Mode Output Research Articles

A planar mounted SQUID full-tensor module for magnetoenterogram denoising detection

Magntoenterogram (MENG) is a functional imaging method with great clinical potential for non-invasive diagnosis of intestinal diseases. In measurements of MENG, the cardiac artifacts are a strong biological source of interference. Based on wire-wound SQUID gradiometers, we constructed a planar mounted full-tensor compensation module for MENG system, which can obtain cardiac artifacts while suppressing ambient noise, so as to denoise MENG signals without increasing external ECG recording channels. The cardiac artifacts are regressed out from the full-tensor compensation module outputs, which can denoise the MENG measurements. Using this module, the SNR is about 70 dB better than the raw data. At last, 36-point array MENG signals were detected successfully and the contour map of jejunal slow-wave frequency (SWF) was consistent with the jejunal area (one part of the small intestine). This method will save costs and avoid the new electromagnetic interferences introduced by ECG equipment. It will lay a good foundation for the rapid clinical application of the MENG system.

Research on key circuits of high‐precision servo channel loading card for wing fatigue testing

AbstractThe key circuits of servo channel loading card with high‐precision data acquisition and output functions for wing fatigue testing are studied and improved in this paper. To solve the problem of large offset voltage in some op‐amps, non‐inverting combined amplifier circuit (NCAC) and inverting combination amplifier circuit (ICAC) are proposed by utilizing precision op‐amps combined with wide voltage and high‐power output op‐amps. In the force signal acquisition section, a non‐inverting combined zeroing filter circuit (NCZFC) is proposed to improve the precision of signal acquisition. In the excitation source output section, a non‐inverting combined excitation output circuit (NCEOC) is proposed to improve the precision of the positive excitation output; an inverting combined excitation output circuit (ICEOC) is also proposed, which improves the precision of the negative excitation output and also realizes the inversion of the input and output signals. In the servo drive output section, a non‐inverting combined voltage and current conversion circuit is proposed to improve the precision of voltage and current outputs, which has two modes of non‐inverting combined voltage output (NCVO) and non‐inverting combined current output (NCIO) modes. Simulation experiments in Multisim and actual tests have been carried out on the above improved circuits to verify the stability and precision.

True random number generation based on temporal fluctuations of abalone shell coherent random lasers.

The output modes of random lasers exhibit randomness, making them a potential high-quality physical entropy source for generating random numbers. In this paper, we controlled a low-cost and easily fabricated abalone shell random laser, generating forward and backward coherent random lasers simultaneously in a single channel, resulting in highly diverse mode variations. After post-processing steps such as third-order difference calculations and exclusive-or (XOR) logic operations, we generated a random number sequence for the first time, to the best of our knowledge, based on the temporal fluctuations of biomimetic random laser coherent modes. The instantaneous generation rate reached a preliminary 40 Gbps. Moreover, the random bits satisfy requirements such as random distribution, independence, and absence of bias, successfully passing the NIST SP800-22 standard test, confirming the high quality of the random number sequence.

Tunable optical matrix convolution of 20-Gbit/s QPSK 2-D data with a kernel using optical wave mixing.

Compared to its electronic counterpart, optically performed matrix convolution can accommodate phase-encoded data at high rates while avoiding optical-to-electronic-to-optical (OEO) conversions. We experimentally demonstrate a reconfigurable matrix convolution of quadrature phase-shift keying (QPSK)-encoded input data. The two-dimensional (2-D) input data is serialized, and its time-shifted replicas are generated. This 2-D data is convolved with a 1-D kernel with coefficients, which are applied by adjusting the relative phase and amplitude of the kernel pumps. Time-shifted data replicas (TSDRs) and kernel pumps are coherently mixed using nonlinear wave mixing in a periodically poled lithium niobate (PPLN) waveguide. To show the tunability and reconfigurability of this approach, we vary the kernel coefficients, kernel sizes (e.g., 2 × 1 or 3 × 1), and input data rates (e.g., 6-20 Gbit/s). The convolution results are verified to be error-free under an applied: (a) 2 × 1 kernel, resulting in a 16-quadrature amplitude modulation (QAM) output with an error vector magnitude (EVM) of ∼5.1-8.5%; and (b) 3 × 1 kernel, resulting in a 64-QAM output with an EVM of ∼4.9-5.5%.

