Search results for: computer vision on embedded systems

Authors: Md Habibur Rahman, Jaeho Kim

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Efficient process scheduling is a crucial factor in ensuring optimal system performance and resource utilization in computer systems. While various algorithms have been proposed over the years, there are still limitations to their effectiveness. This paper introduces a new Wait-Optimized Scheduler (WOS) algorithm that aims to minimize process waiting time by dividing them into two layers and considering both process time and waiting time. The WOS algorithm is non-preemptive and prioritizes processes with the shortest WOS. In the first layer, each process runs for a predetermined duration, and any unfinished process is subsequently moved to the second layer, resulting in a decrease in response time. Whenever the first layer is free or the number of processes in the second layer is twice that of the first layer, the algorithm sorts all the processes in the second layer based on their remaining time minus waiting time and sends one process to the first layer to run. This ensures that all processes eventually run, optimizing waiting time. To evaluate the performance of the WOS algorithm, we conducted experiments comparing its performance with traditional scheduling algorithms such as First-Come-First-Serve (FCFS) and Shortest-Job-First (SJF). The results showed that the WOS algorithm outperformed the traditional algorithms in reducing the waiting time of processes, particularly in scenarios with a large number of short tasks with long wait times. Our study highlights the effectiveness of the WOS algorithm in improving process scheduling efficiency in computer systems. By reducing process waiting time, the WOS algorithm can improve system performance and resource utilization. The findings of this study provide valuable insights for researchers and practitioners in developing and implementing efficient process scheduling algorithms.

Keywords: process scheduling, wait-optimized scheduler, response time, non-preemptive, waiting time, traditional scheduling algorithms, first-come-first-serve, shortest-job-first, system performance, resource utilization

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Authors: Fatemeh Nokhodchi Bonab

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Bioinformatics, in its broad sense, involves application of computer processes to solve biological problems. A wide range of computational tools are needed to effectively and efficiently process large amounts of data being generated as a result of recent technological innovations in biology and medicine. A number of computational tools have been developed or adapted to deal with the experimental riches of complex and multivariate data and transition from data collection to information or knowledge. These bioinformatics tools are being evaluated and applied in various medical areas including early detection, risk assessment, classification, and prognosis of cancer. The goal of these efforts is to develop and identify bioinformatics methods with optimal sensitivity, specificity, and predictive capabilities. The recent flood of data from genome sequences and functional genomics has given rise to new field, bioinformatics, which combines elements of biology and computer science. Bioinformatics is conceptualizing biology in terms of macromolecules (in the sense of physical-chemistry) and then applying "informatics" techniques (derived from disciplines such as applied maths, computer science, and statistics) to understand and organize the information associated with these molecules, on a large-scale. Here we propose a definition for this new field and review some of the research that is being pursued, particularly in relation to transcriptional regulatory systems.

Keywords: methods, applications, transcriptional regulatory systems, techniques

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Authors: Ahmet Faruk Akyuz, Hasan Sakir Bilge

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Depth estimation using stereo images is a challenging problem in computer vision. Many different studies have been carried out to solve this problem. With advancing machine learning, tackling this problem is often done with neural network-based solutions. The images used in these studies are mostly in the visible spectrum. However, the need to use the Infrared (IR) spectrum for depth estimation has emerged because it gives better results than visible spectra in some conditions. At this point, we recommend using thermal-thermal (IR) image pairs for depth estimation. In this study, we used two well-known networks (PSMNet, FADNet) with minor modifications to demonstrate the viability of this idea.

Keywords: thermal stereo matching, deep neural networks, CNN, Depth estimation

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Authors: Tania Breton, Dorina Simeonov, Shauna MacEachern

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Ontario’s 10 Year Mental Health and Addictions Strategy has included the establishment of 18 Service Collaborative across the province; cross-sector tables in a specific region coming together to explore mental health and addiction system needs and adopting an intervention to address that need. The process is community led and supported by implementation teams from the Centre for Addiction and Mental Health (CAMH), using the framework of implementation science (IS) to enable evidence-based and sustained change. These justice initiatives are focused on the intersection of the justice system and the mental health and addiction systems. In this presentation, we will share the learnings, achievements and challenges of implementing innovative practices to the mental health and addictions needs of Ontarians within the justice system. Specifically, we will focus on the key points across the justice system - from early intervention and trauma-informed, culturally appropriate services to post-sentence support and community reintegration. Our approach to this work involves external implementation support from the CAMH team including coaching, knowledge exchange, evaluation, Aboriginal engagement and health equity expertise. Agencies supported the implementation of tools and processes which changed practice at the local level. These practices are being scaled up across Ontario and community agencies have come together in an unprecedented collaboration and there is a shared vision of the issues overlapping between the mental health, addictions and justice systems. Working with ministry partners has allowed space for innovation and created an environment where better approaches can be nurtured and spread.

