Search results for: vision computing

Authors: Xiaoyuan Suo

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3D object reconstruction is a broad research area within the computer vision field involving many stages and still open problems. One of the existing challenges in this field lies with micromotion, such as the facial expressions on the appearance of the human or animal face. Similar literatures in this field focuses on 3D reconstruction in stable conditions such as an existing image or photos taken in a rather static environment, while the purpose of this work is to discuss a flexible scan system using multiple cameras that can correctly reconstruct 3D stable and moving objects -- human face with expression in particular. Further, a mathematical model is proposed at the end of this literature to automate the 3D object reconstruction process. The reconstruction process takes several stages. Firstly, a set of simple 2D lines would be projected onto the object and hence a set of uneven curvy lines can be obtained, which represents the 3D numerical data of the surface. The lines and their shapes will help to identify object’s 3D construction in pixels. With the two-recorded angles and their distance from the camera, a simple mathematical calculation would give the resulting coordinate of each projected line in an absolute 3D space. This proposed research will benefit many practical areas, including but not limited to biometric identification, authentications, cybersecurity, preservation of cultural heritage, drama acting especially those with rapid and complex facial gestures, and many others. Specifically, this will (I) provide a brief survey of comparable techniques existing in this field. (II) discuss a set of specialized methodologies or algorithms for effective reconstruction of 3D objects. (III)implement, and testing the developed methodologies. (IV) verify findings with data collected from experiments. (V) conclude with lessons learned and final thoughts.

Keywords: 3D photogrammetry, 3D object reconstruction, facial expression recognition, facial recognition

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Authors: Siffat Ullah Khan, Rahmat Ullah Khan, Rafiq Ahmad Khan, Habibullah Khan

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Green IT or green computing has emerged as a fast growing business paradigm in recent years in order to develop energy-efficient Software and peripheral devices. With the constant evolution of technology and the world critical environmental status, all private and public information technology (IT) businesses are moving towards sustainability. We identified, through systematic literature review and questionnaire survey, 9 motivators, in total, faced by vendors in IT-Outsourcing relationship. Amongst these motivators 7 were ranked as critical motivators. We also identified 21, in total, practices for addressing these critical motivators. Based on these inputs we have developed Green IT-Outsourcing Assurance Model (GITAM) for IT-Outsourcing vendors. The model comprises four different levels. i.e. Initial, White, Green and Grey. Each level comprises different critical motivators and their relevant practices. We conclude that our model, GITAM, will assist IT-Outsourcing vendors in gauging their level in order to manage IT-Outsourcing activities in a green and sustainable fashion to assist the environment and to reduce the carbon emission. The model will assist vendors in improving their current level by suggesting various practices. The model will contribute to the body of knowledge in the field of Green IT.

Keywords: Green IT-outsourcing Assurance Model (GITAM), Systematic Literature Review, Empirical Study, Case Study

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Authors: Hu Zhenxing, Gao Jianxin

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Stereo-based digital image correlation (also referred to as three-dimensional (3D) digital image correlation (DIC)) is a technique for both 3D shape and surface deformation measurement of a component, which has found increasing applications in academia and industries. The accuracy of the reconstructed coordinate depends on many factors such as configuration of the setup, stereo-matching, distortion, etc. Most of these factors have been investigated in literature. For instance, the configuration of a binocular vision system determines the systematic errors. The stereo-matching errors depend on the speckle quality and the matching algorithm, which can only be controlled in a limited range. And the distortion is non-linear particularly in a complex imaging acquisition system. Thus, the distortion correction should be carefully considered. Moreover, the distortion function is difficult to formulate in a complex imaging acquisition system using conventional models in such cases where microscopes and other complex lenses are involved. The errors of the distortion correction will propagate to the reconstructed 3D coordinates. To address the problem, an accurate mapping method based on 2D B-spline functions is proposed in this study. The mapping functions are used to convert the distorted coordinates into an ideal plane without distortions. This approach is suitable for any image acquisition distortion models. It is used as a prior process to convert the distorted coordinate to an ideal position, which enables the camera to conform to the pin-hole model. A procedure of this approach is presented for stereo-based DIC. Using 3D speckle image generation, numerical simulations were carried out to compare the accuracy of both the conventional method and the proposed approach.

Keywords: distortion, stereo-based digital image correlation, b-spline, 3D, 2D

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Authors: Greg Turner, Bin Lu, Cheer-Sun Yang

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As computing technology advances, smartphone applications can assist in student learning in a pervasive way. For example, the idea of using a mobile apps for the PA Common Trees, Pests, Pathogens, in the field as a reference tool allows middle school students to learn about trees and associated pests/pathogens without bringing a textbook. In the past, some researches study the mobile software Mobile Application Software Development Life Cycle (MADLC) including traditional models such as the waterfall model, or more recent Agile Methods. Others study the issues related to the software development process. Very little research is on the development of three heterogenous mobile systems simultaneously in a small university where the availability of developers is an issue. In this paper, we propose to use a hybride model of Waterfall Model and the Agile Model, known as the Relay Race Methodology (RRM) in practice, to reflect the concept of racing and relaying for scheduling. Based on the development project, we observe that the modeling of the transition between any two phases is manifested naturally. Thus, we claim that the RRM model can provide a de fecto rather than a de jure basis for the core concept in the MADLC. In this paper, the background of the project is introduced first. Then, the challenges are pointed out followed by our solutions. Finally, the experiences learned and the future work are presented.

