Search results for: cone of vision

Authors: Hengameh Hosseini

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Design of large healthcare facilities – such as hospitals, multi-service line clinics, and nursing facilities - that can accommodate patients with wide-ranging disabilities is a challenging endeavor and one that is poorly understood among healthcare facility managers, administrators, and executives. An even less-understood extension of this problem is the implications of weakly or insufficiently accommodative design of facilities for healthcare workers in physically-intensive jobs who may also suffer from a range of disabilities and who are therefore at increased risk of workplace accident and injury. Combine this reality with the vast range of facility types, ages, and designs, and the problem of universal accommodation becomes even more daunting and complex. In this study, we focus on the implication of facility design for healthcare workers suffering with low vision who also have physically active jobs. The points of difficulty are myriad and could span health service infrastructure, the equipment used in health facilities, and transport to and from appointments and other services can all pose a barrier to health care if they are inaccessible, less accessible, or even simply less comfortable for people with various disabilities. We conduct a series of surveys and interviews with employees and administrators of 7 facilities of a range of sizes and ownership models in the Northeastern United States and combine that corpus with in-facility observations and data collection to identify five major points of failure common to all the facilities that we concluded could pose safety threats to employees with vision impairments, ranging from very minor to severe. We determine that lack of design empathy is a major commonality among facility management and ownership. We subsequently propose three methods for remedying this lack of empathy-informed design, to remedy the dangers posed to employees: the use of an existing open-sourced Augmented Reality application to simulate the low-vision experience for designers and managers; the use of a machine learning model we develop to automatically infer facility shortcomings from large datasets of recorded patient and employee reviews and feedback; and the use of a computer vision model fine tuned on images of each facility to infer and predict facility features, locations, and workflows, that could again pose meaningful dangers to visually impaired employees of each facility. After conducting a series of real-world comparative experiments with each of these approaches, we conclude that each of these are viable solutions under particular sets of conditions, and finally characterize the range of facility types, workforce composition profiles, and work conditions under which each of these methods would be most apt and successful.

Keywords: artificial intelligence, healthcare workers, facility design, disability, visually impaired, workplace safety

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Authors: Nor Hidayah Reduwan, Jira Chindasombatjaroen, Suchaya Pornprasersuk-Damrongsri, Sopee Pomsawat

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INTRODUCTION: Odontogenic keratocyst (OKC) remain as a controversial pathologic entity under the scrutiny of many researchers and maxillofacial surgeons alike. The high recurrence rate and relatively aggressive nature of this lesion demand a meticulous analysis of the radiographic characteristic of OKC leading to the formulation of an accurate diagnosis. OBJECTIVE: This study aims to determine the radiographic characteristic of odontogenic keratocyst (OKC) using conventional radiographs and cone beam computed tomography (CBCT) images. MATERIALS AND METHODS: Patients histopathologically diagnosed as OKC from 2003 to 2016 by Oral and Maxillofacial Pathology Department were retrospectively reviewed. Radiographs of these cases from the archives of the Department of Oral and Maxillofacial Radiology, Faculty of Dentistry Mahidol University were retrieved. Assessment of the location, shape, border, cortication, locularity, the relationship of lesion to embedded tooth, displacement of adjacent tooth, root resorption and bony expansion of the lesion were conducted. RESULTS: Radiographs of 91 patients (44 males, 47 females) with the mean age of 31 years old (10 to 84 years) were analyzed. Among all patients, 5 cases were syndromic patients. Hence, a total of 103 OKCs were studied. The most common location was at the ramus of mandible (32%) followed by posterior maxilla (29%). Most cases presented as a well-defined unilocular radiolucency with smooth and corticated border. The lesion was in associated with embedded tooth in 48 lesions (47%). Eighty five percent of embedded tooth are impacted 3rd molar. Thirty-seven percentage of embedded tooth were entirely encapsulated in the lesion. The lesion attached to the embedded tooth at the cementoenamel junction (CEJ) in 40% and extended to part of root in 23% of cases. Teeth displacement and root resorption were found in 29% and 6% of cases, respectively. Bony expansion in bucco-lingual dimension was seen in 63% of cases. CONCLUSION: OKCs were predominant in the posterior region of the mandible with radiographic features of a well-defined, unilocular radiolucency with smooth and corticated margin. The lesions might relate to an embedded tooth by surrounding an entire tooth, attached to the CEJ level or extending to part of root. Bony expansion could be found but teeth displacement and root resorption were not common. These features might help in giving the differential diagnosis.

