Search Results (94)

The demand for real-time 4K video streaming has introduced technical challenges due to the high bandwidth, low latency, and minimal jitter required for high-quality user experience. Traditional single-path networks often fail to meet these requirements, especially under network congestion and packet loss conditions, which degrade video quality and disrupt streaming stability. This study evaluates Multipath tunnel- Generic Routing Encapsulation (MPT-GRE), a technology designed to address these challenges by enabling simultaneous data transmission across multiple network paths. By aggregating bandwidth and adapting dynamically to network conditions, MPT-GRE enhances resilience, maintains quality during network disruptions, and offers throughput nearly equal to the sum of its physical paths’ throughput. This feature ensures that even if one path fails, the technology seamlessly continues streaming through the remaining path, significantly reducing interruptions. We measured key video quality metrics to assess MPT-GRE’s performance: Structural Similarity Index Measure (SSIM), Mean Squared Error (MSE), and Peak Signal-to-Noise Ratio (PSNR). Our results confirm that the MPT-GRE tunnel effectively improves SSIM, PSNR, and reduces MSE compared to single-path streaming, offering a more stable, high-quality viewing experience. Our results indicate that analyzing the SSIM, MSE, and PSNR values for 4K video streaming using the MPT tunnel demonstrates a significant performance improvement compared to a single path. The improvement percentages of the SSIM and PSNR values for the MPT tunnel are (29.05% and 29.04%) higher than that of the single path, while MSE is reduced by 81.17% compared to the single path. Full article

The Malaysian Department of Occupational Safety and Health (DOSH) reported that noise-induced hearing loss (NIHL) accounted for 92% of occupational diseases in 2019. To address this, accurate risk assessment is crucial. The current noise evaluation methods are complex and time-consuming, relying on manual calculations and field measurements. An easy-to-use, open-source noise simulator that directly compares the output with national standards would help mitigate this issue. This research aims to develop an advanced noise evaluation tool to assess and predict unregulated workplace noise, providing tailored safety recommendations. Using a representative plant layout, the Sound Pressure Level (SPL) is calculated using MATLAB’s ray tracing propagation model. The model simulates all possible transmission paths from the source to the receiver to derive the resultant SPL. A noise simulation application featuring a graphical user interface (GUI) built with MATLAB’s App Designer (version: R2024a) automates these computations. The simulation results are validated against the DOSH’s safety standards in Malaysia. Additional safety metrics, such as the recommended maximum exposure time and the required Noise Reduction Rating (NRR) for hearing protection, are calculated based on the SPLs for hazardous locations. The simulation algorithm’s functionality is validated against manual calculations, with an average deviation of just 3.06 dB, demonstrating the model’s precision. This tool can assess and predict indoor noise levels, provide information on optimal exposure limits, and recommend necessary protective measures, ultimately reducing the risk of NIHL in factory environments. It can potentially optimise plant floor operations for existing and new facilities, ensuring safer shift operations and reducing worker noise hazard exposure. Full article

Multiband (MB) optical transmission targets increasing the capacity of operators’ optical transport networks. However, nonlinear impairments (NLI) affect each optical channel in the C+L+S bands differently, and, therefore, the routing and spectrum assignment (RSA) problem needs to be complemented with fast and accurate tools to consider the quality of transmission (QoT) within the provisioning process. This paper proposes a digital twin-assisted approach for lightpath provisioning to provide a complete solution for the RSA problem that ensures the required QoT in MB optical networks. The OCATA time domain digital twin is proposed, not only to estimate the QoT of a selected path but also to support the QoT-based channel assignment process. OCATA is based on a Deep Neural Network (DNN) to model the propagation of the optical signal. However, because of the different impacts of nonlinear noise on each channel and the large number of channels that need to be considered in C+L+S MB scenarios, OCATA needs to be adapted to make it scalable, while keeping its high accuracy and fast QoT estimation characteristics. In consequence, a complete methodology is proposed in this work that limits the number of channels being modeled to just a few. Moreover, OCATA-MB helps to mitigate NLI noise by programming the receiver at the provisioning time and thus with very little complexity compared to its equivalent implemented during the operation. NLI noise mitigation can be applied in the case when a lightpath cannot be provisioned because none of the available channels can provide the required QoT, making it an advantageous tool for reducing connection blocking. Exhaustive simulation results demonstrate the remarkable accuracy of OCATA-MB in estimating the QoT for any channel. Interestingly, by utilizing the proposed OCATA-MB-assisted lightpath provisioning approach, a reduction of the blocking ratio exceeding 50% when compared to traditional approaches is shown when NLI noise mitigation is not applied. If NLI mitigation is implemented, an additional over 50% blocking reduction is achieved. Full article

