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M. Naim Yunus

dblp:328/6656 · DBLP profile ↗
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2ranked-venue papers
2as first author
0since 2021 · last 1987
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 2 · 2 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
2 papers
Network performance modeling · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Network performance modeling › teletraffic engineering
overflow traffic		 0.021987
Approximation for mean of overflow traffic with discrete time-dependent input · Proc. IEEE 1987
Transient mean and variance of overflow traffic in a loss system · Proc. IEEE 1987
Network performance modeling › teletraffic engineering
teletraffic analysis		 0.021987
Approximation for mean of overflow traffic with discrete time-dependent input · Proc. IEEE 1987
Transient mean and variance of overflow traffic in a loss system · Proc. IEEE 1987
Network performance modeling
loss systems		 0.021987
Approximation for mean of overflow traffic with discrete time-dependent input · Proc. IEEE 1987
Transient mean and variance of overflow traffic in a loss system · Proc. IEEE 1987
Network performance modeling
queueing analysis		 0.021987
Approximation for mean of overflow traffic with discrete time-dependent input · Proc. IEEE 1987
Transient mean and variance of overflow traffic in a loss system · Proc. IEEE 1987

Methods — techniques the papers use, named apart from their topics

step function approximation · 0.0peakedness · 0.0interrupted poisson process · 0.0
YearPublicationVenuePosition
1987 Transient mean and variance of overflow traffic in a loss system
abstract
Simple techniques to approximate the mean and variance of the overflow traffic in a telephone system are outlined. The mean is first approximated, and by using the peakedness value obtained via the interrupted Poisson process, the variance is then approximated.
M. Naim Yunus
 Proc. IEEE1
1987 Approximation for mean of overflow traffic with discrete time-dependent input
abstract
Real-life arrival traffic pattern can be approximated by a sequence of step functions with durations of about a quarter-hour. For this interval, a stationary arrival rate will be assumed, and transience will be present. An approximation to the mean of the traffic overflowing from a loss system when the arrival traffic consists of such a sequence of step functions will be given.
M. Naim Yunus
 Proc. IEEE1