Keywords: cone beam computed tomography, imaging dentistry, odontogenic keratocyst, radiographic features

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Authors: Merav Hayakac, Orit Avidov-Ungarab

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The current study adopted an interpretive-constructivist approach to examine how senior academics from a large sample of Israeli teacher education colleges serving general or religious populations perceived the integration of digital games into their teacher instruction and what their policy and vision were in this regard in the context of the COVID-19 pandemic. Half the participants expressed a desire to integrate digital games into their teaching and learning but acknowledged that this practice was uncommon. Only a small minority believed they had achieved successful integration, with doubt and skepticism expressed by some religious colleges. Most colleges had policies encouraging technology integration supported by ongoing funding. Although a considerable gap between policy and implementation remained, the COVID-19 pandemic was viewed as having accelerated the integration of digital games into pre-service teacher instruction. The findings suggest that discussions around technology-related vision and policy and their translation into practice should relate to the specific cultural needs and academic preparedness of the population(s) served by the college.

Keywords: COVID-19, digital games, pedagogy, teacher education colleges

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Authors: Xingdu Wang, Charlie Gere, Emma Rose, Yuxuan Zhao

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The study introduces a fresh perspective on the digital realm through an examination of the Chinese concept of Xiang, elucidating how it can build an understanding of pixels and textures on screens as digital trigrams. This concept attempts to offer an outlook on the intersection of digital technology and the natural world, thereby contributing to discussions about the harmonious relationship between humans and technology. The study looks for the ancient Chinese theory of Xiang as a key to establishing the theories and practices to respond to the problem of Contemporary Chinese technics. Xiang is a Chinese method of understanding the essentials of things through appearances, which differs from the method of science in the Westen. Xiang, the basement of Chinese visual art, is rooted in ancient Chinese philosophy and connected to the eight trigrams. The discussion of Xiang connects art, philosophy, and technology. This paper connects the meaning of Xiang with the 'truth appearing' philosophically through the analysis of the concepts of phenomenon and noumenon and the unique Chinese way of observing. Hereafter, the historical interconnection between ancient painting and writing in China emphasizes their relationship between technical craftsmanship and artistic expression. In digital, the paper blurs the traditional boundaries between images and text on digital screens in theory. Lastly, this study identified an ensemble concept relating to pixels and textures in computer vision, drawing inspiration from AI image recognition in Chinese paintings. In art practice, by presenting a fluid visual experience in the form of pixels, which mimics the flow of lines in traditional calligraphy and painting, it is hoped that the viewer will be brought back to the process of the truth appearing as defined by the 'Xiang’.

Keywords: Chinese cosmotechnics, computer vision, contemporary Neo-Confucianism, texture and pixel, Xiang

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Authors: Promise Ekpo Osaine, Daniel Melesse

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Image captioning is an active area of research in the multi-modal artificial intelligence (AI) community as it connects vision and language understanding, especially in settings where it is required that a model understands the content shown in an image and generates semantically and grammatically correct descriptions. In this project, we followed a standard approach to a deep learning-based image captioning model, injecting architecture for the encoder-decoder setup, where the encoder extracts image features, and the decoder generates a sequence of words that represents the image content. As such, we investigated image encoders, which are ResNet101, InceptionResNetV2, EfficientNetB7, EfficientNetV2M, and CLIP. As a caption generation structure, we explored long short-term memory (LSTM). The CLIP-LSTM model demonstrated superior performance compared to the encoder-decoder models, achieving a BLEU-1 score of 0.904 and a BLEU-4 score of 0.640. Additionally, among the CNN-LSTM models, EfficientNetV2M-LSTM exhibited the highest performance with a BLEU-1 score of 0.896 and a BLEU-4 score of 0.586 while using a single-layer LSTM.

Keywords: multi-modal AI systems, image captioning, encoder, decoder, BLUE score

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Authors: Aline Giardin, Maria Rosa Chitolina, Maria Catarina Zanini

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In this paper, we analyze the conceptions of high school students about mental health issues, and discuss the creation of mental basic health programs in schools. We base our findings in a quantitative survey carried out by us with 156 high school students of CTISM (Colégio Técnico Industrial de Santa Maria) school, located in Santa Maria city, Brazil. We have found that: (a) 28 students relate the subject ‘mental health’ with psychiatric hospitals and lunatic asylums; (b) 28 students have relatives affected by mental diseases; (c) 76 students believe that mental patients, if treated, can live a healthy life; (d) depression, schizophrenia and bipolar disorder are the most cited diseases; (e) 84 students have contact with mental patients, but know nothing about the disease; (f) 123 students have never been instructed about mental diseases while in the school; and (g) 135 students think that a mental health program would be important in the school. We argue that these numbers reflect a vision of mental health that can be related to the reductionist education still present in schools and to the lack of integration between health professionals, sciences teachers, and students. Furthermore, this vision can also be related to a stigmatization process, which interferes with the interactions and with the representations regarding mental disorders and mental patients in society.

Keywords: mental health, schools, mental illness, conception

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Authors: Babar Khan, Wang Zhijie

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Machine Vision has been playing a significant role in Industrial Automation, to imitate the wide variety of human functions, providing improved safety, reduced labour cost, the elimination of human error and/or subjective judgments, and the creation of timely statistical product data. Despite the intensive research, there have not been any attempts to classify fabric prints based on printed texture and colour, most of the researches so far encompasses only black and white or grey scale images. We proposed a biologically inspired processing architecture to classify fabrics w.r.t. the fabric print texture and colour. We created a texture descriptor based on the HMAX model for machine vision, and incorporated colour descriptor based on opponent colour channels simulating the single opponent and double opponent neuronal function of the brain. We found that our algorithm not only outperformed the original HMAX algorithm on classification of fabric print texture and colour, but we also achieved a recognition accuracy of 85-100% on different colour and different texture fabric.

