Channel Characteristics Research Articles

Knowledge Graph-Based Multi-Objective Recommendation for a 6G Air Interface: A Digital Twin Empowered Approach

In future sixth-generation (6G) communication systems, it is foreseen that complex communication scenarios and critical performance requirements will necessitate more flexible air interface configurations. Traditional air interface adaptation will no longer be applicable to 6G due to issues such as high computational complexity, sub-optimal trade-offs among multi-objective performance metrics, outdated configurations due to fast-varying channels, etc. In this paper, the relevant user behaviors, communication environment, and system are virtualized via the digital twinning technique. Then, a knowledge graph-based multi-objective recommendation framework is proposed to configure the digital twinning air interface to adapt to channel conditions, while balancing various service requirements. First, the knowledge graph is applied to reveal complex dependencies between the air interface and the service requirements, and more importantly, to reconcile possibly contradictory performance targets. Furthermore, the air interface configuration, empowered by the digital twin technique, is able to exploit predicted prior knowledge about user behavior and the channel characteristics, thus improving the utilization efficiency of wireless resources promptly. Moreover, the digital twin technique allows the candidate air interfaces to be virtually verified and compared with little effort. Finally, two case studies are presented to demonstrate the potential of the knowledge graph-based recommendation method for the digital twinning air interface.

Numerical Simulation and Optimization Study on the Flow Field Characteristics of a Double-Slot Spillway

To investigate the flow characteristics of a novel dual-slot overflow channel, a research approach integrating physical experiments and numerical simulations was adopted. A three-dimensional model of the overflow channel was developed, employing the RNG k-ε turbulence model and the VOF two-phase flow model to optimize the numerical simulation of the high-low dual-slot flow field. Physical experiments were conducted to verify and analyze the hydraulic characteristics of the high-low overflow channel, including the longitudinal water surface profile and flow patterns. The numerical simulation results aligned well with the physical model test results. By analyzing the flow field of the dual-slot counterflow spillway, the flow characteristics at both the spillway and outlet sections were identified. This study focused on the water surface profile along the spillway, the pressure distribution, and the counterflow characteristics of the protruding water tongue, and explored optimization strategies for the WES surface and spillway design. Physical model tests were conducted on the final optimized design, yielding good agreement between the theoretical predictions and experimental results, thereby confirming the feasibility of the energy dissipation methods for both high and low spillways. The research outcomes offer valuable references for related engineering applications.

NTS-YOLO: A Nocturnal Traffic Sign Detection Method Based on Improved YOLOv5

Accurate traffic sign recognition is one of the core technologies of intelligent driving systems, which face multiple challenges such as insufficient light and shadow interference at night. In this paper, we improve the YOLOv5 model for small, fuzzy, and partially occluded traffic sign targets at night and propose a high-precision nighttime traffic sign recognition method, “NTS-YOLO”. The method firstly preprocessed the traffic sign dataset by adopting an unsupervised nighttime image enhancement method to improve the image quality under low-light conditions; secondly, it introduced the Convolutional Block Attention Module (CBAM) attentional mechanism, which focuses on the shape of the traffic sign by weighting the channel and spatial features inside the model and color to improve the perception under complex background and uneven illumination conditions; and finally, the Optimal Transport Assignment (OTA) loss function was adopted to optimize the accuracy of predicting the bounding box and thus improve the performance of the model by comparing the difference between two probability distributions, i.e., minimizing the difference. In order to evaluate the effectiveness of the method, 154 samples of typical traffic signs containing small targets and fuzzy and partially occluded traffic signs with different lighting conditions at nighttime were collected, and the data samples were subjected to the CBAM, OTA, and a combination of the two methods, respectively, and comparative experiments were conducted with the traditional YOLOv5 algorithm. The experimental results showed that “NTS-YOLO” achieved a significant performance improvement in nighttime traffic sign recognition, with a mean average accuracy improvement of 0.95% for the target detection of traffic signs and 0.17% for instance segmentation.

