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This Conference Presentation, "Impacts of pulse pileup, charge sharing, and anti-charge sharing on the noise power spectra of photon counting detectors: theoretical analysis and experimental demonstrations," was recorded at Medical Imaging 2020 held in Houston, Texas, United States.

As the rapid development of back-illuminated CMOS (BI-CMOS) image sensor technology in recent years, its application prospect in the field of Low-Light-Level (LLL) night vision has been widely concerned. Therefore, LLL imaging module was developed based on BICMOS, whose 3-D noise data was obtained under different illumination conditions. The test results show that, the signal-to-noise ratio (SNR) of imaging module becomes worse with the decreasing of illumination. According to the judgement of noise, the noise power of the image in low illumination is mainly Gaussian distribution. And the image processed by spatial filtering, which efficiently reducing the imaging noise and improving the imaging quality.

In FDK reconstruction, distribution of noise power is different along the axial (i.e., high pass noise) and coronal slice (i.e., low pass or white noise), which may results in different detectability of same objects. In this work, we examined denoising performance of convolutional neural network trained using axial and coronal slice images separately, and how the direction of image slice affects the detectability of small objects in denoised images. We used the modified version of U-Net. For network training, we used Adam optimizer with a learning rate of 0.001, batch size of 4, and VGG loss was used. The XCAT simulator was used to generate the training, validation, and test dataset. Projection data was acquired by Siddon’s method for the XCAT phantoms, and different levels of Poisson noise was added to the projection data to generate quarter dose and normal dose CT images, which were then reconstructed by FDK algorithm. The reconstructed quarter dose and normal dose CT images were used as training, validation, and test dataset for our network. The performance of denoised output images from U-Net-Axial (i.e., network trained using axial images) and U-Net-Coronal (i.e., network trained using coronal images) were evaluated using structural similarity (SSIM) and mean square error (MSE). The results showed that output images from both U-Net-Axial and U-Net-Coronal shows the improved image quality compared to quarter dose images. However, it was observed that the detectability of small objects were higher in U-Net-Coronal.

This work presents and tests a new method of adapting mammography images to appear as if acquired using different radiographic factors by changing the signal and noise within the images. A Hologic Selenia Dimensions was used for the validation of the conversion method. Two set of images were acquired for the validation, one set was of 30, 45 and 60 mm thick polymethyl methacrylate (PMMA) acquired for a range of beam qualities while the other set was the same except for the addition of a contrast object of 2 and 4 mm thick pieces of PMMA. One set of images was then adapted to appear the same as a target image acquired with a higher or lower tube voltage. The normalized noise power spectra (NNPS) and standard deviation of the flat field images and the contrast-to-noise ratio (CNR) of the other images were calculated for the simulated and target images. The NNPS and standard deviation for the target and the simulated images were found to be within 2%. The CNRs of the target and simulated images were found to be within 4% of each other. The methodology successfully converted the images and can be used in observer studies to examine the impact of radiographic factors on lesion detection.

The photodetection intensity noise to demodulated phase noise conversion process of fiber optic sensors using phase generated carrier (PGC) scheme is investigated through theoretical calculation and experimental verification. Several categories of intensity noise are calculated, according to their relation between intensity noise power spectral density and photodetection current. The results revealed that demodulated phase noise power level increases by several decibels over the relative intensity noise power level of detected light power signal. Phase noise power level could also fluctuate with demodulated phase signal. Increase of phase noise power level varies according to the noise type, as well as the fluctuation amplitude. For noises that intensity noise power level unrelated to detected light power, such as electronic noise of detector circuit, phase noise power level increases by about 3.7 dB and do not fluctuate with demodulated phase signal. For noises that intensity noise power level proportional to detected light power, such as signal-amplified spontaneous emission(ASE) beat noise of optical amplifiers, phase noise power level fluctuates with demodulated phase signal by about 5.7 decibels and averagely increases about 3.7 decibels over a 2π period of demodulated phase signal. For noises that intensity noise power level proportional to square of detected light power, such as light source relative intensity noise, phase noise power level fluctuates with demodulated phase signal by about 9.0 decibels and averagely increases about 4.8 decibels over a 2π period of demodulated phase signal. Verification experiments are demonstrated on electronic noise, ASE-signal beat noise and light source relative intensity noise separately.

Damped sinusoidal signals occur in many fields of science and practical issues. One of them are adaptive optics systems where such signals are undesirable and often diminish the system performance. One of solutions to reject these signals is a method called AVC which is based on the estimation of vibrations parameters. In recent years, an universal, fast and accurate estimation method has been presented. It can be used to estimate multifrequency signals and can be useful in many various cases where the estimation method plays a crucial role. The main idea of this paper is using it in the AVC method to increase the system performance. There can be distinguished several measurement parameters that affect the accuracy and the speed of the estimation method: CiR (number of signal cycles in the estimation process), N (number of signal samples in a measurement window), H (time window order). There are also parameters that are especially important in practical situations (damped signals with noise and harmonics): SNR, THD, γ (changed in time a damping ratio). Total estimation errors consist of systematic errors and random errors. This paper is focusing on the second component, i.e. when the signal with γ ≠ 0 is distorted by noise. Results can be very useful from a practical point of view because they give information about the estimation accuracy in dependence of noise power for various damping ratio values. The value of the empirical MSE of the frequency estimator is approximately 10 Λ -3 Hz for SNR = 30 dB H = 2 and γ = 0.01%.

Although non-linear CT systems offer improved image quality over conventional linear systems, they disrupt certain assumptions of the dependency of noise and resolution on radiation dose that are true of linear systems. As such, simplistic phantoms do not fully represent the actual performance of current systems in the clinic. Assessing image quality from clinical images address this limitation, but full realization of image quality attributes, particularly noise, requires the knowledge of the exact heterogeneous anatomy of the patient (not knowable) and/or repeated imaging (ethically unattainable). This limitation can be overcome through realistic simulations enabled by virtual clinical trials (VCTs). This study aimed to characterize the noise properties of CT images reconstructed with filtered back-projection (FBP) and non-linear iterative reconstruction (IR) algorithms through a VCT. The study deployed a new generation version of the Extended Cardio-Torso (XCAT) phantom enhanced with anatomically-based intra-organ heterogeneities. The phantom was virtually “imaged” using a scanner-specific simulator, with fifty repeats, and reconstructed using clinical FBP and IR algorithms. The FBP and IR noise magnitude maps and the relative noise reduction maps were calculated to quantify the amount of noise reduction achieved by IR. Moreover, the 2D noise power spectra were measured for both FBP and IR images. The noise reduction maps showed that IR images have lower noise magnitude in uniform regions but higher noise magnitude at edge voxels, thus the noise reduction attributed to IR is less than what could be expected from uniform phantoms (29% versus 60%). This work demonstrates the utility of our CT simulator and “textured” XCAT phantoms in performing VCT that would be otherwise infeasible.

