Multi-channel Sensor Research Articles

Design and analysis of Gold-nanowires based multi-channel SPR sensor

This research reports the simultaneous multi-analyte sensing capabilities of a d-shaped multi-channel surface plasmon resonance (SPR) sensor. Three channels are truncated in a U-shaped pattern at the side-polished surface of the d-shaped three-core fiber. Three Gold-nanowires (AuNWs) are positioned at the bottom of each sensing channel. To examine the SPR sensor's multi-channel characteristics, a finite element method (FEM) is applied. In order to detect a variety of analytes, y-polarized modes-multiplexing is used, which offers a sufficient wavelength range. At an infiltrated refractive index (RI) of 1.35, 1.38, and 1.41, respectively, the maximal RI sensitivities of c-1 (channel-one), c-2 (channel-two), and c-3 (channel-three) are found as 7,611 nm/RIU, 5,128 nm/RIU, and 12,974 nm/RIU, respectively. Moreover, the sensing structure with three external sensing channels is being demonstrated first time, to the best of our knowledge, using multicore fiber for the detection and discrimination of three different analytes simultaneously. According to expectations, the proposed sensor may be helpful for the detection of a wide range of chemicals and bio-analytes.

Multichannel Gradient Piezoelectric Transducer Assisted with Deep Learning for Broadband Acoustic Sensing.

As an important part of human-machine interfaces, piezoelectric voice recognition has received extensive attention due to its unique self-powered nature. However, conventional voice recognition devices exhibit a limited response frequency band due to the intrinsic hardness and brittleness of piezoelectric ceramics or the flexibility of piezoelectric fibers. Here, we propose a cochlear-inspired multichannel piezoelectric acoustic sensor (MAS) based on gradient PVDF piezoelectric nanofibers for broadband voice recognition by a programmable electrospinning technique. Compared with the common electrospun PVDF membrane-based acoustic sensor, the developed MAS demonstrates the greatly 300%-broadened frequency band and the substantially 334.6%-enhanced piezoelectric output. More importantly, this MAS can serve as a high-fidelity auditory platform for music recording and human voice recognition, in which the classification accuracy rate can reach up to 100% in coordination with deep learning. The programmable bionic gradient piezoelectric nanofiber may provide a universal strategy for the development of intelligent bioelectronics.

A portable wireless‐sensor system from all‐in‐one sensor array based hybrid solid contact layer for point‐of‐care ion monitoring in river basin

AbstractThe sensor arrays are considered as a promising way to aim at point‐of‐care (POC) testing for the detection of different ions in environment through the use of solid‐contact ion‐selective electrodes (SC‐ISEs). However, the exorbitant equipment and intricate process for the production of SC‐ISEs arrays has limit its application. Herein, the Au with nano‐branched structure (Au NBS) and poly (3,4‐ethylenedioxythiophene): poly (styrenesulfonate) (PEDOT : PSS) were decorated on the surface of bare pad of traditional printed circuit boards coated with Au (Au‐PCB) to construct a hybrid solid‐contact layer including double‐layer capacitance and pseudocapacitance of SC‐ISEs synergistically, thus the multichannel SC‐ISEs exhibited high stability and specificity. Overall, based on portable multichannel sensor and homemade APP (Ion Analysis), a wireless all‐in‐one sensor system was constructed to detect ions with the limit of NO3−, Cl−, Na+, K+, Ca2+ and Mg2+ as 0.047 mM, 0.039 mM, 0.022 mM, 0.066 mM, 0.045 mM and 0.024 mM, respectively. Besides, it can detect such ions in river basin successfully. With the cost of each electrode less than 0.5 $, this sensor arrays enable a wide range of hazardous ion monitoring in water environment.

