Multi-channel Operation Research Articles

Fusion innovation: Multi-scale dilated collaborative model of ConvNeXt and MSDA for fault diagnosis

Bearings and gears are critical components in modern industry, and cross-domain diagnosis of these elements is of great significance. However, in practical applications, challenges such as insufficient training data and variability between equipment arise. To address this issue, this study proposes an innovative neural network structure, ConvNeXt, and a Multi-Scale Dilated Attention (MSDA) mechanism to improve the accuracy problem caused by inadequate feature extraction. ConvNeXt improves upon traditional convolutional neural networks by introducing a multi-scale attention mechanism to enhance the model's performance and expressiveness. Through parallel multi-channel convolution operations, ConvNeXt can capture dependencies between different channels and reduce the number of parameters. Meanwhile, the MSDA mechanism allows signals to interact and exchange information at different scales, effectively extracting complex features in one-dimensional signals. Experimental results demonstrate a significant performance improvement in one-dimensional signal processing using ConvNeXt and MSDA, better capturing relationships between global and local features in one-dimensional signals and enhancing model accuracy. The joint application of ConvNeXt and MSDA brings new solutions to one-dimensional signal processing, offering potential opportunities for effective monitoring of critical components in rotating machinery. Experimental results show that this method achieves high diagnostic accuracy in various transfer tasks, with an average accuracy of 94.28%, providing reliable support for bearing fault diagnosis.

Consumer Social Connectedness and Persuasiveness of Collaborative-Filtering Recommender Systems: Evidence From an Online-to-Offline Recommendation App

Consumers often rely on their social connections or social technologies, such as (automated) system-generated recommender systems, to navigate the proliferation of diverse products and services offered in online and offline markets and cope with the corresponding choice overload. In this study, we investigate the relationship between the consumers’ social connectedness and the economic impact of recommender systems. Specifically, we examine whether the social connectedness levels of consumers moderate the effectiveness of online recommendations toward increasing product demand levels. We study this novel research question using a combination of datasets and a demand-estimation model. Interestingly, the empirical results show a positive moderating effect of social connectedness on the demand effect of online-to-offline recommendations. Further delving into the findings, we also provide empirical evidence that social identification might explain why denser social connectedness with local users accentuates the effects of collaborative-filtering online-to-offline recommendations. Our study enhances the understanding of community factors affecting the efficacy of social technologies in multichannel operations while also extending the social identity theory in operations in the digital realm. The results also have intriguing operational implications for operations managers and practitioners, while suggesting several interesting avenues for future research on social technologies and operations management.

Mode-pairing quantum key distribution based on wavelength division multiplexing in multi-user networks

Mode-pairing quantum key distribution (MP-QKD), a protocol that combines high performance and flexibility, not only eliminates the need for global phase locking but also beats the rate-transmittance bound. Such remarkable characteristics are poised to further advance the practical application of quantum communication networks. In this paper, MP-QKD is extended to the scenario of multi-user communication networks. MP-QKD enables concurrent operation across multiple channels by integrating wavelength division multiplexing (WDM) technology, facilitating secure communication among multiple users. The performance of MP-QKD in multi-channel concurrent operation is analyzed through simulating various experimental conditions. The asymmetric MP-QKD case is also considered and pulse intensity optimization improves performance for asymmetric network channels. These results delineate the performance of MP-QKD with WDM technology, highlighting its significant potential for application in quantum communication networks.

Multi-Channel Operation for the Release 2 of ETSI Cooperative Intelligent Transport Systems

Multi-Channel Operation for the Release 2 of ETSI Cooperative Intelligent Transport Systems

A Multi-Channel Packet Scheduling Approach to Improving Video Delivery Performance in Vehicular Networks

When working with the Wireless Access in Vehicular Environment (WAVE) protocol stack, the multi-channel operation mechanism of the IEEE 1609.4 protocol may impact the overall network performance, especially when using video streaming applications. In general, packets delivered from the application layer during a Control Channel (CCH) time slot have to wait for transmission until the next Service Channel (SCH) time slot arrives. The accumulation of packets at the beginning of the latter time slot may introduce additional delays and higher contention when all the network nodes try, at the same time, to obtain access to the shared channel in order to send the delayed packets as soon as possible. In this work, we have analyzed these performance issues and proposed a new method, which we call SkipCCH, that helps the MAC layer to overcome the high contention produced by the packet transmission bursts at the beginning of every SCH slot. This high contention implies an increase in the number of packet losses, which directly impacts the overall network performance. With our proposal, streaming video in vehicular networks will provide a better quality of reconstructed video at the receiver side under the same network conditions. Furthermore, this method has particularly proven its benefits when working with Quality of Service (QoS) techniques, not only by increasing the received video quality but also because it avoids starvation of the lower-priority traffic.

