Window Method Research Articles

Efficient Error Detection Schemes for ECSM Window Method Benchmarked on FPGAs

Efficient Error Detection Schemes for ECSM Window Method Benchmarked on FPGAs

Deep learning model to predict lupus nephritis renal flare based on dynamic multivariable time-series data

ObjectivesTo develop an interpretable deep learning model of lupus nephritis (LN) relapse prediction based on dynamic multivariable time-series data.DesignA single-centre, retrospective cohort study in China.SettingA Chinese central tertiary hospital.ParticipantsThe cohort...

Transfer-Learning-Based Human Activity Recognition Using Antenna Array

Due to its low cost and privacy protection, Channel-State-Information (CSI)-based activity detection has gained interest recently. However, to achieve high accuracy, which is challenging in practice, a significant number of training samples are required. To address the issues of the small sample size and cross-scenario in neural network training, this paper proposes a WiFi human activity-recognition system based on transfer learning using an antenna array: Wi-AR. First, the Intel5300 network card collects CSI signal measurements through an antenna array and processes them with a low-pass filter to reduce noise. Then, a threshold-based sliding window method is applied to extract the signal of independent activities, which is further transformed into time–frequency diagrams. Finally, the produced diagrams are used as input to a pretrained ResNet18 to recognize human activities. The proposed Wi-AR was evaluated using a dataset collected in three different room layouts. The testing results showed that the suggested Wi-AR recognizes human activities with a consistent accuracy of about 94%, outperforming the other conventional convolutional neural network approach.

Changes in Community Structure of Brain Dynamic Functional Connectivity States in Mild Cognitive Impairment

Recent researches have noted many changes of short-term dynamic modalities in mild cognitive impairment (MCI) patients' brain functional networks. In this study, the dynamic functional brain networks of 82 MCI patients and 85 individuals in the normal control (NC) group were constructed using the sliding window method and Pearson correlation. The window size was determined using single-scale time-dependent (SSTD) method. Subsequently, k-means was applied to cluster all window samples, identifying three dynamic functional connectivity (DFC) states. Collective sparse symmetric non-negative matrix factorization (cssNMF) was then used to perform community detection on these states and quantify differences in brain regions. Finally, metrics such as within-community connectivity strength, community strength, and node diversity were calculated for further analysis. The results indicated high similarity between the two groups in state 2, with no significant differences in optimal community quantity and functional segregation (p < 0.05). However, for state 1 and state 3, the optimal community quantity was smaller in MCI patients compared to the NC group. In state 1, MCI patients had lower within-community connectivity strength and overall strength than the NC group, whereas state 3 showed results opposite to state 1. Brain regions with statistical difference included MFG.L, ORBinf.R, STG.R, IFGtriang.L, CUN.L, CUN.R, LING.R, SOG.L, and PCUN.R. This study on DFC states explores changes in the brain functional networks of patients with MCI from the perspective of alterations in the community structures of DFC states. The findings could provide new insights into the pathological changes in the brains of MCI patients.

Hardware Implementation of FIR Filter for ECG Signal Processing: Design, Optimization, and Performance Analysis on an FPGA

Electrocardiogram (ECG) signals are commonly used to diagnose heart-related diseases. However, noise induced during the measurement process can affect the accuracy of the diagnosis. Digital filters, such as the Finite Impulse Response (FIR) filter, are widely used to filter out noise from the ECG signal. Nevertheless, the processing speed of software-based FIR filters is slow for large ECG datasets due to serial processing. This paper presents a hardware implementation of the FIR filter for ECG signal processing to overcome the processing speed issue. The filter is designed using the Kaiser Window method and implemented on the Intel Cyclone IV Field Programmable Gate Array (FPGA). The filter is first designed in MATLAB to obtain the filter coefficients where the ECG data were obtained from Physionet database. From the difference equation, we designed the signal flow graph (SDFG) and then mapped into hardware logics to enable parallel processing. Simulation results of the software (MATLAB) and hardware (FPGA) implementations are obtained and compared. The results show that the FPGA-based FIR filter can process the ECG signal up to 1,250 times faster than software implementations. To further optimize the design and reduce hardware cost, we introduce optimized designs by applying the operation scheduling and constrained resource allocation techniques. The maximum operating frequency, logic utilization, and power consumption of each design were analysed and compared. This study demonstrates that custom-designed hardware logic for digital signal processing can significantly outperform software implementations due to its parallel processing capabilities. The proposed optimization techniques reduce the hardware cost while maintaining high processing speed and accuracy. The hardware implementation of the FIR filter for ECG signal processing has numerous applications in diagnosing heart-related diseases and real-time monitoring of ECG signals in critical care settings.

