Search Results (217)

There has been growing interest in constructing mid- and high-rise wooden buildings in recent years. To ensure the feasibility of these structures, it is necessary to provide evidence that their long-term reliability can be guaranteed. While long-term testing is typically necessary, a continuous monitoring system for the moisture content of wood materials used in buildings has been proposed as an alternative. The proposed method measures the change in the local moisture content using the equilibrium moisture content calculated from the temperature and humidity measured using temperature and humidity sensors. The study used Japanese cypress specimens with dimensions of 50 mm, 75 mm, and 100 mm cubes and Douglas fir specimens of 50 mm cubes. The moisture content was measured under various external environments. Results showed that this system effectively captured changes in local moisture content, reflecting fluctuations in temperature and humidity in a controlled thermo-hygrostat over a three-day moisture absorption environment (20 °C, 95% humidity). Additionally, it was observed that higher moisture content levels yielded correspondingly higher local moisture content measurements compared to those obtained using the oven-drying method. Full article

This paper presents a novel analytical approach for the efficient design of a particular class of 2D FIR filters, having a frequency response with an elliptically shaped support in the frequency plane. The filter design is based on a Gaussian shaped prototype filter, which is frequently used in signal and image processing. In order to express the Gaussian prototype frequency response as a trigonometric polynomial, we developed it into a Fourier series up to a specified order, given by the imposed approximation precision. We determined analytically a 1D to 2D frequency transformation, which was applied to the factored frequency response of the prototype, yielding directly the factored frequency response of a directional, elliptically shaped 2D filter, with specified selectivity and an orientation angle. The designed filters have accurate shapes and negligible distortions. We also designed a 2D uniform filter bank of elliptical filters, which was then applied in decomposing a test image into sub-band images, thus proving its usefulness as an analysis filter bank. Then, the original image was accurately reconstructed from its sub-band images. Very selective directional elliptical filters can be used in efficiently extracting straight lines with specified orientations from images, as shown in simulation examples. A computationally efficient implementation at the system level was also discussed, based on a polyphase and block filtering approach. The proposed implementation is illustrated for a smaller size of the filter kernel and input image and is shown to have reduced computational complexity due to its parallel structure, being much more arithmetically efficient compared not only to the direct filtering approach but also with the most recent similar implementations. Full article

This study presents a comprehensive performance evaluation of field-programmable gate array (FPGA), graphics processing unit (GPU), and central processing unit (CPU) platforms for implementing finite impulse response (FIR) filters in semiconductor-based digital signal processing (DSP) systems. Utilizing a standardized FIR filter designed with the Kaiser window method, we compare computational efficiency, latency, and energy consumption across the ZYNQ XC7Z020 FPGA, Tesla K80 GPU, and Arm-based CPU, achieving processing times of 0.004 s, 0.008 s, and 0.107 s, respectively, with FPGA power consumption of 1.431 W and comparable energy profiles for GPU and CPU. The FPGA is 27 times faster than the CPU and 2 times faster than the GPU, demonstrating its suitability for low-latency DSP tasks. A detailed analysis of resource utilization and scalability underscores the FPGA’s reconfigurability for optimized DSP implementations. This work provides novel insights into platform-specific optimizations, addressing the demand for energy-efficient solutions in edge computing and IoT applications, with implications for advancing sustainable DSP architectures. Full article

