Search results for: point cloud imaging

Authors: Leping Chen, Daoxiang An, Xiaotao Huang

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Automatic vehicle detection using synthetic aperture radar (SAR) image has been widely researched, as well as using optical remote sensing images. However, most researches treat the detection as an independent problem, failing to make full use of SAR data information. In circular SAR (CSAR), the two long borders of vehicle will shrink if the imaging surface is set higher than the reference one. Based on above variance, an automatic vehicle detection using CSAR image is proposed to enhance detection ability under complex environment, such as vehicles’ closely packing, which confuses the detector. The detection method uses the multiple images generated by different height plane to obtain an energy-concentrated image for detecting and then uses the maximally stable extremal regions method (MSER) to detect vehicles. A result of vehicles’ detection is given to verify the effectiveness and correctness of proposed method.

Keywords: circular SAR, vehicle detection, automatic, imaging

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Authors: Assem Abdel Hamed Mousa

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The Internet of Things probably already influences your life. And if it doesn’t, it soon will, say computer scientists; Ubiquitous computing names the third wave in computing, just now beginning. First were mainframes, each shared by lots of people. Now we are in the personal computing era, person and machine staring uneasily at each other across the desktop. Next comes ubiquitous computing, or the age of calm technology, when technology recedes into the background of our lives. Alan Kay of Apple calls this "Third Paradigm" computing. Ubiquitous computing is essentially the term for human interaction with computers in virtually everything. Ubiquitous computing is roughly the opposite of virtual reality. Where virtual reality puts people inside a computer-generated world, ubiquitous computing forces the computer to live out here in the world with people. Virtual reality is primarily a horse power problem; ubiquitous computing is a very difficult integration of human factors, computer science, engineering, and social sciences. The approach: Activate the world. Provide hundreds of wireless computing devices per person per office, of all scales (from 1" displays to wall sized). This has required new work in operating systems, user interfaces, networks, wireless, displays, and many other areas. We call our work "ubiquitous computing". This is different from PDA's, dynabooks, or information at your fingertips. It is invisible; everywhere computing that does not live on a personal device of any sort, but is in the woodwork everywhere. The initial incarnation of ubiquitous computing was in the form of "tabs", "pads", and "boards" built at Xerox PARC, 1988-1994. Several papers describe this work, and there are web pages for the Tabs and for the Boards (which are a commercial product now): Ubiquitous computing will drastically reduce the cost of digital devices and tasks for the average consumer. With labor intensive components such as processors and hard drives stored in the remote data centers powering the cloud , and with pooled resources giving individual consumers the benefits of economies of scale, monthly fees similar to a cable bill for services that feed into a consumer’s phone.

Keywords: internet, cloud computing, ubiquitous computing, big data

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Authors: M. S. Sukumar

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Our objective is to develop a full-fledged system for exploring and studying nature of fossils and to extend this to underwater archaeology and mineral mapping. This includes aerial surveying, imaging techniques, artefact extraction and spectrum analysing techniques. These techniques help in regular monitoring of fossils and also the sensing system. The ROV was designed to complete several tasks which simulate collecting data and samples. Given the time constraints, the ROV was engineered for efficiency and speed in performing tasks. Its other major design consideration was modularity, allowing the team to distribute the building process, to easily test systems as they were completed and troubleshoot and replace systems as necessary. Our design itself had several challenges of on-board waterproofed sensor mounting, waterproofing of motors, ROV stability criteria, camera mounting and hydrophone sound acquisition.

Keywords: remotely operated vehicle (ROV) dragonair, underwater archaeology, full-fledged system, aerial imaging and detection

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Authors: Sonia Mahajan Dinesh, Anant Dinesh, Madhavi Tripathi, Vinod Kumar Ramteke, Rajnish Sharma, Anupam Mondal

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Background: Neo-adjuvant chemotherapy plays an important role in treatment of breast cancer by decreasing the tumour load and it offers an opportunity to evaluate response of primary tumour to chemotherapy. Standard anatomical imaging modalities are unable to accurately reflect the response to chemotherapy until several cycles of drug treatment have been completed. Metabolic imaging using tracers like 18F-fluorodeoxyglucose (FDG) as a marker of glucose metabolism or amino acid tracers like L-methyl-11C methionine (MET) have potential role for the measurement of treatment response. In this study, our objective was to compare these two PET tracers for assessment of response to neoadjuvant chemotherapy, in locally advanced breast carcinoma. Methods: In our prospective study, 20 female patients with histology proven locally advanced breast carcinoma underwent PET-CT imaging using FDG and MET before and after three cycles of neoadjuvant chemotherapy (CAF regimen). Thereafter, all patients were taken for MRM and the resected specimen was sent for histo-pathological analysis. Tumour response to the neoadjuvant chemotherapy was evaluated by PET-CT imaging using PERCIST criteria and correlated with histological results. Responses calculated were compared for statistical significance using paired t- test. Results: Mean SUVmax for primary lesion in FDG PET and MET PET was 15.88±11.12 and 5.01±2.14 respectively (p<0.001) and for axillary lymph nodes was 7.61±7.31 and 2.75±2.27 respectively (p=0.001). Statistically significant response in primary tumour and axilla was noted on both FDG and MET PET after three cycles of NAC. Complete response in primary tumour was seen in only 1 patient in FDG and 7 patients in MET PET (p=0.001) whereas there was no histological complete resolution of tumor in any patient. Response to therapy in axillary nodes noted on both PET scans were similar (p=0.45) and correlated well with histological findings. Conclusions: For the primary breast tumour, FDG PET has a higher sensitivity and accuracy than MET PET and for axilla both have comparable sensitivity and specificity. FDG PET shows higher target to background ratios so response is better predicted for primary breast tumour and axilla. Also, FDG-PET is widely available and has the advantage of a whole body evaluation in one study.