Keywords: implementation, innovation, early identification, mental health and addictions, prevention, systems

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Authors: Varsha V. Rohini, P. R. Shreya, B. Renukadevi

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In the web analytics the information delivery and the web usage is optimized and the analysis of data is done. The analytics is the measurement, collection and analysis of webpage data. Page statistics and user metrics are the important factor in most of the web analytics tool. This is the limitation of the existing tools. It does not provide design inputs for the optimization of information. This paper aims at providing an extension for the scope of web analytics to provide analysis and statistics of each segment of a webpage. The number of click count is calculated and the concentration of links in a web page is obtained. Its user metrics are used to help in proper design of the displayed content in a webpage by Vision Based Page Segmentation (VIPS) algorithm. When the algorithm is applied on the web page it divides the entire web page into the visual block tree. The visual block tree generated will further divide the web page into visual blocks or segments which help us to understand the usage of each segment in a page and its content. The dynamic web pages and deep web pages are used to extend the scope of web page segment analytics. Space optimization concept is used with the help of the output obtained from the Vision Based Page Segmentation (VIPS) algorithm. This technique provides us the visibility of the user interaction with the WebPages and helps us to place the important links in the appropriate segments of the webpage and effectively manage space in a page and the concentration of links.

Keywords: analytics, design optimization, visual block trees, vision based technology

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Authors: Ahmed Soliman Nagiub, Mahmoud Fayez, Heba Khaled, Said Ghoniemy

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Accurate perception is a critical component in enabling autonomous vehicles to understand their driving environment. The acquisition of 3D information about objects, including their location and pose, is essential for achieving this understanding. This survey paper presents a comprehensive review of 3D object detection techniques specifically tailored for autonomous vehicles. The survey begins with an introduction to 3D object detection, elucidating the significance of the third dimension in perceiving the driving environment. It explores the types of sensors utilized in this context and the corresponding data extracted from these sensors. Additionally, the survey investigates the different types of datasets employed, including their formats, sizes, and provides a comparative analysis. Furthermore, the paper categorizes and thoroughly examines the perception methods employed for 3D object detection based on the diverse range of sensors utilized. Each method is evaluated based on its effectiveness in accurately detecting objects in a three-dimensional space. Additionally, the evaluation metrics used to assess the performance of these methods are discussed. By offering a comprehensive overview of 3D object detection techniques for autonomous vehicles, this survey aims to advance the field of perception systems. It serves as a valuable resource for researchers and practitioners, providing insights into the techniques, sensors, and evaluation metrics employed in 3D object detection for autonomous vehicles.

Keywords: computer vision, 3D object detection, autonomous vehicles, deep learning

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Authors: Erick C. Valverde, Wansu Lim

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Facial emotion recognition (FER) system has been recently developed for more advanced computer vision applications. The ability to identify human emotions would enable smart healthcare facility to diagnose mental health illnesses (e.g., depression and stress) as well as better human social interactions with smart technologies. The FER system involves two steps: 1) face detection task and 2) facial emotion recognition task. It classifies the human expression in various categories such as angry, disgust, fear, happy, sad, surprise, and neutral. This system requires intensive research to address issues with human diversity, various unique human expressions, and variety of human facial features due to age differences. These issues generally affect the ability of the FER system to detect human emotions with high accuracy. Early stage of FER systems used simple supervised classification task algorithms like K-nearest neighbors (KNN) and artificial neural networks (ANN). These conventional FER systems have issues with low accuracy due to its inefficiency to extract significant features of several human emotions. To increase the accuracy of FER systems, deep learning (DL)-based methods, like convolutional neural networks (CNN), are proposed. These methods can find more complex features in the human face by means of the deeper connections within its architectures. However, the inference speed and computational costs of a DL-based FER system is often disregarded in exchange for higher accuracy results. To cope with this drawback, an optimized DL-based FER system is proposed in this study.An extreme version of Inception V3, known as Xception model, is leveraged by applying different network optimization methods. Specifically, network pruning and quantization are used to enable lower computational costs and reduce memory usage, respectively. To support low resource requirements, a 68-landmark face detector from Dlib is used in the early step of the FER system.Furthermore, a DL compiler is utilized to incorporate advanced optimization techniques to the Xception model to improve the inference speed of the FER system. In comparison to VGG-Net and ResNet50, the proposed optimized DL-based FER system experimentally demonstrates the objectives of the network optimization methods used. As a result, the proposed approach can be used to create an efficient and real-time FER system.

