Sensor Output Research Articles

Linking Seismic Measurements to the International System of Units

AbstractThe current state of the art in the calibration of seismometers is given by internal calibration procedures which give only incomplete information about a seismometer’s response and are based on transfer functions supplied by manufacturers. Calibrations traceable to the International System of Units (SI) provide an independent and comprehensible characterization of seismometers. These calibrations take part in a laboratory using an electrodynamic shaker or shake table. To overcome the issue that seismometers placed in seismic stations are not supposed to be moved to a calibration laboratory, novel on-site calibration methods incorporating a reference seismometer were developed. Such a reference is placed near the sensor to be calibrated, and the transfer function of the seismometer under test can be derived based on the output of both sensors.

Design of PMN-PT-based dual-resonance acoustic emission sensor for partial discharge detection

This paper presents the design, fabrication, and testing of a dual-resonance acoustic emission (AE) sensor based on PMN-PT single crystal for partial discharge (PD) detection. The use of high-performance PMN-PT single crystal enhances the sensor sensitivity. Resonance peak coupling is utilized to increase the bandwidth of the sensor effectively. The effects of piezoelectric vibrator size, vibrator spacing, and external circuitry on the sensor output are analyzed using equivalent circuits and finite element models (FEM) for sensor design. After calibration, the self-made sensor has a peak sensitivity of 76.4 dB and a bandwidth of 20 kHz to 150 kHz, which meets the demand of PD detection. The PD detection experiments verify that the self-made sensors can accurately detect PD with a high signal-to-noise ratio of 15.8 dB. In the end, the reliability test proves that the sensors have good stability and temperature characteristics. Besides above sensor’s performance improvement, our design has the great potential to reduce the sensor’s cross section, resulting better suitability on curved surfaces. The feasibility of employing PMN-PT as a replacement for PZT in the fabrication of AE sensors is proven by these good test results.

Design of Static Output Feedback Suspension Controllers for Ride Comfort Improvement and Motion Sickness Reduction

This paper presents a method to design a static output feedback active suspension controller for ride comfort improvement and motion sickness reduction in a real vehicle system. Full-state feedback controller has shown good performance for active suspension control. However, it requires a lot of states to be measured, which is very difficult in real vehicles. To avoid this problem, a static output feedback (SOF) controller is adopted in this paper. This controller requires only three sensor outputs, vertical velocity, roll and pitch rates, which are relatively easy to measure in real vehicles. Three types of SOF controller are proposed and optimized with linear quadratic optimal control and the simulation optimization method. Two of these controllers have only three gains to be tuned, which are much smaller than those of full-state feedback. To validate the performance of the proposed SOF controllers, a simulation is carried out on a vehicle simulation package. From the results, the proposed SOF controllers are quite good at improving ride comfort and reducing motion sickness.

Design, fabrication and experimental analysis of piezoresistive bidirectional acoustic sensor

PurposeIdentification of the direction of the sound source is very important for human–machine interfacing in the applications such as target detection on military applications and wildlife conservation. Considering its vast applications, this study aims to design, simulate, fabricate and test a bidirectional acoustic sensor having two cantilever structures coated with piezoresistive material for sensing has been designed, simulated, fabricated and tested.Design/methodology/approachThe structure is a piezoresistive acoustic pressure sensor, which consists of two Kapton diaphragms with four piezoresistors arranged in Wheatstone bridge arrangement. The applied acoustic pressure causes diaphragm deflection and stress in diaphragm hinge, which is sensed by the piezoresistors positioned on the diaphragm. The piezoresistive material such as carbon or graphene is deposited at maximum stress area. Furthermore, the Wheatstone bridge arrangement has been formed to sense the change in resistance resulting into imbalanced bridge and two cantilever structures add directional properties to the acoustic sensor. The structure is designed, fabricated and tested and the dimensions of the structure are chosen to enable ease of fabrication without clean room facilities. This structure is tested with static and dynamic calibration for variation in resistance leading to bridge output voltage variation and directional properties.FindingsThis paper provides the experimental results that indicate sensor output variation in terms of a Wheatstone bridge output voltage from 0.45 V to 1.618 V for a variation in pressure from 0.59 mbar to 100 mbar. The device is also tested for directionality using vibration source and was found to respond as per the design.Research limitations/implicationsThe fabricated devices could not be tested for practical acoustic sources due to lack of facilities. They have been tested for a vibration source in place of acoustic source.Practical implicationsThe piezoresistive bidirectional sensor can be used for detection of direction of the sound source.Social implicationsIn defense applications, it is important to detect the direction of the acoustic signal. This sensor is suited for such applications.Originality/valueThe present paper discusses a novel yet simple design of a cantilever beam-based bidirectional acoustic pressure sensor. This sensor fabrication does not require sophisticated cleanroom for fabrication and characterization facility for testing. The fabricated device has good repeatability and is able to detect the direction of the acoustic source in external environment.