Two-layer optimal scheduling method for regional integrated energy system considering flexibility characteristics of CHP system

A regional integrated energy system (RIES) guarantees the fulfillment of a diversified load demand of users by coordinating all types of energy equipment and is a two-layer optimal scheduling method that takes into consideration the flexibility of the characteristics of the combined heat and power (CHP) system is proposed in this paper. First, the thermoelectric output characteristics of hydrogen fuel cell (HFC) and CHP units are analyzed to establish an energy equipment model that takes into consideration the variability of the operating conditions. Subsequently, a two-layer optimal scheduling model of the RIES is established, where the upper layer takes into consideration the operating costs, carbon emission penalties, and renewable energy consumption capacity to determine the strategy for allocating the energy flow, and the lower layer considers the energy efficiency as the optimization objective to determine the output modes of the HFC and CHP units. Finally, an adaptive closed-loop control strategy is used to avoid the continuous startup and shutdown of the CHP unit. The simulation results show that the proposed method can effectively reduce operating costs and carbon emissions, improve energy efficiency and renewable energy consumption, and promote the efficient utilization of integrated energy resources.

Photovoltaic modules fault detection, power output, and parameter estimation: A deep learning approach based on electroluminescence images

Accurately detecting faults in photovoltaic modules/cells and estimating their effective power output and parameters of the equivalent circuit representation of photovoltaic modules is becoming increasingly critical for both the reliability of associated systems and the efficiency of electricity production from renewable energy sources. Existing studies often work with datasets containing photovoltaic cells that exhibit one fault at a time, leading to the classification of photovoltaic cells with multiple faults as “mixed” faults. Moreover, factors such as cell alignment and specific fault types, collectively called “cell level features”, are not considered in current studies estimating the power output of a photovoltaic module. Therefore, this paper focuses on a comprehensive deep-learning pipeline to separately detect three types of faults in photovoltaic modules/cells using electroluminescence images. Furthermore, it addresses the estimation of the output power of photovoltaic modules and the series resistance of their equivalent circuit, considering the cell-level characteristics extracted from the electroluminescence images. The proposed model demonstrates its ability to detect “black core”, “crack”, and “edge” faults with global accuracies of 0.93, 0.868, and 0.95, respectively. Furthermore, the proposed model estimates the power output of photovoltaic modules with a normalized mean absolute error of 0.03547 and a normalized root mean squared error of 0.04892. This outperforms the base model that relies solely on non-pre-processed detected faults and significantly larger models adept at extracting features from the electroluminescence images. Moreover, the VGG16-based model estimates the series resistance in the equivalent circuit representation of photovoltaic modules with a normalized mean absolute error of 0.04472 and a normalized root mean squared error of 0.0622.

A self-powered sensor based on hollow bimetallic sulfide and double-strand TSDR amplification achieves sensitive miRNA-21 detection

MicroRNA (miRNA) sensitive detection is significant for cancer diagnosis. Currently, self-powered biosensors in clinical miRNA detection show the advantages of no external power supply, mild reaction conditions, low cost and portability. In this work, the bimetallic sulfide heterojunction hollow nanocubes are prepared as the substrate material, which can significantly improve the electrochemical reaction rate and increase the enzyme load. Meanwhile, we design the independent duplex-chain assisted toehold mediated strand displacement reaction (TSDR) strategy. The two substable double chains trigger a continuous chain displacement reaction, and the current signal amplification is realized based on molecular recognition. The constructed sensor has a linear response range of 0.05–10000 fM and a detection limit (LOD) of 8.3 aM. In addition, we use commercial apps to present signals, fundamentally changing the signal output mode of electrochemical sensors, and facilitating the construction of a portable real-time sensor platform. The self-powered biosensor has the characteristics of good biocompatibility, real-time monitoring and economic efficiency in miRNA detection, which provides a new idea for sensitive detection of miRNA. Moreover, it has far-reaching reference significance for the development of other sensing technologies, which is expected to extend from the medical field to environmental monitoring, food safety and other fields.

