Search Results (44)

Wireless communications in industrial environments are challenged by severe multipath propagation, which causes significant signal distortion. Conventional mitigation techniques, such as complex equalizers, are unsuitable as they contradict the stringent low-power and low-complexity requirements of Reduced Capability (RedCap) devices. This paper introduces a novel method for optimizing single-carrier pulse shapes under a distortion constraint to combat multipath propagation. The performance was evaluated through simulations in MATLAB 2023b using a ray-traced warehouse model. The results show that the proposed optimal pulses achieve a significant reduction in Error Vector Magnitude (EVM) (up to 40% in non-line-of-sight scenarios) compared to conventional root-raised cosine (RRC) pulses, while adhering to the 20 MHz RedCap bandwidth requirement. Furthermore, this performance is attainable with a low-complexity scaling equalizer. EVM degradation under Doppler shift is estimated and the pilot period required to maintain the target distortion level is specified. The resulting bit rate of approximately 2.9 Mbps supports industrial sensor networks and low-definition video streaming, confirming the approach’s suitability for resource-constrained industrial applications. Full article

In-band full-duplex (IBFD) communication systems offer a promising means of improving spectral efficiency by enabling simultaneous transmission and reception on the same frequency channel. Despite this advantage, self-interference (SI) remains a major challenge to their practical deployment. Among the different SI cancellation (SIC) techniques, this paper focuses on digital SIC methodologies tailored for multiple-input multiple-output (MIMO) wireless transceivers operating under digital beamforming architectures. Two distinct digital SIC approaches are evaluated, employing a generalized memory polynomial (GMP) model augmented with Itô–Hermite polynomial basis functions and a phase-normalized neural network (PNN) to effectively model the nonlinearities and memory effects introduced by transmitter and receiver hardware impairments. The robustness of the SIC is further evaluated under both single off-line training and closed-loop real-time adaptation, employing estimation techniques such as least squares (LS), least mean squares (LMS), and fast Kalman (FK) for model coefficient estimation. The performance of the proposed digital SIC techniques is evaluated through detailed simulations that incorporate realistic power amplifier (PA) characteristics, channel conditions, and high-order modulation schemes. Metrics such as error vector magnitude (EVM) and total bit error rate (BER) are used to assess the quality of the received signal after SIC under different signal-to-interference ratio (SIR) and signal-to-noise ratio (SNR) conditions. The results show that, for time-variant scenarios, a low-complexity adaptive SIC can be realized using a GMP model with FK parameter estimation. However, in time-invariant scenarios, an open-loop SIC approach based on PNN offers superior performance and maintains robustness across various modulation schemes. Full article

With the evolution of 6G technology toward global coverage and multidimensional integration, OTFS modulation has become a research focus due to its advantages in high-mobility scenarios. However, existing OTFS signal detection algorithms face challenges such as pilot contamination, Doppler spread degradation, and diverse interference in complex environments. This paper proposes the SPARSE-OTFS-Net algorithm, which establishes a comprehensive signal detection solution by innovatively integrating sparse random pilot design, compressive sensing-based frequency offset estimation with closed-loop cancellation, and joint denoising techniques combining an autoencoder, residual learning, and multi-scale feature fusion. The algorithm employs deep learning to dynamically generate non-uniform pilot distributions, reducing pilot contamination by 60%. Through orthogonal matching pursuit algorithms, it achieves super-resolution frequency offset estimation with tracking errors controlled within 20 Hz, effectively addressing Doppler spread degradation. The multi-stage denoising mechanism of deep neural networks suppresses various interferences while preserving time-frequency domain signal sparsity. Simulation results demonstrate: Under large frequency offset, multipath, and low SNR conditions, multi-kernel convolution technology achieves significant computational complexity reduction while exhibiting outstanding performance in tracking error and weak multipath detection. In 1000 km/h high-speed mobility scenarios, Doppler error estimation accuracy reaches ±25 Hz (approaching the Cramér-Rao bound), with BER performance of 5.0 × 10⁻⁶ (7× improvement over single-Gaussian CNN’s 3.5 × 10⁻⁵). In 1024-user interference scenarios with BER = 10⁻⁵ requirements, SNR demand decreases from 11.4 dB to 9.2 dB (2.2 dB reduction), while maintaining EVM at 6.5% under 1024-user concurrency (compared to 16.5% for conventional MMSE), effectively increasing concurrent user capacity in 6G ultra-massive connectivity scenarios. These results validate the superior performance of SPARSE-OTFS-Net in 6G ultra-massive connectivity applications and provide critical technical support for realizing integrated space–air–ground networks. Full article

