with larger differences between the dynamic ranges of the two ears. No relationship was found between amplitude modulation detection thresholds or symmetry of loudness growth and the binaural measures. The results suggest that some of the variability in binaural hearing performance within listeners across stimulation sites can be explained by factors nonspecific to binaural processing. The results are consistent with the idea that dynamic range and comfortable levels relate to peripheral neural survival and the width of the excitation pattern which could affect the fidelity with which central binaural nuclei process bilateral inputs.

System and method of modulating electrical signals using photoconductive wide bandgap semiconductors as variable resistors

DOEpatents

Harris, John Richardson; Caporaso, George J; Sampayan, Stephen E

2013-10-22

A system and method for producing modulated electrical signals. The system uses a variable resistor having a photoconductive wide bandgap semiconductor material construction whose conduction response to changes in amplitude of incident radiation is substantially linear throughout a non-saturation region to enable operation in non-avalanche mode. The system also includes a modulated radiation source, such as a modulated laser, for producing amplitude-modulated radiation with which to direct upon the variable resistor and modulate its conduction response. A voltage source and an output port, are both operably connected to the variable resistor so that an electrical signal may be produced at the output port by way of the variable resistor, either generated by activation of the variable resistor or propagating through the variable resistor. In this manner, the electrical signal is modulated by the variable resistor so as to have a waveform substantially similar to the amplitude-modulated radiation.

Parallel optimization of signal detection in active magnetospheric signal injection experiments

NASA Astrophysics Data System (ADS)

Gowanlock, Michael; Li, Justin D.; Rude, Cody M.; Pankratius, Victor

2018-05-01

Signal detection and extraction requires substantial manual parameter tuning at different stages in the processing pipeline. Time-series data depends on domain-specific signal properties, necessitating unique parameter selection for a given problem. The large potential search space makes this parameter selection process time-consuming and subject to variability. We introduce a technique to search and prune such parameter search spaces in parallel and select parameters for time series filters using breadth- and depth-first search strategies to increase the likelihood of detecting signals of interest in the field of magnetospheric physics. We focus on studying geomagnetic activity in the extremely and very low frequency ranges (ELF/VLF) using ELF/VLF transmissions from Siple Station, Antarctica, received at Québec, Canada. Our technique successfully detects amplified transmissions and achieves substantial speedup performance gains as compared to an exhaustive parameter search. We present examples where our algorithmic approach reduces the search from hundreds of seconds down to less than 1 s, with a ranked signal detection in the top 99th percentile, thus making it valuable for real-time monitoring. We also present empirical performance models quantifying the trade-off between the quality of signal recovered and the algorithm response time required for signal extraction. In the future, improved signal extraction in scenarios like the Siple experiment will enable better real-time diagnostics of conditions of the Earth's magnetosphere for monitoring space weather activity.

Learning to perceptually organize speech signals in native fashion.

PubMed

Nittrouer, Susan; Lowenstein, Joanna H

2010-03-01

The ability to recognize speech involves sensory, perceptual, and cognitive processes. For much of the history of speech perception research, investigators have focused on the first and third of these, asking how much and what kinds of sensory information are used by normal and impaired listeners, as well as how effective amounts of that information are altered by "top-down" cognitive processes. This experiment focused on perceptual processes, asking what accounts for how the sensory information in the speech signal gets organized. Two types of speech signals processed to remove properties that could be considered traditional acoustic cues (amplitude envelopes and sine wave replicas) were presented to 100 listeners in five groups: native English-speaking (L1) adults, 7-, 5-, and 3-year-olds, and native Mandarin-speaking adults who were excellent second-language (L2) users of English. The L2 adults performed more poorly than L1 adults with both kinds of signals. Children performed more poorly than L1 adults but showed disproportionately better performance for the sine waves than for the amplitude envelopes compared to both groups of adults. Sentence context had similar effects across groups, so variability in recognition was attributed to differences in perceptual organization of the sensory information, presumed to arise from native language experience.

Do radio frequencies of medical instruments common in the operating room interfere with near-infrared spectroscopy signals?

NASA Astrophysics Data System (ADS)

Shadgan, Babak; Molavi, Behnam; Reid, W. D.; Dumont, Guy; Macnab, Andrew J.

2010-02-01

Background: Medical and diagnostic applications of near infrared spectroscopy (NIRS) are increasing, especially in operating rooms (OR). Since NIRS is an optical technique, radio frequency (RF) interference from other instruments is unlikely to affect the raw optical data, however, NIRS data processing and signal output could be affected. Methods: We investigated the potential for three common OR instruments: an electrical cautery, an orthopaedic drill and an imaging system, to generate electromagnetic interference (EMI) that could potentially influence NIRS signals. The time of onset and duration of every operation of each device was recorded during surgery. To remove the effects of slow changing physiological variables, we first used a lowpass filter and then selected 2 windows with variable lengths around the moment of device onset. For each instant, variances (energy) and means of the signals in the 2 windows were compared. Results: Twenty patients were studied during ankle surgery. Analysis shows no statistically significant difference in the means and variance of the NIRS signals (p < 0.01) during operation of any of the three devices for all surgeries. Conclusion: This method confirms the instruments evaluated caused no significant interference. NIRS can potentially be used without EMI in clinical environments such as the OR.

Accuracy of heart rate variability estimation by photoplethysmography using an smartphone: Processing optimization and fiducial point selection.

PubMed

Ferrer-Mileo, V; Guede-Fernandez, F; Fernandez-Chimeno, M; Ramos-Castro, J; Garcia-Gonzalez, M A

2015-08-01

This work compares several fiducial points to detect the arrival of a new pulse in a photoplethysmographic signal using the built-in camera of smartphones or a photoplethysmograph. Also, an optimization process for the signal preprocessing stage has been done. Finally we characterize the error produced when we use the best cutoff frequencies and fiducial point for smartphones and photopletysmograph and compare if the error of smartphones can be reasonably be explained by variations in pulse transit time. The results have revealed that the peak of the first derivative and the minimum of the second derivative of the pulse wave have the lowest error. Moreover, for these points, high pass filtering the signal between 0.1 to 0.8 Hz and low pass around 2.7 Hz or 3.5 Hz are the best cutoff frequencies. Finally, the error in smartphones is slightly higher than in a photoplethysmograph.

Transport induced by mean-eddy interaction: II. Analysis of transport processes

NASA Astrophysics Data System (ADS)

Ide, Kayo; Wiggins, Stephen

2015-03-01

We present a framework for the analysis of transport processes resulting from the mean-eddy interaction in a flow. The framework is based on the Transport Induced by the Mean-Eddy Interaction (TIME) method presented in a companion paper (Ide and Wiggins, 2014) [1]. The TIME method estimates the (Lagrangian) transport across stationary (Eulerian) boundaries defined by chosen streamlines of the mean flow. Our framework proceeds after first carrying out a sequence of preparatory steps that link the flow dynamics to the transport processes. This includes the construction of the so-called "instantaneous flux" as the Hovmöller diagram. Transport processes are studied by linking the signals of the instantaneous flux field to the dynamical variability of the flow. This linkage also reveals how the variability of the flow contributes to the transport. The spatio-temporal analysis of the flux diagram can be used to assess the efficiency of the variability in transport processes. We apply the method to the double-gyre ocean circulation model in the situation where the Rossby-wave mode dominates the dynamic variability. The spatio-temporal analysis shows that the inter-gyre transport is controlled by the circulating eddy vortices in the fast eastward jet region, whereas the basin-scale Rossby waves have very little impact.

Characterizing individual differences in reward sensitivity from the brain networks involved in response inhibition.

PubMed

Fuentes-Claramonte, Paola; Ávila, César; Rodríguez-Pujadas, Aina; Costumero, Víctor; Ventura-Campos, Noelia; Bustamante, Juan Carlos; Rosell-Negre, Patricia; Barrós-Loscertales, Alfonso

2016-01-01

A "disinhibited" cognitive profile has been proposed for individuals with high reward sensitivity, characterized by increased engagement in goal-directed responses and reduced processing of negative or unexpected cues, which impairs adequate behavioral regulation after feedback in these individuals. This pattern is manifested through deficits in inhibitory control and/or increases in RT variability. In the present work, we aimed to test whether this profile is associated with the activity of functional networks during a stop-signal task using independent component analysis (ICA). Sixty-one participants underwent fMRI while performing a stop-signal task, during which a manual response had to be inhibited. ICA was used to mainly replicate the functional networks involved in the task (Zhang and Li, 2012): two motor networks involved in the go response, the left and right fronto-parietal networks for stopping, a midline error-processing network, and the default-mode network (DMN), which was further subdivided into its anterior and posterior parts. Reward sensitivity was mainly associated with greater activity of motor networks, reduced activity in the midline network during correct stop trials and, behaviorally, increased RT variability. All these variables explained 36% of variance of the SR scores. This pattern of associations suggests that reward sensitivity involves greater motor engagement in the dominant response, more distractibility and reduced processing of salient or unexpected events, which may lead to disinhibited behavior. Copyright © 2015 Elsevier Inc. All rights reserved.

Feature extraction of performance variables in elite half-pipe snowboarding using body mounted inertial sensors

NASA Astrophysics Data System (ADS)

Harding, J. W.; Small, J. W.; James, D. A.

2007-12-01

Recent analysis of elite-level half-pipe snowboard competition has revealed a number of sport specific key performance variables (KPV's) that correlate well to score. Information on these variables is difficult to acquire and analyse, relying on collection and labour intensive manual post processing of video data. This paper presents the use of inertial sensors as a user-friendly alternative and subsequently implements signal processing routines to ultimately provide automated, sport specific feedback to coaches and athletes. The author has recently shown that the key performance variables (KPV's) of total air-time (TAT) and average degree of rotation (ADR) achieved during elite half-pipe snowboarding competition show strong correlation with an athlete's subjectively judged score. Utilising Micro-Electrochemical System (MEMS) sensors (tri-axial accelerometers) this paper demonstrates that air-time (AT) achieved during half-pipe snowboarding can be detected and calculated accurately using basic signal processing techniques. Characterisation of the variations in aerial acrobatic manoeuvres and the associated calculation of exact degree of rotation (DR) achieved is a likely extension of this research. The technique developed used a two-pass method to detect locations of half-pipe snowboard runs using power density in the frequency domain and subsequently utilises a threshold based search algorithm in the time domain to calculate air-times associated with individual aerial acrobatic manoeuvres. This technique correctly identified the air-times of 100 percent of aerial acrobatic manoeuvres within each half-pipe snowboarding run (n = 92 aerial acrobatic manoeuvres from 4 subjects) and displayed a very strong correlation with a video based reference standard for air-time calculation (r = 0.78 +/- 0.08; p value < 0.0001; SEE = 0.08 ×/÷ 1.16; mean bias = -0.03 +/- 0.02s) (value +/- or ×/÷ 95% CL).

Attentional bias between modalities: effect on the internal clock, memory, and decision stages used in animal time discrimination.

PubMed

Meck, W H

1984-01-01

Both the presentation of unbalanced stimulus probabilities and the insertion of a predictive cue prior to the signal on each trial apparently induces a strong bias to use a particular stimulus modality in order to select a temporal criterion and response rule. This attentional bias toward one modality is apparently independent of the modality of the stimulus being timed and is strongly influenced by stimulus probabilities or prior warning cues. These techniques may be useful to control trial-by-trial sequential effects that influence a subject's perceptual and response biases when signals from more than one modality are used in duration discrimination tasks. Cross-procedural generality of the effects of attentional bias was observed. An asymmetrical modality effect on the latency to begin timing was observed with both the temporal bisection and the peak procedure. The latency to begin timing light signals, but not the latency to begin timing sound signals, was increased when the signal modality was unexpected. This asymmetrical effect was explained with the assumption that sound signals close the mode switch automatically, but that light signals close the mode switch only if attention is directed to the light. The time required to switch attention is reflected in a reduction of the number of pulses from the pacemaker that enter the accumulator. One positive aspect of this work is the demonstration that procedures similar to those used to study human cognition can be used with animal subjects with similar results. Perhaps these similarities will stimulate animal research on the physiological basis of various cognitive capacities. Animal subjects would be preferred for such physiological experimentation if it were established that they possessed some of the cognitive processes described by investigators of human information processing. One of the negative aspects of this work is that only one combination of modalities was used and variables such as stimulus intensity, stimulus probability, and range of signal durations have not been adequately investigated at present. Future work might test additional combinations of modalities and vary stimulus intensity and stimulus probability within a signal detection theory (SDT) framework to determine the effects of these variables on attentional bias.

Anticipatory reward processing in addicted populations: a focus on the monetary incentive delay task.

PubMed

Balodis, Iris M; Potenza, Marc N

2015-03-01

Advances in brain imaging techniques have allowed neurobiological research to temporally analyze signals coding for the anticipation of reward. In addicted populations, both hyporesponsiveness and hyperresponsiveness of brain regions (e.g., ventral striatum) implicated in drug effects and reward system processing have been reported during anticipation of generalized reward. We discuss the current state of knowledge of reward processing in addictive disorders from a widely used and validated task: the monetary incentive delay task. Only studies applying the monetary incentive delay task in addicted and at-risk adult populations are reviewed, with a focus on anticipatory processing and striatal regions activated during task performance as well as the relationship of these regions with individual difference (e.g., impulsivity) and treatment outcome variables. We further review drug influences in challenge studies as a means to examine acute influences on reward processing in abstinent, recreationally using, and addicted populations. Generalized reward processing in addicted and at-risk populations is often characterized by divergent anticipatory signaling in the ventral striatum. Although methodologic and task variations may underlie some discrepant findings, anticipatory signaling in the ventral striatum may also be influenced by smoking status, drug metabolites, and treatment status in addicted populations. Divergent results across abstinent, recreationally using, and addicted populations demonstrate complexities in interpreting findings. Future studies would benefit from focusing on characterizing how impulsivity and other addiction-related features relate to anticipatory striatal signaling over time. Additionally, identifying how anticipatory signals recover or adjust after protracted abstinence will be important in understanding recovery processes. Copyright © 2015 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Local-scale changes in mean and heavy precipitation in Western Europe, climate change or internal variability?

NASA Astrophysics Data System (ADS)

Aalbers, Emma E.; Lenderink, Geert; van Meijgaard, Erik; van den Hurk, Bart J. J. M.

2018-06-01

High-resolution climate information provided by e.g. regional climate models (RCMs) is valuable for exploring the changing weather under global warming, and assessing the local impact of climate change. While there is generally more confidence in the representativeness of simulated processes at higher resolutions, internal variability of the climate system—`noise', intrinsic to the chaotic nature of atmospheric and oceanic processes—is larger at smaller spatial scales as well, limiting the predictability of the climate signal. To quantify the internal variability and robustly estimate the climate signal, large initial-condition ensembles of climate simulations conducted with a single model provide essential information. We analyze a regional downscaling of a 16-member initial-condition ensemble over western Europe and the Alps at 0.11° resolution, similar to the highest resolution EURO-CORDEX simulations. We examine the strength of the forced climate response (signal) in mean and extreme daily precipitation with respect to noise due to internal variability, and find robust small-scale geographical features in the forced response, indicating regional differences in changes in the probability of events. However, individual ensemble members provide only limited information on the forced climate response, even for high levels of global warming. Although the results are based on a single RCM-GCM chain, we believe that they have general value in providing insight in the fraction of the uncertainty in high-resolution climate information that is irreducible, and can assist in the correct interpretation of fine-scale information in multi-model ensembles in terms of a forced response and noise due to internal variability.

Local-scale changes in mean and heavy precipitation in Western Europe, climate change or internal variability?

NASA Astrophysics Data System (ADS)

Aalbers, Emma E.; Lenderink, Geert; van Meijgaard, Erik; van den Hurk, Bart J. J. M.

2017-09-01

High-resolution climate information provided by e.g. regional climate models (RCMs) is valuable for exploring the changing weather under global warming, and assessing the local impact of climate change. While there is generally more confidence in the representativeness of simulated processes at higher resolutions, internal variability of the climate system—`noise', intrinsic to the chaotic nature of atmospheric and oceanic processes—is larger at smaller spatial scales as well, limiting the predictability of the climate signal. To quantify the internal variability and robustly estimate the climate signal, large initial-condition ensembles of climate simulations conducted with a single model provide essential information. We analyze a regional downscaling of a 16-member initial-condition ensemble over western Europe and the Alps at 0.11° resolution, similar to the highest resolution EURO-CORDEX simulations. We examine the strength of the forced climate response (signal) in mean and extreme daily precipitation with respect to noise due to internal variability, and find robust small-scale geographical features in the forced response, indicating regional differences in changes in the probability of events. However, individual ensemble members provide only limited information on the forced climate response, even for high levels of global warming. Although the results are based on a single RCM-GCM chain, we believe that they have general value in providing insight in the fraction of the uncertainty in high-resolution climate information that is irreducible, and can assist in the correct interpretation of fine-scale information in multi-model ensembles in terms of a forced response and noise due to internal variability.

Determinants of cell-to-cell variability in protein kinase signaling.

PubMed

Jeschke, Matthias; Baumgärtner, Stephan; Legewie, Stefan

2013-01-01

Cells reliably sense environmental changes despite internal and external fluctuations, but the mechanisms underlying robustness remain unclear. We analyzed how fluctuations in signaling protein concentrations give rise to cell-to-cell variability in protein kinase signaling using analytical theory and numerical simulations. We characterized the dose-response behavior of signaling cascades by calculating the stimulus level at which a pathway responds ('pathway sensitivity') and the maximal activation level upon strong stimulation. Minimal kinase cascades with gradual dose-response behavior show strong variability, because the pathway sensitivity and the maximal activation level cannot be simultaneously invariant. Negative feedback regulation resolves this trade-off and coordinately reduces fluctuations in the pathway sensitivity and maximal activation. Feedbacks acting at different levels in the cascade control different aspects of the dose-response curve, thereby synergistically reducing the variability. We also investigated more complex, ultrasensitive signaling cascades capable of switch-like decision making, and found that these can be inherently robust to protein concentration fluctuations. We describe how the cell-to-cell variability of ultrasensitive signaling systems can be actively regulated, e.g., by altering the expression of phosphatase(s) or by feedback/feedforward loops. Our calculations reveal that slow transcriptional negative feedback loops allow for variability suppression while maintaining switch-like decision making. Taken together, we describe design principles of signaling cascades that promote robustness. Our results may explain why certain signaling cascades like the yeast pheromone pathway show switch-like decision making with little cell-to-cell variability.

Glutamatergic model psychoses: prediction error, learning, and inference.

PubMed

Corlett, Philip R; Honey, Garry D; Krystal, John H; Fletcher, Paul C

2011-01-01

Modulating glutamatergic neurotransmission induces alterations in conscious experience that mimic the symptoms of early psychotic illness. We review studies that use intravenous administration of ketamine, focusing on interindividual variability in the profundity of the ketamine experience. We will consider this individual variability within a hypothetical model of brain and cognitive function centered upon learning and inference. Within this model, the brains, neural systems, and even single neurons specify expectations about their inputs and responding to violations of those expectations with new learning that renders future inputs more predictable. We argue that ketamine temporarily deranges this ability by perturbing both the ways in which prior expectations are specified and the ways in which expectancy violations are signaled. We suggest that the former effect is predominantly mediated by NMDA blockade and the latter by augmented and inappropriate feedforward glutamatergic signaling. We suggest that the observed interindividual variability emerges from individual differences in neural circuits that normally underpin the learning and inference processes described. The exact source for that variability is uncertain, although it is likely to arise not only from genetic variation but also from subjects' previous experiences and prior learning. Furthermore, we argue that chronic, unlike acute, NMDA blockade alters the specification of expectancies more profoundly and permanently. Scrutinizing individual differences in the effects of acute and chronic ketamine administration in the context of the Bayesian brain model may generate new insights about the symptoms of psychosis; their underlying cognitive processes and neurocircuitry.

USAF/SCEEE (United States Air Force/Southeastern Center for Electrical Engineering Education) Research Initiation Program Research Reports. Volume 1.

DTIC Science & Technology

1985-03-01

comparison of samples would be difficult. (5) A restrictive random sample allows the sample to be irregularly spaced throughout the auxiliary variable space ...looking or downward-looking probes and the very low background radiation from space contribute to high signal-to-noise ratio and allow the...sunshine and earthshine, chemiluminescent processes, and radiation to space , in addition to collisional processes, determine the vibrational

Hydrogen loss during N-15 nuclear reaction analysis of high strength steel

NASA Astrophysics Data System (ADS)

Larochelle, J. S.; Désilets-Benoit, A.; Borduas, G.; Laliberté-Riverin, S.; Roorda, S.; Brochu, M.

2017-10-01

High strength steel samples were analysed by N-15 nuclear reaction analysis in order to detect hydrogen that may have been introduced by electroplating process. The NRA signal decreased during exposure of the ion beam and residual gas analysis showed that the gas was desorbed by the beam interaction. The variable hydrogen signal could be well described as the sum of a constant concentration and a fraction susceptible to second order desorption. A mechanically polished bevel allowed measurements to be extended to a depth of 0.2 mm.

Reduced error signalling in medication-naive children with ADHD: associations with behavioural variability and post-error adaptations

PubMed Central

Plessen, Kerstin J.; Allen, Elena A.; Eichele, Heike; van Wageningen, Heidi; Høvik, Marie Farstad; Sørensen, Lin; Worren, Marius Kalsås; Hugdahl, Kenneth; Eichele, Tom

2016-01-01

Background We examined the blood-oxygen level–dependent (BOLD) activation in brain regions that signal errors and their association with intraindividual behavioural variability and adaptation to errors in children with attention-deficit/hyperactivity disorder (ADHD). Methods We acquired functional MRI data during a Flanker task in medication-naive children with ADHD and healthy controls aged 8–12 years and analyzed the data using independent component analysis. For components corresponding to performance monitoring networks, we compared activations across groups and conditions and correlated them with reaction times (RT). Additionally, we analyzed post-error adaptations in behaviour and motor component activations. Results We included 25 children with ADHD and 29 controls in our analysis. Children with ADHD displayed reduced activation to errors in cingulo-opercular regions and higher RT variability, but no differences of interference control. Larger BOLD amplitude to error trials significantly predicted reduced RT variability across all participants. Neither group showed evidence of post-error response slowing; however, post-error adaptation in motor networks was significantly reduced in children with ADHD. This adaptation was inversely related to activation of the right-lateralized ventral attention network (VAN) on error trials and to task-driven connectivity between the cingulo-opercular system and the VAN. Limitations Our study was limited by the modest sample size and imperfect matching across groups. Conclusion Our findings show a deficit in cingulo-opercular activation in children with ADHD that could relate to reduced signalling for errors. Moreover, the reduced orienting of the VAN signal may mediate deficient post-error motor adaptions. Pinpointing general performance monitoring problems to specific brain regions and operations in error processing may help to guide the targets of future treatments for ADHD. PMID:26441332

Processes Affecting the Variability of Fluorescence Signals from Benthic Targets in Shallow Waters

DTIC Science & Technology

2001-09-30

symbiotic corals. - Limnol. and Oceanogr., 46: 75-85. Gorbunov M.Y. and Falkowski P.G. 2001. Photoreceptors in the cnidarian hosts allow symbiotic...Gorbunov M.Y. and Falkowski P.G. 2001. Photoreceptors in the cnidarian hosts allow symbiotic corals to sense blue moonlight. – Limnol. and Oceanogr

The mechanics behind plant development.

PubMed

Hamant, Olivier; Traas, Jan

2010-01-01

Morphogenesis in living organisms relies on the integration of both biochemical and mechanical signals. During the last decade, attention has been mainly focused on the role of biochemical signals in patterning and morphogenesis, leaving the contribution of mechanics largely unexplored. Fortunately, the development of new tools and approaches has made it possible to re-examine these processes. In plants, shape is defined by two local variables: growth rate and growth direction. At the level of the cell, these variables depend on both the cell wall and turgor pressure. Multidisciplinary approaches have been used to understand how these cellular processes are integrated in the growing tissues. These include quantitative live imaging to measure growth rate and direction in tissues with cellular resolution. In parallel, stress patterns have been artificially modified and their impact on strain and cell behavior been analysed. Importantly, computational models based on analogies with continuum mechanics systems have been useful in interpreting the results. In this review, we will discuss these issues focusing on the shoot apical meristem, a population of stem cells that is responsible for the initiation of the aerial organs of the plant.

Supervised normalization of microarrays

PubMed Central

Mecham, Brigham H.; Nelson, Peter S.; Storey, John D.

2010-01-01

Motivation: A major challenge in utilizing microarray technologies to measure nucleic acid abundances is ‘normalization’, the goal of which is to separate biologically meaningful signal from other confounding sources of signal, often due to unavoidable technical factors. It is intuitively clear that true biological signal and confounding factors need to be simultaneously considered when performing normalization. However, the most popular normalization approaches do not utilize what is known about the study, both in terms of the biological variables of interest and the known technical factors in the study, such as batch or array processing date. Results: We show here that failing to include all study-specific biological and technical variables when performing normalization leads to biased downstream analyses. We propose a general normalization framework that fits a study-specific model employing every known variable that is relevant to the expression study. The proposed method is generally applicable to the full range of existing probe designs, as well as to both single-channel and dual-channel arrays. We show through real and simulated examples that the method has favorable operating characteristics in comparison to some of the most highly used normalization methods. Availability: An R package called snm implementing the methodology will be made available from Bioconductor (http://bioconductor.org). Contact: jstorey@princeton.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:20363728

Essential Role of the m2R-RGS6-IKACh Pathway in Controlling Intrinsic Heart Rate Variability

PubMed Central

Posokhova, Ekaterina; Ng, David; Opel, Aaisha; Masuho, Ikuo; Tinker, Andrew; Biesecker, Leslie G.; Wickman, Kevin; Martemyanov, Kirill A.

2013-01-01

Normal heart function requires generation of a regular rhythm by sinoatrial pacemaker cells and the alteration of this spontaneous heart rate by the autonomic input to match physiological demand. However, the molecular mechanisms that ensure consistent periodicity of cardiac contractions and fine tuning of this process by autonomic system are not completely understood. Here we examined the contribution of the m2R-IKACh intracellular signaling pathway, which mediates the negative chronotropic effect of parasympathetic stimulation, to the regulation of the cardiac pacemaking rhythm. Using isolated heart preparations and single-cell recordings we show that the m2R-IKACh signaling pathway controls the excitability and firing pattern of the sinoatrial cardiomyocytes and determines variability of cardiac rhythm in a manner independent from the autonomic input. Ablation of the major regulator of this pathway, Rgs6, in mice results in irregular cardiac rhythmicity and increases susceptibility to atrial fibrillation. We further identify several human subjects with variants in the RGS6 gene and show that the loss of function in RGS6 correlates with increased heart rate variability. These findings identify the essential role of the m2R-IKACh signaling pathway in the regulation of cardiac sinus rhythm and implicate RGS6 in arrhythmia pathogenesis. PMID:24204714

[A basis for application of cardiac contractility variability in the Evaluation and assessment of exercise and fitness].

PubMed

Bu, Bin; Wang, Aihua; Han, Haijun; Xiao, Shouzhong

2010-06-01

Cardiac contractility variability (CCV) is a new concept which is introduced in the research field of cardiac contractility in recent years, that is to say, there are some disparities between cardiac contractilities when heart contracts. The changing signals of cardiac contractility contain a plenty of information on the cardiovascular function and disorder. In order to collect and analyze the message, we could quantitatively evaluate the tonicity and equilibrium of cardiac sympathetic nerve and parasympathetic nerve, and the effects of bio-molecular mechanism on the cardiovascular activities. By analyzing CCV, we could further understand the background of human being's heritage characteristics, nerve types, the adjusting mechanism, the molecular biology, and the adjustment of cardiac automatic nerve. With the development of the computing techniques, the digital signal processing method and its application in medical field, this analysis has been progressing greatly. By now, the assessment of CCV, just like the analysis of heart rate variability, is mainly via time domain and frequency domain analysis. CCV is one of the latest research fields in human cardiac signals being scarcely reported in the field of sports medicine; however, its research progresses are of important value for cardiac physiology and pathology in sports medicine and rehabilitation medicine.

The technique of entropy optimization in motor current signature analysis and its application in the fault diagnosis of gear transmission

NASA Astrophysics Data System (ADS)

Chen, Xiaoguang; Liang, Lin; Liu, Fei; Xu, Guanghua; Luo, Ailing; Zhang, Sicong

2012-05-01

Nowadays, Motor Current Signature Analysis (MCSA) is widely used in the fault diagnosis and condition monitoring of machine tools. However, although the current signal has lower SNR (Signal Noise Ratio), it is difficult to identify the feature frequencies of machine tools from complex current spectrum that the feature frequencies are often dense and overlapping by traditional signal processing method such as FFT transformation. With the study in the Motor Current Signature Analysis (MCSA), it is found that the entropy is of importance for frequency identification, which is associated with the probability distribution of any random variable. Therefore, it plays an important role in the signal processing. In order to solve the problem that the feature frequencies are difficult to be identified, an entropy optimization technique based on motor current signal is presented in this paper for extracting the typical feature frequencies of machine tools which can effectively suppress the disturbances. Some simulated current signals were made by MATLAB, and a current signal was obtained from a complex gearbox of an iron works made in Luxembourg. In diagnosis the MCSA is combined with entropy optimization. Both simulated and experimental results show that this technique is efficient, accurate and reliable enough to extract the feature frequencies of current signal, which provides a new strategy for the fault diagnosis and the condition monitoring of machine tools.

Networks of Interacting Processes: Relationships Between Drivers and Deltaic Variables to Understand Water and Sediment Transport in Wax Lake Delta, Coastal Louisiana

NASA Astrophysics Data System (ADS)

Sendrowski, A.; Passalacqua, P.; Wagner, W.; Mohrig, D. C.; Meselhe, E. A.; Sadid, K. M.; Castañeda-Moya, E.; Twilley, R.

2017-12-01

Studying distributary channel networks in river deltaic systems provides important insight into deltaic functioning and evolution. This view of networks highlights the physical connection along channels and can also encompass the structural link between channels and deltaic islands (termed structural connectivity). An alternate view of the deltaic network is one composed of interacting processes, such as relationships between external drivers (e.g., river discharge, tides, and wind) and internal deltaic response variables (e.g., water level and sediment concentration). This network, also referred to as process connectivity, is dynamic across space and time, often comprises nonlinear relationships, and contributes to the development of complex channel networks and ecologically rich island platforms. The importance of process connectivity has been acknowledged, however, few studies have directly quantified these network interactions. In this work, we quantify process connections in Wax Lake Delta (WLD), coastal Louisiana. WLD is a naturally prograding delta that serves as an analogue for river diversion projects, thus it provides an excellent setting for understanding the influence of river discharge, tides, and wind on water and sediment in a delta. Time series of water level and sediment concentration were collected in three channels from November 2013 to February 2014, while water level and turbidity were collected on an island from April 2014 to August 2015. Additionally, a model run on WLD bathymetry generated two years of sediment concentration time series in multiple channels. River discharge, tide, and wind measurements were collected from the USGS and NOAA, respectively. We analyze this data with information theory (IT), a set of statistics that measure uncertainty in signals and communication between signals. Using IT, the timescale, strength, and direction of network links are quantified by measuring the synchronization and direct influence from one variable to another. We compare channel and island process connections, which show distinct differences. Our study captures the temporal evolution of variable transport at multiple locations. While WLD is river dominated, tides and wind show unique transport signatures related to tidal spring and neap transitions and wind events.

Variability Analysis of Therapeutic Movements using Wearable Inertial Sensors.

PubMed

López-Nava, Irvin Hussein; Arnrich, Bert; Muñoz-Meléndez, Angélica; Güneysu, Arzu

2017-01-01

A variability analysis of upper limb therapeutic movements using wearable inertial sensors is presented. Five healthy young adults were asked to perform a set of movements using two sensors placed on the upper arm and forearm. Reference data were obtained from three therapists. The goal of the study is to determine an intra and inter-group difference between a number of given movements performed by young people with respect to the movements of therapists. This effort is directed toward studying other groups characterized by motion impairments, and it is relevant to obtain a quantified measure of the quality of movement of a patient to follow his/her recovery. The sensor signals were processed by applying two approaches, time-domain features and similarity distance between each pair of signals. The data analysis was divided into classification and variability using features and distances calculated previously. The classification analysis was made to determine if the movements performed by the test subjects of both groups are distinguishable among them. The variability analysis was conducted to measure the similarity of the movements. According to the results, the flexion/extension movement had a high intra-group variability. In addition, meaningful information were provided in terms of change of velocity and rotational motions for each individual.

Characterization of large-scale fluctuations and short-term variability of Seine river daily streamflow (France) over the period 1950-2008 by empirical mode decomposition and the Hilbert-Huang transform

NASA Astrophysics Data System (ADS)

Massei, N.; Fournier, M.

2010-12-01

Daily Seine river flow from 1950 to 2008 was analyzed using Hilbert-Huang Tranform (HHT). For the last ten years, this method which combines the so-called Empirical Mode Decomposition (EMD) multiresolution analysis and the Hilbert transform has proven its efficiency for the analysis of transient oscillatory signals, although the mathematical definition of the EMD is not totally established yet. HHT also provides an interesting alternative to other time-frequency or time-scale analysis of non-stationary signals, the most famous of which being wavelet-based approaches. In this application of HHT to the analysis of the hydrological variability of the Seine river, we seek to characterize the interannual patterns of daily flow, differenciate them from the short-term dynamics and eventually interpret them in the context of regional climate regime fluctuations. In this aim, HHT is also applied to the North-Atlantic Oscillation (NAO) through the annual winter-months NAO index time series. For both hydrological and climatic signals, dominant variability scales are extracted and their temporal variations analyzed by determination of the intantaneous frequency of each component. When compared to previous ones obtained from continuous wavelet transform (CWT) on the same data, HHT results highlighted the same scales and somewhat the same internal components for each signal. However, HHT allowed the identification and extraction of much more similar features during the 1950-2008 period (e.g., around 7-yr, between NAO and Seine flow than what was obtained from CWT, which comes to say that variability scales in flow likely to originate from climatic regime fluctuations were much properly identified in river flow. In addition, a more accurate determination of singularities in the natural processes analyzed were authorized by HHT compared to CWT, in which case the time-frequency resolution partly depends on the basic properties of the filter (i.e., the reference wavelet chosen initially). Compared to CWT or even to discrete wavelet multiresolution analysis, HHT is auto-adaptive, non-parametric, allows an orthogonal decomposition of the signal analyzed and provides a more accurate estimation of changing variability scales across time for highly transient signals.

Suspended sediment load in northwestern South America (Colombia): A new view on variability and fluxes into the Caribbean Sea

NASA Astrophysics Data System (ADS)

Restrepo López, Juan Camilo; Orejarena R, Andrés F.; Torregroza, Ana Carolina

2017-12-01

Monthly averaged suspended sediment load data from seven rivers in northern Colombia (Caribbean alluvial plain) draining into the Caribbean Sea were analyzed to quantify magnitudes, estimate long-term trends, and evaluate variability patterns of suspended sediment load. Collectively these rivers deliver an average of around 146.3 × 106 t yr-1 of suspended sediments to the Colombian Caribbean coast. The largest sediment supply is provided by the Magdalena River, with a mean suspended sediment load of 142.6 × 106 t yr-1, or 38% of the total fluvial discharge estimated for the whole Caribbean littoral zone. Between 2000 and 2010, the annual suspended sediment load of these rivers increased by as much as 36%. Wavelet spectral analyses identified periods of intense variability between 1987-1990 and 1994-2002, where major oscillation processes appeared simultaneously. The semi-annual, annual and quasi-decadal bands are the main factors controlling suspended sediment load variability in fluvial systems, whereas the quasi-biennial and interannual bands constitute second-order sources of variability. The climatic and oceanographic drivers of the oscillations identified through wavelet spectral analyses define a signal of medium-long-term variability for the suspended sediment load, while the physiographic and environmental characteristics of the basins determine their ability to magnify, attenuate or modify this signal.

Partitioning the impact of environment and spatial structure on alpha and beta components of taxonomic, functional, and phylogenetic diversity in European ants.

PubMed

Arnan, Xavier; Cerdá, Xim; Retana, Javier

2015-01-01

We analyze the relative contribution of environmental and spatial variables to the alpha and beta components of taxonomic (TD), phylogenetic (PD), and functional (FD) diversity in ant communities found along different climate and anthropogenic disturbance gradients across western and central Europe, in order to assess the mechanisms structuring ant biodiversity. To this aim we calculated alpha and beta TD, PD, and FD for 349 ant communities, which included a total of 155 ant species; we examined 10 functional traits and phylogenetic relatedness. Variation partitioning was used to examine how much variation in ant diversity was explained by environmental and spatial variables. Autocorrelation in diversity measures and each trait's phylogenetic signal were also analyzed. We found strong autocorrelation in diversity measures. Both environmental and spatial variables significantly contributed to variation in TD, PD, and FD at both alpha and beta scales; spatial structure had the larger influence. The different facets of diversity showed similar patterns along environmental gradients. Environment explained a much larger percentage of variation in FD than in TD or PD. All traits demonstrated strong phylogenetic signals. Our results indicate that environmental filtering and dispersal limitations structure all types of diversity in ant communities. Strong dispersal limitations appear to have led to clustering of TD, PD, and FD in western and central Europe, probably because different historical and evolutionary processes generated different pools of species. Remarkably, these three facets of diversity showed parallel patterns along environmental gradients. Trait-mediated species sorting and niche conservatism appear to structure ant diversity, as evidenced by the fact that more variation was explained for FD and that all traits had strong phylogenetic signals. Since environmental variables explained much more variation in FD than in PD, functional diversity should be a better indicator of community assembly processes than phylogenetic diversity.

Steel characteristics measurement system using Barkhausen jump sum rate and magnetic field intensity and method of using same

DOEpatents

Kohn, Gabriel; Hicho, George; Swartzendruber, Lydon

1997-01-01

A steel hardness measurement system and method of using same are provided for measuring at least one mechanical or magnetic characteristic of a ferromagnetic sample as a function of at least one magnetic characteristic of the sample. A magnetic field generator subjects the sample to a variable external magnetic field. The magnetic field intensity of the magnetic field generated by the magnetic field generating means is measured and a signal sensor is provided for measuring Barkhausen signals from the sample when the sample is subjected to the external magnetic field. A signal processing unit calculates a jump sum rate first moment as a function of the Barkhausen signals measured by the signal sensor and the magnetic field intensity, and for determining the at least one mechanical or magnetic characteristic as a function of the jump sum rate first moment.

Steel characteristics measurement system using Barkhausen jump sum rate and magnetic field intensity and method of using same

DOEpatents

Kohn, G.; Hicho, G.; Swartzendruber, L.

1997-04-08

A steel hardness measurement system and method of using same are provided for measuring at least one mechanical or magnetic characteristic of a ferromagnetic sample as a function of at least one magnetic characteristic of the sample. A magnetic field generator subjects the sample to a variable external magnetic field. The magnetic field intensity of the magnetic field generated by the magnetic field generating means is measured and a signal sensor is provided for measuring Barkhausen signals from the sample when the sample is subjected to the external magnetic field. A signal processing unit calculates a jump sum rate first moment as a function of the Barkhausen signals measured by the signal sensor and the magnetic field intensity, and for determining the at least one mechanical or magnetic characteristic as a function of the jump sum rate first moment. 7 figs.

Apparatus and method for prevention of cracking in welded brittle alloys

DOEpatents

Kronberg, James W.; Younkins, Robert M.

2000-01-01

An apparatus and method for reducing cracking in a heated material as the material cools. The apparatus includes a variable frequency electric signal generator that is coupled to a transducer. The transducer produces a variable frequency acoustic signal in response to the variable frequency electric signal, which is applied to the heated material to reduce cracking as the material cools.

Improving EEG-Based Motor Imagery Classification for Real-Time Applications Using the QSA Method.

PubMed

Batres-Mendoza, Patricia; Ibarra-Manzano, Mario A; Guerra-Hernandez, Erick I; Almanza-Ojeda, Dora L; Montoro-Sanjose, Carlos R; Romero-Troncoso, Rene J; Rostro-Gonzalez, Horacio

2017-01-01

We present an improvement to the quaternion-based signal analysis (QSA) technique to extract electroencephalography (EEG) signal features with a view to developing real-time applications, particularly in motor imagery (IM) cognitive processes. The proposed methodology (iQSA, improved QSA) extracts features such as the average, variance, homogeneity, and contrast of EEG signals related to motor imagery in a more efficient manner (i.e., by reducing the number of samples needed to classify the signal and improving the classification percentage) compared to the original QSA technique. Specifically, we can sample the signal in variable time periods (from 0.5 s to 3 s, in half-a-second intervals) to determine the relationship between the number of samples and their effectiveness in classifying signals. In addition, to strengthen the classification process a number of boosting-technique-based decision trees were implemented. The results show an 82.30% accuracy rate for 0.5 s samples and 73.16% for 3 s samples. This is a significant improvement compared to the original QSA technique that offered results from 33.31% to 40.82% without sampling window and from 33.44% to 41.07% with sampling window, respectively. We can thus conclude that iQSA is better suited to develop real-time applications.

Improving EEG-Based Motor Imagery Classification for Real-Time Applications Using the QSA Method

PubMed Central

Batres-Mendoza, Patricia; Guerra-Hernandez, Erick I.; Almanza-Ojeda, Dora L.; Montoro-Sanjose, Carlos R.

2017-01-01

We present an improvement to the quaternion-based signal analysis (QSA) technique to extract electroencephalography (EEG) signal features with a view to developing real-time applications, particularly in motor imagery (IM) cognitive processes. The proposed methodology (iQSA, improved QSA) extracts features such as the average, variance, homogeneity, and contrast of EEG signals related to motor imagery in a more efficient manner (i.e., by reducing the number of samples needed to classify the signal and improving the classification percentage) compared to the original QSA technique. Specifically, we can sample the signal in variable time periods (from 0.5 s to 3 s, in half-a-second intervals) to determine the relationship between the number of samples and their effectiveness in classifying signals. In addition, to strengthen the classification process a number of boosting-technique-based decision trees were implemented. The results show an 82.30% accuracy rate for 0.5 s samples and 73.16% for 3 s samples. This is a significant improvement compared to the original QSA technique that offered results from 33.31% to 40.82% without sampling window and from 33.44% to 41.07% with sampling window, respectively. We can thus conclude that iQSA is better suited to develop real-time applications. PMID:29348744

The Relationship Between Intensity Coding and Binaural Sensitivity in Adults With Cochlear Implants

PubMed Central

Todd, Ann E.; Goupell, Matthew J.; Litovsky, Ruth Y.

2016-01-01

Objectives Many bilateral cochlear implant users show sensitivity to binaural information when stimulation is provided using a pair of synchronized electrodes. However, there is large variability in binaural sensitivity between and within participants across stimulation sites in the cochlea. It was hypothesized that within-participant variability in binaural sensitivity is in part affected by limitations and characteristics of the auditory periphery which may be reflected by monaural hearing performance. The objective of this study was to examine the relationship between monaural and binaural hearing performance within participants with bilateral cochlear implants. Design Binaural measures included dichotic signal detection and interaural time difference discrimination thresholds. Diotic signal detection thresholds were also measured. Monaural measures included dynamic range and amplitude modulation detection. In addition, loudness growth was compared between ears. Measures were made at three stimulation sites per listener. Results Greater binaural sensitivity was found with larger dynamic ranges. Poorer interaural time difference discrimination was found with larger difference between comfortable levels of the two ears. In addition, poorer diotic signal detection thresholds were found with larger differences between the dynamic ranges of the two ears. No relationship was found between amplitude modulation detection thresholds or symmetry of loudness growth and the binaural measures. Conclusions The results suggest that some of the variability in binaural hearing performance within listeners across stimulation sites can be explained by factors non-specific to binaural processing. The results are consistent with the idea that dynamic range and comfortable levels relate to peripheral neural survival and the width of the excitation pattern which could affect the fidelity with which central binaural nuclei process bilateral inputs. PMID:27787393

The transfer function of neuron spike.

PubMed

Palmieri, Igor; Monteiro, Luiz H A; Miranda, Maria D

2015-08-01

The mathematical modeling of neuronal signals is a relevant problem in neuroscience. The complexity of the neuron behavior, however, makes this problem a particularly difficult task. Here, we propose a discrete-time linear time-invariant (LTI) model with a rational function in order to represent the neuronal spike detected by an electrode located in the surroundings of the nerve cell. The model is presented as a cascade association of two subsystems: one that generates an action potential from an input stimulus, and one that represents the medium between the cell and the electrode. The suggested approach employs system identification and signal processing concepts, and is dissociated from any considerations about the biophysical processes of the neuronal cell, providing a low-complexity alternative to model the neuronal spike. The model is validated by using in vivo experimental readings of intracellular and extracellular signals. A computational simulation of the model is presented in order to assess its proximity to the neuronal signal and to observe the variability of the estimated parameters. The implications of the results are discussed in the context of spike sorting. Copyright © 2015 Elsevier Ltd. All rights reserved.

Mandarin Chinese Tone Identification in Cochlear Implants: Predictions from Acoustic Models

PubMed Central

Morton, Kenneth D.; Torrione, Peter A.; Throckmorton, Chandra S.; Collins, Leslie M.

2015-01-01

It has been established that current cochlear implants do not supply adequate spectral information for perception of tonal languages. Comprehension of a tonal language, such as Mandarin Chinese, requires recognition of lexical tones. New strategies of cochlear stimulation such as variable stimulation rate and current steering may provide the means of delivering more spectral information and thus may provide the auditory fine structure required for tone recognition. Several cochlear implant signal processing strategies are examined in this study, the continuous interleaved sampling (CIS) algorithm, the frequency amplitude modulation encoding (FAME) algorithm, and the multiple carrier frequency algorithm (MCFA). These strategies provide different types and amounts of spectral information. Pattern recognition techniques can be applied to data from Mandarin Chinese tone recognition tasks using acoustic models as a means of testing the abilities of these algorithms to transmit the changes in fundamental frequency indicative of the four lexical tones. The ability of processed Mandarin Chinese tones to be correctly classified may predict trends in the effectiveness of different signal processing algorithms in cochlear implants. The proposed techniques can predict trends in performance of the signal processing techniques in quiet conditions but fail to do so in noise. PMID:18706497

Music preferences with hearing aids: effects of signal properties, compression settings, and listener characteristics.

PubMed

Croghan, Naomi B H; Arehart, Kathryn H; Kates, James M

2014-01-01

Current knowledge of how to design and fit hearing aids to optimize music listening is limited. Many hearing-aid users listen to recorded music, which often undergoes compression limiting (CL) in the music industry. Therefore, hearing-aid users may experience twofold effects of compression when listening to recorded music: music-industry CL and hearing-aid wide dynamic-range compression (WDRC). The goal of this study was to examine the roles of input-signal properties, hearing-aid processing, and individual variability in the perception of recorded music, with a focus on the effects of dynamic-range compression. A group of 18 experienced hearing-aid users made paired-comparison preference judgments for classical and rock music samples using simulated hearing aids. Music samples were either unprocessed before hearing-aid input or had different levels of music-industry CL. Hearing-aid conditions included linear gain and individually fitted WDRC. Combinations of four WDRC parameters were included: fast release time (50 msec), slow release time (1,000 msec), three channels, and 18 channels. Listeners also completed several psychophysical tasks. Acoustic analyses showed that CL and WDRC reduced temporal envelope contrasts, changed amplitude distributions across the acoustic spectrum, and smoothed the peaks of the modulation spectrum. Listener judgments revealed that fast WDRC was least preferred for both genres of music. For classical music, linear processing and slow WDRC were equally preferred, and the main effect of number of channels was not significant. For rock music, linear processing was preferred over slow WDRC, and three channels were preferred to 18 channels. Heavy CL was least preferred for classical music, but the amount of CL did not change the patterns of WDRC preferences for either genre. Auditory filter bandwidth as estimated from psychophysical tuning curves was associated with variability in listeners' preferences for classical music. Fast, multichannel WDRC often leads to poor music quality, whereas linear processing or slow WDRC are generally preferred. Furthermore, the effect of WDRC is more important for music preferences than music-industry CL applied to signals before the hearing-aid input stage. Variability in hearing-aid users' perceptions of music quality may be partially explained by frequency resolution abilities.

Differentially Variable Component Analysis (dVCA): Identifying Multiple Evoked Components using Trial-to-Trial Variability

NASA Technical Reports Server (NTRS)

Knuth, Kevin H.; Shah, Ankoor S.; Truccolo, Wilson; Ding, Ming-Zhou; Bressler, Steven L.; Schroeder, Charles E.

2003-01-01

Electric potentials and magnetic fields generated by ensembles of synchronously active neurons in response to external stimuli provide information essential to understanding the processes underlying cognitive and sensorimotor activity. Interpreting recordings of these potentials and fields is difficult as each detector records signals simultaneously generated by various regions throughout the brain. We introduce the differentially Variable Component Analysis (dVCA) algorithm, which relies on trial-to-trial variability in response amplitude and latency to identify multiple components. Using simulations we evaluate the importance of response variability to component identification, the robustness of dVCA to noise, and its ability to characterize single-trial data. Finally, we evaluate the technique using visually evoked field potentials recorded at incremental depths across the layers of cortical area VI, in an awake, behaving macaque monkey.

The zipper mechanism in phagocytosis: energetic requirements and variability in phagocytic cup shape

PubMed Central

2010-01-01

Background Phagocytosis is the fundamental cellular process by which eukaryotic cells bind and engulf particles by their cell membrane. Particle engulfment involves particle recognition by cell-surface receptors, signaling and remodeling of the actin cytoskeleton to guide the membrane around the particle in a zipper-like fashion. Despite the signaling complexity, phagocytosis also depends strongly on biophysical parameters, such as particle shape, and the need for actin-driven force generation remains poorly understood. Results Here, we propose a novel, three-dimensional and stochastic biophysical model of phagocytosis, and study the engulfment of particles of various sizes and shapes, including spiral and rod-shaped particles reminiscent of bacteria. Highly curved shapes are not taken up, in line with recent experimental results. Furthermore, we surprisingly find that even without actin-driven force generation, engulfment proceeds in a large regime of parameter values, albeit more slowly and with highly variable phagocytic cups. We experimentally confirm these predictions using fibroblasts, transfected with immunoreceptor FcγRIIa for engulfment of immunoglobulin G-opsonized particles. Specifically, we compare the wild-type receptor with a mutant receptor, unable to signal to the actin cytoskeleton. Based on the reconstruction of phagocytic cups from imaging data, we indeed show that cells are able to engulf small particles even without support from biological actin-driven processes. Conclusions This suggests that biochemical pathways render the evolutionary ancient process of phagocytic highly robust, allowing cells to engulf even very large particles. The particle-shape dependence of phagocytosis makes a systematic investigation of host-pathogen interactions and an efficient design of a vehicle for drug delivery possible. PMID:21059234

Optical signal processing techniques and applications of optical phase modulation in high-speed communication systems

NASA Astrophysics Data System (ADS)

Deng, Ning

In recent years, optical phase modulation has attracted much research attention in the field of fiber optic communications. Compared with the traditional optical intensity-modulated signal, one of the main merits of the optical phase-modulated signal is the better transmission performance. For optical phase modulation, in spite of the comprehensive study of its transmission performance, only a little research has been carried out in terms of its functions, applications and signal processing for future optical networks. These issues are systematically investigated in this thesis. The research findings suggest that optical phase modulation and its signal processing can greatly facilitate flexible network functions and high bandwidth which can be enjoyed by end users. In the thesis, the most important physical-layer technology, signal processing and multiplexing, are investigated with optical phase-modulated signals. Novel and advantageous signal processing and multiplexing approaches are proposed and studied. Experimental investigations are also reported and discussed in the thesis. Optical time-division multiplexing and demultiplexing. With the ever-increasing demand on communication bandwidth, optical time division multiplexing (OTDM) is an effective approach to upgrade the capacity of each wavelength channel in current optical systems. OTDM multiplexing can be simply realized, however, the demultiplexing requires relatively complicated signal processing and stringent timing control, and thus hinders its practicability. To tackle this problem, in this thesis a new OTDM scheme with hybrid DPSK and OOK signals is proposed. Experimental investigation shows this scheme can greatly enhance the demultiplexing timing misalignment and improve the demultiplexing performance, and thus make OTDM more practical and cost effective. All-optical signal processing. In current and future optical communication systems and networks, the data rate per wavelength has been approaching the speed limitation of electronics. Thus, all-optical signal processing techniques are highly desirable to support the necessary optical switching functionalities in future ultrahigh-speed optical packet-switching networks. To cope with the wide use of optical phase-modulated signals, in the thesis, an all-optical logic for DPSK or PSK input signals is developed, for the first time. Based on four-wave mixing in semiconductor optical amplifier, the structure of the logic gate is simple, compact, and capable of supporting ultrafast operation. In addition to the general logic processing, a simple label recognition scheme, as a specific signal processing function, is proposed for phase-modulated label signals. The proposed scheme can recognize any incoming label pattern according to the local pattern, and is potentially capable of handling variable-length label patterns. Optical access network with multicast overlay and centralized light sources. In the arena of optical access networks, wavelength division multiplexing passive optical network (WDM-PON) is a promising technology to deliver high-speed data traffic. However, most of proposed WDM-PONs only support conventional point-to-point service, and cannot meet the requirement of increasing demand on broadcast and multicast service. In this thesis, a simple network upgrade is proposed based on the traditional PON architecture to support both point-to-point and multicast service. In addition, the two service signals are modulated on the same lightwave carrier. The upstream signal is also remodulated on the same carrier at the optical network unit, which can significantly relax the requirement on wavelength management at the network unit.

