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Articles 1 - 5 of 5
Full-Text Articles in Engineering
Smart Parking Systems: Reviewing The Literature, Architecture And Ways Forward, Can Biyik, Zaheer Allam, Gabriele Pieri, Davide Moroni, Muftah O' Fraifer, Eoin O' Connell, Stephan Olariu, Muhammad Khalid
Smart Parking Systems: Reviewing The Literature, Architecture And Ways Forward, Can Biyik, Zaheer Allam, Gabriele Pieri, Davide Moroni, Muftah O' Fraifer, Eoin O' Connell, Stephan Olariu, Muhammad Khalid
Computer Science Faculty Publications
The Internet of Things (IoT) has come of age, and complex solutions can now be implemented seamlessly within urban governance and management frameworks and processes. For cities, growing rates of car ownership are rendering parking availability a challenge and lowering the quality of life through increased carbon emissions. The development of smart parking solutions is thus necessary to reduce the time spent looking for parking and to reduce greenhouse gas emissions. The principal role of this research paper is to analyze smart parking solutions from a technical perspective, underlining the systems and sensors that are available, as documented in the …
Parallel Anisotropic Unstructured Grid Adaptation, Christos Tsolakis, Nikos Chrisochoides, Michael A. Park, Adrien Loseille, Todd Michal
Parallel Anisotropic Unstructured Grid Adaptation, Christos Tsolakis, Nikos Chrisochoides, Michael A. Park, Adrien Loseille, Todd Michal
Computer Science Faculty Publications
Computational fluid dynamics (CFD) has become critical to the design and analysis of aerospace vehicles. Parallel grid adaptation that resolves multiple scales with anisotropy is identified as one of the challenges in the CFD Vision 2030 Study to increase the capacity and capability of CFD simulation. The study also cautions that computer architectures are undergoing a radical change, and dramatic increases in algorithm concurrency will be required to exploit full performance. This paper reviews four different methods to parallel anisotropic grid adaptation. They cover both ends of the spectrum: 1) using existing state-of-the-art software optimized for a single core and …
Combining Cryo-Em Density Map And Residue Contact For Protein Secondary Structure Topologies, Maytha Alshammari, Jing He
Combining Cryo-Em Density Map And Residue Contact For Protein Secondary Structure Topologies, Maytha Alshammari, Jing He
Computer Science Faculty Publications
Although atomic structures have been determined directly from cryo-EM density maps with high resolutions, current structure determination methods for medium resolution (5 to 10 Å) cryo-EM maps are limited by the availability of structure templates. Secondary structure traces are lines detected from a cryo-EM density map for α-helices and β-strands of a protein. A topology of secondary structures defines the mapping between a set of sequence segments and a set of traces of secondary structures in three-dimensional space. In order to enhance accuracy in ranking secondary structure topologies, we explored a method that combines three sources of information: a set …
See-Trend: Secure Traffic-Related Event Detection In Smart Communities, Stephan Olariu, Dimitrie C. Popescu
See-Trend: Secure Traffic-Related Event Detection In Smart Communities, Stephan Olariu, Dimitrie C. Popescu
Computer Science Faculty Publications
It has been widely recognized that one of the critical services provided by Smart Cities and Smart Communities is Smart Mobility. This paper lays the theoretical foundations of SEE-TREND, a system for Secure Early Traffic-Related EveNt Detection in Smart Cities and Smart Communities. SEE-TREND promotes Smart Mobility by implementing an anonymous, probabilistic collection of traffic-related data from passing vehicles. The collected data are then aggregated and used by its inference engine to build beliefs about the state of the traffic, to detect traffic trends, and to disseminate relevant traffic-related information along the roadway to help the driving public make informed …
Fmri Feature Extraction Model For Adhd Classification Using Convolutional Neural Network, Senuri De Silva, Sanuwani Udara Dayarathna, Gangani Ariyarathne, Dulani Meedeniya, Sampath Jayarathna
Fmri Feature Extraction Model For Adhd Classification Using Convolutional Neural Network, Senuri De Silva, Sanuwani Udara Dayarathna, Gangani Ariyarathne, Dulani Meedeniya, Sampath Jayarathna
Computer Science Faculty Publications
Biomedical intelligence provides a predictive mechanism for the automatic diagnosis of diseases and disorders. With the advancements of computational biology, neuroimaging techniques have been used extensively in clinical data analysis. Attention deficit hyperactivity disorder (ADHD) is a psychiatric disorder, with the symptomology of inattention, impulsivity, and hyperactivity, in which early diagnosis is crucial to prevent unwelcome outcomes. This study addresses ADHD identification using functional magnetic resonance imaging (fMRI) data for the resting state brain by evaluating multiple feature extraction methods. The features of seed-based correlation (SBC), fractional amplitude of low-frequency fluctuation (fALFF), and regional homogeneity (ReHo) are comparatively applied to …