Search results for: dual-layer spectral computed tomography

Authors: Vincent Kheswa

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X-rays are ionizing electromagnetic radiation that is used in various industries such as computed tomography scans, dental X-rays, and screening freight trains. However, they pose health risks to humans if they are not shielded properly. In recent years, many researchers around the globe have been searching for nontoxic best possible glass materials for shielding X-rays. In this work, the x-ray shielding properties of 45Na₂O + 10 Bi₂O₃ + (5 - x)TiO₂+ (x) Nb₂O₅ + 40 P₂O₅, were x = 0, 1, 3, 5 mol%, glass materials were studied. The results revealed that the glass sample with the highest TiO2 content has the highest mass and linear attenuation coefficients and lowest half-value thickness, tenth-value thickness and mean-free path in the 20 to 80 keV energy region. The sample with 3 mol% of Nb₂O₅ has the highest mass and linear attenuation coefficients and the lowest half-value thickness, tenth-value thickness, and mean-free path at 15 keV and photon energies between 80 to 300 keV. It was, therefore, concluded that 45Na₂O + 10 Bi₂O₃ + 5 TiO₂ + 40 P₂O₅ glass is best for shielding x-rays of energies between 20 and 80 keV, while 45Na₂O + 10 Bi₂O₃ + 2 TiO₂ + 3 Nb₂O₅ + 40 P₂O₅ is best for shielding 15 keV x-rays and x-rays of energies between 80 keV and 300 keV.

Keywords: bismuth-titanium-phosphate glass, x-ray shielding, LAC, MAC, radiation shielding

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Authors: M. A. Talha, M. Osman Gani, M. Ferdows

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This paper focuses on the study of two dimensional magnetohydrodynamic (MHD) steady incompressible viscous Williamson nanofluid with exponential internal heat generation containing gyrotactic microorganism over a stretching sheet. The governing equations and auxiliary conditions are reduced to a set of non-linear coupled differential equations with the appropriate boundary conditions using similarity transformation. The transformed equations are solved numerically through spectral relaxation method. The influences of various parameters such as Williamson parameter γ, power constant λ, Prandtl number P_r, magnetic field parameter M, Peclet number P_e, Lewis number Le, Bioconvection Lewis number Lb, Brownian motion parameter Nb, thermophoresis parameter Nt, and bioconvection constant σ are studied to obtain the momentum, heat, mass and microorganism distributions. Moment, heat, mass and gyrotactic microorganism profiles are explored through graphs and tables. We computed the heat transfer rate, mass flux rate and the density number of the motile microorganism near the surface. Our numerical results are in better agreement in comparison with existing calculations. The Residual error of our obtained solutions is determined in order to see the convergence rate against iteration. Faster convergence is achieved when internal heat generation is absent. The effect of magnetic parameter M decreases the momentum boundary layer thickness but increases the thermal boundary layer thickness. It is apparent that bioconvection Lewis number and bioconvection parameter has a pronounced effect on microorganism boundary. Increasing brownian motion parameter and Lewis number decreases the thermal boundary layer. Furthermore, magnetic field parameter and thermophoresis parameter has an induced effect on concentration profiles.

Keywords: convection flow, similarity, numerical analysis, spectral method, Williamson nanofluid, internal heat generation

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Authors: Radhe Tado, Ashish B. Deoghare, K. M. Pandey

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The aim of this study is to estimate the effect of blood flow through the coronary artery in human heart so as to assess the coronary artery disease.Velocity, wall shear stress (WSS), strain rate and wall pressure distribution are some of the important hemodynamic parameters that are non-invasively assessed with computational fluid dynamics (CFD). These parameters are used to identify the mechanical factors responsible for the plaque progression and/or rupture in left coronary arteries (LCA) in coronary arteries.The initial step for CFD simulations was the construction of a geometrical model of the LCA. Patient specific artery model is constructed using computed tomography (CT) scan data with the help of MIMICS Research 19.0. For CFD analysis ANSYS FLUENT-14.5 is used.Hemodynamic parameters were quantified and flow patterns were visualized both in the absence and presence of coronary plaques. The wall pressure continuously decreased towards distal segments and showed pressure drops in stenotic segments. Areas of high WSS and high flow velocities were found adjacent to plaques deposition.

Keywords: angiography, computational fluid dynamics (CFD), time-average wall shear stress (TAWSS), wall pressure, wall shear stress (WSS)

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Authors: S. S. Sathiesh

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The aim of this project is to study the radiation mechanism synchrotron and Inverse Compton scattering. Theoretically, we discussed spectral energy distribution for both. Programming is done for plotting the graph of Power-law spectrum for synchrotron Radiation using fortran90. The importance of power law spectrum was discussed and studied to infer its physical parameters from the model fitting. We also discussed how to infer the physical parameters from the theoretically drawn graph, we have seen how one can infer B (magnetic field of the source), γ min, γ max, spectral indices (p1, p2) while fitting the curve to the observed data.

