Amplitude Histograms Research Articles

Characterization of Rembrandt self-portraits through digital-chiaroscuro statistics

Abstract In order to develop an analytical tool to aid connoisseurs in authentication of paintings we introduce a digital computational technique based on luminosity histograms of high-resolution digitized images of paintings. This exploratory approach emerged from the widely-held hypothesis that every individual artist (master, student, copier, or forger) will tend to have a personal hand/eye spatial “signature” in shading and contrast (e.g., chiaroscuro and sfumato) that may be revealed and quantified by means of histogram statistics. A preliminary database library is built of amplitude histogram data of the digital images of authenticated works by the Dutch painter Rembrandt van Rijn against which copies and forgeries then are compared. It illuminates the individual chiaroscuro or spatial-blending characteristics of the master-created paintings to be analyzed. We show preliminary results from our analyses of 36 self-portraits that at various times have been attributed to Rembrandt and were chosen by us as a consequence of the prodigious historical, scientific, and stylistic scrutiny that have focused on these works in recent decades. Our statistical results demonstrate probative conformity for paintings that have been established as authentic self-portraits by Rembrandt and reveal statistical differences when compared to copies and forgeries as well as paintings of the Rembrandt workshop.

Renormalisation of global mantle dynamic topography predictions using residual topography measurements for “normal” oceanic crust

We compare mantle dynamic topography predicted from mantle convection modelling with residual topography measurements for oceanic regions, where crustal basement thickness is 10.0 km or less. Measurements of residual topography, calculated by removing the isostatic effects of crustal thickness variation, bathymetry, sediments, ice and lithosphere thermal anomalies, from the observed topography, are inaccurate for continents and oceanic plateaus due to uncertainties in determining their crustal thickness and density. As a consequence, residual topography measurements for these regions are unsuitable for testing mantle dynamic topography predictions. Residual topography is more accurately determined for oceanic crust. We use global mapping of crustal basement thickness using gravity anomaly inversion to identify oceanic crust of 10.0 km thickness or less to select measured residual topography for comparison with predicted mantle dynamic topography. For these oceanic regions we compare mantle dynamic topography and residual topography and, using amplitude histogram matching and grid searches, compute the amplitude rescaling and shift which needs to be applied to predicted mantle dynamic topography to fit the observed residual topography. We examine three global compilations which use different approaches to determine mantle dynamic topography: (i) Steinberger (2007), which uses seismic topography deeper than 220 km to determine mantle density; (ii) Flament et al. (2013), which uses plate velocity and subduction history; and (iii) Steinberger et al. (2017), which uses seismic tomography, including that above 220 km, to determine shallow upper mantle densities. Our analysis shows that for the Steinberger (2007) and Flament et al. (2013) compilations, the predicted mantle dynamic topography for oceanic regions requires a rescaling of approximately ×0.5 and a negative shift of approximately −500 m to match the observed residual topography. In contrast Steinberger et al. (2017), which includes shallow upper mantle densities above 220 km, requires only a small shift (+50 m) but a greater scaling of ×0.375. Maps of renormalised (rescaled and shifted) mantle dynamic topography for Steinberger et al. (2017) show a close resemblance to measured residual topography.

Patch clamp data driven stochastic modeling and simulation of hTREK1 potassium ion channel gating

Chemical reactions involving small number of molecules are noisy and are simulated using stochastic simulation algorithms, which are valid when the reaction environments are well mixed. However, biological media are crowded reaction environments where alternative simulation methods have to be used. Single molecule experiments have revealed non-markovian nature of biological reactions due to a phenomenon called dynamic disorder which makes the rate constants random. This happens when there are additional slow scale conformational transitions, giving the molecule a memory of its previous states. Traditionally, ion channel gating is modeled as Markovian transitions between fixed states. hTREK1 channel, a two pore domain potassium channel, has been shown to have long term memory in its kinetics under the influence of trichloroethanol, an anaesthetic. Here we show that lactate, a metabolite that increases in concentration during ischemia, is also able to induce memory in the channel. We have provided a simple diffusion model for its gating that assumes ion channel diffusion through a continuum of states on its potential energy landscape, derived from the steady state probability distribution of ionic current recorded from patch clamp experiments. A stochastic differential equation (SDE) driven by Gaussian white noise is proposed to model this motion in an asymmetric double well potential which can reproduce the amplitude histogram very well. Such cases where ligands like lactate are added, the SDE is modified by introducing an auxiliary variable to run on coloured noise such that increasing the noise correlation with ligand concentration improves the fits to experimental data. These methods unlike the Markovian models are true to the physical picture of gating and are able to efficiently reproduce the whole raw data trace over a few seconds’ interval.