Monitoring Peripheral Oxygen Saturation (SpO₂) is an important vital sign both in Intensive Care Units (ICUs), during surgery and convalescence, and as part of remote medical consultations after of the COVID-19 pandemic. This has made the development of new SpO₂-measurement tools an area of active research and opportunity. In this paper, we present a new Deep Learning (DL) combined strategy to estimate SpO₂ without contact, using pre-magnified facial videos to reveal subtle color changes related to blood flow and with no calibration per subject required. We applied the Eulerian Video Magnification technique using the Hermite Transform (EVM-HT) as a feature detector to feed a Three-Dimensional Convolutional Neural Network (3D-CNN). Additionally, parameters and hyperparameter Bayesian optimization and an ensemble technique over the dataset magnified were applied. We tested the method on 18 healthy subjects, where facial videos of the subjects, including the automatic detection of the reference from a contact pulse oximeter device, were acquired. As performance metrics for the SpO₂-estimation proposal, we calculated the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and other parameters from the Bland–Altman (BA) analysis with respect to the reference. Therefore, a significant improvement was observed by adding the ensemble technique with respect to the only optimization, obtaining 14.32% in RMSE (reduction from 0.6204 to 0.5315) and 13.23% in MAE (reduction from 0.4323 to 0.3751). On the other hand, regarding Bland–Altman analysis, the upper and lower limits of agreement for the Mean of Differences (MOD) between the estimation and the ground truth were 1.04 and −1.05, with an MOD (bias) of −0.00175; therefore, MOD $\pm 1.96\sigma$ = −0.00175 ± 1.04. Thus, by leveraging Bayesian optimization for hyperparameter tuning and integrating a Bagging Ensemble, we achieved a significant reduction in the training error (bias), achieving a better generalization over the test set, and reducing the variance in comparison with the baseline model for SpO₂ estimation. Full article

This article presents the decision feedback equalizer (DFE), the maximum likelihood detection (MLD), and the radius-directed equalization (RDE) algorithms designed in MATLAB-R2018a to equalize the received signal in a dispersive optical link up to 120 km. DFE is essential for improving signal quality in several communication systems, including WiFi networks, cable modems, and long-term evolution (LTE) systems. Its capacity to mitigate inter-symbol interference (ISI) and rapidly adjust to channel variations renders it a flexible option for high-speed data transfer and wireless communications. Conversely, MLD is utilized in applications that require great precision and dependability, including multi-input–multi-output (MIMO) systems, satellite communications, and radar technology. The ability of MLD to optimize the probability of accurate symbol detection in complex, high-dimensional environments renders it crucial for systems where signal integrity and precision are critical. Lastly, RDE is implemented as an alternative algorithm to the CMA-based equalizer, utilizing the idea of adjusting the amplitude of the received distorted symbol so that its modulus is closer to the ideal value for that symbol. The algorithms are tested using a converged 5G mm-wave analog radio-over-fiber (A-RoF) system at 60 GHz. Their performance is measured regarding error vector magnitude (EVM) values before and after equalization for different optical fiber lengths and modulation formats (QPSK, 16-QAM, 64-QAM, and 128-QAM) and shows a clear performance improvement of the output signal. Moreover, the performance of the proposed algorithms is compared to three commonly used algorithms: the simple least mean square (LMS) algorithm, the constant modulus algorithm (CMA), and the adaptive median filtering (AMF), demonstrating superior results in both QPSK and 16-QAM and extending the transmission distance up to 120 km. DFE has a significant advantage over LMS and AMF in reducing the inter-symbol interference (ISI) in a dispersive channel by using previous decision feedback, resulting in quicker convergence and more precise equalization. MLD, on the other hand, is highly effective in improving detection accuracy by taking into account the probability of various symbol sequences achieving lower error rates and enhancing performance in advanced modulation schemes. RDE performs best for QPSK and 16-QAM constellations among all the other algorithms. Furthermore, DFE and MLD are particularly suitable for higher-order modulation formats like 64-QAM and 128-QAM, where accurate equalization and error detection are of utmost importance. The enhanced functionalities of DFE, RDE, and MLD in managing greater modulation orders and expanding transmission range highlight their efficacy in improving the performance and dependability of our system. Full article

