Search

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Coal Geology & Exploration is the property of Xian Research Institute of China Coal Research Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to the large band gap and quantum size effect, most of reported fluorescent graphite phase carbon nitride (GCN) nanosheets/quantum dot emits blue fluorescence under ultraviolet, which limits its further application in bio‐imaging. However, diversified strategies of PL wavelength tailored are rare except for heteroatom or molecule doping. In this paper, yellowish carbon nitride colloid with 508 nm PL emission was produced through structural self‐modulation method using melamine as sole precursor. The obtained colloid demonstrate nontoxicity, stability (up to 10 month), good dispersibility in water and green fluorescent under 365 nm light excitation. Associated with its broad emission peak excited by different wavelengths of light, it shows good performance in biological imaging and importantly, realizes blue, green and red three‐channels imaging. To the best of our knowledge, this is the first time to report multi‐channel biological imaging based on GCN polymer with long PL wavelength by structural self‐modulation strategy. [ABSTRACT FROM AUTHOR]

Underwater acoustic channels are characterized by long propagation delay, limited available bandwidth and high energy costs. These unique characteristics bring challenges to design media access control (MAC) protocol for underwater acoustic sensor networks (UASNs). Especially in data-collection-oriented UASNs, data generated at underwater nodes are transmitted hop-by-hop to the sink node. The traffic loads undertaken by nodes of different depths are different. However, most existing MAC protocols do not consider the traffic load imbalance in data-collection-oriented UASNs, resulting in unfairness in how the nodes transmit their own generated data. In this paper, we propose a novel traffic-aware fair MAC protocol for layered data-collection-oriented UASNs, called TF-MAC. TF-MAC accesses a medium by assigning time slots of different lengths to each layer via different traffic loads to achieve traffic fairness of nodes. To improve throughput and avoid collision in the network, an overlapping time slot division mechanism for different layers and multi-channel allocation scheme within each single layer is employed. Considering the time-varying traffic loads of the nodes, an adaptive packet length algorithm is proposed by taking advantage of the spatial-temporal uncertainty of underwater channels. A sea experiment was conducted to prove the spatial-temporal uncertainty of UASNs, which provides a feasibility basis for the proposed algorithm. Simulation results show that TF-MAC can greatly improve the network performance in terms of throughput, delay, energy consumption, and fairness in the layered data-collection-oriented UASNs. [ABSTRACT FROM AUTHOR]

To address the issue that the point-value prediction method does not make full use of the historical cycle features and contextual information of VM,and the prediction accuracy is not high, this paper proposed a dynamic sliding window multi-channel Bi-LSTM for VM fault prediction. The model firstly used dynamic sliding windows to dynamically capture the contextual features of the VM failure process, then it constructed a multi-channel Bi-LSTM to simultaneously learn the correlation features between different indicator classes, so as to predict the VM failure in the next cycle. Finally it judged the prediction results according to OCSVM and interval skewness methods to derive specific failure types. Experiments show that the model outperforms the baseline model in three metrics, namely prediction accuracy, recall and F-value, verifying the effectiveness of the model for VM fault prediction. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Emerging biomagnetism imaging longing for multi-channel miniaturized spin-exchange relaxation-free (SERF) atomic magnetometers that promise high resolution magnetocardiogram (MCG) and magnetoencephalogram (MEG). However, conventional multi-channel schemes (single vapor cell) rely heavily on manipulating pumping intensity distribution, which is susceptible to crosstalk caused by atomic thermal diffusion, hindering the potential to improve spatial resolution further. In this study, spatial inhomogeneous atomic polarization is achieved within a 3 × 3 × 3 mm 3 Rb vapor cell through a spatially variable hysteresis (SVR) plate and a quarter wave plate (QWP), leading to a four-channel SERF magnetometer with measurement volume of 1. 5 × 1. 5 × 3 mm 3 for each channel. The spacing between the center of adjacent channels is 850 μ m and the average sensitivity measured is 24.1 f T / H z . Additionally, unique fictitious magnetic fields generated by the inhomogeneous polarized pumping beam are utilized to compensate the remaining magnetic field (3 nT) after the adjustment of laser to resonance frequency (377.089 Thz). This study presents a novel approach to the miniaturization of four-channel optically pumped atomic sensors, together with a theoretical model to investigate crosstalk in a single vapor cell. This paves the way for high-resolution biomagnetism imaging and for portable atomic sensing applications. • An ultra-compact four-channel miniaturized SERF magnetometer. • Spatial inhomogeneous atomic polarization. • Suppress atomic diffusion-induced crosstalk. • Compensate the remaining magnetic field by unique fictitious magnetic fields. [ABSTRACT FROM AUTHOR]

