Search Results (241)

This paper presents the design, optimization, and performance evaluation of a Satellite-Based Augmentation System (SBAS) ground segment tailored to Colombia’s air navigation infrastructure, with emphasis on ionospheric anomalies in equatorial latitudes. The configuration comprises six Reference Stations (RIMS), strategically sited via geometric dilution of precision (GDOP) minimization and airspace demand models from ADS-B data. A simulation suite—integrating STK^®, Radio Mobile™, and Stanford-ESA certified monitors—quantifies service volume, link margins, and protection level compliance. Ionospheric threat characterization uses regional scintillation datasets (σln ≈ 0.36, ROTI₉₅ ≈ 85 mm/km), informing GIVE inflation and dual-frequency pseudorange integrity validation. Simulations confirm the system sustains ≥ 99.8% APV-I availability over the CAR/SAM FIR, with Horizontal and Vertical Protection Levels (HPL/VPL) bounded below 28 m and 46 m. Uplink integrity and GEO broadcast continuity are modelled under worst-case masking and multipath, confirming ICAO Annex 10 SARPs compliance. The architecture achieves a high performance-to-cost ratio, enabling nationwide SBAS coverage with a 65% cost reduction versus legacy navaids. The system is forward-compatible with dual-frequency multi-constellation SBAS (DFMC), supporting future APV-II scalability. These results position Colombia as a regional node for GNSS augmentation, fostering safety, efficiency, and procedural harmonization. Full article

The shift toward Industry 5.0 places human-centred and digitally integrated metrology at the core of modern manufacturing, particularly in the automotive sector, where portable Laser Line Triangulation (LLT) systems must combine accuracy with operator usability. This study addresses the challenge of operator-induced variability by evaluating how algorithmic strategies and mechanical support features jointly influence the performance of a portable LLT device derived from the G3F sensor. A comprehensive Measurement System Analysis was performed to compare three feature extraction algorithms—GC, FIR, and Steger—and to assess the effect of a masking device designed to improve mechanical alignment during manual measurements. The results highlight distinct algorithm-dependent behaviours in terms of repeatability, reproducibility, and computational efficiency. More sophisticated algorithms demonstrate improved sensitivity and feature localisation under controlled conditions, whereas simpler gradient-based strategies provide more stable performance and shorter processing times when measurement conditions deviate from the ideal. These differences indicate a trade-off between algorithmic complexity and operational robustness that is particularly relevant for portable, operator-assisted metrology. The presence of mechanical alignment aids was found to contribute to improved measurement consistency across all algorithms. Overall, the findings highlight the need for an integrated co-design of algorithms, calibration procedures, and ergonomic aids to enhance repeatability and support operator-friendly LLT systems aligned with Industry 5.0 principles. Full article

The Soft Yoke Mooring (SYM) system is a critical single-point mooring method for Floating Production Storage and Offloading systems (FPSOs) in shallow waters. Its articulated thrust roller bearing operates long-term in harsh marine environments, making it prone to failure and difficult to diagnose. To address the issues of non-stationary signals and fault features submerged in strong noise caused by the bearing’s non-rotational oscillatory motion, this paper proposes an adaptive improved diagnosis scheme based on Minimum Entropy Deconvolution (MED). By optimizing Finite Impulse Response (FIR) filter parameters to adapt to the oscillatory operating conditions and combining joint analysis of time-domain indicators and envelope spectra, precise identification of bearing faults is achieved. Research shows that this method effectively enhances fault impact components. After MED processing, the kurtosis value of the fault signal can be significantly increased from approximately 2.6 to over 8.6. Its effectiveness in noisy environments was verified through simulation. Experiments conducted on a 1:10 scale soft yoke model demonstrated that the MED denoising and filtering signal analysis method can effectively identify damage in the thrust roller bearing of the SYM system under marine conditions characterized by high noise and complex frequencies. This study provides an efficient and reliable method for fault diagnosis of non-rotational oscillatory bearings in complex marine environments, holding significant engineering application value. Full article