In response to the urgent demands of the Internet of Things for precise indoor target positioning and information interaction, this paper proposes a multi-band analog radio-over-fiber mobile fronthaul system. The objective is to obtain the target’s location in indoor environments while integrating remote beamforming capabilities to achieve wireless access to the targets. Vector signals centered at 3, 4, 5, and 6 GHz for indoor positioning and centered at 30 GHz for wireless access are generated centrally in the distributed unit (DU) and fiber-distributed to the active antenna unit (AAU) in the multi-band analog radio-over-fiber mobile fronthaul system. Target positioning is achieved by radiating electromagnetic waves indoors through four omnidirectional antennas in conjunction with a pre-trained neural network, while high-speed wireless communication is realized through a phased array antenna (PAA) comprising four antenna elements. Remote beamforming for the PAA is implemented through the integration of an optical true time delay pool in the multi-band analog radio-over-fiber mobile fronthaul system. This integration decouples the weight control of beamforming from the AAU, enabling centralized control of beam direction at the DU and thereby reducing the complexity and cost of the AAU. Simulation results show that the average accuracy of localization classification can reach 86.92%, and six discrete beam directions are achieved via the optical true time delay pool. In the optical transmission layer, when the received optical power is 10 dBm, the error vector magnitudes (EVMs) of vector signals in all frequency bands remain below 3%. In the wireless transmission layer, two beam directions were selected for verification. Once the beam is aligned with the target device at maximum gain and the received signal is properly processed, the EVM of millimeter-wave vector signals remains below 11%. Full article

Conceptually, this paper aims to help reduce the communication blind spots originating from the design of millimeter-wave (mmW) beamforming by deploying radio units of an open radio access network (O-RAN) with free-space optics (FSOs) as the backhaul and the fiber-optic link as the fronthaul. At frequencies exceeding 24 GHz, the transmission reach of 5G/6G beamforming is limited to a few hundred meters, and the periphery area of the sector operational range of beamforming introduces a communication blind spot. Using FSOs as the backhaul and a fiber-optic link as the fronthaul, O-RAN empowers the radio unit to extend over greater distances to supplement the communication range that mmW beamforming cannot adequately cover. Notably, O-RAN is a prime example of next-generation wireless networks renowned for their adaptability and open architecture to enhance the cost-effectiveness of this integration. A 200 meter-long FSO link for backhaul and a fiber-optic link of up to 10 km for fronthaul were erected, thereby enabling the reach of communication services from urban centers to suburban and remote rural areas. Furthermore, in the context of beamforming, reinforcement learning (RL) was employed to optimize the error vector magnitude (EVM) by dynamically adjusting the beamforming phase based on the communication user’s location. In summary, the integration of RL-based mmW beamforming with the proposed O-RAN communication setup is operational. It lends scalability and cost-effectiveness to current and future communication infrastructures in urban, peri-urban, and rural areas. Full article

The utilization of form-stable phase change materials (PCMs) represents a reliable technology for achieving energy conversion. In this study, starch was impregnated into the layers of expanded vermiculite (EVM) and subsequently carbonized at high temperatures to produce in situ carbon layers modified materials (EVMC), which enhance heat storage efficiency. The EVMC, characterized by its carbon network, acted as encapsulated material, leading to the development of paraffin (P)/EVCM-based shape-stable composite PCM (EVMCP). The latent heat of the EVMCP was measured at 179 J/g, surpassing that of EVMP at 144.8 J/g. This finding suggested that in situ porous carbon significantly improves the heat storage ability. Furthermore, non-isothermal crystallization curves indicated that EVMC markedly accelerated the nucleation and simultaneously restricted the non-isothermal crystal growth. Full article

