bluuebunny/crossref_metadata_2025 huggingface dataset:
Point RCNN: An Angle-Free Framework for Rotated Object Detection
Authors: Zhou Qiang , Yu Chaohui
Published: May 2022

Rotated object detection in aerial images is still challenging due to arbitrary orientations, large scale and aspect ratio variations, and extreme density of objects. Existing state-of-the-art rotated object detection methods mainly rely on angle-based detectors. However, angle-based detectors can easily suffer from a long-standing boundary problem. To tackle this problem, we propose a purely angle-free framework for rotated object detection, called Point RCNN. Point RCNN is a two-stage detector including both PointRPN and PointReg which are angle-free. Given an input aerial image, first, the backbone-FPN extracts hierarchical features, then, the PointRPN module generates an accurate rotated region of interests (RRoIs) by converting the learned representative points of each rotated object using the MinAreaRect function of OpenCV. Motivated by RepPoints, we designed a coarse-to-fine process to regress and refine the representative points for more accurate RRoIs. Next, based on the learned RRoIs of PointRPN, the PointReg module learns to regress and refine the corner points of each RRoI to perform more accurate rotated object detection. Finally, the final rotated bounding box of each rotated object can be attained based on the learned four corner points. In addition, aerial images are often severely unbalanced in categories, and existing rotated object detection methods almost ignore this problem. To tackle the severely unbalanced dataset problem, we propose a balanced dataset strategy. We experimentally verified that re-sampling the images of the rare categories can stabilize the training procedure and further improve the detection performance. Specifically, the performance was improved from 80.37 mAP to 80.71 mAP in DOTA-v1.0. Without unnecessary elaboration, our Point RCNN method achieved new state-of-the-art detection performance on multiple large-scale aerial image datasets, including DOTA-v1.0, DOTA-v1.5, HRSC2016, and UCAS-AOD. Specifically, in DOTA-v1.0, our Point RCNN achieved better detection performance of 80.71 mAP. In DOTA-v1.5, Point RCNN achieved 79.31 mAP, which significantly improved the performance by 2.86 mAP (from ReDet’s 76.45 to our 79.31). In HRSC2016 and UCAS-AOD, our Point RCNN achieved higher performance of 90.53 mAP and 90.04 mAP, respectively.
Fine-Grained Urban Air Quality Mapping from Sparse Mobile Air Pollution Measurements and Dense Traffic Density
Authors: Qin Xuening , Do Tien Huu , Hofman Jelle , Bonet Esther Rodrigo , La Manna Valerio Panzica , Deligiannis Nikos , Philips Wilfried
Published: May 2022

Urban air quality mapping has been widely applied in urban planning, air pollution control and personal air pollution exposure assessment. Urban air quality maps are traditionally derived using measurements from fixed monitoring stations. Due to high cost, these stations are generally sparsely deployed in a few representative locations, leading to a highly generalized air quality map. In addition, urban air quality varies rapidly over short distances (<1 km) and is influenced by meteorological conditions, road network and traffic flow. These variations are not well represented in coarse-grained air quality maps generated by conventional fixed-site monitoring methods but have important implications for characterizing heterogeneous personal air pollution exposures and identifying localized air pollution hotspots. Therefore, fine-grained urban air quality mapping is indispensable. In this context, supplementary low-cost mobile sensors make mobile air quality monitoring a promising alternative. Using sparse air quality measurements collected by mobile sensors and various contextual factors, especially traffic flow, we propose a context-aware locally adapted deep forest (CLADF) model to infer the distribution of NO2 by 100 m and 1 h resolution for fine-grained air quality mapping. The CLADF model exploits deep forest to construct a local model for each cluster consisting of nearest neighbor measurements in contextual feature space, and considers traffic flow as an important contextual feature. Extensive validation experiments were conducted using mobile NO2 measurements collected by 17 postal vans equipped with low-cost sensors operating in Antwerp, Belgium. The experimental results demonstrate that the CLADF model achieves the lowest RMSE as well as advances in accuracy and correlation, compared with various benchmark models, including random forest, deep forest, extreme gradient boosting and support vector regression.
Machine Learning Classification of Fused Sentinel-1 and Sentinel-2 Image Data towards Mapping Fruit Plantations in Highly Heterogenous Landscapes
Authors: Chabalala Yingisani , Adam Elhadi , Ali Khalid Adem
Published: May 2022

