SNR Characteristics Research Articles

γCygni SNR morphology viewed in the electromagnetic spectrum

AbstractThe investigation of very high energy gamma‐ray sources touches on the problem of the cosmic ray origin and the role of the Galaxy in their generation. The SHALON observations have yielded the results on Cygni SNR Galactic supernova remnant. The observation results are presented with spectral energy distribution and emission map by SHALON in comparison with other experiment data obtained by ROSAT in x‐ray energy range, radio‐data by CGPS, and also observations of GeV–TeV gamma‐rays by Fermi LAT. The overall Cygni SNR characteristics detected in radio, x‐rays, and GeV–TeV gamma‐rays can be a result of the shocks at the interaction of the supernova ejecta and the surrounding medium. The collected experimental data help to make clear the origin of TeV ‐ray emission in the Cygni SNR. The density of target material in the SNR surroundings is enough to produce the observable TeV gamma‐ray flux via the shock acceleration of hadrons in the detected regions. The detection of gamma‐ray emission at 0.8–60 TeV from the North‐West and South‐East shells of Cygni SNR by SHALON would favor the hadronic origin of the gamma‐rays in this supernova remnant.

Adaptive noise reduction for dual-energy x-ray imaging based on spatial variations in beam attenuation

Purpose. The main goal of this work is to improve the previously proposed patient-specific pixel-based dual-energy (PP-DE) algorithm by developing an adaptive anti-correlated noise reduction (ACNR) method, resulting in reduced image noise. Methods. Theoretical models of contrast-to-noise (CNR) and signal-to-noise (SNR) ratio were developed as functions of weighting factors for DE bone ω Bn or soft tissue ω ST cancellation. These analytical expressions describe CNR and SNR properties of dual-energy (DE) images, obtained with both simple log subtraction (SLS) and ACNR algorithms, and allow for a direct comparison between experimental and theoretical results. The theoretical models demonstrate the importance of ACNR weighting factor (ω A ) optimization leading to the maximization of the SNR of the final image. A step phantom was constructed, which consisted of overlapping slabs of solid water (0-30 cm) and bone-mimicking material (0-6 cm), resulting in a total of 7 × 7 regions. High-energy (HE) and low-energy (LE) images were acquired at 140 kVp and 60 kVp with a clinical ExacTrac imaging system. The CNR and SNR were obtained for the DE images as functions of ω Bn,ST and noise reduction weighting factor ω A for different combinations of thicknesses. Weighting factors for bone cancellation were optimized for each region of interest (ROI) by finding zeros of CNR function for DE images between soft tissue only and soft tissue plus bone regions (and vice versa for soft tissue cancellation). The weighting factor for the ACNR algorithm ω A was then optimized by maximizing the SNR function for each ROI. HE and LE images for an anthropomorphic Rando phantom were obtained with the same acquisition parameters as for the step phantom. DE images for bone only and soft tissue only were obtained with three algorithms: SLS and PP-DE with conventional ACNR (uniform ω A ), and PP-DE with adaptive ACNR (region-varying ω A ). Weighting factor maps for PP-DE and adaptive ACNR methods were obtained for Rando phantom geometry (which was determined from its CT scans) by interpolation (or extrapolation) of weighting factors for the step phantom. CNR values were calculated for different regions. Results. The CNR and SNR characteristics as functions of material cancellation and noise reduction weighting factors were obtained from theoretical models and experimental data from the step phantom. This showed a good qualitative validation of the models. For the ANCR algorithm, both the theory and experiment demonstrated that the material cancellation weighting factors (ω Bn,ST ) can be optimized independently of the noise cancellation weighting factors (ω A ), which can be optimized by maximizing SNR. For each ROI (with different overlapping bone and soft tissue thicknesses) the weighting factors ω Bn,ST were determined as well as corresponding optimal weighting factors ω A for noise reduction. For the Rando phantom, CNR values for regions representing different anatomical structures (ribs, spine, and tumor) were evaluated. It was shown that the proposed adaptive ACNR further improves image quality, compared to the conventional ACNR algorithm. The improvement is maximized for regions with bones (ribs or spine), where the largest attenuation is observed. Conclusion. The ACNR weighting factors are dependent on the material thicknesses due to varying beam attenuation leading to different levels of quantum noise. This was shown with the derived theoretical expressions of the CNR and SNR functions and was validated by experimental data. The adaptive ANCR DE algorithm was developed, which allows for an increase in image quality by spatially varying weighting factors for noise reduction. This algorithm complements the previously developed PP-DE algorithm to obtain better quality DE images.