Keywords: automatic classification, texture descriptor, colour descriptor, opponent colour channel

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Authors: Yifan Lu, Ying Cui, Ying Zhu, Edward S. Lu, Rebecca Zeng, Rohan Bajaj, Raviv Katz, Rongrong Le, Jay C. Wang, John B. Miller

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Purpose: Retinal artery occlusion (RAO) is an ophthalmic emergency that can lead to poor visual outcome and is associated with an increased risk of cerebral stroke and cardiovascular events. Fluorescein angiography (FA) is the traditional diagnostic tool for RAO; however, wide-field swept-source optical coherence tomography angiography (WF SS-OCTA), as a nascent imaging technology, is able to provide quick and non-invasive angiographic information with a wide field of view. In this study, we looked for associations between OCT-A vascular metrics and visual acuity in patients with prior diagnosis of RAO. Methods: Patients with diagnoses of central retinal artery occlusion (CRAO) or branched retinal artery occlusion (BRAO) were included. A 6mm x 6mm Angio and a 15mm x 15mm AngioPlex Montage OCT-A image were obtained for both eyes in each patient using the Zeiss Plex Elite 9000 WF SS-OCTA device. Each 6mm x 6mm image was divided into nine Early Treatment Diabetic Retinopathy Study (ETDRS) subfields. The average measurement of the central foveal subfield, inner ring, and outer ring was calculated for each parameter. Non-perfusion area (NPA) was manually measured using 15mm x 15mm Montage images. A linear regression model was utilized to identify a correlation between the imaging metrics and visual acuity. A P-value less than 0.05 was considered to be statistically significant. Results: Twenty-five subjects were included in the study. For RAO eyes, there was a statistically significant negative correlation between vision and retinal thickness as well as superficial capillary plexus vessel density (SCP VD). A negative correlation was found between vision and deep capillary plexus vessel density (DCP VD) without statistical significance. There was a positive correlation between vision and choroidal thickness as well as choroidal volume without statistical significance. No statistically significant correlation was found between vision and the above metrics in contralateral eyes. For NPA measurements, no significant correlation was found between vision and NPA. Conclusions: This is the first study to our best knowledge to investigate the utility of WF SS-OCTA in RAO and to demonstrate correlations between various retinal vascular imaging metrics and visual outcomes. Further investigations should explore the associations between these imaging findings and cardiovascular risk as RAO patients are at elevated risk for symptomatic stroke. The results of this study provide a basis to understand the structural changes involved in visual outcomes in RAO. Furthermore, they may help guide management of RAO and prevention of cerebral stroke and cardiovascular accidents in patients with RAO.

Keywords: OCTA, swept-source OCT, retinal artery occlusion, Zeiss Plex Elite

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Authors: Lihua Zhu, Jianfeng Du, Yu Wang, Zhiqiang Wu

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Collision avoidance problems of a swarm of unmanned air vehicles (UAVs) flying in an obstacle-laden environment are investigated in this paper. Given that the UAV swarm needs to adapt to the obstacle distribution in dynamic operation, a priority configuration is designed to guide the UAVs to pass through the obstacles in turn. Based on the collision cone approach and the prediction of the collision time, a collision evaluation model is established to judge the urgency of the imminent collision of each UAV, and the evaluation result is used to assign the priority of each UAV to further instruct them going through the obstacles in descending order. At last, the simulation results provide the promising validation in terms of the efficiency and scalability of the proposed approach.

Keywords: UAV swarm, collision avoidance, complex environment, online priority design

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Authors: Simiao Ren, En Wei

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Drowning is a significant safety issue worldwide, and a robust computer vision-based alert system can easily prevent such tragedies in swimming pools. However, due to domain shift caused by the visual gap (potentially due to lighting, indoor scene change, pool floor color etc.) between the training swimming pool and the test swimming pool, the robustness of such algorithms has been questionable. The annotation cost for labeling each new swimming pool is too expensive for mass adoption of such a technique. To address this issue, we propose a domain-aware data augmentation pipeline based on Gaussian Poisson Generative Adversarial Network (GP-GAN). Combined with YOLOv8, we demonstrate that such a domain adaptation technique can significantly improve the model performance (from 0.24 mAP to 0.82 mAP) on new test scenes. As the augmentation method only require background imagery from the new domain (no annotation needed), we believe this is a promising, practical route for preventing swimming pool drowning.

Keywords: computer vision, deep learning, YOLOv8, detection, swimming pool, drowning, domain adaptation, generative adversarial network, GAN, GP-GAN

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Authors: Kashika P. H.

