Modeling Shielded Gamma-ray Source Spectra using Non-negative Matrix Factorization

The improvements in ship detection performance that can be obtained by using polarimetric synthetic aperture radar (SAR) data as compared to single-channel SAR data are investigated. Statistical decision theory is used to define decision variables that quantify the trade-off between the probability of missed detection and the probability of false alarm; performance is characterized by calculating receiver operating characteristics from single-channel and polarimetric SAR data by using likelihood ratio tests with the Neyman-Pearson criterion. It is shown that improvements in detection performance can be obtained with polarimetric SAR data as compared to single channel SAR data. We evaluate the results of these algorithms applied to single channel, dual channel amplitude only, dual channel with amplitude and phase, and fully polarimetric SAR data of known ships. In this way, the relative improvement in ship detection performance that is realized by introducing polarimetric information is quantified

Analysis of complex gamma-ray spectra using particle swarm optimization

NaI(Tl) scintillation detectors have been widely applied for gamma-ray spectrum measurements owing to advantages such as high detection efficiency and low price. However, the mitigation of the limited energy resolution of these detectors, which detracts from an accurate analysis of the instrument spectra obtained, remains a crucial need. Based on the physical properties and spectrum formation processes of NaI(Tl) scintillation detectors, the detector response to gamma photons with different energies is represented by photopeaks that are approximately Gaussian in shape with unique full-width-at-half-maximum (FWHM) values. The FWHM is established as a detector parameter based on resolution calibrations and is used in the construction of a general Gaussian response matrix, which is employed for the inverse decomposition of gamma spectra obtained from the detector. The Gold and Boosted Gold iterative algorithms are employed to accelerate the decomposition of the measured spectrum. Tests of the inverse decomposition method on multiple simulated overlapping peaks and on experimentally obtained U and Th radionuclide series spectra verify the practicability of the method, particularly in the low-energy region of the spectrum, providing for the accurate qualitative and quantitative analysis of radionuclides.

Precise gamma-ray spectrum stabilization using full spectral information

Maximum-likelihood fitting by the expectation maximization deconvolution method is presented to analyse gamma-ray spectra recorded using an NaI(Tl) detector for a water monitoring system. The applicability of the method was tested by deconvolving measured spectra taken using an industry standard 3'' x 3'' cylindrical NaI(Tl) detector in a model water tank with several calibration sources. The results show significant removal of the Compton continuum counts and efficient transfer of the counts into the corresponding photo-peaks. The peak-to-total count ratio and the number of counts in the photo-peaks in the deconvolved spectra increased approximately 4.67 and 5.29 times, respectively, compared with those of measured spectra taken using an NaI(Tl) scintillation detector in the case of (137)Cs.

Equatorial secondary cosmic ray observatory to study space weather and terrestrial events

This paper presents a novel spatial texture prediction method based on non-negative matrix factorization. As an extension of template matching, approximation based iterative texture prediction methods have recently been considered for image prediction. These approaches rely on the assumption that the given basis functions (atoms) span the signal residue space at each iteration of the algorithm. However, in the case of signal prediction with a sup port region approximation, the atoms may not approximate residue signals very well even though the dictionary has been well adapted in the spatial domain. The underlying main idea is to consider a factorization based algorithm in which the given atoms approximate the signal without going further into signal residue space. The proposed spatial prediction method has first been assessed against the prediction methods based on template matching and sparse approximations. It has then been assessed in a compression scheme where the prediction residue is transform encoded. Experimental results obtained show that the proposed method outperforms the template matching and sparse approximations based techniques in terms of encoding efficiency.

Experimental evaluation of gamma radiation attenuation properties of Ni0.2MgxZn0.8-xFe2O4

Compton back-scattering technique is used to determine the liquid-liquid interface and measurement of physical density of solution by using 662 keV gamma photons. In the present work, 3″×3″ NaI(Tl) scintillation detector is used which is placed at an angle of 1800 with the incident gamma beam to detect the back-scattered gamma photons from immiscible liquids of varying density. Calibration curve is drawn for different solutions with respect to back-scattered counts with their densities and is used to find out the density of any unknown liquid under the density range of 0.7 ≤ ρ ≤ 1.5 g/cm3. Response function of 3″×3″ NaI(Tl) scintillation detector is applied on the recorded pulse-height distribution in order to have accurate experimental results.