An equal-TE ultrafast 3D gradient-echo imaging method with high tolerance to magnetic susceptibility artifacts: Application to BOLD functional MRI.

To develop an ultrafast 3D gradient echo-based MRI method with constant TE and high tolerance to B0 inhomogeneity, dubbed ERASE (equal-TE rapid acquisition with sequential excitation), and to introduce its use in BOLD functional MRI (fMRI). Essential features of ERASE, including spin behavior, were characterized, and a comparison study was conducted with conventional EPI. To demonstrate high tolerance to B0 inhomogeneity, in vivo imaging of the mouse brain with a fiber-optic implant was performed at 9.4 T, and human brain imaging (including the orbitofrontal cortex) was performed at 3 T and 7 T. To evaluate the performance of ERASE in BOLD-fMRI, the characteristics of SNR and temporal SNR were analyzed for in vivo rat brains at 9.4 T in comparison with multislice gradient-echo EPI. Percent signal changes and t-scores are also presented. For both mouse brain and human brain imaging, ERASE exhibited a high tolerance to magnetic susceptibility artifacts, showing much lower distortion and signal dropout, especially in the regions involving large magnetic susceptibility effects. For BOLD-fMRI, ERASE provided higher temporal SNR and t-scores than EPI, but exhibited similar percent signal changes in in vivo rat brains at 9.4 T. When compared with conventional EPI, ERASE is much less sensitive, not only to EPI-related artifacts such as Nyquist ghosting, but also to B0 inhomogeneity including magnetic susceptibility effects. It is promising for use in BOLD-fMRI, providing higher temporal SNR and t-scores with constant TE when compared with EPI, although further optimization is needed for human fMRI.

Heterogeneous LPWAN Communication for Electric Vehicle Charging Infrastructure

Many countries worldwide are set to include Electric Vehicles (EV) as a prime element in transportation policy owing to political polarization to combat climate changes. Circumstances are conducive to a sustainable mobility paradigm and have accelerated the adoption of EVs. The situation demands the deployment of charging infrastructure at a faster pace. Smart Grid provides the pervasive support for the deployment of charge stations. But the charging infrastructure should scale beyond the frontiers of metropolitan geography. The paper proposes a heterogeneous network to support mobility to EV users utilizing the paradigm of the Internet of Things (IoT). A heterogeneous ad-hoc network of Low Power Wide Area Networks (LPWAN) and mobile data communication is proposed to support mobility and scalability of the infrastructure. The proposed ad-hoc communication infrastructure utilizes LoRaWAN, a promising LPWAN technology characterized by a long-range at a narrow bandwidth. LoRaWAN employs Chirp Spread Spectrum (CSS) in its physical layer. The paper peeps into standards and protocols supporting Smart Grid and EV charge infrastructure. An SNR characteristic of LoRaWAN is simulated for suitability and Hardware for communication node is proposed.

Efficient reduction of PLI in ECG signal using new variable step size least mean fourth adaptive algorithm

It is very important in remote cardiac diagnosis to extract pure ECG signal from the contaminated recordings of the signal. When recording the ECG signal in the laboratory, the signal is affected by numerous artifacts. Varies artifacts generally degrades the signal quality are PLI, EM, MA and EM. In addition to these, the channel noise also added when transmitting signal from remote location to diagnosis center for analyzing the signal. There are several approaches are used to reduce the noise present in the ECG signal. From the literature it is proven that compared to non adaptive filters, adaptive filters play vital role to trace the random changes in the corrupted signals. In this paper, we proposed efficient Variable step size leaky least mean fourth algorithm and its sign versions for reducing the complexity. These algorithms shows that it gives low steady state error due to least mean fourth and fast convergence rate that is it tracks the input signal quickly because of its variable step size is high at initial iterations of signal compared to the LMS algorithm. The performance of the algorithm is evaluated using SNR, frequency spectrum, MSE, misadjustment and convergence characteristics.