Mapping smallholder fruit plantations using optical data is challenging due to morphological landscape heterogeneity and crop types having overlapping spectral signatures. Furthermore, cloud covers limit the use of optical sensing, especially in subtropical climates where they are persistent. This research assessed the effectiveness of Sentinel-1 (S1) and Sentinel-2 (S2) data for mapping fruit trees and co-existing land-use types by using support vector machine (SVM) and random forest (RF) classifiers independently. These classifiers were also applied to fused data from the two sensors. Feature ranks were extracted using the RF mean decrease accuracy (MDA) and forward variable selection (FVS) to identify optimal spectral windows to classify fruit trees. Based on RF MDA and FVS, the SVM classifier resulted in relatively high classification accuracy with overall accuracy (OA) = 0.91.6% and kappa coefficient = 0.91% when applied to the fused satellite data. Application of SVM to S1, S2, S2 selected variables and S1S2 fusion independently produced OA = 27.64, Kappa coefficient = 0.13%; OA= 87%, Kappa coefficient = 86.89%; OA = 69.33, Kappa coefficient = 69. %; OA = 87.01%, Kappa coefficient = 87%, respectively. Results also indicated that the optimal spectral bands for fruit trees mapping are green (B3) and SWIR_2 (B10) for S2, whereas for S1, the vertical-horizontal (VH) polarization band. Including the textural metrics from the VV channel improved crop discrimination and co-existing land use cover types. The fusion approach proved robust and well suited for accurate smallholder fruit plantation mapping.
Characterizing Spatiotemporal Patterns of Land Deformation in the Santa Ana Basin, Los Angeles, from InSAR Time Series and Independent Component Analysis
Authors: Zhu Kang , Zhang Xing , Sun Qian , Wang Hai , Hu Jun
Published: May 2022

The excessive extraction and recharge of groundwater lead to long-time seasonal land subsidence in Los Angeles, USA, and especially in the Santa Ana basin. The rate of land subsidence in the Santa Ana basin has been rising, which could pose a danger to infrastructure and human lives. However, the most recent research on land surface deformation in the area was conducted using the traditional parameter estimation method, resulting in little understanding of the regional spatiotemporal characteristics. The parametric method consists of a least square linear inversion, using the pre-defined mathematical geometric or geophysical theoretical models to describe groundwater deformation, and it requires precise external environmental variables and accurate geophysical parameters, which are more difficult to implement. In this study, multitemporal InSAR-derived deformation time series are analyzed by using 69 descending C-band Sentinel-1A SAR scenes acquired from 2015 to 2018. A method based on independent component analysis (ICA) is applied to characterize the spatial pattern and temporal evolution of land subsidence in the Santa Ana basin. The results reveal two different spatial and temporal deformation patterns in the basin. First, a widespread seasonal deformation is identified by the first component, related to annual seasonal groundwater level changes, and the overall deformation shows a concentrated spatial pattern. The second component captures a long-term signal with a large-scale spatial pattern. For quantitative assessment, the obtained deformation time series are compared with the GNSS data, validating an accuracy of millimeters. We further calculate the cross-correlation coefficient and the elastic skeletal storage coefficient from the ICA-derived seasonal deformation and groundwater level, which reveals that the deformation responds quickly (i.e., a lag of 8 days) to the change in groundwater and the Santa Ana aquifer retains almost the same elasticity for at least 15 years. Quantifying the spatiotemporal characteristics of the deformation in the Santa Ana basin can provide a reference for the monitoring and managing of groundwater.
TLSO with Graphene Sensors—An Application to Measurements of Corrective Forces in the Prototype of Intelligent Brace
Authors: Tymińska Patrycja , Zaborowska-Sapeta Katarzyna , Janczak Daniel , Giżewski Tomasz
Published: May 2022

This study presents a prototype of Intelligent Brace—the gold medal winner in the 68th edition of the International EUREKA 2019 Competition, in Valencia, Spain. It shows how to improve the effectiveness of a static orthopedic brace, with modern technology based on advanced electronic solutions. The research uses in-house-made thin-film graphene sensors, an electronic system with a microcontroller and derotating pads, a mobile application for Android system, and a remote database. The article presents a description of the real project, the system principle of operation, and the layout of the system elements in the orthosis. A prototype device was designed and built that was tested not only in laboratory conditions, but also during trials with the first patient. Approximately two months of data were collected and interpreted. The collected research results provided basic knowledge about the range of forces exerted by the brace on the patient’s body, as well as the regularity of wearing the orthosis by the patient and compliance with the doctor’s recommendations.
Acoustic Estimation of the Direction of Arrival of an Unmanned Aerial Vehicle Based on Frequency Tracking in the Time-Frequency Plane
Authors: Itare Nathan , Thomas Jean-Hugh , Raoof Kosai , Blanchard Torea
Published: May 2022

The development of unmanned aerial vehicles (UAVs) opens up a lot of opportunities but also brings some threats. Dealing with these threats is not easy and requires some good techniques. Knowing the location of the threat is essential to deal with an UAV that is displaying disturbing behavior. Many methods exist but can be very limited due to the size of UAVs or due to technological improvements over the years. However, the noise produced by the UAVs is still predominant, so it gives a good opening for the development of acoustic methods. The method presented here takes advantage of a microphone array with a processing based on time domain Delay and Sum Beamforming. In order to obtain a better signal to noise ratio, the UAV’s acoustic signature is taken into account in the processing by using a time-frequency representation of the beamformer’s output. Then, only the content related to this signature is considered to calculate the energy in one direction. This method enables to have a good robustness to noise and to localize an UAV with a poor spectral content or to separate two UAVs with different spectral contents. Simulation results and those of a real flight experiment are reported.
Improved Feature-Based Gaze Estimation Using Self-Attention Module and Synthetic Eye Images
Authors: Oh Jaekwang , Lee Youngkeun , Yoo Jisang , Kwon Soonchul
Published: May 2022