An artificial bridge circuit approach between two biological neurons using nanoscale topologies towards paralytic disorders

The advent of Nanoscale IC technology towards pulse-based neural systems reactivates the dead nervous about restoring the functionality of paralytic disorders. This work reports in first time a design of a novel CMOS biological neuron system, which replaces a dead neuron between two neurons to restore communication in paralyzed individuals. The work binds into three stages: design of a spiking leaky Integrator and Fire (LIF) neuron with refractory period mechanisms, which achieves a low power consumption of 2.4 μW, in the first stage; an adaptive homeostatic synapse with short and long-term spike plasticity, that reconfigure the spiking neuron networks of multichannel sensor electrodes to record the electric signal from the active cell as second stage; the final stage presents a low-power common source current reuse regulated cascode (CS-CR-RGC) TIA for amplifying the weak synapse current signal, which achieves a high gain of 135.71 dBΩ with an optimized noise performance of 0.19 pA/Hz. The entire work is designed and implemented using a CMOS 65 nm commercial process that occupies a die area of 400 μm × 120 μm.

The application of a multi-channel sensor network to decompose the local and background sources and quantify their contributions

The construction site is one of the typical sources of anthropogenic dust and profoundly responsible for generating a high concentration of particles in the surrounding air. To assess the detailed attribution of construction dust to the environment, it is necessary to distinguish the emissions and underlying pollution levels from the measurements. This study demonstrated the feasibility of using multi-channel particulate matter (PM) sensors to measure the concentration of PM in different sizes at a near-source site in real time. A total of five sensors were placed at different locations throughout the construction site for the intensive air quality monitoring from April 12 to April 26, 2021, during the extensive construction activities. Meanwhile, iterative discrete wavelet transform (IDWT) was used to decompose pollutant time series into different components based on low/high frequency filtering. Overall, baseline component was closely related to regional background concentration, showing mild diurnal variation, while the emission component showed a significant increase during working hours from 8:00 to 18:00, especially for coarse particles. The baseline and long-period emission components were mainly from the rapid accumulation of fine particles, but when the particle size was larger than 2.5 μm, the short-period emissions still increased rapidly. The correlation matrix clustering supports the conclusion that the different components were mainly determined by three potential sources. Our results may help to understand the profiles of different-sized PM in construction sites and identify potential source attributions.

Diagnostic Techniques for Electrical Discharge Plasma Used in PVD Coating Processes

This article discusses the possibilities of two methods for monitoring Physical Vapor Deposition (PVD) process parameters: multi-grid probe, which makes it possible, in particular, to determine the energy distribution of ions of one- or two-component plasma and spectrum analyzer of the glow discharge plasma electromagnetic radiation signal based on the Prony–Fourier multichannel inductive spectral analysis sensor. The energy distribution curves of argon ions in the low-voltage operation mode of ion sources with closed electron current have been analyzed. With a decline in the discharge current, the average ion energy decreases, and the source efficiency (the ratio of the average ion energy W to the discharge voltage U) remains approximately at the same level of W/U ≈ 0.68, …, 0.71 in the operating voltage range of the source. The spectrum analyzer system can obtain not only the spectra at the output of the sensor, but also the deconvolution of the spectrum of the electromagnetic radiation signal of the glow discharge plasma. The scheme of a spectrum analyzer is considered, which can be used both for monitoring and for controlling the processing process, including in automated PVD installations.

Highly Sensitive Multichannel Fano Resonance-Based Plasmonic Sensor for Refractive Index and Temperature Sensing Application

We propose a susceptible multichannel plasmonic sensor for sensing refractive index (RI) and temperature media working in the visible to near-infrared range. The proposed structure’s resonator consists of an elliptical-shaped ring with two stubs at two sides and four metal nanorods side-coupled to two separated metal–insulator–metal waveguides. The optical responses of the structure, including transmittance spectra and magnetic and electric field distributions, are investigated using the finite element method (FEM) to obtain the optimal structural parameters. The designed structure supports five channels of Fano resonance modes because of the interaction between the narrowband mode of the elliptical-shaped ring resonator and the broadband mode of two separated MIM WGs. The maximum sensitivity values can reach 4500 nm/RIU for RI sensing, and the temperature sensitivity can get 1.00 nm/°C. The designed device exhibits excellent sensing performance and could pave the way for sensing devices with significantly higher sensitivity.

