Multichannel System Research Articles

A speed decoupling control method for multi-channel pressure-flow coupling system

Abstract The main engine of construction machinery often involves the compound action of multi-action, such as excavator bucket rod, shovel bucket, and moving arm. Due to the matching relationship between the constant pressure differential valve and main valve, constant power limitation of a pump, load change, and other factors, it will cause the load with low pressure to move first and the load with high pressure to move later. To solve this problem, the existing speed control (or flow control) in the industry generally adopts pre-valve or post-valve compensation load-sensitive systems, positive flow control, negative flow control, constant power control, and so on. The speed control method can solve the coarse flow distribution under multi-load conditions, but is not suitable for working conditions with high precision of flow control or flow distribution, such as speed tracking, trajectory control, etc., and cannot meet the accurate control of flow or speed. In this paper, a speed decoupling control method based on hydraulic factors (such as pressure drop, flow rate, oil temperature, hydraulic pump speed, system pressure, etc.) is proposed. Through accurate modeling and simulation technology, the accuracy of compound action flow control is improved, and the anti-interference energy of the compound action system is enhanced, which lays a foundation for hydraulic speed tracking, displacement tracking, and so on.

Self-powered porous polymer sensors with high sensitivity for machine learning-assisted motion and rehabilitation monitoring

Muscle contraction and relaxation inherently contains valuable information crucial for monitoring physical states, rehabilitation, and injury prevention. However, the majority of flexible wearable devices struggle to offer customizable sensors with high sensitivity, durability, and stability, resulting in suboptimal biofeedback performance. Herein, the machine learning-assisted smart motion and rehabilitation monitoring system (SMRMS) is demonstrated for full-body motion recognition and rehabilitation assessment using a designed porous triboelectric nanogenerator (TENG) array. A theoretical model of porous materials is built to demonstrate the effect mechanism of porosity on TENG output. Through pore design on the tribolayer, theoretical simulation and experimental are performed to determine the key features of porous TENG sensors with high sensitivity (1.76 kPa−1), fast response time (50 ms) and high durability (over 100,000 cycles). A ring-shaped TENG (RS-TENG) is fabricated based on the porous TENG sensor, enabling real-time recording of leg/arm force without additional attachment. By integrating the RS-TENG array with a multichannel signal acquisition system, an SMRMS is designed to capture motion information during human subjects’ exercise and rehabilitation training. These data are then fused for machine learning analytics, resulting in significantly improved accuracy in motion pattern recognition (98.75 %) and rehabilitation monitoring (100 %). The simple, precise and durable porous sensors could help mitigate the risk of excessive exercise-induced muscle injuries, expanding self-powered wearable functionalities and adaptabilities.

Miniaturized high signal-to-noise ratio optical air data system using a compact multi-axis parallel transceiver for airborne application

We have developed a miniaturized multi-channel parallel optical air data system with high signal-to-noise ratio for airborne application. In the system, we designed a fiber amplifier with multi-channel high-energy output that was respectively used as the transmitting signals and a compact multi-axis transceiver with an entrance pupil diameter of 70 mm that was used to receive multi-channel signals simultaneously. We demonstrated the performance of our system both on ground and on board. On ground, the measured line-of-sight speed had an average error of 0.02 m/s and a standard deviation of 0.15 m/s. On board, the standard deviation between the true air speed, angle of attack, and angle of sideslip measured by our system and a commercial Swiss air data system was 1 kt, 0.68°, and 0.54°, respectively, and those standard deviation between our system and a system with the same design but employing multiple single-axis telescopes with entrance pupil diameter of 30 mm was 0.34 kt, 0.36°, and 0.28°, respectively. The signal-to-noise ratio of our system was 4.5 times higher than that of the system with small single-axis telescopes. Our system is very promising for airborne applications because of its small volume, high signal-to-noise ratio, and high data rate.