Keywords: blazars/quasars, beaming, synchrotron radiation, Synchrotron Self Compton, inverse Compton scattering, mrk421

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Authors: Ibrahim A. Elsayed, Mohamed H. Elalfy, Mostafa M. Abdalla

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A strain-based failure criterion for composite notched laminates is introduced where the most critical stress concentration factor for the anisotropic notched laminates could be related to the failure of the corresponding quasi-isotropic laminate and the anisotropy ratio of the laminate. The proposed criterion will simplify the design of composites to meet notched failure requirements by eliminating the need for the detailed specifications of the stacking sequence at the preliminary design stage. The designer will be able to design based on the stiffness of the laminate, then at a later stage, select an appropriate stacking sequence to meet the stiffness requirements. The failure strains for the notched laminates are computed using the material’s Omni-strain envelope. The concept of Omni-strain envelope concerns the region of average strain where the laminate is safe regardless of ply orientation. In this work, we use Hashin’s failure criteria and the strains around the hole are computed using Savin’s analytic solution. A progressive damage analysis study has been conducted where the failure loads for the notched laminates are computed using finite element analysis. The failure strains are computed and used to estimate the concentration factor. It is found that the correlation found using Savin’s analytic solution predicts the same ratio of concentration factors between anisotropic and quasi-isotropic laminates as the more expensive progressive failure analysis.

Keywords: anisotropy ratio, failure criteria, notched laminates, Omni-strain envelope, savin’s solution

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Authors: Joseph M. Rich, Vinay A. Duddalwar, Assad A. Oberai

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Prostate adenocarcinoma is the most common cancer in males, with osseous metastases as the commonest site of metastatic prostate carcinoma (mPC). Treatment monitoring is based on the evaluation and characterization of lesions on multiple imaging studies, including Computed Tomography (CT). Monitoring of the osseous disease burden, including follow-up of lesions and identification and characterization of new lesions, is a laborious task for radiologists. Deep learning algorithms are increasingly used to perform tasks such as identification and segmentation for osseous metastatic disease and provide accurate information regarding metastatic burden. Here, nnUNet was used to produce a model which can segment CT scan images of prostate adenocarcinoma vertebral bone metastatic lesions. nnUNet is an open-source Python package that adds optimizations to deep learning-based UNet architecture but has not been extensively combined with transfer learning techniques due to the absence of a readily available functionality of this method. The IRB-approved study data set includes imaging studies from patients with mPC who were enrolled in clinical trials at the University of Southern California (USC) Health Science Campus and Los Angeles County (LAC)/USC medical center. Manual segmentation of metastatic lesions was completed by an expert radiologist Dr. Vinay Duddalwar (20+ years in radiology and oncologic imaging), to serve as ground truths for the automated segmentation. Despite nnUNet’s success on some medical segmentation tasks, it only produced an average Dice Similarity Coefficient (DSC) of 0.31 on the USC dataset. DSC results fell in a bimodal distribution, with most scores falling either over 0.66 (reasonably accurate) or at 0 (no lesion detected). Applying more aggressive data augmentation techniques dropped the DSC to 0.15, and reducing the number of epochs reduced the DSC to below 0.1. Datasets have been identified for transfer learning, which involve balancing between size and similarity of the dataset. Identified datasets include the Pancreas data from the Medical Segmentation Decathlon, Pelvic Reference Data, and CT volumes with multiple organ segmentations (CT-ORG). Some of the challenges of producing an accurate model from the USC dataset include small dataset size (115 images), 2D data (as nnUNet generally performs better on 3D data), and the limited amount of public data capturing annotated CT images of bone lesions. Optimizations and improvements will be made by applying transfer learning and generative methods, including incorporating generative adversarial networks and diffusion models in order to augment the dataset. Performance with different libraries, including MONAI and custom architectures with Pytorch, will be compared. In the future, molecular correlations will be tracked with radiologic features for the purpose of multimodal composite biomarker identification. Once validated, these models will be incorporated into evaluation workflows to optimize radiologist evaluation. Our work demonstrates the challenges of applying automated image segmentation to small medical datasets and lays a foundation for techniques to improve performance. As machine learning models become increasingly incorporated into the workflow of radiologists, these findings will help improve the speed and accuracy of vertebral metastatic lesions detection.

Keywords: deep learning, image segmentation, medicine, nnUNet, prostate carcinoma, radiomics

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Authors: Siriluk Ruangrungrote

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Analyses of available data from MFR-7 measurement were performed and discussed on the study of tropospheric aerosol and its consequence in Thailand. Since, ASSA (w) is one of the most important parameters for a determination of aerosol effect on radioactive forcing. Here the estimation of w was directly determined in terms of the ratio of aerosol scattering optical depth to aerosol extinction optical depth (ωscat/ωext) without any utilization of aerosol computer code models. This is of benefit for providing the elimination of uncertainty causing by the modeling assumptions and the estimation of actual aerosol input data. Diurnal w of 5 cloudless-days in winter and early summer at 5 distinct wavelengths of 415, 500, 615, 673 and 870 nm with the consideration of Rayleigh scattering and atmospheric column NO2 and Ozone contents were investigated, respectively. Besides, the tendency of spectral dependence of ω representing two seasons was observed. The characteristic of spectral results reveals that during wintertime the atmosphere of the inland rural vicinity for the period of measurement possibly dominated with a lesser amount of soil dust aerosols loading than one in early summer. Hence, the major aerosol loading particularly in summer was subject to a mixture of both soil dust and biomass burning aerosols.