Keywords: agile methods, mobile apps, software process model, waterfall model

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Authors: P. Priya Madhuri, J. Kamini, S. C. Jayanthi

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Assessment of land use and transportation networks is essential for sustainable urban growth, urban planning, efficient public transportation systems, and reducing traffic congestion. The study focuses on land use, population density, and their correlation with the road network for future development. The scope of the study covers inventory and assessment of the road network dataset (line) at the city, zonal, or ward level, which is extracted from very high-resolution satellite data (spatial resolution < 0.5 m) at 1:4000 map scale and ground truth verification. Road network assessment is carried out by computing various indices that measure road coverage and connectivity. In this study, an assessment of the road network is carried out for the study region at the municipal and ward levels. In order to identify gaps, road coverage and connectivity were associated with urban land use, built-up area, and population density in the study area. Ward-wise road connectivity and coverage maps have been prepared. To assess the relationship between road network metrics, correlation analysis is applied. The study's conclusions are extremely beneficial for effective road network planning and detecting gaps in the road network at the ward level in association with urban land use, existing built-up, and population.

Keywords: road connectivity, road coverage, road network, urban land use, transportation analysis

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Authors: Ching-Feng Chen, Ching-Chih Tsai

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With the size of the transistor gradually approaching the physical limit, it challenges the persistence of Moore’s Law due to the development of the high numerical aperture (high-NA) lithography equipment and other issues such as short channel effects. In the context of the ever-increasing technical requirements of portable devices and high-performance computing, relying on the law continuation to enhance the chip density will no longer support the prospects of the electronics industry. Weighing the chip’s power consumption-performance-area-cost-cycle time to market (PPACC) is an updated benchmark to drive the evolution of the advanced wafer nanometer (nm). The advent of two and half- and three-dimensional (2.5 and 3D)- Very-Large-Scale Integration (VLSI) packaging based on Through Silicon Via (TSV) technology has updated the traditional die assembly methods and provided the solution. This overview investigates the up-to-date and cutting-edge packaging technologies for 2.5D and 3D integrated circuits (ICs) based on the updated transistor structure and technology nodes. The author concludes that multi-chip solutions for 2.5D and 3D IC packagings are feasible to prolong Moore’s Law.

Keywords: moore’s law, high numerical aperture, power consumption-performance-area-cost-cycle time to market, 2.5 and 3D- very-large-scale integration, packaging, through silicon via

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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: Véronique Georlette, Sebastien Bette, Sylvain Brohez, Nicolas Point, Veronique Moeyaert

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Visible light communication (VLC) is a communication technology that is part of the optical wireless communication (OWC) family. It uses the visible and infrared spectrums to send data. For now, this technology has widely been studied for indoor use-cases, but it is sufficiently mature nowadays to consider the outdoor environment potentials. The main outdoor challenges are the meteorological conditions and the presence of smoke due to fire or pollutants in urban areas. This paper proposes a methodology to assess the robustness of an outdoor VLC system given the outdoor conditions. This methodology is put into practice in two realistic scenarios, a VLC bus stop, and a VLC streetlight. The methodology consists of computing the power margin available in the system, given all the characteristics of the VLC system and its surroundings. This is done thanks to an outdoor VLC communication channel simulator developed in Python. This simulator is able to quantify the effects of fog and smoke thanks to models taken from environmental and fire engineering scientific literature as well as the optical power reaching the receiver. These two phenomena impact the communication by increasing the total attenuation of the medium. The main conclusion drawn in this paper is that the levels of attenuation due to fog and smoke are in the same order of magnitude. The attenuation of fog being the highest under the visibility of 1 km. This gives a promising prospect for the deployment of outdoor VLC uses-cases in the near future.

Keywords: channel modeling, fog modeling, meteorological conditions, optical wireless communication, smoke modeling, visible light communication

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Authors: Aishwarya Ravindra Fursule, Shruti Kshirsagar

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In the intersection of artificial intelligence and human-centered computing, this paper delves into speech emotion recognition (SER). It presents a comparative analysis of machine learning models such as K-Nearest Neighbors (KNN),logistic regression, support vector machines (SVM), decision trees, ensemble classifiers, and random forests, applied to SER. The research employs four datasets: Crema D, SAVEE, TESS, and RAVDESS. It focuses on extracting salient audio signal features like Zero Crossing Rate (ZCR), Chroma_stft, Mel Frequency Cepstral Coefficients (MFCC), root mean square (RMS) value, and MelSpectogram. These features are used to train and evaluate the models’ ability to recognize eight types of emotions from speech: happy, sad, neutral, angry, calm, disgust, fear, and surprise. Among the models, the Random Forest algorithm demonstrated superior performance, achieving approximately 79% accuracy. This suggests its suitability for SER within the parameters of this study. The research contributes to SER by showcasing the effectiveness of various machine learning algorithms and feature extraction techniques. The findings hold promise for the development of more precise emotion recognition systems in the future. This abstract provides a succinct overview of the paper’s content, methods, and results.