Physical reservoir computing with graphene-based solid electric double layer transistor and the information processing capacity analysis

Abstract Physical reservoir computing (PRC) is helpful for power reduction in machine learning technology, although the challenge is to improve computational performance. In this study, we developed a PRC device utilizing ion-electron coupled dynamics in an electric double layer transistor (EDLT) consisting of monolayer graphene channels and Li+ conducting inorganic oxide thin film. The ambipolar transfer characteristics of graphene channels in the EDLT obtained complex and diverse drain current responses, providing high information processing capacity and high PRC performance in the nonlinear autoregressive moving average (NARMA) task.

Identification and pharmacological characterization of pH-sensitive chloride channels in the fall armyworm, Spodoptera frugiperda.

The pH-sensitive chloride channels (pHCls) are unique to invertebrates and play crucial roles in fluid regulation, food selection, and intake. In this study, we identified and isolated two cDNAs encoding the SfpHCl1 and SfpHCl2 subunits from the fall armyworm, Spodoptera frugiperda. Both subunits exhibited similar expression patterns. When expressed in Xenopus laevis oocytes, SfpHCl1 and SfpHCl2 formed functional chloride channels with reversal potentials indicative of chloride selectivity. Shifts in extracellular pH from acidic to alkaline conditions induced inward currents in both SfpHCl1 and SfpHCl2, with EC50 values of pH 8.24 and 8.49, respectively. Zinc ions (Zn2⁺) and the insecticide emamectin benzoate (EB) also activated concentration-dependent inward currents in these channels, whether expressed individually or co-expressed. Notably, SfpHCl1 and SfpHCl2 channels exhibited significant differences in their activation and deactivation time constants. Molecular docking simulations indicated that Zn2⁺ binds to both SfpHCl1 and SfpHCl2 through three hydrogen bonds, while EB interacts with these channels via hydrogen bonds and a salt bridge. These findings elucidate the biophysical and pharmacological characteristics of pH-sensitive, zinc-gated chloride channels, which, being exclusive to invertebrates, present a promising target for the development of highly specific insecticides.

Sub-Terahertz Channel Characterization in a Lecture Room From Measurements With Phase Drift by a Rotating Horn Antenna

Sub-Terahertz Channel Characterization in a Lecture Room From Measurements With Phase Drift by a Rotating Horn Antenna

Research on Blood Cell Image Detection Method Based on Fourier Ptychographic Microscopy

Autonomous Fourier Ptychographic Microscopy (FPM) is a technology widely used in the field of pathology. It is compatible with high resolution and large field-of-view imaging and can observe more image details. Red blood cells play an indispensable role in assessing the oxygen-carrying capacity of the human body and in screening for clinical diagnosis and treatment needs. In this paper, the blood cell data set is constructed based on the FPM system experimental platform. Before training, four enhancement strategies are adopted for the blood cell image data to improve the generalization and robustness of the model. A blood cell detection algorithm based on SCD-YOLOv7 is proposed. Firstly, the C-MP (Convolutional Max Pooling) module and DELAN (Deep Efficient Learning Automotive Network) module are used in the feature extraction network to optimize the feature extraction process and improve the extraction ability of overlapping cell features by considering the characteristics of channels and spatial dimensions. Secondly, through the Sim-Head detection head, the global information of the deep feature map (mean average precision) and the local details of the shallow feature map are fully utilized to improve the performance of the algorithm for small target detection. MAP is a comprehensive indicator for evaluating the performance of object detection algorithms, which measures the accuracy and robustness of a model by calculating the average precision (AP) under different categories or thresholds. Finally, the Focal-EIoU (Focal Extended Intersection over Union) loss function is introduced, which not only improves the convergence speed of the model but also significantly improves the accuracy of blood cell detection. Through quantitative and qualitative analysis of ablation experiments and comparative experimental results, the detection accuracy of the SCD-YOLOv7 algorithm on the blood cell data set reached 92.4%, increased by 7.2%, and the calculation amount was reduced by 14.6 G.

