Search results for: medical resonance (MR) images

Authors: Wanda Luen-Wun Siu, Xiaowen Zhang

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The outbreak of the COVID-19 epidemic has caused great difficulties for South-South cooperation, but there are also opportunities. China’s health diplomacy has changed from dispatching medical teams, assisting in the construction of hospitals, and encouraging medical investment in the Africa health sector. This paper adopted a retrospective review of China’s health diplomacy in Africa from 1963 to 2020. Findings suggested that China has a preference for aiding Africa health infrastructure and sending medical teams to African countries. China’s health diplomacy in Africa is a success and has established secure diplomatic relations with African countries, thanks to the medical and health assistance to Africa over 60 years. This research contributes to the literature of health diplomacy and foreign relations and indicates that China’s health aid has fostered cooperation at the medical and diplomatic levels.

Keywords: Africa, china’s health diplomacy, COVID-19, bilateral relations

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Authors: Qingjin Liu, Jinpeng Niu, Kangjia Chen, Jiao Li, Huafu Chen, Wei Liao

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Functional connectomics is essential in cognitive science and neuropsychiatry, offering insights into the brain's complex network structures and dynamic interactions. Although neuroimaging has uncovered functional connectivity issues in Major Depressive Disorder (MDD) patients, the dynamic shifts in connectome topology and their link to gene expression are yet to be fully understood. To explore the differences in dynamic connectome topology between MDD patients and healthy individuals, we conducted an extensive analysis of resting-state functional magnetic resonance imaging (fMRI) data from 434 participants (226 MDD patients and 208 controls). We used multilayer network models to evaluate brain module dynamics and examined the association between whole-brain gene expression and dynamic module variability in MDD using publicly available transcriptomic data. Our findings revealed that compared to healthy individuals, MDD patients showed lower global mean values and higher standard deviations, indicating unstable patterns and increased regional differentiation. Notably, MDD patients exhibited more frequent module switching, primarily within the executive control network (ECN), particularly in the left dorsolateral prefrontal cortex and right fronto-insular regions, whereas the default mode network (DMN), including the superior frontal gyrus, temporal lobe, and right medial prefrontal cortex, displayed lower variability. These brain dynamics predicted the severity of depressive symptoms. Analyzing human brain gene expression data, we found that the spatial distribution of MDD-related gene expression correlated with dynamic module differences. Cell type-specific gene analyses identified oligodendrocytes (OPCs) as major contributors to the transcriptional relationships underlying module variability in MDD. To the best of our knowledge, this is the first comprehensive description of altered brain module dynamics in MDD patients linked to depressive symptom severity and changes in whole-brain gene expression profiles.

Keywords: major depressive disorder, module dynamics, magnetic resonance imaging, transcriptomic

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Authors: Joe Amalraj, M. Arunkumar

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Haze is an atmospheric phenomenon that signicantly degrades the visibility of outdoor scenes. This is mainly due to the atmosphere particles that absorb and scatter the light. This paper introduces a novel single image approach that enhances the visibility of such degraded images. In this method is a fusion-based strategy that derives from two original hazy image inputs by applying a white balance and a contrast enhancing procedure. To blend effectively the information of the derived inputs to preserve the regions with good visibility, we filter their important features by computing three measures (weight maps): luminance, chromaticity, and saliency. To minimize artifacts introduced by the weight maps, our approach is designed in a multiscale fashion, using a Laplacian pyramid representation. This paper demonstrates the utility and effectiveness of a fusion-based technique for de-hazing based on a single degraded image. The method performs in a per-pixel fashion, which is straightforward to implement. The experimental results demonstrate that the method yields results comparative to and even better than the more complex state-of-the-art techniques, having the advantage of being appropriate for real-time applications.

Keywords: single image de-hazing, outdoor images, enhancing, DSP

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Authors: Ahmedou Moulaye Idriss, Yahya Tfeil

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Medical imaging technology has become an indispensable tool in many branches of the biomedical, health area, and research and is vitally important for the training of professionals in these fields. It is not only about the tools, technologies, and knowledge provided but also about the community that this training project proposes. In order to be able to raise the level of anatomy teaching in the medical school of Nouakchott in Mauritania, it is necessary and even urgent to facilitate access to modern technology for African countries. The role of technology as a key driver of justifiable development has long been recognized. Anatomy is an essential discipline for the training of medical students; it is a key element for the training of medical specialists. The quality and results of the work of a young surgeon depend on his better knowledge of anatomical structures. The teaching of anatomy is difficult as the discipline is being neglected by medical students in many academic institutions. However, anatomy remains a vital part of any medical education program. When anatomy is presented in various planes medical students approve of difficulties in understanding. They do not increase their ability to visualize and mentally manipulate 3D structures. They are sometimes not able to correctly identify neighbouring or associated structures. This is the case when they have to make the identification of structures related to the caudate lobe when the liver is moved to different positions. In recent decades, some modern educational tools using digital sources tend to replace old methods. One of the main reasons for this change is the lack of cadavers in laboratories with poorly qualified staff. The emergence of increasingly sophisticated mathematical models, image processing, and visualization tools in biomedical imaging research have enabled sophisticated three-dimensional (3D) representations of anatomical structures. In this paper, we report our current experience in the Faculty of Medicine in Nouakchott Mauritania. One of our main aims is to create a local learning community in the fields of anatomy. The main technological platform used in this project is called 3D Slicer. 3D Slicer platform is an open-source application available for free for viewing, analysis, and interaction with biomedical imaging data. Using the 3D Slicer platform, we created from real medical images anatomical atlases of parts of the human body, including head, thorax, abdomen, liver, and pelvis, upper and lower limbs. Data were collected from several local hospitals and also from the website. We used MRI and CT-Scan imaging data from children and adults. Many different anatomy atlases exist, both in print and digital forms. Anatomy Atlas displays three-dimensional anatomical models, image cross-sections of labelled structures and source radiological imaging, and a text-based hierarchy of structures. Open and free online anatomical atlases developed by our anatomy laboratory team will be available to our students. This will allow pedagogical autonomy and remedy the shortcomings by responding more fully to the objectives of sustainable local development of quality education and good health at the national level. To make this work a reality, our team produced several atlases available in our faculty in the form of research projects.

