Sensor Topology Research Articles

Biomimetic multiscale structure with hierarchically entangled topologies of cellulose-based hydrogel sensors for human-computer interaction

The development of high-performance cellulose-based sensors with superior interfacial compatibility, flexibility, and strength has always been challenging. Drawing inspiration from the intricate multiscale hierarchy found in resilient natural materials, the incorporation of this structure into cellulose-based hydrogels using biomimetic strategies is anticipated to enhance their properties. Therefore, the cellulose/polyacrylamide (PAM) hybrid hydrogels are fabricated using the aqueous AlCl3/ZnCl2 system through an all-green one-pot method at room temperature, achieving efficient dissolution of cellulose at multiple scales and in-situ polymerization of polyacrylamide. The multiscale cellulose/PAM hydrogels achieve good interfacial compatibility, with the reinforcing mechanism being confirmed through a combination of experimental validation and atomic simulations. The findings underscore the key elements that drive the multiscale behavior of hydrogels, encompassing a range of factors such as multiple interfacial interactions, hierarchical molecular entanglements, and microskeleton reinforcement. The hydrogels exhibit satisfactory stretchability (412 %), compressive strength (1.2 MPa), conductivity (1.65 S m−1), and ultralong circulability (> 12,000 cycles), rendering them suitable for various scenarios of human-computer interaction. This work establishes a comprehensive interpretation of the strengthening mechanism in cellulose-based multiscale hierarchical structures, thereby providing novel insights for the advanced design strategies of other promising hierarchical materials.

Electrochemical Impedance Spectroscopy for Ion Sensors with Interdigitated Electrodes: Capacitance Calculations, Equivalent Circuit Models and Design Optimizations.

Electrochemical impedance spectroscopy (EIS) is becoming more and more relevant for the characterization of biosensors employing interdigitated electrodes. We compare four different sensor topologies for an exemplary use case of ion sensing to extract recommendations for the design optimizations of impedimetric biosensors. Therefore, we first extract how sensor design parameters affect the sensor capacitance using analytical calculations and finite element (FEM) simulations. Moreover, we develop equivalent circuit models for our sensor topologies and validate them using FEM simulations. As a result, the impedimetric sensor response is better understood, and sensitive and selective frequency ranges can be determined for a given sensor topology. From this, we extract design optimizations for different sensing principles.

Multi-Fault Diagnosis of Electric Vehicle Power Battery Based on Double Fault Window Location and Fast Classification

As energy supply units, lithium-ion batteries have been widely used in the electric vehicle industry. However, the safety of lithium-ion batteries remains a significant factor limiting their development. To achieve rapid fault diagnosis of lithium-ion batteries, this paper presents a comprehensive fault diagnosis process. Firstly, an interleaved voltage sensor topology structure is utilized to acquire battery voltage data. An improved complete ensemble empirical mode decomposition with adaptive noise method is introduced to process data. Then, the reconstructed voltage data sequence is used to eliminate the influence of noise. A fault location is performed using dichotomy correlation coefficient and time window correlation coefficient. Afterwards, principal component analysis is used to select the principal components with high contribution rate as classification features. The gray wolf optimization algorithm is used to find the parameters of the least squares support vector machine, constructing an optimal classifier for fault classification. A fault experiment platform is established to realize the physical triggering of faults such as external short circuit, internal circuit, and connection of experimental battery packs. Finally, the accuracy and reliability of the method are verified by the results of fault localization and fault type determination.

Varactor-Based Tunable Sensor for Dielectric Measurements of Solid and Liquid Materials

In this article, a tunable RF sensor is presented for the measurement of dielectric materials (liquids and solids) based on a metamaterial resonator. The proposed novel configuration sensor has a microstrip line-loaded metamaterial resonator with tunable characteristics by utilizing a single varactor diode in the series of the resonator. CST Microwave studio is employed for 3D simulations of the tunable sensor, and the desired performance is attained by optimizing various structural parameters to enhance the transmission coefficient (S21 magnitude) notch depth performance. The proposed RF sensor can be tuned in L and S-bands using the varactor diode biasing voltage range of 0–20 V. To validate the performance of the sensor, the proposed design has been simulated, fabricated, and tested for the dielectric characterization of different solid and liquid materials. Material testing is performed in the bandwidth of 1354 MHz by incorporating a single metamaterial resonator-based sensor. Agilent’s Network Analyzer is used for measuring the S-parameters of the proposed sensor topology under loaded and unloaded conditions. Simulated and measured S-parameter results correspond substantially in the 1.79 to 3.15 GHz frequency band during the testing of the fabricated sensor. This novel tunable resonator design has various applications in modulators, phase shifters, and filters as well as in biosensors for liquid materials.

