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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]

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]

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]

Purpose: This empirical study aims to examine luxury fashion retailers' localised multiple channel distribution strategies in China. Design/methodology/approach: Through case studies of 15 participating retailers, qualitative data were collected from 33 semi-structured interviews. Findings: Strong impacts of internationalisation strategies, distribution strategies and channel length towards multiple channel retailing are revealed. Multi-channel retailing is widely employed by firms who have entered China and further developed their businesses through local partnerships and adopted a selective distribution strategy via relatively longer channels. Omni-channel retailing is only suitable for the few retailers using an exclusive distribution strategy through direct marketing and wholly owned customer relationship management. As a dynamic transformation from multi- to omni-channel retailing, cross-channel retailing is adopted by those who are withdrawing from local partnerships and shifting to wholly owned expansions and operations in host markets. Research limitations/implications: The results are potentially challenged by relatively small sample size. Practical implications: Practitioners are suggested to adapt multiple channel retailing to their international expansion strategies, distribution strategies and channel length in the host markets. Originality/value: This paper contributes to the literature in both multiple channel retailing and international retailing by offering insights into the motives, development patterns and suitability of multiple channel retailing in the international retail marketing context. [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]

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

Copyright of 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]

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]

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]

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]

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.

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.

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.

Highly homogeneous magnetic fields in magnetic resonance spectrometers are an important guarantee for improving the quality of spectra for chemical structure analysis and kinetic information. The active shimming process using a shimming power supply to drive a shimming coil to produce a compensating electromagnetic field is an essential way to improve the homogeneity of the magnetic field. The high accuracy and stability of the shimming power supply’s output are crucial indicators for improving the active shimming capability and maintaining the shimming results. The paper develops a high-precision, high-stability, low-ripple active shimming power supply system for magnetic resonance spectrometers, and designs a shimming current driver (constant current source) with output state monitoring based on negative feedback control, coupled with an MCU control platform and host computer system to complete the closed-loop transmission of control commands and data between hardware and software, to achieve digital constant current output control. Under R=2.84 Ω, L=25.5 µH shimming coil load, the full-scale output response time is less than 18.9 µs, the output ripple peak-to-peak control is 30 mV, the positive and negative current output symmetry is good, and the maximum output deviation is 4.8‰ for long time operation. This power supply was used in a nuclear magnetic resonance spectrometer system using 0.5 T-Halbach configuration magnets, and the magnetic field homogeneity was optimised from 24.48 ppm (10-6) to 2.72 ppm by driving a first-order shimming coil. This work contributes to the integration of compact nuclear magnetic resonance spectrometer systems and the applications related to active shimming.

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]

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.)

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]

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.

Summary: Data broadcast is a fundamental communication pattern in wireless IoT networks, in which the messages are disseminated from a source node to the entire network. The problem of minimum latency broadcast scheduling (MLBS) which is aimed to generate a quick and conflict‐free broadcast schedule has not been extensively explored in duty‐cycled networks. The existing works either work in a centralized scheme or rely on a fixed tree for broadcasting. Additionally, they all employ a strict premise that each node can only utilize one channel for both transmitting and receiving messages. Thus, to address the issues mentioned above, we examine the first distributed broadcasting algorithm in multi‐channel duty‐cycled wireless IoT networks, without relying on a predetermined tree. First, the MLBS problem in such networks is defined and proved to be NP‐hard. Then, in order to avoid transmission conflicts between different links locally, two efficient data structures are designed to help compute the earliest time and channel of receiving messages without conflicts. Based on the above data structures, we introduce an efficient distributed broadcasting algorithm, which can generate a latency‐sensitive broadcast tree while calculating a collision‐free broadcast schedule, simultaneously. Finally, the theoretical analysis and simulations demonstrate the efficiency of the proposed algorithm. [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.)

Investigation of the proposed Silicon gate all around dielectric window spaced-multi-channel (Si-GAA-DWS-multi-channel) MOSFET is carried out through 3D-ATLAS TCAD simulator. The proposed device parameter has been compared with the existing devices, mainly with Silicon-Nanowire-Dielectric Pocket Packed MOSFET (Si-NW-DPP FET) and Silicon-Nanowire MOSFET (Si-NW FET). The Si-GAA-DWS-Multi-channel FET is found to be the better device than the other two structures with a threshold voltage of 0.40 V, an on current of 0.326 × 10–3 A, off current of 5.9 × 10–13 A, on/off current ratio of 5.52 × 108, sub-threshold swing of 63.4 mV/dec and DIBL of 26.25 mV/V. Impact of interface fixed charges and multi state defects have been studied for the Si-GAA-DWS-multi-channel MOSFET. Presence of fixed negative charges and multi state defects at semiconductor-oxide interface causes a step in the potential profile which results in the shift of threshold voltage, degradation of drain current, and Ion/Ioff ratio. [ABSTRACT FROM AUTHOR]

