FFT-Based Spectrum Analysis in the Case of Data Loss

Sujbert, László; Orosz, György

doi:10.1109/tim.2015.2508278

Cited by 13 publications

(10 citation statements)

References 24 publications

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“…The spreading of the idea of Internet of Things has amplified the problem, which motivated new research. The actual results are about FFT-based spectrum estimation in the case of data loss [139,140]. Fig.…”

Section: Digital Signal Processingmentioning

confidence: 99%

BME VIK Annual Research Report on Electrical Engineering and Computer Science 2016

Charaf

Harsányi

Poppe

et al. 2017

Period. Polytech. Elec. Eng. Comp. Sci.

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Preface Since being established in 1949, the Faculty of Electrical Engineering and Informatics (VIK) BME has played a flagship role in the development of electronics, IT and computer science in Hungary.We László Jakab (dean, BME VIK) János Levendovszky (vice-dean in charge of scientific affairs, BME VIK) are proud of combining engineering applications with sound scientific results, which is the assurance of high-level industrial collaboration leading to novel results and innovation. The various collaborations with our industrial partners has made it clear that the industry expects methods and results which make their industrial processes more effective and increase productivity and quality. Thus, participation in these collaborations give a competitive edge and ensure the continuous development of VIK. These factors have positioned our Department of Automation and Applied InformaticsDepartment of Automation and Applied Informatics (AUT) is one of the largest and dynamically developing departments at BME with diverse scope competences such as control theory, embedded systems, software modelling, applied software development, Internet of Things, power electronics, mechatronics, and many others. The department's main activities are education, research and development. Training versatile electrical and software engineers with solid practical knowledge is our top priority. Our profile also includes developing high quality software and hardware solutions for industry partners. All of our activities are backed by strong research background.In 2016, the department organized and hosted a number of domestic and international events. In cooperation with the industry, AUT has organized the IoT4U conference. It was dedicated to Internet of Things, cloud and data management as well as the integration of IoT into various formats. RobonAUT, the yearly domestic amateur robotics contest was organized by the department in February 2016. The yearly contest is open to teams of students, organized either in student projects within the department or independent research teams.The next sections summarize the key research results of the department in year 2016. Software Development Methods and Mobile PlatformsThe diversity of mobile platforms necessitates the development of the same mobile application for all major mobile platforms, what requires considerable development effort. Mobile application developers are multiplatform developers, but they still prioritize the platforms, therefore, not all platforms are equally important for them. Appropriate methods, processes and tools are required to support the development in order to achieve better productivity.The research on educational mobile applications that adapt to the mental state of the user required a method for estimating the difficulty of tasks found in the serious games. There are generated or random assignments where the effort required from the user is not known or cannot be estimated in advance.61 (2) IoT and Smart CityThe Internet of Things (IoT) is transforming the surroun...

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Section: Digital Signal Processingmentioning

confidence: 99%

BME VIK Annual Research Report on Electrical Engineering and Computer Science 2016

Charaf

Harsányi

Poppe

et al. 2017

Period. Polytech. Elec. Eng. Comp. Sci.

View full text Add to dashboard Cite

show abstract

“…In [8], three data loss models have been investigated: 1. random independent data loss, 2. random block-based data loss, 3. two-state Markov model-based data loss. The random data loss is one of the most essential data loss models; it is often used because of its simplicity [10].…”

Section: Data Loss Models and Their Spectramentioning

confidence: 99%

“…The Markov model has been proven to be useful, e.g., in the description of data loss pattern in realtime data transmission over Internet [11]. In [8] the two-state model has exhaustively been investigated, but real situations may require more states.…”

Section: Data Loss Models and Their Spectramentioning

confidence: 99%

“…The presence of data loss in measurement systems motivated the investigation of the phenomena from a signal processing point of view. Our recently published paper [8] discussed one of such problems: the handling of data loss in the case of spectrum estimation. In spite of the existing methods concentrating to the spectrum estimation, this paper dealt with the characterization of distortion caused by missing data.…”

Section: Introductionmentioning

confidence: 99%

“…The spectra are calculated by the Fast Fourier Transform (FFT), which makes the method computationally effective. Summing up, our method offers not only the spectral estimation of the lossy signal [8], but also provides an estimate for the data loss model along with its parameters.…”

Section: Introductionmentioning

confidence: 99%

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Frequency Domain Identification of Data Loss Models

Sujbert

Orosz

2017

ACTA IMEKO

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Recently measurement data loss has been of greater interest, due to the spread of sensor networks and the idea of Internet of Things. A procedure is proposed that is able to identify the most frequently employed data loss models. It is assumed that the communication protocol provides information about data loss, i.e. the so-called data availability indicator function is known. The power spectral density (PSD) of the indicator function is representative for the model, and can be used for identification. Spectral estimation is carried out by Fast Fourier Transform (FFT) based techniques. The paper introduces the identification procedure for random independent, random block-based and general Markov model-based data loss patterns. The efficiency of the proposed method is demonstrated by simulation and measurement results.

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2021

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FFT-Based Spectrum Analysis in the Case of Data Loss

Cited by 13 publications

References 24 publications

BME VIK Annual Research Report on Electrical Engineering and Computer Science 2016

BME VIK Annual Research Report on Electrical Engineering and Computer Science 2016

Frequency Domain Identification of Data Loss Models

PSD based high impedance fault detection and classification in distribution system

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