Modern Sensor Technology Research Articles

Development of A Sensor-Based Tool for Measurement of Sensor-Based Capturing Skills

This research aims to develop a tool for measuring catching-throwing skills using modern, effective, and efficient sensor technology. This tool is designed to increase accuracy in measuring catch-throwing skills. This type of research uses (R&D). This research involved three experts in their field: an electrical expert, an evaluation and measurement expert, and a physicist. A total of 30 participants participated in testing in a small group of 12 people and a large group of 18 people with an age range of 15-20 years. Testing is carried out with questionnaires and direct tests. The statistical analysis used is r-correlation with the help of the SPSS application. The test results show a high alpha coefficient, confirming that this tool is consistent and reliable in collecting data. Three experts carried out practicality testing: Electrical Experts, Evaluation and Measurement Experts, and Physicists. The results show that this tool is efficient, with practicality levels of 98%, 92%, and 89%, respectively. In addition, an excellent level of effectiveness is demonstrated by expert assessment results, which reach 99%, 89%, and 85%. With this tool, researchers and teachers can evaluate object control skills more efficiently and accurately and assist in collecting more comprehensive data. Overall, this tool for measuring throwing and catching skills provides significant innovation and is expected to support research and education in measuring and developing children's motor skills.

Fire Fighter Robot Using IOT and Mobile Application

In today's fast-paced world, fire hazards pose significant risks to life, property, and the environment. Traditional fire detection systems often rely on human intervention, leading to delays in response time and exacerbating the damage caused by fire incidents. Our project, the IoT-Based Automatic Fire Detection and Extinguisher Robot, addresses this challenge by utilizing a combination of modern sensor technology, robotics, and IoT to provide an autonomous and real-time solution. The robot is equipped with flame and temperature sensors that allow it to detect fires at an early stage. Once a fire is detected, the robot activates its movement mechanism using DC motors to navigate toward the fire while the integrated servo-controlled water pump precisely sprays water to extinguish the fire. In addition to automatic firefighting, the robot features obstacle detection using ultrasonic sensors, ensuring safe navigation in complex environments. The system includes Bluetooth and Wi-Fi communication, allowing for remote control via an Android mobile app. This app provides both manual and automatic control options and sends real-time fire alerts when the robot detects a fire. This autonomous system minimizes human intervention, speeds up response times, and reduces fire damage effectively. The robot is powered by an ESP32 microcontroller, which processes data from the sensors and controls the motors and water pump. The rescue pod feature allows the robot to perform additional operations like transporting sensitive materials during emergencies. The system's IoT functionality enables real-time alerts and remote monitoring, enhancing its capability as a scalable and cost-effective solution for residential, commercial, and industrial fire safety.

A Cloud Infrastructure for Health Monitoring in Emergency Response Scenarios.

Wearable devices have a significant impact on society, and recent advancements in modern sensor technologies are opening up new possibilities for healthcare applications. Continuous vital sign monitoring using Internet of Things solutions can be a crucial tool for emergency management, reducing risks in rescue operations and ensuring the safety of workers. The massive amounts of data, high network traffic, and computational demands of a typical monitoring application can be challenging to manage with traditional infrastructure. Cloud computing provides a solution with its built-in resilience and elasticity capabilities. This study presents a Cloud-based monitoring architecture for remote vital sign tracking of paramedics and medical workers through the use of a mobile wearable device. The system monitors vital signs such as electrocardiograms and breathing patterns during work sessions, and it is able to manage real-time alarm events to a personnel management center. In this study, 900 paramedics and emergency workers were monitored using wearable devices over a period of 12 months. Data from these devices were collected, processed via Cloud infrastructure, and analyzed to assess the system's reliability and scalability. The results showed a significant improvement in worker safety and operational efficiency. This study demonstrates the potential of Cloud-based systems and Internet of Things devices in enhancing emergency response efforts.

