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Wireless networks need channel estimation (CE) to function well. Deep learning (DL) has improved 5G and future-generation network communication reliability and computational complexity. Due to its lack of statistical channel information, the least squares (LS) estimation approach has significant estimate mistakes despite its cost-effectiveness and widespread usage. The MIMO-OFDM-5G-CS-AGAN architecture improves CE for 5G and beyond networks. The suggested CE architecture uses DL to outperform LS. The suggested architecture has been specifically developed using a multipath channel profile and a MIMO system, to effectively address situations characterized by mobility-induced Doppler effects in 5G and future networks. Because of its adaptability and scalability, the suggested architecture can accommodate almost unlimited transceiver antennas. Conditional Self-Attention Generative Adversarial Networks (CS-AGAN) are at the heart of the proposed method because they reliably provide high-quality CE at low error rates. Python is used for the implementation of the suggested approach. In this case, we have the performance metrics symbol error rate (SER), bit error rate (BER), uplink sum rate, downlink sum rate, Normalized CE Error, and mean square error. When compared to current methods like MIMO-OFDM-5G-Bi-LSTM, MIMO-OFDM-5G-DNN, and MIMO-OFDM-5G-CGAN, the suggested approach achieves 13.4%, 13.02%, 12.21% lower BER and 11.23%, 6.3%, 9.08% lower SER. [ABSTRACT FROM AUTHOR]

Multi-user detection techniques are crucial in minimizing the impact of Multi-Access Interference in Multiple-Input Multiple-Output Orthogonal Frequency-Division Multiplexing (MIMO-OFDM) systems. It is suggested to combine channel estimation with multi-user identification to get rid of different interferences and lower the Bit Error Rate. In this manuscript, Channel Estimation and Data Detection for 5G MIMO Communication in Fading Channels based on Recalling Enhanced Recurrent Neural Network are proposed. The suggested method prioritizes fast communication and strives to provide high reliability even when there is just a small amount of user equipment per base station. The study of the multipath Rayleigh fading channel in MIMO-OFDM systems has a difficulty with frequency selective channel estimate. The primary air interface for 4G and 5G broadband wireless communications is MIMO-OFDM. With its emphasis on the network's active users and its ability to reduce interference, MIMO-OFDM offers further advantages in multi-user identification. When the activity factor is unknown, the deep learning technique is used to identify the active users in the multi-access system. With a high data rate, the RERNN classifier is utilized to evaluate the unknown activity component. The BS receiver scans for active users and deciphers their transmissions. Both pilot and data symbols are used in activity detection. A unique pilot allocation strategy that reduces the common relation of the measurement matrix is proposed for optimum pilot placement. The sparse channel assessment is eventually verified, and the computational cost is decreased, by selecting the best pilot patterns with the aid of improved Mexican axolotl optimization algorithm. The CE and decoder techniques both regulate the impact of sounds and MAI. It accurately decodes the received signal without channel issues using Python implementation. Here the performance metrics like of Symbol Error Rate, Bit Error Rate, Uplink Sum Rate, Downlink Sum Rate, Normalized CE Error and Mean Square Error. The suggested approach demonstrates a reduction in BER by 23.34%, 17.02%, and 32.21%, as well as a reduction in SER by 21.09%, 12.32%, and 19.08%, when compared to conventional methodologies. [ABSTRACT FROM AUTHOR]

A triple band rectangular slotted antenna for THz applications has been reported in this study. Over a 10 dB impedance scale, the suggested antenna covers the frequency spectrums with a centre frequency of 0.73, 0.76, and 0.79 GHz. With a 0.75 GHz fundamental frequency, a rectangular patch antenna is first developed. Later, it is transformed into a rectangular ring, which provides triple band resonance across a 10 dB impedance bandwidth. The intended triple-band THz array that was built has a gain of up to 10 dBi and a directivity of 12 dB. Additionally, the array has a radiation efficiency of up to 85% throughout all spectral bands. After seeing and assessing the impact that the variation in the number of radiating pieces had on both the gain and the return loss, we came to this conclusion. Given this information, the developed antenna has been a potential choice for usage in satellite and space research as well as terrestrial applications. [ABSTRACT FROM AUTHOR]