Slave devices in Ethernet-based fieldbus networks often require extensive reprogramming of applications and replacement of protocol stacks and Ethernet drivers whenever the fieldbus protocol needs to be changed. To address this challenge, we develop a common application programming interface (API) and stack interfaces that enable seamless protocol switching among EtherCAT, PROFINET, and EtherNet/IP without requiring protocol-specific code modifications. The real-time data exchange between the API and each protocol stack is realized in the stack interface by using the synchronization mechanism provided by FreeRTOS. The developed common API and stack interfaces facilitate the development of slave device applications that are universally compatible with multiple protocols, EtherCAT, PROFINET, and EtherNet/IP. Moreover, once a required protocol is selected in the integrated development environment (IDE) software before building the slave device firmware, the corresponding protocol stack and Ethernet drivers are automatically specified and the need to replace protocol stacks or Ethernet drivers is even eliminated when switching protocols. To validate the developed common API and stack interfaces, they were implemented on a slave device using TI’s TMDS243EVM board, and a fieldbus network was built by connecting the slave device to a master device executed by Beckhoff’s TwinCAT on a Windows PC. Experimental results confirmed the API’s functionality, reliability, and practical applicability in streamlining protocol management for Ethernet-based fieldbus networks. Full article

Machine learning technologies have recently emerged as transformative tools for enhancing project management accuracy and efficiency. This study introduces a data-driven model that leverages the hybrid eXtreme Gradient Boosting-Simulated Annealing (XGBoost-SA) algorithm to predict the time and cost of construction projects. By accounting for the complexity of activity networks and uncertainties within project environments, the model aims to address key challenges in project forecasting. Unlike traditional methods such as Earned Value Management (EVM) and Earned Schedule Method (ESM), which rely on static metrics, the XGBoost-SA model adapts dynamically to project data, achieving 92% prediction accuracy. This advanced model offers a more precise forecasting approach by incorporating and optimizing features from historical data. Results reveal that XGBoost-SA reduces cost prediction error by nearly 50% and time prediction error by approximately 80% compared to EVM and ESM, underscoring its effectiveness in complex scenarios. Furthermore, the model’s ability to manage limited and evolving data offers a practical solution for real-time adjustments in project planning. With these capabilities, XGBoost-SA provides project managers with a powerful tool for informed decision-making, efficient resource allocation, and proactive risk management, making it highly applicable to complex construction projects where precision and adaptability are essential. The main limitation of the developed model in this study is the reliance on data from similar projects, which necessitates additional data for application to other industries. Full article

The growing importance of traceability in supply chains requires robust, transparent, and efficient systems to ensure the integrity and authenticity of product journeys. This paper presents a comprehensive characterisation and data model for a generic blockchain-based traceability system, highlighting its implementation using smart contracts on Ethereum-compatible networks, as the Ethereum Virtual Machine (EVM), with its pioneering implementation of smart contracts and its extensive ecosystem; it provides a robust environment for developing decentralised applications. We discuss the advantages of using blockchain technology to notarise traceability activities, ensuring immutability and transparency by exploring two main scenarios, namely one where hash keys (i.e, cryptographic digests) are stored on-chain while detailed data remain off-chain, and another where all traceability data are fully stored on-chain. Each approach is evaluated for its impact on scalability, privacy, storage efficiency, and operational costs. The hash key method offers significant advantages in reducing blockchain storage costs, enhancing privacy, and maintaining data integrity, but it depends on reliable off-chain storage. Conversely, the full on-chain approach guarantees data immutability but at a higher cost and lower scalability. By combining these strategies, a balanced solution can be achieved, leveraging the strengths of both methods to provide a reliable, efficient, and secure blockchain-based traceability system, which is illustrated with a practical implementation to support traceability in the timber sector in Galicia, Spain. This paper aims to provide valuable insights for researchers and practitioners looking to implement or enhance traceability systems using blockchain technology, demonstrating how smart contracts can be effectively utilised to meet the demanding requirements of modern supply chains. Full article