Advanced Pressure-Sending Wristband for Disease Detection Based on Traditional Chinese Medicine Pulse Diagnosis

This paper explores the integration of Traditional Chinese Medicine (TCM) pulse diagnosis with modern sensor technology to develop a sophisticated wristband system for monitoring vital organ health. Leveraging the principles of TCM, the proposed system focuses on the “Three regions and nine indicators” method to accurately detect pulse variations at different wrist positions and depths. By utilizing a pressure sensor array that covers the Cun, Guan, and Chi pulse positions, the wristband aims to enhance diagnostic accuracy beyond conventional wearable technologies. The system incorporates advanced micro-electro-mechanical techniques to minimize size while maintaining functionality, enabling continuous health monitoring through a coplanar crossed sensing array. Additionally, the use of machine learning algorithms facilitates the analysis of pulse data to identify potential health issues related to the heart, lungs, liver, kidneys, and spleen. An accompanying mobile app provides real-time feedback and health insights, promoting proactive health management. This innovative approach merges TCM’s holistic diagnosis methods with cutting-edge technology to offer a powerful tool for early disease detection and health monitoring, potentially transforming the landscape of personal healthcare devices.

Smart Glasses for Visually Impaired People Using IoT

The visually handicapped have a difficult time getting by in a world that is mostly intended for sighted people. Even seemingly simple things like locating obstacles, traversing new places, and keeping spatial awareness can be difficult for them and frequently impede their freedom and movement. Their sense of dependence and loneliness are exacerbated by the absence of real-time support and systems catered to their individual needs, which exacerbates these challenges. Smart glasses are made especially for those who are blind or visually impaired to address these issues. These smart glasses use technologies to improve the wearer's movement and independence while offering real-time help. It seeks to remove the obstacles that visually impaired people encounter when attempting to safely and successfully navigate their environment by utilizing modern sensor technology and creative IoT solutions. [3] Its primary goal is to create a wearable assistive technology with features including proximity alarms, object detection, distance measuring, and location mapping. It seeks to provide a complete and user-friendly system that caters to the unique needs of visually impaired people by integrating a variety of sensors, communication modules, and cloud-based platforms. The goal of the glasses is to let people to live more independently and confidently by removing the limits imposed by visual impairment, which will ultimately improve their overall quality of life.

Tampered energy meter information conveyed to concerned authority by wireless communication

Utility companies face a serious problem with energy metre manipulation, which can result in lost income, inaccurate invoicing, and safety issues. Maintaining fair billing practices and the integrity of energy distribution systems depend on the prompt detection of tampering incidents. Nevertheless, current detection techniques frequently fall short in terms of effectiveness and fail to promptly notify authorities. In order to overcome this difficulty, our research suggests a novel method for identifying energy metre tampering and sending pertinent data via wireless communication technologies. The principal objective is to create a dependable system that can precisely identify instances of tampering in real-time and send alerts to relevant authorities with ease. Our solution is carefully designed to fill in the research gaps by drawing on a thorough assessment of the literature on wireless communication protocols and tampering detection techniques. Our solution delivers accurate and quick detection of tampering situations by utilising complex wireless communication protocols, modern sensor technology, and sophisticated data processing algorithms. The system architecture combines a wireless communication module with tampering detection sensors to quickly transmit notifications to the relevant authorities. Extensive tests are carried out to verify the functionality of the system, assessing critical parameters including alert reliability, response speed, and detection accuracy. Our experimental results highlight the effectiveness and reliability of the suggested approach in real-time alerting authorities and identifying tampered energy metres. This study makes a substantial contribution to the field of energy metre tampering detection and emphasises the critical role that wireless communication technologies play in preserving the integrity and security of energy distribution networks. In conclusion, our study presents a novel method for identifying energy metre tampering and wirelessly communicating vital information to authorities. Our research establishes a new benchmark in energy metre tampering detection by providing improved accuracy, efficiency, and timeliness, which promotes increased resilience and security in energy distribution networks.

The Influence of External Parameters on the Ripeness of Pumpkins.

Growing pumpkins in controlled environments, such as greenhouses, has become increasingly important due to the potential to optimise yield and quality. However, achieving optimal environmental conditions for pumpkin cultivation requires precise monitoring and control, which can be facilitated by modern sensor technologies. The objective of this study was to determine the optimal placement of sensors to determine the influence of external parameters on the maturity of pumpkins. The greenhouse used in the study consisted of a plastic film for growing pumpkins. Five different sensors labeled from A1 to A5 measured the air temperature, humidity, soil temperature, soil humidity, and illumination at five different locations. We used two methods, error-based sensor placement and entropy-based sensor placement, to evaluate optimisation. We selected A3 sensor locations where the monitored data were close to the reference value, i.e., the average data of all measurement locations and parameters. Using this method, we selected sensor positions to monitor the influence of external parameters on the maturity of pumpkins. These methods enable the determination of optimal sensor locations to represent the entire facility environment and detect areas with significant environmental disparities. Our study provides an accurate measurement of the internal environment of a greenhouse and properly selects the base installation locations of sensors in the pumpkin greenhouse.

