Search results for: computer architecture

Authors: Christena Stephen, Biju Kunnumpurath

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This study intends to comprehend the effect of the Tilt Brush (TB) Virtual Reality 3D Painting application on creativity among final year architecture students. The research was done over the course of 30 hours and evaluated the performance of a group of 20 university students. Using a Structured Observation Form (SOF), the researcher assessed the research's progress. Four recently graduated artists, educators, and researchers used a Rubric to assess student designs. During the training, the study group was instructed in the fundamentals of virtual Reality, design principles, and TB. The design process, which began with the construction of a 3D design, progressed with the addition of texture, color, and script to items and culminated in the creation of a finished project. The group in the design process is rated as "Good" by the researcher based on feedback from SOF. The creativity evaluation rubric used by the experts rates their work as "Accomplished." According to the researcher's assessment, the group received a "Good" rating. Based on these findings, it can be said that including virtual reality 3D painting in the curriculum for art and design classes will help students improve their imagination and creativity as well as their 21st-century skills in education.

Keywords: creativity, virtual reality, 3D painting, tilt brush, education

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Authors: Marasovic Branka, Aljinovic Zdravka, Poklepovic Tea

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Numerical methods like binomial and trinomial trees and finite difference methods can be used to price a wide range of options contracts for which there are no known analytical solutions. American options are the most famous of that kind of options. Besides numerical methods, American options can be valued with the approximation formulas, like Bjerksund-Stensland formulas from 1993 and 2002. When the value of American option is approximated by Bjerksund-Stensland formulas, the computer time spent to carry out that calculation is very short. The computer time spent using numerical methods can vary from less than one second to several minutes or even hours. However to be able to conduct a comparative analysis of numerical methods and Bjerksund-Stensland formulas, we will limit computer calculation time of numerical method to less than one second. Therefore, we ask the question: Which method will be most accurate at nearly the same computer calculation time?

Keywords: Bjerksund and Stensland approximations, computational analysis, finance, options pricing, numerical methods

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Authors: Amit Kohli, Pooja Lekhi, Gihan Adel Amin Hafez

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Blockchain is a distributor ledger technology trend that extended to different fields and proved a remarkable success. Blockchain technology is a vital proliferation technique that recuperates the food supply chain traceability process. While tracing is the core of the food supply chain; still, a complex system mitigates the exceptional risk of food contamination, foodborne, food waste, and food fraud. In addition, the upsurge of food supply chain data variance and variety in the traceability system requires complete transparency, a secure, steadfast, sustainable, and efficient approach to face the food supply chain challenges. On the other hand, blockchain technical aspects merged with a detailed implementation plan, the advantages and challenges in food traceability have not been much elucidated for small and medium enterprises (SMEs.) This paper demonstrated the advantages and challenges of applying blockchain in SMEs combined with the success stories of firms implementing blockchain to cover the gap. Moreover, blockchain architecture in SMEs and how technology, organization, and environment frameworks can guarantee the success of blockchain implementation have been revealed.

Keywords: blockchain technology, small and medium enterprises, food traceability, blockchain architecture

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Authors: Parvaneh Ziviyar, Simin Armaghan

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Traditional house in Rasht contains monuments and heritage of ancestors who once lived in these houses. These houses represent the customs, culture and lifestyle of the people of Rasht and bridge the gap between modern people and their past that is being forgotten. Maintenance of the buildings and architectural heritage together with their unique architecture and climatic related construction has an important role in tourism attraction and sustainable development. The purpose of this study was to develop a new definition of vacation shacks that is different with the definition of Cultural Heritage Organization. The place to stay and visit that is rebuilt or renovated based on traditional architectural style of Rasht and yet provides modern amenities so that it would not undermine indigenous traditional sense of the house. Data collection for this study is based on review of literature and field study. Results and the statistics of this study will prove that the research hypothesis is supported and there is a correlation between traditional houses of Rasht, as tourism–accommodation place and tourist attraction. It also indicates the capability and potential of these ancient monuments in the introduction of the culture of this land, and calling people and many tourists come to visit and stay in such places.