Development of a smart artificial muscle using optical fibres

A McKibben artificial muscle is a fluid-driven soft actuator comprising sleeve fibres and rubber tube. However, as typical bulky and rigid displacement sensors are unsuitable as sensor elements in soft actuators, displacement sensing is challenging for the McKibben artificial muscle. Therefore, we propose an optical fibre-based smart artificial muscle (OSAM) to estimate self-displacement from the bending loss of the optical fibre used as the sleeve fibre. The optical fibre can be effortlessly integrated into the OSAM sleeve using a braiding machine, which is generally used for manufacturing strings, easing the mass production process. The radius of curvature of the optical fibre changed when the OSAM was driven. The displacement of the artificial muscle was estimated based on the sensor output. To demonstrate the usefulness of OSAM, displacement feedback control experiments were conducted using the optical fibre sensor integrated into OSAM. From the results, OSAM’s displacement showed a good response to the target displacement. Therefore, the developed artificial muscle can facilitate displacement feedback control without requiring external sensors, which in turn can improve the performance of rehabilitation and wearable devices.

Adaptive output-feedback control for switched nonlinear systems with measurement sensitivity based on double-side event-triggering mechanisms

This article concentrates on the problem of adaptive event-triggered control with double-side event-triggering mechanisms for a family of switched nonlinear systems with measurement sensitivity. In order to circumvent the negative effect of intermittent transmission and sensor measurement sensitivity on the output, an extended system is constructed by introducing a first-order output filter. Then, an input-driven neural switched observer is designed to estimate unmeasurable system states. To deal with the mutual interaction between the triggering and the switching mechanisms and their negative effect on the positive lower bound of inter-execution intervals, an incremental compensation is incorporated in the event-triggering mechanism (ETM) at the output sensor side. At last, a novel event-triggered control scheme built on both sides of the controller and output is proposed, which exhibits superior efficacy compared to the one-side event-triggered control scheme. By using the methods of the average dwell time (ADT) and the backstepping design, it is guaranteed that all signals of the switched closed-loop system are semiglobally uniformly ultimately bounded (SGUUB), and the Zeno phenomenon is excluded. The efficacy of the proposed methodology is validated by using a numerical simulation and a one-link robot system.

Smart Hydrogel Swelling State Detection Based on a Power-Transfer Transduction Principle.