Quantitative Contact-Less Estimation of Energy Expenditure from Video and 3D Imagery.

Measurement of energy expenditure is an important tool in sport science and medicine, especially when trying to estimate the extent and intensity of physical activity. However, most approaches still rely on sensors or markers, placed directly on the body. In this paper, we present a novel approach using a fully contact-less, fully automatic method, that relies on computer vision algorithms and widely available and inexpensive imaging sensors. We rely on the estimation of the optical and scene flow to calculate Histograms of Oriented Optical Flow (HOOF) descriptors, which we subsequently augment with the Histograms of Absolute Flow Amplitude (HAFA). Descriptors are fed into regression model, which allows us to estimate energy consumption, and to a lesser extent, the heart rate. Our method has been tested both in lab environment and in realistic conditions of a sport match. Results confirm that these energy expenditures could be derived from purely contact-less observations. The proposed method can be used with different modalities, including near infrared imagery, which extends its future potential.

A novel modulation format identification based on amplitude histogram space

In this paper, we proposed a novel modulation format identification method for square M-quadrature amplitude modulation (M-QAM) signals which is based on amplitude histogram space of the incoming data after analog-to-digital conversion, chromatic dispersion compensation at the receiver. We demonstrated the identification of quadrature phase-shift keying (QPSK), 16-QAM, 64-QAM formats with an amplitude histogram space. Simulation results show that it achieve 100% identification accuracy when the incoming signal OSNR is 14 dB to identify the modulation format of QPSK, 16-QAM, and 64-QAM signals in digital coherent systems. The method has low complexity and small delay.

Stokes Space Modulation Format Identification for Optical Signals Using Probabilistic Neural Network

A Stokes space modulation format identification (MFI) method using probabilistic neural network (PNN) is proposed for coherent optical receivers. According to amplitude histograms obtained by the distribution of Stokes vectors on the s <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> axis, the incoming signals are first classified into PDM-mPSK, PDM-16QAM, and PDM-64 QAM signals based on PNN. To further identify PDM-mPSK signals, the constellation feature of Stokes vectors on s <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -s <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> plane is extracted by image processing techniques and then processed by PNN. The high identification accuracy is demonstrated via numerical simulations with 28-Gbaud PDM-QPSK, PDM-8PSK, PDM-16QAM, and PDM-64QAM signals over a wide optical signal-to-noise ratio range. Owing to the characteristic of PNN, the proposed MFI method has simple training process, small training data size and a small number of required symbols. Proof-of-concept experiments have been implemented to verify the effectiveness of the proposed MFI method among 28-Gbaud PDM-QPSK, PDM-8PSK, and PDM-16QAM signals.

Improving bedload transport determination by grain-size fraction using the Swiss plate geophone recordings at the Erlenbach stream

Direct bedload samples were taken with a large metal basket at the steep Erlenbach stream in Switzerland. These measurements were compared with the signal of the Swiss impact plate geophone system to derive information about bedload transport. The so-called amplitude histogram (AH) method was developed in an earlier study to estimate the bedload flux for different grain-size classes at the Erlenbach. A new analysis of a larger set of measurements was made here to improve the performance of the AH method. The approach relies on an identification of the transported grain sizes through their dependency on the signal amplitude. As a new element we introduce here the impulse rate, which is found to affect the number of impulses recorded for each amplitude class. As compared to the original method, the new version of the AH method shows a slightly improved performance for total calculated bedload mass, and results in a clearly better agreement between calculated and measured characteristic grain sizes.

