Channel Data Research Articles

A simple quantitative model of neuromodulation, Part I: Ion flow through neural ion channels

We develop a simple model of ionic current through neuronal membranes as a function of membrane potential and extracellular ion concentration. The model combines a simplified Poisson–Nernst–Planck (PNP) model of ion transport through individual ion channels with channel activation functions calibrated from ad hoc in-house experimental data. The simplified PNP model is validated against bacterial gramicidin A ion channel data. The calibrated model accounts for the transport of calcium, sodium, potassium, and chloride and exhibits remarkable agreement with the experimentally measured current–voltage curves for the differentiated human neural cells. All relevant data and code related to the ion flow models are available at Werneck et al. (2023).

Real and fake channel: GAN-based wireless channel modeling and generating

With the increasing complexity of wireless environment, scene, frequency band, antenna scale and other factors, it brings new challenges to wireless channel modeling. On the other hand, wireless communication solutions based on artificial intelligence have been constantly proposed in recent years, which are highly dependent on the quality and quantity of channel data. However, in the actual communication system, the acquisition of real channel data is restricted by the high cost of channel data acquisition. In this paper, an improved Wasserstein generative adversal network with gradient penalty (WGAN-GP) is proposed to solve the channel modeling and generation problem in MIMO communication system. The experimental results show that the improved WGAN-GP can generate fake channel data with the same distribution as the real channel data. In addition, the validity of the fake channel data is cross-verified by a channel feedback scheme based on deep learning.

Tailored Microcantilever Optimization for Multifrequency Force Microscopy.

Microcantilevers are at the heart of atomic force microscopy (AFM) and play a significant role in AFM-based techniques. Recent advancements in multifrequency AFM require the simultaneous excitation and detection of multiple eigenfrequencies of microcantilevers to assess more data channels to quantify the material properties. However, to achieve higher spatiotemporal resolution there is a need to optimize the structure of microcantilevers. In this study, the architecture of the cantilever with gold nanoparticles using a dip-coating method is modified, aiming to tune the higher eigenmodes of the microcantilever as integer multiples of its fundamental frequency. Through the theoretical methodology and simulative model, that integer harmonics improve the coupling in multifrequency AFM measurements is demonstrated, leading to enhanced image quality and resolution. Furthermore, via the combined theoretical-experimental approach, the interplay between induced mass and stiffness change of the modified cantilever depending on the attached particle location, size, mass, and geometry is found. To validate the results of this predictive model, tapping-mode AFM is utilized and bimodal Amplitude Modulation AFM techniques to examine and quantify the impact of tuning higher-order eigenmodes on the imaging quality of a polystyrene-polymethylmethacrylate (PS-PMMA) block co-polymer assembly deposited on a glass slide and Highly Ordered Pyrolytic Graphite (HOPG).

Joint channel and impulse noise estimation based on compressed sensing and Kalman filter for OFDM system

Impulse noise (IN) widely exists in many communication systems, which seriously affects the performance of OFDM communication systems. A joint channel and IN estimation method based on all subcarriers is designed. This method uses a sparse Bayesian learning (SBL) algorithm incorporating forward–backward Kalman filter (FB-Kalman) to tackle the problem of joint channel and IN estimation and data detection for OFDM systems. Firstly, the channel impulse response and IN are regarded as unknown sparse vectors, and a SBL framework using all subcarriers is proposed to estimate the unknown vector. The SBL theory is used based on the prior distribution of variables, and then the forward–backward joint system is established, which applies the data detection simultaneously. We also propose the FB-Kalman implementation algorithm by using the expectation maximization updates. Explicit expressions of mean and covariance matrix of the posterior distribution are derived in the E-step. Simulation results show that the proposed algorithm improves the normalized mean square error and bit error rate performance of OFDM system in the presence of IN communication environment.