This paper focuses on the noise transmission process, presenting a comprehensive noise transfer model for velocity broad-band seismometers, which elucidate the transmission mechanisms of five distinct noise sources. We analyzed the spectral characteristics of the noise transfer functions across the forward path, feedback path, and data acquisition stages, with a focus on gains, corner frequencies, and the 0 dB point. Numerical simulations and experiments using the CS60 seismometer showed excellent agreement with theoretical predictions, validating the proposed model and associated noise optimization strategies. This study identified effective methods to reduce noise transfer gains, including optimizing the input and feedback mechanical constants and refining gains at various stages. Full article

In recent years, ensuring communication security at the physical layer has become increasingly important due to the transmission of sensitive information over various networks. Traditional approaches to physical layer security often rely on artificial noise generation, which may not offer robust solutions against advanced interception techniques. This study addresses these limitations by proposing a novel security technique based on non-orthogonal signaling using Faster-than-Nyquist (FTN) signaling. Unlike conventional FTN methods that utilize fixed symbol intervals, the proposed technique employs variable symbol intervals encoded as secure information, shared only with legitimate receivers. This encoding enables effective interference cancellation and symbol detection at the receiver, while preventing eavesdroppers from deciphering transmitted signals. The performance of the proposed technique was evaluated using the DVB-S2X system, a practical digital video broadcasting standard. Simulation results demonstrated that the proposed method maintains smooth communication with minimal performance degradation compared to traditional methods. Furthermore, eavesdroppers were unable to decode the transmitted signals, confirming the enhanced security. This research presents a new approach to physical layer security that does not depend on generating artificial noise, offering a path to more secure and efficient communication systems. Full article

Rotor unbalance stands as one of the primary causes of vibration and noise in rotating equipment. Accurate identification of unbalanced positions enables targeted measures for balance correction, thereby reducing vibration and noise levels and enhancing the operational efficiency and stability of the equipment. However, the complexity of rotor structures may lead to a diversity of vibration transmission paths, which complicates the identification of unbalanced positions. In this paper, an experimental platform for rotor systems is established to analyze the change patterns of vibration displacement in rotor systems at four unbalanced positions. Additionally, a rotor dynamics model is developed based on the finite element method and verified through experiments. Furthermore, an unbalanced rotor position identification method based on Long Short-Term Memory (LSTM) neural networks is proposed. This method utilizes multiple sets of measured response data and simulated data from unbalanced rotor positions to train the LSTM network, achieving precise identification of unbalanced positions at various rotational speeds. The research results indicate that under subcritical, critical, and supercritical speeds, the identification accuracy based on measured data reaches 95.5%, while the accuracy based on simulated data remains at a high level of 90.5%. These results fully validate the effectiveness and accuracy of the proposed model and identification method, providing new insights and technical means for identifying unbalanced rotor positions. Full article

In this paper, a four-stage gallium nitride (GaN) low noise amplifier (LNA) using coupled-line (CL) feedback in a 0.15-μm GaN-on-SiC process is proposed. The electromagnetic coupling feedback between the drain and source of each transistor is employed to generate an additional signal path for neutralization, which enhances gain and improves stability performance. A series transmission line-capacitor-transmission line (TL-C-TL) network is introduced between stages of the LNA for wider band interstage matching. The measured results show that the designed LNA achieves a 3-dB bandwidth of 23.6 to 29.8 GHz, a peak gain of 23.7 dB at 25.8 GHz, and a minimum noise figure (NF) of 2.2 dB at 27.8 GHz. The output-referred 1-dB compression point (OP1dB) is 13 dBm. The total power consumption of the LNA is 200 mW. Full article

Free-space optics communication (FSO) can be used as a transmission medium for fiber optic sensing signals to make fiber optic sensing easier to implement; however, interference with the sensing signals caused by the optical turbulence and scattering of airborne particles in the FSO path is a potential problem. This work aims to deep denoise sensed signals from fiber Bragg grating (FBG) sensors based on FSO link transmission using advanced denoising deep learning techniques, such as stacked denoising autoencoders (SDAE). Furthermore, it will demodulate the sensed wavelength of FBGs by applying the deep belief network (DBN) technique. This is the first time the real FBG sensing experiment has utilized the actual noise interference caused by the environmental turbulence from an FSO link rather than adding noise through numerical processing. Consequently, the spectrum of the FBG sensors is clearly modulated by the noise and the issue with peak power variation. This complicates the determination of the center wavelengths of multiple stacked FBG spectra, requiring the use of machine learning techniques to predict these wavelengths. The results indicate that SDAE is efficient in denoising from the FBG spectrum, and DBN is effective in demodulating the central wavelength of the overlapped FBG spectrum. Thus, it is beneficial to implement an FSO link-based FBG sensing system in adverse weather conditions or atmospheric turbulence. Full article