• The chirped fiber grating loop ring-down (CFGLRD) structure and overlapping spectrum demodulation technology are used for establishing of the multi-channel strain sensor for the first time. • This design innovatively realizes the temperature compensation for the CFGLRD strain sensor via overlapping spectrum demodulation technology. • A dual-channel strain experimental setup with strain range of 0–3000 με proves the feasibility of the strain sensor. The experimental results show the design has good performances on linearity of curve, measurement range, and temperature robustness. This study proposes a novel multi-channel strain sensor, which combines the chirped fiber grating loop ring-down (CFGLRD) structure and time division multiplexing (TDM) technology. Each channel contains a CFGLRD cavity based on overlapping spectrum demodulation, which can convert measurements into ring-down spectra. All cavities are connected in series by delay fiber. The advantages of the sensor include simple structure, high light utilization, and temperature insensitive. A two-channel strain experimental setup is used in performance testing of the sensor. The strain measurement ranges of both channels extend from 0 to 3000 με. The resolutions of two channels are 18.3 με and 27.9 με, the linear correlation coefficients R2 are 0.997 and 0.996, the strain sensitivities are about 112.97 μs/ε and 280.03 μs/ε, and the temperature sensitivities are 0.37 ns/°C and 0.72 ns/°C. The experimental results show the structure has good performances on linearity of curve, measurement range, and temperature robustness. [ABSTRACT FROM AUTHOR]

• Two pieces of MMF SPR sensors on a quartz glass are side polished simultaneously. • Two sensors work well without spectrum interference. • First time to use five machine learning algorithms to predict resonant wavelength. • Only 120 wavelengths within 760–889 nm are used in predict resonant wavelength. • CatBoost is found to be the best in five algorithms through error analysis. Fiber-optic surface plasmon resonance (SPR) sensors have been increasingly used due to their advantages such as compact size, stable physical and chemical properties, high sensitivity, and resistant to electromagnetic interference. In this paper, a dual-channel side-polished fiber-optic SPR sensor system is established to detect the refractive index of liquids and the liquid temperature. High sensitivity of ∼2000 nm/RIU (refractive index unit) and linear sensitivity of ∼−2.0 nm/°C is achieved for two-channel SPR sensors, which are integrated for simultaneously RI temperature sensing and easily scaled to multi-channel SPR sensor array. The resonant wavelengths of the two sensors are located in the range of 600–700 nm and 700–800 nm respectively, which could avoid overlapping the resonance dips. Then, we propose an approach using five machine learning algorithms to predict the resonant wavelength of the second channel of SPR temperature sensor using the normalized transmission spectrum data with a wavelength range of 760–889 nm. After error analysis, it is found that the Categorical Boosting (CatBoost) is the best among five algorithms in terms of accuracy and resolution. The results provide clear evidence that accurate SPR resonant wavelength can be obtained with machine learning approach using a more compact spectrometer. [ABSTRACT FROM AUTHOR]

• Non-contact detection effectively prevents liquid contamination. • The multi-channel excitation of FOAM greatly enhances detection efficiency. • Near-field enable high-precision detection of RI change. • Far-field achieves synchronous detection of multiple samples at a distance. • The relationship between FOAM image and RI is determined through SWV calculations. Terahertz refractive index (RI) sensor is an important tool for characterization of material properties. However, traditional immersion RI sensors suffer from issues of liquid contamination and low detection efficiency. Therefore, this study designs a non-contact multi-channel terahertz RI sensor based on metasurface which can produce vortex beam with focused orbital angular momentum (FOAM). Computing Spatially Weighted Variance (SWV) of the contours and intensities of the images resulting from different RI demonstrates that the RI value correlates uniquely with the weighted variance. Specifically, in the near-field metasurface (NF metasurface), by employing 3-bit phase binary (PB) encoding and convolution operations on vanadium dioxide (VO 2) metasurfaces, we realized a high-purity FOAM with a focal length of f = 3500 μm. After fixing the sample under test at f = 3500 μm and analyzing the near-field FOAM amplitude images under various RI conditions using the SWV method, the RI detection sensitivity of the NF metasurface was calculated to be 15,775/RIU. To enhance detection efficiency and meet the requirements for far-field detection, we proposed a sensor capable of detecting multiple samples in the far-field. In the far-field metasurface (FF metasurface), when linearly polarized light is incident on this metasurface, the sensitivity of the FOAM to RI detection produced by this metasurface under the left circularly polarized (LCP) and right circularly polarized (RCP) components are 10,554/RIU and 13,292/RIU, respectively. The minimum change of the RI that can be detected by the near-field and far field sensors reaches 10−4 RIU. When VO 2 transitions to its dielectric state, the metasurface switches to specular reflection, thereby endowing the sensor with switching functionality for RI detection. This approach overcomes issues of liquid-contaminated surfaces and enables simultaneous detection of multiple substances, offering broad application prospects across various sensing scenarios. [ABSTRACT FROM AUTHOR]

Orbital angular momentum (OAM) multiplexing of electromagnetic (EM) waves is of great significance for high-speed wireless communication and remote sensing. To achieve high-efficiency OAM multiplexing for multi-channel incident EM waves, this paper presents a novel angle-dispersive meta-atom structure, which can introduce the required anti-symmetric phase dispersion as well as high transmission efficiency for OAM multiplexing. These meta-atoms are then arranged delicately to form an angle-dispersive metasurface working at the X band, which enables three-channel OAM multiplexing by converting highly directional transverse-magnetic (TM) waves incident from 0 and ±45° to coaxial OAM beams with l = 0 and ±2 modes, respectively. The simulation and experimental results reveal that the proposed metasurface can convert a higher proportion of energy to the required OAM modes compared to the conventional OAM multiplexing metasurfaces, which can significantly improve the coaxial transmission efficiency of multi-channel OAM multiplexing.