Keywords: comparison, ML classifiers, KNN, decision tree, SVM, random forest, logistic regression, ensemble classifiers

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Authors: Przemyslaw Nogly, Tobias Weinert, Daniel James, Sergio Carbajo, Dmitry Ozerov, Antonia Furrer, Dardan Gashi, Veniamin Borin, Petr Skopintsev, Kathrin Jaeger, Karol Nass, Petra Bath, Robert Bosman, Jason Koglin, Matthew Seaberg, Thomas Lane, Demet Kekilli, Steffen Brünle, Tomoyuki Tanaka, Wenting Wu, Christopher Milne, Thomas A. White, Anton Barty, Uwe Weierstall, Valerie Panneels, Eriko Nango, So Iwata, Mark Hunter, Igor Schapiro, Gebhard Schertler, Richard Neutze, Jörg Standfuss

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Ultrafast isomerization of retinal is the primary step in a range of photoresponsive biological functions including vision in humans and ion-transport across bacterial membranes. We studied the sub-picosecond structural dynamics of retinal isomerization in the light-driven proton pump bacteriorhodopsin using an X-ray laser. Twenty snapshots with near-atomic spatial and temporal resolution in the femtosecond regime show how the excited all-trans retinal samples conformational states within the protein binding pocket prior to passing through a highly-twisted geometry and emerging in the 13-cis conformation. The aspartic acid residues and functional water molecules in proximity of the retinal Schiff base respond collectively to formation and decay of the initial excited state and retinal isomerization. These observations reveal how the protein scaffold guides this remarkably efficient photochemical reaction.

Keywords: bacteriorhodopsin, free-electron laser, retinal isomerization mechanism, time-resolved crystallography

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Authors: N. Mansouri, A. Asadi

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Data Grid is a geographically distributed environment that deals with data intensive application in scientific and enterprise computing. Data replication is a common method used to achieve efficient and fault-tolerant data access in Grids. In this paper, a dynamic data replication strategy, called Enhanced Latest Access Largest Weight (ELALW) is proposed. This strategy is an enhanced version of Latest Access Largest Weight strategy. However, replication should be used wisely because the storage capacity of each Grid site is limited. Thus, it is important to design an effective strategy for the replication replacement task. ELALW replaces replicas based on the number of requests in future, the size of the replica, and the number of copies of the file. It also improves access latency by selecting the best replica when various sites hold replicas. The proposed replica selection selects the best replica location from among the many replicas based on response time that can be determined by considering the data transfer time, the storage access latency, the replica requests that waiting in the storage queue and the distance between nodes. Simulation results utilizing the OptorSim show our replication strategy achieve better performance overall than other strategies in terms of job execution time, effective network usage and storage resource usage.

Keywords: data grid, data replication, simulation, replica selection, replica placement

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Authors: Pramod Ghadge, Swadesh Srivastava

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Revenue generation of a logistics company depends on how the logistics cost of a shipment is calculated. Logistics cost of a shipment is a function of distance & speed of the shipment travel in a particular network, its volumetric size and dead weight. Logistics billing is based mainly on the consumption of the scarce resource (space or weight carrying capacity of a carrier). Shipment’s size or deadweight is a function of product and packaging weight, dimensions and flexibility. Hence, to arrive at a standard methodology to compute accurate cost to bill the customer, the interplay among above mentioned physical attributes along with their measurement plays a key role. This becomes even more complex for an ecommerce company, like Flipkart, which caters to shipments from both warehouse and marketplace in an unorganized non-standard market like India. In this paper, we will explore various methodologies to define a standard way of billing the non-standard shipments across a wide range of size, shape and deadweight. Those will be, usage of historical volumetric/dead weight data to arrive at a factor which can be used to compute the logistics cost of a shipment, also calculating the real/contour volume of a shipment to address the problem of irregular shipment shapes which cannot be solved by conventional bounding box volume measurements. We will also discuss certain key business practices and operational quality considerations needed to bring standardization and drive appropriate ownership in the ecosystem.