The heterostructure topology of a chemiresistive sensor based on hexagonal BaTiO3 and 2D SnO for toluene detection

The SnO–BaTiO3 bi-layered heterostructured chemiresistive sensor exhibits promising potential for detecting volatile organic compounds (VOCs) under ambient conditions.

Multi-Fault Diagnosis for Series-Connected Lithium-Ion Battery Packs Based on Improved Sensor Topology and Correlation Coefficient Method

Multi-Fault Diagnosis for Series-Connected Lithium-Ion Battery Packs Based on Improved Sensor Topology and Correlation Coefficient Method

Accurate Range Free Location based Partial Derivative and Stochastic Feedforward Neural Network Hyperbolic-based Agriculture Sensor Network Formation

Wireless sensor networks (WSN) authorize the control of different source environmental aspects and crop states as far as precision agriculture is concerned. Nevertheless, the complicated agricultural environment brings about the WSN topology to change often and hence link association likelihood is laborious to predict. Information pertaining to agriculture is an essential distress for localization-based service in the domain of WSNs. Smooth planning and control as the most typical range-free localization method localization performance is said to be good in even distributed networks. Nevertheless, it demonstrated extremely poor accuracy under that in an urgent issue that required to be addressed. In this work a novel topology construction method called, Partial Derivative Laurent Approximation and Stochastic Feedforward Hyperbolic (PDLA-SFH) based Agriculture Sensor Network Formation is proposed. The proposed method is split into three steps. They are agriculture sensor topology construction, average hop size distance validation and position estimation. First topology construction is performed by employing Game Theory Partial Derivative Regression Coefficient-based Topology model. Second, Laurent Approximation-based Hop Size Distance validation model is designed for optimal topology formation. Finally the Stochastic Feedforward Hyperbolic-based Position Estimation is modeled. The simulation results performed in NS3 showed that the proposed localization algorithms can attain better localization performance in terms of accuracy, time and error rate in comparison with other existing methods such as basic Digital Twins and Autonomous Groups Particles Swarm Optimization (AGPSO) under distinct arbitrary network topologies.

The hydrogen sensing capability of zinc oxide-containing sensors: Modeling by the general regression artificial neural network

This research applies general regression (GR) neural networks to model the hydrogen sensing capability of pure zinc oxide (ZnO) and ZnO nanocomposites containing different additives. The sensor's topology/composition, temperature, and hydrogen concentration in the atmosphere are the independent features of the GR models. Sensitivity analysis approves that the Gaussian spread value must adjust to 23.5 × 10−6 to achieve the most accurate predictions. This model forecasts 297 actual responses of ten ZnO-containing sensors with mean absolute error, relative absolute error, and regression coefficient of 0.29, 1.56%, and 0.9977, respectively. These prediction accuracies are better than those obtained by cascade feedforward, radial basis, Elman recurrent, and multilayer perceptron neural networks. The relevancy test shows that the sensor's hydrogen detection ability improves with the sensor geometry and ZnO additive dose. ZnO–Co3O4 is the best sensor composition to detect even low hydrogen concentrations. Also, the nanowire and nanofiber are the best sensor topologies for the considered task.

Ensemble Learning-Based Correlation Coefficient Method for Robust Diagnosis of Voltage Sensor and Short-Circuit Faults in Series Battery Packs

Accurate and reliable multi-fault diagnosis of battery packs is crucial to the safe operation of electric vehicles. To this end, this paper proposes a systematically improved Correlation Coefficient (CC) method by utilizing multivariate statistical analysis and Bayesian probability theory under the framework of ensemble learning. Specifically, different window-widths are first selected in an appropriate value range, and the CC signals between cross-cell voltages for each fixed window-width are calculated to create different local sub-models. Then, in each sub-model, an independent component analysis-based fault diagnosis is implemented to obtain the local diagnostic result, and the results of all sub-models are integrated as an Ensemble Fault Probability (EFP) and an Ensemble Contribution Rate (ECR) through Bayesian probabilistic ensemble interface. Once the EFP exceeds its threshold, a fault is considered to be detected and the ECR is subsequently applied along with the cross-cell sensor topology to identify the fault type (short-circuit or sensor fault) and locate the accurate position of the failed battery cell/sensor. In-depth theoretical analysis and sufficient comparative experiments on a real Lithium-ion battery packs test platform demonstrate that the proposed approach becomes more robust and reliable than the conventional CC-based methods, and also has better physical interpretability.

