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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]

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.)

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]

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]

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.

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]

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]

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.

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.)

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.

This paper minimizes fake news, which has been a hot topic recently, using blockchain and artificial intelligence technology, and verifies it with blockchain. Also, using Artificial Intelligence technology, we want to create an algorithm that predicts how fake news will spread in the future. You can see various attempts at a news media platform based on Blockchain technology. However, the Blockchain news media platform is still not getting the market response we expected. It is questionable whether the reason is simply because it is a new technology, so it takes a long time to gain trust from consumers, whether consumers are not yet expecting an innovative news media platform, or whether the explosive growth of the Blockchain news media platform is difficult for other reasons. Research to answer this or direct research between Blockchain and media platforms is still lacking. In addition, the method of verifying fake news using artificial intelligence was verified, ANN, CBR, and MDA were changed, and the experiment was verified for progress. In addition, the use of 5-fold cross-validation as a comparative method was added as described above to more closely examine the possibility of its usefulness even in general situations. Also, through various fields of artificial intelligence and blockchain, verification work was done with blockchain, and fake news prediction was made using artificial intelligence. Various experiments were conducted and performance tests were performed, while the performance of about 5,000 TTPS was recorded through the third experiment. In the future, we think it is necessary to combine Artificial Intelligence and blockchain technology.

In this article, we introduce a causal low-latency low-complexity approach for binaural multichannel blind speaker separation in noisy reverberant conditions. The model, referred to as Group Communication Binaural Filter and Sum Network (GCBFSnet) predicts complex filters for filter-and-sum beamforming in the time-frequency domain. We apply Group Communication (GC), i.e., latent model variables are split into groups and processed with a shared sequence model with the aim of reducing the complexity of a simple model only containing one convolutional and one recurrent module. With GC we are able to reduce the size of the model by up to 83% and the complexity up to 73% compared to the model without GC, while mostly retaining performance. Even for the smallest model configuration, GCBFSnet matches the performance of a low-complexity TasNet baseline in most metrics despite the larger size and higher number of required operations of the baseline.

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%.

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.

Metasurfaces of quarter-wave plate (QWP) meta-atoms have exhibited high flexibility and versatile functionalities in the manipulation of light fields. However, the generation of multi-channel vortex beams with the QWP meta-atom metasurfaces presents a significant challenge. In this study, we propose dielectric metasurfaces composed of QWP meta-atoms to manipulate multi-channel vortex beams. QWP meta-atoms, systematically arranged in concentric circular rings, are designed to introduce the modulations via the propagation phase and geometric phase, leading to the generation of co- and cross-polarized vortex beams in distinct channels. Theoretical investigations and simulations are employed to analyze the modulation process, confirming the capability of QWP meta-atom metasurfaces for generating the multi-channel vortex beams. This study presents prospective advancements for the compact, integrated, and multifunctional nanophotonic platforms, which have potential applications in classical physics and quantum domains. [ABSTRACT FROM AUTHOR]

Multi-channel and multi-parameter near-infrared spectroscopy (NIRS) has gradually become a new research direction and hot spot due to its ability to provide real-time, continuous, comprehensive indicators of multiple parameters. However, multi-channel and multi-parameter detection may lead to crosstalk between signals. There is still a lack of benchmarks for the evaluation of the reliability, sensitivity, stability and response consistency of the NIRS instruments. In this study, a set of test methods (a human blood model test, ink drop test, multi-channel crosstalk test and multi-parameter crosstalk test) for analyzing crosstalk and verifying the reliability of NIRS was conducted to test experimental verification on a multi-channel (8-channel), multi-parameter (4-parameter) NIRS instrument independently developed by our team. Results show that these tests can be used to analyze the signal crosstalk and verify the reliability, sensitivity, stability and response consistency of the NIRS instrument. This study contributes to the establishment of benchmarks for the NIRS instrument crosstalk and reliability testing. These novel tests have the potential to become the benchmark for NIRS instrument reliability testing. [ABSTRACT FROM AUTHOR]