Keywords: architecture, traditional houses, vacation shacks, tourism

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Authors: H. Z. Ulukan, E. Demircioğlu, M. Erol Genevois

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The scope of this paper and the aim of proposed model were to apply monetary Input –Output (I-O) analysis to point out the importance of reusing know-how and other requirements in order to reduce the production costs in a manufacturing process for a laptop computer. I-O approach using the monetary input-output model is employed to demonstrate the impacts of different factors in a manufacturing process. A sensitivity analysis showing the correlation between these different factors is also presented. It is expected that the recommended model would have an advantageous effect in the cost minimization process.

Keywords: input-output analysis, monetary input-output model, manufacturing process, laptop computer

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Authors: Qingqing Gai, Junxing Shen, Yu Luo

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Due to the hardware architecture characteristics, some operators tend to acquire better performance if the input/output tensor dimensions are aligned to a certain minimum granularity, such as convolution and deconvolution commonly used in deep learning. Furthermore, if the requirements are not met, the general strategy is to pad with 0 to satisfy the requirements, potentially leading to the under-utilization of the hardware resources. Therefore, for the convolution and deconvolution whose input and output channels do not meet the minimum granularity alignment, we propose to transfer the W-dimensional data to the C-dimension for computation (W2C) to enable the C-dimension to meet the hardware requirements. This scheme also reduces the number of computations in the W-dimension. Although this scheme substantially increases computation, the operator’s speed can improve significantly. It achieves remarkable speedups on multiple hardware accelerators, including Nvidia Tensor cores, Qualcomm digital signal processors (DSPs), and Huawei neural processing units (NPUs). All you need to do is modify the network structure and rearrange the operator weights offline without retraining. At the same time, for some operators, such as the Reducemax, we observe that transferring the Cdimensional data to the W-dimension(C2W) and replacing the Reducemax with the Maxpool can accomplish acceleration under certain circumstances.

Keywords: convolution, deconvolution, W2C, C2W, alignment, hardware accelerator

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Authors: Y. Lacroix, J.-F. Malbranque

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We present a chronological evolution for naval telecommunication networks. We distinguish periods: with or without multiplexers, with switch systems, with federative systems, with medium switching, and with medium switching with wireless networks. This highlights the introduction of new layers and technology in the architecture. These architectures are presented using layer models of transmission, in a unified way, which enables us to integrate pre-existing models. A ship of a naval fleet has internal communications (i.e. applications' networks of the edge) and external communications (i.e. the use of the means of transmission between edges). We propose architectures, deduced from the layer model, which are the point of convergence between the networks on board and the HF, UHF radio, and satellite resources. This modelling allows to consider end-to-end naval communications, and in a more global way, that is from the user on board towards the user on shore, including transmission and networks on the shore side. The new architectures need take care of quality of services for end-to-end communications, the more remote control develops a lot and will do so in the future. Naval telecommunications will be more and more complex and will use more and more advanced technologies, it will thus be necessary to establish clear global communication schemes to grant consistency of the architectures. Our latest model has been implemented in a military naval situation, and serves as the basic architecture for the RIFAN2 network.

Keywords: equilibrium beach profile, eastern tombolo of Giens, potential function, erosion

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Authors: Anshika Kankane, Dongshik Kang

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Marine debris has a significant influence on coastal environments, damaging biodiversity, and causing loss and damage to marine and ocean sector. A functional cost-effective and automatic approach has been used to look up at this problem. Computer vision combined with a deep learning-based model is being proposed to identify and categorize marine debris of seven kinds on different beach locations of Japan. This research compares state-of-the-art deep learning models with a suggested model architecture that is utilized as a feature extractor for debris categorization. The model is being proposed to detect seven categories of litter using a manually constructed debris dataset, with the help of Mask R-CNN for instance segmentation and a shape matching network called HOGShape, which can then be cleaned on time by clean-up organizations using warning notifications of the system. The manually constructed dataset for this system is created by annotating the images taken by fixed KaKaXi camera using CVAT annotation tool with seven kinds of category labels. A pre-trained HOG feature extractor on LIBSVM is being used along with multiple templates matching on HOG maps of images and HOG maps of templates to improve the predicted masked images obtained via Mask R-CNN training. This system intends to timely alert the cleanup organizations with the warning notifications using live recorded beach debris data. The suggested network results in the improvement of misclassified debris masks of debris objects with different illuminations, shapes, viewpoints and litter with occlusions which have vague visibility.