Experimental Demonstration of Simultaneous Modulation Format/Symbol Rate Identification and Optical Performance Monitoring for Coherent Optical Systems

Transmission systems that use advanced complex modulation schemes and digital coherent receivers, have been driving the growth of optical communication networks for nearly a decade. In this fact, new strategies for network diagnosis and management are developed. In this paper, we propose and experimentally demonstrate a cost-effective technique that not only exhibits optical performance monitoring functionalities but also enables simultaneous symbol rate and modulation format identification. It is implemented for both dual-polarized quadrature phase shift keying and 16-quadrature amplitude modulation at various symbol rates. Our approach is based on artificial neural networks (ANNs) in conjunction with asynchronous amplitude histogram (AAH) evaluation. Our ANN-based pattern recognition algorithm, with all of the features of AAHs, facilitates both symbol rate and modulation format classification at high data rates. Experimental investigation demonstrates accurate classification of advanced modulation formats, in the presence of various link impairments. The latter include chromatic dispersion (CD) varying from 200 to 1600 ps/nm, differential group delay between 10 and 70 ps, phase noise varying from 10 kHz to 10 MHz and amplified spontaneous emission noise. The results of the aforementioned method demonstrate multiimpairment monitoring over wide monitoring ranges. At high symbol rates, after a precise extraction of monitoring parameters (i.e., $F_{\text{CD}}$ and $F_{\text{OSNR}}$ ), our approach achieved a minimum dispersion parameter close to 0.2 dB and a maximum performance parameter of 6 dB. Our technique is robust and transparent for both the symbol rates and the modulation formats.

Time color map and histogram of electromyography (EMG) sample amplitudes: possible tools for global electromyogram analysis by images

To present a method for surface electromyogram (EMG) analysis by images. 12 healthy sedentary males were studied. Surface EMG (from lateral deltoid) was recorded (1000 samples/s) and analysed during self-selected angular speed shoulder abduction/adduction actions, made at 40% of 1 revolution maximum, up to exhaustion. EMG was rectified and normalized to its maximum (100%). Histogram of sample amplitudes: the 0–100% amplitude range was divided in 20 levels (step 5%). Each sample was assigned to its bin for the histogram generation 2D time color map: the amplitude range was divided in five colored levels: blue (0–20%), green (21–40%), yellow (41–60%), orange (61–80%), red (81–100%). Along the time bar each ms was colored according to the sample relative amplitude. The whole colored bar was divided into 100 ms slices. These short color bars were graphically stacked one on top of each other and interpolated on the x/y plane using a standard contour plotting procedure. The spike shape analysis (SSA) was performed and the average rectified value (ARV) was calculated. From the first to the last action the histogram median amplitude increased from 34.33 ± 12.36 to 46.74 ± 14.04%. On the 2D map the areas of activity hues enlarged in both directions showing more regular recurrence of longer electrical events. As expected, the ARV increased and the SSA reflected the spikes changes at fatigue. Histogram of the EMG sample amplitudes and 2D time color map provide data, at a glance, about the regularity of the EMG spikes occurrence and their amplitude distribution. This is a different, adjunct information with respect to the one provided by traditional EMG time domain analysis measurements.

Nonlinear gray-level co-occurrence matrix texture analysis for improved seismic facies interpretation

Seismic texture analysis is a useful tool for delineating subsurface geologic features from 3D seismic surveys, and the gray-level co-occurrence matrix (GLCM) method has been popularly applied for seismic texture discrimination since its first introduction in the 1990s. The GLCM texture analysis consists of two components: (1) to rescale seismic amplitude by a user-defined number of gray levels and (2) to perform statistical analysis on the spatial arrangement of gray levels within an analysis window. Traditionally, the linear transformation is simply used for amplitude rescaling so that the original reflection patterns could be best preserved. However, the seismic features of interpretational interest often cover only a small portion of its amplitude histogram. For representing such features more effectively, it is helpful to perform a nonlinear rescaling of the amplitude distribution between different seismic features. To achieve such an objective, this study proposes a nonlinear GLCM analysis based on four types of nonlinear gray-level transformation (logarithmic, exponential, sigmoid, and logit) and investigates their implications for seismic facies interpretation. Applications to the 3D seismic data set from offshore Angola (West Africa) demonstrate the added values of the generated nonlinear GLCM attributes in better characterizing the channels, fans, and lobes in a deep-marine turbidite system.

Joint OSNR monitoring and modulation format identification in digital coherent receivers using deep neural networks.