Ultrasensitive strategy for OTA analysis by both recognition-activated multiple isothermal cyclic strand displacement amplification and DNAzyme-mediated cyclic signal reporting mechanism

We propose a recognition-driven ultrasensitive fluorescence sensing strategy for accurate analysis of ochratoxin A (OTA) by taking Mg2+-dependent DNAzyme based catalysis as the final signal output module. Recognition of target OTA destroys the simple double-strand recognition structure and contributes to the liberation of primer probe, which can trigger and activate the primer-mediated multiple isothermal cyclic strand displacement amplification (M-CSDA) process. The designed dual templates (T-P, T-A) can contribute to the generation of enormous nicked products (DNAzyme and primer), which will take effect in the triggering of M-CSDA. Meanwhile, one of the nicked products (DNAzyme) will also take part in the cyclically cleavage of designed hairpin probes (HP) with the presence of the cofactor Mg2+, inducing the recovery of fluorescent signal of quenched hairpin probes. Only the trace amount of target OTA could generate drastic fluorescent signal and ultrasensitive detection of OTA could be well achieved. Under optimized conditions, the detection limit of this method for OTA is 0.59 fg/mL. The characteristics of this method including the simplicity in dual signal amplification design, isothermal operation process and easy fluorescence measurement model exhibit the superior sensitivity and specificity for OTA detection. And this strategy can be further expanded for the design of ultrasensitive aptamer-sensing methods for series analytes.

Semi-Supervised Building Extraction with Optical Flow Correction Based on Satellite Video Data in a Tsunami-Induced Disaster Scene.

Data and reports indicate an increasing frequency and intensity of natural disasters worldwide. Buildings play a crucial role in disaster responses and damage assessments, aiding in planning rescue efforts and evaluating losses. Despite advances in applying deep learning to building extraction, challenges remain in handling complex natural disaster scenes and reducing reliance on labeled datasets. Recent advances in satellite video are opening a new avenue for efficient and accurate building extraction research. By thoroughly mining the characteristics of disaster video data, this work provides a new semantic segmentation model for accurate and efficient building extraction based on a limited number of training data, which consists of two parts: the prediction module and the automatic correction module. The prediction module, based on a base encoder-decoder structure, initially extracts buildings using a limited amount of training data that are obtained instantly. Then, the automatic correction module takes the output of the prediction module as input, constructs a criterion for identifying pixels with erroneous semantic information, and uses optical flow values to extract the accurate corresponding semantic information on the corrected frame. The experimental results demonstrate that the proposed method outperforms other methods in accuracy and computational complexity in complicated natural disaster scenes.

Low-cost, mobile EEG hardware for SSVEP applications

The global shortage of integrated circuits due to the COVID-19 pandemic has made it challenging to build biopotential acquisition devices like electroencephalography (EEG) hardware. To address this issue, a new hardware system using common ICs has been designed, which is cost-effective, precise, and easily accessible from global distributors. The hardware system comprises 8-channel inputs EEG hardware with a mobile headset capable of acquiring 5-30Hz EEG signals. First two channels of the design is enabled for steady-state visual evoked potential (SSVEP) operations, and the remaining channels can be powered up as needed. A small 3D-printable enclosure is also designed for the hardware board, which is attached to protective glasses to be used as a head-mounted device. The board includes an additional green LED, 4 pulse width modulation (PWM) outputs for general-purpose input/output (GPIO), 2 buttons for input, and exposed programming pins and digital-to-analog converter (DAC) output from the microcontroller unit (MCU). The proposed hardware system is expected to enable students and young researchers to experiment with EEG signals, especially SSVEP, before investing in professional equipment with the availability of programming codes.