Gaze is an excellent indicator and has utility in that it can express interest or intention and the condition of an object. Recent deep-learning methods are mainly appearance-based methods that estimate gaze based on a simple regression from entire face and eye images. However, sometimes, this method does not give satisfactory results for gaze estimations in low-resolution and noisy images obtained in unconstrained real-world settings (e.g., places with severe lighting changes). In this study, we propose a method that estimates gaze by detecting eye region landmarks through a single eye image; and this approach is shown to be competitive with recent appearance-based methods. Our approach acquires rich information by extracting more landmarks and including iris and eye edges, similar to the existing feature-based methods. To acquire strong features even at low resolutions, we used the HRNet backbone network to learn representations of images at various resolutions. Furthermore, we used the self-attention module CBAM to obtain a refined feature map with better spatial information, which enhanced the robustness to noisy inputs, thereby yielding a performance of a 3.18% landmark localization error, a 4% improvement over the existing error and A large number of landmarks were acquired and used as inputs for a lightweight neural network to estimate the gaze. We conducted a within-datasets evaluation on the MPIIGaze, which was obtained in a natural environment and achieved a state-of-the-art performance of 4.32 degrees, a 6% improvement over the existing performance.
Design of Transverse Brachiation Robot and Motion Control System for Locomotion between Ledges at Different Elevations
Authors: Lin Chi-Ying , Tian Yong-Jie
Published: May 2022

Bio-inspired transverse brachiation robots mimic the movement of human climbers as they traverse along ledges on a vertical wall. The constraints on the locomotion of these robots differ considerably from those of conventional brachiation robots due primarily to the need for robust hand-eye coordination. This paper describes the development of a motion control strategy for a brachiation robot navigating between wall ledges positioned on a level plane or at different elevations. We based our robot on a four-link arm-body-tail system performing a four-phase movement, including a release phase, body reversal phase, swing-up phase, and grasping phase. We designed a gripper that uses passive wrist joint motion to grasp the ledge during the tail swing. We also developed a dynamic model by which to coordinate the swing-up movement, define the phase switching conditions, and time the grasping action of the grippers. In experiments, the robot proved highly effective in traversing between wall ledges of the same or different elevations.
A Novel Approach for the Creation of Electrically Controlled LC:PDMS Microstructures
Authors: Rutkowska Katarzyna A. , Sobotka Piotr , Grom Monika , Baczyński Szymon , Juchniewicz Marcin , Marchlewicz Kasper , Dybko Artur
Published: May 2022

This work presents research on unique optofluidic systems in the form of air channels fabricated in PDMS and infiltrated with liquid crystalline material. The proposed LC:PDMS structures represent an innovative solution due to the use of microchannel electrodes filled with a liquid metal alloy. The latter allows for the easy and dynamic reconfiguration of the system and eliminates technological issues experienced by other research groups. The paper discusses the design, fabrication, and testing methods for tunable LC:PDMS structures. Particular emphasis was placed on determining their properties after applying an external electric field, depending on the geometrical parameters of the system. The conclusions of the performed investigations may contribute to the definition of guidelines for both LC:PDMS devices and a new class of potential sensing elements utilizing polymers and liquid crystals in their structures.
Non-Intrusive Pipeline Flow Detection Based on Distributed Fiber Turbulent Vibration Sensing
Authors: Shang Ying , Wang Chen , Zhang Yongkang , Zhao Wenan , Ni Jiasheng , Peng Gangding
Published: May 2022

We demonstrate a non-intrusive dynamic monitoring method of oil well flow based on distributed optical fiber acoustic sensing (DAS) technology and the turbulent vibration. The quantitative measurement of the flow rate is theoretically acquired though the amplitude of the demodulated phase changes from DAS based on the flow impact in the tube on the pipe wall. The experimental results show that the relationships between the flow rate and the demodulated phase changes, in both a whole frequency region and in a sensitive-response frequency region, fit the quadratic equation well, with a max R2 of 0.997, which is consistent with the theoretical simulation results. The detectable flow rate is from 0.73 m3/h to 2.48 m3/h. The experiments verify the feasibility of DAS system flow monitoring and provide technical support for the practical application of the downhole flow measurement.
High Electrocaloric Effect in Lead Scandium Tantalate Thin Films with Interdigitated Electrodes
Authors: Kovacova Veronika , Glinsek Sebastjan , Girod Stephanie , Defay Emmanuel
Published: May 2022