A Binaural CI Research Platform for Oticon Medical SP/XP Implants Enabling ITD/ILD and Variable Rate Processing

PubMed Central

Adiloğlu, K.; Herzke, T.

2015-01-01

We present the first portable, binaural, real-time research platform compatible with Oticon Medical SP and XP generation cochlear implants. The platform consists of (a) a pair of behind-the-ear devices, each containing front and rear calibrated microphones, (b) a four-channel USB analog-to-digital converter, (c) real-time PC-based sound processing software called the Master Hearing Aid, and (d) USB-connected hardware and output coils capable of driving two implants simultaneously. The platform is capable of processing signals from the four microphones simultaneously and producing synchronized binaural cochlear implant outputs that drive two (bilaterally implanted) SP or XP implants. Both audio signal preprocessing algorithms (such as binaural beamforming) and novel binaural stimulation strategies (within the implant limitations) can be programmed by researchers. When the whole research platform is combined with Oticon Medical SP implants, interaural electrode timing can be controlled on individual electrodes to within ±1 µs and interaural electrode energy differences can be controlled to within ±2%. Hence, this new platform is particularly well suited to performing experiments related to interaural time differences in combination with interaural level differences in real-time. The platform also supports instantaneously variable stimulation rates and thereby enables investigations such as the effect of changing the stimulation rate on pitch perception. Because the processing can be changed on the fly, researchers can use this platform to study perceptual changes resulting from different processing strategies acutely. PMID:26721923

A Binaural CI Research Platform for Oticon Medical SP/XP Implants Enabling ITD/ILD and Variable Rate Processing.

PubMed

Backus, B; Adiloğlu, K; Herzke, T

2015-12-30

We present the first portable, binaural, real-time research platform compatible with Oticon Medical SP and XP generation cochlear implants. The platform consists of (a) a pair of behind-the-ear devices, each containing front and rear calibrated microphones, (b) a four-channel USB analog-to-digital converter, (c) real-time PC-based sound processing software called the Master Hearing Aid, and (d) USB-connected hardware and output coils capable of driving two implants simultaneously. The platform is capable of processing signals from the four microphones simultaneously and producing synchronized binaural cochlear implant outputs that drive two (bilaterally implanted) SP or XP implants. Both audio signal preprocessing algorithms (such as binaural beamforming) and novel binaural stimulation strategies (within the implant limitations) can be programmed by researchers. When the whole research platform is combined with Oticon Medical SP implants, interaural electrode timing can be controlled on individual electrodes to within ±1 µs and interaural electrode energy differences can be controlled to within ±2%. Hence, this new platform is particularly well suited to performing experiments related to interaural time differences in combination with interaural level differences in real-time. The platform also supports instantaneously variable stimulation rates and thereby enables investigations such as the effect of changing the stimulation rate on pitch perception. Because the processing can be changed on the fly, researchers can use this platform to study perceptual changes resulting from different processing strategies acutely. © The Author(s) 2015.

Human Language Technology: Opportunities and Challenges

DTIC Science & Technology

2005-01-01

because of the connections to and reliance on signal processing. Audio diarization critically includes indexing of speakers [12], since speaker ...to reduce inter- speaker variability in training. Standard techniques include vocal-tract length normalization, adaptation of acoustic models using...maximum likelihood linear regression (MLLR), and speaker -adaptive training based on MLLR. The acoustic models are mixtures of Gaussians, typically with

Sequence information signal processor for local and global string comparisons

DOEpatents

Peterson, John C.; Chow, Edward T.; Waterman, Michael S.; Hunkapillar, Timothy J.

1997-01-01

A sequence information signal processing integrated circuit chip designed to perform high speed calculation of a dynamic programming algorithm based upon the algorithm defined by Waterman and Smith. The signal processing chip of the present invention is designed to be a building block of a linear systolic array, the performance of which can be increased by connecting additional sequence information signal processing chips to the array. The chip provides a high speed, low cost linear array processor that can locate highly similar global sequences or segments thereof such as contiguous subsequences from two different DNA or protein sequences. The chip is implemented in a preferred embodiment using CMOS VLSI technology to provide the equivalent of about 400,000 transistors or 100,000 gates. Each chip provides 16 processing elements, and is designed to provide 16 bit, two's compliment operation for maximum score precision of between -32,768 and +32,767. It is designed to provide a comparison between sequences as long as 4,194,304 elements without external software and between sequences of unlimited numbers of elements with the aid of external software. Each sequence can be assigned different deletion and insertion weight functions. Each processor is provided with a similarity measure device which is independently variable. Thus, each processor can contribute to maximum value score calculation using a different similarity measure.

Detection of buried mines with seismic sonar

NASA Astrophysics Data System (ADS)

Muir, Thomas G.; Baker, Steven R.; Gaghan, Frederick E.; Fitzpatrick, Sean M.; Hall, Patrick W.; Sheetz, Kraig E.; Guy, Jeremie

2003-10-01

Prior research on seismo-acoustic sonar for detection of buried targets [J. Acoust. Soc. Am. 103, 2333-2343 (1998)] has continued with examination of the target strengths of buried test targets as well as targets of interest, and has also examined detection and confirmatory classification of these, all using arrays of seismic sources and receivers as well as signal processing techniques to enhance target recognition. The target strengths of two test targets (one a steel gas bottle, the other an aluminum powder keg), buried in a sand beach, were examined as a function of internal mass load, to evaluate theory developed for seismic sonar target strength [J. Acoust. Soc. Am. 103, 2344-2353 (1998)]. The detection of buried naval and military targets of interest was achieved with an array of 7 shaker sources and 5, three-axis seismometers, at a range of 5 m. Vector polarization filtering was the main signal processing technique for detection. It capitalizes on the fact that the vertical and horizontal components in Rayleigh wave echoes are 90 deg out of phase, enabling complex variable processing to obtain the imaginary component of the signal power versus time, which is unique to Rayleigh waves. Gabor matrix processing of this signal component was the main technique used to determine whether the target was man-made or just a natural target in the environment. [Work sponsored by ONR.

Correction of I/Q channel errors without calibration

DOEpatents

Doerry, Armin W.; Tise, Bertice L.

2002-01-01

A method of providing a balanced demodular output for a signal such as a Doppler radar having an analog pulsed input; includes adding a variable phase shift as a function of time to the input signal, applying the phase shifted input signal to a demodulator; and generating a baseband signal from the input signal. The baseband signal is low-pass filtered and converted to a digital output signal. By removing the variable phase shift from the digital output signal, a complex data output is formed that is representative of the output of a balanced demodulator.

Acoustic emission characteristics of a single cylinder diesel generator at various loads and with a failing injector

NASA Astrophysics Data System (ADS)

Dykas, Brian; Harris, James

2017-09-01

Acoustic emission sensing techniques have been applied in recent years to dynamic machinery with varying degrees of success in diagnosing various component faults and distinguishing between operating conditions. This work explores basic properties of acoustic emission signals measured on a small single cylinder diesel engine in a laboratory setting. As reported in other works in the open literature, the measured acoustic emission on the engine is mostly continuous mode and individual burst events are generally not readily identifiable. Therefore, the AE are processed into the local (instantaneous) root mean square (rms) value of the signal which is averaged over many cycles to obtain a mean rms AE in the crank angle domain. Crank-resolved spectral representation of the AE is also given but rigorous investigation of the AE spectral qualities is left to future study. Cycle-to-cycle statistical dispersion of the AE signal is considered to highlight highly variable engine processes. Engine speed was held constant but load conditions are varied to investigate AE signal sensitivity to operating condition. Furthermore, during the course of testing the fuel injector developed a fault and acoustic emission signals were captured and several signal attributes were successful in distinguishing this altered condition. The sampling and use of instantaneous rms acoustic emission signal demonstrated promise for non-intrusive and economical change detection of engine injection, combustion and valve events.

Rho'ing in and out of cells: viral interactions with Rho GTPase signaling.

PubMed

Van den Broeke, Céline; Jacob, Thary; Favoreel, Herman W

2014-01-01

Rho GTPases are key regulators of actin and microtubule dynamics and organization. Increasing evidence shows that many viruses have evolved diverse interactions with Rho GTPase signaling and manipulate them for their own benefit. In this review, we discuss how Rho GTPase signaling interferes with many steps in the viral replication cycle, especially entry, replication, and spread. Seen the diversity between viruses, it is not surprising that there is considerable variability in viral interactions with Rho GTPase signaling. However, several largely common effects on Rho GTPases and actin architecture and microtubule dynamics have been reported. For some of these processes, the molecular signaling and biological consequences are well documented while for others we just begin to understand them. A better knowledge and identification of common threads in the different viral interactions with Rho GTPase signaling and their ultimate consequences for virus and host may pave the way toward the development of new antiviral drugs that may target different viruses.

Automated process control for plasma etching

NASA Astrophysics Data System (ADS)

McGeown, Margaret; Arshak, Khalil I.; Murphy, Eamonn

1992-06-01

This paper discusses the development and implementation of a rule-based system which assists in providing automated process control for plasma etching. The heart of the system is to establish a correspondence between a particular data pattern -- sensor or data signals -- and one or more modes of failure, i.e., a data-driven monitoring approach. The objective of this rule based system, PLETCHSY, is to create a program combining statistical process control (SPC) and fault diagnosis to help control a manufacturing process which varies over time. This can be achieved by building a process control system (PCS) with the following characteristics. A facility to monitor the performance of the process by obtaining and analyzing the data relating to the appropriate process variables. Process sensor/status signals are input into an SPC module. If trends are present, the SPC module outputs the last seven control points, a pattern which is represented by either regression or scoring. The pattern is passed to the rule-based module. When the rule-based system recognizes a pattern, it starts the diagnostic process using the pattern. If the process is considered to be going out of control, advice is provided about actions which should be taken to bring the process back into control.

A Surrogate Technique for Investigating Deterministic Dynamics in Discrete Human Movement.

PubMed

Taylor, Paul G; Small, Michael; Lee, Kwee-Yum; Landeo, Raul; O'Meara, Damien M; Millett, Emma L

2016-10-01

Entropy is an effective tool for investigation of human movement variability. However, before applying entropy, it can be beneficial to employ analyses to confirm that observed data are not solely the result of stochastic processes. This can be achieved by contrasting observed data with that produced using surrogate methods. Unlike continuous movement, no appropriate method has been applied to discrete human movement. This article proposes a novel surrogate method for discrete movement data, outlining the processes for determining its critical values. The proposed technique reliably generated surrogates for discrete joint angle time series, destroying fine-scale dynamics of the observed signal, while maintaining macro structural characteristics. Comparison of entropy estimates indicated observed signals had greater regularity than surrogates and were not only the result of stochastic but also deterministic processes. The proposed surrogate method is both a valid and reliable technique to investigate determinism in other discrete human movement time series.

Optical sensor for real-time weld defect detection

NASA Astrophysics Data System (ADS)

Ancona, Antonio; Maggipinto, Tommaso; Spagnolo, Vincenzo; Ferrara, Michele; Lugara, Pietro M.

2002-04-01

In this work we present an innovative optical sensor for on- line and non-intrusive welding process monitoring. It is based on the spectroscopic analysis of the optical VIS emission of the welding plasma plume generated in the laser- metal interaction zone. Plasma electron temperature has been measured for different chemical species composing the plume. Temperature signal evolution has been recorded and analyzed during several CO2-laser welding processes, under variable operating conditions. We have developed a suitable software able to real time detect a wide range of weld defects like crater formation, lack of fusion, excessive penetration, seam oxidation. The same spectroscopic approach has been applied for electric arc welding process monitoring. We assembled our optical sensor in a torch for manual Gas Tungsten Arc Welding procedures and tested the prototype in a manufacturing industry production line. Even in this case we found a clear correlation between the signal behavior and the welded joint quality.

Incomes, Attitudes, and Occurrences of Invasive Species: An Application to Signal Crayfish in Sweden

NASA Astrophysics Data System (ADS)

Gren, Ing-Marie; Campos, Monica; Edsman, Lennart; Bohman, Patrik

2009-02-01

This article analyzes and carries out an econometric test of the explanatory power of economic and attitude variables for occurrences of the nonnative signal crayfish in Swedish waters. Signal crayfish are a carrier of plague which threatens the native noble crayfish with extinction. Crayfish are associated with recreational and cultural traditions in Sweden, which may run against environmental preferences for preserving native species. Econometric analysis is carried out using panel data at the municipality level with economic factors and attitudes as explanatory variables, which are derived from a simple dynamic harvesting model. A log-normal model is used for the regression analysis, and the results indicate significant impacts on occurrences of waters with signal crayfish of changes in both economic and attitude variables. Variables reflecting environmental and recreational preferences have unexpected signs, where the former variable has a positive and the latter a negative impact on occurrences of waters with signal crayfish. These effects are, however, counteracted by their respective interaction effect with income.

Joint Maximum Likelihood Time Delay Estimation of Unknown Event-Related Potential Signals for EEG Sensor Signal Quality Enhancement

PubMed Central

Kim, Kyungsoo; Lim, Sung-Ho; Lee, Jaeseok; Kang, Won-Seok; Moon, Cheil; Choi, Ji-Woong

2016-01-01

Electroencephalograms (EEGs) measure a brain signal that contains abundant information about the human brain function and health. For this reason, recent clinical brain research and brain computer interface (BCI) studies use EEG signals in many applications. Due to the significant noise in EEG traces, signal processing to enhance the signal to noise power ratio (SNR) is necessary for EEG analysis, especially for non-invasive EEG. A typical method to improve the SNR is averaging many trials of event related potential (ERP) signal that represents a brain’s response to a particular stimulus or a task. The averaging, however, is very sensitive to variable delays. In this study, we propose two time delay estimation (TDE) schemes based on a joint maximum likelihood (ML) criterion to compensate the uncertain delays which may be different in each trial. We evaluate the performance for different types of signals such as random, deterministic, and real EEG signals. The results show that the proposed schemes provide better performance than other conventional schemes employing averaged signal as a reference, e.g., up to 4 dB gain at the expected delay error of 10°. PMID:27322267

Dimensionality Reduction in Big Data with Nonnegative Matrix Factorization

DTIC Science & Technology

2017-06-20

appli- cations of data mining, signal processing , computer vision, bioinformatics, etc. Fun- damentally, NMF has two main purposes. First, it reduces...shape of the function becomes more spherical because ∂ 2g ∂y2i = 1, ∀i, and g(y) is convex. This part aims to make the post- processing parts more...maxStop = 0 for each thread of computation */; 3 /*Re-scaling variables*/; 4 Q = H√ diag(H)diag(H)T ; q = h√ diag(H) ; 5 /*Solving NQP: minimizingf(x

Waveform Design and Diversity for Advanced Space-Time Adaptive Processing and Multiple Input Multiple Output Systems

DTIC Science & Technology

2012-08-01

It suggests that a smart use of some a-priori information about the operating environment, when processing the received signal and designing the...random variable with the same variance of the backscattering target amplitude αT , and D ( αT , α G T ) is the Kullback − Leibler divergence, see [65...MI . Proof. See Appendix 3.6.6. Thus, we can use the optimization procedure of Algorithm 4 to optimize the Mutual Information between the target

Hammerstein system represention of financial volatility processes

NASA Astrophysics Data System (ADS)

Capobianco, E.

2002-05-01

We show new modeling aspects of stock return volatility processes, by first representing them through Hammerstein Systems, and by then approximating the observed and transformed dynamics with wavelet-based atomic dictionaries. We thus propose an hybrid statistical methodology for volatility approximation and non-parametric estimation, and aim to use the information embedded in a bank of volatility sources obtained by decomposing the observed signal with multiresolution techniques. Scale dependent information refers both to market activity inherent to different temporally aggregated trading horizons, and to a variable degree of sparsity in representing the signal. A decomposition of the expansion coefficients in least dependent coordinates is then implemented through Independent Component Analysis. Based on the described steps, the features of volatility can be more effectively detected through global and greedy algorithms.

High-temperature optical fiber instrumentation for gas flow monitoring in gas turbine engines

NASA Astrophysics Data System (ADS)

Roberts, Adrian; May, Russell G.; Pickrell, Gary R.; Wang, Anbo

2002-02-01

In the design and testing of gas turbine engines, real-time data about such physical variables as temperature, pressure and acoustics are of critical importance. The high temperature environment experienced in the engines makes conventional electronic sensors devices difficult to apply. Therefore, there is a need for innovative sensors that can reliably operate under the high temperature conditions and with the desirable resolution and frequency response. A fiber optic high temperature sensor system for dynamic pressure measurement is presented in this paper. This sensor is based on a new sensor technology - the self-calibrated interferometric/intensity-based (SCIIB) sensor, recently developed at Virginia Tech. State-of-the-art digital signal processing (DSP) methods are applied to process the signal from the sensor to acquire high-speed frequency response.

A scalable SIMD digital signal processor for high-quality multifunctional printer systems

NASA Astrophysics Data System (ADS)

Kang, Hyeong-Ju; Choi, Yongwoo; Kim, Kimo; Park, In-Cheol; Kim, Jung-Wook; Lee, Eul-Hwan; Gahang, Goo-Soo

2005-01-01

This paper describes a high-performance scalable SIMD digital signal processor (DSP) developed for multifunctional printer systems. The DSP supports a variable number of datapaths to cover a wide range of performance and maintain a RISC-like pipeline structure. Many special instructions suitable for image processing algorithms are included in the DSP. Quad/dual instructions are introduced for 8-bit or 16-bit data, and bit-field extraction/insertion instructions are supported to process various data types. Conditional instructions are supported to deal with complex relative conditions efficiently. In addition, an intelligent DMA block is integrated to align data in the course of data reading. Experimental results show that the proposed DSP outperforms a high-end printer-system DSP by at least two times.

Dynamics of chromatic visual system processing differ in complexity between children and adults.

PubMed

Boon, Mei Ying; Suttle, Catherine M; Henry, Bruce I; Dain, Stephen J

2009-06-30

Measures of chromatic contrast sensitivity in children are lower than those of adults. This may be related to immaturities in signal processing at or near threshold. We have found that children's VEPs in response to low contrast supra-threshold chromatic stimuli are more intra-individually variable than those recorded from adults. Here, we report on linear and nonlinear analyses of chromatic VEPs recorded from children and adults. Two measures of signal-to-noise ratio are similar between the adults and children, suggesting that relatively high noise is unlikely to account for the poor clarity of negative and positive peak components in the children's VEPs. Nonlinear analysis indicates higher complexity of adults' than children's chromatic VEPs, at levels of chromatic contrast around and well above threshold.

Some-or-none recollection: Evidence from item and source memory.

PubMed

Onyper, Serge V; Zhang, Yaofei X; Howard, Marc W

2010-05-01

Dual-process theory hypothesizes that recognition memory depends on 2 distinguishable memory signals. Recollection reflects conscious recovery of detailed information about the learning episode. Familiarity reflects a memory signal that is not accompanied by a vivid conscious experience but nonetheless enables participants to distinguish recently experienced probe items from novel ones. This dual-process explanation of recognition memory has gained wide acceptance among cognitive neuroscientists and some cognitive psychologists. Nonetheless, its difficulty in providing a quantitatively satisfactory description of performance has precluded a consensus not only regarding the theoretical structure of recognition memory but also about how to best measure recognition accuracy. In 2 experiments we show that neither the standard formulation of dual-process signal detection (DPSD) theory nor a widely used single-process model called the unequal-variance signal-detection (UVSD) model provides a satisfactory explanation of recognition memory across different types of stimuli (words and travel scenes). In the variable-recollection dual-process (VRDP) model, recollection fails for some old probe items, as in standard formulations of DPSD, but gives rise to a continuous distribution of memory strengths when it succeeds. The VRDP can approximate both the DPSD and UVSD. In both experiments it provides a consistently superior fit across materials to the superset of the DPSD and UVSD. The VRDP offers a simple explanation of the form of conjoint item-source judgments, something neither the DPSD nor UVSD accomplishes. The success of the VRDP supports the core assumptions of dual-process theory by providing an excellent quantitative description of recognition performance across materials and response criteria.

Investigation of column flotation process on sulphide ore using 2-electrode capacitance sensor: The effect of air flow rate and solid percentage

NASA Astrophysics Data System (ADS)

Haryono, Didied; Harjanto, Sri; Wijaya, Rifky; Oediyani, Soesaptri; Nugraha, Harisma; Huda, Mahfudz Al; Taruno, Warsito Purwo

2018-04-01

Investigation of column flotation process on sulphide ore using 2-electrode capacitance sensor is presented in this paper. The effect of air flow rate and solid percentage on column flotation process has been experimentally investigated. The purpose of this paper is to understand the capacitance signal characteristic affected by the air flow rate and the solid percentage which can be used to determine the metallurgical performance. Experiments were performed using a laboratory column flotation cell which has a diameter of 5 cm and the total height of 140 cm. The sintered ceramic sparger and wash water were installed at the bottom and above of the column. Two-electrode concave type capacitance sensor was also installed at a distance of 50 cm from the sparger. The sensor was attached to the outer wall of the column, connected to data acquisition system, manufactured by CTECH Labs Edwar Technology and personal computer for further data processing. Feed consisting ZnS and SiO2 with the ratio of 3:2 was mixed with some reagents to make 1 litre of slurry. The slurry was fed into the aerated column at 100 cm above the sparger with a constant rate and the capacitance signals were captured during the process. In this paper, 7.5 and 10% of solid and 2-4 L/min of air flow rate with 0.5 L/min intervals were used as independent variables. The results show that the capacitance signal characteristics between the 7.5 and 10% of solid are different at any given air flow rate in which the 10% solid produced signals higher than those of 7.5%. Metallurgical performance and capacitance signal exhibit a good correlation.

Phase processing for quantitative susceptibility mapping of regions with large susceptibility and lack of signal.

PubMed

Fortier, Véronique; Levesque, Ives R

2018-06-01

Phase processing impacts the accuracy of quantitative susceptibility mapping (QSM). Techniques for phase unwrapping and background removal have been proposed and demonstrated mostly in brain. In this work, phase processing was evaluated in the context of large susceptibility variations (Δχ) and negligible signal, in particular for susceptibility estimation using the iterative phase replacement (IPR) algorithm. Continuous Laplacian, region-growing, and quality-guided unwrapping were evaluated. For background removal, Laplacian boundary value (LBV), projection onto dipole fields (PDF), sophisticated harmonic artifact reduction for phase data (SHARP), variable-kernel sophisticated harmonic artifact reduction for phase data (V-SHARP), regularization enabled sophisticated harmonic artifact reduction for phase data (RESHARP), and 3D quadratic polynomial field removal were studied. Each algorithm was quantitatively evaluated in simulation and qualitatively in vivo. Additionally, IPR-QSM maps were produced to evaluate the impact of phase processing on the susceptibility in the context of large Δχ with negligible signal. Quality-guided unwrapping was the most accurate technique, whereas continuous Laplacian performed poorly in this context. All background removal algorithms tested resulted in important phase inaccuracies, suggesting that techniques used for brain do not translate well to situations where large Δχ and no or low signal are expected. LBV produced the smallest errors, followed closely by PDF. Results suggest that quality-guided unwrapping should be preferred, with PDF or LBV for background removal, for QSM in regions with large Δχ and negligible signal. This reduces the susceptibility inaccuracy introduced by phase processing. Accurate background removal remains an open question. Magn Reson Med 79:3103-3113, 2017. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Direct measurement of the propagation velocity of defects using coherent X-rays

DOE PAGES

Ulbrandt, Jeffrey G.; Rainville, Meliha G.; Wagenbach, Christa; ...

2016-03-28

The properties of artificially grown thin films are often strongly affected by the dynamic relationships between surface growth processes and subsurface structure. Coherent mixing of X-ray signals promises to provide an approach to better understand such processes. Here, we demonstrate the continuously variable mixing of surface and bulk scattering signals during realtime studies of sputter deposition of a-Si and a-WSi2 films by controlling the X-ray penetration and escape depths in coherent grazing-incidence small-angle X-ray scattering. Under conditions where the X-ray signal comes from both the growth surface and the thin film bulk, oscillations in temporal correlations arise from coherent interferencemore » between scattering from stationary bulk features and from the advancing surface. We also observe evidence that elongated bulk features propagate upwards at the same velocity as the surface. Moreover, a highly surface-sensitive mode is demonstrated that can access the surface dynamics independently of the subsurface structure.« less

Eddy current nondestructive testing device for measuring variable characteristics of a sample utilizing Walsh functions

DOEpatents

Libby, Hugo L.; Hildebrand, Bernard P.

1978-01-01

An eddy current testing device for measuring variable characteristics of a sample generates a signal which varies with variations in such characteristics. A signal expander samples at least a portion of this generated signal and expands the sampled signal on a selected basis of square waves or Walsh functions to produce a plurality of signal components representative of the sampled signal. A network combines these components to provide a display of at least one of the characteristics of the sample.

Relationship Among Signal Fidelity, Hearing Loss, and Working Memory for Digital Noise Suppression.

PubMed

Arehart, Kathryn; Souza, Pamela; Kates, James; Lunner, Thomas; Pedersen, Michael Syskind

2015-01-01

This study considered speech modified by additive babble combined with noise-suppression processing. The purpose was to determine the relative importance of the signal modifications, individual peripheral hearing loss, and individual cognitive capacity on speech intelligibility and speech quality. The participant group consisted of 31 individuals with moderate high-frequency hearing loss ranging in age from 51 to 89 years (mean = 69.6 years). Speech intelligibility and speech quality were measured using low-context sentences presented in babble at several signal-to-noise ratios. Speech stimuli were processed with a binary mask noise-suppression strategy with systematic manipulations of two parameters (error rate and attenuation values). The cumulative effects of signal modification produced by babble and signal processing were quantified using an envelope-distortion metric. Working memory capacity was assessed with a reading span test. Analysis of variance was used to determine the effects of signal processing parameters on perceptual scores. Hierarchical linear modeling was used to determine the role of degree of hearing loss and working memory capacity in individual listener response to the processed noisy speech. The model also considered improvements in envelope fidelity caused by the binary mask and the degradations to envelope caused by error and noise. The participants showed significant benefits in terms of intelligibility scores and quality ratings for noisy speech processed by the ideal binary mask noise-suppression strategy. This benefit was observed across a range of signal-to-noise ratios and persisted when up to a 30% error rate was introduced into the processing. Average intelligibility scores and average quality ratings were well predicted by an objective metric of envelope fidelity. Degree of hearing loss and working memory capacity were significant factors in explaining individual listener's intelligibility scores for binary mask processing applied to speech in babble. Degree of hearing loss and working memory capacity did not predict listeners' quality ratings. The results indicate that envelope fidelity is a primary factor in determining the combined effects of noise and binary mask processing for intelligibility and quality of speech presented in babble noise. Degree of hearing loss and working memory capacity are significant factors in explaining variability in listeners' speech intelligibility scores but not in quality ratings.

Ventromedial prefrontal cortex activity and pathological worry in generalised anxiety disorder.

PubMed

Via, E; Fullana, M A; Goldberg, X; Tinoco-González, D; Martínez-Zalacaín, I; Soriano-Mas, C; Davey, C G; Menchón, J M; Straube, B; Kircher, T; Pujol, J; Cardoner, N; Harrison, B J

2018-05-09

Pathological worry is a hallmark feature of generalised anxiety disorder (GAD), associated with dysfunctional emotional processing. The ventromedial prefrontal cortex (vmPFC) is involved in the regulation of such processes, but the link between vmPFC emotional responses and pathological v. adaptive worry has not yet been examined.AimsTo study the association between worry and vmPFC activity evoked by the processing of learned safety and threat signals. In total, 27 unmedicated patients with GAD and 56 healthy controls (HC) underwent a differential fear conditioning paradigm during functional magnetic resonance imaging. Compared to HC, the GAD group demonstrated reduced vmPFC activation to safety signals and no safety-threat processing differentiation. This response was positively correlated with worry severity in GAD, whereas the same variables showed a negative and weak correlation in HC. Poor vmPFC safety-threat differentiation might characterise GAD, and its distinctive association with GAD worries suggests a neural-based qualitative difference between healthy and pathological worries.Declaration of interestNone.

Sequencing batch-reactor control using Gaussian-process models.

PubMed

Kocijan, Juš; Hvala, Nadja

2013-06-01

This paper presents a Gaussian-process (GP) model for the design of sequencing batch-reactor (SBR) control for wastewater treatment. The GP model is a probabilistic, nonparametric model with uncertainty predictions. In the case of SBR control, it is used for the on-line optimisation of the batch-phases duration. The control algorithm follows the course of the indirect process variables (pH, redox potential and dissolved oxygen concentration) and recognises the characteristic patterns in their time profile. The control algorithm uses GP-based regression to smooth the signals and GP-based classification for the pattern recognition. When tested on the signals from an SBR laboratory pilot plant, the control algorithm provided a satisfactory agreement between the proposed completion times and the actual termination times of the biodegradation processes. In a set of tested batches the final ammonia and nitrate concentrations were below 1 and 0.5 mg L(-1), respectively, while the aeration time was shortened considerably. Copyright © 2013 Elsevier Ltd. All rights reserved.

Design and performance of optimal detectors for guided wave structural health monitoring

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dib, G.; Udpa, L.

2016-01-01

Ultrasonic guided wave measurements in a long term structural health monitoring system are affected by measurement noise, environmental conditions, transducer aging and malfunction. This results in measurement variability which affects detection performance, especially in complex structures where baseline data comparison is required. This paper derives the optimal detector structure, within the framework of detection theory, where a guided wave signal at the sensor is represented by a single feature value that can be used for comparison with a threshold. Three different types of detectors are derived depending on the underlying structure’s complexity: (i) Simple structures where defect reflections can bemore » identified without the need for baseline data; (ii) Simple structures that require baseline data due to overlap of defect scatter with scatter from structural features; (iii) Complex structure with dense structural features that require baseline data. The detectors are derived by modeling the effects of variabilities and uncertainties as random processes. Analytical solutions for the performance of detectors in terms of the probability of detection and false alarm are derived. A finite element model is used to generate guided wave signals and the performance results of a Monte-Carlo simulation are compared with the theoretical performance. initial results demonstrate that the problems of signal complexity and environmental variability can in fact be exploited to improve detection performance.« less

The Paleoclimate Uncertainty Cascade: Tracking Proxy Errors Via Proxy System Models.

NASA Astrophysics Data System (ADS)

Emile-Geay, J.; Dee, S. G.; Evans, M. N.; Adkins, J. F.

2014-12-01

Paleoclimatic observations are, by nature, imperfect recorders of climate variables. Empirical approaches to their calibration are challenged by the presence of multiple sources of uncertainty, which may confound the interpretation of signals and the identifiability of the noise. In this talk, I will demonstrate the utility of proxy system models (PSMs, Evans et al, 2013, 10.1016/j.quascirev.2013.05.024) to quantify the impact of all known sources of uncertainty. PSMs explicitly encode the mechanistic knowledge of the physical, chemical, biological and geological processes from which paleoclimatic observations arise. PSMs may be divided into sensor, archive and observation components, all of which may conspire to obscure climate signals in actual paleo-observations. As an example, we couple a PSM for the δ18O of speleothem calcite to an isotope-enabled climate model (Dee et al, submitted) to analyze the potential of this measurement as a proxy for precipitation amount. A simple soil/karst model (Partin et al, 2013, 10.1130/G34718.1) is used as sensor model, while a hiatus-permitting chronological model (Haslett & Parnell, 2008, 10.1111/j.1467-9876.2008.00623.x) is used as part of the observation model. This subdivision allows us to explicitly model the transformation from precipitation amount to speleothem calcite δ18O as a multi-stage process via a physical and chemical sensor model, and a stochastic archive model. By illustrating the PSM's behavior within the context of the climate simulations, we show how estimates of climate variability may be affected by each submodel's transformation of the signal. By specifying idealized climate signals(periodic vs. episodic, slow vs. fast) to the PSM, we investigate how frequency and amplitude patterns are modulated by sensor and archive submodels. To the extent that the PSM and the climate models are representative of real world processes, then the results may help us more accurately interpret existing paleodata, characterize their uncertainties, and design sampling strategies that exploit their strengths while mitigating their weaknesses.

Multivariate and multiorgan analysis of cardiorespiratory variability signals: the CAP sleep case.

PubMed

Bianchi, Anna M; Ferini-Strambi, Luigi; Castronovo, Vincenza; Cerutti, Sergio

2006-10-01

Signals from different systems are analyzed during sleep on a beat-to-beat basis to provide a quantitative measure of synchronization with the heart rate variability (HRV) signal, oscillations of which reflect the action of the autonomic nervous system. Beat-to-beat variability signals synchronized to QRS occurrence on ECG signals were extracted from respiration, electroencephalogram (EEG) and electromyogram (EMG) traces. The analysis was restricted to sleep stage 2. Cyclic alternating pattern (CAP) periods were detected from EEG signals and the following conditions were identified: stage 2 non-CAP (2 NCAP), stage 2 CAP (2 CAP) and stage 2 CAP with myoclonus (2 CAP MC). The coupling relationships between pairs of variability signals were studied in both the time and frequency domains. Passing from 2 NCAP to 2 CAP, sympathetic activation is indicated by tachycardia and reduced respiratory arrhythmia in the heart rate signal. At the same time, we observed a marked link between EEG and HRV at the CAP frequency. During 2 CAP MC, the increased synchronization involved myoclonus and respiration. The underlying mechanism seems to be related to a global control system at the central level that involves the different systems.

Global scale stratospheric processes as measured by the infrasound IMS network

NASA Astrophysics Data System (ADS)

Le Pichon, A.; Ceranna, L.; Kechut, P.

2012-04-01

IMS infrasound array data are routinely processed at the International Data Center (IDC). The wave parameters of the detected signals are estimated with the Progressive Multi-Channel Correlation method (PMCC). This new implementation of the PMCC algorithm allows the full frequency range of interest (0.01-5 Hz) to be processed efficiently in a single computational run. We have processed continuous recordings from 41 certified IMS stations from 2005 to 2010. We show that microbaroms are the dominant source of signals and are near-continuously globally detected. The observed azimuthal seasonal trend correlates well with the variation of the effective sound speed ratio which is a proxy for the combined effects of refraction due to sound speed gradients and advection due to along-path wind on infrasound propagation. A general trend in signal backazimuth is observed between winter and summer, driven by the seasonal reversal of the stratospheric winds. Combined with propagation modeling, we show that such an analysis enables a characterization of the wind and temperature structure above the stratosphere and may provide detailed information on upper atmospheric processes (e.g., large-scale planetary waves, stratospheric warming effects). We correlate perturbations and deviations from the seasonal trend to short time-scale variability of the atmosphere. We discuss the potential benefit of long-term infrasound monitoring to infer stratospheric processes for the first time on a global scale.

Tuned Normalization Explains the Size of Attention Modulations

PubMed Central

Ni, Amy M.; Ray, Supratim; Maunsell, John H. R.

2012-01-01

SUMMARY The effect of attention on firing rates varies considerably within a single cortical area. The firing rate of some neurons is greatly modulated by attention while others are hardly affected. The reason for this variability across neurons is unknown. We found that the variability in attention modulation across neurons in area MT of macaques can be well explained by variability in the strength of tuned normalization across neurons. The presence of tuned normalization also explains a striking asymmetry in attention effects within neurons: when two stimuli are in a neuron’s receptive field, directing attention to the preferred stimulus modulates firing rates more than directing attention to the non-preferred stimulus. These findings show that much of the neuron-to-neuron variability in modulation of responses by attention depends on variability in the way the neurons process multiple stimuli, rather than differences in the influence of top-down signals related to attention. PMID:22365552

Tuned normalization explains the size of attention modulations.

PubMed

Ni, Amy M; Ray, Supratim; Maunsell, John H R

2012-02-23

The effect of attention on firing rates varies considerably within a single cortical area. The firing rate of some neurons is greatly modulated by attention while others are hardly affected. The reason for this variability across neurons is unknown. We found that the variability in attention modulation across neurons in area MT of macaques can be well explained by variability in the strength of tuned normalization across neurons. The presence of tuned normalization also explains a striking asymmetry in attention effects within neurons: when two stimuli are in a neuron's receptive field, directing attention to the preferred stimulus modulates firing rates more than directing attention to the nonpreferred stimulus. These findings show that much of the neuron-to-neuron variability in modulation of responses by attention depends on variability in the way the neurons process multiple stimuli, rather than differences in the influence of top-down signals related to attention. Copyright © 2012 Elsevier Inc. All rights reserved.

eCTG: an automatic procedure to extract digital cardiotocographic signals from digital images.

PubMed

Sbrollini, Agnese; Agostinelli, Angela; Marcantoni, Ilaria; Morettini, Micaela; Burattini, Luca; Di Nardo, Francesco; Fioretti, Sandro; Burattini, Laura

2018-03-01

Cardiotocography (CTG), consisting in the simultaneous recording of fetal heart rate (FHR) and maternal uterine contractions (UC), is a popular clinical test to assess fetal health status. Typically, CTG machines provide paper reports that are visually interpreted by clinicians. Consequently, visual CTG interpretation depends on clinician's experience and has a poor reproducibility. The lack of databases containing digital CTG signals has limited number and importance of retrospective studies finalized to set up procedures for automatic CTG analysis that could contrast visual CTG interpretation subjectivity. In order to help overcoming this problem, this study proposes an electronic procedure, termed eCTG, to extract digital CTG signals from digital CTG images, possibly obtainable by scanning paper CTG reports. eCTG was specifically designed to extract digital CTG signals from digital CTG images. It includes four main steps: pre-processing, Otsu's global thresholding, signal extraction and signal calibration. Its validation was performed by means of the "CTU-UHB Intrapartum Cardiotocography Database" by Physionet, that contains digital signals of 552 CTG recordings. Using MATLAB, each signal was plotted and saved as a digital image that was then submitted to eCTG. Digital CTG signals extracted by eCTG were eventually compared to corresponding signals directly available in the database. Comparison occurred in terms of signal similarity (evaluated by the correlation coefficient ρ, and the mean signal error MSE) and clinical features (including FHR baseline and variability; number, amplitude and duration of tachycardia, bradycardia, acceleration and deceleration episodes; number of early, variable, late and prolonged decelerations; and UC number, amplitude, duration and period). The value of ρ between eCTG and reference signals was 0.85 (P < 10 -560 ) for FHR and 0.97 (P < 10 -560 ) for UC. On average, MSE value was 0.00 for both FHR and UC. No CTG feature was found significantly different when measured in eCTG vs. reference signals. eCTG procedure is a promising useful tool to accurately extract digital FHR and UC signals from digital CTG images. Copyright © 2018 Elsevier B.V. All rights reserved.

The beat of social cognition: Exploring the role of heart rate variability as marker of mentalizing abilities.

PubMed

Okruszek, Łukasz; Dolan, Kirsty; Lawrence, Megan; Cella, Matteo

2017-10-01

There is a long-standing debate on the influence of physiological signals on social behavior. Recent studies suggested that heart rate variability (HRV) may be a marker of social cognitive processes. However, this evidence is preliminary and limited to laboratory studies. In this study, 25 participants were assessed with a social cognition battery and asked to wear a wearable device measuring HRV for 6 consecutive days. The results showed that reduced HRV correlated with higher hostility attribution bias. However, no relationship was found between HRV and other social cognitive measures including facial emotion recognition, theory of mind or emotional intelligence. These results suggest that HRV may be linked to specific social cognitive processes requiring online emotional processing, in particular those related to social threat. These findings are discussed in the context of the neurovisceral integration model.

Effect of variability of sequence length of go trials preceding a stop trial on ability of response inhibition in stop-signal task.

PubMed

Hiraoka, Koichi; Kinoshita, Atsushi; Kunimura, Hiroshi; Matsuoka, Masakazu

2018-05-31

This study investigated whether the variability of the sequence length of the go trials preceding a stop trial enhanced or interfered with inhibitory control. The hypotheses tested were either inhibitory control improves when the sequence length of the go trials varies as a consequence of increased preparatory effort or it degrades as a consequence of the switching cost from the go trial to the stop trial. The right-handed participants abducted the left or right index finger in response to a go cue during the go trials. A stop cue was given at 50, 90, or 130 ms after the go cue, with 0.25 probability in the stop trial. In the less variable session, a stop trial was presented after two, three, or four consecutive go trials. In the variable session, a stop trial was presented after one, two, three, four, or five consecutive go trials. The reaction time and stop-signal reaction time were not significantly different between the sessions and between the response sides. Nevertheless, the probability of successful inhibition of the right-hand response in the variable session was higher than that in the less variable session when the stop cue was given 50 ms after a go cue. This finding supports the view that preparatory effort due to less predictability of the chance of a forthcoming response inhibition enhances the ability of the right-hand response inhibition when the stop process begins earlier.

Method and system for detecting a failure or performance degradation in a dynamic system such as a flight vehicle

NASA Technical Reports Server (NTRS)

Miller, Robert H. (Inventor); Ribbens, William B. (Inventor)

2003-01-01

A method and system for detecting a failure or performance degradation in a dynamic system having sensors for measuring state variables and providing corresponding output signals in response to one or more system input signals are provided. The method includes calculating estimated gains of a filter and selecting an appropriate linear model for processing the output signals based on the input signals. The step of calculating utilizes one or more models of the dynamic system to obtain estimated signals. The method further includes calculating output error residuals based on the output signals and the estimated signals. The method also includes detecting one or more hypothesized failures or performance degradations of a component or subsystem of the dynamic system based on the error residuals. The step of calculating the estimated values is performed optimally with respect to one or more of: noise, uncertainty of parameters of the models and un-modeled dynamics of the dynamic system which may be a flight vehicle or financial market or modeled financial system.

Phenotypic variability in unicellular organisms: from calcium signalling to social behaviour

PubMed Central

Vogel, David; Nicolis, Stamatios C.; Perez-Escudero, Alfonso; Nanjundiah, Vidyanand; Sumpter, David J. T.; Dussutour, Audrey

2015-01-01

Historically, research has focused on the mean and often neglected the variance. However, variability in nature is observable at all scales: among cells within an individual, among individuals within a population and among populations within a species. A fundamental quest in biology now is to find the mechanisms that underlie variability. Here, we investigated behavioural variability in a unique unicellular organism, Physarum polycephalum. We combined experiments and models to show that variability in cell signalling contributes to major differences in behaviour underpinning some aspects of social interactions. First, following thousands of cells under various contexts, we identified distinct behavioural phenotypes: ‘slow–regular–social’, ‘fast–regular–social’ and ‘fast–irregular–asocial’. Second, coupling chemical analysis and behavioural assays we found that calcium signalling is responsible for these behavioural phenotypes. Finally, we show that differences in signalling and behaviour led to alternative social strategies. Our results have considerable implications for our understanding of the emergence of variability in living organisms. PMID:26609088

Phenotypic variability in unicellular organisms: from calcium signalling to social behaviour.

PubMed

Vogel, David; Nicolis, Stamatios C; Perez-Escudero, Alfonso; Nanjundiah, Vidyanand; Sumpter, David J T; Dussutour, Audrey

2015-11-22

Historically, research has focused on the mean and often neglected the variance. However, variability in nature is observable at all scales: among cells within an individual, among individuals within a population and among populations within a species. A fundamental quest in biology now is to find the mechanisms that underlie variability. Here, we investigated behavioural variability in a unique unicellular organism, Physarum polycephalum. We combined experiments and models to show that variability in cell signalling contributes to major differences in behaviour underpinning some aspects of social interactions. First, following thousands of cells under various contexts, we identified distinct behavioural phenotypes: 'slow-regular-social', 'fast-regular-social' and 'fast-irregular-asocial'. Second, coupling chemical analysis and behavioural assays we found that calcium signalling is responsible for these behavioural phenotypes. Finally, we show that differences in signalling and behaviour led to alternative social strategies. Our results have considerable implications for our understanding of the emergence of variability in living organisms. © 2015 The Author(s).

Detection of essential hypertension with physiological signals from wearable devices.

PubMed

Ghosh, Arindam; Torres, Juan Manuel Mayor; Danieli, Morena; Riccardi, Giuseppe

2015-08-01

Early detection of essential hypertension can support the prevention of cardiovascular disease, a leading cause of death. The traditional method of identification of hypertension involves periodic blood pressure measurement using brachial cuff-based measurement devices. While these devices are non-invasive, they require manual setup for each measurement and they are not suitable for continuous monitoring. Research has shown that physiological signals such as Heart Rate Variability, which is a measure of the cardiac autonomic activity, is correlated with blood pressure. Wearable devices capable of measuring physiological signals such as Heart Rate, Galvanic Skin Response, Skin Temperature have recently become ubiquitous. However, these signals are not accurate and are prone to noise due to different artifacts. In this paper a) we present a data collection protocol for continuous non-invasive monitoring of physiological signals from wearable devices; b) we implement signal processing techniques for signal estimation; c) we explore how the continuous monitoring of these physiological signals can be used to identify hypertensive patients; d) We conduct a pilot study with a group of normotensive and hypertensive patients to test our techniques. We show that physiological signals extracted from wearable devices can distinguish between these two groups with high accuracy.

Design of quantum efficiency measurement system for variable doping GaAs photocathode

NASA Astrophysics Data System (ADS)

Chen, Liang; Yang, Kai; Liu, HongLin; Chang, Benkang

2008-03-01

To achieve high quantum efficiency and good stability has been a main direction to develop GaAs photocathode recently. Through early research, we proved that variable doping structure is executable and practical, and has great potential. In order to optimize variable doping GaAs photocathode preparation techniques and study the variable doping theory deeply, a real-time quantum efficiency measurement system for GaAs Photocathode has been designed. The system uses FPGA (Field-programmable gate array) device, and high speed A/D converter to design a high signal noise ratio and high speed data acquisition card. ARM (Advanced RISC Machines) core processor s3c2410 and real-time embedded system are used to obtain and show measurement results. The measurement precision of photocurrent could reach 1nA, and measurement range of spectral response curve is within 400~1000nm. GaAs photocathode preparation process can be real-time monitored by using this system. This system could easily be added other functions to show the physic variation of photocathode during the preparation process more roundly in the future.

[Prescribing monitoring in clinical practice: from enlightened empiricism to rational strategies].

PubMed

Buclin, Thierry; Herzig, Lilli

2013-05-15

Monitoring of a medical condition is the periodic measurement of one or several physiological or biological variables to detect a signal regarding its clinical progression or its response to treatment. We distinguish different medical situations between diagnostic, clinical and therapeutic process to apply monitoring. Many clinical, variables can be used for monitoring, once their intrinsic properties (normal range, critical difference, kinetics, reactivity) and external validity (pathophysiological importance, predictive power for clinical outcomes) are established. A formal conceptualization of monitoring is being developed and should support the rational development of monitoring strategies and their validation through appropriate clinical trials.

Method for using polarization gating to measure a scattering sample

DOEpatents

Baba, Justin S.

2015-08-04

Described herein are systems, devices, and methods facilitating optical characterization of scattering samples. A polarized optical beam can be directed to pass through a sample to be tested. The optical beam exiting the sample can then be analyzed to determine its degree of polarization, from which other properties of the sample can be determined. In some cases, an apparatus can include a source of an optical beam, an input polarizer, a sample, an output polarizer, and a photodetector. In some cases, a signal from a photodetector can be processed through attenuation, variable offset, and variable gain.

Affective Computing and the Impact of Gender and Age

PubMed Central

Rukavina, Stefanie; Gruss, Sascha; Hoffmann, Holger; Tan, Jun-Wen; Walter, Steffen; Traue, Harald C.

2016-01-01

Affective computing aims at the detection of users’ mental states, in particular, emotions and dispositions during human-computer interactions. Detection can be achieved by measuring multimodal signals, namely, speech, facial expressions and/or psychobiology. Over the past years, one major approach was to identify the best features for each signal using different classification methods. Although this is of high priority, other subject-specific variables should not be neglected. In our study, we analyzed the effect of gender, age, personality and gender roles on the extracted psychobiological features (derived from skin conductance level, facial electromyography and heart rate variability) as well as the influence on the classification results. In an experimental human-computer interaction, five different affective states with picture material from the International Affective Picture System and ULM pictures were induced. A total of 127 subjects participated in the study. Among all potentially influencing variables (gender has been reported to be influential), age was the only variable that correlated significantly with psychobiological responses. In summary, the conducted classification processes resulted in 20% classification accuracy differences according to age and gender, especially when comparing the neutral condition with four other affective states. We suggest taking age and gender specifically into account for future studies in affective computing, as these may lead to an improvement of emotion recognition accuracy. PMID:26939129

Linking Inflammation, Cardiorespiratory Variability, and Neural Control in Acute Inflammation via Computational Modeling

PubMed Central

Dick, Thomas E.; Molkov, Yaroslav I.; Nieman, Gary; Hsieh, Yee-Hsee; Jacono, Frank J.; Doyle, John; Scheff, Jeremy D.; Calvano, Steve E.; Androulakis, Ioannis P.; An, Gary; Vodovotz, Yoram

2012-01-01

Acute inflammation leads to organ failure by engaging catastrophic feedback loops in which stressed tissue evokes an inflammatory response and, in turn, inflammation damages tissue. Manifestations of this maladaptive inflammatory response include cardio-respiratory dysfunction that may be reflected in reduced heart rate and ventilatory pattern variabilities. We have developed signal-processing algorithms that quantify non-linear deterministic characteristics of variability in biologic signals. Now, coalescing under the aegis of the NIH Computational Biology Program and the Society for Complexity in Acute Illness, two research teams performed iterative experiments and computational modeling on inflammation and cardio-pulmonary dysfunction in sepsis as well as on neural control of respiration and ventilatory pattern variability. These teams, with additional collaborators, have recently formed a multi-institutional, interdisciplinary consortium, whose goal is to delineate the fundamental interrelationship between the inflammatory response and physiologic variability. Multi-scale mathematical modeling and complementary physiological experiments will provide insight into autonomic neural mechanisms that may modulate the inflammatory response to sepsis and simultaneously reduce heart rate and ventilatory pattern variabilities associated with sepsis. This approach integrates computational models of neural control of breathing and cardio-respiratory coupling with models that combine inflammation, cardiovascular function, and heart rate variability. The resulting integrated model will provide mechanistic explanations for the phenomena of respiratory sinus-arrhythmia and cardio-ventilatory coupling observed under normal conditions, and the loss of these properties during sepsis. This approach holds the potential of modeling cross-scale physiological interactions to improve both basic knowledge and clinical management of acute inflammatory diseases such as sepsis and trauma. PMID:22783197

Linking Inflammation, Cardiorespiratory Variability, and Neural Control in Acute Inflammation via Computational Modeling.

