Search Results (24,485)

Internet of Things (IoT) devices play a crucial role in the real-time acquisition of photoplethysmography (PPG) signals, facilitating seamless data transmission to cloud-based platforms for analysis. Atrial fibrillation (AF), affecting approximately 1–2% of the global population, requires accurate detection methods due to its prevalence and health impact. This study employs IoT devices to capture PPG signals and implements comprehensive preprocessing steps, including windowing, filtering, and artifact removal, to extract relevant features for classification. We explored a broad range of machine learning (ML) and deep learning (DL) approaches. Our results demonstrate superior performance, achieving an accuracy of 97.7%, surpassing state-of-the-art methods, including those with FDA clearance. Key strengths of our proposal include the use of shortened 15-second traces and validation using publicly available datasets. This research advances the design of cost-effective IoT devices for AF detection by leveraging diverse ML and DL techniques to enhance classification accuracy and robustness. Full article

To achieve the in situ capacity expansion of the post-denitrification biological aerated filter (BAF-DN), the integration of BAF with the anammox process (BAF/AX) was proposed. With the objective of maximizing retaining ammonia nitrogen, the operational optimization of BAF was achieved by two distinct strategies. The treatment performance of BAF demonstrated that the removal efficiencies of chemical oxygen demand (COD) and ammonia nitrogen () was 66.3~67.3% and 4~12%, respectively, under conditions of low aeration intensity (0.4 m³·m⁻²·h⁻¹) or a shortened empty bed residence time (EBRT) of 30 min. Residual in the BAF effluent served as the ammonia substrate for the subsequent anammox process, which was successfully launched by using ceramic particles and sponges as carriers. Notably, the sponge carrier facilitated a shorter start-up period of 41 to 44 days. Furthermore, the sponge-based anammox reactor exhibited a superior removal capacity (≥85.7%), under operations of a shorter EBRT of 40 min, low influent concentrations (≤30 mg/L), and COD levels of ≤67 mg/L. In addition, a comprehensive evaluation of the BAF/AX process was conducted, which considered performance, cost-effectiveness, and engineering feasibility. The performance results illustrated that the effluent quality met the standard well (with a COD level of ≤50 mg/L, and a TN of ≤3.1~10.5 mg/L). Following a comparison against the low aeration intensity operation, it was recommended to operate BAF at a low EBRT within the BAF/AX process. Consequently, the treated volume was double the volume of the standalone BAF-DN, synchronously achieving low costs (0.413 yuan/m³). Full article

The Gobi Desert ecosystem is currently experiencing the impacts of persistent climate warming and extreme weather. However, the relative influences of factors such as soil, climate, and spatial variables on the β-diversity of desert plants and their key components have not been systematically studied. In this research, the Dunhuang North Mountain and Mazong Mountain areas were selected as study areas, with a total of 79 plant community plots systematically established. The aim was to explore intercommunity β-diversity and its components and to analyze the interrelationships with climate factors, soil factors, and geographic distance. The results indicate that (1) there is a geographic decay pattern and significant differences among plant communities in the Dunhuang North Mountain and Mazong Mountain areas, with β-diversity primarily driven by replacement components. (2) Climate, soil, and geographic distance significantly influence β-diversity and its replacement components, with climate factors exerting the greatest influence and geographic distance the least. (3) Multiple regression analysis (MRM) reveals differential effects of climate factors, soil factors, and geographic distance on β-diversity and its replacement components, with climate and soil factors exerting a much greater influence than geographic distance. In summary, the β-diversity of plant communities and their replacement components in the Dunhuang North Mountain and Mazong Mountain areas result from the combined effects of habitat filtering and dispersal limitation, with habitat filtering having a greater impact, while environmental heterogeneity is an important factor influencing species differences in this region. Full article

This paper is concerned with the state estimation problem based on non-fragile set-membership filtering for a class of measurement-saturated memristive neural networks (MNNs) with unknown but bounded (UBB) noises, mixed time delays and missing measurements (MMs), subject to cyber-attacks under the framework of weighted try-once-discard protocol (WTOD protocol). Considering bandwidth-limited open networks, this paper proposes an improved set-membership filtering based on WTOD protocol to partially solve the problem that multiple sensor-related problems and multiple network-induced phenomena influence the state estimation performance of MNNs. Moreover, this paper also discusses the gain perturbations of the estimator and proposes an improved non-fragile estimation framework based on set-membership filtering, which enhances the robustness of the estimation approach. The proposed estimation framework can effectively estimate the state of MNNs with UBB noises, estimator gain perturbations, mixed time-delays, cyber-attacks, measurement saturations and MMs. This paper first utilizes mathematical induction to provide the sufficient conditions for the existence of the desired estimator, and obtains the estimator gain by solving a set of linear matrix inequalities. Then, a recursive optimization algorithm is utilized to achieve optimal estimation performance. The effectiveness of the theoretical results is verified by comparative numerical simulation examples. Full article

