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As per bulk-edge correspondence, the junction between two topologically distinct lattices in a topological optical fiber supports topological interface states, which enable highly stable, scattering-free, robust propagation of light. We investigate the propagation characteristics of a multi-channel topological optical fiber having co-existing multiple topological interface states in the presence of potential structural defects and deformation in shape. The tolerance analysis has been carried out by deliberately introducing structural imperfections upto ⩽4% with reference to the annular area at the topological junction of trivial and non-trivial lattices. These studies demonstrate robust properties of multiple topological interface states despite the presence of such structural deformations. Furthermore, a deliberately introduced lateral misalignment upto a maximum of 800 nm of the topological junction exhibits a low splice loss of 0.3 dB and high confinement properties of the multiple topological interface states. These results clearly indicate that the propagation characteristics and properties of the co-existing multiple topological interface states exhibit exceptional tolerance to structural defects, junction deformations, and lateral misalignments, surpassing the performance of state-of-the-art conventional optical fibers. Hence, topological optical fibers could outperform conventional optical fibers in terms of undistorted signal transmission irrespective of fabrication-related potential structural defects. [ABSTRACT FROM AUTHOR]

Titanium alloys are difficult to machine using conventional metal cutting methods due to their low thermal conductivity and high chemical reactivity. This study explores the new multi-channel discharge machining of Ti-6Al-4V using silicon electrodes, leveraging their internal resistivity to generate potential differences for multi-channel discharges. To investigate the underlying machining mechanism, the equivalent circuit model was developed and a theoretical simulation was carried out. Comparative experiments with silicon and conventional copper electrodes under identical parameters were also conducted to analyze discharge waveforms, material removal rate, surface quality, and heat-affected zones (HAZ). The results demonstrate that the bulk resistance of silicon is the main mechanism for generating multi-channel discharges. This process efficiently disperses the discharge energy of the single discharge pulse, resulting in smaller craters, smoother machined surfaces, and shallower recast layers and HAZ. [ABSTRACT FROM AUTHOR]

This paper addresses the rental car allocation problem in which a car rental company rents its cars through various channels. This study proposes a constrained model based on the nested-allocation structure to allocate the rental cars to the car rental system to maximize the total rental revenues. The nested allocation strategy uses the concept of whether the number of sales accumulated to a certain level exceeds a threshold corresponding to that sharer. This approach allows the quota of low-profit channels to support the leasing demand of high-profit channels at any time, thereby increasing revenue. The proposed model can dynamically adjust the nested strategy as the dynamic programming model. However, it avoids the issues of ample storage space and long calculation time commonly encountered by the dynamic programming model. Computational results show that the proposed nested approach substantially outperforms the traditional exclusive allocation approach. [ABSTRACT FROM AUTHOR]

This article introduces a three-channel half-mode substrate-integrated waveguide (HMSIW) link. The link achieves three independent channels by stacking two HMSIWs vertically, with one of them utilizing both the fundamental and the first higher-order modes ($TE_{0.50}$ and $TE_{1.50}$). Methods for selective excitation of both modes with good overall cross-modal isolation are introduced and presented. Furthermore, real-time eye-diagram measurements of the link using a pseudo-random binary sequence (PRBS) and on-off keying (OOK) modulation are presented in addition to a full MATLAB simulation model of the link showing its performance under quadrature amplitude modulation (QAM). The measured 10-dB return loss (RL) BW of the channels are more than 4 GHz (12–16 GHz) for the $TE_{0.50}$ modes along the two HMSIWs, and 3.6 GHz of the first higher-order mode $TE_{1.50}$ (12.1–15.7 GHz), corresponding to a concurrent bandwidth of 3.6 GHz with more than 15 dB of isolation between the channels. To the best of our knowledge, this is the first demonstration of a three-channel link using HMSIWs. The structure is fabricated and measurements agree well with the electromagnetic simulations.