PubMed

Dick, Thomas E; Molkov, Yaroslav I; Nieman, Gary; Hsieh, Yee-Hsee; Jacono, Frank J; Doyle, John; Scheff, Jeremy D; Calvano, Steve E; Androulakis, Ioannis P; An, Gary; Vodovotz, Yoram

2012-01-01

Acute inflammation leads to organ failure by engaging catastrophic feedback loops in which stressed tissue evokes an inflammatory response and, in turn, inflammation damages tissue. Manifestations of this maladaptive inflammatory response include cardio-respiratory dysfunction that may be reflected in reduced heart rate and ventilatory pattern variabilities. We have developed signal-processing algorithms that quantify non-linear deterministic characteristics of variability in biologic signals. Now, coalescing under the aegis of the NIH Computational Biology Program and the Society for Complexity in Acute Illness, two research teams performed iterative experiments and computational modeling on inflammation and cardio-pulmonary dysfunction in sepsis as well as on neural control of respiration and ventilatory pattern variability. These teams, with additional collaborators, have recently formed a multi-institutional, interdisciplinary consortium, whose goal is to delineate the fundamental interrelationship between the inflammatory response and physiologic variability. Multi-scale mathematical modeling and complementary physiological experiments will provide insight into autonomic neural mechanisms that may modulate the inflammatory response to sepsis and simultaneously reduce heart rate and ventilatory pattern variabilities associated with sepsis. This approach integrates computational models of neural control of breathing and cardio-respiratory coupling with models that combine inflammation, cardiovascular function, and heart rate variability. The resulting integrated model will provide mechanistic explanations for the phenomena of respiratory sinus-arrhythmia and cardio-ventilatory coupling observed under normal conditions, and the loss of these properties during sepsis. This approach holds the potential of modeling cross-scale physiological interactions to improve both basic knowledge and clinical management of acute inflammatory diseases such as sepsis and trauma.

Advances in heart rate variability signal analysis: joint position statement by the e-Cardiology ESC Working Group and the European Heart Rhythm Association co-endorsed by the Asia Pacific Heart Rhythm Society.

PubMed

Sassi, Roberto; Cerutti, Sergio; Lombardi, Federico; Malik, Marek; Huikuri, Heikki V; Peng, Chung-Kang; Schmidt, Georg; Yamamoto, Yoshiharu

2015-09-01

Following the publication of the Task Force document on heart rate variability (HRV) in 1996, a number of articles have been published to describe new HRV methodologies and their application in different physiological and clinical studies. This document presents a critical review of the new methods. A particular attention has been paid to methodologies that have not been reported in the 1996 standardization document but have been more recently tested in sufficiently sized populations. The following methods were considered: Long-range correlation and fractal analysis; Short-term complexity; Entropy and regularity; and Nonlinear dynamical systems and chaotic behaviour. For each of these methods, technical aspects, clinical achievements, and suggestions for clinical application were reviewed. While the novel approaches have contributed in the technical understanding of the signal character of HRV, their success in developing new clinical tools, such as those for the identification of high-risk patients, has been rather limited. Available results obtained in selected populations of patients by specialized laboratories are nevertheless of interest but new prospective studies are needed. The investigation of new parameters, descriptive of the complex regulation mechanisms of heart rate, has to be encouraged because not all information in the HRV signal is captured by traditional methods. The new technologies thus could provide after proper validation, additional physiological, and clinical meaning. Multidisciplinary dialogue and specialized courses in the combination of clinical cardiology and complex signal processing methods seem warranted for further advances in studies of cardiac oscillations and in the understanding normal and abnormal cardiac control processes. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2015. For permissions please email: journals.permissions@oup.com.

Development of gait segmentation methods for wearable foot pressure sensors.

PubMed

Crea, S; De Rossi, S M M; Donati, M; Reberšek, P; Novak, D; Vitiello, N; Lenzi, T; Podobnik, J; Munih, M; Carrozza, M C

2012-01-01

We present an automated segmentation method based on the analysis of plantar pressure signals recorded from two synchronized wireless foot insoles. Given the strict limits on computational power and power consumption typical of wearable electronic components, our aim is to investigate the capability of a Hidden Markov Model machine-learning method, to detect gait phases with different levels of complexity in the processing of the wearable pressure sensors signals. Therefore three different datasets are developed: raw voltage values, calibrated sensor signals and a calibrated estimation of total ground reaction force and position of the plantar center of pressure. The method is tested on a pool of 5 healthy subjects, through a leave-one-out cross validation. The results show high classification performances achieved using estimated biomechanical variables, being on average the 96%. Calibrated signals and raw voltage values show higher delays and dispersions in phase transition detection, suggesting a lower reliability for online applications.

System level analysis and control of manufacturing process variation

DOEpatents

Hamada, Michael S.; Martz, Harry F.; Eleswarpu, Jay K.; Preissler, Michael J.

2005-05-31

A computer-implemented method is implemented for determining the variability of a manufacturing system having a plurality of subsystems. Each subsystem of the plurality of subsystems is characterized by signal factors, noise factors, control factors, and an output response, all having mean and variance values. Response models are then fitted to each subsystem to determine unknown coefficients for use in the response models that characterize the relationship between the signal factors, noise factors, control factors, and the corresponding output response having mean and variance values that are related to the signal factors, noise factors, and control factors. The response models for each subsystem are coupled to model the output of the manufacturing system as a whole. The coefficients of the fitted response models are randomly varied to propagate variances through the plurality of subsystems and values of signal factors and control factors are found to optimize the output of the manufacturing system to meet a specified criterion.

Two systems analyses of SETI. [microwave Search for Extra-Terrestrial Intelligence

NASA Technical Reports Server (NTRS)

Machol, R. E.

1976-01-01

The problem of receiving and identifying a single microwave signal transmitted by extraterrestrial intelligent beings is analyzed in the cases where the signal is designed to catch our attention and the signal is designed for internal purposes of another civilization. Six variables which yield uncertainty as to the exact signal which should be searched for are described: polarization, modulation, flux level, direction, frequency (including bandwidth and drift rate), and time. It is shown that if all reasonable variations of these parameters are to be examined sequentially for 1000 seconds, the search would take over a million times longer than the age of the Universe. Ways to simplify the search are considered, including widening the frequency bin, selecting specific targets, cutting the observation time, using a Fourier transform device for data processing, and building larger antennas as well as better low-noise receivers.

The Taguchi methodology as a statistical tool for biotechnological applications: a critical appraisal.

PubMed

Rao, Ravella Sreenivas; Kumar, C Ganesh; Prakasham, R Shetty; Hobbs, Phil J

2008-04-01

Success in experiments and/or technology mainly depends on a properly designed process or product. The traditional method of process optimization involves the study of one variable at a time, which requires a number of combinations of experiments that are time, cost and labor intensive. The Taguchi method of design of experiments is a simple statistical tool involving a system of tabulated designs (arrays) that allows a maximum number of main effects to be estimated in an unbiased (orthogonal) fashion with a minimum number of experimental runs. It has been applied to predict the significant contribution of the design variable(s) and the optimum combination of each variable by conducting experiments on a real-time basis. The modeling that is performed essentially relates signal-to-noise ratio to the control variables in a 'main effect only' approach. This approach enables both multiple response and dynamic problems to be studied by handling noise factors. Taguchi principles and concepts have made extensive contributions to industry by bringing focused awareness to robustness, noise and quality. This methodology has been widely applied in many industrial sectors; however, its application in biological sciences has been limited. In the present review, the application and comparison of the Taguchi methodology has been emphasized with specific case studies in the field of biotechnology, particularly in diverse areas like fermentation, food processing, molecular biology, wastewater treatment and bioremediation.

Networks for image acquisition, processing and display

NASA Technical Reports Server (NTRS)

Ahumada, Albert J., Jr.

1990-01-01

The human visual system comprises layers of networks which sample, process, and code images. Understanding these networks is a valuable means of understanding human vision and of designing autonomous vision systems based on network processing. Ames Research Center has an ongoing program to develop computational models of such networks. The models predict human performance in detection of targets and in discrimination of displayed information. In addition, the models are artificial vision systems sharing properties with biological vision that has been tuned by evolution for high performance. Properties include variable density sampling, noise immunity, multi-resolution coding, and fault-tolerance. The research stresses analysis of noise in visual networks, including sampling, photon, and processing unit noises. Specific accomplishments include: models of sampling array growth with variable density and irregularity comparable to that of the retinal cone mosaic; noise models of networks with signal-dependent and independent noise; models of network connection development for preserving spatial registration and interpolation; multi-resolution encoding models based on hexagonal arrays (HOP transform); and mathematical procedures for simplifying analysis of large networks.

Origins and implications of pluripotent stem cell variability and heterogeneity

PubMed Central

Cahan, Patrick; Daley, George Q.

2014-01-01

Pluripotent stem cells constitute a platform to model disease and developmental processes and can potentially be used in regenerative medicine. However, not all pluripotent cell lines are equal in their capacity to differentiate into desired cell types in vitro. Genetic and epigenetic variations contribute to functional variability between cell lines and heterogeneity within clones. These genetic and epigenetic variations could ‘lock’ the pluripotency network resulting in residual pluripotent cells or alter the signalling response of developmental pathways leading to lineage bias. The molecular contributors to functional variability and heterogeneity in both embryonic stem (ES) cells and induced pluripotent stem (iPS) cells are only beginning to emerge, yet they are crucial to the future of the stem cell field. PMID:23673969

Wireless plataforms for the monitoring of biomedical variables

NASA Astrophysics Data System (ADS)

Bianco, Román; Laprovitta, Agustín; Misa, Alberto; Toselli, Eduardo; Castagnola, Juan Luis

2007-11-01

The present paper aims to analyze and to compare two wireless platforms for the monitoring of biomedical variables. They must obtain the vital signals of the patients, transmit them through a radio frequency bond and centralize them for their process, storage and monitoring in real time. The implementation of this system permit us to obtain two important benefits; The patient will enjoy greater comfort during the internment, and the doctors will be able to know the state of the biomedical variables of each patient, in simultaneous form. In order to achieve the objective of this work, two communication systems for wireless transmissions data were developed and implemented. The CC1000 transceiver was used in the first system and the Bluetooth module was used in the other system.

Cholinergic enhancement reduces functional connectivity and BOLD variability in visual extrastriate cortex during selective attention.

PubMed

Ricciardi, Emiliano; Handjaras, Giacomo; Bernardi, Giulio; Pietrini, Pietro; Furey, Maura L

2013-01-01

Enhancing cholinergic function improves performance on various cognitive tasks and alters neural responses in task specific brain regions. We have hypothesized that the changes in neural activity observed during increased cholinergic function reflect an increase in neural efficiency that leads to improved task performance. The current study tested this hypothesis by assessing neural efficiency based on cholinergically-mediated effects on regional brain connectivity and BOLD signal variability. Nine subjects participated in a double-blind, placebo-controlled crossover fMRI study. Following an infusion of physostigmine (1 mg/h) or placebo, echo-planar imaging (EPI) was conducted as participants performed a selective attention task. During the task, two images comprised of superimposed pictures of faces and houses were presented. Subjects were instructed periodically to shift their attention from one stimulus component to the other and to perform a matching task using hand held response buttons. A control condition included phase-scrambled images of superimposed faces and houses that were presented in the same temporal and spatial manner as the attention task; participants were instructed to perform a matching task. Cholinergic enhancement improved performance during the selective attention task, with no change during the control task. Functional connectivity analyses showed that the strength of connectivity between ventral visual processing areas and task-related occipital, parietal and prefrontal regions reduced significantly during cholinergic enhancement, exclusively during the selective attention task. Physostigmine administration also reduced BOLD signal temporal variability relative to placebo throughout temporal and occipital visual processing areas, again during the selective attention task only. Together with the observed behavioral improvement, the decreases in connectivity strength throughout task-relevant regions and BOLD variability within stimulus processing regions support the hypothesis that cholinergic augmentation results in enhanced neural efficiency. This article is part of a Special Issue entitled 'Cognitive Enhancers'. Copyright © 2012 Elsevier Ltd. All rights reserved.

Cholinergic enhancement reduces functional connectivity and BOLD variability in visual extrastriate cortex during selective attention

PubMed Central

Ricciardi, Emiliano; Handjaras, Giacomo; Bernardi, Giulio; Pietrini, Pietro; Furey, Maura L.

2012-01-01

Enhancing cholinergic function improves performance on various cognitive tasks and alters neural responses in task specific brain regions. Previous findings by our group strongly suggested that the changes in neural activity observed during increased cholinergic function may reflect an increase in neural efficiency that leads to improved task performance. The current study was designed to assess the effects of cholinergic enhancement on regional brain connectivity and BOLD signal variability. Nine subjects participated in a double-blind, placebo-controlled crossover functional magnetic resonance imaging (fMRI) study. Following an infusion of physostigmine (1mg/hr) or placebo, echo-planar imaging (EPI) was conducted as participants performed a selective attention task. During the task, two images comprised of superimposed pictures of faces and houses were presented. Subjects were instructed periodically to shift their attention from one stimulus component to the other and to perform a matching task using hand held response buttons. A control condition included phase-scrambled images of superimposed faces and houses that were presented in the same temporal and spatial manner as the attention task; participants were instructed to perform a matching task. Cholinergic enhancement improved performance during the selective attention task, with no change during the control task. Functional connectivity analyses showed that the strength of connectivity between ventral visual processing areas and task-related occipital, parietal and prefrontal regions was reduced significantly during cholinergic enhancement, exclusively during the selective attention task. Cholinergic enhancement also reduced BOLD signal temporal variability relative to placebo throughout temporal and occipital visual processing areas, again during the selective attention task only. Together with the observed behavioral improvement, the decreases in connectivity strength throughout task-relevant regions and BOLD variability within stimulus processing regions provide further support to the hypothesis that cholinergic augmentation results in enhanced neural efficiency. PMID:22906685

Variability of Relative Sea Level Rise: Spatial and Temporal Correlations in Northwest Gulf of Mexico

NASA Astrophysics Data System (ADS)

Tissot, P.; Reisinger, A. S.; Besonen, M. R.

2017-12-01

While our understanding of global sea level rise and its budget has made great progress over the past decade, the spatial and temporal variability of relative sea level rise along the coasts still needs to be better understood and quantified. We developed a technique to reduce the confidence intervals associated with relative sea level rise (RSLR) estimates for 15 tide gauges located along the Texas coast for the period 1993-2016. Seasonally detrended monthly mean water levels are highly correlated after removal of station-specific RSLR trends, which allows for the quantification of a common, low frequency oceanic signal. RSLR confidence intervals are reduced from over 1.9 mm/yr, on average 2.3mm, to less than 1.1 mm/yr, on average 0.7 mm/yr after removing this common signal. The resulting RSLR rates range from 3.0 to 8.4 mm/yr. The range is wider than the longer-term rates of 5.3, 3.8 and 1.9 mm/yr measured from north to south by the three National Water Level Observation Network (NWLON) stations covering the study area (over different and longer time spans). The results emphasize the importance of the spatial variability of the vertical land motion component of RSLR. The temporal variability of the coherent oceanic signal is not significantly correlated to the ENSO signal for the study period and is only weakly correlated to the AMO and PDO climate indices. The coherence of the signal is further investigated by comparison with other locations along the Gulf of Mexico and along the Northeast Atlantic coast. The results are discussed while considering strong local processes along the Northwest Gulf of Mexico, such as wind forcing and intermittent eddies and the spatially broader influence of the Gulf Stream. The local significance of the RSLR spatial and temporal differences are discussed in terms of the differences in inundation frequency for nuisance type flooding including comparing the time span to reach a probability of at least one nuisance flood event per year.

CHAM: weak signals detection through a new multivariate algorithm for process control

NASA Astrophysics Data System (ADS)

Bergeret, François; Soual, Carole; Le Gratiet, B.

2016-10-01

Derivatives technologies based on core CMOS processes are significantly aggressive in term of design rules and process control requirements. Process control plan is a derived from Process Assumption (PA) calculations which result in a design rule based on known process variability capabilities, taking into account enough margin to be safe not only for yield but especially for reliability. Even though process assumptions are calculated with a 4 sigma known process capability margin, efficient and competitive designs are challenging the process especially for derivatives technologies in 40 and 28nm nodes. For wafer fab process control, PA are declined in monovariate (layer1 CD, layer2 CD, layer2 to layer1 overlay, layer3 CD etc….) control charts with appropriated specifications and control limits which all together are securing the silicon. This is so far working fine but such system is not really sensitive to weak signals coming from interactions of multiple key parameters (high layer2 CD combined with high layer3 CD as an example). CHAM is a software using an advanced statistical algorithm specifically designed to detect small signals, especially when there are many parameters to control and when the parameters can interact to create yield issues. In this presentation we will first present the CHAM algorithm, then the case-study on critical dimensions, with the results, and we will conclude on future work. This partnership between Ippon and STM is part of E450LMDAP, European project dedicated to metrology and lithography development for future technology nodes, especially 10nm.

Synchronization transmission of laser pattern signal within uncertain switched network

NASA Astrophysics Data System (ADS)

Lü, Ling; Li, Chengren; Li, Gang; Sun, Ao; Yan, Zhe; Rong, Tingting; Gao, Yan

2017-06-01

We propose a new technology for synchronization transmission of laser pattern signal within uncertain network with controllable topology. In synchronization process, the connection of dynamic network can vary at all time according to different demands. Especially, we construct the Lyapunov function of network through designing a special semi-positive definite function, and the synchronization transmission of laser pattern signal within uncertain network with controllable topology can be realized perfectly, which effectively avoids the complicated calculation for solving the second largest eignvalue of the coupling matrix of the dynamic network in order to obtain the network synchronization condition. At the same time, the uncertain parameters in dynamic equations belonging to network nodes can also be identified accurately via designing the identification laws of uncertain parameters. In addition, there are not any limitations for the synchronization target of network in the new technology, in other words, the target can either be a state variable signal of an arbitrary node within the network or an exterior signal.

Electrical capacitance clearanceometer

NASA Technical Reports Server (NTRS)

Hester, Norbert J. (Inventor); Hornbeck, Charles E. (Inventor); Young, Joseph C. (Inventor)

1992-01-01

A hot gas turbine engine capacitive probe clearanceometer is employed to measure the clearance gap or distance between blade tips on a rotor wheel and its confining casing under operating conditions. A braze sealed tip of the probe carries a capacitor electrode which is electrically connected to an electrical inductor within the probe which is inserted into a turbine casing to position its electrode at the inner surface of the casing. Electrical power is supplied through a voltage controlled variable frequency oscillator having a tuned circuit in which the probe is a component. The oscillator signal is modulated by a change in electrical capacitance between the probe electrode and a passing blade tip surface while an automatic feedback correction circuit corrects oscillator signal drift. A change in distance between a blade tip and the probe electrode is a change in capacitance therebetween which frequency modulates the oscillator signal. The modulated oscillator signal which is then processed through a phase detector and related circuitry to provide an electrical signal is proportional to the clearance gap.

Hardware implementation of fuzzy Petri net as a controller.

PubMed

Gniewek, Lesław; Kluska, Jacek

2004-06-01

The paper presents a new approach to fuzzy Petri net (FPN) and its hardware implementation. The authors' motivation is as follows. Complex industrial processes can be often decomposed into many parallelly working subprocesses, which can, in turn, be modeled using Petri nets. If all the process variables (or events) are assumed to be two-valued signals, then it is possible to obtain a hardware or software control device, which works according to the algorithm described by conventional Petri net. However, the values of real signals are contained in some bounded interval and can be interpreted as events which are not only true or false, but rather true in some degree from the interval [0, 1]. Such a natural interpretation from multivalued logic (fuzzy logic) point of view, concerns sensor outputs, control signals, time expiration, etc. It leads to the idea of FPN as a controller, which one can rather simply obtain, and which would be able to process both analog, and binary signals. In the paper both graphical, and algebraic representations of the proposed FPN are given. The conditions under which transitions can be fired are described. The algebraic description of the net and a theorem which enables computation of new marking in the net, based on current marking, are formulated. Hardware implementation of the FPN, which uses fuzzy JK flip-flops and fuzzy gates, are proposed. An example illustrating usefulness of the proposed FPN for control algorithm description and its synthesis as a controller device for the concrete production process are presented.

Synaptic unreliability facilitates information transmission in balanced cortical populations

NASA Astrophysics Data System (ADS)

Gatys, Leon A.; Ecker, Alexander S.; Tchumatchenko, Tatjana; Bethge, Matthias

2015-06-01

Synaptic unreliability is one of the major sources of biophysical noise in the brain. In the context of neural information processing, it is a central question how neural systems can afford this unreliability. Here we examine how synaptic noise affects signal transmission in cortical circuits, where excitation and inhibition are thought to be tightly balanced. Surprisingly, we find that in this balanced state synaptic response variability actually facilitates information transmission, rather than impairing it. In particular, the transmission of fast-varying signals benefits from synaptic noise, as it instantaneously increases the amount of information shared between presynaptic signal and postsynaptic current. Furthermore we show that the beneficial effect of noise is based on a very general mechanism which contrary to stochastic resonance does not reach an optimum at a finite noise level.

Assessment of tobacco smoke effects on neonatal cardiorespiratory control using a semi-automated processing approach.

PubMed

Al-Omar, Sally; Le Rolle, Virginie; Beuchée, Alain; Samson, Nathalie; Praud, Jean-Paul; Carrault, Guy

2018-05-10

A semi-automated processing approach was developed to assess the effects of early postnatal environmental tobacco smoke (ETS) on the cardiorespiratory control of newborn lambs. The system consists of several steps beginning with artifact rejection, followed by the selection of stationary segments, and ending with feature extraction. This approach was used in six lambs exposed to 20 cigarettes/day for the first 15 days of life, while another six control lambs were exposed to room air. On postnatal day 16, electrocardiograph and respiratory signals were obtained from a 6-h polysomnographic recording. The effects of postnatal ETS exposure on heart rate variability, respiratory rate variability, and cardiorespiratory interrelations were explored. The unique results suggest that early postnatal ETS exposure increases respiratory rate variability and decreases the coupling between cardiac and respiratory systems. Potentially harmful consequences in early life include unstable breathing and decreased adaptability of cardiorespiratory function, particularly during early life challenges, such as prematurity or viral infection. Graphical abstract ᅟ.

A scalable moment-closure approximation for large-scale biochemical reaction networks

PubMed Central

Kazeroonian, Atefeh; Theis, Fabian J.; Hasenauer, Jan

2017-01-01

Abstract Motivation: Stochastic molecular processes are a leading cause of cell-to-cell variability. Their dynamics are often described by continuous-time discrete-state Markov chains and simulated using stochastic simulation algorithms. As these stochastic simulations are computationally demanding, ordinary differential equation models for the dynamics of the statistical moments have been developed. The number of state variables of these approximating models, however, grows at least quadratically with the number of biochemical species. This limits their application to small- and medium-sized processes. Results: In this article, we present a scalable moment-closure approximation (sMA) for the simulation of statistical moments of large-scale stochastic processes. The sMA exploits the structure of the biochemical reaction network to reduce the covariance matrix. We prove that sMA yields approximating models whose number of state variables depends predominantly on local properties, i.e. the average node degree of the reaction network, instead of the overall network size. The resulting complexity reduction is assessed by studying a range of medium- and large-scale biochemical reaction networks. To evaluate the approximation accuracy and the improvement in computational efficiency, we study models for JAK2/STAT5 signalling and NFκB signalling. Our method is applicable to generic biochemical reaction networks and we provide an implementation, including an SBML interface, which renders the sMA easily accessible. Availability and implementation: The sMA is implemented in the open-source MATLAB toolbox CERENA and is available from https://github.com/CERENADevelopers/CERENA. Contact: jan.hasenauer@helmholtz-muenchen.de or atefeh.kazeroonian@tum.de Supplementary information: Supplementary data are available at Bioinformatics online. PMID:28881983

Reliability of the Parabola Approximation Method in Heart Rate Variability Analysis Using Low-Sampling-Rate Photoplethysmography.

PubMed

Baek, Hyun Jae; Shin, JaeWook; Jin, Gunwoo; Cho, Jaegeol

2017-10-24

Photoplethysmographic signals are useful for heart rate variability analysis in practical ambulatory applications. While reducing the sampling rate of signals is an important consideration for modern wearable devices that enable 24/7 continuous monitoring, there have not been many studies that have investigated how to compensate the low timing resolution of low-sampling-rate signals for accurate heart rate variability analysis. In this study, we utilized the parabola approximation method and measured it against the conventional cubic spline interpolation method for the time, frequency, and nonlinear domain variables of heart rate variability. For each parameter, the intra-class correlation, standard error of measurement, Bland-Altman 95% limits of agreement and root mean squared relative error were presented. Also, elapsed time taken to compute each interpolation algorithm was investigated. The results indicated that parabola approximation is a simple, fast, and accurate algorithm-based method for compensating the low timing resolution of pulse beat intervals. In addition, the method showed comparable performance with the conventional cubic spline interpolation method. Even though the absolute value of the heart rate variability variables calculated using a signal sampled at 20 Hz were not exactly matched with those calculated using a reference signal sampled at 250 Hz, the parabola approximation method remains a good interpolation method for assessing trends in HRV measurements for low-power wearable applications.

Obstacle Avoidance and Target Acquisition for Robot Navigation Using a Mixed Signal Analog/Digital Neuromorphic Processing System

PubMed Central

Milde, Moritz B.; Blum, Hermann; Dietmüller, Alexander; Sumislawska, Dora; Conradt, Jörg; Indiveri, Giacomo; Sandamirskaya, Yulia

2017-01-01

Neuromorphic hardware emulates dynamics of biological neural networks in electronic circuits offering an alternative to the von Neumann computing architecture that is low-power, inherently parallel, and event-driven. This hardware allows to implement neural-network based robotic controllers in an energy-efficient way with low latency, but requires solving the problem of device variability, characteristic for analog electronic circuits. In this work, we interfaced a mixed-signal analog-digital neuromorphic processor ROLLS to a neuromorphic dynamic vision sensor (DVS) mounted on a robotic vehicle and developed an autonomous neuromorphic agent that is able to perform neurally inspired obstacle-avoidance and target acquisition. We developed a neural network architecture that can cope with device variability and verified its robustness in different environmental situations, e.g., moving obstacles, moving target, clutter, and poor light conditions. We demonstrate how this network, combined with the properties of the DVS, allows the robot to avoid obstacles using a simple biologically-inspired dynamics. We also show how a Dynamic Neural Field for target acquisition can be implemented in spiking neuromorphic hardware. This work demonstrates an implementation of working obstacle avoidance and target acquisition using mixed signal analog/digital neuromorphic hardware. PMID:28747883

Obstacle Avoidance and Target Acquisition for Robot Navigation Using a Mixed Signal Analog/Digital Neuromorphic Processing System.

PubMed

Milde, Moritz B; Blum, Hermann; Dietmüller, Alexander; Sumislawska, Dora; Conradt, Jörg; Indiveri, Giacomo; Sandamirskaya, Yulia

2017-01-01

Neuromorphic hardware emulates dynamics of biological neural networks in electronic circuits offering an alternative to the von Neumann computing architecture that is low-power, inherently parallel, and event-driven. This hardware allows to implement neural-network based robotic controllers in an energy-efficient way with low latency, but requires solving the problem of device variability, characteristic for analog electronic circuits. In this work, we interfaced a mixed-signal analog-digital neuromorphic processor ROLLS to a neuromorphic dynamic vision sensor (DVS) mounted on a robotic vehicle and developed an autonomous neuromorphic agent that is able to perform neurally inspired obstacle-avoidance and target acquisition. We developed a neural network architecture that can cope with device variability and verified its robustness in different environmental situations, e.g., moving obstacles, moving target, clutter, and poor light conditions. We demonstrate how this network, combined with the properties of the DVS, allows the robot to avoid obstacles using a simple biologically-inspired dynamics. We also show how a Dynamic Neural Field for target acquisition can be implemented in spiking neuromorphic hardware. This work demonstrates an implementation of working obstacle avoidance and target acquisition using mixed signal analog/digital neuromorphic hardware.

Envelope analysis of rotating machine vibrations in variable speed conditions: A comprehensive treatment

NASA Astrophysics Data System (ADS)

Abboud, D.; Antoni, J.; Sieg-Zieba, S.; Eltabach, M.

2017-02-01

Nowadays, the vibration analysis of rotating machine signals is a well-established methodology, rooted on powerful tools offered, in particular, by the theory of cyclostationary (CS) processes. Among them, the squared envelope spectrum (SES) is probably the most popular to detect random CS components which are typical symptoms, for instance, of rolling element bearing faults. Recent researches are shifted towards the extension of existing CS tools - originally devised in constant speed conditions - to the case of variable speed conditions. Many of these works combine the SES with computed order tracking after some preprocessing steps. The principal object of this paper is to organize these dispersed researches into a structured comprehensive framework. Three original features are furnished. First, a model of rotating machine signals is introduced which sheds light on the various components to be expected in the SES. Second, a critical comparison is made of three sophisticated methods, namely, the improved synchronous average, the cepstrum prewhitening, and the generalized synchronous average, used for suppressing the deterministic part. Also, a general envelope enhancement methodology which combines the latter two techniques with a time-domain filtering operation is revisited. All theoretical findings are experimentally validated on simulated and real-world vibration signals.

Neural Architecture of Selective Stopping Strategies: Distinct Brain Activity Patterns Are Associated with Attentional Capture But Not with Outright Stopping.

PubMed

Sebastian, Alexandra; Rössler, Kora; Wibral, Michael; Mobascher, Arian; Lieb, Klaus; Jung, Patrick; Tüscher, Oliver

2017-10-04

In stimulus-selective stop-signal tasks, the salient stop signal needs attentional processing before genuine response inhibition is completed. Differential prefrontal involvement in attentional capture and response inhibition has been linked to the right inferior frontal junction (IFJ) and ventrolateral prefrontal cortex (VLPFC), respectively. Recently, it has been suggested that stimulus-selective stopping may be accomplished by the following different strategies: individuals may selectively inhibit their response only upon detecting a stop signal (independent discriminate then stop strategy) or unselectively whenever detecting a stop or attentional capture signal (stop then discriminate strategy). Alternatively, the discrimination process of the critical signal (stop vs attentional capture signal) may interact with the go process (dependent discriminate then stop strategy). Those different strategies might differentially involve attention- and stopping-related processes that might be implemented by divergent neural networks. This should lead to divergent activation patterns and, if disregarded, interfere with analyses in neuroimaging studies. To clarify this crucial issue, we studied 87 human participants of both sexes during a stimulus-selective stop-signal task and performed strategy-dependent functional magnetic resonance imaging analyses. We found that, regardless of the strategy applied, outright stopping displayed indistinguishable brain activation patterns. However, during attentional capture different strategies resulted in divergent neural activation patterns with variable activation of right IFJ and bilateral VLPFC. In conclusion, the neural network involved in outright stopping is ubiquitous and independent of strategy, while different strategies impact on attention-related processes and underlying neural network usage. Strategic differences should therefore be taken into account particularly when studying attention-related processes in stimulus-selective stopping. SIGNIFICANCE STATEMENT Dissociating inhibition from attention has been a major challenge for the cognitive neuroscience of executive functions. Selective stopping tasks have been instrumental in addressing this question. However, recent theoretical, cognitive and behavioral research suggests that different strategies are applied in successful execution of the task. The underlying strategy-dependent neural networks might differ substantially. Here, we show evidence that, regardless of the strategy used, the neural network involved in outright stopping is ubiquitous. However, significant differences can only be found in the attention-related processes underlying those different strategies. Thus, when studying attentional processing of salient stop signals, strategic differences should be considered. In contrast, the neural networks implementing outright stopping seem less or not at all affected by strategic differences. Copyright © 2017 the authors 0270-6474/17/379786-10$15.00/0.

Variability of Neuronal Responses: Types and Functional Significance in Neuroplasticity and Neural Darwinism

PubMed Central

Chervyakov, Alexander V.; Sinitsyn, Dmitry O.; Piradov, Michael A.

2016-01-01

HIGHLIGHTS We suggest classifying variability of neuronal responses as follows: false (associated with a lack of knowledge about the influential factors), “genuine harmful” (noise), “genuine neutral” (synonyms, repeats), and “genuine useful” (the basis of neuroplasticity and learning).The genuine neutral variability is considered in terms of the phenomenon of degeneracy.Of particular importance is the genuine useful variability that is considered as a potential basis for neuroplasticity and learning. This type of variability is considered in terms of the neural Darwinism theory. In many cases, neural signals detected under the same external experimental conditions significantly change from trial to trial. The variability phenomenon, which complicates extraction of reproducible results and is ignored in many studies by averaging, has attracted attention of researchers in recent years. In this paper, we classify possible types of variability based on its functional significance and describe features of each type. We describe the key adaptive significance of variability at the neural network level and the degeneracy phenomenon that may be important for learning processes in connection with the principle of neuronal group selection. PMID:27932969

Variability of Neuronal Responses: Types and Functional Significance in Neuroplasticity and Neural Darwinism.

PubMed

Chervyakov, Alexander V; Sinitsyn, Dmitry O; Piradov, Michael A

2016-01-01

HIGHLIGHTS We suggest classifying variability of neuronal responses as follows: false (associated with a lack of knowledge about the influential factors), "genuine harmful" (noise), "genuine neutral" (synonyms, repeats), and "genuine useful" (the basis of neuroplasticity and learning).The genuine neutral variability is considered in terms of the phenomenon of degeneracy.Of particular importance is the genuine useful variability that is considered as a potential basis for neuroplasticity and learning. This type of variability is considered in terms of the neural Darwinism theory. In many cases, neural signals detected under the same external experimental conditions significantly change from trial to trial. The variability phenomenon, which complicates extraction of reproducible results and is ignored in many studies by averaging, has attracted attention of researchers in recent years. In this paper, we classify possible types of variability based on its functional significance and describe features of each type. We describe the key adaptive significance of variability at the neural network level and the degeneracy phenomenon that may be important for learning processes in connection with the principle of neuronal group selection.

In-situ Condition Monitoring of Components in Small Modular Reactors Using Process and Electrical Signature Analysis. Final report, volume 1. Development of experimental flow control loop, data analysis and plant monitoring

DOE Office of Scientific and Technical Information (OSTI.GOV)

Upadhyaya, Belle; Hines, J. Wesley; Damiano, Brian

The research and development under this project was focused on the following three major objectives: Objective 1: Identification of critical in-vessel SMR components for remote monitoring and development of their low-order dynamic models, along with a simulation model of an integral pressurized water reactor (iPWR). Objective 2: Development of an experimental flow control loop with motor-driven valves and pumps, incorporating data acquisition and on-line monitoring interface. Objective 3: Development of stationary and transient signal processing methods for electrical signatures, machinery vibration, and for characterizing process variables for equipment monitoring. This objective includes the development of a data analysis toolbox. Themore » following is a summary of the technical accomplishments under this project: - A detailed literature review of various SMR types and electrical signature analysis of motor-driven systems was completed. A bibliography of literature is provided at the end of this report. Assistance was provided by ORNL in identifying some key references. - A review of literature on pump-motor modeling and digital signal processing methods was performed. - An existing flow control loop was upgraded with new instrumentation, data acquisition hardware and software. The upgrading of the experimental loop included the installation of a new submersible pump driven by a three-phase induction motor. All the sensors were calibrated before full-scale experimental runs were performed. - MATLAB-Simulink model of a three-phase induction motor and pump system was completed. The model was used to simulate normal operation and fault conditions in the motor-pump system, and to identify changes in the electrical signatures. - A simulation model of an integral PWR (iPWR) was updated and the MATLAB-Simulink model was validated for known transients. The pump-motor model was interfaced with the iPWR model for testing the impact of primary flow perturbations (upsets) on plant parameters and the pump electrical signatures. Additionally, the reactor simulation is being used to generate normal operation data and data with instrumentation faults and process anomalies. A frequency controller was interfaced with the motor power supply in order to vary the electrical supply frequency. The experimental flow control loop was used to generate operational data under varying motor performance characteristics. Coolant leakage events were simulated by varying the bypass loop flow rate. The accuracy of motor power calculation was improved by incorporating the power factor, computed from motor current and voltage in each phase of the induction motor.- A variety of experimental runs were made for steady-state and transient pump operating conditions. Process, vibration, and electrical signatures were measured using a submersible pump with variable supply frequency. High correlation was seen between motor current and pump discharge pressure signal; similar high correlation was exhibited between pump motor power and flow rate. Wide-band analysis indicated high coherence (in the frequency domain) between motor current and vibration signals. - Wide-band operational data from a PWR were acquired from AMS Corporation and used to develop time-series models, and to estimate signal spectrum and sensor time constant. All the data were from different pressure transmitters in the system, including primary and secondary loops. These signals were pre-processed using the wavelet transform for filtering both low-frequency and high-frequency bands. This technique of signal pre-processing provides minimum distortion of the data, and results in a more optimal estimation of time constants of plant sensors using time-series modeling techniques.« less

Behavioral Signal Processing: Deriving Human Behavioral Informatics From Speech and Language

PubMed Central

Narayanan, Shrikanth; Georgiou, Panayiotis G.

2013-01-01

The expression and experience of human behavior are complex and multimodal and characterized by individual and contextual heterogeneity and variability. Speech and spoken language communication cues offer an important means for measuring and modeling human behavior. Observational research and practice across a variety of domains from commerce to healthcare rely on speech- and language-based informatics for crucial assessment and diagnostic information and for planning and tracking response to an intervention. In this paper, we describe some of the opportunities as well as emerging methodologies and applications of human behavioral signal processing (BSP) technology and algorithms for quantitatively understanding and modeling typical, atypical, and distressed human behavior with a specific focus on speech- and language-based communicative, affective, and social behavior. We describe the three important BSP components of acquiring behavioral data in an ecologically valid manner across laboratory to real-world settings, extracting and analyzing behavioral cues from measured data, and developing models offering predictive and decision-making support. We highlight both the foundational speech and language processing building blocks as well as the novel processing and modeling opportunities. Using examples drawn from specific real-world applications ranging from literacy assessment and autism diagnostics to psychotherapy for addiction and marital well being, we illustrate behavioral informatics applications of these signal processing techniques that contribute to quantifying higher level, often subjectively described, human behavior in a domain-sensitive fashion. PMID:24039277

Improved Uncertainty Quantification in Groundwater Flux Estimation Using GRACE

NASA Astrophysics Data System (ADS)

Reager, J. T., II; Rao, P.; Famiglietti, J. S.; Turmon, M.

2015-12-01

Groundwater change is difficult to monitor over large scales. One of the most successful approaches is in the remote sensing of time-variable gravity using NASA Gravity Recovery and Climate Experiment (GRACE) mission data, and successful case studies have created the opportunity to move towards a global groundwater monitoring framework for the world's largest aquifers. To achieve these estimates, several approximations are applied, including those in GRACE processing corrections, the formulation of the formal GRACE errors, destriping and signal recovery, and the numerical model estimation of snow water, surface water and soil moisture storage states used to isolate a groundwater component. A major weakness in these approaches is inconsistency: different studies have used different sources of primary and ancillary data, and may achieve different results based on alternative choices in these approximations. In this study, we present two cases of groundwater change estimation in California and the Colorado River basin, selected for their good data availability and varied climates. We achieve a robust numerical estimate of post-processing uncertainties resulting from land-surface model structural shortcomings and model resolution errors. Groundwater variations should demonstrate less variability than the overlying soil moisture state does, as groundwater has a longer memory of past events due to buffering by infiltration and drainage rate limits. We apply a model ensemble approach in a Bayesian framework constrained by the assumption of decreasing signal variability with depth in the soil column. We also discuss time variable errors vs. time constant errors, across-scale errors v. across-model errors, and error spectral content (across scales and across model). More robust uncertainty quantification for GRACE-based groundwater estimates would take all of these issues into account, allowing for more fair use in management applications and for better integration of GRACE-based measurements with observations from other sources.

Multi-scale variability and long-range memory in indoor Radon concentrations from Coimbra, Portugal

NASA Astrophysics Data System (ADS)

Donner, Reik V.; Potirakis, Stelios; Barbosa, Susana

2014-05-01

The presence or absence of long-range correlations in the variations of indoor Radon concentrations has recently attracted considerable interest. As a radioactive gas naturally emitted from the ground in certain geological settings, understanding environmental factors controlling Radon concentrations and their dynamics is important for estimating its effect on human health and the efficiency of possible measures for reducing the corresponding exposition. In this work, we re-analyze two high-resolution records of indoor Radon concentrations from Coimbra, Portugal, each of which spans several months of continuous measurements. In order to evaluate the presence of long-range correlations and fractal scaling, we utilize a multiplicity of complementary methods, including power spectral analysis, ARFIMA modeling, classical and multi-fractal detrended fluctuation analysis, and two different estimators of the signals' fractal dimensions. Power spectra and fluctuation functions reveal some complex behavior with qualitatively different properties on different time-scales: white noise in the high-frequency part, indications of some long-range correlated process dominating time scales of several hours to days, and pronounced low-frequency variability associated with tidal and/or meteorological forcing. In order to further decompose these different scales of variability, we apply two different approaches. On the one hand, applying multi-resolution analysis based on the discrete wavelet transform allows separately studying contributions on different time scales and characterize their specific correlation and scaling properties. On the other hand, singular system analysis (SSA) provides a reconstruction of the essential modes of variability. Specifically, by considering only the first leading SSA modes, we achieve an efficient de-noising of our environmental signals, highlighting the low-frequency variations together with some distinct scaling on sub-daily time-scales resembling the properties of a long-range correlated process.

A Variance Distribution Model of Surface EMG Signals Based on Inverse Gamma Distribution.

PubMed

Hayashi, Hideaki; Furui, Akira; Kurita, Yuichi; Tsuji, Toshio

2017-11-01

Objective: This paper describes the formulation of a surface electromyogram (EMG) model capable of representing the variance distribution of EMG signals. Methods: In the model, EMG signals are handled based on a Gaussian white noise process with a mean of zero for each variance value. EMG signal variance is taken as a random variable that follows inverse gamma distribution, allowing the representation of noise superimposed onto this variance. Variance distribution estimation based on marginal likelihood maximization is also outlined in this paper. The procedure can be approximated using rectified and smoothed EMG signals, thereby allowing the determination of distribution parameters in real time at low computational cost. Results: A simulation experiment was performed to evaluate the accuracy of distribution estimation using artificially generated EMG signals, with results demonstrating that the proposed model's accuracy is higher than that of maximum-likelihood-based estimation. Analysis of variance distribution using real EMG data also suggested a relationship between variance distribution and signal-dependent noise. Conclusion: The study reported here was conducted to examine the performance of a proposed surface EMG model capable of representing variance distribution and a related distribution parameter estimation method. Experiments using artificial and real EMG data demonstrated the validity of the model. Significance: Variance distribution estimated using the proposed model exhibits potential in the estimation of muscle force. Objective: This paper describes the formulation of a surface electromyogram (EMG) model capable of representing the variance distribution of EMG signals. Methods: In the model, EMG signals are handled based on a Gaussian white noise process with a mean of zero for each variance value. EMG signal variance is taken as a random variable that follows inverse gamma distribution, allowing the representation of noise superimposed onto this variance. Variance distribution estimation based on marginal likelihood maximization is also outlined in this paper. The procedure can be approximated using rectified and smoothed EMG signals, thereby allowing the determination of distribution parameters in real time at low computational cost. Results: A simulation experiment was performed to evaluate the accuracy of distribution estimation using artificially generated EMG signals, with results demonstrating that the proposed model's accuracy is higher than that of maximum-likelihood-based estimation. Analysis of variance distribution using real EMG data also suggested a relationship between variance distribution and signal-dependent noise. Conclusion: The study reported here was conducted to examine the performance of a proposed surface EMG model capable of representing variance distribution and a related distribution parameter estimation method. Experiments using artificial and real EMG data demonstrated the validity of the model. Significance: Variance distribution estimated using the proposed model exhibits potential in the estimation of muscle force.

Moment-to-Moment BOLD Signal Variability Reflects Regional Changes in Neural Flexibility across the Lifespan.

PubMed

Nomi, Jason S; Bolt, Taylor S; Ezie, C E Chiemeka; Uddin, Lucina Q; Heller, Aaron S

2017-05-31

Variability of neuronal responses is thought to underlie flexible and optimal brain function. Because previous work investigating BOLD signal variability has been conducted within task-based fMRI contexts on adults and older individuals, very little is currently known regarding regional changes in spontaneous BOLD signal variability in the human brain across the lifespan. The current study used resting-state fMRI data from a large sample of male and female human participants covering a wide age range (6-85 years) across two different fMRI acquisition parameters (TR = 0.645 and 1.4 s). Variability in brain regions including a key node of the salience network (anterior insula) increased linearly across the lifespan across datasets. In contrast, variability in most other large-scale networks decreased linearly over the lifespan. These results demonstrate unique lifespan trajectories of BOLD variability related to specific regions of the brain and add to a growing literature demonstrating the importance of identifying normative trajectories of functional brain maturation. SIGNIFICANCE STATEMENT Although brain signal variability has traditionally been considered a source of unwanted noise, recent work demonstrates that variability in brain signals during task performance is related to brain maturation in old age as well as individual differences in behavioral performance. The current results demonstrate that intrinsic fluctuations in resting-state variability exhibit unique maturation trajectories in specific brain regions and systems, particularly those supporting salience detection. These results have implications for investigations of brain development and aging, as well as interpretations of brain function underlying behavioral changes across the lifespan. Copyright © 2017 the authors 0270-6474/17/375539-10$15.00/0.

Transiting Planet Search in the Kepler Pipeline

NASA Technical Reports Server (NTRS)

Jenkins, Jon M.; Chandrasekaran, Hema; McCauliff, Sean D.; Caldwell, Douglas A.; Tenebaum, Peter; Li, Jie; Klaus, Todd C.; Cote, Mile T.; Middour, Christopher

2010-01-01

The Kepler Mission simultaneously measures the brightness of more than 160,000 stars every 29.4 minutes over a 3.5-year mission to search for transiting planets. Detecting transits is a signal-detection problem where the signal of interest is a periodic pulse train and the predominant noise source is non-white, non-stationary (1/f) type process of stellar variability. Many stars also exhibit coherent or quasi-coherent oscillations. The detection algorithm first identifies and removes strong oscillations followed by an adaptive, wavelet-based matched filter. We discuss how we obtain super-resolution detection statistics and the effectiveness of the algorithm for Kepler flight data.

Methyl jasmonate as a vital substance in plants.

PubMed

Cheong, Jong-Joo; Choi, Yang Do

2003-07-01

The plant floral scent methyl jasmonate (MeJA) has been identified as a vital cellular regulator that mediates diverse developmental processes and defense responses against biotic and abiotic stresses. The pleiotropic effects of MeJA have raised numerous questions about its regulation for biogenesis and mode of action. Characterization of the gene encoding jasmonic acid carboxyl methyltransferase has provided basic information on the role(s) of this phytohormone in gene-activation control and systemic long-distance signaling. Recent approaches using functional genomics and bioinformatics have identified a whole set of MeJA-responsive genes, and provide insights into how plants use volatile signals to withstand diverse and variable environments.

Explaining neural signals in human visual cortex with an associative learning model.

PubMed

Jiang, Jiefeng; Schmajuk, Nestor; Egner, Tobias

2012-08-01

"Predictive coding" models posit a key role for associative learning in visual cognition, viewing perceptual inference as a process of matching (learned) top-down predictions (or expectations) against bottom-up sensory evidence. At the neural level, these models propose that each region along the visual processing hierarchy entails one set of processing units encoding predictions of bottom-up input, and another set computing mismatches (prediction error or surprise) between predictions and evidence. This contrasts with traditional views of visual neurons operating purely as bottom-up feature detectors. In support of the predictive coding hypothesis, a recent human neuroimaging study (Egner, Monti, & Summerfield, 2010) showed that neural population responses to expected and unexpected face and house stimuli in the "fusiform face area" (FFA) could be well-described as a summation of hypothetical face-expectation and -surprise signals, but not by feature detector responses. Here, we used computer simulations to test whether these imaging data could be formally explained within the broader framework of a mathematical neural network model of associative learning (Schmajuk, Gray, & Lam, 1996). Results show that FFA responses could be fit very closely by model variables coding for conditional predictions (and their violations) of stimuli that unconditionally activate the FFA. These data document that neural population signals in the ventral visual stream that deviate from classic feature detection responses can formally be explained by associative prediction and surprise signals.

First-Pass Processing of Value Cues in the Ventral Visual Pathway.

PubMed

Sasikumar, Dennis; Emeric, Erik; Stuphorn, Veit; Connor, Charles E

2018-02-19

Real-world value often depends on subtle, continuously variable visual cues specific to particular object categories, like the tailoring of a suit, the condition of an automobile, or the construction of a house. Here, we used microelectrode recording in behaving monkeys to test two possible mechanisms for category-specific value-cue processing: (1) previous findings suggest that prefrontal cortex (PFC) identifies object categories, and based on category identity, PFC could use top-down attentional modulation to enhance visual processing of category-specific value cues, providing signals to PFC for calculating value, and (2) a faster mechanism would be first-pass visual processing of category-specific value cues, immediately providing the necessary visual information to PFC. This, however, would require learned mechanisms for processing the appropriate cues in a given object category. To test these hypotheses, we trained monkeys to discriminate value in four letter-like stimulus categories. Each category had a different, continuously variable shape cue that signified value (liquid reward amount) as well as other cues that were irrelevant. Monkeys chose between stimuli of different reward values. Consistent with the first-pass hypothesis, we found early signals for category-specific value cues in area TE (the final stage in monkey ventral visual pathway) beginning 81 ms after stimulus onset-essentially at the start of TE responses. Task-related activity emerged in lateral PFC approximately 40 ms later and consisted mainly of category-invariant value tuning. Our results show that, for familiar, behaviorally relevant object categories, high-level ventral pathway cortex can implement rapid, first-pass processing of category-specific value cues. Copyright © 2018 Elsevier Ltd. All rights reserved.

Kepler AutoRegressive Planet Search

NASA Astrophysics Data System (ADS)

Caceres, Gabriel Antonio; Feigelson, Eric

2016-01-01

The Kepler AutoRegressive Planet Search (KARPS) project uses statistical methodology associated with autoregressive (AR) processes to model Kepler lightcurves in order to improve exoplanet transit detection in systems with high stellar variability. We also introduce a planet-search algorithm to detect transits in time-series residuals after application of the AR models. One of the main obstacles in detecting faint planetary transits is the intrinsic stellar variability of the host star. The variability displayed by many stars may have autoregressive properties, wherein later flux values are correlated with previous ones in some manner. Our analysis procedure consisting of three steps: pre-processing of the data to remove discontinuities, gaps and outliers; AR-type model selection and fitting; and transit signal search of the residuals using a new Transit Comb Filter (TCF) that replaces traditional box-finding algorithms. The analysis procedures of the project are applied to a portion of the publicly available Kepler light curve data for the full 4-year mission duration. Tests of the methods have been made on a subset of Kepler Objects of Interest (KOI) systems, classified both as planetary `candidates' and `false positives' by the Kepler Team, as well as a random sample of unclassified systems. We find that the ARMA-type modeling successfully reduces the stellar variability, by a factor of 10 or more in active stars and by smaller factors in more quiescent stars. A typical quiescent Kepler star has an interquartile range (IQR) of ~10 e-/sec, which may improve slightly after modeling, while those with IQR ranging from 20 to 50 e-/sec, have improvements from 20% up to 70%. High activity stars (IQR exceeding 100) markedly improve. A periodogram based on the TCF is constructed to concentrate the signal of these periodic spikes. When a periodic transit is found, the model is displayed on a standard period-folded averaged light curve. Our findings to date on real-data tests of the KARPS methodology will be discussed including confirmation of some Kepler Team `candidate' planets. We also present cases of new possible planetary signals.

