Here, we examine the behavior of micropolar fluids as they travel through a curved stretching surface. We integrate thermo-diffusion and diffusion-thermo effects into the temperature and concentration equations to study the impact of cross-diffusion gradients on velocities, temperatures, and concentrations. The equations governing the flow are nonlinear, thus by applying practical similarity transformations, we obtain a system of nonlinear ordinary differential equations that can be solved. Using the Runge-Kutta numerical procedure, we are able to determine an answer to the modified system. The profiles of velocity, concentration, and temperature are shown, along with the influence of non-dimensional parameters on those variables. The skin friction coefficient, as well as the Nusselt and Sherwood numbers, is computed numerically, and their fluctuations as a function of various parameters are investigated. The implications of these findings for engineering and industry are greater.
Speech enhancement (SE) aims to improve the intelligibility and perceptual quality of speech contaminated by noise signals through spectral or temporal changes. Deep learning models achieve speech enhancement and estimate the magnitude spectrum. This paper proposes a novel and computationally efficient deep learning model to enhance noisy speech. The model pre-processes the noisy speech magnitude by redistributing energy from high-energy voiced segments to low-energy unvoiced segments using an adaptive power law transformation while maintaining the total energy of the speech signals constant. A U-shaped fuzzy long short-term memory (UFLSTM) estimates the magnitude of a time-frequency (T-F) mask by using the pre-processed data. Residual connections to the similar-shaped layers are added to avoid gradient decay. Attention process is adopted by modifying the forget gate of UFLSTM. To make a causal speech enhancement system, the processing does not include any future audio frames. We compare the proposed speech enhancement to other deep learning models in different noisy environments with signal-to-noise ratios of 0 dB, 5 dB, and 10 dB. The experiments show that the proposed SE system outscores the competing deep learning models and considerably improves speech intelligibility and quality. In terms of STOI and PESQ, the LibriSpeech database improves results by (0.211) 21.1% and (0.95) 36.39%, respectively, over noisy speech in seen noisy conditions, and by (0.199) 19.9% and (0.94) 35.69% over noisy speech in unseen noisy conditions. Further, the cross-corpus analysis shows that proposed SE system performs better when trained with the DNS dataset as compared to the LibriSpeech, VoiceBank, and TIMIT datasets.
To detect sustainable changes in the production processes, memory-type control charts are frequently utilized. This study is conducted to assess the performance of the Bayesian adaptive exponentially weighted moving average (AEWMA) control chart using ranked set sampling schemes following two different loss functions in the presence of a measurement error for posterior and posterior predictive distributions using conjugate priors. This study is based on the covariate model and multiple measurement methods in the presence of a measurement error (ME). The performance of the proposed Bayesian-AEWMA control chart with ME has been evaluated through the average run length and the standard deviation of the run length. Finally, a real-life application in semiconductor manufacturing was conducted to evaluate the effectiveness of the proposed Bayesian-AEWMA control chart with a measurement error based on different ranked set sampling schemes. The results demonstrate that the proposed control chart, in the presence of a measurement error, performed well in detecting out-of-control signals compared to the existing control chart. However, the median ranked set sampling scheme (MRSS) proved to be better than the other two schemes in the presence of a measurement error.
It is crucial to have a dependable and precise channel model in order to study the properties of millimeter wave (mmWave) propagation. The Quasi-deterministic (QD) channel model is employed in this viewpoint, which describes the propagation of mm-Wave as a group of reflected and scattered rays originating from a complex environmental setup. These rays are assumed to travel in clusters, with each cluster consisting of a deterministic ray followed by postcursor rays and preceded by precursor rays. The summation of these rays is the number of multiple path components (MPCs) of each cluster. However, this comes at the cost of higher computational complexity for the channel model, which can hinder the simulation's scalability. To simplify the QD channel model while maintaining accuracy, one option is to decrease the number of MPCs. In this paper, we present an analysis of path gains (PGs) of specular and diffused rays to reduce the total number of MPCs. Specifically, we propose two different reduction methods namely: i) the reduced post rays (RPR) method ii) the removed surfaces post rays (RSPR) method. The computational performance of the proposed methods is investigated in terms of computational time, and complexity. Additionally, the accuracy validation compared to the original QD model is evaluated in terms of PG cumulative distribution function (CDF), signal-to-noise ratio (SNR), and intra-cluster statistics. The proposed methods' complexity and accuracy were assessed by examining measured data from indoor and outdoor environments at 60 GHz and 28 GHz, respectively. Both first and second-order reflection orders were tested to illustrate the balance between the two variables. The simplified methods suggested can decrease computational time by approximately 16% and 11% for RSPR and RPR schemes, respectively, when compared to the original QD.INDEX TERMS Diffused rays, Millimeter wave propagation, Multipath components, Path gain, Quasideterministic channel models, intra-cluster statistics, Ray tracer.
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