Photonics-assisted methods for microwave frequency measurement (MFM) show great potential for overcoming electronic bottlenecks and offer promising applications in radar and communication due to their wide bandwidth and immunity to electromagnetic interference. In common photonics-assisted MFM methods, the frequency-to-time mapping (FTTM) method has the capability to measure various types of signals, but with a trade-off between measurement error, measurement range, and real-time performance, while the frequency-to-power mapping (FTPM) method offers low measurement error but faces great difficulty in measuring signal types other than single-tone signals. In this paper, a two-step high-precision MFM method based on the combination of FTTM and FTPM is proposed, which balances real-time performance with measurement precision and resolution compared with other similar works based on the FTTM method. By utilizing high-speed optical sweeping and an optical filter based on stimulated Brillouin scattering (SBS), FTTM is accomplished, enabling the rough identification of multiple different signals. Next, based on the results from the previous step, more precise measurement results can be calculated from several additional sampling points according to the FTPM principle. The demonstration system can perform optical sweeping at a speed of 20 GHz/μs in the measurement range of 1–18 GHz, with a measurement error of less than 10 MHz and a frequency resolution of 40 MHz. Full article

Falls are a significant health concern leading to increased morbidity and healthcare costs, especially for the elderly. Early and accurate detection of fall events is critical for timely intervention and preventing severe complications. This study presents a novel approach to triaxial accelerometer signals by employing data-adaptive Gaussian average filtering (DAGAF) decomposition in conjunction with machine learning techniques for fall detection. The triaxial accelerometer signals from the FallAllD dataset were decomposed into intrinsic mode functions (IMFs) and a residual component, from which feature vectors were extracted to train support vector machine (SVM) and -nearest neighbor (NN) classifiers. Experimental results demonstrate that the combination of the first and the third IMFs with the residual component yields the highest classification accuracy of 96.34%, with SVM outperforming NN across all performance metrics. This approach significantly improves fall detection accuracy compared to using raw accelerometer signals, highlighting its potential in enhancing wearable fall detection systems. The proposed DAGAF decomposition method not only enhances feature extraction but also provides a promising advancement in the field, suggesting its potential to increase the reliability and accuracy of fall detection in practical applications. Full article

The performance of marine radar constant false alarm rate (CFAR) detection method is significantly influenced by the modeling of sea clutter distribution and detector decision rules. The false alarm rate and detection rate are therefore unstable. In order to address low CFAR detection performance and the modeling problem of non-uniform, non-Gaussian, and non-stationary sea clutter distribution in marine radar images, in this paper, a CFAR detection method in generalized extreme value distribution modeling based on marine radar space-time filtering background clutter is proposed. Initially, three-dimensional (3D) frequency wave-number (space-time) domain adaptive filter is employed to filter the original radar image, so as to obtain uniform and stable background clutter. Subsequently, generalized extreme value (GEV) distribution is introduced to integrally model the filtered background clutter. Finally, Inclusion/Exclusion (IE) with the best performance under the GEV distribution is selected as the clutter range profile CFAR (CRP-CFAR) detector decision rule in the final detection. The proposed method is verified by utilizing real marine radar image data. The results indicate that when the $P_{fa}$ is set at 0.0001, the proposed method exhibits an average improvement in $P_D$ of 2.3% compared to STAF-RCBD-CFAR, and a 6.2% improvement compared to STCS-WL-CFAR. When the $P_{fa}$ is set at 0.001, the proposed method exhibits an average improvement in $P_D$ of 6.9% compared to STAF-RCBD-CFAR, and a 9.6% improvement compared to STCS-WL-CFAR. Full article

TextNetTopics is a novel topic modeling-based topic selection approach that finds highly ranked discriminative topics for training text classification models, where a topic is a set of semantically related words. However, it suffers from several limitations, including the retention of redundant or irrelevant features within topics, a computationally intensive topic-scoring mechanism, and a lack of explicit semantic modeling. In order to address these shortcomings, this paper proposes fasTNT, an enhanced version of TextNetTopics grounded in the Grouping–Scoring–Modeling approach. FasTNT aims to improve the topic selection process by preserving only informative features within topics, reforming LDA topics using fastText word embeddings, and introducing an efficient scoring method that considers topic interactions using Random Forest feature importance. Experimental results on four diverse datasets demonstrate that fasTNT outperforms the original TextNetTopics method in classification performance and feature reduction. Full article