The lunar eclipse at Udaipur (27◦ 43’ 12.00” N, 75◦ 28’ 48.01” E), India was experimentally observed on June16, 2011 using ground based NaI (Tl) Scintillation detector. We interpret such decrement of secondary gamma radiation flux (SGRF) on the basis of obstruction effect produced by Earth and Moon during lunar eclipse on radiation. Objective: To observe secondary gamma radiation flux during lunar eclipse day on comparison to pre and post eclipse days. Methods: For this experimental study we used ground based NaI (Tl) Scintillation detector. The data files were stored in computer for thirty minutes duration from 1.00 AM to 2.00 AM (Indian Time) on pre-eclipse normal days June 14 and 15, 2011 as well as on post eclipse normal day June 17 and also on eclipse day June 16, 2011. Finding: The analyzed data reveal significant decrement of secondary gamma radiation flux (SGRF) on lunar eclipse day about 6% on comparison to pre and post lunar eclipse days. Novelty: In this experimental study first time we reported about 6% decrement of secondary gamma radiation flux on lunar eclipse day on comparison to pre and post lunar eclipse days. Keywords: Lunar eclipse; cosmic radiation; reflected solar radiation; secondary gamma radiation; obstruction effect by Earth and Moon

The detection efficiency study of NaI(Tl) scintillation detector with the different numbers of SiPMs

Simulation of a NaI(Tl) detector model using OpenMC modified on code source and validation using experimental and simulated results

In this dissertation, a novel approach of integrating template matching with statistical modeling is proposed to improve continuous speech recognition. Hidden Markov Modeling (HMMs) has been the dominant approach in statistical speech recognition since it provides a principled way of jointly modeling speech spectral variations and time dynamics. However, HMMs have the shortcoming of assuming the observations being independent within each state, which makes it ineffective in modeling the details of speech temporal evolutions that are important for characterizing nonstationary speech sounds. Template-based methods make comparisons between a test pattern and the templates derived from training data, and therefore they are able to capture speech dynamics and time correlation of speech frames better than HMM based methods. However, template matching requires large memory space and computational time since feature vectors of training data need to be stored in computer memory for access at the recognition stage, which is difficult in large vocabulary continuous speech recognition (LVCSR). Our proposed approach takes advantages of both statistical modeling and template matching, which overcomes the weakness of conventional template-based method and is feasible for LVCSR. We use multiple Gaussian Mixture Model (GMM) indices to represent each frame of speech templates, and define the template unit to be context-dependent phone segments (triphone context). We also use phonetic decision trees borrowed from those commonly used in HMMs to tie triphone templates and predict triphones unseen in training data. Two local distances, log likelihood ratio (LLR) and Kullback-Leibler (KL) divergence, are proposed for dynamic time warping (DTW) based template matching. In order to reduce computational complexity and storage space, we propose methods of minimum distance template selection (MDTS) and maximum log-likelihood template selection (MLTS), and investigate a template compression method on top of template selection to further improve recognition performance. The template based methods were used to rescore lattices generated by baseline HMMs on the tasks of TIMIT continuous phone recognition and teleheath LVCSR and experimental results demonstrated that the proposed approach of integrating template matching with statistical modeling significantly improved recognition performances over the HMM baselines. The template selection methods also provided significant recognition accuracy improvements over the HMM baseline while largely reducing the computation and storage complexities. When all templates or MDTS were used, using the LLR local distance obtained better recognition performance than the KL divergence local distance. For MLTS and template compression, KL divergence local distance provided better performance than the LLR local distance, and the template compression method made further improvements over KL based MLTS. Since the templates were constructed based on the GMM indices extracted from HMM baselines, we also validated the effectiveness of the proposed template methods based on enhanced HMM baselines. Experimental results showed that LLR based all template method was able to consistently improve TIMIT phone recognition accuracies based on four enhanced HMM baselines. Prosodic features such as duration, energy, and pitch can reflect longer span information of speech than conventional single frame vectors but they have commonly been ignored by HMMs. Template based methods provide possibilities to conveniently integrate prosodic features into speech recognition, which has not been well studied in the past. In this dissertation, we investigate combining template based methods with the speech prosodic features of duration, energy and pitch to further improve speech recognition accuracy. The scores of prosodic information were computed by a GMM based method and a non-parametric method, and the prosodic scores were combined with the acoustic scores in triphone template matching. Experimental results obtained on the telehealth task showed that prosodic information had positive effects on vowel sound recognition.