Lead scandium tantalate, Pb(Sc,Ta)O3, is an excellent electrocaloric material showing large temperature variations, good efficiency, and a broad operating temperature window. In form of multilayer ceramic capacitors integrated into a cooling device, the device can generate a temperature difference larger than 13 K. Here, we investigate Pb(Sc,Ta)O3 in form of thin films prepared using the sol–gel chemical solution deposition method. We report the detailed fabrication process of high-quality films on various substrates such as c-sapphire and fused silica. The main originality of this research is the use of interdigitated top electrodes, enabling the application of very large electric fields in PST. We provide structural and electrical characterisation, as well as electrocaloric temperature variation, using the Maxwell relation approach. Films do not show a B-site ordering. The temperature variation from 7.2 to 15.7 K was measured on the Pb(Sc,Ta)O3 film on a c-sapphire substrate under the electric field of 1330 kV/cm between 14.5 °C and 50 °C. This temperature variation is the highest reported so far in Pb(Sc,Ta)O3 thin films. Moreover, stress seems to have an effect on the maximum permittivity temperature and thus electrocaloric temperature variation with temperature in Pb(Sc,Ta)O3 films. Tensile stress induced by fused silica shifts the “transition” of Pb(Sc,Ta)O3 to lower temperatures. This study shows the possibility for electrocaloric temperature variation tuning with stress conditions.
Low-Frequency Vibrational Spectroscopy Characteristic of Pharmaceutical Carbamazepine Co-Crystals with Nicotinamide and Saccharin
Authors: Ge Meilan , Wang Yuye , Zhu Junfeng , Wu Bin , Xu Degang , Yao Jianquan
Published: May 2022

The pharmaceutical co-crystal has attracted increasing interest due to the improvement of physicochemical properties of active pharmaceutical ingredients. The characterization of pharmaceutical co-crystal is an integral part of the pharmaceutical field. In this paper, the low-frequency vibrational properties for carbamazepine co-crystals with nicotinamide and saccharin (CBZ-NIC and CBZ-SAC) have been characterized by combining the THz spectroscopy with low-wavenumber Raman spectroscopy. The experiment results show that, compared with the individual constituents, CBZ-NIC and CBZ-SAC co-crystals not only have different characteristic absorption peaks in the 0.3-2.5 THz region, but also have significant low-wavenumber Raman characteristic peaks in 0–100 cm−1. Density functional theory was performed to simulate the terahertz and low-wavenumber Raman spectra of the two co-crystals, where the calculation agreed well with the measured vibrational peak positions. The vibrational modes of CBZ-NIC and CBZ-SAC co-crystals were assigned through comparing theoretical results with the experimental spectra. Meanwhile, the low-frequency infrared and/or Raman active of characteristic peaks for such co-crystals were discussed. The results indicate the combination of THz spectroscopy and low-wavenumber Raman spectroscopy can provide more comprehensive low-frequency vibrational information for pharmaceutical co-crystals, such as collective vibration and skeleton vibration, which could play an important role in pharmaceutical science.
Artificial Intelligence, Machine Learning and Smart Technologies for Nondestructive Evaluation
Authors: Taheri Hossein , Gonzalez Bocanegra Maria , Taheri Mohammad
Published: May 2022

Nondestructive evaluation (NDE) techniques are used in many industries to evaluate the properties of components and inspect for flaws and anomalies in structures without altering the part’s integrity or causing damage to the component being tested. This includes monitoring materials’ condition (Material State Awareness (MSA)) and health of structures (Structural Health Monitoring (SHM)). NDE techniques are highly valuable tools to help prevent potential losses and hazards arising from the failure of a component while saving time and cost by not compromising its future usage. On the other hand, Artificial Intelligence (AI) and Machine Learning (ML) techniques are useful tools which can help automating data collection and analyses, providing new insights, and potentially improving detection performance in a quick and low effort manner with great cost savings. This paper presents a survey on state of the art AI-ML techniques for NDE and the application of related smart technologies including Machine Vision (MV) and Digital Twins in NDE.
Application of Improved CycleGAN in Laser-Visible Face Image Translation
Authors: Qin Mingyu , Fan Youchen , Guo Huichao , Wang Mingqian
Published: May 2022

CycleGAN is widely used in various image translations, such as thermal-infrared–visible-image translation, near-infrared–visible-image translation, and shortwave-infrared–visible-image translation. However, most image translations are used for infrared-to-visible translation, and the wide application of laser imaging has an increasingly strong demand for laser–visible image translation. In addition, the current image translation is mainly aimed at frontal face images, which cannot be effectively utilized to translate faces at a certain angle. In this paper, we construct a laser-visible face mapping dataset; in case of the gradient dispersion of the objective function of the original adversarial loss, the least squares loss function is used to replace the cross-entropy loss function and an identity loss function is added to strengthen the network constraints on the generator. The experimental results indicate that the SSIM value of the improved model increases by 1.25%, 8%, 0, 8%, the PSNR value is not much different, and the FID value decreases by 11.22, 12.85, 43.37 and 72.19, respectively, compared with the CycleGAN, Pix2pix, U-GAN-IT and StarGAN models. In the profile image translation, in view of the poor extraction effect of CycleGAN’s original feature extraction module ResNet, the RRDB module is used to replace it based on the first improvement. The experimental results show that, compared with the CycleGAN, Pix2pix, U-GAN-IT, StarGAN and the first improved model, the SSIM value of the improved model increased by 3.75%, 10.67%, 2.47%, 10.67% and 2.47%, respectively; the PSNR value increased by 1.02, 2.74, 0.32, 0.66 and 0.02, respectively; the FID value reduced by 26.32, 27.95, 58.47, 87.29 and 15.1, respectively. Subjectively, the contour features and facial features were better conserved.
Nuclei-Guided Network for Breast Cancer Grading in HE-Stained Pathological Images
Authors: Yan Rui , Ren Fei , Li Jintao , Rao Xiaosong , Lv Zhilong , Zheng Chunhou , Zhang Fa
Published: May 2022