History of blood pressure measurement in newborns and infants.

The development of methods for measuring blood pressure (BP) in newborns and small children has a rich history. Methods for BP measuring in adults had to be adapted to this age group. For measuring BP in direct invasive way, a suitable approach had to be found to access the arterial circulation through the umbilical and later radialis artery. Currently, results obtained from direct invasive BP measurement are considered the "gold standard". The development of non-invasive methods for BP measuring in newborns and children began with the use of von Basch's sphygmomanometer (1880). In 1899, Gustav Gärtner constructed the device, which was the basis for the flush method. After the discovery of the palpation and auscultation methods, these methods were also used for BP measurement in newborns and children, however, the BP values obtained in these ways were typically underestimated using excessively wide cuffs. From the auscultation method, methods utilizing ultrasound and infrasound to detect arterial wall movement and blood flow were later developed. The oscillometric method for BP measurement was introduced by E. J. Marey so early as in 1876. In 1912, P. Balard used the oscillometric technique to measure blood pressure in a large group of newborns. Through different types of oscillometers using various methods for detecting vascular oscillations (such as xylol method, impedance and volume plethysmography, etc.), the development has continued to assessment of vascular oscillations by modern sensor technology and software. For continuous non-invasive blood pressure measurement, the volume-clamp method, first described by Jan Peňáz in 1968, was developed. After modification for use in newborns, application of the cuff to the wrist instead of the finger, it is primarily used in clinical physiological studies to evaluate beat-to-beat BP and heart rate pressure variability, such as in the determination of the baroreflex sensitivity.

Optical Networks Automation Overview: A Survey

The increasing demand for data has driven the advancement of optical networks from traditional architectures to more flexible, dynamic and efficient solutions. This includes technologies like flexgrid reconfigurable optical add-drop multiplexers (ROADMs), variable bandwidth transponders (VBTs) providing different modulation, coding schemes and baud rates. These advancements have brought about new challenges that concerns to the routing and spectrum allocation (RSA), fragmented spectrum, need for rapid and efficient channel restoration, and operation and maintenance management of optical networks. To address these challenges, a dynamic and flexible network requires a highly advanced network operational system (OS) capable of efficiently managing and allocating network resources. It relies on network abstraction, sensors, actuators, and software-defined networking (SDN) to enable algorithms, management, control, and decision-making. Improving the sensing capabilities of the network is crucial. Modern hardware and sensor technology can help forecast fiber breaks, equipment failures, and other potential issues in advance, allowing for proactive actions to be taken. Machine learning (ML) methods have been proposed in the literature to enhance the accuracy of quality of transmission (QoT) estimation, mitigate nonlinearities and provide decisions. This reduces the need for conservative design margins, maximizes the capacity of optical network systems and reduces the investment in infrastructure. Failure management is a critical aspect of optical networks. Providing early-warning and proactive protection is essential. This includes detecting failures, localizing them, identifying the root causes, and estimating their magnitude. Quick response to failures is vital to maintaining network reliability

Design and Implementation of Environmental Monitoring System for Substation Based on Internet of Things

At present, the environmental monitoring of the substation suffers from complex wiring, low intelligence, high staff work intensity, and a lack of information-based overall management. Moreover, the distribution of substations is relatively scattered, making it complex and costly to set up a dedicated communication network. In response to these problems, this paper combines modern communication technology, sensor technology, and mobile intelligent terminal development technology to study an overall information-based substation environmental monitoring system. The monitoring system proposed in this paper includes a main controller, a data collection terminal, and a mobile communication terminal. The environmental data collected by the main controller is sent to the server through the communication method of subscription and substation using wireless communication technology, and the interface of the mobile communication terminal can display indoor environmental monitoring data. When the environmental data of the Substation exceeds the set threshold value to issue an early warning alert, the user can control it remotely through the mobile communication terminal to improve the indoor environment of the Substation. The system has a simple operation interface, low power consumption, and strong expansion compatibility, which effectively realizes remote monitoring and control of the substation environment and has good engineering application value.