Aim: Since dose reduction techniques such as tube current modulation may impact noise and consequently the performance that can be achieved with CT images, it is important to establish quality monitoring. We have studied whether it would be possible and relevant to implement an (automatical) procedure to retrieve and possibly alert for patients with relatively high noise levels in CT in comparison to similar cases. Proper alerting would make clinical quality supervision more efficient. Material and methods: Two homogeneous phantoms consisting of different diameters were scanned following a routine CT thorax protocol on a Siemens SOMATOM Force scanner and noise power spectra were calculated for the different phantom sizes. Next, forty-four patients, scanned with the same CT thorax protocol and reconstructed with a hard kernel (lung) and soft kernel (liver), were retrieved from PACS. Noise power spectra (NPS) were calculated for regions in the lung and liver, and evaluated over different frequency ranges. We hypothesized that the high frequency part correlates better with dose than the low frequency part that is determined by anatomical noise. Therefore we focused on the correlation of high frequency noise and dose versus patient size. Water equivalent diameters (WED) were calculated as a metric of patient size. Additionally, all patients were rated subjectively by an experienced thorax radiologist for their overall image quality and presence of diagnostically acceptable noise. Statistical correlations and outliers were investigated. Results: While the correlations between NPS and dose and patient size were not significant for the lung, a positive correlation of NPS measured in the liver with CTDIvol and WED was found (e.g. R 2 = 0.31 for NPS(high frequencies) versus WED). The combined visualization of NPS at high frequencies, WED and CTDI vol showed some interesting outliers, however they did not receive lower image quality ratings. Conclusions: This work described how the Siemens SOMATOM Force scanner balances patient size, dose and image noise for a routine CT thorax protocol. However, since the outliers in both dose and (high frequency) noise levels still result in adequate to very good image quality scores, it is suggested that (straightforward) dose outlier based alerting should be the first task in dose-quality surveys on the particular scanner.

Assessing the image resolution of the differential phase (dP) and the dark field (DF) image in grating-based phase-contrast imaging (GB-PCI) requires a precision machined wedge and edge respectively in order to generate an accurate edge spread function (ESF) from which the modulation transfer function (MTF) can be calculated. Imperfect test objects, i.e. truncated wedges (dP), unsharp or too thick edges (DF), can lead both to an over-or an underestimation of image sharpness, making the MTF potentially test object dependent. Here the object free method of Kuhls-Gilcrist, which resolves the MTF from noise power spectra (NPS method), is applied to the transmission, dark field and differential phase image in GB-PCI. In parallel to the NPS method, each MTF was also determined using a test object based approach. Good agreement was found between both approaches. Moreover, it was possible to distinguish unsuitable test objects and this work illustrated that selecting DF test objects can be difficult. This sharpness measurement method offers a robust alternative to test object approaches and can be used to verify sharpness of test objects or to generate an accurate MTF if no such object is available.

The receiver operating characteristic (ROC curve), which is a plot of the probability of detection as a function of the probability of false alarm, plays a key role in the classical analysis of detector performance. However, meaningful characterization of the ROC curve is challenging when practically important complications such as variations in source emissions, environmental impacts on the signal propagation, uncertainties in the sensor response, and multiple sources of interference are considered. In this paper, a relatively simple but realistic model for scattered signals is employed to explore how parametric uncertainties impact the ROC curve. In particular, we show that parametric uncertainties in the mean signal and noise power substantially raise the tails of the distributions; since receiver operation with a very low probability of false alarm and a high probability of detection is normally desired, these tails lead to severely degraded performance. Because full a priori knowledge of such parametric uncertainties is rarely available in practice, analyses must typically be based on a finite sample of environmental states, which only partially characterize the range of parameter variations. We show how this effect can lead to misleading assessments of system performance. For the cases considered, approximately 64 or more statistically independent samples of the uncertain parameters are needed to accurately predict the probabilities of detection and false alarm. A connection is also described between selection of suitable distributions for the uncertain parameters, and Bayesian adaptive methods for inferring the parameters.

In many settings, multiple Magnetic Resonance Imaging (MRI) scans are performed with different contrast characteristics at a single patient visit. Unfortunately, MRI data-acquisition is inherently slow creating a persistent need to accelerate scans. Multi-contrast reconstruction deals with the joint reconstruction of different contrasts simultaneously. Previous approaches suggest solving a regularized optimization problem using group sparsity and/or color total variation, using composite-splitting denoising and FISTA. Yet, there is significant room for improvement in existing methods regarding computation time, ease of parameter selection, and robustness in reconstructed image quality. Selection of sparsifying transformations is critical in applications of compressed sensing. Here we propose using non-convex p-norm group sparsity (with p < 1), and apply color total variation (CTV). Our method is readily applicable to magnitude images rather than each of the real and imaginary parts separately. We use the constrained form of the problem, which allows an easier choice of data-fidelity error-bound (based on noise power determined from a noise-only scan without any RF excitation). We solve the problem using an adaptation of Alternating Direction Method of Multipliers (ADMM), which provides faster convergence in terms of CPU-time. We demonstrated the effectiveness of the method on two MR image sets (numerical brain phantom images and SRI24 atlas data) in terms of CPU-time and image quality. We show that a non-convex group sparsity function that uses the p-norm instead of the convex counterpart accelerates convergence and improves the peak-Signal-to-Noise-Ratio (pSNR), especially for highly undersampled data. © (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

Atmospheric turbulence has a great influence on the performance of the atmospheric laser communication system reducing the signal to noise ratio (SNR) and increasing the bit error rate (BER). However, there is rarely study on the effect of atmospheric turbulence on the power spectrum of the rectangular pulse. In this paper, a spectral analyzing method is used to analyze the influence of atmospheric turbulence on the signal. An experiment of laser beam propagation characteristic is carried out on a 6km horizontal atmospheric link, the wavelength is 808 nm. The signal is 100MHz rectangular pulse. The waveform of the rectangular pulse is collected by the oscilloscope, and the power spectral density of the signal is calculated and analyzed by the method of periodogram. Experimental results show that the response and noise characteristics of the laser and photoelectric detector have a great influence on the signal power spectrum distribution which can increase the noise component in the 10^6 Hz frequency range. After the atmospheric turbulence propagation, the signal power decreases in the whole frequency range. However, as the existence of atmospheric turbulence, the signal power increases in the atmospheric turbulence characteristic frequency (tens to hundreds of Hz). The noise power increases in the high frequency range (10^7~10^8 Hz).

