bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimal Structure of Real Estate Portfolio Using EVA: A Stochastic Markowitz Model Using Data from Greek Real Estate Market
Authors: Petropoulos Theofanis , Liapis Konstantinos , Thalassinos Eleftherios
Published: February 2023

The purpose of this paper is to examine the issue of portfolio optimization. Optimization consists of minimizing the risk for a given rate of return or achieving a bigger return for a given level of risk. We use historical data from the Bank of Greece to calculate the net return and the standard deviation (std) for each type of property that is available. The objective is to maximize the economic value added (EVA) of a property’s assets portfolio under a specific rate of standard deviation, following the classic Markowitz model (M-V). The stochastic procedure entry in the model uses the Monte Carlo Simulation method with debt to equity (DTE) following PERT distribution for the portfolio’s invested budget, and the net return for the normal distribution with the mean of the expected return and std are taken from historical data, correspondingly. The returns verify that they follow the base assumption of normality through the Lilliefors test in the Greek real estate market. We observe the maximization of EVA and the expected return maximizing concurrently, but the minimizing risk of EVA is diversified with the minimization of portfolio risk. We observe that the max weight that a residential asset takes is 22.7% because a bigger percent reduces both mean and std. The study provides an explicit portfolio optimization procedure under uncertainty in the real estate market and enriches the academic debate about EVA and revenue.
GOCE Downward Continuation to the Earth’s Surface and Improvements to Local Geoid Modeling by FFT and LSC
Authors: Natsiopoulos Dimitrios A. , Mamagiannou Elisavet G. , Pitenis Eleftherios A. , Vergos Georgios S. , Tziavos Ilias N.
Published: February 2023

One of the main applications of the gravity field and steady-state Ocean Circulation Explorer (GOCE) satellite data is their combination with local gravity anomalies for geoid and gravity field modeling purposes. The aim of the present paper was the determination of an improved geoid model for the wider Hellenic area, using original GOCE SGG data filtered to retain only useful signals inside the measurement bandwidth (MBW) of the satellite. The filtered SGGs, originally at the satellite altitude, were projected to a mean orbit (MO) and then downward continued to the Earth’s surface (ES) in order to be combined with local gravity anomalies. For the projection to an MO, grids of disturbing gravity gradients from a global geopotential model (GGM) were used, computed per 1 km from the maximum satellite altitude to that of the MO. The downward continuation process was then undertaken using an iterative Monte Carlo (MC) simulated annealing method with GGM gravity anomalies on the ES used as ground truth data. The final geoid model over the wider Hellenic area was estimated, employing the remove–compute–restore method and both Fast Fourier Transform (FFT) and Least Squares Collocation (LSC). Gravity-only, GOCE-only and combined models using local gravity and GOCE data were determined and evaluation of the results was carried out against available GNSS/levelling data in the study area. From the results achieved, it was concluded that even when FFT is used, so that a combined grid of local gravity and GOCE data is used, improvements to the differences regarding GNSS/levelling data by 14.53% to 27.78% can be achieved. The geoid determination with LSC was focused on three different areas over Greece, with different characteristics in the topography and gravity variability. From these results, improvements from 14.73%, for the well-surveyed local data of Thessaly, to 32.88%, over the mountainous area of Pindos, and 57.10% for the island of Crete for 57.10% were found.
In Situ Experimental Study of Cloud-Precipitation Interference by Low-Frequency Acoustic Waves
Authors: Shi Yang , Qiao Zhen , Wang Guangqian , Wei Jiahua
Published: February 2023

Since acoustic agglomeration is an effective pre-treatment technique for removing fine particles, it can be considered as a potential technology for applications in aerosol pollution control, industrial dust and mist removal, and cloud and precipitation interference. In this study, the cloud-precipitation interference effect was evaluated in situ based on a multi-dimensional multi-scale monitoring system. The variations in the spatial and temporal distribution of rainfall near the surface and the characteristics of precipitation droplets in the air were investigated. The results indicate that strong low-frequency acoustic waves had a significant impact on the macro-characteristics of rainfall clouds, the microphysical structure of rain droplets and near-surface precipitation, and various microwave parameters. In terms of physical structure, the precipitation cloud’s base height decreased significantly upon opening the acoustic device, while agglomeration and de-agglomeration of raindrops were in a dynamic equilibrium. When the sound generator was on, the particle concentration at a sampling attitude of 500−1700 m and the proportion of particles with diameters of 1–1.5 mm decreased significantly (by 1–5 ln [1/m3·mm]). In contrast, the particle concentration increased by 1–3 ln [1/m3·mm] at a sampling attitude below 400 m. Moreover, during acoustic interference, the reflectivity factor surged by 2.71 dBZ within 1200 m of the operation centre. Overall, the spatial and temporal distributions of rainfall rates and cumulative precipitation within 5 km of acoustic operation were uneven and influenced by local terrain and background winds.
Updating Active Deformation Inventory Maps in Mining Areas by Integrating InSAR and LiDAR Datasets
Authors: Hu Liuru , Tomás Roberto , Tang Xinming , López Vinielles Juan , Herrera Gerardo , Li Tao , Liu Zhiwei
Published: February 2023

