bluuebunny/crossref_metadata_2025 huggingface dataset:
Preparation and Properties of Multi-Responsive Liquid Crystalline Poly(urethane-acrylate)s and Its Composite Membranes
Authors: Zhou Liming , Wang Ziwen , Gao Lijun , Yang Hongcheng , Fang Shaoming
Published: July 2024

In this work, a kind of side chain liquid crystalline poly(urethane-acrylate)s was synthesized by free polymerization based on self-made liquid crystalline monomers, and a series of liquid crystalline polyurethane/shape memory polyurethane composite membranes were prepared by electrospinning. The synthesized liquid crystalline poly(urethane-acrylate)s have excellent thermal stability. Due to the regular arrangement of azobenzene on the side chains, polymers can rapidly undergo a photoinduced transition from trans-isomerism to cis-isomerism in THF solution and restore reversible configurational changes under visible light. The composite membranes prepared by electrospinning can also undergo photoinduced deformation within 6 s, and the deformation slowly returns under visible light. Meanwhile, the composites have shape memory, and after deformation caused by stretching, the membranes can quickly recover their original shape under thermal stimulation. These results indicate that the composites have triple response performances of photoinduced deformation, photo-, and thermal recovery.
Microbial Biomass in Mesophilic and Thermophilic High-Rate Biodigestion of Sugarcane Vinasse: Similar in Quantity, Different in Composition
Authors: Fuess Lucas Tadeu , de Araujo Matheus Neves , Saia Flávia Talarico , Gregoracci Gustavo Bueno , Zaiat Marcelo , Lens Piet N. L.
Published: July 2024

This study compared the behavior of the biomass in two fixed-film anaerobic reactors operated under equivalent organic loading rates but at different temperatures, i.e., 30 °C (RMM) and 55 °C (RMT). The reactors were fed with sugarcane vinasse and molasses (both fermented) in a simulation of sequential periods of season and off-season. The dynamics of biomass growth and retention, as well as the microbial composition, were assessed throughout 171 days of continuous operation, coupled with an additional 10-day test assessing the microbial activity in the bed region. Despite the different inoculum sources used (mesophilic granular vs. thermophilic flocculent sludge types), the biomass growth yield was identical (0.036–0.038 g VSS g−1COD) in both systems. The retention rates (higher in RMT) were regulated according to the initial amount of biomass provided in the inoculation, resulting in similar amounts of total retained biomass (46.8 vs. 43.3 g VSS in RMT and RMM) and biomass distribution patterns (30–35% in the feeding zone) at the end of the operation. Meanwhile, hydrogenotrophic methanogenesis mediated by Methanothermobacter coupled to syntrophic acetate oxidation prevailed in RMT, while the Methanosaeta-mediated acetoclastic pathway occurred in RMM. The results show that different anaerobic consortia can behave similarly in quantitative terms when subjected to equivalent organic loads, regardless of the prevailing methane-producing pathway. The community grows and reaches a balance (or a given cell activity level) defined by the amount of substrate available for conversion. In other words, while the metabolic pathway may differ, the endpoint (the amount of biomass) remains the same if operational stability is maintained.
Artificial Neural Network Modeling Techniques for Drying Kinetics of Citrus medica Fruit during the Freeze-Drying Process
Authors: Topal Muhammed Emin , Şahin Birol , Vela Serkan
Published: July 2024

The main objective of this study is to analyze the drying kinetics of Citrus medica by using the freeze-drying method at various thicknesses (3, 5, and 7 mm) and cabin pressures (0.008, 0.010, and 0.012 mbar). Additionally, the study aims to evaluate the efficacy of an artificial neural network (ANN) in estimating crucial parameters like dimensionless mass loss ratio (MR), moisture content, and drying rate. Feedforward multilayer perceptron (MLP) neural network architecture was employed to model the freeze-drying process of Citrus medica. The ANN architecture was trained using a dataset covering various drying conditions and product characteristics. The training process, including hyperparameter optimization, is detailed and the performance of the ANN is evaluated using robust metrics such as RMSE and R2. As a result of comparing the experimental MR with the predicted MR of the ANN modeling created by considering various product thicknesses and cabin pressures, the R2 was found to be 0.998 and the RMSE was 0.010574. Additionally, color change, water activity, and effective moisture diffusivity were examined in this study. As a result of the experiments, the color change in freeze-dried Citrus medica fruits was between 6.9 ± 0.2 and 21.0 ± 0.6, water activity was between 0.4086 ± 0.0104 and 0.5925 ± 0.0064, effective moisture diffusivity was between 4.19 × 10−11 and 21.4 × 10−11, respectively. In freeze-drying experiments conducted at various cabin pressures, it was observed that increasing the slice thickness of Citrus medica fruit resulted in longer drying times, higher water activity, greater color changes, and increased effective moisture diffusivity. By applying the experimental data to mathematical models and an ANN, the optimal process conditions were determined. The results of this study indicate that ANNs can potentially be applied to characterize the freeze-drying process of Citrus medica.
Study on Microwave Freeze-Drying of Krill
Authors: Chang Hao-Cheng , Chen Su-Der
Published: July 2024