Keywords: anatomy, education, medical imaging, three dimensional

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Authors: Y. Y. Babanyara, D. B. Ibrahim, T. Garba

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Proper management of medical waste is a crucial issue for maintaining human health and the environment. The waste generated in the hospitals has the potential for spreading infections and causing diseases. The study is aimed at assessing the medical waste management practices in Nigeria. Three instruments, questionnaire administration, in-depth interview and observation method for data collection were adopted in the study. The results revealed that the hospital does not quantify medical waste. Segregation of medical wastes is not conducted according to definite rules and standards. Wheeled trolleys are used for on-site transportation of waste from the points of production to the temporary storage area. Offsite transportation of the hospital waste is undertaken by a private waste management company. Small pickups are mainly used to transport waste daily to an off-site area for treatment and disposal. The main treatment method used in the final disposal of infectious waste is incineration. Non-infectious waste is disposed off using land disposal method. The study showed that the hospital does not have a policy and plan in place for managing medical waste. The study revealed number of problems the hospital faces in terms of medical waste management, including; lack of necessary rules, regulations and instructions on the different aspects of collections and disposal of waste, failure to quantify the waste generated in reliable records, lack of use of coloured bags by limiting the bags to only one colour for all waste, the absence of a dedicated waste manager, and no committee responsible for monitoring the management of medical waste. Recommendations are given with the aim of improving medical waste management in the hospital.

Keywords: medical waste, treatment, disposal, public health

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Authors: Suleyman Karabal, Timothy L. Burnett, Egemen Avcu, Andrew H. Sherry, Philip J. Withers

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SA508 Grade 3 steel is widely used in the construction of nuclear pressure vessels, where its fracture toughness plays a critical role in ensuring operational safety and reliability. Understanding the ductile failure mechanisms in this steel grade is crucial for designing robust pressure vessels that can withstand severe nuclear environment conditions. In the present study, round bar specimens of SA508 Grade 3 steel with four distinct notch geometries were subjected to tensile loading while capturing continuous 2D images at 5-second intervals in order to monitor any alterations in their geometries to construct true stress-strain curves of the specimens. 3D reconstructions of X-ray computed tomography (CT) images at high-resolution (a spatial resolution of 0.82 μm) allowed for a comprehensive assessment of the influences of second-phase particles (i.e., manganese sulfide inclusions and cementite particles) on ductile failure initiation as a function of applied plastic strain. Additionally, based on 2D and 3D images, plasticity modeling was executed, and the results were compared to experimental data. A specific ‘two-parameter criterion’ was established and calibrated based on the correlation between stress triaxiality and equivalent plastic strain at failure initiation. The proposed criterion demonstrated substantial agreement with the experimental results, thus enhancing our knowledge of ductile fracture behavior in this steel grade. The implementation of X-ray CT and fractography analysis provided new insights into the diverse roles played by different populations of second-phase particles in fracture initiation under varying stress triaxiality conditions.

Keywords: ductile fracture, two-parameter criterion, x-ray computed tomography, stress triaxiality

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Authors: Rinlee Butch M. Cervera, Princess Stephanie P. Llanos

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Lithium iron phosphate (LiFePO₄) is a potential cathode material for lithium-ion batteries due to its promising characteristics. In this study, pure LiFePO₄ (LFP) and carbon-coated nanograined LiFePO₄ (LFP-C) is synthesized and characterized for its microstructural properties. X-ray diffraction patterns of the synthesized samples can be indexed to an orthorhombic LFP structure with about 63 nm crystallite size as calculated by using Scherrer’s equation. Agglomerated particles that range from 200 nm to 300 nm are observed from scanning electron microscopy images. Transmission electron microscopy images confirm the crystalline structure of LFP and coating of amorphous carbon layer. Elemental mapping using energy dispersive spectroscopy analysis revealed the homogeneous dispersion of the compositional elements. In addition, galvanostatic charge and discharge measurements were investigated for the cathode performance of the synthesized LFP and LFP-C samples. The results showed that the carbon-coated sample demonstrated the highest capacity of about 140 mAhg^-1 as compared to non-coated and micrograined sized commercial LFP.

Keywords: ceramics, energy storage, electrochemical measurements, transmission electron microscope