Methods to detect, characterize, and remove motion artifact in resting state fMRI

PubMed Central

Power, Jonathan D; Mitra, Anish; Laumann, Timothy O; Snyder, Abraham Z; Schlaggar, Bradley L; Petersen, Steven E

2013-01-01

Head motion systematically alters correlations in resting state functional connectivity fMRI (RSFC). In this report we examine impact of motion on signal intensity and RSFC correlations. We find that motion-induced signal changes (1) are often complex and variable waveforms, (2) are often shared across nearly all brain voxels, and (3) often persist more than 10 seconds after motion ceases. These signal changes, both during and after motion, increase observed RSFC correlations in a distance-dependent manner. Motion-related signal changes are not removed by a variety of motion-based regressors, but are effectively reduced by global signal regression. We link several measures of data quality to motion, changes in signal intensity, and changes in RSFC correlations. We demonstrate that improvements in data quality measures during processing may represent cosmetic improvements rather than true correction of the data. We demonstrate a within-subject, censoring-based artifact removal strategy based on volume censoring that reduces group differences due to motion to chance levels. We note conditions under which group-level regressions do and do not correct motion-related effects. PMID:23994314

Novel measures of response performance and inhibition in children with ADHD.

PubMed

Morein-Zamir, Sharon; Hommersen, Paul; Johnston, Charlotte; Kingstone, Alan

2008-11-01

Fifteen children with ADHD aged 8 to 12 years and age and gender matched controls performed two different stopping tasks to examine response performance and inhibition and their respective moment-to-moment variability. One task was the well-established stop-signal task, while the other was a novel tracking task where the children tracked a spaceship on the screen until an alarm indicated they should stop. Although performance was discrete in the stop signal task and continuous in the tracking task, in both tasks latencies to the stop signal were significantly slowed in children with ADHD. Go performance and variability did not significantly differ between ADHD and control children in either task. Importantly, stopping latency in the novel spaceship tracking task also was more variable in children with ADHD. As stopping variability cannot be measured using the standard stop signal task, the new task offers compelling support for the heretofore untested prediction that stopping is both slowed and more variable in children with ADHD. The results support a response inhibition impairment in ADHD, whilst limiting the extent of an intra-trial variability deficit.

Working memory, age, and hearing loss: susceptibility to hearing aid distortion.

PubMed

Arehart, Kathryn H; Souza, Pamela; Baca, Rosalinda; Kates, James M

2013-01-01

Hearing aids use complex processing intended to improve speech recognition. Although many listeners benefit from such processing, it can also introduce distortion that offsets or cancels intended benefits for some individuals. The purpose of the present study was to determine the effects of cognitive ability (working memory) on individual listeners' responses to distortion caused by frequency compression applied to noisy speech. The present study analyzed a large data set of intelligibility scores for frequency-compressed speech presented in quiet and at a range of signal-to-babble ratios. The intelligibility data set was based on scores from 26 adults with hearing loss with ages ranging from 62 to 92 years. The listeners were grouped based on working memory ability. The amount of signal modification (distortion) caused by frequency compression and noise was measured using a sound quality metric. Analysis of variance and hierarchical linear modeling were used to identify meaningful differences between subject groups as a function of signal distortion caused by frequency compression and noise. Working memory was a significant factor in listeners' intelligibility of sentences presented in babble noise and processed with frequency compression based on sinusoidal modeling. At maximum signal modification (caused by both frequency compression and babble noise), the factor of working memory (when controlling for age and hearing loss) accounted for 29.3% of the variance in intelligibility scores. Combining working memory, age, and hearing loss accounted for a total of 47.5% of the variability in intelligibility scores. Furthermore, as the total amount of signal distortion increased, listeners with higher working memory performed better on the intelligibility task than listeners with lower working memory did. Working memory is a significant factor in listeners' responses to total signal distortion caused by cumulative effects of babble noise and frequency compression implemented with sinusoidal modeling. These results, together with other studies focused on wide-dynamic range compression, suggest that older listeners with hearing loss and poor working memory are more susceptible to distortions caused by at least some types of hearing aid signal-processing algorithms and by noise, and that this increased susceptibility should be considered in the hearing aid fitting process.

Role of optical computers in aeronautical control applications

NASA Technical Reports Server (NTRS)

Baumbick, R. J.

1981-01-01

The role that optical computers play in aircraft control is determined. The optical computer has the potential high speed capability required, especially for matrix/matrix operations. The optical computer also has the potential for handling nonlinear simulations in real time. They are also more compatible with fiber optic signal transmission. Optics also permit the use of passive sensors to measure process variables. No electrical energy need be supplied to the sensor. Complex interfacing between optical sensors and the optical computer is avoided if the optical sensor outputs can be directly processed by the optical computer.

Functional Dysregulation of CDC42 Causes Diverse Developmental Phenotypes.

PubMed

Martinelli, Simone; Krumbach, Oliver H F; Pantaleoni, Francesca; Coppola, Simona; Amin, Ehsan; Pannone, Luca; Nouri, Kazem; Farina, Luciapia; Dvorsky, Radovan; Lepri, Francesca; Buchholzer, Marcel; Konopatzki, Raphael; Walsh, Laurence; Payne, Katelyn; Pierpont, Mary Ella; Vergano, Samantha Schrier; Langley, Katherine G; Larsen, Douglas; Farwell, Kelly D; Tang, Sha; Mroske, Cameron; Gallotta, Ivan; Di Schiavi, Elia; Della Monica, Matteo; Lugli, Licia; Rossi, Cesare; Seri, Marco; Cocchi, Guido; Henderson, Lindsay; Baskin, Berivan; Alders, Mariëlle; Mendoza-Londono, Roberto; Dupuis, Lucie; Nickerson, Deborah A; Chong, Jessica X; Meeks, Naomi; Brown, Kathleen; Causey, Tahnee; Cho, Megan T; Demuth, Stephanie; Digilio, Maria Cristina; Gelb, Bruce D; Bamshad, Michael J; Zenker, Martin; Ahmadian, Mohammad Reza; Hennekam, Raoul C; Tartaglia, Marco; Mirzaa, Ghayda M

2018-01-17

Exome sequencing has markedly enhanced the discovery of genes implicated in Mendelian disorders, particularly for individuals in whom a known clinical entity could not be assigned. This has led to the recognition that phenotypic heterogeneity resulting from allelic mutations occurs more commonly than previously appreciated. Here, we report that missense variants in CDC42, a gene encoding a small GTPase functioning as an intracellular signaling node, underlie a clinically heterogeneous group of phenotypes characterized by variable growth dysregulation, facial dysmorphism, and neurodevelopmental, immunological, and hematological anomalies, including a phenotype resembling Noonan syndrome, a developmental disorder caused by dysregulated RAS signaling. In silico, in vitro, and in vivo analyses demonstrate that mutations variably perturb CDC42 function by altering the switch between the active and inactive states of the GTPase and/or affecting CDC42 interaction with effectors, and differentially disturb cellular and developmental processes. These findings reveal the remarkably variable impact that dominantly acting CDC42 mutations have on cell function and development, creating challenges in syndrome definition, and exemplify the importance of functional profiling for syndrome recognition and delineation. Copyright © 2017 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

The constant current loop - A new paradigm for resistance signal conditioning

NASA Astrophysics Data System (ADS)

Anderson, Karl F.

A practical single constant current loop circuit for the signal conditioning of variable-resistance transducers has been synthesized, analyzed, and demonstrated. The strain gage and the resistance temperature device are examples of variable-resistance sensors. Lead wires connect variable-resistance sensors to remotely located signal-conditioning hardware. The presence of lead wires in the conventional Wheatstone bridge signal-conditioning circuit introduces undesired effects that reduce the quality of the data from the remote sensors. A practical approach is presented for suppressing essentially all lead wire resistance effects while indicating only the change in resistance value. An adaptation of the current loop circuit is presented that simultaneously provides an output signal voltage directly proportional to transducer resistance change and provides temperature information that is unaffected by transducer and lead wire resistance variations.

The constant current loop - A new paradigm for resistance signal conditioning

NASA Technical Reports Server (NTRS)

Anderson, Karl F.

1993-01-01

A practical single constant current loop circuit for the signal conditioning of variable-resistance transducers has been synthesized, analyzed, and demonstrated. The strain gage and the resistance temperature device are examples of variable-resistance sensors. Lead wires connect variable-resistance sensors to remotely located signal-conditioning hardware. The presence of lead wires in the conventional Wheatstone bridge signal-conditioning circuit introduces undesired effects that reduce the quality of the data from the remote sensors. A practical approach is presented for suppressing essentially all lead wire resistance effects while indicating only the change in resistance value. An adaptation of the current loop circuit is presented that simultaneously provides an output signal voltage directly proportional to transducer resistance change and provides temperature information that is unaffected by transducer and lead wire resistance variations.

Interannual Variations In the Low-Degree Components of the Geopotential derived from SLR and the Connections With Geophysical/Climatic Processes

NASA Technical Reports Server (NTRS)

Chao, Benjamin F.; Cox, Christopher M.; Au, Andrew Y.

2004-01-01

Recent Satellite Laser Ranging derived long wavelength gravity time series analysis has focused to a large extent on the effects of the recent large changes in the Earth s 52, and the potential causes. However, it is difficult to determine whether there are corresponding signals in the shorter wavelength zonals from the existing SLR-derived time variable gravity results, although it appears that geophysical fluid transport is being observed. For example, the recovered J3 time series shows remarkable agreement with NCEP-derived estimates of atmospheric gravity variations. Likewise, some of the non-zonal spherical harmonic coefficient series have significant interannual signal that appears to be related to mass transport. The non-zonal degree 2 terms show reasonable correlation with atmospheric signals, as well as climatic effects such as El Nino Southern Oscillation. While the formal uncertainty of these terms is significantly higher than that for J2, it is also clear that there is useful signal to be extracted. Consequently, the SLR time series is being reprocessed to improve the time variable gravity field recovery. We will present recent updates on the J2 evolution, as well as a look at other components of the interannual variations of the gravity field, complete through degree 4, and possible geophysical and climatic causes.

Cognitive strategies regulate fictive, but not reward prediction error signals in a sequential investment task.

PubMed

Gu, Xiaosi; Kirk, Ulrich; Lohrenz, Terry M; Montague, P Read

2014-08-01

Computational models of reward processing suggest that foregone or fictive outcomes serve as important information sources for learning and augment those generated by experienced rewards (e.g. reward prediction errors). An outstanding question is how these learning signals interact with top-down cognitive influences, such as cognitive reappraisal strategies. Using a sequential investment task and functional magnetic resonance imaging, we show that the reappraisal strategy selectively attenuates the influence of fictive, but not reward prediction error signals on investment behavior; such behavioral effect is accompanied by changes in neural activity and connectivity in the anterior insular cortex, a brain region thought to integrate subjective feelings with high-order cognition. Furthermore, individuals differ in the extent to which their behaviors are driven by fictive errors versus reward prediction errors, and the reappraisal strategy interacts with such individual differences; a finding also accompanied by distinct underlying neural mechanisms. These findings suggest that the variable interaction of cognitive strategies with two important classes of computational learning signals (fictive, reward prediction error) represent one contributing substrate for the variable capacity of individuals to control their behavior based on foregone rewards. These findings also expose important possibilities for understanding the lack of control in addiction based on possibly foregone rewarding outcomes. Copyright © 2013 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc.

Neural and vascular variability and the fMRI-BOLD response in normal aging

PubMed Central

Kannurpatti, Sridhar S.; Motes, Michael A.; Rypma, Bart; Biswal, Bharat B.

2010-01-01

Neural, vascular and structural variables contributing to the BOLD signal response variability were investigated in younger and older humans. Twelve younger healthy human subjects (6M and 6F; mean age: 24 years; range: 19–27 years) and twelve older healthy subjects (5M and 7F; mean age: 58 years; range: 55–71 years) with no history of head trauma and neurological disease were scanned. FMRI measurements using the BOLD contrast were made when participants performed a motor, cognitive or a breath hold task. Activation volume and the BOLD response amplitude were estimated for the younger and older at both group and subject levels. Mean activation volume was reduced by 45, 40 and 38% in the elderly group during the motor, cognitive and breath hold tasks respectively compared to the younger. Reduction in activation volume was substantially higher compared to the reduction in the gray matter volume of 14% in the older compared to the younger. A significantly larger variability in the inter-subject BOLD signal change occurred during the motor task, compared to the cognitive task. BH-induced BOLD signal change between subjects was significantly less-variable in the motor task-activated areas in the younger compared to older whereas such a difference between age groups was not observed during the cognitive task. Hemodynamic scaling using the BH signal substantially reduced the BOLD signal variability during the motor task compared to the cognitive task. The results indicate that the origin of the BOLD signal variability between subjects was predominantly vascular during the motor task while being principally a consequence of neural variability during the cognitive task. Thus, in addition to gray matter differences, the type of task performed can have different vascular variability weighting that can influence age-related differences in brain functional response. PMID:20117893

Neural and vascular variability and the fMRI-BOLD response in normal aging.

PubMed

Kannurpatti, Sridhar S; Motes, Michael A; Rypma, Bart; Biswal, Bharat B

2010-05-01

Neural, vascular and structural variables contributing to the blood oxygen level-dependent (BOLD) signal response variability were investigated in younger and older humans. Twelve younger healthy human subjects (six male and six female; mean age: 24 years; range: 19-27 years) and 12 older healthy subjects (five male and seven female; mean age: 58 years; range: 55-71 years) with no history of head trauma and neurological disease were scanned. Functional magnetic resonance imaging measurements using the BOLD contrast were made when participants performed a motor, cognitive or a breath hold (BH) task. Activation volume and the BOLD response amplitude were estimated for the younger and older at both group and subject levels. Mean activation volume was reduced by 45%, 40% and 38% in the elderly group during the motor, cognitive and BH tasks, respectively, compared to the younger. Reduction in activation volume was substantially higher compared to the reduction in the gray matter volume of 14% in the older compared to the younger. A significantly larger variability in the intersubject BOLD signal change occurred during the motor task, compared to the cognitive task. BH-induced BOLD signal change between subjects was significantly less-variable in the motor task-activated areas in the younger compared to older whereas such a difference between age groups was not observed during the cognitive task. Hemodynamic scaling using the BH signal substantially reduced the BOLD signal variability during the motor task compared to the cognitive task. The results indicate that the origin of the BOLD signal variability between subjects was predominantly vascular during the motor task while being principally a consequence of neural variability during the cognitive task. Thus, in addition to gray matter differences, the type of task performed can have different vascular variability weighting that can influence age-related differences in brain functional response. 2010 Elsevier Inc. All rights reserved.

Statistical learning and adaptive decision-making underlie human response time variability in inhibitory control.

PubMed

Ma, Ning; Yu, Angela J

2015-01-01

Response time (RT) is an oft-reported behavioral measure in psychological and neurocognitive experiments, but the high level of observed trial-to-trial variability in this measure has often limited its usefulness. Here, we combine computational modeling and psychophysics to examine the hypothesis that fluctuations in this noisy measure reflect dynamic computations in human statistical learning and corresponding cognitive adjustments. We present data from the stop-signal task (SST), in which subjects respond to a go stimulus on each trial, unless instructed not to by a subsequent, infrequently presented stop signal. We model across-trial learning of stop signal frequency, P(stop), and stop-signal onset time, SSD (stop-signal delay), with a Bayesian hidden Markov model, and within-trial decision-making with an optimal stochastic control model. The combined model predicts that RT should increase with both expected P(stop) and SSD. The human behavioral data (n = 20) bear out this prediction, showing P(stop) and SSD both to be significant, independent predictors of RT, with P(stop) being a more prominent predictor in 75% of the subjects, and SSD being more prominent in the remaining 25%. The results demonstrate that humans indeed readily internalize environmental statistics and adjust their cognitive/behavioral strategy accordingly, and that subtle patterns in RT variability can serve as a valuable tool for validating models of statistical learning and decision-making. More broadly, the modeling tools presented in this work can be generalized to a large body of behavioral paradigms, in order to extract insights about cognitive and neural processing from apparently quite noisy behavioral measures. We also discuss how this behaviorally validated model can then be used to conduct model-based analysis of neural data, in order to help identify specific brain areas for representing and encoding key computational quantities in learning and decision-making.

Retrieving Tract Variables From Acoustics: A Comparison of Different Machine Learning Strategies.

PubMed

Mitra, Vikramjit; Nam, Hosung; Espy-Wilson, Carol Y; Saltzman, Elliot; Goldstein, Louis

2010-09-13

Many different studies have claimed that articulatory information can be used to improve the performance of automatic speech recognition systems. Unfortunately, such articulatory information is not readily available in typical speaker-listener situations. Consequently, such information has to be estimated from the acoustic signal in a process which is usually termed "speech-inversion." This study aims to propose and compare various machine learning strategies for speech inversion: Trajectory mixture density networks (TMDNs), feedforward artificial neural networks (FF-ANN), support vector regression (SVR), autoregressive artificial neural network (AR-ANN), and distal supervised learning (DSL). Further, using a database generated by the Haskins Laboratories speech production model, we test the claim that information regarding constrictions produced by the distinct organs of the vocal tract (vocal tract variables) is superior to flesh-point information (articulatory pellet trajectories) for the inversion process.

Seismologic applications of GRACE time-variable gravity measurements

NASA Astrophysics Data System (ADS)

Li, Jin; Chen, Jianli; Zhang, Zizhan

2014-04-01

The Gravity Recovery and Climate Experiment (GRACE) has been measuring temporal and spatial variations of mass redistribution within the Earth system since 2002. As large earthquakes cause significant mass changes on and under the Earth's surface, GRACE provides a new means from space to observe mass redistribution due to earthquake deformations. GRACE serves as a good complement to other earthquake measurements because of its extensive spatial coverage and being free from terrestrial restriction. During its over 10 years mission, GRACE has successfully detected seismic gravitational changes of several giant earthquakes, which include the 2004 Sumatra-Andaman earthquake, 2010 Maule (Chile) earthquake, and 2011 Tohoku-Oki (Japan) earthquake. In this review, we describe by examples how to process GRACE time-variable gravity data to retrieve seismic signals, and summarize the results of recent studies that apply GRACE observations to detect co- and post-seismic signals and constrain fault slip models and viscous lithospheric structures. We also discuss major problems and give an outlook in this field of GRACE application.

smRithm: Graphical user interface for heart rate variability analysis.

PubMed

Nara, Sanjeev; Kaur, Manvinder; Datta, Saurav

2015-01-01

Over the past 25 years, Heart rate variability (HRV) has become a non-invasive research and clinical tool for indirectly carrying out investigation of both cardiac and autonomic system function in both healthy and diseased. It provides valuable information about a wide range of cardiovascular disorders, pulmonary diseases, neurological diseases, etc. Its primary purpose is to access the functioning of the nervous system. The source of information for HRV analysis is the continuous beat to beat measurement of inter-beat intervals. The electrocardiography (ECG or EKG) is considered as the best way to measure inter-beat intervals. This paper proposes an open source Graphical User Interface (GUI): smRithm developed in MATLAB for HRV analysis that will apply effective techniques on the raw ECG signals to process and decompose it in a simpler manner to obtain more useful information out of signals that can be utilized for more powerful and efficient applications in the near future related to HRV.

Theory and simulations of covariance mapping in multiple dimensions for data analysis in high-event-rate experiments

NASA Astrophysics Data System (ADS)

Zhaunerchyk, V.; Frasinski, L. J.; Eland, J. H. D.; Feifel, R.

2014-05-01

Multidimensional covariance analysis and its validity for correlation of processes leading to multiple products are investigated from a theoretical point of view. The need to correct for false correlations induced by experimental parameters which fluctuate from shot to shot, such as the intensity of self-amplified spontaneous emission x-ray free-electron laser pulses, is emphasized. Threefold covariance analysis based on simple extension of the two-variable formulation is shown to be valid for variables exhibiting Poisson statistics. In this case, false correlations arising from fluctuations in an unstable experimental parameter that scale linearly with signals can be eliminated by threefold partial covariance analysis, as defined here. Fourfold covariance based on the same simple extension is found to be invalid in general. Where fluctuations in an unstable parameter induce nonlinear signal variations, a technique of contingent covariance analysis is proposed here to suppress false correlations. In this paper we also show a method to eliminate false correlations associated with fluctuations of several unstable experimental parameters.

New Perspectives on the Role of Internal Variability in Regional Climate Change and Climate Model Evaluation

NASA Astrophysics Data System (ADS)

Deser, C.

2017-12-01

Natural climate variability occurs over a wide range of time and space scales as a result of processes intrinsic to the atmosphere, the ocean, and their coupled interactions. Such internally generated climate fluctuations pose significant challenges for the identification of externally forced climate signals such as those driven by volcanic eruptions or anthropogenic increases in greenhouse gases. This challenge is exacerbated for regional climate responses evaluated from short (< 50 years) data records. The limited duration of the observations also places strong constraints on how well the spatial and temporal characteristics of natural climate variability are known, especially on multi-decadal time scales. The observational constraints, in turn, pose challenges for evaluation of climate models, including their representation of internal variability and assessing the accuracy of their responses to natural and anthropogenic radiative forcings. A promising new approach to climate model assessment is the advent of large (10-100 member) "initial-condition" ensembles of climate change simulations with individual models. Such ensembles allow for accurate determination, and straightforward separation, of externally forced climate signals and internal climate variability on regional scales. The range of climate trajectories in a given model ensemble results from the fact that each simulation represents a particular sequence of internal variability superimposed upon a common forced response. This makes clear that nature's single realization is only one of many that could have unfolded. This perspective leads to a rethinking of approaches to climate model evaluation that incorporate observational uncertainty due to limited sampling of internal variability. Illustrative examples across a range of well-known climate phenomena including ENSO, volcanic eruptions, and anthropogenic climate change will be discussed.

The development and evaluation of accident predictive models

NASA Astrophysics Data System (ADS)

Maleck, T. L.

1980-12-01

A mathematical model that will predict the incremental change in the dependent variables (accident types) resulting from changes in the independent variables is developed. The end product is a tool for estimating the expected number and type of accidents for a given highway segment. The data segments (accidents) are separated in exclusive groups via a branching process and variance is further reduced using stepwise multiple regression. The standard error of the estimate is calculated for each model. The dependent variables are the frequency, density, and rate of 18 types of accidents among the independent variables are: district, county, highway geometry, land use, type of zone, speed limit, signal code, type of intersection, number of intersection legs, number of turn lanes, left-turn control, all-red interval, average daily traffic, and outlier code. Models for nonintersectional accidents did not fit nor validate as well as models for intersectional accidents.

Subwavelength grating enabled on-chip ultra-compact optical true time delay line

PubMed Central

Wang, Junjia; Ashrafi, Reza; Adams, Rhys; Glesk, Ivan; Gasulla, Ivana; Capmany, José; Chen, Lawrence R.

2016-01-01

An optical true time delay line (OTTDL) is a basic photonic building block that enables many microwave photonic and optical processing operations. The conventional design for an integrated OTTDL that is based on spatial diversity uses a length-variable waveguide array to create the optical time delays, which can introduce complexities in the integrated circuit design. Here we report the first ever demonstration of an integrated index-variable OTTDL that exploits spatial diversity in an equal length waveguide array. The approach uses subwavelength grating waveguides in silicon-on-insulator (SOI), which enables the realization of OTTDLs having a simple geometry and that occupy a compact chip area. Moreover, compared to conventional wavelength-variable delay lines with a few THz operation bandwidth, our index-variable OTTDL has an extremely broad operation bandwidth practically exceeding several tens of THz, which supports operation for various input optical signals with broad ranges of central wavelength and bandwidth. PMID:27457024

Subwavelength grating enabled on-chip ultra-compact optical true time delay line.

PubMed

Wang, Junjia; Ashrafi, Reza; Adams, Rhys; Glesk, Ivan; Gasulla, Ivana; Capmany, José; Chen, Lawrence R

2016-07-26

An optical true time delay line (OTTDL) is a basic photonic building block that enables many microwave photonic and optical processing operations. The conventional design for an integrated OTTDL that is based on spatial diversity uses a length-variable waveguide array to create the optical time delays, which can introduce complexities in the integrated circuit design. Here we report the first ever demonstration of an integrated index-variable OTTDL that exploits spatial diversity in an equal length waveguide array. The approach uses subwavelength grating waveguides in silicon-on-insulator (SOI), which enables the realization of OTTDLs having a simple geometry and that occupy a compact chip area. Moreover, compared to conventional wavelength-variable delay lines with a few THz operation bandwidth, our index-variable OTTDL has an extremely broad operation bandwidth practically exceeding several tens of THz, which supports operation for various input optical signals with broad ranges of central wavelength and bandwidth.

Annual Research Review: Reaction time variability in ADHD and autism spectrum disorders: measurement and mechanisms of a proposed trans-diagnostic phenotype

PubMed Central

Karalunas, Sarah L.; Geurts, Hilde M.; Konrad, Kerstin; Bender, Stephan; Nigg, Joel T.

2014-01-01

Background Intraindividual variability in reaction time (RT) has received extensive discussion as an indicator of cognitive performance, a putative intermediate phenotype of many clinical disorders, and a possible trans-diagnostic phenotype that may elucidate shared risk factors for mechanisms of psychiatric illnesses. Scope and Methodology Using the examples of attention deficit hyperactivity disorder (ADHD) and autism spectrum disorders (ASD), we discuss RT variability. We first present a new meta-analysis of RT variability in ASD with and without comorbid ADHD. We then discuss potential mechanisms that may account for RT variability and statistical models that disentangle the cognitive processes affecting RTs. We then report a second meta-analysis comparing ADHD and non-ADHD children on diffusion model parameters. We consider how findings inform the search for neural correlates of RT variability. Findings Results suggest that RT variability is increased in ASD only when children with comorbid ADHD are included in the sample. Furthermore, RT variability in ADHD is explained by moderate to large increases (d = 0.63–0.99) in the ex-Gaussian parameter τ and the diffusion parameter drift rate, as well as by smaller differences (d = 0.32) in the diffusion parameter of nondecision time. The former may suggest problems in state regulation or arousal and difficulty detecting signal from noise, whereas the latter may reflect contributions from deficits in motor organization or output. The neuroimaging literature converges with this multicomponent interpretation and also highlights the role of top-down control circuits. Conclusion We underscore the importance of considering the interactions between top-down control, state regulation (e.g. arousal), and motor preparation when interpreting RT variability and conclude that decomposition of the RT signal provides superior interpretive power and suggests mechanisms convergent with those implicated using other cognitive paradigms. We conclude with specific recommendations for the field for next steps in the study of RT variability in neurodevelopmental disorders. PMID:24628425

Nature and Nurture of Human Pain

PubMed Central

2013-01-01

Humans are very different when it comes to pain. Some get painful piercings and tattoos; others can not stand even a flu shot. Interindividual variability is one of the main characteristics of human pain on every level including the processing of nociceptive impulses at the periphery, modification of pain signal in the central nervous system, perception of pain, and response to analgesic strategies. As for many other complex behaviors, the sources of this variability come from both nurture (environment) and nature (genes). Here, I will discuss how these factors contribute to human pain separately and via interplay and how epigenetic mechanisms add to the complexity of their effects. PMID:24278778

Seasonal Evolution and Variability Associated with the West African Monsoon System

NASA Technical Reports Server (NTRS)

Gu, Guojun; Adler, Robert F.

2003-01-01

In this study, we investigate the seasonal variations in surface rainfall and associated large-scale processes in the tropical eastern Atlantic and West African region. The 5-yr (1998-2002) high-quality TRMM rainfall, sea surface temperature (SST), water vapor and cloud liquid water observations are applied along with the NCEP/NCAR reanalysis wind components and a 3-yr (2000-2002) Quickscat satellite-observed surface wind product. Major mean rainfall over West Africa tends to be concentrated in two regions and is observed in two different seasons, manifesting an abrupt shift of the mean rainfall zone during June-July. (i) Near the Gulf of Guinea (about 5 degN), intense convection and rainfall are seen during April-June and roughly follow the seasonality of SST in the tropical eastern Atlantic. (ii) Along the latitudes of about 10 deg. N over the interior West African continent, a second intense rain belt begins to develop from July and remains there during the later summer season. This belt co-exists with a northwardmoved African Easterly Jet (AEJ) and its accompanying horizonal and vertical shear zones, the appearance and intensification of an upper tropospheric Tropical Easterly Jet (TEJ), and a strong low-level westerly flow. Westward-propagating wave signals [ i e . , African easterly waves (AEWs)] dominate the synoptic-scale variability during July-September, in contrast to the evident eastward-propagating wave signals during May- June. The abrupt shift of mean rainfall zone thus turns out to be a combination of two different physical processes: (i) Evident seasonal cycles in the tropical eastern Atlantic ocean which modulate convection and rainfall in the Gulf of Guinea by means of SST thermal forcing and SST-related meridional gradient; (ii) The interaction among the AEJ, TEJ, low-level westerly flow, moist convection and AEWs during July-September which modulates rainfall variability in the interior West Africa, primarily within the ITCZ rain band. Evident seasonality in synoptic-scale wave signals is shown to be a good evidence for this seasonal evolution.

Chrestenson transform FPGA embedded factorizations.

PubMed

Corinthios, Michael J

2016-01-01

Chrestenson generalized Walsh transform factorizations for parallel processing imbedded implementations on field programmable gate arrays are presented. This general base transform, sometimes referred to as the Discrete Chrestenson transform, has received special attention in recent years. In fact, the Discrete Fourier transform and Walsh-Hadamard transform are but special cases of the Chrestenson generalized Walsh transform. Rotations of a base-p hypercube, where p is an arbitrary integer, are shown to produce dynamic contention-free memory allocation, in processor architecture. The approach is illustrated by factorizations involving the processing of matrices of the transform which are function of four variables. Parallel operations are implemented matrix multiplications. Each matrix, of dimension N × N, where N = p (n) , n integer, has a structure that depends on a variable parameter k that denotes the iteration number in the factorization process. The level of parallelism, in the form of M = p (m) processors can be chosen arbitrarily by varying m between zero to its maximum value of n - 1. The result is an equation describing the generalised parallelism factorization as a function of the four variables n, p, k and m. Applications of the approach are shown in relation to configuring field programmable gate arrays for digital signal processing applications.

Disorders of dysregulated signal traffic through the RAS-MAPK pathway: phenotypic spectrum and molecular mechanisms.

PubMed

Tartaglia, Marco; Gelb, Bruce D

2010-12-01

RAS GTPases control a major signaling network implicated in several cellular functions, including cell fate determination, proliferation, survival, differentiation, migration, and senescence. Within this network, signal flow through the RAF-MEK-ERK pathway-the first identified mitogen-associated protein kinase (MAPK) cascade-mediates early and late developmental processes controlling morphology determination, organogenesis, synaptic plasticity, and growth. Signaling through the RAS-MAPK cascade is tightly controlled; and its enhanced activation represents a well-known event in oncogenesis. Unexpectedly, in the past few years, inherited dysregulation of this pathway has been recognized as the cause underlying a group of clinically related disorders sharing facial dysmorphism, cardiac defects, reduced postnatal growth, ectodermal anomalies, variable cognitive deficits, and susceptibility to certain malignancies as major features. These disorders are caused by heterozygosity for mutations in genes encoding RAS proteins, regulators of RAS function, modulators of RAS interaction with effectors, or downstream signal transducers. Here, we provide an overview of the phenotypic spectrum associated with germline mutations perturbing RAS-MAPK signaling, the unpredicted molecular mechanisms converging toward the dysregulation of this signaling cascade, and major genotype-phenotype correlations. © 2010 New York Academy of Sciences.

A High-Linearity Low-Noise Amplifier with Variable Bandwidth for Neural Recoding Systems

NASA Astrophysics Data System (ADS)

Yoshida, Takeshi; Sueishi, Katsuya; Iwata, Atsushi; Matsushita, Kojiro; Hirata, Masayuki; Suzuki, Takafumi

2011-04-01

This paper describes a low-noise amplifier with multiple adjustable parameters for neural recording applications. An adjustable pseudo-resistor implemented by cascade metal-oxide-silicon field-effect transistors (MOSFETs) is proposed to achieve low-signal distortion and wide variable bandwidth range. The amplifier has been implemented in 0.18 µm standard complementary metal-oxide-semiconductor (CMOS) process and occupies 0.09 mm2 on chip. The amplifier achieved a selectable voltage gain of 28 and 40 dB, variable bandwidth from 0.04 to 2.6 Hz, total harmonic distortion (THD) of 0.2% with 200 mV output swing, input referred noise of 2.5 µVrms over 0.1-100 Hz and 18.7 µW power consumption at a supply voltage of 1.8 V.

Source mechanics for monochromatic icequakes produced during iceberg calving at Columbia Glacier, AK

USGS Publications Warehouse

O'Neel, Shad; Pfeffer, W.T.

2007-01-01

Seismograms recorded during iceberg calving contain information pertaining to source processes during calving events. However, locally variable material properties may cause signal distortions, known as site and path effects, which must be eliminated prior to commenting on source mechanics. We applied the technique of horizontal/vertical spectral ratios to passive seismic data collected at Columbia Glacier, AK, and found no dominant site or path effects. Rather, monochromatic waveforms generated by calving appear to result from source processes. We hypothesize that a fluid-filled crack source model offers a potential mechanism for observed seismograms produced by calving, and fracture-processes preceding calving.

How mechanisms of perceptual decision-making affect the psychometric function

PubMed Central

Gold, Joshua I.; Ding, Long

2012-01-01

Psychometric functions are often interpreted in the context of Signal Detection Theory, which emphasizes a distinction between sensory processing and non-sensory decision rules in the brain. This framework has helped to relate perceptual sensitivity to the “neurometric” sensitivity of sensory-driven neural activity. However, perceptual sensitivity, as interpreted via Signal Detection Theory, is based on not just how the brain represents relevant sensory information, but also how that information is read out to form the decision variable to which the decision rule is applied. Here we discuss recent advances in our understanding of this readout process and describe its effects on the psychometric function. In particular, we show that particular aspects of the readout process can have specific, identifiable effects on the threshold, slope, upper asymptote, time dependence, and choice dependence of psychometric functions. To illustrate these points, we emphasize studies of perceptual learning that have identified changes in the readout process that can lead to changes in these aspects of the psychometric function. We also discuss methods that have been used to distinguish contributions of the sensory representation versus its readout to psychophysical performance. PMID:22609483

A latent low-dimensional common input drives a pool of motor neurons: a probabilistic latent state-space model.

PubMed

Feeney, Daniel F; Meyer, François G; Noone, Nicholas; Enoka, Roger M

2017-10-01

Motor neurons appear to be activated with a common input signal that modulates the discharge activity of all neurons in the motor nucleus. It has proven difficult for neurophysiologists to quantify the variability in a common input signal, but characterization of such a signal may improve our understanding of how the activation signal varies across motor tasks. Contemporary methods of quantifying the common input to motor neurons rely on compiling discrete action potentials into continuous time series, assuming the motor pool acts as a linear filter, and requiring signals to be of sufficient duration for frequency analysis. We introduce a space-state model in which the discharge activity of motor neurons is modeled as inhomogeneous Poisson processes and propose a method to quantify an abstract latent trajectory that represents the common input received by motor neurons. The approach also approximates the variation in synaptic noise in the common input signal. The model is validated with four data sets: a simulation of 120 motor units, a pair of integrate-and-fire neurons with a Renshaw cell providing inhibitory feedback, the discharge activity of 10 integrate-and-fire neurons, and the discharge times of concurrently active motor units during an isometric voluntary contraction. The simulations revealed that a latent state-space model is able to quantify the trajectory and variability of the common input signal across all four conditions. When compared with the cumulative spike train method of characterizing common input, the state-space approach was more sensitive to the details of the common input current and was less influenced by the duration of the signal. The state-space approach appears to be capable of detecting rather modest changes in common input signals across conditions. NEW & NOTEWORTHY We propose a state-space model that explicitly delineates a common input signal sent to motor neurons and the physiological noise inherent in synaptic signal transmission. This is the first application of a deterministic state-space model to represent the discharge characteristics of motor units during voluntary contractions. Copyright © 2017 the American Physiological Society.

Estimating the signal-to-noise ratio of AVIRIS data

NASA Technical Reports Server (NTRS)

Curran, Paul J.; Dungan, Jennifer L.

1988-01-01

To make the best use of narrowband airborne visible/infrared imaging spectrometer (AVIRIS) data, an investigator needs to know the ratio of signal to random variability or noise (signal-to-noise ratio or SNR). The signal is land cover dependent and varies with both wavelength and atmospheric absorption; random noise comprises sensor noise and intrapixel variability (i.e., variability within a pixel). The three existing methods for estimating the SNR are inadequate, since typical laboratory methods inflate while dark current and image methods deflate the SNR. A new procedure is proposed called the geostatistical method. It is based on the removal of periodic noise by notch filtering in the frequency domain and the isolation of sensor noise and intrapixel variability using the semi-variogram. This procedure was applied easily and successfully to five sets of AVIRIS data from the 1987 flying season and could be applied to remotely sensed data from broadband sensors.

Biogeochemical control of marine productivity in the Mediterranean Sea during the last 50 years

PubMed Central

Macias, Diego; Garcia-Gorriz, Elisa; Piroddi, Chiara; Stips, Adolf

2014-01-01

The temporal dynamics of biogeochemical variables derived from a coupled 3-D model of the Mediterranean Sea are evaluated for the last 50 years (1960–2010) against independent data on fisheries catch per unit effort (CPUE) for the same time period. Concordant patterns are found in the time series of all of the biological variables (from the model and from fisheries statistics), with low values at the beginning of the series, a later increase, with maximum levels reached at the end of the 1990s, and a posterior stabilization. Spectral analysis of the annual biological time series reveals coincident low-frequency signals in all of them. The first, more energetic signal peaks around the year 2000, while the second, less energetic signal peaks near 1982. Almost identical low-frequency signals are found in the nutrient loads of the rivers and in the integrated nutrient levels in the surface marine ecosystem. Nitrate concentration shows a maximum level in 1998, with a later stabilization to present-day values, coincident with the first low-frequency signal found in the biological series. Phosphate shows maximum concentrations around 1982 and a posterior sharp decline, in concordance with the second low-frequency signal observed in the biological series. That result seems to indicate that the control of marine productivity (plankton to fish) in the Mediterranean is principally mediated through bottom-up processes that could be traced back to the characteristics of riverine discharges. The high sensitivity of CPUE time series to environmental conditions might be another indicator of the overexploitation of this marine ecosystem. Key Points Biogeochemical evolution of the Mediterranean over the past 50 years River nutrient loads drive primary and secondary productions Strong link between low trophic levels and fisheries PMID:26180286

A Robust Distributed Multipoint Fiber Optic Gas Sensor System Based on AGC Amplifier Structure.

PubMed

Zhu, Cunguang; Wang, Rende; Tao, Xuechen; Wang, Guangwei; Wang, Pengpeng

2016-07-28

A harsh environment-oriented distributed multipoint fiber optic gas sensor system realized by automatic gain control (AGC) technology is proposed. To improve the photoelectric signal reliability, the electronic variable gain can be modified in real time by an AGC closed-loop feedback structure to compensate for optical transmission loss which is caused by the fiber bend loss or other reasons. The deviation of the system based on AGC structure is below 4.02% when photoelectric signal decays due to fiber bending loss for bending radius of 5 mm, which is 20 times lower than the ordinary differential system. In addition, the AGC circuit with the same electric parameters can keep the baseline intensity of signals in different channels of the distributed multipoint sensor system at the same level. This avoids repetitive calibrations and streamlines the installation process.

Analysis of Utility and Use of a Web-Based Tool for Digital Signal Processing Teaching by Means of a Technological Acceptance Model

ERIC Educational Resources Information Center

Toral, S. L.; Barrero, F.; Martinez-Torres, M. R.

2007-01-01

This paper presents an exploratory study about the development of a structural and measurement model for the technological acceptance (TAM) of a web-based educational tool. The aim consists of measuring not only the use of this tool, but also the external variables with a significant influence in its use for planning future improvements. The tool,…

Processes Affecting the Variability of Fluorescence Signals from Benthic Targets in Shallow Waters

DTIC Science & Technology

1998-09-30

analysis of zooxanthellae isolated from coral samples. We examined fluorescence lifetimes from benthic targets using femtosecond laser based Single...a temporal resolution 5 ps. The laser set-up was employed for laboratory studies of fluorescence lifetimes from model targets such as zooxanthellae ...seagrasses. Over 200 measurements on isolated zooxanthellae were conducted with a bench-top FRR fluorometer and Phase Shift Fluorometer. During the CoBOP-98

Epistasis between dopamine regulating genes identifies a nonlinear response of the human hippocampus during memory tasks.

PubMed

Bertolino, Alessandro; Di Giorgio, Annabella; Blasi, Giuseppe; Sambataro, Fabio; Caforio, Grazia; Sinibaldi, Lorenzo; Latorre, Valeria; Rampino, Antonio; Taurisano, Paolo; Fazio, Leonardo; Romano, Raffaella; Douzgou, Sofia; Popolizio, Teresa; Kolachana, Bhaskar; Nardini, Marcello; Weinberger, Daniel R; Dallapiccola, Bruno

2008-08-01

Dopamine modulation of neuronal activity in prefrontal cortex maps to an inverted U-curve. Dopamine is also an important factor in regulation of hippocampal mediated memory processing. Here, we investigated the effect of genetic variation of dopamine inactivation via catechol-O-methyltransferase (COMT) and the dopamine transporter (DAT) on hippocampal activity in healthy humans during different memory conditions. Using blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) in 82 subjects matched for a series of demographic and genetic variables, we studied the effect of the COMT valine (Val)(158)methionine (Met) and the DAT 3' variable number tandem repeat (VNTR) polymorphisms on function of the hippocampus during encoding of recognition memory and during working memory. Our results consistently demonstrated a double dissociation so that DAT 9-repeat carrier alleles modulated activity in the hippocampus in the exact opposite direction of DAT 10/10-repeat alleles based on COMT Val(158)Met genotype during different memory conditions. Similar results were evident in ventrolateral and dorsolateral prefrontal cortex. These findings suggest that genetically determined dopamine signaling during memory processing maps to a nonlinear relationship also in the hippocampus. Our data also demonstrate in human brain epistasis of two genes implicated in dopamine signaling on brain activity during different memory conditions.

Age-related Multiscale Changes in Brain Signal Variability in Pre-task versus Post-task Resting-state EEG.

PubMed

Wang, Hongye; McIntosh, Anthony R; Kovacevic, Natasa; Karachalios, Maria; Protzner, Andrea B

2016-07-01

Recent empirical work suggests that, during healthy aging, the variability of network dynamics changes during task performance. Such variability appears to reflect the spontaneous formation and dissolution of different functional networks. We sought to extend these observations into resting-state dynamics. We recorded EEG in young, middle-aged, and older adults during a "rest-task-rest" design and investigated if aging modifies the interaction between resting-state activity and external stimulus-induced activity. Using multiscale entropy as our measure of variability, we found that, with increasing age, resting-state dynamics shifts from distributed to more local neural processing, especially at posterior sources. In the young group, resting-state dynamics also changed from pre- to post-task, where fine-scale entropy increased in task-positive regions and coarse-scale entropy increased in the posterior cingulate, a key region associated with the default mode network. Lastly, pre- and post-task resting-state dynamics were linked to performance on the intervening task for all age groups, but this relationship became weaker with increasing age. Our results suggest that age-related changes in resting-state dynamics occur across different spatial and temporal scales and have consequences for information processing capacity.

Multiple serial picture presentation with millisecond resolution using a three-way LC-shutter-tachistoscope

PubMed Central

Fischmeister, Florian Ph.S.; Leodolter, Ulrich; Windischberger, Christian; Kasess, Christian H.; Schöpf, Veronika; Moser, Ewald; Bauer, Herbert

2010-01-01

Throughout recent years there has been an increasing interest in studying unconscious visual processes. Such conditions of unawareness are typically achieved by either a sufficient reduction of the stimulus presentation time or visual masking. However, there are growing concerns about the reliability of the presentation devices used. As all these devices show great variability in presentation parameters, the processing of visual stimuli becomes dependent on the display-device, e.g. minimal changes in the physical stimulus properties may have an enormous impact on stimulus processing by the sensory system and on the actual experience of the stimulus. Here we present a custom-built three-way LC-shutter-tachistoscope which allows experimental setups with both, precise and reliable stimulus delivery, and millisecond resolution. This tachistoscope consists of three LCD-projectors equipped with zoom lenses to enable stimulus presentation via a built-in mirror-system onto a back projection screen from an adjacent room. Two high-speed liquid crystal shutters are mounted serially in front of each projector to control the stimulus duration. To verify the intended properties empirically, different sequences of presentation times were performed while changes in optical power were measured using a photoreceiver. The obtained results demonstrate that interfering variabilities in stimulus parameters and stimulus rendering are markedly reduced. Together with the possibility to collect external signals and to send trigger-signals to other devices, this tachistoscope represents a highly flexible and easy to set up research tool not only for the study of unconscious processing in the brain but for vision research in general. PMID:20122963

Exposure and response prevention process predicts treatment outcome in youth with OCD.

PubMed

Kircanski, Katharina; Peris, Tara S

2015-04-01

Recent research on the treatment of adults with anxiety disorders suggests that aspects of the in-session exposure therapy process are relevant to clinical outcomes. However, few comprehensive studies have been conducted with children and adolescents. In the present study, 35 youth diagnosed with primary obsessive-compulsive disorder (OCD; M age = 12.9 years, 49% male, 63% Caucasian) completed 12 sessions of exposure and response prevention (ERP) in one of two treatment conditions as part of a pilot randomized controlled testing of a family focused intervention for OCD. Key exposure process variables, including youth self-reported distress during ERP and the quantity and quality of ERP completed, were computed. These variables were examined as predictors of treatment outcomes assessed at mid-treatment, post-treatment, and three-month follow-up, partialing treatment condition. In general, greater variability of distress during ERP and completing a greater proportion of combined exposures (i.e., exposures targeting more than one OC symptom at once) were predictive of better outcomes. Conversely, greater distress at the end of treatment was generally predictive of poorer outcomes. Finally, several variables, including within- and between-session decreases in distress during ERP, were not consistently predictive of outcomes. Findings signal potentially important facets of exposure for youth with OCD and have implications for treatment. A number of results also parallel recent findings in the adult literature, suggesting that there may be some continuity in exposure processes from child to adult development. Future work should examine additional measures of exposure process, such as psychophysiological arousal during exposure, in youth.

Impacts of short-time scale water column variability on broadband high-frequency acoustic wave propagation

NASA Astrophysics Data System (ADS)

Eickmeier, Justin

Acoustical oceanography is one way to study the ocean, its internal layers, boundaries and all processes occurring within using underwater acoustics. Acoustical sensing techniques allows for the measurement of ocean processes from within that logistically or financially preclude traditional in-situ measurements. Acoustic signals propagate as pressure wavefronts from a source to a receiver through an ocean medium with variable physical parameters. The water column physical parameters that change acoustic wave propagation in the ocean include temperature, salinity, current, surface roughness, seafloor bathymetry, and vertical stratification over variable time scales. The impacts of short-time scale water column variability on acoustic wave propagation include coherent and incoherent surface reflections, wavefront arrival time delay, focusing or defocusing of the intensity of acoustic beams and refraction of acoustic rays. This study focuses on high-frequency broadband acoustic waves, and examines the influence of short-time scale water column variability on broadband high-frequency acoustics, wavefronts, from 7 to 28 kHz, in shallow water. Short-time scale variability is on the order of seconds to hours and the short-spatial scale variability is on the order of few centimeters. Experimental results were collected during an acoustic experiment along 100 m isobaths and data analysis was conducted using available acoustic wave propagation models. Three main topics are studied to show that acoustic waves are viable as a remote sensing tool to measure oceanographic parameters in shallow water. First, coherent surface reflections forming striation patterns, from multipath receptions, through rough surface interaction of broadband acoustic signals with the dynamic sea surface are analyzed. Matched filtered results of received acoustic waves are compared with a ray tracing numerical model using a sea surface boundary generated from measured water wave spectra at the time of signal propagation. It is determined that on a time scale of seconds, corresponding to typical periods of surface water waves, the arrival time of reflected acoustic signals from surface waves appear as striation patterns in measured data and can be accurately modelled by ray tracing. Second, changes in acoustic beam arrival angle and acoustic ray path influenced by isotherm depth oscillations are analyzed using an 8-element delay-sum beamformer. The results are compared with outputs from a two-dimensional (2-D) parabolic equation (PE) model using measured sound speed profiles (SSPs) in the water column. Using the method of beamforming on the received signal, the arrival time and angle of an acoustic beam was obtained for measured acoustic signals. It is determined that the acoustic ray path, acoustic beam intensity and angular spread are a function of vertical isotherm oscillations on a time scale of minutes and can be modeled accurately by a 2-D PE model. Third, a forward problem is introduced which uses acoustic wavefronts received on a vertical line array, 1.48 km from the source, in the lower part of the water column to infer range dependence or independence in the SSP. The matched filtering results of received acoustic wavefronts at all hydrophone depths are compared with a ray tracing routine augmented to calculate only direct path and bottom reflected signals. It is determined that the SSP range dependence can be inferred on a time scale of hours using an array of hydrophones spanning the water column. Sound speed profiles in the acoustic field were found to be range independent for 11 of the 23 hours in the measurements. A SSP cumulative reconstruction process, conducted from the seafloor to the sea surface, layer-by-layer, identifies critical segments in the SSP that define the ray path, arrival time and boundary interactions. Data-model comparison between matched filtered arrival time spread and arrival time output from the ray tracing was robust when the SSP measured at the receiver was input to the model. When the SSP measured nearest the source (at the same instant in time) was input to the ray tracing model, the data-model comparison was poor. It was determined that the cumulative sound speed change in the SSP near the source was 1.041 m/s greater than that of the SSP at the receiver and resulted in the poor data-model comparison. In this study, the influences on broadband acoustic wave propagation in the frequency range of 7 to 28 kHz of spatial and temporal changes in the oceanography of shallow water regions are addressed. Acoustic waves can be used as remote sensing tools to measure oceanographic parameters in shallow water and data-model comparison results show a direct relationship between the oceanographic variations and acoustic wave propagations.

Role of calcium permeable channels in dendritic cell migration.

PubMed

Sáez, Pablo J; Sáez, Juan C; Lennon-Duménil, Ana-María; Vargas, Pablo

2018-06-01

Calcium ion (Ca 2+ ) is an essential second messenger involved in multiple cellular and subcellular processes. Ca 2+ can be released and sensed globally or locally within cells, providing complex signals of variable amplitudes and time-scales. The key function of Ca 2+ in the regulation of acto-myosin contractility has provided a simple explanation for its role in the regulation of immune cell migration. However, many questions remain, including the identity of the Ca 2+ stores, channels and upstream signals involved in this process. Here, we focus on dendritic cells (DCs), because their immune sentinel function heavily relies on their capacity to migrate within tissues and later on between tissues and lymphoid organs. Deciphering the mechanisms by which cytoplasmic Ca 2+ regulate DC migration should shed light on their role in initiating and tuning immune responses. Copyright © 2018 Elsevier Ltd. All rights reserved.

Estimation of tool wear during CNC milling using neural network-based sensor fusion

NASA Astrophysics Data System (ADS)

Ghosh, N.; Ravi, Y. B.; Patra, A.; Mukhopadhyay, S.; Paul, S.; Mohanty, A. R.; Chattopadhyay, A. B.