Keywords: contour volume, logistics, real volume, volumetric weight

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Authors: Paul Barasa Wanyama, Tom Wanyama

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Governments and development partners in low-income countries often prioritize building Water Sanitation and Hygiene (WaSH) infrastructure of healthcare facilities to improve maternal healthcare outcomes. However, the operation, maintenance, and utilization of this infrastructure is almost never considered. Many healthcare facilities in these countries use untreated water that is not monitored for quality or quantity. Consequently, it is common to run out of water with a patient is on their way to, or in, the operating theater. Further, the handwashing stations in healthcare facilities regularly run out of water or soap for months, and the latrines are typically not clean, in part due to the lack of water. In this chapter, we present a system that uses Internet of Things (IoT), big data, cloud computing and AI to initiate WaSH security in healthcare facilities, with a specific focus on maternal health. We have implemented smart sensors and actuators to monitor and control WaSH systems from afar to ensure their objectives are achieved. We have also developed a cloud-based system to analyze WaSH data in real time and communicate relevant information back to the healthcare facilities and their stakeholders (e.g., medical personnel, NGOs, ministry of health officials, facilities managers, community leaders, pregnant women, and new mothers and their families) to avert or mitigate problems before they occur.

Keywords: WaSH, internet of things, artificial intelligence, maternal health, rural communities, healthcare facilities

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Authors: Zhangquan Zhou, Guilin Qi

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Materialization is an important reasoning service for applications built on the Web Ontology Language (OWL). To make materialization efficient in practice, current research focuses on deciding tractability of an ontology language and designing parallel reasoning algorithms. However, some well-known large-scale ontologies, such as YAGO, have been shown to have good performance for parallel reasoning, but they are expressed in ontology languages that are not parallelly tractable, i.e., the reasoning is inherently sequential in the worst case. This motivates us to study the problem of parallel tractability of ontology materialization from a theoretical perspective. That is we aim to identify the ontologies for which materialization is parallelly tractable, i.e., in the NC complexity. Since the NC complexity is defined based on Boolean circuit that is widely used to investigate parallel computing problems, we first transform the problem of materialization to evaluation of Boolean circuits, and then study the problem of parallel tractability based on circuits. In this work, we focus on datalog rewritable ontology languages. We use Boolean circuits to identify two classes of datalog rewritable ontologies (called parallelly tractable classes) such that materialization over them is parallelly tractable. We further investigate the parallel tractability of materialization of a datalog rewritable OWL fragment DHL (Description Horn Logic). Based on the above results, we analyze real-world datasets and show that many ontologies expressed in DHL belong to the parallelly tractable classes.

Keywords: ontology materialization, parallel reasoning, datalog, Boolean circuit

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Authors: Kamal KC, Zhendong Yin, Dasen Li, Zhilu Wu

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Intra-plant disease classification based on leaf images is a challenging computer vision task due to similarities in texture, color, and shape of leaves with a slight variation of leaf spot; and external environmental changes such as lighting and background noises. Deep convolutional neural network (DCNN) has proven to be an effective tool for binary classification. In this paper, two methods for binary classification of diseased plant leaves using DCNN are presented; model created from scratch and transfer learning. Our main contribution is a thorough evaluation of 4 networks created from scratch and transfer learning of 5 pre-trained models. Training and testing of these models were performed on a plant leaf images dataset belonging to 16 distinct classes, containing a total of 22,265 images from 8 different plants, consisting of a pair of healthy and diseased leaves. We introduce a deep CNN model, Optimized MobileNet. This model with depthwise separable CNN as a building block attained an average test accuracy of 99.77%. We also present a fine-tuning method by introducing the concept of a convolutional block, which is a collection of different deep neural layers. Fine-tuned models proved to be efficient in terms of accuracy and computational cost. Fine-tuned MobileNet achieved an average test accuracy of 99.89% on 8 pairs of [healthy, diseased] leaf ImageSet.

Keywords: deep convolution neural network, depthwise separable convolution, fine-grained classification, MobileNet, plant disease, transfer learning

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Authors: Belgherbi Aicha, Hadjidj Ismahen, Bessaid Abdelhafid

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The phase of segmentation is an important step in the processing and interpretation of medical images. In this paper, we focus on the segmentation of liver and spleen from the abdomen computed tomography (CT) images. The importance of our study comes from the fact that the segmentation of ROI from CT images is usually a difficult task. This difficulty is the gray’s level of which is similar to the other organ also the ROI are connected to the ribs, heart, kidneys, etc. Our proposed method is based on the anatomical information and mathematical morphology tools used in the image processing field. At first, we try to remove the surrounding and connected organs and tissues by applying morphological filters. This first step makes the extraction of interest regions easier. The second step consists of improving the quality of the image gradient. In this step, we propose a method for improving the image gradient to reduce these deficiencies by applying the spatial filters followed by the morphological filters. Thereafter we proceed to the segmentation of the liver, spleen. To validate the segmentation technique proposed, we have tested it on several images. Our segmentation approach is evaluated by comparing our results with the manual segmentation performed by an expert. The experimental results are described in the last part of this work.The system has been evaluated by computing the sensitivity and specificity between the semi-automatically segmented (liver and spleen) contour and the manually contour traced by radiological experts.