Model predictive control of 3L‐NPC inverter to enhance fault ride through capability under unbalanced grid conditions

AbstractUnbalanced voltages are becoming increasingly common in distribution networks due to the growing integrations of distributed renewable energy resources as well as large loads such as electric vehicle chargers. Such unbalanced voltages introduce significant control challenges as they produce ripples in the controlling signals, impacting the operation of inverters during the unbalanced conditions. Therefore, this paper proposes a controlling algorithm for three‐level inverters aimed at suppressing these ripples on controlling signals; improving the overall performance of inverters, and consequently enhancing the fault ride‐through capability under unbalanced conditions and faults. The proposed controlling algorithm enables inverters to operate in either a balanced current output mode or a suppressed‐ripple active power output mode, depending on the specific event scenario and demands from the grid operators. Current harmonics are suppressed even under severe faults. For improving the voltage balance on the DC capacitors in three‐level inverters, an enhanced method of neutral point current prediction is presented. The proposed method ensures higher balancing accuracy through the use of weighted switching costs, avoiding the requirement of a balancing controller or current extrapolation. Simulation and experimental results demonstrate the effectiveness of the proposed controller for reliable grid operation.

Recent Advances in Aptamer‐Based Sensors for In Vitro Detection of Small Molecules

AbstractSensitive and accurate detection of small molecules from complex matrix has aroused increasing interest in many fields, yet remains an open challenge. Recent years have witnessed a considerable advance of aptasensors for diagnostic assay development towards diverse small molecules because aptamer is one of the most powerful classes of molecular receptors with advanced affinity and specificity. Herein, we reviewed the small‐molecule aptasensors in the past five years, focusing on the principles to specific applications in clinical diagnosis, food safety, and environmental monitoring. The first introductory section on the development of aptasensors in historical view and its analytical features contextualizes essential health‐related small molecules. The second part highlights the basic components of aptasensor and the detection principles of different sensors based on signal output modes. The subsequent part systematically discusses various small‐molecule sensing platforms by interfacing aptamers with diverse signal amplification strategies. Finally, challenges and perspectives for improving the aptasensor performance are also discussed. By describing biochemical and analytical procedures, this review highlights the optimal use of aptamers in the detection, quantification, and imaging of important health‐related small molecules and presents new insights, technical advances, and engineering strategies for practical applications.

Research on the intensity profiles of high-order Laguerre-Gaussian mode laser beams

Here we analyzed the intensity profile properties of the Laguerre-Gaussian (LG) mode laser beams, to facilitate the intracavity intensity modulation for high-order LG mode laser output. The commonly used definitions of ring-like beam size are discussed, including the second moment of light intensity, power in the bucket, and the peak intensity radius of the ring-like beam. The differences between these methods underscore the importance of selecting appropriate beam size definitions when estimating the mode order, which is particularly worthy of attention when employing certain mode-selection techniques, such as using a defect spot, an aperture, or inducing spherical aberration (SA), to achieve high-order mode outputs. In the experiment, high-order mode LG0,±m output were obtained by utilizing the SA of a short-focal lens in the cavity of an end-pumped Nd:YVO4 laser at 1064 nm. The highest angular indices m reached 355, which is the highest order of LG mode output obtained directly from a laser cavity. The experimental relationship between the mode-order and cavity parameters matched well with the theoretical results, validating the analysis of the intensity profile properties.

Spiking mode-based neural networks.

Spiking neural networks play an important role in brainlike neuromorphic computations and in studying working mechanisms of neural circuits. One drawback of training a large-scale spiking neural network is that updating all weights is quite expensive. Furthermore, after training, all information related to the computational task is hidden into the weight matrix, prohibiting us from a transparent understanding of circuit mechanisms. Therefore, in this work, we address these challenges by proposing a spiking mode-based training protocol, where the recurrent weight matrix is explained as a Hopfield-like multiplication of three matrices: input modes, output modes, and a score matrix. The first advantage is that the weight is interpreted by input and output modes and their associated scores characterizing the importance of each decomposition term. The number of modes is thus adjustable, allowing more degrees of freedom for modeling the experimental data. This significantly reduces the training cost because of significantly reduced space complexity for learning. Training spiking networks is thus carried out in the mode-score space. The second advantage is that one can project the high-dimensional neural activity (filtered spike train) in the state space onto the mode space which is typically of a low dimension, e.g., a few modes are sufficient to capture the shape of the underlying neural manifolds. We successfully apply our framework in two computational tasks-digit classification and selective sensory integration tasks. Our method thus accelerates the training of spiking neural networks by a Hopfield-like decomposition, and moreover this training leads to low-dimensional attractor structures of high-dimensional neural dynamics.