Lightweight UAV Landing Model Based on Visual Positioning

In order to enhance the precision of UAV (unmanned aerial vehicle) landings and realize the convenient and rapid deployment of the model to the mobile terminal, this study proposes a Land-YOLO lightweight UAV-guided landing algorithm based on the YOLOv8 n model. Firstly, GhostConv replaces standard convolutions in the backbone network, leveraging existing feature maps to create additional “ghost” feature maps via low-cost linear transformations, thereby lightening the network structure. Additionally, the CSP structure of the neck network is enhanced by incorporating the PartialConv structure. This integration allows for the transmission of certain channel characteristics through identity mapping, effectively reducing both the number of parameters and the computational load of the model. Finally, the bidirectional feature pyramid network (BiFPN) module is introduced, and the accuracy and average accuracy of the model recognition landing mark are improved through the bidirectional feature fusion and weighted fusion mechanism. The experimental results show that for the landing-sign data sets collected in real and virtual environments, the Land-YOLO algorithm in this paper is 1.4% higher in precision and 0.91% higher in mAP0.5 than the original YOLOv8n baseline, which can meet the detection requirements of landing signs. The model’s memory usage and floating-point operations per second (FLOPs) have been reduced by 42.8% and 32.4%, respectively. This makes it more suitable for deployment on the mobile terminal of a UAV.

MFFOD: Multidomain Feature Fusion Object Detector for Infrared Images

Abstract Infrared imagery surpasses the limitations of visible light images and finds widespread applications in fields such as military reconnaissance and security surveillance. Recent studies on infrared target detection aim to preserve local features and global representations to the greatest extent. However, compared to visible light images, infrared images exhibit inherent challenges such as insufficient texture information and coarse boundaries, which introduce new difficulties to this research. To address these issues, this paper introduces additional information cues from the perspective of enriching feature map information. Specifically, we propose a multidomain feature fusion object detector (MFFOD), whose backbone feature extraction network consists of a convolutional branch and a fast Fourier transform (FFT) branch. This hybrid domain representation enables the extraction of both domain-specific information and global high-frequency and low-frequency information with minimal computational overhead. Furthermore, in the intermediate layers of the network, we have carefully designed a feature injection module that enables comprehensive interaction between channel features and spatial features within a single feature map. Experimental results demonstrate that MFFOD achieves average detection accuracies of 88.97%, 90.32%, and 99.25% on three significant infrared scene datasets, outperforming existing target detection methods. We hope that this general detection algorithm will provide a robust reference for future infrared target detection research.

SDGSAT-1 Cloud Detection Algorithm Based On RDE-SegNeXt

This paper proposes an efficient cloud detection algorithm for Sustainable Development Scientific Satellite (SDGSAT-1) data. The core work includes the following: (1) constructing a SDGSAT-1 cloud detection dataset containing five types of elements: clouds, cloud shadow, snow, water body, and land, with a total of 15,000 samples; (2) designing a multi-scale convolutional attention unit (RDE-MSCA) based on a gated linear unit (GLU), with parallel re-parameterized convolution (RepConv) and detail-enhanced convolution (DEConv). This design focuses on improving the feature representation and edge detail capture capabilities of targets such as clouds, cloud shadow, and snow. Specifically, the RepConv branch focuses on learning a new global representation, reconstructing the original multi-branch deep convolution into a single-branch structure that can efficiently fuse channel features, reducing computational and memory overhead. The DEConv branch, on the other hand, uses differential convolution to enhance the extraction of high-frequency information, and is equivalent to a normal convolution in the form of re-parameterization during the inference stage without additional overhead; GLU then realizes adaptive channel-level information regulation during the multi-branch fusion process, which further enhances the model’s discriminative power for easily confused objects. It is integrated into the SegNeXt architecture based on RDE-MSCA and proposed as RDE-SegNeXt. Experiments show that this model can achieve 71.85% mIoU on the SDGSAT-1 dataset with only about 1/12 the computational complexity of the Swin-L model (a 2.71% improvement over Swin-L and a 5.26% improvement over the benchmark SegNeXt-T). It also significantly improves the detection of clouds, cloud shadow, and snow. It achieved competitive results on both the 38-Cloud and LoveDA public datasets, verifying its effectiveness and versatility.

