Optimal Channel Configuration Research Articles

High-density ear-EEG for understanding ear-centered EEG

Background. Mobile ear-EEG provides the opportunity to record EEG unobtrusively in everyday life. However, in real-life, the EEG data quickly becomes difficult to interpret, as the neural signal is contaminated by other, non-neural signal contributions. Due to the small number of electrodes in ear-EEG devices, the interpretation of the EEG becomes even more difficult. For meaningful and reliable ear-EEG, it is crucial that the brain signals we wish to record in real life are well-understood and that we make optimal use of the available electrodes. Their placement should be guided by prior knowledge about the characteristics of the signal of interest. Objective. We want to understand the signal we record with ear-EEG and make recommendations on how to optimally place a limited number of electrodes. Approach. We built a high-density ear-EEG with 31 channels spaced densely around one ear. We used it to record four auditory event-related potentials (ERPs): the mismatch negativity, the P300, the N100 and the N400. With this data, we gain an understanding of how different stages of auditory processing are reflected in ear-EEG. We investigate the electrode configurations that carry the most information and use a mass univariate ERP analysis to identify the optimal channel configuration. We additionally use a multivariate approach to investigate the added value of multi-channel recordings. Main results. We find significant condition differences for all ERPs. The different ERPs vary considerably in their spatial extent and different electrode positions are necessary to optimally capture each component. In the multivariate analysis, we find that the investigation of the ERPs benefits strongly from multi-channel ear-EEG. Significance. Our work emphasizes the importance of a strong theoretical and practical background when building and using ear-EEG. We provide recommendations on finding the optimal electrode positions. These results will guide future research employing ear-EEG in real-life scenarios.

Optimal online channel configuration for a Manufacturer under price and lead time-sensitive demand

Optimal online channel configuration for a Manufacturer under price and lead time-sensitive demand

Deep Transferable Intelligence for Spatial Variability Characterization and Data-Efficient Learning in Biomechanical Measurement

Biomechanical measurement is of promising value for rehabilitation, assisted-living, and lifestyle management applications. Nevertheless, the understanding is still limited on the spatial variability of biomechanical dynamics that is essential for optimal motion sensor configuration. Besides, training physical activity detectors is usually data-heavy and time-consuming. Targeting these two challenges, in this study, we propose a novel deep transfer intelligence framework, which leverages deep learning to characterize spatial variability of different motion sensors on diverse body locations, and further leverages inter-subject transfer learning to maximize data-efficiency in challenging scarce data learning. More specifically, to characterize the spatial variability, we propose deep convolutional neural networks to investigate capabilities of both different sensor locations and channels, on physical activity measurement. The characterization determines both optimal sensor configuration and optimal channel configuration. Further, we propose a transfer learning approach to mine inter-subject similarity and then share learned knowledge among subjects, thereby minimizing the training effort and maximizing the data-efficiency in the wearable scarce data learning scenario. Our evaluation experiments have determined the optimal sensor location from seven options as thigh, and the optimal sensor&channel configuration from 42 options as thigh-accelerometer-axis-Y. Our experiments have further demonstrated that, with transfer learning under the optimal sensor&channel configuration, only 10% of data from the target subject for model fine-tuning can yield a physical activity detection accuracy up to 91.6%, with a performance boosting of 9% compared with direct learning without transfer learning. Therefore, the deep transferable learning framework will greatly advance spatial variability characterization for optimal sensor and channel configuration, and efficient scarce data learning in biomedical measurement.

Ventral and Dorsal Stream EEG Channels: Key Features for EEG-Based Object Recognition and Identification.