We experimentally demonstrate the use of deep neural networks (DNNs) in combination with signals' amplitude histograms (AHs) for simultaneous optical signal-to-noise ratio (OSNR) monitoring and modulation format identification (MFI) in digital coherent receivers. The proposed technique automatically extracts OSNR and modulation format dependent features of AHs, obtained after constant modulus algorithm (CMA) equalization, and exploits them for the joint estimation of these parameters. Experimental results for 112 Gbps polarization-multiplexed (PM) quadrature phase-shift keying (QPSK), 112 Gbps PM 16 quadrature amplitude modulation (16-QAM), and 240 Gbps PM 64-QAM signals demonstrate OSNR monitoring with mean estimation errors of 1.2 dB, 0.4 dB, and 1 dB, respectively. Similarly, the results for MFI show 100% identification accuracy for all three modulation formats. The proposed technique applies deep machine learning algorithms inside standard digital coherent receiver and does not require any additional hardware. Therefore, it is attractive for cost-effective multi-parameter estimation in next-generation elastic optical networks (EONs).

Influences of signal processing methods on photon number resolving capability of multi-pixel photon counter

Influences of signal processing methods on photon number resolving capability of multi-pixel photon counter (MPPC) were studied in this work. Results show that the photon number resolving (PNR) capability of MPPC can be greatly improved by waveform integration of the avalanche pulses of MPPC, relative to the histograms of the output pulse amplitudes. Up to 47 photon-equivalent peaks can be distinguished in the PNR spectrum with pulsed light repetition frequency of 80[Formula: see text]MHz and 5[Formula: see text]ns time gate. The analog to digital converter (ADC) in oscilloscope with more bit resolution may be beneficial for the PNR of MPPC.

Modulation Format Identification in Coherent Receivers Using Deep Machine Learning

We propose a novel technique for modulation format identification (MFI) in digital coherent receivers by applying deep neural network (DNN) based pattern recognition on signals’ amplitude histograms obtained after constant modulus algorithm (CMA) equalization. Experimental results for three commonly-used modulation formats demonstrate MFI with an accuracy of 100% over a wide optical signal-to-noise ratio (OSNR) range. The effects of fiber nonlinearity on the performance of MFI technique are also investigated. The proposed technique is non-data-aided (NDA) and avoids any additional hardware on top of standard digital coherent receiver. Therefore, it is ideal for simple and cost-effective MFI in future heterogeneous optical networks.

Calibration and Gray-level Image Generation for the SR4500 ToF Camera

This paper proposes a novel distance calibration and gray-level image generation method for the SR4500 Time of Flight (ToF) camera. For the distance measurement, the Gaussian Process Regression (GPR) model is proposed to calibrate the range error. With this model, fewer parameters need to be estimated and the model complexity is reduced. In order to eliminate the error data caused by the object edge, the distance threshold which determined by the range data of the current pixel is selected to remove the mixed pixel. To acquire gray-level images from the amplitude data, the amplitude histogram is used to select the amplitude threshold of the reflected light, and grayscale images can be generated through normalizing the amplitude value with this threshold. The experiment results illustrate the feasibility and effectiveness of the proposed algorithms.

Nanoscale measurements of proton tracks using fluorescent nuclear track detectors.

The authors describe a method in which fluorescence nuclear track detectors (FNTDs), novel track detectors with nanoscale spatial resolution, are used to determine the linear energy transfer (LET) of individual proton tracks from proton therapy beams by allowing visualization and 3D reconstruction of such tracks. FNTDs were exposed to proton therapy beams with nominal energies ranging from 100 to 250 MeV. Proton track images were then recorded by confocal microscopy of the FNTDs. Proton tracks in the FNTD images were fit by using a Gaussian function to extract fluorescence amplitudes. Histograms of fluorescence amplitudes were then compared with LET spectra. The authors successfully used FNTDs to register individual proton tracks from high-energy proton therapy beams, allowing reconstruction of 3D images of proton tracks along with delta rays. The track amplitudes from FNTDs could be used to parameterize LET spectra, allowing the LET of individual proton tracks from therapeutic proton beams to be determined. FNTDs can be used to directly visualize proton tracks and their delta rays at the nanoscale level. Because the track intensities in the FNTDs correlate with LET, they could be used further to measure LET of individual proton tracks. This method may be useful for measuring nanoscale radiation quantities and for measuring the LET of individual proton tracks in radiation biology experiments.