Objective With the increasing number of rail transit vehicles in operation, the maintenance workload for data is also increasing. The existing data landing methods have many drawbacks, necessitating research into new data transmission methods to reduce the failure rate of trains. Method By comparing Wi-Fi and 4G technologies, it is determined to utilize the both. Considering the need for network coverage on main lines and in depots, three channels are ultimately established to ensure data transmission under different conditions and locations. Meanwhile, to ensure the security of data transmission, several security measures are implemented to prevent channel attacks. Result & Conclusion Experimental results demonstrate that the proposed approach can effectively and reliably download data to ground servers. Moreover, in the event of a channel failure, the remaining channels can continue to operate, ensuring uninterrupted data transmission.

Studies employing neural networks to classify emotions from brain waves and other biological signals provide a quantitative perspective on understanding human physiological phenomena. Typically, multimodal networks process combined data without considering the relationships between electrodes, such as in electroencephalograms (EEG) where data are gathered from multiple inputs. However, incorporating electrode relationships when combining data may improve the model accuracy. This study explores EEG data, often treated as a single modality, input into networks of varied structures as a multimodal data stream to evaluate accuracy. Additionally, it investigates the effect of input electrode combination patterns on accuracy. The results underscore the importance of designing appropriate electrode models when integrating EEG data into networks with various structures. Under the conditions of this study, the highest accuracy of 89.08% was obtained by the most appropriate model, significantly surpassing others. Therefore, when incorporating multi-channel EEG data into neural networks, the structure of the model’s specific section receiving the EEG signal must be considered.

Multi-channel neural electrodes as a crucial means are of great significance for information exchange between the brain and computers. Herein, we present a 16-channel Si-based active neural probe system that achieves a monolithic integration between the electrodes and circuits in a single probe, making it a standalone integrated electrophysiology recording system. The ASIC prepared on a base (2×2mm2) is a 16-channel analog frontend (AFE) for neural recording, and each channel has a low-noise amplifier (LNA), a bandpass filter (BPF), a buffer and a current bias circuit. The 258 neural signal recording electrodes (22×24μm2) are densely packed on a 50 μm thick, 100 μm wide, and 3 mm long shank. The ASIC of neural probe, internal interconnecting wires are all implemented in commercial SMIC 0.18 μm CMOS technology. The neural probe system achieves a 3.6 μVrms input-referred noise (IRN) in a bandwidth of 1.1Hz-10 kHz, 70.8 μW power consumption, 0.0785 mm2 area per channel, as well as an AFE gain of 58.1 dB Furthermore, the impedances of the Au electrodes can be obtained as 0.5–2.1 MΩ at a frequency of 1 kHz. The functionality of a 16-channel silicon-based neural probe is validated in an in-vivo experiment on lab rats.

AbstractThermal errors of machine tools account for 40–70% of machining errors, with motorized spindles and linear motors being the primary heat sources adversely impacting machine tools’ precision. This article aims to solve the issue of significant thermal errors brought about by numerous internal heat sources, complex heat transfer processes, and uneven temperature field distributions. This article proposes a cooling control strategy that employs multiple channels with varying water temperature and velocity. The article also presents the design of the cooling system for a machine tool. The established multi-channel cooling control equation is then used to optimize the cooling water parameters of different channels. Ultimately, finite element simulations and cooling experiments verify the system’s efficacy. Based on the data collected from simulations and cooling experiments, it has been demonstrated that the motorized spindle motor’s thermal balance temperature decreased by 58.5% after cooling, and the shaft deformation was reduced by 70.2%. Similarly, the linear motor’s thermal balance temperature decreased by 37.13%, and the parallelism error of the machine tool guide rail was reduced by 72.3%. These results demonstrate convincingly that the machine tool cooling system can achieve more accurate and efficient cooling, which can provide a reference for machine tool thermal error control technology. [ABSTRACT FROM AUTHOR]

This paper explores the significant impact of e-commerce and investigates manufacturers' decision-making processes regarding the opening of e-shops and the adoption of online and offline coordination strategies to enhance profits in an e-commerce environment. A novel three-channel model with asymmetric retailers is proposed, and three scenarios are analyzed: the initial dual-channel scenario, the multi-channel scenario with e-platform, and the "Order online, Pickup offline" coordinated scenario. By comparing optimal profits across these scenarios, decision-making inequalities for manufacturers are derived. Subsequently, numerical experiments validate that manufacturers, aiming to maximize profits, opt to establish an e-commerce channel and coordinate with offline retailers. Additionally, the study emphasizes the importance of e-platforms in setting commission rates to drive increased profits. The findings offer practical implications for manufacturers to engage in interest-balanced coordination, enabling them to adapt to evolving retail dynamics in the advancing e-commerce landscape from both theoretical and practical perspectives. [ABSTRACT FROM AUTHOR]

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

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office 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.)