Analysis of Possible Interference Effects Minimization in IEEE 802.11ax Network by Partial Allocation of Resource Units

Statement of the problem: one of the main tasks in IEEE 802.11 networks planning is adjacent channel interference mitigation. The purpose of the work is to synthesize a model and analyze the possibility of adjacent channel effects minimization by partial allocation of OFDMA resource units allocation. Methods used: mathematical analysis methods and spectrum modeling. Novelty: in contrast to the existing approaches, quantative estimations of data rate gain has been calculated and a corresponding step-by-step method has been proposed. Result: in this paper an analysis of partial allocation of resourve units within IEEE 802.11ax channel has been carried out. A model of the adjacent channel interference has been proposed, where signal to noise ratio is estimated via spectrum calculation of target and interferer signals. The model parameters range is estimated which corresponds to the gain of total multi user data rate gain in comparison to full target channel resource units utilization. Theoretical and practical relevance: method may be used to additional increase of total data rate in multi user channel operation mode in case of IEEE 802.11 networks interference presence.

Bionic-inspired oil price prediction: Auditory multi-feature collaboration network

Predictions of the oil price are critical to support intelligent decision-making for individual investors, governments, and corporations, but a challenging task since there are complex nonlinear and random fluctuations involved in series. Additionally, most existing methods mainly focus on the data-layer fitting, and inherit uninterpretable design flowcharts and unclear layer functions. Based on the fact that living things evolve numerous advanced cognition processes after long-term interactions with environments, it is a promising problem to design bionic deep prediction networks. In this paper, an auditory-inspired multi-feature collaboration network (AMFC-Net) is explored to predict the oil price, and includes the feature extraction, brain memory, and comprehensive prediction blocks. Specifically, through imitating that complex sounds are captured by auditory canals and converted into electrical impulses, the first block adopts four typical activation functions to extract multi-group complementary features for detecting nonlinear changes. Through imitating that electrical impulses are transformed to the left and right hemispheres for handling and analyzing, the second block realizes gating and cooperation mechanisms to learn long short-term dependencies and highlight core information. Through imitating that information is sent to higher cerebral cortices for sensing the environment, the third block maps relationships from features to targets for producing final predictions. In summary, multi-channel convolution operations establish the imitations of both form (clear functional layer) and spirit (layer-by-layer collaboration), which not only ensure the prediction effectiveness but also improve the AMFC-Net interpretability. Three experiments (comparison with 12 machine learning methods), three experiments (comparison with 8 hybrid methods under decomposition plus ensemble framework), and seven discussions under two real-world oil price datasets all indicate that the proposed AMFC-Net is superior and suitable to predict the oil price.

Analyzing Peak-to-Average Power Ratio Characteristics in Multi-Channel Intensity Modulation and Direct Detection Flexible Transceivers Deploying Inverse Fast Fourier Transform/Fast Fourier Transform-Based Processing.

Cascaded inverse fast Fourier transform/fast Fourier transform (IFFT/FFT)-based multi-channel aggregation/de-aggregation offers a promising solution in constructing highly desirable flexible optical transceivers for considerably improving optical networks' elasticity, flexibility, and adaptability. However, the multi-channel aggregation operation unavoidably results in generated signals having high peak-to-average power ratios (PAPRs). To solve this technical challenge, this paper first explores the PAPR characteristics of the corresponding flexible transceivers in optical back-to-back (B2B) and 20 km intensity modulation and direct detection (IMDD) transmission systems, and then numerically investigates the feasibility and effectiveness of utilizing the conventional clipping techniques in reducing their PAPR reductions. The results show that the last IFFT operation size is the primary factor determining the PAPRs rather than the channel count and modulation format. For a given last IFFT operation size, the optimal clipping ratio can be identified, which is independent of channel count. With the identified optimal clipping ratio, when the channel count is >4, every two-channel increase in the channel count can only lead to <1.2 Gb/s decreases in the maximum aggregated signal transmission capacity.

