Estimating the number of components in an OSL decay curve using the Bayesian Information Criterion

Abstract The optically stimulated luminescence (OSL) decay curve is assumed to consist of a number of first-order exponential components. Improper estimation of the number of components leads to under-or over-fitting of the curve under consideration. Hence, correct estimation of the number of components is important to accurately analyze an OSL decay curve. In this study, we investigated the possibility of using the Bayesian Information Criterion to estimate the optimal number of components in an OSL decay curve. We tested the reliability of this method using several hundred measured decay curves and three simulation scenarios. Our results demonstrate that the quality of the identification can be influenced by several factors: the measurement time and the number of channels; the variability of the decay constants; and the signal-to-noise ratios of a decaying component. The results also suggest that the Bayesian Information Criterion has great potential to estimate the number of components in an OSL decay curve with a moderate to high signal-to-noise ratio.

Download Full-text

Assisted traveltime picking of crosshole GPR data

Geophysics ◽

10.1190/1.3141002 ◽

2009 ◽

Vol 74 (4) ◽

pp. J35-J48 ◽

Cited By ~ 9

Author(s):

Bernard Giroux ◽

Abderrezak Bouchedda ◽

Michel Chouteau

Keyword(s):

Time Window ◽

Signal To Noise Ratio ◽

Real Data ◽

Information Criterion ◽

High Signal ◽

Data Sets ◽

Signal To Noise ◽

Arrival Times ◽

Common Time ◽

Waveform Similarity

We introduce two new traveltime picking schemes developed specifically for crosshole ground-penetrating radar (GPR) applications. The main objective is to automate, at least partially, the traveltime picking procedure and to provide first-arrival times that are closer in quality to those of manual picking approaches. The first scheme is an adaptation of a method based on cross-correlation of radar traces collated in gathers according to their associated transmitter-receiver angle. A detector is added to isolate the first cycle of the radar wave and to suppress secon-dary arrivals that might be mistaken for first arrivals. To improve the accuracy of the arrival times obtained from the crosscorrelation lags, a time-rescaling scheme is implemented to resize the radar wavelets to a common time-window length. The second method is based on the Akaike information criterion(AIC) and continuous wavelet transform (CWT). It is not tied to the restrictive criterion of waveform similarity that underlies crosscorrelation approaches, which is not guaranteed for traces sorted in common ray-angle gathers. It has the advantage of being automated fully. Performances of the new algorithms are tested with synthetic and real data. In all tests, the approach that adds first-cycle isolation to the original crosscorrelation scheme improves the results. In contrast, the time-rescaling approach brings limited benefits, except when strong dispersion is present in the data. In addition, the performance of crosscorrelation picking schemes degrades for data sets with disparate waveforms despite the high signal-to-noise ratio of the data. In general, the AIC-CWT approach is more versatile and performs well on all data sets. Only with data showing low signal-to-noise ratios is the AIC-CWT superseded by the modified crosscorrelation picker.

Download Full-text

Getting the model right: an information criterion for spectroscopy

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/staa3551 ◽

2020 ◽

Vol 501 (2) ◽

pp. 2268-2278

Author(s):

John K Webb ◽

Chung-Chi Lee ◽

Robert F Carswell ◽

Dinko Milaković

Keyword(s):

Line Strength ◽

Detection Threshold ◽

Information Criterion ◽

Optimal Number ◽

Information Criteria ◽

High Signal ◽

Model Parameters ◽

Signal To Noise ◽

Data Set ◽

Noise Data

ABSTRACT Robust model-fitting to spectroscopic transitions is a requirement across many fields of science. The corrected Akaike and Bayesian information criteria (AICc and BIC) are most frequently used to select the optimal number of fitting parameters. In general, AICc modelling is thought to overfit (too many model parameters) and BIC underfits. For spectroscopic modelling, both AICc and BIC lack in two important respects: (a) no penalty distinction is made according to line strength such that parameters of weak lines close to the detection threshold are treated with equal importance as strong lines and (b) no account is taken of the way in which a narrow spectral line impacts only on a very small section of the overall data. In this paper, we introduce a new information criterion that addresses these shortcomings, the Spectral Information Criterion (SpIC). Spectral simulations are used to compare performances. The main findings are (i) SpIC clearly outperforms AICc for high signal-to-noise data, (ii) SpIC and AICc work equally well for lower signal-to-noise data, although SpIC achieves this with fewer parameters, and (iii) BIC does not perform well (for this application) and should be avoided. The new method should be of broader applicability (beyond spectroscopy), wherever different model parameters influence separated small ranges within a larger data set and/or have widely varying sensitivities.

Download Full-text

Effect of Scan Time on Neuro 18F-Fluorodeoxyglucose Positron Emission Tomography Image Generated Using Deep Learning

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2021.3316 ◽

2021 ◽

Vol 11 (3) ◽

pp. 681-687

Author(s):

Jaewon Kim ◽

Sungsik Kang ◽

Konsu Lee ◽

Jin Ho Jung ◽

Garam Kim ◽

...