In order to capture the large-scale interferometric signal generated by the space-borne interferometric infrared Fourier spectrometer (IRIFS) in real time, and overcome the limitations of insufficient sampling rate, transmission rate, and significant signal noise in current equipment, a multi-channel high-speed acquisition system for large-scale interferometric signals is designed. A high-performance analog-to-digital converter (ADC) oversampling scheme is designed, which can realize up to 8 synchronous acquisition channels and has a maximum sampling rate of 125 Msps/Ch to ensure the acquisition of interferometric signals. The scheme of jesd204b inter-board transmission and optical fiber terminal transmission is designed. The inter-board transmission rate is 12.5 Gbps, and the terminal transmission rate is 10 GB/s to ensure high-speed data transmission. A hardware filter is designed to realize spatial noise processing of interference signals and ensure the accuracy of acquisition results. The dynamic performance of the data acquisition (DAQ) card is analyzed using discrete Fourier transform in the frequency domain. The spurious free dynamic range (SFDR) is 84 dB, the signal-to-noise ratio (SNR) is 72.7 dB, and the cross-talk is −81.6 dB, which verifies the dynamic stability of the DAQ card. Finally, the infrared radiation in real space is measured. The average Δ N E S R of long wave reaches 48 mW ∗ m − 2 ∗ sr − 1 , and the average Δ N E S R of medium wave reaches 12.3 mW ∗ m − 2 ∗ sr − 1 , which verifies the reliability of the system performance. The system is of great significance for large-scale infrared interferometric signal acquisition, and has strong practical application value in multi-channel synchronization, real-time high-speed acquisition, and high-speed data transmission. [ABSTRACT FROM AUTHOR]

With the widespread adoption of virtual reality and 360-degree video, there is a pressing need for objective metrics to assess quality in this immersive panoramic format reliably. However, existing image quality assessment models developed for traditional fixed-viewpoint content do not fully consider the specific perceptual issues involved in 360-degree viewing. This paper proposes a 360-degree image full-reference quality assessment (FR-IQA) methodology based on a multi-channel architecture. The proposed 360-degree FR-IQA method further optimizes and identifies the distorted image quality using two easily obtained useful saliency and depth-aware image features. The convolutional neural network (CNN) is designed for training. Furthermore, the proposed method accounts for predicting user viewing behaviors within 360-degree images, which will further benefit the multi-channel CNN architecture and enable the weighted average pooling of the predicted FR-IQA scores. The performance is evaluated on publicly available databases to demonstrate the advantages brought by the proposed multi-channel model in performance evaluation and cross-database evaluation experiments, where it outperforms other state-of-the-art ones. Moreover, an ablation study exhibits good generalization ability and robustness. [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.

A very sensitive multi-channel refractive index-based biosensor with a detection range of 1.26 to 1.36 has been proposed. Titanium oxide (TiO2) was utilized as the dielectric, and gold as the plasmonic material in the sensor. The suggested sensor achieved a maximum sensitivity value of 48,000 nm/RIU and 7220 RIU-1 using the wavelength and amplitude interrogation techniques with a pitch layer variation of 5.55–5.95 μm and a metal layer variation of 30–50 nm. The suggested sensor can detect many compounds with amazing sensitivity, such as glucose, sucrose, ethanol, methanol, human intestinal mucosa, sevoflurane, and bio-chemicals. Additionally, a deep learning model that forecasts six optical properties has been proposed. The hyperparameters of the deep neural network model are tuned extensively to maximize accuracy. Our model had a Mean Squared Error of 0.006 and was significantly faster than conventional techniques. The results show that deep learning can predict optical features in photonic sensors, which can identify specific analytes or biomolecules in a sample without costly and time-consuming simulations. Photonic sensor researchers and engineers may utilize the suggested model on vast datasets and adjust it to new settings. This study may open the door to the use of deep learning-based optimization techniques to enhance biosensors.

