Sensor Area Research Articles

The Vibration Dynamic Model for Blister Detection in Medium-Density Fiberboard

Ultrasonic detection is currently used in the industry of medium-density fiberboard to detect blister defects. Due to the small detection area of a single sensor, multiple sensors need to be used, which results in high costs. Starting from elastic thin plate vibration theory, this paper builds a vibration dynamic model to detect blisters. The size and depth of the blister area can be established by determining the natural frequency of the thin plate vibration in the blister area. In this model, if the elastic modulus and density are known, the natural frequency of the thin plate vibration at the blister place is directly proportional to the blister depth and inversely proportional to the square of the blister radius. The size and depth of the blister can be determined by measuring the first third-order natural frequency of this area of research. A total of 25 specimens with blister sizes and depths were simulated, and the natural frequencies of the specimens were measured. Subsequently, the detection model was verified by comparing its experimental data with theoretical values. The theoretical value was highly consistent with the measured data. The measured values of the first, second, and third-order natural frequencies were slightly smaller than the theoretical calculated values, with average relative deviations of −1.6%, −1.34%, and −1.03%, respectively. As the order progressed, the deviation exhibited a downward trend, and the third-order natural frequency displayed the smallest deviation and highest accuracy. The proposed vibration dynamic model can detect larger blister areas by measuring the natural frequency, which can overcome the shortcomings of small ultrasonic detection areas in current actual industries. Thus, the practical online blister detection device is expected to be further developed.

A federated learning model for intelligent cattle health monitoring system using body area sensors and IoT

The Sustainable Development Goals (SDGs) emphasize synchronizing technology and routine life for sustainability. Food and water shortage, and exponentially increasing environmental pollution are the biggest challenges for sustainability. Livestock plays a vital role in developing countries’ economies; the most profitable businesses are breeding dairy and non-dairy products. The productivity of cattle farms is dependent on the health conditions of cattle. Identifying unhealthy cattle and providing suitable treatment is critical. Hence, deploying the Internet of Things (IoT) along with AI systems is one of the potential solutions. This cattle health monitoring system provides monitoring of cattle health to ensure the minimum human intervention. A system has been designed and developed to aid the intelligent cattle health monitoring system by using machine learning techniques. The system includes multiple sensor nodes, each having a body area sensor that is connected to the IoT platform through a controller. As a novelty, the prototype has been trained and evaluated using a federated learning technique. The system warns the owner about specific diseases such as fever, mastitis, foot and mouth disease, and ketosis. The presented results validate the proposal as it diagnoses the prescribed viral diseases precisely. We have implemented the Gaussian Naïve Bayes classifier for this multiclass problem. Considering the federated learning model, three different datasets are considered as three different clients with 70% train and 30% test data. Client 1, Client 2, and Client 3 represent the cattle farm, veterinary hospital, and veterinary respectively. The sensor nodes are placed on key points of the cattle body while each node collects physiological parameters that are further used to train the prediction system. Additionally, we have developed a user-friendly Android application for the owner to control cattle well-being. A comprehensive comparative analysis demonstrates that the proposed system outperforms existing state-of-the-art systems by showing good accuracy.

New insights into the signal transduction mechanism of O2-sensing FixL and other biological heme-based sensor proteins

Recent structural and biophysical studies of O2-sensing FixL, NO-sensing soluble guanylate cyclase, and other biological heme-based sensing proteins have begun to reveal the details of their molecular mechanisms and shed light on how nature regulates important biological processes such as nitrogen fixation, blood pressure, neurotransmission, photosynthesis and circadian rhythm. The O2-sensing FixL protein from S. meliloti, the eukaryotic NO-sensing protein sGC, and the CO-sensing CooA protein from R. rubrum transmit their biological signals through gas-binding to the heme domain of these proteins, which inhibits or activates the regulatory, enzymatic domain. These proteins appear to propagate their signal by specific structural changes in the heme sensor domain initiated by the appropriate gas binding to the heme, which is then propagated through a coiled-coil linker or other domain to the regulatory, enzymatic domain that sends out the biological signal. The current understanding of the signal transduction mechanisms of O2-sensing FixL, NO-sensing sGC, CO-sensing CooA and other biological heme-based gas sensing proteins and their mechanistic themes are discussed, with recommendations for future work to further understand this rapidly growing area of biological heme-based gas sensors.