This paper consider the problem of detecting range-distributed targets using high resolution radar(HRR) in compound-Gaussian clutter without secondary data. To overcome the lack of training data, we first assume that clutter returns can be clustered into groups of cells sharing the same value of the noise power. Then an adaptive modified generalized likelihood ratio test (A-GLRT) detector is proposed by replacing the unknown parameters with their maximum likelihood estimations (MLEs). The proposed A-GLRT detector do not need secondary data and ensures constant false alarm rate (CFAR) property with respect to the unknown statistics of the clutter. Performances of this proposed detectors are assessed through Monte Carlo simulations and are shown to have better detection performance compared with existing similar modified generalized likelihood ratio test (MGLRT) detector.

A prototype of scientific CCD detector system is designed, implemented and tested for the extreme environment in Antarctic, including clocks and biases driver for CCD chip, video pre-amplifier, video sampling circuit and ultra-low noise power. The low temperature influence is fully considered in the electronics design. Low noise readout system with CCD47-20 is tested, and the readout noise is as low as 5e- when the CCD readout speed is 100kpixs/s. We simulated the extreme low temperature environment of Antarctic to test the system, and verified that the system has the ability of long-term working in the extreme low temperature environment as low as -80°C.

A small animal micro-CT system was built using an EMCCD detectors having complex pre-digitization amplification technology, high-resolution, high-sensitivity and low-noise. Noise in CBCT reconstructed images when using pre-digitization amplification behaves differently than commonly used detectors and warrants a detailed investigation. In this study, noise power and contrast sensitivity were estimated for the newly built system. Noise analysis was performed by scanning a water phantom. Tube voltage was lowered to minimum delivered by the tube (20 kVp and 0.5 mA) and detector gain was varied. Contrast sensitivity was analyzed by using a phantom containing different iodine contrast solutions (20% to 70%) filled in six different tubes. First, we scanned the phantom using various x-ray exposures at 40 kVp while changing the gain to maintain the background air value of the projection images constant. Next, the exposure was varied while the detector gain was maintained constant. Radial NPS plots show that noise power level increases as gain increases. Contrast sensitivity was analyzed by calculating ratio of signal-to-noise ratios (SNR) for increased gain with those of low constant gain at each exposure. The SNR value at low constant gain was always lower than SNR of high detector gain at all x-ray settings and iodine contrast. The largest increase of SNR approached 1.3 for low contrast feature for an iodine concentration of 20%. Despite an increase in noise level as gain increases, the SNR improvement shows that signal level also increases because of the unique on-chip gain of the detector.

Large X-ray beam collimation in computed tomography (CT) opens the way to new image acquisition techniques and improves patient management for several clinical indications. The systems that offer large X-ray beam collimation enable, in particular, a whole region of interest to be investigated with an excellent temporal resolution. However, one of the potential drawbacks of this option might be a noticeable difference in image quality along the z-axis when compared with the standard helical acquisition mode using more restricted X-ray beam collimations. The aim of this project is to investigate the impact of the use of large X-ray beam collimation and new iterative reconstruction on noise properties, spatial resolution and low contrast detectability (LCD). An anthropomorphic phantom and a custom made phantom were scanned on a GE Revolution CT. The images were reconstructed respectively with ASIR-V at 0% and 50%. Noise power spectra, to evaluate the noise properties, and Target Transfer Functions, to evaluate the spatial resolution, were computed. Then, a Channelized Hotelling Observer with Gabor and Dense Difference of Gaussian channels was used to evaluate the LCD using the Percentage correct as a figure of merit. Noticeable differences of 3D noise power spectra and MTF have been recorded; however no significant difference appeared when dealing with the LCD criteria. As expected the use of iterative reconstruction, for a given CTDIvol level, allowed a significant gain in LCD in comparison to ASIR-V 0%. In addition, the outcomes of the NPS and TTF metrics led to results that would contradict the outcomes of CHO model observers if used for a NPWE model observer (Non- Prewhitening With Eye filter). The unit investigated provides major advantages for cardiac diagnosis without impairing the image quality level of standard chest or abdominal acquisitions.

Jet engine noise can be a health hazard and environmental pollutant, particularly affecting personnel working in close proximity to jet engines, such as airline mechanics. Mitigating noise could reduce the potential for hearing loss in runway workers; however, there exists a very complex relationship between jet engine design parameters, operating conditions, and resultant noise power levels, and understanding and characterizing this relationship is a key step in mitigating jet engine noise effects. We demonstrate initial results highlighting the utility of high-speed imaging (hypertemporal imaging) in correlating the infrared signatures of jet engines with acoustic noise. This paper builds on prior theoretical analysis of jet engine infrared signatures and their potential relationships to jet engine acoustic emissions. This previous work identified the region of the jet plume most likely to emit both in infrared and in acoustic domains, and it prompted the investigation of wave packets as a physical construct tying together acoustic and infrared energy emissions. As a means of verifying these assertions, a field campaign to collect relevant data was proposed, and data collection was carried out with a bank of infrared instruments imaging a T700 turboshaft engine undergoing routine operational testing. The detection of hypertemporal signatures in association with acoustic signatures of jet engines enables the use of a new domain in characterizing jet engine noise. This may in turn enable new methods of predicting or mitigating jet engine noise, which could lead to socioeconomic benefits for airlines and other operators of large numbers of jet engines.

We propose a novel method for optical signal-to-noise ratio (OSNR) monitoring by the correction to the error of OSNR in high speed coherent optical communication systems. We calculate the total power and the total noise power by the real and imaginary part of the coherent detection signal. Then, the error of OSNR is corrected by a polynomial function. The simulation results demonstrate that, with the input power of 0.0dBm and 2.0dBm of 15-35GB QPSK optical fiber communication systems, the error of estimated OSNR is in the range of ±1dB with the reference OSNR of 15-25dB.