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Authors: Farhan A. Alenizi

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Digital watermarking has evolved in the past years as an important means for data authentication and ownership protection. The images and video watermarking was well known in the field of multimedia processing; however, 3D objects' watermarking techniques have emerged as an important means for the same purposes, as 3D mesh models are in increasing use in different areas of scientific, industrial, and medical applications. Like the image watermarking techniques, 3D watermarking can take place in either space or transform domains. Unlike images and video watermarking, where the frames have regular structures in both space and temporal domains, 3D objects are represented in different ways as meshes that are basically irregular samplings of surfaces; moreover, meshes can undergo a large variety of alterations which may be hard to tackle. This makes the watermarking process more challenging. While the transform domain watermarking is preferable in images and videos, they are still difficult to implement in 3d meshes due to the huge number of vertices involved and the complicated topology and geometry, and hence the difficulty to perform the spectral decomposition, even though significant work was done in the field. Spatial domain watermarking has attracted significant attention in the past years; they can either act on the topology or on the geometry of the model. Exploiting the statistical characteristics in the 3D mesh models from both geometrical and topological aspects was useful in hiding data. However, doing that with minimal surface distortions to the mesh attracted significant research in the field. A 3D mesh blind watermarking technique is proposed in this research. The watermarking method depends on modifying the vertices' positions with respect to the center of the object. An optimal method will be developed to reduce the errors, minimizing the distortions that the 3d object may experience due to the watermarking process, and reducing the computational complexity due to the iterations and other factors. The technique relies on the displacement process of the vertices' locations depending on the modification of the variances of the vertices’ norms. Statistical analyses were performed to establish the proper distributions that best fit each mesh, and hence establishing the bins sizes. Several optimizing approaches were introduced in the realms of mesh local roughness, the statistical distributions of the norms, and the displacements in the mesh centers. To evaluate the algorithm's robustness against other common geometry and connectivity attacks, the watermarked objects were subjected to uniform noise, Laplacian smoothing, vertices quantization, simplification, and cropping. Experimental results showed that the approach is robust in terms of both perceptual and quantitative qualities. It was also robust against both geometry and connectivity attacks. Moreover, the probability of true positive detection versus the probability of false-positive detection was evaluated. To validate the accuracy of the test cases, the receiver operating characteristics (ROC) curves were drawn, and they’ve shown robustness from this aspect. 3D watermarking is still a new field but still a promising one.

Keywords: watermarking, mesh objects, local roughness, Laplacian Smoothing

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Authors: António J. R. Neves, Rui Garcia, Paulo Dias, Alina Trifan

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With the aging of the world population and the continuous growth in technology, service robots are more and more explored nowadays as alternatives to healthcare givers or personal assistants for the elderly or disabled people. Any service robot should be capable of interacting with the human companion, receive commands, navigate through the environment, either known or unknown, and recognize objects. This paper proposes an approach for object recognition based on the use of depth information and color images for a service robot. We present a study on two of the most used methods for object detection, where 3D data is used to detect the position of objects to classify that are found on horizontal surfaces. Since most of the objects of interest accessible for service robots are on these surfaces, the proposed 3D segmentation reduces the processing time and simplifies the scene for object recognition. The first approach for object recognition is based on color histograms, while the second is based on the use of the SIFT and SURF feature descriptors. We present comparative experimental results obtained with a real service robot.

Keywords: object detection, feature, descriptors, SIFT, SURF, depth images, service robots

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Authors: Zahra Asgharpour, Eric Renteria, Sebastian De Boodt

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There is a growing trend towards personalization of medical care, as evidenced by the emphasis on outcomes based medicine, the latest developments in CT and MR imaging and personalized treatment in a variety of surgical disciplines. 3D Printing has been introduced and applied in the medical field since 2000. The first applications were in the field of dental implants and custom prosthetics. According to recent publications, 3D printing in the medical field has been used in a wide range of applications which can be organized into several categories including implants, prosthetics, anatomical models and tissue bioprinting. Some of these categories are still in their infancy stage of the concept of proof while others are in application phase such as the design and manufacturing of customized implants and prosthesis. The approach of 3D printing in this category has been successfully used in the health care sector to make both standard and complex implants within a reasonable amount of time. In this study, some of the clinical applications of 3D printing in design and manufacturing of a patient-specific hip implant would be explained. In cases where patients have complex bone geometries or are undergoing a complex revision on hip replacement, the traditional surgical methods are not efficient, and hence these patients require patient-specific approaches. There are major advantages in using this new technology for medical applications, however, in order to get this technology widely accepted in medical device industry, there is a need for gaining more acceptance from the medical device regulatory offices. This is a challenge that is moving onward and will help the technology find its way at the end as an accepted manufacturing method for medical device industry in an international scale. The discussion will conclude with some examples describing the future directions of 3D Medical Printing.

Keywords: CT/MRI, image processing, 3D printing, medical devices, patient specific implants

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Authors: Temenuzhka Spasova, Nadya Yanakieva

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Snow modulates the hydrological cycle and influences the functioning of ecosystems and is a significant resource for many populations whose water is harvested from cold regions. Snow observations are important for validating climate models. The accumulation and rapid melt of snow are two of the most dynamical seasonal environmental changes on the Earth’s surface. The actuality of this research is related to the modern tendencies of the remote sensing application in the solution of problems of different nature in the ecological monitoring of the environment. The subject of the study is the dynamic during the different seasons on Livingstone Island, South Shetland Islands in Antarctica and on Svalbard in Arctic. The objects were analyzed and mapped according to the Еuropean Space Agency data (ESA), acquired by sensors Sentinel-1 SAR (Synthetic Aperture Radar), Sentinel 2 MSI and GIS. Results have been obtained for changes in snow coverage during the summer-winter transition and its dynamics in the two hemispheres. The data used is of high time-spatial resolution, which is an advantage when looking at the snow cover. The MSI images are with different spatial resolution at the Earth surface range. The changes of the environmental objects are shown with the SAR images and different processing approaches. The results clearly show that snow and snow melting can be best registered by using SAR data via hh- horizontal polarization. The effect of the researcher on aerospace data and technology enables us to obtain different digital models, structuring and analyzing results excluding the subjective factor. Because of the large extent of terrestrial snow coverage and the difficulties in obtaining ground measurements over cold regions, remote sensing and GIS represent an important tool for studying snow areas and properties from regional to global scales.

Keywords: climate changes, GIS, remote sensing, SAR images, snow coverage

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Authors: Mohammed S. Al-Rawi, J. Bastos, J. Rodriguez

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Object detection and object recognition are essential components of every computer vision system. Despite the high computational complexity and other problems related to numerical stability and accuracy, Zernike moments of 2D images (ZMs) have shown resilience when used in object recognition and have been used in various image analysis applications. In this work, we propose a novel method for computing ZMs via Fast Fourier Transform (FFT). Notably, this is the first algorithm that can generate ZMs up to extremely high orders accurately, e.g., it can be used to generate ZMs for orders up to 1000 or even higher. Furthermore, the proposed method is also simpler and faster than the other methods due to the availability of FFT software and/or hardware. The accuracies and numerical stability of ZMs computed via FFT have been confirmed using the orthogonality property. We also introduce normalizing ZMs with Neumann factor when the image is embedded in a larger grid, and color image reconstruction based on RGB normalization of the reconstructed images. Astonishingly, higher-order image reconstruction experiments show that the proposed methods are superior, both quantitatively and subjectively, compared to the q-recursive method.