Abstract Background Stochastic optical reconstruction microscopy (STORM), a super-resolution microscopy technique based on single-molecule localizations, has become popular to characterize sub-diffraction limit targets. However, due to lengthy image acquisition, STORM recordings are prone to sample drift. Existing cross-correlation or fiducial marker-based algorithms allow correcting the drift within each channel, but misalignment between channels remains due to interchannel drift accumulating during sequential channel acquisition. This is a major drawback in multi-color STORM, a technique of utmost importance for the characterization of various biological interactions. Results We developed RegiSTORM, a software for reducing channel misalignment by accurately registering STORM channels utilizing fiducial markers in the sample. RegiSTORM identifies fiducials from the STORM localization data based on their non-blinking nature and uses them as landmarks for channel registration. We first demonstrated accurate registration on recordings of fiducials only, as evidenced by significantly reduced target registration error with all the tested channel combinations. Next, we validated the performance in a more practically relevant setup on cells multi-stained for tubulin. Finally, we showed that RegiSTORM successfully registers two-color STORM recordings of cargo-loaded lipid nanoparticles without fiducials, demonstrating the broader applicability of this software. Conclusions The developed RegiSTORM software was demonstrated to be able to accurately register multiple STORM channels and is freely available as open-source (MIT license) at https://github.com/oystein676/RegiSTORM.git and https://doi.org/10.5281/zenodo.5509861 (archived), and runs as a standalone executable (Windows) or via Python (Mac OS, Linux).

The automatic artery/vein (A/V) classification in retinal fundus images plays a significant role in detecting vascular abnormalities and could speed up the diagnosis of various systemic diseases. Deep-learning methods have been extensively employed in this task. However, due to the lack of annotated data and the serious data imbalance, the performance of the existing methods is constricted. To address these limitations, we propose a novel multi-channel multi-scale fusion network (MMF-Net) that employs the enhancement of vessel structural information to constrain the A/V classification. First, the newly designed multi-channel (MM) module could extract the vessel structure from the original fundus image by the frequency filters, increasing the proportion of blood vessel pixels and reducing the influence caused by the background pixels. Second, the MMF-Net introduces a multi-scale transformation (MT) module, which could efficiently extract the information from the multi-channel feature representations. Third, the MMF-Net utilizes a multi-feature fusion (MF) module to improve the robustness of A/V classification by splitting and reorganizing the pixel feature from different scales. We validate our results on several public benchmark datasets. The experimental results show that the proposed method could achieve the best result compared with the existing state-of-the-art methods, which demonstrate the superior performance of the MMF-Net. The highly optimized Python implementations of our method is released at: https://github.com/chenchouyu/MMF_Net. [ABSTRACT FROM AUTHOR]

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

Simple Summary: Tree trunk damage can be influenced by multiple factors, among which trunk-boring insect infestation plays a significant role. Early external detection of such damage poses challenges. Manual observation remains the prevailing method for controlling tree trunk pests, but it demands a substantial workforce and yields limited outcomes. To address these limitations, acoustic technology has gained popularity, using vibration probes embedded in tree trunks to capture vibrations produced by insect larvae feeding, thereby facilitating the detection of pest larvae. However, traditional methods primarily rely on single-channel vibration signal acquisition, often assuming the proximity of the vibration probe to the sound source. Nevertheless, when the probe's position exceeds a certain distance from the original, capturing effective drilling vibration signals becomes difficult due to noise interference and other factors. To overcome this constraint, we have developed a novel multi-channel drilling vibration signal acquisition board that enables the distribution of multiple vibration probes at different locations on the tree trunk, allowing simultaneous collection of vibration signals from diverse probes. Additionally, we have devised a multi-channel signal separation model based on attention mechanisms, which effectively denoises and recovers clean target signals from noisy recordings. Experimental results demonstrate that this approach significantly enhances the detection efficiency of trunk-boring insects. The larvae of certain wood-boring beetles typically inhabit the interior of trees and feed on the wood, leaving almost no external traces during the early stages of infestation. Acoustic techniques are commonly employed to detect the vibrations produced by these larvae while they feed on wood, significantly increasing detection efficiency compared to traditional methods. However, this method's accuracy is greatly affected by environmental noise interference. To address the impact of environmental noise, this paper introduces a signal separation system based on a multi-channel attention mechanism. The system utilizes multiple sensors to receive wood-boring vibration signals and employs the attention mechanism to adjust the weights of relevant channels. By utilizing beamforming techniques, the system successfully removes noise from the wood-boring vibration signals and separates the clean wood-boring vibration signals from the noisy ones. The data used in this study were collected from both field and laboratory environments, ensuring the authenticity of the dataset. Experimental results demonstrate that this system can efficiently separate the wood-boring vibration signals from the mixed noisy signals. [ABSTRACT FROM AUTHOR]