2007-01-01

Cutting tool wear degrades the product quality in manufacturing processes. Monitoring tool wear value online is therefore needed to prevent degradation in machining quality. Unfortunately there is no direct way of measuring the tool wear online. Therefore one has to adopt an indirect method wherein the tool wear is estimated from several sensors measuring related process variables. In this work, a neural network-based sensor fusion model has been developed for tool condition monitoring (TCM). Features extracted from a number of machining zone signals, namely cutting forces, spindle vibration, spindle current, and sound pressure level have been fused to estimate the average flank wear of the main cutting edge. Novel strategies such as, signal level segmentation for temporal registration, feature space filtering, outlier removal, and estimation space filtering have been proposed. The proposed approach has been validated by both laboratory and industrial implementations.

Foraging for brain stimulation: toward a neurobiology of computation.

PubMed

Gallistel, C R

1994-01-01

The self-stimulating rat performs foraging tasks mediated by simple computations that use interreward intervals and subjective reward magnitudes to determine stay durations. This is a simplified preparation in which to study the neurobiology of the elementary computational operations that make cognition possible, because the neural signal specifying the value of a computationally relevant variable is produced by direct electrical stimulation of a neural pathway. Newly developed measurement methods yield functions relating the subjective reward magnitude to the parameters of the neural signal. These measurements also show that the decision process that governs foraging behavior divides the subjective reward magnitude by the most recent interreward interval to determine the preferability of an option (a foraging patch). The decision process sets the parameters that determine stay durations (durations of visits to foraging patches) so that the ratios of the stay durations match the ratios of the preferabilities.

C, N, P export regimes from headwater catchments to downstream reaches

NASA Astrophysics Data System (ADS)

Dupas, R.; Musolff, A.; Jawitz, J. W.; Rao, P. S.; Jaeger, C. G.; Fleckenstein, J. H.; Rode, M.; Borchardt, D.

2017-12-01

Excessive amounts of nutrients and dissolved organic matter in freshwater bodies affect aquatic ecosystems. In this study, the spatial and temporal variability in nitrate (NO3), dissolved organic carbon (DOC) and soluble reactive phosphorus (SRP) was analyzed in the Selke river continuum from headwaters draining 1 - 3 km² catchments to downstream reaches representing spatially integrated signals from 184 - 456 km² catchments (part of TERENO - Terrestrial Environmental Observatories, in Germany). Three headwater catchments were selected as archetypes of the main landscape units (land use x lithology) present in the Selke catchment. Export regimes in headwater catchments were interpreted in terms of NO3, DOC and SRP land-to-stream transfer processes. Headwater signals were subtracted from downstream signals, with the differences interpreted in terms of in-stream processes and contribution of point-source emissions. The seasonal dynamics for NO3 were opposite those of DOC and SRP in all three headwater catchments, and spatial differences also showed NO3 contrasting with DOC and SRP. These dynamics were interpreted as the result of the interplay of hydrological and biogeochemical processes, for which riparian zones were hypothesized to play a determining role. In the two downstream reaches, NO3 was transported almost conservatively, whereas DOC was consumed and produced in the upper and lower river sections, respectively. The natural export regime of SRP in the three headwater catchments mimicked a point-source signal, which may lead to overestimation of domestic contributions in the downstream reaches. Monitoring the river continuum from headwaters to downstream reaches proved effective to investigate jointly land-to-stream and in-stream transport and transformation processes.

Designing Experiments to Discriminate Families of Logic Models.

PubMed

Videla, Santiago; Konokotina, Irina; Alexopoulos, Leonidas G; Saez-Rodriguez, Julio; Schaub, Torsten; Siegel, Anne; Guziolowski, Carito

2015-01-01

Logic models of signaling pathways are a promising way of building effective in silico functional models of a cell, in particular of signaling pathways. The automated learning of Boolean logic models describing signaling pathways can be achieved by training to phosphoproteomics data, which is particularly useful if it is measured upon different combinations of perturbations in a high-throughput fashion. However, in practice, the number and type of allowed perturbations are not exhaustive. Moreover, experimental data are unavoidably subjected to noise. As a result, the learning process results in a family of feasible logical networks rather than in a single model. This family is composed of logic models implementing different internal wirings for the system and therefore the predictions of experiments from this family may present a significant level of variability, and hence uncertainty. In this paper, we introduce a method based on Answer Set Programming to propose an optimal experimental design that aims to narrow down the variability (in terms of input-output behaviors) within families of logical models learned from experimental data. We study how the fitness with respect to the data can be improved after an optimal selection of signaling perturbations and how we learn optimal logic models with minimal number of experiments. The methods are applied on signaling pathways in human liver cells and phosphoproteomics experimental data. Using 25% of the experiments, we obtained logical models with fitness scores (mean square error) 15% close to the ones obtained using all experiments, illustrating the impact that our approach can have on the design of experiments for efficient model calibration.

Model-based analysis of δ34S signatures to trace sedimentary pyrite oxidation during managed aquifer recharge in a heterogeneous aquifer

NASA Astrophysics Data System (ADS)

Seibert, Simone; Descourvieres, Carlos; Skrzypek, Grzegorz; Deng, Hailin; Prommer, Henning

2017-05-01

The oxidation of pyrite is often one of the main drivers affecting groundwater quality during managed aquifer recharge in deep aquifers. Data and techniques that allow detailed identification and quantification of pyrite oxidation are therefore crucial for assessing and predicting the adverse water quality changes that may be associated with this process. In this study, we explore the benefits of combining stable sulphur isotope analysis with reactive transport modelling to improve the identification and characterisation of pyrite oxidation during an aquifer storage and recovery experiment in a chemically and physically heterogeneous aquifer. We characterise the stable sulphur isotope signal (δ34S) in both the ambient groundwater and the injectant as well as its spatial distribution within the sedimentary sulphur species. The identified stable sulphur isotope signal for pyrite was found to vary between -32 and +34‰, while the signal of the injectant ranged between +9.06 and +14.45‰ during the injection phase of the experiment. Both isotope and hydrochemical data together suggest a substantial contribution of pyrite oxidation to the observed, temporally variable δ34S signals. The variability of the δ34S signal in pyrite and the injectant were both found to complicate the analysis of the stable isotope data. However, the incorporation of the data into a numerical modelling approach allowed to successfully employ the δ34S signatures as a valuable additional constraint for identifying and quantifying the contribution of pyrite oxidation to the redox transformations that occur in response to the injection of oxygenated water.

Underlying neural alpha frequency patterns associated with intra-hemispheric inhibition during an interhemispheric transfer task.

PubMed

Simon-Dack, Stephanie L; Kraus, Brian; Walter, Zachary; Smith, Shelby; Cadle, Chelsea

2018-05-18

Interhemispheric transfer measured via differences in right- or left-handed motoric responses to lateralized visual stimuli, known as the crossed-uncrossed difference (CUD), is one way of identifying patterns of processing that are vital for understanding the transfer of neural signals. Examination of interhemispheric transfer by means of the CUD is not entirely explained by simple measures of response time. Multiple processes contribute to wide variability observed in CUD reaction times. Prior research has suggested that intra-hemispheric inhibitory processes may be involved in regulation of speed of transfer. Our study examined electroencephalography recordings and time-locked alpha frequency activity while 18 participants responded to lateralized targets during performance of the Poffenberger Paradigm. Our results suggest that there are alpha frequency differences at fronto-central lateral electrodes based on target, hand-of-response, and receiving hemisphere. These findings suggest that early motoric inhibitory mechanisms may help explain the wide range of variability typically seen with the CUD. Copyright © 2018 Elsevier B.V. All rights reserved.

Connecting the dots across time: reconstruction of single-cell signalling trajectories using time-stamped data

NASA Astrophysics Data System (ADS)

Mukherjee, Sayak; Stewart, David; Stewart, William; Lanier, Lewis L.; Das, Jayajit

2017-08-01

Single-cell responses are shaped by the geometry of signalling kinetic trajectories carved in a multidimensional space spanned by signalling protein abundances. It is, however, challenging to assay a large number (more than 3) of signalling species in live-cell imaging, which makes it difficult to probe single-cell signalling kinetic trajectories in large dimensions. Flow and mass cytometry techniques can measure a large number (4 to more than 40) of signalling species but are unable to track single cells. Thus, cytometry experiments provide detailed time-stamped snapshots of single-cell signalling kinetics. Is it possible to use the time-stamped cytometry data to reconstruct single-cell signalling trajectories? Borrowing concepts of conserved and slow variables from non-equilibrium statistical physics we develop an approach to reconstruct signalling trajectories using snapshot data by creating new variables that remain invariant or vary slowly during the signalling kinetics. We apply this approach to reconstruct trajectories using snapshot data obtained from in silico simulations, live-cell imaging measurements, and, synthetic flow cytometry datasets. The application of invariants and slow variables to reconstruct trajectories provides a radically different way to track objects using snapshot data. The approach is likely to have implications for solving matching problems in a wide range of disciplines.

Monitoring fetal heart rate during pregnancy: contributions from advanced signal processing and wearable technology.

PubMed

Signorini, Maria G; Fanelli, Andrea; Magenes, Giovanni

2014-01-01

Monitoring procedures are the basis to evaluate the clinical state of patients and to assess changes in their conditions, thus providing necessary interventions in time. Both these two objectives can be achieved by integrating technological development with methodological tools, thus allowing accurate classification and extraction of useful diagnostic information. The paper is focused on monitoring procedures applied to fetal heart rate variability (FHRV) signals, collected during pregnancy, in order to assess fetal well-being. The use of linear time and frequency techniques as well as the computation of non linear indices can contribute to enhancing the diagnostic power and reliability of fetal monitoring. The paper shows how advanced signal processing approaches can contribute to developing new diagnostic and classification indices. Their usefulness is evaluated by comparing two selected populations: normal fetuses and intra uterine growth restricted (IUGR) fetuses. Results show that the computation of different indices on FHRV signals, either linear and nonlinear, gives helpful indications to describe pathophysiological mechanisms involved in the cardiovascular and neural system controlling the fetal heart. As a further contribution, the paper briefly describes how the introduction of wearable systems for fetal ECG recording could provide new technological solutions improving the quality and usability of prenatal monitoring.

Histogram Matching Extends Acceptable Signal Strength Range on Optical Coherence Tomography Images

PubMed Central

Chen, Chieh-Li; Ishikawa, Hiroshi; Wollstein, Gadi; Bilonick, Richard A.; Sigal, Ian A.; Kagemann, Larry; Schuman, Joel S.

2015-01-01

Purpose. We minimized the influence of image quality variability, as measured by signal strength (SS), on optical coherence tomography (OCT) thickness measurements using the histogram matching (HM) method. Methods. We scanned 12 eyes from 12 healthy subjects with the Cirrus HD-OCT device to obtain a series of OCT images with a wide range of SS (maximal range, 1–10) at the same visit. For each eye, the histogram of an image with the highest SS (best image quality) was set as the reference. We applied HM to the images with lower SS by shaping the input histogram into the reference histogram. Retinal nerve fiber layer (RNFL) thickness was automatically measured before and after HM processing (defined as original and HM measurements), and compared to the device output (device measurements). Nonlinear mixed effects models were used to analyze the relationship between RNFL thickness and SS. In addition, the lowest tolerable SSs, which gave the RNFL thickness within the variability margin of manufacturer recommended SS range (6–10), were determined for device, original, and HM measurements. Results. The HM measurements showed less variability across a wide range of image quality than the original and device measurements (slope = 1.17 vs. 4.89 and 1.72 μm/SS, respectively). The lowest tolerable SS was successfully reduced to 4.5 after HM processing. Conclusions. The HM method successfully extended the acceptable SS range on OCT images. This would qualify more OCT images with low SS for clinical assessment, broadening the OCT application to a wider range of subjects. PMID:26066749

Thermo-elastic wave model of the photothermal and photoacoustic signal

DOE Office of Scientific and Technical Information (OSTI.GOV)

Meja, P.; Steiger, B.; Delsanto, P.P.

1996-12-31

By means of the thermo-elastic wave equation the dynamical propagation of mechanical stress and temperature can be described and applied to model the photothermal and photoacoustic signal. Analytical solutions exist only in particular cases. Using massively parallel computers it is possible to simulate the photothermal and photoacoustic signal in a most sufficient way. In this paper the method of local interaction simulation approach (LISA) is presented and selected examples of its application are given. The advantages of this method, which is particularly suitable for parallel processing, consist in reduced computation time and simple description of the photoacoustic signal in opticalmore » materials. The present contribution introduces the authors model, the formalism and some results in the 1 D case for homogeneous nonattenuative materials. The photoacoustic wave can be understood as a wave with locally limited displacement. This displacement corresponds to a temperature variation. Both variables are usually measured in photoacoustics and photothermal measurements. Therefore the temperature and displacement dependence on optical, elastic and thermal constants is analysed.« less

Spectroscopy of T Tauri stars with UVES. Observations and analysis of RU Lup

NASA Astrophysics Data System (ADS)

Stempels, H. C.; Piskunov, N.

2002-08-01

We present the first results of our observations of classical T Tauri Stars with UVES/VLT. The data consists of high signal-to-noise (ge 150) and high spectral resolution (R ~ 60 000) spectra. A large simultaneous wavelength coverage throughout most of the visible spectrum and comparatively short integration times allow us to study variability on short time-scales, using a number of diagnostics reflecting a wide range of physical processes. In particular we concentrate on the properties and geometry of the accretion process in the strongly accreting and highly variable CTTS RU Lup. We use the evolution of the level of veiling, the shapes of absorption and emission lines, and correlations between these diagnostics, to make new measurements of the fundamental stellar parameters as well as constraints on the accretion process and its geometry. We also derive the shortest time-scale of incoherent changes, which has implications for the nature of the accretion process in RU Lup. Based on observations collected at the European Southern Observatory, Chile (proposal 65.I-0404).

Design of overload vehicle monitoring and response system based on DSP

NASA Astrophysics Data System (ADS)

Yu, Yan; Liu, Yiheng; Zhao, Xuefeng

2014-03-01

The overload vehicles are making much more damage to the road surface than the regular ones. Many roads and bridges are equipped with structural health monitoring system (SHM) to provide early-warning to these damage and evaluate the safety of road and bridge. However, because of the complex nature of SHM system, it's expensive to manufacture, difficult to install and not well-suited for the regular bridges and roads. Based on this application background, this paper designs a compact structural health monitoring system based on DSP, which is highly integrated, low-power, easy to install and inexpensive to manufacture. The designed system is made up of sensor arrays, the charge amplifier module, the DSP processing unit, the alarm system for overload, and the estimate for damage of the road and bridge structure. The signals coming from sensor arrays go through the charge amplifier. DSP processing unit will receive the amplified signals, estimate whether it is an overload signal or not, and convert analog variables into digital ones so that they are compatible with the back-end digital circuit for further processing. The system will also restrict certain vehicles that are overweight, by taking image of the car brand, sending the alarm, and transferring the collected pressure data to remote data center for further monitoring analysis by rain-flow counting method.

Efficient high light acclimation involves rapid processes at multiple mechanistic levels.

PubMed

Dietz, Karl-Josef

2015-05-01

Like no other chemical or physical parameter, the natural light environment of plants changes with high speed and jumps of enormous intensity. To cope with this variability, photosynthetic organisms have evolved sensing and response mechanisms that allow efficient acclimation. Most signals originate from the chloroplast itself. In addition to very fast photochemical regulation, intensive molecular communication is realized within the photosynthesizing cell, optimizing the acclimation process. Current research has opened up new perspectives on plausible but mostly unexpected complexity in signalling events, crosstalk, and process adjustments. Within seconds and minutes, redox states, levels of reactive oxygen species, metabolites, and hormones change and transmit information to the cytosol, modifying metabolic activity, gene expression, translation activity, and alternative splicing events. Signalling pathways on an intermediate time scale of several minutes to a few hours pave the way for long-term acclimation. Thereby, a new steady state of the transcriptome, proteome, and metabolism is realized within rather short time periods irrespective of the previous acclimation history to shade or sun conditions. This review provides a time line of events during six hours in the 'stressful' life of a plant. © The Author 2015. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Wave-variable framework for networked robotic systems with time delays and packet losses

NASA Astrophysics Data System (ADS)

Puah, Seng-Ming; Liu, Yen-Chen

2017-05-01

This paper investigates the problem of networked control system for nonlinear robotic manipulators under time delays and packet loss by using passivity technique. With the utilisation of wave variables and a passive remote controller, the networked robotic system is demonstrated to be stable with guaranteed position regulation. For the input/output signals of robotic systems, a discretisation block is exploited to convert continuous-time signals to discrete-time signals, and vice versa. Subsequently, we propose a packet management, called wave-variable modulation, to cope with the proposed networked robotic system under time delays and packet losses. Numerical examples and experimental results are presented to demonstrate the performance of the proposed wave-variable-based networked robotic systems.

Design and prototyping of a wristband-type wireless photoplethysmographic device for heart rate variability signal analysis.

PubMed

Ghamari, M; Soltanpur, C; Cabrera, S; Romero, R; Martinek, R; Nazeran, H

2016-08-01

Heart Rate Variability (HRV) signal analysis provides a quantitative marker of the Autonomic Nervous System (ANS) function. A wristband-type wireless photoplethysmographic (PPG) device was custom-designed to collect and analyze the arterial pulse in the wrist. The proposed device is comprised of an optical sensor to monitor arterial pulse, a signal conditioning unit to filter and amplify the analog PPG signal, a microcontroller to digitize the analog PPG signal, and a Bluetooth module to transfer the data to a smart device. This paper proposes a novel model to represent the PPG signal as the summation of two Gaussian functions. The paper concludes with a verification procedure for HRV signal analysis during sedentary activities.

Top-Down Inhibition of BMP Signaling Enables Robust Induction of hPSCs Into Neural Crest in Fully Defined, Xeno-free Conditions.

PubMed

Hackland, James O S; Frith, Tom J R; Thompson, Oliver; Marin Navarro, Ana; Garcia-Castro, Martin I; Unger, Christian; Andrews, Peter W

2017-10-10

Defects in neural crest development have been implicated in many human disorders, but information about human neural crest formation mostly depends on extrapolation from model organisms. Human pluripotent stem cells (hPSCs) can be differentiated into in vitro counterparts of the neural crest, and some of the signals known to induce neural crest formation in vivo are required during this process. However, the protocols in current use tend to produce variable results, and there is no consensus as to the precise signals required for optimal neural crest differentiation. Using a fully defined culture system, we have now found that the efficient differentiation of hPSCs to neural crest depends on precise levels of BMP signaling, which are vulnerable to fluctuations in endogenous BMP production. We present a method that controls for this phenomenon and could be applied to other systems where endogenous signaling can also affect the outcome of differentiation protocols. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Signal Prediction With Input Identification

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan; Chen, Ya-Chin

1999-01-01

A novel coding technique is presented for signal prediction with applications including speech coding, system identification, and estimation of input excitation. The approach is based on the blind equalization method for speech signal processing in conjunction with the geometric subspace projection theory to formulate the basic prediction equation. The speech-coding problem is often divided into two parts, a linear prediction model and excitation input. The parameter coefficients of the linear predictor and the input excitation are solved simultaneously and recursively by a conventional recursive least-squares algorithm. The excitation input is computed by coding all possible outcomes into a binary codebook. The coefficients of the linear predictor and excitation, and the index of the codebook can then be used to represent the signal. In addition, a variable-frame concept is proposed to block the same excitation signal in sequence in order to reduce the storage size and increase the transmission rate. The results of this work can be easily extended to the problem of disturbance identification. The basic principles are outlined in this report and differences from other existing methods are discussed. Simulations are included to demonstrate the proposed method.

Signal extraction and wave field separation in tunnel seismic prediction by independent component analysis

NASA Astrophysics Data System (ADS)

Yue, Y.; Jiang, T.; Zhou, Q.

2017-12-01

In order to ensure the rationality and the safety of tunnel excavation, the advanced geological prediction has been become an indispensable step in tunneling. However, the extraction of signal and the separation of P and S waves directly influence the accuracy of geological prediction. Generally, the raw data collected in TSP system is low quality because of the numerous disturb factors in tunnel projects, such as the power interference and machine vibration interference. It's difficult for traditional method (band-pass filtering) to remove interference effectively as well as bring little loss to signal. The power interference, machine vibration interference and the signal are original variables and x, y, z component as observation signals, each component of the representation is a linear combination of the original variables, which satisfy applicable conditions of independent component analysis (ICA). We perform finite-difference simulations of elastic wave propagation to synthetic a tunnel seismic reflection record. The method of ICA was adopted to process the three-component data, and the results show that extract the estimates of signal and the signals are highly correlated (the coefficient correlation is up to more than 0.93). In addition, the estimates of interference that separated from ICA and the interference signals are also highly correlated, and the coefficient correlation is up to more than 0.99. Thus, simulation results showed that the ICA is an ideal method for extracting high quality data from mixed signals. For the separation of P and S waves, the conventional separation techniques are based on physical characteristics of wave propagation, which require knowledge of the near-surface P and S waves velocities and density. Whereas the ICA approach is entirely based on statistical differences between P and S waves, and the statistical technique does not require a priori information. The concrete results of the wave field separation will be presented in the meeting. In summary, we can safely draw the conclusion that ICA can not only extract high quality data from the mixed signals, but also can separate P and S waves effectively.

Performance comparison of optimal fractional order hybrid fuzzy PID controllers for handling oscillatory fractional order processes with dead time.

PubMed

Das, Saptarshi; Pan, Indranil; Das, Shantanu

2013-07-01

Fuzzy logic based PID controllers have been studied in this paper, considering several combinations of hybrid controllers by grouping the proportional, integral and derivative actions with fuzzy inferencing in different forms. Fractional order (FO) rate of error signal and FO integral of control signal have been used in the design of a family of decomposed hybrid FO fuzzy PID controllers. The input and output scaling factors (SF) along with the integro-differential operators are tuned with real coded genetic algorithm (GA) to produce optimum closed loop performance by simultaneous consideration of the control loop error index and the control signal. Three different classes of fractional order oscillatory processes with various levels of relative dominance between time constant and time delay have been used to test the comparative merits of the proposed family of hybrid fractional order fuzzy PID controllers. Performance comparison of the different FO fuzzy PID controller structures has been done in terms of optimal set-point tracking, load disturbance rejection and minimal variation of manipulated variable or smaller actuator requirement etc. In addition, multi-objective Non-dominated Sorting Genetic Algorithm (NSGA-II) has been used to study the Pareto optimal trade-offs between the set point tracking and control signal, and the set point tracking and load disturbance performance for each of the controller structure to handle the three different types of processes. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Redox Control of Inflammation in Macrophages

PubMed Central

Dehne, Nathalie; Grossmann, Nina; Jung, Michaela; Namgaladze, Dmitry; Schmid, Tobias; von Knethen, Andreas; Weigert, Andreas

2013-01-01

Abstract Macrophages are present throughout the human body, constitute important immune effector cells, and have variable roles in a great number of pathological, but also physiological, settings. It is apparent that macrophages need to adjust their activation profile toward a steadily changing environment that requires altering their phenotype, a process known as macrophage polarization. Formation of reactive oxygen species (ROS), derived from NADPH-oxidases, mitochondria, or NO-producing enzymes, are not necessarily toxic, but rather compose a network signaling system, known as redox regulation. Formation of redox signals in classically versus alternatively activated macrophages, their action and interaction at the level of key targets, and the resulting physiology still are insufficiently understood. We review the identity, source, and biological activities of ROS produced during macrophage activation, and discuss how they shape the key transcriptional responses evoked by hypoxia-inducible transcription factors, nuclear-erythroid 2-p45-related factor 2 (Nrf2), and peroxisome proliferator-activated receptor-γ. We summarize the mechanisms how redox signals add to the process of macrophage polarization and reprogramming, how this is controlled by the interaction of macrophages with their environment, and addresses the outcome of the polarization process in health and disease. Future studies need to tackle the option whether we can use the knowledge of redox biology in macrophages to shape their mediator profile in pathophysiology, to accelerate healing in injured tissue, to fight the invading pathogens, or to eliminate settings of altered self in tumors. Antioxid. Redox Signal. 19, 595–637. PMID:23311665

Development of Advanced Signal Processing and Source Imaging Methods for Superparamagnetic Relaxometry

PubMed Central

Huang, Ming-Xiong; Anderson, Bill; Huang, Charles W.; Kunde, Gerd J.; Vreeland, Erika C.; Huang, Jeffrey W.; Matlashov, Andrei N.; Karaulanov, Todor; Nettles, Christopher P.; Gomez, Andrew; Minser, Kayla; Weldon, Caroline; Paciotti, Giulio; Harsh, Michael; Lee, Roland R.; Flynn, Edward R.

2017-01-01

Superparamagnetic Relaxometry (SPMR) is a highly sensitive technique for the in vivo detection of tumor cells and may improve early stage detection of cancers. SPMR employs superparamagnetic iron oxide nanoparticles (SPION). After a brief magnetizing pulse is used to align the SPION, SPMR measures the time decay of SPION using Super-conducting Quantum Interference Device (SQUID) sensors. Substantial research has been carried out in developing the SQUID hardware and in improving the properties of the SPION. However, little research has been done in the pre-processing of sensor signals and post-processing source modeling in SPMR. In the present study, we illustrate new pre-processing tools that were developed to: 1) remove trials contaminated with artifacts, 2) evaluate and ensure that a single decay process associated with bounded SPION exists in the data, 3) automatically detect and correct flux jumps, and 4) accurately fit the sensor signals with different decay models. Furthermore, we developed an automated approach based on multi-start dipole imaging technique to obtain the locations and magnitudes of multiple magnetic sources, without initial guesses from the users. A regularization process was implemented to solve the ambiguity issue related to the SPMR source variables. A procedure based on reduced chi-square cost-function was introduced to objectively obtain the adequate number of dipoles that describe the data. The new pre-processing tools and multi-start source imaging approach have been successfully evaluated using phantom data. In conclusion, these tools and multi-start source modeling approach substantially enhance the accuracy and sensitivity in detecting and localizing sources from the SPMR signals. Furthermore, multi-start approach with regularization provided robust and accurate solutions for a poor SNR condition similar to the SPMR detection sensitivity in the order of 1000 cells. We believe such algorithms will help establishing the industrial standards for SPMR when applying the technique in pre-clinical and clinical settings. PMID:28072579

A Novel Feature Level Fusion for Heart Rate Variability Classification Using Correntropy and Cauchy-Schwarz Divergence.

PubMed

Goshvarpour, Ateke; Goshvarpour, Atefeh

2018-04-30

Heart rate variability (HRV) analysis has become a widely used tool for monitoring pathological and psychological states in medical applications. In a typical classification problem, information fusion is a process whereby the effective combination of the data can achieve a more accurate system. The purpose of this article was to provide an accurate algorithm for classifying HRV signals in various psychological states. Therefore, a novel feature level fusion approach was proposed. First, using the theory of information, two similarity indicators of the signal were extracted, including correntropy and Cauchy-Schwarz divergence. Applying probabilistic neural network (PNN) and k-nearest neighbor (kNN), the performance of each index in the classification of meditators and non-meditators HRV signals was appraised. Then, three fusion rules, including division, product, and weighted sum rules were used to combine the information of both similarity measures. For the first time, we propose an algorithm to define the weights of each feature based on the statistical p-values. The performance of HRV classification using combined features was compared with the non-combined features. Totally, the accuracy of 100% was obtained for discriminating all states. The results showed the strong ability and proficiency of division and weighted sum rules in the improvement of the classifier accuracies.

Approaches to Observe Anthropogenic Aerosol-Cloud Interactions.

PubMed

Quaas, Johannes

Anthropogenic aerosol particles exert an-quantitatively very uncertain-effective radiative forcing due to aerosol-cloud interactions via an immediate altering of cloud albedo on the one hand and via rapid adjustments by alteration of cloud processes and by changes in thermodynamic profiles on the other hand. Large variability in cloud cover and properties and the therefore low signal-to-noise ratio for aerosol-induced perturbations hamper the identification of effects in observations. Six approaches are discussed as a means to isolate the impact of anthropogenic aerosol on clouds from natural cloud variability to estimate or constrain the effective forcing. These are (i) intentional cloud modification, (ii) ship tracks, (iii) differences between the hemispheres, (iv) trace gases, (v) weekly cycles and (vi) trends. Ship track analysis is recommendable for detailed process understanding, and the analysis of weekly cycles and long-term trends is most promising to derive estimates or constraints on the effective radiative forcing.

Exploiting single-cell variability to infer the dynamics of immune responses

NASA Astrophysics Data System (ADS)

Höfer, Thomas

Cell division, differentiation, migration and death determine the dynamics of immune responses. These processes are regulated by a multitude of biochemical signals which, at present, cannot faithfully be reconstituted outside the living organism. However, quantitative measurements in living organisms have been limited. In recent years experimental techniques for the ``fate mapping'' of single immune cells have been developed that allow performing parallel single-cell experiments in an experimental animal. The resulting data are more informative about underlying biological processes than traditional measurements. I will show how the theory of stochastic dynamical systems can be used to infer the topology and dynamics of cell differentiation pathways from such data. The focus will be on joint theoretical and experimental work addressing: (i) the development of immune cells during hematopoiesis, and (ii) T cell responses to diverse pathogens. I will discuss questions on the nature of cellular variability that are posed by these new findings.

Experimental studies of breaking of elastic tired wheel under variable normal load

NASA Astrophysics Data System (ADS)

Fedotov, A. I.; Zedgenizov, V. G.; Ovchinnikova, N. I.

2017-10-01

The paper analyzes the braking of a vehicle wheel subjected to disturbances of normal load variations. Experimental tests and methods for developing test modes as sinusoidal force disturbances of the normal wheel load were used. Measuring methods for digital and analogue signals were used as well. Stabilization of vehicle wheel braking subjected to disturbances of normal load variations is a topical issue. The paper suggests a method for analyzing wheel braking processes under disturbances of normal load variations. A method to control wheel baking processes subjected to disturbances of normal load variations was developed.

An approach to predict Sudden Cardiac Death (SCD) using time domain and bispectrum features from HRV signal.

PubMed

Houshyarifar, Vahid; Chehel Amirani, Mehdi

2016-08-12

In this paper we present a method to predict Sudden Cardiac Arrest (SCA) with higher order spectral (HOS) and linear (Time) features extracted from heart rate variability (HRV) signal. Predicting the occurrence of SCA is important in order to avoid the probability of Sudden Cardiac Death (SCD). This work is a challenge to predict five minutes before SCA onset. The method consists of four steps: pre-processing, feature extraction, feature reduction, and classification. In the first step, the QRS complexes are detected from the electrocardiogram (ECG) signal and then the HRV signal is extracted. In second step, bispectrum features of HRV signal and time-domain features are obtained. Six features are extracted from bispectrum and two features from time-domain. In the next step, these features are reduced to one feature by the linear discriminant analysis (LDA) technique. Finally, KNN and support vector machine-based classifiers are used to classify the HRV signals. We used two database named, MIT/BIH Sudden Cardiac Death (SCD) Database and Physiobank Normal Sinus Rhythm (NSR). In this work we achieved prediction of SCD occurrence for six minutes before the SCA with the accuracy over 91%.

Amphetamine modulates brain signal variability and working memory in younger and older adults.

PubMed

Garrett, Douglas D; Nagel, Irene E; Preuschhof, Claudia; Burzynska, Agnieszka Z; Marchner, Janina; Wiegert, Steffen; Jungehülsing, Gerhard J; Nyberg, Lars; Villringer, Arno; Li, Shu-Chen; Heekeren, Hauke R; Bäckman, Lars; Lindenberger, Ulman

2015-06-16

Better-performing younger adults typically express greater brain signal variability relative to older, poorer performers. Mechanisms for age and performance-graded differences in brain dynamics have, however, not yet been uncovered. Given the age-related decline of the dopamine (DA) system in normal cognitive aging, DA neuromodulation is one plausible mechanism. Hence, agents that boost systemic DA [such as d-amphetamine (AMPH)] may help to restore deficient signal variability levels. Furthermore, despite the standard practice of counterbalancing drug session order (AMPH first vs. placebo first), it remains understudied how AMPH may interact with practice effects, possibly influencing whether DA up-regulation is functional. We examined the effects of AMPH on functional-MRI-based blood oxygen level-dependent (BOLD) signal variability (SD(BOLD)) in younger and older adults during a working memory task (letter n-back). Older adults expressed lower brain signal variability at placebo, but met or exceeded young adult SD(BOLD) levels in the presence of AMPH. Drug session order greatly moderated change-change relations between AMPH-driven SD(BOLD) and reaction time means (RT(mean)) and SDs (RT(SD)). Older adults who received AMPH in the first session tended to improve in RT(mean) and RT(SD) when SD(BOLD) was boosted on AMPH, whereas younger and older adults who received AMPH in the second session showed either a performance improvement when SD(BOLD) decreased (for RT(mean)) or no effect at all (for RT(SD)). The present findings support the hypothesis that age differences in brain signal variability reflect aging-induced changes in dopaminergic neuromodulation. The observed interactions among AMPH, age, and session order highlight the state- and practice-dependent neurochemical basis of human brain dynamics.

Amphetamine modulates brain signal variability and working memory in younger and older adults

PubMed Central

Garrett, Douglas D.; Nagel, Irene E.; Preuschhof, Claudia; Burzynska, Agnieszka Z.; Marchner, Janina; Wiegert, Steffen; Jungehülsing, Gerhard J.; Nyberg, Lars; Villringer, Arno; Li, Shu-Chen; Heekeren, Hauke R.; Bäckman, Lars; Lindenberger, Ulman

2015-01-01

Better-performing younger adults typically express greater brain signal variability relative to older, poorer performers. Mechanisms for age and performance-graded differences in brain dynamics have, however, not yet been uncovered. Given the age-related decline of the dopamine (DA) system in normal cognitive aging, DA neuromodulation is one plausible mechanism. Hence, agents that boost systemic DA [such as d-amphetamine (AMPH)] may help to restore deficient signal variability levels. Furthermore, despite the standard practice of counterbalancing drug session order (AMPH first vs. placebo first), it remains understudied how AMPH may interact with practice effects, possibly influencing whether DA up-regulation is functional. We examined the effects of AMPH on functional-MRI–based blood oxygen level-dependent (BOLD) signal variability (SDBOLD) in younger and older adults during a working memory task (letter n-back). Older adults expressed lower brain signal variability at placebo, but met or exceeded young adult SDBOLD levels in the presence of AMPH. Drug session order greatly moderated change–change relations between AMPH-driven SDBOLD and reaction time means (RTmean) and SDs (RTSD). Older adults who received AMPH in the first session tended to improve in RTmean and RTSD when SDBOLD was boosted on AMPH, whereas younger and older adults who received AMPH in the second session showed either a performance improvement when SDBOLD decreased (for RTmean) or no effect at all (for RTSD). The present findings support the hypothesis that age differences in brain signal variability reflect aging-induced changes in dopaminergic neuromodulation. The observed interactions among AMPH, age, and session order highlight the state- and practice-dependent neurochemical basis of human brain dynamics. PMID:26034283

Hilbert-Huang spectral analysis for characterizing the intrinsic time-scales of variability in decennial time-series of surface solar radiation

NASA Astrophysics Data System (ADS)

Bengulescu, Marc; Blanc, Philippe; Wald, Lucien

2016-04-01

An analysis of the variability of the surface solar irradiance (SSI) at different local time-scales is presented in this study. Since geophysical signals, such as long-term measurements of the SSI, are often produced by the non-linear interaction of deterministic physical processes that may also be under the influence of non-stationary external forcings, the Hilbert-Huang transform (HHT), an adaptive, noise-assisted, data-driven technique, is employed to extract locally - in time and in space - the embedded intrinsic scales at which a signal oscillates. The transform consists of two distinct steps. First, by means of the Empirical Mode Decomposition (EMD), the time-series is "de-constructed" into a finite number - often small - of zero-mean components that have distinct temporal scales of variability, termed hereinafter the Intrinsic Mode Functions (IMFs). The signal model of the components is an amplitude modulation - frequency modulation (AM - FM) one, and can also be thought of as an extension of a Fourier series having both time varying amplitude and frequency. Following the decomposition, Hilbert spectral analysis is then employed on the IMFs, yielding a time-frequency-energy representation that portrays changes in the spectral contents of the original data, with respect to time. As measurements of surface solar irradiance may possibly be contaminated by the manifestation of different type of stochastic processes (i.e. noise), the identification of real, physical processes from this background of random fluctuations is of interest. To this end, an adaptive background noise null hypothesis is assumed, based on the robust statistical properties of the EMD when applied to time-series of different classes of noise (e.g. white, red or fractional Gaussian). Since the algorithm acts as an efficient constant-Q dyadic, "wavelet-like", filter bank, the different noise inputs are decomposed into components having the same spectral shape, but that are translated to the next lower octave in the spectral domain. Thus, when the sampling step is increased, the spectral shape of IMFs cannot remain at its original position, due to the new lower Nyquist frequency, and is instead pushed toward the lower scaled frequency. Based on these features, the identification of potential signals within the data should become possible without any prior knowledge of the background noises. When applying the above outlined procedure to decennial time-series of surface solar irradiance, only the component that has an annual time-scale of variability is shown to have statistical properties that diverge from those of noise. Nevertheless, the noise-like components are not completely devoid of information, as it is found that their AM components have a non-null rank correlation coefficient with the annual mode, i.e. the background noise intensity seems to be modulated by the seasonal cycle. The findings have possible implications on the modelling and forecast of the surface solar irradiance, by discriminating its deterministic from its quasi-stochastic constituents, at distinct local time-scales.

Estimation of Ocean and Seabed Parameters and Processes Using Low Frequency Acoustic Signals

DTIC Science & Technology

2011-09-01

Dr. Mohsen Badiey (University of Delaware), Kevin Smith (Naval Postgraduate School), Dr. James F. Lynch and Dr. Y.-T. Lin (Woods Hole Oceanographic...Wilson (ARL, University of Texas) in this topic. 3. Finite Element Modeling of wave propagation: Doctoral student, Hui- Kwan Kim, is modeling wave...student Hui- Kwan Kim is focusing on finite element modeling of wave propagation. RESULTS 1. Acoustic variability in the presence of internal waves

Economic Value Biases Uncertain Perceptual Choices in the Parietal and Prefrontal Cortices

PubMed Central

Summerfield, Christopher; Koechlin, Etienne

2010-01-01

An observer detecting a noisy sensory signal is biased by the costs and benefits associated with its presence or absence. When these costs and benefits are asymmetric, sensory, and economic information must be integrated to inform the final choice. However, it remains unknown how this information is combined at the neural or computational levels. To address this question, we asked healthy human observers to judge the presence or absence of a noisy sensory signal under economic conditions that favored yes responses (liberal blocks), no responses (conservative blocks), or neither response (neutral blocks). Economic information biased fast choices more than slow choices, suggesting that value and sensory information are integrated early in the decision epoch. More formal simulation analyses using an Ornstein–Uhlenbeck process demonstrated that the influence of economic information was best captured by shifting the origin of evidence accumulation toward the more valuable bound. We then used the computational model to generate trial-by-trial estimates of decision-related evidence that were based on combined sensory and economic information (the decision variable or DV), and regressed these against fMRI activity recorded whilst participants performed the task. Extrastriate visual regions responded to the level of sensory input (momentary evidence), but fMRI signals in the parietal and prefrontal cortices responded to the decision variable. These findings support recent single-neuron data suggesting that economic information biases decision-related signals in higher cortical regions. PMID:21267421

Transceivers and receivers for quantum key distribution and methods pertaining thereto

DOE Office of Scientific and Technical Information (OSTI.GOV)

DeRose, Christopher; Sarovar, Mohan; Soh, Daniel B.S.

Various technologies for performing continuous-variable (CV) and discrete-variable (DV) quantum key distribution (QKD) with integrated electro-optical circuits are described herein. An integrated DV-QKD system uses Mach-Zehnder modulators to modulate a polarization of photons at a transmitter and select a photon polarization measurement basis at a receiver. An integrated CV-QKD system uses wavelength division multiplexing to send and receive amplitude-modulated and phase-modulated optical signals with a local oscillator signal while maintaining phase coherence between the modulated signals and the local oscillator signal.

Empirical and Theoretical Aspects of Generation and Transfer of Information in a Neuromagnetic Source Network

PubMed Central

Vakorin, Vasily A.; Mišić, Bratislav; Krakovska, Olga; McIntosh, Anthony Randal

2011-01-01

Variability in source dynamics across the sources in an activated network may be indicative of how the information is processed within a network. Information-theoretic tools allow one not only to characterize local brain dynamics but also to describe interactions between distributed brain activity. This study follows such a framework and explores the relations between signal variability and asymmetry in mutual interdependencies in a data-driven pipeline of non-linear analysis of neuromagnetic sources reconstructed from human magnetoencephalographic (MEG) data collected as a reaction to a face recognition task. Asymmetry in non-linear interdependencies in the network was analyzed using transfer entropy, which quantifies predictive information transfer between the sources. Variability of the source activity was estimated using multi-scale entropy, quantifying the rate of which information is generated. The empirical results are supported by an analysis of synthetic data based on the dynamics of coupled systems with time delay in coupling. We found that the amount of information transferred from one source to another was correlated with the difference in variability between the dynamics of these two sources, with the directionality of net information transfer depending on the time scale at which the sample entropy was computed. The results based on synthetic data suggest that both time delay and strength of coupling can contribute to the relations between variability of brain signals and information transfer between them. Our findings support the previous attempts to characterize functional organization of the activated brain, based on a combination of non-linear dynamics and temporal features of brain connectivity, such as time delay. PMID:22131968

Monitoring the fetal heart rate variability during labor.

PubMed

Moslem, B; Mohydeen, A; Bazzi, O

2015-08-01

In respect to the main goal of our ongoing work for estimating the heart rate variability (HRV) from fetal electrocardiogram (FECG) signals for monitoring the health of the fetus, we investigate in this paper the possibility of extracting the fetal heart rate variability (HRV) directly from the abdominal composite recordings. Our proposed approach is based on a combination of two techniques: Periodic Component Analysis (PiCA) and recursive least square (RLS) adaptive filtering. The Fetal HRV of the estimated FECG signal is compared to a reference value extracted from an FECG signal recorded by using a spiral electrode attached directly to the fetal scalp. The results obtained show that the fetal HRV can be directly evaluated from the abdominal composite recordings without the need of recording an external reference signal.

Deconstructed transverse mass variables

DOE PAGES

Ismail, Ahmed; Schwienhorst, Reinhard; Virzi, Joseph S.; ...

2015-04-02

Traditional searches for R-parity conserving natural supersymmetry (SUSY) require large transverse mass and missing energy cuts to separate the signal from large backgrounds. SUSY models with compressed spectra inherently produce signal events with small amounts of missing energy that are hard to explore. We use this difficulty to motivate the construction of "deconstructed" transverse mass variables which are designed preserve information on both the norm and direction of the missing momentum. Here, we demonstrate the effectiveness of these variables in searches for the pair production of supersymmetric top-quark partners which subsequently decay into a final state with an isolated lepton,more » jets and missing energy. We show that the use of deconstructed transverse mass variables extends the accessible compressed spectra parameter space beyond the region probed by traditional methods. The parameter space can further be expanded to neutralino masses that are larger than the difference between the stop and top masses. In addition, we also discuss how these variables allow for novel searches of single stop production, in order to directly probe unconstrained stealth stops in the small stop-and neutralino-mass regime. We also demonstrate the utility of these variables for generic gluino and stop searches in all-hadronic final states. Overall, we demonstrate that deconstructed transverse variables are essential to any search wanting to maximize signal separation from the background when the signal has undetected particles in the final state.« less

A wide-range variable-frequency resonant tunneling diode oscillator using a variable resonator suitable for simple MEMS process

NASA Astrophysics Data System (ADS)

Yamashita, Takashi; Nakano, Daisuke; Mori, Masayuki; Maezawa, Koichi

2018-04-01

A resonant tunneling diode oscillator having a wide frequency variation range based on a novel MEMS resonator was proposed, which exploits the change in the signal propagation velocity on a coplanar waveguide according to a movable ground plane. First, we discussed the velocity modulation mechanism, and clarified the importance of the dielectric constant of the substrate. Then, a prototype device oscillating in a 10 to 20 GHz frequency range was fabricated to demonstrate the basic operation. A large and continuous increase in the oscillation frequency of about two times was achieved with this device. This is promising for various applications including THz spectroscopy.

Assessment of heart rate variability based on mobile device for planning physical activity

NASA Astrophysics Data System (ADS)

Svirin, I. S.; Epishina, E. V.; Voronin, V. V.; Semenishchev, E. A.; Solodova, E. N.; Nabilskaya, N. V.

2015-05-01

In this paper we present a method for the functional analysis of human heart based on electrocardiography (ECG) signals. The approach using the apparatus of analytical and differential geometry and correlation and regression analysis. ECG contains information on the current condition of the cardiovascular system as well as on the pathological changes in the heart. Mathematical processing of the heart rate variability allows to obtain a great set of mathematical and statistical characteristics. These characteristics of the heart rate are used when solving research problems to study physiological changes that determine functional changes of an individual. The proposed method implemented for up-to-date mobile Android and iOS based devices.

Separating Decision and Encoding Noise in Signal Detection Tasks

PubMed Central

Cabrera, Carlos Alexander; Lu, Zhong-Lin; Dosher, Barbara Anne

2015-01-01

In this paper we develop an extension to the Signal Detection Theory (SDT) framework to separately estimate internal noise arising from representational and decision processes. Our approach constrains SDT models with decision noise by combining a multi-pass external noise paradigm with confidence rating responses. In a simulation study we present evidence that representation and decision noise can be separately estimated over a range of representative underlying representational and decision noise level configurations. These results also hold across a number of decision rules and show resilience to rule miss-specification. The new theoretical framework is applied to a visual detection confidence-rating task with three and five response categories. This study compliments and extends the recent efforts of researchers (Benjamin, Diaz, & Wee, 2009; Mueller & Weidemann, 2008; Rosner & Kochanski, 2009, Kellen, Klauer, & Singmann, 2012) to separate and quantify underlying sources of response variability in signal detection tasks. PMID:26120907

Layer detection and snowpack stratigraphy characterisation from digital penetrometer signals

NASA Astrophysics Data System (ADS)

Floyer, James Antony

Forecasting for slab avalanches benefits from precise measurements of snow stratigraphy. Snow penetrometers offer the possibility of providing detailed information about snowpack structure; however, their use has yet to be adopted by avalanche forecasting operations in Canada. A manually driven, variable rate force-resistance penetrometer is tested for its ability to measure snowpack information suitable for avalanche forecasting and for spatial variability studies on snowpack properties. Subsequent to modifications, weak layers of 5 mm thick are reliably detected from the penetrometer signals. Rate effects are investigated and found to be insignificant for push velocities between 0.5 to 100 cm s-1 for dry snow. An analysis of snow deformation below the penetrometer tip is presented using particle image velocimetry and two zones associated with particle deflection are identified. The compacted zone is a region of densified snow that is pushed ahead of the penetrometer tip; the deformation zone is a broader zone surrounding the compacted zone, where deformation is in compression and in shear. Initial formation of the compacted zone is responsible for pronounced force spikes in the penetrometer signal. A layer tracing algorithm for tracing weak layers, crusts and interfaces across transects or grids of penetrometer profiles is presented. This algorithm uses Wiener spiking deconvolution to detect a portion of the signal manually identified as a layer in one profile across to an adjacent profile. Layer tracing is found to be most effective for tracing crusts and prominent weak layers, although weak layers close to crusts were not well traced. A framework for extending this method for detecting weak layers with no prior knowledge of weak layer existence is also presented. A study relating the fracture character of layers identified in compression tests is presented. A multivariate model is presented that distinguishes between sudden and other fracture characters 80% of the time. Transects of penetrometer profiles are presented over several alpine terrain features commonly associated with spatial variability of snowpack properties. Physical processes relating to the variability of certain snowpack properties revealed in the transects is discussed. The importance of characteristic signatures for training avalanche practitioners to recognise potentially unstable terrain is also discussed.

Talker and accent variability effects on spoken word recognition

NASA Astrophysics Data System (ADS)

Nyang, Edna E.; Rogers, Catherine L.; Nishi, Kanae

2003-04-01

A number of studies have shown that words in a list are recognized less accurately in noise and with longer response latencies when they are spoken by multiple talkers, rather than a single talker. These results have been interpreted as support for an exemplar-based model of speech perception, in which it is assumed that detailed information regarding the speaker's voice is preserved in memory and used in recognition, rather than being eliminated via normalization. In the present study, the effects of varying both accent and talker are investigated using lists of words spoken by (a) a single native English speaker, (b) six native English speakers, (c) three native English speakers and three Japanese-accented English speakers. Twelve /hVd/ words were mixed with multi-speaker babble at three signal-to-noise ratios (+10, +5, and 0 dB) to create the word lists. Native English-speaking listeners' percent-correct recognition for words produced by native English speakers across the three talker conditions (single talker native, multi-talker native, and multi-talker mixed native and non-native) and three signal-to-noise ratios will be compared to determine whether sources of speaker variability other than voice alone add to the processing demands imposed by simple (i.e., single accent) speaker variability in spoken word recognition.

The research on visual industrial robot which adopts fuzzy PID control algorithm

NASA Astrophysics Data System (ADS)

Feng, Yifei; Lu, Guoping; Yue, Lulin; Jiang, Weifeng; Zhang, Ye

2017-03-01

The control system of six degrees of freedom visual industrial robot based on the control mode of multi-axis motion control cards and PC was researched. For the variable, non-linear characteristics of industrial robot`s servo system, adaptive fuzzy PID controller was adopted. It achieved better control effort. In the vision system, a CCD camera was used to acquire signals and send them to video processing card. After processing, PC controls the six joints` motion by motion control cards. By experiment, manipulator can operate with machine tool and vision system to realize the function of grasp, process and verify. It has influence on the manufacturing of the industrial robot.

Application of multi response optimization with grey relational analysis and fuzzy logic method

NASA Astrophysics Data System (ADS)

Winarni, Sri; Wahyu Indratno, Sapto

2018-01-01

Multi-response optimization is an optimization process by considering multiple responses simultaneously. The purpose of this research is to get the optimum point on multi-response optimization process using grey relational analysis and fuzzy logic method. The optimum point is determined from the Fuzzy-GRG (Grey Relational Grade) variable which is the conversion of the Signal to Noise Ratio of the responses involved. The case study used in this research are case optimization of electrical process parameters in electrical disharge machining. It was found that the combination of treatments resulting to optimum MRR and SR was a 70 V gap voltage factor, peak current 9 A and duty factor 0.8.

Global Assessment of New GRACE Mascons Solutions for Hydrologic Applications

NASA Astrophysics Data System (ADS)

Save, H.; Zhang, Z.; Scanlon, B. R.; Wiese, D. N.; Landerer, F. W.; Long, D.; Longuevergne, L.; Chen, J.

2016-12-01

Advances in GRACE (Gravity Recovery and Climate Experiment) satellite data processing using new mass concentration (mascon) solutions have greatly increased the spatial localization and amplitude of recovered total Terrestrial Water Storage (TWS) signals; however, limited testing has been conduct on land hydrologic applications. In this study we compared TWS anomalies from (1) Center for Space Research mascons (CSR-M) solution with (2) NASA JPL mascon (JPL-M) solution, and with (3) a CSR gridded spherical harmonic rescaled (sf) solution from Tellus (CSRT-GSH.sf) in 176 river basins covering 80% of the global land area. There is good correspondence in TWS anomalies from mascons (CSR-M and JPL-M) and SH solutions based on high correlations between time series (rank correlation coefficients mostly >0.9). The long-term trends in basin TWS anomalies represent a relatively small signal (up to ±20 mm/yr) with differences among GRACE solutions and inter-basin variability increasing with decreasing basin size. Long-term TWS declines are greatest in (semi)arid and irrigated basins. Annual and semiannual signals have much larger amplitudes (up to ±250 mm). There is generally good agreement among GRACE solutions, increasing confidence in seasonal fluctuations from GRACE data. Rescaling spherical harmonics to restore lost signal increases agreement with mascons solutions for long-term trends and seasonal fluctuations. There are many advantages to using GRACE mascons solutions relative to SH solutions, such as reduced leakage from land to ocean increasing signal amplitude, and constraining results by applying geophysical data during processing with little or no post-processing requirements, making mascons more user friendly for non-geodetic users. This inter-comparison of various GRACE solutions should allow hydrologists to better select suitable GRACE products for hydrologic applications.