Keyword(s):

Deep Learning ◽

Performance Test ◽

Signal To Noise Ratio ◽

Ground Truth ◽

High Signal ◽

Ct Scanner ◽

Signal To Noise ◽

Scan Time ◽

Mean Square Errors

The purpose of this study was to generate the PET images with high signal-to-noise ratio (SNR) acquired for typical scan durations (H-PET) from short scan time PET images with low SNR (L-PET) using deep learning and to evaluate the effect of scan time on the quality of predicted PET image. A convolutional neural network (CNN) with a concatenated connection and residual learning framework was implemented. PET data from 27 patients were acquired for 900 s, starting 60 minutes after the intravenous administration of FDG using a commercial PET/CT scanner. To investigate the effect of scan time on the quality of the predicted H-PETs, 10 s, 30 s, 60 s, and 120 s PET data were generated by sorting the 900 s LMF data into the LMF data acquired for each scan time. Twenty-three of the 27 patient images were used for training of the proposed CNN and the remaining four patient images were used for test of the CNN. The predicted H-PETs generated by the CNN were compared to ground-truth H-PETs, L-PETs, and filtered L-PETs processed with four commonly used denoising algorithms. The peak signal-to-noise ratios (PSNRs), normalized root mean square errors (NRMSEs), and average regionof- interest (ROI) differences as a function of scan time were calculated. The quality of the predicted H-PETs generated by the CNN was superior to that of the L-PETs and filtered L-PETs. Lower NRMSEs and higher PSNRs were also obtained from predicted H-PETs compared to the L-PETs and filtered L-PETs. ROI differences in the predicted H-PETs were smaller than those of the L-PETs. The quality of the predicted H-PETs gradually improved with increasing scan times. The lowest NRMSEs, highest PSNRs, and smallest ROI differences were obtained using the predicted H-PETs for 120 s. Various performance test results for the proposed CNN indicate that it is possible to generate H-PETs from neuro FDG L-PETs using the proposed CNN method, which might allow reductions in both scan time and injection dose.

Download Full-text

A compact guided-wave EMAT with pulsed electromagnet for ferromagnetic tube inspection

International Journal of Applied Electromagnetics and Mechanics ◽

10.3233/jae-209409 ◽

2020 ◽

Vol 64 (1-4) ◽

pp. 951-958

Author(s):

Tianhao Liu ◽

Yu Jin ◽

Cuixiang Pei ◽

Jie Han ◽

Zhenmao Chen

Keyword(s):

Power Plants ◽

Signal To Noise Ratio ◽

Small Diameter ◽

Performance Testing ◽

Guided Wave ◽

High Signal ◽

Receiver Coil ◽

Signal To Noise ◽

Corrosion Defects

Small-diameter tubes that are widely used in petroleum industries and power plants experience corrosion during long-term services. In this paper, a compact inserted guided-wave EMAT with a pulsed electromagnet is proposed for small-diameter tube inspection. The proposed transducer is noncontact, compact with high signal-to-noise ratio and unattractive to ferromagnetic tubes. The proposed EMAT is designed with coils-only configuration, which consists of a pulsed electromagnet and a meander pulser/receiver coil. Both the numerical simulation and experimental results validate its feasibility on generating and receiving L(0,2) mode guided wave. The parameters for driving the proposed EMAT are optimized by performance testing. Finally, feasibility on quantification evaluation for corrosion defects was verified by experiments.

Download Full-text

Image Quality Enhancing by Efficient Histogram Equalization

Wasit Journal of Engineering Sciences ◽

10.31185/ejuow.vol2.iss2.29 ◽

2014 ◽

Vol 2 (2) ◽

pp. 47-58

Author(s):

Ismail Sh. Baqer

Keyword(s):

Image Quality ◽

Contrast Enhancement ◽

Mean Squared Error ◽

Signal To Noise Ratio ◽

Histogram Equalization ◽

Gray Level ◽

Signal To Noise ◽

Squared Error ◽

Noise Ratio

A two Level Image Quality enhancement is proposed in this paper. In the first level, Dualistic Sub-Image Histogram Equalization DSIHE method decomposes the original image into two sub-images based on median of original images. The second level deals with spikes shaped noise that may appear in the image after processing. We presents three methods of image enhancement GHE, LHE and proposed DSIHE that improve the visual quality of images. A comparative calculations is being carried out on above mentioned techniques to examine objective and subjective image quality parameters e.g. Peak Signal-to-Noise Ratio PSNR values, entropy H and mean squared error MSE to measure the quality of gray scale enhanced images. For handling gray-level images, convenient Histogram Equalization methods e.g. GHE and LHE tend to change the mean brightness of an image to middle level of the gray-level range limiting their appropriateness for contrast enhancement in consumer electronics such as TV monitors. The DSIHE methods seem to overcome this disadvantage as they tend to preserve both, the brightness and contrast enhancement. Experimental results show that the proposed technique gives better results in terms of Discrete Entropy, Signal to Noise ratio and Mean Squared Error values than the Global and Local histogram-based equalization methods