At present, most of the researches set amplitude of driving force fixed when detecting weak signal buried in noise utilizing Duffing oscillator. In this paper, we find the critical value of driving force corresponding to critical state varies with noise power. Taken this into consideration, a new adaptive method to detect weak signal is proposed. In this method, the amplitude of driving force is determined by input power. The simulation results indicate that lowest threshold of SNR can be acquired in this method is lower than that in the methods proposed in most papers.

Peaky template matching (PTM) is a special case of a general algorithm known as multinomial pattern matching originally developed for automatic target recognition of synthetic aperture radar data. The algorithm is a model- based approach that first quantizes pixel values into N q = 2 discrete values yielding generative Beta-Bernoulli models as class-conditional templates. Here, we consider the case of classification of target chips in AWGN and develop approximations to image-to-template classification performance as a function of the noise power. We focus specifically on the case of a uniform quantization" scheme, where a fixed number of the largest pixels are quantized high as opposed to using a fixed threshold. This quantization method reduces sensitivity to the scaling of pixel intensities and quantization in general reduces sensitivity to various nuisance parameters difficult to account for a priori. Our performance expressions are verified using forward-looking infrared imagery from the Army Research Laboratory Comanche dataset.

Software breast phantoms are increasingly seeing use in preclinical validation of breast image acquisition systems and image analysis methods. Phantom realism has been proven sufficient for numerous specific validation tasks. A challenge is the generation of suitably realistic small-scale breast structures that could further improve the quality of phantom images. Power law noise follows the noise power characteristics of breast tissue, but may not sufficiently represent certain (e.g., non-Gaussian) properties seen in clinical breast images. The purpose of this work was to investigate the utility of fractal Perlin noise in generating more realistic breast tissue through investigation of its power spectrum and visual characteristics. Perlin noise is an algorithm that creates smoothly varying random structures of an arbitrary frequency. Through the use of a technique known as fractal noise or fractional Brownian motion (fBm), octaves of noise with different frequency are combined to generate coherent noise with a broad frequency range. fBm is controlled by two parameters – lacunarity and persistence – related to the frequency and amplitude of successive octaves, respectively. Average noise power spectra were calculated and beta parameters estimated in sample volumes of fractal Perlin noise with different combinations of lacunarity and persistence. Certain combinations of parameters resulted in noise volumes with beta values between 2 and 3, corresponding to reported measurements in real breast tissue. Different combinations of parameters resulted in different visual appearances. In conclusion, Perlin noise offers a flexible tool for generating breast tissue with realistic properties.

In medical imaging systems, several factors (e.g., reconstruction algorithm, noise structures, target size, contrast, etc) affect the detection performance and need to be considered for object detection. In a cone beam CT system, FDK reconstruction produces different noise structures in axial and coronal slices, and thus we analyzed directional dependent detectability of objects using detection SNR of Channelized Hotelling observer. To calculate the detection SNR, difference-of-Gaussian channel model with 10 channels was implemented, and 20 sphere objects with different radius (i.e., 0.25 (mm) to 5 (mm) equally spaced by 0.25 (mm)), reconstructed by FDK algorithm, were used as object templates. Covariance matrix in axial and coronal direction was estimated from 3000 reconstructed noise volumes, and then the SNR ratio between axial and coronal direction was calculated. Corresponding 2D noise power spectrum was also calculated. The results show that as the object size increases, the SNR ratio decreases, especially lower than 1 when the object size is larger than 2.5 mm radius. The reason is because the axial (coronal) noise power is higher in high (low) frequency band, and therefore the detectability of a small (large) object is higher in coronal (axial) images. Our results indicate that it is more beneficial to use coronal slices in order to improve the detectability of a small object in a cone beam CT system.

We propose an all-optical fiber-based device able to accomplish both polarization control and OSNR enhancement of an amplitude modulated optical signal, affected by unpolarized additive white Gaussian noise, at the same time. The proposed noise cleaning device is made of a nonlinear lossless polarizer (NLP), that performs polarization control, followed by an ideal polarizing filter that removes the orthogonally polarized half of additive noise. The NLP transforms every input signal polarization into a unique, well defined output polarization (without any loss of signal energy) and its task is to impose a signal polarization aligned with the transparent eigenstate of the polarizing filter. In order to effectively control the polarization of the modulated signal, we show that two different NLP configurations (with counter- or co-propagating pump laser) are needed, as a function of the signal polarization coherence time. The NLP is designed so that polarization attraction is effective only on the "noiseless" (i.e., information-bearing) component of the signal and not on noise, that remains unpolarized at the NLP output. Hence, the proposed device is able to discriminate signal power (that is preserved) from in-band noise power (that is partly suppressed). Since signal repolarization is detrimental if applied to polarization-multiplexed formats, the noise cleaner application is limited here to "legacy" links, with 10 Gb/s OOK modulation, still representing the most common format in deployed networks. By employing the appropriate NLP configurations, we obtain an OSNR gain close to 3dB. Furthermore, we show how the achievable OSNR gain can be estimated theoretically.

A set of three dimensionless parameters is proposed to characterize lidar systems. Two of them are based on an asymptotic approximation of the output signal-to-noise ratio as a function of the input optical power reaching the photoreceiver when there is no background radiation. Of these, one is defined as the ratio between the input signal power level coming from a reference range in a reference atmosphere (reference power level) and the input power level that would produce a reference output signal-to-noise ratio if the photoreceiver operated always in signal-shot noise limited regime. The other is defined as the ratio between the reference power level and the input power level for which the signal-induced shot noise power equals the receiver noise power. A third parameter, defined as the ratio between the background optical power at the photoreceiver input and the reference power level, quantifies the effect of background radiation. With these three parameters a good approximation to the output signal-to-noise ratio of the lidar can be calculated as a function of the power reduction with respect to the power reaching the photodetector in the reference situation. These parameters can also be used to compare and rank the performance of different systems.

