Low Noise Amplifiers (LNAs) play a pivotal role in advancing sensor networks, IoT (Internet of Things), smart cities, and health monitoring systems. In the context of microwave sensors employed for detection, ranging, and communication within these domains, LNAs are crucial for ensuring reliable and accurate data transmission. These sensors, widely deployed in smart city infrastructure, IoT devices, and health monitoring systems, require exceptional environmental adaptability, particularly to varying temperature conditions, to ensure uninterrupted operation.Moreover, heightened sensitivity is imperative for capturing weak signals in dense urban environments or within the human body for health monitoring. By incorporating temperature- and process-insensitive LNAs with high Output Third-Order Intercept Point (OIP3) into the receiving systems of these microwave sensors, we can significantly enhance their sensitivity, enabling more precise data capture across diverse environments.This study proposes a highly linear LNA design, impervious to process and temperature variations, tailored specifically for sensor networks, IoT, smart cities, and health monitoring applications. The circuit's linearity is bolstered through derivative superposition technology, while an on-chip active bias circuit dynamically stabilizes the transconductance of the common-source transistor, mitigating IIP3 fluctuations due to process and temperature changes.Simulated using a 0.13μm CMOS process from DongBu High-Tech at the post-layout level, this LNA demonstrates exceptional performance with a 33.9dBm OIP3, operating efficiently at 42mW from a 2.8V supply. It achieves a stable gain of 16 dB, a low noise figure of 0.91dB, and excellent input/output return losses of less than -8 dB and -10 dB, respectively. This technology advancement fosters large-scale integration, empowering sensor networks, IoT devices, smart cities, and health monitoring systems to function seamlessly and reliably in the most challenging of environments.

This paper presents a comprehensive study on enhancing Industrial Internet of Things (IIoT) and Industrial Metaverse Applications through the integration of On-Device Automatic Speech Recognition (ASR) using Microsoft HoloLens 2 smart glasses. Specifically, this paper focuses on the utilization of the HoloLens 2's microphone array and sound capture APIs to benchmark the performance and accuracy of on-device ASR models. The evaluation of these models includes metrics such as Character Error Rate (CER), Word Error Rate (WER), and latency. Furthermore, the paper explores various optimization techniques, including quantization tools and model refinement strategies, aimed at minimizing latency while maintaining high accuracy. The study also emphasizes the importance of supporting low-resource languages, using Galician—a language spoken by less than 3 million people worldwide—as a case study. By benchmarking different variations of a Wav2Vec2.0-based ASR model fine-tuned for Galician, the research identifies the most effective models and their optimal runtime configurations. This work underscores the critical role of low-latency, on-device ASR systems in real-time IIoT and Industrial Metaverse applications, highlighting how these technologies can enhance operational efficiency, privacy, and user experience in industrial environments. The findings contribute to the broader applicability of ASR's potential in supporting emerging Metaverse applications across various industrial contexts.

Since ancient times, medicinal plants were used for the treatment of human diseases. The wide range of bioactive compounds contained in plants stipulates the development of novel phytopharmaceuticals. Traditional phytotherapy is also still applied as part of complex treatment. Antioxidants are one of the largest groups of bioactive compounds of plant origin. Thus, evaluation of the antioxidant capacity of medicinal plant extracts used in phytotherapy is of practical interest. Water extracts from 11 plants obtained by sonication for 30 min have been studied by cyclic voltammetry at bare glassy carbon electrode and electrode modified with mixture of 1 mg mL^–1 CeO₂ and SnO₂ nanoparticles dispersed in 0.10 mM cetylpyridinium bromide. Electrode surface modification provides improvement of the extract response in the shape of oxidation steps and their currents, which is caused by increase in the effective surface area of the modified electrode as voltammetric data confirm. For all extracts under study, the first oxidation step occurs in the range of 310-400 mV and the second one – in the range of 520-900 mV that agrees well with the classification of antioxidants by their reducing power. As far as only components with relatively high concentration give response on the voltammograms, a two-step chronoamperometric approach has been developed for estimation of the antioxidant capacity of medicinal plant extracts. Components with low concentration also give impact in the analytical response in this case. Two steps at 400 and 900 mV for 75 s each one have been applied and antioxidant capacity has been expressed as current recalculated per 100 mL of the extract. The comparison of the data obtained with the standard antioxidant parameters (total phenolics contents and antioxidant capacity toward 2,2-diphenyl-1-picrylhydrazyl) has shown a strong and very strong correlation level.