Stimulus-responsive (smart) hydrogels are a promising sensing material for biomedical contexts due to their reversible swelling change in response to target analytes. The design of application-specific sensors that utilize this behavior requires the development of suitable transduction concepts. The presented study investigates a power-transfer-based readout approach that is sensitive to small volumetric changes of the smart hydrogel. The concept employs two thin film polyimide substrates with embedded conductive strip lines, which are shielded from each other except at the tip region, where the smart hydrogel is sandwiched in between. The hydrogel's volume change in response to a target analyte alters the distance and orientation of the thin films, affecting the amount of transferred power between the two transducer parts and, consequently, the measured sensor output voltage. With proper calibration, the output signal can be used to determine the swelling change of the hydrogel and, consequently, to quantify the stimulus. In proof-of-principle experiments with glucose- and pH-sensitive smart hydrogels, high sensitivity to small analyte concentration changes was found along with very good reproducibility and stability. The concept was tested with two exemplary hydrogels, but the transduction principle in general is independent of the specific hydrogel material, as long as it exhibits a stimulus-dependent volume change. The application vision of the presented research is to integrate in situ blood analyte monitoring capabilities into standard (micro)catheters. The developed sensor is designed to fit into a catheter without obstructing its normal use and, therefore, offers great potential for providing a universally applicable transducer platform for smart catheter-based sensing.

Diagnostic Machine safety with IoT Monitoring

After analyzing the current situation of safety of diagnostic machine, it is realized that there is a need to develop a system that could inform the users about various faults in the machine, thereby protecting from severe damages. The proposed project presents a novel system that monitors machines and detects faults. This project is a model for a real time monitoring system using contemporary technologies like Internet of Things (IoT)-based sensors that are promising for efficient machine monitoring. The proposed project is a model for a real-time monitoring system using IoT-based sensors accessible via webpages. The system involves connecting the machine to two sensors: a current sensor to continuously monitor current flow and a temperature sensor to gauge heat production. These sensors provide input signals to an Arduino controller, which processes them into real values based on sensor output electrical signals. Subsequently, these values are translated from machine-level language to low-level language and displayed on an LCD. A Wi-Fi module facilitates transmission of these values over the internet to cloud storage. In this system "ThingSpeak" is opted as cloud platform, offering data collection. This platform stores the data, enabling access to values worldwide via webpages. It also generates graphs depicting sensor values over time. Key Words: Fault detection, Machinery monitoring, Arduino controller, Cloud storage, Real-time monitoring

Continuous glucose monitoring using wearable non-enzymatic sensors in a physiological environment

A novel non-enzymatic sensor has been developed for continuous glucose measurement in physiological body fluids using a Step-wise; amperometric method. Unlike; traditional nickel-based catalysts, this sensor overcomes challenges in biological and neutral pH environments. It utilizes a carbon fiber microelectrode modified with gold and nickel nanoparticles, reinforced by a biopolymer layer derived from quince seed mucilage (QSM). By applying a negative pretreatment potential step, hydroxide ions are locally generated, creating a partially alkaline environment that activates the nickel nanoparticles. Glucose concentration is determined by measuring the current at the electrocatalytic potential of nickel, which directly oxidizes glucose. To clean and reactivate the sensor, a positive pulse potential step is applied at the end of each cycle. The sensor exhibits high sensitivity (13.8 μA mM−1.mm−2) and a low limit of detection (11.3 μM) in neutral pH (7.4). These results demonstrate the promising performance of the sensor for continuous glucose measurement in physiological body fluids. Using eco-friendly and biocompatible QSM as a reinforcing layer enhances its potential for biomedical applications. Additionally, a wearable compact electronic module was designed and fabricated to apply the potential, read the sensor's output currents, and monitor and record the results. The module's performance in measuring glucose in blood plasma is comparable to that of a commercial glucometer.

Research on Vibration Accumulation Self-Powered Downhole Sensor Based on Triboelectric Nanogenerators.