Antarctic krill (Euphausua superba) need to undergo freeze-drying to facilitate lipid extraction, but freeze-drying is time-consuming and energy-intensive, resulting in high processing costs. Microwave heating technology can reduce freeze-drying time and lower energy consumption costs. The objective of this study was to establish a drying kinetic model to help the microwave freeze-drying process by predicting krill drying time and evaluating the impact of the drying process on krill quality. The results showed that changing the microwave power did not alter the total energy requirement to complete drying when the sample weight was fixed. The total energy requirement for microwave drying increases with the sample weight. Comparing the three methods of freeze-drying (FD), microwave freeze-drying (MWFD), and hot-air drying at 55 °C (HAD) showed that they took 18, 0.67, and 16 h, respectively, to reach the drying endpoint for krill. Overall, HAD resulted in browning, shrinkage, and quality degradation of krill due to its high temperature and long duration. While the appearance and active ingredient contents of FD krill are slightly better than those of MWFD krill, FD requires a longer process and more energy. MWFD can reduce drying time by 20 times and energy consumption by 95% compared to FD.
The Influence of Hand Dimensions on Finger Flexion during Lower Paleolithic Stone Tool Use in a Comfortable Grip
Authors: Fedato Annapaola , Silva-Gago María , Terradillos-Bernal Marcos , Alonso-Alcalde Rodrigo , Bruner Emiliano
Published: July 2024

Considering the biomechanical and cognitive aspects involved in tool manipulation, hand size emerges as a critical factor. Males, on average, exhibit greater grip strength attributed to larger hand dimensions. Beyond mere physical factors, cognitive components tied to visuospatial abilities also influence stone tool use. However, the intricate relationship between hand size, grip strength, and ergonomic patterns necessitates further exploration. Here, we study the ergonomic pattern of phalanx flexion during the manipulation of Lower Paleolithic stone tools (choppers and handaxes) to understand the nuanced interplay between hand dimensions and grasping behaviors in Lower Paleolithic stone tool use. The static hand posture during the comfortable grasping of each tool is measured using a motion capture hand glove. Flexions are measured at the metacarpophalangeal joint, the proximal interphalangeal joint and the distal interphalangeal joint of each finger. Our investigation into Lower Paleolithic stone tool manipulation reveals gender-based differences in phalanx flexion, with hand dimensions showing correlation only in pooled samples. However, these associations diminish when analyzing males and females separately. This study suggests a minimal link between hand size and grasping behavior within our sample, hinting at the influence of cognitive, behavioral, and motor factors. Exploring lifestyle and psychometric profiles could provide further insights. In the context of early human technology, our results prompt considerations on the evolution of the hand-tool interaction system, linking our tool-dependent culture to our phylogenetic history.
Pre-Earthquake Oscillating and Accelerating Patterns in the Lithosphere–Atmosphere–Ionosphere Coupling (LAIC) before the 2022 Luding (China) Ms6.8 Earthquake
Authors: Zhang Xuemin , De Santis Angelo , Liu Jing , Campuzano Saioa A. , Yang Na , Cianchini Gianfranco , Ouyang Xinyan , D’Arcangelo Serena , Yang Muping , De Caro Mariagrazia , Li Xinyan , Fidani Cristiano , Liu Hong , Orlando Martina , Nie Lei , Perrone Loredana , Piscini Alessandro , Dong Lei , Sabbagh Dario , Soldani Maurizio , Xiong Pan
Published: July 2024

The coupling processes among the lithosphere, atmosphere, and ionosphere (LAI) during the earthquake preparation phase are still an open scientific debate. Comprehensive LAI coupling effects around the 2022 Ms6.8 Luding earthquake in China are investigated with a multi-parameter and multi-layer approach, including the b-value, revised accelerated moment release, Earth resistivity, ELF magnetic field emissions, atmospheric electric field, surface temperature, foF2 from ionosonde, GNSS TEC, Ne and magnetic field from CSES and Swarm satellites, and energetic electrons from CSES and NOAA satellites. It is found that the anomalies start from the lithospheric parameters as Earth resistivity and b-values 1–2 years before to reflect the local stress loading in the seismic region, then the ionospheric and atmospheric disturbances occur and accelerate −50 days before and −15 days before, and finally the electrons precipitate a few days before. The simultaneous perturbations in LAI illustrate the thermodynamic coupling channel, such as on 24 August, −12 days before. Meanwhile, the abundant developed ionospheric anomalies without atmospheric disturbances demonstrate the electromagnetic coupling way from the lithosphere to the ionosphere directly. Finally, the results demonstrate a two-way model of LAIC: one way is characterized by a slow chain of processes, of thermodynamic nature, starting from the ground and proceeding to the above atmosphere and ionosphere, showing an exponential trend in the cumulative number of anomalies; the second way is characterized by oscillating electromagnetic coupling between the lithosphere and ionosphere, showing intermittent fluctuations in the corresponding cumulative number of anomalies.
Matching Satellite Sun-Induced Chlorophyll Fluorescence to Flux Footprints Improves Its Relationship with Gross Primary Productivity
Authors: Zhao Liang , Sun Rui , Zhang Jingyu , Liu Zhigang , Li Shirui
Published: July 2024