Spatial planning constraints will mitigate the fragmentation trajectory of natural and semi-natural landscapes: a case of Lushan City, China

ContextThe biodiversity faces an underlying threat from landscape fragmentation resulting from rapid urbanization. Examining the future trajectory of landscape fragmentation is imperative to understanding the impact of current spatial planning constraints on natural and semi-natural habitat preservation, ecosystem services, and sustainability.MethodsWe employed a Patch-generating Land Use Simulation (PLUS) model to simulate and predict the land use and landscape pattern changes in Lushan City under two distinct scenarios: “Planning Constraints (PC)” and “Natural Development (ND)”. We then identified an appropriate landscape fragmentation index (LFI) that effectively captured the fragmentation essence. To determine the optimal scale, we adopted an experimental approach using both the moving window (MW) method and the semi-variance function. By constructing a LFI spatiotemporal sequence and conducting trend analysis, we identified the potential fragmentation areas (PFA) with significant fragmentation tendencies.ResultsThe spatial planning constraints will (1) prevent the encroachment of construction land into 2.14 km2 of cropland, 0.21 km2 of forest, and 0.13 km2 of grassland; (2) shift the highly fragmented area from the northeastern portion of Lushan to the planned area defined by the development boundary; (3) mitigate and decelerate the trend of landscape fragmentation in natural and semi-natural landscapes, decrease PFA by 7.74 km2 and preserve 15.61 km2 of natural landscapes. (4) still leave 29.42% of forest and 22.82% of grassland at risk of fragmentation.ConclusionsSpatial planning constraints will effectively control the potential fragmentation in natural and semi-natural landscapes by changing the spatial distribution of LFI and PFA. This control mechanism will greatly exclude the anthropogenic impact and ensure the conservation of habitats. The habitats remaining within PFA should be focused in future eco-management optimization.

A multi-subsystem collaborative Bi-LSTM-based adaptive soft sensor for global prediction of ammonia-nitrogen concentration in wastewater treatment processes

Ammonia-nitrogen concentration is a key water quality indicator, which reflects changes in pollutant components during wastewater treatment processes. The timely and accurate detection results contribute to optimizing control and operational management of wastewater treatment plants (WWTPs), but current detection methods only focus on the effluent location. This paper proposes a multi-subsystem collaborative Bi-LSTM-based adaptive soft sensor to achieve the global prediction of ammonia-nitrogen concentration. Firstly, the wastewater treatment process is divided into several independent subsystems depending on the reaction mechanism, and the variable selection is performed using mutual information. Subsequently, the bidirectional long short-term memory network (Bi-LSTM) is employed to construct a model for predicting ammonia-nitrogen concentration within each subsystem, and the outputs between neighboring subsystems are incorporated as a set of new variables added into the training dataset to strengthen their connection. Finally, to address performance degradation caused by environmental factors, a probability density function (PDF)-based dynamic moving window method is proposed to enhance the robustness. The effectiveness and superiority of the proposed soft sensor are validated in the Benchmark Simulation Model no. 1 (BSM1). The experimental results demonstrate that the proposed soft sensor can accurately predict the global ammonia-nitrogen concentration in the face of different weather conditions including sunny, rainy, and stormy days. This study contributes to the stable operation of WWTPs with higher treatment efficiency and lower economic costs.

Preparation study and simulation of high transmittance W–Sn–Zr Co-doped VO2 thin films prepared by sol-gel method for energy-saving smart windows

The thermochromic properties of vanadium dioxide (VO2) hold great promise for applications in smart glass windows. VO2 films exhibit a high phase transition temperature (Tc), low visible transmission (Tlum), and low solar modulating ability (ΔTsol), making them inadequate for the requirements of smart glass windows. While some improvement in VO2 film performance can be achieved through single-element doping, these limited enhancements still fall short of ideal results. Therefore, there is a pressing need for the improvement of VO2 films. In this study, we utilized the sol-gel method to prepare VO2 samples simultaneously doped with three elements: W, Sn, and Zr. Microscopic characterization confirmed the successful incorporation of these elements. Optical measurements, including ultraviolet–visible–near-infrared spectroscopy, were conducted to assess the optical properties of the films. Ultimately, outstanding Tlum (69%) and ΔTsol (14%) were achieved, along with an appropriate Tc (27.6 °C). Energy consumption simulations were performed using the EnergyPlus model with experimental data, revealing that compared to conventional glass, VO2 glass reduces total cooling energy consumption by 9.8%. In comparison to previous work, the multi-element-doped films push the performance limits of VO2 films, offering a viable approach for fabricating superior VO2 smart glass windows.

