Experimental characterization of media noise based on equalized, synchronized drive data

DeGroat, R.D.; Vis, M.; Bliss, W.G.

doi:10.1109/20.917593

Cited by 4 publications

(5 citation statements)

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“…In the network environment, the form of social public opinion is network public opinion, which has the characteristics of fast expression, multiple information and interactive way [4][5]. It fundamentally changes the relationship between the disseminator and the receiver, and has the incomparable advantages of traditional media, which is the deconstruction and subversion of the traditional media communication mode.…”

Section: Introductionmentioning

confidence: 99%

Impact of Network Public Opinion Disposal Model on Public Value Factors under the Background of Big Data

Bo¹

2021

converter

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It is the key for the government to control the degree of information alienation to study the mechanism and control model of network public opinion information alienation for big data. This provides a theoretical basis for the government to deal with and manage the network public opinion. This paper uses qualitative analysis of the information alienation mechanism of network public opinion under the big data environment, and expands the evolution mechanism model of network public opinion to the information alienation control model. On this basis, the classification of government control information alienation is studied by numerical simulation. This paper takes the actual forum, blog, website with news comment function as the research object, and proposes a prediction platform construction scheme based on Java, which integrates a variety of prediction models. This provides useful exploration and ideas for quantitative research on the complex social phenomenon of network public opinion.

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Section: Introductionmentioning

confidence: 99%

Impact of Network Public Opinion Disposal Model on Public Value Factors under the Background of Big Data

Bo¹

2021

converter

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“…The improvement of economic income makes people pay more attention to the quality of life, and medical and health services have always been the focus of people's attention. Combined with the background of the current social and economic reform, it can be found that the demand for high-quality medical and health services in social life is increasing [1]. This practical demand has a very important impact on higher medical education.…”

Section: Introductionmentioning

confidence: 99%

The Development of Medical Education Under 5G Technology

Yang¹

2021

JFCR

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This paper studies the development of medical education under 5g technology, and explores a new mixed teaching mode under the background of medical reform based on 5g network technology and competency. This paper supports students' real-time streaming media and real-time online interaction in the form of multipoint and multi terminal, and carries out a series of medical integration education courses based on 5G network. In addition, formative assessment is conducted after each class. A comprehensive evaluation system is formed through feedback reflection guidance and summative evaluation in the last class. This can help students improve their core competitiveness. The results show that 5G network provides experienced data rate and meets the bandwidth requirements of downlink and uplink throughput in the course. The formative assessment and feedback cycle supported by 5G network can be used on the platform to predict medical students' learning habits and scores more quickly and accurately, which is helpful to improve the quality of education and teaching. The realization of medical education through 5G network technology is a new exploration of teaching mode. It can not only improve the learning initiative and enthusiasm of medical students, but also cultivate and improve the core competence of medical students.

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“…Media noise is typically associated with the write process whereas additive noise is associated with the read process [1]. Because the write process is determined by the input data, media noise is strongly data-dependent.…”

Section: Introductionmentioning

confidence: 99%

“…Many existing characterization procedures make use of this flexibility by tailoring the procedure to the specific experimental recording application. For example, to achieve a proper decomposition of the noise sources, the write noise is extracted from the readback signals by appropriately averaging over many reads and writes of a periodic pattern [1].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Decomposition of Noise Sources in Digital Recording Systems With Media Noise

2007

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In digital recording systems, the total amount of data-dependent media noise increases considerably as recording densities increase. A proper noise characterization is crucial for the design of receivers for high-density storage systems. This characterization involves the selection of a proper noise model and subsequently the accurate estimation of the parameters of the selected model. The estimation algorithm proposed in this paper jointly estimates the parameters of both media and additive noise with a high accuracy. The proposed algorithm makes use of the data dependency of the media noise to distinguish between the different noise sources. The algorithm is simple and as a result can be implemented in recording systems, with only a limited amount of complexity, as an easy "add-on" to read-channel ICs. From the simulation results and the analytical derivation of the estimation algorithm, we can clearly indicate which data patterns yield near-optimal estimation performance. These patterns are the ideal test patterns in experimental systems. We propose and discuss test patterns for magnetic and optical storage systems.

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Experimental characterization of media noise based on equalized, synchronized drive data

Cited by 4 publications

References 5 publications

Impact of Network Public Opinion Disposal Model on Public Value Factors under the Background of Big Data

Impact of Network Public Opinion Disposal Model on Public Value Factors under the Background of Big Data

The Development of Medical Education Under 5G Technology

Adaptive Decomposition of Noise Sources in Digital Recording Systems With Media Noise

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