Multi-channel Sensor Research Articles

A Multi-Path MAC Scheduling Scheme for Multi-Channel Wireless Sensor Networks.

Designing reasonable MAC scheduling strategies is an important means to ensure transmission quality in wireless sensor networks (WSNs). When there exist multiple available routes from the source to the destination, it is necessary to combine a data traffic allocation mechanism and design a multi-path MAC scheduling scheme in order to ensure QoS. This paper develops a multi-path resource allocation method for multi-channel wireless sensor networks, which uses random-access technology to complete MAC scheduling and selects the transmission path for each packet according to the probability. Through theoretical analysis and simulation experiments, it can be found that the proposed strategy can provide a reliable throughput capacity region. Meanwhile, due to the use of random-access technology, the computational complexity of the proposed algorithm can be independent of the number of links and channels.

Missing signal imputation for multi-channel sensing signals on rotary machinery by tensor factorization

Multi-channel sensor fusion can be challenging for real-time machinery fault identification and diagnosis when a substantial amount of missing data exists. Usually, some (or even all) sensors may not function correctly during real-time data acquisition due to sensor malfunction or transmission issues. Additionally, multi-channel sensor fusion yields a large volume of data. Imputation of missing entries can also be challenging with a large volume of data, which can predominantly affect the accuracy of machinery fault diagnosis. However, how to impute a substantial amount of missing data for machinery fault identification is an open research question. In light of the above challenges, this paper proposes constructing time-domain tensors based on heterogeneous sensor signals. Subsequently, the fully Bayesian CANDECOMP/PARAFAC (FBCP) factorization method is adopted for missing data imputation of diverse bearing faults signals. To validate the effectiveness of this proposed method, a machinery fault simulator was used to collect diverse bearing fault signals by incorporating both acoustics and vibration sensors. A varying percentage of continuous missing signal scenarios are introduced at the random locations among different acoustics and vibration channels to construct incomplete tensors. Subsequently, the FBCP method was leveraged to complete the incomplete tensors and calculate estimated tensors. To evaluate the performance of continuous missing data imputation, relative standard errors are computed based on the estimated and actual time-domain tensors. Experimental results show that this proposed method can effectively impute a substantial portion of continuous missing data from diverse bearing fault scenarios.

Reconstruction of spectral irradiance in a real application with a multi-channel spectral sensor using convolutional neural networks.

Lighting is not only a key mediator for the perception of the architectural space but also plays a crucial role regarding the long-term well-being of its human occupants. Future lighting solutions must therefore be capable of monitoring lighting parameters to allow for a dynamic compensation of temporal changes from the optimal or intended conditions. Although mostly based on synthetic data, previous studies adopting small, low-cost, multi-band color sensors for this kind of parameter estimation have reported some promising preliminary results. Building up on these findings, the present work introduces a new methodology for estimating the absolute spectral irradiances of real-world lighting scenarios from the responses of a 10-channel spectral sensor by using a convolutional neural network approach. The lighting scenarios considered here are based on a tunable white floor lamp system set up at three different indoor locations and comprise combinations of LED, fluorescent, tungsten, and daylight lighting conditions. For white light mixtures of the various spectral components, the proposed reconstruction methodology yields estimates of the spectral power distribution with an average root-mean-square error of 1.6%, an average Δu'v' of less than 0.001, and an average illuminance accuracy of 2.7%. Sensor metamerism is discussed as a limiting factor for the achievable spectral reconstruction accuracy with certain light mixtures.

Retracted: Common Pathogens and Drug Resistance of Neonatal Pneumonia with New Multichannel Sensor.

[This retracts the article DOI: 10.1155/2022/2208636.].

A Multichannel Fluorescent Array Sensor for Discrimination of Different Types of Drug-Induced Kidney Injury.