Keywords: deep learning, face detection, facial emotion recognition, network optimization methods

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Authors: Peyman Sheikholharam Mashhadi

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Difficulties in analyzing real world images in classical image processing and machine vision framework have motivated researchers towards considering the biology-based vision. It is a common belief that mammalian visual cortex has been adapted to the statistics of the real world images through the evolution process. There are two well-known successful models of mammalian visual cortical cells: Independent Component Analysis (ICA) and Independent Subspace Analysis (ISA). In this paper, we statistically analyze the dependencies which remain in the components after applying these models to the natural images. Also, we investigate the response of feature detectors to gratings with various parameters in order to find optimal parameters of the feature detectors. Finally, the selectiveness of feature detectors to phase, in both models is considered.

Keywords: statistics, independent component analysis, independent subspace analysis, phase, natural images

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Authors: Pushpendra Kumar, Sanjeev Kumar, R. Balasubramanian

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Estimation of optical flow from a sequence of images using variational methods is one of the most successful approach. Discontinuity between different motions is one of the challenging problem in flow estimation. In this paper, we design a new anisotropic diffusion operator, which is able to provide smooth flow over a region and efficiently preserve discontinuity in optical flow. This operator is designed on the basis of intensity differences of the pixels and isotropic operator using exponential function. The combination of these are used to control the propagation of flow. Experimental results on the different datasets verify the robustness and accuracy of the algorithm and also validate the effect of anisotropic operator in the discontinuity preserving.

Keywords: optical flow, variational methods, computer vision, anisotropic operator

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Authors: Anwesha Pathak

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With the increasing number of satellites and space debris, space traffic management (STM) becomes crucial. AI can aid in STM by predicting and preventing potential collisions, optimizing satellite trajectories, and managing orbital slots. Governance frameworks need to address the integration of AI algorithms in STM to ensure safe and sustainable satellite activities. AI and governance play significant roles in the context of satellite activities in space. Artificial intelligence (AI) technologies, such as machine learning and computer vision, can be utilized to process vast amounts of data received from satellites. AI algorithms can analyse satellite imagery, detect patterns, and extract valuable information for applications like weather forecasting, urban planning, agriculture, disaster management, and environmental monitoring. AI can assist in automating and optimizing satellite operations. Autonomous decision-making systems can be developed using AI to handle routine tasks like orbit control, collision avoidance, and antenna pointing. These systems can improve efficiency, reduce human error, and enable real-time responsiveness in satellite operations. AI technologies can be leveraged to enhance the security of satellite systems. AI algorithms can analyze satellite telemetry data to detect anomalies, identify potential cyber threats, and mitigate vulnerabilities. Governance frameworks should encompass regulations and standards for securing satellite systems against cyberattacks and ensuring data privacy. AI can optimize resource allocation and utilization in satellite constellations. By analyzing user demands, traffic patterns, and satellite performance data, AI algorithms can dynamically adjust the deployment and routing of satellites to maximize coverage and minimize latency. Governance frameworks need to address fair and efficient resource allocation among satellite operators to avoid monopolistic practices. Satellite activities involve multiple countries and organizations. Governance frameworks should encourage international cooperation, information sharing, and standardization to address common challenges, ensure interoperability, and prevent conflicts. AI can facilitate cross-border collaborations by providing data analytics and decision support tools for shared satellite missions and data sharing initiatives. AI and governance are critical aspects of satellite activities in space. They enable efficient and secure operations, ensure responsible and ethical use of AI technologies, and promote international cooperation for the benefit of all stakeholders involved in the satellite industry.

Keywords: satellite, space debris, traffic, threats, cyber security.