Objective: This study aimed to investigate the efficacy of far-infrared (FIR) garments in enhancing recovery following resistance exercise in recreationally active individuals. Methods: Ten recreationally active adults (six females, four males; aged 20.7 ± 3.2 years) completed a resistance exercise protocol and were randomly selected to wear either FIR (n = 5) or placebo (n = 5) tights post-exercise. The FIR garments incorporated Celliant-based fibers emitting wavelengths in the 2.5–20 µm range. The participants’ recovery was assessed using countermovement jump (CMJ) metrics, including their jump height, takeoff velocity, and modified reactive strength index (mRSI), along with their fatigue biomarkers and subjective recovery perceptions. The CMJ performance was tested immediately post-exercise and at 24 and 48 h. Results: The FIR garments led to significant improvements in neuromuscular recovery, with greater increases in the jump height, takeoff velocity, and mRSI observed at 48 h post-exercise (p < 0.05). Notably, the mRSI showed earlier improvements at 24 h. The fatigue biomarkers did not differ between the groups (p > 0.05), suggesting localized rather than systemic recovery effects. The participants in the FIR group reported faster subjective recovery, with a readiness to resume activity perceived within 48 h, compared to slower recovery in the placebo group. Conclusions: FIR garments may enhance neuromuscular recovery and subjective recovery perceptions following resistance exercise, likely by improving the peripheral blood flow, metabolic clearance, and tissue oxygenation. These findings suggest that FIR garments may be effective in enhancing both neuromuscular and perceived recovery following resistance exercise, supporting their potential use as a post-exercise recovery tool. Full article

The knowledge of the radiative properties of clouds and the atmospheric state is of fundamental importance in modelling phenomena in numerical weather predictions and climate models. In this study, we show the results of the retrieval of cloud properties, along with the atmospheric state and the surface temperature, from far-infrared spectral radiances, in the 100–1000 cm⁻¹ range, measured by the Far-Infrared Radiation Mobile Observation System-Balloon version (FIRMOS-B) spectroradiometer from a stratospheric balloon launched from Timmins (Canada) in August 2022 within the HEMERA 3 programme. The retrieval study is performed with the Optimal Estimation inversion approach, using three different forward models and retrieval codes to compare the results. Cloud optical depth, particle effective size, and cloud top height are retrieved with good accuracy, despite the relatively high measurement noise of the FIRMOS-B observations used for this study. The retrieved atmospheric profiles, computed simultaneously with cloud parameters, are in good agreement with both co-located radiosonde measurements and ERA-5 profiles, under all-sky conditions. The findings are very promising for the development of an optimised retrieval procedure to analyse the high-precision FIR spectral measurements, which will be delivered by the ESA FORUM mission. Full article

Amid global environmental change, the intensification of nitrogen (N) deposition exerts critical impacts on the growth of forest vegetation and the structure and function of ecosystems in subtropical China. However, the physiological and growth response mechanisms of subtropical tree species remain poorly understood. This study explored adaptive mechanisms of typical subtropical tree species to N deposition, analyzing biomass accumulation, root plasticity, and nutrient/photosynthate allocation strategies. One-year-old potted seedlings of Phyllostachys edulis (moso bamboo) and Cunninghamia lanceolata (Chinese fir) were subjected to four N-addition treatments (N0: 0, N1: 6 g·m⁻²·a⁻¹, N2: 12 g·m⁻²·a⁻¹, N3: 18 g·m⁻²·a⁻¹) for one year. In July and December, measurements were conducted on seedling organ biomass, root morphological and architectural traits, as well as nutrient elements (N and phosphorus(P)) and non-structural carbohydrate (soluble sugars and starch) contents in roots, stems, and leaves. Our results demonstrate that the Chinese fir exhibits stronger tolerance to N deposition and greater root morphological plasticity than moso bamboo. It adapts to N deposition by developing root systems with a higher finer root (diameter ≤ 0.2 mm) ratio, lower construction cost, greater branching intensity and angle, and architecture approaching dichotomous branching. Although N deposition promotes short-term biomass and N accumulation in both species, it reduces P and soluble sugars contents, leading to N/P imbalance and adverse effects on long-term growth. Under conditions of P and photosynthate scarcity, the Chinese fir preferentially allocates soluble sugars to leaves, while moso bamboo prioritizes P and soluble sugars to roots. In the first half of the growing season, moso bamboo allocates more biomass and N to aboveground parts, whereas in the second half, it allocates more biomass and P to roots to adapt to N deposition. This study reveals that Chinese fir enhances its tolerance to N deposition through the plasticity of root morphology and architecture, while moso bamboo exhibits dynamic resource allocation strategies. The research identifies highly adaptive root morphological and architectural patterns, demonstrating that optimizing the allocation of elements and photosynthates and avoiding elemental balance risks represent critical survival mechanisms for subtropical tree species under intensified N deposition. Full article