An encouraging development is the quick expansion of renewable energy extraction. Harnessing renewable energy is economically feasible at the current rate of technological advancement. Traditional energy sources, such as coal, petroleum, and hydrocarbons, which have negative effects on the environment, are coming under more social and financial pressure. Companies need more solar and wind power because this calls for a well-balanced mix of renewable resources and a higher proportion of alternative energy sources. Sustainable energy can be captured using a variety of techniques. Massive scale and small-sized are the two most prevalent techniques. No renewable energy source possesses an inherent property that restricts how it may be managed or how it can be planned to produce electricity. A number of factors have contributed to a growth in the use of alternative sources, one of which is to mitigate the effects of rising temperatures. To improve the ability to estimate renewable energy, various modeling approaches have been created. This region might use an HRES to give many sources with the inclusion of different energy sources. The inventiveness of solar and wind power and the brilliant ability of neural networks to handle complex time-series data signals have both aided in the prediction of sustainable energy. Therefore, this research will examine the numerous information models in order to determine which proposed models can provide accurate projections of renewable energy output, such as sunlight, wind, or pumped storage. In the fields of sustainable energy predictions, a number of machine learning methods, such as multilayer perceptions MLP, RNN CNN, and LSTM designs, are frequently utilized. This form of modeling uses historical data to predict potential values and can predict short-term patterns in solar and wind generation. [ABSTRACT FROM AUTHOR]

The World Wide Web(WWW) comprises a wide range of information, and it is mainly operated on the principles of keyword matching which often reduces accurate information retrieval. Automatic query expansion is one of the primary methods for information retrieval, and it handles the vocabulary mismatch problem often faced by the information retrieval systems to retrieve an appropriate document using the keywords. This paper proposed a novel approach of hybrid COOT-based Cat and Mouse Optimization (CMO) algorithm named as hybrid COOT-CMO for the appropriate selection of optimal candidate terms in the automatic query expansion process. To improve the accuracy of the Cat and Mouse Optimization (CMO) algorithm, the parameters are tuned with the help of the Coot algorithm. The best suitable expanded query is identified from the available expanded query sets also known as candidate query pools. All feasible combinations in this candidate query pool should be obtained from the top retrieved documents. Benchmark datasets such as the GOV2 Test Collection, the Cranfield Collections, and the NTCIR Test Collection are utilized to assess the performance of the proposed hybrid COOT-CMO method for automatic query expansion. This proposed method surpasses the existing state-of-the-art techniques using many performance measures such as F-score, precision, and mean average precision (MAP). [ABSTRACT FROM AUTHOR]

Facial expression recognition (FER) plays a vital role in image processing according to the widespread development of human interactive applications. In the past few years, various researchers have focused on FER for implementing it in different applications. The existing system suffers from various complexities such as low accuracy, computational cost, and poor recognition performances. In this article, we proposed a novel concept to recognize facial expressions. The proposed work comprises three sections that are pre-processing, feature extraction, and classification. The pre-processing techniques remove the unwanted data from the original image and enhance the crucial details for further processing. The Convolutional Neural Network (CNN) is used for feature extraction. But, it yields lower performance in terms of feature extraction due to the shortage of hyperparameter tuning. Hence, the Multi-strategy Gravitational Search Algorithm (M-GSA) is utilized to extract the facial expression features from the eyebrow movement, nose, chin, and lip corner of the facial images. The facial expressions are classified via the Support Vector Machine (SVM) classifier. In this work, the top five facial expressions such as surprise, sad, happy, fear, and angry with three facial expression datasets such as FER-2013 dataset, CK+ dataset, and JAFFE dataset. Ultimately, the proposed method demonstrates better classification accuracy and recognition rates than different kinds of state-of-art methods. [ABSTRACT FROM AUTHOR]

In this work, we study the propagation of optical solitons in a tapered optical fiber with attenuating effects under the effect of erbium dopants. Through the constructed Lax pair, two soliton solutions are obtained with the aid of Darboux transformation. Using Mathematica tool, obtained two soliton solution is graphically represented as 2D and 3D plots. The generated two solitons are manipulated through some dispersion profiles with attenuating effects. Results are meaningful to understand the propagation of optical soliton in the concept of lossy optical medium. Moreover, the proposed approach can be used to control the soliton with lossy effects via dispersion management scheme. Our results reveals that the changes of the soliton width, amplitude, and phases are potentially influenced by the variable coefficients particularly dispersion parameter. The GVD profile mainly affects the intensity of optical solitons which leads to control the attenuation due to absorption of optical medium. These results will be helpful to understand the dynamics of attenuated soliton transmission in an inhomogeneous optical fiber medium. [ABSTRACT FROM AUTHOR]