Keywords: aerosol scattering optical depth, aerosol extinction optical depth, biomass burning aerosol, soil dust aerosol

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Authors: Perumalla Naveen Kumar

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Global Positioning System (GPS) is used for civilian and military user positioning applications. The accuracy of GPS is degrading mainly because of ionospheric error. It is very important to analyze the effects of ionosphere on the performance of satellite systems especially in the low latitude regions. These variations depend on the Total Electron Content (TEC) in the ionosphere. To investigate the variations in the atmosphere, a mini satellite known as YOUTHSAT is launched by India. This is the outcome of the collaboration between India and USSR. One of the YOUTHSAT Indian payload is RaBIT (Radio Beacon for Ionospheric Tomography). In this paper, YOUTHSAT RaBIT payload data for the three typical days of 2011 are considered. The analysis is carried out for four Indian stations. The variations of Slant TEC, elevation angle and azimuth angles are analyzed with respect to local time. The obtained results are encouraging.

Keywords: Global Positioning System (GPS), Total Electron Content (TEC), YOUTHSAT, Radio Beacon for Ionospheric Tomography (RaBIT)

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Authors: N. Fuad, M. N. Taib, R. Jailani, M. E. Marwan

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This paper discusses on power spectral density (PSD) characteristics which are extracted from three-dimensional (3D) electroencephalogram (EEG) models. The EEG signal recording was conducted on 150 healthy subjects. Development of 3D EEG models involves pre-processing of raw EEG signals and construction of spectrogram images. Then, the values of maximum PSD were extracted as features from the model. These features are analysed using mean relative power (MRP) and different mean relative power (DMRP) technique to observe the pattern among different brain balancing indexes. The results showed that by implementing these techniques, the pattern of brain balancing indexes can be clearly observed. Some patterns are indicates between index 1 to index 5 for left frontal (LF) and right frontal (RF).

Keywords: power spectral density, 3D EEG model, brain balancing, mean relative power, different mean relative power

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Authors: M. Harir, A. Bendiabdellah, A. Chaouch, N. Benouzza

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This paper deals with the modelling and simulation of the squirrel cage induction motor by taking into account all space harmonic components, as well as the introduction of the bars skew, in the calculation of the linear evolution of the magnetomotive force (MMF) between the slots extremities. The model used is based on multiple coupled circuits and the modified winding function approach (MWFA). The effect of skewing is included in the calculation of motors inductances with an axial asymmetry in the rotor. The simulation results in both time and spectral domains show the effectiveness and merits of the model and the error that may be caused if the skew of the bars is neglected.

Keywords: modeling, MWFA, skew effect, squirrel cage induction motor, spectral domain

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Authors: Amir Peyman Soleymani, Jasna Jankovic

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The augmented demand of the clean and renewable energy has necessitated the fuel cell and battery industries to produce more efficient devices at the lower prices, which can be achieved through the improvement of the electrode. Microstructural characterization, as one of the main materials development tools, plays a pivotal role in the production of better clean energy devices. In this study, methods for characterization and studying of the defects and components distribution were performed on the polymer electrolyte membrane fuel cell (PEMFC) and Li-ion battery (LIB) electrodes in 2D and 3D. The particles distribution, porosity, mechanical defects, and component distribution were studied by Scanning Electron Microscope (SEM), SEM-Focused Ion Beam (SEM-FIB), and Scanning Transmission Electron Microscope equipped with Energy Dispersive Spectroscopy (STEM-EDS). The 3D results obtained from X-ray Computed Tomography (XCT) revealed the pathways for electron and ion conductivity and defects progression maps. Computer-aided methods (Avizo) were employed to simulate the properties and performance of the microstructure in the electrodes. The suggestions were provided to improve the performance of PEMFCs and LIBs by adjusting the microstructure and the distribution of the components in the electrodes.

Keywords: PEM fuel cells, Li-ion batteries, 2D and 3D imaging, materials characterizations

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Authors: Igor Belykh

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Anti-scatter grids used in radiographic imaging for the contrast enhancement leave specific artifacts. Those artifacts may be visible or may cause Moiré effect when a digital image is resized on a diagnostic monitor. In this paper, we propose an automated grid artifacts detection and suppression algorithm which is still an actual problem. Grid artifacts detection is based on statistical approach in spatial domain. Grid artifacts suppression is based on Kaiser bandstop filter transfer function design and application avoiding ringing artifacts. Experimental results are discussed and concluded with description of advantages over existing approaches.

Keywords: grid, computed radiography, pattern recognition, image processing, filtering

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Authors: Vineel Inampudi, Arjun Prakash, Joseph Vinod

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Aims and Objectives: To evaluate the interrater reliability in grading ASPECTS score, between emergency medicine physician at first contact and radiologist among patients with acute ischemic stroke. Materials and Methods: We conducted a retrospective analysis of 86 acute ischemic stroke cases referred to the Department of Radiodiagnosis during November 2014 to January 2016. The imaging (plain CT scan) was performed using GE Bright Speed Elite 16 Slice CT Scanner. ASPECTS score was calculated separately by an emergency medicine physician and radiologist. Interrater reliability for total and dichotomized ASPECTS (≥ 6 and < 6) scores were assessed using statistical analysis (ICC and Cohen ĸ coefficients) on SPSS software (v17.0). Results: Interrater agreement for total and dichotomized ASPECTS was substantial (ICC 0.79 and Cohen ĸ 0.68) between the emergency physician and radiologist. Mean difference in ASPECTS between the two readers was only 0.15 with standard deviation of 1.58. No proportionality bias was detected. Bland Altman plot was constructed to demonstrate the distribution of ASPECT differences between the two readers. Conclusion: Substantial interrater agreement was noted in grading ASPECTS between emergency medicine physician at first contact and radiologist thereby confirming its robustness even in a rural setting.