Abstract Clinical sleep diagnosis traditionally relies on polysomnography (PSG) and expert manual classification of sleep stages. Recent advancements in deep learning have shown promise in automating sleep stage classification using a single PSG channel. However, variations in PSG acquisition devices and environments mean that the number of PSG channels can differ across sleep centers. To integrate a sleep staging method into clinical practice effectively, it must accommodate a flexible number of PSG channels. In this paper, we proposed FlexSleepTransformer, a transformer-based model designed to handle varying number of input channels, making it adaptable to diverse sleep staging datasets. We evaluated FlexSleepTransformer using two distinct datasets: the public SleepEDF-78 dataset and the local SleepUHS dataset. Notably, FlexSleepTransformer is the first model capable of simultaneously training on datasets with differing number of PSG channels. Our experiments showed that FlexSleepTransformer trained on both datasets together achieved 98% of the accuracy compared to models trained on each dataset individually. Furthermore, it outperformed models trained exclusively on one dataset when tested on the other dataset. Additionally, FlexSleepTransformer surpassed state-of-the-art CNN and RNN-based models on both datasets. Due to its adaptability with varying channels numbers, FlexSleepTransformer holds significant potential for clinical adoption, especially when trained with data from a wide range of sleep centers.

Objective In the multi-level road network, junction stations of lines with different signal systems face quite a number of challenges. In order to meet the new demands of multi-network interoperation, it is necessary to conduct in-depth research and propose interlocking functions and interface configuration solutions for junction stations. Method By comparing the differences between interlocking systems under CBTC and CTCS-2 signal systems, signal control function, construction and operation demands are analyzed in detail, and functional requirements of the junction station interlocking system are clarified. On this basis, the interface objects and interface contents of the interlocking system are further analyzed. Finally, a configuration scheme for the interlocking interface of the junction station is constructed. Result & Conclusion The junction station interlocking system not only needs to complete the basic route safety protection function, but is also able to adapt to the requirements of various signal systems for basic information provision, capable of mobile blocking adaptability, as well as comprehensive functions such as section block combination, platform door control, over lap section protection, automatic return route processing, flexible operation adjustment and adaptation. At the same time, the junction station interlocking system should support multi-channel interfaces such as signal safety data network, wired ring network, wireless network, relay, etc. to ensure the compatibility and reliability of the system.

With the escalating flow of information and digital communication, information security has become an increasingly important issue. Traditional cryptographic methods are being threatened by advancing progress in computing, while physical encryption methods are favored as a viable and compelling avenue. Metasurfaces, which are known for their extraordinary ability to manipulate optical parameters at the nanoscale, exhibit significant potential for the revolution of optical devices, making them a highly promising candidate for optical encryption applications. Here, a single-sized metasurface with four independent channels is proposed for conducting steganography and multi-key information encryption. More specifically, plaintext is transformed into a ciphertext image, which is encoded into a metasurface, while the decryption key is discretely integrated into another channel within the same metasurface. Two different keys for steganographic image unveiling are also encoded into the metasurface and can be retrieved with different channels and spatial positions. This distributed multi-key encryption approach can enhance security, while strategically distributing images across distinct spatial zones serves as an additional measure to reduce the risk of information leakage. This minimalist designed metasurface, with its advantages of high information density and robust security, holds promise across applications including portable encryption, high-camouflaged image display, and high-density optical storage.

Despite the fact that the text image-based optical character recognition (OCR) methods have been applied to a wide range of applications, they do suffer from performance degradation when the image is contaminated with moiré patterns for the sake of interference between the display screen and the camera. To tackle this problem, we propose a novel network for text image demoiréing. Specifically, to encourage our study on text images, we collected a dataset including a number of pairs of images with/without moiré patterns, which is specific for text image demoiréing. In addition, due to the statistical differences among various channels on moiré patterns, a multi-channel strategy is proposed, which roughly extracts the information associated with moiré patterns and subsequently contributes to moiré removal. In addition, our purpose on the text image is to increase the OCR accuracy, while other background pixels are insignificant. Instead of restoring all pixels like those in natural images, a character attention module is conducted, allowing the network to pay more attention on the optical character-associated pixels and also achieving a consistent image style. As a result from this method, characters can be more easily detected and more accurately recognized. Dramatic experimental results on our conducted dataset demonstrate the significance of our study and the superiority of our proposed method compared with state-of-the-art image restoration approaches. Specifically, the metrics of recall and F1-measure on recognition are increased from 56.32%/70.18% to 85.34%/89.36%. [ABSTRACT FROM AUTHOR]

