Abstract Energy efficiency is widely recognized as one of the most significant and economical ways to lower greenhouse gas (GHG) emissions. The aims and goals are that smart meters can evaluate and communicate in-depth real-time electricity usage, enable remote real-time monitoring and management of power consumption, and provide consumers with real-time pricing and analyzed usage information. The house energy management controller decides which loads will be powered based on the real home energy demands and the predefined load priorities. Artificial intelligence (AI) is being used increasingly in control applications due to its great effectiveness and efficiency. As a result, in this work, the author designed, simulated, and optimized an artificial neural network-based model simulation framework that simulates a home with a variety of home appliances and optimizes the total energy consumption of the home realistically through intelligent control of home appliances. The MATLAB application was used to model and examine the performance of four common household appliances: the water heater (WH), washing machine (WM), air conditioner (AC), and refrigerator (RG). The result shows a considerable reduction and savings in energy consumption without a decrease in consumer comfort.

Abstract The methods of estimating the coordinates of sensor nodes based on the measurements made at the “anchor” nodes are widely used in WSNs. In particular, such methods include the RSS method, which is based on measuring the power of signals coming from sensors. The article shows that a similar method can be used for estimating the coordinates of an observation object in the WSN. The efficiency of measuring the coordinates of such an object in the presence of power measurement errors is analyzed. The conditions for increasing this efficiency have been identified. It is shown that the estimation is biased, but the magnitude of the bias is practically independent of the observational conditions and, therefore, can be easily compensated.

Abstract The detection of leaf diseases using modern technology has significant importance in agriculture and artificial intelligence. Deep learning, specifically, plays a crucial role in this field, as it enables accurate and efficient disease classification. Early detection of leaf diseases is vital to implementing timely treatments and preventing widespread damage to leaves. Leaf diseases can be caused by various factors, including bacteria, fungi, viruses, and other pathogens. Among them, bacteria and viruses are the most invasive and can lead to substantial yield losses if not identified and treated promptly. Bacterial and viral infections are common in agricultural settings, affecting leaves of all types and ages. Our research aims to propose a transfer learning-based model for predicting leaf diseases using a dataset of leaf images. The images will be classified into healthy or diseased leaves based on extracted features. The proposed model, named Leaf Disease Transfer Learning Algorithm (LDTLA), demonstrates promising results with an average accuracy of 97.37% on the dataset. Utilizing convolutional neural networks (CNN) and deep learning techniques, our LDTLA model outperforms previous quantitative and qualitative research studies in leaf disease detection. This advanced approach to leaf disease identification holds the potential to revolutionize agriculture by enabling farmers to make informed decisions, implement targeted treatments, and minimize leaf losses caused by diseases.

Abstract A compact, circularly polarized 8 × 8 antenna array is designed for the 60 GHz band. The array comprises circularly polarized magneto-electric dipoles (CP-ME-Dipole) excited by narrow slots. The slots are fed by a printed gap waveguide (PGWG) cooperative network optimized based on the termination of the effective impedance of the array elements. Thus, it accounts for the space mutual coupling of the antenna elements. A procedure based on the full-wave analysis of a 4 ´ 4 array is used to estimate each element’s 8 × 8 array effective port impedance. The cooperative feeding network is designed based on the known effective impedances. The array is divided into two half subarrays out of phase from each other, and a rectangular waveguide feeds both sides. The commonly measured bandwidth of 18.3% achieves return loss better than 10 dB and an axial ratio below 3 dB (AR) of less than 3 dB. A maximum gain of 26.2 dBic with a high radiation efficiency of 82% radiation efficiency.

Abstract Diabetes mellitus (DM) affects the hormone insulin, which causes improper glucose metabolism and raises the body’s blood sugar levels. With 4.2 million fatalities in 2019, DM is one of the top 10 global causes of mortality. Early detection of DM will aid in its treatment and avert complications. There must be a quick and simple technique to diagnose it. Such diseases can be managed, and human lives can be saved with early diagnosis. Smart prediction techniques like machine learning (ML) have produced encouraging outcomes in predictive classifications. There has been a lot of interest in ML-based decision-support platforms for the prediction of chronic illnesses to provide improved diagnosis and prognosis help to medical professionals and the general population. By building predictive models using diagnostic medical datasets gathered from DM patients, ML algorithms efficiently extract knowledge that helps predict diabetic individuals. The association between DM and a healthy lifestyle is used in the model. In this study, the NHANES (National Health and Nutrition Examination Survey) data set is utilized, along with five ML methods such as Artificial Neural Networks (ANN), CATBoost, XGBoost, XGBoost-histogram, and Light GBM to predict DM. The results of the experiment demonstrate that the XGB-h model outperformed other ML methods regarding area under the receiver operating characteristic curve (AUC-ROC) and accuracy. The most effective XGB-h framework can be used in a mobile app and a website to rapidly forecast DM. Real-time prediction using details delivered by the model at runtime can be developed as a whole bundle as a product. Clinicians can quickly determine who is likely to get diabetes using the proposed strategy, which will facilitate prompt intervention and caring.

Abstract This review paper presents a comprehensive study of commonly used power amplifier (PA) structures. In recent years, with the development of modern wireless telecommunications and their dramatic challenges, new requirements are needed. In addition, some applications, like cell phones and tablets, for example, need new considerations, especially in terms of power consumption. Also, linearity is another major factor in designing a PA. Furthermore, fabrication technologies such as complementary metal-oxide semiconductors (CMOS), silicon on insulators (SOI), gallium nitride (GaN), gallium arsenide (GaAs), etc. play a crucial role in terms of power consumption. Therefore, it is necessary for PAs to meet these considerations. This paper reviews design considerations, fabrication technologies, and common PA structures, including envelope tracking (ET), envelope elimination and restoration (EER), Doherty, linear amplification with nonlinear components (LINC), feedback, and feedforward linearization techniques with their pros and cons. This review focuses on the significant achievements, techniques, structures, and characteristics of each. Also, this review focuses on the significant achievements, techniques, structures, and characteristics of each. Also, this paper tries to provide a brief overview of the various methods with the advantages and disadvantages of each. This review paper tries to make readers familiar with common structures so that readers know the advantages and disadvantages of each and choose the desired structure based on their priorities.

Abstract The opioid epidemic is a serious national public health crisis. Although effective medications are available to treat opioid use disorder, there are low rates of uptake and treatment retention. To mitigate these problems, novel engineering devices, such as using virtual reality (VR), warrant examination. Certain opioid use disorder (OUD) populations may especially benefit from virtual reality to assist with treatment initiation and retention, such as incarcerated persons living in pre-release facilities, adolescents and young adults, and patients of methadone treatment facilities. However, prior to implementing VR in research and the community, issues such as side effects (e.g., VR-related nausea) need to be considered. This manuscript provides a brief review, identifies potential OUD-related populations that might most benefit from VR, and discusses considerations needing addressing prior to widescale implementation of VR for OUD.