Dual functionalized Co3O4 porous cages with Pd and Co-MOF for acetone gas sensing under high humidity

Metal oxide semiconductor (MOS) gas sensors play a crucial role in gas sensors. However, the effect of higher ambient humidity on the sensitivity of MOS gas sensors is still largely an unsolved problem, limiting the application areas of sensors. In this work, an ultra-stable and sensitive acetone gas sensor based on dual functionalized Co3O4 porous cages with Pd and Co-MOF at high humidity is reported. The dual functionalized porous cages are made of MOF-derived Co3O4 porous cages substrate, Pd particles and hydrophobic Co-MOF coatings. The prepared porous cages composite exhibits humidity-independent and highly sensitive gas-sensing behavior to acetone under 70–90 % relative humidity (RH). The results indicate that the hydrophobic Co-MOF coatings not only offer excellent anti-humidity, but also show the ability of enrichment of low concentration acetone gas molecules, thus improving the sensitivity and response time of acetone detection. Meanwhile, due to the electronic spillover effect of Pd, porous cages composite possesses remarkable properties of high sensitivity, short response and recovery time, as well as low working temperature. The unique sensing characteristic makes the dual functionalized porous cages composite sensor a potential candidate for reliable noninvasive diagnosis of diabetes via exhaled breath analyses.

A self-regulation blades wind energy harvester system for self-powered wireless monitoring sensors

Using wind energy in the natural environment provides a promising solution for wireless sensor power supply for ecological, meteorological, environmental, and infrastructure monitoring. However, the uncertainty and disorder of natural wind restrict the further development of wind energy harvester systems and self-powered wireless sensor technology. Hence, this paper proposes a self-regulation blade wind energy harvester system (SBWEHS) for self-powered wireless monitoring sensors in remote field areas with power shortages. The system is mainly composed of three parts: wind harvesting mechanism, generator module, and energy storage module. The device can control the blade overlap ratio according to the wind speed while generating electricity to maximize the power coefficient. The system can control the blade’s closure in bad weather to protect the device. Based on the computational fluid dynamics technology of Ansys Fluent software, this study evaluated the impact of wind speed and blade overlap ratio on the two-stage blades. Experiments revealed that when the overlap ratio of the blades is fixed at 0.2 and the wind speed is set at 16 m/s, the maximum average power will reach 0.79 W, which fulfills the power requirements of wireless sensors. These results illustrate that the SBWEHS can effectively supply power for wireless monitoring sensors, especially in remote natural environments.

56‐3: On‐Cell Plus: A New Touch Display Module

"On‐Cell Plus", is designed by setting the same size of active area (AA) of the touch sensor and display, and then uses random mesh pattern to prevent moiré issues. Accordingly, the touch sensor and display cell can be designed and produced independently and demonstrate better touch sensitivity.

Preparation of a novel DNA-imprinted sensor based on chitosan and its highly sensitive detection of Pb2+

Lead ion is very harmful to the environment, so it is very important to study its detection methods. In this study, a novel electrochemical sensor was constructed by modifying deoxyribonucleic acid (DNA) on the electrode, which can be used for the detection of Pb2+ in the environment. Part of the mixed solution of chitosan (CS) and Pb2+ template ions was dropped onto the surface of a glassy carbon electrode. CS-Pb2+ film was cross-linked through sodium tripolyphosphate. And a novel DNA-imprinted sensor was prepared by electrodepositing CS-Pb2+ thin film with gold nanoparticles (AuNPs), removing Pb2+ templates, and immobilizing specific double-stranded DNA. The electroactive area, surface morphology, sensitivity, and electrochemical reaction mechanism of the DNA-imprinted sensor were analyzed. The elementary reaction steps were studied through electrochemical reaction kinetics analysis. The experimental results indicate that the DNA-imprinted electrochemical biosensor can quantitatively detect Pb2+ in the range of 10–100 μM (R2 = 0.9935), and its detection limit is 6.5074 μM (3σ/slope). The sensitivity of the electrochemical biosensor is 1.55233 × 10−6 A/μM, and its active areas is 6.233 cm2. The desorption mechanism and adsorption mechanism have been explored through dynamic parameter analysis. The novel DNA imprinted electrochemical biosensor developed in this paper provides a robust method for detecting lead ions in solution. Additionally, it establishes a solid groundwork for detecting other metal ions.