LieWaves: dataset for lie detection based on EEG signals and wavelets.

This study introduces an electroencephalography (EEG)-based dataset to analyze lie detection. Various analyses or detections can be performed using EEG signals. Lie detection using EEG data has recently become a significant topic. In every aspect of life, people find the need to tell lies to each other. While lies told daily may not have significant societal impacts, lie detection becomes crucial in legal, security, job interviews, or situations that could affect the community. This study aims to obtain EEG signals for lie detection, create a dataset, and analyze this dataset using signal processing techniques and deep learning methods. EEG signals were acquired from 27 individuals using a wearable EEG device called Emotiv Insight with 5 channels (AF3, T7, Pz, T8, AF4). Each person took part in two trials: one where they were honest and another where they were deceitful. During each experiment, participants evaluated beads they saw before the experiment and stole from them in front of a video clip. This study consisted of four stages. In the first stage, the LieWaves dataset was created with the EEG data obtained during these experiments. In the second stage, preprocessing was carried out. In this stage, the automatic and tunable artifact removal (ATAR) algorithm was applied to remove the artifacts from the EEG signals. Later, the overlapping sliding window (OSW) method was used for data augmentation. In the third stage, feature extraction was performed. To achieve this, EEG signals were analyzed by combining discrete wavelet transform (DWT) and fast Fourier transform (FFT) including statistical methods (SM). In the last stage, each obtained feature vector was classified separately using Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and CNNLSTM hybrid algorithms. At the study's conclusion, the most accurate result, achieving a 99.88% accuracy score, was produced using the LSTM and DWT techniques. With this study, a new data set was introduced to the literature, and it was aimed to eliminate the deficiencies in this field with this data set. Evaluation results obtained from the data set have shown that this data set can be effective in this field.

Global forecasts in reservoir computers.

A reservoir computer is a machine learning model that can be used to predict the future state(s) of time-dependent processes, e.g., dynamical systems. In practice, data in the form of an input-signal are fed into the reservoir. The trained reservoir is then used to predict the future state of this signal. We develop a new method for not only predicting the future dynamics of the input-signal but also the future dynamics starting at an arbitrary initial condition of a system. The systems we consider are the Lorenz, Rossler, and Thomas systems restricted to their attractors. This method, which creates a global forecast, still uses only a single input-signal to train the reservoir but breaks the signal into many smaller windowed signals. We examine how well this windowed method is able to forecast the dynamics of a system starting at an arbitrary point on a system's attractor and compare this to the standard method without windows. We find that the standard method has almost no ability to forecast anything but the original input-signal while the windowed method can capture the dynamics starting at most points on an attractor with significant accuracy.

Helmholtz decomposition based windowed Green function methods for elastic scattering problems on a half-space

This paper proposes a new Helmholtz decomposition based windowed Green function (HD-WGF) method for solving the time-harmonic elastic scattering problems on a half-space with Dirichlet boundary conditions in both 2D and 3D. The Helmholtz decomposition is applied to separate the pressure and shear waves, which satisfy the Helmholtz and Helmholtz/Maxwell equations, respectively, and the corresponding boundary integral equations of type (I+T)ϕ=f, that couple these two waves on the unbounded surface, are derived based on the free-space fundamental solution of Helmholtz equation. This approach avoids the treatment of the complex elastic displacement tensor and traction operator that involved in the classical integral equation method for elastic problems. Then a smooth “slow-rise” windowing function is introduced to truncate the boundary integral equations and a “correction” strategy is proposed to ensure the uniformly fast convergence for all incident angles of plane incidence. Numerical experiments for both two and three dimensional problems are presented to demonstrate the accuracy and efficiency of the proposed method.

Synthesis of stochastic algorithms for image registration by the criterion of maximum mutual information

We discuss a synthesis of stochastic algorithms, obtaining expressions for gradients of Shannon, Renyi and Tsallis mutual information on the basis of the mathematical apparatus of stochastic gradient adaptation of algorithms for estimating image registration parameters. To obtain the expressions, derivatives of the image entropy with respect to the estimated parameters are used. The entropies are calculated using a Parzen window method. A comparative study of the synthesized algorithms in terms of stability and accuracy of the registration parameter estimates, including in conditions of additive noise, is carried out.