This paper describes recent engineering designs that allow full-duplex SerDes connectivity between a number of cascaded Xilinx radio frequency system-on-chip (RF-SoC) and VCU FPGA systems. The design allows for unlimited scalability with all-to-all connectivity across FPGA systems and RF-SoCs that allow for bidirectional data transport in streaming mode at a capacity of 50 Gbps per ADC-DAC channel. A custom massively parallel systolic-array architecture supporting 8 parallel data streams from time-interleaved ADC/DACs allow real-time matrix–vector-multiplication (MVM). The MVM can be 8 × 8, 8 × 16, …, 8 × 1024 in supported matrix size, and is demonstrated in real time sustained throughput of 1 TeraMAC/second, for matrix size 8 × 512. The MVM is the building block supporting machine learning and filtering, with the computational graph split across FPGA systems using the SerDes connections. The RF data processed by the FPGA chain can be further utilized for higher-level AI workloads on an NVIDIA DGX Spark platform connected to the system. We demonstrate two platforms in which ZCU111 and ZCU1285 RF-SoC boards perform direct-RF data acquisition, while compute engines operating in real time on VCU128 and VCU129 FPGA boards showcase both digital beamforming and polyphase FIR filterbanking in a real-time bandwidth of 1.0 GHz. Full article

Sustainable strategies for revalorizing food industry by-products are increasingly relevant in the development of modern experimental dermato-cosmetic formulations. In this study, two semisolid cosmetic creams (R10 and EM-R10) were designed using recycled palm oil—physically purified after intensive frying—as the lipid phase. The recycled oil was incorporated strictly within a controlled experimental framework and does not imply cosmetic-grade regulatory compliance. The formulations incorporated distinct bioactive profiles: R10 combined apricot and pineapple extracts with lime essential oil, while EM-R10 integrated fir bud and green tea extracts alongside the same essential oil. Both preparations contained Fragard as a preservative and niacinamide and panthenol as vitaminic components. The physicochemical properties of the formulations were assessed through rheology, confocal microscopy, ATR-FTIR, SEM, DSC, and contact angle measurements. Antimicrobial activity was evaluated against Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, and Candida albicans using disk diffusion and broth microdilution assays. The results demonstrate that, despite partial thermal degradation, recycled palm oil retains modified structural features that influence formulation-related properties relevant to topical systems. EM-R10 showed superior spreadability, adhesion, stability, and diffusion-related performance, as well as improved antimicrobial activity, within the investigated experimental conditions, highlighting recycled palm oil as a promising sustainable lipid phase for experimental dermato-cosmetic formulations, pending further purification, toxicological evaluation, and regulatory compliance assessment. Full article

Adaptive filtering remains a cornerstone of modern signal processing but faces fundamental challenges when confronted with rapidly changing or nonlinear environments. This work investigates the integration of deep learning into adaptive-filter architectures to enhance tracking capability and robustness in non-stationary conditions. After reviewing and analyzing classical algorithms—LMS, NLMS, RLS, and a variable step-size LMS (VSS-LMS)—their theoretical stability and mean-square error behavior are formalized under a slow-variation system model. Comprehensive simulations using drifting autoregressive (AR(2)) processes, piecewise-stationary FIR systems, and time-varying sinusoidal signals confirm the classical trade-off between performance and complexity: RLS achieves the lowest steady-state error, at a quadratic cost, whereas LMS remains computationally efficient with slower adaptation. A stabilized VSS-LMS algorithm is proposed to balance these extremes; the results show that it maintains numerical stability under abrupt parameter jumps while attaining steady-state MSEs that are comparable to RLS (approximately 3 × 10⁻²) and superior robustness to noise. These findings are validated by theoretical tracking-error bounds that are derived for bounded parameter drift. Building on this foundation, a deep-learning-driven adaptive filter is introduced, where the update rule is parameterized by a neural function, $U_{\theta }$, that generalizes the classical gradient descent. This approach offers a pathway toward adaptive filters that are capable of self-tuning and context-aware learning, aligning with emerging trends in AI-augmented system architectures and next-generation computing. Future work will focus on online learning and FPGA/ASIC implementations for real-time deployment. Full article