Thresholding-accelerated convolutional neural network for aeroengine turbine blade segmentation

Turbine blades can only be detected nondestructively and precisely using industrial computed tomography (CT). The accuracy of CT image segmentation, which is a key step in CT detection, significantly affects CT detection precision. However, CT images of turbine blades cannot be segmented precisely because they contain inhomogeneity, noise, artifacts, and low-contrast areas. In addition, it is crucial to consider both efficiency and precision in industrial applications. Unfortunately, large-size networks often tend to be inefficient, while lightweight networks lack the necessary accuracy. To address these issues, we propose an innovative approach utilizing an ultra-lightweight neural network that fuses a threshold-based segmentation algorithm with a convolutional neural network for acceleration. The integrated thresholding algorithm was developed through the convolution of the Phansakar method. The convolutional neural network was designed with a simplified multichannel convolution operation to minimize computational cost. Notably, the proposed neural network utilizes only 0.11 M learnable parameters, which is significantly less than the number utilized by most classical deep neural networks. Experimental results for CFM56-7BE and Pratt & Whitney F100 aeroengine hollow turbine blade CT images demonstrate that compared to state-of-the-art algorithms, our proposed network can achieve 0.44 % mean intersection over union and 0.23 % mean Dive similarity coefficient improvements on testing datasets with significantly reduced computational cost. Therefore, the proposed method has high practical application significance and can achieve real-time detection.

Modeling and Evaluation of a Dynamic Channel Selection Framework for Multi-Channel Operation in ITS-G5

Many years ago, it seemed inconceivable that our cars could drive autonomously or communicate with each other to form a self-organizing convoy or platoon. By 2023, however, technological advances have taken us to the point where most of these goals will be achieved. In the time of what was initially known as Day 1, single-channel Intelligent Transport System (ITS) devices fully met the requirements for safe communication. The trends show that with the rapid development and the emergence of new, more robust Vehicle-to-Everything (V2X) applications, which require higher bandwidth (collectively called Day 2), the current single-channel medium access method in the available ITS bands will no longer achieve the desired capacities. The main reason is that Day 2 and beyond V2X information dissemination protocols introduce increasing packet sizes and sending frequencies. To complete a resource-friendly and more efficient operation with Day 2 or other advanced V2X services, ITS standards present the Multi-Channel Operation (MCO) constellation as a potential solution. In the case of MCO, we use two or more channels simultaneously, thus preventing the radio medium from saturating its capacity. However, there are still several pending questions about MCO applicability, practical usage, configuration, and deployment, to name a few. The primary purpose of this article is to present a dynamic channel selection framework design and implementation capable of modeling and simulating advanced multi-channel communication use cases. We used this framework to investigate Channel Busy Ratio (CBR) based dynamic channel switching within the Artery/OMNeT++ simulation environment.

Multichannel Biolayer Interferometry to Probe the Binding of Microbial Extracellular Polymeric Substances to Sulfamethoxazole

Microbial extracellular polymeric substances (EPS) can bind to sulfonamides, influencing the removal of antibiotics in biological wastewater treatment systems. However, information about the binding kinetics and affinities of EPS for sulfonamides remains unavailable due to the lack of an effective approach. In this study, biolayer interferometry (BLI), a multichannel sensing technique, is first established to probe the binding of the antibiotic sulfamethoxazole (SMX) to microbial EPS. The influence of environmental conditions, such as pH, ionic strength, and temperature, on this binding interaction is investigated. The results indicate that multichannel BLI can rapidly and accurately determine the association/dissociation rate constants and binding affinities of the binding interactions between EPS and SMX. The high reproducibility of the sensorgrams demonstrates the reliability of the BLI approach. Via comparison of the obtained parameters, the binding interactions between EPS and SMX are significantly improved under acidic conditions compared to those under neutral or alkaline conditions. A higher ionic strength and a lower temperature yield stronger binding between EPS and SMX. BLI analysis allows multichannel operations concurrently, substantially reducing the testing time. The BLI approach developed in this study provides a powerful tool for probing the binding interactions between complex environmental samples.

Online or offline? Spillover effect of customer-to-customer interaction in a multichannel background

PurposeEnterprises' multichannel operations provide various avenues for customer interaction; however, existing literature investigating customer-to-customer interaction (CCI) mainly focuses on a single channel. The purpose of this paper is to investigate the spillover effect of CCI and potential underlying mediating mechanisms in different information channels.Design/methodology/approachThree between-subjects experiments with 946 participants were employed to empirically validate the proposed hypotheses in the context of an experiential product and a material product.FindingsResults suggest the clear spillover effect of CCI, indicating that positive CCI improves focal customers' satisfaction and purchase intention, whereas negative CCI reduces focal customers' satisfaction and purchase intention. Moreover, CCI's spillover effect varies based on the CCI channel. Offline CCI has a stronger positive spillover effect than online CCI. Contrarily, online CCI has a stronger negative spillover effect than offline CCI. Customer experience and trust are demonstrated to have mediating roles in this process.Originality/valueThis study is the first to comprehensively understand and compare the CCI spillover effect of the two information channels. The findings add to the existing knowledge of information processing in the psychological mechanisms influencing the belief in addition to providing insights for companies engaged in multichannel operations management across different channels.