In drilling operations, measuring vibration parameters is crucial for enhancing drilling efficiency and ensuring safety. Nevertheless, the conventional vibration measurement sensor significantly extends the drilling cycle due to its dependence on an external power source. Therefore, we propose a vibration-accumulation-type self-powered sensor in this research, aiming to address these needs. By leveraging vibration accumulation and electromagnetic power generation to accelerate charging, the sensor's output performance is enhanced through a complementary charging mode. The experimental results regarding sensing performance demonstrate that the sensor possesses a measurement range spanning from 0 to 11 Hz, with a linearity of 3.2% and a sensitivity of 1.032. Additionally, it exhibits a maximum average measurement error of less than 4%. The experimental results of output performance measurement indicate that the sensor unit and generator set exhibit a maximum output power of 0.258 μW and 25.5 mW, respectively, and eight LED lights can be lit at the same time. When the sensor unit and power generation unit output together, the maximum output power of the sensor is also 25.5 mW. Furthermore, we conducted tests on the sensor's output signal in conditions of high temperature and humidity, confirming its continued functionality in such environments. This sensor not only achieves self-powered sensing capabilities, addressing the power supply challenges faced by traditional downhole sensors, but also integrates energy accumulation with electromagnetic power generation to enhance its output performance. This innovation enables the sensor to harness downhole vibration energy for powering other micro-power devices, showcasing promising application prospects.

Data Preprocessing Techniques for AI and Machine Learning Readiness: Scoping Review of Wearable Sensor Data in Cancer Care.

Wearable sensors are increasingly being explored in health care, including in cancer care, for their potential in continuously monitoring patients. Despite their growing adoption, significant challenges remain in the quality and consistency of data collected from wearable sensors. Moreover, preprocessing pipelines to clean, transform, normalize, and standardize raw data have not yet been fully optimized. This study aims to conduct a scoping review of preprocessing techniques used on raw wearable sensor data in cancer care, specifically focusing on methods implemented to ensure their readiness for artificial intelligence and machine learning (AI/ML) applications. We sought to understand the current landscape of approaches for handling issues, such as noise, missing values, normalization or standardization, and transformation, as well as techniques for extracting meaningful features from raw sensor outputs and converting them into usable formats for subsequent AI/ML analysis. We systematically searched IEEE Xplore, PubMed, Embase, and Scopus to identify potentially relevant studies for this review. The eligibility criteria included (1) mobile health and wearable sensor studies in cancer, (2) written and published in English, (3) published between January 2018 and December 2023, (4) full text available rather than abstracts, and (5) original studies published in peer-reviewed journals or conferences. The initial search yielded 2147 articles, of which 20 (0.93%) met the inclusion criteria. Three major categories of preprocessing techniques were identified: data transformation (used in 12/20, 60% of selected studies), data normalization and standardization (used in 8/20, 40% of the selected studies), and data cleaning (used in 8/20, 40% of the selected studies). Transformation methods aimed to convert raw data into more informative formats for analysis, such as by segmenting sensor streams or extracting statistical features. Normalization and standardization techniques usually normalize the range of features to improve comparability and model convergence. Cleaning methods focused on enhancing data reliability by handling artifacts like missing values, outliers, and inconsistencies. While wearable sensors are gaining traction in cancer care, realizing their full potential hinges on the ability to reliably translate raw outputs into high-quality data suitable for AI/ML applications. This review found that researchers are using various preprocessing techniques to address this challenge, but there remains a lack of standardized best practices. Our findings suggest a pressing need to develop and adopt uniform data quality and preprocessing workflows of wearable sensor data that can support the breadth of cancer research and varied patient populations. Given the diverse preprocessing techniques identified in the literature, there is an urgency for a framework that can guide researchers and clinicians in preparing wearable sensor data for AI/ML applications. For the scoping review as well as our research, we propose a general framework for preprocessing wearable sensor data, designed to be adaptable across different disease settings, moving beyond cancer care.