Keywords: 11C-methionine, 18F-FDG, breast carcinoma, neoadjuvant chemotherapy

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Authors: Hary Aprianto Wijaya Siahaan, Bayu Edo Pratama

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Sea fishery's potential to become one of the nation's assets which very contributed to Indonesia's economy. This fishery potential not in spite of the availability of the chlorophyll in the territorial waters of Indonesia. The research was conducted using three methods, namely: statistics, comparative and analytical. The data used include MODIS sea temperature data imaging results in Aqua satellite with a resolution of 4 km in 2002-2015, MODIS data of chlorophyll-a imaging results in Aqua satellite with a resolution of 4 km in 2002-2015, and Imaging results data ASCAT on MetOp and NOAA satellites with 27 km resolution in 2002-2015. The results of the processing of the data show that the incidence of upwelling in the south of East Java Sea began to happen in June identified with sea surface temperature anomaly below normal, the mass of the air that moves from the East to the West, and chlorophyll-a concentrations are high. In July the region upwelling events are increasingly expanding towards the West and reached its peak in August. Chlorophyll-a concentration prediction using multiple linear regression equations demonstrate excellent results to chlorophyll-a concentrations prediction in 2002 until 2015 with the correlation of predicted chlorophyll-a concentration indicate a value of 0.8 and 0.3 with RMSE value. On the chlorophyll-a concentration prediction in 2016 indicate good results despite a decline in the value of the correlation, where the correlation of predicted chlorophyll-a concentration in the year 2016 indicate a value 0.6, but showed improvement in RMSE values with 0.2.

Keywords: satellite, sea surface temperature, upwelling, wind stress

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Authors: G. Yoga Sairam Teja, K. Harsha Vardhan, A. Vinay Kumar, K. Nithish Kumar, Ch. Shanthi Priyag

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The emergence of the Internet of Things (IoT) has facilitated better device control and monitoring in the modern world. The constant monitoring of a patient would be drastically altered by the usage of IoT in healthcare. As we've seen in the case of the COVID-19 pandemic, it's important to keep oneself untouched while continuously checking on the patient's heart rate and temperature. Additionally, patients with paralysis should be closely watched, especially if they are elderly and in need of special care. Our "IoT BASED PATIENT HEALTH MONITORING SYSTEM" project uses IoT to track patient health conditions in an effort to address these issues. In this project, the main board is an 8051 microcontroller that connects a number of sensors, including a heart rate sensor, a temperature sensor (LM-35), and a saline water measuring circuit. These sensors are connected via an ESP832 (WiFi) module, which enables the sending of recorded data directly to the cloud so that the patient's health status can be regularly monitored. An LCD is used to monitor the data in offline mode, and a buzzer will sound if any variation from the regular readings occurs. The data in the cloud may be viewed as a graph, making it simple for a user to spot any unusual conditions.

Keywords: IoT, ESP8266, 8051 microcontrollers, sensors

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Authors: Samira-Sadat JamaliDinan, Haidar Almohri, Mohammad-Reza Nazem-Zadeh

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Lateralization of temporal lobe epilepsy (TLE) is very important for positive surgical outcomes. We propose a machine learning framework to ultimately identify the epileptogenic hemisphere for temporal lobe epilepsy (TLE) cases using magnetoencephalography (MEG) coherence source imaging (CSI) and diffusion tensor imaging (DTI). Unlike most studies that use classification algorithms, we propose an effective clustering approach to distinguish between normal and TLE cases. We apply the famous Minkowski weighted K-Means (MWK-Means) technique as the clustering framework. To overcome the problem of poor initialization of K-Means, we use particle swarm optimization (PSO) to effectively select the initial centroids of clusters prior to applying MWK-Means. We demonstrate that compared to K-means and MWK-means independently, this approach is able to improve the result of a benchmark data set.

