| 51. |
- Adolfsson, Vilhelm, et al.
(författare)
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Localized Galerkin Estimates for Boundary Integral Equations on Lipschitz Domanis
- 1992
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Ingår i: SIAM Journal on Mathematical Analysis. - : SIAM. ; 5:23, s. 751-764
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Tidskriftsartikel (refereegranskat)abstract
- The Galerkin method is studied for solving the boundary integral equations associated with the Laplace operator on nonsmooth domains. Convergence is established with a condition on the meshsize, which involves the local curvature on certain approximating domains. Error estimates are also proved, and the results are generalized to systems of equations.
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| 52. |
- Advaita, Advaita, et al.
(författare)
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Outage Probability of MIMO Cognitive Cooperative Radio Networks with Multiple AF Relays Using Orthogonal Space-Time Block Codes
- 2017
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Ingår i: 2017 IEEE 13TH INTERNATIONAL CONFERENCE ON WIRELESS AND MOBILE COMPUTING, NETWORKING AND COMMUNICATIONS (WIMOB). - : IEEE. - 9781538638392 ; , s. 84-89
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Konferensbidrag (refereegranskat)abstract
- In this paper, we analyze the outage probability of multiple-input multiple-output cognitive cooperative radio networks (CCRNs) with multiple opportunistic amplify-and-forward relays. The CCRN applies underlay spectrum access accounting for the interference power constraint of a primary network and utilizes orthogonal space-time block coding to transmit multiple data streams across a number of antennas over several time slots. As such, the system exploits both time and space diversity to improve the transmission reliability over Nakagami.. fading. The CCRN applies opportunistic relaying in which the relay offering the highest signal-to-noise ratio at the receiver is selected to forward the transmit signal. Furthermore, selection combining is adopted at the secondary receiver to process the signal from the direct and relaying transmissions. To evaluate system performance, we derive an expression for the outage probability which is valid for an arbitrary number of antennas at the source, relays, and receiver of the CCRN. Selected numerical results are provided using Mathematica for analysis and Matlab for simulations, to reveal the effect of network parameters on the outage probability of the system.
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| 53. |
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| 54. |
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| 55. |
- Aeddula, Omsri, 1993-, et al.
(författare)
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A Solution with Bluetooth Low Energy Technology to Support Oral Healthcare Decisions for improving Oral Hygiene
- 2021
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Ingår i: ACM International Conference Proceeding Series. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450389846 ; , s. 134-139
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Konferensbidrag (refereegranskat)abstract
- The advent of powered toothbrushes and associated mobile health applications provides an opportunity to collect and monitor the data, however collecting reliable and standardized data from large populations has been associated with efforts from the participants and researchers. Finding a way to collect data autonomously and without the need for cooperation imparts the potential to build large knowledge banks. A solution with Bluetooth low energy technology is designed to pair a powered toothbrush with a single-core processor to collect raw data in a real-time scenario, eliminating the manual transfer of powered toothbrush data with mobile health applications. Associating powered toothbrush with a single-core processor is believed to provide reliable and comprehensible data of toothbrush use and propensities can be a guide to improve individual exhortation and general plans on oral hygiene quantifies that can prompt improved oral wellbeing. The method makes a case for an expanded chance to plan assistant capacities to protect or improve factors that influence oral wellbeing in individuals with mild cognitive impairment. The proposed framework assists with determining various parameters, which makes it adaptable and conceivable to execute in various oral care contexts
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| 56. |
- Aeddula, Omsri, 1993-, et al.
(författare)
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AI-Driven Comprehension of Autonomous Construction Equipment Behavior for Improved PSS Development
- 2024
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Ingår i: Proceedings of the 57th Annual Hawaii International Conference on System Sciences. - : University of Hawai'i at Manoa. - 9780998133171 ; , s. 1017-1026
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Konferensbidrag (refereegranskat)abstract
- This paper presents an approach that utilizes artificial intelligence techniques to identify autonomous machine behavior patterns. The context for investigation involves a fleet of prototype autonomous haulers as part of a Product Service System solution under development in the construction and mining industry. The approach involves using deep learning-based object detection and computer vision to understand how prototype machines operate in different situations. The trained model accurately predicts and tracks the loaded and unloaded machines and helps to identify the data patterns such as course deviations, machine failures, unexpected slowdowns, battery life, machine activity, number of cycles per charge, and speed. PSS solutions hinge on efficiently allocating resources to meet the required site-level output. Solution providers can make more informed decisions at the earlier stages of development by using the AI techniques outlined in the paper, considering asset management and reallocation of resources to account for unplanned stoppages or unexpected slowdowns. Understanding machine behavioral aspects in early-stage PSS development could enable more efficient and customized PSS solutions.
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| 57. |
- Aeddula, Omsri, 1993-, et al.