Keywords: CT images, liver and spleen segmentation, anisotropic diffusion filter, morphological filters, watershed algorithm

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Authors: Mohammad H. Yarmohamadian, Reza Safdari, Nahid Tavakoli

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In recent years, many countries have suffered various natural disasters. Disaster response continues to face the challenges in health care sector in all countries. Information and communication management is a significant challenge in disaster scene. During the last decades, rapid advances in information technology have led to manage information effectively and improve communication in health care setting. Information technology is a vital solution for effective response to disasters and emergencies so that if an efficient ICT-based health information system is available, it will be highly valuable in such situation. Of that, mobile technology represents a nearly computing technology infrastructure that is accessible, convenient, inexpensive and easy to use. Most projects have not yet reached the deployment stage, but evaluation exercises show that mHealth should allow faster processing and transport of patients, improved accuracy of triage and better monitoring of unattended patients at a disaster scene. Since there is a high prevalence of cell phones among world population, it is expected the health care providers and managers to take measures for applying this technology for improvement patient safety and public health in disasters. At present there are challenges in the utilization of mhealth in disasters such as lack of structural and financial issues in our country. In this paper we will discuss about benefits and challenges of mhealth technology in disaster setting considering connectivity, usability, intelligibility, communication and teaching for implementing this technology for disaster response.

Keywords: information technology, mhealth, disaster, effective response

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Authors: Anthony Proschka, Deepak Mishra, Merlyn Ramanan, Zurab Baratashvili

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Small and medium-sized businesses receive over 160 billion invoices every year. Since these documents exhibit many subtle differences in layout and text, extracting structured fields such as sender name, amount, and VAT rate from them automatically is an open research question. In this paper, existing work in template-based document extraction is extended, and a system is devised that is able to reliably extract all required fields for up to 70% of all documents in the data set, more than any other previously reported method. The approaches are described for 1) detecting through visual features which template a given document belongs to, 2) automatically generating extraction rules for a given new template by composing regular expressions from multiple components, and 3) computing confidence scores that indicate the accuracy of the automatic extractions. The system can generate templates with as little as one training sample and only requires the ground truth field values instead of detailed annotations such as bounding boxes that are hard to obtain. The system is deployed and used inside a commercial accounting software.

Keywords: data mining, information retrieval, business, feature extraction, layout, business data processing, document handling, end-user trained information extraction, document archiving, scanned business documents, automated document processing, F1-measure, commercial accounting software

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Authors: Ravneet Kaur, Joydeep Acharya, Sudhanshu Gaur

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Artificial Intelligence (AI) has the potential to transform manufacturing by improving shop floor processes such as production, maintenance and quality. However, industrial datasets are notoriously difficult to extract in a real-time, streaming fashion thus, negating potential AI benefits. The main example is some specialized industrial controllers that are operated by custom software which complicates the process of connecting them to an Information Technology (IT) based data acquisition network. Security concerns may also limit direct physical access to these controllers for data acquisition. To connect the Operational Technology (OT) data stored in these controllers to an AI application in a secure, reliable and available way, we propose a novel Industrial IoT (IIoT) solution in this paper. In this solution, we demonstrate how video cameras can be installed in a factory shop floor to continuously obtain images of the controller HMIs. We propose image pre-processing to segment the HMI into regions of streaming data and regions of fixed meta-data. We then evaluate the performance of multiple Optical Character Recognition (OCR) technologies such as Tesseract and Google vision to recognize the streaming data and test it for typical factory HMIs and realistic lighting conditions. Finally, we use the meta-data to match the OCR output with the temporal, domain-dependent context of the data to improve the accuracy of the output. Our IIoT solution enables reliable and efficient data extraction which will improve the performance of subsequent AI applications.

Keywords: human machine interface, industrial internet of things, internet of things, optical character recognition, video analytics

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Authors: Saut Samuel Simamora

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PURPOSE: Nucleus drop is one of the most feared and severe complications of modern cataract surgery. The lens material may drop through iatrogenic breaks of the posterior capsule. The incidence of the nucleus as the complication of phacoemulsification increases concomitant to the increased frequency of phacoemulsification. Pars plana vitrectomy (PPV) followed by endofragmentation and secondary intraocular lens (IOL) implantation is the choice of management procedure. This case report aims to present the outcome of PPV for the treatment dropped nucleus after cataract surgery METHODS: A 65 year old female patient came to Vitreoretina department with chief complaints blurry vision in her left eye after phacoemulsification one month before. Ophthalmological examination revealed visual acuity of the right eye (VA RE) was 6/15, and the left eye (VA LE) was hand movement. The intraocular pressure (IOP) on the right eye was 18 mmHg, and on the left eye was 59 mmHg. On her left eye, there were aphakic, dropped lens nucleus and secondary glaucoma.RESULTS: The patient got antiglaucoma agent until her IOP was decreased. She underwent pars plana vitrectomy to remove dropped nucleus and iris fixated IOL. One week post operative evaluation revealed VA LE was 6/7.5 and iris fixated IOL in proper position. CONCLUSIONS: Nucleus drop generally occurs in phacoemulsification cataract surgery techniques. Retained lens nucleus or fragments in the vitreous may cause severe intraocular inflammation leading to secondary glaucoma. The proper and good management for retained lens fragments in nucleus drop give excellent outcome to patient.