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Authors: Brahmia Nadjet

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The growing needs of displacements led advanced countries in this field install new specific transport systems, able to palliate any deficiencies, especially when regular public transport does not adequately meet the requests of users. In this context, on-demand transport systems (ODT) are very efficient. They rely on techniques based on the location of trip generators which should be assured effectively with the use of operators responsible for the advance reservation, planning and organization, and studying the different ODT criteria (organizational, technical, geographical, etc.). As the advanced countries in the field of transport, some developing countries are involved in the adaptation of the new technologies to reduce the deficit in their communication system. This paper presents the study of an ODT implementation in the west of Algeria, by developing the geomatics side of the study. This part requires the use of specific systems such as Geographic Information System (GIS), Road Database Management System (RDBMS). So, we developed the process through an application in an environment of mobility by using the computer tools dedicated to the management of the entities related to the transport field.

Keywords: ODT, geomatics, GIS, transport systems

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Authors: Mina Boroujerdi, Javad Tavakoli

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Hydrogels are extensively studied as matrices for the controlled release of drugs. To evaluate the mobility of embedded molecules, these drug delivery systems are usually characterized by release studies. In this contribution, an electronic device for swelling pressure measurement during drug release from hydrogel network was developed. Also, poly acrylic acid micro particles were prepared for prolonged and sustained controlled acetaminophen release. Effect of swelling pressure on drug release from micro particles studied under different environment pH in order to predict release profile in gastro-intestine medium. Swelling ratio and swelling pressure were measured in different pH.

Keywords: swelling pressure, drug delivery, hydrogel, polyelectrolyte

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Authors: Alex Hughes

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Patients with advanced heart failure develop specific palliative care needs due to the progressive symptom burden and unpredictable disease trajectory. NICE guidance advises that palliative care should be provided to patients with both cancer and non-cancer conditions as and when required. However, there is some way to go before this guidance is consistently and effectively implemented nationwide in conditions such as heart failure. The Ambitions for Palliative and End of Life Care: A national framework for local action in England provides a set of foundations and ambitions which outline a vision for what high-quality palliative and end-of-life care look like in England. This poster aims to critically consider how to improve access to palliative care for heart failure patients in England by analysing the foundations taken from this framework to generate specific recommendations using Soft Systems Methodology (SSM). The eight foundations analysed are: ‘Personalised care planning’, ‘Shared records’, ‘Evidence and information’, ‘Involving, supporting and caring for those important to the dying Person’, ‘Education and training’, ‘24/7 access’, ‘Co-design’ and ‘Leadership.’ A number of specific recommendations have been generated which highlight a need to close the evidence-policy gap and implement policy with sufficient evidence. These recommendations, alongside the creation of an evidence-based national strategy for palliative care and heart failure, should improve access to palliative care for heart failure patients in England. Once implemented, it will be necessary to evaluate the effect of these proposals to understand if access to palliative care for heart failure patients actually improves.

Keywords: access, health systems, heart failure, palliative care

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Authors: Rahul Mohite, Lahcen Ouarbya

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This paper focuses on the interplay between humans and computer systems and the ability of these systems to understand and respond to human emotions, including non-verbal communication. Current emotion recognition systems are based solely on either facial or verbal expressions. The limitation of these systems is that it requires large training data sets. The paper proposes a system for recognizing human emotions that combines both speech and emotion recognition. The system utilizes advanced techniques such as deep learning and image recognition to identify facial expressions and comprehend emotions. The results show that the proposed system, based on the combination of facial expression and speech, outperforms existing ones, which are based solely either on facial or verbal expressions. The proposed system detects human emotion with an accuracy of 86%, whereas the existing systems have an accuracy of 70% using verbal expression only and 76% using facial expression only. In this paper, the increasing significance and demand for facial recognition technology in emotion recognition are also discussed.

Keywords: facial reputation, expression reputation, deep gaining knowledge of, photo reputation, facial technology, sign processing, photo type

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Authors: Snehal G. Teli, R. J. Shelke

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CNN and MultiUNet models are the framework for the proposed method for enhancing and reconstructing underwater images. Multiscale merging of features and regeneration are both performed by the MultiUNet. CNN collects relevant features. Extensive tests on benchmark datasets show that the proposed strategy performs better than the latest methods. As a result of this work, underwater images can be represented and interpreted in a number of underwater applications with greater clarity. This strategy will advance underwater exploration and marine research by enhancing real-time underwater image processing systems, underwater robotic vision, and underwater surveillance.

