In the majority of analytical and imitation models analyzing the processes of communication networks functioning, input streams are associated with the well known Poisson's model, describing the most unfavorable c...
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In the majority of analytical and imitation models analyzing the processes of communication networks functioning, input streams are associated with the well known Poisson's model, describing the most unfavorable case of stationary random streams. In practice the input streams are characterized mainly with strong nonstationary processes, impacting the final results of modeling to a significant degree. Having this in mind this report presents an input stream model for simulation modeling and analysis of communication networks.
Due to the convenience of pervasive information environment, many people use various computing devices to perform plenty kinds of tasks. In the field of education, there are various applications to facilitate learner,...
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Due to the convenience of pervasive information environment, many people use various computing devices to perform plenty kinds of tasks. In the field of education, there are various applications to facilitate learner, especially for e-learning. However, some computing devices suffer from the limited resources and cannot accept rich information content. Therefore, the information content has to be tailored into different kinds of presentation depending on the types of computing devices. Context sensitivity is an application software system's ability to sense and analyze context from various sources. In this paper, we aim to customize static documents using context-sensitive middleware (CSM) to sense the computing device, and then using the agent-based parser to provide suitable content representation dynamically.
The user observed latency of retrieving Web documents is one of limiting factors while using the Internet as an information data source. Prefetching became important technique to reduce the average Web access latency....
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The user observed latency of retrieving Web documents is one of limiting factors while using the Internet as an information data source. Prefetching became important technique to reduce the average Web access latency. Existing prefetching methods are based predominantly on URL graphs. They use the graphical nature of HTTP links to determine the possible paths through a hypertext system. Although the URL graph-based approaches are effective in the prefetching of frequently accessed documents, few of them can pre-fetch those URLs that are rarely visited. In our paper we aim to propose a new prefetching algorithm that would increase the efficiency of Web prefetching and that will embody the new demands for Web personalisation and Web search assistance. The aim of the research is to design a system for web page prefetching. The system should use user's link path history in combination with the semantic path history. To enable this, semantically annotated web pages are necessary. We cannot rely on the web documents' creators thus one part of the work must be the design and implementation of simple annotator based on WordNet just for purposes of our research.
As Mobile Learning (M-Learning) has had a great impact on online education, more and more mobile applications are designed and developed for the M-Learning. In this paper, a novel mobile-optimized application architec...
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This paper presents a camera calibration method in order to estimate the lane detection and inter-vehicle distance estimation system for an automotive safety driving system. In order to implement the lane detection an...
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The integration of Industry 4.0 technologies in agriculture will reduce the increasing challenges of agricultural process around the globe. The real-time farm management with high degree of automation will greatly imp...
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The integration of Industry 4.0 technologies in agriculture will reduce the increasing challenges of agricultural process around the globe. The real-time farm management with high degree of automation will greatly improve productivity, agri-food supply chain efficiency and food safety. This paper describes a fully customized LoRa-based IoT system that aims for a low-cost, low power and wide range wireless sensor network targeted for smart farms. The presented system integrates already existing Programmable Logic Controllers (PLC) typically used to control multiple processes and devices such as water pumps, certain machinery, etc. along with a newly developed network of wireless LoRA sensors distributed over the farm. A Telegram bot is also included as novelty for automated user communication via this mobile phone messaging application. The network structure was deployed and tested. The developed integrated system also includes a cloud-based monitoring application to provide remote visualization and control for all the farming environment.
The UAV-mounted base station has been integrated into 5G and beyond to provide communication in an emergency when conventional systems fail due to some reason. Moreover, it is also used in coverage enhancement and for...
The UAV-mounted base station has been integrated into 5G and beyond to provide communication in an emergency when conventional systems fail due to some reason. Moreover, it is also used in coverage enhancement and for other strategic purposes. In this work, we have considered a MIMO-OFDM based UAV system that is inclined to fast-fading channels due to the continuous motion of UAVs. Due to the mobility of UAVs, there is a frequency and timing offset which must be estimated for proper and seamless communication. For that purpose, we have proposed a data-aided maximum likelihood-based scheme that efficiently estimates the carrier frequency and timing offset. Results show that our algorithm outperforms the state-of-the-art system in terms of MSE.
A parallel distributed approach to refine a mesh while preserving the curvature of a target geometry is presented. Our approach starts by generating a coarse linear mesh of the computational domain. Second, the former...
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A parallel distributed approach to refine a mesh while preserving the curvature of a target geometry is presented. Our approach starts by generating a coarse linear mesh of the computational domain. Second, the former coarse mesh is curved to match the curvature of the target geometry. Then, the curved mesh is partitioned and the subdomain meshes are sent to the slaves. Finally, the curved elements are uniformly subdivided in parallel targeting the geometry approximated by the curved mesh. The result is a distributed finer linear mesh featuring improved geometric accuracy. The key ingredient of our implementation is to approximate the target geometry as a linear mesh equipped with an elemental field corresponding to an element-wise polynomial geometry representation. Thus, the distribution of the curved geometry is equivalent to partitioning the linear mesh and sending the subdomain meshes and the elemental fields to the slaves. The main application of the obtained finer linear mesh is to compute in parallel steady state flow solutions on real topographies. The qualitative results show that for 2D and 3D steady state flow solutions, on real and synthetic topographies, our parallel subdivision approach mitigates the artificial artifacts that might appear with standard straight-sided subdivision methods. We also check the parallel performance of the implementation by performing a weak scalability test in 2D.
Musical note onset detection is a building component for several MIR related tasks. The ambiguity in the definition of a note onset and the lack of a standard way to annotate onsets, introduce differences in datasets ...
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Musical note onset detection is a building component for several MIR related tasks. The ambiguity in the definition of a note onset and the lack of a standard way to annotate onsets, introduce differences in datasets labeling, which in turn makes evaluations of note onset detection algorithms difficult to compare. This paper gives an overview of the parameters influencing the commonly used onset detection evaluation measure, i.e. the F1-score, pointing out a consistently missing parameter which is the overall time shift in annotations. This paper shows how crucial this parameter is in making reported F1-scores comparable among different algorithms and datasets, achieving a more reliable evaluation. As several MIR applications are concerned with the relative location of onsets to each other and not their absolute location, this paper suggests to include the overall time shift as a parameter when evaluating the algorithm performance. Experiments show a strong variability in the reported F1-score and up to 50% increase in the best-case F1-score when varying the overall time shift. Optimizing the time shift turns out to be crucial when training or testing algorithms with datasets that are annotated differently (e.g. manually, automatically, and with different annotators) and especially when using deep learning algorithms.
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