A new hybrid optimization algorithm for maximizing area coverage in wireless sensor networks

ABSTRACT The rapid advancements in technology influence develop our lives day-by-day. However, the area coverage issue exhibited by sensor nodes in WSN still seems a complicated issue even if the area coverage enhancement models are developed. This research work is developed on the basis of implementing an efficient area coverage maximising approach with an advanced optimisation model, Coati Modernized Beluga Whale Optimisation (CMBWO). The whole process of the area coverage maximising approach revolves in two important steps: (i) Sensor node coverage model and (ii) Area coverage optimisation. In the first step, a 2D WSN monitoring area set-up is considered and in which target monitoring points are assumed to solve the coverage issue. Most importantly, the distance between the sensor node and the target monitoring point is calculated using weighted Manhattan distance evaluation instead of conventional Euclidean distance evaluation. This improvement is introduced to calculate the precise position of the target monitoring point from the position of the sensor node. The next, area coverage optimisation step is handled by the CMBWO algorithm for maximising area coverage in WSN. This hybridisation is possible by modernising BWO via COA. This efficient hybridisation led to maximising the area coverage of sensor nodes in WSN.

Emotion Detection Through Electrocardiogram Signal Classification in an IOT Environment with Deep Neural Networks

An ECG detects the health and rhythm of the heart by measuring the electric activity of the heart. It has also been demonstrated that a person's emotions may influence the electrical activity of the heart. As a result, studying the electrical behaviour of the heart may simply determine a person's cardiac state and emotional wellness. IoT is a new technology that is quickly gaining acceptance throughout the world. Anybody, at any time, from anywhere, may connect to any network or service because to the extraordinary power and capacity of IoT. IoT-enabled devices have revolutionized the medical business by providing new capabilities such as remote patient monitoring and self-monitoring. This research proposed an IoT-based ECG monitoring system that employs a heart rate sensor to generate data and an intelligent hybrid classification algorithm to categorize the data. ECG monitoring has become a widely used method for detecting cardiac problems. The following are the primary contributions of this paper: To begin, this paper describes WISE (Wearable IoT-cloud-based health monitoring system), a one-of-a-kind system for real-time personal health monitoring. WISE makes use of the BASN (body area sensor network) technology to provide real-time health monitoring. WISE rapidly transfers data from the BASN to the cloud, and a lightweight wearable LCD may be included to enable quick access to real-time data. This model can address the issue of class imbalance in the ECG dataset, assisting in the development of an IoT-based smart and accurate healthcare system. Pre-processing, feature extraction, and classification are the three steps in any classification technique, whether it is emotion classification or heart health classification. Sensors are used to collect an ECG signal from a person's outside body. The provided ECG signal is first pre-processed using the Butterworth Filtering Method, which effectively reduces noise from the signal. Following pre-processing, the Adaptive Discrete Wavelet Transform technique is used to anticipate the signal's attributes. Lastly, a decision making classification approach based on relational weights is used to determine if the ECG signal is normal or abnormal.