Breast cancer grading methods based on hematoxylin-eosin (HE) stained pathological images can be summarized into two categories. The first category is to directly extract the pathological image features for breast cancer grading. However, unlike the coarse-grained problem of breast cancer classification, breast cancer grading is a fine-grained classification problem, so general methods cannot achieve satisfactory results. The second category is to apply the three evaluation criteria of the Nottingham Grading System (NGS) separately, and then integrate the results of the three criteria to obtain the final grading result. However, NGS is only a semiquantitative evaluation method, and there may be far more image features related to breast cancer grading. In this paper, we proposed a Nuclei-Guided Network (NGNet) for breast invasive ductal carcinoma (IDC) grading in pathological images. The proposed nuclei-guided attention module plays the role of nucleus attention, so as to learn more nuclei-related feature representations for breast IDC grading. In addition, the proposed nuclei-guided fusion module in the fusion process of different branches can further enable the network to focus on learning nuclei-related features. Overall, under the guidance of nuclei-related features, the entire NGNet can learn more fine-grained features for breast IDC grading. The experimental results show that the performance of the proposed method is better than that of state-of-the-art method. In addition, we released a well-labeled dataset with 3644 pathological images for breast IDC grading. This dataset is currently the largest publicly available breast IDC grading dataset and can serve as a benchmark to facilitate a broader study of breast IDC grading.
Predicting Energy Consumption Using LSTM, Multi-Layer GRU and Drop-GRU Neural Networks
Authors: Mahjoub Sameh , Chrifi-Alaoui Larbi , Marhic Bruno , Delahoche Laurent
Published: May 2022

With the steep rise in the development of smart grids and the current advancement in developing measuring infrastructure, short term power consumption forecasting has recently gained increasing attention. In fact, the prediction of future power loads turns out to be a key issue to avoid energy wastage and to build effective power management strategies. Furthermore, energy consumption information can be considered historical time series data that are required to extract all meaningful knowledge and then forecast the future consumption. In this work, we aim to model and to compare three different machine learning algorithms in making a time series power forecast. The proposed models are the Long Short-Term Memory (LSTM), the Gated Recurrent Unit (GRU) and the Drop-GRU. We are going to use the power consumption data as our time series dataset and make predictions accordingly. The LSTM neural network has been favored in this work to predict the future load consumption and prevent consumption peaks. To provide a comprehensive evaluation of this method, we have performed several experiments using real data power consumption in some French cities. Experimental results on various time horizons show that the LSTM model produces a better result than the GRU and the Drop-GRU forecasting methods. There are fewer prediction errors and its precision is finer. Therefore, these predictions based on the LSTM method will allow us to make decisions in advance and trigger load shedding in cases where consumption exceeds the authorized threshold. This will have a significant impact on planning the power quality and the maintenance of power equipment.
A Lightweight Pose Sensing Scheme for Contactless Abnormal Gait Behavior Measurement
Authors: Zhao Yuliang , Li Jian , Wang Xiaoai , Liu Fan , Shan Peng , Li Lianjiang , Fu Qiang
Published: May 2022

The recognition of abnormal gait behavior is important in the field of motion assessment and disease diagnosis. Currently, abnormal gait behavior is primarily recognized by pressure and inertial data obtained from wearable sensors. However, the data drift and wearing difficulties for patients have impeded the application of these wearable sensors. Here, we propose a contactless abnormal gait behavior recognition method that captures human pose data using a monocular camera. A lightweight OpenPose (OP) model is generated with Depthwise Separable Convolution to recognize joint points and extract their coordinates during walking in real time. For the walking data errors extracted in the 2D plane, a 3D reconstruction is performed on the walking data, and a total of 11 types of abnormal gait features are extracted by the OP model. Finally, the XGBoost algorithm is used for feature screening. The final experimental results show that the Random Forest (RF) algorithm in combination with 3D features delivers the highest precision (92.13%) for abnormal gait behavior recognition. The proposed scheme overcomes the data drift of inertial sensors and sensor wearing challenges in the elderly while reducing the hardware requirements for model deployment. With excellent real-time and contactless capabilities, the scheme is expected to enjoy a wide range of applications in the field of abnormal gait measurement.
Parameter Optimization of a Magnetic Coupled Piezoelectric Energy Harvester with the Homogenized Material—Numerical Approach and Experimental Study
Authors: Koszewnik Andrzej , Ołdziej Daniel , Amaro Mário B.
Published: May 2022

This paper presents the process optimization of some key parameters, such as beam spacing, flux density and optimal impedance load matching of magnetic coupled piezoelectric harvesters. In order to do this, the distributed parameters model of this structure, containing macro-fiber components (MFC) with homogenous material in the piezoelectric fiber layer, was determined. Next, the computational model of this structure was designed on the basis of the first-order shear theory (FOST). The performed analysis of the calculated voltage outputs on the basis of the theoretical approach and finite element model by various beam spacing allowed us to indicate that optimized parameters play an important role in enhancing the efficiency of the system. Experiments carried out in a laboratory stand for this structure, allowed for the verification of the numerical results. In the effect, it can be noted that magnetic coupled harvesters will be relevant for a wide range of application sectors, as well as useful for the evolving composite industry.
X-ray Image Enhancement Based on Nonsubsampled Shearlet Transform and Gradient Domain Guided Filtering
Authors: Zhao Tao , Zhang Si-Xiang
Published: May 2022