Keywords: ASPECTS, computed tomography, MCA territory, stroke

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Authors: Khaled Soliman, Abdullah Alrushoud, Abdulrahman Alkhalifah, Raed Albathi, Salman Altymiat

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Radiation dose received by patients undergoing Computed Tomography (CT) examination of the cervical spine was evaluated using Gafchromic XR-QA2 films and CT-EXPO software (ver. 2.3), in order to document our clinical dose values and to compare our results with other benchmarks reported in the current literature. Radiochromic films were recently used as practical dosimetry tool that provides dose profile information not available using the standard ionisation chamber routinely used in CT dosimetry. We have developed an in-house program to use the films in order to calculate the Entrance Dose Length Product (EDLP) in (mGy.cm) and to relate the EDLP to various organ doses calculated using the CT-EXPO software. We also calculated conversion factor in (mSv/mGy.cm) relating the EDLP to the effective dose (ED) from the examination using CT-EXPO software. Variability among different types of CT scanners and dose modulation methods are reported from at least three major CT brands available at our medical institution. Our work describes the dosimetry method and results are reported. The method can be used as in-vivo dosimetry method. But this work only reports results obtained from adult female anthropomorphic Phantom studies.

Keywords: CT dosimetry, gafchromic films, XR-QA2, CT-Expo software

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Authors: Veronica Sorgato, Jeremy Belhassen, Philippe Chartier, Roddy Sihanath, Nicolas Docquiere, Jean-Yves Giraud

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We analyzed the image-guided radiotherapy method used by the TomoTherapy® System (Accuray Corp.) for patient repositioning in clinical routine. The TomoTherapy® System computes X, Y, Z and roll displacements to match the reference CT, on which the dosimetry has been performed, with the pre-treatment MV CT. The accuracy of the repositioning method has been studied according to the treatment localization. For this, a database of 18774 treatment sessions, performed during 2 consecutive years (2016-2017 period) has been used. The database includes the X, Y, Z and roll displacements proposed by TomoTherapy® System as well as the manual correction of these proposals applied by the radiation therapist. This manual correction aims to further improve the repositioning based on the clinical situation and depends on the structures surrounding the target tumor tissue. The statistical analysis performed on the database aims to define repositioning limits to be used as security and guiding tool for the manual adjustment implemented by the radiation therapist. This tool will participate not only to notify potential repositioning errors but also to further improve patient positioning for optimal treatment.

Keywords: accuracy, IGRT MVCT, image-guided radiotherapy megavoltage computed tomography, statistical analysis, tomotherapy, localization

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Authors: Mian Huang, Chi Ma, Junyu Lin, William Lu

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Introduction: Bone health attracts more attention recently and an intelligent question and answer (QA) chatbot for osteoporosis is helpful for science popularization. With Generative Pretrained Transformer (GPT) technology developing, we build an osteoporosis corpus dataset and then fine-tune LLaMA, a famous open-source GPT foundation large language model(LLM), on our self-constructed osteoporosis corpus. Evaluated by clinical orthopedic experts, our fine-tuned model outperforms vanilla LLaMA on osteoporosis QA task in Chinese. Three-dimensional quantitative computed tomography (QCT) measured bone mineral density (BMD) is considered as more accurate than DXA for BMD measurement in recent years. We develop an automatic Phantom-less QCT(PL-QCT) that is more efficient for BMD measurement since no need of an external phantom for calibration. Combined with LLM on osteoporosis, our PL-QCT provides efficient and accurate BMD measurement for our chatbot users. Material and Methods: We build an osteoporosis corpus containing about 30,000 Chinese literatures whose titles are related to osteoporosis. The whole process is done automatically, including crawling literatures in .pdf format, localizing text/figure/table region by layout segmentation algorithm and recognizing text by OCR algorithm. We train our model by continuous pre-training with Low-rank Adaptation (LoRA, rank=10) technology to adapt LLaMA-7B model to osteoporosis domain, whose basic principle is to mask the next word in the text and make the model predict that word. The loss function is defined as cross-entropy between the predicted and ground-truth word. Experiment is implemented on single NVIDIA A800 GPU for 15 days. Our automatic PL-QCT BMD measurement adopt AI-associated region-of-interest (ROI) generation algorithm for localizing vertebrae-parallel cylinder in cancellous bone. Due to no phantom for BMD calibration, we calculate ROI BMD by CT-BMD of personal muscle and fat. Results & Discussion: Clinical orthopaedic experts are invited to design 5 osteoporosis questions in Chinese, evaluating performance of vanilla LLaMA and our fine-tuned model. Our model outperforms LLaMA on over 80% of these questions, understanding ‘Expert Consensus on Osteoporosis’, ‘QCT for osteoporosis diagnosis’ and ‘Effect of age on osteoporosis’. Detailed results are shown in appendix. Future work may be done by training a larger LLM on the whole orthopaedics with more high-quality domain data, or a multi-modal GPT combining and understanding X-ray and medical text for orthopaedic computer-aided-diagnosis. However, GPT model gives unexpected outputs sometimes, such as repetitive text or seemingly normal but wrong answer (called ‘hallucination’). Even though GPT give correct answers, it cannot be considered as valid clinical diagnoses instead of clinical doctors. The PL-QCT BMD system provided by Bone’s QCT(Bone’s Technology(Shenzhen) Limited) achieves 0.1448mg/cm2(spine) and 0.0002 mg/cm2(hip) mean absolute error(MAE) and linear correlation coefficient R2=0.9970(spine) and R2=0.9991(hip)(compared to QCT-Pro(Mindways)) on 155 patients in three-center clinical trial in Guangzhou, China. Conclusion: This study builds a Chinese osteoporosis corpus and develops a fine-tuned and domain-adapted LLM as well as a PL-QCT BMD measurement system. Our fine-tuned GPT model shows better capability than LLaMA model on most testing questions on osteoporosis. Combined with our PL-QCT BMD system, we are looking forward to providing science popularization and early morning screening for potential osteoporotic patients.