Nonlinear impairment compensation in multi-channel communication systems based on correlated digital backpropagation with separation of walk-off effect

Digital backpropagation (DBP) is a commonly used method to compensate for the nonlinear impairments of optical fiber channels in the electrical domain, offering a new approach to enhancing the data capacity without affecting the system performance. However, due to the repeated Fourier transform and inverse transform of the split-step Fourier method (SSFM), the complexity of DBP algorithm has increased significantly. In this paper, a correlated backpropagation algorithm with the separation of the walk-off effect is developed, which improves the computational step and significantly reduces the complexity of the algorithm. Simulation results demonstrate that in a 6-channel PDM-16QAM system, the proposed hybrid algorithm reduces the computational complexity by 73.96 % compared to the traditional DBP algorithm for an 800 km transmission.

Evaluation of Visual Cortex Activity Using Functional Near-Infrared Spectroscopy in Primary Open Angle and Primary Angle Closure Glaucoma: A Pilot Study.

Functional near-infrared spectroscopy (fNIRS) was used to assess visual cortical activity in patients with primary open angle (POAG) and primary angle closure (PACG) glaucomas. There was decreased activity in the visual cortex of glaucoma patients correlating with the severity of glaucoma. To evaluate visual cortex activity using fNIRS in POAG and PACG compared with healthy controls. A total of 30 POAGs, 31 PACGs, and 30 healthy aged-matched controls from a single centre were recruited in this cross-sectional observational pilot study with purposive sampling. The POAG and PACG groups were age-matched but were not matched for disease severity at recruitment. All participants underwent fNIRS testing using a multichannel continuous-wave near-infrared system NIRSport 8×7 device (NIRx Medizintechnik GmbH). The visual cortex activity was evaluated in terms of the maximum amplitude of change in oxyhemoglobin (OxyHb) concentration over 10 seconds, and a comparison was done among 3 groups. Both POAG and PACG groups were combined (termed as glaucoma group) to assess the relationship of visual cortical activity with disease severity (by visual field defect (mean deviation) and retinal nerve fibre layer thickness). All participants showed the characteristic response of increased OxyHb and decreased deoxyhemoglobin during stimulus presentation. The maximum amplitude of change in OxyHb concentration over 10 seconds was significantly lower in both POAG and PACG groups compared with control in the right and left middle occipital gyri ( P < 0.05). There was no significant difference between PACG and POAG. Importantly, there was a negative correlation between the visual cortex activity with the visual field defects (mean deviation; P < 0.05) and a positive correlation with retinal nerve fibre layer thickness in the glaucoma group ( P < 0.05). In patients with glaucoma, a reduction in visual cortical activity was observed, which may be indicative of neuronal degeneration occurring in the occipital cortex. Disease severity in glaucoma appears to be closely correlated with visual cortex activity. fNIRS can serve as a useful neuroimaging modality for assessing the hemodynamic and neurodegenerative changes in glaucoma.

A Novel Energy-Efficient Coding Based on Coordinated Group Signal Transformation for Image Compression in Energy-Starved Systems

This paper introduces a new method for compressing images in energy-starved systems, like satellites, unmanned aerial vehicles, and Internet of Things nodes, which is based on coordinated group signal transformation (CGST). The transformation algorithm is a type of difference coding and may be classified as a non-transform-based image-compression method. CGST simplifies the difference signal conversion scheme using a single group codec for all signals. It considers color channels as correlated signals of a multi-channel communication system. The performance of CGST was evaluated using a dataset of 128 × 128 pixel images from satellite remote sensing systems. To adapt CGST to image compression, some modifications were introduced to the algorithm, such as fixing the procedure of the difference signals calculation to prevent any “zeroing” of brightness and supplementing the group codec with a neural network to improve the quality of restored images. The following types of neural networks were considered: fully connected, recurrent, convolution, and convolution in the Fourier space. Based on the simulation results, fully connected neural networks are recommended if the goal is to minimize processing delay time. These networks have a response time of 13 ms. Conversely, suppose the priority is to improve quality in cases where delays are not critical. In that case, convolution neural networks in the Fourier space should be used, providing an image compression ratio of 4.8 with better minimum square error and Mikowsky norm values than JPEG with the same compression ratio.