Sensor Topology Optimization in Dense IoT Environments by Applying Neural Network Configuration

In dense IoT deployments of wireless sensor networks (WSNs), sensor placement, coverage, connectivity, and energy constraints determine the overall network lifetime. In large-size WSNs, it is difficult to maintain a trade-off among these conflicting constraints and, thus, scaling is difficult. In the related research literature, various solutions are proposed that attempt to address near-optimal behavior in polynomial time, the majority of which relies on heuristics. In this paper, we formulate a topology control and lifetime extension problem regarding sensor placement, under coverage and energy constraints, and solve it by applying and testing several neural network configurations. To do so, the neural network dynamically proposes and handles sensor placement coordinates in a 2D plane, having the ultimate goal to extend network lifetime. Simulation results show that our proposed algorithm improves network lifetime, while maintaining communication and energy constraints, for medium- and large-scale deployments.

Automated Machine Learning Strategies for Multi-Parameter Optimisation of a Caesium-Based Portable Zero-Field Magnetometer.

Machine learning (ML) is an effective tool to interrogate complex systems to find optimal parameters more efficiently than through manual methods. This efficiency is particularly important for systems with complex dynamics between multiple parameters and a subsequent high number of parameter configurations, where an exhaustive optimisation search would be impractical. Here we present a number of automated machine learning strategies utilised for optimisation of a single-beam caesium (Cs) spin exchange relaxation free (SERF) optically pumped magnetometer (OPM). The sensitivity of the OPM (T/Hz), is optimised through direct measurement of the noise floor, and indirectly through measurement of the on-resonance demodulated gradient (mV/nT) of the zero-field resonance. Both methods provide a viable strategy for the optimisation of sensitivity through effective control of the OPM's operational parameters. Ultimately, this machine learning approach increased the optimal sensitivity from 500 fT/Hz to <109fT/Hz. The flexibility and efficiency of the ML approaches can be utilised to benchmark SERF OPM sensor hardware improvements, such as cell geometry, alkali species and sensor topologies.

Graph Neural Networks in IoT: A Survey

The Internet of Things (IoT) boom has revolutionized almost every corner of people’s daily lives: healthcare, environment, transportation, manufacturing, supply chain, and so on. With the recent development of sensor and communication technology, IoT artifacts, including smart wearables, cameras, smartwatches, and autonomous systems can accurately measure and perceive their surrounding environment. Continuous sensing generates massive amounts of data and presents challenges for machine learning. Deep learning models (e.g., convolution neural networks and recurrent neural networks) have been extensively employed in solving IoT tasks by learning patterns from multi-modal sensory data. Graph neural networks (GNNs), an emerging and fast-growing family of neural network models, can capture complex interactions within sensor topology and have been demonstrated to achieve state-of-the-art results in numerous IoT learning tasks. In this survey, we present a comprehensive review of recent advances in the application of GNNs to the IoT field, including a deep dive analysis of GNN design in various IoT sensing environments, an overarching list of public data and source codes from the collected publications, and future research directions. To keep track of newly published works, we collect representative papers and their open-source implementations and create a Github repository at GNN4IoT.

METHODS FOR THE FORMATION OF SPATIAL TOPOLOGY AND INTERROGATION OF FIBER-OPTIC SENSORS FOR DIAGNOSTICS OF COMPOSITE STRUCTURES (Review)

The paper formulates general requirements for the formation of spatial topologies of quasi-distributed fiber-optic systems of embedded monitoring based on fiber Bragg gratings for monolithic and three-layer structures made of polymer composite materials. The main types of spatial topologies of fiber-optic sensors for the implementation of an effective quasi-distributed system of embedded diagnostics of composite structures are considered. The main methods for interrogating arrays of fiber-optic sensors that form a quasi-distributed system of embedded diagnostics of composite structures are analyzed, an overview and analysis of the technical characteristics of foreign and domestic interrogation equipment is given. It has been established that for practical applications, taking into account the relative simplicity of physical implementation and the availability of interrogation equipment, it is advisable to use a mixed topology of fiber-optic sensors, which includes elements of serial and star topologies, with he spectral channel separation method being the most optimal.

