Abstract The amount of energy accessible is greatly impacted by technical losses that occur when electrical energy produced in strategic centers is sent to consumers via transmission networks. These losses generate a lot of issues for grid operators because this energy is already produced insufficiently, which results in load shedding that is permanent in developing nations. Local energy adoption is being driven by the need to develop alternate energy sources to meet the ever-increasing demand. The goal of the rapidly expanding discipline of DER modeling research is to optimize decentralized energy management. Indeed, a large number of tiny energy production facilities are now dispersed throughout the world (territory) due to the increased development of renewable energies like solar, wind, and energy storage systems. It is feasible to forecast these DERs’ output, integrate them into the electrical grid, and effectively regulate them by modeling them. It considers factors including consumer consumption, weather, and technological limitations. In order to maintain grid stability and forward the energy transition to a more resilient and sustainable system, this modeling is crucial. This document aims to provide a methodological framework for modeling renewable energy sources and energy storage systems as part of an integrated approach to decentralized energy management.

Abstract This paper presents a low-cost circular patch antenna sensor developed for measurement of moisture content in lentils and rice. The proposed sensor was modeled and simulated in HFSS after that the prototype was fabricated by chemically etching the FR4 material of 1.6 mm thickness. Measurements were conducted using a Vector Network Analyzer. The presented antenna sensor is resonating at 2.45 GHz frequency. Calibration equations were obtained by utilizing moisture content and reflection coefficients values. The moisture content varied between 2.04 and 11.11% based on wet weight. A good agreement between the actual and calculated values of moisture content validates that this proposed antenna sensor could serve as a moisture sensor for rice and lentil grains. For rice, the sensitivity is found to be 0.0071, and the regression coefficient is equal to 0.9976. In case of lentil, the values of the regression coefficient and sensitivity are 0.9981 and 0.0088, respectively. Furthermore, a real-time moisture sensing setup has made using a laptop with LabVIEW software, ESP32 microcontroller, VNA, and an Ethernet connection. The moisture content values can be seamlessly displayed in real time on mobile devices or laptop for convenient monitoring and analysis.

Abstract The inability of visually impaired individuals to navigate their environment independently can lead to a loss of independence and quality of life. Existing solutions do not address the specific needs of individuals who speak Yorùbá. Therefore, there is need to develop a smart walking stick that can detect obstacles and communicate in Yorùbá. The development process begins with creating an object detection dataset, featuring annotated images of common obstacles in Yorùbá, to ensure cultural and linguistic relevance. A Convolutional Neural Network is then trained using this dataset to achieve precise obstacle detection and classification. The model is subsequently deployed to Render's cloud server to leverage advanced computational resources for efficient processing. The final stage involves integrating the trained model with the ESP32. The model achieved accuracy, precision, recall, and F1-score of 0.8969, 0.9110, 0.9915, and 0.8969 in obstacle recognition and offers about 6.23 h of continuous use on a full battery charge. This work demonstrates the viability of integrating cloud-based machine learning into assistive devices for visually impaired users. This study has the potential to significantly impact on the lives of visually impaired individuals, contribute to the advancement of assistive technology, and promote cultural inclusivity. It will also provide opportunities for language learning and engagement.

Abstract Segmentation plays a key role in designing a CAD system. Therefore, we need to implement highly accurate image segmentation to extract the shape of the lung from X-rays. Lung segmentation is challenging due to significant shape variations and unclear lung regions caused by severe lung diseases. Many CNN models give better results in segmentation, but every model has its limitations. Our paper proposes a new, improved model that builds upon the base model, SegNet. SegNet is the best model for semantic segmentation. Our proposed model is a combination of SegNet and ResNet 50. We are replacing the encoder of the SegNet with ResNet50. We modified the base model to get better performance and results. We utilized the Shenzhen dataset, a publicly available dataset, for training and testing the model. The model performance is evaluated in terms of global accuracy, Jaccard index, and dice similarity coefficient. The proposed model achieved a global accuracy of 97.73%, a Jaccard index of 97.32%, a Dice similarity coefficient of 97.33%, a precision of 98.74%, a recall of 98.58%, and an F1-score of 97.37%. The experimental results showed that our model performed better than the other models in terms of global accuracy, Jaccard index, precision, recall, F1-score, and dice similarity coefficient.

Abstract Free driving with electrically powered micromobility vehicles such as e-mopeds and e-scooters is an emerging mobility trend. This trend has also been visible in Germany since the ordinance on the use of electric microvehicles on public roads came into force in 2019. Car sharing and station-based bike sharing have been scientifically studied more often than the usage patterns and behavior of e-moped and e-scooter customers. Insights into usage patterns and customer behavior can be used to improve customer satisfaction and the business model, for example, to increase the utilization rate or distribution of e-mopeds or to offer customers more targeted incentives to perform operational activities. Similar to the existing scientific work, the data set of an e-moped supplier in Stuttgart, Germany is analyzed. Data were analyzed according to the (CRISP-DM) and a (RFM) analysis was performed. The data were clustered using different clustering configurations depending on the model used and the number of clusters. Clusters resulting from the highest performing configuration according to Calinski–Harabasz index (k-Means with 4 clusters) were further analyzed. The resulting clustering allows conclusions to be drawn about how customer usage patterns and behavior have changed compared to previous analyses in the literature. Examples of further findings are that one in five customers abandoned the registration process, or that early adopters were between 40 and 55 years old.