Tutorial on MUedit: An open-source software for identifying and analysing the discharge timing of motor units from electromyographic signals

We introduce the open-source software MUedit and we describe its use for identifying the discharge timing of motor units from all types of electromyographic (EMG) signals recorded with multi-channel systems. MUedit performs EMG decomposition using a blind-source separation approach. Following this, users can display the estimated motor unit pulse trains and inspect the accuracy of the automatic detection of discharge times. When necessary, users can correct the automatic detection of discharge times and recalculate the motor unit pulse train with an updated separation vector. Here, we provide an open-source software and a tutorial that guides the user through (i) the parameters and steps of the decomposition algorithm, and (ii) the manual editing of motor unit pulse trains. Further, we provide simulated and experimental EMG signals recorded with grids of surface electrodes and intramuscular electrode arrays to benchmark the performance of MUedit. Finally, we discuss advantages and limitations of the blind-source separation approach for the study of motor unit behaviour during tonic muscle contractions.

Multi-channel decentralized decoupling FxLMS algorithm and active vibration control experiment

When vibrations generated by marine machinery propagate through a ship’s hull into the ocean, they produce low-frequency radiated noise with distinct “acoustic fingerprint” characteristics. This noise, characterized by stable and concentrated energy, long transmission distances, and difficulty in elimination, becomes the primary target for enemy sonar detection. Active vibration isolation serves as a critical method for reducing low-frequency vibrations in ships and enhancing their acoustic stealth performance. However, control challenges persist, including multi-frequency excitation, frequency fluctuation, multi-channel coupling, and slow convergence speed. To address these issues, this paper introduced an innovative multi-channel decentralized decoupling filtered-x least mean square (DMFxLMS) algorithm. Firstly, a recursive least squares identification algorithm with a forgetting factor was proposed, taking into account the characteristics of single-input, multi-output and multi-input, and multi-output control systems, effectively enhancing the algorithm’s convergence speed and control accuracy. Secondly, based on the decentralized decoupling control concept, the multi-channel control system was simplified into parallel single-channel control loops. The control weight coefficient updates were only related to adjacent error signals, significantly reducing the algorithm’s computational complexity. Thirdly, an anti-impact link was designed to improve the algorithm’s robustness, considering the interference caused by other mechanical equipment during the control process. The influence of abnormal error signals in the control weight coefficient correction term was suppressed, and a percentage function was introduced to limit the output signal. Finally, the feasibility and effectiveness of the DMFxLMS algorithm were verified through simulations and experiments. The results demonstrated that the DMFxLMS algorithm achieved significant control effects for both constant frequency line spectrum excitation and frequency fluctuating line spectrum excitation, fulfilling the objective of reducing base vibration. The DMFxLMS algorithm exhibited fast convergence and excellent robustness, making it suitable for practical engineering applications.

Quantifying changes in oxygen saturation of the internal jugular vein in vivo using deep neural networks and subject-specific three-dimensional Monte Carlo models.

Central venous oxygen saturation (ScvO2) is an important parameter for assessing global oxygen usage and guiding clinical interventions. However, measuring ScvO2 requires invasive catheterization. As an alternative, we aim to noninvasively and continuously measure changes in oxygen saturation of the internal jugular vein (SijvO2) by a multi-channel near-infrared spectroscopy system. The relation between the measured reflectance and changes in SijvO2 is modeled by Monte Carlo simulations and used to build a prediction model using deep neural networks (DNNs). The prediction model is tested with simulated data to show robustness to individual variations in tissue optical properties. The proposed technique is promising to provide a noninvasive tool for monitoring the stability of brain oxygenation in broad patient populations.