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Modulation format identification in heterogeneous fiber-optic networks using artificial neural networks and genetic algorithms

The use of genetic algorithm (GA) to simplify the structures of artificial neural network-based modulation format identification is proposed in next-generation dynamic and heterogeneous fiber-optic networks. Simulation results show that with 80 asynchronous amplitude histogram bins, by virtue of GA, the identification error rate decreases from 4.24 to 1.04 %.

Acoustic emission signals resulting from the drying-induced fractures of Phyllostachys pubescens bamboo: evidence of scale free phenomena

Acoustic Emission (AE) is a noninvasive tool for evaluation of damage and fracture. In this paper, results of AE study of the drying process of Phyllostachys pubescens bamboo are reported. The histogram of experimental squared voltage distributions follows a power law with an exponent of $$-1.16$$ . The histogram of maximum amplitudes $$V_\mathrm{max}$$ usually studied in the AE literature also shows a power law with a slope of $$-2.4$$ (b-value of 2.4). This is reminiscent of scale free phenomena. Another novel aspect for bamboo literature is the calculation of the average signal shape for different time spans of the system. The shape has an asymmetrical shape for both long- and short-span signals, but shows a symmetrical shape for middle-span signals. This is an indication of a reinforcing feedback process, known in the statistical mechanics of fracture as an avalanche.

Measuring Bed Load Transport Rates by Grain-Size Fraction Using the Swiss Plate Geophone Signal at the Erlenbach

AbstractThe Swiss plate geophone system is a bed load surrogate monitoring technique that is used to quantify bed load transport in steep streams. The amplitude of the signal recorded by the Swiss plate geophone contains information about the grain-size distribution of the transported bed load. To extract this information, the authors computed the number of impulses and packets (representing a single particle impact) registered for different amplitude ranges, the so-called amplitude histograms, for 46 samples when bed load was measured independently with automatic basket samplers at the Erlenbach. Amplitude histograms can be interpreted as a statistical distribution of the signal’s amplitude over a given time interval. Using the number of bed load particles per unit mass, absolute bed load masses for each grain-size class can be calculated. The results show that the grain-size distribution of the transported bed load for particles larger than 9.5 mm can be continuously monitored at the Erlenbach with the ...

Enhanced Ca(2+)-induced Ca(2+) release from intracellular stores contributes to catecholamine hypersecretion in adrenal chromaffin cells from spontaneously hypertensive rats.

Adrenal chromaffin cells (CCs) from spontaneously hypertensive rats (SHRs) secrete more catecholamine (CA) upon stimulation than CCs from normotensive Wistar Kyoto rats (WKY). Unitary CA exocytosis events, both spontaneous and stimulated, were amperometrically recorded from cultured WKY and SHR CCs. Both strains display spontaneous amperometric spikes but SHR CCs produce more spikes and of higher mean amplitude. After a brief stimulation with high K(+) or caffeine which produces voltage-gated Ca(2+) influx or intracellular Ca(2+) release, respectively, more spikes and of greater mean amplitude and unitary charge were recorded in SHR CCs. Consequently, peak cumulative charge was ~2-fold higher in SHR CCs. Ryanodine (10μM), a specific blocker of the ryanodine receptors reduced depolarization-induced peak cumulative charge by ~10% in WKY and ~77% in SHR CCs, suggesting, a larger contribution of Ca(2+)-induced Ca(2+) release to CA exocytosis in SHR CCs. Accordingly, Ca(2+) imaging showed larger [Ca(2+)]i signals induced both by depolarization and caffeine in SHR CCs. Distribution amplitude histograms showed that small amperometric spikes (0-50pA) are more frequent in WKY than in SHR CCs. Conversely, medium (50-190pA) and large (190-290pA) spikes are more numerous in SHR than in WKY CCs. This study reveals that the enhanced CA secretion in SHR CCs results from a combination of (1) larger depolarization-induced Ca(2+) transients, due to a greater Ca(2+)-induced intracellular Ca(2+) release, (2) more exocytosis events per time unit, and (3) a greater proportion of medium and large amperometric spikes probably due to a higher mean CA content per granule. Enhanced CA release by excessive amplification by Ca(2+) induced Ca(2+) release and larger granule catecholamine content contributes to the increased CA plasma levels and vasomotor tone in SHRs.