Keywords: GPT, phantom-less QCT, large language model, osteoporosis

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Authors: Deepthy Ann Joy, N. Sreedevi

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Hearing impairment (HI) at an early age, identified before the onset of language development can reduce the negative effect on speech and language development of children. Early rehabilitation is very important in the improvement of speech production in children with HI. Other than conventional hearing aids, Cochlear Implants are being used in the rehabilitation of children with HI. However, delay in acquisition of speech and language milestones persist in children with Cochlear Implant (CI). Delay in speech milestones are reflected through speech sound errors. These errors reflect the temporal and spectral characteristics of speech. Hence, acoustical analysis of the speech sounds will provide a better representation of speech production skills in children with CI. The present study aimed at investigating the acoustic characteristics of vowels in Malayalam speaking children with a cochlear implant. The participants of the study consisted of 20 Malayalam speaking children in the age range of four and seven years. The experimental group consisted of 10 children with CI, and the control group consisted of 10 typically developing children. Acoustic analysis was carried out for 5 short (/a/, /i/, /u/, /e/, /o/) and 5 long vowels (/a:/, /i:/, /u:/, /e:/, /o:/) in word-initial position. The responses were recorded and analyzed for acoustic parameters such as Vowel duration, Ratio of the duration of a short and long vowel, Formant frequencies (F₁ and F₂) and Formant Centralization Ratio (FCR) computed using the formula (F₂u+F₂a+F₁i+F₁u)/(F₂i+F₁a). Findings of the present study indicated that the values for vowel duration were higher in experimental group compared to the control group for all the vowels except for /u/. Ratio of duration of short and long vowel was also found to be higher in experimental group compared to control group except for /i/. Further F₁ for all vowels was found to be higher in experimental group with variability noticed in F₂ values. FCR was found be higher in experimental group, indicating vowel centralization. Further, the results of independent t-test revealed no significant difference across the parameters in both the groups. It was found that the spectral and temporal measures in children with CI moved towards normal range. The result emphasizes the significance of early rehabilitation in children with hearing impairment. The role of rehabilitation related aspects are also discussed in detail which can be clinically incorporated for the betterment of speech therapeutic services in children with CI.

Keywords: acoustics, cochlear implant, Malayalam, vowels

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Authors: Levent Basayigit, Sinan Demir, Yusuf Ucar, Burhan Kara

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Many research projects require accurate delineation of the different land cover type of the agricultural area. Especially it is critically important for the definition of specific plants like cannabis. However, the complexity of vegetation stands structure, abundant vegetation species, and the smooth transition between different seconder section stages make vegetation classification difficult when using traditional approaches such as the maximum likelihood classifier. Most of the time, classification distinguishes only between trees/annual or grain. It has been difficult to accurately determine the cannabis mixed with other plants. In this paper, a mixed distribution models approach is applied to classify pure and mix cannabis parcels using Worldview-2 imagery in the Lakes region of Turkey. Five different land use types (i.e. sunflower, maize, bare soil, and cannabis) were identified in the image. A constrained Gaussian mixture discriminant analysis (GMDA) was used to unmix the image. In the study, 255 reflectance ratios derived from spectral signatures of seven bands (Blue-Green-Yellow-Red-Rededge-NIR1-NIR2) were randomly arranged as 80% for training and 20% for test data. Gaussian mixed distribution model approach is proved to be an effective and convenient way to combine very high spatial resolution imagery for distinguishing cannabis vegetation. Based on the overall accuracies of the classification, the Gaussian mixed distribution model was found to be very successful to achieve image classification tasks. This approach is sensitive to capture the illegal cannabis planting areas in the large plain. This approach can also be used for monitoring and determination with spectral reflections in illegal cannabis planting areas.

Keywords: Gaussian mixture discriminant analysis, spectral mixture model, Worldview-2, land parcels

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Authors: Rabia Saleem

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This research has a recently proposed strategy to find the exact inflationary solution of the Friedman equations in the context of the Rastall theory of gravity (RTG), known as constant-roll warm inflation, including dissipation effects. We establish the model to evaluate the effective potential of inflation and entropy. We develop the inflationary observable like scalar-tensor power spectra, scalar-tensor spectral indices, tensor-to-scalar ratio, and running of spectral-index. The theory parameter $\lambda$ is constrained to observe the compatibility of our model with Planck 2013, Planck TT, TE, EE+lowP (2015), and Planck 2018 bounds. The results are feasible and interesting up to the 2$\sigma$ confidence level.