The differences in urinary proteins could provide a novel opportunity to distinguish the different types of drug-induced kidney injury (DIKI). In this research, Au nanoparticles-polyethyleneimine (AuNPs-PEI) and the three fluorophore-labeled proteins (FLPs) have been constructed as a multichannel fluorescent array sensor via electrostatic interaction, which was used to detect the subtle changes in urine collected from the pathological state of DIKI. Once the urine from different types of DIKI was introduced, the binding equilibrium between AuNPs-PEI and FLPs would be broken due to the competitive binding of urinary protein, and the corresponding fluorescence response pattern would be generated. Depending on the different fluorescence response patterns, the different types of DIKI were successfully identified by principal component analysis (PCA) and linear discriminant analysis (LDA). Accordingly, the strategy was expected to be a powerful technique for evaluating the potential unclear mechanisms of nephrotoxic drugs, which would provide a promising method for screening potential renal-protective drugs.

Single-Beam Double-Pass Miniaturized Atomic Magnetometer for Biomagnetic Imaging Systems

Miniaturized atomic magnetometers (AMs), particularly spin-exchange relaxation-free (SERF) AMs, have been emerging in clinical imaging applications such as magnetocardiography (MCG) and magnetoencephalography (MEG). Miniaturization, portability, and low cost are primary development targets for biomagnetic imaging technologies, as well as high sensitivity, temporal, and spatial resolution. In this article, we propose a low-cost solution for a biomagnetic imaging system based on AMs, in which one laser source is used for a multichannel AM sensor array. A novel design is demonstrated for a miniaturized SERF AM, consisting of a single-beam double-pass configuration based on an optical fiber circulator. The effects of temperature and laser power on the zero-field magnetic resonance linewidth are characterized, and the experimental results show that the present design achieves better performance, compared with a traditional single-beam single-pass configuration. The magnetic field noise spectrum shows that such single-beam double-pass miniaturized AMs reach a sensitivity of approximately 86 fT/Hz <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{{1}/{2}}$ </tex-math></inline-formula> at a frequency of 10 Hz when operating in the single-channel mode and a sensitivity of approximately 40 fT/Hz <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{{1}/{2}}$ </tex-math></inline-formula> when operating in magnetic differential mode with two sensors sharing one laser source and a 6-mm baseline. A typical adult MCG signal with all the visible P, QRS-complex, and T waves is recorded by the magnetometer in the single-channel mode. This design is especially suitable for AMs operating in arrays as a basic building element for low-cost biomagnetic imaging systems.

Accurate Multi-Channel QCM Sensor Measurement Enabled by FPGA-Based Embedded System Using GPS

This paper presents a design and implementation proposal for a real-time frequency measurement system for high-precision, multi-channel quartz crystal microbalance (QCM) sensors using a field programmable gate array (FPGA). The key contribution of this work lies in the integration of a frequency measurement and mass resolution computation based on Global Positioning System (GPS) signals within a single FPGA chip, utilizing Input/Output Blocks to incorporate logic QCM oscillator circuits. The FPGA design enables parallel processing, ensuring accurate measurements, faster calculations, and reduced hardware complexity by minimizing the need for external components. As a result, a cost-effective and accurate multi-channel sensor system is developed, serving as a reconfigurable standalone measurement platform with communication capabilities. The system is implemented and tested using the FPGA Xilinx Virtex-6, along with multiple QCM sensors. The implementation on a Xilinx XC6VLX240T FPGA achieves a maximum frequency of 324 MHz and consumes a dynamic power of 120 mW. Notably, the design utilizes a modest number of resources, requiring only 188 slices, 733 flip-flops, and 13 IOBs to perform a double-channel sensor microbalance. The proposed system meets the precision measurement requirements for QCM sensor applications, exhibiting low measurement error when monitoring QCM frequencies ranging from 1 to 50 MHz, with an accuracy of 0.2 ppm and less than 0.1 Hz.

High-availability displacement sensing with multi-channel self mixing interferometry.