Keywords: Chebyshev polynomial, fourier transform, fast algorithms, image recognition, pseudo Zernike moments, Zernike moments

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Authors: Masashi Kanazawa, Takaaki Nakano, Masaaki Takemoto, Tomonori Imamura, Mamiko Sugimura, Toshitaka Ito

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Introduction: We experienced a massive disaster due to inhalation of water-soluble paint. Sixteen patients were brought to our emergency room, and pulmonary edema was revealed on the CT images of 12 cases. Purpose: Transition of chest CT-scan findings in cases with pulmonary edema was examined. Method: CT-scans were performed on the 1st, 2nd, 5th, and 19th days after the inhalation event. Patients whose pulmonary edema showed amelioration or exacerbation were classified into the improvement or the exacerbation group, respectively. Those with lung edema findings appearing at different sites after the second day were classified into the changing group. Results: Eight, one and three patients were in the improvement, exacerbation and changing groups, respectively. In all cases, the pulmonary edema had disappeared from CT images on the 19th day after the inhalation event. Conclusion: Inhalation of water-soluble paints is considered to be relatively safe. However, our observations in these emergency cases suggest that, even if pulmonary edema is not severe immediately after the exposure, new lesions may appear later and existing lesions may worsen. Follow-up imaging is thus necessary for about two weeks.

Keywords: CT scan, intoxication, pulmonary edema, water-soluble paint

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Authors: Tienfuan Kerh, Hsienchang Lu, Rob Saunders

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Shoreline erosion problems caused by global warming and sea level rising may result in losing of land areas, so it should be examined regularly to reduce possible negative impacts. Initially in this study, three sets of survey images obtained from the years of 1990, 2001, and 2010, respectively, are digitalized by using graphical software to establish the spatial coordinates of six major beaches around the island of Taiwan. Then, by overlaying the known multi-period images, the change of shoreline can be observed from their distribution of coordinates. In addition, the neural network approximation is used to develop a model for predicting shoreline variation in the years of 2015 and 2020. The comparison results show that there is no significant change of total sandy area for all beaches in the three different periods. However, the prediction results show that two beaches may exhibit an increasing of total sandy areas under a statistical 95% confidence interval. The proposed method adopted in this study may be applicable to other shorelines of interest around the world.

Keywords: digitalized shoreline coordinates, survey image overlaying, neural network approximation, total beach sandy areas

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Authors: Zeynep Sener, Mehtap Dursun

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Supplier evaluation and selection is one of the most important components of an effective supply chain management system. Due to the expanding competition in healthcare, selecting the right medical device suppliers offers great potential for increasing quality while decreasing costs. This paper proposes a fuzzy decision making approach for medical supplier selection. A real-world medical device supplier selection problem is presented to illustrate the application of the proposed decision methodology.

Keywords: fuzzy decision making, fuzzy multiple objective programming, medical supply chain, supplier selection

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Authors: Brayden Mi

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Reverse time migration has been widely used in the Petroleum exploration industry to reveal subsurface images and to detect rock and fluid properties since the early 1980s. The seismic technology involves the construction of a velocity model through interpretive model construction, seismic tomography, or full waveform inversion, and the application of the reverse-time propagation of acquired seismic data and the original wavelet used in the acquisition. The methodology has matured from 2D, simple media to present-day to handle full 3D imaging challenges in extremely complex geological conditions. Conventional Ultrasound computed tomography (USCT) utilize travel-time-inversion to reconstruct the velocity structure of an organ. With the velocity structure, USCT data can be migrated with the “bend-ray” method, also known as migration. Its seismic application counterpart is called Kirchhoff depth migration, in which the source of reflective energy is traced by ray-tracing and summed to produce a subsurface image. It is well known that ray-tracing-based migration has severe limitations in strongly heterogeneous media and irregular acquisition geometries. Reverse time migration (RTM), on the other hand, fully accounts for the wave phenomena, including multiple arrives and turning rays due to complex velocity structure. It has the capability to fully reconstruct the image detectable in its acquisition aperture. The RTM algorithms typically require a rather accurate velocity model and demand high computing powers, and may not be applicable to real-time imaging as normally required in day-to-day medical operations. However, with the improvement of computing technology, such a computational bottleneck may not present a challenge in the near future. The present-day (RTM) algorithms are typically implemented from a flat datum for the seismic industry. It can be modified to accommodate any acquisition geometry and aperture, as long as sufficient illumination is provided. Such flexibility of RTM can be conveniently implemented for the application in USCT imaging if the spatial coordinates of the transmitters and receivers are known and enough data is collected to provide full illumination. This paper proposes an implementation of a full 3D RTM algorithm for USCT imaging to produce an accurate 3D acoustic image based on the Phase-shift-plus-interpolation (PSPI) method for wavefield extrapolation. In this method, each acquired data set (shot) is propagated back in time, and a known ultrasound wavelet is propagated forward in time, with PSPI wavefield extrapolation and a piece-wise constant velocity model of the organ (breast). The imaging condition is then applied to produce a partial image. Although each image is subject to the limitation of its own illumination aperture, the stack of multiple partial images will produce a full image of the organ, with a much-reduced noise level if compared with individual partial images.