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The development of computer vision systems has been greatly aided by the efficient and precise detection of images and videos. Although the ability to recognize and comprehend images is a strength of the human brain, employing technology to tackle this issue is exceedingly challenging. In the past few years, the use of Deep Learning algorithms to treat object detection has dramatically expanded. One of the key issues in the realm of image recognition is the recognition and detection of certain notable people from randomly acquired photographs. Face recognition uses a way to identify, assess, and compare faces for a variety of purposes, including user identification, user counting, and classification. With the aid of an accessible deep learning-based API, this article intends to recognize various faces of people and their facial descriptors more accurately. The purpose of this study is to locate suitable individuals and deliver accurate information about them by using the Amazon Rekognition system to identify a specific human from a vast image dataset. We have chosen the Amazon Rekognition system, which allows for more accurate face analysis, face comparison, and face search, to tackle this difficulty.

Keywords: Amazon rekognition, API, deep learning, computer vision, face detection, text detection

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Authors: Smitha Haridasan, Zelalem Demissie, Atri Dutta, Ajita Rattani

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Aided by the wind, all it takes is one ember and a few minutes to create a wildfire. Wildfires are growing in frequency and size due to climate change. Wildfires and its consequences are one of the major environmental concerns. Every year, millions of hectares of forests are destroyed over the world, causing mass destruction and human casualties. Thus early detection of wildfire becomes a critical component to mitigate this threat. Many computer vision-based techniques have been proposed for the early detection of forest fire using video surveillance. Several computer vision-based methods have been proposed to predict and detect forest fires at various spectrums, namely, RGB, HSV, and YCbCr. The aim of this paper is to propose a multi-spectral deep learning model that combines information from different spectrums at intermediate layers for accurate fire detection. A heterogeneous dataset assembled from publicly available datasets is used for model training and evaluation in this study. The experimental results show that multi-spectral deep learning models could obtain an improvement of about 4.68 % over those based on a single spectrum for fire detection.

Keywords: deep learning, forest fire detection, multi-spectral learning, natural hazard detection

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Authors: Massimo Miccoli, Luca Marangoni, Alberto Aniello Scaringi, Alessandro Marceddu, Alessandro Amicone

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The susceptibility of deep neural networks (DNNs) to adversarial manipulations is a recognized challenge within the computer vision domain. Adversarial examples, crafted by adding subtle yet malicious alterations to benign images, exploit this vulnerability. Various defense strategies have been proposed to safeguard DNNs against such attacks, stemming from diverse research hypotheses. Building upon prior work, our approach involves the utilization of autoencoder models. Autoencoders, a type of neural network, are trained to learn representations of training data and reconstruct inputs from these representations, typically minimizing reconstruction errors like mean squared error (MSE). Our autoencoder was trained on a dataset of benign examples; learning features specific to them. Consequently, when presented with significantly perturbed adversarial examples, the autoencoder exhibited high reconstruction errors. The architecture of the autoencoder was tailored to the dimensions of the images under evaluation. We considered various image sizes, constructing models differently for 256x256 and 512x512 images. Moreover, the choice of the computer vision model is crucial, as most adversarial attacks are designed with specific AI structures in mind. To mitigate this, we proposed a method to replace image-specific dimensions with a structure independent of both dimensions and neural network models, thereby enhancing robustness. Our multi-modal autoencoder reconstructs the spectral representation of images across the red-green-blue (RGB) color channels. To validate our approach, we conducted experiments using diverse datasets and subjected them to adversarial attacks using models such as ResNet50 and ViT_L_16 from the torch vision library. The autoencoder extracted features used in a classification model, resulting in an MSE (RGB) of 0.014, a classification accuracy of 97.33%, and a precision of 99%.

Keywords: adversarial attacks, malicious images detector, binary classifier, multimodal transformer autoencoder

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Authors: Omer Cahana, Maya Herman, Ofer Levi

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Magnetic resonance imaging (MRI) is a long medical scan that stems from a long acquisition time. This length is mainly due to the traditional sampling theorem, which defines a lower boundary for sampling. However, it is still possible to accelerate the scan by using a different approach, such as compress sensing (CS) or parallel imaging (PI). These two complementary methods can be combined to achieve a faster scan with high-fidelity imaging. In order to achieve that, two properties have to exist: i) the signal must be sparse under a known transform domain, ii) the sampling method must be incoherent. In addition, a nonlinear reconstruction algorithm needs to be applied to recover the signal. While the rapid advance in the deep learning (DL) field, which has demonstrated tremendous successes in various computer vision task’s, the field of MRI reconstruction is still in an early stage. In this paper, we present an extension of the state-of-the-art model in MRI reconstruction -VarNet. We utilize VarNet by using dilated convolution in different scales, which extends the receptive field to capture more contextual information. Moreover, we simplified the sensitivity map estimation (SME), for it holds many unnecessary layers for this task. Those improvements have shown significant decreases in computation costs as well as higher accuracy.