Accurate estimation of construction costs is of foremost importance in construction management processes. Considering the changes and unexpected situations, cost estimations should be revised during the construction process. This study investigates the predictability of earned value management (EVM)-based approaches using machine learning (ML) methods. A total of 2318 data points via 19 EVM-based cost estimation methods were created and six ML methods were used for the analyses. The planned and actual project data of the rough construction activities of a housing project completed in Türkiye were used. The ML methods considered consisted of adaptive neuro-fuzzy inference systems (ANFISs), artificial neural networks (ANNs), Gaussian process regression (GPR), long-short-term memory (LSTM), M5 model trees (M5TREEs), and support vector machines (SVMs). The created models were compared using performance criteria such as mean absolute percentage error (MAPE), relative root means square error (RRMSE), coefficient of determination (R²), Nash–Sutcliffe efficiency coefficient (NSE), and overall index of model performance (OI). Moreover, radar charts, trend graphs, Taylor diagrams, violin plots, and error boxplots were used to evaluate the performance of the estimation models. The results revealed that the classical ANN model outperforms EVM-based cost methods that utilize current ML methods. Full article

Improving the powdery mildew resistance of bitter gourd is highly important for achieving high yield and high quality. To better understand the genetic basis of powdery mildew resistance in bitter gourd, this study analyzed 300 lines of recombinant inbred lines (RILs) formed by hybridizing the powdery mildew-resistant material MC18 and the powdery mildew-susceptible material MC402. A high-density genetic map of 1222.04 cM was constructed via incorporating 1,996,505 SNPs generated by resequencing data from 180 lines, and quantitative trait locus (QTL) positioning was performed using phenotypic data at different inoculation stages. A total of seven QTLs related to powdery mildew resistance were identified on four chromosomes, among which qPm-3-1 was detected multiple times and at multiple stages after inoculation. By selecting 18 KASP markers that were evenly distributed throughout the region, 250 lines and parents were genotyped, and the interval was narrowed to 207.22 kb, which explained 13.91% of the phenotypic variation. Through RNA-seq analysis of the parents, 11,868 differentially expressed genes (DEGs) were screened. By combining genetic analysis, gene coexpression, and sequence comparison analysis of extreme materials, two candidate genes controlling powdery mildew resistance in bitter gourd were identified (evm.TU.chr3.2934 (C3H) and evm.TU.chr3.2946 (F-box-LRR)). These results represent a step forward in understanding the genetic regulatory network of powdery mildew resistance in bitter gourd and lay a molecular foundation for the genetic improvement in powdery mildew resistance. Full article

Software-Defined Networking (SDN) has revolutionized network management by providing unprecedented flexibility, control, and efficiency. However, its centralized architecture introduces critical security vulnerabilities. This paper introduces a novel approach to securing SDN environments using IOTA 2.0 smart contracts. The proposed system utilizes the IOTA Tangle, a directed acyclic graph (DAG) structure, to improve scalability and efficiency while eliminating transaction fees and reducing energy consumption. We introduce three smart contracts: Authority, Access Control, and DoS Detector, to ensure trusted and secure network operations, prevent unauthorized access, maintain the integrity of control data, and mitigate denial-of-service attacks. Through comprehensive simulations using Mininet and the ShimmerEVM IOTA Test Network, we demonstrate the efficacy of our approach in enhancing SDN security. Our findings highlight the potential of IOTA 2.0 smart contracts to provide a robust, decentralized solution for securing SDN environments, paving the way for the further integration of blockchain technologies in network management. Full article

Blockchain technology is becoming more prominent and is being used in many different industries. Data islands have emerged as a result of the difficulty in transferring assets and exchanging information between blockchains because of differences in the underlying technology. Cross-chain technology is becoming increasingly prevalent as a solution to the data security problem. Decentralized blockchain networks frequently use the Hashed Timelock Contract (HTLC) to solve the problem of balancing atomicity and time sensitivity. However, it suffers from drawbacks such as limited security and privacy protection capabilities. To overcome these limitations, a secure and fully functional system named the Exchange Smart Contract (ExchangeSC) has been developed; the ExchangeSC can integrate smart contracts and Paillier homomorphic encryption into the Mid-Account HTLC (MA-HTLC) cross-chain protocol. This integration effectively resolves the problem of low security and privacy protection in the HTLC cross-chain protocol. Specifically, the locked information in the solution is encrypted using homomorphic encryption before uploading to the blockchain, which is operated by participating nodes in the ciphertext domain. The ExchangeSC demonstrates reasonable performance on the official testing network’s EVM platform. Further evaluation of the ExchangeSC-based HTLC cross-chain reveals its superior security and lower time cost compared to the BitXHub cross-chain project. Full article