Download Full-text

Singular Values Decomposition and Lifting Wavelet Transform for Speech Signal Embedding into Digital Image

Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) ◽

10.2174/2352096511666180511151646 ◽

2019 ◽

Vol 12 (2) ◽

pp. 138-151

Author(s):

Mourad Talbi ◽

Med Salim Bouhlel

Keyword(s):

Wavelet Transform ◽

Speech Signal ◽

Signal To Noise Ratio ◽

Perceptual Quality ◽

Lifting Wavelet Transform ◽

Signal To Noise ◽

Perceptual Evaluation ◽

Lifting Wavelet ◽

Noise Ratio

Background: In this paper, we propose a secure image watermarking technique which is applied to grayscale and color images. It consists in applying the SVD (Singular Value Decomposition) in the Lifting Wavelet Transform domain for embedding a speech image (the watermark) into the host image. Methods: It also uses signature in the embedding and extraction steps. Its performance is justified by the computation of PSNR (Pick Signal to Noise Ratio), SSIM (Structural Similarity), SNR (Signal to Noise Ratio), SegSNR (Segmental SNR) and PESQ (Perceptual Evaluation Speech Quality). Results: The PSNR and SSIM are used for evaluating the perceptual quality of the watermarked image compared to the original image. The SNR, SegSNR and PESQ are used for evaluating the perceptual quality of the reconstructed or extracted speech signal compared to the original speech signal. Conclusion: The Results obtained from computation of PSNR, SSIM, SNR, SegSNR and PESQ show the performance of the proposed technique.

Download Full-text

Understanding PI-QUAL for prostate MRI quality: a practical primer for radiologists

Insights into Imaging ◽

10.1186/s13244-021-00996-6 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Francesco Giganti ◽

Alex Kirkham ◽

Veeru Kasivisvanathan ◽

Marianthi-Vasiliki Papoutsaki ◽

Shonit Punwani ◽

...

Keyword(s):

Diffusion Weighted Imaging ◽

Signal To Noise Ratio ◽

High Signal ◽

Diagnostic Quality ◽

Prostate Mri ◽

Prostate Imaging ◽

Dynamic Contrast Enhanced ◽

Diffusion Weighted ◽

Contrast Enhanced

AbstractProstate magnetic resonance imaging (MRI) of high diagnostic quality is a key determinant for either detection or exclusion of prostate cancer. Adequate high spatial resolution on T2-weighted imaging, good diffusion-weighted imaging and dynamic contrast-enhanced sequences of high signal-to-noise ratio are the prerequisite for a high-quality MRI study of the prostate. The Prostate Imaging Quality (PI-QUAL) score was created to assess the diagnostic quality of a scan against a set of objective criteria as per Prostate Imaging-Reporting and Data System recommendations, together with criteria obtained from the image. The PI-QUAL score is a 1-to-5 scale where a score of 1 indicates that all MR sequences (T2-weighted imaging, diffusion-weighted imaging and dynamic contrast-enhanced sequences) are below the minimum standard of diagnostic quality, a score of 3 means that the scan is of sufficient diagnostic quality, and a score of 5 implies that all three sequences are of optimal diagnostic quality. The purpose of this educational review is to provide a practical guide to assess the quality of prostate MRI using PI-QUAL and to familiarise the radiologist and all those involved in prostate MRI with this scoring system. A variety of images are also presented to demonstrate the difference between suboptimal and good prostate MR scans.

Download Full-text

Narrow-frequency sharp-angular filters using all-dielectric cascaded meta-gratings

Nanophotonics ◽

10.1515/nanoph-2020-0141 ◽

2020 ◽

Vol 9 (10) ◽

pp. 3443-3450 ◽

Cited By ~ 1

Author(s):

Wei-Nan Liu ◽

Rui Chen ◽

Wei-Yi Shi ◽

Ke-Bo Zeng ◽

Fu-Li Zhao ◽

...

Keyword(s):

Thin Film ◽

Signal To Noise Ratio ◽

Incident Angle ◽

Design Strategy ◽

Transmission Peak ◽

High Signal ◽

Waveguide Array ◽

Signal To Noise ◽

Center Wavelength ◽

Noise Ratio

AbstractSelective transmission or filtering always responds to either frequency or incident angle, so as hardly to maximize signal-to-noise ratio in communication, detection and sensing. Here, we propose compact meta-filters of narrow-frequency sharp-angular transmission peak along with broad omnidirectional reflection sidebands, in all-dielectric cascaded subwavelength meta-gratings. The inherent collective resonance of waveguide-array modes and thin film approximation of meta-grating are employed as the design strategy. A unity transmission peak, locating at the incident angle of 44.4° and the center wavelength of 1550 nm, is demonstrated in a silicon meta-filter consisting of two-layer silicon rectangular meta-grating. These findings provide possibilities in cascaded meta-gratings spectroscopic design and alternative utilities for high signal-to-noise ratio applications in focus-free spatial filtering and anti-noise systems in telecommunications.

Download Full-text