Various speech enhancement Algorithms (SEA) have been developed in the last few decades. Each algorithm has its advantages and disadvantages because the speech signal is affected by environmental situations. Distortion of speech results in the loss of important features that make this signal challenging to understand. SEA aims to improve the intelligibility and quality of speech that different types of noise have degraded. In most applications, quality improvement is highly desirable as it can reduce listener fatigue, especially when the listener is exposed to high noise levels for extended periods (e.g., manufacturing). SEA reduces or suppresses the background noise to some degree, sometimes called noise suppression algorithms. In this research, the design of SEA based on different speech models (Laplacian model or Gaussian model) has been implemented using two types of discrete transforms, which are Discrete Tchebichef Transform and Discrete Tchebichef-Krawtchouk Transforms. The proposed estimator consists of dual stages of a wiener filter that can effectively estimate the clean speech signal. The evaluation measures' results show the proposed SEA's ability to enhance the noisy speech signal based on a comparison with other types of speech models and a self-comparison based on different types and levels of noise. The presented algorithm's improvements ratio regarding the average SNRseq are 1.96, 2.12, and 2.03 for Buccaneer, White, and Pink noise, respectively.
Often phenomena suffer from disturbances in their data as well as the difficulty of formulation, especially with a lack of clarity in the response, or the large number of essential differences plaguing the experimental units that have been taking this data from them. Thus emerged the need to include an estimation method implicit rating of these experimental units using the method of discrimination or create blocks for each item of these experimental units in the hope of controlling their responses and make it more homogeneous. Because of the development in the field of computers and taking the principle of the integration of sciences it has been found that modern algorithms used in the field of Computer Science genetic algorithm or ant colo
... Show MoreThis research deals with the role of Qur’anic intents in facilitating and facilitating the understanding of the reader and the seeker of knowledge of the verses of the Holy Qur’an, particularly in the doctrinal investigations (prophecies), and the feature that distinguishes reference to the books of the intentions or the intentional interpretations is that it sings from referring to the books of speakers and delving into their differences in contractual issues and facilitating access To the meanings, purposes and wisdom that the wise street wanted directly from the rulings and orders contained in the verses of the wise Qur’an.
Mass transfer was studied using a rotating cylinder electrode with different lengths of legs acting as turbulence promoters. Two types of rotating cylinder ,made of brass, were examined : an enhanced cylinder one, with four rectangular extensions 10 mm long, 10 mm wide, and 1mm thick, and an enhanced cylinder two with four longitudes 30 mm long,10 mm wide, and 1mm thick. The best performance was obtained for enhanced cylinder two at low rotation speeds while enhanced cylinder one was realized at high rotation speeds. The mass transfer enhancement as compared with a normal rotating cylinder electrode, devoid of promoters, is 53% or 58% higher. The enhancement percentage decreased as rotation speeds increased further, since, seemingly, ful
... Show MoreArtificial Neural networks (ANN) are powerful and effective tools in time-series applications. The first aim of this paper is to diagnose better and more efficient ANN models (Back Propagation, Radial Basis Function Neural networks (RBF), and Recurrent neural networks) in solving the linear and nonlinear time-series behavior. The second aim is dealing with finding accurate estimators as the convergence sometimes is stack in the local minima. It is one of the problems that can bias the test of the robustness of the ANN in time series forecasting. To determine the best or the optimal ANN models, forecast Skill (SS) employed to measure the efficiency of the performance of ANN models. The mean square error and
... Show More<span>Dust is a common cause of health risks and also a cause of climate change, one of the most threatening problems to humans. In the recent decade, climate change in Iraq, typified by increased droughts and deserts, has generated numerous environmental issues. This study forecasts dust in five central Iraqi districts using machine learning and five regression algorithm supervised learning system framework. It was assessed using an Iraqi meteorological organization and seismology (IMOS) dataset. Simulation results show that the gradient boosting regressor (GBR) has a mean square error of 8.345 and a total accuracy ratio of 91.65%. Moreover, the results show that the decision tree (DT), where the mean square error is 8.965, c
... Show MoreThe Artificial Neural Network methodology is a very important & new subjects that build's the models for Analyzing, Data Evaluation, Forecasting & Controlling without depending on an old model or classic statistic method that describe the behavior of statistic phenomenon, the methodology works by simulating the data to reach a robust optimum model that represent the statistic phenomenon & we can use the model in any time & states, we used the Box-Jenkins (ARMAX) approach for comparing, in this paper depends on the received power to build a robust model for forecasting, analyzing & controlling in the sod power, the received power come from
... Show MoreCO2 geo-storage efficiency is strongly influenced by the wettability of the CO2-brine-mineral system. With decreasing water-wetness, both, structural and residual trapping capacities are substantially reduced. This constitutes a serious limitation for CO2 storage particularly in oil-wet formations (which are CO2-wet). To overcome this, we treated CO2-wet calcite surfaces with nanofluids (nanoparticles dispersed in base fluid) and found that the systems turned strongly water-wet state, indicating a significant wettability alteration and thus a drastic improvement in storage potential. We thus conclude that CO2 storage capacity can be significantly enhanced by nanofluid priming.
With the spread of global markets for modern technical education and the diversity of programs for the requirements of the local and global market for information and communication technology, the universities began to race among themselves to earn their academic reputation. In addition, they want to enhance their technological development by developing IMT systems with integrated technology as the security and fastest response with the speed of providing the required service and sure information and linking it The network and using social networking programs with wireless networks which in turn is a driver of the emerging economies of technical education. All of these facilities opened the way to expand the number of students and s
... Show MoreThe first successful implementation of Artificial Neural Networks (ANNs) was published a little over a decade ago. It is time to review the progress that has been made in this research area. This paper provides taxonomy for classifying Field Programmable Gate Arrays (FPGAs) implementation of ANNs. Different implementation techniques and design issues are discussed, such as obtaining a suitable activation function and numerical truncation technique trade-off, the improvement of the learning algorithm to reduce the cost of neuron and in result the total cost and the total speed of the complete ANN. Finally, the implementation of a complete very fast circuit for the pattern of English Digit Numbers NN has four layers of 70 nodes (neurons) o
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