Synthetic dyes of various classes are widely applied in food production due to the bright and reproducible colors, high stability, and improvement of the foodstuff appearance. The dye content in food is strictly regulated due to a wide range of possible negative health effects. Thus, reliable and simple methods are in demand for food quality control. The presence of electroactive fragments in the structure of synthetic dyes makes possible development of voltammetric sensors for their quantification. In current work, Sunset Yellow FCF, Brilliant Blue FCF, and Quinoline Yellow have been studied as analytes. Novel sensitive and selective voltammetric sensors based on glassy carbon electrodes modified with mixtures of metal oxide nanoparticles dispersed in water or surfactant media have been developed for the first time. Mixtures of cerium and tin dioxide nanoparticles dispersed in cationic hexadecylpyridium bromide or non-ionic Brij® 35 surfactants have been shown to be the best sensing layers for the determination of Sunset Yellow FCF and Quinoline Yellow, respectively. Voltammetric sensor based on the mixture of cerium dioxide and iron(III) oxide nanoparticles dispersed in water allows determination of Brilliant Blue FCF. Sensors are characterized by scanning electron microscopy, electrochemical impedance spectroscopy and voltammetry. A significant increase in the electroactive surface area and electron transfer rate has been confirmed. Differential pulse voltammetry in Britton-Robinson buffer has been applied for dye quantification. The analytical parameters achieved are improved vs. existing ones and sufficient for application to real samples. The sensors developed have been successfully tested in beverage analysis. The results obtained have been compared to data of the independent methods.

This research delves into the advanced domain of fault detection in distributed motors within the Internet of Electrical Drives framework. The primary objective is to achieve precise and dependable fault detection in industrial motors by harnessing artificial neural networks (ANN) and leveraging data from a network of distributed devices. This study introduces a novel approach through the design and development of a comprehensive cyber-physical system (CPS) architecture, coupled with an optimized mathematical modeling framework for fault detection. The mathematical model is meticulously crafted to capture the intricate interactions within the CPS, emphasizing the dynamic relationships between distributed motors and their edge controllers. Signal processing employs Fast Fourier Transform (FFT) to extract critical frequency features that signal potential motor faults. The integration of an ANN-based fault detection system enhances the framework's capability to learn complex patterns and adapt to various motor conditions. The proposed framework and model undergo rigorous validation through experimental evaluations across multiple fault scenarios, assessing system performance in terms of accuracy, sensitivity, and false positive rates. The findings highlight the robustness and efficacy of this innovative approach, demonstrating its potential to significantly enhance the reliability and efficiency of fault detection in distributed motor systems. This research makes a valuable contribution to the field of industrial automation and smart manufacturing, offering a promising solution for improving operational efficiency and minimizing downtime in industrial environments.

Pose tracking and captioning are extensively employed for motion capturing and activity description in daylight vision scenarios. Activity detection through camera systems presents a complex challenge, necessitating the refinement of numerous algorithms to ensure accurate functionality. Even though there are notable characteristics, IP cameras lack integrated models for effective human activity detection. With this motivation, this paper presents a gait-driven OpenCV and MediaPipe machine-learning framework for human pose and movement captioning. This is implemented by incorporating the Generative 3D Human Shape (GHUM 3D) model which can classify human bones while Python can classify the human movements as either usual or unusual. This model is fed into a website equipped with camera input, activity detection, and gait posture analysis for pose tracking and movement captioning. The proposed approach comprises four modules, two for pose tracking and the remaining two for generating natural language descriptions of movements. The implementation is carried out on two publicly available datasets, CASIA-A and CASIA-B. The proposed methodology emphasizes the diagnostic ability of video analysis by dividing video data available in the datasets into 15-frame segments for detailed examination, where each segment represents a time frame with detailed scrutiny of human movement. Features such as spatial-temporal descriptors, motion characteristics, or key point coordinates are derived from each frame to detect key pose landmarks, focusing on the left shoulder, elbow, and wrist. By calculating the angle between these landmarks, the proposed method classifies the activities as "Walking" (angle between -45 and 45 degrees), "Clapping" (angles below -120 or above 120 degrees), and "Running" (angles below -150 or above 150 degrees). Angles outside these ranges are categorized as "Abnormal," indicating abnormal activities. The experimental results show that the proposed method is robust for individual activity recognition.

An advanced IoT-based Liquefied Petroleum Gas (LPG) cylinder monitoring and safety system is presented in this work. The proposed technique provides continuous monitoring of residential gas usage and detects any potential leakage. It utilizes an MQ135 gas sensor for gas leakage detection, a load cell to monitor the weight of the cylinder, and a DHT22 sensor for temperature sensing. The sensors are mounted on a customized trolley for domestic LPG cylinders. All the sensors are connected to NodeMCU microcontroller, which exchanges sensor data with a cloud platform using HHTP GET and POST method to transmit the data to a cloud-based MySQL database. Unlike other existing methods, the proposed approach does not necessitate any modifications to the existing setup, which includes the gas cylinder, regulating valve, and distribution pipe.

