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Your search keyword '"Georgios Bouloukakis"' showing total 88 results
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88 results on '"Georgios Bouloukakis"'

1. Performance Analysis of Internet of Things Interactions via Simulation-Based Queueing Models

Author

Georgios Bouloukakis, Ioannis Moscholios, Nikolaos Georgantas, and Valérie Issarny

Subjects
Internet of Things, queueing models, middleware, QoS analysis, Information technology, T58.5-58.64
Abstract

Numerous middleware application programming interfaces (APIs) and protocols were introduced in the literature in order to facilitate the application development of the Internet of Things (IoT). Such applications are built on reliable or even unreliable protocols that may implement different quality-of-service (QoS) delivery modes. The exploitation of these protocols, APIs and QoS modes, can satisfy QoS requirements in critical IoT applications (e.g., emergency response operations). To study QoS in IoT applications, it is essential to leverage a performance analysis methodology. Queueing-network models offer a modeling and analysis framework that can be adopted for the IoT interactions of QoS representation through either analytical or simulation models. In this paper, various types of queueing models are presented that can be used for the representation of various QoS settings of IoT interactions. In particular, we propose queueing models to represent message-drop probabilities, intermittent mobile connectivity, message availability or validity, the prioritization of important information, and the processing or transmission of messages. Our simulation models demonstrate the significant effect on delivery success rates and response times when QoS settings are varied.

Published
2021
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6. REMOD: a computational tool for remodeling neuronal dendrites

Author

Panagiotis Bozelos, Georgios Bouloukakis, Stefanos S. Stefanou, and Panayiota Poirazi

Subjects
Dendrites, dendritic morphology, neuronal function, neural computations, dendritic computations, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

In recent years, several modeling studies have indicated that dendritic morphology is a key determinant of how individual neurons acquire a unique signal processing profile. The highly branched dendritic structure that originates from the cell body, explores the surrounding 3D space in a fractal-like manner, until it reaches a certain amount of complexity. Its shape undergoes significant alterations not only in various neuropathological conditions, but in physiological, too. Yet, despite the profound effect that these alterations can have on neuronal function, the causal relationship between structure and function remains largely elusive. The lack of a systematic approach for remodeling neuronal cells and their dendritic trees is a key limitation that contributes to this problem. In this context, we developed a computational tool that allows the remodeling of any type of neurons, given a set of exemplar morphologies. The tool is written in Python and provides a simple GUI that guides the user through various options to manipulate selected neuronal morphologies. It provides the ability to load one or more morphology files (.swc or .hoc) and choose specific dendrites to operate one of the following actions: shrink, remove, extend or branch (as shown in Figure 1). The user retains complete control over the extent of each alteration and if a chosen action is not possible due to pre-existing structural constraints, appropriate warnings are produced. Importantly, the tool can also be used to extract morphology statistics for one or multiple morphologies, including features such as the total dendritic length, path length to the root, branch order, diameter tapering, etc. Finally, an experimental utility enables the user to remodel entire dendritic trees based on preloaded statistics from a database of cell-type specific neuronal morphologies. To our knowledge, this is the first tool that allows (a) the remodeling of existing –as opposed to the de novo generation of- neuronal morphologies both ad hoc and based on predefined statistics and (b) the extraction of morphological feature statistics. Thus, REMOD allows the implementation of a systematic approach for altering neuronal morphologies that will promote further research into understanding the hidden associations between critical morphology parameters and the distinct electrophysiological patterns that individual neurons exhibit.

Published
2014
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