Cryo-EM structures of a bathy phytochrome histidine kinase reveal a unique light-dependent activation mechanism

Phytochromes are photoreceptor proteins in plants, fungi, and bacteria. They can adopt two photochromic states with differential biochemical responses. The structural changes transducing the signal from the chromophore to the biochemical output modules are poorly understood due to challenges in capturing structures of the dynamic, full-length protein. Here, we present cryoelectron microscopy (cryo-EM) structures of the phytochrome from Pseudomonas aeruginosa (PaBphP) in its resting (Pfr) and photoactivated (Pr) state. The kinase-active Pr state has an asymmetric, dimeric structure, whereas the kinase-inactive Pfr state opens up. This behavior is different from other known phytochromes and we explain it with the unusually short connection between the photosensory and output modules. Multiple sequence alignment of this region suggests evolutionary optimization for different modes of signal transduction in sensor proteins. The results establish a new mechanism for light-sensing by phytochrome histidine kinases and provide input for the design of optogenetic phytochrome variants.

FARN: Fetal Anatomy Reasoning Network for Detection With Global Context Semantic and Local Topology Relationship.

Accurate recognition of fetal anatomical structure is a pivotal task in ultrasound (US) image analysis. Sonographers naturally apply anatomical knowledge and clinical expertise to recognizing key anatomical structures in complex US images. However, mainstream object detection approaches usually treat each structure recognition separately, overlooking anatomical correlations between different structures in fetal US planes. In this work, we propose a Fetal Anatomy Reasoning Network (FARN) that incorporates two kinds of relationship forms: a global context semantic block summarized with visual similarity and a local topology relationship block depicting structural pair constraints. Specifically, by designing the Adaptive Relation Graph Reasoning (ARGR) module, anatomical structures are treated as nodes, with two kinds of relationships between nodes modeled as edges. The flexibility of the model is enhanced by constructing the adaptive relationship graph in a data-driven way, enabling adaptation to various data samples without the need for predefined additional constraints. The feature representation is further refined by aggregating the outputs of the ARGR module. Comprehensive experimental results demonstrate that FARN achieves promising performance in detecting 37 anatomical structures across key US planes in tertiary obstetric screening. FARN effectively utilizes key relationships to improve detection performance, demonstrates robustness to small-scale, similar, and indistinct structures, and avoids some detection errors that deviate from anatomical norms. Overall, our study serves as a resource for developing efficient and concise approaches to model inter-anatomy relationships.

Research on influence of random vibration on closed-loop drive control of MEMS gyroscope

Abstract In response to the unclear impact of random vibration on the drive mode of MEMS gyroscopes, this paper analyzes the effects of vibration on the use of proportional control and PID control in self-excited oscillation close-loop drive through theoretical derivation and experimental comparison. By deducing the principles of synovial comb and self-excited oscillation closed-loop drive, the performance degradation of closed-loop drive control caused by vibration is analyzed theoretically. Data was collected by PCI-4461. The result shows that the peak-peak value of the drive mode output signal of the PID control loop under vibration is 43% of that of the proportional control loop. Compared with a proportional control loop, a PID control loop can better suppress the impact of random vibration.