Passive optical network (PON) is considered as the most appealing access network architecture in terms of cost-effectiveness, bandwidth management flexibility, scalability and durability. And to further reduce the cost per subscriber, a Fabry-Perot (FP) laser diode is preferred as the transmitter at the optical network units (ONUs) because of its lower cost compared to distributed feedback (DFB) laser diode. However, the mode partition noise (MPN) associated with the multi-longitudinal-mode FP laser diode becomes the limiting factor in the network. This paper studies the MPN characteristics of the FP laser diode using the time-domain simulation of noise-driven multi-mode laser rate equation. The probability density functions are calculated for each longitudinal mode. The paper focuses on the investigation of the k-factor, which is a simple yet important measure of the noise power, but is usually taken as a fitted or assumed value in the penalty calculations. In this paper, the sources of the k-factor are studied with simulation, including the intrinsic source of the laser Langevin noise, and the extrinsic source of the bit pattern. The photon waveforms are shown under four simulation conditions for regular or random bit pattern, and with or without Langevin noise. The k-factors contributed by those sources are studied with a variety of bias current and modulation current. Simulation results are illustrated in figures, and show that the contribution of Langevin noise to the k-factor is larger than that of the random bit pattern, and is more dominant at lower bias current or higher modulation current.

Jet engine noise can be a hazard and environmental pollutant, affecting personnel working in close proximity to jet engines. Mitigating the effects of jet engine noise could reduce the potential for hearing loss in runway workers, but engine noise is not yet sufficiently well-characterized that it can easily be mitigated for new engine designs. That is, there exists a very complex relationship between jet engine design parameters, operating conditions, and resultant noise power levels. In this paper, we propose to evaluate the utility of high-speed imaging (also called hypertemporal imaging) in correlating the infrared signatures of jet aircraft engines with acoustic noise from the jet engines. This paper will focus on a theoretical analysis of jet engine infrared signatures, and will define potentially-detectable characteristics of such signatures in the hypertemporal domain. A systematic test campaign to determine whether such signatures actually exist and can be correlated with acoustic jet engine characteristics will be proposed. The detection of any hypertemporal signatures in association with acoustic signatures of jet engines will enable the use of a new domain in characterizing jet engine noise. This may in turn enable new methods of predicting or mitigating jet engine noise, which could lead to benefits for operators of large numbers of jet engines.

A SWIR FPA was designed and manufactured with 640*512 pixels, 20 μm pitch and InGaAs detectors for electroluminescence characterization and astronomical applications in the [0.9 – 1.55 μm] range. The FPA is mounted in a liquid nitrogen dewar and is operated by a low noise frontend electronics. One of the biggest problem in designing sensors and cameras for electro-luminescence measurements is the autoillumination of the detectors by the readout circuit. Besides of proper shielding of the detectors, the ROIC shall be optimized for minimal electrical activity during the integration time of the very-weak signals coming from the circuit under test. For this reason a SFD (or Source Follower per Detector) architecture (like in the Hawaii sensor) was selected, resulting in a background limited performance of the detector. The pixel has a (somewhat arbitrary) full well capacity of 400 000 e - and a sensitivity of 2.17 μV/e - . The dark signal is app. 1 e-/pixel/sec and with the appropriate Fowler sampling the dark noise lowers below 5 e - rms . The power consumption of the circuit is limited 2 mW, allowing more than 24 hours of operation on less than 1 l of liquid nitrogen. The FPA is equipped with 4 outputs (optional readout on one single channel) and is capable of achieving 3 frames per second. Due to the non-destructive readout it is possible to determine in a dynamic way the optimal integration time for each observation. The Cougar camera is equipped with ultra-low noise power supply and bias lines; the electronics contain also a 24 bit AD converter to fully exploit the sensitivity of the FPA and the camera.

Estimation and prediction of noise power are very important for communication anti-jamming and efficient allocation of spectrum resources in adaptive wireless communication and cognitive radio. In order to estimate and predict the time-varying noise power caused by natural factors and jamming in the high frequency channel, Variational Bayesian algorithm and adaptive ARMA time series are proposed. Through establishing the time-varying noise power model, which controlled by the noise variance rate, the noise power can be estimated with Variational Bayesian algorithm, and the results show that the estimation error is related to observation interval. What’s more, through the analysis of the correlation characteristics of the estimation power, noise power can be predicted based on adaptive ARMA time series, and the results show that it will be available to predict the noise power in next 5 intervals with the proportional error less than 0.2.

The statistical model based iterative reconstruction (MBIR) method has been introduced to clinical CT systems. Due to the nonlinearity of this method, the noise characteristics of MBIR are expected to differ from those of filtered backprojection (FBP). This paper reports an experimental characterization of the noise performance of MBIR equipped on several state-of-the-art clinical CT scanners at our institution. The thoracic section of an anthropomorphic phantom was scanned 50 times to generate image ensembles for noise analysis. Noise power spectra (NPS) and noise standard deviation maps were assessed locally at different anatomical locations. It was found that MBIR lead to significant reduction in noise magnitude and improvement in noise spatial uniformity when compared with FBP. Meanwhile, MBIR shifted the NPS of the reconstructed CT images towards lower frequencies along both the axial and the z frequency axes. This effect was confirmed by a relaxed slice thicknesstradeoff relationship shown in our experimental data. The unique noise characteristics of MBIR imply that extra effort must be made to optimize CT scanning parameters for MBIR to maximize its potential clinical benefits.

Speaker recognition is used to identify a speaker's voice from among a group of known speakers. A common method of speaker recognition is a classification based on cepstral coefficients of the speaker's voice, using a Gaussian mixture model (GMM) to model each speaker. In this paper we try to fool a speaker recognition system using additive noise such that an intruder is recognized as a target user. Our attack uses a mixture selected from a target user's GMM model, inverting the cepstral transformation to produce noise samples. In our 5 speaker data base, we achieve an attack success rate of 50% with a noise signal at 10dB SNR, and 95% by increasing noise power to 0dB SNR. The importance of this attack is its simplicity and flexibility: it can be employed in real time with no processing of an attacker's voice, and little computation is needed at the moment of detection, allowing the attack to be performed by a small portable device. For any target user, knowing that user's model or voice sample is sufficient to compute the attack signal, and it is enough that the intruder plays it while he/she is uttering to be classiffed as the victim.