Semantic segmentation of aerial images plays a pivotal role in extracting detailed information about land cover, infrastructure, and natural features. Traditional single-channel segmentation models struggle to harness the rich information present in multi-channel aerial images, such as multi-spectral or hyperspectral data. While the DeepLabV3 + architecture has shown remarkable success in semantic segmentation tasks by exploiting multi-scale context and atrous convolutions, its performance on aerial images remains suboptimal due to the unique challenges of this domain. With the motive of addressing the difficulties in the existing semantic segmentation techniques for aerial images, the adoption of deep learning techniques is utilized. A multi-objective derived Adaptive Multichannel deeplabv3 + (AMC-Deeplabv3 +) with a new meta-heuristic algorithm called Improved Beluga whale optimization (IBWO) algorithm is proposed in this paper. Here, the hyperparameters of Multichannel deeplabv3 + are optimized by the IBWO algorithm and this model intends to set new benchmarks in the accuracy and contextual understanding of aerial image segmentation by integrating multi-channel data processing techniques and preserving spatial context. The proposed model attains improved accuracies of 98.65% & 98.72% for dataset 1 and 2 respectively and also achieves the dice coefficient of 98.73% & 98.85% respectively, with a computation time of 113.0123 s. The evolutional outcomes of the proposed model show significantly better than the state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

In order to increase the flexibility of the algorithm as well as improve the performance of the filter, a set of optimal non-causal filters and filtering matrices are developed based on the existing single-channel noise reduction algorithms in Short-Time Fourier Transform (STFT) domain which takes the interframe correlation into account. Specifically, this paper designs a set of single-channel non-causal filters by introducing the non-causal concept to achieve the purpose of noise reduction by exploiting both the past and future information. Based on that, several optimal single-channel filtering matrices are also derived to make the computational burden of the algorithms lower. Combining the aforementioned ideas, a set of single-channel non-causal filtering matrices are deduced. Furthermore, the proposed algorithms are generalized to the multi-channel case to achieve a better performance. Finally, the algorithms developed previously are investigated through simulations. The results show that the proposed algorithms can effectively improve the signal-to-noise ratio of the noisy signal (input signal) and enhance the speech quality compared with the traditional algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a multi-gate junctionless field-effect transistor with a quad gate and multiple channels has been proposed and thoroughly simulated. The proposed device offers a lower Ioff current of 4.34 × 10−19 A, an Ion current of 4.17 × 10−06 A with an Ion/Ioff current ratio of 9.65 × 1012, a threshold voltage of 0.86 V, a transconductance of 5 × 10−05 S, a drain-induced barrier lowering (DIBL) of 22 mV/V, and a subthreshold slope (SS) of 61.51 mV/dec. The visual contour plots of the device are also included in this study to understand the working mechanism. The larger tunneling width between the channel drain and barrier height between the source–channel interface exhibits a lower leakage current and better performance. The performance of the proposed device is also studied when the work function between the semiconductor and gate material is altered. The work function difference improves the performance metrics. Aside from that, the effects of temperature variation on the device's performance characteristics are also being studied. When the temperature varies by 33% the threshold voltage of the proposed device drops by only 7% which shows a minimum variation in the proposed device. The results obtained from the proposed device exhibit the potential of using multi-gate multi-channel junctionless field effect transistor (MCMG) device in low-power logic circuits and memory devices. [ABSTRACT FROM AUTHOR]