Keywords: temporal lobe epilepsy, machine learning, clustering, magnetoencephalography

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Authors: Aljon Pangan

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The Philippines being an archipelagic country, is surrounded by coastlines (36,289 km), coastal waters (226,000 km²), oceanic waters (1.93 million km²) and territorial waters (2.2 million km²). Studies show that the Philippine coastal ecosystems are the most productive and biologically diverse in the world, however, plagued by degradation problems due to over-exploitation and illegal activities. The existence of coastal degradation issues in the country led to the emergence of Coastal Resource Management (CRM) as an approach to both national and local government in providing solutions for sustainable coastal resource utilization. CRM applies the idea of planning, implementing and monitoring through the lens of collaborative governance. It utilizes collective action and decision-making to achieve sustainable use of coastal resources. The Municipality of Bagac in Bataan is one of the coastal municipalities in the country who crafts its own CRM Program as a solution to coastal resource degradation and problems. Information and Communications Technology (ICT), particularly Integrated Imaging Management System (IIMS) is one approach that can be applied in the formula of collaborative governance which entails the Government, Private Sector, and Civil Society. IIMS can help policymakers, managers, and citizens in managing coastal resources through analyzed spatial data describing the physical, biological, and socioeconomic characteristics of the coastal areas. Moreover, this study will apply the qualitative approach in deciphering possible impacts of the application of IIMS in the Coastal Resource Management policy making and implementation of the Municipality of Bagac.

Keywords: coastal resource management, collaborative governance, integrated imaging management system, information and communication technology

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Authors: Maysoon Nasser Samhan, Somaya Alkiswani, Abdullah Alzibdeh

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Background: Routine lumbar MRIs for back pain may yield normal results despite persistent symptoms, which means the possibility of other causes for this pain, which was not shown on the routine images. Research suggests including coronal STIR imaging to detect additional pathologies like sacroiliitis. Objectives: This study aims to enhance diagnostic accuracy and aid in determining treatment processes for patients with persistent back pain who have normal routine lumbar MRI (T1 and T2 images) by incorporating coronal STIR into the examination. Methods: A prospectively conducted study involving 274 patients, 115 males and 159 females, with an age range of 6–92 years, reviewed their medical records and imaging data following a lumbar spine MRI. This study included patients with back pain and sciatica as their primary complaints, all of whom underwent lumbar spine MRIs at our hospital to identify potential pathologies. Using a GE Signa HD 1.5T MRI System, each patient received a standard MRI protocol that included T1 and T2 sagittal and axial sequences, as well as a coronal STIR sequence. We collected relevant MRI findings, including abnormalities and structural variations, from radiology reports. We classified these findings into tables and documented them as counts and percentages, using Fisher’s exact test to assess differences between categorical variables. We conducted a statistical analysis using Prism GraphPad software version 10.1.2. The study adhered to ethical guidelines, institutional review board approvals, and patient confidentiality regulations. Results: Exclusion of the coronal STIR sequence led to 83 subjects (30.29%) being classified as within normal limits on MRI examination. 36 patients without abnormalities on T1 and T2 sequences showed abnormalities on the coronal STIR sequence, with 26 cases attributed to spinal pathologies and 10 to non-spinal pathologies. In addition to that, Fisher's exact test demonstrated a significant association between sacroiliitis diagnosis and abnormalities identified solely through the coronal STIR sequence (P < 0.0001). Conclusion: Implementing coronal STIR imaging as part of routine lumbar MRI protocols has the potential to improve patient care by facilitating a more comprehensive evaluation and management of persistent back pain.

Keywords: magnetic resonance imaging, lumber MRI, radiology, neurology

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Authors: Awais Asif

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This article investigates fixed point theorems of Chatterjea type F-contraction in the setting of F-metric space. We relax the conditions of F-contraction and define modified F-contraction for two mappings. The study provides fixed point results for both single-valued and multivalued mappings. The results are further extended to common fixed point theorems for two mappings. Moreover, to discuss the applicability of our results, an application is provided, which shows the role of our results in finding the solution to functional equations in dynamic programming. Our results generalize and extend the existing results in the literature.

Keywords: Chatterjea type F-contraction, F-cauchy sequence, F-convergent, multi valued mappings

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Authors: Sherine Farrag

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Magnetic resonance imaging (MRI) presents safety hazards, with the general physical environment. The principal hazard of the MRI is the presence of static magnetic fields. Proper architectural design of MRI’s room ensure environment and health care staff safety. This research paper presents an easy approach for numerical computation of fringe static magnetic fields. Iso-gauss line of different MR intensities (0.3, 0.5, 1, 1.5 Tesla) was mapped and a polynomial function of the 7th degree was generated and tested. Matlab script was successfully applied for MRI SMF mapping. This method can be valid for any kind of commercial scanner because it requires only the knowledge of the MR scanner room map with iso-gauss lines. Results help to develop guidelines to guide healthcare architects to design of a safer Magnetic resonance imaging suite.

Keywords: designing MRI suite, MRI safety, radiology occupational exposure, static magnetic fields

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Authors: Abdelazziz Aouiche, El Moundher Aouiche, Mouhamed Salah Soudani

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Renewable energy sources now significantly contribute to the replacement of traditional fossil fuel energy sources. One of the most potent types of renewable energy that has developed quickly in recent years is photovoltaic energy. We all know that solar energy, which is sustainable and non-depleting, is the best knowledge form of energy that we have at our disposal. Due to changing weather conditions, the primary drawback of conventional solar PV cells is their inability to track their maximum power point. In this study, we apply artificial neural networks (ANN) to automatically track and measure the maximum power point (MPP) of solar panels. In MATLAB, the complete system is simulated, and the results are adjusted for the external environment. The results are better performance than traditional MPPT methods and the results demonstrate the advantages of using neural networks in solar PV systems.