Far-infrared (FIR)-emitting textiles are increasingly used in sleepwear; however, their influence on sleep physiology has not been comprehensively evaluated with multi-modal wearable sensing. This randomized, double-blind, placebo-controlled crossover study examined whether FIR-emitting garments modulate nocturnal thermoregulation, autonomic activity, and sleep architecture. Fifteen healthy young men completed two overnight laboratory sleep sessions wearing either FIR-emitting garments or visually matched polyester controls. Tympanic membrane temperature (TMT), sweating rate, skin temperature, and humidity were continuously monitored using wearable sensors, and sleep stages and heart rate variability (HRV) were assessed using validated portable systems. Compared with control garments, FIR garments produced consistently lower TMT across the night (p = 0.004) and reduced mid-sleep sweating (condition × time interaction: p = 0.026). The proportion of rapid eye movement (REM) sleep was higher in the FIR condition (22.2% ± 6.5% vs. 18.6% ± 6.5%, p = 0.027), despite no changes in total sleep time or sleep efficiency. A transient increase in low-frequency power during early sleep (p = 0.027) suggested baroreflex-related thermal adjustments without sympathetic activation. These findings indicate that FIR-emitting garments facilitate mild nocturnal heat dissipation and support REM expression, demonstrating their potential as a passive intervention to improve sleep-related thermal environments. Full article

Disease-specific diversity in RNA transcripts stems from RNA splicing, ribosomal abnormalities, and other factors. However, the mechanisms underlying the regulation of rRNA expression in the nucleolus and mRNA expression in the cytoplasm during cancer and neuronal differentiation remain largely unknown. In this article, we review current knowledge and discuss the regulatory mechanisms of rRNA and mRNA expression in human diseases using the splicing model of PUF60 (poly(U) binding splicing factor 60)—also known as FUSE-binding protein-interacting repressor (FIR) (FUBP1-interacting repressor), RoBPI, SIAHBP1, and VRJS (Gene ID: 22827). Noncoding RNAs, much like coding RNAs, have been found to be translated into proteins with significant physiological functions. Splicing is also involved in dominant ORF RNAs implicated in the expression of both noncoding and coding RNAs. Here, we analyze recent findings regarding gene splicing, ribosome formation, and the determination of selected ORFs (dominant ORFs) in a system modeled on FIR splicing in two databases (RefSeq and ENSEMBL). rRNA transcription affects ribosomes, whereas mRNA expression and splicing affect the intracellular proteome. Our objective is to develop efficient methods for identifying biomarkers for disease diagnosis and therapeutic targets. In the field of cancer treatment, therapeutic drugs targeting intracellular signaling have proven effective. Full article

With the explosive growth of global data traffic, long-distance fiber optic transmission systems are continuously evolving towards higher capacity and longer distances. However, to overcome the high complexity of fiber dispersion compensation algorithms, various dispersion compensation techniques have emerged. This paper aims to systematically review and summarize dispersion compensation algorithms in long-distance fiber optic transmission. First, we briefly introduce the physical mechanism of fiber dispersion. Then, this paper focuses on digital domain compensation algorithms, dividing them into two major categories: compensation algorithms without penalty and with penalty. For compensation algorithms without penalty, we elaborate on traditional block processing strategies such as Overlap-Save (OLS), and various enhanced strategies combining intelligent filter segmentation and optimized frequency domain workflows. For compensation algorithms with penalty, we focus on analyzing a scheme that redesigns chromatic dispersion compensation (CDC) algorithm into a hardware-friendly structure using geometric clustering of taps, and finite-impulse-response (FIR) filters based on frequency response approximating the ideal inverse chromatic dispersion (CD) transfer function. By numerical simulation, we analyze the core principles, computational complexity, and compensation performance of each type of algorithm. Finally, this paper summarizes the limitations and development trends of existing dispersion compensation algorithms, pointing out that low-complexity and small-scale deployment algorithm structures will be an important research direction in the future. Full article