Analysis and comparative assessment of charging dynamics in vertical multi-channel latent heat storage system with corrugated wavy channels

Latent heat thermal energy storage (LHTES) is a promising technology, but thermal response limitations caused by the relatively poor thermal conductivity of phase change materials (PCMs) remains an issue. In recent years, computational fluid dynamics (CFD) has proven to be a powerful and reliable tool in design optimization of different systems and problems in the energy sector. Building on that, the present work will use CFD to investigate the thermal performance enhancements achieved using vertical double-tube LHTES systems with multi-channel sinusoidal wavy geometries. The main parameters investigated here are the wavy channel height (H), pitch (P) and the number of channels (N) and the results are assessed in terms of melting rates and heat transfer coefficients. The analysis found that increasing the height from 1 mm to 3 mm, accelerated the phase change by 21 %, while reducing the pitch from 12.5 mm to 5 mm, enhanced the heat storage rates by 28 %. In addition, increasing the number of channels from 3 to 7 improved the charging rate by 64 %. Compared to a conventional straight channel, the enhanced wavy channel (N = 7, H = 3 mm, P = 5 mm) achieved 80 % higher thermal storage rate and 42 % faster charging time. The above findings provide new insight into the potential of utilizing compact vertical LHTES systems via customized wavy channels, with significant impact towards enhancing the existing thermal energy storage systems.

New Vacuum Solar Telescope Achieves Narrowband Infrared Solar Imaging Observation at He i 10830 Å

The near-infrared imaging channel constitutes a crucial component of the multichannel high-resolution imaging system of the New Vacuum Solar Telescope (NVST). We have successfully achieved high-resolution, narrowband imaging of the chromosphere using He i 10830 Å triplet within this channel, which significantly enhances the imaging observation capabilities of NVST. This paper provides a concise overview of the optical system associated with the near-infrared imaging channel, detailing data processing procedures and presenting several observed images. Leveraging a high-resolution image reconstruction algorithm, we were able to generate a narrowband image near the diffraction limit at 10830 Å with a temporal resolution of less than 10 s.

A low crosstalk 768-channel of 14-bit analog to digital converters for high resolution array of detectors

This paper presents the design and measurement results of a 768-channel of a 14-bit analog to digital converters. Each sampling channel is equivalent to a pitch of only 8.5 μm with a possible sampling rate from 40 KS/s up to 100 KS/s. Test results show crosstalk of just +/-1 LSB. The circuit architecture and layout structure make it scalable to an exceptionally large format of detectors beyond 1000 channels. The circuit is designed to be used as a side element for multi-channel readout systems or as an IP for transfer to very dense integrated circuits.

Trimethylamine-N-oxide aggravated sympathetic activation in D-galactose induced aging rats by down-regulating P2Y12 receptor in microglia

This study aimed to determine whether trimethylamine N-oxide (TMAO) was involved in sympathetic activation in aging and the underline mechanisms. Our hypothesis is that TMAO reduces P2Y12 receptor and induces microglia-mediated inflammation in the paraventricular nucleus, then leading to sympathetic activation in aging. Eighteen young adults (20-40 years old) and sixteen old adults (65-90 years old) were involved in the study. Aging rats were established by injecting D-galactose (200 mg/kg/d) subcutaneously for 12 weeks. TMAO (120 mg/kg/d) or 1% 3, 3-dimethyl-l-butanol (DMB, a structural analog of choline, inhibiting TMA formation) was administrated in drinking water for 12 weeks to investigate their effects on neuroinflammation and sympathetic activation in aging rats. Heart rate variability was analyzed by 24 h ambulatory ECG multichannel recording system (CT-083S Holter ECG recorder, BENEWARE, Hangzhou) in young and old adults. The PowerLab 15T data acquisition system (AD Instruments, Australia) was used to record blood pressure and renal sympathetic nerve activity in the rats. Plasma levels of TMAO, noradrenaline, and IL-1β in old adults were higher than those in young group. In addition, standard deviation of all normal to normal intervals and standard deviation of the average of normal to normal intervals were lower in old adults and negative correlated with TMAO, which indicating sympathetic activation in old adults and related to the increased TMAO. Rats treatment with D-gal showed increased senescence-associated protein, microglia-mediated inflammation levels and decreased P2Y12 receptor in the paraventricular nucleus. Plasma levels of TMAO, noradrenaline, and IL-1β were increased, accompanied by enhanced renal sympathetic nerve activity. While TMAO supplementation aggravated the above phenomenons, and DMB abated them. These findings suggested that TMAO might contribute to the sympathetic hyperactivity of aging via downregulating P2Y12 receptor in microglia and increasing inflammation in the paraventricular nucleus. These results may provide a new target for the prevention and treatment of aging and aging-related diseases. This work was supported by the Program for the National Natural Science Foundationof China (32271155 and 31671185), the Science and Technology Project of the Hebei Education Department (ZD2021076), the Youth Top Talent Foundation of Hebei Province of China and the Program of Clinical Medicine Innovation Research Team of Hebei Medical University (2022LCTD-A1). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Decentralized Design and Plug-and-Play Distributed Control for Linear Multichannel Systems