Altered dynamic functional network connectivity in rheumatoid arthritis associated with peripheral inflammation and neuropsychiatric disorders

ObjectiveThis study explored the dynamic functional connective (DFC) alterations in patients with rheumatoid arthritis (RA) and investigated the correlation between the neuropsychiatric symptoms, peripheral inflammation and DFC alterations.MethodUsing resting-state functional...

Multichannel energy monitoring based on the sliding window method in an industrial environment

The research on nonintrusive load monitoring (NILM) typically focuses on residential and commercial users. However, high-energy industrial users also require load monitoring analysis to understand their electricity consumption patterns and load operating conditions. Currently used load monitoring methods and parameters are not applicable to industrial environments. In this paper, we propose an innovative multichannel low window sequence-to-point (MLSP) method based on deep neural networks for NILM of high-energy industrial users. First, the data from each channel of the main line terminal are normalized and then aggregated into a time series, which is several times longer than the original data to expand its data volume. Then, the multichannel low window sequence-to-point method is used to select the window data of several times the length, corresponding to the active power of the target device. Finally, the discriminative features of the data are learned using a deep neural network model. The analysis of actual industrial user data shows that the lowest mean absolute error (MAE) of the original sequence-to-point method for related branches is 20.38, and the highest correlation coefficient (CC) is 90.23%. Our proposed method reduces the MAE to 11.02 and increases the CC to 97.42%. The MAE and CC indicators of all branches decreased by 25.8% and increased by 22.4%, respectively. According to Mean Absolute Percentage Error (MAPE), the performance of this model is improved by 39.6% compared with Seq2point. Compared with the original sequence-to-point method, our proposed method achieves better load monitoring results and can more accurately decompose load fluctuations, which are closer to the actual load curve.

Inflation forecasting with rolling windows: An appraisal

AbstractWe examine the performance of rolling windows procedures in forecasting inflation. We implement rolling windows augmented Dickey–Fuller (ADF) tests and then conduct a set of Monte Carlo experiments under stylized forms of structural breaks. We find that as long as the nature of inflation is either stationary or non‐stationary, popular varying‐length window techniques provide little advantage in forecasting over a conventional fixed‐length window approach. However, we also find that varying‐length window techniques tend to outperform the fixed‐length window method under conditions involving a change in the inflation process from stationary to non‐stationary, and vice versa. Finally, we investigate methods that can provide early warnings of structural breaks, a situation for which the available rolling windows procedures are not well suited.

Drying strategies of spent coffee grounds using refractance window method

The objective of this study was to evaluate, during the drying of spent coffee grounds, the application of pretreatments with ethanol and the application of ultrasound assisting the refractive window (RW) drying, and to compare with convective drying by hot air (AC). The effect on the kinetics parameters of the Fick and Page models were evaluated, as well as on the content of total phenolics and antioxidant capacity. For AC drying, samples of spent ground coffee were prepared in the form of fixed rectangular beds 0.7 cm high, which were placed on polyester sheets and pretreated up to 10 times sprayed with ethanol, then dried by AC at 80 °C and 0.8 m/s. For RW drying, the samples prepared in the same way as for AC were used. For this case, the application of ethanol as pretreatment and the use of ultrasound during process were tested. As results, regardless of the conditions applied, drying by RW was up to 50 % faster, evidenced in the highest values of effective diffusivity (from the Fick model) and the kinetic parameter (from the Page model). Regarding the treatments applied and their effect on the drying kinetics, any treatment had a significant effect on AC drying. On the contrary, the strategies applied in RW drying had significant effects, both the application of pretreatment with ethanol as well as the application of ultrasound assisting the process accelerated the drying kinetics. However, the treatment with ethanol and RW drying was the one that best preserved the phenolic compounds and the antioxidant capacity in the samples. Therefore, these strategies could be a good option to improve RW drying by accelerating the process and preserving the bioactive compounds in the spent coffee grounds for subsequent utilization.