Keywords: secondary glaucoma, complication of phacoemulsification, nucleus drop, pars plana vitrectomy

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Authors: Ethan James

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Over one billion people worldwide suffer from some level of vision loss or blindness as a result of progressive retinal diseases. Many patients, particularly in developing areas, are incorrectly diagnosed or undiagnosed whatsoever due to unconventional diagnostic tools and screening methods. Artificial intelligence (AI) based on deep learning (DL) convolutional neural networks (CNN) have recently gained a high interest in ophthalmology for its computer-imaging diagnosis, disease prognosis, and risk assessment. Optical coherence tomography (OCT) is a popular imaging technique used to capture high-resolution cross-sections of retinas. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography, and visual fields, achieving robust classification performance in the detection of various retinal diseases including macular degeneration, diabetic retinopathy, and retinitis pigmentosa. However, there is no complete diagnostic model to analyze these retinal images that provide a diagnostic accuracy above 90%. Thus, the purpose of this project was to develop an AI model that utilizes machine learning techniques to automatically diagnose specific retinal diseases from OCT scans. The algorithm consists of neural network architecture that was trained from a dataset of over 20,000 real-world OCT images to train the robust model to utilize residual neural networks with cyclic pooling. This DL model can ultimately aid ophthalmologists in diagnosing patients with these retinal diseases more quickly and more accurately, therefore facilitating earlier treatment, which results in improved post-treatment outcomes.

Keywords: artificial intelligence, deep learning, imaging, medical devices, ophthalmic devices, ophthalmology, retina

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Authors: Mai Alshareef

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The Saudi government considers the modernisation of the education system as a critical component of the national development plan, Saudi Vision 2030; however, this sudden reform creates tension amongst Saudis. This study examines first the reflection of the social and digital education reform on stakeholders and the general Saudi public, and second, the influence of information and communication technologies (ICTs) on the ethnoreligious conflict in Saudi Arabia. This study employs Cyberconflict theory to examine conflicts in the real world and cyberspace. The findings are based on a qualitative case study methodology that uses netnography, an analysis of 3,750 Twitter posts and semi-structural interviews with 30 individuals, including key actors in the Saudi education sector and Twitter activists during 2019\2020. The methods utilised are guided by thematic analysis to map an understanding of factors that influence societal conflicts in Saudi Arabia, which in this case include religious, national, and gender identity. Elements of Cyberconflict theory are used to better understand how conflicting groups build their identities in connection to their ethnic/religious/cultural differences and competing national identities. The findings correspond to the ethnoreligious components of the Cyberconflict theory. Twitter became a battleground for liberals, conservatives, the Saudi public and elites, and it is used in a novel way to influence public opinion and to challenge the media monopoly. Opposing groups relied heavily on a discourse of exclusion and inclusion and showed ethnic and religious affiliations, national identity, and chauvinism. The findings add to existing knowledge in the cyberconflict field of study, and they also reveal outcomes that are critical to the Saudi Arabian national context.

Keywords: education, cyberconflict, Twitter, national identity

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Authors: J. K. Adedeji, S. T. Ijatuyi

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The application of neural network using pattern recognition to study the fluid dynamics and predict the groundwater reservoirs properties has been used in this research. The essential of geophysical survey using the manual methods has failed in basement environment, hence the need for an intelligent computing such as predicted from neural network is inevitable. A non-linear neural network with an XOR (exclusive OR) output of 8-bits configuration has been used in this research to predict the nature of groundwater reservoirs and fluid dynamics of a typical basement crystalline rock. The control variables are the apparent resistivity of weathered layer (p₁), fractured layer (p₂), and the depth (h), while the dependent variable is the flow parameter (F=λ). The algorithm that was used in training the neural network is the back-propagation coded in C++ language with 300 epoch runs. The neural network was very intelligent to map out the flow channels and detect how they behave to form viable storage within the strata. The neural network model showed that an important variable g_r (gravitational resistance) can be deduced from the elevation and apparent resistivity p_a. The model results from SPSS showed that the coefficients, a, b and c are statistically significant with reduced standard error at 5%.

Keywords: gravitational resistance, neural network, non-linear, pattern recognition

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Authors: Mohammadhossein Moghimi, Robert Stone, Pia Rotshtein

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Recording psychological and physiological correlates of human performance within virtual environments and interpreting their impacts on human engagement, ‘immersion’ and related emotional or ‘effective’ states is both academically and technologically challenging. By exposing participants to an effective, real-time (game-like) virtual environment, designed and evaluated in an earlier study, a psychophysiological database containing the EEG, GSR and Heart Rate of 30 male and female gamers, exposed to 10 games, was constructed. Some 174 features were subsequently identified and extracted from a number of windows, with 28 different timing lengths (e.g. 2, 3, 5, etc. seconds). After reducing the number of features to 30, using a feature selection technique, K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) methods were subsequently employed for the classification process. The classifiers categorised the psychophysiological database into four effective clusters (defined based on a 3-dimensional space – valence, arousal and dominance) and eight emotion labels (relaxed, content, happy, excited, angry, afraid, sad, and bored). The KNN and SVM classifiers achieved average cross-validation accuracies of 97.01% (±1.3%) and 92.84% (±3.67%), respectively. However, no significant differences were found in the classification process based on effective clusters or emotion labels.