(författare)
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ARTIFICIAL NEURAL NETWORKS SUPPORTING CAUSE AND EFFECT STUDIES IN PRODUCT-SERVICE SYSTEM DEVELOPMENT
- 2021
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Ingår i: Design for Tomorrow—Volume 1. - Singapore : Springer. - 9789811600418 - 9789811600401
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Konferensbidrag (refereegranskat)abstract
- A data analysis method based on artificial neural networks aiming to support cause-and-effect analysis in design exploration studies is presented. The method clusters and aggregates the effects of multiple design variables based on the structural hierarchy of the evaluated system. The proposed method is exemplified in a case study showing that the predictive capability of the created, clustered, a dataset is comparable to the original, unmodified, one. The proposed method is evaluated using coefficient-of-determination, root mean square error, average relative error, and mean square error. Data analysis approach with artificial neural networks is believed to significantly improve the comprehensibility of the evaluated cause-and-effect relationships studying PSS concepts in a cross-functional team and thereby assisting the difficult and resource-demanding negotiations process at the conceptual stage of the design.
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| 58. |
- Aeddula, Omsri, 1993-
(författare)
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Data-Driven Decision Support Systems for Product Development - A Data Exploration Study Using Machine Learning
- 2021
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Modern product development is a complex chain of events and decisions. The ongoing digital transformation of society, increasing demands in innovative solutions puts pressure on organizations to maintain, or increase competitiveness. As a consequence, a major challenge in the product development is the search for information, analysis, and the build of knowledge. This is even more challenging when the design element comprises complex structural hierarchy and limited data generation capabilities. This challenge is even more pronounced in the conceptual stage of product development where information is scarce, vague, and potentially conflicting. The ability to conduct exploration of high-level useful information using a machine learning approach in the conceptual design stage would hence enhance be of importance to support the design decision-makers, where the decisions made at this stage impact the success of overall product development process.The thesis aims to investigate the conceptual stage of product development, proposing methods and tools in order to support the decision-making process by the building of data-driven decision support systems. The study highlights how the data can be utilized and visualized to extract useful information in design exploration studies at the conceptual stage of product development. The ability to build data-driven decision support systems in the early phases facilitates more informed decisions.The thesis presents initial descriptive study findings from the empirical studies, showing the capabilities of the machine learning approaches in extracting useful information, and building data-driven decision support systems. The thesis initially describes how the linear regression model and artificial neural networks extract useful information in design exploration, providing support for the decision-makers to understand the consequences of the design choices through cause-and-effect relationships on a detailed level. Furthermore, the presented approach also provides input to a novel visualization construct intended to enhance comprehensibility within cross-functional design teams. The thesis further studies how the data can be augmented and analyzed to extract the necessary information from an existing design element to support the decision-making process in an oral healthcare context.
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| 59. |
- Aeddula, Omsri, 1993-, et al.
(författare)
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Image-Based Localization System
- 2020
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Ingår i: Proceedings of the 8th ICIECE 2019. - Singapore : Springer. ; , s. 535-541
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Konferensbidrag (refereegranskat)abstract
- The position of a vehicle is essential for navigation of the vehicle along the desired path without a human interference. A good positioning system should have both good positioning accuracy and reliability. Global Positioning System (GPS) employed for navigation in a vehicle may lose significant power due to signal attenuation caused by construction buildings or other obstacles. In this paper, a novel real-time indoor positioning system using a static camera is presented. The proposed positioning system exploits gradient information evaluated on the camera video stream to recognize the contours of the vehicle. Subsequently, the mass center of the vehicle contour is used for simultaneous localization of the vehicle. This solution minimizes the design and computational complexity of the positioning system. The experimental evaluation of the proposed approach has demonstrated the positioned accuracy of 92.26%. © Springer Nature Singapore Pte Ltd. 2020.
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| 60. |
- Afzal, Wasif, et al.
(författare)
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A Comparative Evaluation of Using Genetic Programming for Predicting Fault Count Data
- 2008
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Ingår i: Proceedings - The 3rd International Conference on Software Engineering Advances, ICSEA 2008, Includes ENTISY 2008: International Workshop on Enterprise Information Systems. - : IEEE. - 9780769533728 ; , s. 407-414
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Konferensbidrag (refereegranskat)abstract
- There have been a number of software reliability growth models (SRGMs) proposed in literature. Due to several reasons, such as violation of models' assumptions and complexity of models, the practitioners face difficulties in knowing which models to apply in practice. This paper presents a comparative evaluation of traditional models and use of genetic programming (GP) for modeling software reliability growth based on weekly fault count data of three different industrial projects. The motivation of using a GP approach is its ability to evolve a model based entirely on prior data without the need of making underlying assumptions. The results show the strengths of using GP for predicting fault count data.
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