Keywords: MRI, deep learning, variational network, computer vision, compress sensing

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Authors: Obulesh Avuku, Satwik Sunnam, Sri Charan Mohan Janthuka, Keerthi Yalamaddi

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In the United States alone, there are over 1.5 million spine fractures per year, resulting in about 17,730 spinal cord injuries. The cervical spine is where fractures in the spine most frequently occur. The prevalence of spinal fractures in the elderly has increased, and in this population, fractures may be harder to see on imaging because of coexisting degenerative illness and osteoporosis. Nowadays, computed tomography (CT) is almost completely used instead of radiography for the imaging diagnosis of adult spine fractures (x-rays). To stop neurologic degeneration and paralysis following trauma, it is vital to trace any vertebral fractures at the earliest. Many approaches have been proposed for the classification of the cervical spine [2d models]. We are here in this paper trying to break the bounds and use the vision transformers, a State-Of-The-Art- Model in image classification, by making minimal changes possible to the architecture of ViT and making it 3D-enabled architecture and this is evaluated using a weighted multi-label logarithmic loss. We have taken this problem statement from a previously held Kaggle competition, i.e., RSNA 2022 Cervical Spine Fracture Detection.

Keywords: cervical spine, spinal fractures, osteoporosis, computed tomography, 2d-models, ViT, multi-label logarithmic loss, Kaggle, public score, private score

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Authors: Sathyamangalam Rangasamy Guru Prasath

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Due to the increasing homogeneity in product offerings, the attendant services provided are emerging as a key differentiator in the mind of the consumers. Services marketing are a sub field of marketing which covers the marketing of both goods and services. Service marketing differs from product marketing due to the face that services are intangible and typically require personal interaction with the customer. Relationships are a key factor when it comes to the marketing of services. The role of interpersonal relationships distinguishes service and product marketing in strategic vision and organizational considerations. This paper explores some of the trends in service marketing as they relate to strategic vision, operational and organizational changes, and marketing tactics. The presence of the customer in the service facility means that capacity management becomes an important driver of the firm’s profitability service marketing is a process from the organization’s point of view, but an experience from the customer’s perspective. The quality of the experience is a function of the careful design of customer service processes, adoption of standardized procedures, rigorous management of service quality, high standards of training and automation. Services marketing helps to ensure that these processes are designed from the customer’s perspective. Services marketing includes customer loyalty, managing relationships, complaint handling, improving service quality and productivity of service operations, and how to become a service leader in your industry.

Keywords: customer perspective, product marketing, service marketing, rigorous management

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Authors: Shreyansh Jain Jeetmal, Shobith P. Chadaga, Shreyas H. Srinivas

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Internet of Things (IoT) is seen as the next major step in the ongoing revolution in the Information Age. It is predicted that in the near future billions of embedded devices will be communicating with each other to perform a plethora of tasks with or without human intervention. One of the major ongoing hotbed of research activity in IoT is Human Computer Interaction (HCI). HCI is used to facilitate communication between an intelligent system and a user. An intelligent system typically comprises of a system consisting of various sensors, actuators and embedded controllers which communicate with each other to monitor data collected from the environment. Communication by the user to the system is typically done using voice. One of the major ongoing applications of HCI is in home automation as a personal assistant. The prime objective of our project is to implement a use case of HCI for home automation. Our system is designed to detect and recognize the users and personalize the appliances in the house according to their individual preferences. Our HCI system is also capable of speaking with the user when certain commands are spoken such as searching on the web for information and controlling appliances. Our system can also monitor the environment in the house such as air quality and gas leakages for added safety.

Keywords: human computer interaction, internet of things, computer vision, sensor networks, speech to text, text to speech, android

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Authors: Ashraf Eid

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Many formulas are set to estimate the pile load capacity based on the in-situ pile load tests. However, discrepancy is obvious between the results of these applications. Some formulas are more adequate than others with respect to soil formation and its characteristics. In this research, attempts were undertaken to evaluate the adequacy of the most well-known formulas based on a series of pile load tests carried out in Port Said city in the northeast of Egypt for major residential projects. Comparisons were undertaken between the different formulas supported by the results of in-situ Cone Penetration Tests (CPT). Based on this study, a guide for engineers for using the proper formula can be adopted with consideration of soil type and characteristics. The Egyptian Code which relies on the results of some formulas is involved in the study as a guiding aspect in the pile design.

Keywords: pile load formula, load test of piles, CPT, Egyptian code

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Authors: T. Qasim, M. Obeidat, S. Al-Sharairi

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The objective of this study is to investigate the effect of the excessive use of smartphones. Smartphones have enormous effects on the human body in that some musculoskeletal disorders (MSDs) and health problems might evolve. These days, there is a wide use of the smartphones among all age groups of society, thus, the focus on smartphone effects on human behavior and health, especially on the young and elderly people, becomes a crucial issue. This study was conducted in Jordan on smartphone users for different genders and ages, by conducting a survey to collect data related to the symptoms and MSDs that are resulted from the excessive use of smartphones. A total of 357 responses were used in the analysis. The main related symptoms were numbness, fingers pain, and pain in arm, all linked to age and gender for comparative reasons. A statistical analysis was performed to find the effects of extensive usage of a smartphone for long periods of time on the human body. Results show that the significant variables were the vision problems and the time spent when using the smartphone that cause vision problems. Other variables including age of user and ear problems due to the use of the headsets were found to be a border line significant.

