Channel Data Research Articles

Calibration and Zeta Functions for the Wacom DTU1141b

To accurately examine and compare Digitally Captured Signature data, the provided data channels (X, Y, F and T) need to be expressed in comparable units. The Force channel data is routinely expressed in unnormalized Pressure Levels hindering the accurate comparison between data collected from different devices. The normalization method using the Zeta Function is used to calibrate and calculate the Zeta Functions of the Wacom DTU1141b.

Ball Grid Array Package Intermittent Partial Connection Defect Analysis in DDR4 Data Channel

Ball Grid Array Package Intermittent Partial Connection Defect Analysis in DDR4 Data Channel

Solar Energy Datasets of Deep Learning Models Incorporating with GK-2A and ASOS Ground Measurements

This study presents the construction and evaluation of a dataset for estimating solar energy using the GK<sup>-2</sup>A satellite and deep learning. The GK<sup>-2</sup>A is currently utilized in real-time for weather observations over the Korean Peninsula. The GK<sup>-2</sup>A satellite features 16 channels, producing radiative channel images at spatial resolutions ranging from 500 m to 2 km, with temporal intervals as short as 2 minutes depending on the area. These satellite data are used in various fields, including meteorology, oceanography, vegetation monitoring, and renewable energy. In this study, we used spectral channel data from the GK<sup>-2</sup>A expended local area satellite from January 2021 to December 2022. For training and evaluating the accuracy of the deep learning model, we utilized data from 98 automated synoptic observing system ground observation sites operated by the Korea Meteorological Administration. A back-propagation neural network model, which showed meaningful results in estimating solar energy, was applied. Various hyperparameters were optimized, and data preprocessing and separation were conducted to optimize the model. The study also compared the performance of the deep learning model with physical models. The BPNN deep learning model achieved a statistical accuracy of root mean squared error (RMSE) 77.32 Wm<sup>-2</sup>, mean bias error (MBE) -0.48 Wm<sup>-2</sup>, and R<sup>2</sup> 0.91, indicating high accuracy. In contrast, the physical model showed an RMSE of 132.01 Wm<sup>-2</sup>, MBE -76.51 Wm<sup>-2</sup>, and an R<sup>2</sup> of 0.74, displaying relatively lower accuracy compared to the deep learning model. Additionally, the spatio-temporal map of solar energy generated by the deep learning model successfully captured the attenuation of radiation due to clouds and the variation in solar energy based on the position of the sun. The solar energy data produced in this study are expected to be useful as input data for various fields such as meteorology, agriculture, environmental monitoring, and marine sciences.

Mapping of Soil Salinity in Absheron Soil Based on SENTINEL-2 Using LightGBM

This paper reflects the results of assessing the degree of salinity of Absheron soils based on remote sensing technologies. The research was conducted in Sumgayit, Guzdek down station, and Yashma districts located in Absheron region of Azerbaijan. Certain areas of the Absheron region are ecologically clean areas with high tourism potential in the future. Classification of soils by salinity degree was performed using indices calculated from data in Sentinel-2 spectral channels. Previously obtained field measurements and ground observations using LightGBM machine learning algorithm were used to confirm the degree of soil salinization in the areas identified on satellite images. Based on a comparative analysis of machine learning and remote sensing methods, it was found that they provide similar results for soil salinity assessment when the study area does not cover a large area of land. This work has important implications for understanding soil salinity dynamics and could be used in further research on land and agricultural management at the regional level. For a country like Azerbaijan, the result obtained by the staff of the Soil Institute is of a local nature.

The Impact of Mobility, Beam Sweeping and Smart Jammers on Security Vulnerabilities of 5G Cells

The vulnerability of 5G networks to jamming attacks has emerged as a significant concern. This paper contributes in two primary aspects. Firstly, it investigates the effect of a multi-jammer on 5G cell metrics, specifically throughput and goodput. The investigation is conducted within the context of a mobility model for user equipment (UE), with a focus on scenarios involving connected vehicles (CVs) engaged in a mission. Secondly, the vulnerability of synchronization signal block (SSB) components is examined concerning jamming power and beam sweeping. Notably, the study reveals that increasing jamming power beyond 40 dBm blackin our specific scenario configuration no longer decreases network throughput due to the re-transmission of packets through the hybrid automatic repeat request (HARQ) process. Furthermore, it is observed that under the same jamming power, the physical downlink shared channel (PDSCH) is more vulnerable than the primary synchronization signal (PSS) and secondary synchronization signal (SSS). However, a smart jammer can disrupt the cell search process by injecting less power and targeting PSS-SSS or physical broadcast channel (PBCH) data compared to a barrage jammer. On the other hand, beam sweeping proves effective in mitigating the impact of a smart jammer, reducing the error vector magnitude root mean square from 51.59% to 23.36% under the same jamming power.