Leak detection in nuclear reactor coolant systems is crucial for maintaining the safety and operational integrity of nuclear power plants. Traditional leak detection methods, such as acoustic emission sensors and spectroscopy, face challenges in sensitivity, response time, and accurate leak localization, particularly in complex piping systems. In this study, we propose a novel leak detection approach that incorporates a rigid guide tube into the insulation layer surrounding reactor coolant pipes and combines this with an advanced detection criterion based on Frequency Center of Gravity shifts and Signal-to-Noise Ratio analysis. This dual-method strategy significantly improves the sensitivity and accuracy of leak detection by providing a stable transmission path for ultrasonic signals and enabling robust signal analysis. The rigid guide tube-based system, along with the integrated criteria, addresses several limitations of existing technologies, including the detection of minor leaks and the complexity of installation and maintenance. By enhancing the early detection of leaks and enabling precise localization, this approach contributes to increased reactor safety, reduced downtime, and lower operational costs. Experimental evaluations demonstrate the system’s effectiveness, focusing on its potential as a valuable addition to the current array of nuclear power plant maintenance technologies. Future research will focus on optimizing key parameters, such as the threshold frequency shift (Δf) and the number of randomly selected frequencies (N), using machine learning techniques to further enhance the system’s accuracy and reliability in various reactor environments. Full article

Electrocardiographic signals (ECG) are ubiquitous, which justifies the research of their optimal storage and transmission. However, proposals for non-uniform signal sampling must take into account the priority of diagnostic data accuracy and record integrity, as well as robustness to noise and interference. In this study, two novel methods are introduced, each utilizing a distinct neural network architecture for optimizing non-uniform sampling of ECG signal. A transformer model refines each time point selection through an iterative process using gradient descent optimization, with the goal of minimizing the mean squared error between the original and resampled signals. It adaptively modifies time points, which improves the alignment between both signals. In contrast, the Temporal Convolutional Network model trains on the original signal, and gradient descent optimization is utilized to improve the selection of time points. Evaluation of both strategies’ efficacy is performed by calculating signal distances at lower and higher sampling rates. First, a collection of synthetic data points that resembled the P-QRS-T wave was used to train the model. Then, the ECG-ID database for real data analysis was used. Filtering to remove baseline wander followed by evaluation and testing were carried out in the real patient data. The results, in particular MSE = 0.0005, RMSE = 0.0216, and Pearson’s CC = 0.9904 for 120 sps in the case of the transformer patient data model, provide viable paths for maintaining the precision and dependability of ECG-based diagnostic systems at much lower sampling rate. Outcomes indicate that both techniques are effective at improving the fidelity between the original and modified ECG signals. Full article

Conserving water resources from scarcity and pollution is the basis of water resource management and water quality monitoring programs. However, due to industrialization and population growth in Malaysia, which have resulted in poor water quality in many areas, this program needs to be improved. A smart water quality monitoring system based on the internet of things (IoT) paradigm was designed to analyze water conditions in real time and enable effective water management. Long-range (LoRa) application of the low-power, wide-area networking concept has become a phenomenon in IoT smart monitoring applications. This study proposes the implementation of a LoRa network in a water quality monitoring system-based IoT approach. The LoRa nodes were embedded with measuring sensors pH, turbidity, temperature, total dissolved solids, and dissolved oxygen, in the designated water stations. They operate at a transmission power of 14 dB and a bandwidth of 125 kHz. The network properties were tested with two different antenna gains of 2.1 dBi and 3 dBi, with three different spread factors of 7, 9, and 12. The water stations were located on the Sungai Pantai and Sungai Anak Air Batu rivers on the Universiti Malaya campus, Malaysia. Following a dashboard display and K-means analysis of the water quality data received by the LoRa gateway, it was determined that both rivers are Class II B rivers. The results from the evaluation of LoRa performance on the received strength signal indicator, signal noise ratio, loss packet, and path loss at best were −83 dBm, 7 dB, <0%, and 64.41 dB, respectively, with a minimum received sensitivity of −129.1 dBm. LoRa has demonstrated its efficiency in an urban environment for smart river monitoring purposes. Full article