Electromyography (EMG) and acoustic myography (AMG) play crucial roles in assessing muscle function, providing valuable insights into neural control, muscle activation, and fatigue levels. However, the current landscape reveals a gap in dual‐modal stretchable sensing patches, highlighting the need for comfortable and wearable solutions in the field of in situ monitoring of synchronous EMG and AMG sensing. In this work, a dual‐modal stretchable patch (DMSP) is developed that fully integrates a multi‐channel EMG electrode with AMG sensors. It possesses stable electromechanical properties even at strains exceeding 200%. With an overall modulus of under 40 kPa and a skin adhesion force surpassing 30 N m−1, the DMSP exhibits exceptional durability, enduring 1000 stretching cycles. The DMSP enables in situ simultaneous monitoring of EMG and AMG signals from sternocleidomastoid muscle during neck rotation where the mechanical strain reaches up to 30%. The DMSP proves instrumental in characterizing muscle activity, facilitating in‐depth research on muscle movement, and thus enhancing clinical outcomes in rehabilitation medicine and physical therapy. [ABSTRACT FROM AUTHOR]

Chaos-based encryption is promising for safeguarding digital images. Nonetheless, existing chaos-based encryption algorithms still exhibit certain shortcomings. Given this, we propose a novel multi-channel image encryption algorithm that leverages pixel reorganization and hyperchaotic maps (MIEA-PRHM). Our MIEA-PRHM algorithm employs two hyperchaotic maps to jointly generate chaotic sequences, ensuring a larger key space and better randomness. During the encryption process, we first convert input images into two fused matrices through pixel reorganization. Then, we apply two rounds of scrambling and diffusion operations, coupled with one round of substitution operations, to the high 4-bit matrix. For the low 4-bit matrix, we conduct one round of substitution and diffusion operations. Extensive experiments and comparisons demonstrate that MIEA-PRHM outperforms many recent encryption algorithms in various aspects, especially in encryption efficiency. [ABSTRACT FROM AUTHOR]

Aiming to address the challenge of difficulty in extracting damage features from gear signals due to significant noise interference, a method combining adaptive successive multivariate variational mode decomposition (ASMVMD) and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) was proposed. Firstly, the optimization of SMVMD decomposition parameters was adaptively conducted using the weighted chimpanzee optimization algorithm. The optimal values for the maximum penalty factor α and the maximum decomposition mode number k were determined by using the negative sum of the average crest factor of envelope spectrum (Ec) of each channel component after successive multivariate variational mode decomposition as the fitness function for optimization. Then, the ASMVMD method was used to adaptively decompose the multi-channel fault data of gears. Specific components were then extracted from each channel based on the Ec index, and these components were summed for signal reconstruction. Finally, the signal was reconstructed using MOMEDA deconvolution processing to further enhance the impact characteristics of gear faults, and envelope spectrum analysis was used to deconvolve the signal and extract the characteristic frequency of gear faults. The research results show that through the analysis of simulated signals and experimental signals, it can be concluded that the combined method of ASMVMD-MOMEDA has a significant denoising effect on the signal. This effectively suppresses the impact of irrelevant interference components, allowing the fault frequencies and their first few harmonics to be clearly observed in the envelope spectrum. When comparing with the method that combines multivariate empirical mode decomposition (MEMD)-MOMEDA, the envelope spectrum obtained by the ASMVMD-MOMEDA method is cleaner and its harmonics are more distinct. This further demonstrates that the ASMVMD-MOMEDA method can accurately extract gear fault characteristics. [ABSTRACT FROM AUTHOR]

A multi-channel differential synchronous demodulator is proposed for third harmonics suppression in three-phase receiving coils based linear inductive position sensor, which is realized by taking envelope differences of every two induced signals on three-phase receiving coils as differential signals. The proposed demodulator includes a differential synchronous envelope detector (DSED), and a time division MUX (TDM). By using the DSED, the envelope differences are synchronously demodulated as differential signals. The TDM including a multi-channel selector, a MUX and a voltage follower, is used for independently outputting the corresponding differential signals for linear position calculation. The proposed demodulator was designed in a 180 nm CMOS process and verified by Spectre simulator. Simulation results show third harmonics of differential signals are improved by 17.4 dB, indicating the proposed demodulator could be a candidate for designing the high performance analog front-end of inductive position sensor. [ABSTRACT FROM AUTHOR]