Keywords: illumination, reverse time migration (RTM), ultrasound computed tomography (USCT), wavefield extrapolation

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Authors: Harsh Vikram Singh, Ankur Rai, Anand Mohan

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In this paper, we propose secure and robust data hiding algorithm based on DCT by Arnold transform and chaotic sequence. The watermark image is scrambled by Arnold cat map to increases its security and then the chaotic map is used for watermark signal spread in middle band of DCT coefficients of the cover image The chaotic map can be used as pseudo-random generator for digital data hiding, to increase security and robustness .Performance evaluation for robustness and imperceptibility of proposed algorithm has been made using bit error rate (BER), normalized correlation (NC), and peak signal to noise ratio (PSNR) value for different watermark and cover images such as Lena, Girl, Tank images and gain factor .We use a binary logo image and text image as watermark. The experimental results demonstrate that the proposed algorithm achieves higher security and robustness against JPEG compression as well as other attacks such as addition of noise, low pass filtering and cropping attacks compared to other existing algorithm using DCT coefficients. Moreover, to recover watermarks in proposed algorithm, there is no need to original cover image.

Keywords: data hiding, watermarking, DCT, chaotic sequence, arnold transforms

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Authors: Riikka Elina Kallio

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“The walls have ears” phrase is defining the era of dictatorship in Portugal (1926–1974) and political unrest decades in Finland (1917–1948). The phrase is referring to the policing of the political, secret police, PIDE (Policia Internacional e de Defesa do Estado 1933–1969) in Portugal and VALPO (Valtiollinen Poliisi 1939–1948) in Finland. Free speech at any public space and even in private events could be fatal. The members of the PIDE/VALPO or informers/collaborators could be listening. Strict censorship under the Salazar´s regime was controlling media for example newspapers, music, and the film industry. Similarly, the politically affected censorship influenced the media in Finland in those unrest decades. This article examines the similarities and the differences in the images of the political police in Finland and Portugal, by analyzing Finnish and Portuguese films from the nineteen-thirties to nineteensixties. The text addresses two main research questions: what are the common and different features in the representations of the Finnish and Portuguese political police in films between the 1930s and 1960s, and how did the national censorship affect these representations? This study approach is interdisciplinary, and it combines film studies and criminology. Close reading is a practical qualitative method for analyzing films and in this study, close reading emphasizes the features of the police officer. Criminology provides the methodological tools for analysis of the police universal features and European common policies. The characterization of the police in this study is based on Robert Reiner´s 1980s and Timo Korander´s 2010s definitions of the police officer. The research material consisted of the Portuguese films from online film archives and Finnish films from Movie Making Finland -project´s metadata which offered suitable material by data mining the keywords such as poliisi, poliisipäällikkö and konstaapeli (police, police chief, police constable). The findings of this study suggest that even though there are common features of the images of the political police in Finland and Portugal, there are still national and cultural differences in the representations of the political police and policing.

Keywords: censorship, film studies, images, PIDE, political police, VALPO

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Authors: Amaladhithyan Krishnamoorthy, Ruvaitha Banu

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Vision is considered as the most important sense in humans, without which leading a normal can be often difficult. There are many existing smart canes for visually impaired with obstacle detection using ultrasonic transducer to help them navigate. Though the basic smart cane increases the safety of the users, it does not help in filling the void of visual loss. This paper introduces the concept of Digi-Buddy which is an evolved smart cane for visually impaired. The cane consists for several modules, apart from the basic obstacle detection features; the Digi-Buddy assists the user by capturing video/images and streams them to the server using a wide-angled camera, which then detects the objects using Deep Convolutional Neural Network. In addition to determining what the particular image/object is, the distance of the object is assessed by the ultrasonic transducer. The sound generation application, modelled with the help of Natural Language Processing is used to convert the processed images/object into audio. The object detected is signified by its name which is transmitted to the user with the help of Bluetooth hear phones. The object detection is extended to facial recognition which maps the faces of the person the user meets in the database of face images and alerts the user about the person. One of other crucial function consists of an automatic-intimation-alarm which is triggered when the user is in an emergency. If the user recovers within a set time, a button is provisioned in the cane to stop the alarm. Else an automatic intimation is sent to friends and family about the whereabouts of the user using GPS. In addition to safety and security by the existing smart canes, the proposed concept devices to be implemented as a prototype helping visually-impaired visualize their surroundings through audio more in an amicable way.

Keywords: artificial intelligence, facial recognition, natural language processing, internet of things

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Authors: Hadeer R. M. Tawfik, Rania A. K. Birry, Amani A. Saad

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Eyes are considered to be the most sensitive and important organ for human being. Thus, any eye disorder will affect the patient in all aspects of life. Cataract is one of those eye disorders that lead to blindness if not treated correctly and quickly. This paper demonstrates a model for automatic detection, classification, and grading of cataracts based on image processing techniques and artificial intelligence. The proposed system is developed to ease the cataract diagnosis process for both ophthalmologists and patients. The wavelet transform combined with 2D Log Gabor Wavelet transform was used as feature extraction techniques for a dataset of 120 eye images followed by a classification process that classified the image set into three classes; normal, early, and advanced stage. A comparison between the two used classifiers, the support vector machine SVM and the artificial neural network ANN were done for the same dataset of 120 eye images. It was concluded that SVM gave better results than ANN. SVM success rate result was 96.8% accuracy where ANN success rate result was 92.3% accuracy.