Over the past decade, there has been a meteoric evolution in Internet Messaging Services and although these services have become ingrained in our everyday life, SMS service remains an essential form of communication service. The omnipresence of SMS has also given rise to unsolicited and junk messages which has motivated researchers to use machine learning and deep learning to detect such spam messages. Studies using deep learning have shown promising results for spam classification, and in this paper, extending these studies, we have proposed a Multi-Channel CNN architecture with static and dynamic embeddings for SMS spam classification. UCI’s SMS spam collection dataset along with several personally collected text messages are used to create a rich dataset for training the models. The proposed model has an accuracy of 96.12% and overcomes certain disadvantages associated with some of the state-of-the-art deep learning models.

A multi-wavelength bandstop filter is proposed and numerically demonstrated using the sum-frequency generation (SFG) process in a waveguide of periodically poled lithium niobate (PPLN). This proposed device achieves channels number reconfigurable, central filtering wavelength of each filtering channel independently tunable and extinction ratios (ERs) equalized via all-optical methods.

In cold pulse experiments in J-TEXT, not only are rapid electron temperature increases in the core observed, but also steep rises in the inner density are found. Moreover, some evidence of acceleration of the core toroidal rotation is also observed during the non-local transport process of electron temperature. These new findings of cold pulse experiments in J-TEXT suggest that turbulence spreading is a possible mechanism for the non-local transport dynamics.

Network intrusion data are characterized by high feature dimensionality, extreme category imbalance, and complex nonlinear relationships between features and categories. The actual detection accuracy of existing supervised intrusion-detection models performs poorly. To address this problem, this paper proposes a multi-channel contrastive learning network-based intrusion-detection method (MCLDM), which combines feature learning in the multi-channel supervised contrastive learning stage and feature extraction in the multi-channel unsupervised contrastive learning stage to train an effective intrusion-detection model. The objective is to research whether feature enrichment and the use of contrastive learning for specific classes of network intrusion data can improve the accuracy of the model. The model is based on an autoencoder to achieve feature reconstruction with supervised contrastive learning and for implementing multi-channel data reconstruction. In the next stage of unsupervised contrastive learning, the extraction of features is implemented using triplet convolutional neural networks (TCNN) to achieve the classification of intrusion data. Through experimental analysis, the multichannel contrastive learning network-based intrusion-detection method achieves 98.43% accuracy in dataset CICIDS17 and 93.94% accuracy in dataset KDDCUP99. [ABSTRACT FROM AUTHOR]

Side-scan sonar (SSS) target recognition is an important part of building an underwater detection system and ensuring a high-precision perception of underwater information. In this paper, a novel multi-channel multi-location attention mechanism is proposed for a multi-modal phased transfer side-scan sonar target recognition model. Optical images from the ImageNet database, synthetic aperture radar (SAR) images and SSS images are used as the training datasets. The backbone network for feature extraction is transferred and learned by a staged transfer learning method. The head network used to predict the type of target extracts the attention features of SSS through a multi-channel and multi-position attention mechanism, and subsequently performs target recognition. The proposed model is tested on the SSS test dataset and evaluated using several metrics, and compared with different recognition algorithms as well. The results show that the model has better recognition accuracy and robustness for SSS targets. [ABSTRACT FROM AUTHOR]

In this paper, a parallel computing method is proposed to perform the background denoising and wheezing detection from a multi-channel recording captured during the auscultation process. The proposed system is based on a non-negative matrix factorization (NMF) approach and a detection strategy. Moreover, the initialization of the proposed model is based on singular value decomposition to avoid dependence on the initial values of the NMF parameters. Additionally, novel update rules to simultaneously address the multichannel denoising while preserving an orthogonal constraint to maximize source separation have been designed. The proposed system has been evaluated for the task of wheezing detection showing a significant improvement over state-of-the-art algorithms when noisy sound sources are present. Moreover, parallel and high-performance techniques have been used to speedup the execution of the proposed system, showing that it is possible to achieve fast execution times, which enables its implementation in real-world scenarios. [ABSTRACT FROM AUTHOR]

Skeleton-based action sequences are widely used for human behaviour understanding due to their compact characteristics. Most existing work designed Graph Convolutional Networks and integrated multiple input channels rather than the original motion sequence to improve the final performance. However, few of them have been reported on the detailed effects of such multiple input channels. In contrast to them, we systemically study the impact of different input channels and construct a more efficient GCN framework. We have identified the complementary effect between the local frame channel and global sequence channel, which is essential to improve the action recognition accuracy. By coupling local frame and global sequence information with a classical spatial–temporal graph neural network, e.g. MS-G3D, it achieves competitive performance compared with SOTA methods on challenging benchmark datasets. Related code would be available at https://github.com/movearbitrarily/multi-stream. • In this paper, we propose an effective multi-channel mechanism that exploiting global and local complementary information, which could significantly improve GCN models' performance on skeleton based action recognition. • We conduct comprehensive evaluations on two large scale benchmark datasets and achieved a competitive result compared with SOTA approaches. • We show that the performance of GCN models could be improved in a simple way. • Notably, the proposed multi-channel mechanism is generic and robust to seamlessly work with any existing GCN framework, which is essential that the reasonable performance could be achieved in a simple way. [ABSTRACT FROM AUTHOR]