FEMTOSAT-Based Air Quality Monitoring: Leveraging Satellite Data and LoRa Communication for Improved AQI Predictions

The exponential growth of the global population has given rise to an alarming surge in air pollution levels, casting profound repercussions on economies, ecosystems, and human well-being. The predominant method for assessing air quality involves the deployment of sensors atop buildings at regular intervals. However, this approach faces significant drawbacks, including heightened energy consumption associated with each building's sensor infrastructure and its inapplicability in sparsely populated rural areas. Addressing these limitations, the utilization of FEMTOSAT technology emerges as a solution, capitalizing on satellite data for autonomous air pollution monitoring, analysis, and mitigation. Amidst the evolving scientific landscape, the integration of Low Power Wide Area Network (LoRa) technology assumes a pivotal role within the Internet of Things (IoT), facilitating long-range data communication with minimal power consumption. In this context, LoRa communication serves as the conduit for transmitting and receiving data from satellites via RF signals. The satellite-derived environmental data, thus collected, serves as the foundation for computing the Air Quality Index (AQI) at specific locations, a critical metric that informs us about air quality conditions, whether pristine or contaminated. The AQI computation factors in various pollutants, including NO2, CO, O3, PM2.5, SO2, and PM10, all of which significantly influence air quality. This study employs a range of machine learning (ML) techniques, including time series analysis, linear regression, Support Vector Machines (SVM), and logistic regression, to predict and forecast AQI values. These models amalgamate AQI data from diverse sources, yielding robust and dependable AQI prediction models. Notably, modern sensor technology simplifies and enhances data collection accuracy. In the realm of environmental data analysis, only ML algorithms can grapple with the complexity of processing vast datasets to generate precise and trustworthy predictions. The incorporation of integrated sensors as payload in the Femto Sat mission epitomizes the mission's objectives. This system is characterized by its cost-effectiveness, lightweight design, durability, redundancy, and user-friendly interface, requiring minimal power consumption for operation.

A Novel Approach to Discovering Hygrothermal Transfer Patterns in Wooden Building Exterior Walls

To maintain the life of building materials, it is critical to understand the hygrothermal transfer mechanisms (HTM) between the walls and the layers inside the walls. Due to the extreme instability of weather data, the actual data models of the HTM—the data being collected for actual buildings using modern sensor technologies—would appear to be a great difference from any theoretical models, in particular, for wood building materials. In this paper, we aim to consider a variety of data analysis tools for hygrothermal transfer features. A novel approach for peak and valley detection is proposed based on the discrete differentiation of the original data. Not to be limited to the measure of peak and valley delays for HTM, we propose a cross-correlation analysis to obtain the general delay between two daily time series, which seems to be representative of the delay in the daily time series. Furthermore, the seasonal pattern of the hygrothermal transfer combined with the correlation analysis reveals a reasonable relationship between the delays and the indoor and outdoor climates.

High-resolution mapping of forest structure from integrated SAR and optical images using an enhanced U-net method

Forest structure is an essential part of biodiversity and ecological analysis and provides crucial insights to address challenges in these areas. Modern sensor technologies unlock new possibilities for more advanced vegetation monitoring. This study examines the potential of single high resolution X-band synthetic aperture radar (SAR) and optical images for pixel-wise mapping of four forest structure attributes (height, average height, fractional cover, and density) at a striking 0.5 m resolution. The study site is situated in Western Norway, hosting trees from flatlands to elevated mountainous areas and in-between. The proposed model architecture, called PSE-UNet, is a modified UNet incorporating key components from state-of-the-art deep learning from the field of forest structure monitoring. A comparative analysis involving state-of-the-art models shows promising results with MAE% between 21.5 and 24.7, depending on the variable.