Keywords: modified gravity, warm inflation, constant-roll limit, dissipation

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Authors: Amina Sultan, Mohamed Ghonim, Eman Oweida, Aya Abdelaziz

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Objective: To assess the diagnostic reliability of multi-detector CT in pre and post-operative evaluation of cochlear implant candidates. Material and Methods: The study includes 40 patients (18 males and 22 females); mean age 5.6 years. They were classified into two groups: Group A (20 patients): cochlear implant device was Nucleus-22 and Group B (20 patients): the device was MED-EL. Cochlear length (CL) and cochlear height (CH) were measured pre-operatively by 128-multidetector CT. Electrode length (EL) and insertion depth angle (α) were measured post-operatively by MDCT. Results: For Group A mean CL was 9.1 mm ± 0.4 SD; mean CH was 4.1 ± 0.3 SD; mean EL was 18 ± 2.7 SD; mean α angle was 299.05 ± 37 SD. Significant statistical correlation (P < 0.05) was found between preoperative CL and post-operative EL (r²=0.6); as well as EL and α angle (r²=0.7). Group B's mean CL was 9.1 mm ± 0.3 SD; mean CH was 4.1 ± 0.4 SD; mean EL was 27 ± 2.1 SD; mean α angle was 287.6 ± 41.7 SD. Significant statistical correlation was found between CL and EL (r²= 0.6) and α angle (r²=0.5). Also, a strong correlation was found between EL and α angle (r²=0.8). Significant statistical difference was detected between the two devices as regards to the electrode length. Conclusion: Multidetector CT is a reliable tool for preoperative planning and post-operative evaluation of the outcomes of cochlear implant operations. Cochlear length is a valuable prognostic parameter for prediction of the depth of electrode array insertion which can influence criteria of device selection.

Keywords: angle of insertion (α angle), cochlear implant (CI), cochlear length (CL), Multidetector Computed Tomography (MDCT)

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Authors: Aleksandra Zysk, Pawel Badura

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Recognizing and controlling vocal registers during singing is a difficult task for beginner vocalist. It requires among others identifying which part of natural resonators is being used when a sound propagates through the body. Thus, an application has been designed allowing for sound recording, automatic vocal register recognition (VRR), and a graphical user interface providing real-time visualization of the signal and recognition results. Six spectral features are determined for each time frame and passed to the support vector machine classifier yielding a binary decision on the head or chest register assignment of the segment. The classification training and testing data have been recorded by ten professional female singers (soprano, aged 19-29) performing sounds for both chest and head register. The classification accuracy exceeded 93% in each of various validation schemes. Apart from a hard two-class clustering, the support vector classifier returns also information on the distance between particular feature vector and the discrimination hyperplane in a feature space. Such an information reflects the level of certainty of the vocal register classification in a fuzzy way. Thus, the designed recognition and training application is able to assess and visualize the continuous trend in singing in a user-friendly graphical mode providing an easy way to control the vocal emission.

Keywords: classification, singing, spectral analysis, vocal emission, vocal register

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Authors: Siti Nurhidayah Naqiah Abdull Rani, Herlina Abdul Rahim

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Near infrared (NIR) spectroscopy has always been of great interest in the food and agriculture industries. The development of predictive models has facilitated the estimation process in recent years. In this research, 176 crude palm oil (CPO) samples acquired from Felda Johor Bulker Sdn Bhd were studied. A FOSS NIRSystem was used to tak e absorbance measurements from the sample. The wavelength range for the spectral measurement is taken at 1600nm to 1900nm. Partial Least Square Regression (PLSR) prediction model with 50 optimal number of principal components was implemented to study the relationship between the measured Free Fatty Acid (FFA) values and the measured spectral absorption. PLSR showed predictive ability of FFA values with correlative coefficient (R) of 0.9808 for the training set and 0.9684 for the testing set.

Keywords: palm oil, fatty acid, NIRS, PLSR

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Authors: Aminu Abdullahi Isyaku

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Semi-arid Bornu basin in northeast Nigeria is characterised with flat topography, thick cover sediments and lack of continuous bedrock outcrops discernible for field geology. This paper presents the methodology for the characterisation of neotectonic surface structures and surface lithology in the north-eastern Bornu basin in northeast Nigeria as an alternative approach to field geological mapping using free multispectral Landsat 7 ETM+, SRTM DEM and ASAR Earth Observation datasets. Spectral lithological mapping herein developed utilised spectral discrimination of the surface features identified on Landsat 7 ETM+ images to infer on the lithology using four steps including; computations of band combination images; band ratio images; supervised image classification and inferences of the lithological compositions. Two complementary approaches to lineament mapping are carried out in this study involving manual digitization and automatic lineament extraction to validate the structural lineaments extracted from the Landsat 7 ETM+ image mosaic covering the study. A comparison between the mapped surface lineaments and lineament zones show good geospatial correlation and identified the predominant NE-SW and NW-SE structural trends in the basin. Topographic profiles across different parts of the Bama Beach Ridge palaeoshorelines in the basin appear to show different elevations across the feature. It is determined that most of the drainage systems in the northeastern Bornu basin are structurally controlled with drainage lines terminating against the paleo-lake border and emptying into the Lake Chad mainly arising from the extensive topographic high-stand Bama Beach Ridge palaeoshoreline.