Urea-based selective catalytic reduction (SCR) is highly efficient for NOx abatement within a diesel aftertreatment system. However, abnormally high NOx emissions in the aftertreatment system tailpipe during WHSC (World Harmonized Steady-State Cycle) evaluation have been observed due to insufficient urea decomposition or mixing, which cannot be predicted by the current uniform 1D (one-dimensional) modelling approach with different urea dosing ratios. As a result, a multi-channel model has been developed to investigate the effect of urea maldistribution on aftertreatment system performance, where the uniformity index (UI) is used as a characteristic parameter to describe urea mixing efficiency. It was found that NOx emissions at the tailpipe can be successfully described with the multi-channel model even with a relatively high UI (UI = 0.95). Additionally, an improved segment UI factor as a function of mass flow rate has also been applied for maldistribution description, wherein better correlation with the measured NOx emission can be obtained. [ABSTRACT FROM AUTHOR]

Radar forward-looking imaging is critical in many civil and military fields, such as aircraft landing, autonomous driving, and geological exploration. Although the super-resolution forward-looking imaging algorithm based on spectral estimation has the potential to discriminate multiple targets within the same beam, the estimation of the angle and magnitude of the targets are not accurate due to the influence of sidelobe leakage. This paper proposes a multi-channel super-resolution forward-looking imaging algorithm based on the improved Fast Iterative Interpolated Beamforming (FIIB) algorithm to solve the problem. First, the number of targets and the coarse estimates of angle and magnitude are obtained from the iterative adaptive approach (IAA). Then, the accurate estimates of angle and magnitude are achieved by the strategy of iterative interpolation and leakage subtraction in FIIB. Finally, a high-resolution forward-looking image is obtained through non-coherent accumulation. The simulation results of point targets and scenes show that the proposed algorithm can distinguish multiple targets in the same beam, effectively improve the azimuthal resolution of forward-looking imaging, and attain the accurate reconstruction of point targets and the contour reconstruction of extended targets. [ABSTRACT FROM AUTHOR]

Decoupling of phase and polarization is significant for multidimensional light manipulation. However, this requires traditional cascaded and parallel optical elements, resulting in a bulky and complex system. Metasurfaces can potentially replace complex optical modules as novel ultrathin planar optical elements. Herein, an ultrathin single‐layer metasurface is proposed to decouple the phase and polarization based on the Pancharatnam–Berry phase. The rotation angles of the two nanofins are employed to manipulate the phase and polarization independently, significantly simplifying the design process. Diffraction‐limited focusing with a degree of linear polarization higher than 95% is achieved for wavelengths in the 405–633 nm range. Furthermore, a metasurface can arbitrarily manipulate the relative intensity between different channels. The intensity ratio is adjusted from 0.12 to 6.62. These results will pave the way for miniaturized, integrated, and multi‐channel optical elements. [ABSTRACT FROM AUTHOR]

Clinical sleep diagnosis traditionally relies on polysomnography (PSG) and expert manual classification of sleep stages. Recent advancements in deep learning have shown promise in automating sleep stage classification using a single PSG channel. However, variations in PSG acquisition devices and environments mean that the number of PSG channels can differ across sleep centers. To integrate a sleep staging method into clinical practice effectively, it must accommodate a flexible number of PSG channels. In this paper, we proposed FlexSleepTransformer, a transformer-based model designed to handle varying number of input channels, making it adaptable to diverse sleep staging datasets. We evaluated FlexSleepTransformer using two distinct datasets: the public SleepEDF-78 dataset and the local SleepUHS dataset. Notably, FlexSleepTransformer is the first model capable of simultaneously training on datasets with differing number of PSG channels. Our experiments showed that FlexSleepTransformer trained on both datasets together achieved 98% of the accuracy compared to models trained on each dataset individually. Furthermore, it outperformed models trained exclusively on one dataset when tested on the other dataset. Additionally, FlexSleepTransformer surpassed state-of-the-art CNN and RNN-based models on both datasets. Due to its adaptability with varying channels numbers, FlexSleepTransformer holds significant potential for clinical adoption, especially when trained with data from a wide range of sleep centers. [ABSTRACT FROM AUTHOR]