Decentralized Design and Plug-and-Play Distributed Control for Linear Multichannel Systems

Measuring Human Auditory Evoked Fields with a Flexible Multi-Channel OPM-Based MEG System.

Magnetoencephalography (MEG) is a non-invasive imaging technique for directly measuring the external magnetic field generated from synchronously activated pyramidal neurons in the brain. The optically pumped magnetometer (OPM) is known for its less expensive, non-cryogenic, movable and user-friendly custom-design provides the potential for a change in functional neuroimaging based on MEG. An array of OPMs covering the opposite sides of a subject's head is placed inside a magnetically shielded room (MSR) and responses evoked from the auditory cortices are measured. High signal-to-noise ratio auditory evoked response fields (AEFs) were detected by a wearable OPM-MEG system in a MSR, for which a flexible helmet was specially designed to minimize the sensor-to-head distance, along with a set of bi-planar coils developed for background field and gradient nulling. Neuronal current sources activated in AEF experiments were localized and the auditory cortices showed the highest activities. Performance of the hybrid optically pumped magnetometer-magnetoencephalography/electroencephalography (OPM-MEG/EEG) system was also assessed. The multi-channel OPM-MEG system performs well in a custom built MSR equipped with bi-planar coils and detects human AEFs with a flexible helmet. Moreover, the similarities and differences of auditory evoked potentials (AEPs) and AEFs are discussed, while the operation of OPM-MEG sensors in conjunction with EEG electrodes provides an encouraging combination for the exploration of hybrid OPM-MEG/EEG systems.

Pulse Compression Shape-Based ADC/DAC Chain Synchronization Measurement Algorithm with Sub-Sampling Resolution.

In this article, we address the problem of synchronizing multiple analog-to-digital converter (ADC) and digital-to-analog converter (DAC) chains in a multi-channel system, which is constrained by the sampling frequency and inconsistencies among the components during system integration. To evaluate and compensate for the synchronization differences, we propose a pulse compression shape-based algorithm to measure the entire delay parameter of the ADC/DAC chain, which achieves sub-sampling resolution by mapping the shape of the discrete pulse compression peak to the signal propagation delay. Moreover, owing to the matched filtering in the pulse compression process, the algorithm exhibits good noise performance and is suitable for wireless scenarios. Experiments verified that the algorithm can achieve precise measurements with sub-sampling resolution in scenarios where the signal-to-noise ratio (SNR) is greater than -10 dB.