Pedestrian detection is an important research topic in the field of visual cognition and autonomous driving systems. The proposal of the YOLO model has significantly improved the speed and accuracy of detection. To achieve full day detection performance, multimodal YOLO models based on RGB-FIR image pairs have become a research hotspot. Existing work has focused on the design of fusion modules after feature extraction of RGB and FIR branch backbone networks, achieving a multimodal backbone network framework based on back-end fusion. However, these methods overlook the complementarity and prior knowledge between modalities and scales in the front-end raw feature extraction of RGB and FIR branch backbone networks. As a result, the performance of the backend fusion framework largely depends on the representation ability of the raw features of each modality in the front-end. This paper proposes a novel RGB-FIR multimodal backbone network framework based on a cross-modality context attentional model (CCAM). Different from the existing works, a multi-level fusion framework is designed. At the front-end of the RGB-FIR parallel backbone network, the CCAM model is constructed for the raw features of each scale. The RGB-FIR feature fusion results of the lower-level features of the RGB and FIR branch backbone networks are fully utilized to optimize the spatial weight of the upper level RGB and FIR features, to achieve cross-modality and cross-scale complementarity between adjacent scale feature extraction modules. At the back-end of the RGB-FIR parallel network, a channel-space joint attention model (CBAM) and self-attention models are combined to obtain the final RGB-FIR fusion features at each scale for those RGB and FIR features optimized by CCAM. Compared with the current RGB-FIR multimodal YOLO model, comparative experiments on different performance evaluation indicators on multiple RGB-FIR public datasets indicate that this method can significantly enhance the accuracy and robustness of pedestrian detection. Full article

As the proportion of renewable energy continues to increase, the large-scale grid integration of photovoltaic (PV) generation presents new technical challenges for reactive power balance in power systems. This paper proposes a coordinated reactive power and voltage optimization method based on Filtered Multiband Decomposition (FMD). First, to address the stochastic fluctuations of PV power, an improved FMD-based prediction model is developed. The model employs an adaptive finite impulse response (FIR) filter to decompose signals and captures periodicity and uncertainty through kurtosis-based feature extraction. By utilizing adaptive function windows for multiband signal decomposition, combined with kernel principal component analysis (KPCA) for dimensionality reduction and a long short-term memory (LSTM) network for prediction, the model significantly enhances forecasting accuracy. Second, to tackle the challenges of integrating high-penetration distributed PV while maintaining reactive power balance, a multi-head attention-based velocity update strategy is introduced within a multi-objective particle swarm optimization (MOPSO) framework. This strategy quantifies the spatial distance and fitness differences of historical best solutions, constructing a dynamic weight allocation mechanism to adaptively adjust particle search direction and step size. Finally, the effectiveness of the proposed method is validated through an improved IEEE 33-bus test case. Full article

The Laser Light Screen System faces critical technical challenges in high-speed, long-range target detection: when a target passes through the light screen, weak light flux variations lead to significantly degraded signal-to-noise ratios (SNRs). Traditional signal processing algorithms fail to effectively suppress phase distortion and boundary effects under extremely low SNR conditions, creating a technical bottleneck that severely constrains system detection performance. To address this problem, this paper proposes a Multi-stage Collaborative Filtering Chain (MCFC) signal processing framework incorporating three key innovations: (1) the design of zero-phase FIR bandpass filtering with forward–backward processing and dynamic phase compensation mechanisms to effectively suppress phase distortion; (2) the implementation of a four-stage cascaded collaborative filtering strategy, combining adaptive sampling and anti-aliasing techniques to significantly enhance signal quality; and (3) the development of a multi-scale adaptive transform algorithm based on fourth-order Daubechies wavelets to achieve high-precision signal reconstruction. The experimental results demonstrate that under −20 dB conditions, the method achieves a 25 dB SNR improvement and boundary artifact suppression while reducing the processing time from 0.42 to 0.04 s. These results validate the proposed method’s effectiveness in high-speed target detection under low SNR conditions. Full article