Gamma rays camera is widely used in many studies, including the image diagnostics of the radiation sources, flash photography, and nondestructive assessment (NDA), etc. As a major component of the high sensitivity gamma rays camera, the MCP image intensifier is characterized in the intensified image, tunable shutter time and gain. The gamma rays camera is consisting with rays-fluorescence convertor, the optical imaging system, the MCP image intensifier, CCD and other devices. The performance of the gamma rays camera is mainly dependent on such parameters as the modulation transfer function (MTF), the noise power spectrum (NPS), and the detective quantum efficiency (DQE), etc. All of the parameters are somewhat limited by the noise characteristics of the system. Compared with the standard derivative noise distribution, the NPS, which can reflect the evolution characteristics of the noise of the imaging system with the change of the spatial frequency, could convey more information on the noise distribution in the system. In this paper, theoretical analysis is presented on the major sources of the noise in the gamma rays camera. Based on the analysis, the noise power spectra of the gamma rays camera were calibrated under various radiation dosages respectively with the visible light and gamma rays radiation sources (0.2MeV and 1.25MeV in energy, respectively). As indicated by the experimental results, the noise is majorly induced by the fluctuations of the gain of the MCP image intensifier. And the remarkable noise peak occurs nearby the spatial frequency of about 0.633 Hz/mm. And almost the same phenomena were found with both the 0.2MeV and 1.25MeV radiation energy. Besides, the noise power spectra are in circular symmetrical distribution, whose intensities are rapidly decreased with the increasing spatial frequencies.

Few-mode fiber (FMF) transmission system has become an emerging technology to overcome next capacity crunch. To compensate the large accumulated differential mode group delay (DMGD) and random mode coupling in FMF transmission systems, frequency domain least mean square (FD-LMS) algorithm has been proposed and proven as the most hardware efficient approach. Except for the hardware complexity, convergence speed is another major consideration of adaptive FD-LMS algorithm, especially in FMF system with large accumulated DMGD. We propose two algorithms to improve the convergence speed of the adaptive FD-LMS in FMF transmission systems. One is signal power spectrum density (PSD) dependent adaptive algorithm, which adopts variable step size that is the reciprocal of the power level in each frequency bin. The other is noise power directed adaptive FD-LMS algorithm, which adopts the step size of each frequency bin rendering the posterior errors that will converge to background noise in additive white Gaussian noise channel. Our simulation results show that, in a 3000 km FMF transmission system with 35-ps/km DMGD and optical signal to noise ratio (OSNR) of 14 dB, the noise power directed algorithm and PSD dependent algorithm can improve the convergence speed by 54% and 35% over conventional adaptive FD-LMS algorithm with negligible increase in hardware complexity. We also proposed a complexity reduced single-stage adaptive equalizer for compensating both chromatic dispersion (CD) and DMGD simultaneously. The single-stage method can save 6% complex multiplication over conventional two-stage equalization method in a 1000 km FMF transmission system with 76-ps/km DMGD.

It is very common for noise to have an influence on analog circuits. In order to preserve the quality of measurements taken by specific sensors and any noise dependent amplifiers which are correlated to them, all of these devices must be powered by low-noise power supplies. Therefore a necessity exists to develop new ultra-low noise power supplies which can cooperate with specified amplifiers and preamplifiers. Many well-known power supplies are particularly expensive and yet still have their disadvantages. This paper proposes a simple and inexpensive solution, which fulfills a specific criteria and can be treated as a base for improvement.

A blind parameters estimation algorithm based on forward difference of power spectral density is presented to solve the problems in co-channel multiple signals processing. The proposed algorithm can fast and accurately estimate such parameters as number of signals, center frequency, signal power, noise power and signal to noise ratio of the mixed signals. Both theoretical analysis and simulation results show that the algorithm is effective.

Compressed Sensing (CS) that can effectively extract the information contained in the signal is a new sampling theory based on signal sparseness. This paper applies CS theory in digital speech signal enhancement processing, proposes an adaptive quantile method for the noise power estimation and combines the improved double-threshold orthogonal matching pursuit algorithm for speech reconstruction, then achieves speech enhancement processing. Compared with the simulation results of the spectral subtraction and the subspace algorithm, the experiment results verify the feasibility and effectiveness of the algorithm proposed in this paper applied to speech enhancement processing.

The aim of this work is to characterise the image quality of a mammography system in both planar and tomosynthesis imaging modes for the purpose of realistic image simulation. The simulation technique will be applied to projected images from voxelised breast phantoms to investigate the imaging properties of different configurations of tomosynthesis systems. Methods: A Hologic Dimensions mammography system was char acterised in terms of no ise, sharpness, and lag in both planar and tomosythesis modes. The noise power spectra (NPS) were measured for both imaging modes. The contributions (noise coefficients) to the NPS from el ectronic, quantum and struct ure noise were calculated. Results: The MTF was shown to be affected by the focal spot size and th e movement of the X-ray tube. These effects can be modelled in the tomosynthesis projection images using an extended source. The quantum noise coefficients for the two imaging modes showed a close match up to the Nyquist frequency of the tomosynthesis mode, while the electronic and structure noise showed differences. The effect of lag and ghosting caused the signal in the last projection of a tomosynthesis run to be between 2 and 4% higher than the first. Conclusions: We have characterised the noise and resolution properties of images from a Hologic Dimensions tomosynthesis system. This forms the basis of further work required to create a model for applying imaging characteristics to mathematically produced images. More work is required on detector lag and ghosting and the influence of oblique X-ray incidence. Keywords: tomosynthesis, simulation, noise, lag, NPS, MTF

The grating-based x-ray differential phase contrast (DPC) CT is emerging as a new technology with the potential for extensive preclinical and clinical applications. In general, the performance of an imaging system is jointly determined by its signal property (modulation transfer function-MTF(k)) and noise property (noise power spectrum-NPS(k)), which is characterized by its spectrum of noise equivalent quanta. As reported by us previously, owing to an adoption of the Hilbert filtering for image reconstruction in the fashion of filtered backprojection (FBP), the noise property of DPC-CT characterized by its NPS(k) differs drastically from that of the conventional attenuation-based CT (1/|k| trait vs. |k| trait). In this work, via system analysis, modeling and simulated phantom study, we initially investigate the signal property of DPC-CT characterized by its MTF(k) and compare it with that of the conventional CT. In addition, we investigate the DPC-CT's spectrum of noise equivalent quanta NEQ(k) - the most important figure of merit (FOM) in the assessment of an imaging system's performance - by taking the MTF(k) and NPS(k) jointly into account. Through such a thorough investigation into both the signal and noise properties, the imaging performance of DPC-CT and its potential over the conventional attenuation-based CT can be fully understood and appreciated.