Laser self-mixing is in principle a simple and robust general purpose interferometric method, with the additional expressivity which results from nonlinearity. However, it is rather sensitive to unwanted changes in target reflectivity, which often hinders applications with non-cooperative targets. Here we analyze experimentally a multi-channel sensor based on three independent self-mixing signals processed by a small neural network. We show that it provides high-availability motion sensing, robust not only to measurement noise but also to complete loss of signal in some channels. As a form of hybrid sensing based on nonlinear photonics and neural networks, it also opens perspectives for fully multimodal complex photonics sensing.

Multi-channel anion and cation sensing conduct of a terpyridyl-imidazole receptor: Experimental demonstration and implication of artificial intelligence tools for analysis and data prediction

A terpyridyl-imidazole based receptor (tpy-HImzPh3NMe2) is designed here for selective sensing of cations and anions. Coupling of NMe2 groups and electron-acceptor terpyridine unit induces ligand to ligand charge transfer (LLCT) character in the probe whose photophysical properties is significantly altered by treating with selected anions, cations, acid as well as by solvents of varying polarity. The receptor acts as multi-channel optical sensor for F− and CN− in organic solvents whereas highly selective probe for CN− in predominantly aqueous medium. Additionally, the receptor can visually recognize only Fe2+ among the other bivalent 3d metals. Density functional theory (DFT) and time-dependent-DFT calculations are executed to acquire better perception of the electronic structure and for appropriate assignment of the spectral bands of the probe. By utilizing the optical outputs, the receptor demonstrates NOT and YES Boolean logic operations. Additionally, to overcome the difficulty associated with carrying out exhaustive ion-sensing experiments, we applied several machine learning (ML) and artificial intelligence (AI) techniques such as Fuzzy logic (FL), Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Decision tree regression (DTR) to anticipate the complete ion-sensing conduct of the receptor. Comparison was also made among the models’ outputs and the experimental values.

Multichannel microfluidic virus sensor for rapid detection of respiratory viruses using virus-imprinted polymer for digital livestock farming

This paper reports a multichannel microfluidic sensor for detecting H1N1 swine influenza A virus (IAV). This device integrates six independent sensing elements, measurement chambers, and passive mixers on a microfluidic platform to enable parallel processing and analysis of multiple samples. The sensing element uses a conducting virus-imprinted polymer (VIP) to recognize, immobilize, and qualify the target H1N1 virus. The VIP is formed via electropolymerization of 2-amino-1,3,4-thiadiazole monomers and template H1N1 virus particles and subsequent removal of the template particles. The formed cavities are complementary to H1N1 virus in shape and size. High selectivity, conductance, and charge transfer characteristics make the VIP suitable for electrochemical detection of H1N1 virus. The sensor demonstrates a linear response to the logarithmic concentration of H1N1 virus in a concentration range of up to 5 × 106 TCID50/mL with a limit of detection of 9 TCID50/mL. The sensor exhibits high selectivity to H1N1 virus in the presence of several non-specific swine pathogens. Further, the sensor can be regenerated and reused by simply washing the sensing surface with acetic acid. The device demonstrates parallel detection of H1N1 virus in nasal swabs, oral fluids, and lung homogenates sampled from swine with low consumption of reagents and samples. Data AvailabilityData will be made available on request

Yttrium-Iron Garnet Magnetometer in MEG: Advance towards Multi-Channel Arrays.

Recently, a new kind of sensor applicable in magnetoencephalography (MEG) has been presented: a solid-state yttrium-iron garnet magnetometer (YIGM). The feasibility of yttrium-iron garnet magnetometers (YIGMs) was demonstrated in an alpha-rhythm registration experiment. In this paper, we propose the analysis of lead-field matrices for different possible multi-channel on-scalp sensor layouts using YIGMs with respect to information theory. Real noise levels of the new sensor were used to compute signal-to-noise ratio (SNR) and total information capacity (TiC), and compared with corresponding metrics that can be obtained with well-established MEG systems based on superconducting quantum interference devices (SQUIDs) and optically pumped magnetometers (OPMs). The results showed that due to YIGMs' proximity to the subject's scalp, they outperform SQUIDs and OPMs at their respective noise levels in terms of SNR and TiC. However, the current noise levels of YIGM sensors are unfortunately insufficient for constructing a multichannel YIG-MEG system. This simulation study provides insight into the direction for further development of YIGM sensors to create a multi-channel MEG system, namely, by decreasing the noise levels of sensors.