Keywords: smart phone, age group, musculoskeletal disorders (MSDs), health problems

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Authors: Faisal Mahroogi, Mahmoud Bady, Yaser H. Alahmadi, Ahmed Alsisi, Sunny Narayan, Muhammad Usman Kaisan

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The Kingdom of Saudi Arabia established Saudi Vision 2030, an initiative of the government with the goal of promoting more socioeconomic as well as cultural diversity. The kingdom, which is dedicated to sustainable development and clean energy, uses cutting-edge approaches to address energy-related issues, including the circular carbon economy (CCE) and a more varied energy mix. In order for Saudi Arabia to achieve its Vision 2030 goal of having a net zero future by 2060, sustainability is essential. By addressing the energy and climate issues of the modern world with responsibility and innovation, Vision 2030 is turning into a global role model for the transition to a sustainable future. As per the Ambitions of the National Environment Strategy of the Saudi Ministry of Environment, Agriculture, and Water (MEWA), raising environmental compliance across all sectors and reducing pollution and adverse environmental impacts are critical focus areas. As a result, the current study presents an experimental analysis of the performance and exhaust emissions of a diesel engine running mostly on waste cooking oil (WCO). A one-cylinder direct-injection diesel engine with constant speed and natural aspiration is the engine type utilized. Research was done on how the engine performed and emission parameters when fueled with a mixture of 10% butanol, 10% diesel, 10% WCO, and 10% diethyl ether (D70B10W10DD10). The study's findings demonstrated that engine emissions of nitrogen oxides (NOX) and carbon monoxide (CO) varied significantly depending on the load being applied. The brake thermal efficiency, cylinder pressure, and the brake power of the engine were all impacted by load change.

Keywords: ICE, waste cooking oil, fuel additives, butanol, combustion, emission characteristics

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Authors: Arne Hitzmann, Philipp Wentscher, Alexander Gabel, Reinhard Gerndt

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Autonomous mobile robots can be found in a wide field of applications. Their types range from household robots over workshop robots to autonomous cars and many more. All of them undergo a number of testing steps during development, production and maintenance. This paper describes an approach to improve testing of robot behavior. It was inspired by the RoboCup @work competition that itself reflects a robotics benchmark for industrial robotics. There, scaled down versions of mobile industrial robots have to navigate through a workshop-like environment or operation area and have to perform tasks of manipulating and transporting work pieces. This paper will introduce an approach of automated vision-based testing of the behavior of the so called youBot robot, which is the most widely used robot platform in the RoboCup @work competition. The proposed system allows automated testing of multiple tries of the robot to perform a specific missions and it allows for the flexibility of the robot, e.g. selecting different paths between two tasks within a mission. The approach is based on a multi-camera setup using, off the shelf cameras and optical markers. It has been applied for test-driven development (TDD) and maintenance-like verification of the robot behavior and performance.

Keywords: supervisory control, testing, markers, mono vision, automation

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Authors: Ming-Yen Chang, Sheng-Hung Ke

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This research focuses on the development of a speed bump identification system for real-time control of adjustable shock absorbers in vehicular suspension systems. The study initially involved the collection of images of various speed bumps, and rubber speed bump profiles found on roadways. These images were utilized for training and recognition purposes through the deep learning object detection algorithm YOLOv5. Subsequently, the trained speed bump identification program was integrated with an in-vehicle camera system for live image capture during driving. These images were instantly transmitted to a computer for processing. Using the principles of monocular vision ranging, the distance between the vehicle and an approaching speed bump was determined. The appropriate control distance was established through both practical vehicle measurements and theoretical calculations. Collaboratively, with the electronically adjustable shock absorbers equipped in the vehicle, a shock absorber control system was devised to dynamically adapt the damping force just prior to encountering a speed bump. This system effectively mitigates passenger discomfort and enhances ride quality.

Keywords: adjustable shock absorbers, image recognition, monocular vision ranging, ride

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Authors: Nadir Ayrilmis

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Wood and wood-based panels are one of the oldest structural materials used in the construction industry due to their significant advantages such as good mechanical properties, low density, renewable material, low-cost, recycling, etc. However, they burn when exposed to a flame source or high temperatures. This is very important when the wood products are used as structural or hemi-structural materials in the construction industry, furniture industry, so on. For this reason, the fire resistance is demanded property for wood products. They can be impregnated with fire retardants to improve their fire resistance. The most used fire retardants, fire-retardant mechanism, and fire-testing systems, and national and international fire-durability classifications and standard requirements for fire-durability of wood and wood-based panels were given in this study.