Orthogonal affine precoding and decoding based channel estimation and data detection in frequency reversed Alamouti coded FBMC-OQAM system

Orthogonal affine precoding and decoding based channel estimation and data detection in frequency reversed Alamouti coded FBMC-OQAM system

Spatial Distribution of Water Quality in Welang, Gembong and Rejoso Rivers, Pasuruan, East Java, Indonesia

A river is a naturally formed freshwater stream that traverses land and eventually flows into a lake, sea, or another body of water. River provides fresh water for human activities such as irrigation for their paddy fields, aquaculture, industrial purposes, and many other purposes. At the same time, there exists an inherent disparity in the demand, availability, and quality of river water, often giving rise to significant challenges and issues. Environmental experts, commonly use a multivariate statistical method such as Principal Component Analysis (PCA), Storage and Retrieval (STORET), and cluster analysis for water quality analysis. However, those methods are numerical and limited in spatial visualization. Inverse Distance Weighting (IDW) interpolation, Voronoi, and Kriging were applied to obtain the spatial representation of water quality distribution Welang, Gembong, and Rejoso rivers in Pasuruan as study. The objectives are to locate on a map any river segments that experienced poor water quality throughout the observation period. We successively combined STORET with those spatial interpolation. The result shows that IDW interpolation, Voronoi, and Kriging can visualize and map river segments that had poor water quality during the observation time. However, due to the limited input data, the interpolation results exhibit variability. For instance, at a measured location with a STORET value of -28, IDW yielded -28, Voronoi -28, and Kriging -27. Beyond the measurement points, each interpolation method began to produce less accurate values. This study involves interpolating dynamic objects with limited measurements data in narrow channels, which differs from interpolating elevation in broader area, in terms of the accuracy of representation or visualization obtained from this spatial analysis still remain unresolved in this study.

Iterative channel estimation and data detection algorithm for MIMO-OTFS systems

Channel estimation in high-mobility environments is a challenging problem for advanced mobile communication systems (5G and beyond). In this manuscript, we first propose an iterative algorithm for channel estimation and data detection in the delay-Doppler domain for multiple-input multiple-output orthogonal time frequency space system. Then, in order to increase the spectral efficiency of the system, we use a superimposed pilot pattern. The proposed algorithm takes advantage from the sparse nature of the channel in the delay-Doppler domain and iterates between message passing-aided data detection and data-aided channel estimation. For channel estimation, we propose two algorithms. The first one consists in estimating all channel parameters, including the number of path gains, delay taps, Doppler taps, and channel gains by using a mean-field approximation and the variational Bayesian expectation maximization algorithm. The second one, based on the fact that delay and Doppler taps remain unchanged for a rather long period of time, uses an MMSE approach combined with Cholesky decomposition to only estimate channel gains in each transmitted frame. For data detection, we adapt the message-passing algorithm proposed in the literature. We also derive a lower bound on the signal-to-interference-plus-noise ratio of the proposed scheme, and maximize it by optimally allocating power between pilots and data symbols. Finally, we compare the complexity and the performance in terms of normalized mean square error, bit error rate, and spectral efficiency against existing methods. Simulation results, conducted in high-mobility scenarios show that the proposed algorithm achieves a good compromise between complexity and performance.

The Effect of Data Empowerment on Channel Resilience: The Roles of Ambidextrous Learning，Guanxi Inertia and Enterprise Openness

ABSTRACT Data empowerment is a critical component of enterprises’ digital transformations and plays a pivotal role in the creation of enterprise channel resilience. This paper investigates the process mechanism of how data empowerment affects enterprises’ channel resilience by utilizing the theoretical perspectives of ambidextrous learning and resource conservation. To test the hypothesized relationships and conceptual model, survey data was collected from 263 manufacturing enterprises in China, and SPSS PROCESS macro was used for analysis. The empirical findings revealed that data empowerment has both a direct positive impact on channel resilience and an indirect effect through the mediating role of ambidextrous learning. The relationship between ambidextrous learning and channel resilience was moderated by guanxi inertia and enterprise openness. This study contributes to understanding the process mechanism and contextual boundary of the relationship between data empowerment and channel resilience. The findings also offer practical guidance for enterprises to improve channel resilience and foster win-win cooperation among channel members.

