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In reference[142], a parametric model is proposed that is based on a simple method of estimating MSE that occurs due to a given pattern of packet loss.
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It was also observed that a similar phenomenon occurs in problems concerning traffic patterns of packet flows in high-speed data networks such as the Internet (see [4, 5]) and in macroeconomics and finance (see [6]).
The DCR part can handle certain patterns of packet-reordering, but not all possible patterns.
By knowing the access pattern of the packet methods and the contention resolution mechanism on shared memory channel, worst-case memory access times can be determined analytically.
A degraded speech sequence is created according to the dictated pattern of missing packets.
After the handover, the loss pattern changes to coexistence of packet losses and/or packet-reordering.
This traffic pattern increases the possibility of packet loss, resulting in throughput reduction and fluctuating performance; a significant number of TCP packets are dropped during the data interchange.
To overcome this shortage, Roychoudhuri and Al-Shaer [10] proposed a subtle grained speech quality assessor, denoted as Genome, that more accurately considers the pattern of dropped voice packets.
This time includes the execution of pattern reading, packet reading, pattern encoding, MapReduce scheduler initialization, and the searching of the Map function.
This explains why we calculate and store the actual PLR and MBLS values for each couple of packet loss pattern and speech sequence (similarly as what it is done in [16] for video quality assessment).
Our method features a novel metric, namely unpredictability index (UPI), to capture repeating patterns by quantifying the predictability of packet arrivals' temporal structure from the perspective of hierarchical clustering.
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