Keywords: Bornu Basin, lineaments, spectral lithology, tectonics

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Authors: M. A. Ben Messaoud, A. Bouzid, N. Ellouze

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Speech Segmentation is the measure of the change point detection for partitioning an input speech signal into regions each of which accords to only one speaker. In this paper, we apply two features based on multi-scale product (MP) of the clean speech, namely the spectral centroid of MP, and the zero crossings rate of MP. We focus on multi-scale product analysis as an important tool for segmentation extraction. The multi-scale product is based on making the product of the speech wavelet transform coefficients at three successive dyadic scales. We have evaluated our method on the Keele database. Experimental results show the effectiveness of our method presenting a good performance. It shows that the two simple features can find word boundaries, and extracted the segments of the clean speech.

Keywords: multiscale product, spectral centroid, speech segmentation, zero crossings rate

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Authors: Rehan Siddiqui, Brendan Quine

Abstract:

The Radiance Enhancement (RE) and integrated absorption technique is applied to develop a synthetic model to determine the enhancement in radiance due to cloud scene and Shortwave upwelling Radiances (SHupR) by O2, H2O, CO2 and CH4. This new model is used to estimate the magnitude variation for RE and SHupR over spectral range of 900 nm to 1700 nm by varying surface altitude, mixing ratios and surface reflectivity. In this work, we employ satellite real observation of space orbiting Argus 1000 especially for O2, H2O, CO2 and CH4 together with synthetic model by using line by line GENSPECT radiative transfer model. All the radiative transfer simulations have been performed by varying over a different range of percentages of water vapor contents and carbon dioxide with the fixed concentration oxygen and methane. We calculate and compare both the synthetic and real measured observed data set of different week per pass of Argus flight. Results are found to be comparable for both approaches, after allowing for the differences with the real and synthetic technique. The methodology based on RE and SHupR of the space spectral data can be promising for the instant and reliable classification of the cloud scenes.

Keywords: radiance enhancement, radiative transfer, shortwave upwelling radiative flux, cloud reflectivity, greenhouse gases

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Authors: Eric Yi-Hsiu Huang, Meng-Chun Kao, Wen-Chuan Kuo

Abstract:

For every laparoscopic surgery, a safe pneumoperitoneumcreation (gaining access to the peritoneal cavity) is the first and essential step. However, closed pneumoperitoneum is usually obtained by blind insertion of a Veress needle into the peritoneal cavity, which may carry potential risks suchas bowel and vascular injury.Until now, there remains no definite measure to visually confirm the position of the needle tip inside the peritoneal cavity. Therefore, this study established an image-guided Veress needle method by combining a fiber probe with optical coherence tomography (OCT). An algorithm was also proposed for determining the exact location of the needle tip through the acquisition of OCT images. Our method not only generates a series of “live” two-dimensional (2D) images during the needle puncture toward the peritoneal cavity but also can eliminate operator variation in image judgment, thus improving peritoneal access safety. This study was approved by the Ethics Committee of Taipei Veterans General Hospital (Taipei VGH IACUC 2020-144). A total of 2400 in vivo OCT images, independent of each other, were acquired from experiments of forty peritoneal punctures on two piglets. Characteristic OCT image patterns could be observed during the puncturing process. The ROC curve demonstrates the discrimination capability of these quantitative image features of the classifier, showing the accuracy of the classifier for determining the inside vs. outside of the peritoneal was 98% (AUC=0.98). In summary, the present study demonstrates the ability of the combination of our proposed automatic identification method and OCT imaging for automatically and objectively identifying the location of the needle tip. OCT images translate the blind closed technique of peritoneal access into a visualized procedure, thus improving peritoneal access safety.

Keywords: pneumoperitoneum, optical coherence tomography, automatic identification, veress needle

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Authors: Boumediene Hamzi, Turky N. AlOtaiby, Saleh AlShebeili, Arwa AlAnqary

Abstract:

We introduce a data-driven method for seizure detection drawing on recent progress in Machine Learning. The method is based on embedding probability measures in a high (or infinite) dimensional reproducing kernel Hilbert space (RKHS) where the Maximum Mean Discrepancy (MMD) is computed. The MMD is metric between probability measures that are computed as the difference between the means of probability measures after being embedded in an RKHS. Working in RKHS provides a convenient, general functional-analytical framework for theoretical understanding of data. We apply this approach to the problem of seizure detection.