A Unified Joint Optimization of Training Sequences and Transceivers Based on Matrix-Monotonic Optimization

Channel estimation and data transmission constitute the most fundamental functional modules of multiple-input multiple-output (MIMO) communication systems. The underlying key tasks corresponding to these modules are training sequence optimization and transceiver optimization. Hence, we jointly optimize the linear transmit precoder and the training sequence of MIMO systems using the metrics of their effective mutual information (MI), effective mean squared error (MSE), effective weighted MI, effective weighted MSE, as well as their effective generic Schur-convex and Schur-concave functions. Both statistical channel state information (CSI) and estimated CSI are considered at the transmitter in the joint optimization. A unified framework termed as joint matrix-monotonic optimization is proposed. Based on this, the optimal precoder matrix and training matrix structures can be derived for both CSI scenarios. Then, based on the optimal matrix structures, our linear transceivers and their training sequences can be jointly optimized. Compared to state-of-the-art benchmark algorithms, the proposed algorithms visualize the bold explicit relationships between the attainable system performance of our linear transceivers conceived and their training sequences, leading to implementation ready recipes. Finally, several numerical results are provided, which corroborate our theoretical results and demonstrate the compelling benefits of our proposed pilot-aided MIMO solutions.

On Flow Control and Optimized Back-Off in Non-Saturated CSMA

Medium Access Control (MAC) main functions encompass contention for channel access, packet scheduling, error control, and data integrity. Channel contention is a collective function involving all stations in the network, while data integrity pertains to data flows of each individual station. We propose a design where contention related functions are separated from other data management functions. The hinge connecting contention and other data management functions is a flow control algorithm, aiming at guaranteeing stability of contention queues and load on the MAC channel. With reference to Carrier-Sense Multiple Access (CSMA), we define an analytical model of contention queues under non saturated traffic. An asymptotic analysis of the model for large number of stations yields a closed form of the optimal flow control rate. The insight gained from the model is used to design an adaptive flow control algorithm that guarantees throughput optimality for all values of the number of stations.

Quantized Feedback Control of Discrete-Time MIMO Linear Systems With Input and Output Quantization Over Finite Data Rate Channels

This paper considers a control problem of discrete-time MIMO linear systems involving input quantization (for the output of the controller) and output quantization (for the output of the plant). Based on spherical polar coordinate quantizer, a quantization method with finite data rate is proposed for handling the nested quantization and achieving the convergence to the origin of the system with input and output quantization. Further, an optimalization problem is developed for each quantizer, of which the optimal solution set contains a virtual augmented vector. The quantizers quantize their respective virtual augmented vectors to obtain the estimates of the outputs of the plant and the controller. We present the analytical expression of the optimal solution set for each quantizer. Finally, a method is presented to achieve the parameters of the quantizers and the controller.

Real-time, adaptive microphone subset selection for a 128-elementarray

Some advantages of high-sensor count microphone arrays include gain over random noise, increased multiple-source resolution, and potential increases in effective frequency bandwidth. But in real-time applications, computational capabilities of embedded computing hardware might limit the number data channels that can be processed without data loss. For example, given a specific processing pipeline and sampling rate, perhaps only 24 of 128 channels can be supported in real-time. Therefore, the question of which 24-element subset of the 128 possible elements should be being used to detect and track multiple moving acoustic sources. In this presentation, principles of genetic search algorithms are used to develop a method to adaptively select optimal subsets of sensors as data is being processed. Discussion concerning optimality criteria, types of genetic algorithms, and algorithm performance will be presented as well as results corresponding to a 3-D 128-element array of digital MEMS microphones when multiple types of moving acoustic sources were nearby.

A Very Large Cardiac Channel Data Database (VLCD) Used to Evaluate Global Image Coherence (GIC) as an In Vivo Image Quality Metric.