A modified formulation of eDQE for digital radiographic imaging

The aim of this study was to characterize SNR transfer ability of a digital radiographic system for patient equivalent thicknesses that cover a wide range of clinical examinations Detector MTF, NNPS and DQE measured for different beam qualities using IEC methodology. These measurements were repeated with the grid but aluminum filters of the RQA beam quality were replaced with PMMA slabs of 5, 10, 15, 20 and 25 cm thicknesses. The effects of focus unsharpness were investigated by repeating these measurements with these slabs and positioning the edge phantom at the patient input plane, without the presence of scattering material. eDQE was evaluated for different thicknesses of PMMA slabs using standard and different MTF measurement method. A large region of interest was used to form the edge spread function (ESF), thus including the influence of scattered radiation on the MTF curve; this was termed MTFsys. The system DQE was calculated using MTFsys and abbreviated as modified eDQE (eDQEmod) and compared to the standard method of eDQE. The effect of scatter on the system SNR characteristics is a low spatial frequency effect, while focus unsharpness becomes the main degrading signal transfer factor at higher spatial frequencies. Reasonable agreement was found between modified eDQE (eDQEmod) and eDQE that calculated with standard method for all beam qualities. Inclusion of scattered radiation in the MTF measurement, rather than explicit measurement of scatter fraction (SF) is a quick and reasonably accurate means of estimating the eDQE.

Effectiveness of BPSK Modulation With Peak Noise Avoidance Algorithm in Smart Street Lighting Communications Based on PLC

Abstract This work reports the measurement results of long-terms tests obtained in real smart street lighting LV networks and in lab test bed. The measurements results are presented in the form of PER vs. SNR characteristics for BPSK coded and BPSK coded with peak noise avoidance algorithm. The surprising results obtained from real LV networks prompted the Authors to carry out detailed lab tests. The setup for lab tests is also presented in this work. To carry out lab tests a suitable software was prepared, it allowed to obtain results in 72 times shorter time comparing to real conditions; it is why this work can present characteristics of PER vs. SNR even for the values of PER at the level of 10−9. The work concludes with disadvantages of using the peak noise avoidance algorithm in PLC communication for smart street lighting.

G181.1+9.5, a new high-latitude low-surface brightness supernova remnant

More than 90% of the known Milky Way supernova remnants are within 5 degrees of the Galactic Plane. We present the discovery of the supernova remnant G181.1+9.5, a new high-latitude SNR, serendipitously discovered in an ongoing survey of the Galactic Anti-centre High-Velocity Cloud complex, observed with the DRAO Synthesis Telescope in the 21~cm radio continuum and HI spectral line. We use radio continuum observations (including the linearly polarized component) at 1420~MHz (observed with the DRAO ST) and 4850~MHz (observed with the Effelsberg 100-m radio telescope) to map G181.1+9.5 and determine its nature as a SNR. High-resolution 21~cm HI line observations and HI emission and absorption spectra reveal the physical characteristics of its local interstellar environment. Finally, we estimate the basic physical parameters of G181.1+9.5 using models for highly-evolved SNRs. G181.1+9.5 has a circular shell-like morphology with a radius of about 16~pc at a distance of 1.5 kpc some 250 pc above the mid-plane. The radio observations reveal highly linearly polarized emission with a non-thermal spectrum. Archival ROSAT X-ray data reveal high-energy emission from the interior of G181.1+9.5 indicative of the presence of shock-heated ejecta. The SNR is in the advanced radiative phase of SNR evolution, expanding into the HVC inter-cloud medium with a density of 1$~cm$^{-3}$. Basic physical attributes of G181.1+9.5 calculated with radiative SNR models show an upper-limit age of 16,000 years, a swept-up mass of more than 300 solar masses, and an ambient density in agreement with that estimated from HI observations. G181.1+9.5 shows all characteristics of a typical mature shell-type SNR, but its observed faintness is unusual and requires further study.