Keywords: cataract, classification, detection, feature extraction, grading, log-gabor, neural networks, support vector machines, wavelet

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Authors: Jianhong Xiang, Hao Xiang, Linyu Wang

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Compressed sensing (CS) has a wide range of applications in sparse signal reconstruction. Aiming at the problems of low recovery accuracy and long reconstruction time of existing reconstruction algorithms in medical imaging, this paper proposes a corrected smoothing L0 algorithm based on compressed sensing (CSL0). First, an approximate hyperbolic tangent function (AHTF) that is more similar to the L0 norm is proposed to approximate the L0 norm. Secondly, in view of the "sawtooth phenomenon" in the steepest descent method and the problem of sensitivity to the initial value selection in the modified Newton method, the use of the steepest descent method and the modified Newton method are jointly optimized to improve the reconstruction accuracy. Finally, the CSL0 algorithm is simulated on various images. The results show that the algorithm proposed in this paper improves the reconstruction accuracy of the test image by 0-0. 98dB.

Keywords: smoothed L0, compressed sensing, image processing, sparse reconstruction

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Authors: Luan Trong Nguyen, M. Azizur Rahman, Yusuke Tamenori, Toshihiro Yoshimura, Nozomu Iwasaki, Hiroshi Hasegawa

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This study investigated the distribution of magnesium (Mg), phosphorus (P), sulfur (S) and strontium (Sr) using micro X-ray fluorescence (µ-XRF) along the annual growth rings in the skeleton of Japanese red coral Paracorallium japonicum. The Mg, P and S distribution in µ-XRF mapping images correspond to the dark and light bands along the annual growth rings observed in microscopic images of the coral skeleton. The µ-XRF mapping data showed a positive correlation (r = 0.6) between P and S distribution in the coral skeleton. A contrasting distribution pattern of S and Mg along the axial skeleton of P. japonicum indicates a weak negative correlation (r = -0.2) between these two trace elements. The distribution pattern of S, P and Mg reveals linkage between their distributions and the formation of dark/light bands along the annual growth rings in the axial skeleton of P. japonicum. Sulfur and P were distributed in the organic matrix rich dark bands, while Mg was distributed in the light bands of the annual growth rings.

Keywords: µ-XRF, trace element, precious coral, Paracorallium japonicum

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Authors: Hossein Aghababaei, Sergio Vitale, Giampaolo Ferraioli

Abstract:

In the context of Synthetic Aperture Radar (SAR) data, polarization is an important source of information for Earth's surface monitoring. SAR Systems are often considered to transmit only one polarization. This constraint leads to either single or dual polarimetric SAR imaging modalities. Single polarimetric systems operate with a fixed single polarization of both transmitted and received electromagnetic (EM) waves, resulting in a single acquisition channel. Dual polarimetric systems, on the other hand, transmit in one fixed polarization and receive in two orthogonal polarizations, resulting in two acquisition channels. Dual polarimetric systems are obviously more informative than single polarimetric systems and are increasingly being used for a variety of remote sensing applications. In dual polarimetric systems, the choice of polarizations for the transmitter and the receiver is open. The choice of circular transmit polarization and coherent dual linear receive polarizations forms a special dual polarimetric system called hybrid polarimetry, which brings the properties of rotational invariance to geometrical orientations of features in the scene and optimizes the design of the radar in terms of reliability, mass, and power constraints. The complete characterization of target scattering, however, requires fully polarimetric data, which can be acquired with systems that transmit two orthogonal polarizations. This adds further complexity to data acquisition and shortens the coverage area or swath of fully polarimetric images compared to the swath of dual or hybrid polarimetric images. The search for solutions to augment dual polarimetric data to full polarimetric data will therefore take advantage of full characterization and exploitation of the backscattered field over a wider coverage with less system complexity. Several methods for reconstructing fully polarimetric images using hybrid polarimetric data can be found in the literature. Although the improvements achieved by the newly investigated and experimented reconstruction techniques are undeniable, the existing methods are, however, mostly based upon model assumptions (especially the assumption of reflectance symmetry), which may limit their reliability and applicability to vegetation and forest scenarios. To overcome the problems of these techniques, this paper proposes a new framework for reconstructing fully polarimetric information from hybrid polarimetric data. The framework uses Deep Learning solutions to augment hybrid polarimetric data without relying on model assumptions. A convolutional neural network (CNN) with a specific architecture and loss function is defined for this augmentation problem by focusing on different scattering properties of the polarimetric data. In particular, the method controls the CNN training process with respect to several characteristic features of polarimetric images defined by the combination of different terms in the cost or loss function. The proposed method is experimentally validated with real data sets and compared with a well-known and standard approach from the literature. From the experiments, the reconstruction performance of the proposed framework is superior to conventional reconstruction methods. The pseudo fully polarimetric data reconstructed by the proposed method also agree well with the actual fully polarimetric images acquired by radar systems, confirming the reliability and efficiency of the proposed method.

Keywords: SAR image, polarimetric SAR image, convolutional neural network, deep learnig, deep neural network

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Authors: Ali Ben Abbes, Imed Riadh Farah

Abstract:

Due to the considerable advances in Multi-Temporal Satellite Images (MTSI), remote sensing application became more accurate. Recently, many advances in modeling MTSI are developed using various models. The purpose of this article is to present an overview of studies using Hidden Markov Model (HMM). First of all, we provide a background of using HMM and their applications in this context. A comparison of the different works is discussed, and possible areas and challenges are highlighted. Secondly, we discussed the difference on vegetation monitoring as well as urban growth. Nevertheless, most research efforts have been used only stationary data. From another point of view, in this paper, we describe a new non-stationarity HMM, that is defined with a set of parts of the time series e.g. seasonal, trend and random. In addition, a new approach giving more accurate results and improve the applicability of the HMM in modeling a non-stationary data series. In order to assess the performance of the HMM, different experiments are carried out using Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI time series of the northwestern region of Tunisia and Landsat time series of tres Cantos-Madrid in Spain.

