Search results

One of the fundamental tasks in the field of image processing is image denoising. Images are often corrupted by different types of noise and the restoration of images degraded with random-valued impulse noise is still a challenging task. The paper aims to discuss these issues.

This paper presents an adaptive threshold-based impulse noise detection following by a novel selective window median filter for restoration of RVIN pixels.

The proposed method emphasis a local image statistics using an exponential nonlinear function with an adaptive threshold is derived from the rank-ordered trimmed median absolute difference (ROTMAD) are deliberated to detect the noisy pixels. In the filtering stage, a selective 3×3 moving window median filter is applied to restore the detected noisy pixel.

Experimental result shows that the proposed algorithm outperforms the existing state-of-art techniques in terms of noise removal and quantitative metrics such as peak signal to noise ratio (PSNR), mean absolute error (MAE), structural similarity index metric (SSIM) and miss and false detection rate.

Wireless sensor networks (WSNs) are periodically collecting data through randomly dispersed sensors (motes), which typically consume high energy in radio communication that mainly leans on data transmission within the network. Furthermore, dissemination mode in WSN usually produces noisy values, incorrect measurements or missing information that affect the behaviour of WSN. In this article, a Distributed Data Predictive Model (DDPM) was proposed to extend the network lifetime by decreasing the consumption in the energy of sensor nodes. It was built upon a distributive clustering model for predicting dissemination-faults in WSN. The proposed model was developed using Recursive least squares (RLS) adaptive filter integrated with a Finite Impulse Response (FIR) filter, for removing unwanted reflections and noise accompanying of the transferred signals among the sensors, aiming to minimize the size of transferred data for providing energy efficient. The experimental results demonstrated that DDPM reduced the rate of data transmission to ∼20%. Also, it decreased the energy consumption to 95% throughout the dataset sample and upgraded the performance of the sensory network by about 19.5%. Thus, it prolonged the lifetime of the network.

High voltage direct current (HVDC) cable is an important part in the electric power transmission and distribution systems. However, very little research has been carried out on partial discharge under direct current (DC) conditions. Niemeyer’s model has been widely used under alternating current (AC) conditions. This paper aims to intend to modify the Niemeyer’s model considering both electric field and charge dynamics under DC conditions, and therefore proposes a numerical model describing partial discharge characteristics in HVDC cable.

This paper intends to understand partial discharge characteristics under DC conditions through numerical modelling. Niemeyer’s model that has been widely used under AC conditions has been modified, taking both electric field and charge dynamics under DC conditions into consideration. The effects of loading level or current through the conductor, cavity location and material properties on partial discharges have also been studied.

Electrical conductivity is important in determining the characteristics of partial discharge under DC conditions and discharges tend to happen in short when the cavity field exceeds the inception level under the parameter values studied in the paper.

Building the numerical model is the purpose of the paper, and there is lack in experiment and the comparison between the simulation results and experiment.

The proposed model provides the numerical model describing partial discharge in HVDC cable and helps understand the partial discharge mechanism under DC voltage.

To the best of the author’s knowledge, this paper is a very early research on the numerical modelling work on partial discharge under DC voltage.

Nowadays, the speech emotion recognition (SER) model has enhanced as the main research topic in various fields including human–computer interaction as well as speech processing. Generally, it focuses on utilizing the models of machine learning for predicting the exact emotional status from speech. The advanced SER applications go successful in affective computing and human–computer interaction, which is making as the main component of computer system's next generation. This is because the natural human machine interface could grant the automatic service provisions, which need a better appreciation of user's emotional states.

This paper implements a new SER model that incorporates both gender and emotion recognition. Certain features are extracted and subjected for classification of emotions. For this, this paper uses deep belief network DBN model.

Through the performance analysis, it is observed that the developed method attains high accuracy rate (for best case) when compared to other methods, and it is 1.02% superior to whale optimization algorithm (WOA), 0.32% better from firefly (FF), 23.45% superior to particle swarm optimization (PSO) and 23.41% superior to genetic algorithm (GA). In case of worst scenario, the mean update of particle swarm and whale optimization (MUPW) in terms of accuracy is 15.63, 15.98, 16.06% and 16.03% superior to WOA, FF, PSO and GA, respectively. Under the mean case, the performance of MUPW is high, and it is 16.67, 10.38, 22.30 and 22.47% better from existing methods like WOA, FF, PSO, as well as GA, respectively.