Fault‐tolerant control of unmanned marine vehicles with unknown parametric dynamics via integral sliding mode output feedback control

AbstractThis paper focuses on fault‐tolerant control (FTC) for unmanned marine vehicles (UMVs) subject to unknown parametric dynamics, thruster faults, and external disturbances. A novel FTC strategy based on the integral sliding mode output feedback method is proposed. Based on a high gain compensator and output information, a novel integral sliding mode fault‐tolerant controller is constructed to guarantee the dynamic positioning (DP) of UMVs. An attraction region that is related to the known positive constants with respect to dynamic uncertainties has been revealed for the first time. Finally, the closed‐loop stability can be guaranteed from the every initial time in despite of unknown parametric dynamics, thruster faults, and external disturbances. Simulation though a typical floating production ship model has verified the effectiveness of the proposed method.

Insight into motor fatigue mechanisms in natalizumab treated multiple sclerosis patients with wearing off

Motor fatigue in Multiple Sclerosis (MS) is due to reduced motor cortex (M1) output and altered sensorimotor network (SMN) modulation. Natalizumab, a disease-modifying therapy, reduces neuroinflammation and improves fatigue. However, some patients treated with natalizumab experience fatigue recurrence (‘wearing-off’) before subsequent infusions. Wearing-off provides a valuable window into MS-related motor fatigue mechanisms in a controlled, clinically stable, setting. This study investigates whether wearing-off is associated with worsening motor fatigue and its neurophysiological mechanisms and assesses natalizumab’s effect on MS-related fatigue. Forty-five relapsing–remitting MS patients with wearing-off symptoms were evaluated pre- and post-natalizumab infusion. Assessments included evaluating disability levels, depressive symptoms, and the impact of fatigue symptoms on cognitive, physical, and psychosocial functioning. The motor fatigue index was computed through the number of blocks completed during a fatiguing task and peripheral, central, and supraspinal fatigue (M1 output) were evaluated by measuring the superimposed twitches evoked by peripheral nerve and transcranial magnetic stimulation of M1. Transcranial magnetic stimulation-electroencephalography assessed M1 effective connectivity by measuring TMS-evoked potentials (TEPs) within the SMN before- and after the task. We found that wearing-off was associated with increased motor fatigue index, increased central and supraspinal fatigue, and diminished task-related modulation of TEPs compared to post-natalizumab infusion. Wearing-off was also associated with worsened fatigue impact and depression symptom scores. We conclude that the wearing-off phenomenon is associated with worsening motor fatigue due to altered M1 output and modulation of the SMN. Motor fatigue in MS may reflect reversible, inflammation-related changes in the SMN that natalizumab can modulate. Our findings apply primarily to MS patients receiving natalizumab, emphasizing the need for further research on other treatments with wearing-off.

Synergy of Target-Induced Magnetic Network and Single-Drop Chromogenic System for Ultrasensitive "All-in-Tube" Detection of miRNA in Whole Blood.

The development of liquid biopsy methods for the accurate and reliable detection of miRNAs in whole blood is critical for the early diagnosis and monitoring of diseases. However, accurate quantification of miRNA expression levels remains challenging due to the complex matrix and low abundance of miRNAs in blood samples. Herein, we report a contactless signal output strategy with low background interference that ensures "zero-contact" between the reaction system and the colorimetry system. The designed target-induced magnetic ZnS/ZIF-90/ZnS network can serve as a unique signal amplifier and transducer. It releases hydrogen sulfide (H2S) gas in an acidic solution which can be concentrated in a droplet of only a few microliters in volume, etching the silver layer of Au@Ag nanostars (NSTs) in the droplet. This will lead to changes in the localized surface plasmon resonance signals of the NSTs. Finally, quantitative detection of let-7a is realized by measuring the offset value of the UV-vis absorption peak. Therefore, by virtue of the synergistic action of quadruple signal amplification methods, including catalytic hairpin assembly, ZnS/ZIF-90/ZnS, magnetic separation, and microextraction, the "All-in-Tube" ultrasensitive detection of low-abundance let-7a in whole blood is achieved with a detection limit as low as the aM level. In addition, the "zero-contact" signal output mode effectively solves the problem of complex matrix interference, demonstrating the great potential of this method for miRNA quantification in complex samples, such as whole blood.