Keywords: fire resistance, wood-based panels, cone calorimeter, wood

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Authors: Harshan Baskar, Anirudh S. Chakravarthy, Prateek Garg, Divyam Goel, Abhijith S. Raj, Kshitij Kumar, Lakshya, Ravichandra Parvatham, V. Sushant, Bijay Kumar Rout

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In this paper, we introduce a new computer vision task called nighttime dehaze-enhancement. This task aims to jointly perform dehazing and lightness enhancement. Our task fundamentally differs from nighttime dehazing – our goal is to jointly dehaze and enhance scenes, while nighttime dehazing aims to dehaze scenes under a nighttime setting. In order to facilitate further research on this task, we release a new benchmark dataset called Reside-β Night dataset, consisting of 4122 nighttime hazed images from 2061 scenes and 2061 ground truth images. Moreover, we also propose a new network called NDENet (Nighttime Dehaze-Enhancement Network), which jointly performs dehazing and low-light enhancement in an end-to-end manner. We evaluate our method on the proposed benchmark and achieve SSIM of 0.8962 and PSNR of 26.25. We also compare our network with other baseline networks on our benchmark to demonstrate the effectiveness of our approach. We believe that nighttime dehaze-enhancement is an essential task, particularly for autonomous navigation applications, and we hope that our work will open up new frontiers in research. Our dataset and code will be made publicly available upon acceptance of our paper.

Keywords: dehazing, image enhancement, nighttime, computer vision

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Authors: Wolfgang Haupt

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When considering the future city one repeatedly comes across two sometimes sparsely differentiated terms: Sustainable and smart. ‘A European Strategy for Smart, Sustainable, and Inclusive Growth’, this is how the European Commission named its current growth strategy. Thus, Europe should become smarter and more sustainable. Both, the smart and the sustainable city represent a positive vision of urban development as well as a subject area for contemporary and future urban policies. However, more clarity on what is actually behind these terminologies is required. The paper analyses how the terms are defined academically and how this academic understanding is represented in the funding mechanisms of European urban policies. The theoretical framework is mainly based on sources such as journal articles and policy reports. It became clear that despite some similarities, such as the broad field of work or the tendency to operationalize the terms by defining sub-categories, both ideas are distinctly different in terms of the development history, the main driving forces behind and the theoretical scope. Moreover, the significantly more comprehensively defined term sustainability has found its way into the centre of European regional funding policies. On the contrary, the smart city vision still lacks terminological and content-related clarity and as a consequence, the corresponding European funding landscape is more small-scaled and less customized.

Keywords: European spatial policy, European union, smart city, urban sustainable development

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Authors: M. Nghizaderokni, A. Janalizadechobbasty, M. Azizi, M. Naghizaderokni

Abstract:

The liquefaction resistance of soils can be evaluated using laboratory tests such as cyclic simple shear, cyclic triaxial, cyclic tensional shear, and field methods such as Standard Penetration Test (SPT), Cone Penetration Test (CPT), and Shear Wave Velocity (Vs). This paper outlines a great correlation between shear wave velocity and standard penetration resistance of granular soils was obtained. Using Seeds standard penetration test (SPT) based soil liquefaction charts, new charts of soil liquefaction evaluation based on shear wave velocity data were developed for various magnitude earthquakes.

Keywords: soil, liquefaction, shear wave velocity, standard penetration resistance

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Authors: Tsega Asresa, Getahun Tigistu, Melaku Bayih

Abstract:

Computer vision is a subfield of artificial intelligence that allows computers and systems to extract meaning from digital images and video. It is used in a wide range of fields of study, including self-driving cars, video surveillance, medical diagnosis, manufacturing, law, agriculture, quality control, health care, facial recognition, and military applications. Aromatic medicinal plants are botanical raw materials used in cosmetics, medicines, health foods, essential oils, decoration, cleaning, and other natural health products for therapeutic and Aromatic culinary purposes. These plants and their products not only serve as a valuable source of income for farmers and entrepreneurs but also going to export for valuable foreign currency exchange. In Ethiopia, there is a lack of technologies for the classification and identification of Aromatic medicinal plant parts and disease type cured by aromatic medicinal plants. Farmers, industry personnel, academicians, and pharmacists find it difficult to identify plant parts and disease types cured by plants before ingredient extraction in the laboratory. Manual plant identification is a time-consuming, labor-intensive, and lengthy process. To alleviate these challenges, few studies have been conducted in the area to address these issues. One way to overcome these problems is to develop a deep learning model for efficient identification of Aromatic medicinal plant parts with their corresponding disease type. The objective of the proposed study is to identify the aromatic medicinal plant parts and their disease type classification using computer vision technology. Therefore, this research initiated a model for the classification of aromatic medicinal plant parts and their disease type by exploring computer vision technology. Morphological characteristics are still the most important tools for the identification of plants. Leaves are the most widely used parts of plants besides roots, flowers, fruits, and latex. For this study, the researcher used RGB leaf images with a size of 128x128 x3. In this study, the researchers trained five cutting-edge models: convolutional neural network, Inception V3, Residual Neural Network, Mobile Network, and Visual Geometry Group. Those models were chosen after a comprehensive review of the best-performing models. The 80/20 percentage split is used to evaluate the model, and classification metrics are used to compare models. The pre-trained Inception V3 model outperforms well, with training and validation accuracy of 99.8% and 98.7%, respectively.

