Deviation Of Noise Research Articles

Image tamper detection based on noise estimation and lacunarity texture

Aiming at the problem of image tampering, a novel detection method is proposed based on the image noise and lacunarity. As there exist differences in image sensor pattern noise and image lacunarity between real image and tampered image, standard deviation of noise, relative frequency lacunarity (RFL), relative frequency mean (RFM) and relative frequency variance (RFV) are extracted from the suspected image to construct feature space. By using LIBSVM classifier, the image is detected if it is tampered or not. Experimental results and analysis show that it can effectively be used for the detection of real image and tampered image, natural image and computer generated graphics. Furthermore, it can be implemented for the detection of artificial blurring in the image with high precision.

Fusion of Quantized and Unquantized Sensor Data for Estimation

This letter investigates the usefulness of quantized data for estimation problems in which unquantized data is already available. A worst case scenario is considered in which a fusion center has access to continuous and binary-valued measurements of the same uniformly distributed parameter observed in Gaussian noise. The difference in mean squared error between a minimum mean squared error estimate using unquantized data and a minimum mean squared error estimate using both quantized and unquantized data is used to quantify the value of fusing the two kinds of data. Discussion of the Cramer-Rao Bound predicts how noise in the quantized data affects the reduction in estimate mean squared error from fusing the data types. It is then determined that the maximum reduction in estimate mean squared error from fusion can be approximated as a rational function of the ratio of the standard deviations of the measurement noise in the two data types. Finally, similarities between the approximation to the reduction in estimate mean squared error for the most favorable uniform prior width and a closed form expression based on the Cramer-Rao Bound are discussed.

Automated and optimal detection of 3D articular cartilage using undecimated wavelets in MRI

Articular cartilage is a thin and curvilinear structure whose image may get corrupted with noise during acquisition process. Image segmentation of this structure depends on expertise of an operator. In this paper, we propose an automated cartilage detection technique using 3D histogram and wavelet multiscale singularity analysis. 3D undecimated wavelet transform is implemented on MRI volume to obtain wavelet coefficients, which are used to determine an adaptive threshold for a given local resolution and a global threshold value. Local threshold helps to segment foreground cartilage edge details and is obtained using maximum likelihood estimate of the 3D histogram for a selected confidence level of intensity histogram. A global threshold is used to optimize coefficients using wavelet multiresolution singularity. The final wavelet coefficients are used to obtain a 3D model of cartilage tissue. The proposed method has been tested and validated using MRI and phantom datasets of articular cartilage. Quantitative analysis has been performed using mean square error (MSE), signal-to-noise ratio (SNR) and volumetric estimation of the datasets for different confidence and noise levels. The proposed method displays reduction in MSE for both denoised and noisy MRI volumes at different standard deviations of noise with overall improvement in SNR.

Preliminary clinical application of an adaptive iterative statistical reconstruction algorithm inhead and neck computed tomography angiography with low tube voltage and a low concentration of contrast medium

Abstract Objective To evaluate the feasibility of using a low concentration of contrast medium (Visipaque 270 mgI/mL), low tube voltage, and an advanced image reconstruction algorithm in head and neck computed tomography angiography (CTA). Methods Forty patients (22 men and 18 women; average age 48.7 ± 14.25 years; average body mass index 23.9 ± 3.7 kg/m2) undergoing CTA for suspected vascular diseases were randomly assigned into two groups. Group A (n = 20) was administered 370 mgI/mL contrast medium, and group B (n = 20) was administered 270 mgI/mL contrast medium. Both groups were administered at a rate of 4.8 mL/s and an injection volume of 0.8 mL/kg. Images of group A were obtained with 120 kVp and filtered back projection (FBP) reconstruction, whereas images of group B were obtained with 80 kVp and 80% adaptive iterative statistical reconstruction algorithm (ASiR). The CT values and standard deviations of intracranial arteries and image noise on the corona radiata were measured to calculate the contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR). The beam-hardening artifacts (BHAs) around the skull base were calculated. Two readers evaluated the image quality with volume rendered images using scores from 1 to 5. The values between the two groups were statistically compared. Results The mean CT value of the intracranial arteries in group B was significantly higher than that in group A (P < 0.001). The CNR and SNR values in group B were also statistically higher than those in group A (P < 0.001). Image noise and BHAs were not significantly different between the two groups. The image quality score of VR images of in group B was significantly higher than that in group A (P = 0.001). However, the quality scores of axial enhancement images in group B became significantly smaller than those in group A (P< 0.001). The CT dose index volume and dose-length product were decreased by 63.8% and 64%, respectively, in group B (P < 0.001 for both). Conclusion Visipaque combined with 80 kVp and 80% ASiR provided similar image quality in intracranial CTA with 64% radiation dose reduction compared with the use of Iopamidol, 120 kVp, and FBP reconstruction.