The transmission system is a key component to ensure the stable operation of high-speed trains. Thus, it is significant to monitor its condition to ensure the operation safety. Nowadays sparse representation is widely used in fault diagnosis. However, as the number of sensors is increasing, the existing method destroys the internal structure of multi-channel signals and cannot effectively deal with the fault diagnosis of multi-channel signals in parallel. Therefore, this article extends the existing sparse representation method to tensor space to extract the coupling information between channels and realize the fault diagnosis of multi-channel. First, a tensor sparse representation model is proposed to achieve data-level multi-channel signal fusion and complete inter-channel fault feature extraction. Then, a multimodal dictionary learning algorithm is proposed to adaptively design the data-driven dictionary to achieve data-driven feature extraction. Finally, a tensor sparse representation classification method is proposed to achieve the purpose of intelligent diagnosis. Fault experiments verify the effectiveness and superiority of the method. [ABSTRACT FROM AUTHOR]

Beaconing is a fundamental task in IoT networks, where each node tries to locally broadcast a packet to all its neighbors. Unfortunately, the problem of Minimum Latency Beaconing Schedule (MLBS), which tries to obtain the fastest and collision-free schedule, is not well studied when the IoT devices employ the duty-cycled working mode. The existing works have rigid assumptions that there exists a single channel and can only work in a centralized fashion. Aiming at making the work more practical and general, in this paper, we investigate the first distributed method for the MLBS problem in Multi-channel asynchronous Duty-cycled IoT networks (MLBSMD problem). The MLBSMD problem is first formulated and proved to be NP-hard. To avoid collisions locally, several special structures are designed which can work in O (Δ 2) time, where Δ denotes the maximum node degree in the network. Then, a distributed beaconing scheduling method that can compute a low-latency and collision-free schedule is proposed with a theoretical bound, taking the active time slots of each node into account. Finally, the extensive simulation results demonstrate the effectiveness of the proposed algorithm in terms of latency. [ABSTRACT FROM AUTHOR]

This study investigates the diffusion and network structure of hydrogen technology from 1980 to 2022. Hydrogen fuel cell technology has the shortest diffusion cycle and high global acceptance. Production technology shows significant global demand, particularly within regional patent families. In contrast, hydrogen storage and fuel cell technologies have more concentrated diffusion, indicating higher adaptation thresholds. Japanese firms are crucial in spreading storage and production technologies, while U.S. enhances diffusion through licensing and transfer. Hydrogen refrigeration technology exhibits a more dispersed bidirectional network. Hydrogen production and fuel cell technologies demonstrate notable activity and network scale in the global market. This research provides valuable insights into the global development of hydrogen technology. [Display omitted] • The evolution of hydrogen technology diffusion was evaluated from 1980 to 2022 using patent data. • Hydrogen fuel cell technology shows the shortest diffusion cycle, hydrogen storage and fuel cell technologies have concentrated diffusion in core countries. • Hydrogen production technology and hydrogen refrigeration technology exhibits broader diffusion. [ABSTRACT FROM AUTHOR]

[Objective] In the multi-level road network, junction stations of lines with different signal systems face quite a number of challenges. In order to meet the new demands of multi-network interoperation, it is necessary to conduct indepth research and propose interlocking functions and interface configuration solutions for junction stations. [Method] By comparing the differences between interlocking systems under CBTC and CTCS-2 signal systems, signal control function, construction and operation demands are analyzed in detail, and functional requirements of the junction station interlocking system are clarified. On this basis, the interface objects and interface contents of the interlocking system are further analyzed. Finally, a configuration scheme for the interlocking interface of the junction station is constructed. [Result & Conclusion] The junction station interlocking system not only needs to complete the basic route safety protection function, but is also able to adapt to the requirements of various signal systems for basic information provision, capable of mobile blocking adaptability, as well as comprehensive functions such as section block combination, platform door control, over lap section protection, automatic return route processing, flexible operation adjustment and adaptation. At the same time, the junction station interlocking system should support multi-channel interfaces such as signal safety data network, wired ring network, wireless network, relay, etc. to ensure the compatibility and reliability of the system. [ABSTRACT FROM AUTHOR]

Copyright of Cyber Security & Data Governance is the property of Editorial Office of Information Technology & Network Security 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.)