This paper presents a comparative assessment of interaction analysis methods applied to a multi-variant offshore power system model. Complementary analytical techniques—eigenvalue analysis, frequency–response characteristics, RGA, DRGA, and GDRG—are used to quantify interactions across electromechanical and electromagnetic frequency ranges. The main novelty of this study is a modified DRGA approach that incorporates a hybrid FIR/IIR digital filtering stage, significantly improving the accuracy of interaction evaluations. The results show that no single method provides complete data and that the enhanced DRGA and GDRG techniques are essential for interaction analysis. The proposed framework offers practical guidelines for analyzing and coordinating control loops in offshore grids. Full article

This paper presents a novel approach to optimal input signal design for open-loop fractional-order system identification, using an integer-order approximation of the fractional operators to minimize the average input power. This is obtained by formulating the problem as an LMI (Linear Matrix Inequality) optimization problem with the limitation of achieving at least a specified model accuracy. The ORA (Oustaloup Recursive Approximation) method has been employed to model the fractional-order differentiation operator in discrete integer-order Output Error model form. The optimal input design is executed using finite-dimensional FIR (Finite Impulse Response) filter spectrum parameterization, where the decision variables are calculated through convex optimization. The A-optimality criterion has been used to examine the relationship between the input signal spectrum power and the accuracy of estimated models. Finally, numerical examples illustrate the proposed approach, confirming the method’s suitability for fractional-order system identification. Full article

We investigate analog and digital pre-emphasis for ultra-high-bit-rate coherent dual-polarization 64-QAM (DP-64QAM) transmission using OptiSystem. Two representative single-wavelength configurations are studied: 64 Gbaud (600 Gb/s payload, 768 Gb/s line rate) and 100 Gbaud (1000 Gb/s payload, 1.2 Tb/s line rate). The transmitter employs raised-cosine pulse shaping (roll-off 0.1) and a 9-bit DAC, while the receiver uses a 9-bit ADC; bandwidth-limiting Bessel/Gaussian filters emulate practical transmitter (Tx) and receiver (Rx) front-end constraints. Analog pre-emphasis (APE) is realized by uploading a measured analog filter response immediately after the DAC to compensate high-frequency roll-off. Digital pre-emphasis (DPE) is implemented before the DAC as a finite-impulse-response (FIR) pre-distortion stage, with taps obtained from the measured frequency response via spectrum mirroring, inverse FFT, Hamming-window smoothing, and normalization. We compare four cases: (i) ideal reference without bandwidth limits; (ii) bandwidth-limited without pre-emphasis; (iii) APE; and (iv) DPE. Bit-error-rate–versus–optical signal-to-noise ratio (OSNR) results show that both APE and DPE substantially mitigate bandwidth-induced penalties and approach the theoretical bound, reducing the OSNR gap to 5.8 dB at 64 Gbaud and 6.6 dB at 100 Gbaud, with operation near the forward error correction (FEC) threshold ($\mathrm{BER}={10}^{-2}$). While DPE offers full programmability, it increases peak-to-average power ratio (PAPR) and may require additional gain headroom. Overall, APE provides an effective rapid-prototyping step prior to DPE deployment, confirming the feasibility of 768 Gb/s and 1.2 Tb/s DP-64QAM links with commercially realistic components, including a 150 GSa/s DAC operating at 1.5 samples/symbol for 100 Gbaud. Full article