Statistical Properties of SNR for Compressed Measurements

Some basic statistical properties of the compressed measurements are investigated. It is well known that the statistical properties are a foundation for analyzing the performance of signal detection and the applications of compressed sensing in communication signal processing. Firstly, we discuss the statistical properties of the compressed signal, the compressed noise, and their corresponding energy. And then, the statistical characteristics of SNR of the compressed measurements are calculated, including the mean and the variance. Finally, probability density function and cumulative distribution function of SNR are derived for the cases of the Gamma distribution and the Gaussian distribution. Numerical simulation results demonstrate the correctness of the theoretical analysis.

OCT Signal Processing Method Based on Sequential Kalman Filtering

Optical current transformer (OCT) measured current signal which is mixed with strong random noise. The measured readings can’t accurately reflect the value of the measured current. Since the optical current transformer noise inside the band is basically where the measured current signal overlap,we can not use the traditional method to filter it out. This paper describes the measurement principle based on the Faraday effect of optical current transformer and signal to noise characteristics. Considering optical current transformer for low SNR characteristics, and embedded systems do not have the characteristics of a matrix library, we proposed using sequential Kalman filter to improve the real-time output signal to noise ratio. In the measured current for DC and AC conditions,we established an appropriate state space model Kalman filter.,and conduct simulation on matlab. Practice shows that the sequential Kalman filter algorithm can effectively improve the output signal to noise ratio and accuracy.

Sparse Bayesian Inference of White Matter Fiber Orientations from Compressed Multi-resolution Diffusion MRI.

The RubiX [1] algorithm combines high SNR characteristics of low resolution data with high spacial specificity of high resolution data, to extract microstructural tissue parameters from diffusion MRI. In this paper we focus on estimating crossing fiber orientations and introduce sparsity to the RubiX algorithm, making it suitable for reconstruction from compressed (under-sampled) data. We propose a sparse Bayesian algorithm for estimation of fiber orientations and volume fractions from compressed diffusion MRI. The data at high resolution is modeled using a parametric spherical deconvolution approach and represented using a dictionary created with the exponential decay components along different possible directions. Volume fractions of fibers along these orientations define the dictionary weights. The data at low resolution is modeled using a spatial partial volume representation. The proposed dictionary representation and sparsity priors consider the dependence between fiber orientations and the spatial redundancy in data representation. Our method exploits the sparsity of fiber orientations, therefore facilitating inference from under-sampled data. Experimental results show improved accuracy and decreased uncertainty in fiber orientation estimates. For under-sampled data, the proposed method is also shown to produce more robust estimates of fiber orientations.

The Wavelet De-Noising of Vibration Signals for Aircraft Rolling Bearings

Aiming at air rolling bearing vibration signals low SNR and nonstationary characteristics, taking wavelet theory and principles of the wavelet noise reduction for air vibration signals of rolling bearings to conduct wavelet noise reduction processing.By means of the simulation signal wavelet noise reduction processing and fast Fourier transform, the contrast analysis of the vibration signals after wavelet noise reduction and FFT transform and the original signal directly to the result of the fast Fourier transform, and thus prove the validity of the vibration signal wavelet noise reduction. Through the actual vibration signals of bearing conductnoise reduction processing, the result is a further indication of the superiority of wavelet noise reduction in eliminate noise interference.

Feedback characteristics between implantable microphone and transducer in middle ear cavity

With the advent of implantable hearing aids, implementation and acoustic sensing strategy of the implantable microphone becomes an important issue; among the many types of implantable microphone, placing the microphone in middle ear cavity (MEC) has advantages including simple operation and insensitive to skin touching or chewing motion. In this paper, an implantable microphone was implemented and researched feedback characteristic when both the implantable microphone and the transducer were placed in the MEC. Analytical and finite element analysis were conducted to design the microphone to have a natural frequency of 7kHz and showed good characteristics of SNR and sensitivity. For the feedback test, simple analytical and finite element analysis were calculated and compared with in vitro experiments (n = 4). From the experiments, the open-loop gain and feedback factor were measured and the minimum gain margin measured as 14.3dB.