Keywords: computer vision, debris, deep learning, fixed live camera images, histogram of gradients feature extractor, instance segmentation, manually annotated dataset, multiple template matching

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Authors: Ayşe Nihan Avcı, İpek Memikoğlu

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Lighting systems in interior architecture need to be designed according to the function of the space, the type of task within the space, user comfort and needs. Desired and comfortable lighting levels increase task efficiency. When natural lighting is inadequate in a space, artificial lighting is additionally used to support the level of light. With the technological developments, the characteristics of light are being researched comprehensively and several business segments have focused on its qualitative and quantitative characteristics. These studies have increased awareness and usage of artificial lighting systems and researchers have investigated the effects of lighting on physical and psychological aspects of human in various ways. The aim of this study is to research the effects of illuminance levels of LED lighting on user visual comfort. Eighty participants from the Department of Interior Architecture of Çankaya University participated in three lighting scenarios consisting of 200 lux, 500 lux and 800 lux that are created with LED lighting. Each lighting scenario is evaluated according to six visual comfort criteria in which a reading task is performed. The results of the study indicated that LED lighting with three different illuminance levels affect visual comfort in different ways. The results are limited to the participants and questions that are attended and used in this study.

Keywords: illuminance levels, LED lighting, reading task, visual comfort criteria

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Authors: Mohammadjavad Mahdavinejad, Hadi Yazdi

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The greatest influence we have from the world is shaped through the visual form, thus light is an inseparable element in human life. The use of daylight in visual perception and environment readability is an important issue for users. With regard to the hazards of greenhouse gas emissions from fossil fuels, and in line with the attitudes on the reduction of energy consumption, the correct use of daylight results in lower levels of energy consumed by artificial lighting, heating and cooling systems. Windows are usually the starting points for analysis and simulations to achieve visual comfort and energy optimization; therefore, attention should be paid to the orientation of buildings to minimize electrical energy and maximize the use of daylight. In this paper, by using the Design Builder Software, the effect of the orientation of an 18m²(3m*6m) room with 3m height in city of Tehran has been investigated considering the design constraint limitations. In these simulations, the dimensions of the building have been changed with one degree and the window is located on the smaller face (3m*3m) of the building with 80% ratio. The results indicate that the orientation of building has a lot to do with energy efficiency to meet high-performance architecture and planning goals and objectives.

Keywords: daylight, window, orientation, energy consumption, design builder

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Authors: Marjan Ghazisaeedi, Azadeh Bashiri

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Background: Attention-Deficit/Hyperactivity Disorder(ADHD), is one of the most common psychiatric disorders in early childhood that in addition to its main symptoms provide significant deficits in the areas of educational, social and individual relationship. Considering the importance of rehabilitation in ADHD patients to control these problems, this study investigated the advantages of computer-based cognitive training in these patients. Methods: This review article has been conducted by searching articles since 2005 in scientific databases and e-Journals and by using keywords including computerized cognitive rehabilitation, computer-based training and ADHD. Results: Since drugs have short term effects and also they have many side effects in the rehabilitation of ADHD patients, using supplementary methods such as computer-based cognitive training is one of the best solutions. This approach has quick feedback and also has no side effects. So, it provides promising results in cognitive rehabilitation of ADHD especially on the working memory and attention. Conclusion: Considering different cognitive dysfunctions in ADHD patients, application of the computerized cognitive training has the potential to improve cognitive functions and consequently social, academic and behavioral performances in patients with this disorder.

Keywords: ADHD, computer-based cognitive training, cognitive functions, rehabilitation

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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: Aaron Yan

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Tennis elbow has been a leading injury and problem among amateur and even professional players. Many factors contribute to tennis elbow. In this research, we apply state of the art sensor-less computer vision and AI technology to study the biomechanics of a player’s tennis movements during training and competition as they relate to the causes of tennis elbow. We provide a framework for the analysis of key biomechanical parameters and their correlations with specific tennis stroke and movements that can lead to tennis elbow or elbow injury. We also devise a method for using AI to automatically detect player’s forms that can lead to tennis elbow development for on-court injury prevention.