Furthermore, a mobile application that emphasizes the needs of the user is developed to enable a wider range of functionalities using cloud data collected from the sensors. The application facilitates the real-time monitoring of gas levels, provides comprehensive usage records for daily, weekly, and monthly intervals, issues immediate alarms in the event of gas leakage, low gas levels, and detects any unauthorized movement of the LPG cylinder such as theft. The proposed technique not only improves user safety but also streamlines gas cylinder management with predictive analytics based on gas consumption trends and projected days of usage. Moreover, the application includes a functionality that automatically orders a new cylinder with the vendor when the gas level drops below the predetermined threshold, therefore ensuring continuous availability of gas supply.

With the emergence of visual sensors and their widespread application in intelligent systems, precise and interpretable visual explanations have become essential for ensuring the reliability and effectiveness of these systems. Sensor data, such as that from cameras operating in different spectra, LiDAR, or other imaging modalities, is often processed using complex deep learning methods, whose decision-making processes can be unclear. Accurate interpretation of network decisions is particularly critical in domains such as autonomous vehicles, medical imaging, and security systems. Moreover, during the development and deployment of deep learning architectures, the ability to accurately interpret results is crucial for identifying and mitigating any sources of bias in the training data, thereby ensuring fairness and robustness in the model's performance. Explainable AI (XAI) techniques have garnered significant interest for their ability to reveal the rationale behind network decisions. In this work, we propose leveraging entropy information to enhance Class Activation Maps (CAMs). We explore two novel approaches: the first replaces the traditional gradient averaging scheme with entropy values to generate feature map weights, while the second directly utilizes entropy to weigh and sum feature maps, thereby reducing reliance on gradient-based methods, which can sometimes be unreliable. Our results demonstrate that entropy-based CAMs offer significant improvements in highlighting relevant regions of the input across various scenarios.

In this work, we investigate the dynamic response of the Maglev vehicle-guideway system at medium-low speed, in the presence of geometric and guideway irregularities. Such irregularities are recognized to be of importance, as they can be linked to the different stiffness of adjacent girders. In the analysis, the vehicle model is a multi-rigid body one while finite elements are adopted for the guideway system to allow for local effects of the F-type rail; the suspension control is instead based on a state observer, to compute the interaction between the vehicle and the guideway system. The ultra-low stiffness steel beam is used for the first time to carry out field tests, featuring a bending stiffness and a weight respectively reduced by 65% and around 80%, if compared to that of the adjacent ordinary concrete beams. The support piers of the steel beams are manually jacked up by 5, 10, 15, and 20 mm during the experimental campaign, to sense the system response and the effects on the suspension gap when the track geometry is uneven due to aforementioned irregularities and to environmental factors. The outcome of field tests and the numerical results have shown that the stiffness of the guideway system and the irregularities have significant effects on the monitored Maglev system. Appropriate arrangements of the stiffness relationship between adjacent girders and a clear matching among guideway, vehicle and suspension control parameters can be then used to effectively reduce the vibrations and improve vehicle comfort.

Introduction: The fertile soil is having a balanced nutrient value of pH, potassium, phosphorous, nitrogen, water retention capability, and organic substances. A fertile soil allows for better growth of plants and in turn leads to better production. The soil fertility needs vary from crop to crop such as nutrient needs, pH requirements, soil texture availability, etc . It is essential to identify the soil fertile level as per the crop.

Objective: Our objective of this paper is to develop a robust model that is capable of predicting soil fertility. The model is integrated with the IoT-generated data and federated learning-based feature selection techniques to improve the accuracy of the dataset.

Material/Methods: A soil mineral dataset was collected through the IoT sensors of different agricultural fields. It collects the parameters of the soil and these are moisture, pH, nutrient levels, etc. Further, it is transformed into the dataset which is divided into several parts(partitions). We used federated learning for the training of the ML model on the partitioned datasets which are collected from the IoT sensors. The model updates are aggregated centrally without sharing raw data, preserving data privacy. Initially, we applied different feature selection techniques on the dataset such as Variance Threshold, Chi-Square, Recursive Feature Elimination (RFE), PCA Loadings, Random Forest (RF) Importance, Mutual Information, and Lasso Coefficients) to suggest the best model. Then we applied machine learning algorithms such as Logistic Regression, Decision Trees, Naive Bayes, and their ensemble to analyze and improve the performance.

Result: From our experimental observation, we analyzed the performance metrics of different ML classifiers and it revealed that the ensemble of logistic regression and decision tree has better performance compared to other models. The model achieved a precision of 87%, accuracy of 87%, recall of 87%, and F1-Score of 86%.