Object recognition and object identification are multifaceted cognitive operations that require various brain regions to synthesize and process information. Prior research has evidenced the activity of both visual and temporal cortices during these tasks. Notwithstanding their similarities, object recognition and identification are recognized as separate brain functions. Drawing from the two-stream hypothesis, our investigation aims to understand whether the channels within the ventral and dorsal streams contain pertinent information for effective model learning regarding object recognition and identification tasks. By utilizing the data we collected during the object recognition and identification experiment, we scrutinized EEGNet models, trained using channels that replicate the two-stream hypothesis pathways, against a model trained using all available channels. The outcomes reveal that the model trained solely using the temporal region delivered a high accuracy level in classifying four distinct object categories. Specifically, the object recognition and object identification models achieved an accuracy of 89% and 85%, respectively. By incorporating the channels that mimic the ventral stream, the model's accuracy was further improved, with the object recognition model and object identification model achieving an accuracy of 95% and 94%, respectively. Furthermore, the Grad-CAM result of the trained models revealed a significant contribution from the ventral and dorsal stream channels toward the training of the EEGNet model. The aim of our study is to pinpoint the optimal channel configuration that provides a swift and accurate brain-computer interface system for object recognition and identification.

Impact of layer thickness on the operating characteristics of In2O3/ZnO heterojunction thin-film transistors

Combining low-dimensional layers of dissimilar metal oxide materials to form a heterojunction structure offers a potent strategy to improve the performance and stability of thin-film transistors (TFTs). Here, we study the impact of channel layer thicknesses on the operating characteristics of In2O3/ZnO heterojunction TFTs prepared via sputtering. The conduction band offset present at the In2O3/ZnO heterointerface affects the device's operating characteristics, as is the thickness of the individual oxide layers. The latter is investigated using a variety of experimental and computational modeling techniques. An average field-effect mobility (μFE) of >50 cm2 V−1 s−1, accompanied by a low threshold voltage and a high on/off ratio (∼108), is achieved using an optimal channel configuration. The high μFE in these TFTs is found to correlate with the presence of a quasi-two-dimensional electron gas at the In2O3/ZnO interface. This work provides important insight into the operating principles of heterojunction metal oxide TFTs, which can aid further developments.

Selection of the optimal channel configuration for implementing wearable EEG devices for the diagnosis of mild cognitive impairment

BackgroundEarly diagnosis of mild cognitive impairment (MCI) is essential for timely treatment planning. With recent advances in the wearable technology, interest has increasingly shifted toward computer-aided self-diagnosis of MCI using wearable electroencephalography (EEG) devices in daily life. However, no study so far has investigated the optimal electrode configurations for the efficient diagnosis of MCI while considering the design factors of wearable EEG devices. In this study, we aimed to determine the optimal channel configurations of wearable EEG devices for the computer-aided diagnosis of MCI.MethodWe employed an EEG dataset collected from 21 patients with MCI and 21 healthy control subjects. After evaluating the classification accuracies for all possible electrode configurations for the two-, four-, six-, and eight-electrode conditions using a support vector machine, the optimal electrode configurations that provide the highest diagnostic accuracy were suggested for each electrode condition.ResultsThe highest classification accuracies of 74.04% ± 4.82, 82.43% ± 6.14, 86.28% ± 2.81, and 86.85% ± 4.97 were achieved for the optimal two-, four-, six-, and eight-electrode configurations, respectively, which demonstrated the possibility of precise machine-learning-based diagnosis of MCI with a limited number of EEG electrodes. Additionally, further simulations with the EEG dataset revealed that the optimal electrode configurations had significantly higher classification accuracies than commercial EEG devices with the same number of electrodes, which suggested the importance of electrode configuration optimization for wearable EEG devices based on clinical EEG datasets.ConclusionsThis study highlighted that the optimization of the electrode configuration, assuming the wearable EEG devices can potentially be utilized for daily life monitoring of MCI, is necessary to enhance the performance and portability.