Chromolaena Odorata Leaves Derived Carbon Dot‐Based Fluorescence/Second‐Order Scattering Ratiometric Sensor for Fe3+ and Vitamin C

Herein, a ratiometric fluorescence and second‐order scattering sensor with plant leaf‐derived carbon dots (CDs) for Fe3+ and vitamin C is reported. The CDs are prepared from Chromolaena odorata leaves through a one‐pot hydrothermal method. The synthesized CDs have a quantum yield of 22.6%. The spherical dots have an average size of 3 nm and the chemical composition is found as nitrogen (7%) being the highest heteroatom present along with carbon and oxygen atoms in it. The ratio between fluorescence intensity at 445 nm and second‐order scattering constitutes the sensor output. A linear relationship is found between the output and analyte concentration in the range 0–70 and 0–10 μM with limit of detection 0.12 and 1.2 μM for Fe3+ and vitamin C, respectively. Static mechanism along with inner filter effect has been identified as the underlying quenching mechanism for Fe3+ activity. The strong chelation of Fe3+ by vitamin C further enhances the fluorescence. The change in the second‐order scattering intensity is explained by the formation and disruption of CDs/Fe3+ complex. The sensor also shows satisfactory performance with borewell water (Fe3+) and commercial tablet (vitamin C) with recovery rate ranging between 98.6% and 103.5%.

LAMP-visualized photofuel cell self-powered dual-mode sensing platform for detection of transmissible gastroenteritis virus

The detection of transmissible gastroenteritis virus (TGEV) is of great significance to reduce the loss of pig industry. A LAMP-visualization/PFC self-powered dual-mode output sensor platform was constructed to detect TGEV by combining a simple and intuitive photoelectrochromic material with a highly sensitive PFC self-powered sensing platform without external power supply. The PFC sensing substrate was constructed using CdS nanoparticles modified ZnO NRs (CdS/ZnO NRs) as the photoanode, which exhibited high photoactivity, and Prussian blue (PB) as the cathode. After LAMP reaction on the optical anode, visual signals caused by PB discolorimetry can be detected semi-quantitatively, or PFC power density electrical signals collected by electrochemical workstation can be used. The output power density value is logarithm of TGEV concentration. The linear relationship was good within the detection range of 0.075 fg/μL-7.5 ng/μL, with a detection limit of 0.025 fg/μL (S/N = 3). This multi-signal output sensing platform provides more choices for quantifying TGEV detection results, and the two methods can be mutually verified, which meets the needs of different scenarios and improves the reliability of detection. It has a good effect in the actual sample detection, without the use of expensive and complex instruments, and has a broad application prospect.

High Sensitivity and Wide Linear Range Flexible Piezoresistive Pressure Sensor with Microspheres as Spacers for Pronunciation Recognition.

Flexible piezoresistive pressure sensors have received great popularity in flexible electronics due to their simple structure and promising applications in health monitoring and artificial intelligence. However, the contradiction between sensitivity and detection range limits the application of the sensors in the medium-pressure regime. Here, a flexible piezoresistive pressure sensor is fabricated by combining a hierarchical spinous microstructure sensitive layer and a periodic microsphere array spacer. The sensor achieves high sensitivity (39.1 kPa-1) and outstanding linearity (0.99, R2 coefficient) in a medium-pressure regime, as well as a wide range of detection (100 Pa-160.0 kPa), high detection precision (<0.63‰ full scale), and excellent durability (>5000 cycles). The mechanism of the microsphere array spacer in improving sensitivity and detection range was revealed through finite element analysis. Furthermore, the sensors have been utilized to detect muscle and joint movements, spatial pressure distributions, and throat movements during pronouncing words. By means of a full-connect artificial neural network for machine learning, the sensor's output of different pronounced words can be precisely distinguished and classified with an overall accuracy of 96.0%. Overall, the high-performance flexible pressure sensor based on a microsphere array spacer has great potential in health monitoring, human-machine interface, and artificial intelligence of medium-pressure regime.

