Video frame rate control for non-guaranteed network services with explicit rate feedback

We consider a modification of TCP congestion control in which the congestion window is adapted to explicit bottleneck rate feedback; we call this RATCP (rate adaptive TCP). Our goal in this paper is to study the performance of RATCP (using analytical models, and an experimental test-bed) in various network scenarios, and to compare the performance of RATCP and TCP, both with and without fast-retransmit and fast-recovery. We find that when sessions with the same round trip times share a bottleneck link then, even with ideal fair rate feedback, the performance of RATCP is only slightly better than that of TCP. RATCP, however, does reduce losses significantly. When there are random losses, however, RATCP with fast-recovery provides substantially better throughput than plain TCP. Further, when sessions with different round trip times share the bottleneck link, as expected, RATCP ensures fairness. Finally, we suggest a practical situation in which RATCP can be useful for improving Web access performance.

In this paper, several reported blind adaptive beam- forming algorithms, including the stochastic gradient (SG) methods and the recursive least squares (RLS) methods are reviewed. Then, the conjugate gradient (CG) algorithm is introduced as a tradeoff between SG and RLS algorithms. On the basis of this idea, a novel constrained constant modulus (CCM) design criterion is described and employed for deriving a modified conjugate gradient (CG) optimization algorithm, which does not require the information of the desired user beforehand. Simulations are performed to compare the performance of the proposed method with both SG and RLS methods. The results indicate that the proposed approach has a significant faster convergence rate and superior resolution compared with the SG algorithms and lower computation complexity compared with the RLS algorithms.

Letter to the editor

In this thesis, four methods specifically designed for sparse systems estimation are originally developed and presented, which were called here: Relaxations method, Successive Expansions method, l 1 -norm Minimization method and Automatic Adjustment of the Regularization Factor method. The four proposed methods are based on the Least Squares (LS) Estimation method and incorporate techniques related to convex optimization and to the theory of compressive sensing. The simulation results show that the proposed methods herein present superior performance than the ordinary LS estimation method and the Recursive Least Squares (RLS) with convex regularization method (l 1 -RLS), in many cases achieving the same optimal performance presented by the LS Oracle method. Furthermore, the proposed methods demand lower computational cost than the l 1 -RLS method.

Vanadium redox flow battery (VRB) energy storage system has been widely utilized in renewable energy applications such as wind power integration and green buildings. An online electrical model of VRB is needed to monitor and estimate battery states by using real time data since the internal parameters of battery are varying during operation. In this paper, an improved second-order Thevenin model is proposed to describe the operating characteristics of VRB. To overcome the drawback of recursive least squares (RLS) algorithm in tracking parameter identification, this paper proposes a method based on time-varying forgetting factor recursive least Squares (TFF-RLS) algorithm to identify model parameters. According to the data collected by the pulse charging experiment, the RLS algorithm and the TFF-RLS algorithm are used to identify the model parameters. Experiments are carried out on a 5kWh/3kWh VRB experimental platform in Wuhan University of Technology. Two online identification methods are compared. The results show that the TFF-RLS method demonstrates a better performance on tracking the variation of parameters dynamically, and improving estimation accuracy compared with the RLS method.

Recursive identification of multiple-input single-output fractional-order Hammerstein model with time delay

System identification, which includes parameter identification and non-parameter identification, is to estimate its mathematical model based on the input and output observation in system. This paper discusses the system identification theory and establishes a transfer function of 1/4 vehicle’s second-order vibration system model. Through the discrete transfer function, the system’s difference equation can be obtained to identify the system in two ways, RLS (recursive least squares) and RELS (extended recursive least squares). Finally, the paper makes a comparative analysis about RLS and RELS in connection with the vehicle model. The results show that RELS method is more accurate and has stronger convergence than RLS method, which provides the basis for the researching of control system’s algorithm, simulation and making control strategy.

Electrocardiogram (ECG) is easily submerged in noise of the complex environment during remote medical treatment, and this affects the intelligent diagnosis of cardiovascular diseases. Considering this situation, this paper proposes an echo state network (ESN) denoising algorithm based on recursive least square (RLS) for ECG signals. The algorithm trains the ESN through the RLS method, and can automatically learn the deep nonlinear and differentiated characteristics in the noisy ECG data, and then the network can use these characteristic to separate out clear ECG signals automatically. In the experiment, the proposed method is compared with the wavelet transform with subband dependent threshold and the S-transform method by evaluating the signal-to-noise ratio and root mean square error. Experimental results show that the denoising accuracy is better and the low frequency component of the signal is well preserved. This method can effectively filter out complex noise and effectively preserve the effective information of ECG signals, which lays a foundation for the recognition of ECG signal feature waveform and the intelligent diagnosis of cardiovascular disease.

A single-input and single-output (SISO) controlled autoregressive moving average system with scaled input-output data is considered here. Recursive least squares (RLSs) methods were used to estimate the nanosized parameters of a SISO linear model using input-output scaling factors. Thus, a general identification technique, through scaling data, was produced. Different variations of the RLS method were tested and compared. The first RLS method used a forgetting factor and the second method integrated a Kalman filter covariance. Using the described method, in order to estimate the resistance, time constant and inductance, the latter two lying within the nano range, the input signal must have both a high frequency and a high sampling rate, in relation to the time constant. The method developed here can be used to identify the nano parameters characterising the linear model, while allowing for a broader sampling rate and an input signal with lower frequency. Simulation results indicate that the proposed algorithm is both effective and robust at estimating the nano range parameters. The most powerful contribution contained here is the provision of a scaled identification bandwidth and sampling rate for the detecting signal in the identification process.

Online identification of mechanistic milling force models

In this paper, a new type of high specific energy ratio power storage element supercapacitor battery is studied. In order to accurately estimate the state of charge of the battery, based on the in-depth analysis of the working principle of the supercapacitor battery, an equivalent circuit model describing the charging and discharging characteristics and relaxation characteristics of the supercapacitor battery is proposed and established. Firstly, the components of supercapacitor battery are modeled respectively. Then the overall equivalent model is constructed. Secondly, the identification method of equivalent model parameters is discussed, and the online identification method of recursive least squares (RLS) is used for parameter identification. The supercapacitor battery was tested on the charging and discharging experimental platform according to relevant standards. Finally, the established model is built and simulated in Matlab/Simulink using experimental data.

In ad hoc networks of nodes equipped with multiple antennas, the tradeoff between spatial multiplexing and diversity gains in each link impacts the overall network capacity. An optimal algorithm is developed for adaptive rate and power control for a communication link over multiple channels in a Poi

Intra-frame plays a significant role in controlling video quality for H.264/AVC rate control. In this paper, we present an adaptive current buffer fullness ratio-based intra-frame rate control for H.264/AVC. The proposed algorithm adaptively adjusts the quantization step (QP) of the intra-frame by taking current buffer fullness ratio and scene change into consideration. Experimental results show that compared with JM11.0, the proposed algorithm increases average PSNR up to 2.59 dB and significantly reduces PSNR deviation and frame skipping. It improves objective and subjective reconstructed video qualities.

Feature enhancement method of rolling bearing acoustic signal based on RLS-RSSD

Approximately 0.3% of the world's population is suffering from a focal epilepsy. In this paper the bioelectrical activity of the human brain in epilepsy will be analyzed with Volterra-systems (VS), whereas the kernels of different orders will be determined by using an RLS (recursive least squares) method of reduced computation complexity. The prediction gain for data segments obtained in presurgical evaluations will be given and criteria for a detection of distinct changes of the prediction coefficients will be proposed.