Online High-Resolution EIS of Lithium-Ion Batteries by Means of Compact and Low Power ASIC

A compact electronic circuit capable of performing Electrochemical Impedance Spectroscopy (EIS) on either single Lithium-ion cells or modules formed by the series of two cells is presented. The proposed device, named Double Cell Management Unit (DCMU), constitutes an important improvement to a recently proposed cell management unit, which combined EIS acquisition functions with a multichannel sensor interface compatible with thermistors, strain-gauges and moisture detectors. The proposed circuit maintains the versatility of the previous version and significantly extends the EIS frequency range, allowing vector impedance measurements from 0.1 Hz to about 15 kHz. The capability of handling both single Lithium-ion cells or series of two cells is obtained by adding a few external components to the previous version. This also allowed increasing the stimulation current to a maximum amplitude of 200 mA, resulting in improved resolution. Experiments consisting in EIS acquisition performed on batteries of different capacity at different temperatures and states of charge are described. Estimated impedance resolution (standard deviation) is 20 μΩ obtained at 1 kHz with a stimulation current of 100 mA amplitude.

Abaxial leaf surface-mounted multimodal wearable sensor for continuous plant physiology monitoring.

Wearable plant sensors hold tremendous potential for smart agriculture. We report a lower leaf surface-attached multimodal wearable sensor for continuous monitoring of plant physiology by tracking both biochemical and biophysical signals of the plant and its microenvironment. Sensors for detecting volatile organic compounds (VOCs), temperature, and humidity are integrated into a single platform. The abaxial leaf attachment position is selected on the basis of the stomata density to improve the sensor signal strength. This versatile platform enables various stress monitoring applications, ranging from tracking plant water loss to early detection of plant pathogens. A machine learning model was also developed to analyze multichannel sensor data for quantitative detection of tomato spotted wilt virus as early as 4 days after inoculation. The model also evaluates different sensor combinations for early disease detection and predicts that minimally three sensors are required including the VOC sensors.

Multi-channel capacitive sensing system for cross bore detection and classification by machine learning

Cross bore is an intersection of an existing underground pipeline such as a sewer pipeline by a second utility such as a gas pipeline installed using the trenchless technique, which could lead to severe incidents like explosions. There are hundreds of thousands of existing cross bores in the U.S., at an estimated average rate of 0.4 per mile of pipeline reported by Cross Bore Safety Association, which is at high risk and a significant threat to public safety if not well detected, characterized, and mitigated. The research on high-accuracy nondestructive evaluation (NDE) methods that can identify cross bore areas more effectively and efficiently under challenging field environment is of vital importance. This paper proposes a multi-channel capacitive sensing system for cross bores detection and classification, which passes through the gas pipe to inspect the dielectric property changes of surrounding materials and indicates the existence of cross bores. A 3-D simulation modeling tool was developed to optimize the footprint of multi-channel sensor, and the effect of four cross bore types on the received capacitance values was investigated. The lab-scale experiments were performed using the developed multi-channel capacitive sensing system through a soil box setup, and the experimental results indicated that the developed system can identify the four types of cross bores. To achieve an automated decision making for the cross bores detection and classification, machine learning (ML) algorithms were developed through the experimental dataset, and it was found that the subspace k-nearest neighbors (SKNN) performed better with a high classification accuracy. Finally, field test validation was performed at the pipe farm (Gas Technology Institute, IL, USA) and the superior capability of the developed sensing system in identifying cross bores as well as other key parts in gas pipelines including butt fusion and saddle fitting with coil was demonstrated.