In order to solve the problem of effective verification of radar real-time signal processing function and performance, a wideband and narrowband integrated radar echo playback system with multiple channels is designed and implemented. The system transfers the radar echo data to the real-time signal processing system according to the time sequence and data rate of the radar operation, so as to reproduce the target tracking scene and realize the effective verification of the real-time signal processing function and performance of the radar. The data playback system comprises a disk array server and two PCIe fiber boards. Based on the Xilinx series products, the PCIe DMA control process and response mechanism are further adjusted according to the system characteristics, and the high-speed batch transmission of radar echo data from the server to the fiber board is realized. According to the working mechanism of the radar and the echo data structure, the FPGA playback control state machine and related function modules are designed, and the radar echo data is transmitted from the optical fiber board to the real-time processing system in strict accordance with the working sequence and data rate of the radar. To ensure the synchronization between multiple boards when wideband and narrowband echo playback is carried out at the same time, the study proposes two methods based on software and hardware respectively. The test results show that the system realizes the synchronous playback of wide-band and narrow-band radar echo data between boards, each data type supports 6 parallel data channels, and the peak data playback rate can reach 1 920 MB·s-1.The designed system can be directly integrated in the origin radar hardware platform, without adding extra circuit boards or cables, which has good generality and convenience. The system has been successfully applied in a multi-function phased array radar. [ABSTRACT FROM AUTHOR]

Background: Voxel-based morphometry (VBM) using magnetic resonance imaging (MR) has been used to estimate cortical atrophy associated with various diseases. However, there are mis-segmentations of segmented gray matter image in VBM. Purpose: To study a twofold evaluation of single- and multi-channel segmentation using synthetic MR images: (1) mis-segmentation of segmented gray matter images in transverse and cavernous sinuses; and (2) accuracy and repeatability of segmented gray matter images. Material and Methods: A total of 13 healthy individuals were scanned with 3D quantification using an interleaved Look–Locker acquisition sequence with a T2 preparation pulse (3D-QALAS) sequence on a 1.5-T scanner. Three of the 13 healthy participants were scanned five consecutive times for evaluation of repeatability. We used SyMRI software to create images with three contrasts: T1-weighted (T1W), T2-weighted (T2W), and proton density-weighted (PDW) images. Manual regions of interest (ROI) on T1W imaging were individually set as the gold standard in the transverse sinus, cavernous sinus, and putamen. Single-channel (T1W) and multi-channel (T1W + T2W, T1W + PDW, and T1W + T2W + PDW imaging) segmentations were performed with statistical parametric mapping 12 software. Results: We found that mis-segmentations in both the transverse and cavernous sinuses were large in single-channel segmentation compared with multi-channel segmentations. Furthermore, the accuracy of segmented gray matter images in the putamen was high in both multi-channel T1W + PDW and T1W + T2W + PDW segmentations compared with other segmentations. Finally, the highest repeatability of left putamen volumetry was found with multi-channel segmentation T1WI + PDWI. Conclusion: Multi-channel segmentation with T1WI + PDWI provides good results for VBM compared with single-channel and other multi-channel segmentations. [ABSTRACT FROM AUTHOR]

At present, the congestion of highways in China is severe. Traffic flow prediction plays an important role in the intelligent transportation system. If it can achieve high-precision prediction, it will be able to efficiently manage traffic and alleviate congestion. To solve this issue, this paper proposed a multi-channel traffic flow prediction method (MCST-Transformer)considering spatiotemporal correlation. Firstly, it used Transformer to extract the internal laws of different data, and then introduced a spatial correlation module to mine the association features of different data. Finally, it integrated global information through channel attention. Using the data of Guangdong province highway, the proposed method realized the traffic flow prediction of 92 toll stations within two hours with high precision. The results show that MCST-Transformer is superior to traditional machine learning methods and time series models based on the attention mechanism. Under the prediction horizon of 120 min, MAPE decreases by 5. 1 % compared with Bayesian regression. Compared with deep learning algorithms like Seq2Seq-Att and Seq2Seq, the overall MAPE of the proposed method can also be reduced by 0. 5%. It indicates that the multi-channel approach can distinguish the characteristics of different data, so as to acquire better performance. [ABSTRACT FROM AUTHOR]

It is essential to estimate the quality of transmission (QoT) of lightpaths before their establishment for efficient planning and operation of optical networks. Due to the nonlinear effect of fibers, the deployed lightpaths influence the QoT of each other; thus, multi-channel QoT estimation is necessary, which provides complete QoT information for network optimization. Moreover, the different interfering channels have different effects on the channel under test. However, the existing artificial-neural-network-based multi-channel QoT estimators (ANN-QoT-E) neglect the different effects of the interfering channels in their input layer, which affects their estimation accuracy severely. In this paper, we propose a self-attention mechanism-based multi-channel QoT estimator (SA-QoT-E) to improve the estimation accuracy of the ANN-QoT-E. In the SA-QoT-E, the input features are designed as a sequence of feature vectors of channels that route the same path, and the self-attention mechanism dynamically assigns weights to the feature vectors of interfering channels according to their effects on the channel under test. Moreover, a hyperparameter search method is used to optimize the SA-QoT-E. The simulation results show that, compared with the ANN-QoT-E, our proposed SA-QoT-E achieves higher estimation accuracy, and can be directly applied to the network wavelength expansion scenarios without retraining. [ABSTRACT FROM AUTHOR]