Ultrasound image quality is of utmost importance for a clinician to reach a correct diagnosis. Conventionally, image quality is evaluated using metrics to determine the contrast and resolution. These metrics require localization of specific regions and targets in the image such as a region of interest (ROI), a background region, and/or a point scatterer. Such objects can all be difficult to identify in in-vivo images, especially for automatic evaluation of image quality in large amounts of data. Using a matrix array probe, we have recorded a Very Large cardiac Channel data Database (VLCD) to evaluate coherence as an in vivo image quality metric. The VLCD consists of 33280 individual image frames from 538 recordings of 106 patients. We also introduce a global image coherence (GIC), an in vivo image quality metric that does not require any identified ROI since it is defined as an average coherence value calculated from all the data pixels used to form the image, below a preselected range. The GIC is shown to be a quantitative metric for in vivo image quality when applied to the VLCD. We demonstrate, on a subset of the dataset, that the GIC correlates well with the conventional metrics contrast ratio (CR) and the generalized contrast-to-noise ratio (gCNR) with R = 0.74 ( ) and R = 0.62 ( ), respectively. There exist multiple methods to estimate the coherence of the received signal across the ultrasound array. We further show that all coherence measures investigated in this study are highly correlated ( 0.9 and ) when applied to the VLCD. Thus, even though there are differences in the implementation of coherence measures, all quantify the similarity of the signal across the array and can be averaged into a GIC to evaluate image quality automatically and quantitatively.

NEAR FIELD BEAM FORMING IN TERAHERTZ WIDEBAND MASSIVE MIMO SYSTEMS USING CODEBOOK LEARNING

Terahertz (THz) communication technology holds immense potential for enabling ultra-high data rates in wireless communication networks. However, the highly directional nature of THz waves poses significant challenges for achieving precise beam focusing, especially in near-field scenarios. In this study, we propose a novel approach leveraging deep learning techniques for near-field beam focusing in Terahertz Wideband Massive Multiple Input, Multiple Output (MIMO) systems. Our methodology's main novelty is the combination of codebook learning with deep neural networks. We use a convolutional neural network (CNN) architecture to learn complex spatial properties of THz channels and optimise beamforming weights. During the training phase, the codebook, which represents a discrete collection of beamforming vectors, is adaptively modified, allowing for dynamic response to changing channel circumstances. Extensive tests are being carried out to confirm the efficacy of our strategy, employing cutting-edge THz transceivers and MIMO technology. The proposed deep learning model is trained and evaluated using real-world channel data. Comparative comparisons with traditional beamforming approaches demonstrate our methodology's better performance, revealing significant advances in spectral efficiency and signal quality. This paper highlights the transformational influence of codebook learning linked with deep neural networks in boosting the performance of near-field beam focusing in Terahertz Wideband Massive MIMO systems. It not only provides a major achievement in the field of THz communications. Key Words: THz Communication, deep neural networks, MIMO, beamforming, deep learning.

Flood simulation using the hydrological model and the hydrological–hydrodynamic coupling model in a small watershed in semi-arid and sub-humid region, North China

Abstract Flood disasters occur frequently in semi-arid and sub-humid mountain watersheds, and their formation mechanism is affected by many factors, resulting in low simulation accuracy. The main purpose of this study is to evaluate the impact of adding actual river channel data on the accuracy of flood simulation. In order to obtain higher-resolution terrain data, an unmanned aerial vehicle (UAV) was used to survey the study area. Taking the Liulin Watershed in Xingtai City, Hebei Province as the research area, the HEC-HMS hydrological model and the HEC-HMS and HEC-RAS coupling model were constructed, respectively, to simulate 26 flood events during the period from 1982 to 2016. The results indicate that the coupled model can reflect the evolution process of river floods. When simulating certain major flood events, the percentage of flood peak error and Nash–Sutcliffe efficiency coefficient (NSE) have improved, such as the NSE of No. 160812 floods increasing from 0.9 to 0.95. However, the simulation accuracy of these two models for small floods in the watershed is relatively low. Future research should focus on how to accurately evaluate the parameter curve number (CN) of the watershed before rainfall and obtain more accurate runoff yield.