In this paper, we propose an image enhancement algorithm combining non-subsampled shearlet transform and gradient-domain guided filtering to address the problems of low resolution, noise amplification, missing details, and weak edge gradient retention in the X-ray image enhancement process. First, we decompose histogram equalization and nonsubsampled shearlet transform to the original image. We get a low-frequency sub-band and several high-frequency sub-bands. Adaptive gamma correction with weighting distribution is used for the low-frequency sub-band to highlight image contour information and improve the overall contrast of the image. The gradient-domain guided filtering is conducted for the high-frequency sub-bands to suppress image noise and highlight detail and edge information. Finally, we reconstruct all the effectively processed sub-bands by the inverse non-subsampled shearlet transform and obtain the final enhanced image. The experimental results show that the proposed algorithm has good results in X-ray image enhancement, and its objective index also has evident advantages over some classical algorithms.
Hybrid and Deep Learning Approach for Early Diagnosis of Lower Gastrointestinal Diseases
Authors: Fati Suliman Mohamed , Senan Ebrahim Mohammed , Azar Ahmad Taher
Published: May 2022

Every year, nearly two million people die as a result of gastrointestinal (GI) disorders. Lower gastrointestinal tract tumors are one of the leading causes of death worldwide. Thus, early detection of the type of tumor is of great importance in the survival of patients. Additionally, removing benign tumors in their early stages has more risks than benefits. Video endoscopy technology is essential for imaging the GI tract and identifying disorders such as bleeding, ulcers, polyps, and malignant tumors. Videography generates 5000 frames, which require extensive analysis and take a long time to follow all frames. Thus, artificial intelligence techniques, which have a higher ability to diagnose and assist physicians in making accurate diagnostic decisions, solve these challenges. In this study, many multi-methodologies were developed, where the work was divided into four proposed systems; each system has more than one diagnostic method. The first proposed system utilizes artificial neural networks (ANN) and feed-forward neural networks (FFNN) algorithms based on extracting hybrid features by three algorithms: local binary pattern (LBP), gray level co-occurrence matrix (GLCM), and fuzzy color histogram (FCH) algorithms. The second proposed system uses pre-trained CNN models which are the GoogLeNet and AlexNet based on the extraction of deep feature maps and their classification with high accuracy. The third proposed method uses hybrid techniques consisting of two blocks: the first block of CNN models (GoogLeNet and AlexNet) to extract feature maps; the second block is the support vector machine (SVM) algorithm for classifying deep feature maps. The fourth proposed system uses ANN and FFNN based on the hybrid features between CNN models (GoogLeNet and AlexNet) and LBP, GLCM and FCH algorithms. All the proposed systems achieved superior results in diagnosing endoscopic images for the early detection of lower gastrointestinal diseases. All systems produced promising results; the FFNN classifier based on the hybrid features extracted by GoogLeNet, LBP, GLCM and FCH achieved an accuracy of 99.3%, precision of 99.2%, sensitivity of 99%, specificity of 100%, and AUC of 99.87%.
Effects of Paradigm Color and Screen Brightness on Visual Fatigue in Light Environment of Night Based on Eye Tracker and EEG Acquisition Equipment
Authors: Tian Peiyuan , Xu Guanghua , Han Chengcheng , Zheng Xiaowei , Zhang Kai , Du Chenghang , Wei Fan , Zhang Sicong
Published: May 2022

Nowadays, more people tend to go to bed late and spend their sleep time with various electronic devices. At the same time, the BCI (brain–computer interface) rehabilitation equipment uses a visual display, thus it is necessary to evaluate the problem of visual fatigue to avoid the impact on the training effect. Therefore, it is very important to understand the impact of using electronic devices in a dark environment at night on human visual fatigue. This paper uses Matlab to write different color paradigm stimulations, uses a 4K display with an adjustable screen brightness to jointly design the experiment, uses eye tracker and g.tec Electroencephalogram (EEG) equipment to collect the signal, and then carries out data processing and analysis, finally obtaining the influence of the combination of different colors and different screen brightness on human visual fatigue in a dark environment. In this study, subjects were asked to evaluate their subjective (Likert scale) perception, and objective signals (pupil diameter, θ + α frequency band data) were collected in a dark environment (<3 lx). The Likert scale showed that a low screen brightness in the dark environment could reduce the visual fatigue of the subjects, and participants preferred blue to red. The pupil data revealed that visual perception sensitivity was more vulnerable to stimulation at a medium and high screen brightness, which is easier to deepen visual fatigue. EEG frequency band data concluded that there was no significant difference between paradigm colors and screen brightness on visual fatigue. On this basis, this paper puts forward a new index—the visual anti-fatigue index, which provides a valuable reference for the optimization of the indoor living environment, the improvement of satisfaction with the use of electronic equipment and BCI rehabilitation equipment, and the protection of human eyes.
Marker-Free, Molecule Sensitive Mapping of Disturbed Falling Fluid Films Using Raman Imaging
Authors: Nachtmann Marcel , Feger Daniel , Sold Sebastian , Wühler Felix , Scholl Stephan , Rädle Matthias
Published: May 2022