Photoacoustic tomography based on the Green's function retrieval with ultrasound interferometry for sample partially behind an acoustically scattering layer

Acoustically inhomogeneous mediums with multiple scattering are often the nightmare of photoacoustic tomography. In order to break this limitation, a photoacoustic tomography scheme combining ultrasound interferometry and time reversal is proposed to achieve images in acoustically scattering medium. An ultrasound interferometry is developed to determine the unknown Green's function of strong scattering tissue. Using the determined Greens' function, a time-reversal process is carried out to restore images behind an acoustically inhomogeneous layer from the scattering photoacoustic signals. This method effectively decreases the false contrast, noise, and position deviation of images induced by the multiple scattering. Phantom experiment is carried out to validate the method. Therefore, the proposed method could have potential value in extending the biomedical applications of photoacoustic tomography in acoustically inhomogeneous tissue.

Performance analysis of a digital capacitance measuring circuit.

This paper presents the design and study of a digital capacitance measuring circuit with theoretical analysis, numerical simulation, and experimental evaluation. The static and dynamic performances of the capacitance measuring circuit are first defined, including signal-to-noise ratio (SNR), standard deviation, accuracy, linearity, sensitivity, and response time, within a given measurement range. Then numerical simulation is carried out to analyze the SNR and standard deviation of the circuit, followed by experiments to validate the overall performance of the circuit. The simulation results show that when the standard deviation of noise is 0.08 mV and the measured capacitance decreases from 6 pF to 3 fF, the SNR decreases from 90 dB to 22 dB and the standard deviation is between 0.17 fF and 0.24 fF. The experimental results show that when the measured capacitance decreases from 6 pF to 40 fF and the data sampled in a single period are used for demodulation, the SNR decreases from 88 dB to 40 dB and the standard deviation is between 0.18 fF and 0.25 fF. The maximum absolute error and relative error are 5.12 fF and 1.26%, respectively. The SNR and standard deviation can be further improved if the data sampled in more than one period are used for demodulation by the circuit.

A piezoelectric energy harvester based on internal resonance

A vibration-based energy harvester is essentially a resonator working in a limited frequency range. To increase the working frequency range is a challenging problem. This paper reveals a novel possibility for enhancing energy harvesting via internal resonance. An internal resonance energy harvester is proposed. The excitation is successively assumed as the Gaussian white noise, the colored noise defined by a second-order filter, the narrow-band noise, and exponentially correlated noise. The corresponding averaged root-mean-square output voltages are computed. Numerical results demonstrate that the internal resonance increases the operating bandwidth and the output voltage. Averaged root-mean-square voltage via standard deviation of white noise for five designs

The effect of surface roughness on corona-generated electromagnetic interference for long-term operating conductors

This paper presents a research of the surface roughness effects on corona-generated electromagnetic interference of long-term operating conductors. The detailed surface morphologies and roughness of typical long-term operating conductors were measured and analyzed. The radio noise measurement system based on the corona cage was discussed and the actual radio noise generated by the conductor in the measured cage was obtained, based on the parameters of the corona cage and the measured data. The radio noise tests were carried out under both dry and wet conditions. Under two types of conditions, the corona intensity was higher on the long-term operating conductors contrasted to the new conductors as a result of the anomalistic surfaces of the long-term operating conductors. The rate of decrease of corona inception voltages between the long-term operating conductors and new conductors was discovered to increase with the raise of surface roughness degree Ra in linear relationship. Approximate linear relation was also discovered between the averaged radio noise deviation and Ra. The rate of decrease of corona inception voltages under wet conditions was slightly higher (about 1%) than that under dry conditions. The difference between the radio noise of long-term operating conductors and that of new conductors was larger (1.3 dB) under wet conditions compared to dry conditions.