In this study, a closed multi-channel air-blowing plug seedling pick-up device and a combined plug tray were designed to address the issues of complex structure, high seedling damage rates and low pick-up efficiency in fully automated vegetable transplanter systems. The device operates by sealing the plug seedlings in a seedling cup, where compressed air is channeled into the sealed cavity through multiple passages during the seedling pick-up process. The upper surface of the seedling plug is subjected to uniform force, overcoming the friction and adhesion between the plug seedlings and the tray. This process presses the seedlings into the guide tube, completing the pick-up operation. A mechanical model for the plug seedlings was developed, and the kinetics of the pick-up process were analyzed. The multi-channel high-pressure airflow was simulated and evaluated, identifying three key parameters affecting seedling pick-up performance: water content of the seedling plug, air pressure during pick-up, and air-blowing duration. Using these factors as variables, and with seedling pick-up rate and substrate loss rate as evaluation indicators, single-factor experiments and a three-factor, three-level orthogonal experiment were conducted. The experiments' results showed that the best seedling pick-up performance was achieved when the water content of the plug was 20%, the air pressure was 0.3 MPa, and the air-blowing time was 30 ms. Under these conditions, the seedling pick-up success rate was 97.22%, and the substrate loss rate was 10.46%. [ABSTRACT FROM AUTHOR]

A multi-channel computational ghost imaging (GI) method based on multi-scale speckle optimization is proposed. We not only reduce imaging time and enhance imaging quality but also reduce interference among different channels. Using one bucket detector to receive total light intensity, the color speckle is formed by combining components obtained through the singular value decomposition of three self-designed multi-scale measurement matrices. Simulation and experimental results demonstrate that our designed method contributes to reducing imaging time and enhancing imaging quality, achieving improved visual quality even at low sampling rates. This approach enhances GI flexibility and holds potential for diverse applications, including target recognition and biomedical imaging. [ABSTRACT FROM AUTHOR]

Polarization is the nature of optics. Exploiting metasurface's polarizations can enhance the multiplexing capacity but face a limited number of channels. For example, a single‐layer metasurface can offer three independent channels with six degrees of freedom (DoFs) including the amplitude and phase of Ex, Exy(yx), and Ey of the Jones matrix. In this work, it is theoretically demonstrated that the degrees of freedom of dual‐layer metasurfaces can reach eight, overcoming the polarization multiplexing constraints of single‐layer metasurfaces and that the cross‐talk can be largely reduced as compared with single‐layer metasurfaces when the channel number is larger than three. Numerical calculations manifest a decrease of 100%, 63%, and 50% for the cross‐talk of channels 4, 5, and 6, respectively. As a proof‐of‐concept demonstration of high‐capacity polarization multiplexing, an arbitrary‐polarization‐controlled 5‐channel dual‐layer metasurface exhibiting five nanoprintings and five holographic images with reduced cross‐talk under different incident and output polarizations is successfully designed. Thus, the research highlighting the high‐multiplexing‐capacity of dual‐layer metasurfaces can significantly advance multifunctional optical devices with high efficiency, simple integration, and ease of manipulation. [ABSTRACT FROM AUTHOR]

Spinal cord injury (SCI) is a catastrophic condition that disrupts neurons within the spinal cord, leading to severe motor and sensory deficits. While current treatments can alleviate pain, they do not promote neural regeneration or functional recovery. Three-dimensional (3D) bioprinting offers promising solutions for SCI repair by enabling the creation of complex neural tissue constructs. This review provides a comprehensive overview of 3D bioprinting techniques, bioinks, and stem cell applications in SCI repair. Additionally, it highlights recent advancements in 3D bioprinted scaffolds, including the integration of conductive materials, the incorporation of bioactive molecules like neurotrophic factors, drugs, and exosomes, and the design of innovative structures such as multi-channel and axial scaffolds. These innovative strategies in 3D bioprinting can offer a comprehensive approach to optimizing the spinal cord microenvironment, advancing SCI repair. This review highlights a comprehensive understanding of the current state of 3D bioprinting in SCI repair, offering insights into future directions in the field of regenerative medicine. [ABSTRACT FROM AUTHOR]