As a model predictive control (MPC) technique, dynamic matrix control (DMC) has gained widespread industrial adoption due to its straightforward model construction and clear physical interpretation. However, its effectiveness relies on the accuracy of the predictive model, where measurement inaccuracies or excessive noise in step-response coefficients may significantly degrade control performance. This study enhances robustness of DMC by implementing finite impulse response (FIR) filters on measured step-response coefficients while providing theoretical proof of its stability. The improved algorithm is applied to cold helium temperature control of the modular High-Temperature Gas-Cooled Reactor (mHTGR). A cascade control structure is adopted, where the inner loop uses a PID controller to ensure system stability, while the outer loop uses DMC to adjust the setpoint of the hot helium temperature, thereby controlling the cold helium temperature. Numerical simulation results demonstrate significant improvements in temperature control performance and enhanced robustness of the modified DMC method. Full article

Background: The importance and etiology of meconium-stained amniotic fluid (MSAF) in preterm pregnancies are still poorly understood. Among other factors, intrauterine inflammation is proposed to be a pathophysiological change associated with MSAF. To study the extent of intrauterine inflammation, histological evaluation represents the “gold standard” of diagnostics. Objectives: To investigate the concomitant occurrence of MSAF and histological chorioamnionitis (HCA) and fetal inflammatory response (FIR). To investigate the incidence of short-term neonatal outcomes in preterm infants born from MSAF. Materials and methods: We conducted a single-center retrospective study in a tertiary neonatal intensive care unit between 2020 and 2022. 237 preterm infants born ≤ 32 weeks or with ≤1500 g birthweight were investigated. The group of infants born from MSAF was compared to the group of infants born from clear amniotic fluid (CAF). The variables measured were the following: HCA, FIR, maternal and fetal vascular malformations (MVM, FVM), maternal clinical and laboratory signs of chorioamnionitis (CA), early neonatal outcomes, neonatal white blood cell count (WBC) in the first day of life, and neonatal c-reactive protein (CRP) level on the second day of life. Histological evaluation of the placenta and the umbilical cord was based on the recommendation of the 2014 Amsterdam Placental Workshop Group Consensus Statement (APWGCS). Results: Out of 237 preterm infants (mean gestational age: 28.6 (95% CI: 28.2; 28.9) weeks, mean birth weight: 1165 (95% CI: 1110; 1218) grams), 22 were born from MSAF. There was no difference between the perinatal characteristics of the two groups. A higher incidence of HCA (54.5% vs. 32.6%; p: <0.001), a higher incidence of stage 3 HCA (45.4% vs. 9.3%), a higher incidence of FIR (50% vs. 16.7%; p: <0.001), and a higher incidence of stage 3 FIR (18.2% vs. 1.9%) were found in the MSAF group in comparison with the CAF group. A higher incidence of elevated (>30 mg/L) maternal CRP level (36.8% vs. 15.3%; p: 0.02) and elevated (>15 mg/L) neonatal CRP level (31.8% vs. 14.4%; p: 0.03) was detected in the MSAF group. Among neonatal complications, severe (Stage III/IV) intraventricular hemorrhage (IVH) had a higher incidence in the MSAF group (22.2% vs. 5.1%; p: 0.005). Conclusion: MSAF in preterm pregnancies is associated with a severe maternal and fetal inflammatory response in the placenta and the umbilical cord. MSAF is also accompanied by elevated systemic inflammatory parameters and a higher incidence of severe neonatal IVH as well. Full article