Keywords: convolutional neural network, image enhancement, machine learning, multiunet, underwater images

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Authors: Mohammad Y. Abualhoul, Edgar Talavera Munoz, Fawzi Nashashibi

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The new emerging Visible Light Communication (VLC) technology has been subjected to intensive investigation, evaluation, and lately, deployed in the context of convoy-based applications for Intelligent Transportations Systems (ITS). The technology limitations were defined and supported by different solutions proposals to enhance the crucial alignment and mobility limitations. In this paper, we propose the incorporation of VLC technology and Lane-Centering (LC) technique to assure the VLC-connectivity by keeping the autonomous vehicle aligned to the lane center using vision-based lane detection in a convoy-based formation. Such combination can ensure the optical communication connectivity with a lateral error less than 30 cm. As soon as the road lanes are detectable, the evaluated system showed stable behavior independently from the inter-vehicle distances and without the need for any exchanged information of the remote vehicles. The evaluation of the proposed system is verified using VLC prototype and an empirical result of LC running application over 60 km in Madrid M40 highway.

Keywords: visible light communication, lane-centerin, platooning, intelligent transportation systems, road safety applications

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Authors: Araceli Martínez Ortiz

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An educational research effort is analyzed using systems theory to document the power of collective impact when addressing multiple factors contributing towards the retention of students majoring in science, technology, engineering and mathematics (STEM) academic programs. This research promotes understanding on how networked communities may work effectively toward a shared vision and mutually aligned activities that result in sustained, large scale change. The actions of a team of researchers in their third year of collaboration are presented to describe a model that positively aligns work efforts resulting in greater total gains. The goals of the multiple programs managed by the funded program team are to: 1) expand the number of students who choose to study a STEM field of study; 2) promote student collaborative learning; 3) support faculty understanding of the funds of knowledge of diverse students and 4) establish innovative and robust STEM education research that will lead to the development of nationally replicable, scalable models for broadening participation in STEM. The impacts of this research effort are measured through quantitative statistical analysis of the changes in second-year STEM undergraduate student retention rates and representation rates of women, Hispanics and African American STEM majors.

Keywords: collaborative impact, diversity, student retention, systems theory, STEM education

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Authors: Aishrith Rao

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COVID 19 is a pandemic which has resulted in thousands of deaths around the world and a huge impact on the global economy. Testing is a huge issue as the test kits have limited availability and are expensive to manufacture. Using deep learning methods on radiology images in the detection of the coronavirus as these images contain information about the spread of the virus in the lungs is extremely economical and time-saving as it can be used in areas with a lack of testing facilities. This paper focuses on binary classification and multi-class classification of COVID 19 and other diseases such as pneumonia, tuberculosis, etc. Different deep learning methods such as VGG-19, COVID-Net, ResNET+ SVM, Deep CNN, DarkCovidnet, etc., have been used, and their accuracy has been compared using the Chest X-Ray dataset.

Keywords: deep learning, computer vision, radiology, COVID-19, ResNet, VGG-19, deep neural networks

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Authors: Sergo Tsiramua, Sulkhan Sulkhanishvili, Elisabed Asabashvili, Lazare Kvirtia

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The paper presents logical-probabilistic methods, models, and algorithms for reliability assessment of complex systems, based on which a web application for structural analysis and reliability assessment of systems was created. It is important to design systems based on structural analysis, research, and evaluation of efficiency indicators. One of the important efficiency criteria is the reliability of the system, which depends on the components of the structure. Quantifying the reliability of large-scale systems is a computationally complex process, and it is advisable to perform it with the help of a computer. Logical-probabilistic modeling is one of the effective means of describing the structure of a complex system and quantitatively evaluating its reliability, which was the basis of our application. The reliability assessment process included the following stages, which were reflected in the application: 1) Construction of a graphical scheme of the structural reliability of the system; 2) Transformation of the graphic scheme into a logical representation and modeling of the shortest ways of successful functioning of the system; 3) Description of system operability condition with logical function in the form of disjunctive normal form (DNF); 4) Transformation of DNF into orthogonal disjunction normal form (ODNF) using the orthogonalization algorithm; 5) Replacing logical elements with probabilistic elements in ODNF, obtaining a reliability estimation polynomial and quantifying reliability; 6) Calculation of “weights” of elements of system. Using the logical-probabilistic methods, models and algorithms discussed in the paper, a special software was created, by means of which a quantitative assessment of the reliability of systems of a complex structure is produced. As a result, structural analysis of systems, research, and designing of optimal structure systems are carried out.