Enhanced selective delayless subband algorithm independent of primary disturbance configuration for multi-channel active noise control system in vehicles

The selective delayless subband structure stands out as a promising algorithmic choice for the multi-channel active control of vehicle interior noise, particularly in the context of road noise. This type of algorithm reduces the eigenvalue spread of the autocorrelation matrix of the signal by decomposing the signal into subbands, and the desired subbands are activated selectively, thus achieving a significant performance improvement while consuming less computational resources compared with the traditional fullband algorithms. Nevertheless, the effectiveness of the subband algorithm appears to be closely tied to the configuration of the primary disturbance signal. In cases where the primary disturbance signal encompasses both broadband and tonal components, the performance advantage of the subband algorithm becomes constrained. In this paper, we conduct a thorough comparative analysis of two extensively employed subband algorithms, namely the Morgan and the Milani methods, through simulations employing data obtained from a multi-channel active noise control (ANC) headrest system. The results indicate that the Morgan method outperforms the Milani method when configured optimally. Subsequently, we propose an enhanced version of the subband algorithm based on the Morgan method. The enhanced algorithm incorporates an additional sinusoidal noise canceller (SNC) subsystem and a narrowband active noise control (NANC) subsystem based on local secondary path modeling to address tonal components, while the selective subband structure is employed for controlling broadband components. In addition, the computational complexity of the algorithm is analyzed. The effectiveness of the proposed algorithm is validated through numerous simulations and the ANC tests conducted on an actual vehicle utilizing the multi-channel ANC headrest system. The performance of the proposed algorithm surpasses that of traditional selective subband and fullband algorithms, and balanced binaural noise reduction is achieved.

Real-Time Monitoring of Multistep Batch and Flow Synthesis Processes of a Key Intermediate of Lifitegrast by a Combined Spectrometer System with Both Near-Infrared and Raman Spectroscopies Coupled to Partial Least-Squares.

Process analytical technology (PAT) has been successfully applied in numerous chemical synthesis cases and is an important tool in pharmaceutical process research and development. PAT brings new methods and opportunities for the real-time monitoring of chemical processes. In multistep synthesis, real-time monitoring of the complex reaction mixtures is a significant challenge but provides an opportunity to enhance reaction understanding and control. In this study, a combined multichannel spectrometer system with both near-infrared and Raman spectroscopy was built, and calibration models were developed to quantify the desired products, intermediates, and impurities in real-time at multiple points along the synthetic pathway. The capabilities of the system have been demonstrated by operating dynamic experiments in both batch and continuous-flow processes. It represents a significant step forward in data-driven, multistep pharmaceutical ingredient synthesis.

Multi-channel ANC System with Online Secondary Path Modeling for Turboprop Aircraft Cabin

Multi-channel ANC System with Online Secondary Path Modeling for Turboprop Aircraft Cabin

Automatic Object Detection in Radargrams of Multi-Antenna GPR Systems Based on Simulation Data for Railway Infrastructure Analysis

Ground-penetrating radar (GPR) is a non-invasive technology that uses electromagnetic pulses for subsurface exploration. In the railroad sector, it is crucial to assessing soil layers and infrastructure, offering insights into soil stratification and geological features and aiding in identifying subsurface hazards. However, the automation of radargram analysis is impeded by the lack of ground truth—accurate real-world data used to validate machine learning models—thus affecting the deployment of advanced algorithms. This study focuses on generating high-quality simulated data to address the shortage of real-world data in the context of object detection along railroad tracks and presents a fully automated pipeline that includes data generation, algorithm training, and validation using real-world data. By doing so, it paves the way for significantly easing the future task of object detection algorithms in the railway sector. A simulation environment, including the digital twin of a GPR antenna, was developed for artificial data generation. The process involves pre- and post-processing techniques to transform the three-dimensional data from the multichannel GPR system into two-dimensional datasets. This ensures minimal information loss and suitability for established two-dimensional object detection algorithms like the well-known YOLO (You Only Look Once) framework. Validation involved real-world measurements on a track with predefined buried objects. The entire pipeline, encompassing data generation, processing, training, and application, was automated for efficient algorithm testing and implementation. Artificial data show promise for better performance with increased training. Future AI and sensor advancements will enhance subsurface exploration, contributing to safer and more reliable railroad operations.