Slope failures, subsidence, earthworks, consolidation of waste dumps, and erosion are typical active deformation processes that pose a significant hazard in current and abandoned mining areas, given their considerable potential to produce damage and affect the population at large. This work proves the potential of exploiting space-borne InSAR and airborne LiDAR techniques, combined with data inferred through a simple slope stability geotechnical model, to obtain and update inventory maps of active deformations in mining areas. The proposed approach is illustrated by analyzing the region of Sierra de Cartagena-La Union (Murcia), a mountainous mining area in southeast Spain. Firstly, we processed Sentinel-1 InSAR imagery acquired both in ascending and descending orbits covering the period from October 2016 to November 2021. The obtained ascending and descending deformation velocities were then separately post-processed to semi-automatically generate two active deformation areas (ADA) maps by using ADATool. Subsequently, the PS-InSAR LOS displacements of the ascending and descending tracks were decomposed into vertical and east-west components. Complementarily, open-access, and non-customized LiDAR point clouds were used to analyze surface changes and movements. Furthermore, a slope stability safety factor (SF) map was obtained over the study area adopting a simple infinite slope stability model. Finally, the InSAR-derived maps, the LiDAR-derived map, and the SF map were integrated to update a previously published landslides’ inventory map and to perform a preliminary classification of the different active deformation areas with the support of optical images and a geological map. Complementarily, a level of activity index is defined to state the reliability of the detected ADA. A total of 28, 19, 5, and 12 ADAs were identified through ascending, descending, horizontal, and vertical InSAR datasets, respectively, and 58 ADAs from the LiDAR change detection map. The subsequent preliminary classification of the ADA enabled the identification of eight areas of consolidation of waste dumps, 11 zones in which earthworks were performed, three areas affected by erosion processes, 17 landslides, two mining subsidence zone, seven areas affected by compound processes, and 23 possible false positive ADAs. The results highlight the effectiveness of these two remote sensing techniques (i.e., InSAR and LiDAR) in conjunction with simple geotechnical models and with the support of orthophotos and geological information to update inventory maps of active deformation areas in mining zones.
Application of a Novel Multiscale Global Graph Convolutional Neural Network to Improve the Accuracy of Forest Type Classification Using Aerial Photographs
Authors: Pei Huiqing , Owari Toshiaki , Tsuyuki Satoshi , Zhong Yunfang
Published: February 2023

The accurate classification of forest types is critical for sustainable forest management. In this study, a novel multiscale global graph convolutional neural network (MSG-GCN) was compared with random forest (RF), U-Net, and U-Net++ models in terms of the classification of natural mixed forest (NMX), natural broadleaved forest (NBL), and conifer plantation (CP) using very high-resolution aerial photographs from the University of Tokyo Chiba Forest in central Japan. Our MSG-GCN architecture is novel in the following respects: The convolutional kernel scale of the encoder is unlike those of other models; local attention replaces the conventional U-Net++ skip connection; a multiscale graph convolutional neural block is embedded into the end layer of the encoder module; and various decoding layers are spliced to preserve high- and low-level feature information and to improve the decision capacity for boundary cells. The MSG-GCN achieved higher classification accuracy than other state-of-the-art (SOTA) methods. The classification accuracy in terms of NMX was lower compared with NBL and CP. The RF method produced severe salt-and-pepper noise. The U-Net and U-Net++ methods frequently produced error patches and the edges between different forest types were rough and blurred. In contrast, the MSG-GCN method had fewer misclassification patches and showed clear edges between different forest types. Most areas misclassified by MSG-GCN were on edges, while misclassification patches were randomly distributed in internal areas for U-Net and U-Net++. We made full use of artificial intelligence and very high-resolution remote sensing data to create accurate maps to aid forest management and facilitate efficient and accurate forest resource inventory taking in Japan.
Performance and Consistency of Final Global Ionospheric Maps from Different IGS Analysis Centers
Authors: Li Wei , Wang Keke , Yuan Kaitian
Published: February 2023