Keywords: complex systems, logical-probabilistic methods, orthogonalization algorithm, reliability of systems, “weights” of elements

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Authors: H. Paredes Gutiérrez, S. T. Pérez-Merchancano

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Motivated by recent experimental and theoretical developments, we investigate the influence of embedded quantum dot (EQD) of different geometries (lens, ring and pyramidal) in a double barrier heterostructure (DBH). We work with a general theory of quantum transport that accounts the tight-binding model for the spin dependent resonant tunneling in a semiconductor nanostructure, and Rashba spin orbital to study the spin orbit coupling. In this context, we use the second quantization theory for Rashba effect and the standard Green functions method. We calculate the current density as a function of the voltage without and in the presence of quantum dots. In the second case, we considered the size and shape of the quantum dot, and in the two cases, we worked considering the spin polarization affected by external electric fields. We found that the EQD generates significant changes in current when we consider different morphologies of EQD, as those described above. The first thing shown is that the current decreases significantly, such as the geometry of EQD is changed, prevailing the geometrical confinement. Likewise, we see that the current density decreases when the voltage is increased, showing that the quantum system studied here is more efficient when the morphology of the quantum dot changes.

Keywords: quantum semiconductors, nanostructures, quantum dots, spin polarization

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Authors: I. Gahlouz, A. Chellil

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The paper concerns the estimation of system components faults by using an unknown inputs observer. To reach this goal, we used the Bond Graph approach to physical modelling. We showed that this graphical tool is allowing the representation of system components faults as unknown inputs within the state representation of the considered physical system. The study of the causal and structural features of the system (controllability, observability, finite structure, and infinite structure) based on the Bond Graph approach was hence fulfilled in order to design an unknown inputs observer which is used for the system component fault estimation.

Keywords: estimation, bond graph, controllability, observability

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Authors: Young-Min Ko, Seung-Jun Yu, Seongjoo Lee, Hyoung-Kyu Song

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The Internet of things (IoT) system has attracted attention. IoT is a technology to connect all the objects to the internet as well as computer. IoT makes it possible for providing more data interoperability methods for an application purpose. Among the IoT technology, the research of devices so that they can communicate without power supply has been actively conducted. Batteryless system permits us to communicate without power supply devices. In this paper, batteryless backscatter system is used as a tag. And mobile devices which are embedded wireless fidelity (Wi-Fi) chipset are used as a reader. The backscatter tag can be obtained Internet connectivity from the reader. Conventional Wi-Fi backscatter system has limitation in the transmission range. In this paper, the proposed algorithm can be obtained improved reliability as well as overcoming the limitation about transmission range.

Keywords: Ambient RF, Backscatter, Batteryless communication, Energy-harvesting, IoT, RFID, Tag, Wi-Fi

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Authors: Bushra Suhail, Laith Kadim

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The behavior of model piles embedded in a very high expansive soil was investigated, a specially manufactured saturation-drying tank was used to apply three cycles of wetting and drying to the expansive soil surrounding the model straight shaft and under reamed piles, the relative movement of the piles with respect to the soil surface was recorded with time, also the exerted uplift pressure of the piles due to soil swelling was recorded. The behavior of unloaded straight shaft and under reamed piles was investigated. Two design charts were presented for straight shaft and under reamed piles one for the required pile depth for zero upward movement due to soil swelling, the other for the required pile depth to exert zero uplift pressure when the soil swells. Under reamed piles showed a decrease in upward movement of 20% to 40%, and an uplift pressure decrease of 10% to 30%.

Keywords: expansive soil, piles, under reamed, structural and geotechnical engineering

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Authors: Naim Izet Kajtazi

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Context: Increased intracranial pressure and associated symptoms such as headache, papilledema, motor or sensory deficits, seizures, and conscious disturbance are well-known in acute CVT. However, visual loss is not commonly associated with this disease, except in the case of secondary IIH associated with it. Process: We report a case of a 40-year-old male with Behçet’s disease and cerebral venous thrombosis, and other multiple comorbidities admitted with a four-day history of increasing headache and rapidly progressive visual loss bilaterally. The neurological examination was positive for bilateral papilledema of grade 3 with light perception on the left eye and counting fingers on the right eye. Brain imaging showed old findings of cerebral venous thrombosis without any intraparenchymal lesions to suggest a flare-up of Behçet’s disease. The lumbar puncture, followed by the lumbar drain insertion, gave no benefit in headache or vision. However, he completely lost sight. The right optic nerve sheath fenestration did not result in vision improvement. The acute spinal shock complicated the lumbar drain removal due to epidural hematoma. An urgent lumbar laminectomy with hematoma evacuation undertook. Intra-operatively, the neurosurgeon noted suspicious abnormal vessels at conus medullaris with the possibility of an arteriovenous malformation. Outcome: In a few days following the spinal surgery, the patient vision started to improve. Further improvement was achieved after plasma exchange sessions followed by cyclophosphamide. In the recent follow-up in the clinic, he reported better vision, drove, and completed his Ph.D. studies. Relevance: Visual loss in patients with Behçet’s disease should always be anticipated and taken reasonable care of, ensuring that they receive well-combined immunosuppression with anticoagulation and agents to reduce intracranial pressure. This patient’s story is significant for a high disease burden and complicated hospital course by acute spinal shock due to spinal lumbar drain removal with a possible underlying spinal arteriovenous malformation.