This paper presents a new model for SER that aids both gender and emotion recognition. For the classification purpose, DBN is used and the weight of DBN is used and this is the first work uses MUPW algorithm for finding the optimal weight of DBN model.

The purpose of this paper is to investigate the dynamic relationship between inflation, interest rate differential, the exchange trade and exchange rate parities, i.e. (USD/TND, EUR/TND and JPY/TND).

Given the existing non-linear form between the different time series in this study, the empirical analysis is based on the using of non-parametric method such as the artificial neural networks. In order to detect the causality relationship between the variables, the authors use an NARX model.

Mixed results were found; there is a bidirectional relationship between inflation and exchange rate among others. Results also show that there is a strong correlation between the terms of trade and inflation, which says that trade openness increases the demand for imported goods and, therefore, causes more inflation for Tunisia.

After these results, it is important for policymakers to know which factors influence exchange rate stability, especially in developing countries like Tunisia.

Snacking typically occurs as an automatic, consciously uncontrolled process which can lead to unintended health consequences. Grounded cognition informs about the multifaceted drivers of such automatic consumption processes. By integrating situation-, stimulus-, and person-specific factors, this study provides a holistic account of snacking.

A combined psychophysiological and behavioral experiment is conducted wherein participants can casually snack chocolate while participating in a survey setting. Implicit cognitions are assessed with the Implicit Association Test. The percentage of consumed chocolate serves as dependent variable in a Tobit regression with predictors at situation, stimulus and person level.

Chocolate snacking is positively influenced by personal craving tendencies, implicit food associations and situational contingency. We condense the results into an overarching framework in line with grounded cognition literature.

The multidimensional framework can guide consumer protection efforts to reduce excessive snacking habits based on situation, stimulus and person.

This study integrates theory from social cognition, consumer research, and behavioral food research and, thereby, extends the existing body of knowledge on grounded cognitions underlying snacking consumption.

The purpose is to present a new approach for studying the phenomenon of traveling bubble cavitation.

A flow around a rigid, 2D hydrofoil (NACA‐0012) with a smooth surface is analyzed computationally. The Rayleigh‐Plesset equation is numerically integrated to simulate the growth and collapse of a cavitation bubble moving in a varying pressure field. The analysis is performed for both incompressible and compressible fluid cases. Considering the initial bubble radius as a uniformly distributed random variable, the probability density function of the maximum collapse pressure is determined.

The significance of the liquid compressibility during bubble collapse is illustrated. Furthermore, it is shown that the initial size of the bubble has a significant effect on the maximum pressure generated during the bubble collapse. The maximum local pressure developed during cavitation bubble collapse is of the order of 10⁴ atm.

A single bubble model that does not account for the effect of neighboring bubbles is used in this analysis. A spherical bubble is assumed.

A new approach has been developed to simulate traveling bubble cavitation by interfacing a CFD solver for simulating a flow with a program simulating the growth and collapse of the bubble. Probabilistic analysis of the local pressure due to bubble collapse has been performed.

Prediction accuracies are usually affected by the techniques and devices used as well as the algorithms applied. This work aims to attempt to further devise a better positioning accuracy based on location fingerprinting taking advantage of two important mobile fingerprints, namely signal strength (SS) and signal quality (SQ) and subsequently building a model based on extreme learning machine (ELM), a new learning algorithm for single‐hidden‐layer neural networks.

Prediction approach to location determination based on historical data has attracted a lot of attention in recent studies, the reason being that it offers the convenience of using previously accumulated location data to subsequently determine locations using predictive algorithms. There have been various approaches to location positioning to further improve mobile user location determination accuracy. This work examines the location determination techniques by attempting to determine the location of mobile users by taking advantage of SS and SQ history data and modeling the locations using the ELM algorithm. The empirical results show that the proposed model based on the ELM algorithm noticeably outperforms k‐Nearest Neighbor approaches.

WiFi's SS contributes more in accuracy to the prediction of user location than WiFi's SQ. Moreover, the new framework based on ELM has been compared with the k‐Nearest Neighbor and the results have shown that the proposed model based on the extreme learning algorithm outperforms the k‐Nearest Neighbor approach.

A new computational intelligence modeling scheme, based on the ELM has been investigated, developed and implemented, as an efficient and more accurate predictive solution for determining position of mobile users based on location fingerprint data (SS and SQ).