In this paper, a multi-channel speech enhancement structure combining beamformers and lightweight neural networks is proposed. By using a first-order differential beamformer to improve the signal-to-noise ratio of the target speech, the burden on the subsequent neural network is reduced, thus lowering the complexity of the required neural network. A two-stage gated recurrent neural network is employed, where the first stage recurrent neural network processes the channel characteristics of the speech and the second handles the frequency domain features of the speech. The frequency band division combined with convolution is used to further compress the network scale. With suitable loss function, the speech enhancement performance of the model is further improved. The proposed model structure is trained, evaluated, and validated using simulated multi-channel microphone array datasets generated from the public TIMIT dataset. The results demonstrate that our model achieves good multi-channel speech enhancement performance with relatively small parameter and computational requirements when compared to popular existing approaches. [ABSTRACT FROM AUTHOR]

Peripheral nerve modulation via electrical stimulation shows promise for treating several diseases, but current approaches lack selectivity, leading to side effects. Exploring selective neuromodulation with commercially available nerve cuffs is impractical due to their high cost and limited spatial resolution. While custom cuffs reported in the literature achieve high spatial resolutions, they require specialized microfabrication equipment and significant effort to produce even a single design. This inability to rapidly and cost-effectively prototype novel cuff designs impedes research into selective neuromodulation therapies in acute studies. To address this, we developed a reproducible method to easily create multi-channel epineural nerve cuffs for selective fascicular neuromodulation. Leveraging commercial flexible printed circuit (FPC) technology, we created cuffs with high spatial resolution (50 μm) and customizable parameters like electrode size, channel count, and cuff diameter. We designed cuffs to accommodate adult mouse or rat sciatic nerves (300–1500 μm diameter). We coated the electrodes with PEDOT:PSS to improve the charge injection capacity. We demonstrated selective neuromodulation in both rats and mice, achieving preferential activation of the tibialis anterior (TA) and lateral gastrocnemius (LG) muscles. Selectivity was confirmed through micro-computed tomography (μCT) and quantified through a selectivity index. These results demonstrate the potential of this fabrication method for enabling selective neuromodulation studies while significantly reducing production time and costs compared to traditional approaches. [ABSTRACT FROM AUTHOR]

Copyright of Piezoelectrics & Acoustooptics is the property of Piezoelectric & Acoustooptic 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.)

The article addresses document-level event extraction challenges, proposing a novel framework with a token-event-role data structure and a multi-channel prediction module. Topics include the significance of document-level event extraction, challenges in multi-event detection and argument identification, and previous methods' limitations.

Speech enhancement in 3D reverberant environments is a challenging and significant problem for many downstream applications, such as speech recognition, speaker identification, and audio analysis. Existing deep neural network models have shown efficacy for 3D speech enhancement tasks, but they often introduce distortions or unnatural artifacts in the enhanced speech. In this work, we propose a novel two-stage refiner system that integrates a neural beamforming network and a diffusion model for robust 3D speech enhancement. The neural beamforming network performs spatial filtering to suppress the noise and reverberation; while, the diffusion model leverages its generative capability to restore the missing or distorted speech components from the beamformed output. To the best of our knowledge, this is the first work that applies the diffusion model as a backend refiner to 3D speech enhancement. We investigate the effect of training the diffusion model with either enhanced speech or clean speech, and find that clean speech can better capture the prior knowledge of speech components and improve the speech recovery. We evaluate our proposed system on different datasets and beamformer architectures, and show that it achieves consistent improvements in metrics like WER and NISQA, indicating that the diffusion model has strong generalization ability and can serve as a backend refinement module for 3D speech enhancement, regardless of the front-end beamforming network. Our work demonstrates the effectiveness of integrating discriminative and generative models for robust 3D speech enhancement, and also opens up a new direction for applying generative diffusion models to 3D speech processing tasks, which can be used as a backend to various beamforming enhancement methods. [ABSTRACT FROM AUTHOR]