Comparison of End-to-End Neural Network Architectures and Data Augmentation Methods for Automatic Infant Motility Assessment Using Wearable Sensors

Infant motility assessment using intelligent wearables is a promising new approach for assessment of infant neurophysiological development, and where efficient signal analysis plays a central role. This study investigates the use of different end-to-end neural network architectures for processing infant motility data from wearable sensors. We focus on the performance and computational burden of alternative sensor encoder and time series modeling modules and their combinations. In addition, we explore the benefits of data augmentation methods in ideal and nonideal recording conditions. The experiments are conducted using a dataset of multisensor movement recordings from 7-month-old infants, as captured by a recently proposed smart jumpsuit for infant motility assessment. Our results indicate that the choice of the encoder module has a major impact on classifier performance. For sensor encoders, the best performance was obtained with parallel two-dimensional convolutions for intrasensor channel fusion with shared weights for all sensors. The results also indicate that a relatively compact feature representation is obtainable for within-sensor feature extraction without a drastic loss to classifier performance. Comparison of time series models revealed that feedforward dilated convolutions with residual and skip connections outperformed all recurrent neural network (RNN)-based models in performance, training time, and training stability. The experiments also indicate that data augmentation improves model robustness in simulated packet loss or sensor dropout scenarios. In particular, signal- and sensor-dropout-based augmentation strategies provided considerable boosts to performance without negatively affecting the baseline performance. Overall, the results provide tangible suggestions on how to optimize end-to-end neural network training for multichannel movement sensor data.

Characteristics of Response to Extracted Solution of Rice using Multichannel Taste Sensor

The purpose of this study is to reproduce tastes expect the five basic tastes with a multichannel taste sensor. The sensor can isolate and detect five tastes. The basic taste evaluation is expressed in terms of the concentration of typical tasting substances. For example, if the taste is salty, the sensor output for sodium chloride is obtained data and converted to an equivalent sodium chloride concentration from the sensor output of the unknown taste solution. In the case of rice taste, we found it difficult to describe it in terms of a combination of the five basic tastes. In the existing sensory test of rice, there are no items defined that correspond to taste in the narrow sense. We estimated that rice tastes other than the five basic tastes. Therefore, we first analyzed to find out what the taste substances were in the rice. The response characteristics of a multichannel taste sensor for rice were determined. The inorganic ionic components in rice were analyzed, and a mixed solution containing the obtained taste substances, NaH2PO4 and CaCl2, was prepared, and it was shown to have the same sensor output as that of the rice extract. The results of this study suggest that as the mixture is made from two substances, a quantitative evaluation of rice taste can be made on two axes: the taste sensor output corresponding to the concentration of each substance.

Recognition Engineering-Mediated Multichannel Sensor Array for Gut Microbiota Sensing.

The composition and activity of the gut microbiota are crucial for health management and disease treatment. Herein, we develop a rapid and robust multichannel sensor array via a recognition engineering strategy using antimicrobial agent (vancomycin, bacitracin, and lysozyme) functional gold nanoclusters and gluconamide-modified Ti3C2 MXenes, which provide superior fingerprint patterns to distinguish gut-derived bacteria. The discrimination ability of the sensor array was highly improved via the synergistic recognition between the bacteria and the various antimicrobial agents. Five gut-derived bacteria, including probiotics, neutral, and pathogenic bacteria were clearly differentiated and discriminated from the bacteria mixtures. Furthermore, the sensing system was successfully applied for the accurate classification of human colorectal cancer samples from healthy individuals rapidly (30 min) with clinically relevant specificity. The rapidity, simplicity, and economic cost of this strategy offers a robust platform for gut microbiota analysis.