Keywords: modeling, photovoltaic panel, artificial neural networks, maximum power point tracking

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Authors: Sidi M. A. Ghaly, Mohammad O. Khan, Mohammed Shalaby, Khaled A. Al-Snaie

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Electrical capacitance tomography (ECT) is a valuable, non-invasive technique used to monitor multiphase flow processes, especially within industrial pipelines. This study focuses on the design, testing, and performance comparison of ECT sensors configured with 8, 12, and 16 electrodes, aiming to evaluate their effectiveness in imaging accuracy, resolution, and sensitivity. Each sensor configuration was designed to capture the spatial permittivity distribution within a pipeline cross-section, enabling visualization of phase distribution and flow characteristics such as oil and water interactions. The sensor designs were implemented and tested in closed pipes to assess their response to varying flow regimes. Capacitance data collected from each electrode configuration were reconstructed into cross-sectional images, enabling a comparison of image resolution, noise levels, and computational demands. Results indicate that the 16-electrode configuration yields higher image resolution and sensitivity to phase boundaries compared to the 8- and 12-electrode setups, making it more suitable for complex flow visualization. However, the 8 and 12-electrode sensors demonstrated advantages in processing speed and lower computational requirements. This comparative analysis provides critical insights into optimizing ECT sensor design based on specific industrial requirements, from high-resolution imaging to real-time monitoring needs.

Keywords: capacitance tomography, modeling, simulation, electrode, permittivity, fluid dynamics, imaging sensitivity measurement

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Authors: Samir Hessas

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Modern medicine is witnessing a profound transformation as advanced technology reshapes surgical environments. Hybrid operating rooms, where state-of-the-art medical equipment, advanced imaging solutions, and Artificial Intelligence (AI) converge, are at the forefront of this revolution. In this comprehensive exploration, we scrutinize the multifaceted facets of AI and delve into an array of groundbreaking technologies. We also discuss visionary concepts that hold the potential to revolutionize hybrid operating rooms, making them more efficient and patient-centered. These innovations encompass real-time imaging, surgical simulation, IoT and remote monitoring, 3D printing, telemedicine, quantum computing, and nanotechnology. The outcome of this fusion of technology and imagination is a promising future of surgical precision, individualized patient care, and unprecedented medical advances in hybrid operating rooms.

Keywords: artificial intelligence, hybrid operating rooms, telemedicine, monitoring

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Authors: Aseem Rajvanshi, Pankaj Kumar Pandey

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Increase in population has increased the demand of energy to fulfill all its needs. This will result in burden on fossil fuels especially crude oil. Waste oil due to its disposal problem creates environmental degradation. In this context, this paper studies utilization of waste cooking oil and waste motor sludge for making lubricating grease. Experimental studies have been performed by variation in time and concentration of mixture of waste cooking oil and waste motor sludge. The samples were analyzed using penetration test (ASTM D-217), dropping point (ASTM D-566), work penetration (ASTM D-217) and copper strip test (ASTM D-408). Among 6 samples, sample 6 gives the best results with a good drop point and a fine penetration value. The dropping point and penetration test values were found to be 205 °C and 315, respectively. The penetration value falls under the category of NLGI (National Lubricating Grease Institute) consistency number 1.

Keywords: crude oil, copper strip corrosion test, dropping point, penetration test

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Authors: Aniket Ganvir, Ritarani Sahu, Suchismita Chinara

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The rapid digitization of various aspects of life is leading to the creation of smart IoT ecosystems, where interconnected devices generate significant amounts of valuable data. However, these IoT devices face constraints such as limited computational resources and bandwidth. Cloud computing emerges as a solution by offering ample resources for offloading tasks efficiently despite introducing latency issues, especially for time-sensitive applications like fog computing. Fog computing (FC) addresses latency concerns by bringing computation and storage closer to the network edge, minimizing data travel distance, and enhancing efficiency. Offloading tasks to fog nodes or the cloud can conserve energy and extend IoT device lifespan. The offloading process is intricate, with tasks categorized as full or partial, and its optimization presents an NP-hard problem. Traditional greedy search methods struggle to address the complexity of task offloading efficiently. To overcome this, the efficient crow search algorithm (ECSA) has been proposed as a meta-heuristic optimization algorithm. ECSA aims to effectively optimize computation offloading, providing solutions to this challenging problem.

Keywords: IoT, fog computing, task offloading, efficient crow search algorithm

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Authors: Hassina Hafida Boukhalfa, Mathieu Massinon, Fréderic Lebeau, Mohamed Belhamra

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Drop behaviour during impact affects retention. The increase of adhesion is usually seen as the objective when applying crop protection products, while bouncing and shattering are seen as detrimental to spray retention. However, observation of drop impacts using high speed shadow graphy shows that fragmentation can occur in Wenzel wetting regime. In this case, a part of the drop sticks on the surface, what contributes to retention. Using simultaneous measurements of drop impacts with high speed imaging and of retention with fluorometry for 3 spray mixtures on excised barley leaves allowed us to observe that about 50% of the drops fragmented in Wenzel state remain on the leaf. Depending on spray mixture, these impact outcomes accounted for 25 to 50% of retention, the higher contribution being correlated with bigger VMD (Volume Median Diameter). This contribution is non-negligible and should be considered when a modelling of spray retention process is performed.