Speech Recognition in Noise by Children with and without Dyslexia: How is it Related to Reading?

PubMed

Nittrouer, Susan; Krieg, Letitia M; Lowenstein, Joanna H

2018-06-01

Developmental dyslexia is commonly viewed as a phonological deficit that makes it difficult to decode written language. But children with dyslexia typically exhibit other problems, as well, including poor speech recognition in noise. The purpose of this study was to examine whether the speech-in-noise problems of children with dyslexia are related to their reading problems, and if so, if a common underlying factor might explain both. The specific hypothesis examined was that a spectral processing disorder results in these children receiving smeared signals, which could explain both the diminished sensitivity to phonological structure - leading to reading problems - and the speech recognition in noise difficulties. The alternative hypothesis tested in this study was that children with dyslexia simply have broadly based language deficits. Ninety-seven children between the ages of 7 years; 10 months and 12 years; 9 months participated: 46 with dyslexia and 51 without dyslexia. Children were tested on two dependent measures: word reading and recognition in noise with two types of sentence materials: as unprocessed (UP) signals, and as spectrally smeared (SM) signals. Data were collected for four predictor variables: phonological awareness, vocabulary, grammatical knowledge, and digit span. Children with dyslexia showed deficits on both dependent and all predictor variables. Their scores for speech recognition in noise were poorer than those of children without dyslexia for both the UP and SM signals, but by equivalent amounts across signal conditions indicating that they were not disproportionately hindered by spectral distortion. Correlation analyses on scores from children with dyslexia showed that reading ability and speech-in-noise recognition were only mildly correlated, and each skill was related to different underlying abilities. No substantial evidence was found to support the suggestion that the reading and speech recognition in noise problems of children with dyslexia arise from a single factor that could be defined as a spectral processing disorder. The reading and speech recognition in noise deficits of these children appeared to be largely independent. Copyright © 2018 Elsevier Ltd. All rights reserved.

Signal processing for smart cards

NASA Astrophysics Data System (ADS)

Quisquater, Jean-Jacques; Samyde, David

2003-06-01

In 1998, Paul Kocher showed that when a smart card computes cryptographic algorithms, for signatures or encryption, its consumption or its radiations leak information. The keys or the secrets hidden in the card can then be recovered using a differential measurement based on the intercorrelation function. A lot of silicon manufacturers use desynchronization countermeasures to defeat power analysis. In this article we detail a new resynchronization technic. This method can be used to facilitate the use of a neural network to do the code recognition. It becomes possible to reverse engineer a software code automatically. Using data and clock separation methods, we show how to optimize the synchronization using signal processing. Then we compare these methods with watermarking methods for 1D and 2D signal. The very last watermarking detection improvements can be applied to signal processing for smart cards with very few modifications. Bayesian processing is one of the best ways to do Differential Power Analysis, and it is possible to extract a PIN code from a smart card in very few samples. So this article shows the need to continue to set up effective countermeasures for cryptographic processors. Although the idea to use advanced signal processing operators has been commonly known for a long time, no publication explains that results can be obtained. The main idea of differential measurement is to use the cross-correlation of two random variables and to repeat consumption measurements on the processor to be analyzed. We use two processors clocked at the same external frequency and computing the same data. The applications of our design are numerous. Two measurements provide the inputs of a central operator. With the most accurate operator we can improve the signal noise ratio, re-synchronize the acquisition clock with the internal one, or remove jitter. The analysis based on consumption or electromagnetic measurements can be improved using our structure. At first sight the same results can be obtained with only one smart card, but this idea is not completely true because the statistical properties of the signal are not the same. As the two smart cards are submitted to the same external noise during the measurement, it is more easy to reduce the influence of perturbations. This paper shows the importance of accurate countermeasures against differential analysis.

Determining the Ocean's Role on the Variable Gravity Field on Earth Rotation

NASA Technical Reports Server (NTRS)

Ponte, Rui M.

1999-01-01

A number of ocean models of different complexity have been used to study changes in the oceanic mass field and angular momentum and their relation to the variable Earth rotation and gravity field. Time scales examined range from seasonal to a few days. Results point to the importance of oceanic signals in driving polar motion, in particular the Chandler and annual wobbles. Results also show that oceanic signals have a measurable impact on length-of-day variations. Various circulation features and associated mass signals, including the North Pacific subtropical gyre, the equatorial currents, and the Antarctic Circumpolar Current play a significant role in oceanic angular momentum variability.

Nonlinear digital signal processing in mental health: characterization of major depression using instantaneous entropy measures of heartbeat dynamics.

PubMed

Valenza, Gaetano; Garcia, Ronald G; Citi, Luca; Scilingo, Enzo P; Tomaz, Carlos A; Barbieri, Riccardo

2015-01-01

Nonlinear digital signal processing methods that address system complexity have provided useful computational tools for helping in the diagnosis and treatment of a wide range of pathologies. More specifically, nonlinear measures have been successful in characterizing patients with mental disorders such as Major Depression (MD). In this study, we propose the use of instantaneous measures of entropy, namely the inhomogeneous point-process approximate entropy (ipApEn) and the inhomogeneous point-process sample entropy (ipSampEn), to describe a novel characterization of MD patients undergoing affective elicitation. Because these measures are built within a nonlinear point-process model, they allow for the assessment of complexity in cardiovascular dynamics at each moment in time. Heartbeat dynamics were characterized from 48 healthy controls and 48 patients with MD while emotionally elicited through either neutral or arousing audiovisual stimuli. Experimental results coming from the arousing tasks show that ipApEn measures are able to instantaneously track heartbeat complexity as well as discern between healthy subjects and MD patients. Conversely, standard heart rate variability (HRV) analysis performed in both time and frequency domains did not show any statistical significance. We conclude that measures of entropy based on nonlinear point-process models might contribute to devising useful computational tools for care in mental health.

Rapid Prototyping of a Smart Device-based Wireless Reflectance Photoplethysmograph

PubMed Central

Ghamari, M.; Aguilar, C.; Soltanpur, C.; Nazeran, H.

2017-01-01

This paper presents the design, fabrication, and testing of a wireless heart rate (HR) monitoring device based on photoplethysmography (PPG) and smart devices. PPG sensors use infrared (IR) light to obtain vital information to assess cardiac health and other physiologic conditions. The PPG data that are transferred to a computer undergo further processing to derive the Heart Rate Variability (HRV) signal, which is analyzed to generate quantitative markers of the Autonomic Nervous System (ANS). The HRV signal has numerous monitoring and diagnostic applications. To this end, wireless connectivity plays an important role in such biomedical instruments. The photoplethysmograph consists of an optical sensor to detect the changes in the light intensity reflected from the illuminated tissue, a signal conditioning unit to prepare the reflected light for further signal conditioning through amplification and filtering, a low-power microcontroller to control and digitize the analog PPG signal, and a Bluetooth module to transmit the digital data to a Bluetooth-based smart device such as a tablet. An Android app is then used to enable the smart device to acquire and digitally display the received analog PPG signal in real-time on the smart device. This article is concluded with the prototyping of the wireless PPG followed by the verification procedures of the PPG and HRV signals acquired in a laboratory environment. PMID:28959119

Rapid Prototyping of a Smart Device-based Wireless Reflectance Photoplethysmograph.

PubMed

Ghamari, M; Aguilar, C; Soltanpur, C; Nazeran, H

2016-03-01

This paper presents the design, fabrication, and testing of a wireless heart rate (HR) monitoring device based on photoplethysmography (PPG) and smart devices. PPG sensors use infrared (IR) light to obtain vital information to assess cardiac health and other physiologic conditions. The PPG data that are transferred to a computer undergo further processing to derive the Heart Rate Variability (HRV) signal, which is analyzed to generate quantitative markers of the Autonomic Nervous System (ANS). The HRV signal has numerous monitoring and diagnostic applications. To this end, wireless connectivity plays an important role in such biomedical instruments. The photoplethysmograph consists of an optical sensor to detect the changes in the light intensity reflected from the illuminated tissue, a signal conditioning unit to prepare the reflected light for further signal conditioning through amplification and filtering, a low-power microcontroller to control and digitize the analog PPG signal, and a Bluetooth module to transmit the digital data to a Bluetooth-based smart device such as a tablet. An Android app is then used to enable the smart device to acquire and digitally display the received analog PPG signal in real-time on the smart device. This article is concluded with the prototyping of the wireless PPG followed by the verification procedures of the PPG and HRV signals acquired in a laboratory environment.

A comparison of annual vertical crustal displacements from GPS and Gravity Recovery and Climate Experiment (GRACE) over Europe

NASA Astrophysics Data System (ADS)

van Dam, T.; Wahr, J.; LavalléE, David

2007-03-01

We compare approximately 3 years of GPS height residuals (with respect to the International Terrestrial Reference Frame) with predictions of vertical surface displacements derived from the Gravity Recovery and Climate Experiment (GRACE) gravity fields for stations in Europe. An annual signal fit to the residual monthly heights, corrected for atmospheric pressure and barotropic ocean loading effects, should primarily represent surface displacements due to long-wavelength variations in water storage. A comparison of the annual height signal from GPS and GRACE over Europe indicates that at most sites, the annual signals do not agree in amplitude or phase. We find that unlike the annual signal predicted from GRACE, the annual signal in the GPS heights is not coherent over the region, displaying significant variability from site to site. Confidence in the GRACE data and the unlikely possibility of large-amplitude small-scale features in the load field not captured by the GRACE data leads us to conclude that some of the discrepancy between the GPS and GRACE observations is due to technique errors in the GPS data processing. This is evidenced by the fact that the disagreement between GPS and GRACE is largest at coastal sites, where mismodeling of the semidiurnal ocean tidal loading signal can result in spurious annual signals.

An innovative nonintrusive driver assistance system for vital signal monitoring.

PubMed

Sun, Ye; Yu, Xiong Bill

2014-11-01

This paper describes an in-vehicle nonintrusive biopotential measurement system for driver health monitoring and fatigue detection. Previous research has found that the physiological signals including eye features, electrocardiography (ECG), electroencephalography (EEG) and their secondary parameters such as heart rate and HR variability are good indicators of health state as well as driver fatigue. A conventional biopotential measurement system requires the electrodes to be in contact with human body. This not only interferes with the driver operation, but also is not feasible for long-term monitoring purpose. The driver assistance system in this paper can remotely detect the biopotential signals with no physical contact with human skin. With delicate sensor and electronic design, ECG, EEG, and eye blinking can be measured. Experiments were conducted on a high fidelity driving simulator to validate the system performance. The system was found to be able to detect the ECG/EEG signals through cloth or hair with no contact with skin. Eye blinking activities can also be detected at a distance of 10 cm. Digital signal processing algorithms were developed to decimate the signal noise and extract the physiological features. The extracted features from the vital signals were further analyzed to assess the potential criterion for alertness and drowsiness determination.

Developing a complex independent component analysis technique to extract non-stationary patterns from geophysical time-series

NASA Astrophysics Data System (ADS)

Forootan, Ehsan; Kusche, Jürgen

2016-04-01

Geodetic/geophysical observations, such as the time series of global terrestrial water storage change or sea level and temperature change, represent samples of physical processes and therefore contain information about complex physical interactionswith many inherent time scales. Extracting relevant information from these samples, for example quantifying the seasonality of a physical process or its variability due to large-scale ocean-atmosphere interactions, is not possible by rendering simple time series approaches. In the last decades, decomposition techniques have found increasing interest for extracting patterns from geophysical observations. Traditionally, principal component analysis (PCA) and more recently independent component analysis (ICA) are common techniques to extract statistical orthogonal (uncorrelated) and independent modes that represent the maximum variance of observations, respectively. PCA and ICA can be classified as stationary signal decomposition techniques since they are based on decomposing the auto-covariance matrix or diagonalizing higher (than two)-order statistical tensors from centered time series. However, the stationary assumption is obviously not justifiable for many geophysical and climate variables even after removing cyclic components e.g., the seasonal cycles. In this paper, we present a new decomposition method, the complex independent component analysis (CICA, Forootan, PhD-2014), which can be applied to extract to non-stationary (changing in space and time) patterns from geophysical time series. Here, CICA is derived as an extension of real-valued ICA (Forootan and Kusche, JoG-2012), where we (i) define a new complex data set using a Hilbert transformation. The complex time series contain the observed values in their real part, and the temporal rate of variability in their imaginary part. (ii) An ICA algorithm based on diagonalization of fourth-order cumulants is then applied to decompose the new complex data set in (i). (iii) Dominant non-stationary patterns are recognized as independent complex patterns that can be used to represent the space and time amplitude and phase propagations. We present the results of CICA on simulated and real cases e.g., for quantifying the impact of large-scale ocean-atmosphere interaction on global mass changes. Forootan (PhD-2014) Statistical signal decomposition techniques for analyzing time-variable satellite gravimetry data, PhD Thesis, University of Bonn, http://hss.ulb.uni-bonn.de/2014/3766/3766.htm Forootan and Kusche (JoG-2012) Separation of global time-variable gravity signals into maximally independent components, Journal of Geodesy 86 (7), 477-497, doi: 10.1007/s00190-011-0532-5

Distribution and Variability of Satellite-Derived Signals of Isolated Convection Initiation Events Over Central Eastern China

NASA Astrophysics Data System (ADS)

Huang, Yipeng; Meng, Zhiyong; Li, Jing; Li, Wanbiao; Bai, Lanqiang; Zhang, Murong; Wang, Xi

2017-11-01

This study combined measurements from the Chinese operational geostationary satellite Fengyun-2E (FY-2E) and ground-based weather radars to conduct a statistical survey of isolated convection initiation (CI) over central eastern China (CEC). The convective environment in CEC is modulated by the complex topography and monsoon climate. From May to August 2010, a total of 1,630 isolated CI signals were derived from FY-2E using a semiautomated method. The formation of these satellite-derived CI signals peaks in the early afternoon and occurs with high frequency in areas with remarkable terrain inhomogeneity (e.g., mountain, water, and mountain-water areas). The high signal frequency areas shift from northwest CEC (dry, high altitude) in early summer to southeast CEC (humid, low altitude) in midsummer along with an increasing monthly mean frequency. The satellite-derived CI signals tend to have longer lead times (the time difference between satellite-derived signal formation and radar-based CI) in the late morning and afternoon than in the early morning and night. During the early morning and night, the distinction between cloud top signatures and background terrestrial radiation becomes less apparent, resulting in delayed identification of the signals and thus short and even negative lead times. A decline in the lead time is observed from May to August, likely due to the increasing cloud growth rate and warm-rain processes. Results show increasing lead times with increasing landscape elevation, likely due to more warm-rain processes over the coastal sea and plain, along with a decreasing cloud growth rate from hill and mountain to the plateau.

Sensing device and method for measuring emission time delay during irradiation of targeted samples utilizing variable phase tracking

NASA Technical Reports Server (NTRS)

Danielson, J. D. Sheldon (Inventor)

2006-01-01

An apparatus for measuring emission time delay during irradiation of targeted samples by utilizing digital signal processing to determine the emission phase shift caused by the sample is disclosed. The apparatus includes a source of electromagnetic radiation adapted to irradiate a target sample. A mechanism generates first and second digital input signals of known frequencies with a known phase relationship, and a device then converts the first and second digital input signals to analog sinusoidal signals. An element is provided to direct the first input signal to the electromagnetic radiation source to modulate the source by the frequency thereof to irradiate the target sample and generate a target sample emission. A device detects the target sample emission and produces a corresponding first output signal having a phase shift relative to the phase of the first input signal, the phase shift being caused by the irradiation time delay in the sample. A member produces a known phase shift in the second input signal to create a second output signal. A mechanism is then provided for converting each of the first and second analog output signals to digital signals. A mixer receives the first and second digital output signals and compares the signal phase relationship therebetween to produce a signal indicative of the change in phase relationship between the first and second output signals caused by the target sample emission. Finally, a feedback arrangement alters the phase of the second input signal based on the mixer signal to ultimately place the first and second output signals in quadrature. Mechanisms for enhancing this phase comparison and adjustment technique are also disclosed.

Non-native Listeners’ Recognition of High-Variability Speech Using PRESTO

PubMed Central

Tamati, Terrin N.; Pisoni, David B.

2015-01-01

Background Natural variability in speech is a significant challenge to robust successful spoken word recognition. In everyday listening environments, listeners must quickly adapt and adjust to multiple sources of variability in both the signal and listening environments. High-variability speech may be particularly difficult to understand for non-native listeners, who have less experience with the second language (L2) phonological system and less detailed knowledge of sociolinguistic variation of the L2. Purpose The purpose of this study was to investigate the effects of high-variability sentences on non-native speech recognition and to explore the underlying sources of individual differences in speech recognition abilities of non-native listeners. Research Design Participants completed two sentence recognition tasks involving high-variability and low-variability sentences. They also completed a battery of behavioral tasks and self-report questionnaires designed to assess their indexical processing skills, vocabulary knowledge, and several core neurocognitive abilities. Study Sample Native speakers of Mandarin (n = 25) living in the United States recruited from the Indiana University community participated in the current study. A native comparison group consisted of scores obtained from native speakers of English (n = 21) in the Indiana University community taken from an earlier study. Data Collection and Analysis Speech recognition in high-variability listening conditions was assessed with a sentence recognition task using sentences from PRESTO (Perceptually Robust English Sentence Test Open-Set) mixed in 6-talker multitalker babble. Speech recognition in low-variability listening conditions was assessed using sentences from HINT (Hearing In Noise Test) mixed in 6-talker multitalker babble. Indexical processing skills were measured using a talker discrimination task, a gender discrimination task, and a forced-choice regional dialect categorization task. Vocabulary knowledge was assessed with the WordFam word familiarity test, and executive functioning was assessed with the BRIEF-A (Behavioral Rating Inventory of Executive Function – Adult Version) self-report questionnaire. Scores from the non-native listeners on behavioral tasks and self-report questionnaires were compared with scores obtained from native listeners tested in a previous study and were examined for individual differences. Results Non-native keyword recognition scores were significantly lower on PRESTO sentences than on HINT sentences. Non-native listeners’ keyword recognition scores were also lower than native listeners’ scores on both sentence recognition tasks. Differences in performance on the sentence recognition tasks between non-native and native listeners were larger on PRESTO than on HINT, although group differences varied by signal-to-noise ratio. The non-native and native groups also differed in the ability to categorize talkers by region of origin and in vocabulary knowledge. Individual non-native word recognition accuracy on PRESTO sentences in multitalker babble at more favorable signal-to-noise ratios was found to be related to several BRIEF-A subscales and composite scores. However, non-native performance on PRESTO was not related to regional dialect categorization, talker and gender discrimination, or vocabulary knowledge. Conclusions High-variability sentences in multitalker babble were particularly challenging for non-native listeners. Difficulty under high-variability testing conditions was related to lack of experience with the L2, especially L2 sociolinguistic information, compared with native listeners. Individual differences among the non-native listeners were related to weaknesses in core neurocognitive abilities affecting behavioral control in everyday life. PMID:25405842

Characterization of 67P/Churyumov-Gerasimenko interior from CONSERT signal amplitude variability

NASA Astrophysics Data System (ADS)

Zine, Sonia; Kofman, Wlodek; Herique, Alain; Hahnel, Ronny; Plettemeier, Dirk; Rogez, Yves; Statz, Christoph; Ciarletti, Valerie

2016-04-01

The bistatic radar CONSERT on Rosetta and Philae operated for 9 hours during Philae's First Science Sequence (FSS), on 12 and 13 November 2014. A strong signal was detected for 30 min at the beginning of the sequence, and for 80 min at the end. The signal propagated through the smaller lobe of the nucleus, with a length of propagation ranging between 200 and 800m, and a rapid decrease of its amplitude. First results have been published, based on the study of the signal propagation delay and the propagation path (Kofman et al., Science 2015; Ciarletti et al, A&A, 2015). This work focuses on the study of the signal amplitude, which shows variability throughout the acquisition sequence. The cause of this variability is twofold: (1) losses within the comet interior; (2) depolarization due to both antennas' varying relative attitudes. We simulate the depolarization by taking into account Rosetta's position and attitude on its orbit and by making assumptions on Philae's position, attitude, and close environment on the comet (dielectric properties). Then we assess the variability due to losses within the medium, and infer a characterization of the composition of the comet interior.

Yield impact for wafer shape misregistration-based binning for overlay APC diagnostic enhancement

NASA Astrophysics Data System (ADS)

Jayez, David; Jock, Kevin; Zhou, Yue; Govindarajulu, Venugopal; Zhang, Zhen; Anis, Fatima; Tijiwa-Birk, Felipe; Agarwal, Shivam

2018-03-01

The importance of traditionally acceptable sources of variation has started to become more critical as semiconductor technologies continue to push into smaller technology nodes. New metrology techniques are needed to pursue the process uniformity requirements needed for controllable lithography. Process control for lithography has the advantage of being able to adjust for cross-wafer variability, but this requires that all processes are close in matching between process tools/chambers for each process. When this is not the case, the cumulative line variability creates identifiable groups of wafers1 . This cumulative shape based effect is described as impacting overlay measurements and alignment by creating misregistration of the overlay marks. It is necessary to understand what requirements might go into developing a high volume manufacturing approach which leverages this grouping methodology, the key inputs and outputs, and what can be extracted from such an approach. It will be shown that this line variability can be quantified into a loss of electrical yield primarily at the edge of the wafer and proposes a methodology for root cause identification and improvement. This paper will cover the concept of wafer shape based grouping as a diagnostic tool for overlay control and containment, the challenges in implementing this in a manufacturing setting, and the limitations of this approach. This will be accomplished by showing that there are identifiable wafer shape based signatures. These shape based wafer signatures will be shown to be correlated to overlay misregistration, primarily at the edge. It will also be shown that by adjusting for this wafer shape signal, improvements can be made to both overlay as well as electrical yield. These improvements show an increase in edge yield, and a reduction in yield variability.

Variable classification in the LSST era: exploring a model for quasi-periodic light curves

NASA Astrophysics Data System (ADS)

Zinn, J. C.; Kochanek, C. S.; Kozłowski, S.; Udalski, A.; Szymański, M. K.; Soszyński, I.; Wyrzykowski, Ł.; Ulaczyk, K.; Poleski, R.; Pietrukowicz, P.; Skowron, J.; Mróz, P.; Pawlak, M.

2017-06-01

The Large Synoptic Survey Telescope (LSST) is expected to yield ˜107 light curves over the course of its mission, which will require a concerted effort in automated classification. Stochastic processes provide one means of quantitatively describing variability with the potential advantage over simple light-curve statistics that the parameters may be physically meaningful. Here, we survey a large sample of periodic, quasi-periodic and stochastic Optical Gravitational Lensing Experiment-III variables using the damped random walk (DRW; CARMA(1,0)) and quasi-periodic oscillation (QPO; CARMA(2,1)) stochastic process models. The QPO model is described by an amplitude, a period and a coherence time-scale, while the DRW has only an amplitude and a time-scale. We find that the periodic and quasi-periodic stellar variables are generally better described by a QPO than a DRW, while quasars are better described by the DRW model. There are ambiguities in interpreting the QPO coherence time due to non-sinusoidal light-curve shapes, signal-to-noise ratio, error mischaracterizations and cadence. Higher order implementations of the QPO model that better capture light-curve shapes are necessary for the coherence time to have its implied physical meaning. Independent of physical meaning, the extra parameter of the QPO model successfully distinguishes most of the classes of periodic and quasi-periodic variables we consider.

An Investigation into the Periodic Optical Variability of Radio Detected Ultracool Dwarfs using the GUFI Photometer

NASA Astrophysics Data System (ADS)

Boyle, Richard P.; Harding, L. K.; Hallinan, G.; Butler, R. F.; Golden, A.

2011-05-01

In the past ten years or so, radio observations of ultracool dwarfs have yielded the detection of both quiescent and time-variable radio emission in the late-M and L dwarf regime. Four of these dwarfs have been found to produce periodic pulses, determined to be associated with the dwarf's rotation. More recently, two of these radio pulsing dwarfs have been shown to be periodically variable in broadband optical photometry, where the detected periods match the periods of the radio pulses. For one of these dwarfs in particular, it has been established that the mechanism which is driving the optical and radio periodic variability are possibly linked, being a consequence of a magnetically-driven auroral process. We therefore undertook a campaign to investigate the ubiquity of optical periodicity for known radio detected ultracool dwarfs, via multi-color photometric monitoring. To facilitate this research, the GUFI instrument (Galway Ultra Fast Imager) was commissioned on the 1.8m VATT observatory, on Mt. Graham, Arizona. We present the recently published results from this observation campaign, where we have confirmed periodic variability for five of these dwarfs, three of which have been detected for the first time by GUFI. These data provide an insight into the cause of this optical emission, its connection to the radio processes, and most importantly determine whether optical periodic signals are present only in radio pulsing dwarfs.

Hybrid online sensor error detection and functional redundancy for systems with time-varying parameters.

PubMed

Feng, Jianyuan; Turksoy, Kamuran; Samadi, Sediqeh; Hajizadeh, Iman; Littlejohn, Elizabeth; Cinar, Ali

2017-12-01

Supervision and control systems rely on signals from sensors to receive information to monitor the operation of a system and adjust manipulated variables to achieve the control objective. However, sensor performance is often limited by their working conditions and sensors may also be subjected to interference by other devices. Many different types of sensor errors such as outliers, missing values, drifts and corruption with noise may occur during process operation. A hybrid online sensor error detection and functional redundancy system is developed to detect errors in online signals, and replace erroneous or missing values detected with model-based estimates. The proposed hybrid system relies on two techniques, an outlier-robust Kalman filter (ORKF) and a locally-weighted partial least squares (LW-PLS) regression model, which leverage the advantages of automatic measurement error elimination with ORKF and data-driven prediction with LW-PLS. The system includes a nominal angle analysis (NAA) method to distinguish between signal faults and large changes in sensor values caused by real dynamic changes in process operation. The performance of the system is illustrated with clinical data continuous glucose monitoring (CGM) sensors from people with type 1 diabetes. More than 50,000 CGM sensor errors were added to original CGM signals from 25 clinical experiments, then the performance of error detection and functional redundancy algorithms were analyzed. The results indicate that the proposed system can successfully detect most of the erroneous signals and substitute them with reasonable estimated values computed by functional redundancy system.

Speech production variability in fricatives of children and adults: Results of functional data analysis

PubMed Central

Koenig, Laura L.; Lucero, Jorge C.; Perlman, Elizabeth

2008-01-01

This study investigates token-to-token variability in fricative production of 5 year olds, 10 year olds, and adults. Previous studies have reported higher intrasubject variability in children than adults, in speech as well as nonspeech tasks, but authors have disagreed on the causes and implications of this finding. The current work assessed the characteristics of age-related variability across articulators (larynx and tongue) as well as in temporal versus spatial domains. Oral airflow signals, which reflect changes in both laryngeal and supralaryngeal apertures, were obtained for multiple productions of ∕h s z∕. The data were processed using functional data analysis, which provides a means of obtaining relatively independent indices of amplitude and temporal (phasing) variability. Consistent with past work, both temporal and amplitude variabilities were higher in children than adults, but the temporal indices were generally less adultlike than the amplitude indices for both groups of children. Quantitative and qualitative analyses showed considerable speaker- and consonant-specific patterns of variability. The data indicate that variability in ∕s∕ may represent laryngeal as well as supralaryngeal control and further that a simple random noise factor, higher in children than in adults, is insufficient to explain developmental differences in speech production variability. PMID:19045800

Experiments with recursive estimation in astronomical image processing

NASA Technical Reports Server (NTRS)

Busko, I.

1992-01-01

Recursive estimation concepts were applied to image enhancement problems since the 70's. However, very few applications in the particular area of astronomical image processing are known. These concepts were derived, for 2-dimensional images, from the well-known theory of Kalman filtering in one dimension. The historic reasons for application of these techniques to digital images are related to the images' scanned nature, in which the temporal output of a scanner device can be processed on-line by techniques borrowed directly from 1-dimensional recursive signal analysis. However, recursive estimation has particular properties that make it attractive even in modern days, when big computer memories make the full scanned image available to the processor at any given time. One particularly important aspect is the ability of recursive techniques to deal with non-stationary phenomena, that is, phenomena which have their statistical properties variable in time (or position in a 2-D image). Many image processing methods make underlying stationary assumptions either for the stochastic field being imaged, for the imaging system properties, or both. They will underperform, or even fail, when applied to images that deviate significantly from stationarity. Recursive methods, on the contrary, make it feasible to perform adaptive processing, that is, to process the image by a processor with properties tuned to the image's local statistical properties. Recursive estimation can be used to build estimates of images degraded by such phenomena as noise and blur. We show examples of recursive adaptive processing of astronomical images, using several local statistical properties to drive the adaptive processor, as average signal intensity, signal-to-noise and autocorrelation function. Software was developed under IRAF, and as such will be made available to interested users.

Nonword repetition in lexical decision: support for two opposing processes.

PubMed

Wagenmakers, Eric-Jan; Zeelenberg, René; Steyvers, Mark; Shiffrin, Richard; Raaijmakers, Jeroen

2004-10-01

We tested and confirmed the hypothesis that the prior presentation of nonwords in lexical decision is the net result of two opposing processes: (1) a relatively fast inhibitory process based on global familiarity; and (2) a relatively slow facilitatory process based on the retrieval of specific episodic information. In three studies, we manipulated speed-stress to influence the balance between the two processes. Experiment 1 showed item-specific improvement for repeated nonwords in a standard "respond-when-ready" lexical decision task. Experiment 2 used a 400-ms deadline procedure and showed performance for nonwords to be unaffected by up to four prior presentations. In Experiment 3 we used a signal-to-respond procedure with variable time intervals and found negative repetition priming for repeated nonwords. These results can be accounted for by dual-process models of lexical decision.

An adaptive confidence limit for periodic non-steady conditions fault detection

NASA Astrophysics Data System (ADS)

Wang, Tianzhen; Wu, Hao; Ni, Mengqi; Zhang, Milu; Dong, Jingjing; Benbouzid, Mohamed El Hachemi; Hu, Xiong

2016-05-01

System monitoring has become a major concern in batch process due to the fact that failure rate in non-steady conditions is much higher than in steady ones. A series of approaches based on PCA have already solved problems such as data dimensionality reduction, multivariable decorrelation, and processing non-changing signal. However, if the data follows non-Gaussian distribution or the variables contain some signal changes, the above approaches are not applicable. To deal with these concerns and to enhance performance in multiperiod data processing, this paper proposes a fault detection method using adaptive confidence limit (ACL) in periodic non-steady conditions. The proposed ACL method achieves four main enhancements: Longitudinal-Standardization could convert non-Gaussian sampling data to Gaussian ones; the multiperiod PCA algorithm could reduce dimensionality, remove correlation, and improve the monitoring accuracy; the adaptive confidence limit could detect faults under non-steady conditions; the fault sections determination procedure could select the appropriate parameter of the adaptive confidence limit. The achieved result analysis clearly shows that the proposed ACL method is superior to other fault detection approaches under periodic non-steady conditions.

Impaired auditory temporal selectivity in the inferior colliculus of aged Mongolian gerbils.

PubMed

Khouri, Leila; Lesica, Nicholas A; Grothe, Benedikt

2011-07-06

Aged humans show severe difficulties in temporal auditory processing tasks (e.g., speech recognition in noise, low-frequency sound localization, gap detection). A degradation of auditory function with age is also evident in experimental animals. To investigate age-related changes in temporal processing, we compared extracellular responses to temporally variable pulse trains and human speech in the inferior colliculus of young adult (3 month) and aged (3 years) Mongolian gerbils. We observed a significant decrease of selectivity to the pulse trains in neuronal responses from aged animals. This decrease in selectivity led, on the population level, to an increase in signal correlations and therefore a decrease in heterogeneity of temporal receptive fields and a decreased efficiency in encoding of speech signals. A decrease in selectivity to temporal modulations is consistent with a downregulation of the inhibitory transmitter system in aged animals. These alterations in temporal processing could underlie declines in the aging auditory system, which are unrelated to peripheral hearing loss. These declines cannot be compensated by traditional hearing aids (that rely on amplification of sound) but may rather require pharmacological treatment.

Characterizing the spatial variability of local and background concentration signals for air pollution at the neighbourhood scale

NASA Astrophysics Data System (ADS)

Shairsingh, Kerolyn K.; Jeong, Cheol-Heon; Wang, Jonathan M.; Evans, Greg J.

2018-06-01

Vehicle emissions represent a major source of air pollution in urban districts, producing highly variable concentrations of some pollutants within cities. The main goal of this study was to identify a deconvolving method so as to characterize variability in local, neighbourhood and regional background concentration signals. This method was validated by examining how traffic-related and non-traffic-related sources influenced the different signals. Sampling with a mobile monitoring platform was conducted across the Greater Toronto Area over a seven-day period during summer 2015. This mobile monitoring platform was equipped with instruments for measuring a wide range of pollutants at time resolutions of 1 s (ultrafine particles, black carbon) to 20 s (nitric oxide, nitrogen oxides). The monitored neighbourhoods were selected based on their land use categories (e.g. industrial, commercial, parks and residential areas). The high time-resolution data allowed pollutant concentrations to be separated into signals representing background and local concentrations. The background signals were determined using a spline of minimums; local signals were derived by subtracting the background concentration from the total concentration. Our study showed that temporal scales of 500 s and 2400 s were associated with the neighbourhood and regional background signals respectively. The percent contribution of the pollutant concentration that was attributed to local signals was highest for nitric oxide (NO) (37-95%) and lowest for ultrafine particles (9-58%); the ultrafine particles were predominantly regional (32-87%) in origin on these days. Local concentrations showed stronger associations than total concentrations with traffic intensity in a 100 m buffer (ρ:0.21-0.44). The neighbourhood scale signal also showed stronger associations with industrial facilities than the total concentrations. Given that the signals show stronger associations with different land use suggests that resolving the ambient concentrations differentiates which emission sources drive the variability in each signal. The benefit of this deconvolution method is that it may reduce exposure misclassification when coupled with predictive models.

Spectro-Temporal Electrocardiogram Analysis for Noise-Robust Heart Rate and Heart Rate Variability Measurement

PubMed Central

Tobón, Diana P.; Jayaraman, Srinivasan

2017-01-01

The last few years has seen a proliferation of wearable electrocardiogram (ECG) devices in the market with applications in fitness tracking, patient monitoring, athletic performance assessment, stress and fatigue detection, and biometrics, to name a few. The majority of these applications rely on the computation of the heart rate (HR) and the so-called heart rate variability (HRV) index via time-, frequency-, or non-linear-domain approaches. Wearable/portable devices, however, are highly susceptible to artifacts, particularly those resultant from movement. These artifacts can hamper HR/HRV measurement, thus pose a serious threat to cardiac monitoring applications. While current solutions rely on ECG enhancement as a pre-processing step prior to HR/HRV calculation, existing artifact removal algorithms still perform poorly under extremely noisy scenarios. To overcome this limitation, we take an alternate approach and propose the use of a spectro-temporal ECG signal representation that we show separates cardiac components from artifacts. More specifically, by quantifying the rate-of-change of ECG spectral components over time, we show that heart rate estimates can be reliably obtained even in extremely noisy signals, thus bypassing the need for ECG enhancement. With such HR measurements in hands, we then propose a new noise-robust HRV index termed MD-HRV (modulation-domain HRV) computed as the standard deviation of the obtained HR values. Experiments with synthetic ECG signals corrupted at various different signal-to-noise levels, as well as recorded noisy signals show the proposed measure outperforming several HRV benchmark parameters computed post wavelet-based enhancement. These findings suggest that the proposed HR measures and derived MD-HRV metric are well-suited for ambulant cardiac monitoring applications, particularly those involving intense movement (e.g., elite athletic training). PMID:29255653

Multicenter evaluation of signalment and comorbid conditions associated with aortic thrombotic disease in dogs.

PubMed

Winter, Randolph L; Budke, Christine M

2017-08-15

OBJECTIVE To assess signalment and concurrent disease processes in dogs with aortic thrombotic disease (ATD). DESIGN Retrospective case-control study. ANIMALS Dogs examined at North American veterinary teaching hospitals from 1985 through 2011 with medical records submitted to the Veterinary Medical Database. PROCEDURES Medical records were reviewed to identify dogs with a diagnosis of ATD (case dogs). Five control dogs without a diagnosis of ATD were then identified for every case dog. Data were collected regarding dog age, sex, breed, body weight, and concurrent disease processes. RESULTS ATD was diagnosed in 291 of the 984,973 (0.03%) dogs included in the database. The odds of a dog having ATD did not differ significantly by sex, age, or body weight. Compared with mixed-breed dogs, Shetland Sheepdogs had a significantly higher odds of ATD (OR, 2.59). Protein-losing nephropathy (64/291 [22%]) was the most commonly recorded concurrent disease in dogs with ATD. CONCLUSIONS AND CLINICAL RELEVANCE Dogs with ATD did not differ significantly from dogs without ATD in most signalment variables. Contrary to previous reports, cardiac disease was not a common concurrent diagnosis in dogs with ATD.

Stochastic characterization of small-scale algorithms for human sensory processing

NASA Astrophysics Data System (ADS)

Neri, Peter

2010-12-01

Human sensory processing can be viewed as a functional H mapping a stimulus vector s into a decisional variable r. We currently have no direct access to r; rather, the human makes a decision based on r in order to drive subsequent behavior. It is this (typically binary) decision that we can measure. For example, there may be two external stimuli s[0] and s[1], mapped onto r[0] and r[1] by the sensory apparatus H; the human chooses the stimulus associated with largest r. This kind of decisional transduction poses a major challenge for an accurate characterization of H. In this article, we explore a specific approach based on a behavioral variant of reverse correlation techniques, where the input s contains a target signal corrupted by a controlled noisy perturbation. The presence of the target signal poses an additional challenge because it distorts the otherwise unbiased nature of the noise source. We consider issues arising from both the decisional transducer and the target signal, their impact on system identification, and ways to handle them effectively for system characterizations that extend to second-order functional approximations with associated small-scale cascade models.

Digital Intermediate Frequency Receiver Module For Use In Airborne Sar Applications

DOEpatents

Tise, Bertice L.; Dubbert, Dale F.

2005-03-08

A digital IF receiver (DRX) module directly compatible with advanced radar systems such as synthetic aperture radar (SAR) systems. The DRX can combine a 1 G-Sample/sec 8-bit ADC with high-speed digital signal processor, such as high gate-count FPGA technology or ASICs to realize a wideband IF receiver. DSP operations implemented in the DRX can include quadrature demodulation and multi-rate, variable-bandwidth IF filtering. Pulse-to-pulse (Doppler domain) filtering can also be implemented in the form of a presummer (accumulator) and an azimuth prefilter. An out of band noise source can be employed to provide a dither signal to the ADC, and later be removed by digital signal processing. Both the range and Doppler domain filtering operations can be implemented using a unique pane architecture which allows on-the-fly selection of the filter decimation factor, and hence, the filter bandwidth. The DRX module can include a standard VME-64 interface for control, status, and programming. An interface can provide phase history data to the real-time image formation processors. A third front-panel data port (FPDP) interface can send wide bandwidth, raw phase histories to a real-time phase history recorder for ground processing.

Apparatus for and method of testing an electrical ground fault circuit interrupt device

DOEpatents

Andrews, L.B.

1998-08-18

An apparatus for testing a ground fault circuit interrupt device includes a processor, an input device connected to the processor for receiving input from an operator, a storage media connected to the processor for storing test data, an output device connected to the processor for outputting information corresponding to the test data to the operator, and a calibrated variable load circuit connected between the processor and the ground fault circuit interrupt device. The ground fault circuit interrupt device is configured to trip a corresponding circuit breaker. The processor is configured to receive signals from the calibrated variable load circuit and to process the signals to determine a trip threshold current and/or a trip time. A method of testing the ground fault circuit interrupt device includes a first step of providing an identification for the ground fault circuit interrupt device. Test data is then recorded in accordance with the identification. By comparing test data from an initial test with test data from a subsequent test, a trend of performance for the ground fault circuit interrupt device is determined. 17 figs.

Apparatus for and method of testing an electrical ground fault circuit interrupt device

DOEpatents

Andrews, Lowell B.

1998-01-01

An apparatus for testing a ground fault circuit interrupt device includes a processor, an input device connected to the processor for receiving input from an operator, a storage media connected to the processor for storing test data, an output device connected to the processor for outputting information corresponding to the test data to the operator, and a calibrated variable load circuit connected between the processor and the ground fault circuit interrupt device. The ground fault circuit interrupt device is configured to trip a corresponding circuit breaker. The processor is configured to receive signals from the calibrated variable load circuit and to process the signals to determine a trip threshold current and/or a trip time. A method of testing the ground fault circuit interrupt device includes a first step of providing an identification for the ground fault circuit interrupt device. Test data is then recorded in accordance with the identification. By comparing test data from an initial test with test data from a subsequent test, a trend of performance for the ground fault circuit interrupt device is determined.

An Intelligent Monitoring Network for Detection of Cracks in Anvils of High-Press Apparatus.

PubMed

Tian, Hao; Yan, Zhaoli; Yang, Jun

2018-04-09

Due to the endurance of alternating high pressure and temperature, the carbide anvils of the high-press apparatus, which are widely used in the synthetic diamond industry, are prone to crack. In this paper, an acoustic method is used to monitor the crack events, and the intelligent monitoring network is proposed to classify the sound samples. The pulse sound signals produced by such cracking are first extracted based on a short-time energy threshold. Then, the signals are processed with the proposed intelligent monitoring network to identify the operation condition of the anvil of the high-pressure apparatus. The monitoring network is an improved convolutional neural network that solves the problems that may occur in practice. The length of pulse sound excited by the crack growth is variable, so a spatial pyramid pooling layer is adopted to solve the variable-length input problem. An adaptive weighted algorithm for loss function is proposed in this method to handle the class imbalance problem. The good performance regarding the accuracy and balance of the proposed intelligent monitoring network is validated through the experiments finally.

Recent advancements in GRACE mascon regularization and uncertainty assessment

NASA Astrophysics Data System (ADS)

Loomis, B. D.; Luthcke, S. B.

2017-12-01

The latest release of the NASA Goddard Space Flight Center (GSFC) global time-variable gravity mascon product applies a new regularization strategy along with new methods for estimating noise and leakage uncertainties. The critical design component of mascon estimation is the construction of the applied regularization matrices, and different strategies exist between the different centers that produce mascon solutions. The new approach from GSFC directly applies the pre-fit Level 1B inter-satellite range-acceleration residuals in the design of time-dependent regularization matrices, which are recomputed at each step of our iterative solution method. We summarize this new approach, demonstrating the simultaneous increase in recovered time-variable gravity signal and reduction in the post-fit inter-satellite residual magnitudes, until solution convergence occurs. We also present our new approach for estimating mascon noise uncertainties, which are calibrated to the post-fit inter-satellite residuals. Lastly, we present a new technique for end users to quickly estimate the signal leakage errors for any selected grouping of mascons, and we test the viability of this leakage assessment procedure on the mascon solutions produced by other processing centers.

Neural Correlation Is Stimulus Modulated by Feedforward Inhibitory Circuitry

PubMed Central

Middleton, Jason W.; Omar, Cyrus; Doiron, Brent; Simons, Daniel J.

2012-01-01

Correlated variability of neural spiking activity has important consequences for signal processing. How incoming sensory signals shape correlations of population responses remains unclear. Cross-correlations between spiking of different neurons may be particularly consequential in sparsely firing neural populations such as those found in layer 2/3 of sensory cortex. In rat whisker barrel cortex, we found that pairs of excitatory layer 2/3 neurons exhibit similarly low levels of spike count correlation during both spontaneous and sensory-evoked states. The spontaneous activity of excitatory–inhibitory neuron pairs is positively correlated, while sensory stimuli actively decorrelate joint responses. Computational modeling shows how threshold nonlinearities and local inhibition form the basis of a general decorrelating mechanism. We show that inhibitory population activity maintains low correlations in excitatory populations, especially during periods of sensory-evoked coactivation. The role of feedforward inhibition has been previously described in the context of trial-averaged phenomena. Our findings reveal a novel role for inhibition to shape correlations of neural variability and thereby prevent excessive correlations in the face of feedforward sensory-evoked activation. PMID:22238086

An Efficient Biometric-Based Algorithm Using Heart Rate Variability for Securing Body Sensor Networks

PubMed Central

Pirbhulal, Sandeep; Zhang, Heye; Mukhopadhyay, Subhas Chandra; Li, Chunyue; Wang, Yumei; Li, Guanglin; Wu, Wanqing; Zhang, Yuan-Ting

2015-01-01

Body Sensor Network (BSN) is a network of several associated sensor nodes on, inside or around the human body to monitor vital signals, such as, Electroencephalogram (EEG), Photoplethysmography (PPG), Electrocardiogram (ECG), etc. Each sensor node in BSN delivers major information; therefore, it is very significant to provide data confidentiality and security. All existing approaches to secure BSN are based on complex cryptographic key generation procedures, which not only demands high resource utilization and computation time, but also consumes large amount of energy, power and memory during data transmission. However, it is indispensable to put forward energy efficient and computationally less complex authentication technique for BSN. In this paper, a novel biometric-based algorithm is proposed, which utilizes Heart Rate Variability (HRV) for simple key generation process to secure BSN. Our proposed algorithm is compared with three data authentication techniques, namely Physiological Signal based Key Agreement (PSKA), Data Encryption Standard (DES) and Rivest Shamir Adleman (RSA). Simulation is performed in Matlab and results suggest that proposed algorithm is quite efficient in terms of transmission time utilization, average remaining energy and total power consumption. PMID:26131666

An Efficient Biometric-Based Algorithm Using Heart Rate Variability for Securing Body Sensor Networks.

PubMed

Pirbhulal, Sandeep; Zhang, Heye; Mukhopadhyay, Subhas Chandra; Li, Chunyue; Wang, Yumei; Li, Guanglin; Wu, Wanqing; Zhang, Yuan-Ting

2015-06-26

Body Sensor Network (BSN) is a network of several associated sensor nodes on, inside or around the human body to monitor vital signals, such as, Electroencephalogram (EEG), Photoplethysmography (PPG), Electrocardiogram (ECG), etc. Each sensor node in BSN delivers major information; therefore, it is very significant to provide data confidentiality and security. All existing approaches to secure BSN are based on complex cryptographic key generation procedures, which not only demands high resource utilization and computation time, but also consumes large amount of energy, power and memory during data transmission. However, it is indispensable to put forward energy efficient and computationally less complex authentication technique for BSN. In this paper, a novel biometric-based algorithm is proposed, which utilizes Heart Rate Variability (HRV) for simple key generation process to secure BSN. Our proposed algorithm is compared with three data authentication techniques, namely Physiological Signal based Key Agreement (PSKA), Data Encryption Standard (DES) and Rivest Shamir Adleman (RSA). Simulation is performed in Matlab and results suggest that proposed algorithm is quite efficient in terms of transmission time utilization, average remaining energy and total power consumption.

How mechanisms of perceptual decision-making affect the psychometric function.

PubMed

Gold, Joshua I; Ding, Long

2013-04-01

Psychometric functions are often interpreted in the context of Signal Detection Theory, which emphasizes a distinction between sensory processing and non-sensory decision rules in the brain. This framework has helped to relate perceptual sensitivity to the "neurometric" sensitivity of sensory-driven neural activity. However, perceptual sensitivity, as interpreted via Signal Detection Theory, is based on not just how the brain represents relevant sensory information, but also how that information is read out to form the decision variable to which the decision rule is applied. Here we discuss recent advances in our understanding of this readout process and describe its effects on the psychometric function. In particular, we show that particular aspects of the readout process can have specific, identifiable effects on the threshold, slope, upper asymptote, time dependence, and choice dependence of psychometric functions. To illustrate these points, we emphasize studies of perceptual learning that have identified changes in the readout process that can lead to changes in these aspects of the psychometric function. We also discuss methods that have been used to distinguish contributions of the sensory representation versus its readout to psychophysical performance. Copyright © 2012 Elsevier Ltd. All rights reserved.

Light-Mediated Hormonal Regulation of Plant Growth and Development.

PubMed

de Wit, Mieke; Galvão, Vinicius Costa; Fankhauser, Christian

2016-04-29

Light is crucial for plant life, and perception of the light environment dictates plant growth, morphology, and developmental changes. Such adjustments in growth and development in response to light conditions are often established through changes in hormone levels and signaling. This review discusses examples of light-regulated processes throughout a plant's life cycle for which it is known how light signals lead to hormonal regulation. Light acts as an important developmental switch in germination, photomorphogenesis, and transition to flowering, and light cues are essential to ensure light capture through architectural changes during phototropism and the shade avoidance response. In describing well-established links between light perception and hormonal changes, we aim to give insight into the mechanisms that enable plants to thrive in variable light environments.

EMG-based speech recognition using hidden markov models with global control variables.

PubMed

Lee, Ki-Seung

2008-03-01

It is well known that a strong relationship exists between human voices and the movement of articulatory facial muscles. In this paper, we utilize this knowledge to implement an automatic speech recognition scheme which uses solely surface electromyogram (EMG) signals. The sequence of EMG signals for each word is modelled by a hidden Markov model (HMM) framework. The main objective of the work involves building a model for state observation density when multichannel observation sequences are given. The proposed model reflects the dependencies between each of the EMG signals, which are described by introducing a global control variable. We also develop an efficient model training method, based on a maximum likelihood criterion. In a preliminary study, 60 isolated words were used as recognition variables. EMG signals were acquired from three articulatory facial muscles. The findings indicate that such a system may have the capacity to recognize speech signals with an accuracy of up to 87.07%, which is superior to the independent probabilistic model.

Coupled Research in Ocean Acoustics and Signal Processing for the Next Generation of Underwater Acoustic Communication Systems

DTIC Science & Technology

2016-08-05

technique which used unobserved ”intermediate” variables to break a high-dimensional estimation problem such as least- squares (LS) optimization of a large...Least Squares (GEM-LS). The estimator is iterative and the work in this time period focused on characterizing the convergence properties of this...ap- proach by relaxing the statistical assumptions which is termed the Relaxed Approximate Graph-Structured Recursive Least Squares (RAGS-RLS). This

Increased Intra-Participant Variability in Children with Autistic Spectrum Disorders: Evidence from Single-Trial Analysis of Evoked EEG

PubMed Central

Milne, Elizabeth

2011-01-01

Intra-participant variability in clinical conditions such as autistic spectrum disorder (ASD) is an important indicator of pathophysiological processing. The data reported here illustrate that trial-by-trial variability can be reliably measured from EEG, and that intra-participant EEG variability is significantly greater in those with ASD than in neuro-typical matched controls. EEG recorded at the scalp is a linear mixture of activity arising from muscle artifacts and numerous concurrent brain processes. To minimize these additional sources of variability, EEG data were subjected to two different methods of spatial filtering. (i) The data were decomposed using infomax independent component analysis, a method of blind source separation which un-mixes the EEG signal into components with maximally independent time-courses, and (ii) a surface Laplacian transform was performed (current source density interpolation) in order to reduce the effects of volume conduction. Data are presented from 13 high functioning adolescents with ASD without co-morbid ADHD, and 12 neuro-typical age-, IQ-, and gender-matched controls. Comparison of variability between the ASD and neuro-typical groups indicated that intra-participant variability of P1 latency and P1 amplitude was greater in the participants with ASD, and inter-trial α-band phase coherence was lower in the participants with ASD. These data support the suggestion that individuals with ASD are less able to synchronize the activity of stimulus-related cell assemblies than neuro-typical individuals, and provide empirical evidence in support of theories of increased neural noise in ASD. PMID:21716921

Emotional voices in context: A neurobiological model of multimodal affective information processing

NASA Astrophysics Data System (ADS)

Brück, Carolin; Kreifelts, Benjamin; Wildgruber, Dirk

2011-12-01

Just as eyes are often considered a gateway to the soul, the human voice offers a window through which we gain access to our fellow human beings' minds - their attitudes, intentions and feelings. Whether in talking or singing, crying or laughing, sighing or screaming, the sheer sound of a voice communicates a wealth of information that, in turn, may serve the observant listener as valuable guidepost in social interaction. But how do human beings extract information from the tone of a voice? In an attempt to answer this question, the present article reviews empirical evidence detailing the cerebral processes that underlie our ability to decode emotional information from vocal signals. The review will focus primarily on two prominent classes of vocal emotion cues: laughter and speech prosody (i.e. the tone of voice while speaking). Following a brief introduction, behavioral as well as neuroimaging data will be summarized that allows to outline cerebral mechanisms associated with the decoding of emotional voice cues, as well as the influence of various context variables (e.g. co-occurring facial and verbal emotional signals, attention focus, person-specific parameters such as gender and personality) on the respective processes. Building on the presented evidence, a cerebral network model will be introduced that proposes a differential contribution of various cortical and subcortical brain structures to the processing of emotional voice signals both in isolation and in context of accompanying (facial and verbal) emotional cues.