Effects of Bottom Channel Coverage Ratio on Leakage Current and Static Power Consumption of Vertically Stacked GAA Si NS FETs

Abstract The performance of vertically stacked gate-all-around silicon nanosheet (GAA Si NS) field-effect transistors (FETs) is significantly impacted by the non-ideal characteristics of the bottom channel, primarily due to etching process limitations. These issues lead to variations in the coverage ratio of the bottom channel, which impacts key device characteristics like leakage current and static power consumption. In this study, we used experimentally calibrated 3-D device simulations to analyze the effects of varying bottom channel coverage ratios from 60% to 100% on the GAA Si NS n-/p-type FETs for sub-2-nm technology nodes. Our results reveal an inverse relationship between the coverage ratio and leakage current and static power consumption. Notably, n-/p-type devices at 60% bottom channel coverage ratio exhibiting leakage currents 75.6 and 102.7 times higher than those with 100% bottom channel coverage ratio. This increase is linked to substantial variations in the off-state conduction (18.5%, 75 meV for n-type FETs) and valance (15.3%, 57 meV for p-type FETs) band energies. An 80% bottom channel coverage ratio proves to be an effective compromise, reducing parasitic leakage while addressing manufacturing feasibility. However, achieving a 100% bottom channel coverage ratio remains a critical challenge, highlighting the need for further research on fabrication optimization.

Characteristics of UHF Propagation Channels Due to the Impact of Vegetation on WSN in The Tropical Forests

Characteristics of UHF Propagation Channels Due to the Impact of Vegetation on WSN in The Tropical Forests

CLTNet: A Hybrid Deep Learning Model for Motor Imagery Classification

Background: Brain–computer interface (BCI) technology opens up new avenues for human–machine interaction and rehabilitation by connecting the brain to machines. Electroencephalography (EEG)-based motor imagery (MI) classification is a key component of BCI technology, which is capable of translating neural activity in the brain into commands for controlling external devices. Despite the great potential of BCI technology, the challenges of extracting and decoding brain signals limit its wide application. Methods: To address this challenge, this study proposes a novel hybrid deep learning model, CLTNet, which focuses on solving the feature extraction problem to improve the classification of MI-EEG signals. In the preliminary feature extraction stage, CLTNet uses a convolutional neural network (CNN) to extract time series, channel, and spatial features of EEG signals to obtain important local information. In the deep feature extraction stage, the model combines the long short-term memory (LSTM) network and the Transformer module to capture time-series data and global dependencies in the EEG. The LSTM explains the dynamics of the brain activity, while the Transformer’s self-attention mechanism reveals the global features of the time series. Ultimately, the CLTNet model classifies motor imagery EEG signals through a fully connected layer. Results: The model achieved an average accuracy of 83.02% and a Kappa value of 0.77 on the BCI IV 2a dataset, and 87.11% and a Kappa value of 0.74 on the BCI IV 2b dataset, both of which outperformed the traditional methods. Conclusions: The innovation of the CLTNet model is that it integrates multiple network architectures, which offers a more comprehensive understanding of the characteristics of the EEG signals during motor imagery, providing a more comprehensive perspective and establishing a new benchmark for future research in this area.

Ray tracing simulation and experimental validation for millimeter-wave massive MIMO systems

Abstract Accurate channel characterization is extremely helpful in channel estimation, channel coding, and many other parts of communication system design and can effectively reduce overhead. Ray tracing (RT) shows accurate channel reconstruction for specific maps, but the multipath propagation in indoor scenes is far more complex than in outdoor scenes leading to a challenge for RT. This work presents and validates an RT tool for a massive multiple-input multiple-output (MIMO) system in the millimeter-wave frequency bands with the associated channel beamforming algorithm and provides ideas for channel estimation algorithm in subsequent MIMO systems. The impact of the order of interactions, e.g. reflections and diffractions on the channel impulse response reconstruction are analyzed in the RT simulation. The comparison between RT simulated and measured results shows a reasonable level of agreement. The presented RT tool that can provide complete and accurate channel information is of high value for the design of reliable communication systems.