Features of creating a system of simultaneous built-in testing of deformation and temperature of composite structures by fiber-optic sensors

This article describes the relevance of improving existing and creating new effective methods of non-destructive testing and technical diagnostics of highly loaded aircraft structures made of polymer composite materials to ensure safe operation. Approaches to the creation of efficient systems for the simultaneous embedded testing of deformations and temperature of structures made of polymer composite materials by an optical method using embedded fiber-optic sensors based on fiber Bragg gratings are considered. The world experience in creating such systems, the methodology of non-destructive testing are analyzed, taking into account the creation of a spatial topology of fiber-optic sensors in a real product. It is shown that to solve this problem, it is most expedient to use the method of two optical fibers with different sensitivity to deformation and temperature, or to one of these parameters. The results of experimental studies on the simultaneous control by the proposed method of deformation and temperature of a structurally similar sample from a carbon composite processed by a vacuum method from a prepreg are presented. It has been established that the use of a quadratic model for optical control of structurally similar samples of carbon composite makes it possible to increase the accuracy of deformation and temperature measurements in comparison with the linear control model. It is confirmed that the proposed experimental technique allows for simultaneous control of deformation and temperature of structurally similar samples from carbon composites, while it can be adjusted and adapted to the actual operating conditions of a particular structure.

Data-Driven Diagnosis of Multiple Faults in Series Battery Packs Based on Cross-Cell Voltage Correlation and Feature Principal Components

This article develops an efficient fault diagnostic scheme for battery packs using a novel sensor topology and signal processing procedure. Cross-cell voltages are measured to capture electrical abnormalities, and recursive correlation coefficients between adjacent voltages are calculated to embody system state. Then discrete wavelet packet transform is applied on the correlation sequences to extract diverse characteristic indexes, wherein the most representative components are refined as fault features by principal component analysis. Afterward, resorting to multiclass relevance vector machine, sparse classification models are constructed to cognize fault patterns, and accordingly, fault types and grades are evaluated. Common faults, including external and internal short-circuit, thermal abuse, and loose connection, are physically triggered on a series pack to acquire realistic data set. Experimental verifications under different conditions and algorithmic configurations suggest that the proposed diagnosis scheme can give accurate and reliable assessments on different fault specifics, with a fault isolation success rate of 84% and a fault severity grading success rate of 90%.

Enhanced Half-Mode SIW Loaded With Interdigital Capacitor for Permittivity Measurements

This paper presents a novel sensor topology employing an Interdigital Capacitor (IDC) integrated in Half Mode Substrate Integrated Waveguide (HMSIW) for permittivity measurement of solid materials. HMSIW with around 50% smaller size compared with SIW offers a compact solution for hosting the sensing section. Capacitive-like characteristic of IDC further improves the size reduction up to 71.6%. In addition, IDC with enhancing the electric field concentration is exploited for sensing section. Design details of the hosting section and the sensing section are provided along with extraction and analysis of the equivalent circuit model of the sensor. Design and simulations are carried out by means of design tools, and the proposed sensor is fabricated on a 50×24×1.6 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> printed circuit board (PCB). Measurements demonstrates that the fabricated sensor prototype shows 113 MHz frequency shift per unit permittivity change around 3.5 GHz, and it has a normalized sensitivity of 3.16% within a wide range of permittivity values, which is relatively better than similar sensors in the literature.