Smart Crops Irrigation System with Low Energy Consumption

The COVID-19 preventive measurements restrict farm-workers from travel to their farms, social distancing and lockdown to prevent them from getting infected. Consequently, farming and irrigation activities are dramatically reduced due to shortage of farm-workers and reduction of crops production. To find solutions, various technologies have been adopted to remotely connect farmers with their farm-fields from their homes and perform the same work as they are in the farm. An Internet of Things (IoT) approach, modern sensor technologies, and smart irrigation equipment are coupled to allow the collection of the field data for automatic irrigation of the field. This paper presents the IoT system based on Fuzzy logic controller that assists farmers to receive and monitor necessary irrigation data from the farm field through their smart phones and PCs. The Wireless Sensor Networks (WSNs) are deployed in the field to measure the water content in the soil and water used for plants by measuring the soil moisture dynamics throughout the entire duration of the crop growing season in the Maize crop farm. The Fuzzy logic controller was used for data analytics and intelligent decision making as well as Fuzzy logic toolbox in MATLAB to simulate irrigation scheduling process. The system saves 50% of water and energy consumption for irrigation, 80% reduction of farm-workers and increase in 20% of crop production.

ЗАСТОСУВАННЯ ПЛАЗМОНІКИ ДЛЯ АНАЛІЗУ ПОЛІДИСПЕРСНИХ СИСТЕМ (ОГЛЯД)

Modern sensor technologies have advantages compared to other classical methods of analysis of liquid and gas environments, in particular, fast analysis and a small volume of the analyzed sample, high sensitivity, the ability to detect the analyzed substance in a polydisperse environment due to the specificity of the sensor. Plasmonics is one of the leading scientific directions, which is actively used in sensor technologies for the development of highly sensitive analytical devices and systems. Precision analytical devices based on SPR are used for research in the food, chemical, pharmaceutical industry, agriculture, medicine, and ecology. However, the interpretation of the measurement result is somewhat complicated when analyzing complex polydisperse systems and the presence of an interferent in the sample, which affects the value of the sensor signal. Therefore, there is a need to study, in particular, liquid polydisperse systems using models of approximate environments and using additional technologies to increase the specificity of the analyzed component by using receptor surfaces. The review provides information on the current state of polydisperse systems research using the example of milk - one of the most common food products. Sensitive, fast and efficient quantification and monitoring systems play a key role in the determination of harmful impurities in dairy products. The need to detect antibiotics with a minimum concentration in food products, in particular in dairy raw materials, which are complex polydisperse systems, is an important scientific problem. Such requirements give rise to the need to develop reliable, highly sensitive analytical methods. In addition, there is a huge demand for rapid analytical devices to detect antibiotic residues in milk samples on the farm before the raw material is sent to the factory, because after sending directly to the factory, in case of antibiotic detection, a large batch of milk is missing, which leads to excessive costs.

Airflow vibration of diaphragmatic breathing: model and demonstration using optical biosensor

Optical fiber is increasingly popular and appreciated as a modern sensor technology in various sectors, one of which is for medical functions. This study was conducted to detect human diaphragmatic breathing flow using theoretical and experimental approaches. Initially, the lung model was formed using the finite element method and the Navier-Stokes equation by applying the principles of momentum and continuity. Furthermore, fiber Bragg grating (FBG) and single mode fiber (SMF) were experimentally designed with sinusoidal patterned macro-scale bending as a stretch sensor in a breathing belt applied to the diaphragm. The simulation model shows the airflow velocity increases up to 4 m/s when it flows into smaller branches. While the experimental results show that the largest power loss occurs at a buffer diameter of 0.8 cm. The power loss detected in SMF is a maximum of -0.18 dBm during inhalation and a minimum of -0.28 dBm during expiration. However, FBG bending is superior with high sensitivity.

Maneuvering Extended Object Tracking with Modified Star-Convex Random Hypersurface Model Based on Minimum Cosine Distance

Maneuvering extended object tracking is a new research field due to the rapid development of modern sensor technology. Multiple measurements may be resolved from different unknown sources on an object by using a high-resolution radar. In this case, the object should be regarded as an extended one with object extension, e.g., its shape may be described by the star-convex random hypersurface model. This model is usually specified by a one-dimensional radial function. However, the divergence of the shape estimation and a high error of the kinematic state estimation are likely to occur when an extended object maneuvers. This is because the radial function may take a negative value after Fourier series expansion, which leads to unpredictable estimation results. Unfortunately, the model itself is unable to solve this problem via the subsequent iterations. In this paper, we proposed a modified shape estimation approach to track an extended object with a star-convex random hypersurface model based on minimum cosine distance. Both the extension state and kinematic state at the current time are reinitialized once the radial function takes a negative value. Moreover, a mathematical model was constructed by using the principle of minimum cosine distance, so as to obtain more reasonable weight distribution coefficients for the correction of the extension state. Simulation results in different scenarios demonstrated the effectiveness of the proposed tracking approach.

