Voice service is one of today's most important communication network services. The E-model standardized by ITU-T offers the rating factor R. The R-value is used to detect and assess the speech quality. Two algorit...
详细信息
Voice service is one of today's most important communication network services. The E-model standardized by ITU-T offers the rating factor R. The R-value is used to detect and assess the speech quality. Two algorithms are used - packet dropping and ACK pacing. This paper improves the two algorithms for improvement of the VoIP QoS (Quality of Service). The simulations use Web traffic, FTP traffic, and Bit Torrent traffic as the background traffic. The simulator NS-2 is used to accomplish our work. Simulation results indicate that the voice quality of VoIP in ADSL access network has been improved effectively by the proposed adaptive algorithm.
In the present paper, we introduce a self-adaptive algorithm for solving the split common fixed point problem of demicontractive operators in real Hilbert spaces. Weak convergence result is discussed under suitable as...
详细信息
Efficient memory usage in high performance timesharing computing systems is a considerable challenge. Some research areas on adaptive algorithms concerning memory page replacement, analyze the memory access behavior s...
详细信息
Efficient memory usage in high performance timesharing computing systems is a considerable challenge. Some research areas on adaptive algorithms concerning memory page replacement, analyze the memory access behavior seeking to store the pages that will be used in a near future and discarding the others. This is important due to the high cost of treating page faults. The proposal is to analyze the influence of page access frequency on adaptive algorithms, using its structure and applying page replacement with access frequency analysis, as a function of its execution state. The comparative performance analysis is conducted by using trace files that represent different memory access behaviors.
Underwater signal transmission is a challenging task since the usable frequency range is limited to low frequency and the transmission of electromagnetic waves is impossible due to its high attenuation nature. Hence l...
详细信息
Underwater signal transmission is a challenging task since the usable frequency range is limited to low frequency and the transmission of electromagnetic waves is impossible due to its high attenuation nature. Hence low frequency acoustic signal is more suited for transmission in underwater. Underwater transmission is highly affected by wind noise which is predominant at low frequency. The real time data collected from Indian Seas at Chennai (Bay of Bengal) are studied in detail using Welch, Bartlett and Blackman estimation methods and the results shows the effect of wind over 0-8 kHz range. Various adaptive algorithms are analyzed in detail and the Signal to Noise Ratio (SNR) values are tabulated for different wind speeds. The results shows that Recursive Mean Square (RLS) works better when compared to others. The maximum Signal to Noise Ratio (SNR) of about 42-51 dB is achieved.
To solve a general class of elliptic operator equations, Cohen et al. (2001) [2] designed two adaptive wavelet algorithms that yield an approximate solution with error O(N-s). In this paper, we modify the error analys...
详细信息
To solve a general class of elliptic operator equations, Cohen et al. (2001) [2] designed two adaptive wavelet algorithms that yield an approximate solution with error O(N-s). In this paper, we modify the error analysis for the second algorithm and optimize the upper bound. Numerical experiments demonstrate that our approximation has some advantages compared to that of the reference. (C) 2012 Elsevier Inc. All rights reserved.
This paper presents a cost-effective adaptive feedback Active Noise Control (FANC) method for controlling functional Magnetic Resonance Imaging (fMRI) acoustic noise by decomposing it into dominant periodic components...
详细信息
This paper presents a cost-effective adaptive feedback Active Noise Control (FANC) method for controlling functional Magnetic Resonance Imaging (fMRI) acoustic noise by decomposing it into dominant periodic components and residual random components. Periodicity of fMRI acoustic noise is exploited by using linear prediction (LP) filtering to achieve signal decomposition. A hybrid combination of adaptive filters-Recursive Least Squares (RLS) and Normalized Least Mean Squares (NLMS) are then used to effectively control each component separately. Performance of the proposed FANC system is analyzed and Noise attenuation levels (NAL) up to 32.27dB obtained by simulation are presented which confirm the effectiveness of the proposed FANC method.
We propose a general framework to online learning for classification problems with time-varying potential functions in the adversarial setting. This framework allows to design and prove relative mistake bounds for any...
详细信息
ISBN:
(纸本)9781617823800
We propose a general framework to online learning for classification problems with time-varying potential functions in the adversarial setting. This framework allows to design and prove relative mistake bounds for any generic loss function. The mistake bounds can be specialized for the hinge loss, allowing to recover and improve the bounds of known online classification algorithms. By optimizing the general bound we derive a new online classification algorithm, called NAROW, that hybridly uses adaptive- and fixed- second order information. We analyze the properties of the algorithm and illustrate its performance using synthetic dataset.
In recent years a number of novel distributed parameter estimation algorithms have been developed stimulated by applications in cognitive radio, robotics, wireless networks and sensor networks. However, existing perfo...
详细信息
ISBN:
(纸本)9781479978878
In recent years a number of novel distributed parameter estimation algorithms have been developed stimulated by applications in cognitive radio, robotics, wireless networks and sensor networks. However, existing performance analyses have used white noise assumptions extensively. Here, we analyse for the first time a class of diffusion LMS strategies under autocorrelation assumptions. Further, we obtain a result in the white noise setting which provides a new understanding of existing steady state mean square error results. We treat the Adapt-then-Combine (ATC) algorithm.
—This paper presents a novel distributed low-rank scheme and adaptive algorithms for distributed estimation over wireless networks. The proposed distributed scheme is based on a transformation that performs dimension...
详细信息
In this paper a method for adapting the stepsize in on-line network training is presented. The proposed technique derives from the stochastic gradient descent proposed by Almeida et al. [On-line Learning in Neural Net...
详细信息
In this paper a method for adapting the stepsize in on-line network training is presented. The proposed technique derives from the stochastic gradient descent proposed by Almeida et al. [On-line Learning in Neural Networks, 111-134, Cambridge University Press, 1998]. The new aspect of our approach consists in taking into consideration previously computed pieces of information regarding the adaptation of the stepsize. The proposed algorithm has been implemented, tested and compared against other on-line methods in three problems. The results shown that it behaves predictably and reliably, and possesses a satisfactory average performance.
暂无评论