Keywords: kernel methods, maximum mean discrepancy, seizure detection, machine learning

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Authors: Paola Causin, Andrea Aspri, Alessandro Benfenati

Abstract:

Diffuse Optical Tomography (DOT) is an emergent medical imaging technique which employs NIR light to estimate the spatial distribution of optical coefficients in biological tissues for diagnostic purposes, in a noninvasive and non-ionizing manner. DOT reconstruction is a severely ill-conditioned problem due to prevalent scattering of light in the tissue. In this contribution, we present our research in adopting hybrid knowledgedriven/data-driven approaches which exploit the existence of well assessed physical models and build upon them neural networks integrating the availability of data. Namely, since in this context regularization procedures are mandatory to obtain a reasonable reconstruction [1], we explore the use of neural networks as tools to include prior information on the solution. 2. Materials and Methods The idea underlying our approach is to leverage neural networks to solve PDE-constrained inverse problems of the form 𝒒 ∗ = 𝒂𝒓𝒈 𝒎𝒊𝒏𝒒 𝐃(𝒚, 𝒚̃), (1) where D is a loss function which typically contains a discrepancy measure (or data fidelity) term plus other possible ad-hoc designed terms enforcing specific constraints. In the context of inverse problems like (1), one seeks the optimal set of physical parameters q, given the set of observations y. Moreover, 𝑦̃ is the computable approximation of y, which may be as well obtained from a neural network but also in a classic way via the resolution of a PDE with given input coefficients (forward problem, Fig.1 box ). Due to the severe ill conditioning of the reconstruction problem, we adopt a two-fold approach: i) we restrict the solutions (optical coefficients) to lie in a lower-dimensional subspace generated by auto-decoder type networks. This procedure forms priors of the solution (Fig.1 box ); ii) we use regularization procedures of type 𝒒̂ ∗ = 𝒂𝒓𝒈𝒎𝒊𝒏𝒒 𝐃(𝒚, 𝒚̃)+ 𝑹(𝒒), where 𝑹(𝒒) is a regularization functional depending on regularization parameters which can be fixed a-priori or learned via a neural network in a data-driven modality. To further improve the generalizability of the proposed framework, we also infuse physics knowledge via soft penalty constraints (Fig.1 box ) in the overall optimization procedure (Fig.1 box ). 3. Discussion and Conclusion DOT reconstruction is severely hindered by ill-conditioning. The combined use of data-driven and knowledgedriven elements is beneficial and allows to obtain improved results, especially with a restricted dataset and in presence of variable sources of noise.

Keywords: inverse problem in tomography, deep learning, diffuse optical tomography, regularization

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Authors: Nuridayanti Che Khalib, Kaliyaperumal Thanigaimani, Suhana Arshad, Ibrahim Abdul Razak

Abstract:

The 1:1 co-crystal of 2-amino-4-chloro-6-methylpyrimidine (2A4C6MP) with 4-methylbenzoic acid (4MBA) (I) has been prepared by slow evaporation method in methanol, which was crystallized in monoclinic C2/c space group, Z = 8, a = 28.431 (2) Å, b = 7.3098 (5) Å, c = 14.2622 (10) Å, and β = 109.618 (3)°. The presence of unionized –COOH functional group in co-crystal I was identified both by spectral methods (1H and 13C NMR, FTIR) and X-ray diffraction structural analysis. The 2A4C6MP molecule interact with the carboxylic group of the respective 4MBA molecule through N—H⋯O and O—H⋯N hydrogen bonds, forming a cyclic hydrogen –bonded motif R22(8). The crystal structure was stabilized by Npyrimidine-H⋯O=C and C=O-H⋯Npyrimidine types hydrogen bonding interactions. Theoretical investigations have been computed by HF and density function (B3LYP) method with 6-311+G(d,p) basis set. The vibrational frequencies together with 1H and 13C NMR chemical shifts have been calculated on the fully optimized geometry of co-crystal I. Theoretical calculations are in good agreement with the experimental results. Solvent-free formation of this co-crystal I is confirmed by powder X-ray diffraction analysis.

Keywords: supramolecular co-crystal, 2-amino-4-chloro-6-methylpyrimidine, Harthree-Fock and DFT studies, spectroscopic analysis

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Authors: Ainouna Bouziane

Abstract:

The ability of Electron Tomography to recover the 3D structure of catalysts, with spatial resolution in the subnanometer scale, has been widely explored and reviewed in the last decades. A variety of experimental techniques, based either on Transmission Electron Microscopy (TEM) or Scanning Transmission Electron Microscopy (STEM) have been used to reveal different features of nanostructured catalysts in 3D, but High Angle Annular Dark Field imaging in STEM mode (HAADF-STEM) stands out as the most frequently used, given its chemical sensitivity and avoidance of imaging artifacts related to diffraction phenomena when dealing with crystalline materials. In this regard, our group has developed a methodology that combines image denoising by undecimated wavelet transforms (UWT) with automated, advanced segmentation procedures and parameter selection methods using CS-TVM (Compressed Sensing-total variation minimization) algorithms to reveal more reliable quantitative information out of the 3D characterization studies. However, evaluating the accuracy of the magnitudes estimated from the segmented volumes is also an important issue that has not been properly addressed yet, because a perfectly known reference is needed. The problem particularly complicates in the case of multicomponent material systems. To tackle this key question, we have developed a methodology that incorporates volume reconstruction/segmentation methods. In particular, we have established an approach to evaluate, in quantitative terms, the accuracy of TVM reconstructions, which considers the influence of relevant experimental parameters like the range of tilt angles, image noise level or object orientation. The approach is based on the analysis of material-realistic, 3D phantoms, which include the most relevant features of the system under analysis.

Keywords: electron tomography, supported catalysts, nanometrology, error assessment

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