Highly Sensitive Multi-Channel Biosensor for Low-Interference Simultaneous Detection

In this paper, we propose a multi-channel photonic crystal fiber sensor, which adopts dual-polarization and multiple materials to effectively reduce the mutual interference between channels and enhance the surface plasmon resonance, thus achieving simultaneous detection of a multi-channel with low interference. Four channels are polished around the cylindrical fiber, and then different metal films (gold or silver) and plasmonic materials (titanium dioxide, thallium pentoxide, or graphene) are added to the sensing area of each channel. All channels detect refractive indices in the range of 1.34 to 1.42. The sensing performance of the fiber optic sensor was numerically investigated using the full vector finite element method. After the optimization of structural parameters, the maximum wavelength sensitivity of channel-1, channel-2, channel-3, and channel-4 are 49,800 nm/RIU, 49,000 nm/RIU, 35,900 nm/RIU, and 36,800 nm/RIU, respectively. We have theoretically analyzed the sensor's capabilities for partial bio-detection and simulated its detection capability with a wavelength sensitivity of 11,500 nm/RIU for normal red blood cells and 12,200 nm/RIU for MCF-7 cancerous cells. Our proposed sensor has a novel design, can detect multiple channels simultaneously, has strong anti-interference capability and high sensitivity, and has good sensing characteristics.

Multi-channel self-powered attitude sensor based on triboelectric nanogenerator and inertia

It is particularly important to detect and control the attitude of a moving object especially in the fields of marine navigation and aviation. In this work, a multi-channel self-powered sensor based on triboelectric nanogenerator (TENG) and inertia is designed. The sensor consists of a square box with rings on three sides and stainless-steel balls covered with FEP (Fluorinated ethylene propylene). Each ring is divided into 18 channels according to the angle. It is found that the sensor can sensitively detect the moving attitude by outputting notable signals of voltage, current and charge. Multiple channels can work independently without interference. This work presents the application of multi-channel self-powered sensors for pose sensing as well as three-dimensional motion monitoring, which is of great practical importance.

Research on Rapid and Low-Cost Spectral Device for the Estimation of the Quality Attributes of Tea Tree Leaves.

Tea polyphenols, amino acids, soluble sugars, and other ingredients in fresh tea leaves are the key parameters of tea quality. In this research, a tea leaf ingredient estimation sensor was developed based on a multi-channel spectral sensor. The experiment showed that the device could effectively acquire 700-1000 nm spectral data of tea tree leaves and could display the ingredients of leaf samples in real time through the visual interactive interface. The spectral data of Fuding white tea tree leaves acquired by the detection device were used to build an ingredient content prediction model based on the ridge regression model and random forest algorithm. As a result, the prediction model based on the random forest algorithm with better prediction performance was loaded into the ingredient detection device. Verification experiment showed that the root mean square error (RMSE) and determination coefficient (R2) in the prediction were, respectively, as follows: moisture content (1.61 and 0.35), free amino acid content (0.16 and 0.79), tea polyphenol content (1.35 and 0.28), sugar content (0.14 and 0.33), nitrogen content (1.15 and 0.91), and chlorophyll content (0.02 and 0.97). As a result, the device can predict some parameters with high accuracy (nitrogen, chlorophyll, free amino acid) but some of them with lower accuracy (moisture, polyphenol, sugar) based on the R2 values. The tea leaf ingredient estimation sensor could realize rapid non-destructive detection of key ingredients affecting tea quality, which is conducive to real-time monitoring of the current quality of tea leaves, evaluating the status during tea tree growth, and improving the quality of tea production. The application of this research will be helpful for the automatic management of tea plantations.

Light-GBM based signal correction method for surface myoelectropotential measured by multi-channel band-type EMG sensor

The aim of this research is to develop a user interface using myoelectrical signals, a type of biological signal. A multi-channel band-type EMG sensor is used to measure this signal. The goal is to make it easier to attach and detach the EMG sensor, while still obtaining sufficiently accurate measurements even if the sensor is placed slightly off the standard position. To achieve this, a Light-GBM based method is proposed to estimate the degree of deviation of the EMG sensor from the standard position. Once the deviation is estimated, a filter is constructed to compensate for the effect of the deviation on the myoelectrical signal. Experimental results using this method show that the sensor misalignment can be estimated with over 90% accuracy, and that correcting the myoelectrical signal based on this information improves joint angle estimation by approximately 13% in terms of root mean square error.