The precise monitoring of bearings is crucial for the timely detection of issues in rotating mechanical systems. However, the high complexity of the structures makes the paths of vibration signal transmission exceedingly intricate, posing significant challenges in diagnosing aero-engine bearing faults. Therefore, a Rotational-Spectrum-informed Scale-aware Robustness (RSSR) neural network is proposed in this study to address intricate fault characteristics and significant noise interference. The RSSR algorithm amalgamates a scale-aware feature extraction block, a non-activation convolutional network, and an innovative channel attention block, striking a balance between simplicity and efficacy. We provide a comprehensive analysis by comparing traditional CNNs, transformers, and their respective variants. Our strategy not only elevates diagnostic precision but also judiciously moderates the network’s parameter count and computational intensity, mitigating the propensity for overfitting. To assess the efficacy of our proposed network, we performed rigorous testing using two complex, publicly available datasets, with additional artificial noise introductions to simulate challenging operational environments. On the noise-free dataset, our technique increased the accuracy by 5.11% on the aero-engine dataset compared with the current mainstream methods. Even under maximal noise conditions, it enhances the average accuracy by 4.49% compared with other contemporary approaches. The results demonstrate that our approach outperforms other techniques in terms of diagnostic performance and generalization ability. Full article

The C+L band elastic optical networks (C+L-EONs) increase the network capacity significantly. However, the introduction of an L band enhances the inter-channel stimulated Raman scattering effect (ISRS), consequently deteriorating the quality of transmission (QoT) of the signal. Furthermore, spectrum allocation leads to spectrum fragmentation inevitably, which escalates the bandwidth blocking rate. In addition, in C+L-EONs, a single fiber carries more services, and once one of the links fails, a huge number of requests will be interrupted, resulting in huge economic losses. Therefore, this paper proposes a survivability routing, band, modulation, and spectrum allocation (RBMSA) algorithm that effectively guarantees service survivability and reduces the impact of ISRS and spectrum fragmentation. The algorithm employs shared backup path protection and a band partitioning method, whereby the spectrum resource of the primary path is assigned in the L band and the backup path is assigned in the C band in order to minimize the impact of ISRS on the QoT of the request while ensuring the survivability of the network. Furthermore, a fragmentation metric accounting for both the free and shared spectrum resource is proposed to mitigate both free and shared spectrum fragmentation. The simulation results reveal that the proposed RBMSA algorithm reduces the bandwidth blocking probability (BBP) and the fragmentation rate (FR) by 47.7% and 21.3%, respectively, and improves the optical signal-to-noise ratio (OSNR) by 4.17 dB in NSFNET. In COST239, the BBP, FR, and OSNR are 22.1%, 21.5%, and 4.71 dB, respectively. Full article

This study utilizes the Kerr effect in the analysis of a pulsed electric field (intensity ~10⁸ V/m, limited by the liquid dielectric strength) in deionized water at the sub-nanosecond time scale. The results provide information about voltage waveforms at the field-producing anode (160 kV peak, du/dt > 70 kV/ns). The analysis is based on detecting the phase shifts between measured and reference pulsed laser beams (pulse width, 35 ps; wavelength, 532 nm) using a Mach–Zehnder interferometer. The signal-to-noise ratio of the detected phase shift is maximized by an appropriate geometry of the field-producing anode, which creates a correctly oriented strong electric field along the interaction path and simultaneously does not electrically load the feeding transmission line. The described method has a spatial resolution of ~1 μm, and its time resolution is determined by the laser pulse duration. Full article

Vibration signal analysis is an important means for bearing fault diagnosis. Affected by the vibration of other machine parts, external noise and the vibration transmission path, the impulses induced by a bearing defect in the measured vibrations are very weak. Blind deconvolution (BD) methods can counteract the effect of the transmission path and enhance the fault impulses. Most BD methods highlight fault features of the filtered signals by impulse-featured objective functions (OFs). However, residual noise in the filtered signals has not been well tackled. To overcome this problem, a fuzzy entropy-assisted deconvolution (FEAD) method is proposed. First, FEAD takes advantage of the high noise sensitivity of fuzzy entropy (FuzzyEn) and constructs a weighted FuzzyEn–kurtosis OF to enhance the fault impulses while suppressing noise interference. Then, the PSO algorithm is used to iteratively solve the optimal inverse deconvolution filter. Finally, envelope spectrum analysis is performed on the filtered signal to realize bearing fault diagnosis. The feasibility of FEAD was first verified by the bearing fault simulation signals at constant and variable speeds. The bearing test signals from Case Western Reserve University (CWRU), the railway wheelset and the test bench validated the good performance of FEAD in fault feature enhancement. A comparison with and quantitative results for the other state-of-the-art BD methods indicated the superiority of the proposed method. Full article