A frequent and well-founded criticism of the maximum a posteriori (MAP) and minimum mean squared error (MMSE) estimates of a continuous parameter γ taking values in a differentiable manifold Γ is that they are not invariant to arbitrary ‘reparametrizations’ of Γ. This paper clarifies the issues surrounding this problem, by pointing out the difference between coordinate invariance, which is a si...

— Consider observations where random signals are randomly present or absent in independent and additive white Gaussian noise (AWGN). By using a recently established limit theorem, we introduce a new estimator for the estimation of the noise standard deviation when the signals are less present than absent and have unknown probability distributions. The bias, the consistency and the minimum attai...

This letter introduces a joint design method for uplinkdownlink multiple-input multiple-output (MIMO) relay communication systems in which the source nodes transmit information to the destination nodes with the help of a relay. We propose a signal forwarding schceme based on the minimum mean-square error (MMSE) approach in uplink relay systems. Exploiting the duality of relay systems, we also p...

Abstract The majority of water pipelines are subjected to serious deterioration and degradation challenges. This research examines the application optimized neural network models for estimating condition in Shaker Al-Bahery, Egypt. proposed hybrid compared against classical network, adaptive neuro-fuzzy inference system, group method data handling using four evaluation metrics. These metrics ar...

this paper presents a new multi-sensor data fusion method based on the combination of wavelettransform (wt) and extended kalman filter (ekf). input data are first filtered by a wavelettransform via daubechies wavelet “db4” functions and the filtered data are then fused based onvariance weights in terms of minimum mean square error. the fused data are finally treated byextended kalman filter for...

Accurately forecasting wind speed on a short-term scale has become essential in the field of power energy. In this paper, multi-variable long memory network model (MV-LSTM) based Pearson correlation coefficient feature selection is proposed to predict speed. The method utilizes multiple historical meteorological variables, such as speed, temperature, humidity, and air pressure, next hour. Hourl...

With the increase in population and progress of industrialization, rational use energy heating systems has become a research topic for many scholars. The accurate prediction heat load provides us with scientific solution. Due to complexity difficulty forecasting systems, this paper proposes short-term method based on Bayesian algorithm-optimized long- memory network (BO-LSTM). moving average da...