Prediction of Potato Rot Level by Using Electronic Nose Based on Data Augmentation and Channel Attention Conditional Convolutional Neural Networks

Prediction of Potato Rot Level by Using Electronic Nose Based on Data Augmentation and Channel Attention Conditional Convolutional Neural Networks

Linear Detection and Circuit Structure Based on ZF, MMSE Matrix Iteration and Operations

The Multiple-Input Multiple-Output (MIMO) technology substantially boosts the reliability and data transfer rates of communication links by enabling the parallel transmission of multiple data signals over the same frequency bandwidth, utilizing multiple transmit and receive antennas.Despite these enhancements, traditional MIMO systems are approaching their theoretical throughput limits, which underscores the need for developing new algorithms to tackle computational complexity.This paper categorizes the primary technical approaches into three types: iterative approximation methods, matrix operation-based methods, and the emerging Joint channel Estimation and Data detection (JED) algorithms.It delves into the Neumann series approximation for efficient matrix inversion in large-scale MIMO systems, the Gauss-Seidel iterative approach for soft-output detection, and LDLT decomposition-based algorithms for matrix inversion efficiency. Additionally, the paper emphasizes the JED algorithm, such as PROX, which presents a low-complexity solution for large Single-Input Multiple-Output (SIMO) wireless systems.

Aberration correction—impact on image quality and chamber quantification in transthoracic echocardiography

Abstract Aims To improve image quality (IQ) in echocardiography, an aberration correction (AC) algorithm has recently been implemented in commercial scanners. We aimed to study (i) the correlation of a subjective IQ-score and an objective IQ-metric [global image coherence (GIC)], (ii) if AC improved IQ; (iii) if AC affected average values and interobserver agreement of left ventricular (LV) size, LV longitudinal strain, and left atrial (LA) volume. Methods and results From 50 adult patients, where 45 (90%) had cardiovascular disease, unprocessed image data (channel data) were acquired from six standard transthoracic views. The data were processed with and without AC, resulting in 300 pairs of cine-loops. The cine-loops were randomly presented one-by-one to two blinded raters experienced in echocardiography. Both raters scored IQ subjectively from 1 (poor) to 4 (very good) and quantified LV dimensions, volumes and longitudinal strain, and LA volume. IQ-score correlated with GIC, Spearman rho 0.72, P < 0.001. AC improved median IQ-score from 2.5 to 3.0 (Wilcoxon signed rank: P < 0.001). The differences in average values of LV size, LV longitudinal strain, or LA volume with and without AC were not statistically significant and numerically minimal. Measured by intraclass correlation, interobserver agreement of these values was not significantly affected by AC. Conclusion Image quality-score strongly correlated with GIC. Aberration correction improved IQ. However, AC did not lead to statistically significant changes in average values or interobserver agreement of LV size, LV longitudinal strain or LA volume quantification. Likely, the major benefit of AC is enhanced visualization of anatomical details.

Flow characteristics in compound channels with skewed and inclined floodplains

ABSTRACT Understanding the flow characteristics of rivers in both their inbank and overbank flow conditions is an important task for engineers. So far, many studies have been carried out in compound channels with prismatic and non-prismatic floodplains. However, we still don’t understand the effect of the floodplain’s side slope on the flow field in compound channels. This paper presents the results of experiments on compound channels with skewed and inclined floodplains (lateral slope of 0.075). Velocity and boundary shear stress were measured at various experimental sections for different relative depths and two skew angles of 3.81º and 11.31º. Using the measured data, the secondary currents pattern, the apparent shear forces at the vertical interface between the main channel and floodplains, and the discharge distribution in the main channel and the floodplains along the flume have been investigated. The results of the experiments were then compared to the experimental data of the skewed compound channel with flat floodplains. The research shows that due to lateral flow and momentum exchange between flume subsections, the flow velocity, bed shear stress, and percentage discharge on the diverging floodplain are greater than those on the converging floodplain with the same cross-sectional geometry.