Abstract This article introduces a 6-bit Symbol Sequence Modulation (6-SSM) scheme developed to enhance communication security and efficiency through sequence-based modulation. The 6-SSM is designed to exploit symbol sequences, enabling improved secrecy performance through time-slot diversity. To analytically evaluate the performance of the proposed 6-SSM system, an exponent-based Q-function approximation (QFA) is also developed. This QFA employs a composite Gauss–Legendre quadrature applied to the polar form of the Q function. By dividing the integration range into intervals $$N$$ N and using $$n$$ n nodes per interval, the approximation error is significantly reduced to a factor of $$\frac{1}{N^{2n}}$$ 1 N 2 n . A practical configuration with two intervals and two nodes yields a four-exponent QFA that offers high accuracy, low relative error, and reduced complexity compared to existing approximations. The QFA is used to derive closed-form expressions for the bit error rate (BER) of the 6-SSM scheme under generalised fading channels, including Nakagami-m, $$\kappa$$ κ – $$\mu$$ μ , and $$\eta$$ η – $$\mu$$ μ distributions. The analytical results match closely Monte Carlo simulations, confirming the precision of the proposed QFA. Additionally, BER expressions are employed to analyse the secrecy rate, revealing that higher-order SSM offers improved physical layer security.

Abstract Of all howling control techniques, the adaptive feedback control (AFC) scheme promises high signal quality and high maximum stable gain (MSG) increase figures at the expense of high computational complexity requirements. The high computational complexity requirement of the AFC scheme generally limits its application in long acoustic feedback path applications because of the significant number of calculations per update cycle required. In this work, the feedback compensation technique having low computational complexity requirements is presented. A MATLAB® model of the proposed technique was developed and simulated for both speech and audio input signals. With a signal analysis frame size and acoustic path model coefficients of 128 and 4410, respectively, the technique requires 1025 multiplication operations; in comparison, the AFC scheme using the NLMS algorithm requires 17,646 calculations. The modified average signal quality figures of the developed technique are 2.3299 and 1.1859 for speech and audio signals respectively. The MSG increase figures are 8.428 dB and 7.76 dB for both speech and audio signals, respectively. These figures show that the developed technique has reduced computational complexity requirements at the expense of poor sound quality figures and less-than-infinite MSG increase figures.

Abstract Because of their dependability and effectiveness, three-phase induction motors are frequently utilized in industrial settings. They are susceptible to asymmetrical thermal flow, though, which can have a negative impact on longevity, performance, and efficiency. Even though this problem is common, its precise thermal impact and mitigation techniques are frequently ignored in the literature that is currently available. Using ANSYS Motor-CAD 2025 R1.1, this study fills this gap by creating and analysing a computational model of a three-phase induction motor in order to assess the impact of thermal asymmetry and provide practical thermal management options. In order to model heat generation and dissipation in crucial motor components including the stator, rotor, and windings under asymmetrical conditions, the study uses finite element analysis. The findings indicate that thermal imbalances cause higher winding resistance, decreased efficiency, localized overheating, and hastened insulation degradation. With observed temperature changes of 74.7 °C, 76.7 °C, 88.7 °C, and 86.5 °C, the permissible thermal range of 10 °C to 40 °C was greatly exceeded. The suggested tactics, which include improved cooling, redesigned airflow, and material advancements, show a great deal of promise in reducing these impacts. This research adds to the continuous endeavours to improve the energy efficiency and thermal dependability of induction motors in actual industrial settings.

Abstract The widespread adoption of smart meters has significantly made available detailed energy consumption data, providing valuable insights into consumer usage patterns. This paper takes advantage of this fact and presents a comprehensive time series analysis of energy usage patterns using smart meters time series data from high-usage power consumption companies in Tanzania. To the best of our knowledge, the study builds upon no any other data-driven research that uniquely focuses on TANESCO smart meter data to draw insights and forecast usage trends. Therefore, a systematic and scientific study is initiated to explore energy usage trends to enhance the power services by understanding customer consumption behavior. Our study introduces robust methods to handle missing data, a prevalent issue in Tanzania smart meter datasets due to power interruptions, ensuring robust energy consumption analysis. It, however, addresses the challenges of inconsistent data availability and significant discrepancies in power usage among different companies in Tanzania by developing methods to work with fragmented data and draw meaningful conclusions about long-term energy trends. The study devises an approach to unify discontinuous datasets for time series studies and analyzes variations in energy consumption patterns to reveal operational and sectoral influences on energy demand, providing insights into the underlying factors driving these variations and their implications for energy management. Our findings suggest that a detailed understanding of temporal consumption patterns can lead to more efficient demand-side management, encompassing improved demand response, power management, power monitoring, and distribution efficiency, resulting in cost-savings and a possibility of enhanced integration of renewable energy sources.

Abstract Early detection of incipient faults in three-phase induction motors is crucial to enhance system reliability and to minimize unplanned operational interruptions in industrial environments. Traditional diagnostic techniques often struggle to detect incipient faults, especially under fluctuating load conditions and may require complex signal processing or multiple sensors. The paper introduces a method for early detection of faults in three-phase induction motors using Wavelet Kernel-enabled convolutional neural networks (CNNs). The proposed system accurately identifies stator interturn faults in single or multiple phases and broken rotor bar faults, even under varying operating conditions such as load variations. By employing 14 mother wavelets as convolution filters, the method effectively extracts critical features from stator current signatures, streamlining the fault detection and classification process. This technique leverages the deep structures of CNNs to autonomously learn features from current signals, achieving a notable accuracy of above 97% in tests with both simulated model and two different hardware motor setup. The experimental result shows that it is capable of detecting as low as 1–2% of stator interturn fault with varying impedance in short circuit path as well as one broken rotor bar fault. Overall, the proposed method proves to be a powerful tool for the early diagnosis of incipient faults in induction motors with high degree of reliability and effectiveness.