Ultrasensitive detection of chlortetracycline in animal-origin food using molecularly imprinted electrochemical sensor based on SnS2/ZnCo-MOF and AuNPs

The chlortetracycline (CTC) residue in food poses a threat to human health. Therefore, developing sensitive, convenient and selective analytical methods for CTC detection is crucial. This study innovatively uses tin disulfide/bimetallic organic framework (SnS2/ZnCo-MOF) nanocomposites in conjunction with gold nanoparticles (AuNPs) to co-modify a glassy carbon electrode (GCE). Further, a molecularly imprinted polymer (MIP)-based electrochemical sensing platform Au-MIP/SnS2/ZnCo-MOF/Au/GCE (AZG) was fabricated for selective CTC detection. SnS2/ZnCo-MOF enhanced the stability and surface area of the AZG sensor. The presence of AuNPs facilitated electron transport between the probe and the electrode across the insulating MIP layer. The fixation of AuNPs and MIP via electropolymerization enhanced the selective recognition of this sensor and amplified its output signal. The AZG sensor demonstrated a wide linear detection range (0.1–100 μM), low detection limit (0.072 nM), and high sensitivity (0.830 μA μM−1). It has been used for detecting CTC in animal-origin food with good recovery (96.08%–104.60%).

Highly integrable planar-structured printed circularly polarized antenna for emerging wideband internet of things applications in the millimeter-wave band

This paper proposes a numerically and experimentally validated printed wideband antenna with a planar geometry for Internet of Things (IoT) applications. This design tackles the challenges associated with deploying IoT sensors in remote areas or across extensive geographical regions. The proposed design exploits a coplanar-waveguide-fed modified microstrip line monopole for excitation of circularly polarized waves radiating in the broadside direction. The primary design is based on perturbations of the microstrip line protracted from a grounded coplanar waveguide. The capacitively coupled short rectangular stubs are periodically inserted alternately and excited asymmetrically on each side of the microstrip line parallel to the direction of the electric field vector. The sequential phase excitation of the periodic stubs generates a rectangular-cascaded electric field, which suppresses the stop band at the open end. As a result, the antenna radiates in the broadside direction. The impedance bandwidth of the antenna exceeds 8 GHz in the 28 GHz mm-wave band, i.e., it ranged from 25 to 33.5 GHz. Additionally, an axial ratio below 3 dB is achieved within the operating band from 26 to 33.5 GHz with the alterations of the surface current using straightforward topological adjustments of the physical parameters. The average in-band realized gain of the antenna is 10 dBic when measured in the broadside direction. These results indicate that the proposed design has the potential to improve the connectivity between IoT devices and the constantly varying orientation of satellites by mitigating the polarization mismatch.

De-embedding method for a sensing area characterization of planar microstrip sensors without evaluating error networks

A de-embedding method for determining all scattering (S-) parameters (e.g., characterization) of a sensing area of planar microstrip sensors (two-port network or line) is proposed using measurements of S-parameters with no calibration. The method requires only (partially known) non-reflecting line and reflecting line standards to accomplish such a characterization. It utilizes uncalibrated S-parameter measurements of a reflecting line, direct and reversed configurations of a non-reflecting line, and direct and reversed configurations of the sensing area. As different from previous similar studies, it performs such a characterization without any sign ambiguity. The method is first validated by extracting the S-parameters of a bianisotropic metamaterial slab, as for a two-port network (line), constructed by split-ring-resonators (SRRs) from waveguide measurements. Then, it is applied for determining the S-parameters of a sensing area of a microstrip sensor involving double SRRs next to a microstrip line. The root-mean-square-error (RMSE) analysis was utilized to analyze the accuracy of our method in comparison with other techniques in the literature. It has been observed from such an analysis that our proposed de-embedding technique has the lowest RMSE values for the extracted S-parameters of the sensing area of the designed sensor in comparison with those of the compared other de-embedding techniques in the literature, and have similar RMSE values in reference to those of the thru-reflect-line calibration technique. For example, while RMSE values of real and imaginary parts of the forward reflection S-parameter of this sensing area are, respectively, around 0.0271 and 0.0279 for our de-embedding method, those of one of the compared de-embedding techniques approach as high as 0.0318 and 0.0324.

