This paper proposes a novel adaptive filtering algorithm which converges faster than the Krylov proportionate normalized least mean square (KPNLMS) algorithm. KPNLMS is known to exhibit faster convergence than the standard NLMS algorithm for all unknown systems. Our algorithm is named Krylov proportionate normalized least mean fourth (KPNLMF) and it deals with mean fourth minimization of the er...

This paper performs an experiment to forecast stock market movement in India using Artificial Neural Network (ANN) and Genetic Algorithm (GA). This model is named Genetically optimized Neural Network (GNN). We have tested this newly created model against traditional ARCH/GARCH models using hypothesis testing (z-test).We have used different error metrics like Average Absolute Error (AAE), Mean A...

In the arena of, high speed data transmission and requirement of large data to store in minimum available space, compression is very prominent aspect. There are various techniques, which have been applied for this purpose. One of most useful technique is fractal image compression. In which the main errand is to lessen the transmission time and storage capacity. In this paper, the primary object...

We propose a new ratio estimator using two auxiliary variables in simple random sampling. We obtain mean square error (MSE) equation of this estimator and theoretically show that our proposed estimator is more efficient than the traditional multivariate ratio estimator under a defined condition. In addition, we support this theoretical result with the aid of a numerical example.

In this paper, a generalization of the Misspecified Cramér-Rao Bound (MCRB) and of the Constrained MCRB (CMCRB) to complex parameter vectors is presented. Our derivation aims at providing lower bounds on the Mean Square Error (MSE) for both circular and non-circular, MS-unbiased, mismatched estimators. A simple toy example is also presented to clarify the theoretical findings.

This paper presents an empirical mode decomposition (EMD) based adaptive filter (AF) for channel estimation in OFDM system.  In this method, length of channel impulse response (CIR) is first approximated using Akaike information criterion (AIC). Then, CIR is estimated using adaptive filter with EMD decomposed IMF of the received OFDM symbol. The correlation and kurtosis measures are used to sel...

The paper aims to create a most efficient and accurate cab fare prediction system using machine learning algorithms comparing them. are Random forest algorithm Linear regression the r-square, mean square error (MSE), Root MSE Mean Squared Logarithmic Error (RMSLE) values. We implement linear predict prices of get best accuracy when both algorithms. should be trips before starting trip. sample s...

Implicit feedback is an approach that utilizes uplink channel state information (CSI) for downlink transmit beamforming on multiple-input multiple-output (MIMO) systems, relying on over-the-air channel reciprocity. The implicit feedback improves throughput efficiency because overhead of CSI feedback for change of over-the-air channel responses is omitted. However, it is necessary for the implic...

This paper investigates the properties of the performance surface for the problem of nonlinear mean-square estimation of a random sequence. The problem studied has direct application to the study of active noise control (ANC) systems when the transducers are driven into a nonlinear behavior. A deterministic expression is derived for the mean-square error (MSE) surface as a function of the syste...

To mitigate the multipath delay effect of the received signal, the information of the time-varying channel is required at the receiver to determine the equalizer co-efficients. In this paper two basic algorithms, known as Linear Minimum Mean Square (LMMSE) and Least Square Error (LSE), are discussed which make use of the channel statistics in time domain. To reduce the complexity, different var...