Critical and maximally informative encoding between neural populations in the retina

Computation in the brain involves multiple types of neurons, yet the organizing principles for how these neurons work together remain unclear. Information theory has offered explanations for how different types of neurons can maximize the transmitted information by encoding different stimulus features. However, recent experiments indicate that separate neuronal types exist that encode the same filtered version of the stimulus, but then the different cell types signal the presence of that stimulus feature with different thresholds. Here we show that the emergence of these neuronal types can be quantitatively described by the theory of transitions between different phases of matter. The two key parameters that control the separation of neurons into subclasses are the mean and standard deviation (SD) of noise affecting neural responses. The average noise across the neural population plays the role of temperature in the classic theory of phase transitions, whereas the SD is equivalent to pressure or magnetic field, in the case of liquid-gas and magnetic transitions, respectively. Our results account for properties of two recently discovered types of salamander Off retinal ganglion cells, as well as the absence of multiple types of On cells. We further show that, across visual stimulus contrasts, retinal circuits continued to operate near the critical point whose quantitative characteristics matched those expected near a liquid-gas critical point and described by the nearest-neighbor Ising model in three dimensions. By operating near a critical point, neural circuits can maximize information transmission in a given environment while retaining the ability to quickly adapt to a new environment.

Detection of myocardial edema with diffusion weighted imaging in patients with acute myocarditis

Methods We have analyzed 26 consecutive patients: 22 male, average age: 27 years (range 13-43) with clinical diagnosis of acute myocarditis. The CMR examinations were performed on a 1,5 T scanner using an eight-channel phasedarray coil combined with 4-6 elements of spinal coil. All patients underwent assessment of myocardial oedema: T2weighted triple inversion recovery (STIR), T1-weighted turbo spin echo pre and post contrast, function (cine Steady State Free Precession) and scar (Late Gadolinium Enhancement). Additionally DWI EPI sequence with b = 50 sec/mm was acquired before contrast administration. The sequence parameters were as follows: slice thickness 10 mm, repetition time (depending on patient breath cycle) 3-4 s, echo time 78 ms, bandwidth 1,736 Hz/Px. The DWI sequence was ECG-gated and synchronized to the respiratory cycle using PACE technique. For all patients T1 and T2 ratio were calculated and presence of LGE areas were reported. For STIR and DWI contrast between healthy myocardium and edema was calculated as a difference between edematous and normal myocardial muscle divided by standard deviation of image noise. Results We managed to acquired good quality DWI images in all 26 patients, average acquisition time was 120s per slice, distortion artifacts occurred in 5 patients but did not impaired diagnostic value of analyzed images. Increased signal intensity in DWI images occurred in all patients in the area of LGE enhancement and were consistent with areas of increased signal in STIR. All patients met at least two out of three criteria for inflammatory activity and injury. T2 ratio was increased (≥ 2) in 24 patients, T1 ratio (≥ 4) in 23 cases, all patients had focal non ischemic enhancement in LGE. CNR was higher in DWI than in STIR: 23,8 vs. 17,6 respectively.

Calibrate without Calibrating: An Iterative Approach in Participatory Sensing Network

With widespread usages of smart phones, participatory sensing becomes mainstream, especially for applications requiring pervasive deployments with massive sensors. However, the sensors on smart phones are prone to the unknown measurement errors, requiring automatic calibration among uncooperative participants. Current methods need either collaboration or explicit calibration process. However, due to the uncooperative and uncontrollable nature of the participants, these methods fail to calibrate sensor nodes effectively. We investigate sensor calibration in monitoring pollution sources, without explicit calibration process in uncooperative environment. We leverage the opportunity in sensing diversity, where a participant will sense multiple pollution sources when roaming in the area. Further, inspired by expectation maximization (EM) method, we propose a two-level iterative algorithm to estimate the source presences, source parameters and sensor noise iteratively. The key insight is that, only based on the participatory observations, we can “calibrate sensors without explicit or cooperative calibrating process”. Theoretical analysis proves that, our method can converge to the optimal estimation of sensor noise, where the likelihood of observations is maximized. Also, extensive simulations show that, ours improves the estimation accuracy of sensor bias up to 20 percent and that of sensor noise deviation up to 30 percent, compared with three baseline methods.

Segmentation of Bone with Region Based Active Contour Model in PD Weighted MR Images of Shoulder.