Keywords: Tennis Elbow, Computer Vision, AI, 3DAT

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Authors: Sajeeha Ansar, Asad Ali Safi, Sheikh Ziauddin, Ahmad R. Shahid, Faraz Ahsan

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Gliomas are most challenging and aggressive type of tumors which appear in different sizes, locations, and scattered boundaries. CNN is most efficient deep learning approach with outstanding capability of solving image analysis problems. A fully automatic deep learning based 2D-CNN model for brain tumor segmentation is presented in this paper. We used small convolution filters (3 x 3) to make architecture deeper. We increased convolutional layers for efficient learning of complex features from large dataset. We achieved better results by pushing convolutional layers up to 16 layers for HGG model. We achieved reliable and accurate results through fine-tuning among dataset and hyper-parameters. Pre-processing of this model includes generation of brain pipeline, intensity normalization, bias correction and data augmentation. We used the BRATS-2015, and Dice Similarity Coefficient (DSC) is used as performance measure for the evaluation of the proposed method. Our method achieved DSC score of 0.81 for complete, 0.79 for core, 0.80 for enhanced tumor regions. However, these results are comparable with methods already implemented 2D CNN architecture.

Keywords: brain tumor segmentation, convolutional neural networks, deep learning, HGG

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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: Mariana Sabino, Janes Cleiton de Oliveira, Carlos Luna de Melo

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This paper presents a discussion about the importance and influence of the environment in the patient’s recovery process. Some users (employees and patients) were submitted to a questionnaire that helps to diagnoses the major problems of the hospital, specially related to comfort (aesthetic, thermal, acoustic, light, ergonomic), well-being, how does the flow of patients and employees works in the hospital and wayfinding as well. After a short literature review presenting the topic, the hospital will be characterized, showing photos, the projects available and describing the hospital as well (how many rooms, functions of each one, receptions, waiting rooms, between other things.), than the questionnaire will be applied to patients and to the employees. Lastly the results of the answers given will be analyzed in graphics, and it will help to identify which are the major improvements needed immediately. This paper has the intention to propose a methodology to diagnose architecture problems in a cancer hospital in Brasilia, Brazil, besides to open a space to hear the people that use the building to tell about their discomforts and perceptions of the environment, it also will give an opportunity to apply the possible improvements. It is important to tell that it will be considered if the hospital has a healing environment, and it will also be considered the ergonomic issues about comfort and the way the system of this particular hospital works in general.

Keywords: cancer hospital, comfort, diagnose, healing environment

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Authors: Zhepeng Rui, Zhenyu Gu, Caitilin de Bérigny

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In the current study, we designed an interactive meditation and brain painting application to cultivate users’ creativity, promote meditation, reduce stress, and improve cognition while attempting to learn a foreign language. User tests and data analyses were conducted on 42 male and 42 female participants to better understand sex-associated psychological and aesthetic differences. Our method utilized brain-computer interfaces to import meditation and attention data to create artwork in meditation-based applications. Female participants showed statistically significantly different language learning outcomes following three meditation paradigms. The art style of brain painting helped females with language memory. Our results suggest that the most ideal methods for promoting memory attention were meditation methods and brain painting exercises contributing to language learning, memory concentration promotion, and foreign word memorization. We conclude that a short period of meditation practice can help in learning a foreign language. These findings provide new insights into meditation, creative language education, brain-computer interface, and human-computer interactions.

Keywords: brain-computer interface, creative thinking, meditation, mental health

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Authors: Nauman Idrees

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Modeling and simulation during the conceptual design phase are the most effective means of system testing resulting in time and cost savings as compared to the testing of hardware prototypes, which are mostly not available during the conceptual design phase. This paper uses the model-based design (MBD) method in designing the fly-by-wire flight controls system of a fighter aircraft using Simulink. The process begins with system definition and layout where modeling requirements and system components were identified, followed by hierarchical system layout to identify the sequence of operation and interfaces of system with external environment as well as the internal interface between the components. In the second step, each component within the system architecture was modeled along with its physical and functional behavior. Finally, all modeled components were combined to form the fly-by-wire flight controls system of a fighter aircraft as per system architecture developed. The system model developed using this method can be simulated using any simulation software to ensure that desired requirements are met even without the development of a physical prototype resulting in time and cost savings.