Sun-induced chlorophyll fluorescence (SIF) holds enormous potential for accurately estimating terrestrial gross primary productivity (GPP). However, current studies often overlook the spatial representativeness of satellite SIF and GPP observations. This research downscaled TROPOMI SIF (TROPOSIF) and its enhanced product (eSIF) in China’s Saihanba Forest Region to obtain high-resolution SIF data. SIF was simulated using the SCOPE model, and the downscaled SIF’s reliability was validated at two forest eddy covariance (EC) sites (SHB1 and SHB2) in the study area. Subsequently, the downscaled SIF data were matched to the EC footprint of the two forest sites, and the relationship between SIF and GPP was compared at various observational scales. Additionally, the ability of downscaled TROPOSIF and eSIF to track GPP was compared, along with the correlations among several vegetation indices (VIs) and GPP. The findings reveal the following: (1) Downscaled TROPOSIF and eSIF showed a strong linear relationship with SCOPE-modeled SIF (R2 ≥ 0.86). The eSIF closely matched the SCOPE simulation (RMSE: 0.06 mw m−2 nm−1 sr−1) and displayed a more consistent seasonal variation pattern with GPP. (2) Comparisons among coarse-resolution SIF, EC footprint-averaged SIF (SIFECA), and EC footprint-weighted SIF (SIFECW) demonstrated significant improvements in the linear relationship between downscaled SIF and GPP (the R2 increased from the range of 0.47–0.65 to 0.78–0.85). SIFECW exhibited the strongest relationship with GPP, indicating that matching SIF to flux footprints improves their relationship. (3) As the distance from the flux tower increased, the relationship between SIF and GPP weakened, reaching its lowest point beyond 1 km from the tower. Moreover, in the highly heterogeneous landscape of the SHB2 site, the relationship between VIs and GPP was poor, with no clear pattern as distance from the flux tower increased. In conclusion, the strong spatial dependency of SIF and tower-based GPP emphasizes the importance of using high-resolution SIF to accurately quantify their relationship.
Developing a Semi-Automated Near-Coastal, Water Quality-Retrieval Process from Global Multi-Spectral Data: South-Eastern Australia
Authors: Nandy Avik , Phinn Stuart , Grinham Alistair , Albert Simon
Published: July 2024

The estimation of water quality properties through satellite remote sensing relies on (1) the optical characteristics of the water body, (2) the resolutions (spatial, spectral, radiometric and temporal) of the sensor and (3) algorithm(s) applied. More than 80% of global water bodies fall under Case I (open ocean) waters, dominated by scattering and absorption associated with phytoplankton in the water column. Globally, previous studies show significant correlations between satellite-based retrieval methods and field measurements of absorbing and scattering constituents, while limited research from Australian coastal water bodies appears. This study presents a methodology to extract chlorophyll a properties from surface waters from near-coastal environments, within 2 km of coastline, in Tasmania, south-eastern Australia. We use general purpose, global, long-time series, multi-spectral satellite data, as opposed to ocean colour-specific sensor data. This approach may offer globally applicable tools for combining global satellite image archives with in situ field sensors for water quality monitoring. To enable applications from local to global scales, a cloud-based geospatial analysis workflow was developed and tested on several sites. This work represents the initial stage in developing a semi-automated near-coastal water-quality workflow using easily accessed, fully corrected global multi-spectral datasets alongside large-scale computation and delivery capabilities. Our results indicated a strong correlation between the in situ chlorophyll concentration data and blue-green band ratios from the multi-spectral sensor. In line with published research, environment-specific empirical models exhibited the highest correlations between in situ and satellite measurements, underscoring the importance of tailoring models to specific coastal waters. Our findings may provide the basis for developing this workflow for other sites in Australia. We acknowledge the use of general purpose multi-spectral data such as the Sentinel-2 and Landsat Series, their corrections and algorithms may not be as accurate and precise as ocean colour satellites. The data we are using are more readily accessible and also have true global coverage with global historic archives and regular, global collection will continue at least 10 years in the future. Regardless of sensor specifications, the retrieval method relies on localised algorithm calibration and validation using in situ measurements, which demonstrates close-to-realistic outputs. We hope this approach enables future applications to also consider these globally accessible and regularly updated datasets that are suited to coastal environments.
FEFN: Feature Enhancement Feedforward Network for Lightweight Object Detection in Remote Sensing Images
Authors: Wu Jing , Ni Rixiang , Chen Zhenhua , Huang Feng , Chen Liqiong
Published: July 2024

Object detection in remote sensing images has become a crucial component of computer vision. It has been employed in multiple domains, including military surveillance, maritime rescue, and military operations. However, the high density of small objects in remote sensing images makes it challenging for existing networks to accurately distinguish objects from shallow image features. These factors contribute to many object detection networks that produce missed detections and false alarms, particularly for densely arranged objects and small objects. To address the above problems, this paper proposes a feature enhancement feedforward network (FEFN), based on a lightweight channel feedforward module (LCFM) and a feature enhancement module (FEM). First, the FEFN captures shallow spatial information in images through a lightweight channel feedforward module that can extract the edge information of small objects such as ships. Next, it enhances the feature interaction and representation by utilizing a feature enhancement module that can achieve more accurate detection results for densely arranged objects and small objects. Finally, comparative experiments on two publicly challenging remote sensing datasets demonstrate the effectiveness of the proposed method.
Q-A2NN: Quantized All-Adder Neural Networks for Onboard Remote Sensing Scene Classification
Authors: Zhang Ning , Chen He , Chen Liang , Wang Jue , Wang Guoqing , Liu Wenchao
Published: July 2024