Proton density (PD) weighted MR images present inhomogeneity problem, low signal to noise ratio (SNR) and cannot define bone borders clearly. Segmentation of PD weighted images is hampered with these properties of PD weighted images which even limit the visual inspection. The purpose of this study is to determine the effectiveness of segmentation of humeral head from axial PD MR images with active contour without edge (ACWE) model. We included 219 images from our original data set. We extended the use of speckle reducing anisotropic diffusion (SRAD) in PD MR images by estimation of standard deviation of noise (SDN) from ROI. To overcome the problem of initialization of the initial contour of these region based methods, the location of the initial contour was automatically determined with use of circular Hough transform. For comparison, signed pressure force (SPF), fuzzy C-means, and Gaussian mixture models were applied and segmentation results of all four methods were also compared with the manual segmentation results of an expert. Experimental results on our own database show promising results. This is the first study in the literature to segment normal and pathological humeral heads from PD weighted MR images.

Performance investigation of the pulse and Campbelling modes of a fission chamber using a Poisson pulse train simulation code

The detectors of the neutron flux monitoring system of the foreseen French GEN-IV sodium-cooled fast reactor (SFR) will be high temperature fission chambers placed in the reactor vessel in the vicinity of the core. The operation of a fission chamber over a wide-range neutron flux will be feasible provided that the overlap of the applicability of its pulse and Campbelling operational modes is ensured. This paper addresses the question of the linearity of these two modes and it also presents our recent efforts to develop a specific code for the simulation of fission chamber pulse trains. Our developed simulation code is described and its overall verification is shown.An extensive quantitative investigation was performed to explore the applicability limits of these two standard modes. It was found that for short pulses the overlap between the pulse and Campbelling modes can be guaranteed if the standard deviation of the background noise is not higher than 5% of the pulse amplitude. It was also shown that the Campbelling mode is sensitive to parasitic noise, while the performance of the pulse mode is affected by the stochastic amplitude distributions.

No-reference image quality assessment based on noise, blurring and blocking effect

Since existing no-reference image quality assessment (IQA) algorithms are not consistent with subjective assessment, a novel no-reference image quality assessment method is proposed by introducing three types of image distortion, including noise, blur degree and blocking effects. Firstly, the standard deviation of image noise is estimated by modified wavelet medium estimation. Secondly, the blur degree of image is obtained by counting edge pixel points. Thirdly, blocking effect is represented by characteristics of image pixel blocks. Finally, the assessment model is established by combining these three distortion types. Combining the differential mean opinion scores (DMOS) provided in the LIVE IQA database, the weighting coefficients are obtained. The experimental results indicate that these evaluation values of this algorithm not only agree with PSNR in objective assessment, but also are consistent with the DMOS in subjective assessment.

Transcranial electrical brain stimulation modulates neuronal tuning curves in perception of numerosity and duration

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation method with many putative applications and reported to effectively modulate behaviour. However, its effects have yet to be considered at a computational level. To address this we modelled the tuning curves underlying the behavioural effects of stimulation in a perceptual task. Participants judged which of the two serially presented images contained more items (numerosity judgement task) or was presented longer (duration judgement task). During presentation of the second image their posterior parietal cortices (PPCs) were stimulated bilaterally with opposite polarities for 1.6s. We also examined the impact of three stimulation conditions on behaviour: anodal right-PPC and cathodal left-PPC (rA-lC), reverse order (lA-rC) and no-stimulation condition. Behavioural results showed that participants were more accurate in numerosity and duration judgement tasks when they were stimulated with lA-rC and rA-lC stimulation conditions respectively. Simultaneously, a decrease in performance on numerosity and duration judgement tasks was observed when the stimulation condition favoured the other task. Thus, our results revealed a double-dissociation of laterality and task. Importantly, we were able to model the effects of stimulation on behaviour. Our computational modelling showed that participants' superior performance was attributable to a narrower tuning curve — smaller standard deviation of detection noise. We believe that this approach may prove useful in understanding the impact of brain stimulation on other cognitive domains.

Advanced Method for Magnetic Microwires Noise Specification

The article points to the e ects of the ambient noise and its possible in uences on quanti cation of the inherent switching eld noise of magnetic microwires. Based on the observed e ects, an advanced methodology was developed to quantify the impact of the ambient noise. During measurements a multi-channel system with applied correlation methods was used. The proposed method determines the percentage contribution of the ambient noise deviation to the magnetic microwires switching eld noise.