Keywords: Behcet disease, optic neuritis, IIH, CVT

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Authors: Jiamin Chen, Liping Yang

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In 2007, sociologist Putnam proposed a pessimistic forecast in the United States' "Social Capital Community Benchmark Survey," suggesting that "ethnic diversity would challenge social unity and undermine social cohesion." If this pessimistic assumption were proven true, it would indicate a risk of division in diverse societies. China, with 56 ethnic groups, is a multi-ethnic country. On May 26, 2014, General Secretary Xi Jinping proposed "building ethnically inter-embedded communities to promote deeper development in interactions, exchanges, and integration among ethnic groups." Researchers unanimously agree that ethnic inter-embedded communities can serve as practical arenas and pathways for solidifying the sense of the Chinese national community However, there is no research providing evidence that ethnic inter-embedded communities can foster the sense of the Chinese national community, and the influencing factors remain unclear. This study adopts a constructivist grounded theory research approach. Convenience sampling and snowball sampling were used in the study. Data were collected in three communities in Kunming City. Twelve individuals were eventually interviewed, and the transcribed interviews totaled 187,000 words. The research has obtained ethical approval from the Ethics Committee of Nanjing Normal University (NNU202310030). The research analyzed the data and constructed theories, employing strategies such as coding, constant comparison, and theoretical sampling. The study found that: firstly, ethnic inter-embedded communities exhibit characteristics of diversity, including ethnic diversity, cultural diversity, and linguistic diversity. Diversity has positive functions, including increased opportunities for contact, promoting self-expansion, and increasing happiness; negative functions of diversity include highlighting ethnic differences, causing ethnic conflicts, and reminding of ethnic boundaries. Secondly, individuals typically engage in interactions within the community using active embedding and passive embedding strategies. Active embedding strategies include maintaining openness, focusing on similarities, and pro-diversity beliefs, which can increase external group identification, intergroup relational identity, and promote ethnic integration. Individuals using passive embedding strategies tend to focus on ethnic stereotypes, perceive stigmatization of their own ethnic group, and adopt an authoritarian-oriented approach to interactions, leading to a perception of more identity threats and ultimately rejecting ethnic integration. Thirdly, the commonality of the Chinese nation is reflected in the 56 ethnic groups as an "identity community" and "interest community," and both active and passive embedding paths affect individual understanding of the commonality of the Chinese nation. Finally, community work and environment can influence the embedding process. The research constructed a social psychological process and mechanism model for solidifying sense of the Chinese national community in ethnic inter-embedded communities. Based on this theoretical model, future research can conduct more micro-level psychological mechanism tests and intervention studies to enhance Chinese national cohesion.

Keywords: diversity, sense of the chinese national community, ethnic inter-embedded communities, ethnic group

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Authors: Alireza Pour Ebrahimi, Vahid Abasi

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Recently by the extension of internet usage, Research on the intrusion detection system takes a significant importance. Many of improvement systems prevent internal and external network attacks by providing security through firewalls and antivirus. In recently years, intrusion detection systems gradually turn from host-based systems and depend on O.S to the distributed systems which are running on multiple O.S. In this work, by considering the diversity of computer networks whit respect to structure, architecture, resource, services, users and also security goals requirement a fully distributed collaborative intrusion detection system based on peer to peer architecture is suggested. in this platform each partner device (matched device) considered as a peer-to-peer network. All transmitted information to network are visible only for device that use security scanning of a source. Experimental results show that the distributed architecture is significantly upgradeable in respect to centralized approach.