Keywords: drop impact, retention, fluorometry, high speed imaging

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Authors: Naridchaya Aberdour, Joanna Kao, Anne Miller, Timothy Shore, Richard Maher, Zhixin Liu

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Background: COVID-19 has and continues to be a strain on healthcare globally, with the number of patients requiring hospitalization exceeding the level of medical support available in many countries. As chest x-rays are the primary respiratory radiological investigation, the Radiological Assessment of Lung Edema (RALE) score was used to quantify the extent of pulmonary infection on baseline imaging. Assessment of RALE score's reproducibility and associations with clinical outcome parameters were then evaluated to determine implications for patient management and prognosis. Methods: A retrospective study was performed with the inclusion of patients testing positive for COVID-19 on nasopharyngeal swab within a single Local Health District in Sydney, Australia and baseline x-ray imaging acquired between January to June 2020. Two independent Radiologists viewed the studies and calculated the RALE scores. Clinical outcome parameters were collected and statistical analysis was performed to assess RALE score reproducibility and possible associations with clinical outcomes. Results: A total of 78 patients met inclusion criteria with the age range of 4 to 91 years old. RALE score concordance between the two independent Radiologists was excellent (interclass correlation coefficient = 0.93, 95% CI = 0.88-0.95, p<0.005). Binomial logistics regression identified a positive correlation with hospital admission (1.87 OR, 95% CI= 1.3-2.6, p<0.005), oxygen requirement (1.48 OR, 95% CI= 1.2-1.8, p<0.005) and invasive ventilation (1.2 OR, 95% CI= 1.0-1.3, p<0.005) for each 1-point increase in RALE score. For each one year increased in age, there was a negative correlation with recovery (0.05 OR, 95% CI= 0.92-1.0, p<0.01). RALE scores above three were positively associated with hospitalization (Youden Index 0.61, sensitivity 0.73, specificity 0.89) and above six were positively associated with ICU admission (Youden Index 0.67, sensitivity 0.91, specificity 0.78). Conclusion: The RALE score can be used as a surrogate to quantify the extent of COVID-19 infection and has an excellent inter-observer agreement. The RALE score could be used to prognosticate and identify patients at high risk of deterioration. Threshold values may also be applied to predict the likelihood of hospital and ICU admission.

Keywords: chest radiography, coronavirus, COVID-19, RALE score

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Authors: Xiaoxiao Yu, Guodu He, Zihua Wu, Yuanyuan Wang, Huaqing Xie

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Core-shell composite structure is a common method to regulate the spectral absorption of nanofluids, but there occur complex preparation processes, which limit the applications in some fields, such as photothermal utilization and catalysis. This work proposed point-mode Cu₂O/TiN plasmonic nanofluids to regulate the spectral capturing ability and simplify the preparation process. Non-noble TiN nanoparticles with the localized surface plasmon resonance effect are dispersed in Cu₂O nanoparticles for forming a multi-point resonance source to enhance the spectral absorption performance. The experimental results indicate that the multiple resonance effect of TiN effectively improves the optical absorption and expands the absorption region. When the radius of Cu₂O nanoparticles is equal to 150nm, the optical absorption of point-mode Cu₂O/TiN plasmonic nanoparticles is best. Moreover, the photothermal conversion efficiency of Cu₂O/TiN plasmonic nanofluid can reach 97.5% at a volume fraction of 0.015% and an optical depth of 10mm. The point-mode nanostructure effectively enhances the optical absorption properties and greatly simplifies the preparation process of the composite nanoparticles, which can promote the application of multi-component photonic nanoparticles in the field of solar energy.

Keywords: solar energy, nanofluid, point-mode structure, Cu₂O/TiN, localized surface plasmon resonance effect

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Authors: Xiaoqin Wang, Li Yin

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Background and purpose: In many practices, one estimates causal effects arising from a complex stochastic process, where a sequence of treatments are assigned to influence a certain outcome of interest, and there exist time-dependent covariates between treatments. When covariates are plentiful and/or continuous, statistical modeling is needed to reduce the huge dimensionality of the problem and allow for the estimation of causal effects. Recently, Wang and Yin (Annals of statistics, 2020) derived a new general formula, which expresses these causal effects in terms of the point effects of treatments in single-point causal inference. As a result, it is possible to conduct the modeling via point effects. The purpose of the work is to study the modeling of these causal effects via point effects. Challenges and solutions: The time-dependent covariates often have influences from earlier treatments as well as on subsequent treatments. Consequently, the standard parameters – i.e., the mean of the outcome given all treatments and covariates-- are essentially all different (null paradox). Furthermore, the dimension of the parameters is huge (curse of dimensionality). Therefore, it can be difficult to conduct the modeling in terms of standard parameters. Instead of standard parameters, we have use point effects of treatments to develop likelihood-based parametric approach to the modeling of these causal effects and are able to model the causal effects of a sequence of treatments by modeling a small number of point effects of individual treatment Achievements: We are able to conduct the modeling of the causal effects from a sequence of treatments in the familiar framework of single-point causal inference. The simulation shows that our method achieves not only an unbiased estimate for the causal effect but also the nominal level of type I error and a low level of type II error for the hypothesis testing. We have applied this method to a longitudinal study of COVID-19 mortality among Scandinavian countries and found that the Swedish approach performed far worse than the other countries' approach for COVID-19 mortality and the poor performance was largely due to its early measure during the initial period of the pandemic.