Design of a Multi-Channel Gas Sensor Detection System Based on STM32 Microcontroller and LabVIEW

To address the needs of gas sensitivity performance testing, a multi-channel gas sensor detection system based on an STM32 microcontroller and LabVIEW was designed. This system combines signal acquisition, data processing, date display, and storage into a single unit. The target gases are detected using the sensor array, and the regulated electrical signal is supplied to the MCU. Through the built-in A/D converter, the electrical signal is converted to digital data, which is then evaluated and processed. Finally, the serial communication function is performed in the upper computer to realize the OLED display and dynamic display of the data waveform graph. The test results from the fixed resistance test system error demonstrate that: the system test error between 1.4%–2.0%, to achieve multi-channel gas sensor signal collection and detection, and the design of low cost, good stability, to fulfil the gas sensor detection needs.

Development of a portable optical device with a multi-channel spectrometer sensor for quantification of glycerol in wine: a maker approach for on-site analysis.

In this work we present a novel and environmentally friendly approach for quantifying glycerol in wine samples using a portable optical device based on the maker concept and do-it-yourself (DIY) principles. This method offers significant advantages, including cost-effectiveness, reduced sample and reagent consumption, and the potential for integrating IoT (Internet of Things) technology. The chemical strategy involves the oxidation of glycerol using periodate, followed by the formation of the 3,5-diacetyl-1,4-dihydrolutidine (DDL) compound through a reaction with acetylacetone. The utilization of a cost-effective AS7341 color sensor as a detector enables accurate and sensitive detection of glycerol levels in wine samples. The optimized procedure demonstrates adequate analytical performance for glycerol determination in wine samples, encompassing a wide linear range (0.5 mg L-1 to 40.0 mg L-1), high correlation coefficient (r = 0.998), and low limits of detection (0.050 mg L-1). The method exhibits excellent precision, with the coefficient of variation estimated to be 0.1% for 10 independent measurements of a 20 mg L-1 solution. These features render it suitable not only for routine glycerol analysis in the wine industry, but also for addressing challenges related to wine adulteration and counterfeiting.

Runtime Tracking-Based Replication of On-Chip Embedded Software Using Transfer Function Learning for Dust Particle Sensing Systems

A digital twin is a widely used method that uses digitized simulations of the real-world characteristics because it is effective in predicting results at a low cost. In digital twin analysis, the transfer function between the input and output data is an important research subject. In this study, we intend to investigate the application of the digital twin method to dust particle sensing. A high-performance multi-channel reference dust particle sensor provides particle count as well as particulate matter information, whereas a lightweight embedded test device only provides a particle count. The particulate matter acquisition algorithm for a reference device is unknown and complex. Instead of that, we propose a simple method to calculate the transfer function using singular-value decomposition. In the experimental results, using singular-value decomposition, the predicted particulate matter of the test device was similar to that of the reference device. The obtained transfer function shows similar measurement results of the two dust particle sensor devices, confirming that particulate matter environmental information can be digitized even with low-power and lightweight sensor-embedded devices. In addition, the power consumption of the test device was approximately ten times lower than that of the reference device.

Rapid identification of molecular subtyping of breast cancer cell lines using a multi-channel sensor array

A multi-channel sensor array composed of three fluorescent units is constructed to differentiate and identify molecular subtypes of breast cancer.

Computational study: Windmill-shaped multi-channel SPR sensor for simultaneous detection of multi-analyte

A novel windmill-shaped multi-channel surface plasmon resonance (SPR) sensor for the simultaneous detection and quantification of multi-analyte is reported. The sensor comprises extrinsic multi-channel and is structured as a windmill-shaped structure. Each channel contains a plasmonic Gold (Au) nanowire that induces SPR phenomenon. The sensor has no dependency on polarization, it functions just using y-polarized modes for multi-analyte detection. The maximum wavelength sensitivities of 3,292 nm/RIU (refractive index unit), 6,664 nm/RIU, and 10,243 nm/RIU are obtained at the analyte refractive index (RI) of 1.34, 1.39, and 1.43, with the SPR sensor channel-one (Ch-1), channel-two (Ch-2), and channel-three (Ch-3), respectively. To the best of our knowledge, the proposed windmill-shaped SPR sensor with three extrinsic channels is being demonstrated for the first time by utilizing the multi-analyte sensing capabilities and high-sensitivity nature. The placement of the proposed sensor is anticipated in various potential applications of chemical and bio-analyte detection.

