New sequential partial-update least mean M-estimate algorithms for robust adaptive system identification in impulsive noise

The sequential partial-update least mean square (S-LMS)-based algorithms are efficient methods for reducing the arithmetic complexity in adaptive system identification and other industrial informatics applications. They are also attractive in acoustic applications where long impulse responses are encountered. A limitation of these algorithms is their degraded performances in an impulsive noise environment. This paper proposes new robust counterparts for the S-LMS family based on M-estimation. The proposed sequential least mean M-estimate (S-LMM) family of algorithms employ nonlinearity to improve their robustness to impulsive noise. Another contribution of this paper is the presentation of a convergence performance analysis for the S-LMS/S-LMM family for Gaussian inputs and additive Gaussian or contaminated Gaussian noises. The analysis is important for engineers to understand the behaviors of these algorithms and to select appropriate parameters for practical realizations. The theoretical analyses reveal the advantages of input normalization and the M-estimation in combating impulsive noise. Computer simulations on system identification and joint active noise and acoustic echo cancellations in automobiles with double-talk are conducted to verify the theoretical results and the effectiveness of the proposed algorithms. © 2010 IEEE.published_or_final_versio

A new proportionate fast LMS/Newton algorithm for adaptive filtering

This paper proposes a new proportionate adaptive filtering algorithm which exploits the advantageous features of the generalized proportionate NLMS (GP-NLMS) algorithm and the fast LMS/Newton algorithm. By means of an efficient switching mechanism, the new algorithm works alternately between the GP-NLMS and the fast LMS/Newton algorithms in order to combine their respective advantages. The overall converging speed and steady state performance for both sparse and dispersive channels as well as tracking performance are thus significantly improved. Computer simulations on an echo cancellation problem verify the superior performance of the new algorithm over both the GP-NLMS algorithm and the conventional fast LMS/Newton algorithm. ©2005 IEEE.published_or_final_versio

On bursty packet loss model for TCP performance analysis

In this paper, we study the timeout probability of TCP Reno under the bursty packet loss model, which is widely used to represent the loss characteristics of TCP under drop-tail FIFO queues. With a detailed analysis on the three timeout reasons for TCP Reno, we show that the impact of timeout has been underestimated in the existing literature. Surprisingly, we find that this more precise representation of timeout probability does not match the actual performance of TCP under drop-tail FIFO queues. Therefore we conclude that the bursty loss model is incapable of capturing the behavior of drop-tail FIFO queues, and using bursty loss model to analyze TCP performance is flawed. © 2005 IEEE.published_or_final_versio

An improved approximate QR-LS based second-order Volterra filter

This paper proposes a new transform-domain approximate QR least-squares-based (TA-QR-LS) algorithm for adaptive Volterra filtering (AVF). It improves the approximate QR least-squares (A-QR-LS) algorithm for multichannel adaptive filtering by introducing a unitary transformation to decorrelate the input signal vector so as to achieve better convergence and tracking performances. Further, the Givens rotation is used instead of the Householder transformation to reduce the arithmetic complexity. Simulation results show that the proposed algorithm has much better initial convergence and steady state performance than the LMS-based algorithm. The fast RLS AVF algorithm [J. Lee and V. J. Mathews, Mar 1993] was found to exhibit superior steady state performance when the forgetting factor is chosen to be 0.995, but the tracking performance of the TA-QR-LS algorithm was found to be considerably better.published_or_final_versio

P-XCP: A transport layer protocol for satellite IP networks

Explicit Control Protocol (XCP) is a promising transport layer protocol for satellite IP networks. Nevertheless, two problems of XCP are identified in this paper, namely, low throughput under high link error rate conditions, and output link underutilization in the presence of rate-limited connections. To address the first problem, we propose to maintain the transmission rate of an XCP sender when triple duplicate ACK is detected. To solve the second problem, we propose to adjust the aggregated feedback based on the ratio of the number of rate-limited connections to the total number of connections sharing the link. We then combine our proposed solutions to form a new protocol, called P-XCP. Simulation results show that P-XCP overcomes the two problems of XCP. When packet error rate is over 0.1, P-XCP is shown to enjoy a throughput almost double that of XCP. © 2004 IEEE.published_or_final_versio

A new block exact fast LMS/newton adaptive filtering algorithm

The 47th Midwest Symposium on Circuits and Systems Conference, Salt Lake City, Utah, USA, 25-28 July 2004This paper proposes a new block exact fast LMS/Newton algorithm for adaptive filtering. It is obtained by exploiting the shifting property of the whitened input of the fast LMS/Newton algorithm so that a block exact update can be carried out in the LMS part of the algorithm. The proposed algorithm has significantly reduced arithmetic complexity than but exact arithmetic equivalence to the LMS/Newton algorithm. Since short block length is allowed, the processing delay introduced is not excessively large as in conventional block filtering generalization, Implementation issues and the experimental results are given to illustrate the principle and efficiency of the proposed algorithm.published_or_final_versio

A wavelet based partial update fast LMS/Newton algorithm

This paper studies a wavelet based partial update fast LMS/Newton algorithm. Different from the conventional fast LMS/Newton algorithm, the proposed algorithm first uses a shorter-order, partial Haar transform-based NLMS adaptive filter to estimate the peak position of the long, sparse channel impulse response, and then employs the fast LMS/Newton algorithm integrated with partial update technique to fulfill the rest convergence task. The experimental results demonstrate the proposed algorithm outperforms its conventional counterpart in convergence performance and possesses a significantly lower computational complexity. © 2005 IEEE.published_or_final_versio

Throughput modeling of TCP with slow-start and fast recovery

Despite the rich literature on modeling TCP, we find two common deficiencies with the existing approaches. First, none of the work gives sufficient treatment to slow-start, although almost all of them show that retransmission timeout events are common. Second, the probability that retransmission timeout occurs has been underestimated, because retransmission timeout is coupled with fast recovery but fast recovery has not been properly modeled in the previous work. In this paper, new analytical models for predicting the steady state throughput of TCP flows are proposed. All major TCP mechanisms, including slow-start, congestion avoidance, fast retransmit, and fast recovery, are jointly considered under both bursty and independent loss models. We show that our proposed throughput models capture TCP performance more accurately. © 2005 IEEE.published_or_final_versio

Decision support for personalized cloud service selection through multi-attribute trustworthiness evaluation

Facing a customer market with rising demands for cloud service dependability and security, trustworthiness evaluation techniques are becoming essential to cloud service selection. But these methods are out of the reach to most customers as they require considerable expertise. Additionally, since the cloud service evaluation is often a costly and time-consuming process, it is not practical to measure trustworthy attributes of all candidates for each customer. Many existing models cannot easily deal with cloud services which have very few historical records. In this paper, we propose a novel service selection approach in which the missing value prediction and the multi-attribute trustworthiness evaluation are commonly taken into account. By simply collecting limited historical records, the current approach is able to support the personalized trustworthy service selection. The experimental results also show that our approach performs much better than other competing ones with respect to the customer preference and expectation in trustworthiness assessment. © 2014 Ding et al

Paleoclimate and bubonic plague: a forewarning of future risk?

Pandemics of bubonic plague have occurred in Eurasia since the sixth century ad. Climatic variations in Central Asia affect the population size and activity of the plague bacterium's reservoir rodent species, influencing the probability of human infection. Using innovative time-series analysis of surrogate climate records spanning 1,500 years, a study in BMC Biology concludes that climatic fluctuations may have influenced these pandemics. This has potential implications for health risks from future climate change