Keywords: virtual reality, effective computing, effective VR, emotion-based effective physiological database

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Authors: Nita Nurlaila Kadarwaty

Abstract:

Purpose: This study presents a rare case of bilateral Acute Primary Angle Closure Glaucoma (PACG). Method: A case report of a 64-year-old woman with a good outcome Acute PACG in both eyes who underwent phacotrabeculectomy surgery. Result: A 64-year-old woman complained of acute pain in both eyes, accompanied by decreased vision, photophobia, and seeing halos for three weeks. There was no history of trauma, steroid or other systemic drugs used, or intraocular surgery before. Ophthalmologic examination revealed a right eye (RE) visual acuity of 0.1, left eye (LE) 0.2. RE intraocular pressure (IOP) was 12 mmhg and LE: 36.4 mmHg in medication of timolol maleat ED and acetazolamide oral. Both eyes' anterior segments revealed mixed injection, corneal edema, shallow anterior chamber, posterior synechiae, mid-dilatation pupil with negative pupillary reflection, and cloudy lens without intumescent. There was a glaucomatous optic and closed iridocorneal angle on the gonioscopy. Initial treatments included oral acetazolamide and potassium aspartate 250 mg three times a day, timolol maleate ED 0.5% twice a day, and prednisolone acetate ED 1% four times a day. This patient underwent trabeculectomy, phacoemulsification, and implantation of IOL in both eyes. One week after the surgeries, both eyes showed decreased IOP and good visual improvement. Conclusion: Bilateral simultaneous Acute PACG is generally severe and results in a poor outcome. It causes rapidly progressive visual loss and is often irreversible. Phacotrabeculectomy has more benefits compared to only phacoemulsification for the intervention regarding the reduced IOP post-surgical.

Keywords: acute primary angle closure glaucoma, intraocular pressure, phacotrabeculectomy, glaucoma

Procedia PDF Downloads 71

Authors: Merkineh Markos Lorato, Gedefaw Diress Alene

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Introduction: Visual impairment is any condition of the eye or visual system that results in loss/reduction of visual functioning. It significantly influences the academic routine and social activities of children, and the effect is severe for low-income countries like Ethiopia. So, this study aimed to determine the pooled prevalence of visual impairment among school children in Ethiopia. Methods: Databases such as Medical Literature Analysis and Retrieval System Online, Excerpta Medica dataBASE, World Wide Web of Science, and Cochrane Library searched to retrieve eligible articles. In addition, Google Scholar and a reference list of the retrieved eligible articles were addressed. Studies that reported the prevalence of visual impairment were included to estimate the pooled prevalence. Data were extracted using a standardized data extraction format prepared in Microsoft Excel and analysis was held using STATA 11 statistical software. I² was used to assess the heterogeneity. Because of considerable heterogeneity, a random effect meta-analysis model was used to estimate the pooled prevalence of visual impairment among school children in Ethiopia. Results: The result of 9 eligible studies showed that the pooled prevalence of visual impairment among school children in Ethiopia was 7.01% (95% CI: 5.46, 8.56%). In the subgroup analysis, the highest prevalence was reported in South Nations Nationalities and Tigray region together (7.99%; 3.63, 12.35), while the lowest prevalence was reported in Addis Ababa (5.73%; 3.93, 7.53). Conclusion: The prevalence of visual impairment among school children is significantly high in Ethiopia. If it is not detected and intervened early, it will cause a lifetime threat to visually impaired school children, so that school vision screening program plan and its implementation may cure the life quality of future generations in Ethiopia.

Keywords: visual impairment, school children, Ethiopia, prevalence

Procedia PDF Downloads 32

Authors: Yuanchao Sang, Zhihao Gong, Longsheng Chen, Long Chen

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Clothing fashion represents a human’s aesthetic appreciation towards everyday outfits and appetite for fashion, and it reflects the development of status in society, humanity, and economics. However, modelling fashion by machine is extremely challenging because fashion is too abstract to be efficiently described by machines. Even human beings can hardly reach a consensus about fashion. In this paper, we are dedicated to answering a fundamental fashion-related problem: what image feature best describes clothing fashion? To address this issue, we have designed and evaluated various image features, ranging from traditional low-level hand-crafted features to mid-level style awareness features to various current popular deep neural network-based features, which have shown state-of-the-art performance in various vision tasks. In summary, we tested the following 9 feature representations: color, texture, shape, style, convolutional neural networks (CNNs), CNNs with distance metric learning (CNNs&DML), AutoEncoder, CNNs with multiple layer combination (CNNs&MLC) and CNNs with dynamic feature clustering (CNNs&DFC). Finally, we validated the performance of these features on two publicly available datasets. Quantitative and qualitative experimental results on both intra-domain and inter-domain fashion clothing image retrieval showed that deep learning based feature representations far outweigh traditional hand-crafted feature representation. Additionally, among all deep learning based methods, CNNs with explicit feature clustering performs best, which shows feature clustering is essential for discriminative fashion feature representation.