AutoNAS: Automatic Neural Architecture Search for Hyperspectral Unmixing

Owing to the powerful and automatic representation capabilities, deep learning (DL) techniques have made significant breakthroughs and progress in hyperspectral unmixing (HU). Among the DL approaches, autoencoders (AEs) have become a widely-used and promising network architecture. However, these AE-based methods heavily rely on manual design and may not be a good fit for specific datasets. To unmix hyperspectral images more intelligently, we propose an automatic neural architecture search model for HU, AutoNAS for short, to determine the optimal network architecture by considering channel configurations and convolution kernels simultaneously. In AutoNAS, the self-supervised training mechanism based on hyperspectral images is first designed for generating the training samples of the supernet. Then, the affine parameter sharing strategy is adopted by applying different affine transformations on the supernet weights in the training phase, which enables finding the optimal channel configuration. Furthermore, on the basis of the obtained channel configuration, the evolutionary algorithm with additional computational constraints is introduced into networks to achieve flexible convolution kernel search by evaluating unmixing results of different architectures in the supernet. Extensive experiments conducted on four hyperspectral datasets demonstrate the effectiveness and superiority of the proposed AutoNAS in comparison with several state-of-the-art unmixing algorithms.

Distributed Channel Assignment for Ultra-Dense Wireless Networks Using Belief Propagation

An efficient channel assignment plays an important role in mitigating co-channel interference in ultra-dense wireless networks. A simple solution is to separate interfering network nodes into orthogonal channels to reduce the interference among them. However, determining the optimal channel assignment is considered to be a non-linear problem, which may also be associated with practical implementation issues such as high computational complexity and control signaling issues. In an effort to cope with these challenging issues, we propose a distributed channel assignment algorithm that efficiently finds the optimal channel configuration by utilizing the concept of belief propagation. Based on a message-passing framework, the proposed distributed channel assignment algorithm maximizes the overall sum rate of the ultra-dense network with a low computational load for each network node. In addition, we design a network protocol and frame format to implement the proposed message-passing framework to real-world wireless networks. The main advantage of the proposed approach is that network nodes autonomously determine the optimal channel assignment and rapidly adapt to dynamic changes of the network. Simulation results confirm that the proposed distributed channel assignment algorithm outperforms conventional algorithms in terms of various network performance aspects, such as the sum rate, scalability, latency, and user mobility.

Optimal Channel Configuration for Implementing Remanufacturing Business in a Closed-Loop Supply Chain

This study aims to analyze the optimal channel configuration of a manufacturer in a closed-loop supply chain (CLSC) where the manufacturer is responsible to collect used products and has a competitive dual-channel in selling new and remanufactured products respectively, including through a retailer and a third-party (Model 3PR) or by itself directly and a third-party (Model 3PM) or through a retailer and by itself directly (Model MR). Considering the manufacturer-Stackelberg, it concluded that model MR is the optimal channel configuration in terms of the sales quantity of remanufactured products and model 3PR never be the optimal one. In terms of the overall sales quantity, the profit of the manufacturer and the whole CLSC, whether model 3PM or model MR is the optimal channel configuration depends on the value of the discount perception for remanufactured products, the cost saving of producing remanufactured products, and whether considering the collection process or not.

Fast Start-Up Microfluidic Microbial Fuel Cells With Serpentine Microchannel.

Microfluidic microbial fuel cells (MMFCs) are promising green power sources for future ultra-small electronic devices. The MMFCs with co-laminar microfluidic structure are superior to other MMFCs according to their low internal resistance and relative high power density. However, the area for interfacial electron transfer between the bacteria and the anode is quite limited in the typical Y-shaped device, which apparently restricts the current generation performance. In this study, we developed a membraneless MMFC with serpentine microchannel to enhance the interfacial electron transfer and promote the power generation of the device. Owing to the merit of laminar flow, the proposed MMFC was working well without any proton exchange membrane (PEM). At the same time, the serpentine microchannel greatly increased the power density. The S-MMFC catalyzed by Shewanella putrefaciens CN32 achieves a peak power density of 360 mW/m2 with the optimal channel configuration and the flow rate of 5 ml/h. Meanwhile, this device possesses much shorter start-up time and much longer duration time at high current plateau than the previous reported MMFCs. The presented MMFC appears promising for biochip technology and extends the scope of microfluidic energy.

