Optimizing multi-antenna M-MIMO DM communication systems with advanced linearization techniques for RF front-end nonlinearity compensation in a comprehensive design and performance evaluation study

The study presented in this research focuses on linearization strategies for compensating for nonlinearity in RF front ends in multi-antenna M-MIMO OFDM communication systems. The study includes the design and evaluation of techniques such as analogue pre-distortion (APD), crest factor reduction (CFR), multi-antenna clipping noise cancellation (M-CNC), and multi-clipping noise cancellation (MCNC). Nonlinearities in RF front ends can cause signal distortion, leading to reduced system performance. To address this issue, various linearization methods have been proposed. This research examines the impact of antenna correlation on power amplifier efficiency and bit error rate (BER) of transmissions using these methods. Simulation studies conducted under high signal-to-noise ratio (SNR) regimes reveal that M-CNC and MCNC approaches offer significant improvement in BER performance and PA efficiency compared to other techniques. Additionally, the study explores the influence of clipping level and antenna correlation on the effectiveness of these methods. The findings suggest that appropriate linearization strategies should be selected based on factors such as the number of antennas, SNR, and clipping level of the system

Enhancing child safety with accurate fingerprint identification using deep learning technology

Utilizing deep learning algorithms to differentiate the fingerprints of children can greatly enhance their safety. This advanced technology enables precise identification of individual children, facilitating improved monitoring and tracking of their activities and movements. This can effectively prevent abductions and other forms of harm, while also providing a valuable resource for law enforcement and other organizations responsible for safeguarding children. Furthermore, the use of deep learning algorithms minimizes the potential for errors and enhances the overall accuracy of fingerprint recognition. Overall, implementing this technology has immense potential to significantly improve the safety of children in various settings. Our experiments have demonstrated that deep learning significantly enhances the accuracy of fingerprint recognition for children. The model accurately classified fingerprints with an overall accuracy rate of 93%, surpassing traditional fingerprint recognition techniques by a significant margin. Additionally, it correctly identified individual children's fingerprints with an accuracy rate of 89%, showcasing its ability to distinguish between different sets of fingerprints belonging to different children

Enhancing quality of service in IoT through deep learning techniques

When evaluating an Internet of Things (IoT) platform, it is crucial to consider the quality of service (QoS) as a key criterion. With critical devices relying on IoT technology for both personal and business use, ensuring its security is paramount. However, the vast amount of data generated by IoT devices makes it challenging to manage QoS using conventional techniques, particularly when attempting to extract valuable characteristics from the data. To address this issue, we propose a dynamic-progressive deep reinforcement learning (DPDRL) technique to enhance QoS in IoT. Our approach involves collecting and preprocessing data samples before storing them in the IoT cloud and monitoring user access. We evaluate our framework using metrics such as packet loss, throughput, processing delay, and overall system data rate. Our results show that our developed framework achieved a maximum throughput of 94%, indicating its effectiveness in improving QoS. We believe that our deep learning optimization approach can be further utilized in the future to enhance QoS in IoT platforms

Examining the mediating role of strategic thinking on organizational performance: A quantitative analysis

Currently, private universities are making significant efforts to keep up with changing market demands and societal expectations, particularly in an environment that is rapidly evolving such as Baghdad, Iraq. In order to maintain high standards and achieve excellence, they must continuously seek out the most effective practices and strategies for success. Strategic management has been demonstrated to be an efficient tool for enhancing institutional performance, and the university's organizational culture, which is comprised of traditional institutional elements, is recognized as a significant factor in how organizations operate. Through the utilization of strategic thinking as a mediator, this research aims to evaluate and clarify how organizational culture impacts institutional excellence. A simple random sample was employed to gather information from individuals in managerial positions across multiple sections. Structural Equation Modeling (SEM) was employed to explain the variables under examination. The outcomes and analyses reveal a high level of relative proportions for the variables under investigation and indicate an indirect relationship between the independent and dependent variables. This research recommends several important suggestions and proposes additional research avenues for further investigatio

A Deep learning approach for trust-untrust nodes classification problem in WBAN

The enormous growth in demand for WBAN services has resulted in a new set of security challenges. The capabilities of WBAN are developing to meet these needs. The complexity, heterogeneity, and instability of the mobile context make it difficult to complete these duties successfully. A more secure and flexible WBAN setting can be attained using a trust-untrust nodes classification, which is one method to satisfy the security needs of the WBAN. Considering this, we present a novel Deep Learning (DL) approach for classifying WBAN nodes using spatial attention based iterative DBN (SA-IDBN). Z-score normalization is used to remove repetitive entries from the input data. Then, Linear Discriminate Analysis (LDA) is employed to retrieve the features from the normalized data. In terms of accuracy, latency, recall, and f-measure, the suggested method's performance is examined and contrasted with some other current approaches. Regarding the classification of WBAN nodes, the results are more favorable for the suggested method than for the ones already in use