Keywords: fly-by-wire, flight controls system, model based design, Simulink

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Authors: Marwa Qaraqe, Muhammad Ismail, Erchin Serpedin

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This paper presents a novel architecture for a patient-specific epileptic seizure onset detector using scalp electroencephalography (EEG). The proposed architecture is based on the decision fusion calculated from energy and neural synchronization related features. Specifically, one level of the detector calculates the condition number (CN) of an EEG matrix to evaluate the amount of neural synchronization present within the EEG channels. On a parallel level, the detector evaluates the energy contained in four EEG frequency subbands. The information is then fed into two independent (parallel) classification units based on support vector machines to determine the onset of a seizure event. The decisions from the two classifiers are then combined together according to two fusion techniques to determine a global decision. Experimental results demonstrate that the detector based on the AND fusion technique outperforms existing detectors with a sensitivity of 100%, detection latency of 3 seconds, while it achieves a 2:76 false alarm rate per hour. The OR fusion technique achieves a sensitivity of 100%, and significantly improves delay latency (0:17 seconds), yet it achieves 12 false alarms per hour.

Keywords: epilepsy, EEG, seizure onset, electroencephalography, neuron, detection

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Authors: Ajibade Adeyemo

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The paper examined local terms used for spaces, forms and building practices in southwest Nigeria as cultural semiotics. Housing has more cultural meaning than mere shelter as shown in building terms such as ‘roof over my head’. The study is significant in the study area because its people were traditionally orally centered until ‘culture contact’ led to graphical presentation and appreciation in the form of drawings which is a modern language of architecture. This semiotic study will facilitate the understanding of the wholesomeness of traditional building practices and thoughts. This is in the culture of the traditional multi-sensory appreciation of architecture, urban design and the arts. It will analyze traditional aphoristic words and terms which are like proverbs which are significant in language because of their metaphorical essence. Many of such terms in the dominant Yoruba language of the study area are oftentimes phenomenal reducing universal terms like the earth and heaven to the simple module of housing. These words could be worth investigating because they are symbolic serve as codes which are cultural tool of regional ethnic significance. Sassure’s and Pierce’s concepts of Semiotics in line with Eco’s concept of semiotics of metaphor shall be deployed.

Keywords: traditional terms, spaces, forms, artifacts, cultural semiotics, southwest

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Authors: Zakeia Benmoussa

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Pressure on freshwater systems comes from removing too much water to grow crops; contamination from economic activities, land use practices, and human waste. The paper will be focusing on how water management can influence the design, implementation, and impacts of the ecological principles of biomimicry as sustainable methods in recycling wastewater. At Texas State, United States of America, in particular the lower area of the Trinity River refuge, there is a true example of the diversity to be found in that area, whether when exploring the lands or the waterways. However, as the Trinity River supplies water to the state’s residents, the lower part of the river at Liberty County presents several problem of wastewater discharge in the river. Therefore, conservation efforts are particularly important in the Trinity River basin. Clearly, alternative ways must be considered in order to conserve water to meet future demands. As a result, there should be another system provided rather than the conventional water treatment. Mimicking ecosystem's technologies out of context is not enough, but if we incorporate plants into building architecture, in addition to their beauty, they can filter waste, absorb excess water, and purify air. By providing an architectural proposal center, a living system can be explored through several methods that influence natural resources on the micro-scale in order to impact sustainability on the macro-scale. The center consists of an ecological program of Plant and Water Biomimicry study which becomes a living organism that purifies the river water in a natural way through architecture. Consequently, a rich beautiful nature could be used as an educational destination, observation and adventure, as well as providing unpolluted fresh water to the major cities of Texas. As a result, these facts raise a couple of questions: Why is conservation so rarely practiced by those who must extract a living from the land? Are we sufficiently enlightened to realize that we must now challenge that dogma? Do architects respond to the environment and reflect on it in the correct way through their public projects? The method adopted in this paper consists of general research into careful study of the system of the living machine, in how to integrate it at architectural level, and finally, the consolidation of the all the conclusions formed into design proposal. To summarise, this paper attempts to provide a sustainable alternative perspective in bridging physical and mental interaction with biodiversity to enhance nature by using architecture.