Channel configuration and pay-on-delivery service with the endogenous delivery lead time

This paper investigates the manufacturer’s optimal channel configuration strategy when it provides a pay-on-delivery service directly to consumers through its on-line selling channel. Due to the delivery lead time, we assume that consumers will discount their future utility from the online channel. In particular, the pay-on-delivery service through the direct channel can induce a retailer to lower its price (pricing induction) and/or segment consumers by regulating their valuations of buying products through different channels (consumers segmentation). Furthermore, the firms’ equilibrium strategies are determined by the consumer’s sensitivity to the delivery time, delivery cost and marginal cost of the indirect channel. When selling through an indirect channel is much more costly, the manufacturer’s encroachment on the retail market can achieve Pareto improvement. Compared with the payment scheme of pay-before-delivery, the results show that the manufacturer’s encroachment is more aggressive in the pay-on-delivery scheme. Finally, we extend the pay-on-delivery scheme to different pricing sequences and consider the case in which consumers and the manufacturer have different discount factors.

Energy-efficient resource allocation for hybrid bursty services in multi-relay OFDM networks

In this paper, we propose Mapped Two-way Water Filling (MTWF) scheme to maximize Energy Efficiency (EE) for hybrid bursty services with Quality of Services (QoS) requirements in Two-Way Multi-Relay (TWMR) OFDM networks. The bursty traffic is first analyzed by strictly proved equivalent homogeneous Poisson process, based on which the QoS requirements are converted into sum-rate constraints. The formulated non-convex EE maximization problem, including subcarrier assignment, Relay Selection (RS) and rate allocation, is NP-hard involving combinatorial optimization. To conduct optimal RS on each subcarrier without priori bursty traffic knowledge, we utilize some approximate relationships under high data rate demands to remove its dependence on two-way data rates, and simplify the whole optimization problem as well. After the optimal channel configuration is obtained, which only depends on channel conditions, subcarrier assignment is attained through Elitist Selection Genetic Algorithm (ESGA), and rate allocation of each service is fulfilled by deducing two-way water filling principle. A new equivalent optimization objective function is proposed next as the simple evaluating index in ESGA to reduce complexity. Finally, simulations are carried out to verify the superiority and convergence of our scheme, as well as the applicability for different scenarios.

Investigation of fluid flow and heat transfer in wavy micro-channels with alternating secondary branches

The fluid flow and heat transfer behaviors in the novel sinusoidal micro-channels with secondary branches were examined both numerically and experimentally. Secondary branches, inspired by the oblique fin design were added in an alternating fashion (±45°) to the peaks and the troughs of the wavy micro-channel configuration to enable cross-channel mixing. Two designs with different amplitude of waviness were benchmarked against the optimal wavy channel configuration from the Reynolds number range of 50–200. An increase in heat transfer performance (when compared to the reference conventional wavy design) was observed when secondary branches were added, albeit with a pressure drop penalty. However, when another design with lower amplitude of waviness was introduced and compared to the reference geometry, superior heat transfer performance was observed without pressure drop tradeoff.The velocity and temperature profiles from the simulations were obtained to explain the hydrodynamic and heat transfer behaviors. The combination of Dean’s vortices induced by the wavy channel design and the cross-channel mixing enabled by the secondary branches showed potential to further enhance convective heat transfer (at reasonable pressure drop penalties) due to the increased intensity of span-wise fluid mixing and also boundary layer disruptions. The attractiveness of the introduction of secondary branches lies in the potential to further enhance the heat transfer performance of the already established wavy channel configuration with minimal pressure drop penalty.