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As an emerging technology, wireless power transfer (WPT) has various advantages, which can be enhanced by extending the WPT system into a multi-channel one. With the rapid development of electronic equipment, the demand for multiple independent voltage-source outputs is increasing. In many consumer electronics and industrial applications, voltage sources with multiple independent outputs are required. This paper proposes a wireless charging system with multiple constant-voltage (CV) outputs. The LCC-S (inductor capacitor capacitor-series) topology is adopted to achieve CV outputs. The output voltage can be regulated by the corresponding mutual inductance between the transmitting and receiving coils. The receiving coils are designed with overlapped areas to realize mutual decoupling, thus, each output voltage can be independent of each other. The compensating inductor is designed as a quadruple coil so that it can be integrated and decoupled with the transmitting coil at the same time, meanwhile, it can also be decoupled with the receiving coils. In this way, a multi-channel WPT system with independent CV outputs can be achieved. An experimental prototype with three CV output channels is implemented to validate the proposal. The experimental results have revealed that the three CV outputs can be stable with the load resistance variation in each channel.

Aiming at the problem that the traditional SATA controller has a single interface and cannot give full play to the performance of the SSD, a multi-channel high-speed storage circuit based on PCIE to SATA is designed. This design makes full use of the high-bandwidth and low-latency characteristics of the PCIE bus and follows the AHCI protocol, which can greatly reduce the useless seek times and data search time of the hard disk, and improve the read performance. At the same time, the design can support 4 SATA channels and has good scalability. This design combines the characteristics of PCIE and SATA protocols, introduces the system architecture of PCIE to SATA high-speed storage circuit, and elaborates the data stream transmission process based on AHCI protocol. Finally, the circuit is tested based on the FPGA prototype verification. The single-disk read and write rates of the circuit are 562MB/s and 527MB/s, respectively. Compared with the traditional SATA controller, the read and write performance is greatly improved. The test results show that the designed PCIE to SATA high-speed storage circuit has excellent read and write performance, and has good stability and scalability.

Purpose: This paper aims to investigate the impact of the channel numbers on the performance of B1+ mapping, by using the Bloch-Siegert shift (BSS) method. B1+ mapping plays a crucial role in various brain imaging protocols. Theory and Methods: We simulated the radiofrequency field of the human head model in six groups of multi-channel receive coil with a range of different channel numbers. MR signals were synthesized according to the standard BSS sequence, with quantified Gaussian added. Next, we combined the signals of each channel to reconstruct the B1+ map by weighted averaging and maximum likelihood estimation strategies and evaluate the bias by relative standard deviation of each coil. Results: The simulation results revealed that the accuracy of B1+ maps improved with the increasing of channel numbers, meanwhile the per channel efficiency of B1+maps accuracy gradually decrease. Both trends slowed down when the channel numbers reached 12 or above. Conclusion: Our finding suggests that increasing the channel numbers can improve the accuracy of B1+map. However, a diminishing efficiency of per channel accuracy improvement was overserved, indicating that the relationship between quality of B1+ map and the channel numbers is nonlinear. Based on these findings, our study provides a reference for determining channel numbers to achieve a balance of coil selection and manufacturing cost. It also provides a theoretical basis for evaluating other B1+ mapping techniques.