Molecular electronics represent the cutting-edge and interdisciplinary effort on the future miniaturization of electronic circuits. Benefiting from synthetic chemistry and theoretical insights, molecular circuit studies have promoted devices with increasingly complicated structures. Especially, the evolution of conductive backbones from simple chain-shape single-channel configurations to complex multi-channel architectures marks a pivotal progression. A comprehensive understanding of charge transport and conductance properties in multi-channel molecular devices is crucial for further developing molecular circuits. In this review, we provide an overview of conductance properties, categorizing the influence on conductance as either enhancement or suppression. The underlying mechanisms of conductance modulation are discussed as conductance enhancement attributed to factors of the quantum-interference-based superposition law, the charge-induced self-gating effect, and the facilitation of additional conductive channels through through-space transport; while the conductance suppression originates from the destructive quantum interference in saturated conductive channels and the "electron selective transport" feature within multi-channel structure exhibiting the channels converging on a phenyl group., (© 2025 Wiley‐VCH GmbH.)

Opportunistic Routing (OR), which utilizes the broadcast nature of wireless communication to route packets in a dynamic way, significantly improves the transmission quality. Most of the researches focus on the single channel scenario; however, multi-channel (MC) is widely adopted over wireless communication systems. How to apply OR over MC environments is quite challenging and still an unsolved issue. OR operating over single channel environments collects acknowledgements mainly through overhearing data packets. In contrast, MC environments increase the performance by reducing interference and providing more bandwidth. Since users scatter over multiple channels, the chance of overhearing is reduced. Without overhearing acknowledgements (ACKs), the performance of OR degrades due to duplicate transmissions. To better understand the behavior of OR over MC environments, this paper first surveys current OR methods and constructs a simplified scenario to observe how they behave as the number of interfaces and channels increases. Then, analyze and compare their costs and penalties to find out a potential method. Finally, refine the potential method for MC environments. It is observed that token passing is the most potential method for its high efficiency of utilizing control packets. To apply token passing to OR in MC environments, this paper proposed multi-channel token passing (MCTP) to resolve the hazards of token passing paths and dynamic rate for multiple flows. Simulation results show that MCTP has high performance with low and consistent overheads. In addition, MCTP is scalable with increasing interface and channel numbers, which makes it a prototype for the future OR research in MC environments.

Electrochemical measurement methods are widely used to analyze various biochemical reactions due to their simplicity and versatility. Therefore, the development of potentiostats, electrochemical-based analytical instruments, that are low in cost but maintaining rich analytical features is highly demanding. In this paper, we report the development of a low-cost and portable potentiostat system, named ESPotensio, which supports six different electrochemical measurement methods and a multi-channel electrochemical measurement. The measurement methods it supports include the cyclic voltammetry (CV), linear sweep voltammetry (LSV), square wave voltammetry (SWV), differential pulse voltammetry (DPV), normal pulse voltammetry (NPV), and the observation of electrochemical response current against time based on chronoamperometry (CA) measurement method. Moreover, each of these measurement methods can be run semi-parallelly within three different channels. The electrochemical measurement precision, accuracy, and error of ESPotensio were evaluated based on the repeated measurements and the comparison with the commercial potentiostats. The results showed that it could produce highly accurate electrochemical measurements by having an average accuracy of more than 90% compared to the commercial potentiostat, Emstat Pico. Ultimately, the hardware design was determined accordingly to meet the low-cost demand by costing of only USD 21.4 for the total system realization.

In order to realize the coal mine drilling hole sealing automation, according to the “13th Five-Year” key research and development project “gas control drilling and hole sealing robot” requirements, we put forward the integral multi-channel automatic hole sealing device overall scheme, designed the automatic hole sealing control method, and studied and designed the parameters of grouting channel and hole sealing channel. Finite element method was used to analyze the setting dynamics of the hole sealing device, and the drilling setting and grouting simulation tests were carried out on the prototype of the hole sealing device. The research shows that the whole structure of the hole sealing device can be equipped with the drilling robot to grasp, install and switch the channel automatically to seal the hole automatically. It can meet the requirements of sealing holes with different sealing lengths and different inclination angles. The three-way structure can meet the requirements of setting, exhaust, grouting, gas extraction and other technological processes on pressure and pressure loss; according to the dynamic analysis of the contact stress between the setting seal capsule and the hole wall, the axial friction resistance between the setting seal capsule and the hole wall, as well as the simulation experiment results of sealing the hole, when the setting grouting pressure of the device is 6 MPa, the grouting pressure of the sealing hole can reach 3 MPa, which fully meets the technical requirements of “two blocks and one grouting” sealing hole in the coal mine, and the negative pressure loss of the extraction channel is less than 2 kPa. By meansof sensors and instruments, the monitoring of setting pressure, sealing grouting pressure, extraction flow, pressure and other parameters can be realized.