This paper demonstrates the effectiveness of a diversification mechanism for building a more robust multi-attention system in generic facial action analysis. While previous multi-attention (e.g., visual attention and self-attention) research on facial expression recognition (FER) and Action Unit (AU) detection have been thoroughly studied to focus on “external attention diversification”, where attention branches localize different facial areas, we delve into the realm of “internal attention diversification” and explore the impact of diverse attention patterns within the same Region of Interest (RoI). Our experiments reveal that variability in attention patterns significantly impacts model performance, indicating that unconstrained multi-attention plagued by redundancy and over-parameterization, leading to sub-optimal results. To tackle this issue, we propose a compact module that guides the model to achieve self-diversified multi-attention. Our method is applied to both CNN-based and Transformer-based models, benchmarked on popular databases such as BP4D and DISFA for AU detection, as well as CK+, MMI, BU-3DFE, and BP4D+ for facial expression recognition. We also evaluate the mechanism on Self-attention and Channel-wise attention designs for improving their adaptive capabilities in multi-modal feature fusion tasks. The multi-modal evaluation is conducted on BP4D, BP4D+, and our newly developed large-scale comprehensive emotion database BP4D++, which contains well-synchronized and aligned sensor modalities, addressing the scarcity of annotations and identities in human affective computing. We plan to release the new database to the research community, fostering further advancements in this field. [ABSTRACT FROM AUTHOR]

In this article, analysis of heating effect for a range of thermal resistance (Rth) and different ambient temperatures on the DC parameters of the proposed Silicon‐Gate All Around Dielectric Window Spaced‐Multi‐channel (Si‐GAA‐DWS‐multi‐channel) MOSFET is carried out. The performance of the device is examined through 3D‐ATLAS TCAD simulator, by performing the numerical and electrothermal analysis. Due to cylindrical dimension, the nanowire FETs undergoes undesirable self/lattice‐heating. The lattice heating of the device is calculated with respect to various device parameters such as channel length, drain‐source voltage, thermal resistance, ambient temperature, on‐current Ion, and off‐current Ioff. The proposed device parameter has been compared with the existing devices, mainly with Silicon‐Nanowire‐Dielectric Pocket Packed MOSFET (Si‐NW‐DPP FET) and Silicon‐Nanowire MOSFET (Si‐NW FET). The Si‐GAA‐DWS‐multi‐channel FET is found to be as the better device than the other two structures, as the leakage current of the device is improved by 1 and 2 decades, drain current increases by 1 and 1 decade, a minimum SS of 63.271 mV/dec and a minimum DIBL of 26.172 mV/V, respectively. The lesser the SS, the faster will be the device; hence the proposed structure can be considered as the finest prospect for VLSI/ULSI electronic chips. [ABSTRACT FROM AUTHOR]

With multi‐channel retail competition, some offline retailers implement price matching services to combat consumers' showrooming behavior. With this service, offline retailers cut their prices to match the online price if it is lower. In this paper, we establish a game model that involves an offline and online retailer in the presence of showrooming to analyze the impact of price matching. Specifically, we capture consumers' uncertainty of product value and heterogeneous awareness of price matching and assume that the online retailer provides a free return policy. The results indicate that price matching alleviates showrooming and relaxes competition. Moreover, we find that the online retailer adopts a randomized pricing strategy if the offline visiting cost is moderate. We further show that the price matching strategy does not necessarily benefit offline retailers and hurt online retailers. Subsequently, we identify the offline retailers' optimal conditions for implementing price matching. [ABSTRACT FROM AUTHOR]

Emotion recognition through the fusion of multi-modal sources, including voice and visual cues, holds paramount importance in comprehending human affective states. This research introduces an inventive paradigm, seamlessly uniting the potential of adaptive normalization fusion (ANF) and alpha Gaussian dropout (AGD) within the architecture of a multi-channel convolutional neural network (MCNN). ANF employs a sequence of pre-processing steps, beginning with batch normalization, followed by min–max scaling using the sigmoid function, and further augmented by alpha Gaussian dropout (AGD)—a fusion of Gaussian dropout and alpha dropout. These techniques are seamlessly integrated into multi-channel convolutional neural network (MCNN) architecture, aimed at improving emotion classification accuracy. This synergy fortifies the network's robust generalization capacities while effectively mitigating overfitting concerns. Rigorous experimentation and comprehensive evaluations across datasets that encompass voice and visual-based emotion recognition underscore the remarkable efficiency of the proposed methodology, culminating in notable strides in accuracy and generalization. The culmination of this research delivers a notable achievement, reflecting an impressive 97% accuracy in face emotion recognition and an exceptional 98% accuracy in speech emotion recognition. [ABSTRACT FROM AUTHOR]

Various applications require multi-channel high-voltage sources for their control, e.g. electrostatic adhesion, electrophoresis and artificial muscles such as piezoelectric, hydraulically amplified self-healing electrostatic(HASEL) and dielectric elastomer actuators(DEAs). Further, the ability to simultaneously monitor the state of the actuators either with images, or voltage and current sensing is crucial to characterise their behaviour. In this work, we present the design of a versatile characterisation setup, capable of generating eight HV (15 kV) arbitrary waveforms(rise time of 8 ms and fall time of 80 ms for 60 MΩ load), while synchronously monitoring voltage and current, and record high-speed (120 fps) video. The setup ensures modularity and customisability by consisting of three independent modules: (1) The imaging module includes a Raspberry Pi and a Pi Camera; (2) A 3.3 V analogue interface 16-bit resolution data acquisition module on a PCB that accommodates a microcontroller board, two 8-channel analogue-to-digital converters, and an 8-channel digital-to-analogue converter; (3) Up to 8 DC-to-HVDC converter boards powered by 12 V DC, with 3.3 V analogue interface.