A very sensitive multi-channel refractive index-based biosensor with a detection range of 1.26 to 1.36 has been proposed. Titanium oxide (TiO 2) was utilized as the dielectric, and gold as the plasmonic material in the sensor. The suggested sensor achieved a maximum sensitivity value of 48,000 n m / R I U and 7220 RIU - 1 using the wavelength and amplitude interrogation techniques with a pitch layer variation of 5.55–5.95 μm and a metal layer variation of 30–50 nm. The suggested sensor can detect many compounds with amazing sensitivity, such as glucose, sucrose, ethanol, methanol, human intestinal mucosa, sevoflurane, and bio-chemicals. Additionally, a deep learning model that forecasts six optical properties has been proposed. The hyperparameters of the deep neural network model are tuned extensively to maximize accuracy. Our model had a Mean Squared Error of 0.006 and was significantly faster than conventional techniques. The results show that deep learning can predict optical features in photonic sensors, which can identify specific analytes or biomolecules in a sample without costly and time-consuming simulations. Photonic sensor researchers and engineers may utilize the suggested model on vast datasets and adjust it to new settings. This study may open the door to the use of deep learning-based optimization techniques to enhance biosensors. [ABSTRACT FROM AUTHOR]

Detecting and classifying defects on unpatterned wafers is a key part of wafer front-end inspection. Defect inspection schemes vary depending on the type and location of the defects. In this paper, the structure of the prototype is designed to meet the requirements of wafer surface and edge defect inspection. This prototype has four inspection channels: scattering, reflection, phase, and contour, with two working conditions: surface and edge inspection. The key structure of the prototype was simulated using Ansys. The simulation results show that the maximum deformation of the optical detection subsystem is 19.5 μm and the fundamental frequency of the prototype is 96.9 Hz; thus, these results meet the requirements of optical performance stability and structural design. The experimental results show that the prototype meets the requirements of the inspection sensitivity better than 200 nm equivalent PSL spherical defects. [ABSTRACT FROM AUTHOR]

The efficacy of underwater laser detection is considerably impacted by the intense attenuation of light resulting from the scattering and absorption effects of water. In this study, we present the simulation and design of the underwater Lidar system that integrates the paraxial multi-channel detection strategy to enhance the dynamic range in subsea environments. To evaluate the performance of the system with multiple detection channels, we introduce a multi-channel underwater Lidar simulation (MULS) method based on the radiative transfer Lidar equations. Experimental validations were conducted under varied water conditions to assess the performance of the prototype and validate the simulation results. The measured range accuracy of each channel in the prototype is better than 0.1085 m, and the simulated and measured waveforms exhibit strong correlations, verifying the reliability and validity of the simulation method. The effects of transceiver configuration and the maximum detectable range of different detection methods were also discussed. Preliminary results indicate that the paraxial multi-channel design effectively suppresses near-field backscattering and substantially enhances the maximum detectable range. The findings presented in this study may provide valuable insights for the design and optimization of future underwater laser detection systems. [ABSTRACT FROM AUTHOR]

Integrated frequency routers, which can guide light with different frequencies to different output ports, are an important kind of nanophotonic device. However, frequency routers with both a compact size and multiple channels are difficult to realize, which limits the application of these frequency routers in nanophotonics. Here, a kind of bandgap optimization algorithm, which consists of the finite element method and topology optimization, is proposed to design a multi-channel frequency router. Channels supporting photonic edge states with different frequencies are built through the synthetic dimension of translational deformation. Due to the help of the developed optimization algorithms, the number of channels and output ports can be increased up to nine while maintaining ultracompact device size. The device operates within a working band of 0.585–0.665 c/a, corresponding to 1.504–1.709 μm when the lattice constant is set as 1 μm, covering the telecom wavelength of 1.55 μm. The average crosstalk is about −11.49 dB. The average extinction ratio is around 16.18 dB. Because the bus of the device can be regarded as a part of a topological rainbow, the results show that the structure is robust to fabrication errors. This method is general, which can be used for different materials and different frequency ranges. The all-dielectric planar configuration of our router is compact, robust, and easy to integrate, providing a new method for on-chip multi-channel broadband information processing. [ABSTRACT FROM AUTHOR]