An intelligent approach for enhancing the Quality of Service in IoMT based on 5G

The concept and growth of superior individualized healthcare technologies are influenced in significant ways by the emerging areas of “Artificial Intelligence (AI) and the Internet of Things (IoT)”. Most people use wearable devices for mHealth, hence there are many potential applications for the “Internet of Medical Things (IoMT)”. Only 5G can provide the necessary support for smart medical devices to perform many different types of demanding computing activities. Today, heart disease was the major mortality on a global scale. For patients who need a greater accurate diagnosis and treatment, the advancement of medical innovation has created new obstacles. Although many studies have focused on diagnosing cardiac disease, the findings are often inaccurate and fail to fulfill patients' expectations of quality of service (QoS). So, this paper introduces a novel “feed-forward Bi-directional long-short term memory (FF-Bi-LSTM) algorithm to predict heart disease more accurately with enhanced QoS in IoMT based on 5G”. Linear discriminant analysis (LDA) and min-max normalization are employed, respectively, for preprocessing and feature extraction. The efficacy of the suggested approach is measured using several different metrics, including accuracy, precision, recall, and f1-score. The proposed method is also compared to certain existing techniques. These results show that the suggested strategy outperforms existing strategies in terms of improving QoS

Enhancing smart home energy efficiency through accurate load prediction using deep convolutional neural networks

The method of predicting the electricity load of a home using deep learning techniques is called intelligent home load prediction based on deep convolutional neural networks. This method uses convolutional neural networks to analyze data from various sources such as weather, time of day, and other factors to accurately predict the electricity load of a home. The purpose of this method is to help optimize energy usage and reduce energy costs. The article proposes a deep learning-based approach for nonpermanent residential electrical energy load forecasting that employs temporal convolutional networks (TCN) to model historic load collection with timeseries traits and to study notably dynamic patterns of variants amongst attribute parameters of electrical energy consumption. The method considers the timeseries homes of the information and offers parallelization of large-scale facts processing with magnificent operational efficiency, considering the timeseries aspects of the information and the problematic inherent correlations between variables. The exams have been done using the UCI public dataset, and the experimental findings validate the method's efficacy, which has clear, sensible implications for setting up intelligent strength grid dispatching

A novel nomadic people optimizer-based energy-efficient routing for WBAN

In response to user demand for wearable devices, several WBAN deployments now call for effective communication processes for remote data monitoring in real time. Using sensor networks, intelligent wearable devices have exchanged data that has benefited in the evaluation of possible security hazards. If smart wearables in sensor networks use an excessive amount of power during data transmission, both network lifetime and data transmission performance may suffer. Despite the network's effective data transmission, smart wearable patches include data that has been combined from several sources utilizing common aggregators. Data analysis requires careful network lifespan control throughout the aggregation phase. By using the Nomadic People Optimizer-based Energy-Efficient Routing (NPO-EER) approach, which effectively allows smart wearable patches by minimizing data aggregation time and eliminating routing loops, the network lifetime has been preserved in this research. The obtained findings showed that the NPO method had a great solution. Estimated Aggregation time, Energy consumption, Delay, and throughput have all been shown to be accurate indicators of the system's performance

Exploring the potential of offline cryptography techniques for securing ECG signals in healthcare

In the research, a software for ECG signal based on Chaos encryption based on C#-programmed and Kit of Microsoft Visual Studio Development was implemented. A chaos logic map (ChLMp ) and its initial value are utilized to create Level-1 ECG signal based on Chaos encryption bit streams. A ChLMp, an initial value, a ChLMp bifurcation parameter, and two encryption level parameters are utilized to create level-2 ECG signal based on Chaos encryption bit streams. The level-3 ECG signal based on Chaos encryption software utilizes two parameters for the level of encryption, a permutation mechanism, an initial value, a bifurcation parameter of the level of encryption, and a ChLMp. We assess 16-channel ECG signals with great resolution utilizing encryption software. The level-3 ECG signal based on Chaos encryption program has the slowest and most reliable encryption speed. The encryption effect is superior, according to test findings, and when the right decoding parameter is utilized, the ECG signals may be completely recovered. The high resolution 16-channel ECG signals (HRMCECG) won't be recovered if an invalid input parameter occurred, such as a 0.00001% initial point error, which will result in chaotic encryption bit streams