SU‐F‐T‐35: Optimization of Bladder and Rectal Doses Using a Multi‐Lumen Intracavitary Applicator for Gynecological Brachytherapy

Purpose:Radiation dose delivery for endometrial cancer using HDR techniques is limited by dose to bladder and rectum. A dosimetric study was performed using Varian Capri vaginal brachytherapy applicator to determine the optimal channel configuration which minimizes dose to bladder and rectum, while providing good target coverage.Methods:A total of 17 patients, 63 plans clinically delivered, and 252 simulated plans using Varian BrachyVision planning system were generated to investigate optimal channel configuration which results in minimum dose to bladder and rectum while providing adequate target coverage. The Capri applicator consists of 13 lumens arranged in two concentric rings, one central lumen and six lumens per ring. Manual dose shaping is invariably required to lower the dose to critical organs. Three‐dimensional plans were simulated for 4 channel arrangements, all 13 channels, channel 12 o'clock (close to bladder) and 6 o'clock (close to rectum) deactivated, central channel deactivated, and central channel in addition to 12 o'clock and 6 o'clock deactivated. A relationship between V100, the volume that receives the prescribed dose, and the amount of curie‐seconds required to deliver it, was established.Results:Using all 13 channels results in maximum dose to bladder and rectum. Deactivating central channel in addition to 12 o'clock and 6 o'clock resulted in minimizing bladder and rectum doses but compromised target coverage. The relationship between V100, the volume that receives the prescribed dose, and the curie seconds was found to be linear.Conclusion:Deactivating channels 12 o'clock and 6 o'clock was shown to be the optimal configuration leading to minimum dose to bladder and rectum without compromising target coverage. The linear relationship between V100 and the curie‐ seconds can be used as a verification parameter.

Decoding the attended speech stream with multi-channel EEG: implications for online, daily-life applications

Objective. Recent studies have provided evidence that temporal envelope driven speech decoding from high-density electroencephalography (EEG) and magnetoencephalography recordings can identify the attended speech stream in a multi-speaker scenario. The present work replicated the previous high density EEG study and investigated the necessary technical requirements for practical attended speech decoding with EEG. Approach. Twelve normal hearing participants attended to one out of two simultaneously presented audiobook stories, while high density EEG was recorded. An offline iterative procedure eliminating those channels contributing the least to decoding provided insight into the necessary channel number and optimal cross-subject channel configuration. Aiming towards the future goal of near real-time classification with an individually trained decoder, the minimum duration of training data necessary for successful classification was determined by using a chronological cross-validation approach. Main results. Close replication of the previously reported results confirmed the method robustness. Decoder performance remained stable from 96 channels down to 25. Furthermore, for less than 15 min of training data, the subject-independent (pre-trained) decoder performed better than an individually trained decoder did. Significance. Our study complements previous research and provides information suggesting that efficient low-density EEG online decoding is within reach.

Distributed joint flow-radio and channel assignment using partially overlapping channels in multi-radio wireless mesh networks

Equipping mesh nodes with multiple radios that support multiple wireless channels is considered a promising solution to overcome the capacity limitation of single-radio wireless mesh networks. However, careful and intelligent radio resource management is needed to take full advantage of the extra radios on the mesh nodes. Flow-radio assignment and channel assignment procedures should obey the physical constraints imposed by the radios as well as the topological constraints imposed by routing. Varying numbers of wireless channels are available for the channel assignment procedure for different wireless communication standards. To further complicate the problem, the wireless communication standard implemented by the radios of the wireless mesh network may define overlapping as well as orthogonal channels, as in the case of the IEEE 802.11b/g family of standards. This paper presents Distributed Flow-Radio Channel Assignment, a distributed joint flow-radio and channel assignment scheme and the accompanying distributed protocol in the context of multi-channel multi-radio wireless mesh networks. The scheme’s performance is evaluated on small networks for which the optimal flow-radio and channel configuration can be computed, as well as on large random topologies.