Considering carbon emission cost and consumer loyalty, this paper establishes a three-level multi-channel supply chain composed of the leading manufacturer, the retailer, and the consumer and builds a multi-channel supply chain with centralized decision-making and decentralized decision-making modes, and the optimal decision-making under both decision-making modes is solved. The study found that the carbon emission reduction level of multi-channel centralized decision-making is better than that of decentralized decision-making under the same carbon emission cost, and centralized decision-making can improve the carbon emission reduction level. Manufacturers open a direct channel to help reduce carbon emissions and need to consider the cost of emission reduction in their channel decisions. Consumer loyalty also directly impacts channel decisions. Only when carbon emission costs and consumer loyalty are within a reasonable range can new direct sales channels be opened to bring in new profits. Simultaneously, the total profit of the supply chain is greater than that of decentralized decision-making, but the double marginal utility of both parties decreases with the increase in carbon emission costs. [ABSTRACT FROM AUTHOR]

A 28 GHz fully differential eight-channel beamforming IC (BFIC) with multimode operations is implemented in 65 nm CMOS technology for use in phased array transceivers. The BFIC has an adjustable gain and phase control on each channel to achieve fine beam steering and beam pattern. The BFIC has eight differential beamforming channels each consisting of the two-stage bi-directional amplifier with a precise gain control circuit, a six-bit phase shifter, a three-bit digital step attenuator, and a tuning bit for amplitude and phase variation compensation. The Tx and Rx mode overall gains of the differential eight-channel BFIC are around 11 dB and 9 dB, respectively, at 27.0–29.5 GHz. The return losses of the Tx mode and Rx mode are >10 dB at 27.0–29.5 GHz. The maximum phase of 354° with a phase resolution of 5.6° and the maximum attenuation of 31 dB, including the gain control bits with an attenuation resolution of 1 dB, is achieved at 27.0–29.5 GHz. The root mean square (RMS) phase and amplitude errors are <3.2° and <0.6 dB at 27.0–29.5 GHz, respectively. The chip size is 3.0 × 3.5 mm2, including pads, and Tx mode current consumption is 580 mA at 2.5 V supply voltage. [ABSTRACT FROM AUTHOR]

Background: Stochastic optical reconstruction microscopy (STORM), a super-resolution microscopy technique based on single-molecule localizations, has become popular to characterize sub-diffraction limit targets. However, due to lengthy image acquisition, STORM recordings are prone to sample drift. Existing cross-correlation or fiducial marker-based algorithms allow correcting the drift within each channel, but misalignment between channels remains due to interchannel drift accumulating during sequential channel acquisition. This is a major drawback in multi-color STORM, a technique of utmost importance for the characterization of various biological interactions. Results: We developed RegiSTORM, a software for reducing channel misalignment by accurately registering STORM channels utilizing fiducial markers in the sample. RegiSTORM identifies fiducials from the STORM localization data based on their non-blinking nature and uses them as landmarks for channel registration. We first demonstrated accurate registration on recordings of fiducials only, as evidenced by significantly reduced target registration error with all the tested channel combinations. Next, we validated the performance in a more practically relevant setup on cells multi-stained for tubulin. Finally, we showed that RegiSTORM successfully registers two-color STORM recordings of cargo-loaded lipid nanoparticles without fiducials, demonstrating the broader applicability of this software. Conclusions: The developed RegiSTORM software was demonstrated to be able to accurately register multiple STORM channels and is freely available as open-source (MIT license) at and (archived), and runs as a standalone executable (Windows) or via Python (Mac OS, Linux). [ABSTRACT FROM AUTHOR]