Keywords: Biodiversity, Design with Nature, Sustainable architecture, Waste water treatment.

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Authors: Karen C. Olufokunbi, Odetunji A. Odejobi

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The discourse of this paper is the examination of the generation, accumulation and growth of e-waste in a developing country. Images and other data about computer e-waste were collected using a digital camera, 290 copies of questionnaire and three structured interviews using Obafemi Awolowo University (OAU), Ile-Ife, Nigeria environment as a case study. The numerical data were analysed using R data analysis and process tool. Automata-based techniques and Petri net modeling tool were used to design and simulate a computational model for the recovery of saleable materials from e-waste. The R analysis showed that at a 95 percent confidence level, the computer equipment that will be disposed by 2020 will be 417 units. Compared to the 800 units in circulation in 2014, 50 percent of personal computer components will become e-waste. This indicates that personal computer components were in high demand due to their low costs and will be disposed more rapidly when replaced by new computer equipment Also, 57 percent of the respondents discarded their computer e-waste by throwing it into the garbage bin or by dumping it. The simulated model using Coloured Petri net modelling tool for the process showed that the e-waste dynamics is a forward sequential process in the form of a pipeline meaning that an e-waste recovery of saleable materials process occurs in identifiable discrete stages indicating that e-waste will continue to accumulate and grow in volume with time.

Keywords: Coloured Petri net, computational modelling, electronic waste, electronic waste process dynamics

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Authors: Hervice Roméo Fogno Fotsoa, Germaine Djuidje Kenmoe, Claude Vidal Aloyem Kazé

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One of the main applications of machine learning is the prediction of time series. But a more accurate prediction requires a more optimal model of machine learning. Several optimization techniques have been developed, but without considering the input variables disposition of the system. Thus, this work aims to present a new machine learning architecture optimization technique based on their optimal input variables disposition. The validations are done on the prediction of wind time series, using data collected in Cameroon. The number of possible dispositions with four input variables is determined, i.e., twenty-four. Each of the dispositions is used to perform the prediction, with the main criteria being the training and prediction performances. The results obtained from a static architecture and a dynamic architecture of neural networks have shown that these performances are a function of the input variable's disposition, and this is in a different way from the architectures. This analysis revealed that it is necessary to take into account the input variable's disposition for the development of a more optimal neural network model. Thus, a new neural network training algorithm is proposed by introducing the search for the optimal input variables disposition in the traditional back-propagation algorithm. The results of the application of this new optimization approach on the two single neural network architectures are compared with the previously obtained results step by step. Moreover, this proposed approach is validated in a collaborative optimization method with a single objective optimization technique, i.e., genetic algorithm back-propagation neural networks. From these comparisons, it is concluded that each proposed model outperforms its traditional model in terms of training and prediction performance of time series. Thus the proposed optimization approach can be useful in improving the accuracy of time series forecasts. This proves that the proposed optimization approach can be useful in improving the accuracy of time series prediction based on machine learning.

Keywords: input variable disposition, machine learning, optimization, performance, time series prediction

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Authors: Guantao Bai

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Public space is an indispensable and important component of the urban built environment. How to more accurately evaluate the usage characteristics of public space can help improve its spatial quality. Compared to traditional survey methods, computer vision technology based on deep learning has advantages such as dynamic observation and low cost. This study takes the public space of Shaping Park as an example and, based on deep learning computer vision technology, processes and analyzes the image data of the public space to obtain the spatial usage characteristics and spatiotemporal characteristics of the public space. Research has found that the spontaneous activity time in public spaces is relatively random with a relatively short average activity time, while social activities have a relatively stable activity time with a longer average activity time. Computer vision technology based on deep learning can effectively describe the spatial usage characteristics of the research area, making up for the shortcomings of traditional research methods and providing relevant support for creating a good public space.