Performing remote sensing scene classification (RSSC) directly on satellites can alleviate data downlink burdens and reduce latency. Compared to convolutional neural networks (CNNs), the all-adder neural network (A2NN) is a novel basic neural network that is more suitable for onboard RSSC, enabling lower computational overhead by eliminating multiplication operations in convolutional layers. However, the extensive floating-point data and operations in A2NNs still lead to significant storage overhead and power consumption during hardware deployment. In this article, a shared scaling factor-based de-biasing quantization (SSDQ) method tailored for the quantization of A2NNs is proposed to address this issue, including a powers-of-two (POT)-based shared scaling factor quantization scheme and a multi-dimensional de-biasing (MDD) quantization strategy. Specifically, the POT-based shared scaling factor quantization scheme converts the adder filters in A2NNs to quantized adder filters with hardware-friendly integer input activations, weights, and operations. Thus, quantized A2NNs (Q-A2NNs) composed of quantized adder filters have lower computational and memory overheads than A2NNs, increasing their utility in hardware deployment. Although low-bit-width Q-A2NNs exhibit significantly reduced RSSC accuracy compared to A2NNs, this issue can be alleviated by employing the proposed MDD quantization strategy, which combines a weight-debiasing (WD) strategy, which reduces performance degradation due to deviations in the quantized weights, with a feature-debiasing (FD) strategy, which enhances the classification performance of Q-A2NNs through minimizing deviations among the output features of each layer. Extensive experiments and analyses demonstrate that the proposed SSDQ method can efficiently quantize A2NNs to obtain Q-A2NNs with low computational and memory overheads while maintaining comparable performance to A2NNs, thus having high potential for onboard RSSC.
LOD2-Level+ Low-Rise Building Model Extraction Method for Oblique Photography Data Using U-NET and a Multi-Decision RANSAC Segmentation Algorithm
Authors: He Yufeng , Wu Xiaobian , Pan Weibin , Chen Hui , Zhou Songshan , Lei Shaohua , Gong Xiaoran , Xu Hanzeyu , Sheng Yehua
Published: July 2024

Oblique photography is a regional digital surface model generation technique that can be widely used for building 3D model construction. However, due to the lack of geometric and semantic information about the building, these models make it difficult to differentiate more detailed components in the building, such as roofs and balconies. This paper proposes a deep learning-based method (U-NET) for constructing 3D models of low-rise buildings that address the issues. The method ensures complete geometric and semantic information and conforms to the LOD2 level. First, digital orthophotos are used to perform building extraction based on U-NET, and then a contour optimization method based on the main direction of the building and the center of gravity of the contour is used to obtain the regular building contour. Second, the pure building point cloud model representing a single building is extracted from the whole point cloud scene based on the acquired building contour. Finally, the multi-decision RANSAC algorithm is used to segment the building detail point cloud and construct a triangular mesh of building components, followed by a triangular mesh fusion and splicing method to achieve monolithic building components. The paper presents experimental evidence that the building contour extraction algorithm can achieve a 90.3% success rate and that the resulting single building 3D model contains LOD2 building components, which contain detailed geometric and semantic information.
Bayes R-CNN: An Uncertainty-Aware Bayesian Approach to Object Detection in Remote Sensing Imagery for Enhanced Scene Interpretation
Authors: Sharifuzzaman Sagar A. S. M. , Tanveer Jawad , Chen Yu , Chan Jun Hoong , Kim Hyung Seok , Kallu Karam Dad , Ahmed Shahzad
Published: July 2024

Remote sensing technology has been modernized by artificial intelligence, which has made it possible for deep learning algorithms to extract useful information from images. However, overfitting and lack of uncertainty quantification, high-resolution images, information loss in traditional feature extraction, and background information retrieval for detected objects limit the use of deep learning models in various remote sensing applications. This paper proposes a Bayes by backpropagation (BBB)-based system for scene-driven identification and information retrieval in order to overcome the above-mentioned problems. We present the Bayes R-CNN, a two-stage object detection technique to reduce overfitting while also quantifying uncertainty for each object recognized within a given image. To extract features more successfully, we replace the traditional feature extraction model with our novel Multi-Resolution Extraction Network (MRENet) model. We propose the multi-level feature fusion module (MLFFM) in the inner lateral connection and a Bayesian Distributed Lightweight Attention Module (BDLAM) to reduce information loss in the feature pyramid network (FPN). In addition, our system incorporates a Bayesian image super-resolution model which enhances the quality of the image to improve the prediction accuracy of the Bayes R-CNN. Notably, MRENet is used to classify the background of the detected objects to provide detailed interpretation of the object. Our proposed system is comprehensively trained and assessed utilizing the state-of-the-art DIOR and HRSC2016 datasets. The results demonstrate our system’s ability to detect and retrieve information from remote sensing scene images.
AIRS and MODIS Satellite-Based Assessment of Air Pollution in Southwestern China: Impact of Stratospheric Intrusions and Cross-Border Transport of Biomass Burning
Authors: Lian Puyu , Zhao Kaihui , Yuan Zibing
Published: July 2024