Since the introduction of ASiR, its potential in noise reduction has been reported in various clinical applications. However, the influence of different scan and reconstruction parameters on the trade off between ASiR's blurring effect and noise reduction in low contrast imaging has not been fully studied. Simple measurements on low contrast images, such as CNR or phantom scores could not explore the nuance nature of this problem. We tackled this topic using a method which compares the performance of ASiR in low contrast helical imaging based on an assumed filter layer on top of the FBP reconstruction. Transfer functions of this filter layer were obtained from the noise power spectra (NPS) of corresponding FBP and ASiR images that share the same scan and reconstruction parameters. 2D transfer functions were calculated as sqrt[NPSASiR(u, v)/NPSFBP(u, v)]. Synthesized ACR phantom images were generated by filtering the FBP images with the transfer functions of specific (FBP, ASiR) pairs, and were compared with the ASiR images. It is shown that the transfer functions could predict the deterministic blurring effect of ASiR on low contrast objects, as well as the degree of noise reductions. Using this method, the influence of dose, scan field of view (SFOV), display field of view (DFOV), ASiR level, and Recon Mode on the behavior of ASiR in low contrast imaging was studied. It was found that ASiR level, dose level, and DFOV play more important roles in determining the behavior of ASiR than the other two parameters.

Estimation of the image power spectrum is fundamental to the development of a figure of merit for image performance analysis. We are investigating a new multitaper approach to determine power spectra, which provides a combination of low variance and high spectral resolution in the frequency range of interest. To further reduce the variance, the spectrum estimated by the proposed Local Spectral Adaptive Multitaper Method (LSAMTM) is subsequently smoothed in the frequency domain by bilateral filtering, while keeping the spectral resolution intact. This tool will be especially valuable in power spectrum estimation of images that deviate significantly from uniform white noise. The performance of this approach was evaluated in terms of spectral stability, variance reduction, bias and frequency precision. It was also compared to the conventional power spectrum method in several typical situations, including the noise power spectra (NPS) measurements of simulated projection images of a uniform phantom and NPS measurement of real detector images of a uniform phantom for two clinical digital mammography systems. Examination of variance reduction versus spectral resolution and bias indicates that the LSAMTM with bilateral filtering technique is superior to the conventional estimation methods in variance reduction, spectral resolution and in the prevention of spectrum leakage. It has the ability to keep both low variance and narrow spectral linewidth in the frequency range of interest. Up to 87% more variance reduction can be achieved with proper filtration and no sacrifice of frequency precision has been observed.

The noise properties of differential phase contrast CT (DPC-CT) demonstrate some peculiar features. It has been both theoretically and experimentally demonstrated that the noise variance of DPC-CT scales with spatial resolution following an inverse first order relationship. This is in stark contrast to absorption CT, where the noise variance scales with spatial resolution following an inverse third power. In addition to the scaling relationship, the noise power spectrum (NPS) of DPC-CT is dominated by low spatial frequencies and demonstrates a singular behavior when approaching zero frequency. This focuses the peak noise power within low spatial frequencies while high-frequency noise is suppressed. This is again in contrast to the absorption CT case where the NPS smoothly transitions to zero at zero frequency. The singular behavior of the DPC-CT NPS visually affects image noise texture and may hinder observer perception. In this paper, a method is proposed to improve the noise properties in DPC-CT and potentially improve observer performance. Specifically, the low frequency component of the filtering kernel used in reconstruction has been regularized to modify the noise power at low spatial frequencies. This results in a high-pass filtering of the image. The high-pass filtered image is combined with the original image to generate the final image. As a result of these two operations, the noise power is shifted to the high spatial frequency direction, improving visual perception, while image reconstruction accuracy is maintained. Experimental phantom results are presented to validate the proposed method.

An observer study was run to determine the detection thresholds of several representative examples of column fixed pattern noise, in the presence of varying levels of shot noise, which is known to mask structured noise. The data obtained were fit well at relevant shot noise levels by a simple model based on signal detection theory. Individual metrics of fixed pattern noise and shot noise, used in the masking equation, were computed from one dimensional integrations involving the capture noise power spectra (mapped to CIELAB space); the modulation transfer function of the display; the display pixel pitch; the viewing distance; and the S-CIELAB luminance contrast sensitivity function. The results of this work can be used to predict detection thresholds that can be added to photon transfer curves for the purpose of determining whether fixed pattern noise will be visible.

We report on the electrical stress results in GaN-based heterostructure field-effect transistors (HFETs) with InAlN barriers. We monitored the DC characteristics and low-frequency phase noise behavior for the devices at pre- and poststress conditions for five different wafers with In compositions varying from 12% to 20% in the barriers of the structures. The devices were stressed under off-state conditions with a gate bias of -10V (pinch-off condition) and zero drain bias for 10hr. From the acquired data we observed that at higher In composition, HFETs became less sensitive to the stress. At lower In composition we noted up to 30 dBc/Hz higher low frequency noise for stressed devices over the entire frequency range of 1 Hz-100 kHz. The change in drain current and change in noise power due to electrical stress decrease as the In composition in the barriers of the HFETs increases. The most relevant stress effect is revealed by a drain current reduction which is consistent with higher noise level measured. It was found that the HFET degradation is minimum for nearly lattice matched condition InAlN barriers, i.e.; 17% In composition, at which the sheet electron density (channel current) is comparable with that in lower In composition (12% In). This latter result is promising for power applications in which reliability of devices functioning at higher drain current is crucial. The results may also be beneficial to decouple the effect of off-state stress from the hot electron and self heating effects.© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

Moving target tracking in an infrared (IR) image sequence under high clutter and noise power has been recentlyunder intensive investigation, and the track-before-detect (TBD) technique based-on dynamic programming (DP)is known to be especially attractive in very low SNR environments (3dB). In this paper we present a novel 3-dimensioanl(3D) TBD-DP technique using multiple IR image sensors. Our approach, which does not requirea separate image registration step, uses the pixel intensity values read o jointly from multiple image frames,to compute the merit function value required in the DP process. To overcome the computation burden relatedwith the 3D TBD-DP process, we also propose a novel technique that progressively changes the resolution orthe level-of-detail (LOD) of the image. And we analysis the detection performance of these algorithm, detectionprobability P d and false alarm probability P FA . Our simulation results demonstrate that the proposed algorithmhas good track detection performance with the computation load of less than an order of magnitude comparedwith the straight-forward 3D TBD-DP, not employing the LOD technique.Keywords: 3D track before detect (TBD), LOD technique, performance analysis

The micro-channel plate (MCP) is an important part to imaging quality of image intensifier. In order to obtained high quality of optoelectronic image devices, the microchannel plate (MCP) should be evaluated before assembled in the devices. A new method for noise power factor determination of MCP is described in this paper. The measurements are in accordance with theory and experiments reported. The system consists of vacuum chamber, electron gun, high voltage supply, imaging luminance meter, control units, signal processing circuit, A/D converter, D/A converter, communication unit, industrial computer and measurement software.