This article explores systemic inflammatory response syndrome (SIRS), thromboinflammation, and septic shock in fetuses and neonates, offering a comprehensive examination of their pathophysiology, diagnostic criteria, and clinical implications. It identifies SIRS as an exaggerated response to external stress, disrupting the balance between inflammation and adaptive mechanisms, driven by cytokines such as TNF-α and IL-1. The fetal inflammatory response syndrome (FIRS), a subset of SIRS, is noted for its role in adverse neonatal outcomes, including organ damage, inflammation, and long-term developmental disorders. The article discusses the extensive effects of FIRS on critical systems, including the blood, lungs, central nervous system, and kidneys. It highlights the challenges in diagnosing and managing septic shock in neonates, focusing on the relationship between inflammation and the hemostatic system. Additionally, the paper points out recent advancements, such as the convergent model of coagulation and emerging biomarkers like microRNAs for early detection. Despite this progress, gaps remain in understanding the molecular mechanisms underlying these conditions and in developing effective therapeutic strategies. This highlights the necessity for targeted research to mitigate the morbidity and mortality associated with septic shock in neonates. Full article

This study investigates the use of surface electromyography (sEMG) sensors in measuring muscle activity and mapping it onto wearable textile stretch sensors using a basic deep learning model, the Multi-Layer Perceptron (MLP). Wearable sensors are gaining attention for their ability to monitor physiological data while maintaining user comfort. A three-stage experimental approach was employed to evaluate the mapping process. In the first stage, the impact of applying a low-pass finite impulse response (FIR) filter was assessed by comparing filtered and unfiltered sEMG data. The results showed minimal impact on accuracy (R-squared ~ 0.77), as RMS preprocessing effectively reduced noise. In the second stage, adding tensile velocity data improved the model’s predictive performance (R-squared ~ 0.80), emphasizing the importance of integrating dynamic variables. In the third stage, data from multiple muscle groups, including the biceps brachii, forearm muscles, and triceps brachii, were incorporated, achieving the highest R-squared value of ~0.94. These findings establish wearable textile stretch sensors as reliable tools for monitoring muscle activity during exercise. By demonstrating improved accuracy with a basic MLP model, this study provides a foundation for advancing wearable health monitoring systems and exploring additional physiological parameters and activities. Full article

Trajectory tracking is a critical component of autonomous driving and robotic motion control. This paper proposes a novel robust finite impulse response (FIR) filter for linear time-invariant systems, aimed at enhancing the accuracy and robustness of trajectory tracking. To address the limitations of infinite impulse response (IIR) filters in complex environments, we integrate a cumulative risk–sensitive criterion with an FIR structure. The proposed filter effectively mitigates model mismatches and temporary modeling uncertainties, making it highly suitable for trajectory tracking in dynamic and uncertain environments. To validate its performance, a comprehensive vehicle trajectory tracking experiment is conducted. The experimental results demonstrate that, compared to the Kalman filter (KF), risk–sensitive filter (RSF), and unbiased FIR (UFIR) filter, the proposed algorithm significantly reduces the average tracking error and exhibits superior robustness in complex scenarios. This work provides a new and effective solution for trajectory tracking applications, with broad potential for practical implementation. Full article

This paper proposes an accuracy enhancement method for fiber-longitudinal power profile estimation (PPE) based on convolutional neural networks (CNN). Two types of CNNs are designed. The first network treats different polarization streams identically and is denoted as CNN. The second network considers the difference between the contributions of different polarization streams to the nonlinear phase shift and is denoted as enhanced CNN (ECNN). The numerical simulation results confirm the effectiveness of the method for a 64 Gbaud/s quadrature phase-shift keying (QPSK) polarization-division-multiplexed (PDM) coherent optical communication system with a fiber length of 320 km. The effects of finite impulse response (FIR) filter length, power into the fiber, and polarization mode dispersion on the PPE accuracy are examined. Finally, the results of the proposed method are monitored in the presence of several simultaneous power attenuation anomalies in the fiber optic link. It is found that the accuracy of the PPE substantially improves after using the proposed method, achieving a relative gain of up to 71%. When the modulation format is changed from QPSK to 16-ary quadrature amplitude modulation (16-QAM), and the fiber length is increased from 360 km to 480 km, the proposed method is still effective. This work provides a feasible solution for implementing fiber-longitudinal PPE, enabling significantly improved estimation accuracy in practical applications. Full article