Keywords: causal effect, point effect, statistical modelling, sequential causal inference

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Authors: Kulwinder Singh Mann

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The innovative features of information system, known as Radiology Information System (RIS), for electronic medical records has shown a good impact in the hospital. The objective is to help and make their work easier; such as for a physician to access the patient’s data and for a patient to check their bill transparently. The interoperability of RIS with the other intra-hospital information systems it interacts with, dealing with the compatibility and open architecture issues, are accomplished by two novel mechanisms. The first one is the particular message handling system that is applied for the exchange of information, according to the Health Level Seven (HL7) protocol’s specifications and serves the transfer of medical and administrative data among the RIS applications and data store unit. The second one implements the translation of information between the formats that HL7 and Digital Imaging and Communication in Medicine (DICOM) protocols specify, providing the communication between RIS and Picture and Archive Communication System (PACS) which is used for the increasing incorporation of modern medical imaging equipment.

Keywords: RIS, PACS, HIS, HL7, DICOM, messaging service, interoperability, digital images

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Authors: Laura Boschis, Elena D. Ozzello, Enzo Mastromatteo

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Point of care diagnostic finds growing interest in medicine and agri-food because of faster intervention and prevention. EliChip is a microfluidic platform to perform Point of Care immunoenzymatic assay based on ready-to-use kits and a portable instrument to manage fluidics and read reliable quantitative results. Thanks to miniaturization, analyses are faster and more sensible than conventional ELISA. EliChip is one of the crucial assets of the Europen-founded Flamingo project for in-line measuring inflammatory markers.

Keywords: lab on chip, point of care, immunoenzymatic analysis, synovial arthritis

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Authors: Kaushik Sathupadi, Sandesh Achar

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Human action recognition modeling is a critical task in machine learning. These systems require better techniques for recognizing body parts and selecting optimal features based on vision sensors to identify complex action patterns efficiently. Still, there is a considerable gap and challenges between images and videos, such as brightness, motion variation, and random clutters. This paper proposes a robust approach for classifying human actions over cloud-based image data. First, we apply pre-processing and detection, human and outer shape detection techniques. Next, we extract valuable information in terms of cues. We extract two distinct features: fuzzy local binary patterns and sequence representation. Then, we applied a greedy, randomized adaptive search procedure for data optimization and dimension reduction, and for classification, we used a random forest. We tested our model on two benchmark datasets, AAMAZ and the KTH Multi-view football datasets. Our HMR framework significantly outperforms the other state-of-the-art approaches and achieves a better recognition rate of 91% and 89.6% over the AAMAZ and KTH multi-view football datasets, respectively.

Keywords: computer vision, human motion analysis, random forest, machine learning

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Authors: Lassi Lehto, Jaakko Kahkonen, Juha Oksanen, Tapani Sarjakoski

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The use of raster-formatted data sets in geospatial analysis is increasing rapidly. At the same time, geographic data are being introduced into disciplines outside the traditional domain of geoinformatics, like climate change, intelligent transport, and immigration studies. These developments call for better methods to deliver raster geodata in an efficient and easy-to-use manner. Data cube technologies have traditionally been used in the geospatial domain for managing Earth Observation data sets that have strict requirements for effective handling of time series. The same approach and methodologies can also be applied in managing other types of geospatial data sets. A cloud service-based geodata cube, called GeoCubes Finland, has been developed to support online delivery and analysis of most important geospatial data sets with national coverage. The main target group of the service is the academic research institutes in the country. The most significant aspects of the GeoCubes data repository include the use of multiple resolution levels, cloud-optimized file structure, and a customized, flexible content access API. Input data sets are pre-processed while being ingested into the repository to bring them into a harmonized form in aspects like georeferencing, sampling resolutions, spatial subdivision, and value encoding. All the resolution levels are created using an appropriate generalization method, selected depending on the nature of the source data set. Multiple pre-processed resolutions enable new kinds of online analysis approaches to be introduced. Analysis processes based on interactive visual exploration can be effectively carried out, as the level of resolution most close to the visual scale can always be used. In the same way, statistical analysis can be carried out on resolution levels that best reflect the scale of the phenomenon being studied. Access times remain close to constant, independent of the scale applied in the application. The cloud service-based approach, applied in the GeoCubes Finland repository, enables analysis operations to be performed on the server platform, thus making high-performance computing facilities easily accessible. The developed GeoCubes API supports this kind of approach for online analysis. The use of cloud-optimized file structures in data storage enables the fast extraction of subareas. The access API allows for the use of vector-formatted administrative areas and user-defined polygons as definitions of subareas for data retrieval. Administrative areas of the country in four levels are available readily from the GeoCubes platform. In addition to direct delivery of raster data, the service also supports the so-called virtual file format, in which only a small text file is first downloaded. The text file contains links to the raster content on the service platform. The actual raster data is downloaded on demand, from the spatial area and resolution level required in each stage of the application. By the geodata cube approach, pre-harmonized geospatial data sets are made accessible to new categories of inexperienced users in an easy-to-use manner. At the same time, the multiresolution nature of the GeoCubes repository facilitates expert users to introduce new kinds of interactive online analysis operations.