Development of bipolar plates with different flow channel configurations based on plant vein for fuel cell

In this paper, a kind of proton exchange membrane fuel cell (PEMFC) bipolar plate based on plant vein is developed through biomimetic analogy theory. Fluent software is employed to test the performance characteristics of this newly designed bipolar plate. It is found from the numerical simulation results that the PEMFC performance will be influenced by the number and location of the biomimetic flow channel branches. The distribution pattern of branches has great impact on the outlet velocity. The more the branch number is, the more favorable for water removal. Finally, different operating parameters, such as temperature, pressure, relative humidity and stoichiometric ratio, are chosen based on the optimal flow channel configuration to improve PEMFC performance. Copyright © 2013 John Wiley & Sons, Ltd.

Improving Myoelectric Pattern Recognition Robustness to Electrode Shift by Changing Interelectrode Distance and Electrode Configuration

Pattern recognition of myoelectric signals for prosthesis control has been extensively studied in research settings and is close to clinical implementation. These systems are capable of intuitively controlling the next generation of dexterous prosthetic hands. However, pattern recognition systems perform poorly in the presence of electrode shift, defined as movement of surface electrodes with respect to the underlying muscles. This paper focused on investigating the optimal interelectrode distance, channel configuration, and electromyography feature sets for myoelectric pattern recognition in the presence of electrode shift. Increasing interelectrode distance from 2 to 4 cm improved pattern recognition system performance in terms of classification error and controllability (p < 0.01). Additionally, for a constant number of channels, an electrode configuration that included electrodes oriented both longitudinally and perpendicularly with respect to muscle fibers improved robustness in the presence of electrode shift (p < 0.05). We investigated the effect of the number of recording channels with and without electrode shift and found that four to six channels were sufficient for pattern recognition control. Finally, we investigated different feature sets for pattern recognition control using a linear discriminant analysis classifier and found that an autoregressive set significantly (p < 0.01) reduced sensitivity to electrode shift compared to a traditional time-domain feature set.

SINR Feedback Based Rate Control for Dual-Antenna-Array Multi-Cellular Networks with Cochannel Interference

We propose in this paper a dual-antenna-array (with transmitter antenna array and receiver antenna array) architecture, where the antenna elements are divided into several antenna element sets and each traffic channel is transmitted over an antenna element set, to realize the multiple traffic channels set up by a user. A SINR feedback based algorithm, which can regulate the transmission rate by iteratively adjusting the power on each traffic channel, is proposed to execute the rate control for the proposed dual-antenna-array architecture under cochannel interference. It is shown that the proposed algorithm can make the throughput meet the throughput requirement or achieve the weighted bandwidth sharing for certain fairness. In addition, we further propose a traffic channel configuration algorithm to help the SINR feedback based algorithm find the optimal traffic channel configuration that can meet the throughput requirement for each traffic channel or results in the maximal total throughput for each user.

On Optimal Channel Configurations for SMR-based Brain–Computer Interfaces

One crucial question in the design of electroencephalogram (EEG)-based brain-computer interface (BCI) experiments is the selection of EEG channels. While a setup with few channels is more convenient and requires less preparation time, a dense placement of electrodes provides more detailed information and henceforth could lead to a better classification performance. Here, we investigate this question for a specific setting: a BCI that uses the popular CSP algorithm in order to classify voluntary modulations of sensorimotor rhythms (SMR). In a first approach 13 different fixed channel configurations are compared to the full one consisting of 119 channels. The configuration with 48 channels results to be the best one, while configurations with less channels, from 32 to 8, performed not significantly worse than the best configuration in cases where only few training trials are available. In a second approach an optimal channel configuration is obtained by an iterative procedure in the spirit of stepwise variable selection with nonparametric multiple comparisons. As a surprising result, in the second approach a setting with 22 channels centered over the motor areas was selected. Thanks to the acquisition of a large data set recorded from 80 novice participants using 119 EEG channels, the results of this study can be expected to have a high degree of generalizability.