Keywords: computer vision, deep learning, public spaces, using features

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Authors: Aruna Ramani Grover

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In 1857 the British army in Bengal rose in mutiny. The outcome of a yearlong stifle was the abolition of the East India Company and establishment of Crown rule in 1958. Despite being a political democracy back home, with the declaration of Queen Victoria as Empress of India, the British established themselves as imperialistic successors to the Mughals in India. With the institution of the Crown role in the sub-continent, there was a serious endeavour for systematic governance. This led to infrastructure development and creation of institutions as well. The outcome was many public buildings like courts, railway- stations and headquarters, senates, post offices, banks, libraries, memorial halls, museums, memorials, theatres, government colleges, residential-schools and clock towers. These were built in the old and emerging urban settlements of the sub-continents. In the realm of architecture, like all political masters of the past, the British architects too encountered the living tradition of the sub-continent. A bewildering plethora of buildings in various climatic zones, using local materials and crafted with tools and techniques of the region by local craftsmen had to be understood and assimilated. The19th century British architects who built in India, designed institutional buildings which were functional and responded to the need of the user. In visual terms however, it was a completely different story. Using the manifest elements of the complex and layered indigenous tradition, they fashioned buildings to create an architecture of reconciliation. Treating the traditional architecture as a pattern book, finished buildings was served up to the local population coloured by their understanding of tradition. This paper will discuss a series of building some of which are the Senate House and Law courts at Madras, the Municipal Building and Gateway of India in Bombay, the Muir college in Allahabad, Mayo college Ajmer, the Mubarak Mahal in Jaipur to demonstrate how a visual language of reconciliation was created.

Keywords: infrastructure, British architects, tradition, pattern book, reconciliation

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Authors: Franziska Moser, Martin Trautz, Anna-Lena Beger, Manuel Löwer, Jörg Feldhusen, Jürgen Prell, Alexandra Wormit, Björn Usadel, Christoph Kämpfer, Thomas-Benjamin Seiler, Henner Hollert

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The demand for bio-based materials and components in architecture has increased in recent years due to society’s heightened environmental awareness. Nowadays, most components are being developed via a substitution approach, which aims at replacing conventional components with natural alternatives who are then being processed, shaped and manufactured to fit the desired application. This contribution introduces a novel approach to the development of bio-based products that decreases resource consumption and increases recyclability. In this approach, natural organisms like plants or trees are not being used in a processed form, but grow into a near net shape before then being harvested and utilized as building components. By minimizing the conventional production steps, the amount of resources used in manufacturing decreases whereas the recyclability increases. This paper presents the approach of technical product harvesting, explains the theoretical basis as well as the matching process of product requirements and biological properties, and shows first results of the growth manipulation studies.

Keywords: design with nature, eco manufacturing, sustainable construction materials, technical product harvesting

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Authors: Angeliki Chronopoulou, Eleni Alexandrou

Abstract:

This paper thoroughly investigates residential buildings of the period 1920 – 1960 in Athens and evaluates their bioclimatic response and energy performance. A methodology adapted to the specific context of the city is proposed and applied in order to assess and extract results related to the climate analysis of the city of Athens, the general/architectural design and construction characteristics of the apartment buildings constructed during the period 1920 – 1960, the bioclimatic strategies applied on them, and the achieved thermal comfort based on questionnaires answered by their users. The results of the current study indicate that the residential architecture of that period in the city of Athens is adapted to an extend to the local climate with various climate responsive strategies. As an outcome of the analysis, the most frequently applied depending on the period of construction are presented. For this reason, the examined period is divided into 3 sub – periods: 1st period 1920s – 1930s (late neoclassicism & eclecticism), 2nd period 1930s – 1940s (modernism), 3rd period 1940s – 1960s (postwar modernism).