Keywords: convolutional neural network, feature representation, image processing, machine modelling

Procedia PDF Downloads 136

Authors: Pannathorn Chachvarat, Smarnjit Piromrun

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Aging is the reality for the future world. An urgent priority for the development of the elderlies’ quality living is needed. The promotion of quality the elderly to live longer in their dignity and being independence are essential. The objective of this descriptive research was to study the self-care regarding to the valuable living in Thai elderly. The randomized sample was 100 elderly who live in Muang district of Phayao province. The tools included 2 parts; 1) Personal data (gender, age, income, occupation, marital status, living condition and disease), and 2) the self-care regarding to the valuable living questionnaire consisted of 3 domains, physical (21items), spiritual (13 items) and social domain (12 items). The content validity tool was tested the IOC ranged between 0.60 – 1.00 and the reliability test, Cronbach Alpha was 0.82. The research found that; The most participants were female (60 %), Farmer (37%), and underlying disease (65 %). The range of age was 68 years. Overall of the self-care regarding to the valuable living of physical, spiritual and social were at the high level.The highest level of physical activities was self-taking bath twice a day (morning and evening), and slept at least 5-6 hours at night time.The highest level of spirit activities was a good member of the family, contributions to persons in family, good emotion. Additionally were enjoyable, accepting changes in the body such as the dry skin and the blurred vision, accepting the roles and duties in taking care of house and grandchildren, selecting the applicable activities and practice according to religious Buddhateachingfor the happiness and meditated life.The highest of the social activities were the good relationship between other elderlies and family members, happy to help social activities as of their capacity, and being happy to help other people who have problems.

Keywords: self-care, valuable living, elderly, Thai

Procedia PDF Downloads 284

Authors: Anyeres N. Atehortua Jimenez, J. David Lambraño, Juan Carlos Muñoz

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Navier-Stokes equations (NSE), which describe the dynamics of a fluid, have an important application on modeling waves used for data inversion techniques as full waveform inversion (FWI). In this work a linearized version of NSE and its variables, neglecting deviatoric terms of stress tensor, is presented. In order to get a theoretical modeling of pressure p(x,t) and wave velocity profile c(x,t), a wave equation of visco-acoustic medium (VAE) is written. A change of variables p(x,t)=q(x,t)h(ρ), is made on the equation for the VAE leading to a well known Klein-Gordon equation (KGE) describing waves propagating in variable density medium (ρ) with dispersive term α^2(x). KGE is reduced to a Poisson equation and solved by proposing a specific function for α^2(x) accounting for the energy dissipation and dispersion. Finally, an integral form solution is derived for p(x,t), c(x,t) and kinematics variables like particle velocity v(x,t), displacement u(x,t) and bulk modulus function k_b(x,t). Further, it is compared this visco-acoustic formulation with another form broadly used in the geophysics; it is argued that this formalism is more general and, given its integral form, it may offer several advantages from the modern parallel computing point of view. Applications to minimize the errors in modeling for FWI applied to oils resources in geophysics are discussed.

Keywords: Navier-Stokes equations, modeling, visco-acoustic, inversion FWI

Procedia PDF Downloads 516

Authors: W. Swiderski

Abstract:

In the paper introduced the diagnostic technique making possible the research of internal structures in composite materials reinforced fibres using in different applications. The main reason of damages in structures of these materials is the changing distribution of load in constructions in the lifetime. Appearing defect is largely complicated because of the appearance of disturbing of continuity of reinforced fibres, binder cracks and loss of fibres adhesiveness from binders. Defect in composite materials is usually more complicated than in metals. At present, infrared thermography is the most effective method in non-destructive testing composite. One of IR thermography methods used in non-destructive evaluation is vibrothermography. The vibrothermography is not a new non-destructive method, but the new solution in this test is use ultrasonic waves to thermal stimulation of materials. In this paper, both modelling and experimental results which illustrate the advantages and limitations of ultrasonic IR thermography in inspecting composite materials will be presented. The ThermoSon computer program for computing 3D dynamic temperature distribuions in anisotropic layered solids with subsurface defects subject to ulrasonic stimulation was used to optimise heating parameters in the detection of subsurface defects in composite materials. The program allows for the analysis of transient heat conduction and ultrasonic wave propagation phenomena in solids. The experiments at MIAT were fulfilled by means of FLIR SC 7600 IR camera. Ultrasonic stimulation was performed with the frequency from 15 kHz to 30 kHz with maximum power up to 2 kW.

Keywords: composite material, ultrasonic, infrared thermography, non-destructive testing

Procedia PDF Downloads 292