Ionospheric delay is one of the most problematic errors in satellite-based positioning data processing. The Global Ionospheric Map (GIM), which is publicly available daily in various analysis centers, is thus vitally important for positioning users. There are variations in the accuracy and consistency of GIMs issued by Ionosphere Associate Analysis Centers (IAACs) due to the differences in ionospheric modeling methods and selected tracking stations. In this study on the International GNSS Service’s (IGS) final GIM, the ionospheric total electron content (TEC) (from 243 global navigation satellite system (GNSS) monitoring stations around the world) and the ionospheric TEC (from the Jason-3 altimeter satellite) are selected as reference. By using these three references, we evaluate the performance and consistency of final GIM products from seven IGS IAACs, including the Chinese Academy of Sciences (CAS), the Center for Orbit Determination in Europe (CODE), Natural Resources Canada (EMR), the European Space Agency (ESA), the Jet Propulsion Laboratory (JPL), Universitat Politècnica de Catalunya (UPC), and Wuhan University (WHU) in the mid-solar activity year (2022) and the low-solar activity year (2020). Firstly, the comparison with the IGS final GIM shows that the consistency of each GIMs is basically the same, with the mean value ranging from −0.3 TECu (total electron content unit) to 1.4 TECu. Secondly, the validation with Jason-3 altimeter satellite shows that the accuracy of several GIMs is almost the same, except for the JPL with the worst accuracy and an overall mean deviation (BIAS) between 2 and 6 TECu. Thirdly, the comparison with VTEC extracted from GNSS monitor stations shows that the CAS has the best accuracy in different latitude bands with a root mean square (RMS) of about 2.2–4.7 TECu. In addition, it is found that the accuracy in areas with more stations for ionospheric modelling is better than those with less stations in different latitude bands; meanwhile, the accuracy is closely related to the modeling methods of different GIMs.
A Summary of Hail Events during the Summer of 2022 in Catalonia: A Comparison with the Period of 2013–2021
Authors: Rigo Tomeu , Farnell Carme
Published: February 2023

Hail events are common in Catalonia during the warm season (May to September), but especially between June and August. These cases produce important damages to agriculture and infrastructure. The campaign of 2022 will be remembered by three different phases: the first and last phases, which were very stable and with few events, and the middle phase, which had a large number of episodes. Some of the cases had an important social impact because of the large areas affected or the economical damages. The present analysis used the vertically integrated liquid radar product for estimating the hail swaths. Hail swaths are classified according to different parameters, allowing for the characterization of the campaign and a comparison with the period of 2013–2021. The results show how the month of June had a deficit of cases with respect to the reference period (half of the cases), July presented similar values, and August had a positive anomaly, with five times more cases. In addition, the first ever case of giant hail in Catalonia occurred in August 2022, a month with more than five times the number of cases of severe and very large hail with respect to the average of the period of 2013–2021.
Automatic Detection of Individual Trees in Forests Based on Airborne LiDAR Data with a Tree Region-Based Convolutional Neural Network (RCNN)
Authors: Wang Zhen , Li Pu , Cui Yuancheng , Lei Shuowen , Kang Zhizhong
Published: February 2023

Light Detection and Ranging (LiDAR) has advantages in detecting individual trees because it can obtain information on the vertical structure and even on the lower layers. However, the current methods still cannot detect understory well, because the small trees are often clumped together and overlapped by large trees. To fill this gap, a two-stage network named Tree Region-Based Convolutional Neural Network (RCNN) was proposed to directly detect trees from point clouds. In the first stage, very dense anchors are generated anywhere in a forest. Then, Tree RCNN can directly focus on determining whether an anchor belongs to an individual tree or not and generate tree proposals based on the anchors. In this way, the small trees overlapped by big trees can be detected in the process. In the second stage, multi-position feature extraction is proposed to extract shape features of the tree proposals output in the first stage to refine the tree proposals. The positions and heights of detected trees can be obtained by the refined tree proposals. The performance of our method was estimated by a public dataset. Compared to methods provided by the dataset and the commonly used deep learning methods, Tree RCNN achieved the best performance, especially for the lower-layer trees. The root mean square value of detection rates (RMSass) of all plots of the dataset reached 61%, which was 6 percentage points higher than the best RMSass of other methods. The RMSass of the layers < 5 m, 5–10 m, 10–15 m, and 15–20 reached 20%, 38%, 48%, and 61%, which was 5, 6, 7, and 3 percentage points higher than the best RMSass of other methods, respectively. The results indicated our method can be a useful tool for tree detection.
Light Field Image Super-Resolution Using Deep Residual Networks on Lenslet Images
Authors: Salem Ahmed , Ibrahem Hatem , Kang Hyun-Soo
Published: February 2023