To address the inconsistent output voltage of each branch in the traditional multi-channel wide dynamic microwave rectifier circuit, a four-channel wide dynamic microwave rectifier circuit with even power distribution is hereby designed, which has a double-barreled voltage doubling rectifier circuit that shares a completely same structure adopted for each branch. As a result, the microwave rectification efficiency of the input power is more than 50% within the input power range of 12mW- 274mW, broadened by 274% in comparison to the power dynamic range of a single channel. Besides, the corresponding input power of the optimal rectification efficiency increases from 16dBm to 21dBm, effectively improving the power level of the rectifier circuit. Additionally, the output voltage of each branch is highly consistent, which is conducive to the direct DC power synthesis at the only one load end and is more suitable for battery charging. Such a circuit accesses to the battery through the voltage-stabilized circuit, and is combined with the 16-element antenna array, it is a practical microwave energy collection device. Finally, an intact microwave wireless charging experiment system is carried out using such a device to achieve 3-meter microwave energy collection and storage.

The demand for wireless communication capacity continues to increase with the extensive usage of smartphones, tablets, and devices that are related to the Internet of Things (IoT). However, devices and base stations are diversified, and base stations of various sizes are mixed. In existing cellular networks, the transmission powers of the base stations are different. If the downlink received power from the base station that belongs to the device is maximum, the uplink received power from the device at the base station is not always the maximum. This study maximizes the power that is received from the device through downlink-uplink decoupling (DUDe). DUDe can improve the spectral efficiency by selecting the downlink base station and the uplink base station independently in a network with base stations with different transmission powers. This study focuses on two technologies, DUDe and the dynamic channel assignment (DCA). It proposes an association algorithm to solve the dynamic combinational optimization problem for uplink and downlink cellular networks separately using DUDe. When a user device arrives, it first connects to the base station that has the maximum capacity at that time. Subsequently, by using the base station assignment at that time as an individual, the proposed method performs a more optimal base station assignment with DCA by using a genetic algorithm. The computer simulations demonstrate that the proposed method can achieve up to a 140 % higher spectral efficiency than the existing DUDe in the fixed channel assignment (FCA).

Sensor nodes in linear sensor network (LSN) are deployed linearly due to the linear topology of the monitoring area.Most of the state-of-the-art medium access control (MAC) protocols designed for LSN adopted the duty-cycling and pipelined-forwarding (DCPF) schemes.They can reduce data propagation delay and network energy consumption but may still cause issues such as transmission competition, interference, and energy hole.To address these issues, an improved DCPF MAC protocol was proposed based on multiple channels and redundant-node deployment for LSN.The extensive simulations based on OPNET demonstrate that, compared with existing protocols, the proposed protocol shows a better performance in terms of energy efficiency, packet delivery ratio, throughput, and packet transmission latency.

Total variation (TV) minimum algorithm is an image reconstruction algorithm based on compressed sensing theory, which can realize the reconstruction of images with high accuracy from sparse projection or noisy projection data and has been widely used in computed tomography (CT), magnetic resonance imaging (MRI) and electronic paramagnetic resonance imaging (EPRI). Energy spectrum CT, T1 or T2 weighted MRI and EPRI both belong to multi-channel imaging. The channel-by-channel TV algorithm can achieve high-precision image reconstruction, but it ignores the similarity among the images of each channel while Nuclear TV algorithm is a TV algorithm that considers the image similarity among channels, and can realize high-precision image reconstruction. For multi-channel image reconstruction, taking CT reconstruction as an example, this paper proposes a nuclear TV multi-channel image reconstruction algorithm based on the framework of Chambolle-pock algorithm. Through the reconstruction experiments of simulated phantom and real CT image phantom, the accuracy of the algorithm is verified, the convergence of the algorithm is analyzed, the influence of algorithm parameters on the convergence rate is explored, and the sparse reconstruction ability and noisy projection reconstruction ability of the algorithm are evaluated. The experimental results show that the proposed algorithm can achieve higher reconstruction accuracy than the channel-by-channel TV algorithm. Nuclear TV algorithm is a high-precision multi-channel image reconstruction algorithm, which can be applied to multi-channel reconstruction of various imaging modes.

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