Keywords: multi-temporal satellite image, HMM , nonstationarity, vegetation, urban

Procedia PDF Downloads 348

Authors: Janusz Bobulski, Mariusz Kubanek

Abstract:

The main goal of the research is to develop a multimedia container structure containing three types of images: RGB, lidar and infrared, properly calibrated to each other. An additional goal is to develop program libraries for creating and saving this type of file and for restoring it. It will also be necessary to develop a method of data synchronization from lidar and RGB cameras as well as infrared. This type of file could be used in autonomous vehicles, which would certainly facilitate data processing by the intelligent autonomous vehicle management system. Autonomous cars are increasingly breaking into our consciousness. No one seems to have any doubts that self-driving cars are the future of motoring. Manufacturers promise that moving the first of them to showrooms is the prospect of the next few years. Many experts believe that creating a network of communicating autonomous cars will be able to completely eliminate accidents. However, to make this possible, it is necessary to develop effective methods of detection of objects around the moving vehicle. In bad weather conditions, this task is difficult on the basis of the RGB(red, green, blue) image. Therefore, in such situations, you should be supported by information from other sources, such as lidar or infrared cameras. The problem is the different data formats that individual types of devices return. In addition to these differences, there is a problem with the synchronization of these data and the formatting of this data. The goal of the project is to develop a file structure that could be containing a different type of data. This type of file is calling a multimedia container. A multimedia container is a container that contains many data streams, which allows you to store complete multimedia material in one file. Among the data streams located in such a container should be indicated streams of images, films, sounds, subtitles, as well as additional information, i.e., metadata. This type of file could be used in autonomous vehicles, which would certainly facilitate data processing by the intelligent autonomous vehicle management system. As shown by preliminary studies, the use of combining RGB and InfraRed images with Lidar data allows for easier data analysis. Thanks to this application, it will be possible to display the distance to the object in a color photo. Such information can be very useful for drivers and for systems in autonomous cars.

Keywords: an autonomous car, image processing, lidar, obstacle detection

Procedia PDF Downloads 218

Authors: Bhargav Baruah, Ali Shemsedin Reshad, Pankaj Tiwari

Abstract:

This work presents a detailed study on the thermal degradation kinetics of co-pyrolysis of oil shale of Upper Assam, India with rubber seed shell, and lab-scale pyrolysis to investigate the influence of pyrolysis parameters on product yield and composition of products. The physicochemical characteristics of oil shale and rubber seed shell were studied by proximate analysis, elemental analysis, Fourier transform infrared spectroscopy and X-ray diffraction. The physicochemical study showed the mixture to be of low moisture, high ash, siliceous, sour with the presence of aliphatic, aromatic, and phenolic compounds. The thermal decomposition of the oil shale with rubber seed shell was studied using thermogravimetric analysis at heating rates of 5, 10, 20, 30, and 50 °C/min. The kinetic study of the oil shale pyrolysis process was performed on the thermogravimetric (TGA) data using three model-free isoconversional methods viz. Friedman, Flynn Wall Ozawa (FWO), and Kissinger Akahira Sunnose (KAS). The reaction mechanisms were determined using the Criado master plot. The understanding of the composition of Indian oil shale and rubber seed shell and pyrolysis process kinetics can help to establish the experimental parameters for the extraction of valuable products from the mixture. Response surface methodology (RSM) was employed usinf central composite design (CCD) model to setup the lab-scale experiment using TGA data, and optimization of process parameters viz. heating rate, temperature, and particle size. The samples were pre-dried at 115°C for 24 hours prior to pyrolysis. The pyrolysis temperatures were set from 450 to 650 °C, at heating rates of 2 to 20°C/min. The retention time was set between 2 to 8 hours. The optimum oil yield was observed at 5°C/min and 550°C with a retention time of 5 hours. The pyrolytic oil and gas obtained at optimum conditions were subjected to characterization using Fourier transform infrared spectroscopy (FT-IR) gas chromatography and mass spectrometry (GC-MS) and nuclear magnetic resonance spectroscopy (NMR).

Keywords: Indian oil shale, rubber seed shell, co-pyrolysis, isoconversional methods, gas chromatography, nuclear magnetic resonance, Fourier transform infrared spectroscopy

Procedia PDF Downloads 138

Authors: Jameela Ali Alkrimi, Abdul Rahim Ahmad, Azizah Suliman, Loay E. George

Abstract:

Red blood cells (RBCs) are among the most commonly and intensively studied type of blood cells in cell biology. The lack of RBCs is a condition characterized by lower than normal hemoglobin level; this condition is referred to as 'anemia'. In this study, a software was developed to isolate RBCs by using a machine learning approach to classify anemic RBCs in microscopic images. Several features of RBCs were extracted using image processing algorithms, including principal component analysis (PCA). With the proposed method, RBCs were isolated in 34 second from an image containing 18 to 27 cells. We also proposed that PCA could be performed to increase the speed and efficiency of classification. Our classifier algorithm yielded accuracy rates of 100%, 99.99%, and 96.50% for K-nearest neighbor (K-NN) algorithm, support vector machine (SVM), and neural network ANN, respectively. Classification was evaluated in highly sensitivity, specificity, and kappa statistical parameters. In conclusion, the classification results were obtained for a short time period with more efficient when PCA was used.