EeteS—The EnMAP End-to-End Simulation Tool

The design of future Earth imaging systems, the optimization of fundamental instrument parameters, and the development and evaluation of data pre-processing and scientific-exploitation algorithms require an accurate end-to-end simulation of the entire image generation and processing chain. For this purpose, the end-to-end simulation software EeteS has been developed within the framework of the Environmental Mapping and Analysis Program (EnMAP) mission. This paper presents the EeteS simulation approach and software implementation focusing on calibration and pre-processing. The sequential processing chain of the EnMAP scene simulator consists of four independent parts-the atmospheric, spatial, spectral and radiometric modules. This forward simulator is coupled with a backward simulation branch consisting of calibration modules (non-linearity, dark current and absolute radiometric calibration) and a series of pre-processing modules (radiometric calibration, co-registration, atmospheric correction and orthorectification) forming the complete end-to-end simulation tool. In the result EeteS is capable of simulating EnMAP-like raw image scenes (L0) taking into account a variety of instrumental and environmental configurations. Furthermore, EeteS allows simulations of EnMAP reflectance images carrying out the complete L1 and L2 processing chains. Analysis of the intermediate and final EeteS simulation products has shown the accurate, reliable and consistent performance of the developed modules enabling the system to support technical decision-making processes required for the development of the EnMAP sensor. EeteS has also been used to estimate the SNR characteristics of potential EnMAP products after calibration and pre-processing. Comparing the results to SNR characteristics achieved by the already existing EO-1 Hyperion system has shown a significantly improved SNR which can be expected from future EnMAP data products.

초음파 도플러 컬러 유동 사상에서 신호 대 잡음비 특성의 향상

초음파 컬러 유동 사상에서 가장 많이 사용되는 Kasai 알고리듬은 래그-1 자기상관 방법으로서 적은 계산량으로 비교적 우수하게 도플러 평균주파수를 추정한다. 하지만 특히 깊은 곳을 영상화하는 경우 낮은 신호 대 잡음비로 인해 추정성능이 저하된다. 본 논문에서는 낮은 신호 대 잡음비에서도 Kasai 알고리듬보다 우수한 디에일리어스된 래그-2 자기상관방법을 제안하고 시뮬레이션을 통해 성능을 검증하였다. 제안한 방법은 잡음이 존재하는 경우 도플러 평균주파수 추정 성능을 평균 자승 오차 측면에서 전반적으로 약 2~3dB 정도 개선시켜줌을 확인하였다. 【Being the most widely used in ultrasound Doppler color flow mapping, the Kasai algorithm, also known as lag-1 autocorrelation method, is capable of estimating the Doppler mean frequency relatively accurately with a modest amount of computation. Particularly in the case of imaging deep lying areas, however, its performance suffers due to low signal-to-noise ratios. The purpose of this paper is to propose a dealiased lag-2 autocorrelation method which is superior to the Kasai algorithm even at low signal-to-noise ratios and to compare their performances through simulations. The proposed algorithm is found to be better by about 2 to 3 dB than the Kasai algorithm in terms of Doppler mean frequency estimation error in the presence of measurement noise.】

天基可见光相机探测距离仿真分析

The range of detection is one important specification for expressing the ability of detection and recognition of space-based optical camera.According to the principles of photometry and radiometry,through analysis of the target luminosity characteristics,the sky background characteristics and the CCD SNR characteristics,SNR model was established.According to the connection between the pixel numbers and the detection result,the relationship among SNR,detection distance and detection result was obtained.At last,taking the SBV sensor developed by American,the simulation and calculation were made through Matlab.All of this can provide reference for performance evaluation of the space-based optical detection and the sensor design.

Evaluation of Image Quality of a 32-Channel versus a 12-Channel Head Coil at 1.5T for MR Imaging of the Brain