Advances in 3D Gel Printing for Enzyme Immobilization.

Incorporating enzymes with three-dimensional (3D) printing is an exciting new field of convergence research that holds infinite potential for creating highly customizable components with diverse and efficient biocatalytic properties. Enzymes, nature’s nanoscale protein-based catalysts, perform crucial functions in biological systems and play increasingly important roles in modern chemical processing methods, cascade reactions, and sensor technologies. Immobilizing enzymes on solid carriers facilitates their recovery and reuse, improves stability and longevity, broadens applicability, and reduces overall processing and chemical conversion costs. Three-dimensional printing offers extraordinary flexibility for creating high-resolution complex structures that enable completely new reactor designs with versatile sub-micron functional features in macroscale objects. Immobilizing enzymes on or in 3D printed structures makes it possible to precisely control their spatial location for the optimal catalytic reaction. Combining the rapid advances in these two technologies is leading to completely new levels of control and precision in fabricating immobilized enzyme catalysts. The goal of this review is to promote further research by providing a critical discussion of 3D printed enzyme immobilization methods encompassing both post-printing immobilization and immobilization by physical entrapment during 3D printing. Especially, 3D printed gel matrix techniques offer mild single-step entrapment mechanisms that produce ideal environments for enzymes with high retention of catalytic function and unparalleled fabrication control. Examples from the literature, comparisons of the benefits and challenges of different combinations of the two technologies, novel approaches employed to enhance printed hydrogel physical properties, and an outlook on future directions are included to provide inspiration and insights for pursuing work in this promising field.

A Sensor-Driven Visit Detection System in Older Adults' Homes: Towards Digital Late-Life Depression Marker Extraction.

Modern sensor technology is increasingly used in older adults to not only provide additional safety but also to monitor health status, often by means of sensor derived digital measures or biomarkers. Social isolation is a known risk factor for late-life depression, and a potential component of social-isolation is the lack of home visits. Therefore, home visits may serve as a digital measure for social isolation and late-life depression. Late-life depression is a common mental and emotional disorder in the growing population of older adults. The disorder, if untreated, can significantly decrease quality of life and, amongst other effects, leads to increased mortality. Late-life depression often goes undiagnosed due to associated stigma and the incorrect assumption that it is a normal part of ageing. In this work, we propose a visit detection system that generalizes well to previously unseen apartments - which may differ largely in layout, sensor placement, and size from apartments found in the semi-annotated training dataset. We find that by using a self-training-based domain adaptation strategy, a robust system to extract home visit information can be built (ROC AUC = 0.773). We further show that the resulting visit information correlates well with the common geriatric depression scale screening tool ( ρ = -0.87, p = 0.001), providing further support for the idea of utilizing the extracted information as a potential digital measure or even as a digital biomarker to monitor the risk of late-life depression.

A loss function to evaluate agricultural decision-making under uncertainty: a case study of soil spectroscopy

Modern sensor technologies can provide detailed information about soil variation which allows for more precise application of fertiliser to minimise environmental harm imposed by agriculture. However, growers should lose neither income nor yield from associated uncertainties of predicted nutrient concentrations and thus one must acknowledge and account for uncertainties. A framework is presented that accounts for the uncertainty and determines the cost–benefit of data on available phosphorus (P) and potassium (K) in the soil determined from sensors. For four fields, the uncertainty associated with variation in soil P and K predicted from sensors was determined. Using published fertiliser dose–yield response curves for a horticultural crop the effect of estimation errors from sensor data on expected financial losses was quantified. The expected losses from optimal precise application were compared with the losses expected from uniform fertiliser application (equivalent to little or no knowledge on soil variation). The asymmetry of the loss function meant that underestimation of P and K generally led to greater losses than the losses from overestimation. This study shows that substantial financial gains can be obtained from sensor-based precise application of P and K fertiliser, with savings of up to £121 ha−1 for P and up to £81 ha−1 for K, with concurrent environmental benefits due to a reduction of 4–17 kg ha−1 applied P fertiliser when compared with uniform application.