As unmanned aerial vehicles (UAVs) are widely used in various fields, there is an increasing demand for UAV anti-jamming, multipath mitigation, and covert secrecy. Frequency-hopping binary offset carrier (FH-BOC) signals possess higher anti-jamming and multipath mitigation capabilities than direct-sequence spread spectrum (DSSS) and binary offset carrier (BOC) signals. A prerequisite for constructing communication links between UAVs using FH-BOC signals is the design of efficient acquisition algorithms to capture the signals successfully. In this paper, the modulation and characteristics of the FH-BOC signal are introduced. The maximum relative velocity between UAVs is 5.5 km/s, the maximum acceleration is 50 g, and the maximum plus acceleration is 20 g/s. In this high dynamic environment, the parameters for the parallel code phase and Partial Matched Filter–Fast Fourier Transform (PMF-FFT) acquisition algorithms targeting FH-BOC(10,1) signals are designed, and the acquisition performance of these algorithms is comparatively analyzed. The acquisition time for the first and second algorithms is 4.3317 s and 6.137 s. The number of real additions required by the first and second algorithms is approximately $10.9\times {10}^9$ and $8.9\times {10}^9$, and the number of real multiplications is approximately $7.6\times {10}^9$ and $6.7\times {10}^9$. This helps in selecting the acquisition algorithm when FH-BOC signals are used to build inter-UAV communication links. Full article

In order to improve the observation accuracy of rotor positions in the sensorless control of permanent magnet synchronous motors and to simplify the parameter adjustment process, this paper proposes an improved finite-time adaptive super-twisting sliding mode observer. First, a linear gain term is introduced into the conventional super-twisting sliding mode observer model as a way of improving the identification accuracy of the observer. Then, for the multi-parameter variable problem in the traditional observer model, a rotational speed variable function design is presented, which simplifies the multi-variables into a single adaptive variable. This reduces the complexity of the observer model while further improving the observation accuracy and stability of the improved observer algorithm (which is verified using Lyapunov’s stability theory). A new back EMF filter and an adaptive phase-locked loop are then used to improve the model’s speed tracking capability. Finally, through simulation and experimental tests, the improved algorithm’s ability to quickly observe changes in rotor position and speed, as well as its fast convergence, small jitter and high accuracy characteristics, are verified. Full article

The mesoscale interaction between sea surface temperature (SST) and wind is a crucial factor influencing oceanic and atmospheric conditions. To investigate the mesoscale coupling characteristics of the Yellow Sea and East China Sea, we applied a locally weighted regression filtering method to extract mesoscale signals from Quik-SCAT wind field data and AMSR-E SST data and found that the mesoscale coupling intensity is stronger in the Yellow Sea during the spring and winter seasons. We calculated the mesoscale coupling coefficient to be approximately 0.009 N·m⁻²/°C. Subsequently, the Tikhonov regularization method was used to establish a mesoscale empirical coupling model, and the feedback effect of mesoscale coupling on the ocean was studied. The results show that the mesoscale SST–wind field coupling can lead to the enhancement of upwelling in the offshore area of the East China Sea, a decrease in the upper ocean temperature, and an increase in the eddy kinetic energy in the Yellow Sea. Diagnostic analyses suggested that mesoscale coupling-induced variations in horizontal advection and surface heat flux contribute most to the variation in SST. Moreover, the increase in the wind energy input to the eddy is the main factor explaining the increase in the eddy kinetic energy. Full article

Urban wastewater (UWW) and landfill leachate (LL) co-treatment using membrane bioreactors (MBRs) is a valuable method for managing LL in cities. Coagulants can enhance the filterability of mixed liquor (ML), but the assessment of fouling is still needed. This research aimed to investigate the effects of co-treating synthetic wastewater (SWW) and real LL on an MBR, as well as the impact of adding poly-aluminum chloride (PACl) and Tanfloc SG. Cell-ultrafiltration experiments were conducted with four different feeds: synthetic wastewater, co-treatment with LL (20% v/v), and co-treatment with the addition of 30 mg L⁻¹ coagulants (either PACl or Tanfloc). Co-treatment aggravated flux loss and reduced the recovery rate; however, Tanfloc and PACl improved recovery after cleaning (by 11% and 9%, respectively). Co-treatment also increased cake and irrecoverable/irremovable inorganic resistances, though coagulants reduced the latter, despite a lower fit of the Hermia models during the first hour of filtration. Co-treatment reduced the removal efficiencies of almost all pollutants analyzed, with the most significant impacts observed on the organic fraction. Coagulants, particularly Tanfloc, enhanced overall performance by improving flux recovery and reducing irreversibility, thus benefiting membrane lifespan. In conclusion, Tanfloc addition yielded the best results in terms of filterability and pollutant removal. Full article