A novel Human-Aware Navigation algorithm based on behavioral intention cognition

AbstractIn order to ensure safe and comfortable human–robot navigation in close proximity, it is imperative for robots to possess the capability to understand human behavioral intention. With this objective in mind, this paper introduces a Human-Aware Navigation (HAN) algorithm. The HAN system combines insights from studies on human detection, social behavioral model, and behavior prediction, all while incorporating social distance considerations. This information is integrated into a layer dedicated to human behavior intention cognition, achieved through the fusion of data from laser radar and Kinect sensors, employing Gaussian functions to account for individual private space and movement trend. To cater to the mapping requirements of the HAN system, we have reduced the computational complexity associated with traditional multilayer cost map by implementing a “first-come, first-served” expansion method. Subsequently, we have enhanced the trajectory optimization equation by incorporating an improved dynamic triangle window method that integrates human behavior intention cognition, leading to the determination of an appropriate trajectory for the robot. Finally, experimental evaluations have been conducted to assess and validate the efficacy of the human behavior intention cognition and the HAN system. The results clearly demonstrate that the HAN system outperforms the traditional Dynamic Window Approach algorithm in ensuring the safety and comfort of humans in human–robot coexistence environments.

Plastid genome and its phylogenetic implications of Asiatic Spiraea (Rosaceae).

Spiraea L. is a genus comprising approximately 90 species that are distributed throughout the northern temperate regions. China is recognized as the center of species diversity for this genus, hosting more than 70 species, including 47 endemic species. While Spiraea is well-known for its ornamental value, its taxonomic and phylogenetic studies have been insufficient. In this study, we conducted sequencing and assembly of the plastid genomes (plastomes) of 34 Asiatic Spiraea accessions (representing 27 Asiatic Spiraea species) from China and neighboring regions. The Spiraea plastid genome exhibits typical quadripartite structures and encodes 113-114 genes, including 78-79 protein-coding genes (PCGs), 30 tRNA genes, and 4 rRNA genes. Linear regression analysis revealed a significant correlation between genome size and the length of the SC region. By the sliding windows method, we identified several hypervariable hotspots within the Spiraea plastome, all of which were localized in the SC regions. Our phylogenomic analysis successfully established a robust phylogenetic framework for Spiraea, but it did not support the current defined section boundaries. Additionally, we discovered that the genus underwent diversification after the Early Oligocene (~ 30Ma), followed by a rapid speciation process during the Pliocene and Pleistocene periods. The plastomes of Spiraea provided us invaluable insights into its phylogenetic relationships and evolutionary history. In conjunction with plastome data, further investigations utilizing other genomes, such as the nuclear genome, are urgently needed to enhance our understanding of the evolutionary history of this genus.

Characterization of heterozygosity-rich regions in Italian and worldwide goat breeds

Heterozygosity-rich regions (HRR) are genomic regions of high heterozygosity, which may harbor loci related to key functional traits such as immune response, survival rate, fertility, and other fitness traits. This study considered 30 Italian and 19 worldwide goat breeds genotyped with the Illumina GoatSNP50k BeadChip. The aim of the work was to study inter-breed relationships and HRR patterns using Sliding Window (SW) and Consecutive Runs (CR) detection methods. Genetic relationships highlighted a clear separation between non-European and European breeds, as well as the north–south geographic cline within the latter. The Pearson correlation coefficients between the descriptive HRR parameters obtained with the SW and CR methods were higher than 0.9. A total of 166 HRR islands were detected. CHI1, CHI11, CHI12 and CHI18 were the chromosomes harboring the highest number of HRR islands. The genes annotated in the islands were linked to various factors such as productive, reproductive, immune, and environmental adaptation mechanisms. Notably, the Montecristo feral goat showed the highest number of HRR islands despite the high level of inbreeding, underlining potential balancing selection events characterizing its evolutionary history. Identifying a species-specific HRR pattern could provide a clearer view of the mechanisms regulating the genome modelling following anthropogenic selection combined with environmental interaction.

Online Detection and Adaptation of Concept Drift in Streaming Data Classification

The dynamism of our digital universe systems presents a key challenge for predictive analytics. Ensuring the model’s ability to generalize beyond the training data is crucial for reliable predictions. Recent research has primarily focused on understanding and addressing the phenomenon of concept drift: the changes in the system that afect the model’s accuracy. This paper aims to resolve this issue by introducing an online technique implemented using Light Gradient Boosting Machine (LGBM) classifier for concept drift detection and adaptation. Online LGBM is adjusted incrementally based on the most recent information, allowing it to adapt to changing patterns in the data stream. The proposed technique attempts to leverage incremental learning, ensemble learning, and the sliding window method to deal with concept drift. The experiments on Electricity, Spam and MixedAbrupt Drift datasets result in higher accuracy 86.77%, 97.67%, and 65.25% as compared to the ofine LGBM with lower accuracy 71.52%, 90.86%, and 50.01% respectively. The hyperparameters of LGBM are optimized using the Bayesian method.