The River Runner: a low-cost sensor prototype for continuous dissolved greenhouse gas measurements

Abstract. Freshwater ecosystems are sources of the two most relevant greenhouse gases (GHGs): CO2 and CH4. Understanding the importance of freshwater ecosystems in the global carbon cycle and their role in global warming trends requires the accurate quantification of gas fluxes from the water phase to the atmosphere. These fluxes depend on the gas exchange velocity and the concentration gradient between the phases, which both cause high spatio-temporal variability in fluxes. On a global scale, the estimation of fluxes is limited by the lack of cheap and accurate methods to measure dissolved gas concentrations. Low-cost sensors, as an alternative to expensive gas analysers, are available; however, to date, the in situ performance of such sensors has been poorly examined. Here, we present an inexpensive data-logging sensor prototype that provides continuous measurements of dissolved CO2 and CH4 in submerged environments. Gas measurements are done in a confined gas space, which is rapidly equilibrated with the water phase through a single-layer polytetrafluoroethylene (PTFE) membrane, by a miniature non-dispersive infrared (NDIR) sensor for CO2 (Sunrise sensor, Senseair, Sweden) and a cheap metal oxide sensor for CH4 (TGS2611-E, Figaro Engineering Inc., Japan). Pressure, temperature and humidity are measured to correct raw sensor readings. For freshwater, the dissolved gas concentration is directly obtained from the measured molar fraction and temperature and pressure readings. In air, we measured the molar fraction of CO2 in a range from 400 to 10 000 ppm and the molar fraction of CH4 in a range from 2 to 50 ppm with an accuracy of ± 58 and ± 3 ppm respectively. We successfully used our prototype to measure diurnal variations in dissolved CO2 in a natural stream. We further calibrated the CH4 sensor for in situ use at concentrations ranging from 0.01 to 0.3 µmol L−1. Underwater, we were able to measure the molar fraction of CH4 in the prototype head with an accuracy of ± 13 ppm in the range from 2 to 172 ppm. The underwater measurement error of CH4 is always higher than for the same concentration range in air, and CH4 is highly overestimated below 10 ppm. At low CH4, humidity was the most important influence on the TGS2611-E sensor output in air, whereas temperature became the predominant factor underwater. We describe the response behaviour of low-cost sensors in submerged environments and report calibration methods to correct for temperature and humidity influence on the sensor signal if used underwater. Furthermore, we provide do-it-yourself instructions to build a sensor for submerged continuous measurements of dissolved CO2 and CH4. Our prototype does not rely on an external power source, and we anticipate that such robust low-cost sensors will be useful for future studies of GHG emissions from freshwater environments.

Structurally Aware 3D Gas Distribution Mapping Using Belief Propagation: A Real-Time Algorithm for Robotic Deployment

This paper proposes a new 3D gas distribution mapping technique based on the local message passing of Gaussian belief propagation that is capable of resolving in real time, concentration estimates in 3D space whilst accounting for the obstacle information within the scenario, the first of its kind in the literature. The gas mapping problem is formulated as a 3D factor graph of Gaussian potentials, the connections of which are conditioned on local occupancy values. The Gaussian belief propagation framework is introduced as the solver and a new hybrid message scheduler is introduced to increase the rate of convergence. The factor graph problem is then redesigned as a dynamically expanding inference task, coupling the information of consecutive gas measurements with local spatial structure obtained by the robot. The proposed algorithm is compared to the state of the art methods in 2D and 3D simulations and is found to resolve distribution maps orders of magnitude quicker than typical direct solvers. The proposed framework is then deployed for the first time onboard a ground robot in a 3D mapping and exploration task. The system is shown to be able to resolve multiple sensor inputs and output high resolution 3D gas distribution maps in a GPS denied cluttered scenario in real time. This online inference of complicated plume structures provides a new layer of contextual information over its 2D counterparts and enables autonomous systems to take advantage of real time estimates to inform potential next best sampling locations.

Performance Evaluation of Water Quality Monitoring System using Arduino Uno with IoT Server Based

Clean drinking water is a critical resource, important for the health and well-being of all humans. So it’s very important to monitor the quality of water available in reservoir. For this purpose, we proposed a “Water quality monitoring system using IOT”. 40% of deceases in universal are produced by water contaminations. So, the eminence of the drinking water wants to be restrained in real time although it is provided to customers. In this project, we propose a development and extension of a real time water eminence computing structure at compact cost using Arduino Uno. To figure out the parameters of the water such as temperature, pH, Conductivity, Turbidity, TDS. The sensor output data is sent to the concerned authority for additional stages to advance the water quality.