Emotional voices in context: a neurobiological model of multimodal affective information processing.

PubMed

Brück, Carolin; Kreifelts, Benjamin; Wildgruber, Dirk

2011-12-01

Just as eyes are often considered a gateway to the soul, the human voice offers a window through which we gain access to our fellow human beings' minds - their attitudes, intentions and feelings. Whether in talking or singing, crying or laughing, sighing or screaming, the sheer sound of a voice communicates a wealth of information that, in turn, may serve the observant listener as valuable guidepost in social interaction. But how do human beings extract information from the tone of a voice? In an attempt to answer this question, the present article reviews empirical evidence detailing the cerebral processes that underlie our ability to decode emotional information from vocal signals. The review will focus primarily on two prominent classes of vocal emotion cues: laughter and speech prosody (i.e. the tone of voice while speaking). Following a brief introduction, behavioral as well as neuroimaging data will be summarized that allows to outline cerebral mechanisms associated with the decoding of emotional voice cues, as well as the influence of various context variables (e.g. co-occurring facial and verbal emotional signals, attention focus, person-specific parameters such as gender and personality) on the respective processes. Building on the presented evidence, a cerebral network model will be introduced that proposes a differential contribution of various cortical and subcortical brain structures to the processing of emotional voice signals both in isolation and in context of accompanying (facial and verbal) emotional cues. Copyright © 2011 Elsevier B.V. All rights reserved.

Development of a Double-Gauss Lens Based Setup for Optoacoustic Applications

PubMed Central

Choi, Hojong; Ryu, Jae-Myung; Yeom, Jung-Yeol

2017-01-01

In optoacoustic (photoacoustic) systems, different echo signal intensities such as amplitudes, center frequencies, and bandwidths need to be compensated by utilizing variable gain or time-gain compensation amplifiers. However, such electronic components can increase system complexities and signal noise levels. In this paper, we introduce a double-Gauss lens to generate a large field of view with uniform light intensity due to the low chromatic aberrations of the lens, thus obtaining uniform echo signal intensities across the field of view of the optoacoustic system. In order to validate the uniformity of the echo signal intensities in the system, an in-house transducer was placed at various positions above a tissue sample and echo signals were measured and compared with each other. The custom designed double-Gauss lens demonstrated negligible light intensity variation (±1.5%) across the illumination field of view (~2 cm diameter). When the transducer was used to measure echo signal from an eye of a bigeye tuna within a range of ±1 cm, the peak-to-peak amplitude, center frequency, and their −6 dB bandwidth variations were less than 2 mV, 1 MHz, and 6%, respectively. The custom designed double-Gauss lens can provide uniform light beam across a wide area while generating insignificant echo signal variations, and thus can lower the burden of the receiving electronics or signal processing in the optoacoustic system. PMID:28273794

Human θ burst stimulation enhances subsequent motor learning and increases performance variability.

PubMed

Teo, James T H; Swayne, Orlando B C; Cheeran, Binith; Greenwood, Richard J; Rothwell, John C

2011-07-01

Intermittent theta burst stimulation (iTBS) transiently increases motor cortex excitability in healthy humans by a process thought to involve synaptic long-term potentiation (LTP), and this is enhanced by nicotine. Acquisition of a ballistic motor task is likewise accompanied by increased excitability and presumed intracortical LTP. Here, we test how iTBS and nicotine influences subsequent motor learning. Ten healthy subjects participated in a double-blinded placebo-controlled trial testing the effects of iTBS and nicotine. iTBS alone increased the rate of learning but this increase was blocked by nicotine. We then investigated factors other than synaptic strengthening that may play a role. Behavioral analysis and modeling suggested that iTBS increased performance variability, which correlated with learning outcome. A control experiment confirmed the increase in motor output variability by showing that iTBS increased the dispersion of involuntary transcranial magnetic stimulation-evoked thumb movements. We suggest that in addition to the effect on synaptic plasticity, iTBS may have facilitated performance by increasing motor output variability; nicotine negated this effect on variability perhaps via increasing the signal-to-noise ratio in cerebral cortex.

Behavioral Signal Processing: Deriving Human Behavioral Informatics From Speech and Language: Computational techniques are presented to analyze and model expressed and perceived human behavior-variedly characterized as typical, atypical, distressed, and disordered-from speech and language cues and their applications in health, commerce, education, and beyond.

PubMed

Narayanan, Shrikanth; Georgiou, Panayiotis G

2013-02-07

The expression and experience of human behavior are complex and multimodal and characterized by individual and contextual heterogeneity and variability. Speech and spoken language communication cues offer an important means for measuring and modeling human behavior. Observational research and practice across a variety of domains from commerce to healthcare rely on speech- and language-based informatics for crucial assessment and diagnostic information and for planning and tracking response to an intervention. In this paper, we describe some of the opportunities as well as emerging methodologies and applications of human behavioral signal processing (BSP) technology and algorithms for quantitatively understanding and modeling typical, atypical, and distressed human behavior with a specific focus on speech- and language-based communicative, affective, and social behavior. We describe the three important BSP components of acquiring behavioral data in an ecologically valid manner across laboratory to real-world settings, extracting and analyzing behavioral cues from measured data, and developing models offering predictive and decision-making support. We highlight both the foundational speech and language processing building blocks as well as the novel processing and modeling opportunities. Using examples drawn from specific real-world applications ranging from literacy assessment and autism diagnostics to psychotherapy for addiction and marital well being, we illustrate behavioral informatics applications of these signal processing techniques that contribute to quantifying higher level, often subjectively described, human behavior in a domain-sensitive fashion.

Calibration of visually guided reaching is driven by error-corrective learning and internal dynamics.

PubMed

Cheng, Sen; Sabes, Philip N

2007-04-01

The sensorimotor calibration of visually guided reaching changes on a trial-to-trial basis in response to random shifts in the visual feedback of the hand. We show that a simple linear dynamical system is sufficient to model the dynamics of this adaptive process. In this model, an internal variable represents the current state of sensorimotor calibration. Changes in this state are driven by error feedback signals, which consist of the visually perceived reach error, the artificial shift in visual feedback, or both. Subjects correct for > or =20% of the error observed on each movement, despite being unaware of the visual shift. The state of adaptation is also driven by internal dynamics, consisting of a decay back to a baseline state and a "state noise" process. State noise includes any source of variability that directly affects the state of adaptation, such as variability in sensory feedback processing, the computations that drive learning, or the maintenance of the state. This noise is accumulated in the state across trials, creating temporal correlations in the sequence of reach errors. These correlations allow us to distinguish state noise from sensorimotor performance noise, which arises independently on each trial from random fluctuations in the sensorimotor pathway. We show that these two noise sources contribute comparably to the overall magnitude of movement variability. Finally, the dynamics of adaptation measured with random feedback shifts generalizes to the case of constant feedback shifts, allowing for a direct comparison of our results with more traditional blocked-exposure experiments.

Variability in pigment concentration in warm-core rings as determined by coastal zone color scanner satellite imagery from the Mid-Atlantic Bight

NASA Technical Reports Server (NTRS)

Garcia-Moliner, Graciela; Yoder, James A.

1994-01-01

A time series of coastal zone color scanner (CZCS) derived chlorophyll (CZCS-chl) and sea surface temperature (SST) satellite imagery was developed for the Mid-Atlantic Bight (MAB). Warm-core rings (WCR) were identified by both the warmer SST signal as well as the low pigment concentrations of their cores. The variation in pigment concentrations and SST observed in satellite imagery over the geographic range and life span of four WCRs is investigated. The hypotheses are that pigment concentration increase during the lifetime of the WCR is a response to processes such as convective overturn, upwelling, edge enhancement due to increased vertical mixing, active convergence, or lateral exchange. Empirical orthogonal function analysis (EOF) is used to investigate the relationship between SST and pigment patterns observed in the presence of a WCR. The first two EOF modes explain more than 80% of the variability observed in all four WCRs and in both (SST and pigment) data sets. The results of this study show that, at the synoptic scales of staellite data, the variability observed in the WCRs is greater at the periphery of the rings. These results show that advective entrainment, rather than processes at ring center (e.g., shoaling of the pycnocline/nutricline in response to frictional decay) or at the periphery due to other processes such as vertical mixing, is the mechanism responsible for the observed variability.

Assessing Northern Hemisphere Land-Atmosphere Hotspots Using Dynamical Adjustment

NASA Astrophysics Data System (ADS)

Merrifield, Anna; Lehner, Flavio; Deser, Clara; Xie, Shang-Ping

2017-04-01

Understanding the influence of soil moisture on surface air temperature (SAT) is made more challenging by large-scale, internal atmospheric variability present in the midlatitude summer atmosphere. In this study, dynamical adjustment is used to characterize and remove summer SAT variability associated with large-scale circulation patterns in the Community Earth System Model large ensemble (CESM-LE). The adjustment is performed over North America and Europe with two different circulation indicators: sea level pressure (SLP) and 500mb height (Z500). The removal of dynamical "noise" leaves residual SAT variability in the central U.S. and Mediterranean regions identified as hotspots of land-atmosphere interaction (e.g. Koster et al. 2004, Seneviratne et al. 2006). The residual SAT variability "signal" is not clearly related to modes of sea surface temperature (SST) variability, but is related to local soil moisture, evaporative fraction, and radiation availability. These local relationships suggest that residual SAT variability is representative of the aggregate land surface signal. SLP dynamical adjustment removes ˜15% more variability in the central U.S. hotspot region than Z500 dynamical adjustment. Similar amounts of variability are removed by SLP and Z500 in the Mediterranean region. Differences in SLP and Z500 signal magnitude in the central U.S. are likely due to the modification of SLP by local land surface conditions, while the proximity of European hotspots to the Mediterranean sea mitigates the land surface influence. Variations in the Z500 field more closely resemble large-scale midlatitude circulation patterns and therefore Z500 may be a more suitable circulation indicator for summer dynamical adjustment. Changes in the residual SAT variability signal under increased greenhouse gas forcing will also be explored.

Orexin signaling via the orexin 1 receptor mediates operant responding for food reinforcement.

PubMed

Sharf, Ruth; Sarhan, Maysa; Brayton, Catherine E; Guarnieri, Douglas J; Taylor, Jane R; DiLeone, Ralph J

2010-04-15

Orexin (hypocretin) signaling is implicated in drug addiction and reward, but its role in feeding and food-motivated behavior remains unclear. We investigated orexin's contribution to food-reinforced instrumental responding using an orexin 1 receptor (Ox1r) antagonist, orexin -/- (OKO) and littermate wildtype (WT) mice, and RNAi-mediated knockdown of orexin. C57BL/6J (n = 76) and OKO (n = 39) mice were trained to nose poke for food under a variable ratio schedule of reinforcement. After responding stabilized, a progressive ratio schedule was initiated to evaluate motivation to obtain food reinforcement. Blockade of Ox1r in C57BL/6J mice impaired performance under both the variable ratio and progressive ratio schedules of reinforcement, indicating impaired motivational processes. In contrast, OKO mice initially demonstrated a delay in acquisition but eventually achieved levels of responding similar to those observed in WT animals. Moreover, OKO mice did not differ from WT mice under a progressive ratio schedule, indicating delayed learning processes but no motivational impairments. Considering the differences between pharmacologic blockade of Ox1r and the OKO mice, animals with RNAi mediated knockdown of orexin were then generated and analyzed to eliminate possible developmental effects of missing orexin. Orexin gene knockdown in the lateral hypothalamus in C57BL/6J mice resulted in blunted performance under both the variable ratio and progressive ratio schedules, resembling data obtained following Ox1r antagonism. The behavior seen in OKO mice likely reflects developmental compensation often seen in mutant animals. These data suggest that activation of the Ox1r is a necessary component of food-reinforced responding, motivation, or both in normal mice. Copyright 2010 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Signal analysis and radioholographic methods for airborne radio occultations

NASA Astrophysics Data System (ADS)

Wang, Kuo-Nung

Global Positioning System (GPS) radio occultation (RO) is an atmospheric sounding technique utilizing the change in propagation direction and delay of the GPS signal to measure refractivity, which provides information on temperature and humidity. The GPS-RO technique is now operational on several Low Earth Orbiting (LEO) satellite missions. Nevertheless, when observing localized transient events, such as tropical storms, current LEO satellite systems cannot provide sufficiently high temporal and spatial resolution soundings. An airborne RO (ARO) system has therefore been developed for localized GPS-RO campaigns. The open-loop (OL) tracking in post-processing is used to cross-correlates the received Global Navigation Satellite System (GNSS) signal with an internally generated local carrier signal predicted from a Doppler model and extract the atmospheric refractivity information. OL tracking also allows robust processing of rising GPS signals using backward tracking, which will double the observed occultation event numbers. RO signals in the lower troposphere are adversely affected by rapid phase accelerations and severe signal power fading, however. The negative bias caused by low signal-to-noise ratio (SNR) and multipath ray propagation limits the depth of tracking in the atmosphere. Therefore, we developed a model relating the SNR to the variance in the residual phase of the observed signal produced from OL tracking, and its applicability to airborne data is demonstrated. We then apply this model to set a threshold on refractivity retrieval, based upon the cumulative unwrapping error bias, to determine the altitude limit for reliable signal tracking. To enhance the SNR and decrease the unwrapping error rate, the CIRA-Q climatological model and signal residual phase pre-filtering are utilized to process the ARO residual phase. This more accurately modeled phase and less noisy received signal are shown to greatly reduce the bias caused by unwrapping error at lower altitude. On the other hand, to process the superimposed signal in the lower troposphere with its highly variable moisture distribution, Radio-Holographic (RH) methods such as Phase Matching (PM) have been adapted for ARO platforms to untangle the bending angle of each signal path. Under the assumption of spherically symmetric atmosphere, ARO PM can identify different subsignals using the Method of the Stationary Phase (MSP) and determine the arrival angle for each impact parameter. As a result, each subsignal can be distinguished and its corresponding bending angle can be retrieved without producing a negative bias. The refractivity retrieval results using ARO PM are compared to those using the traditional Geometrical Optics (GO) method. The improvements are shown and discussed in the dissertation. We applied these new methods to the received ARO data collected by the GNSS instrument system for multistatic and occultation sensing (GISMOS) in the 2010 PREDepression Investigation of Cloud systems (PREDICT) campaign. A data set of 5 research flights with 57 occultation events during the formation stage of the Hurricane Karl are processed and analyzed. In this research, the refractivity fractional difference with ERA-I model can be maintained at an average 2% above a height of 2km with a climatological model and ARO PM. Compared to the traditional geometrical optics (GO) method without climatological method assistance, the new ARO processing can effectively decrease the refractivity negative bias and significantly improve the retrieval depth of ARO.

Reward Processing, Neuroeconomics, and Psychopathology.

PubMed

Zald, David H; Treadway, Michael T

2017-05-08

Abnormal reward processing is a prominent transdiagnostic feature of psychopathology. The present review provides a framework for considering the different aspects of reward processing and their assessment, and highlights recent insights from the field of neuroeconomics that may aid in understanding these processes. Although altered reward processing in psychopathology has often been treated as a general hypo- or hyperresponsivity to reward, increasing data indicate that a comprehensive understanding of reward dysfunction requires characterization within more specific reward-processing domains, including subjective valuation, discounting, hedonics, reward anticipation and facilitation, and reinforcement learning. As such, more nuanced models of the nature of these abnormalities are needed. We describe several processing abnormalities capable of producing the types of selective alterations in reward-related behavior observed in different forms of psychopathology, including (mal)adaptive scaling and anchoring, dysfunctional weighting of reward and cost variables, competition between valuation systems, and reward prediction error signaling.

Reward Processing, Neuroeconomics, and Psychopathology

PubMed Central

Zald, David H.; Treadway, Michael

2018-01-01

Abnormal reward processing is a prominent transdiagnostic feature of psychopathology. The present review provides a framework for considering the different aspects of reward processing and their assessment and highlight recent insights from the field of neuroeconomics that may aid in understanding these processes. Although altered reward processing in psychopathology has often been treated as a general hypo- or hyper-responsivity to reward, increasing data indicate that a comprehensive understanding of reward dysfunction requires characterization within more specific reward processing domains, including subjective valuation, discounting, hedonics, reward anticipation and facilitation, and reinforcement learning. As such, more nuanced models of the nature of these abnormalities are needed. We describe several processing abnormalities capable of producing the types of selective alterations in reward related behavior observed in different forms of psychopathology, including (mal)adaptive scaling and anchoring, dysfunctional weighting of reward and cost variables, completion between valuation systems, and positive prediction error signaling. PMID:28301764

Post-Traumatic Stress Constrains the Dynamic Repertoire of Neural Activity.

PubMed

Mišić, Bratislav; Dunkley, Benjamin T; Sedge, Paul A; Da Costa, Leodante; Fatima, Zainab; Berman, Marc G; Doesburg, Sam M; McIntosh, Anthony R; Grodecki, Richard; Jetly, Rakesh; Pang, Elizabeth W; Taylor, Margot J

2016-01-13

Post-traumatic stress disorder (PTSD) is an anxiety disorder arising from exposure to a traumatic event. Although primarily defined in terms of behavioral symptoms, the global neurophysiological effects of traumatic stress are increasingly recognized as a critical facet of the human PTSD phenotype. Here we use magnetoencephalographic recordings to investigate two aspects of information processing: inter-regional communication (measured by functional connectivity) and the dynamic range of neural activity (measured in terms of local signal variability). We find that both measures differentiate soldiers diagnosed with PTSD from soldiers without PTSD, from healthy civilians, and from civilians with mild traumatic brain injury, which is commonly comorbid with PTSD. Specifically, soldiers with PTSD display inter-regional hypersynchrony at high frequencies (80-150 Hz), as well as a concomitant decrease in signal variability. The two patterns are spatially correlated and most pronounced in a left temporal subnetwork, including the hippocampus and amygdala. We hypothesize that the observed hypersynchrony may effectively constrain the expression of local dynamics, resulting in less variable activity and a reduced dynamic repertoire. Thus, the re-experiencing phenomena and affective sequelae in combat-related PTSD may result from functional networks becoming "stuck" in configurations reflecting memories, emotions, and thoughts originating from the traumatizing experience. The present study investigates the effects of post-traumatic stress disorder (PTSD) in combat-exposed soldiers. We find that soldiers with PTSD exhibit hypersynchrony in a circuit of temporal lobe areas associated with learning and memory function. This rigid functional architecture is associated with a decrease in signal variability in the same areas, suggesting that the observed hypersynchrony may constrain the expression of local dynamics, resulting in a reduced dynamic range. Our findings suggest that the re-experiencing of traumatic events in PTSD may result from functional networks becoming locked in configurations that reflect memories, emotions, and thoughts associated with the traumatic experience. Copyright © 2016 the authors 0270-6474/16/360419-13$15.00/0.

Electronic Thermometer Readings

NASA Technical Reports Server (NTRS)

2001-01-01

NASA Stennis' adaptive predictive algorithm for electronic thermometers uses sample readings during the initial rise in temperature and applies an algorithm that accurately and rapidly predicts the steady state temperature. The final steady state temperature of an object can be calculated based on the second-order logarithm of the temperature signals acquired by the sensor and predetermined variables from the sensor characteristics. These variables are calculated during tests of the sensor. Once the variables are determined, relatively little data acquisition and data processing time by the algorithm is required to provide a near-accurate approximation of the final temperature. This reduces the delay in the steady state response time of a temperature sensor. This advanced algorithm can be implemented in existing software or hardware with an erasable programmable read-only memory (EPROM). The capability for easy integration eliminates the expense of developing a whole new system that offers the benefits provided by NASA Stennis' technology.

Extracting Leading Nonlinear Modes of Changing Climate From Global SST Time Series

NASA Astrophysics Data System (ADS)

Mukhin, D.; Gavrilov, A.; Loskutov, E. M.; Feigin, A. M.; Kurths, J.

2017-12-01

Data-driven modeling of climate requires adequate principal variables extracted from observed high-dimensional data. For constructing such variables it is needed to find spatial-temporal patterns explaining a substantial part of the variability and comprising all dynamically related time series from the data. The difficulties of this task rise from the nonlinearity and non-stationarity of the climate dynamical system. The nonlinearity leads to insufficiency of linear methods of data decomposition for separating different processes entangled in the observed time series. On the other hand, various forcings, both anthropogenic and natural, make the dynamics non-stationary, and we should be able to describe the response of the system to such forcings in order to separate the modes explaining the internal variability. The method we present is aimed to overcome both these problems. The method is based on the Nonlinear Dynamical Mode (NDM) decomposition [1,2], but takes into account external forcing signals. An each mode depends on hidden, unknown a priori, time series which, together with external forcing time series, are mapped onto data space. Finding both the hidden signals and the mapping allows us to study the evolution of the modes' structure in changing external conditions and to compare the roles of the internal variability and forcing in the observed behavior. The method is used for extracting of the principal modes of SST variability on inter-annual and multidecadal time scales accounting the external forcings such as CO2, variations of the solar activity and volcanic activity. The structure of the revealed teleconnection patterns as well as their forecast under different CO2 emission scenarios are discussed.[1] Mukhin, D., Gavrilov, A., Feigin, A., Loskutov, E., & Kurths, J. (2015). Principal nonlinear dynamical modes of climate variability. Scientific Reports, 5, 15510. [2] Gavrilov, A., Mukhin, D., Loskutov, E., Volodin, E., Feigin, A., & Kurths, J. (2016). Method for reconstructing nonlinear modes with adaptive structure from multidimensional data. Chaos: An Interdisciplinary Journal of Nonlinear Science, 26(12), 123101.

A customizable system for real-time image processing using the Blackfin DSProcessor and the MicroC/OS-II real-time kernel

NASA Astrophysics Data System (ADS)

Coffey, Stephen; Connell, Joseph

2005-06-01

This paper presents a development platform for real-time image processing based on the ADSP-BF533 Blackfin processor and the MicroC/OS-II real-time operating system (RTOS). MicroC/OS-II is a completely portable, ROMable, pre-emptive, real-time kernel. The Blackfin Digital Signal Processors (DSPs), incorporating the Analog Devices/Intel Micro Signal Architecture (MSA), are a broad family of 16-bit fixed-point products with a dual Multiply Accumulate (MAC) core. In addition, they have a rich instruction set with variable instruction length and both DSP and MCU functionality thus making them ideal for media based applications. Using the MicroC/OS-II for task scheduling and management, the proposed system can capture and process raw RGB data from any standard 8-bit greyscale image sensor in soft real-time and then display the processed result using a simple PC graphical user interface (GUI). Additionally, the GUI allows configuration of the image capture rate and the system and core DSP clock rates thereby allowing connectivity to a selection of image sensors and memory devices. The GUI also allows selection from a set of image processing algorithms based in the embedded operating system.

Smoothing of millennial scale climate variability in European Loess (and other records)

NASA Astrophysics Data System (ADS)

Zeeden, Christian; Obreht, Igor; Hambach, Ulrich; Veres, Daniel; Marković, Slobodan B.; Lehmkuhl, Frank

2017-04-01

Millennial scale climate variability is seen in various records of the northern hemisphere in the last glacial cycle, and their expression represents a correlation tool beyond the resolution of e.g. luminescence dating. Highest (correlative) dating accuracy is a prerequisite of comparing different geoarchives, especially when related to archaeological findings. Here we attempt to constrain the timing of loess geoarchives representing the environmental context of early humans in south-eastern Europe, and discuss the challenge of dealing with smoothed records. In this contribution, we present rock magnetic and grain size data from the Rasova loess record in the Lower Danube basin (Romania), showing millennial scale climate variability. Additionally, we summarize similar data from the Lower and Middle Danube Basins. A comparison of these loess data and reference records from Greenland ice cores and the Mediterranean-Black Sea region indicates a rather unusual expression of millennial scale climate variability recorded in loess. To explain the observed patterns, we experiment with low-pass filters of reference records to simulate a signal smoothing by natural processes such as e.g. bioturbation and pervasive diagenesis. Low-pass filters avoid high frequency oscillations and focus on the longer period (lower frequency) variability, here using cut-off periods from 1-15 kyr. In our opinion low-pass filters represent simple models for the expression of millennial scale climate variability in low sedimentation environments, and in sediments where signals are smoothed by e.g. bioturbation and/or diagenesis. Using different low-pass filter thresholds allows us to (a) explain observed patterns and their relation to millennial scale climate variability, (b) propose these filtered/smoothed signals as correlation targets for records lacking millennial scale recording, but showing smoothed climate variability on supra-millennial scales, and (c) determine which time resolution specific (loess) records can reproduce. Comparing smoothed records to reference data may be a step forward especially for last glacial stratigraphies, where millennial scale patterns are certainly present but not directly recorded in some geoarchives. Interestingly, smoothed datasets from Greenland and the Black Sea-Mediterranean region are most similar in the last ca. 15 ka and again from ca. 30-50 ka. During the cold phase from ca. 30-15 ka records show dissimilarities, challenging robust correlative time scales in this age range. A potential explanation may be related to the expansion of Northern European and Alpine ice sheets influencing atmospheric systems in the North Atlantic and Eurasian regions and thus leading to regionally and temporally differentiated climatic responses.

A flexible continuous-variable QKD system using off-the-shelf components

NASA Astrophysics Data System (ADS)

Comandar, Lucian C.; Brunner, Hans H.; Bettelli, Stefano; Fung, Fred; Karinou, Fotini; Hillerkuss, David; Mikroulis, Spiros; Wang, Dawei; Kuschnerov, Maxim; Xie, Changsong; Poppe, Andreas; Peev, Momtchil

2017-10-01

We present the development of a robust and versatile CV-QKD architecture based on commercially available optical and electronic components. The system uses a pilot tone for phase synchronization with a local oscillator, as well as local feedback loops to mitigate frequency and polarization drifts. Transmit and receive-side digital signal processing is performed fully in software, allowing for rapid protocol reconfiguration. The quantum link is complemented with a software stack for secure-key processing, key storage and encrypted communication. All these features allow for the system to be at the same time a prototype for a future commercial product and a research platform.

P wave detection in ECG signals using an extended Kalman filter: an evaluation in different arrhythmia contexts.

PubMed

Rahimpour, M; Mohammadzadeh Asl, B

2016-07-01

Monitoring atrial activity via P waves, is an important feature of the arrhythmia detection procedure. The aim of this paper is to present an algorithm for P wave detection in normal and some abnormal records by improving existing methods in the field of signal processing. In contrast to the classical approaches, which are completely blind to signal dynamics, our proposed method uses the extended Kalman filter, EKF25, to estimate the state variables of the equations modeling the dynamic of an ECG signal. This method is a modified version of the nonlinear dynamical model previously introduced for a generation of synthetic ECG signals and fiducial point extraction in normal ones. It is capable of estimating the separate types of activity of the heart with reasonable accuracy and performs well in the presence of morphological variations in the waveforms and ectopic beats. The MIT-BIH Arrhythmia and QT databases have been used to evaluate the performance of the proposed method. The results show that this method has Se  =  98.38% and Pr  =  96.74% in the overall records (considering normal and abnormal rhythms).

Water isotope systematics: Improving our palaeoclimate interpretations

USGS Publications Warehouse

Jones, M. D.; Dee, S.; Anderson, L.; Baker, A.; Bowen, G.; Noone, D.

2016-01-01

The stable isotopes of oxygen and hydrogen, measured in a variety of archives, are widely used proxies in Quaternary Science. Understanding the processes that control δ18O change have long been a focus of research (e.g. Shackleton and Opdyke, 1973; Talbot, 1990 ; Leng, 2006). Both the dynamics of water isotope cycling and the appropriate interpretation of geological water-isotope proxy time series remain subjects of active research and debate. It is clear that achieving a complete understanding of the isotope systematics for any given archive type, and ideally each individual archive, is vital if these palaeo-data are to be used to their full potential, including comparison with climate model experiments of the past. Combining information from modern monitoring and process studies, climate models, and proxy data is crucial for improving our statistical constraints on reconstructions of past climate variability.As climate models increasingly incorporate stable water isotope physics, this common language should aid quantitative comparisons between proxy data and climate model output. Water-isotope palaeoclimate data provide crucial metrics for validating GCMs, whereas GCMs provide a tool for exploring the climate variability dominating signals in the proxy data. Several of the studies in this set of papers highlight how collaborations between palaeoclimate experimentalists and modelers may serve to expand the usefulness of palaeoclimate data for climate prediction in future work.This collection of papers follows the session on Water Isotope Systematics held at the 2013 AGU Fall Meeting in San Francisco. Papers in that session, the breadth of which are represented here, discussed such issues as; understanding sub-GNIP scale (Global Network for Isotopes in Precipitation, (IAEA/WMO, 2006)) variability in isotopes in precipitation from different regions, detailed examination of the transfer of isotope signals from precipitation to geological archives, and the implications of advances in understanding in these areas for the interpretation of palaeo records and proxy data – climate model comparison.Here, we briefly review these areas of research, and discuss challenges for the water isotope community in improving our ability to partition climate vs. auxiliary signals in palaeoclimate data.

Drought prediction using co-active neuro-fuzzy inference system, validation, and uncertainty analysis (case study: Birjand, Iran)

NASA Astrophysics Data System (ADS)

Memarian, Hadi; Pourreza Bilondi, Mohsen; Rezaei, Majid

2016-08-01

This work aims to assess the capability of co-active neuro-fuzzy inference system (CANFIS) for drought forecasting of Birjand, Iran through the combination of global climatic signals with rainfall and lagged values of Standardized Precipitation Index (SPI) index. Using stepwise regression and correlation analyses, the signals NINO 1 + 2, NINO 3, Multivariate Enso Index, Tropical Southern Atlantic index, Atlantic Multi-decadal Oscillation index, and NINO 3.4 were recognized as the effective signals on the drought event in Birjand. Based on the results from stepwise regression analysis and regarding the processor limitations, eight models were extracted for further processing by CANFIS. The metrics P-factor and D-factor were utilized for uncertainty analysis, based on the sequential uncertainty fitting algorithm. Sensitivity analysis showed that for all models, NINO indices and rainfall variable had the largest impact on network performance. In model 4 (as the model with the lowest error during training and testing processes), NINO 1 + 2(t-5) with an average sensitivity of 0.7 showed the highest impact on network performance. Next, the variables rainfall, NINO 1 + 2(t), and NINO 3(t-6) with the average sensitivity of 0.59, 0.28, and 0.28, respectively, could have the highest effect on network performance. The findings based on network performance metrics indicated that the global indices with a time lag represented a better correlation with El Niño Southern Oscillation (ENSO). Uncertainty analysis of the model 4 demonstrated that 68 % of the observed data were bracketed by the 95PPU and D-Factor value (0.79) was also within a reasonable range. Therefore, the fourth model with a combination of the input variables NINO 1 + 2 (with 5 months of lag and without any lag), monthly rainfall, and NINO 3 (with 6 months of lag) and correlation coefficient of 0.903 (between observed and simulated SPI) was selected as the most accurate model for drought forecasting using CANFIS in the climatic region of Birjand.

Autogenic geomorphic processes determine the resolution and fidelity of terrestrial paleoclimate records.

PubMed

Foreman, Brady Z; Straub, Kyle M

2017-09-01

Terrestrial paleoclimate records rely on proxies hosted in alluvial strata whose beds are deposited by unsteady and nonlinear geomorphic processes. It is broadly assumed that this renders the resultant time series of terrestrial paleoclimatic variability noisy and incomplete. We evaluate this assumption using a model of oscillating climate and the precise topographic evolution of an experimental alluvial system. We find that geomorphic stochasticity can create aliasing in the time series and spurious climate signals, but these issues are eliminated when the period of climate oscillation is longer than a key time scale of internal dynamics in the geomorphic system. This emergent autogenic geomorphic behavior imparts regularity to deposition and represents a natural discretization interval of the continuous climate signal. We propose that this time scale in nature could be in excess of 10 4 years but would still allow assessments of the rates of climate change at resolutions finer than the existing age model techniques in isolation.

Programmable rate modem utilizing digital signal processing techniques

NASA Technical Reports Server (NTRS)

Bunya, George K.; Wallace, Robert L.

1989-01-01

The engineering development study to follow was written to address the need for a Programmable Rate Digital Satellite Modem capable of supporting both burst and continuous transmission modes with either binary phase shift keying (BPSK) or quadrature phase shift keying (QPSK) modulation. The preferred implementation technique is an all digital one which utilizes as much digital signal processing (DSP) as possible. Here design tradeoffs in each portion of the modulator and demodulator subsystem are outlined, and viable circuit approaches which are easily repeatable, have low implementation losses and have low production costs are identified. The research involved for this study was divided into nine technical papers, each addressing a significant region of concern in a variable rate modem design. Trivial portions and basic support logic designs surrounding the nine major modem blocks were omitted. In brief, the nine topic areas were: (1) Transmit Data Filtering; (2) Transmit Clock Generation; (3) Carrier Synthesizer; (4) Receive AGC; (5) Receive Data Filtering; (6) RF Oscillator Phase Noise; (7) Receive Carrier Selectivity; (8) Carrier Recovery; and (9) Timing Recovery.

Assessment of weaning indexes based on diaphragm activity in mechanically ventilated subjects after cardiovascular surgery. A pilot study

PubMed Central

Ortega, Isabel Cristina Muñoz; Valdivieso, Alher Mauricio Hernández; Lopez, Joan Francesc Alonso; Villanueva, Miguel Ángel Mañanas; Lopez, Luis Horacio Atehortúa

2017-01-01

Objective The aim of this pilot study was to evaluate the feasibility of surface electromyographic signal derived indexes for the prediction of weaning outcomes among mechanically ventilated subjects after cardiac surgery. Methods A sample of 10 postsurgical adult subjects who received cardiovascular surgery that did not meet the criteria for early extubation were included. Surface electromyographic signals from diaphragm and ventilatory variables were recorded during the weaning process, with the moment determined by the medical staff according to their expertise. Several indexes of respiratory muscle expenditure from surface electromyography using linear and non-linear processing techniques were evaluated. Two groups were compared: successfully and unsuccessfully weaned patients. Results The obtained indexes allow estimation of the diaphragm activity of each subject, showing a correlation between high expenditure and weaning test failure. Conclusion Surface electromyography is becoming a promising procedure for assessing the state of mechanically ventilated patients, even in complex situations such as those that involve a patient after cardiovascular surgery. PMID:28977261

Detection in fixed and random noise in foveal and parafoveal vision explained by template learning

NASA Technical Reports Server (NTRS)

Beard, B. L.; Ahumada, A. J. Jr; Watson, A. B. (Principal Investigator)

1999-01-01

Foveal and parafoveal contrast detection thresholds for Gabor and checkerboard targets were measured in white noise by means of a two-interval forced-choice paradigm. Two white-noise conditions were used: fixed and twin. In the fixed noise condition a single noise sample was presented in both intervals of all the trials. In the twin noise condition the same noise sample was used in the two intervals of a trial, but a new sample was generated for each trial. Fixed noise conditions usually resulted in lower thresholds than twin noise. Template learning models are presented that attribute this advantage of fixed over twin noise either to fixed memory templates' reducing uncertainty by incorporation of the noise or to the introduction, by the learning process itself, of more variability in the twin noise condition. Quantitative predictions of the template learning process show that it contributes to the accelerating nonlinear increase in performance with signal amplitude at low signal-to-noise ratios.

Autogenic geomorphic processes determine the resolution and fidelity of terrestrial paleoclimate records

PubMed Central

Foreman, Brady Z.; Straub, Kyle M.

2017-01-01

Terrestrial paleoclimate records rely on proxies hosted in alluvial strata whose beds are deposited by unsteady and nonlinear geomorphic processes. It is broadly assumed that this renders the resultant time series of terrestrial paleoclimatic variability noisy and incomplete. We evaluate this assumption using a model of oscillating climate and the precise topographic evolution of an experimental alluvial system. We find that geomorphic stochasticity can create aliasing in the time series and spurious climate signals, but these issues are eliminated when the period of climate oscillation is longer than a key time scale of internal dynamics in the geomorphic system. This emergent autogenic geomorphic behavior imparts regularity to deposition and represents a natural discretization interval of the continuous climate signal. We propose that this time scale in nature could be in excess of 104 years but would still allow assessments of the rates of climate change at resolutions finer than the existing age model techniques in isolation. PMID:28924607

Missing pulse detector for a variable frequency source

DOEpatents

Ingram, Charles B.; Lawhorn, John H.

1979-01-01

A missing pulse detector is provided which has the capability of monitoring a varying frequency pulse source to detect the loss of a single pulse or total loss of signal from the source. A frequency-to-current converter is used to program the output pulse width of a variable period retriggerable one-shot to maintain a pulse width slightly longer than one-half the present monitored pulse period. The retriggerable one-shot is triggered at twice the input pulse rate by employing a frequency doubler circuit connected between the one-shot input and the variable frequency source being monitored. The one-shot remains in the triggered or unstable state under normal conditions even though the source period is varying. A loss of an input pulse or single period of a fluctuating signal input will cause the one-shot to revert to its stable state, changing the output signal level to indicate a missing pulse or signal.

Statokinesigram normalization method.

PubMed

de Oliveira, José Magalhães

2017-02-01

Stabilometry is a technique that aims to study the body sway of human subjects, employing a force platform. The signal obtained from this technique refers to the position of the foot base ground-reaction vector, known as the center of pressure (CoP). The parameters calculated from the signal are used to quantify the displacement of the CoP over time; there is a large variability, both between and within subjects, which prevents the definition of normative values. The intersubject variability is related to differences between subjects in terms of their anthropometry, in conjunction with their muscle activation patterns (biomechanics); and the intrasubject variability can be caused by a learning effect or fatigue. Age and foot placement on the platform are also known to influence variability. Normalization is the main method used to decrease this variability and to bring distributions of adjusted values into alignment. In 1996, O'Malley proposed three normalization techniques to eliminate the effect of age and anthropometric factors from temporal-distance parameters of gait. These techniques were adopted to normalize the stabilometric signal by some authors. This paper proposes a new method of normalization of stabilometric signals to be applied in balance studies. The method was applied to a data set collected in a previous study, and the results of normalized and nonnormalized signals were compared. The results showed that the new method, if used in a well-designed experiment, can eliminate undesirable correlations between the analyzed parameters and the subjects' characteristics and show only the experimental conditions' effects.

Multidimensional Single-Cell Analysis of BCR Signaling Reveals Proximal Activation Defect As a Hallmark of Chronic Lymphocytic Leukemia B Cells

PubMed Central

Palomba, M. Lia; Piersanti, Kelly; Ziegler, Carly G. K.; Decker, Hugo; Cotari, Jesse W.; Bantilan, Kurt; Rijo, Ivelise; Gardner, Jeff R.; Heaney, Mark; Bemis, Debra; Balderas, Robert; Malek, Sami N.; Seymour, Erlene; Zelenetz, Andrew D.

2014-01-01

Purpose Chronic Lymphocytic Leukemia (CLL) is defined by a perturbed B-cell receptor-mediated signaling machinery. We aimed to model differential signaling behavior between B cells from CLL and healthy individuals to pinpoint modes of dysregulation. Experimental Design We developed an experimental methodology combining immunophenotyping, multiplexed phosphospecific flow cytometry, and multifactorial statistical modeling. Utilizing patterns of signaling network covariance, we modeled BCR signaling in 67 CLL patients using Partial Least Squares Regression (PLSR). Results from multidimensional modeling were validated using an independent test cohort of 38 patients. Results We identified a dynamic and variable imbalance between proximal (pSYK, pBTK) and distal (pPLCγ2, pBLNK, ppERK) phosphoresponses. PLSR identified the relationship between upstream tyrosine kinase SYK and its target, PLCγ2, as maximally predictive and sufficient to distinguish CLL from healthy samples, pointing to this juncture in the signaling pathway as a hallmark of CLL B cells. Specific BCR pathway signaling signatures that correlate with the disease and its degree of aggressiveness were identified. Heterogeneity in the PLSR response variable within the B cell population is both a characteristic mark of healthy samples and predictive of disease aggressiveness. Conclusion Single-cell multidimensional analysis of BCR signaling permitted focused analysis of the variability and heterogeneity of signaling behavior from patient-to-patient, and from cell-to-cell. Disruption of the pSYK/pPLCγ2 relationship is uncovered as a robust hallmark of CLL B cell signaling behavior. Together, these observations implicate novel elements of the BCR signal transduction as potential therapeutic targets. PMID:24489640

In-situ sensing using mass spectrometry and its use for run-to-run control on a W-CVD cluster tool

NASA Astrophysics Data System (ADS)

Gougousi, T.; Sreenivasan, R.; Xu, Y.; Henn-Lecordier, L.; Rubloff, G. W.; Kidder, , J. N.; Zafiriou, E.

2001-01-01

A 300 amu closed-ion-source RGA (Leybold-Inficon Transpector 2) sampling gases directly from the reactor of an ULVAC ERA-1000 cluster tool has been used for real time process monitoring of a W CVD process. The process involves H2 reduction of WF6 at a total pressure of 67 Pa (0.5 torr) to produce W films on Si wafers heated at temperatures around 350 °C. The normalized RGA signals for the H2 reagent depletion and the HF product generation were correlated with the W film weight as measured post-process with an electronic microbalance for the establishment of thin-film weight (thickness) metrology. The metrology uncertainty (about 7% for the HF product) was limited primarily by the very low conversion efficiency of the W CVD process (around 2-3%). The HF metrology was then used to drive a robust run-to-run control algorithm, with the deposition time selected as the manipulated (or controlled) variable. For that purpose, during a 10 wafer run, a systematic process drift was introduced as a -5 °C processing temperature change for each successive wafer, in an otherwise unchanged process recipe. Without adjustment of the deposition time the W film weight (thickness) would have declined by about 50% by the 10th wafer. With the aid of the process control algorithm, an adjusted deposition time was computed so as to maintain constant HF sensing signal, resulting in weight (thickness) control comparable to the accuracy of the thickness metrology. These results suggest that in-situ chemical sensing, and particularly mass spectrometry, provide the basis for wafer state metrology as needed to achieve run-to-run control. Furthermore, since the control accuracy was consistent with the metrology accuracy, we anticipate significant improvements for processes as used in manufacturing, where conversion rates are much higher (40-50%) and corresponding signals for metrology will be much larger.

Physical attraction to reliable, low variability nervous systems: Reaction time variability predicts attractiveness.

PubMed

Butler, Emily E; Saville, Christopher W N; Ward, Robert; Ramsey, Richard

2017-01-01

The human face cues a range of important fitness information, which guides mate selection towards desirable others. Given humans' high investment in the central nervous system (CNS), cues to CNS function should be especially important in social selection. We tested if facial attractiveness preferences are sensitive to the reliability of human nervous system function. Several decades of research suggest an operational measure for CNS reliability is reaction time variability, which is measured by standard deviation of reaction times across trials. Across two experiments, we show that low reaction time variability is associated with facial attractiveness. Moreover, variability in performance made a unique contribution to attractiveness judgements above and beyond both physical health and sex-typicality judgements, which have previously been associated with perceptions of attractiveness. In a third experiment, we empirically estimated the distribution of attractiveness preferences expected by chance and show that the size and direction of our results in Experiments 1 and 2 are statistically unlikely without reference to reaction time variability. We conclude that an operating characteristic of the human nervous system, reliability of information processing, is signalled to others through facial appearance. Copyright © 2016 Elsevier B.V. All rights reserved.

Automated Real-Time Behavioral and Physiological Data Acquisition and Display Integrated with Stimulus Presentation for fMRI

PubMed Central

Voyvodic, James T.; Glover, Gary H.; Greve, Douglas; Gadde, Syam

2011-01-01

Functional magnetic resonance imaging (fMRI) is based on correlating blood oxygen-level dependent (BOLD) signal fluctuations in the brain with other time-varying signals. Although the most common reference for correlation is the timing of a behavioral task performed during the scan, many other behavioral and physiological variables can also influence fMRI signals. Variations in cardiac and respiratory functions in particular are known to contribute significant BOLD signal fluctuations. Variables such as skin conduction, eye movements, and other measures that may be relevant to task performance can also be correlated with BOLD signals and can therefore be used in image analysis to differentiate multiple components in complex brain activity signals. Combining real-time recording and data management of multiple behavioral and physiological signals in a way that can be routinely used with any task stimulus paradigm is a non-trivial software design problem. Here we discuss software methods that allow users control of paradigm-specific audio–visual or other task stimuli combined with automated simultaneous recording of multi-channel behavioral and physiological response variables, all synchronized with sub-millisecond temporal accuracy. We also discuss the implementation and importance of real-time display feedback to ensure data quality of all recorded variables. Finally, we discuss standards and formats for storage of temporal covariate data and its integration into fMRI image analysis. These neuroinformatics methods have been adopted for behavioral task control at all sites in the Functional Biomedical Informatics Research Network (FBIRN) multi-center fMRI study. PMID:22232596

Quantum cryptography with finite resources: unconditional security bound for discrete-variable protocols with one-way postprocessing.

PubMed

Scarani, Valerio; Renner, Renato

2008-05-23

We derive a bound for the security of quantum key distribution with finite resources under one-way postprocessing, based on a definition of security that is composable and has an operational meaning. While our proof relies on the assumption of collective attacks, unconditional security follows immediately for standard protocols such as Bennett-Brassard 1984 and six-states protocol. For single-qubit implementations of such protocols, we find that the secret key rate becomes positive when at least N approximately 10(5) signals are exchanged and processed. For any other discrete-variable protocol, unconditional security can be obtained using the exponential de Finetti theorem, but the additional overhead leads to very pessimistic estimates.

Uncertainty relation for the discrete Fourier transform.

PubMed

Massar, Serge; Spindel, Philippe

2008-05-16

We derive an uncertainty relation for two unitary operators which obey a commutation relation of the form UV=e(i phi) VU. Its most important application is to constrain how much a quantum state can be localized simultaneously in two mutually unbiased bases related by a discrete fourier transform. It provides an uncertainty relation which smoothly interpolates between the well-known cases of the Pauli operators in two dimensions and the continuous variables position and momentum. This work also provides an uncertainty relation for modular variables, and could find applications in signal processing. In the finite dimensional case the minimum uncertainty states, discrete analogues of coherent and squeezed states, are minimum energy solutions of Harper's equation, a discrete version of the harmonic oscillator equation.

Bias and discriminability during emotional signal detection in melancholic depression.

PubMed

Hyett, Matthew; Parker, Gordon; Breakspear, Michael

2014-04-27

Cognitive disturbances in depression are pernicious and so contribute strongly to the burden of the disorder. Cognitive function has been traditionally studied by challenging subjects with modality-specific psychometric tasks and analysing performance using standard analysis of variance. Whilst informative, such an approach may miss deeper perceptual and inferential mechanisms that potentially unify apparently divergent emotional and cognitive deficits. Here, we sought to elucidate basic psychophysical processes underlying the detection of emotionally salient signals across individuals with melancholic and non-melancholic depression. Sixty participants completed an Affective Go/No-Go (AGN) task across negative, positive and neutral target stimuli blocks. We employed hierarchical Bayesian signal detection theory (SDT) to model psychometric performance across three equal groups of those with melancholic depression, those with a non-melancholic depression and healthy controls. This approach estimated likely response profiles (bias) and perceptual sensitivity (discriminability). Differences in the means of these measures speak to differences in the emotional signal detection between individuals across the groups, while differences in the variance reflect the heterogeneity of the groups themselves. Melancholic participants showed significantly decreased sensitivity to positive emotional stimuli compared to those in the non-melancholic group, and also had a significantly lower discriminability than healthy controls during the detection of neutral signals. The melancholic group also showed significantly higher variability in bias to both positive and negative emotionally salient material. Disturbances of emotional signal detection in melancholic depression appear dependent on emotional context, being biased during the detection of positive stimuli, consistent with a noisier representation of neutral stimuli. The greater heterogeneity of the bias across the melancholic group is consistent with a more labile disorder (i.e. variable across the day). Future work will aim to understand how these findings reflect specific individual differences (e.g. prior cognitive biases) and clarify whether such biases change dynamically during cognitive tasks as internal models of the sensorium are refined and updated in response to experience.

FastICA peel-off for ECG interference removal from surface EMG.

PubMed

Chen, Maoqi; Zhang, Xu; Chen, Xiang; Zhu, Mingxing; Li, Guanglin; Zhou, Ping

2016-06-13

Multi-channel recording of surface electromyographyic (EMG) signals is very likely to be contaminated by electrocardiographic (ECG) interference, specifically when the surface electrode is placed on muscles close to the heart. A novel fast independent component analysis (FastICA) based peel-off method is presented to remove ECG interference contaminating multi-channel surface EMG signals. Although demonstrating spatial variability in waveform shape, the ECG interference in different channels shares the same firing instants. Utilizing the firing information estimated from FastICA, ECG interference can be separated from surface EMG by a "peel off" processing. The performance of the method was quantified with synthetic signals by combining a series of experimentally recorded "clean" surface EMG and "pure" ECG interference. It was demonstrated that the new method can remove ECG interference efficiently with little distortion to surface EMG amplitude and frequency. The proposed method was also validated using experimental surface EMG signals contaminated by ECG interference. The proposed FastICA peel-off method can be used as a new and practical solution to eliminating ECG interference from multichannel EMG recordings.

Dopaminergic modulation of hemodynamic signal variability and the functional connectome during cognitive performance.

PubMed

Alavash, Mohsen; Lim, Sung-Joo; Thiel, Christiane; Sehm, Bernhard; Deserno, Lorenz; Obleser, Jonas

2018-05-15

Dopamine underlies important aspects of cognition, and has been suggested to boost cognitive performance. However, how dopamine modulates the large-scale cortical dynamics during cognitive performance has remained elusive. Using functional MRI during a working memory task in healthy young human listeners, we investigated the effect of levodopa (l-dopa) on two aspects of cortical dynamics, blood oxygen-level-dependent (BOLD) signal variability and the functional connectome of large-scale cortical networks. We here show that enhanced dopaminergic signaling modulates the two potentially interrelated aspects of large-scale cortical dynamics during cognitive performance, and the degree of these modulations is able to explain inter-individual differences in l-dopa-induced behavioral benefits. Relative to placebo, l-dopa increased BOLD signal variability in task-relevant temporal, inferior frontal, parietal and cingulate regions. On the connectome level, however, l-dopa diminished functional integration across temporal and cingulo-opercular regions. This hypo-integration was expressed as a reduction in network efficiency and modularity in more than two thirds of the participants and to different degrees. Hypo-integration co-occurred with relative hyper-connectivity in paracentral lobule and precuneus, as well as posterior putamen. Both, l-dopa-induced BOLD signal variability modulation and functional connectome modulations proved predictive of an individual's l-dopa-induced benefits in behavioral performance, namely response speed and perceptual sensitivity. Lastly, l-dopa-induced modulations of BOLD signal variability were correlated with l-dopa-induced modulation of nodal connectivity and network efficiency. Our findings underline the role of dopamine in maintaining the dynamic range of, and communication between, cortical systems, and their explanatory power for inter-individual differences in benefits from dopamine during cognitive performance. Copyright © 2018 Elsevier Inc. All rights reserved.