Spectrum and Power Efficient Anti-jamming Approach for Cognitive Radio Networks Based on Reinforcement Learning

Background: Spectrum scarcity, spectrum efficiency, power constraints, and jamming attacks are core challenges that face wireless networks. While cognitive radio networks (CRNs) enable the sharing of licensed bands when they are unoccupied, the spectrum should be used efficiently by the secondary user (SU) to ensure a high data rate transmission. In addition, the mobility of the SUs makes power consumption a matter of concern in wireless networks. Because of the open environment, the jamming attack can easily deteriorate the performance and disrupt the connections. Objectives: We aim to enhance the performance of CRN and establish more reliable connections for the SU in the presence of smart jammer by ensuring efficient spectrum utilization and extending the network lifetime. Methods: To achieve our objectives, we propose an anti-jamming approach that adopts frequency hopping. Our approach assumes that SUs observe spectrum availability and channel gain. Then, SU learns the jammer behaviour and goes for the appropriate policy in terms of the number of data and control channels that optimize jointly spectrum efficiency and power consumption. Within, the interaction between the SU and the jammer is modelled as a zero-sum stochastic game, and we employ reinforcement learning (RL) to address this game. Results: SUs learn the optimal policy that maximizes the spectrum efficiency and minimizes the power consumption in the presence of a smart jammer. Simulation results show that the low channel gain leads the SU to select a high number of data channels. However, when the channel gain is high, the SU increases the number of control channels to guarantee a more reliable connection. Taking into account the spectrum efficiency, SUs save their energy by decreasing the number of used channels. The proposed strategy achieves better performance in comparison with myopic learning and the random strategy. Conclusion: Under a jamming attack, considering the gain of utilized channels, SUs select the appropriate number of control and data channels to ensure a reliable, efficient, and long-term connection.

3D City Modeling for Flood Inundation Analysis in Mayjen Sungkono Area, Surabaya

Abstract Floods in urban areas are primarily caused by high rainfall combined with inadequate drainage systems, inappropriate land use changes, and insufficient flood mitigation measures. Surabaya, characterized as a seasonal zone with periodic rainfall, frequently experiences flood inundation due to the loss of water retention areas and an inadequate drainage infrastructure. Three-dimensional (3D) city models provide a detailed representation of urban environments, capturing the three-dimensional geometry of objects and general urban structures. This 3D modeling approach can enhance the understanding of flood depths impacting surrounding buildings and infrastructure and assist in estimating the effects on drainage capacity and catchment areas for long-term planning. In this research, 3D flood inundation modeling was conducted using LiDAR data and aerial photography, integrated with other GIS data. The flood inundation simulation was performed using HEC-RAS, while the 3D modeling was carried out using GIS-based software employing semi-automatic methods. The results of the 3D modeling and visualization indicate potential flood inundation depths reaching up to 5 meters, affecting 1,100 buildings and 30 roads. Further research could focus on improving the accuracy of terrain and channel data used in flood modeling to enhance the precision of the results.

Hydraulic simulation on the extensification area of the Katingan I swamp irrigation area, Central Kalimantan Province

Abstract The Katingan I Swamp Irrigation Area (DIR Katingan I) is projected as one of the Food Estate areas in Indonesia. The food sector development program at DIR Katingan I has been implemented since 2021 through the Strategic Irrigation Modernization and Urgent Rehabilitation Project (SIMURP) programs, including extensions of irrigation networks and structures. This study aims to evaluate flow patterns in DIR Katingan I, particularly in the extensification areas with higher elevation than the existing area and has not undergone the leaching processes. The flow pattern of the macro irrigation system is modelled and simulated using Hydrologic Engineering Center’s River Analysis System (HEC-RAS) 6.4.1 software. The modelling is based on channel geometry data and water level elevation recorded in the Katingan River as a downstream boundary condition. Calibration with measured data in the primary channel resulted in a Manning’s coefficient of 0.03, closest to field conditions (R = 0.81, error = 6.63%). Simulation results show that without control structures, the flow in the irrigation network cannot reach the extensification area with the elevation above +3.9 m. Additionally, the primary channels function as a supply channel and the collector channel as a drainage channel cannot be achieved, allowing water from the leaching process to re-enter the rice fields. Thereafter, it is recommended to operate control structures so that the flow can reach the entire extensification area and the supply and drainage flows can be completely separated. Thus, leaching intensity is expected to increase and the soil ripening process will occur more quickly.