Non-invasive glucometer monitoring system through optical based near-infrared sensor method

ABSTRACT Diabetes is a fast-developing medical issue that causes most renal and cardiac illnesses. Thus, diabetes management requires regular glucose monitoring. One potential technology is non-invasive glucometer monitoring. This work aims to develop a user-friendly near-infrared sensor-based non-invasive glucose monitoring system, correlating sensor output voltage variations with glucose levels, to provide accurate and convenient glucose monitoring for diabetes management. The objective is to validate the system’s accuracy against existing fingerpick methods and analyze its performance across different age groups and food intake conditions through experimental testing and Clarke grid analysis. In our research, we propose a near-infrared sensor-based non-invasive-type glucose monitoring technique which is a user-friendly system. The experimental setup and prototype system are designed and implemented for measuring the variation of glucose level with respect to a sensor output voltage. Using Beer Lambert’s law, the established results correlated the absorbance property of light with the sample concentration level. Demonstration of testing for different aged people was done under various food intake conditions. The obtained results are tabulated and validated with the existing fingerpick method and achieved an accuracy of 97.8%. Also, Clarke grid analysis has been done and depicted the pattern obtained.

Baseline Calibration Scheme Embedded in Single-Slope ADC for Gas Sensor Applications

This paper introduces a single-slope analog-to-digital converter (SS ADC) with an embedded digital baseline calibration scheme designed to improve the accuracy and reliability of gas sensor measurements. The proposed SS ADC effectively leverages an up/down counter mechanism to ensure stable signal extraction from gas sensors, despite variations in the baseline distribution. The proposed SS ADC initiates with a down counting operation to capture the initial output value of the gas sensor, which, after A/D conversion, is stored as a reference point for future readings. Subsequent gas sensor output values are derived by performing an up counting operation from this baseline reference. This approach allows for real-time correction of the baseline during the SS A/D conversion process, obviating the need for complex post-processing and baseline correction algorithms. The proposed SS ADC with the baseline calibration scheme was designed using a 0.18 μm standard CMOS process to confirm its feasibility. It demonstrated a signal-to-noise and distortion ratio (SNDR) of 57.56 dB and a spurious-free dynamic range (SFDR) of 59.02 dB, resulting in an effective number of bits (ENOB) of 9.27 bits in the post-simulation level. The proposed SS ADC has a total power consumption of 1.649 mW. This work offers an efficient solution to the baseline distribution problem in gas sensors, facilitating more reliable and accurate gas detection systems.

A novel coreless current sensing mechanism for two-wire power cord

In the development of smart home products, energy monitoring, and control systems, there is a significant need for precise and reliable electric current measurement. Most domestic appliances, whether residential or commercial, are powered by power cords with two wires. A reliable and accurate current probe would be required to detect current in two-wire power cables. Existing current sensors for two-wire power cables are complex, bulky (they use a magnetic core), and need full access to the power cord. In this paper a coreless, contactless current probe that requires only limited access of the space around the two-wire power cable is proposed. The sensor unit presented here measures the current accurately even for certain types of misalignments of the power cord. This makes the current measurement less sensitive to minor mechanical displacements, radius of the conductors, and the overall insulation thickness of power cord. In the proposed current probe, one-of-a-kind and highly reliable arrangement of magnetic field sensors, are used to measure the magnetic flux density caused by the current passing in the power cord. The current is computed using a derived mathematical formula which is expressed in terms of the voltage outputs of the magnetic sensors. To provide a clear understanding of the measurement process, an analysis using the Finite Element Method (FEM) is performed. The functionality of the proposed current probe is theoretically tested. Considering different sources of errors, an accuracy of less than 1.5% is obtained.