Effect of measurement noise and excitation on Generalized Response Surface Model Updating

This paper investigates the robustness of a parameter estimation procedure for nonlinear Finite Element (FE) model updating. Through this procedure, polynomial Response Surface (RS) models are constructed to approximate the response of a nonlinear FE model at every time step of the analysis. Subsequently, the optimization problem of model updating is solved iteratively in time which results in histograms of the updating parameters. With the assumption of White Gaussian measurement noise, it is shown that this parameter estimation technique has low sensitivity to the standard deviation of the measurement noise. In order to validate this, a parametric sensitivity study is performed through numerical simulations of nonlinear systems with single and multiple degrees of freedom. The results show the least sensitivity to measurement noise level, selected time window for model updating, and location of the true model parameters in RS regression domain, when vibration frequency of the system is outside the frequency bandwidth of the load. Further application of this method is also presented through a case study of a steel frame with bilinear material model under seismic loading. The results indicate the robustness of this parameter estimation technique for different cases of input excitation, measurement noise level, and true model parameters.

Design of 10.2299MHz Frequency Source Based on AD9854

To measure frequency stability of 10.23MHz signal, a 10.2299MHz frequency source based on AD9854 was developed. The performance of frequency source was tested using a phase noise and Allan deviation test set 5125A. According to the experiment results, the following conclusion can be drawn that the Allan deviation of the 10.2299MHz frequency signal is about 2.6E-14 at 1s when the 10MHz reference signal was generated by a frequency and phase offset generator HROG-10. The 10.2299MHz frequency source can be used as a common source in dual mixer time difference and as a reference signal in beat frequency method when measuring the frequency stability of 10.23MHz signal.

Non-enhanced T1-weighted liver vessel imaging at 7 Tesla.

ObjectivesAim of the study was to assess the feasibility and to compare three non-enhanced T1-weighted (w) sequences for liver vessel imaging at 7 Tesla (T).Material and Methods12 healthy volunteers were examined on a 7 T whole-body MR-system. The following non-enhanced sequences were acquired: T1w 2D FLASH, T1w 3D FLASH and Time of flight (TOF)-MRA. Qualitative image analysis was performed by two radiologists including over all image quality as well as vessel delineation of the liver arteries, liver veins and portal vein and the presence of artifacts using a five-point scale (5 = excellent vessel delineation to 1 = non-diagnostic). Contrast ratios (CR), SNR und CNR of the above named vessels in correlation to adjacent liver tissue were calculated for quantitative assessment. For statistical analysis, a Wilcoxon Rank Test was applied.ResultsAll three sequences provided a homogenous hyperintense delineation of the assessed liver vessels. Qualitative image analysis demonstrated the superiority of TOF-MRA, providing best overall image quality (TOF 4.17, 2D FLASH 3.42, 3D FLASH 3.46; p<0.01) as well as highest image quality values for all analyzed liver vessel segments. TOF-MRA was least impaired by B1 inhomogeneity (4.13) and susceptibility artifacts (4.63) out of all three sequences (p<0.01). Quantitative image analysis confirmed the superiority of TOF MRA showing significant higher CR values for all liver vessels (e.g. right hepatic artery TOF 0.47, 2D FLASH 0.09, 3D FLASH 0.11 with p = 0.02 and 0.01, respectively). Providing the lowest standard deviation in noise, TOF showed highest values for SNR and CNR.ConclusionsNon-enhanced T1w imaging in general and TOF MRA in particular, appear to be promising techniques for high quality non-enhanced liver vessel assessment at 7 T.

Stochastic Resonance in a Simple Threshold Sensor System with Alpha Stable Noise

We investigate the effect of alpha stable noise on stochastic resonance in a single-threshold sensor system by analytic deduction and stochastic simulation. It is shown that stochastic resonance occurs in the threshold system in alpha stable noise environment, but the resonant effect becomes weakened as the alpha stable index decreases or the skewness parameter of alpha stable distribution increases. In particular, for Cauchy noise a nonlinear relation among the optimal noise deviation parameter, the signal amplitude and the threshold is analytically obtained and illustrated by using the extreme value condition for the output signal-to-noise ratio. The results presented in this communication should have application in signal detection and image restoration in the non-Gaussian noisy environment.