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• A Gibbs sampling-based parameters estimation algorithm for the EGK distribution is presented. • First attempt to address ML-based estimators for the EGK distribution. • Numerical and simulation results show that the proposed method provides an efficient and mathematically tractable approach to achieve this goal. • Compared to the classical moment-matching method, the proposed method can better capture cases of heavy-tail. We present a novel approach for estimating the parameters of the extended generalized- K (EGK) distribution commonly used as a fading model in wireless and optical communications links. The proposed method is based on the Gibbs sampling technique and does not require solving nonlinear equations nor performing numerical integrations. Numerical and simulation results are presented showing that the estimated and original distributions are virtually indistinguishable and formal metrics like Kullback-Leibler (KL) divergence, the mean integrated squared bias (MISB), the mean integrated variance (MIV) and the mean integrated squared error (MISE) all show excellent agreement between the two as well. [ABSTRACT FROM AUTHOR]

Concrete is one of the most versatile and commonly used composite materials in construction, with its properties being influenced by the type and proportion of materials used in its mixing process. Ensuring accurate mixing is crucial for a structure's durability and safety. Therefore, assessing concrete mixture quality, homogeneity, and composition post-hydration is vital to meet application requirements. This study proposes a novel non-destructive approach for concrete classification, focusing on homogeneity, conductivity, and composition analysis in dry concrete mixtures. The proposed technique is based on analyzing changes to Wi-Fi signals that are radiated through concrete specimens. These variations are in the form of changes to the magnitude and phase of the Wi-Fi's Channel State Information (CSI). These data are then utilized to train Convolutional Neural Network (CNN) classifiers for concrete mixture categorization. The proposed method successfully estimates homogeneity across concrete samples with varied particle sizes and achieves a 100% validation accuracy in estimating conductivity and composition. Additionally, distinct CSI responses are observed for mixtures with differing compositions. The validation experiments with new concrete samples demonstrate the models' robustness and generalization capability, achieving a 78.18% correct prediction rate despite variations in dimensions and compositions. While the models exhibit high accuracy in detecting certain materials, such as silica fume and fly ash, NW aggregates, and steel fiber, challenges persist in detecting materials like carbon powder due to potential confusion with other conductive materials. Overall, the trained CNN models demonstrate strong generalization ability and robustness, highlighting their potential for accurate concrete property detection in various scenarios. • Commodity Wi-Fi is utilized to estimate the properties of concrete non-destructive. • System estimates three different concrete properties using the same collected measurement. • Trained convolutional neural networks are used to estimate the properties of concrete. • Channel state information (CSI) measurements of the Wi-Fi signal are used to train CNN and to estimate the concrete properties. [ABSTRACT FROM AUTHOR]

Abstract: A new technique for passive doubly terminated ladder filters transformation has been proposed. The proposed technique depends on current mode realization of doubly terminated LC ladder filters instead of voltage mode realizations presented by Constantinides and Dimopoulos in . Different realizations of passive elements using new technique have been proposed. Design example of a 3rd order low-pass filter has been realized using the proposed technique and compared with the realization introduced by Rathore and Khot using current feedback operational amplifier (CFOA) as an active building block. SPICE simulations have been carried out using 0.18μ m TSMC CMOS technology and supply voltages of±0.9V. The simulation results have been demonstrated and discussed. [Copyright &y& Elsevier]

• This work proposes a novel model for PV generation including dust and cleaning. • The model can be used for identifying the optimal investment and cleaning strategy. • The model relies on generating virtual scenarios of output power including cleaning. • Optimal cleaning results in a 23.9-36.2% added profit compared to no cleaning. • It is shown that variable cleaning frequency is more economic compared to fixed. Stochastic photovoltaic (PV) modeling that can be used for long-term planning of power systems is essential for future renewable power generation. One of the most prevalent problems that PV systems face is the accumulation of dust on the PV panel surface that negatively impacts the output power. Wind speed along with other weather variables including relative humidity, temperature, and precipitation are some of the major factors that contribute to dust accumulation. This paper presents a novel dynamic model of the PV output power profile including the dust accumulation using a Markov chain model. The proposed model incorporates the seasonal variations in ambient temperature, solar irradiance, dust accumulation, and rate of dust accumulation as well as the desired cleaning frequency, which affect the overall energy yield of the PV system. The outcome of the model is virtually generated scenarios that can be used by the investors to decide on the optimal size of the PV system and the optimal cleaning frequency for each season. The model outcome shows an error of less than 5% when compared to actual data collected from the field without cleaning. This error can be reduced by increasing the number of states, which affects the computational time. Various case studies are presented to show the effectiveness of the proposed model and its benefits. [ABSTRACT FROM AUTHOR]