Scaling waveguide-integrated superconducting nanowire single-photon detector solutions to large numbers of independent optical channels.

Superconducting nanowire single-photon detectors are an enabling technology for modern quantum information science and are gaining attractiveness for the most demanding photon counting tasks in other fields. Embedding such detectors in photonic integrated circuits enables additional counting capabilities through nanophotonic functionalization. Here, we show how a scalable number of waveguide-integrated superconducting nanowire single-photon detectors can be interfaced with independent fiber optic channels on the same chip. Our plug-and-play detector package is hosted inside a compact and portable closed-cycle cryostat providing cryogenic signal amplification for up to 64 channels. We demonstrate state-of-the-art multi-channel photon counting performance with average system detection efficiency of (40.5 ± 9.4)% and dark count rate of (123 ± 34) Hz for 32 individually addressable detectors at minimal noise-equivalent power of (5.1 ± 1.2) · 10-18 W/Hz. Our detectors achieve timing jitter as low as 26ps, which increases to (114 ± 17) ps for high-speed multi-channel operation using dedicated time-correlated single photon counting electronics. Our multi-channel single photon receiver offers exciting measurement capabilities for future quantum communication, remote sensing, and imaging applications.

Hybrid-Integrated Comb Source With 16 Wavelengths

Comb sources and multi-wavelength lasers can be used as central sources of light in many communication systems using high channel count wavelength division multiplexing. Here, we demonstrate a 16 wavelengths comb source built through large scale hybrid integration of 16 active gain chips with a passive silica chip that incorporates a 16 channel arrayed waveguide grating. Optical coupling between the 16 gain chips and the silica chip is achieved through 32 ball lenses (two per channel), that are aligned and attached in place with an automated process that we developed. The 16 channels have 100 GHz spacing and are centered in the C-band. We show monomode lasing, both in single-channel and multi-channel operation, with side mode suppression ratio as high as, respectively, 67 dB and 55 dB. The total output power of our comb source is about 17.9 dBm when all the 16 channels are activated simultaneously. The channels of our multi-wavelength laser have very narrow Lorentzian linewidths, with best value as narrow as 880 Hz and worst value of about 3.6 kHz. Our comb source integrates hybridly a large number of optical components, however thanks to the parallel architecture and the possibility of packaging the lenses with an automated process, our integration approach can be scaled to an even higher number of channels.

Improved Image Fusion Method Based on Sparse Decomposition

In the principle of lens imaging, when we project a three-dimensional object onto a photosensitive element through a convex lens, the point intersecting the focal plane can show a clear image of the photosensitive element, and the object point far away from the focal plane presents a fuzzy image point. The imaging position is considered to be clear within the limited size of the front and back of the focal plane. Otherwise, the image is considered to be fuzzy. In microscopic scenes, an electron microscope is usually used as the shooting equipment, which can basically eliminate the factors of defocus between the lens and the object. Most of the blur is caused by the shallow depth of field of the microscope, which makes the image defocused. Based on this, this paper analyzes the causes of defocusing in a video microscope and finds out that the shallow depth of field is the main reason, so we choose the corresponding deblurring method: the multi-focus image fusion method. We proposed a new multi-focus image fusion method based on sparse representation (DWT-SR). The operation burden is reduced by decomposing multiple frequency bands, and multi-channel operation is carried out by GPU parallel operation. The running time of the algorithm is further reduced. The results indicate that the DWT-SR algorithm introduced in this paper is higher in contrast and has much more details. It also solves the problem that dictionary training sparse approximation takes a long time.

Crosstalk suppression for high-density traveling-wave MZM array based on static and dynamic combined analysis and circuit-level designs.

Electrical crosstalk severely degrades the performance of Mach-Zehnder modulator (MZM) array. However, conventional crosstalk suppression techniques incur losses of large amounts of chip area for signal isolation, which becomes a bottleneck of high-density electronic-photonic integrated circuit. In this paper, the electrical crosstalk of Traveling-Wave MZM array is originally analyzed with static and dynamic combined crosstalk coefficients. Circuit-level suppression techniques of differential dual-drive electrode schemes with tightly coupled electrode pairs and a virtual ground structure with full-matching termination circuit are investigated for noise-removing effects. Simulation results show that the dynamic electrical crosstalk coefficient between two adjacent modulators is reduced to below 1.5%, which is five times lower than the baseline. The electro-optical link measurements show that the BER is significantly reduced from 1E-3 to 1E-12 for multi-channel operation, which confirms the effectiveness of the crosstalk suppression techniques.