Indoor localization based on range measurements in wireless sensor networks involves nonlinear measurement models and is susceptible to non-Gaussian noise, which is associated with complex indoor environments. While particle filters (PFs) are well-suited to such systems, they suffer from sample impoverishment, whereby a diminishing sample diversity leads to failures under various conditions. Hence, this paper proposes a novel hybrid localization algorithm that combines a PF, a finite impulse response (FIR) filter, and an artificial neural network. In the proposed algorithm, the PF serves as the main filter for localization because it performs excellently in nonlinear, non-Gaussian systems under normal operation. The neural network is trained to classify whether the system is operating normally or experiencing a failure, based on estimation results from the PF. If a PF failure is detected by the network, the assisting FIR filter is activated to recover the PF from failures. The localization accuracy and reliability of the proposed neural network-aided hybrid particle/FIR filter are confirmed via comparisons with existing algorithms. Full article

Agricultural and ecological systems are threatened by extreme and compound climate extremes such as hotter droughts. These events are characterized by elevated maximum temperatures, leading to atmospheric drought, and reduced precipitation, leading to soil drought. Such conditions reduce plant productivity and are increasing mortality trees in forests worldwide. Some forest types are particularly vulnerable to hotter droughts such as some European mountain silver fir (Abies alba) forests. However, we still lack conceptual frameworks linking hotter droughts and rising VPD with growth decline and tree death. This review elucidates physiological responses to drought in conifers with a focus on silver fir. In silver fir declining populations, prolonged stomatal closure under elevated VPD can lead to reduced growth, and impaired xylem development, potentially triggering positive feedback that exacerbates hydraulic limitations. We also review the ecological significance of xylem vulnerability to embolism, identifying the critical water potential thresholds that determine silver fir survival and hydraulic failure risk under soil water deficit. These findings underscore the importance of both atmospheric and soil drought as physiological stressors causing forest decline, and highlight the need for further research into adaptive strategies and early warning indicators in tree species. Full article

Plant roots influence soil infiltration by altering its properties like porosity and bulk density, which are essential for ecohydrological cycles. Moso bamboo (Phyllostachys edulis), using its well-developed underground root system, invades neighbor forest communities, thereby influencing root characteristics and soil properties. Although Moso bamboo invasion may alter soil hydrology, its specific impact on soil infiltration capacity and water flow connectivity remains unclear. This work took a fir forest (Cunninghamia lanceolata), mixed fir and bamboo forest, and a bamboo forest which represent three different degrees of invasion: uninvaded, partially invaded, and completely invaded, respectively, as study objects, using double-ring dyeing infiltration method to measure soil infiltration capacity and calculating water flow connectivity index for the root zone. To assess the effects of soil properties and root characteristics on soil infiltration capacity and water flow connectivity, we employed random forest and structural equation modeling. The analysis revealed that Moso bamboo invasion significantly enhanced soil infiltration capacity. Specifically, in partially invaded forests, the initial infiltration rate, stable infiltration rate, and average infiltration rate increased by 31.5%, 26.1%, and 28.5%, respectively. In completely invaded forests, the corresponding increases were 6.6%, 35.6%, and 28.5%. Also, Moso bamboo invasion increased water flow connectivity of root zone, compared to the uninvaded forest, the water flow connectivity index increased by 29.4% in the completely invaded forest and by 15.6% in the partially invaded forest. The marked increase in fine root biomass density (RBD1), fine root length density (RLD1), soil organic carbon (SOC), and non-capillary pores (NCP) and the decrease in soil bulk density (SBD) followed by Moso bamboo invasion effectively improved water flow connectivity and soil infiltration capacity. The analysis identified that RBD1, RLD1, NCP, and SBD as the key drivers of soil infiltration capacity, whereas the water flow connectivity index was controlled mainly by SOC, NCP, RLD1, and RBD1. These findings help clarify the mechanistic pathways of Moso bamboo’s effects on soil infiltration. Full article