The exacerbation of air pollution during spring in Yunnan province, China, has attracted widespread attention. However, many studies have focused solely on the impacts of anthropogenic emissions while ignoring the role of natural processes. This study used satellite data spanning 21 years from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Atmospheric Infrared Sounder (AIRS) to reveal two natural processes closely related to springtime ozone (O3) and PM2.5 pollution: stratospheric intrusions (SIs) and cross-border transport of biomass burning (BB). We aimed to assess the mechanisms through which SIs and cross-border BB transport influence O3 and PM2.5 pollution in Southwestern China during the spring. The unique geographical conditions and prevalent southwest winds are considered the key driving factors for SIs and cross-border BB transport. Frequent tropopause folding provides favorable dynamic conditions for SIs in the upper troposphere. In the lower troposphere, the distribution patterns of O3 and stratospheric O3 tracer (O3S) are similar to the terrain, indicating that O3 is more likely to reach the surface with increasing altitude. Using stratospheric tracer tagging methods, we quantified the contributions of SIs to surface O3, ranging from 6 to 31 ppbv and accounting for 10–38% of surface O3 levels. Additionally, as Yunnan is located downwind of Myanmar and has complex terrain, it provides favorable conditions for PM2.5 and O3 generation from cross-border BB transport. The decreasing terrain distribution from north to south in Yunnan facilitates PM2.5 transport to lower-elevation border cities, whereas higher-elevation cities hinder PM2.5 transport, leading to spatial heterogeneity in PM2.5. This study provides scientific support for elucidating the two key processes governing springtime PM2.5 and O3 pollution in Yunnan, SIs and cross-border BB transport, and can assist policymakers in formulating optimal emission reduction strategies.
Spatiotemporal Analysis of Urban Forest in Chattanooga, Tennessee from 1984 to 2021 Using Landsat Satellite Imagery
Authors: Stuart William , Hossain A. K. M. Azad , Hunt Nyssa , Mix Charles , Qin Hong
Published: July 2024

Chattanooga, Tennessee is one of many cities in the Southeastern United States that is experiencing rapid urban growth. As these metropolitan areas continue to grow larger, more and more of Earth’s unique temperate forest, an ecosystem of enormous cultural, ecological, and recreational significance in the Southeastern United States, is destroyed to make way for new urban development. This research takes advantage of the extensive temporal archive of multispectral satellite imagery provided by the Landsat program to conduct a 37-year analysis of urban forest canopy cover across the City of Chattanooga. A time series of seven Landsat 5 scenes and three Landsat 8 scenes were acquired between 1984 and 2021 at an interval of five years or less. Each multispectral image was processed digitally and classified into a four-class thematic raster using a supervised hybrid classification scheme with a support vector machine (SVM) algorithm. The obtained results showed a loss of up to 43% of urban forest canopy and a gain of up to 134% urban land area in the city. Analyzing the multidecade spatiotemporal forest canopy in a rapidly expanding metropolitan center, such as Chattanooga, could help direct sustainable development efforts towards areas urbanizing at an above-average rate.
PointStaClu: A Deep Point Cloud Clustering Method Based on Stable Cluster Discrimination
Authors: Cao Xin , Wang Haoyu , Zhu Qiuquan , Wang Yifan , Liu Xiu , Li Kang , Su Linzhi
Published: July 2024

Potential inconsistencies between the goals of unsupervised representation learning and clustering within multi-stage deep clustering can diminish the effectiveness of these techniques. However, because the goal of unsupervised representation learning is inherently flexible and can be tailored to clustering, we introduce PointStaClu, a novel single-stage point cloud clustering method. This method employs stable cluster discrimination (StaClu) to tackle the inherent instability present in single-stage deep clustering training. It achieves this by constraining the gradient descent updates for negative instances within the cross-entropy loss function, and by updating the cluster centers using the same loss function. Furthermore, we integrate entropy constraints to regulate the distribution entropy of the dataset, thereby enhancing the cluster allocation. Our framework simplifies the process, employing a single loss function and an encoder for deep point cloud clustering. Extensive experiments on the ModelNet40 and ShapeNet dataset demonstrate that PointStaClu significantly narrows the performance gap between unsupervised point cloud clustering and supervised point cloud classification, presenting a novel approach to point cloud classification tasks.
Exploring the Efficacy of Nonlinear Filters in CMOS for 2-D Signal Processing for Image Quality Enhancement
Authors: Bandala-Hernandez Hector , Bautista-Castillo Alejandro , Rocha-Pérez José Miguel , Carbajal Gómez Victor Hugo , Díaz-Sánchez Alejandro
Published: July 2024