The noise characteristics of x-ray differential phase contrast computed tomography (DPC-CT) were investigated. Both theoretical derivation and experimental results demonstrated that the dependence of noise variance on spatial resolution in DPC-CT follows an inverse linear law. This behavior distinguishes DPC-CT from conventional absorption based x-ray CT, where the noise variance varies inversely with the cube of the spatial resolution. This anomalous noise behavior in DPC-CT is due to the Hilbert filtering kernel used in the CT reconstruction algorithm, which equally weights all spatial frequency content. Additionally, we demonstrate that the noise power of DPC-CT is scaled by the inverse of spatial frequency and is highly concentrated at the low spatial frequencies, whereas conventional absorption CT increases in power at the high spatial frequencies.

We present timing jitter measurements of a free-running SESAM modelocked VECSEL generating 8-ps pulses with 1.88-GHz repetition rate and 80-mW average output power. We observed very good performance comparable with iondoped solid-state-lasers which typically show excellent stability. We measured the two-sided noise power spectral density at the 10th harmonic of the laser output with the von der Linde method. The rms timing jitter integrated over an offset frequency range from 100 Hz to 100 kHz gives a free-running timing jitter of ≈400 fs which is an upper limit because the measurement was already system noise limited above 10 kHz.

We report on the low-frequency noise (LFN) measurements on GaN based heterostructure field-effect transistors (HFETs) on sapphire with InAlN and AlGaN barriers to investigate the effects of electrical stress. The HFETs with InAlN barrier undergone a DC stress at bias conditions of VDS=20V and VG= -4.5 for up to 4 hours in aggregate. These devices exhibited an LFN in the form of 1/fγ and a significant increase in the noise spectrum up to 15 dB for 2 hours and then the noise saturated for further stress durations. We also monitored the LFN for the HFETs with AlGaN barriers. The devices were stressed by applying 20V DC drain bias for up to 64 hours at various gate voltages. Stressing at a gate bias (VG) of -2V showed negligible degradation. On the other hand, stressing at VG=0V surprisingly reduced the noise power by about 4 to 15 dB in the frequency range of 1 Hz-100 kHz. Additionally, the InAlN-barrier HFETs exhibited 20-25 dB lower noise power than the ones with the AlGaN layer for the tested devices within the entire frequency range. The results suggest that the trap generation increases due to electrical stress in devices with InAlN barrier, whereas the noise power decreases as a function of stress in AlGaN/GaN HFETs due to an increase in the activation energy of the excess traps.

A system simulation model was used to create scene-dependent noise masks that reflect current performance of mobile phone cameras. Stimuli with different overall magnitudes of noise and with varying mixtures of red, green, blue, and luminance noises were included in the study. Eleven treatments in each of ten pictorial scenes were evaluated by twenty observers using the softcopy ruler method. In addition to determining the quality loss function in just noticeable differences (JNDs) for the average observer and scene, transformations for different combinations of observer sensitivity and scene susceptibility were derived. The psychophysical results were used to optimize an objective metric of isotropic noise based on system noise power spectra (NPS), which were integrated over a visual frequency weighting function to yield perceptually relevant variances and covariances in CIE L*a*b* space. Because the frequency weighting function is expressed in terms of cycles per degree at the retina, it accounts for display pixel size and viewing distance effects, so application-specific predictions can be made. Excellent results were obtained using only L* and a* variances and L*a* covariance, with relative weights of 100, 5, and 12, respectively. The positive a* weight suggests that the luminance (photopic) weighting is slightly narrow on the long wavelength side for predicting perceived noisiness. The L*a* covariance term, which is normally negative, reflects masking between L* and a* noise, as confirmed in informal evaluations. Test targets in linear sRGB and rendered L*a*b* spaces for each treatment are available at http://www.aptina.com/ImArch/ to enable other researchers to test metrics of their own design and calibrate them to JNDs of quality loss without performing additional observer experiments. Such JND-calibrated noise metrics are particularly valuable for comparing the impact of noise and other attributes, and for computing overall image quality.

Noise Power Spectra (NPS) are traditionally measured using uniform areas of tone. Adaptive algorithms, such as noise reduction, demosaicing, and sharpening, can modify their behavior based on underlying image structure. In particular, noise reduction algorithms may suppress noise more strongly in perfectly uniform areas than they would in those with modest variations, as found in actual pictorial images, and so yield unrepresentative NPS. This phenomenon would be similar in nature to the susceptibility of high-contrast-edges to adaptive sharpening and the subsequent over-estimation of effective pictorial modulation transfer function by some targets. Experimentation is described that examines the effect of modern adaptive noise reduction algorithms on the NPS of images containing ramps of varying gradient. Gradients are chosen based on a survey of consumer images from areas where noise is typically noticeable, such as blue sky, walls and faces. Although loss in performance of adaptive noise reduction is observed as gradients increase, the effect is perceptually small when weighted according to the frequency of occurrence of the gradients in pictorial imaging. The significant additional complexity of measuring gradient-based NPS does not appear to be justified; measuring NPS from uniform areas of tone should suffice for most perceptual work.

In this paper, the direct detection behaviors of a superconducting hot electron bolometer integrated with a log spiral antenna are investigated by using Fourier Transform Spectrometer (FTS). We find the response of the bolometer to a modulated signal can be detected by a lock-in amplifier not only from the DC bias current, but also from the output noise power at the IF port of the HEB. We attribute the response in output noise power to Johnson noise and thermal fluctuation noise. Both the current response and the output noise power response measured at different bias voltages can be explained by one dimensional distributed hot spot model. In addition, the frequency response of the hot electron bolometer measured from the response in DC bias current is in good agreement with that in IF output noise power.