This paper describes a novel multistage CIC filter which plays a vital role in software-defined radio (SDR) applications. In order to operate the baseband signal, wireless networking standards demand different sample rates. As a result, sample rate conversion (SRC) turns out as a pivotal method of SDR. Thus, cascaded integrator comb (CIC) filter is a distinct kind of linear-phase FIR filter which can be used as a decimation filter for SRC operation. The architecture of this filter is multiplierless with low passband droop; hence, it requires less area in contrast to other decimation filters. In this brief, the fundamental structure of CIC filter is examined with various parallel prefix adders and also the significant parameters of the CIC filter for different adders are exemplified. The proposed CIC filter with various parallel prefix adders has been designed in Verilog HDL using Xilinx ISE 14.7 and implemented on Kintex7 FPGA. The schemed CIC filter design is compared with the traditional CIC filter in view of area, speed and power consumption. Based on the obtained results on Kintex7 FPGA, the CIC filter with Brent Kung adder outperforms in terms of LUTs by 41.67% and power by 34.78% compared with the classical CIC filter and among the CIC filters using other parallel prefix adders. The proposed CIC uses new polynomial method which provides 33.33% reduction in passband droop and 41.14% reduction in stopband ripple compared to the existing sharpened CIC. [ABSTRACT FROM AUTHOR]

The most important and difficult challenge the digital society has recently faced is ensuring data privacy and security in cloud‐based Internet of Things (IoT) technologies. As a result, many researchers believe that the blockchain's Distributed Ledger Technology (DLT) is a good choice for various clever applications. Nevertheless, it encountered constraints and difficulties with elevated computing expenses, temporal demands, operational intricacy, and diminished security. Therefore, the proposed work aims to develop a Decentralized Identifiable Distributed Ledger Technology‐Blockchain (DIDLT‐BC) framework that is intelligent and effective, requiring the least amount of computing complexity to ensure cloud IoT system safety. In this case, the Rabin algorithm produces the digital signature needed to start the transaction. The public and private keys are then created to verify the transactions. The block is then built using the DIDLT model, which includes the block header information, hash code, timestamp, nonce message, and transaction list. The primary purpose of the Blockchain Consent Algorithm (BCA) is to find solutions for numerous unreliable nodes with varying hash values. The novel contribution of this work is to incorporate the operations of Rabin digital data signature generation, DIDLT‐based blockchain construction, and BCA algorithms for ensuring overall data security in IoT networks. With proper digital signature generation, key generation, blockchain construction and validation operations, secured data storage and retrieval are enabled in the cloud‐IoT systems. By using this integrated DIDLT‐BCA model, the security performance of the proposed system is greatly improved with 98% security, less execution time of up to 150 ms, and reduced mining time of up to 0.98 s. [ABSTRACT FROM AUTHOR]

Background: During the last 25 years, vitamin D -being a member of the superfamily of nuclear steroid transcription regulators - has emerged as a serious candidate in nervous system development and function and a therapeutic tool in a number of neurological pathologies. More recently, experimental and pre-clinical data suggest a link between vitamin D status and cognitive function. Human studies strongly support a correlation between low levels of circulating 25-hydroxyvitamin D (25(OH)D) and cognitive impairment or dementia in aging populations. In parallel, animal studies show that supplementation with vitamin D is protective against biological processes associated with Alzheimer's disease (AD) and enhances learning and memory performance in various animal models of aging and AD. More recently epidemiological associations have been made between low vitamin D and psychiatric disorders not typically associated with abnormalities in brain development such as depression and Alzheimer's disease Aim of the Study: was to review the recent literature to investigate the correlation between vitamin D status and neurocognitive function. Methods: We searched the medical literature to retrieve studies for the review to 30 November 2017. electronic search in the scientific database from 1965 to 2017- (Medline, Embase, AMED, Psych INFO, the Cochrane Library websites were searched for English Publications were obtained from both reprint requests and by searching the database. Data extracted included authors, country, year of publication, characteristics of patients, pathophysiology, risk factors, clinical manifestations, different diagnostic approaches and treatment modalities. Conclusion: Accumulating evidence in the current literature indicate that vitamin D deficiency is highly prevalent among patients with cognitive impairment, and that low vitamin D status may negatively impact cognition and that hypovitaminosis D is usually associated with increased risk of developing AD and dementia. Nevertheless, while vitamin D supplementation is safe for those with cognitive impairment, there is no solid proof that they will see significant improvement in their symptoms. [ABSTRACT FROM AUTHOR]