This study rigorously investigates the effectiveness of nonlinear filters in CMOS for 2-D signal processing to enhance image quality. We comprehensively compare traditional linear filters’ performance, which operate on the principle of linearity, with nonlinear filters, such as the median-median (Med-Med) approach, designed to handle nonlinear data. To ensure the validity of our findings, we use widely accepted metrics like normalized squared error (NSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM) to quantify the differences. Our simulations and experiments, conducted under controlled conditions, demonstrate that nonlinear filters in CMOS outperform linear filters in removing impulse noise and enhancing images. We also address the challenges of implementing these algorithms at the hardware level, focusing on power consumption and chip area optimization. Additionally, we propose a new architecture for the Med-Med filter and validate its functionality through experiments using a 9-pixel image sensor array. Our findings highlight the potential of nonlinear filters in CMOS for real-time image quality enhancement and their applicability in various real-world imaging applications. This research contributes to visual technology by combining theoretical insights with practical implementations, paving the way for more efficient and adaptable imaging systems.
Eddy Current Sensor Array for Electromagnetic Sensing and Crack Reconstruction with High Lift-Off in Railway Tracks
Authors: Shao Yuchun , Xia Zihan , Ding Yiqing , Crocker Bob , Saunders Scott , Bai Xue , Peyton Anthony , Conniffe Daniel , Yin Wuliang
Published: July 2024

A reliable and efficient rail track defect detection system is essential for maintaining rail track integrity and avoiding safety hazards and financial losses. Eddy current (EC) testing is a non-destructive technique that can be employed for this purpose. The trade-off between spatial resolution and lift-off should be carefully considered in practical applications to distinguish closely spaced cracks such as those caused by rolling contact fatigue (RCF). A multi-channel eddy current sensor array has been developed to detect defects on rails. Based on the sensor scanning data, defect reconstruction along the rails is achieved using an inverse algorithm that includes both direct and iterative approaches. In experimental evaluations, the EC system with the developed sensor is used to measure defects on a standard test piece of rail with a probe lift-off of 4–6 mm. The reconstruction results clearly reveal cracks at various depths and spacings on the test piece.
Performance Assessment of Object Detection Models Trained with Synthetic Data: A Case Study on Electrical Equipment Detection
Authors: Santos David O. , Montalvão Jugurta , Araujo Charles A. C. , Lebre Ulisses D. E. S. , Ferreira Tarso V. , Freire Eduardo O.
Published: July 2024

This paper explores a data augmentation approach for images of rigid bodies, particularly focusing on electrical equipment and analogous industrial objects. By leveraging manufacturer-provided datasheets containing precise equipment dimensions, we employed straightforward algorithms to generate synthetic images, permitting the expansion of the training dataset from a potentially unlimited viewpoint. In scenarios lacking genuine target images, we conducted a case study using two well-known detectors, representing two machine-learning paradigms: the Viola–Jones (VJ) and You Only Look Once (YOLO) detectors, trained exclusively on datasets featuring synthetic images as the positive examples of the target equipment, namely lightning rods and potential transformers. Performances of both detectors were assessed using real images in both visible and infrared spectra. YOLO consistently demonstrates F1 scores below 26% in both spectra, while VJ’s scores lie in the interval from 38% to 61%. This performance discrepancy is discussed in view of paradigms’ strengths and weaknesses, whereas the relatively high scores of at least one detector are taken as empirical evidence in favor of the proposed data augmentation approach.
Conductive Hydrogel Tapes for Tripolar EEG: A Promising Solution to Paste-Related Challenges
Authors: Considine Cassidy , Besio Walter
Published: July 2024

Electroencephalography (EEG) remains pivotal in neuroscience for its non-invasive exploration of brain activity, yet traditional electrodes are plagued with artifacts and the application of conductive paste poses practical challenges. Tripolar concentric ring electrode (TCRE) sensors used for EEG (tEEG) attenuate artifacts automatically, improving the signal quality. Hydrogel tapes offer a promising alternative to conductive paste, providing mess-free application and reliable electrode–skin contact in locations without hair. Since the electrodes of the TCRE sensors are only 1.0 mm apart, the impedance of the skin-to-electrode impedance-matching medium is critical. This study evaluates four hydrogel tapes’ efficacies in EEG electrode application, comparing impedance and alpha wave characteristics. Healthy adult participants underwent tEEG recordings using different tapes. The results highlight varying impedances and successful alpha wave detection despite increased tape-induced impedance. MATLAB’s EEGLab facilitated signal processing. This study underscores hydrogel tapes’ potential as a convenient and effective alternative to traditional paste, enriching tEEG research methodologies. Two of the conductive hydrogel tapes had significantly higher alpha wave power than the other tapes, but were never significantly lower.
Impact of Particle Size on the Nonlinear Magnetic Response of Iron Oxide Nanoparticles during Frequency Mixing Magnetic Detection
Authors: Pourshahidi Ali Mohammad , Jean Neha , Kaulen Corinna , Jakobi Simon , Krause Hans-Joachim
Published: July 2024

Magnetic nanoparticles (MNPs), particularly iron oxide nanoparticles (IONPs), play a pivotal role in biomedical applications ranging from magnetic resonance imaging (MRI) enhancement and cancer hyperthermia treatments to biosensing. This study focuses on the synthesis, characterization, and application of IONPs with two different size distributions for frequency mixing magnetic detection (FMMD), a technique that leverages the nonlinear magnetization properties of MNPs for sensitive biosensing. IONPs are synthesized through thermal decomposition and subsequent growth steps. Our findings highlight the critical influence of IONP size on the FMMD signal, demonstrating that larger particles contribute dominantly to the FMMD signal. This research advances our understanding of IONP behavior, underscoring the importance of size in their application in advanced diagnostic tools.
Low-Cost Imaging to Quantify Germination Rate and Seedling Vigor across Lettuce Cultivars
Authors: Iradukunda Mark , van Iersel Marc W. , Seymour Lynne , Lu Guoyu , Ferrarezi Rhuanito Soranz
Published: July 2024