Bremsstrahlung photons measurement show elevated energy emission beyond critical energy in 28-GHz ECR and its nature is not revealed. Thus, the aim of this study is to unveil the nature of high end-point energies at the three measurement locations. Bremsstrahlung photons from a 28-GHz ECR ion source were measured at Busan center of Korean Basic Science Institute (KBSI). The gamma-ray detection system consists of three round type NaI(Tl) scintillation detectors placed 62 cm radially from the beam axis at the three measurement locations namely injection, centre and extraction sides of an ECR ion source, and a NaI(Tl) scintillation detector placed at the view port for monitoring photon intensity along the beam axis. Such design of the experiment was based on the inner structure of the ECR ion source and the shape of the ECR plasma. Also, bremsstrahlung photons were measured by exchanging the detector positions for studying systematic uncertainties. Monte Carlo simulation based on the Geant4 package was performed to take geometrical acceptance and the energydependent detection efficiency into account due to large non-uniformity in the material budget. True bremsstrahlung energy spectra from the 28-GHz ECR ion source were extracted using an inverse matrix unfolding method. The measured high end-point energies at the three measurement locations were 1.68 MeV, 1.72 MeV and 2.04 MeV for the injection, centre and extraction sides, respectively. The high end-point energy values of the measured bremsstrahlung photons beyond the theoretical energy value of 1.33 MeV were found to be correlated with the structure of the ECR ion source and the shape of the ECR plasma. Furthermore, the high end-point energy values resulting from the bremsstrahlung photons measurements in the 28-GHz ECR plasma are due to the higher number of unconfined electrons arriving at the chamber wall easily, and producing higher bremsstrahlung photons. The unconfined electrons are the results of imperfect magnetic confinement that cause electrons to escape from the plasma volume.

Background: Recent technological advances have drastically reduced the cost and increased the speed of DNA sequencing, developments that have been heralded as the beginning of a new era of personalized medicine. However, there still remains a pressing need to translate improved knowledge of cancer genomics into better clinical outcomes. Like many solid tumors, high grade serous ovarian carcinoma (HGS-OvCa) is a heterogeneous entity with a widely variable clinical course even among patients of the same stage and histological subtype. Most prior attempts to stratify tumors into clinically meaningful subtypes have utilized gene expression profiles; however as a whole such methods have not reliably discovered subtypes with significant differences in survival or response to chemotherapy. There has been much effort to stratify tumors based on their somatic mutations; however this has proved difficult due to the great heterogeneity and sparseness of somatic mutations in most solid tumors. For example, in the TCGA HGS-OvCa cohort the average tumor had 61 non-silent mutations spread out over the ~20,000 genes in the human genome; most genes were mutated in one tumor only. Standard consensus clustering approaches applied to this sparse mutational data have repeatedly failed to produce a clinically valuable stratification of patients. Methods: The technique of Network-based Stratification (NBS) combines genome-scale somatic mutation profiles with a genetic interaction network to produce a subdivision of patients into subtypes. Briefly, somatic mutations for each patient are represented as a profile of binary (1,0) states on genes, in which a ‘1’ indicates a mutated gene. For each patient independently we project the mutation profiles onto a human gene interaction network obtained from public databases. Next the technique of network propagation is applied to spread the influence of each subsampled mutation profile over its network neighborhood, resulting in a ‘network-smoothed’ profile. Following this ‘network-smoothing’, profiles are clustered into a predefined number of subtypes using the unsupervised technique of non-negative matrix factorization (NMF). This procedure is repeated for 1000 different subsamples using the technique of consensus clustering to promote robust cluster assignments. This method was repeated with three difference sources of network data: STRING, HumanNet, and PathwayCommons. Results: Using the NBS technique four distinct subtypes were identified from the TCGA HGS-OvCa cohort of 330 exome sequenced and clinically-annotated tumors. The median overall survival (OS) in months for these four subtypes was 36, 48, 56, and not-yet-reached respectively, which was highly significant (Logrank p = 1.59 x 10-5). Subtypes were also predictive of platinum free interval (PFI), (Logrank p = 0.046). No significant differences between subtypes were found in age, tumor stage at presentation, or percentage of patients with an optimal surgical resection, and a cox proportional hazards model was significant even after taking those factors into account (Likelihood ratio test p = 3.3x10-4). Comparing each subtype against the other three, the network of mutated genes for subtype 1, which had the worst overall survival and shortest platinum free interval, was enriched for over 20 genes in the fibroblast growth pathway. The network for subtype 2 was enriched in DNA damage response genes including ATM, ATR, BRCA1/2, RAD51 and Chek2, and also contained the majority of patients with BRCA1/2 germline mutations (15/20 and 5/6, respectively). Conclusions: Network-based Stratification (NBS) combines genome-scale somatic mutation profiles with a genetic interaction network to stratify a cohort of HGS-OvCa tumors into subtypes with significant differences is OS and PFI. Citation Format: Matan Hofree, John Paul Shen, Hannah Carter, Andrew Gross, Trey Ideker. Network-based stratification: Combining genome-scale somatic mutation profiles with genetic interaction networks to identify clinically relevant subtypes in high grade serous ovarian carcinoma. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research: From Concept to Clinic; Sep 18-21, 2013; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2013;19(19 Suppl):Abstract nr A14.