Selecting the most appropriate mining method to recover mineral resources is a critical decision-making task in mining project development. This study introduces an artificial intelligence-based mining methods recommendation system (AI-MMRS) for the pre-selection of underground mining methods. The study integrates and evaluates the capability of two approaches for mining methods selection (MMS): the memory-based collaborative filtering (CF) approach aided by the UBC-MMS system to predict the top-3 relevant mining methods and supervised machine learning (ML) classification algorithms to enhance the effectiveness and novelty of the AI-MMRS, addressing the limitations of the CF approach. The results reveal that the memory-based CF approach achieves an accuracy ranging from 81.8% to 87.9%. Among the classification algorithms, artificial neural network (ANN) and k-nearest neighbors (KNN) classifiers perform the best, with accuracy levels of 66.7% and 63.6%, respectively. These findings demonstrate the effectiveness and viability of both approaches in MMS, acknowledging their limitations and the need for continuous training and optimization. The proposed AI-MMRS for the pre-selection stage supplemented by the direct involvement of mining professionals in later stages of MMS, has the potential to significantly aid in the MMS decision-making, providing data-driven and experience-based recommendations following the ongoing evolution of mining practices. Full article

Usually, hyperspectral data captured from an airborne UAV or satellite contain some noise that can be severe in some channels. Often, channels that are badly affected by the noise are discarded. This is because the corrupted channels cannot be reclaimed by common filtering techniques, making important information in the affected channels different from those of field spectroscopy of similar wavelengths. In this study, a fast-denoising method is implemented on some channels of oil palm hyperspectral data that are badly affected by noise. The amount of noise is unknown, and it varies across the noisy channels from bad to severe. This is different from the data normally used by many studies, which are essentially clean data spiked with mild noise of known variance. The process starts by identifying which noisy channels to filter based on the level of the estimated noise in them. Then, filtering is conducted within each channel and across channels. Once the noise is removed, the improvement in signal-to-noise ratio (SNR) is calculated for each channel. The performance of Kalman, Wiener, Savitzky–Golay, wavelet, and cosine filters is tested in the same framework and the results are compared in terms of execution time, signal-to-noise ratio, and visual quality. The results show that the Kalman filter slightly outperformed the other filters. The proposed scheme was implemented using MATLAB R2023b running on an Intel i7 processor, and the average execution time was less than 1 second for each channel. To the best of our knowledge, this is the first attempt to filter real oil palm hyperspectral data containing speckle noise using a Kalman filter. This technique can be a useful tool to those working in the oil palm industry. Full article

Background: Renal replacement therapy (RRT), widely used in the treatment of renal injury during sepsis, aims to eliminate the toxins and proinflammatory cytokines involved in the pathomechanism underlying septic shock. Dialysis filters are characterized by a high adsorption potential for cytokines in RRT in the case of septic renal injury. For the treatment of sepsis with antibiotics, it is of key importance to achieve the desired values of PK/PD indices. Continuous renal replacement therapy (CRRT) may affect antimicrobial clearance, increasing their elimination in some cases. Methods: The aim of this study was to determine the degree of adsorption for linezolid on three different types of filters used in CRRT. In our in vitro study, a continuous veno-venous hemofiltration (CVVH) was conducted using three types of filters: polysulfone (PS), polyethyleneimine-treated polyacrylonitrile (PAN PEI), and non-PEI-treated polyacrylonitrile (PAN). Each type of filter was used in three CVVH cycles, involving the use of 600 mg of linezolid dissolved in 700 mL of bovine blood or in 700 mL of 0.9% NaCl. In each case, the total volume of the obtained solution was 1000 mL. Blood samples were collected at particular time points to measure their drug concentration. The differences in mean drug/NaCl adsorption and drug/blood adsorption were determined using a one-way ANOVA with multiple comparisons via Tukey’s post hoc test; a p-value of <0.05 was considered significant. Results: A significant adsorption of linezolid was found for PAN PEI filters, both in samples obtained from bovine blood and 0.9% NaCl solutions, at the endpoint. In PAN PEI samples, the concentration of linezolid in 0.9% NaCl solutions decreased from 594.74 μg/mL to 310.66 μg/mL after 120 min (the difference was established at 52%). In blood samples, the initial concentration was 495.18 μg/mL, which then decreased to 359.84 μg/mL (73% of the beginning value). No significant adsorption was demonstrated on PAN or PS filters. Conclusion: There is a need for in vivo research to confirm the effect of filter type on linezolid concentration in patients undergoing CRRT. Full article