Biochemical monitoring of bodily fluidics such as sweat, urine, and tears have been extensively developed, but reliable biochemical analysis of sputum biospecimens remains limited and challenging due to the low abundance of biomarkers in intrinsically viscous sputum. We reported a portable multi-channel sputum-based interdigitated organic electrochemical transistors (SiOECTs) device for noninvasive sputum diagnosis. We tailored the AgNWs-doped organic electrochemical transistors, integrating with multiplexed aptamer-antigen assays, to realize the signal amplification and simultaneous detection of biomarkers in raw sputum biospecimens from lung cancer patients. Clinical validation studies demonstrated favorable correlation coefficients between the sputum and serum biospecimens. By utilizing our portable multi-channel iOECTs devices, lung cancer patients were differentiated from health control with an optimum area under the curve (AUC) of 0.931, sensitivity of 87.0%, and specificity of 86.5%. Our miniaturized and portable device could even realize the continuous in-home tracking of the biomarkers change for lung cancer patients after radiotherapy/chemotherapy. It is envisaged that the SiOECTs will shed light on noninvasive diagnostics platforms for sputum-related diseases.

Nowadays, the utilization of memristors to enhance the dynamical properties of chaotic systems has become a popular research topic. In this paper, we present the design of a novel 2D memristor-enhanced polynomial hyper-chaotic map (2D-MPHM) by utilizing the cross-coupling of two TiO2 memristors. The dynamical properties of the 2D-MPHM were investigated using Lyapunov exponents, bifurcation diagrams, and trajectory diagrams. Additionally, Kolmogorov entropy and sample entropy were also employed to evaluate the complexity of the 2D-MPHM. Numerical analysis has demonstrated the superiority of the 2D-MPHM. Subsequently, the proposed 2D-MPHM was applied to a multi-channel image encryption algorithm (MIEA-MPHM) whose excellent security was demonstrated by key space, key sensitivity, plaintext sensitivity, information entropy, pixel distribution, correlation analysis, and robustness analysis. Finally, the encryption efficiency of the MIEA-MPHM was evaluated via numerous encryption efficiency tests. These tests demonstrate that the MIEA-MPHM not only possesses excellent security but also offers significant efficiency advantages, boasting an average encryption rate of up to 87.2798 Mbps.

In cognitive radio networks (CRNs), secondary users (SUs) are permitted to use the available spectrum in opportunity ways to optimize spectrum utilization. As legal spectrum owners, primary users (PUs) can always interrupt SUs' transmissions when they have transmission needs. To increase SUs' transmission continuity while guaranteeing PUs' transmission quality, we propose a preemption backoff mechanism (PBM) with backoff threshold m and backoff parameter f. Under the centralized spectrum sharing structure, we construct a discrete-time queueing model by developing a 3-dimensional Markov-chain (3DMC). Through the model analysis, the system's steady-state probability distribution is obtained. To assess the comprehensive performance of our proposed mechanism, we deduce various important expressions of performance indices and get the corresponding performance figures. At last, we design a cost function by compromising various performance indices and find the optimum parameter combination under the lowest system cost through the Transient Search Optimization (TSO) algorithm. The numerical and simulation results illustrate our proposed PBM can improve SUs' transmission continuity. For example, when m = 1 and f = 0.05 , under the parameter settings given by the experiments, the proposed PBM can reduce SU packets' outage rate and improve throughput by about 60% at most. [ABSTRACT FROM AUTHOR]

The rising user needs for high speed applications and services have necessitated the requirement of a communication link that can provide the data rate comparable to optical fiber. Free Space Optical (FSO) Communication is an admired means of communication from past few years. To achieve seamless and error free transmission at high speed for heterogeneous multimedia applications at desired bit rates, novel advancements in optical communication technology are being explored along with FSO. However, several constraints such as atmospheric turbulence, uneven terrain, dynamic climatic conditions cause several nonlinearities which limit the connectivity. In this work, a multi channel multi beam FSO communication system by incorporating Reed Solomon (RS) Codes has been proposed at 30 Gbps. The Modified Gamma-Gamma model is being applied for channel turbulences. The application of Forward Error Correction (FEC) coding in the designed system offers ease of error detection and correction with improved performance at higher data rate for implementation over longer link lengths.The system performance has been evaluated for (i) Number of errors corrected, (ii) Bit Error Rate (BER), (iii) Received power, (iv) Geometric path losses and (v) Signal to Noise Ratio (SNR). At an acceptable BER of 10–9, the RS (255, 127, 129) encoded Four channel Four beam FSO communication link is effectively implemented for a distance of 3500 m with − 32.5 dBm received low optical power and 25 dB SNR. [ABSTRACT FROM AUTHOR]