Technical liquid flow films are the basic arrangement for gas fluid transitions of all kinds and are the basis of many chemical processes, such as columns, evaporators, dryers, and different other kinds of fluid/fluid separation units. This publication presents a new method for molecule sensitive, non-contact, and marker-free localized concentration mapping in vertical falling films. Using Raman spectroscopy, no label or marker is needed for the detection of the local composition in liquid mixtures. In the presented cases, the film mapping of sodium sulfate in water on a plain surface as well as an added artificial streaming disruptor with the shape of a small pyramid is scanned in three dimensions. The results show, as a prove of concept, a clear detectable spectroscopic difference between air, back plate, and sodium sulfate for every local point in all three dimensions. In conclusion, contactless Raman scanning on falling films for liquid mapping is realizable without any mechanical film interaction caused by the measuring probe. Surface gloss or optical reflections from a metallic back plate are suppressed by using only inelastic light scattering and the mathematical removal of background noise.
Scheimpflug Camera-Based Technique for Multi-Point Displacement Monitoring of Bridges
Authors: Xing Lei , Dai Wujiao , Zhang Yunsheng
Published: May 2022

Owing to the limited field of view (FOV) and depth of field (DOF) of a conventional camera, it is quite difficult to employ a single conventional camera to simultaneously measure high-precision displacements at many points on a bridge of dozens or hundreds of meters. Researchers have attempted to obtain a large FOV and wide DOF by a multi-camera system; however, with the growth of the camera number, the cost, complexity and instability of multi-camera systems will increase exponentially. This study proposes a multi-point displacement measurement method for bridges based on a low-cost Scheimpflug camera. The Scheimpflug camera, which meets the Scheimpflug condition, can enlarge the depth of field of the camera without reducing the lens aperture and magnification; thus, when the measurement points are aligned in the depth direction, all points can be clearly observed in a single field of view with a high-power zoom lens. To reduce the impact of camera motions, a motion compensation method applied to the Scheimpflug camera is proposed according to the characteristic that the image plane is not perpendicular to the lens axis in the Scheimpflug camera. Several tests were conducted for performance verification under diverse settings. The results showed that the motion errors in x and y directions were reduced by at least 62% and 92%, respectively, using the proposed method, and the measurements of the camera were highly consistent with LiDAR-based measurements.
Removal of Non-Specifically Bound Proteins Using Rayleigh Waves Generated on ST-Quartz Substrates
Authors: Richardson Mandek , Das Pradipta K. , Morrill Samuel , Suthar Kamlesh J. , Sankaranarayanan Subramanian K. R. S. , Bhethanabotla Venkat R.
Published: May 2022

Label-free biosensors are plagued by the issue of non-specific protein binding which negatively affects sensing parameters such as sensitivity, selectivity, and limit-of-detection. In the current work, we explore the possibility of using the Rayleigh waves in ST-Quartz devices to efficiently remove non-specifically bound proteins via acoustic streaming. A coupled-field finite element (FE) fluid structure interaction (FSI) model of a surface acoustic wave (SAW) device based on ST-Quartz substrate in contact with a liquid loading was first used to predict trends in forces related to SAW-induced acoustic streaming. Based on model predictions, it is found that the computed SAW body force is sufficient to overcome adhesive forces between particles and a surface while lift and drag forces prevent reattachment for a range of SAW frequencies. We further performed experiments to validate the model predictions and observe that the excitation of Rayleigh SAWs removed non-specifically bound (NSB) antigens and antibodies from sensing and non-sensing regions, while rinsing and blocking agents were ineffective. An amplified RF signal applied to the device input disrupted the specific interactions between antigens and their capture antibody as well. ST-quartz allows propagation of Rayleigh and leaky SH-SAW waves in orthogonal directions. Thus, the results reported here could allow integration of three important biosensor functions on a single chip, i.e., removal of non-specific binding, mixing, and sensing in the liquid phase.
Dynamic Impedance Analysis of Intestinal Anastomosis during High-Frequency Electric Field Welding Process
Authors: Yin Li , Zhu Caihui , Xu Jianzhi , Zhao Hui , Qiu Jian , Wang Hao , Liu Kefu
Published: May 2022