A highly sensitive electrochemical sensing platform to detect rutin in pharmaceutical and serum samples

In this study, we created a brand-new g-C3N4-MWCNTs/Ag/β-CD-MOF electrode that can be used as a platform for the electrochemical sensing of Rutin (RT). The g-C3N4-MWCNTs/Ag/β-CD-MOF composite was synthesized via calcination treatment and electrodeposition, coupled with hydrothermal reaction. This composite could appreciably enlarge the electrochemically active surface area of the as-prepared sensor to promote electron transfer and result in the enhanced response signals.With the assistance of differential pulse voltammetry (DPV) and cyclic voltammetry (CV), the electrochemical performance of sensor can be greatly enhanced. Under these optimum conditions, the designed electrochemical sensor was able to sense RT at the lower detection limit (LOD) of 0.69 nM (S/N = 3) with a wider range of 5–500 nM and showed high sensitivity of 1.97 μA. nM−1. cm−2 at room temperature. Additionally, the selective investigation on RT was performed with the related interfering molecules, and the g-C3N4-MWCNTs/Ag/β-CD-MOF/GCE showed decent stability and reproducibility. The real-time application of the modified electrode for RT detection was monitored in both pharmaceutical and serum samples with the satisfactory recovery. Overall, this research contributes to the field of the electroanalytical sensing and offers the potential applications in the drug monitoring.

SOLDIERS HEALTH MONTORING AND TRACKING USING GSM AND GPS

In enemy territory soldiers not only have to deal with the physical threat, but also with stress andfatigue caused by protracted operations or lack of sleep. So for the security purpose we need a tool for remote soldier performance and health monitoring. So in this project a tool is implemented using biosensors for health monitoring purpose. Also a GPS system is used in order to track the location of soldier. Additionally a GSM modem is also used to make the system wirelessly compatible. Any abnormalities in the readings of wireless body area sensor network (WBASNs) is considered as a trigger for GSM to establish the connection between the soldier and base unit and send current location and health status to the receiver. By using all this equipments we had tried to implement the basic guarding system for the soldier in low cost, light weighted, portable and precise device.

Intelligent Wearable Systems: Opportunities and Challenges in Health and Sports

Wearable devices, or wearables, designed to be attached to the human body, can gather personalized real-time data and continuously monitor an individual’s health status and physiological disposition in a non-invasive manner. Intelligent wearables integrate advanced machine learning algorithms to process complex data patterns and provide accurate insights. As a result, intelligent wearables have emerged as a ground-breaking innovation in the fields of sports and health, introducing a new paradigm in kinematic analysis and patient data evaluation. For example, virtual coaches offer feedback on athletes’ performance, whereas virtual physicians assist in customizing medication for patients. This article provides an overview of various types of intelligent wearables and their applications in health and sports, categorizes machine learning algorithms, and introduces the wireless body area sensor network (WBASN) used for communication in wearable sensors. Additionally, we discuss potential challenges and development directions that could shape the future of intelligent wearables and propose effective solutions for their continued enhancement. This article offers valuable insights into the exciting potential of intelligent wearables to transform healthcare and sports.

Study on energy capture characteristics of piezoelectric stack energy harvester for railway track

Railway traffic plays a pivotal role within the current transportation system, with ensuring its safety being of paramount importance. Real-time monitoring of train track health through sensor technology has emerged as a significant approach for achieving this objective. However, a persistent challenge lies in providing cost-effective and stable power to sensors in remote areas. This paper proposes the utilization of a novel piezoelectric stack energy harvester in railway systems to address this issue. The substantial and consistent vibration energy produced by railway operations makes it an ideal source for energy harvesting, unaffected by environmental conditions. Through simulation analysis and experimental verification, this study explores the impact of load size, load frequency, and external resistance on the energy harvesting efficiency of the proposed piezoelectric stack energy harvester. The results indicate that the output power of the energy harvester increases with the load size. Furthermore, the frequency significantly influences the energy capture efficiency when below 8 Hz, with minimal impact on frequencies above 8 Hz. Interestingly, the load dramatically affects the energy capture efficiency, even at higher frequencies. With the external resistance set to 250 kΩ, the harvester achieves a maximum output power of 42.76 mW.