Low-PAPR Joint Channel Estimation and Data Detection in ZP-OTFS System

Low-PAPR Joint Channel Estimation and Data Detection in ZP-OTFS System

High-fidelity robust decoding of multiplexed recording by deep learning

Multiplexing information in light’s fundamental attributes to create supplementary orthogonal data channels has been well heralded as an effective means for optical data storage with greatly enhanced capacities. However, robust decoding methods against inevitable crosstalks associated with experimental noise and writing imperfections as the increase of multiplexing dimensions represent a major hurdle preventing the effective practice of multi-dimensional optical recording. Here, we propose a deep-learning-based retrieval approach for robust decoding multiplexed information. An artificial neural network is trained to learn the crosstalks from multiplexed recording in disordered gold nanorod aggregates with loosened orthogonality constraints. The acquired raw readout images are analyzed by the trained neural network, which allows quick, high-fidelity, and reliable information retrieval from polarization-, wavelength-, and 3D spatially multiplexed data. The smart decoding protocol paves the way toward the mass-production ready and wide-spread application of high-capacity multi-dimensional optical storage.

Signalling overhead minimization aware handover execution using ensemble learning in next generation wireless networks

Upcoming smart intelligent heterogeneous wireless networks (HWNs) and their uses can greatly benefit from the merging of long-term evolution (LTE) sub-6 GHz along with millimeter wave (mmWave) frequencies by boosting the coverage, bandwidth, reliability, seamless connectivity, and high quality of service (QoS). Nevertheless, because of the inability of directed waves in terms of coverage, it is difficult to locate the appropriate mmWave remote radio units (RRUs). Therefore, it is crucial to lessen the burden of the handover signaling processes. In meeting research requirements this paper presents signaling overhead minimization aware handover execution (SOMAHE) model. The SOMAHE model first introduces a novel handover mechanism between LTE and mmWave is presented in this research, followed by a machine learning (ML)-based autonomous handover execution technique. To estimate the handover success rate, the model introduces a feature ensemble learning (FEL) model built using XGBoost (XGB) model that makes use of sampling windows channel data. To conclude, combining FEL into the SOMAHE model reduces signaling overhead while simultaneously increasing the handover success-rate. Experiment results with varying mobile terminals, demonstrate that the SOMAHE model significantly outperforms the existing standard deep q-networks (DQN)-based handover-execution method.

Challenges and Recommendations for Improving Blockchain-Based Bank Transfer Security in 5G IoT Applications

Blockchain technology in 5G IoT applications may improve security and efficiency. This paper examines blockchain-based bank transfer security problems and solutions for IoT applications. The security improvement framework we propose uses modern cryptographic algorithms and efficient consensus procedures for 5G-enabled IoT devices. Simulations show a 35% increase in transaction validation speed, from 2.3 seconds to 1.5 seconds. The framework also reduces energy usage per transaction by 42%, making it better for resource-constrained IoT devices. Bank transfer security indicators including fraud detection accuracy and data integrity increased 28% and 22%, respectively. A standardized threat simulation environment shows that the suggested architecture boosts DDoS resistance by 30%. Scalability, interoperability, and regulatory compliance remain issues despite these advances. This article suggests adaptive scaling algorithms, cross-platform integration solutions, and a compliance roadmap. The suggested methods seek to improve bank transfer security in 5G-enabled IoT environments and promote blockchain technology. It also raises awareness about vulnerabilities in IoT networks, including DDoS attacks and man-in-the-middle attacks, and the risks associated with reliance on third-party service providers. The study focuses on designing complete and adjustable protection for secure IoT and sensor networks, with the network layer sending digital information to the IoT system's server, the internet layer acting as a gateway between computers, and the application layer processing information displayed in the user interface. The paper investigates stealthy data exfiltration on IoT and presents a realistic assault approach and potential implementation strategy. The need for collaboration among enterprises, governments, and network operators is discussed, along with the need for standardized security standards, measures like end-to-end encryption and network segmentation, and education and awareness campaigns to address security concerns. The study explores the vulnerability of IoT devices to attacks, focusing on wireless protocols like Bluetooth and Zigbee. Researchers used two samples of smart light bulbs to analyze their light intensity, revealing that patterns can be seen in a single color at all times. Cross-domain attacks, such as clock mute and clock split attacks, are demonstrated using the I2C master-slave interface. The ADobf method, an obfuscated Trojan detection method, is introduced to mitigate clock attacks in I2C communication. Experimental results were evaluated using Verilog HDL and 45nm FreePDK technology. Finally, this work also explores the security risks posed by IoT devices in both civil and industrial applications, identifying protocol flaws and proposing solutions for analog-based hardware Trojans (HT) activity. The authors use Bluetooth low-energy protocols of smart bulbs to covertly exfiltrate data in conversationally secure air-gapped networks, using the GATT and General Access Profile layers of the protocol hierarchy to implement covert data channels and control the device.