Keywords: cloud service, geodata cube, multiresolution, raster geodata

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Authors: Mohamed Khalifa Zayet, Salma Belal Eiid, Mushira Mohamed Dahaba

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Introduction: Malignant tumors may not be easily detected by traditional radiographic techniques especially in an anatomically complex area like maxillofacial region. At the same time, the advent of biological functional MRI was a significant footstep in the diagnostic imaging field. Objective: The purpose of this study was to define the malignant metabolic profile of maxillofacial lesions using diffusion MRI and magnetic resonance spectroscopy, as adjunctive aids for diagnosing of such lesions. Subjects and Methods: Twenty-one patients with twenty-two lesions were enrolled in this study. Both morphological and functional MRI scans were performed, where T1, T2 weighted images, diffusion-weighted MRI with four apparent diffusion coefficient (ADC) maps were constructed for analysis, and magnetic resonance spectroscopy with qualitative and semi-quantitative analyses of choline and lactate peaks were applied. Then, all patients underwent incisional or excisional biopsies within two weeks from MR scans. Results: Statistical analysis revealed that not all the parameters had the same diagnostic performance, where lactate had the highest areas under the curve (AUC) of 0.9 and choline was the lowest with insignificant diagnostic value. The best cut-off value suggested for lactate was 0.125, where any lesion above this value is supposed to be malignant with 90 % sensitivity and 83.3 % specificity. Despite that ADC maps had comparable AUCs still, the statistical measure that had the final say was the interpretation of likelihood ratio. As expected, lactate again showed the best combination of positive and negative likelihood ratios, whereas for the maps, ADC map with 500 and 1000 b-values showed the best realistic combination of likelihood ratios, however, with lower sensitivity and specificity than lactate. Conclusion: Diffusion weighted imaging and magnetic resonance spectroscopy are state-of-art in the diagnostic arena and they manifested themselves as key players in the differentiation process of orofacial tumors. The complete biological profile of malignancy can be decoded as low ADC values, high choline and/or high lactate, whereas that of benign entities can be translated as high ADC values, low choline and no lactate.

Keywords: diffusion magnetic resonance imaging, magnetic resonance spectroscopy, malignant tumors, maxillofacial

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Authors: Mojtaba Valinejadshoubi, Azin Shakibabarough, Ashutosh Bagchi

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Facilities such as residential buildings, office buildings, and hospitals house large density of occupants. Therefore, a low-cost facility management program (FMP) should be used to provide a satisfactory built environment for these occupants. Facility management (FM) has been recently used in building projects as a critical task. It has been effective in reducing operation and maintenance cost of these facilities. Issues of information integration and visualization capabilities are critical for reducing the complexity and cost of FM. Building information modeling (BIM) can be used as a strong visual modeling tool and database in FM. The main objective of this study is to examine the applicability of BIM in the FM process during a building’s operational phase. For this purpose, a seven-storey office building is modeled Autodesk Revit software. Authors integrated the cloud-based environment using a visual programming tool, Dynamo, for the purpose of having a real-time cloud-based communication between the facility managers and the participants involved in the project. An appropriate and effective integrated data source and visual model such as BIM can reduce a building’s operational and maintenance costs by managing the building life cycle properly.

Keywords: building information modeling, facility management, operational phase, building life cycle

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Authors: John Lamb, Robert Epstein, Vasundhara L. Bhupathi, Sanjeev Kumar Marimekala

Abstract:

As we enter the new era of Artificial Intelligence (AI) and Cloud Computing, we mostly rely on the Machine and Natural Language Processing capabilities of AI, and Energy Efficient Hardware and Software Devices in almost every industry sector. In these industry sectors, much emphasis is on developing new and innovative methods for producing and conserving energy and sustaining the depletion of natural resources. The core pillars of sustainability are economic, environmental, and social, which is also informally referred to as the 3 P's (People, Planet and Profits). The 3 P's play a vital role in creating a core Sustainability Model in the Enterprise. Natural resources are continually being depleted, so there is more focus and growing demand for renewable energy. With this growing demand, there is also a growing concern in many industries on how to reduce carbon emissions and conserve natural resources while adopting sustainability in corporate business models and policies. In our paper, we would like to discuss the driving forces such as Climate changes, Natural Disasters, Pandemic, Disruptive Technologies, Corporate Policies, Scaled Business Models and Emerging social media and AI platforms that influence the 3 main pillars of Sustainability (3P’s). Through this paper, we would like to bring an overall perspective on enterprise strategies and the primary focus on bringing cultural shifts in adapting energy-efficient operational models. Overall, many industries across the globe are incorporating core sustainability principles such as reducing energy costs, reducing greenhouse gas (GHG) emissions, reducing waste and increasing recycling, adopting advanced monitoring and metering infrastructure, reducing server footprint and compute resources (Shared IT services, Cloud computing, and Application Modernization) with the vision for a sustainable environment.