Summary: Identifying fundamental characteristics of a structure provide key information for structural health monitoring (SHM). To date, numerous researchers have reported tools and algorithms that can perform system identification, with a large portion of their application being contact‐based sensors. Although dynamic responses of structures can be directly measured from contact‐based sensors, the lifespan of those sensors being much shorter than that of the structure, requiring labor to deploy and maintain the sensors, etc., has led to the use of non‐contact‐based sensors. Among various non‐contact‐based sensors, some researchers have investigated the use of light detection and ranging (Lidar) sensors, which remotely acquire three‐dimensional ranging information, mostly for static displacement measurement during construction. Thus, this paper presents an approach for system identification of structures using dynamic displacement measured from a multi‐channel Lidar sensor. Hardware and mechanical attributes that limit the direct use of raw data from the Lidar are explored. Then, strategies to adjust the tilt axes and reduce the range uncertainties and data synchronization are proposed. Subsequently, two types of laboratory‐scale structures are prepared for validation: a flexible cantilever beam and a four‐story shear building. In both of the structures, the Lidar showed less than 5% error in estimating the first natural frequencies. Also, the mode shape has been estimated with high precision. The results demonstrate the ability of the Lidar for identifying dynamic characteristics of a structure. The potable feature of the Lidar will further allow full‐scale monitoring of a large‐scale civil infrastructure for SHM. [ABSTRACT FROM AUTHOR]

Copyright of CT Theory & Applications is the property of Editorial Department of CT Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Fundamental neuroscience research and high-performance neuro-prostheses require large-scale brain interfaces with ever-greater spatial resolution across a large cortex coverage, which cannot be achieved with current passive (micro) electrocorticography (ECoG) technologies. In this article, we present an active micro-electrocorticography ($\mu $ ECoG) implant system that circumvents these challenges while achieving significantly lower noise compared to other existing active $\mu $ ECoG arrays. The proposed brain implant system is composed of a flexible, actively multiplexed 256-electrode $\mu $ ECoG array and an incremental- $\Delta \Sigma $ readout integrated circuit (ROIC). The 1 cm $\times $ 1 cm $\mu $ ECoG array was fabricated in a 3- $\mu \text{m}$ IGZO thin-film transistor (TFT) technology on a 15- $\mu \text{m}$ flexible foil and coupled to a 1.25 mm $\times $ 1.25 mm CMOS ROIC fabricated in a 22-nm fully depleted silicon on insulator (FDSOI) process. Due to the 256:16 time-division multiplexing achieved in the electrode array, only 16 multiplexed channels are required in the ROIC to acquire signals from the 256 electrodes simultaneously. By combining TFT multiplexing with newly proposed bulk-DAC (BDAC) feedback in the readout channel, we can integrate and address 4 $\times $ more electrodes than other passive arrays, achieve >10 $\times $ less noise than existing active arrays, and obtain >2 $\times $ effective channel area reduction in the ROIC while maintaining comparable electrical performance over current state-of-the-art ($\mu $)ECoG readouts. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Compared with traditional physical commodities, data are intangible and easy to leak, and the related trading process has problems, such as complex participating roles, lengthy information flow, poor supervisory coverage and difficult information traceability. To handle these problems, we construct a distributed supervision model for data trading based on blockchain, and conduct multi-party hierarchical and multi-dimensional supervision of the whole process of data trading through collaborative supervision before the event, at present and after the event. First, the characteristics of information flow in the data trading process are analyzed, and the main subject and key supervision information in the data trading process are sorted out and refined. Secondly, combined with the actual business process of data trading supervision, a multi-channel structure of distributed supervision is proposed by adopting an access–verification–traceability strategy. Finally, under the logical framework of the supervision model, the on-chain hierarchical structure and the data hybrid storage method of "on-chain + off-chain" are designed, and multi-supervisor-oriented hierarchical supervision and post-event traceability are realized through smart contracts. The results show that the constructed blockchain-based distributed supervision model of data trading can effectively isolate and protect sensitive and private information between data trading, so as to realize the whole process, multi-subject and differentiated supervision of key information of data trading, and provide an effective and feasible method for the controllable and safe supervision of data trading. [ABSTRACT FROM AUTHOR]

A multi-channel backscatter communication and radar sensing system is proposed and demonstrated in this paper. Frequency modulated continuous wave (FMCW) radar ranging is integrated with simultaneous uplink data transmission from a self-packaged active radio frequency (RF) tag. A novel package solution is proposed for the RF tag. With the proposed package, the RF tag can transmit a 32-QAM signal up to 2.5 Gbps and QPSK signal up to 8 Gbps. For a multi-tag scenario, we proposed using spread spectrum code to separate the data from each tag. In this case, tags can be placed at arbitrary locations without adjacent channel interference. Proof-of-concept simulations and measurements are demonstrated. A 625 Mbps data rate is achieved in a dual-tag scenario for two tags. [ABSTRACT FROM AUTHOR]

Underwater sensing and detection still rely heavily on acoustic equipment, known as sonar. As an imaging sonar, side-scan sonar can present a specific underwater situation in images, so the application scenario is comprehensive. However, the definition of side scan sonar is low; many objects are in the picture, and the scale is enormous. Therefore, the traditional image segmentation method is not practical. In addition, data acquisition is challenging, and the sample size is insufficient. To solve these problems, we design a semantic segmentation model of side-scan sonar images based on a convolutional neural network, which is used to realize the semantic segmentation of side-scan sonar images with few training samples. The model uses a large convolution kernel to extract large-scale features, adds a parallel channel using a small convolution kernel to obtain multi-scale features, and uses SE-block to focus on the weight of different channels. Finally, we verify the effect of the model on the self-collected side-scan sonar dataset. Experimental results show that, compared with the traditional lightweight semantic segmentation network, the model's performance is improved, and the number of parameters is relatively small, which is easy to transplant to AUV. [ABSTRACT FROM AUTHOR]