Deep Learning-Based Optimization for Maritime Relay Networks

The complexity and uncertainty of the marine environment pose significant challenges to the stability and coverage of communication links. Due to the limited coverage range of traditional onshore base stations (BSs) in marine environments, relay technology has become an essential approach to extending communication coverage. However, the rapid variations in marine wireless channels and the complexity of hydrological conditions make it extremely difficult to obtain accurate channel state information (CSI). In particular, dynamic environmental factors such as waves, tides and wind speed cause channel parameters to fluctuate significantly over time. To address these challenges, this paper proposes a cooperative communication strategy based on ships and designs a novel channel modeling method to accurately capture the characteristics of marine wireless channels. Furthermore, a deep learning-based optimization scheme is proposed, which formulates the relay selection problem as a spatiotemporal classification task. By integrating the spatial positions of candidate relays and their communication conditions, the proposed scheme enables real-time selection of the optimal relay while evaluating link connectivity probabilities under hydrological influences. Simulation results confirm that the proposed method achieves high accuracy even in rapidly changing marine environments.

Pharmacological approaches in drug-resistant pediatric epilepsies caused by pathogenic variants in potassium channel genes

Variants in genes encoding for voltage-gated K+ (Kv) channels are frequent cause of drug-resistant pediatric epilepsies. Obtaining a molecular diagnosis gives the opportunity to assess the efficacy of pharmacological strategies based on in vitro features of mutant channels. In this retrospective observational study, we selected patients with drug-resistant pediatric epilepsies caused by variants in potassium channel encoding genes, followed at the Fondazione IRCCS Istituto Neurologico Carlo Besta of Milan, Italy. After the experimental characterization of variants’ functional properties in transiently transfected Chinese Hamster Ovary (CHO) cells, we identified drugs to be used as pharmacological approaches. We recruited six patients carrying different missense variants in four Kv channels (Kv7.2, Kv7.3, Kv3.1, and KNa1.1). In vitro experiments demonstrated that variants in Kv7 channels induced loss-of-function (LoF) effects, while those affecting Kv3.1 or KNa1.1 led to gain-of-function (GoF). Moreover, we found that the Kv7 channels activator gabapentin was able to revert the LoF effects caused by Kv7.2/Kv7.3 variants, and the potassium channel-blocker fluoxetine counteracted the GoF effects in Kv3.1 or KNa1.1 variants. According to experimental data, patients carrying Kv7 variants were treated with gabapentin. While this treatment resulted successful in two patients (#1, Kv7.2 G310S variant; #3, Kv7.3 V359L + Kv7.3 D542N), it resulted detrimental in the remaining case (#2, Kv7.2 D535E), requiring drug withdrawal. The application in vivo of fluoxetine to counteract GoF effects induced by Kv3.1 or KNa1.1 variants determined a significant reduction of both seizure frequency and behavior disturbances in patient #4 (Kv3.1 V425M), and in both subjects carrying KNa1.1 variants (#5, S937G and #6, R262Q). However, for the latter case, this drug was halted due to severe behavioral side effects. For most of the patients herein reported, pharmacological strategies, selected according to the in vitro functional properties of Kv-channels pathogenic variants, resulted in a significant improvement of both epileptic and cognitive features.

Assessing riparian functioning condition for improved ecosystem services: A case study of the Back Creek watershed (Virginia, USA).

Riparian functioning condition refers to a rating and description of the current ecological status of a reach of a riparian ecosystem in consideration of its potential hydrology, vegetation, and geomorphology. Reach rating options are Proper Functioning Condition (PFC), Functional-At-Risk (FAR), Non-Functional, and apparent or monitored trends. We assessed the functioning condition of flowing riverbank areas of Back Creek located in Virginia (USA) following a PFC protocol developed by the U.S. Department of the Interior and the U.S. Department of Agriculture. The PFC protocol involves 17 qualitative assessment items that address three categories of attributes (hydrology, vegetation, and geomorphology); each is answered as "yes", "no," or "not applicable" with an explanation. We discussed key PFC items driving stream water quality contextualizing the previously modeled riparian buffer zones and ecosystem services tradeoffs in the Back Creek watershed published by U.S. Environmental Protection Agency. Using the remote sensing data in the Geographic Information System, we delineated and characterized 26 Back Creek reaches of 41.1km length. We also analyzed the 38-year (1981-2018) daily datasets of the precipitation, surface runoff, temperature, soil moisture pattern, and vegetation types for the watershed. Then, we conducted the PFC assessments using field reconnaissance of 10 reaches, 19.3km of the Back Creek. The field assessments concluded that 52% of the assessed length of Back Creek was in PFC, attributed to diverse vegetation, maintained channel characteristics, floodplain accessibility (where appropriate), and balanced water and sediment loading supplied by individual headwater streams to the Back Creek. 48% of the assessed length was in FAR condition due to land use changes and urban road network. This study provides an exemplar of nonpoint source (diffused pollution generated by land runoff) methodology for identifying key riparian functioning issues and assessing effectiveness of the non-point source best management practice programs.