Interferometric Synthetic Aperture Radar (InSAR) enables the efficient retrieval of surface elevation and has extensive applications in terrain mapping. Dual/multi-channel InSAR techniques utilize the differences in the elevation ambiguity of different InSAR channels (i.e., baselines and frequencies) to perform Phase Unwrapping (PU). This enables the effective application of InSAR in regions with abrupt terrain changes. In response to the growing demand for efficient and precise PU, this study leverages deep learning and proposes a dual/multi-channel joint PU network, i.e., Multi-Channel-Joint-UNet (MCJ-UNet), which effectively combines multi-channel phase characteristics and their mutual constraint relationships. The proposed network is constructed based on the dual-channel (i.e., dual-frequency and dual-baseline) InSAR observation configuration. It can also be extended to multi-channel InSAR. The core concept of the proposed method can be summarized as follows. First, the method transforms the elevation ambiguity estimation problem in PU into semantic segmentation, and the UNet network is employed to accomplish the segmentation processing. Second, the squeeze-and-excitation module is introduced to dynamically adjust the information weights, enhancing the network’s perception of the required information across different channels. Third, a phase residual optimization loss function is employed in the context of multi-channel joint constraints to achieve network tuning. In addition, to mitigate the effect of edge detail errors in semantic segmentation results on PU performance, a self-correcting approach for PU errors based on multi-channel joint constraints is proposed. The proposed MCJ-UNet is verified by computer simulations based on simulated and real terrains and experiments based on real TerraSAR-X data.

Agriculture is essential for human survival facing critical issues such as limited access to modern machinery, a lack of comprehensive data, and difficulties in implementing insurance programs. To address these challenges, this study explores the potential of blockchain technology, finding that the Hyperledger Fabric can satisfy certain criteria vital for rectifying the existing issues due to its secure and scalable features. Utilizing the channel mechanism of Hyperledger Fabric, we developed a multi-channel network infrastructure to cater to the varying needs in agriculture. Each agricultural service is facilitated through its unique channel, accompanied by an easy-to-navigate webpage, enhancing user experience while ensuring data security. In our experiments, we deployed the system using Docker containers and virtual machines. Performance tests were conducted to evaluate the system’s effectiveness in data sharing, machinery service coordination, and insurance claim processing. The results demonstrated that the system effectively improves data sharing efficiency, streamlines machinery service coordination, and enhances the transparency and reliability of insurance claims processing. These findings highlight the potential of our system to significantly enhance agricultural production efficiency. The flexible and expandable nature of this system offers tremendous potential for future improvements, aiming to fulfill the multifaceted demands of agriculture more effectively, thereby laying a robust foundation for significantly enhancing agricultural production efficiency.

In recent years, Graph Convolutional Networks (GCNs) have emerged as a crucial methodology for handling graph-structured data, exhibiting superior performance in semi-supervised classification tasks. However, most existing GCNs encounter two main issues in real-world scenarios: 1) graph-structured data may be incomplete, for example, containing outlier nodes and noisy edges, which poses a great challenge for GCNs to extract the relation information for classification tasks; 2) the scarcity of labeled data, often limited to a few-shot scenario, hampers the ability of GCNs to learn comprehensive embedding representations. To cope with these issues, we propose a novel framework called pseudo labeling collaborative multi-channel graph convolutional networks (PCM-GCN). First, considering incomplete graph-structured data, we develop two modules: graph generation module and multi-channel fusion module. The graph generation module is designed to extend the raw data to multiple graphs, which avoids being constrained by the effective expression ability of the raw data. Meanwhile, the multi-channel fusion module integrates embeddings from multiple graphs, capturing the complementarity among multiple channels. Second, to address the problem of sparse labels, we develop a confidence-based pseudo labeling module, appending confident data with pseudo label to the labeled set to enlarge the training set. PCM-GCN leverages pseudo labeling to enhance multi-channel embedding fusion, resulting in rich and comprehensive node embedding representation. Extensive experiments on five benchmark datasets have shown that PCM-GCN surpasses other state-of-the-art methods in semi-supervised node classification tasks.