Investigating the impact of network topologies on the iot-based wsn in smart home monitoring system

The object of this research is to present IoT WSN-based smart home monitoring system, which allows users to monitor and manage all of their appliances and home equipment via the Internet using established protocols. IoT is described as the connection of equipment and appliances to the Internet in order to monitor, report, and perform certain tasks. Wireless Sensor Networks (WSN) are considered as a key component in the IoT model's implementation. This research presented the IoT WSN platform using Riverbed Modeler Simulation Program in order to examine the network performance for different Wireless Sensor topologies (Star, Tree and Mesh). This platform consists of a number of scenarios with a number of sensors in each scenario. Each sensor is represented by the ZigBee end device, which sensed and collected data about the smart home and sent the collected data to the controller, which is represented by the ZigBee coordinator. The controller sends the data to the server to be monitored by the users through any gateway (Wi-Fi) after logging in using a specific application with three routing topologies on the controller. The results showed that IoT WSN tree topology is the best topology if the throughput is considered for improvement at the expense of data dropped with acceptable delay. Star topology improves the network performance in terms of data dropped and throughput when the number of sensors was increased. Mesh topology achieved the smallest data dropped with low throughput. Due to their features, these results were effective because they indicated that the selection of suitable routing topology played an important role in improving the degradation of IoT WSN performance due to the interference of Wi-Fi and ZigBee network since they utilized the same frequency band (2.4 GHz).

PCB-Based Planar Inductive Loops for Partial Discharges Detection in Power Cables

Partial discharge (PD) diagnosis tests, including detecting, locating, and identifying, are used to trace defects or faults and assess the degree of aging in order to monitor the insulation condition of medium- and high-voltage power cables. In this context, an experimental evaluation of three different printed circuit board (PCB)-based inductive sensor topologies, with spiral, non-spiral, and meander shapes, is performed. The aim is to assess their capabilities for PD detection along a transmission power cable. First, simulation and experimental characterization are carried out to determine the equivalent electrical circuit and the quality factor of the three sensors. PD activity was studied in the lab on a 10-m-long defective MVAC cable. The three PCB-based sensors were tested in three different positions: directly on the defective cable (P1), at a separation distance of 10 cm to 3 m (P2), and on the ground line (P3). For the three positions, all sensors' outputs present a damped sine wave signal with similar frequencies and durations. Experimental results showed that the best sensitivity was given by the non-spiral inductor, with a peak voltage of around 500 mV in P1, 428 mV in P2, and 45 mV in P3, while the meander sensor had the lowest values, which were approximately 80 mV in P1. The frequency spectrum bandwidth of all sensors was between 10 MHz and 45 MHz. The high sensitivity of the non-spiral inductor could be associated with its interesting properties in terms of quality factor and SFR, which are due to its very low resistivity. To benchmark the performance of the designed three-loop sensors, a comparison with a commercial high-frequency current transformer (HFCT) is also made.

An Innovative and Cost-Effective Traffic Information Collection Scheme Using the Wireless Sniffing Technique

In recent years, the wireless sniffing technique (WST) has become an emerging technique for collecting real-time traffic information. The spatiotemporal variations in wireless signal collection from vehicles provide various types of traffic information, such as travel time, speed, traveling path, and vehicle turning proportion at an intersection, which can be widely used for traffic management applications. However, three problems challenge the applicability of the WST to traffic information collection: the transportation mode classification problem (TMP), lane identification problem (LIP), and multiple devices problem (MDP). In this paper, a WST-based intelligent traffic beacon (ITB) with machine learning methods, including SVM, KNN, and AP, is designed to solve these problems. Several field experiments are conducted to validate the proposed system: three sensor topologies (X-type, rectangle-type, and diamond-type topologies) with two wireless sniffing schemes (Bluetooth and Wi-Fi). Experiment results show that X-type has the best performance among all topologies. For sniffing schemes, Bluetooth outperforms Wi-Fi. With the proposed ITB solution, traffic information can be collected in a more cost-effective way.

Recent Progress in the Topologies of the Surface Acoustic Wave Sensors and the Corresponding Electronic Processing Circuits.

In this paper, we present an overview of the latest achievements in surface acoustic wave (SAW) sensors for gas or liquid fluid, with a focus on the electrodes’ topology and signal processing, as related to the application of the sensing device. Although the progress in this field is mainly due to advances in the materials science and the sensing coatings, the interdigital (IDT) electrodes’ organization is also an important tool for setting the acoustic-wave-distribution mode, and, thus, for improvement of the SAW performance. The signal-conditioning system is of practical interest, as the implementation of the SAW, as a compact and mobile system is dependent on this electronic circuit. The precision of the detection of the SAW platform is related not only to the IDT electrodes’ geometry but also to their location around the sensing layer. The most commonly used architectures are shown in the present paper. Finally, we identify the needs for the future improvement of these prospective sensors.