Deep Learning-Based Link Quality Estimation for RIS-Assisted UAV-Enabled Wireless Communications System.

In recent years, unmanned aerial vehicles (UAVs) have become a valuable platform for many applications, including communication networks. UAV-enabled wireless communication faces challenges in complex urban and dynamic environments. UAVs can suffer from power limitations and path losses caused by non-line-of-sight connections, which may hamper communication performance. To address these issues, reconfigurable intelligent surfaces (RIS) have been proposed as helpful technologies to enhance UAV communication networks. However, due to the high mobility of UAVs, complex channel environments, and dynamic RIS configurations, it is challenging to estimate the link quality of ground users. In this paper, we propose a link quality estimation model using a gated recurrent unit (GRU) to assess the link quality of ground users for a multi-user RIS-assisted UAV-enabled wireless communication system. Our proposed framework uses a time series of user channel data and RIS phase shift information to estimate the quality of the link for each ground user. The simulation results showed that the proposed GRU model can effectively and accurately estimate the link quality of ground users in the RIS-assisted UAV-enabled wireless communication network.

Remote Sensing of the Light-Obscuring Smoke Properties in Real-Scale Fires Using a Photometric Measurement Method

AbstractA new photometric measurement method for the determination of temporally and spatially resolved light extinction coefficients in laboratory fire tests was recently presented. The approach relies on capturing the change in intensity of individual light sources (LEDs) due to fire smoke using a commercially available digital camera. Comparing the results for red light LEDs to measurements of the well-established MIREX system indicates the model is capable of capturing the investigated phenomena. However, a significant underestimation of this reference measurement taken in the infrared range is inconsistent with the expected increase of the extinction coefficients with lower wavelengths. In the context of new experimental investigations, this deficiency was remedied by evaluating multible colour channels of RAW image data instead of the previously used JPG files. Furthermore, extending the experimental setup by several LED strips as well as a second camera allows to verify the hypothesis of a homogeneous smoke layering. The study covers eight experiments including n-heptane fuel in style of the well documented EN 54 TF 5 testfire as well as two additional experiments with an n-heptane-toluene mixture. Considering spatial resolution as well as the high reproducibility of the results, the method appears to be a convenient tool for the validation of numerical visibility models. Nevertheless, a sensitivity analysis identified uncertainties that need to be addressed in upcoming investigations to further improve the accuracy.

Assigning channel weights using an attention mechanism: an EEG interpolation algorithm.

During the acquisition of electroencephalographic (EEG) signals, various factors can influence the data and lead to the presence of one or multiple bad channels. Bad channel interpolation is the use of good channels data to reconstruct bad channel, thereby maintaining the original dimensions of the data for subsequent analysis tasks. The mainstream interpolation algorithm assigns weights to channels based on the physical distance of the electrodes and does not take into account the effect of physiological factors on the EEG signal. The algorithm proposed in this study utilizes an attention mechanism to allocate channel weights (AMACW). The model gets the correlation among channels by learning from good channel data. Interpolation assigns weights based on learned correlations without the need for electrode location information, solving the difficulty that traditional methods cannot interpolate bad channels at unknown locations. To avoid an overly concentrated weight distribution of the model when generating data, we designed the channel masking (CM). This method spreads attention and allows the model to utilize data from multiple channels. We evaluate the reconstruction performance of the model using EEG data with 1 to 5 bad channels. With EEGLAB's interpolation method as a performance reference, tests have shown that the AMACW models can effectively reconstruct bad channels.