Modulation Classification of Satellite Communication Signals Using Cumulants and Neural Networks

NASA Technical Reports Server (NTRS)

Smith, Aaron; Evans, Michael; Downey, Joseph

2017-01-01

National Aeronautics and Space Administration (NASA)'s future communication architecture is evaluating cognitive technologies and increased system intelligence. These technologies are expected to reduce the operational complexity of the network, increase science data return, and reduce interference to self and others. In order to increase situational awareness, signal classification algorithms could be applied to identify users and distinguish sources of interference. A significant amount of previous work has been done in the area of automatic signal classification for military and commercial applications. As a preliminary step, we seek to develop a system with the ability to discern signals typically encountered in satellite communication. Proposed is an automatic modulation classifier which utilizes higher order statistics (cumulants) and an estimate of the signal-to-noise ratio. These features are extracted from baseband symbols and then processed by a neural network for classification. The modulation types considered are phase-shift keying (PSK), amplitude and phase-shift keying (APSK),and quadrature amplitude modulation (QAM). Physical layer properties specific to the Digital Video Broadcasting - Satellite- Second Generation (DVB-S2) standard, such as pilots and variable ring ratios, are also considered. This paper will provide simulation results of a candidate modulation classifier, and performance will be evaluated over a range of signal-to-noise ratios, frequency offsets, and nonlinear amplifier distortions.

Enzyme-Based Logic Gates and Networks with Output Signals Analyzed by Various Methods.

PubMed

Katz, Evgeny

2017-07-05

The paper overviews various methods that are used for the analysis of output signals generated by enzyme-based logic systems. The considered methods include optical techniques (optical absorbance, fluorescence spectroscopy, surface plasmon resonance), electrochemical techniques (cyclic voltammetry, potentiometry, impedance spectroscopy, conductivity measurements, use of field effect transistor devices, pH measurements), and various mechanoelectronic methods (using atomic force microscope, quartz crystal microbalance). Although each of the methods is well known for various bioanalytical applications, their use in combination with the biomolecular logic systems is rather new and sometimes not trivial. Many of the discussed methods have been combined with the use of signal-responsive materials to transduce and amplify biomolecular signals generated by the logic operations. Interfacing of biocomputing logic systems with electronics and "smart" signal-responsive materials allows logic operations be extended to actuation functions; for example, stimulating molecular release and switchable features of bioelectronic devices, such as biofuel cells. The purpose of this review article is to emphasize the broad variability of the bioanalytical systems applied for signal transduction in biocomputing processes. All bioanalytical systems discussed in the article are exemplified with specific logic gates and multi-gate networks realized with enzyme-based biocatalytic cascades. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

THE ALLEN TELESCOPE ARRAY SEARCH FOR ELECTROSTATIC DISCHARGES ON MARS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Anderson, Marin M.; Siemion, Andrew P. V.; Bower, Geoffrey C.

The Allen Telescope Array was used to monitor Mars between 2010 March 9 and June 2, over a total of approximately 30 hr, for radio emission indicative of electrostatic discharge. The search was motivated by the report from Ruf et al. of the detection of non-thermal microwave radiation from Mars characterized by peaks in the power spectrum of the kurtosis, or kurtstrum, at 10 Hz, coinciding with a large dust storm event on 2006 June 8. For these observations, we developed a wideband signal processor at the Center for Astronomy Signal Processing and Electronics Research. This 1024 channel spectrometer calculatesmore » the accumulated power and power-squared, from which the spectral kurtosis is calculated post-observation. Variations in the kurtosis are indicative of non-Gaussianity in the signal, which can be used to detect variable cosmic signals as well as radio frequency interference (RFI). During the three-month period of observations, dust activity occurred on Mars in the form of small-scale dust storms; however, no signals indicating lightning discharge were detected. Frequent signals in the kurtstrum that contain spectral peaks with an approximate 10 Hz fundamental were seen at both 3.2 and 8.0 GHz, but were the result of narrowband RFI with harmonics spread over a broad frequency range.« less

Towards process-informed bias correction of climate change simulations

NASA Astrophysics Data System (ADS)

Maraun, Douglas; Shepherd, Theodore G.; Widmann, Martin; Zappa, Giuseppe; Walton, Daniel; Gutiérrez, José M.; Hagemann, Stefan; Richter, Ingo; Soares, Pedro M. M.; Hall, Alex; Mearns, Linda O.

2017-11-01

Biases in climate model simulations introduce biases in subsequent impact simulations. Therefore, bias correction methods are operationally used to post-process regional climate projections. However, many problems have been identified, and some researchers question the very basis of the approach. Here we demonstrate that a typical cross-validation is unable to identify improper use of bias correction. Several examples show the limited ability of bias correction to correct and to downscale variability, and demonstrate that bias correction can cause implausible climate change signals. Bias correction cannot overcome major model errors, and naive application might result in ill-informed adaptation decisions. We conclude with a list of recommendations and suggestions for future research to reduce, post-process, and cope with climate model biases.

Reduction of produced elementary sulfur in denitrifying sulfide removal process.

PubMed

Zhou, Xu; Liu, Lihong; Chen, Chuan; Ren, Nanqi; Wang, Aijie; Lee, Duu-Jong

2011-05-01

Denitrifying sulfide removal (DSR) processes simultaneously convert sulfide, nitrate, and chemical oxygen demand from industrial wastewater into elemental sulfur, dinitrogen gas, and carbon dioxide, respectively. The failure of a DSR process is signaled by high concentrations of sulfide in reactor effluent. Conventionally, DSR reactor failure is blamed for overcompetition for heterotroph to autotroph communities. This study indicates that the elementary sulfur produced by oxidizing sulfide that is a recoverable resource from sulfide-laden wastewaters can be reduced back to sulfide by sulfur-reducing Methanobacterium sp. The Methanobacterium sp. was stimulated with excess organic carbon (acetate) when nitrite was completely consumed by heterotrophic denitrifiers. Adjusting hydraulic retention time of a DSR reactor when nitrite is completely consumed provides an additional control variable for maximizing DSR performance.

Variable speed wind turbine generator with zero-sequence filter

DOEpatents

Muljadi, Eduard

1998-01-01

A variable speed wind turbine generator system to convert mechanical power into electrical power or energy and to recover the electrical power or energy in the form of three phase alternating current and return the power or energy to a utility or other load with single phase sinusoidal waveform at sixty (60) hertz and unity power factor includes an excitation controller for generating three phase commanded current, a generator, and a zero sequence filter. Each commanded current signal includes two components: a positive sequence variable frequency current signal to provide the balanced three phase excitation currents required in the stator windings of the generator to generate the rotating magnetic field needed to recover an optimum level of real power from the generator; and a zero frequency sixty (60) hertz current signal to allow the real power generated by the generator to be supplied to the utility. The positive sequence current signals are balanced three phase signals and are prevented from entering the utility by the zero sequence filter. The zero sequence current signals have zero phase displacement from each other and are prevented from entering the generator by the star connected stator windings. The zero sequence filter allows the zero sequence current signals to pass through to deliver power to the utility.

Variable Speed Wind Turbine Generator with Zero-sequence Filter

DOEpatents

Muljadi, Eduard

1998-08-25

A variable speed wind turbine generator system to convert mechanical power into electrical power or energy and to recover the electrical power or energy in the form of three phase alternating current and return the power or energy to a utility or other load with single phase sinusoidal waveform at sixty (60) hertz and unity power factor includes an excitation controller for generating three phase commanded current, a generator, and a zero sequence filter. Each commanded current signal includes two components: a positive sequence variable frequency current signal to provide the balanced three phase excitation currents required in the stator windings of the generator to generate the rotating magnetic field needed to recover an optimum level of real power from the generator; and a zero frequency sixty (60) hertz current signal to allow the real power generated by the generator to be supplied to the utility. The positive sequence current signals are balanced three phase signals and are prevented from entering the utility by the zero sequence filter. The zero sequence current signals have zero phase displacement from each other and are prevented from entering the generator by the star connected stator windings. The zero sequence filter allows the zero sequence current signals to pass through to deliver power to the utility.

Variable speed wind turbine generator with zero-sequence filter

DOEpatents

Muljadi, E.

1998-08-25

A variable speed wind turbine generator system to convert mechanical power into electrical power or energy and to recover the electrical power or energy in the form of three phase alternating current and return the power or energy to a utility or other load with single phase sinusoidal waveform at sixty (60) hertz and unity power factor includes an excitation controller for generating three phase commanded current, a generator, and a zero sequence filter. Each commanded current signal includes two components: a positive sequence variable frequency current signal to provide the balanced three phase excitation currents required in the stator windings of the generator to generate the rotating magnetic field needed to recover an optimum level of real power from the generator; and a zero frequency sixty (60) hertz current signal to allow the real power generated by the generator to be supplied to the utility. The positive sequence current signals are balanced three phase signals and are prevented from entering the utility by the zero sequence filter. The zero sequence current signals have zero phase displacement from each other and are prevented from entering the generator by the star connected stator windings. The zero sequence filter allows the zero sequence current signals to pass through to deliver power to the utility. 14 figs.

Fault Tolerant Signal Processing Using Finite Fields and Error-Correcting Codes.

DTIC Science & Technology

1983-06-01

Decimation in Frequency Form, Fast Inverse Transform F-18 F-4 Part of Decimation in Time Form, Fast Inverse Transform F-21 I . LIST OF TABLES fable Title Page...F-2 Intermediate Variables In A Fast Inverse Transform F-14 Accession For NTIS GRA&il DTIC TAB E Unannounced El ** Dist ribut ion/ ____ AvailabilitY...component polynomials may be transformed to an equiva- lent series of multiplications of the related transform ’.. coefficients. The inverse transform of

A particle filter for multi-target tracking in track before detect context

NASA Astrophysics Data System (ADS)

Amrouche, Naima; Khenchaf, Ali; Berkani, Daoud

2016-10-01

The track-before-detect (TBD) approach can be used to track a single target in a highly noisy radar scene. This is because it makes use of unthresholded observations and incorporates a binary target existence variable into its target state estimation process when implemented as a particle filter (PF). This paper proposes the recursive PF-TBD approach to detect multiple targets in low-signal-to noise ratios (SNR). The algorithm's successful performance is demonstrated using a simulated two target example.

Nonlinear Signal Processing and Team Differential Games.

DTIC Science & Technology

1986-03-01

w can be viewed as a control variable belonging to a mythical mini- mizing third party, such that (33) is a Nash strategy. It can be shown that the...Morgenstern [2] the discrete game analysis embodied most of the prin- ciples of the game theory yet recognized as important. But it is not until the...more common variety. Nor can it be treated as a second phase of a bargaining game with coalitions already formed, such as studied by Von Netman and

Human factors research problems in electronic voice warning system design

NASA Technical Reports Server (NTRS)

Simpson, C. A.; Williams, D. H.

1975-01-01

The speech messages issued by voice warning systems must be carefully designed in accordance with general principles of human decision making processes, human speech comprehension, and the conditions in which the warnings can occur. The operator's effectiveness must not be degraded by messages that are either inappropriate or difficult to comprehend. Important experimental variables include message content, linguistic redundancy, signal/noise ratio, interference with concurrent tasks, and listener expectations generated by the pragmatic or real world context in which the messages are presented.

Automatic detection of snow avalanches in continuous seismic data using hidden Markov models

NASA Astrophysics Data System (ADS)

Heck, Matthias; Hammer, Conny; van Herwijnen, Alec; Schweizer, Jürg; Fäh, Donat

2018-01-01

Snow avalanches generate seismic signals as many other mass movements. Detection of avalanches by seismic monitoring is highly relevant to assess avalanche danger. In contrast to other seismic events, signals generated by avalanches do not have a characteristic first arrival nor is it possible to detect different wave phases. In addition, the moving source character of avalanches increases the intricacy of the signals. Although it is possible to visually detect seismic signals produced by avalanches, reliable automatic detection methods for all types of avalanches do not exist yet. We therefore evaluate whether hidden Markov models (HMMs) are suitable for the automatic detection of avalanches in continuous seismic data. We analyzed data recorded during the winter season 2010 by a seismic array deployed in an avalanche starting zone above Davos, Switzerland. We re-evaluated a reference catalogue containing 385 events by grouping the events in seven probability classes. Since most of the data consist of noise, we first applied a simple amplitude threshold to reduce the amount of data. As first classification results were unsatisfying, we analyzed the temporal behavior of the seismic signals for the whole data set and found that there is a high variability in the seismic signals. We therefore applied further post-processing steps to reduce the number of false alarms by defining a minimal duration for the detected event, implementing a voting-based approach and analyzing the coherence of the detected events. We obtained the best classification results for events detected by at least five sensors and with a minimal duration of 12 s. These processing steps allowed identifying two periods of high avalanche activity, suggesting that HMMs are suitable for the automatic detection of avalanches in seismic data. However, our results also showed that more sensitive sensors and more appropriate sensor locations are needed to improve the signal-to-noise ratio of the signals and therefore the classification.

Verbal Working Memory in Children With Cochlear Implants

PubMed Central

Caldwell-Tarr, Amanda; Low, Keri E.; Lowenstein, Joanna H.

2017-01-01

Purpose Verbal working memory in children with cochlear implants and children with normal hearing was examined. Participants Ninety-three fourth graders (47 with normal hearing, 46 with cochlear implants) participated, all of whom were in a longitudinal study and had working memory assessed 2 years earlier. Method A dual-component model of working memory was adopted, and a serial recall task measured storage and processing. Potential predictor variables were phonological awareness, vocabulary knowledge, nonverbal IQ, and several treatment variables. Potential dependent functions were literacy, expressive language, and speech-in-noise recognition. Results Children with cochlear implants showed deficits in storage and processing, similar in size to those at second grade. Predictors of verbal working memory differed across groups: Phonological awareness explained the most variance in children with normal hearing; vocabulary explained the most variance in children with cochlear implants. Treatment variables explained little of the variance. Where potentially dependent functions were concerned, verbal working memory accounted for little variance once the variance explained by other predictors was removed. Conclusions The verbal working memory deficits of children with cochlear implants arise due to signal degradation, which limits their abilities to acquire phonological awareness. That hinders their abilities to store items using a phonological code. PMID:29075747

A Study of Mexican Free-Tailed Bat Chirp Syllables: Bayesian Functional Mixed Models for Nonstationary Acoustic Time Series.

PubMed

Martinez, Josue G; Bohn, Kirsten M; Carroll, Raymond J; Morris, Jeffrey S

2013-06-01

We describe a new approach to analyze chirp syllables of free-tailed bats from two regions of Texas in which they are predominant: Austin and College Station. Our goal is to characterize any systematic regional differences in the mating chirps and assess whether individual bats have signature chirps. The data are analyzed by modeling spectrograms of the chirps as responses in a Bayesian functional mixed model. Given the variable chirp lengths, we compute the spectrograms on a relative time scale interpretable as the relative chirp position, using a variable window overlap based on chirp length. We use 2D wavelet transforms to capture correlation within the spectrogram in our modeling and obtain adaptive regularization of the estimates and inference for the regions-specific spectrograms. Our model includes random effect spectrograms at the bat level to account for correlation among chirps from the same bat, and to assess relative variability in chirp spectrograms within and between bats. The modeling of spectrograms using functional mixed models is a general approach for the analysis of replicated nonstationary time series, such as our acoustical signals, to relate aspects of the signals to various predictors, while accounting for between-signal structure. This can be done on raw spectrograms when all signals are of the same length, and can be done using spectrograms defined on a relative time scale for signals of variable length in settings where the idea of defining correspondence across signals based on relative position is sensible.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Boumaaraf, Abdelâali, E-mail: aboumaaraf@yahoo.fr; University of Farhat Abbas Setif1, Sétif, 19000; Mohamadi, Tayeb

In this paper, we present the FPGA implementation of the multiple pulse width modulation (MPWM) signal generation with repetition of data segments, applied to the variable frequency variable voltage systems and specially at to the photovoltaic water pumping system, in order to generate a signal command very easily between 10 Hz to 60 Hz with a small frequency and reduce the cost of the control system.

Development and application of a microarray meter tool to optimize microarray experiments

PubMed Central

Rouse, Richard JD; Field, Katrine; Lapira, Jennifer; Lee, Allen; Wick, Ivan; Eckhardt, Colleen; Bhasker, C Ramana; Soverchia, Laura; Hardiman, Gary

2008-01-01

Background Successful microarray experimentation requires a complex interplay between the slide chemistry, the printing pins, the nucleic acid probes and targets, and the hybridization milieu. Optimization of these parameters and a careful evaluation of emerging slide chemistries are a prerequisite to any large scale array fabrication effort. We have developed a 'microarray meter' tool which assesses the inherent variations associated with microarray measurement prior to embarking on large scale projects. Findings The microarray meter consists of nucleic acid targets (reference and dynamic range control) and probe components. Different plate designs containing identical probe material were formulated to accommodate different robotic and pin designs. We examined the variability in probe quality and quantity (as judged by the amount of DNA printed and remaining post-hybridization) using three robots equipped with capillary printing pins. Discussion The generation of microarray data with minimal variation requires consistent quality control of the (DNA microarray) manufacturing and experimental processes. Spot reproducibility is a measure primarily of the variations associated with printing. The microarray meter assesses array quality by measuring the DNA content for every feature. It provides a post-hybridization analysis of array quality by scoring probe performance using three metrics, a) a measure of variability in the signal intensities, b) a measure of the signal dynamic range and c) a measure of variability of the spot morphologies. PMID:18710498

Body Morphology, Energy Stores, and Muscle Enzyme Activity Explain Cricket Acoustic Mate Attraction Signaling Variation

PubMed Central

Thomson, Ian R.; Darveau, Charles-A.; Bertram, Susan M.

2014-01-01

High mating success in animals is often dependent on males signalling attractively with high effort. Since males should be selected to maximize their reproductive success, female preferences for these traits should result in minimal signal variation persisting in the population. However, extensive signal variation persists. The genic capture hypothesis proposes genetic variation persists because fitness-conferring traits depend on an individual's basic processes, including underlying physiological, morphological, and biochemical traits, which are themselves genetically variable. To explore the traits underlying signal variation, we quantified among-male differences in signalling, morphology, energy stores, and the activities of key enzymes associated with signalling muscle metabolism in two species of crickets, Gryllus assimilis (chirper: <20 pulses/chirp) and G. texensis (triller: >20 pulses/chirp). Chirping G. assimilis primarily fuelled signalling with carbohydrate metabolism: smaller individuals and individuals with increased thoracic glycogen stores signalled for mates with greater effort; individuals with greater glycogen phosphorylase activity produced more attractive mating signals. Conversely, the more energetic trilling G. texensis fuelled signalling with both lipid and carbohydrate metabolism: individuals with increased β-hydroxyacyl-CoA dehydrogenase activity and increased thoracic free carbohydrate content signalled for mates with greater effort; individuals with higher thoracic and abdominal carbohydrate content and higher abdominal lipid stores produced more attractive signals. Our findings suggest variation in male reproductive success may be driven by hidden physiological trade-offs that affect the ability to uptake, retain, and use essential nutrients, although the results remain correlational in nature. Our findings indicate that a physiological perspective may help us to understand some of the causes of variation in behaviour. PMID:24608102

Brain Tissue Responses to Neural Implants Impact Signal Sensitivity and Intervention Strategies

PubMed Central

2015-01-01

Implantable biosensors are valuable scientific tools for basic neuroscience research and clinical applications. Neurotechnologies provide direct readouts of neurological signal and neurochemical processes. These tools are generally most valuable when performance capacities extend over months and years to facilitate the study of memory, plasticity, and behavior or to monitor patients’ conditions. These needs have generated a variety of device designs from microelectrodes for fast scan cyclic voltammetry (FSCV) and electrophysiology to microdialysis probes for sampling and detecting various neurochemicals. Regardless of the technology used, the breaching of the blood–brain barrier (BBB) to insert devices triggers a cascade of biochemical pathways resulting in complex molecular and cellular responses to implanted devices. Molecular and cellular changes in the microenvironment surrounding an implant include the introduction of mechanical strain, activation of glial cells, loss of perfusion, secondary metabolic injury, and neuronal degeneration. Changes to the tissue microenvironment surrounding the device can dramatically impact electrochemical and electrophysiological signal sensitivity and stability over time. This review summarizes the magnitude, variability, and time course of the dynamic molecular and cellular level neural tissue responses induced by state-of-the-art implantable devices. Studies show that insertion injuries and foreign body response can impact signal quality across all implanted central nervous system (CNS) sensors to varying degrees over both acute (seconds to minutes) and chronic periods (weeks to months). Understanding the underlying biological processes behind the brain tissue response to the devices at the cellular and molecular level leads to a variety of intervention strategies for improving signal sensitivity and longevity. PMID:25546652

Trial-Level Regressor Modulation for Functional Magnetic Resonance Imaging Designs Requiring Strict Periodicity of Stimulus Presentations: Illustrated Using a Go/No-Go Task

PubMed Central

Motes, Michael A; Rao, Neena K; Shokri-Kojori, Ehsan; Chiang, Hsueh-Sheng; Kraut, Michael A; Hart, John

2017-01-01

Computer-based assessment of many cognitive processes (eg, anticipatory and response readiness processes) requires the use of invariant stimulus display times (SDT) and intertrial intervals (ITI). Although designs with invariant SDTs and ITIs have been used in functional magnetic resonance imaging (fMRI) research, such designs are problematic for fMRI studies because of collinearity issues. This study examined regressor modulation with trial-level reaction times (RT) as a method for improving signal detection in a go/no-go task with invariant SDTs and ITIs. The effects of modulating the go regressor were evaluated with respect to the detection of BOLD signal-change for the no-go condition. BOLD signal-change to no-go stimuli was examined when the go regressor was based on a (a) canonical hemodynamic response function (HRF), (b) RT-based amplitude-modulated (AM) HRF, and (c) RT-based amplitude and duration modulated (A&DM) HRF. Reaction time–based modulation reduced the collinearity between the go and no-go regressors, with A&DM producing the greatest reductions in correlations between the regressors, and greater reductions in the correlations between regressors were associated with longer mean RTs and greater RT variability. Reaction time–based modulation increased statistical power for detecting group-level no-go BOLD signal-change across a broad set of brain regions. The findings show the efficacy of using regressor modulation to increase power in detecting BOLD signal-change in fMRI studies in which circumstances dictate the use of temporally invariant stimulus presentations. PMID:29276390

Trial-Level Regressor Modulation for Functional Magnetic Resonance Imaging Designs Requiring Strict Periodicity of Stimulus Presentations: Illustrated Using a Go/No-Go Task.

PubMed

Motes, Michael A; Rao, Neena K; Shokri-Kojori, Ehsan; Chiang, Hsueh-Sheng; Kraut, Michael A; Hart, John

2017-01-01

Computer-based assessment of many cognitive processes (eg, anticipatory and response readiness processes) requires the use of invariant stimulus display times (SDT) and intertrial intervals (ITI). Although designs with invariant SDTs and ITIs have been used in functional magnetic resonance imaging (fMRI) research, such designs are problematic for fMRI studies because of collinearity issues. This study examined regressor modulation with trial-level reaction times (RT) as a method for improving signal detection in a go / no-go task with invariant SDTs and ITIs. The effects of modulating the go regressor were evaluated with respect to the detection of BOLD signal-change for the no-go condition. BOLD signal-change to no-go stimuli was examined when the go regressor was based on a (a) canonical hemodynamic response function (HRF), (b) RT-based amplitude-modulated (AM) HRF, and (c) RT-based amplitude and duration modulated (A&DM) HRF. Reaction time-based modulation reduced the collinearity between the go and no-go regressors, with A&DM producing the greatest reductions in correlations between the regressors, and greater reductions in the correlations between regressors were associated with longer mean RTs and greater RT variability. Reaction time-based modulation increased statistical power for detecting group-level no-go BOLD signal-change across a broad set of brain regions. The findings show the efficacy of using regressor modulation to increase power in detecting BOLD signal-change in fMRI studies in which circumstances dictate the use of temporally invariant stimulus presentations.

Validating of Atmospheric Signals Associated with some of the Major Earthquakes in Asia (2003-2009)

NASA Technical Reports Server (NTRS)

Ouzounov, D. P.; Pulinets, S.; Liu, J. Y.; Hattori, K.; Oarritm N,; Taylor, P. T.

2010-01-01

The recent catastrophic earthquake in Haiti (January 2010) has provided and renewed interest in the important question of the existence of precursory signals related to strong earthquakes. Latest studies (VESTO workshop in Japan 2009) have shown that there were precursory atmospheric signals observed on the ground and in space associated with several recent earthquakes. The major question, still widely debated in the scientific community is whether such signals systematically precede major earthquakes. To address this problem we have started to validate the anomalous atmospheric signals during the occurrence of large earthquakes. Our approach is based on integration analysis of several physical and environmental parameters (thermal infrared radiation, electron concentration in the ionosphere, Radon/ion activities, air temperature and seismicity) that were found to be associated with earthquakes. We performed hind-cast detection over three different regions with high seismicity Taiwan, Japan and Kamchatka for the period of 2003-2009. We are using existing thermal satellite data (Aqua and POES); in situ atmospheric data (NOAA/NCEP); and ionospheric variability data (GPS/TEC and DEMETER). The first part of this validation included 42 major earthquakes (M greater than 5.9): 10 events in Taiwan, 15 events in Japan, 15 events in Kamchatka and four most recent events for M8.0 Wenchuan earthquake (May 2008) in China and M7.9 Samoa earthquakes (Sep 2009). Our initial results suggest a systematic appearance of atmospheric anomalies near the epicentral area, 1 to 5 days prior to the largest earthquakes, that could be explained by a coupling process between the observed physical parameters, and the earthquake preparation processes.

Reaction Time Variability in Children With ADHD Symptoms and/or Dyslexia

PubMed Central

Gooch, Debbie; Snowling, Margaret J.; Hulme, Charles

2012-01-01

Reaction time (RT) variability on a Stop Signal task was examined among children with attention deficit hyperactivity disorder (ADHD) symptoms and/or dyslexia in comparison to typically developing (TD) controls. Children's go-trial RTs were analyzed using a novel ex-Gaussian method. Children with ADHD symptoms had increased variability in the fast but not the slow portions of their RT distributions compared to those without ADHD symptoms. The RT distributions of children with dyslexia were similar to those of TD-controls. It is argued that variability in responding may be underpinned by impairments in response preparation or timing during Stop Signal tasks. PMID:22799763

Decoupling biogeochemical records, extinction, and environmental change during the Cambrian SPICE event

PubMed Central

Schiffbauer, James D.; Huntley, John Warren; Fike, David A.; Jeffrey, Matthew Jarrell; Gregg, Jay M.; Shelton, Kevin L.

2017-01-01

Several positive carbon isotope excursions in Lower Paleozoic rocks, including the prominent Upper Cambrian Steptoean Positive Carbon Isotope Excursion (SPICE), are thought to reflect intermittent perturbations in the hydrosphere-biosphere system. Models explaining these secular changes are abundant, but the synchronicity and regional variation of the isotope signals are not well understood. Examination of cores across a paleodepth gradient in the Upper Cambrian central Missouri intrashelf basin (United States) reveals a time-transgressive, facies-dependent nature of the SPICE. Although the SPICE event may be a global signal, the manner in which it is recorded in rocks should and does vary as a function of facies and carbonate platform geometry. We call for a paradigm shift to better constrain facies, stratigraphic, and biostratigraphic architecture and to apply these observations to the variability in magnitude, stratigraphic extent, and timing of the SPICE signal, as well as other biogeochemical perturbations, to elucidate the complex processes driving the ocean-carbonate system. PMID:28275734

Pulse-height loss in the signal readout circuit of compound semiconductor detectors

NASA Astrophysics Data System (ADS)

Nakhostin, M.; Hitomi, K.

2018-06-01

Compound semiconductor detectors such as CdTe, CdZnTe, HgI2 and TlBr are known to exhibit large variations in their charge collection times. This paper considers the effect of such variations on the measurement of induced charge pulses by using resistive feedback charge-sensitive preamplifiers. It is shown that, due to the finite decay-time constant of the preamplifiers, the capacitive decay during the signal readout leads to a variable deficit in the measurement of ballistic signals and a digital pulse processing method is employed to correct for it. The method is experimentally examined by using sampled pulses from a TlBr detector coupled to a charge-sensitive preamplifier with 150 μs of decay-time constant and 20 % improvement in the energy resolution of the detector at 662 keV is achieved. The implications of the capacitive decay on the correction of charge-trapping effect by using depth-sensing technique are also considered.

Oufti: An integrated software package for high-accuracy, high-throughput quantitative microscopy analysis

PubMed Central

Paintdakhi, Ahmad; Parry, Bradley; Campos, Manuel; Irnov, Irnov; Elf, Johan; Surovtsev, Ivan; Jacobs-Wagner, Christine

2016-01-01

Summary With the realization that bacteria display phenotypic variability among cells and exhibit complex subcellular organization critical for cellular function and behavior, microscopy has re-emerged as a primary tool in bacterial research during the last decade. However, the bottleneck in today’s single-cell studies is quantitative image analysis of cells and fluorescent signals. Here, we address current limitations through the development of Oufti, a stand-alone, open-source software package for automated measurements of microbial cells and fluorescence signals from microscopy images. Oufti provides computational solutions for tracking touching cells in confluent samples, handles various cell morphologies, offers algorithms for quantitative analysis of both diffraction and non-diffraction-limited fluorescence signals, and is scalable for high-throughput analysis of massive datasets, all with subpixel precision. All functionalities are integrated in a single package. The graphical user interface, which includes interactive modules for segmentation, image analysis, and post-processing analysis, makes the software broadly accessible to users irrespective of their computational skills. PMID:26538279

Mesolimbic confidence signals guide perceptual learning in the absence of external feedback

PubMed Central

Guggenmos, Matthias; Wilbertz, Gregor; Hebart, Martin N; Sterzer, Philipp

2016-01-01

It is well established that learning can occur without external feedback, yet normative reinforcement learning theories have difficulties explaining such instances of learning. Here, we propose that human observers are capable of generating their own feedback signals by monitoring internal decision variables. We investigated this hypothesis in a visual perceptual learning task using fMRI and confidence reports as a measure for this monitoring process. Employing a novel computational model in which learning is guided by confidence-based reinforcement signals, we found that mesolimbic brain areas encoded both anticipation and prediction error of confidence—in remarkable similarity to previous findings for external reward-based feedback. We demonstrate that the model accounts for choice and confidence reports and show that the mesolimbic confidence prediction error modulation derived through the model predicts individual learning success. These results provide a mechanistic neurobiological explanation for learning without external feedback by augmenting reinforcement models with confidence-based feedback. DOI: http://dx.doi.org/10.7554/eLife.13388.001 PMID:27021283

Basic Investigations of Dynamic Travel Time Estimation Model for Traffic Signals Control Using Information from Optical Beacons

NASA Astrophysics Data System (ADS)

Okutani, Iwao; Mitsui, Tatsuro; Nakada, Yusuke

In this paper put forward are neuron-type models, i.e., neural network model, wavelet neuron model and three layered wavelet neuron model(WV3), for estimating traveling time between signalized intersections in order to facilitate adaptive setting of traffic signal parameters such as green time and offset. Model validation tests using simulated data reveal that compared to other models, WV3 model works very fast in learning process and can produce more accurate estimates of travel time. Also, it is exhibited that up-link information obtainable from optical beacons, i.e., travel time observed during the former cycle time in this case, makes a crucial input variable to the models in that there isn't any substantial difference between the change of estimated and simulated travel time with the change of green time or offset when up-link information is employed as input while there appears big discrepancy between them when not employed.

Global and regional axial ocean angular momentum signals and length-of-day variations (1985-1996)

NASA Astrophysics Data System (ADS)

Ponte, Rui M.; Stammer, Detlef

2000-07-01

Changes in ocean angular momentum M about the polar axis are related to fluctuations in zonal currents (relative component Mr) and latitudinal shifts in mass (planetary component MΩ). Output from a 1° ocean model is used to calculate global Mr, MΩ, and M time series at 5 day intervals for the period January 1985 to April 1996. The annual cycle in Mr, MΩ, and M is larger than the semiannual cycle, and MΩ amplitudes are nearly twice those of Mr. Year-to-year modulation of the seasonal cycle is present, but interannual variability is weak. The spectrum of M is red (background slope between ω-1 and ω-2) at subseasonal periods, implying a white or blue spectrum for the external torque on the ocean. Comparisons with previous studies indicate the importance of direct atmospheric forcing in inducing subseasonal M signals, relative to instabilities and other internal sources of rapid oceanic signals. Regional angular momentum estimates show that seasonal variability tends to be larger at low latitudes, but many local maxima exist because of the spatial structure of zonal current and mass variability. At seasonal timescales, latitudes ~20°S-10°N contribute substantial variability to MΩ, while signals in Mr can be traced to Antarctic Circumpolar Current transports and associated circulation. Variability in M is found to be small when compared with similar time series for the atmosphere and the solid Earth, but ocean signals are significantly coherent with atmosphere-solid Earth residuals, implying a measurable oceanic impact on length-of-day variations.

Use of signal analysis of heart sounds and murmurs to assess severity of mitral valve regurgitation attributable to myxomatous mitral valve disease in dogs.

PubMed

Ljungvall, Ingrid; Ahlstrom, Christer; Höglund, Katja; Hult, Peter; Kvart, Clarence; Borgarelli, Michele; Ask, Per; Häggström, Jens

2009-05-01

To investigate use of signal analysis of heart sounds and murmurs in assessing severity of mitral valve regurgitation (mitral regurgitation [MR]) in dogs with myxomatous mitral valve disease (MMVD). 77 client-owned dogs. Cardiac sounds were recorded from dogs evaluated by use of auscultatory and echocardiographic classification systems. Signal analysis techniques were developed to extract 7 sound variables (first frequency peak, murmur energy ratio, murmur duration > 200 Hz, sample entropy and first minimum of the auto mutual information function of the murmurs, and energy ratios of the first heart sound [S1] and second heart sound [S2]). Significant associations were detected between severity of MR and all sound variables, except the energy ratio of S1. An increase in severity of MR resulted in greater contribution of higher frequencies, increased signal irregularity, and decreased energy ratio of S2. The optimal combination of variables for distinguishing dogs with high-intensity murmurs from other dogs was energy ratio of S2 and murmur duration > 200 Hz (sensitivity, 79%; specificity, 71%) by use of the auscultatory classification. By use of the echocardiographic classification, corresponding variables were auto mutual information, first frequency peak, and energy ratio of S2 (sensitivity, 88%; specificity, 82%). Most of the investigated sound variables were significantly associated with severity of MR, which indicated a powerful diagnostic potential for monitoring MMVD. Signal analysis techniques could be valuable for clinicians when performing risk assessment or determining whether special care and more extensive examinations are required.

Northern Hemisphere glaciation and the evolution of Plio-Pleistocene climate noise

NASA Astrophysics Data System (ADS)

Meyers, Stephen R.; Hinnov, Linda A.

2010-08-01

Deterministic orbital controls on climate variability are commonly inferred to dominate across timescales of 104-106 years, although some studies have suggested that stochastic processes may be of equal or greater importance. Here we explicitly quantify changes in deterministic orbital processes (forcing and/or pacing) versus stochastic climate processes during the Plio-Pleistocene, via time-frequency analysis of two prominent foraminifera oxygen isotopic stacks. Our results indicate that development of the Northern Hemisphere ice sheet is paralleled by an overall amplification of both deterministic and stochastic climate energy, but their relative dominance is variable. The progression from a more stochastic early Pliocene to a strongly deterministic late Pleistocene is primarily accommodated during two transitory phases of Northern Hemisphere ice sheet growth. This long-term trend is punctuated by “stochastic events,” which we interpret as evidence for abrupt reorganization of the climate system at the initiation and termination of the mid-Pleistocene transition and at the onset of Northern Hemisphere glaciation. In addition to highlighting a complex interplay between deterministic and stochastic climate change during the Plio-Pleistocene, our results support an early onset for Northern Hemisphere glaciation (between 3.5 and 3.7 Ma) and reveal some new characteristics of the orbital signal response, such as the puzzling emergence of 100 ka and 400 ka cyclic climate variability during theoretical eccentricity nodes.

Changes in Intense Precipitation Events in West Africa and the central U.S. under Global Warming

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cook, Kerry H.; Vizy, Edward

The purpose of the proposed project is to improve our understanding of the physical processes and large-scale connectivity of changes in intense precipitation events (high rainfall rates) under global warming in West Africa and the central U.S., including relationships with low-frequency modes of variability. This is in response to the requested subject area #2 “simulation of climate extremes under a changing climate … to better quantify the frequency, duration, and intensity of extreme events under climate change and elucidate the role of low frequency climate variability in modulating extremes.” We will use a regional climate model and emphasize an understandingmore » of the physical processes that lead to an intensification of rainfall. The project objectives are as follows: 1. Understand the processes responsible for simulated changes in warm-season rainfall intensity and frequency over West Africa and the Central U.S. associated with greenhouse gas-induced global warming 2. Understand the relationship between changes in warm-season rainfall intensity and frequency, which generally occur on regional space scales, and the larger-scale global warming signal by considering modifications of low-frequency modes of variability. 3. Relate changes simulated on regional space scales to global-scale theories of how and why atmospheric moisture levels and rainfall should change as climate warms.« less

A first approach to the distortion analysis of nonlinear analog circuits utilizing X-parameters

NASA Astrophysics Data System (ADS)

Weber, H.; Widemann, C.; Mathis, W.

2013-07-01

In this contribution a first approach to the distortion analysis of nonlinear 2-port-networks with X-parameters1 is presented. The X-parameters introduced by Verspecht and Root (2006) offer the possibility to describe nonlinear microwave 2-port-networks under large signal conditions. On the basis of X-parameter measurements with a nonlinear network analyzer (NVNA) behavioral models can be extracted for the networks. These models can be used to consider the nonlinear behavior during the design process of microwave circuits. The idea of the present work is to extract the behavioral models in order to describe the influence of interfering signals on the output behavior of the nonlinear circuits. Hereby, a simulator is used instead of a NVNA to extract the X-parameters. Assuming that the interfering signals are relatively small compared to the nominal input signal, the output signal can be described as a superposition of the effects of each input signal. In order to determine the functional correlation between the scattering variables, a polynomial dependency is assumed. The required datasets for the approximation of the describing functions are simulated by a directional coupler model in Cadence Design Framework. The polynomial coefficients are obtained by a least-square method. The resulting describing functions can be used to predict the system's behavior under certain conditions as well as the effects of the interfering signal on the output signal. 1 X-parameter is a registered trademark of Agilent Technologies, Inc.

Statistical optimisation techniques in fatigue signal editing problem

NASA Astrophysics Data System (ADS)

Nopiah, Z. M.; Osman, M. H.; Baharin, N.; Abdullah, S.

2015-02-01

Success in fatigue signal editing is determined by the level of length reduction without compromising statistical constraints. A great reduction rate can be achieved by removing small amplitude cycles from the recorded signal. The long recorded signal sometimes renders the cycle-to-cycle editing process daunting. This has encouraged researchers to focus on the segment-based approach. This paper discusses joint application of the Running Damage Extraction (RDE) technique and single constrained Genetic Algorithm (GA) in fatigue signal editing optimisation.. In the first section, the RDE technique is used to restructure and summarise the fatigue strain. This technique combines the overlapping window and fatigue strain-life models. It is designed to identify and isolate the fatigue events that exist in the variable amplitude strain data into different segments whereby the retention of statistical parameters and the vibration energy are considered. In the second section, the fatigue data editing problem is formulated as a constrained single optimisation problem that can be solved using GA method. The GA produces the shortest edited fatigue signal by selecting appropriate segments from a pool of labelling segments. Challenges arise due to constraints on the segment selection by deviation level over three signal properties, namely cumulative fatigue damage, root mean square and kurtosis values. Experimental results over several case studies show that the idea of solving fatigue signal editing within a framework of optimisation is effective and automatic, and that the GA is robust for constrained segment selection.

Statistical optimisation techniques in fatigue signal editing problem

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nopiah, Z. M.; Osman, M. H.; Baharin, N.

Success in fatigue signal editing is determined by the level of length reduction without compromising statistical constraints. A great reduction rate can be achieved by removing small amplitude cycles from the recorded signal. The long recorded signal sometimes renders the cycle-to-cycle editing process daunting. This has encouraged researchers to focus on the segment-based approach. This paper discusses joint application of the Running Damage Extraction (RDE) technique and single constrained Genetic Algorithm (GA) in fatigue signal editing optimisation.. In the first section, the RDE technique is used to restructure and summarise the fatigue strain. This technique combines the overlapping window andmore » fatigue strain-life models. It is designed to identify and isolate the fatigue events that exist in the variable amplitude strain data into different segments whereby the retention of statistical parameters and the vibration energy are considered. In the second section, the fatigue data editing problem is formulated as a constrained single optimisation problem that can be solved using GA method. The GA produces the shortest edited fatigue signal by selecting appropriate segments from a pool of labelling segments. Challenges arise due to constraints on the segment selection by deviation level over three signal properties, namely cumulative fatigue damage, root mean square and kurtosis values. Experimental results over several case studies show that the idea of solving fatigue signal editing within a framework of optimisation is effective and automatic, and that the GA is robust for constrained segment selection.« less

SEMICONDUCTOR TECHNOLOGY A signal processing method for the friction-based endpoint detection system of a CMP process

NASA Astrophysics Data System (ADS)

Chi, Xu; Dongming, Guo; Zhuji, Jin; Renke, Kang

2010-12-01

A signal processing method for the friction-based endpoint detection system of a chemical mechanical polishing (CMP) process is presented. The signal process method uses the wavelet threshold denoising method to reduce the noise contained in the measured original signal, extracts the Kalman filter innovation from the denoised signal as the feature signal, and judges the CMP endpoint based on the feature of the Kalman filter innovation sequence during the CMP process. Applying the signal processing method, the endpoint detection experiments of the Cu CMP process were carried out. The results show that the signal processing method can judge the endpoint of the Cu CMP process.

Sequential Modular Position and Momentum Measurements of a Trapped Ion Mechanical Oscillator

NASA Astrophysics Data System (ADS)

Flühmann, C.; Negnevitsky, V.; Marinelli, M.; Home, J. P.

2018-04-01

The noncommutativity of position and momentum observables is a hallmark feature of quantum physics. However, this incompatibility does not extend to observables that are periodic in these base variables. Such modular-variable observables have been suggested as tools for fault-tolerant quantum computing and enhanced quantum sensing. Here, we implement sequential measurements of modular variables in the oscillatory motion of a single trapped ion, using state-dependent displacements and a heralded nondestructive readout. We investigate the commutative nature of modular variable observables by demonstrating no-signaling in time between successive measurements, using a variety of input states. Employing a different periodicity, we observe signaling in time. This also requires wave-packet overlap, resulting in quantum interference that we enhance using squeezed input states. The sequential measurements allow us to extract two-time correlators for modular variables, which we use to violate a Leggett-Garg inequality. Signaling in time and Leggett-Garg inequalities serve as efficient quantum witnesses, which we probe here with a mechanical oscillator, a system that has a natural crossover from the quantum to the classical regime.

What Information Is Necessary for Speech Categorization? Harnessing Variability in the Speech Signal by Integrating Cues Computed Relative to Expectations

ERIC Educational Resources Information Center

McMurray, Bob; Jongman, Allard

2011-01-01

Most theories of categorization emphasize how continuous perceptual information is mapped to categories. However, equally important are the informational assumptions of a model, the type of information subserving this mapping. This is crucial in speech perception where the signal is variable and context dependent. This study assessed the…

The influence of the pressure force control signal on selected parameters of the vehicle continuously variable transmission

NASA Astrophysics Data System (ADS)

Bieniek, A.; Graba, M.; Prażnowski, K.

2016-09-01

The paper presents results of research on the effect of frequency control signal on the course selected operating parameters of the continuously variable transmission CVT. The study used a gear Fuji Hyper M6 with electro-hydraulic control system and proprietary software for control and data acquisition developed in LabView environment.

Computational motor control: feedback and accuracy.

PubMed

Guigon, Emmanuel; Baraduc, Pierre; Desmurget, Michel

2008-02-01

Speed/accuracy trade-off is a ubiquitous phenomenon in motor behaviour, which has been ascribed to the presence of signal-dependent noise (SDN) in motor commands. Although this explanation can provide a quantitative account of many aspects of motor variability, including Fitts' law, the fact that this law is frequently violated, e.g. during the acquisition of new motor skills, remains unexplained. Here, we describe a principled approach to the influence of noise on motor behaviour, in which motor variability results from the interplay between sensory and motor execution noises in an optimal feedback-controlled system. In this framework, we first show that Fitts' law arises due to signal-dependent motor noise (SDN(m)) when sensory (proprioceptive) noise is low, e.g. under visual feedback. Then we show that the terminal variability of non-visually guided movement can be explained by the presence of signal-dependent proprioceptive noise. Finally, we show that movement accuracy can be controlled by opposite changes in signal-dependent sensory (SDN(s)) and SDN(m), a phenomenon that could be ascribed to muscular co-contraction. As the model also explains kinematics, kinetics, muscular and neural characteristics of reaching movements, it provides a unified framework to address motor variability.

Diversity in GABAergic signaling.

PubMed

Vogt, Kaspar

2015-01-01

GABA(A) receptor-mediated synaptic transmission is responsible for inhibitory control of neural function in the brain. Recent progress has shown that GABA(A) receptors also provide a wide range of additional functions beyond simple inhibition. This diversity of functions is mediated by a large variety of different interneuron classes acting on a diverse population of receptor subtypes. Here, I will focus on an additional source of GABAergic signaling diversity, caused by the highly variable ion signaling mechanism of GABA(A) receptors. In concert with the other two sources of GABAergic heterogeneity, this variability in signaling allows for a wide array of GABAergic effects that are crucial for the development of the brain and its function. © 2015 Elsevier Inc. All rights reserved.

System properties, feedback control and effector coordination of human temperature regulation.

PubMed

Werner, Jürgen

2010-05-01

The aim of human temperature regulation is to protect body processes by establishing a relative constancy of deep body temperature (regulated variable), in spite of external and internal influences on it. This is basically achieved by a distributed multi-sensor, multi-processor, multi-effector proportional feedback control system. The paper explains why proportional control implies inherent deviations of the regulated variable from the value in the thermoneutral zone. The concept of feedback of the thermal state of the body, conveniently represented by a high-weighted core temperature (T (c)) and low-weighted peripheral temperatures (T (s)) is equivalent to the control concept of "auxiliary feedback control", using a main (regulated) variable (T (c)), supported by an auxiliary variable (T (s)). This concept implies neither regulation of T (s) nor feedforward control. Steady-states result in the closed control-loop, when the open-loop properties of the (heat transfer) process are compatible with those of the thermoregulatory processors. They are called operating points or balance points and are achieved due to the inherent property of dynamical stability of the thermoregulatory feedback loop. No set-point and no comparison of signals (e.g. actual-set value) are necessary. Metabolic heat production and sweat production, though receiving the same information about the thermal state of the body, are independent effectors with different thresholds and gains. Coordination between one of these effectors and the vasomotor effector is achieved by the fact that changes in the (heat transfer) process evoked by vasomotor control are taken into account by the metabolic/sweat processor.

The theory and method of variable frequency directional seismic wave under the complex geologic conditions

NASA Astrophysics Data System (ADS)

Jiang, T.; Yue, Y.

2017-12-01

It is well known that the mono-frequency directional seismic wave technology can concentrate seismic waves into a beam. However, little work on the method and effect of variable frequency directional seismic wave under complex geological conditions have been done .We studied the variable frequency directional wave theory in several aspects. Firstly, we studied the relation between directional parameters and the direction of the main beam. Secondly, we analyzed the parameters that affect the beam width of main beam significantly, such as spacing of vibrator, wavelet dominant frequency, and number of vibrator. In addition, we will study different characteristics of variable frequency directional seismic wave in typical velocity models. In order to examine the propagation characteristics of directional seismic wave, we designed appropriate parameters according to the character of direction parameters, which is capable to enhance the energy of the main beam direction. Further study on directional seismic wave was discussed in the viewpoint of power spectral. The results indicate that the energy intensity of main beam direction increased 2 to 6 times for a multi-ore body velocity model. It showed us that the variable frequency directional seismic technology provided an effective way to strengthen the target signals under complex geological conditions. For concave interface model, we introduced complicated directional seismic technology which supports multiple main beams to obtain high quality data. Finally, we applied the 9-element variable frequency directional seismic wave technology to process the raw data acquired in a oil-shale exploration area. The results show that the depth of exploration increased 4 times with directional seismic wave method. Based on the above analysis, we draw the conclusion that the variable frequency directional seismic wave technology can improve the target signals of different geologic conditions and increase exploration depth with little cost. Due to inconvenience of hydraulic vibrators in complicated surface area, we suggest that the combination of high frequency portable vibrator and variable frequency directional seismic wave method is an alternative technology to increase depth of exploration or prospecting.

Signal detection with criterion noise: applications to recognition memory.

PubMed

Benjamin, Aaron S; Diaz, Michael; Wee, Serena

2009-01-01

A tacit but fundamental assumption of the theory of signal detection is that criterion placement is a noise-free process. This article challenges that assumption on theoretical and empirical grounds and presents the noisy decision theory of signal detection (ND-TSD). Generalized equations for the isosensitivity function and for measures of discrimination incorporating criterion variability are derived, and the model's relationship with extant models of decision making in discrimination tasks is examined. An experiment evaluating recognition memory for ensembles of word stimuli revealed that criterion noise is not trivial in magnitude and contributes substantially to variance in the slope of the isosensitivity function. The authors discuss how ND-TSD can help explain a number of current and historical puzzles in recognition memory, including the inconsistent relationship between manipulations of learning and the isosensitivity function's slope, the lack of invariance of the slope with manipulations of bias or payoffs, the effects of aging on the decision-making process in recognition, and the nature of responding in remember-know decision tasks. ND-TSD poses novel, theoretically meaningful constraints on theories of recognition and decision making more generally, and provides a mechanism for rapprochement between theories of decision making that employ deterministic response rules and those that postulate probabilistic response rules.

Analysis of cardiovascular regulation.

PubMed

Wilhelm, F H; Grossman, P; Roth, W T

1999-01-01

Adequate characterization of hemodynamic and autonomic responses to physical and mental stress can elucidate underlying mechanisms of cardiovascular disease or anxiety disorders. We developed a physiological signal processing system for analysis of continuously recorded ECG, arterial blood pressure (BP), and respiratory signals using the programming language Matlab. Data collection devices are a 16-channel digital, physiological recorder (Vitaport), a finger arterial pressure transducer (Finapres), and a respiratory inductance plethysmograph (Respitrace). Besides the conventional analysis of the physiological channels, power spectral density and transfer functions of respiration, heart rate, and blood pressure variability are used to characterize respiratory sinus arrhythmia (RSA), 0.10-Hz BP oscillatory activity (Mayer-waves), and baroreflex sensitivity. The arterial pressure transducer waveforms permit noninvasive estimation of stroke volume, cardiac output, and systemic vascular resistance. Time trends in spectral composition of indices are assessed using complex demodulation. Transient dynamic changes of cardiovascular parameters at the onset of stress and recovery periods are quantified using a regression breakpoint model that optimizes piecewise linear curve fitting. Approximate entropy (ApEn) is computed to quantify the degree of chaos in heartbeat dynamics. Using our signal processing system we found distinct response patterns in subgroups of patients with coronary artery disease or anxiety disorders, which were related to specific pharmacological and behavioral factors.