Keywords: aromatic medicinal plant, computer vision, convolutional neural network, deep learning, plant classification, residual neural network

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Authors: Ritesh Verma, Manisha Rathi, Chand Singh Dhull, Sumit Sachdeva, Jitender Phogat

Abstract:

A Rathke's cleft cyst is a benign growth found on the pituitary gland in the brain, specifically a fluid-filled cyst in the posterior portion of the anterior pituitary gland. It occurs when the Rathke's pouch does not develop properly and ranges in size from 2 to 40mm in diameter. A 38-year-old male presented to the outpatient department with loss of vision in the inferior quadrant of the left eye since 15 days. Visual acuity was 6/6 in the right eye and 6/9 in the left eye. Visual field analysis by HFA-24-2 revealed an inferior field defect extending to the supero-temporal quadrant in the left eye. MRI brain and orbit was advised to the patient and it revealed a well defined cystic pituitary adenoma indenting left optic nerve near optic chiasm consistent with the diagnosis of Rathke’s cleft cyst (RCC). The patient was referred to neurosurgery department for further management. Symptoms vary greatly between individuals having RCCs. RCCs can be non-functioning, functioning, or both. Besides headaches, neurocognitive deficits are almost always present but have a high rate of immediate reversal if the cyst is properly treated or drained.

Keywords: pituitary tumors, rathke’s cleft cyst, visual field defects, vision loss

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Authors: Cheng Fang, Lingwei Quan, Cunyue Lu

Abstract:

Human pose estimation and tracking are to accurately identify and locate the positions of human joints in the video. It is a computer vision task which is of great significance for human motion recognition, behavior understanding and scene analysis. There has been remarkable progress on human pose estimation in recent years. However, more researches are needed for human pose tracking especially for online tracking. In this paper, a framework, called PoseSRPN, is proposed for online single-person pose estimation and tracking. We use Siamese network attaching a pose estimation branch to incorporate Single-person Pose Tracking (SPT) and Visual Object Tracking (VOT) into one framework. The pose estimation branch has a simple network structure that replaces the complex upsampling and convolution network structure with deconvolution. By augmenting the loss of fully convolutional Siamese network with the pose estimation task, pose estimation and tracking can be trained in one stage. Once trained, PoseSRPN only relies on a single bounding box initialization and producing human joints location. The experimental results show that while maintaining the good accuracy of pose estimation on COCO and PoseTrack datasets, the proposed method achieves a speed of 59 frame/s, which is superior to other pose tracking frameworks.

Keywords: computer vision, pose estimation, pose tracking, Siamese network

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Authors: Emily S. M. Chow

Abstract:

The word ‘utopia’ was coined by Thomas More in Utopia (1516). Its Greek roots ‘ou’ means ‘not’ and ‘topos’ means ‘place.’ In other words, it literally refers to ‘no-place.’ However, the possibility of having an alternative and better future society has always been appealing. In fact, at the core of every utopianism is the search for a future alternative state with the anticipation of a better life. Nonetheless, the practicalities of such ideas have never ceased to be questioned. At times, building a utopia presents itself as a divisive act. In addition to the violence that must be employed to sweep away the old regime in order to make space for the new, all utopias carry within them the potential for bringing catastrophic consequences to human life. After all, every utopia seeks to remodel the individual in a very particular way for the benefit of the masses. In this sense, utopian thinking has the potential both to create and destroy the future. While writing during a traumatic transitional period in Zimbabwe’s history, Dambudzo Marechera witnessed an age of upheavals in which different parties battled for power over Zimbabwe. Being aware of the fact that all institutionalized narratives, be they originated from the governance of the UK, Ian Smith’s white minority regime or Zimbabwe’s revolutionary parties, revealed themselves to be nothing more than fiction, Marechera realized the impossibility of determining reality absolutely. As such, this thesis concerns the writing of the Zimbabwean maverick, Dambudzo Marechera. It argues that Marechera writes a unique vision of utopia. In short, for Marechera utopia is not a static entity but a moment of perpetual change. He rethinks utopia in the sense that he phrases it as an event that ceaselessly contests institutionalized and naturalized narratives of a post-colonial self and its relationship to society. Marechera writes towards a vision of an alternative future of the country. Yet, it is a vision that does not constitute a fully rounded sense of utopia. Being cautious about the world and the operation of power upon the people, rather than imposing his own utopian ideals, Marechera chooses to instead peeling away the narrative constitution of the self in relation to society in order to turn towards a truly radical utopian thinking that empowers the individual.

Keywords: African literature, Marechera, post-colonial literature, utopian studies

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