Keywords: climate change, pandemic, disruptive technology, government policies, business model, machine learning and natural language processing, AI, social media platform, cloud computing, advanced monitoring, metering infrastructure

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Authors: Kang Shuo-Shuo, Li Jian-Nan, Yang Shiming

Abstract:

Purpose: The three-dimensional anatomical structure of the facial nerve recess and its relationship were measured by high-resolution temporal bone CT to provide imaging reference for cochlear implant operation. Materials and Methods: By analyzing the high-resolution CT of 160 cases (320 pleural ears) of the temporal bone, the following parameters were measured at the axial window niche level: 1. The distance between the facial nerve and chordae tympani nerve d1; 2. Distance between the facial nerve and circular window niche d2; 3. The relative Angle between the facial nerve and the circular window niche a; 4. Distance between the middle point of the face recess and the circular window niche d3; 5. The relative angle between the middle point of the face recess and the circular window niche b. Factors that might influence the anatomy of the facial recess were recorded, including the patient's sex, age, and anatomical variation (e.g., vestibular duct dilation, mastoid gas type, mothoid sinus advancement, jugular bulbar elevation, etc.), and the correlation between these factors and the measured facial recess parameters was analyzed. Result: The mean value of face-drum distance d1 is (3.92 ± 0.26) mm, the mean value of face-niche distance d2 is (5.95 ± 0.62) mm, the mean value of face-niche Angle a is (94.61 ± 9.04) °, and the mean value of fossa - niche distance d3 is (6.46 ± 0.63) mm. The average fossa-niche Angle b was (113.47 ± 7.83) °. Gender, age, and anterior sigmoid sinus were the three factors affecting the width of the opposite recess d1, the Angle of the opposite nerve relative to the circular window niche a, and the Angle of the facial recess relative to the circular window niche b. Conclusion: High-resolution temporal bone CT before cochlear implantation can show the important anatomical relationship of the facial nerve recess, and the measurement results have clinical reference value for the operation of cochlear implantation.

Keywords: cochlear implantation, recess of facial nerve, temporal bone CT, three-dimensional measurement

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Authors: Jong-Hwa Kim, Mu-Wook Pyeon, Yang-dam Eo, Ill-Woong Jang

Abstract:

Construction of geo-spatial information recently tends to develop as multi-dimensional geo-spatial information. People constructing spatial information is also expanding its area to the general public from some experts. As well as, studies are in progress using a variety of devices, with the aim of near real-time update. In this paper, getting the stereo images using GoPro device used widely also to the general public as well as experts. And correcting the distortion of the images, then by using SIFT, DLT, is acquired the point clouds. It presented a possibility that on the basis of this experiment, using a video device that is readily available in real life, to create a real-time digital map.

Keywords: GoPro, SIFT, DLT, point clouds

Procedia PDF Downloads 469

Authors: Jacintha Menezes, Ramalingam Dharmalingam, Palaiahnakote Shivakumara

Abstract:

In the recent years, Omani acid lime cultivation and production has been affected by Citrus greening or Huanglongbing (HLB) disease. HLB disease is one of the most destructive diseases for citrus, with no remedies or countermeasures to stop the disease. Currently used Polymerase chain reaction (PCR) and enzyme-linked immunosorbent assay (ELISA) HLB detection tests require lengthy and labor-intensive laboratory procedures. Furthermore, the equipment and staff needed to carry out the laboratory procedures are frequently specialized hence making them a less optimal solution for the detection of the disease. The current research uses hyperspectral imaging technology for automatic detection of citrus trees with HLB disease. Omani citrus tree leaf images were captured through portable Specim IQ hyperspectral camera. The research considered healthy, nutrition deficient, and HLB infected leaf samples based on the Polymerase chain reaction (PCR) test. The highresolution image samples were sliced to into sub cubes. The sub cubes were further processed to obtain RGB images with spatial features. Similarly, RGB spectral slices were obtained through a moving window on the wavelength. The resized spectral-Spatial RGB images were given to Convolution Neural Networks for deep features extraction. The current research was able to classify a given sample to the appropriate class with 92.86% accuracy indicating the effectiveness of the proposed techniques. The significant bands with a difference in three types of leaves are found to be 560nm, 678nm, 726 nm and 750nm.

Keywords: huanglongbing (HLB), hyperspectral imaging (HSI), · omani citrus, CNN

Procedia PDF Downloads 80