Immune surveillance is dependent on lymphocyte migration and targeted recruitment. This can involve different modes of cell motility ranging from random walk to highly directional environment-guided migration driven by chemotaxis. This study protocol describes a flow-based microfluidic device to perform quantitative multiplex cell migration assays with the potential to investigate in real time the migratory response of T cells at the population or single-cell level. The device also allows for subsequent in situ fixation and direct fluorescence analysis of the cells in the microchannel. [ABSTRACT FROM AUTHOR]

The geophysical quality Q involved in anelastic energy absorption plays a significant role in the study of subsurface pressure, rock types and temperature distributions. However, with respect to newly collected lunar penetrating radar (LPR) data, scholars mainly focused on related parameters such as the permittivity, density and chemical composition, while the Q-value has rarely been estimated. In this study, we improved the multi-channel quality factor Q estimation method to study shallow underground structures of the Moon, interpret the geological structure and provide reference for the exploration of lunar resources. The improved windowed multi-channel Q estimation method uses multiple spatial coherence-constrained inversion to achieve a stable Q estimation of the whole direction profile in the presence of noise. An example of a numerical simulation of the simple lunar geological model illustrates the feasibility of the proposed method in estimating the Q-value. Partial lunar data collected by Chang'e-4 LPR were used to demonstrate its value with respect to the interpretation of the stratigraphic development history, dislocation and relative permittivity of the Moon. [ABSTRACT FROM AUTHOR]

This paper introduces the state-/output-feedback control for multi-channel nonlinear cyber–physical systems (CPSs). Many cyber-attacks are considered such as Denial-of-Service (DoS), replay and deception attacks. The deception cyber-attacks can be treated as measurement additive and multiplicative uncertainties. Both time-varying state-dependent and state-independent sensor additive attacks are considered. As DoS attack makes the CPS states unavailable, the standard modeling​ and control methods cannot be applied directly. Alternatively, as attackers in the replay attack re-transmit previous data and prevent the transmission of the more recent data, a delayed model is generated. To deal with these problems, a new observer at the controller side is proposed. It is used to perform two main tasks. The first is to estimate all system states at every time instant. The second is to exclude some unsecured transmitting channels from affecting the system response. Therefore, all attacks in these channels will have no effect on the system response. Using the estimated states, an anti-cyber-attacks state-feedback controller is investigated. Meanwhile, it is verified that the suggested approach certifies the convergence of all the CPSs states under different cyber-attacks. The effectiveness of the proposed secure control approach against different kinds of cyber-attacks is confirmed through two examples with simulation results. • A new model of DoS, replay, and deception attacks with degradation gain and state-dependent/-independent forms is proposed. • An asymptotic observer at the controller side is synthesized to reconstruct the system states at every time instant. • There is no need to design predictors for predicting the time of occurrence of the DoS attacks. • A new multi-channel transmission method in which some channels are excluded from affecting the system response is proposed. • For state and output feedback, controllers are designed such that asymptotic stability rather than boundedness is ensured. [ABSTRACT FROM AUTHOR]

Usually the edges of the graph contain important information of the graph. However, most of deep learning models for graph learning, such as graph convolutional network (GCN) and graph attention network (GAT), do not fully utilize the characteristics of multi-dimensional edge features. Another problem is that there may be noise in the graph that affects the performance of graph learning. Multilayer perceptron (MLP) was used to denoise and optimize the graph data, and a multi-channel learning edge feature method was introduced on the basis of GCN. The multi- dimensional edge attributes of the graph were encoded, and the attributes contained in the original graph were modeled as multi-channel. Each channel corresponds to an edge feature attribute to constrain the training of graph nodes, which allows the algorithm to learn multi-dimensional edge features in the graph more reasonably. Experiments based on Cora, Tox21, Freesolv and other datasets had proved the effectiveness of denoising methods and multi- channel methods. [ABSTRACT FROM AUTHOR]

Multi-channel GaN power device, consisting of stacking multiple two-dimensional-electron-gas (2DEG) channels, has been demonstrated to achieve unprecedented on-state performance while maintaining high breakdown voltage (${V}_{BR}$). However, the large carrier density (${N}_{s}$) makes it more challenging to achieve high positive threshold voltages (${V}_{TH}$) on multi-channel epitaxies. In this work, we demonstrate enhancement-mode (e-mode) multi-channel GaN transistors based on conformally deposited p-type LiNiO over tri-gates to form a multi-channel junction gate structure. Compared to the normal MOS gate, the p-type LiNiO junction gate provides an additional depletion of the channels to yield a more positive ${V}_{TH}$ , reaching a maximum ${V}_{TH}$ of 1.2 V (defined at $1 ~\mu \text{A}$ /mm). Moreover, high-quality LiNiO provided excellent on-state performance in multi-channel tri-gate devices with a stable operation at high temperature, which present small ${V}_{TH}$ shift and hysteresis, and low off-state leakage current. The e-mode devices in this work presented a small specific ${R}_{ON}$ (${R}_{ON, sp}$) of 0.62 $\text{m}\Omega \cdot $ cm−2 along with a hard breakdown voltage (${V}_{BR}$) of 920 V. This work demonstrates the potential of LiNiO for high-performance e-mode power devices. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)