NAPA-p1: monolithic nanosecond timing pixel for large area sensors, designed for future e + e - colliders

NAPA-p1 is a prototype Monolithic Active Pixel Sensor 'MAPS' developed as a first iteration towards meeting the detectors general requirements for future e + e - colliders. Long-term objective is to develop a wafer-scale sensor in MAPS with an area ∼ 10 cm × 10 cm. This article presents the motivations for the design choices of NAPA-p1, translating the physics requirement into circuit specifications. Simulations show a pixel jitter of < 400 ps-rms and an equivalent noise charge of 13 e -rms with an average power consumption of 1.15 mW/cm2 assuming a 1% duty cycle, meeting the target specifications. The prototype is designed in 65 nm CMOS imaging technology, with dimensions of 1.5 mm × 1.5 mm and a pixel pitch of 25 μm. The prototype chip has been fabricated and characterization results will be available soon.

Pixelwise high-temperature calibration for in-situ temperature measuring in powder bed fusion of metal with laser beam

High-temperature calibration methods in additive manufacturing involve the use of advanced techniques to accurately measure and control the temperature of the build material during the additive manufacturing process. Infrared cameras, blackbody radiation sources and non-linear optimization algorithms are used to correlate the temperature of the material with its emitted thermal radiation. This is essential for ensuring the quality and repeatability of the final product. This paper presents the calibration procedure of an imaging system for in-situ measurement of absolute temperatures and temperature gradients during powder bed fusion of metal with laser beam (PBF-LB/M) in the temperature range of 500 K–1500 K. It describes the design of the optical setup to meet specific requirements in this application area as well as the procedure for accounting the various factors influencing the temperature measurement. These include camera-specific effects such as varying spectral sensitivities of the individual pixels of the sensor as well as influences of the exposure time and the exposed sensor area. Furthermore, influences caused by the complex optical path, such as inhomogeneous transmission properties of the galvanometer scanner as well as angle-dependent transmission properties of the f-theta lens were considered. A two-step fitting algorithm based on Planck's law of radiation was applied to best represent the correlation. With the presented procedure the calibrated thermography system provides the ability to measure absolute temperatures under real process conditions with high accuracy.

Optimizing time resolution and power consumption in a current-mode circuit for SiPMs

Several applications that employ SiPMs require high time precision readout electronics. This work presents a study for the optimization of timing resolution of readout electronics for SiPMs focused on the effect of sensor area, transistor scaling and power consumption on electronic jitter. The design of the most critical stages are presented, specially the front-end input stage in current-mode. The performance of three different technologies (180, 130 and 65 nm) are studied. 65 nm is the best option to obtain good timing resolution with less power consumption. Dividing the sensor into smaller segments improves the Single Photon Electronics Jitter (SPEJ), but does not translate into a better Coincidence Time Resolution (CTR) when keeping the power per unit area constant, performing analog summation or employing an averaging algorithm of the time stamps for small LSO:Ce:%0.2Ca scintillator crystal.

Building Detection from SkySat Images with Transfer Learning: a Case Study over Ankara

AbstractThe detection and continuous updating of buildings in geodatabases has long been a major research area in geographic information science and is an important theme for national mapping agencies. Advancements in machine learning techniques, particularly state-of-the-art deep learning (DL) models, offer promising solutions for extracting and modeling building rooftops from images. However, tasks such as automatic labelling of learning data and the generalizability of models remain challenging. In this study, we assessed the sensor and geographic area adaptation capabilities of a pretrained DL model implemented in the ArcGIS environment using very-high-resolution (50 cm) SkySat imagery. The model was trained for digitizing building footprints via Mask R‑CNN with a ResNet50 backbone using aerial and satellite images from parts of the USA. Here, we utilized images from three different SkySat satellites with various acquisition dates and off-nadir angles and refined the pretrained model using small numbers of buildings as training data (5–53 buildings) over Ankara. We evaluated the buildings in areas with different characteristics, such as urban transformation, slums, regular, and obtained high accuracies with F‑1 scores of 0.92, 0.94, and 0.96 from SkySat 4, 7, and 17, respectively. The study findings showed that the DL model has high transfer learning capability for Ankara using only a few buildings and that the recent SkySat satellites demonstrate superior image quality.