Cladding-Pumped Er/Yb-Co-Doped Fiber Amplifier for Multi-Channel Operation

Cladding-pumped erbium (Er3+)/ytterbium (Yb3+)-co-doped fiber amplifiers are more advantageous at high output powers. However, this amplification technique also has potential in telecom-related applications. These types of amplifiers have complex properties, especially when considering gain profile and a pump conversion efficiency. Such metrics depend on the doped fiber profile, absorption/emission spectra, and the input signal power. In this context, we design, build and characterize an inhouse prototype of cladding-pumped Er3+/Yb3+-co-doped fiber amplifier (EYDFA). Our goal is to identify the EYDFA configuration (a co-doped fiber length, pump power, input signal power) suitable for signal amplification in a multichannel fiber-optic transmission system with a dense wavelength allocation across the C-band (1530–1565 nm). Our approach involves experimentally determining the Er3+/Yb3+-co-doped fiber’s parameters to be used in a simulation setup to decide on an initial EYDFA configuration before moving to a laboratory setup. An experimental EYDFA prototype is tested under different conditions using a 48-channel dense wavelength division multiplexing (DWDM, 100 GHz) system to evaluate the absolute gain and gain uniformity. The obtained results allow the cladding pump amplifier’s suitability for wideband signal amplification to be assessed. The developed prototype provides &gt;21 dB of gain with a 12 dB ripple within 1534–1565 nm. Furthermore, we show that the gain profile can be partially flattened out by using longer EYDF spans. This enhances signal amplification in the upper C-band in exchange for a weaker amplification in the lower C-band, which can be marginally improved with higher pump powers.

Improved Multi-Channel Multi-Interface Routing Protocol for Wireless Mesh Networks

In recent years, Wireless Mesh Networks (WMN) are emerging as a potential technology to enhance the ability and availability of wireless networks. WMN consists of wireless mesh routers and terminals connected by wireless multi-hop communication. Besides, in WMN, each wireless node owns many interfaces, and each interface operates on many channels. However, effectively using multiple orthogonal channels and multiple interfaces to increase the performance of WMN and decrease the radio link transmission interference is of significant challenge. In this paper, we consider routing issues in WMN. Then, we proposed an on-demand routing protocol, adaptive multi-channel multi-interface operation, improved from the AODV protocol. This protocol is capable of effectively managing communication in the WMN and limited effects of co-channel interference. The simulation results on NS2 show that our proposed protocol improves throughput, delay and packet delivery rate compared to existing traditional protocols.

Design and analysis of 1D photonic crystal doped with magnetized cold plasma defect for application of single/multi-channel tunable narrowband filter

The present research demonstrates the tunable filtering properties of a single/multi-channel filter based on 1D photonic crystal (PhC), which is designed with dielectric materials (A, B) and magnetized cold plasma (P). The proposed filter has a configuration of Transmission spectra of the structure is investigated for both right-hand polarization and left-hand polarization configurations of the plasma material by employing the well-known transfer matrix method. It is found that number of periods of the plasma layer (N) decides the single-channel and multi-channel operations of the filter. It is revealed that the external magnetic field can be manipulated to tune the frequency of the filter. Also, it is found that the geometrical parameters such as thickness of the plasma layer, plasma density, and incident angle play a significant role in tuning the defect mode frequency. Moreover, the color map plot is studied to provide a clear insight of shifting nature of the defect mode frequency. Quality factor of the filter is evaluated vis-à-vis different geometrical parameters. Our considered structure can be the mainstay for filtering applications in microwave regions, and can find suitable applications in dense wavelength division multiplexing and high speed signal processing.

RESEARCH OF MULTICHANNEL MODES OF THE NETWORK INTERFACE OF MODULAR COMPUTING SYSTEMS

This paper considers the so-called "blade" server solutions of multiprocessor computing systems, where several same-type mother modules are installed in one enclosure. The main feature of the clustered system, which is considered in this paper, is that the data interchange among computing nodes is carried out in a separate network, which operates at the channel level using channel bonding mechanisms. This provided an increase in the speed of data interchange and reduced the load of the channel that connects the nodes of the cluster. Further reseraches were aimed at implementing a procedure for increasing the efficiency of parallelization by introducing of multichannel modes of the network interface of modular computing systems. The conducted studies have created the prerequisites for a quantitative assessment of the effectiveness of a multiprocessor computing system in the implementation of a multi-channel operation mode of a cluster network.