Cascade algorithms for combined acoustic feedback cancelation and noise reduction

This paper presents three cascade algorithms for combined acoustic feedback cancelation (AFC) and noise reduction (NR) in speech applications. A prediction error method (PEM)-based adaptive feedback cancelation (PEM-based AFC) algorithm is used for the AFC stage, while a multichannel Wiener filter (MWF) is applied for the NR stage. A scenario with M microphones and 1 loudspeaker is considered, without loss of generality. The first algorithm is the baseline algorithm, namely the cascade M-channel rank-1 MWF and PEM-AFC, where a NR stage is performed first using a rank-1 MWF followed by a single-channel AFC stage using a PEM-based AFC algorithm. The second algorithm is the cascade (M+1)-channel rank-2 MWF and PEM-AFC, where again a NR stage is applied first followed by a single-channel AFC stage. The novelty of this algorithm is to consider an (M+1)-channel data model in the MWF formulation with two different desired signals, i.e., the speech component in the reference microphone signal and in the loudspeaker signal, both defined by the speech source signal but not equal to each other. The two desired signal estimates are later used in a single-channel PEM-based AFC stage. The third algorithm is the cascade M-channel PEM-AFC and rank-1 MWF where an M-channel AFC stage is performed first followed by an M-channel NR stage. Although in cascade algorithms where NR is performed first and then AFC the estimation of the feedback path is usually affected by the NR stage, it is shown here that by performing a rank-2 approximation of the speech correlation matrix this issue can be avoided and the feedback path can be correctly estimated. The performance of the algorithms is assessed by means of closed-loop simulations where it is shown that for the considered input signal-to-noise ratios (iSNRs) the cascade (M+1)-channel rank-2 MWF and PEM-AFC and the cascade M-channel PEM-AFC and rank-1 MWF algorithms outperform the cascade M-channel rank-1 MWF and PEM-AFC algorithm in terms of the added stable gain (ASG) and misadjustment (Mis) as well as in terms of perceptual metrics such as the short-time objective intelligibility (STOI), perceptual evaluation of speech quality (PESQ), and signal distortion (SD).

Adaptive machine learning method for photoacoustic computed tomography based on sparse array sensor data

Background and objectivePhotoacoustic computed tomography (PACT) is a non-invasive biomedical imaging technology that has developed rapidly in recent decades, especially has shown potential for small animal studies and early diagnosis of human diseases. To obtain high-quality images, the photoacoustic imaging system needs a high-element-density detector array. However, in practical applications, due to the cost limitation, manufacturing technology, and the system requirement in miniaturization and robustness, it is challenging to achieve sufficient elements and high-quality reconstructed images, which may even suffer from artifacts. Different from the latest machine learning methods based on removing distortions and artifacts to recover high-quality images, this paper proposes an adaptive machine learning method to firstly predict and complement the photoacoustic sensor channel data from sparse array sampling and then reconstruct images through conventional reconstruction algorithms. MethodsWe develop an adaptive machine learning method to predict and complement the photoacoustic sensor channel data. The model consists of XGBoost and a neural network named SS-net. To handle data sets of different sizes and improve the generalization, a tunable parameter is used to control the weights of XGBoost and SS-net outputs. ResultsThe proposed method achieved superior performance as demonstrated by simulation, phantom experiments, and in vivo experiment results. Compared with linear interpolation, XGBoost, CAE, and U-net, the simulation results show that the SSIM value is increased by 12.83%, 6.78%, 21.46%, and 12.33%. Moreover, the median R2 is increased by 34.4%, 8.1%, 28.6%, and 84.1% with the in vivo data. ConclusionsThis model provides a framework to predict the missed photoacoustic sensor data on a sparse ring-shaped array for PACT imaging and has achieved considerable improvements in reconstructing the objects. Compared with linear interpolation and other deep learning methods qualitatively and quantitatively, our proposed methods can effectively suppress artifacts and improve image quality. The advantage of our methods is that there is no need for preparing a large number of images as the training dataset, and the data for training is directly from the sensors. It has the potential to be applied to a wide range of photoacoustic imaging detector arrays for low-cost and user-friendly clinical applications.