Keywords: network, intrusion detection system, peer to peer, internal and external network

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Authors: Bersufekad Wubie

Abstract:

Introduction: Near visual impairment is presenting near visual acuity of the eye worse than N6 at a 40 cm distance. Teachers' regular duties, such as reading books, writing on the blackboard, and recognizing students' faces, need good near vision. If a teacher has near-visual impairment, the work output is unsatisfactory. Objective: The study was aimed to assess the prevalence and associated factors near vision impairment among school teachers at Gondar city Northwest Ethiopia, August 2022. Methods: To select 567 teachers in Gondar city schools, an institutional-based cross-sectional study design with a multistage sampling technique were used. The study was conducted in selected schools from May 1 to May 30, 2022. Trained data collectors used well-structured Amharic and English language questionnaires and ophthalmic instruments for examination. The collected data were checked for completeness and entered into Epi data version 4.6, then exported to SPSS version 26 for further analysis. A binary and multivariate logistic regression model was fitted. And associated factors of the outcome variable. Result: The prevalence of near visual impairment was 64.6%, with a confidence interval of 60.3%–68.4%. Near visual impairment was significantly associated with age >= 35 years (AOR: 4.90 at 95% CI: 3.15, 7.65), having prolonged years of teaching experience (AOR: 3.29 at 95% CI: 1.70, 4.62), having a history of ocular surgery (AOR: 1.96 at 95% CI: 1.10, 4.62), smokers (AOR: 2.21 at 95% CI: 1.22, 4.07), history of ocular trauma (AOR : 1.80 at 95%CI:1.11,3.18 and uncorrected refractive error (AOR:2.01 at 95%CI:1.13,4.03). Conclusion and recommendations: This study showed the prevalence of near vision impairment among school teachers was high, and it is not a problem of the presbyopia age group alone; it also happens at a young age. So teachers' ocular health should be well accommodated in the school's eye health.

Keywords: Gondar, near visual impairment, school, teachers

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Authors: Yoshinori Kitsutaka, Fumiya Ikedo

Abstract:

In this study, the mechanical model of various anchors embedded in gypsum board subjected cyclic shear loading were investigated. Shear tests for anchors embedded in 200 mm square size gypsum board were conducted to measure the load - load displacement curves. The strength of the gypsum board was changed for three conditions and 12 kinds of anchors were selected which were ordinary used for gypsum board anchoring. The loading conditions were a monotonous loading and a cyclic loading controlled by a servo-controlled hydraulic loading system to achieve accurate measurement. The fracture energy for each of the anchors was estimated by the analysis of consumed energy calculated by the load - load displacement curve. The effect of the strength of gypsum board and the types of anchors on the shear properties of gypsum board anchors was cleared. A numerical model to predict the load-unload curve of shear deformation of gypsum board anchors caused by such as the earthquake load was proposed and the validity on the model was proved.

Keywords: gypsum board, anchor, shear test, cyclic loading, load-unload curve

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Authors: Ali A. Al-Jazaairry, Tahsin T. Sabbagh

Abstract:

The stability of a single model pile located adjacent to a continuous cavity was studied. This paper is an attempt to understand the behaviour of axially loaded single pile embedded in clayey soil with the presences of cavities. The performance of piles located in such soils was studied analytically. A verification analysis was carried out on available studies to assess the ability of analytical model to correctly interpret the system behaviour. The study was adopted by finite element program (PLAXIS). The study included many cases; in each case, there is a critical value in which the presence of cavities has shown minimum effect on the pile performance. Figures including the load carrying capacity of pile with the affecting factors are presented. These figures provide beneficial information for pile design constructed close to underground cavities. It was concluded that the load carrying capacity of the pile is reduced by the presence of the cavity within the soil mass. This reduction varies according to the size and location of cavity.

Keywords: axial load, cavity, clay, pile, ultimate capacity

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Authors: Hesheng Wang, Haoyu Wang, Chungang Zhuang

Abstract:

Estimating the 6D pose of objects is a core step for robot bin-picking tasks. The problem is that various objects are usually randomly stacked with heavy occlusion in real applications. In this work, we propose a method to regress 6D poses by predicting three points for each object in the 3D point cloud through deep learning. To solve the ambiguity of symmetric pose, we propose a labeling method to help the network converge better. Based on the predicted pose, an iterative method is employed for pose optimization. In real-world experiments, our method outperforms the classical approach in both precision and recall.

Keywords: pose estimation, deep learning, point cloud, bin-picking, 3D computer vision

Procedia PDF Downloads 137