A hydraulic motor fault diagnosis method based on weighted multi-channel information fusion

Abstract In response to the limitations of the existing single-sensor hydraulic motor fault diagnosis model, which includes significant fluctuations in fault identification accuracy, low data utilization, poor reliability, and insufficient generalization ability under variable working conditions, a novel hydraulic motor fault diagnosis method based on weighted fusion of multi-channel data and migration learning is proposed. Firstly, in order to fully extract the fault information in the multi-channel data set of the hydraulic motor, a multi-channel fusion method based on information entropy weighting is proposed. The information entropy method is employed to calculate the fusion weight of each channel of data, and the sampled data of each channel is weighted and fused. Subsequently, the fusion data from the source domain is employed to pre-train the deep transfer model, with the model parameters obtained from this pre-training serving as the initialization parameters for the target domain model. Further, the parameters of the target domain model’s feature extractor are fixed, and the parameters of its classifier are fine-tuned using the target domain’s fusion data. The distance between the source and target domains is reduced by incorporating an attention mechanism and constructing a loss function. The migration from the source domain to the target domain is achieved, which enables the classifier to adapt to the novel target sample recognition task. Ultimately, the experimental results of hydraulic motor migration diagnosis under variable operating conditions demonstrate that the proposed method is efficacious for hydraulic motor fault diagnosis. In comparison to conventional models such as CNN, LSTM and ResNet, the proposed method exhibits superior migration diagnosis accuracy and strong generalization and robustness under variable operating conditions.

Retraction Note: Unlocking the power of recalling enhanced recurrent neural network: channel estimation and data analysis for 5G MIMO communication system

Retraction Note: Unlocking the power of recalling enhanced recurrent neural network: channel estimation and data analysis for 5G MIMO communication system

SDN-controlled multi-user NOMA-OFDM VLC system based on a resource allocation algorithm.

We propose a novel, to our knowledge, resource allocation algorithm (RAA) for a multi-user non-orthogonal multiple access orthogonal frequency division multiplexing (NOMA-OFDM) visible light communication (VLC) system. The proposed algorithm considers both the interference between users and their channel diversity. Bit allocation to users in each iteration is based on the ratio between the already allocated number of bits and the demanded number of bits of users, rather than their channel gains. In this way, the inherent issue of prioritizing strong users in a conventional RAA is avoided. We implement a software-defined-network (SDN)-controlled VLC system with real-time signal generation to validate the proposed RAA. The allocation results in the SDN platform are used to configure a FPGA-based transmitter. Experiments of a two-user NOMA-OFDM system with 115-191 Mbit/s throughput show that the system can dynamically react to the change of data demand and channel responses of users. We further demonstrate that the proposed algorithm outperforms conventional RAAs regardless of the data demand, receiving angles, and distances of users.

Numerical Simulations Using iRIC Nays2DH for Sediment Transport Behaviors in Dam Breach Tests

After the breach of a landslide dam, the sediment in the breach opening will be carried downstream by the breach flood. The river channel will also be eroded by the flood, resulting in bed load transport. Three large-scale dam breach tests were conducted to investigate the sediment transport behavior after a dam breach. The topography data of the creek channel were measured before and after the dam breach tests to understand the sediment transport behavior. The sediment transport simulations of the dam breach tests were conducted using the iRIC Nays2DH software. The simulations focused on three types of test setups: the single dam, single dam with a spur dike, and double dam models. The terrain (DEM) for the numerical model input was designated based on the LiDAR results, and a flow hydrograph during the dam breach tests was applied. The accuracy of the simulations was assessed using the “coverage index” and “mean absolute percentage error”. A numerical parametrical study was performed to find the major parameters that influenced the simulations. The results showed that the dynamic behavior of water flow and sediment during the dam breach processes were effectively captured by the iRIC Nays2DH simulation, but with limitations. The average flow velocity of the flood in the single dam case was the fastest among the three types of dam breaches. Due to the contraction of the creek channel caused by the spur dike, severe erosion occurred locally, and the flow rate increased in the narrowed section. Water impoundment between the two dams after the first dam breach and the consequent breach of the second dam were also well-simulated for the double dam breach. The findings and simulations in this study help explain dam breaches better and can guide researchers working on sediment transport during dam-breach floods.