The survival and growth of young plants hinge on various factors, such as seed quality and environmental conditions. Assessing seedling potential/vigor for a robust crop yield is crucial but often resource-intensive. This study explores cost-effective imaging techniques for rapid evaluation of seedling vigor, offering a practical solution to a common problem in agricultural research. In the first phase, nine lettuce (Lactuca sativa) cultivars were sown in trays and monitored using chlorophyll fluorescence imaging thrice weekly for two weeks. The second phase involved integrating embedded computers equipped with cameras for phenotyping. These systems captured and analyzed images four times daily, covering the entire growth cycle from seeding to harvest for four specific cultivars. All resulting data were promptly uploaded to the cloud, allowing for remote access and providing real-time information on plant performance. Results consistently showed the ‘Muir’ cultivar to have a larger canopy size and better germination, though ‘Sparx’ and ‘Crispino’ surpassed it in final dry weight. A non-linear model accurately predicted lettuce plant weight using seedling canopy size in the first study. The second study improved prediction accuracy with a sigmoidal growth curve from multiple harvests (R2 = 0.88, RMSE = 0.27, p < 0.001). Utilizing embedded computers in controlled environments offers efficient plant monitoring, provided there is a uniform canopy structure and minimal plant overlap.
MSE-VGG: A Novel Deep Learning Approach Based on EEG for Rapid Ischemic Stroke Detection
Authors: Tong Wei , Yue Weiqi , Chen Fangni , Shi Wei , Zhang Lei , Wan Jian
Published: July 2024

Ischemic stroke is a type of brain dysfunction caused by pathological changes in the blood vessels of the brain which leads to brain tissue ischemia and hypoxia and ultimately results in cell necrosis. Without timely and effective treatment in the early time window, ischemic stroke can lead to long-term disability and even death. Therefore, rapid detection is crucial in patients with ischemic stroke. In this study, we developed a deep learning model based on fusion features extracted from electroencephalography (EEG) signals for the fast detection of ischemic stroke. Specifically, we recruited 20 ischemic stroke patients who underwent EEG examination during the acute phase of stroke and collected EEG signals from 19 adults with no history of stroke as a control group. Afterwards, we constructed correlation-weighted Phase Lag Index (cwPLI), a novel feature, to explore the synchronization information and functional connectivity between EEG channels. Moreover, the spatio-temporal information from functional connectivity and the nonlinear information from complexity were fused by combining the cwPLI matrix and Sample Entropy (SaEn) together to further improve the discriminative ability of the model. Finally, the novel MSE-VGG network was employed as a classifier to distinguish ischemic stroke from non-ischemic stroke data. Five-fold cross-validation experiments demonstrated that the proposed model possesses excellent performance, with accuracy, sensitivity, and specificity reaching 90.17%, 89.86%, and 90.44%, respectively. Experiments on time consumption verified that the proposed method is superior to other state-of-the-art examinations. This study contributes to the advancement of the rapid detection of ischemic stroke, shedding light on the untapped potential of EEG and demonstrating the efficacy of deep learning in ischemic stroke identification.
The Use of Imaging to Quantify the Impact of Seed Aging on Lettuce Seed Germination and Seedling Vigor
Authors: Iradukunda Mark , van Iersel Marc W. , Seymour Lynne , Lu Guoyu , Ferrarezi Rhuanito Soranz
Published: July 2024

The decline in seed quality over time due to natural aging or mishandling requires assessing seed vigor for resilience in adverse conditions. Accelerated aging (AA) methods simulate seed deterioration by subjecting seeds to high temperatures and humidity. Saturated salt accelerated aging (SSAA) is an AA method adopted for small seeds like lettuce (Lactuca sativa). In this study, we subjected seeds of two lettuce cultivars (‘Muir’ and ‘Bauer’) to SSAA by sealing them in a box containing 40 g/100 mL of a sodium chloride (NaCl) solution in a dark growth chamber at 41 °C for 24, 48, and 72 h with a control. We monitored their vigor using embedded computer cameras, tracking the projected canopy size (PCS) daily from sowing to harvest. The cultivar ‘Muir’ exhibited consistent PCS values across the treatments, while ‘Bauer’ showed PCS variations, with notable declines after prolonged aging. The germination rates dropped significantly after 48 and 72 h of SSAA. A nonlinear regression model revealed a strong relationship between PCS and shoot dry weight across harvests and cultivars (R2 = 0.93, RMSE = 0.15, p < 0.001). The research found that the projected canopy size and shoot dry weight increased over time with significant differences in treatments for the cultivar ‘Bauer’ but not for ‘Muir,’ with the canopy size being a strong predictor of dry weight and no significant impact from the SSAA treatments. This study highlights cultivar-specific responses to aging and demonstrates the efficacy of our imaging tool in predicting lettuce dry weight despite treatment variations. Understanding how aging affects different lettuce varieties is crucial for seed management and crop sustainability.
Conforming Capacitive Load Cells for Conical Pick Cutters
Authors: Oltmanns Austin F. , Petruska Andrew J.
Published: July 2024