The success rate of the electrosurgical high-frequency electric field welding technique lies in reasonable control of the welding time. However, the final impedance value used to control the welding time varies due to differences in tissue size and the welding method during the welding process. This study aims to introduce a new reference indicator not limited by impedance size from dynamic impedance to achieve an adequate weld strength with minimal thermal damage, providing feedback on the tissue welding effect in medical power supplies. End-to-end anastomosis experiments were conducted with porcine small intestine tissue under seven levels of compression pressure. The dynamic impedance changes were analyzed, combined with compression pressure, temperature, moisture, and collagen during welding. The welding process was divided into three stages according to the dynamic impedance, with impedance decreasing in Period Ⅰ and impedance increasing in Period Ⅲ. Period Ⅲ was the key to high-strength connections due to water evaporation and collagen reorganization. The dynamic impedance ratio is defined as the final impedance divided by the minimum impedance, and successful welding would be predicted when detecting the dynamic impedance ratio over 4 (n = 70, p < 0.001). Dynamic impedance monitoring can be used as a macroscopic real-time prediction of the anastomosis effect.
Machine-Learning Approach for Automatic Detection of Wild Beluga Whales from Hand-Held Camera Pictures
Authors: Araújo Voncarlos M. , Shukla Ankita , Chion Clément , Gambs Sébastien , Michaud Robert
Published: May 2022

A key aspect of ocean protection consists in estimating the abundance of marine mammal population density within their habitat, which is usually accomplished using visual inspection and cameras from line-transect ships, small boats, and aircraft. However, marine mammal observation through vessel surveys requires significant workforce resources, including for the post-processing of pictures, and is further challenged due to animal bodies being partially hidden underwater, small-scale object size, occlusion among objects, and distracter objects (e.g., waves, sun glare, etc.). To relieve the human expert’s workload while improving the observation accuracy, we propose a novel system for automating the detection of beluga whales (Delphinapterus leucas) in the wild from pictures. Our system relies on a dataset named Beluga-5k, containing more than 5.5 thousand pictures of belugas. First, to improve the dataset’s annotation, we have designed a semi-manual strategy for annotating candidates in images with single (i.e., one beluga) and multiple (i.e., two or more belugas) candidate subjects efficiently. Second, we have studied the performance of three off-the-shelf object-detection algorithms, namely, Mask-RCNN, SSD, and YOLO v3-Tiny, on the Beluga-5k dataset. Afterward, we have set YOLO v3-Tiny as the detector, integrating single- and multiple-individual images into the model training. Our fine-tuned CNN-backbone detector trained with semi-manual annotations is able to detect belugas despite the presence of distracter objects with high accuracy (i.e., 97.05 mAP@0.5). Finally, our proposed method is able to detect overlapped/occluded multiple individuals in images (beluga whales that swim in groups). For instance, it is able to detect 688 out of 706 belugas encountered in 200 multiple images, achieving 98.29% precision and 99.14% recall.
Digital Optical Ballistocardiographic System for Activity, Heart Rate, and Breath Rate Determination during Sleep
Authors: López-Ruiz Nuria , Escobedo Pablo , Ruiz-García Isidoro , Carvajal Miguel A. , Palma Alberto J. , Martínez-Olmos Antonio
Published: May 2022

In this work, we present a ballistocardiographic (BCG) system for the determination of heart and breath rates and activity of a user lying in bed. Our primary goal was to simplify the analog and digital processing usually required in these kinds of systems while retaining high performance. A novel sensing approach is proposed consisting of a white LED facing a digital light detector. This detector provides precise measurements of the variations of the light intensity of the incident light due to the vibrations of the bed produced by the subject’s breathing, heartbeat, or activity. Four small springs, acting as a bandpass filter, connect the boards where the LED and the detector are mounted. Owing to the mechanical bandpass filtering caused by the compressed springs, the proposed system generates a BCG signal that reflects the main frequencies of the heartbeat, breathing, and movement of the lying subject. Without requiring any analog signal processing, this device continuously transmits the measurements to a microcontroller through a two-wire communication protocol, where they are processed to provide an estimation of the parameters of interest in configurable time intervals. The final information of interest is wirelessly sent to the user’s smartphone by means of a Bluetooth connection. For evaluation purposes, the proposed system has been compared with typical BCG systems showing excellent performance for different subject positions. Moreover, applied postprocessing methods have shown good behavior for information separation from a single-channel signal. Therefore, the determination of the heart rate, breathing rate, and activity of the patient is achieved through a highly simplified signal processing without any need for analog signal conditioning.
Mobile Robot Localization and Mapping Algorithm Based on the Fusion of Image and Laser Point Cloud
Authors: Dai Jun , Li Dongfang , Li Yanqin , Zhao Junwei , Li Wenbo , Liu Gang
Published: May 2022

Given the lack of scale information of the image features detected by the visual SLAM (simultaneous localization and mapping) algorithm, the accumulation of many features lacking depth information will cause scale blur, which will lead to degradation and tracking failure. In this paper, we introduce the lidar point cloud to provide additional depth information for the image features in estimating ego-motion to assist visual SLAM. To enhance the stability of the pose estimation, the front-end of visual SLAM based on nonlinear optimization is improved. The pole error is introduced in the pose estimation between frames, and the residuals are calculated according to whether the feature points have depth information. The residuals of features reconstruct the objective function and iteratively solve the robot’s pose. A keyframe-based method is used to optimize the pose locally in reducing the complexity of the optimization problem. The experimental results show that the improved algorithm achieves better results in the KITTI dataset and outdoor scenes. Compared with the pure visual SLAM algorithm, the trajectory error of the mobile robot is reduced by 52.7%. The LV-SLAM algorithm proposed in this paper has good adaptability and robust stability in different environments.