Keywords: Athens, climatic design strategies, residential buildings, middle war and post war architecture, thermal comfort

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Authors: Najm Alotaibi

Abstract:

Human motion capture has become one of the major area of interest in the field of computer vision. Some of the major application areas that have been rapidly evolving include the advanced human interfaces, virtual reality and security/surveillance systems. This study provides a brief overview of the techniques and applications used for the markerless human motion capture, which deals with analyzing the human motion in the form of mathematical formulations. The major contribution of this research is that it classifies the computer vision based techniques of human motion capture based on the taxonomy, and then breaks its down into four systematically different categories of tracking, initialization, pose estimation and recognition. The detailed descriptions and the relationships descriptions are given for the techniques of tracking and pose estimation. The subcategories of each process are further described. Various hypotheses have been used by the researchers in this domain are surveyed and the evolution of these techniques have been explained. It has been concluded in the survey that most researchers have focused on using the mathematical body models for the markerless motion capture.

Keywords: human motion capture, computer vision, vision-based, tracking

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Authors: Jie Zhao, Meng Su

Abstract:

Image recognition, as one of the most critical technologies in computer vision, works to help machine-like robotics understand a scene, that is, if deployed appropriately, will trigger the revolution in remote sensing and industry automation. With the developments of AI technologies, there are many prevailing and sophisticated neural networks as technologies developed for image recognition. However, computer vision platforms as hardware, supporting neural networks for image recognition, as crucial as the neural network technologies, need to be more congruently addressed as the research subjects. In contrast, different computer vision platforms are deterministic to leverage the performance of different neural networks for recognition. In this paper, three different computer vision platforms – Jetson Nano(with 4GB), a standalone laptop(with RTX 3000s, using CUDA), and Google Colab (web-based, using GPU) are explored and four prominent neural network architectures (including AlexNet, VGG(16/19), GoogleNet, and ResNet(18/34/50)), are investigated. In the context of pairwise usage between different computer vision platforms and distinctive neural networks, with the merits of recognition accuracy and time efficiency, the performances are evaluated. In the case study using public imageNets, our findings provide a nuanced perspective on optimizing image recognition tasks across Edge-AI platforms, offering guidance on selecting appropriate neural network structures to maximize performance under hardware constraints.

Keywords: alexNet, VGG, googleNet, resNet, Jetson nano, CUDA, COCO-NET, cifar10, imageNet large scale visual recognition challenge (ILSVRC), google colab

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Authors: Christos Konstantopoulos, Thomas Ussmueller

Abstract:

Digital low drop-out regulators, in contrast to analog counterparts, provide an architecture of sub-1 V regulation with low power consumption, high power efficiency, and system integration. Towards an optimized integration in the ultra-low-power system-on-chip Internet of Things architecture that is operated through a radio frequency energy harvesting scheme, the D-LDO regulator should constitute the main regulator that operates the master-clock and rest loads of the SoC. In this context, we present a D-LDO with linear search coarse regulation and asynchronous fine regulation, which incorporates an in-regulator clock generation unit that provides an autonomous, self-start-up, and power-efficient D-LDO design. In contrast to contemporary D-LDO designs that employ ring-oscillator architecture which start-up time is dependent on the frequency, this work presents a fast start-up burst oscillator based on a high-gain stage with wake-up time independent of coarse regulation frequency. The design is implemented in a 55-nm Global Foundries CMOS process. With the purpose to validate the self-start-up capability of the presented D-LDO in the presence of ultra-low input power, an on-chip test-bench with an RF rectifier is implemented as well, which provides the RF to DC operation and feeds the D-LDO. Power efficiency and load regulation curves of the D-LDO are presented as extracted from the RF to regulated DC operation. The D-LDO regulator presents 83.6 % power efficiency during the RF to DC operation with a 3.65 uA load current and voltage regulator referred input power of -27 dBm. It succeeds 486 nA maximum quiescent current with CL 75 pF, the maximum current efficiency of 99.2%, and 1.16x power efficiency improvement compared to analog voltage regulator counterpart oriented to SoC IoT loads. Complementary, the transient performance of the D-LDO is evaluated under the transient droop test, and the achieved figure-of-merit is compared with state-of-art implementations.

Keywords: D-LDO, Internet of Things, RF energy harvesting, voltage regulators

Procedia PDF Downloads 127