In underground coal mining, machine operators put themselves at risk when getting close to the machine or cutting face to observe the process. To improve the safety and efficiency of machine operators, a cutting force sensor is proposed. A linear cutting machine is used to cut two separate coal samples cast in concrete with conical pick cutters to simulate mining with a continuous miner. Linear and neural network regression models are fit using 100 random 70:30 test/train splits. The normal force exceeds 60 kN during the rock-cutting tests, and it is averaged using a low pass filter with a 10 Hertz cutoff frequency. The sensor uses measurements of the resonant frequency of capacitive cells in a steel case to determine cutting forces. When used in the rock-cutting experiments, the sensor conforms to the tooling and the stiffness and sensitivity are increased compared to the initial configuration. The sensor is able to track the normal force on the conical picks with a mean absolute error less than 6 kN and an R2 score greater than 0.60 using linear regression. A small neural network with a second-order polynomial expansion is able to improve this to a mean absolute error of less than 4 kN and an R2 score of around 0.80. Filtering measurements before regression fitting is explored. This type of sensor could allow operators to assess tool wear and material type using objective force measurements while maintaining a greater distance from the cutting interface.
In Situ Preparation of Metallic Copper Nanosheets/Carbon Paper Sensitive Electrodes for Low-Potential Electrochemical Detection of Nitrite
Authors: Zhao Xing , Zhou Guangfeng , Qin Sitao , Zhang Jingwen , Wang Guanda , Gao Jie , Suo Hui , Zhao Chun
Published: July 2024

In the realm of electrochemical nitrite detection, the potent oxidizing nature of nitrite typically necessitates operation at high detection potentials. However, this study introduces a novel approach to address this challenge by developing a highly sensitive electrochemical sensor with a low reduction detection potential. Specifically, a copper metal nanosheet/carbon paper sensitive electrode (Cu/CP) was fabricated using a one-step electrodeposition method, leveraging the catalytic reduction properties of copper’s high occupancy d-orbital. The Cu/CP sensor exhibited remarkable performance in nitrite detection, featuring a low detection potential of −0.05 V vs. Hg/HgO, a wide linear range of 10~1000 μM, an impressive detection limit of 0.079 μM (S/N = 3), and a high sensitivity of 2140 μA mM−1cm−2. These findings underscore the efficacy of electrochemical nitrite detection through catalytic reduction as a means to reduce the operational voltage of the sensor. By showcasing the successful implementation of this strategy, this work sets a valuable precedent for the advancement of electrochemical low-potential nitrite detection methodologies.
Stiffness-Based Evaluation of Hinge Joints in Prefabricated Assembled Multi-Girder Bridges under Operational Conditions
Authors: Shang Zhiqiang , Gong Fengzong , Shao Zhufeng , Matos Jose , Xin Gongfeng , Xia Ye
Published: July 2024

Presently, the prevailing approaches to assessing hinge joint damage predominantly rely on predefined damage indicators or updating finite element models (FEMs). However, these methods possess certain limitations. The damage indicator method requires high-quality monitoring data and demonstrates variable sensitivities of distinct indicators to damage. On the other hand, the FEM approach mandates a convoluted FEM update procedure. Hinge joint damage represents a major kind of defect in prefabricated assembled multi-girder bridges (AMGBs). Therefore, effective damage detection methods are imperative to identify the damage state of hinge joints. To this end, a stiffness-based method for the performance evaluation of hinge joints of AMGBs is proposed in this paper. The proposed method estimates hinge joint stiffness by solving the characteristic equations of the multi-beam system. In addition, this study introduces a method for determining baseline joint stiffness using design data and FEM. Subsequently, a comprehensive evaluation framework for hinge joints is formulated, coupling a finite element model with the baseline stiffness, thereby introducing a damage indicator rooted in stiffness ratios. To verify the effectiveness of the proposed method, strain and displacement correlations are analyzed using actual bridge monitoring data, and articulation joint stiffness is identified. The results underscore the capability of the proposed method to accurately pinpoint the location and extent of hinge joint damage.
Anomaly Detection and Remaining Useful Life Estimation for the Health and Usage Monitoring Systems 2023 Data Challenge
Authors: Matania Omri , Bechhoefer Eric , Blunt David , Wang Wenyi , Bortman Jacob
Published: July 2024

Gear fault detection and remaining useful life estimation are important tasks for monitoring the health of rotating machinery. In this study, a new benchmark for endurance gear vibration signals is presented and made publicly available. The new dataset was used in the HUMS 2023 conference data challenge to test anomaly detection algorithms. A survey of the suggested techniques is provided, demonstrating that traditional signal processing techniques interestingly outperform deep learning algorithms in this case. Of the 11 participating groups, only those that used traditional approaches achieved good results on most of the channels. Additionally, we introduce a signal processing anomaly detection algorithm and meticulously compare it to a standard deep learning anomaly detection algorithm using data from the HUMS 2023 challenge and simulated signals. The signal processing algorithm surpasses the deep learning algorithm on all tested channels and also on simulated data where there is an abundance of training data. Finally, we present a new digital twin that enables the estimation of the remaining useful life of the tested gear from the HUMS 2023 challenge.