Multichannel phased-array head coils are undergoing exponential escalation of coil element numbers. While previous technical studies have found gains in SNR and spatial resolution with the addition of element coils, it remains to be determined how these gains affect clinical reading. The purpose of this clinical study was to determine if the SNR and spatial resolution characteristics of a 32-channel head coil result in improvements in perceived image quality and lesion evaluation. Twenty-one patients underwent MR imaging of the brain at 1.5T sequentially with both a 12-channel and a 32-channel receive-only phased-array head coil. Axial T2WIs, T1WIs, FLAIR images, and DWIs were acquired. Anonymized images were compared side-by-side and by sequence for image quality, lesion evaluation, and artifacts by 3 neuroradiologists. Results of the comparison were analyzed for the preference for a specific head coil. FLAIR and DWI images acquired with the 32-channel coil showed significant improvement in image quality in several parameters. T2WIs also improved significantly with acquisition by the 32-channel coil, while T1WIs improved in a limited number of parameters. While lesion evaluation also improved with acquisition of images by the 32-channel coil, there was no apparent improvement in diagnostic quality. There was no difference in artifacts between the 2 coils. Improvements in SNR and spatial resolution attributed to image acquisition with a 32-channel head coil are paralleled by perceived improvements in image quality.

Application of multiple attributes fusion technology in the Su-14 Well Block

In this study area the geological conditions are complicated and the effective sandstone is very heterogeneous. The sandstones are thin and lateral and vertical variations are large. We introduce multi-attribute fusion technology based on pre-stack seismic data, pre-stack P- and S-wave inversion results, and post-stack attributes. This method not only can keep the fluid information contained in pre-stack seismic data but also make use of the high SNR characteristics of post-stack data. First, we use a one-step recursive method to get the optimal attribute combination from a number of attributes. Second, we use a probabilistic neural network method to train the nonlinear relationship between log curves and seismic attributes and then use the trained samples to find the natural gamma ray distribution in the Su-14 well block and improve the resolution of seismic data. Finally, we predict the effective reservoir distribution in the Su-14 well block.

TU‐D‐204B‐02: Tumor Motion Assessment with 4D Cone Beam CT Using Prior Image Constrained Compressed Sensing (PICCS)

Purpose: 4D‐CBCT provides capability of in‐room measurement of breathing induced tumor motion before the treatment. However, to ensure sufficient image quality for motion assessment, investigators have proposed to prolong the data acquisition from 1 minute to 4 minutes or more. In this paper, we present a method which could generate high quality 4D‐CBCT images and support tumor motion extraction using the standard 1‐minute protocol with an on‐board imager. Method and Materials: The enabling technique is Prior Image Constrained Compressing Sensing (PICCS) reconstruction method. PICCS maintains high temporal resolution for each phase‐bin image after resorting the projection data. Within each phase bin, although the number of projections is vastly undersampled, PICCS reconstruction ensures high SNR characteristics since the SNR is primarily determined by the prior image reconstructed from all cone‐beam projection data without gating. Patient studies were conducted for validation. The first case was a 1‐minute CBCT scan; the second case, although originally a 4‐minute scan, was used to simulate a 1‐minute scan by down‐sampling the projection data by 4 times. For both cases, both FBP and PICCS algorithms were performed to reconstruct 4DCBCT images; a deformable registration method was also applied to extract the tumor motion. For the second case, a comparison was also made between 4‐minute FBP images and 1‐min PICCS images. Results: For 1‐minutes scans from both studies, PICCS images showed superior image quality to FBP ones; as a result, PICCS images generated physically acceptable tumor motion profiles which agree well with direct observation. Compared with FBP images from the 4‐minute scan, PICCS images from the simulated 1‐minute scan showed higher SNR and more physically accurate motion profile. Conclusion: Clinical feasibility of 1‐minute PICCS‐4DCBCT was demonstrated. The proposed method is able to reconstruct high SNR 4D images for tumor motion assessment, without slowing down the data acquisition.

Accurate Signal-to-Noise Analysis of Derivative and Quadrature Differential FM Discriminators Based on Multi-Sinusoidal AWGN Representation

The noise performances under AWGN channel of the IF-derivative and the quadrature differential FM discriminators, which are widely utilized in modern low power wireless radios, are analyzed and compared. The analysis relies upon the time-domain multi-sinusoidal representation of the noise that facilitates accurate and closed-form analytical SNR characteristics. Derivation of the SNR equations is detailed and discussion based on the analysis results is given to provide insights into the discriminators' performance limitation where it is demonstrated that the differential scheme is considerably more advantageous. Simulated SNR characteristics of practical continuous-phase frequency shift keying (CPFSK) systems using both the FM discriminators are presented as analysis verification.