Multi-channel sensor fusion for real-time bearing fault diagnosis by frequency-domain multilinear principal component analysis

Multi-channel sensor fusion for real-time bearing fault diagnosis by frequency-domain multilinear principal component analysis

Multichannel Directional Recognition SPR Curvature Sensor Based on D-Type Double-Clad Multimode Fiber

A fiber surface plasmon resonance (SPR) curvature sensor has the advantage of being insensitive to axial strain and temperature, but there are still difficulties in multichannel cascade and curvature direction identification. In this article, a multichannel directional recognition SPR curvature sensor based on D-type double-clad multimode fiber was proposed. The asymmetric structure of D-type multimode fiber can well realize directional curvature sensing. When the light in the multimode core leaks to the silica clad, due to the existence of plastic clad, it can still continue to transmit. When the refractive index of UV curable adhesive coated in different sensing areas is different, wavelength-division multiplexing multichannel sensing can be realized. When the sensing probe is bent to be convex, the maximum intensity sensitivity is 0.0057 a.u./m−1, the resonance wavelength is redshift, and the wavelength sensitivity is 0.6114 nm/m−1. When the sensor probe is bent to be concave, the maximum intensity sensitivity is 0.0043 a.u./m−1, the wavelength is blueshift, and its highest sensitivity is 0.5831 nm/m−1. In addition, this article also realizes a three-channel directional recognition SPR curvature sensor based on a D-type double-clad multimode fiber, which provides a new scheme for multichannel directional curvature measurement.

Multi-channel curvature sensor based on fiber bending loss wavelength and SPR.

Fiber Bragg gratings and interferometric curvature sensors are easily disturbed by axial strain and temperature, and cascaded multi-channel curvature sensing is difficult. In this letter, a curvature sensor based on fiber bending loss wavelength and the surface plasmon resonance (SPR) mechanism is proposed, which is insensitive to axial strain and temperature. In addition, fiber bending loss valley wavelength demodulation curvature improves the accuracy of bending loss intensity sensing. Experiments show that the bending loss valley of single-mode fiber with different cut-off wavelengths has different working bands which is combined with a plastic-clad multi-mode fiber SPR curvature sensor to realize a wavelength division multiplexing multi-channel curvature sensor. The bending loss valley wavelength sensitivity of single-mode fiber is 0.8474 nm/m-1, and the intensity sensitivity is 0.0036 a.u./m-1. The resonance valley wavelength sensitivity of the multi-mode fiber SPR curvature sensor is 0.3348 nm/m-1, and the intensity sensitivity is 0.0026 a.u./m-1. The proposed sensor is insensitive to temperature and strain, and the working band is controllable, which provides a new, to the best of our knowledge, solution for wavelength division multiplexing multi-channel fiber curvature sensing.

High-throughput and energy-efficient data gathering in heterogeneous multi-channel wireless sensor networks using genetic algorithm

The need for high throughput in Wireless Sensor Networks (WSNs) necessitates the use of the Multi-Channel (MC) scheme. Existing multi-channel approaches used single or multi-radio sensors to exploit channel resources. The former scheme yields low throughput as well as considerable overhead for channel switching. On the other hand, the latter is costly due to installing multiple radios per node. This study applies the MC scheme to heterogeneous WSNs to bridge this gap. It assumes some super nodes that are equipped with multiple radios. These nodes are used as Cluster Heads (CHs) and are responsible for delivering the gathered data from normal sensors to the Base Station (BS). A high-throughput and energy-efficient algorithm is proposed to perform clustering, routing, and channel assignment in the heterogeneous setting. The proposed method decouples the original problem into two phases and solves them using genetic algorithm. The first phase is devoted to constructing a spanning tree over super nodes and assigning proper channels to their radios. A novel multi-objective cost function is proposed which prolongs network lifetime considerably compared to existing tree construction approaches. It also increases throughput by balancing the observed interference throughout the network. In the second phase of the proposed algorithm, the proper CH and channel per normal node are determined. The extensive simulations demonstrate that the proposed algorithm obtains high throughput using multiple channels. Additionally, it improves total energy consumption and energy exhaustion per delivered bit to the BS by 21.6% and 48.3% compared to the previous schemes.