Keywords: red blood cells, pre-processing image algorithms, classification algorithms, principal component analysis PCA, confusion matrix, kappa statistical parameters, ROC

Procedia PDF Downloads 400

Authors: Hossein Aghababaei, Sergio Vitale, Giampaolo ferraioli

Abstract:

In the context of Synthetic Aperture Radar (SAR) data, polarization is an important source of information for Earth's surface monitoring . SAR Systems are often considered to transmit only one polarization. This constraint leads to either single or dual polarimetric SAR imaging modalities. Single polarimetric systems operate with a fixed single polarization of both transmitted and received electromagnetic (EM) waves, resulting in a single acquisition channel. Dual polarimetric systems, on the other hand, transmit in one fixed polarization and receive in two orthogonal polarizations, resulting in two acquisition channels. Dual polarimetric systems are obviously more informative than single polarimetric systems and are increasingly being used for a variety of remote sensing applications. In dual polarimetric systems, the choice of polarizations for the transmitter and the receiver is open. The choice of circular transmit polarization and coherent dual linear receive polarizations forms a special dual polarimetric system called hybrid polarimetry, which brings the properties of rotational invariance to geometrical orientations of features in the scene and optimizes the design of the radar in terms of reliability, mass, and power constraints. The complete characterization of target scattering, however, requires fully polarimetric data, which can be acquired with systems that transmit two orthogonal polarizations. This adds further complexity to data acquisition and shortens the coverage area or swath of fully polarimetric images compared to the swath of dual or hybrid polarimetric images. The search for solutions to augment dual polarimetric data to full polarimetric data will therefore take advantage of full characterization and exploitation of the backscattered field over a wider coverage with less system complexity. Several methods for reconstructing fully polarimetric images using hybrid polarimetric data can be found in the literature. Although the improvements achieved by the newly investigated and experimented reconstruction techniques are undeniable, the existing methods are, however, mostly based upon model assumptions (especially the assumption of reflectance symmetry), which may limit their reliability and applicability to vegetation and forest scenarios. To overcome the problems of these techniques, this paper proposes a new framework for reconstructing fully polarimetric information from hybrid polarimetric data. The framework uses Deep Learning solutions to augment hybrid polarimetric data without relying on model assumptions. A convolutional neural network (CNN) with a specific architecture and loss function is defined for this augmentation problem by focusing on different scattering properties of the polarimetric data. In particular, the method controls the CNN training process with respect to several characteristic features of polarimetric images defined by the combination of different terms in the cost or loss function. The proposed method is experimentally validated with real data sets and compared with a well-known and standard approach from the literature. From the experiments, the reconstruction performance of the proposed framework is superior to conventional reconstruction methods. The pseudo fully polarimetric data reconstructed by the proposed method also agree well with the actual fully polarimetric images acquired by radar systems, confirming the reliability and efficiency of the proposed method.

Keywords: SAR image, deep learning, convolutional neural network, deep neural network, SAR polarimetry

Procedia PDF Downloads 83

Authors: Muhittin Karaman, Murat Budakoglu

Abstract:

In this study, derivation of lake bathymetry was evaluated using the high resolution Worldview-2 multispectral images in the very shallow hypersaline Lake Acıgöl which does not have a stable water table due to the wet-dry season changes and industrial usage. Every year, a great part of the lake water budget has been consumed for the industrial salt production in the evaporation ponds, which are generally located on the south and north shores of Lake Acıgöl. Therefore, determination of the water level changes from a perspective of remote sensing-based lake water by bathymetry studies has a great importance in the sustainability-control of the lake. While the water table interval is around 1 meter between dry and wet season, dissolved ion concentration, salinity and turbidity also show clear differences during these two distinct seasonal periods. At the same time, with the satellite data acquisition (June 9, 2013), a field study was conducted to collect the salinity values, Secchi disk depths and turbidity levels. Max depth, Secchi disk depth and salinity were determined as 1,7 m, 0,9 m and 43,11 ppt, respectively. Eight-band Worldview-2 image was corrected for atmospheric effects by ATCOR technique. For each sampling point in the image, mean reflectance values in 1*1, 3*3, 5*5, 7*7, 9*9, 11*11, 13*13, 15*15, 17*17, 19*19, 21*21, 51*51 pixel reflectance neighborhoods were calculated separately. A unique image has been derivated for each matrix resolution. Spectral values and depth relation were evaluated for these distinct resolution images. Correlation coefficients were determined for the 1x1 matrix: 0,98, 0,96, 0,95 and 0,90 for the 724 nm, 831 nm, 908 nm and 659 nm, respectively. While 15x5 matrix characteristics with 0,98, 0,97 and 0,97 correlation values for the 724 nm, 908 nm and 831 nm, respectively; 51x51 matrix shows 0,98, 0,97 and 0,96 correlation values for the 724 nm, 831 nm and 659 nm, respectively. Comparison of all matrix resolutions indicates that RedEdge band (724 nm) of the Worldview-2 satellite image has the best correlation with the saline shallow lake of Acıgöl in-situ depth.

Keywords: bathymetry, Worldview-2 satellite image, ATCOR technique, Lake Acıgöl, Denizli, Turkey

Procedia PDF Downloads 436

Authors: Rhay-Hung Weng, Ching-Yuan Huang, Wan-Ping Chen

Abstract:

Recently, many hospitals have put much emphasis upon the development of nurse innovation. The present study aimed to clarify how nurse innovation is related to medical service quality and medical service experience. This study adopted questionnaire-survey method with nurses and customers of the inpatient wards from three Taiwanese hospitals as the research subjects. After pairing, there were 294 valid questionnaires. Hierarchical regression analysis was utilized to test the possible impact of nurse innovation on medical service quality and experience. In terms of the dimensions of nurse innovation, “innovation behavior” ranked the highest (3.24), followed by knowledge creation and innovation diffusion; in terms of the degree of the medical service quality, 'reliability' ranked the highest (4.35). As for the degree of the medical service experience, 'feel experience' ranked the highest (4.44). All dimensions of nurse innovation have no significant effects on medical service quality and medical service experience. Of these three dimensions of nurse innovation, the level of innovation behavior was perceived by the nurses as the highest. The study found that nurse innovation has no significant effects on medical service quality and medical service experience. Managers shall provide sufficient resources and budget for fostering innovation development and encourage their nurses to develop nursing innovation for patents. The education and training courses on “patient-centered ” shall be enhanced among hospital nurses. Health care managers shall also explore the difficulties about innovation diffusion and find the solutions for nurses.

Keywords: innovation, employee innovative behavior, service quality, service experience

Procedia PDF Downloads 331