Streamflow and sediment simulation in the Song River basin using the SWAT model

This study assesses the performance of the Soil and Water Assessment Tool (SWAT) in simulating streamflow and sediment for the Song River watershed, with a focus on calibration, validation, and sensitivity analysis. Thirteen parameters were selected for calibration, with eight identified as highly sensitive, reflecting key hydrological processes of the area. The model was calibrated for the period 1974–1995 and validated from 1996 to 2004, with additional testing using field data collected in 2022–2023 through Acoustic Doppler Current Profiler (ADCP) measurements. Key model adjustments, such as the baseflow recession constant (ALPHA_BF) and channel roughness coefficient (CH_N2), were set to 0.05 and 0.04, respectively, to capture the area’s groundwater dynamics and channel characteristics. The calibration results indicated a strong fit, with R2 values of 0.77, NSE of 0.70, and PBIAS of 17.06, demonstrating good agreement between observed and simulated streamflow. Validation showed slightly lower but acceptable performance, with R2 of 0.75 and NSE of 0.68. Further ADCP validation from field data showed R2 values of 0.79 and 0.78 for two monitoring sites, confirming the model’s reliability. Sediment yield simulations at site-2 yielded R2 values of 0.70 and 0.59 for calibration and validation, with NSE values of 0.53 and 0.52, indicating the model’s capability to simulate both streamflow and sediment accurately. These results demonstrate SWAT’s practical utility for water resource management in similar data-limited regions.

Correlation of instantaneous fading in bidirectional channels of atmospheric laser communication

The instantaneous fading correlation of the bidirectional channel is an important characteristic of the atmospheric random channel. The instantaneous channel state of the transmitting end can be directly obtained through the instantaneous channel state of the receiving end, reducing the overhead of the additional feedback branch. In this paper, an autoregressive (AR) model and an inverse transformation method are used to generate time-dependent optical signals obeying an APD exponential Weibull distribution. Based on the channel reciprocity function, a channel cross-correlation model on the laser communication transmission path is established, and the influence of factors such as transmitting and receiving apertures, zenith angle, transmission distance, altitude, wavelength, and detector responsivity on the cross-correlation coefficient is studied. Based on theoretical research, an experimental system was built to verify the heterogeneous reciprocity model, and the experimental results were basically consistent with the simulation results. A channel state information prediction and evaluation are achieved through a relevant reciprocity theory, providing a basis for the development of adaptive technology in atmospheric laser communications.

Remaining Useful Life Prediction of Rolling Bearings Based on CBAM-CNN-LSTM.

Predicting the Remaining Useful Life (RUL) is vital for ensuring the reliability and safety of equipment and components. This study introduces a novel method for predicting RUL that utilizes the Convolutional Block Attention Module (CBAM) to address the problem that Convolutional Neural Networks (CNNs) do not effectively leverage data channel features and spatial features in residual life prediction. Firstly, Fast Fourier Transform (FFT) is applied to convert the data into the frequency domain. The resulting frequency domain data is then used as input to the convolutional neural network for feature extraction; Then, the weights of channel features and spatial features are assigned to the extracted features by CBAM, and the weighted features are then input into the Long Short-Term Memory (LSTM) network to learn temporal features. Finally, the effectiveness of the proposed model is verified using the PHM2012 bearing dataset. Compared to several existing RUL prediction methods, the mean squared error, mean absolute error, and root mean squared error of the proposed method in this paper are reduced by 53%, 16.87%, and 31.68%, respectively, which verifies the superiority of the method. Meanwhile, the experimental results demonstrate that the proposed method achieves good RUL prediction accuracy across various failure modes.