Multi-Channel MEMS-FAIMS Gas Sensor for VOCs Detection

Aimed at the problems of a large equipment size, long time and high price of environmental VOC gas detection, the FAIMS-VOC gas sensor was designed and prepared according to the principle that the ionization energy of the common VOC gas is less than 10.6 eV. The sensor is small in size, fast in detection, low in power consumption, and can work continuously. The sensor was fabricated through the MEMS process, a specific process which included photolithography, etching, anodic bonding, etc. The sensor is 5160 μm long, 5300 μm wide and 800 μm high. We built a test system to detect two typical VOC gases: isobutylene and acetone. The results show that in the detection of isobutylene gas and acetone gas, the sensor voltage value changes with the change of gas concentration. The linearity of testing isobutylene is 0.961, and the linearity of testing acetone is 0.987. When the isobutylene gas concentration is 50 ppm, the response time is 8 s and the recovery time is 6 s; when the acetone gas concentration is 50 ppm, the response time is 9 s and the recovery time is 10 s. In addition, the sensor demonstrates good repeatability and stability, which are conducive to the detection of VOCs in the environment.

Multi-Source Error Calibration and Observability Analysis of Integrated Inertial Navigation System/Polarization Sensor Navigation System

Accurate polarization information acquisition is the key technique of the integrated inertial navigation system (INS)/polarization sensor (PS) navigation system. To solve the problem of poor performance of polarization sensors that real-time error calibration by using a rotary table, we propose a fast calibration method that polarization sensor combined with inertial navigation systems. Firstly, we construct the calibration model of polarization sensor. A tightly coupled INS/PS calibration model is proposed, specifically, the polarization sensor errors, which are the intensity gain coefficient, the degree of polarization coefficient, and the installation angle error of a polarizer, are augmented into the state of the integrated navigation systems. By introducing the error term of polarization sensor into the state of the integrated navigation systems, the error estimation can be carried out and the speed of error calibration can be improved. Secondly, considering the nonlinearity of error calibration model, we use the Unscented Kalman Filter (UKF) to calibrate the error. On this basis, we make use of the observability analysis to verify its validity of the proposed model in a dynamic scene. Finally, real-world experiments are conducted on the self-developed multi-channel polarization sensor. The experimental results show that the calibration method can be done quickly without the use of a rotary table.

Establishment of an analytical model for remote sensing of typical stratocumulus cloud profiles under various precipitation and entrainment conditions

Abstract. Structural patterns of cloud effective radius (ER) and liquid water content (LWC) profiles are essential variables of cloud lifecycle and precipitation processes, while observing cloud profiles from passive remote-sensing sensors remains highly challenging. Understanding whether there are typical structural patterns of ER and LWC profiles in liquid clouds and how they are linked to cloud entrainment or precipitating status is critical in developing algorithms to derive cloud profiles from passive satellite sensors. This study aims to address these questions and provide a preliminary foundation for the development of liquid cloud profile retrievals for the Multi-viewing, Multi-channel and Multi-polarization Imaging (3MI) sensor aboard the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Polar System-Second Generation (EPS-SG) satellite, which is scheduled to be launched in 2025. Firstly, we simulate a large ensemble of stratocumulus cloud profiles using the Colorado State University (CSU) Regional Atmospheric Modeling System (RAMS). The empirical orthogonal function (EOF) analysis is adopted to describe the shape of simulated profiles with a limited number of elemental profile variations. Our results indicate that the first three EOFs of LWC and ER profiles can explain &gt;90 % of LWC and ER profiles. The profiles are divided into four prominent patterns and all of these patterns can be simplified as triangle-shaped polylines. The frequency of these four patterns is found to relate to intensities of the cloud-top entrainment and precipitation. Based on these analyses, we propose a simplified triangle-shaped cloud profile parameterization scheme allowing us to represent these main patterns of LWC and ER. This simple yet physically realistic analytical model of cloud profiles is expected to facilitate the representation of cloud properties in advanced retrieval algorithms such as those developed for the 3MI/EPS-SG.