Due to its widespread usage in many applications, numerous deep learning algorithms have been proposed to overcome Light Field’s trade-off (LF). The sensor’s low resolution limits angular and spatial resolution, which causes this trade-off. The proposed method should be able to model the non-local properties of the 4D LF data fully to mitigate this problem. Therefore, this paper proposes a different approach to increase spatial and angular information interaction for LF image super-resolution (SR). We achieved this by processing the LF Sub-Aperture Images (SAI) independently to extract the spatial information and the LF Macro-Pixel Image (MPI) to extract the angular information. The MPI or Lenslet LF image is characterized by its ability to integrate more complementary information between different viewpoints (SAIs). In particular, we extract initial features and then process MAI and SAIs alternately to incorporate angular and spatial information. Finally, the interacted features are added to the initial extracted features to reconstruct the final output. We trained the proposed network to minimize the sum of absolute errors between low-resolution (LR) input and high-resolution (HR) output images. Experimental results prove the high performance of our proposed method over the state-of-the-art methods on LFSR for small baseline LF images.
Measurement of Trunk Movement during Sit-to-Stand Motion Using Laser Range Finders: A Preliminary Study
Authors: Toda Haruki , Omori Kiyohiro , Fukui Katsuya , Chin Takaaki
Published: February 2023

The sit-to-stand (STS) motion evaluates physical functions in frail older adults. Mounting sensors or using a camera is necessary to measure trunk movement during STS motion. Therefore, we developed a simple measurement method by embedding laser range finders in the backrests and seats of chairs that can be used in daily life situations. The objective of this study was to validate the performance of the proposed measurement method in comparison with that of the optical motion capture (MoCap) system during STS motion. The STS motions of three healthy young adults were simultaneously measured under seven conditions using a chair with embedded sensors and the optical MoCap system. We evaluated the waveform similarity, absolute error, and relationship of the trunk joint angular excursions between these measurement methods. The experimental results indicated high waveform similarity in the trunk flexion phase regardless of STS conditions. Furthermore, a strong relationship was observed between the two measurement methods with respect to the angular excursion of the trunk flexion. Although the angular excursion of the trunk extension exhibited a large error, the developed chair with embedded sensors evaluated trunk flexion during the STS motion, which is a characteristic of frail older adults.
Uneven Terrain Walking with Linear and Angular Momentum Allocation
Authors: He Zhicheng , Piao Songhao , Leng Xiaokun , Wu Yucong
Published: February 2023

Uneven terrain walking is hard to achieve for most child-size humanoid robots, as they are unable to accurately detect ground conditions. In order to reduce the demand for ground detection accuracy, a walking control framework based on centroidal momentum allocation is studied in this paper, enabling a child-size humanoid robot to walk on uneven terrain without using ground flatness information. The control framework consists of three controllers: momentum decreasing controller, posture controller, admittance controller. First, the momentum decreasing controller is used to quickly stabilize the robot after disturbance. Then, the posture controller restores the robot posture to adapt to the unknown terrain. Finally, the admittance controller aims to decrease contact impact and adapt the robot to the terrain. Note that the robot uses a mems-based inertial measurement unit (IMU) and joint position encoders to calculate centroidal momentum and use force-sensitive resistors (FSR) on the robot foot to perform admittance control. None of these is a high-cost component. Experiments are conducted to test the proposed framework, including standing posture balancing, structured non-flat ground walking, and soft uneven terrain walking, with a speed of 2.8 s per step, showing the effectiveness of the momentum allocation method.
Convergent Validity between Electromyographic Muscle Activity, Ultrasound Muscle Thickness and Dynamometric Force Measurement for Assessing Muscle
Authors: Varol Umut , Navarro-Santana Marcos J. , Valera-Calero Juan Antonio , Antón-Ramírez Sergio , Álvaro-Martínez Javier , Díaz-Arribas María José , Fernández-de-las-Peñas César , Plaza-Manzano Gustavo
Published: February 2023

Muscle fatigue is defined as a reversible decline in performance after intensive use, which largely recovers after a resting period. Surface electromyography (EMG), ultrasound imaging (US) and dynamometry are used to assess muscle activity, muscle morphology and isometric force capacity. This study aimed to assess the convergent validity between these three methods for assessing muscle fatigue during a manual prehension maximal voluntary isometric contraction (MVIC). A diagnostic accuracy study was conducted, enrolling 50 healthy participants for the measurement of simultaneous changes in muscle thickness, muscle activity and isometric force using EMG, US and a hand dynamometer, respectively, during a 15 s MVIC. An adjustment line and its variance (R2) were calculated. Muscle activity and thickness were comparable between genders (p > 0.05). However, men exhibited lower force holding capacity (p < 0.05). No side-to-side or dominance differences were found for any variable. Significant correlations were found for the EMG slope with US (r = 0.359; p < 0.01) and dynamometry (r = 0.305; p < 0.01) slopes and between dynamometry and US slopes (r = 0.227; p < 0.05). The sample of this study was characterized by comparable muscle activity and muscle thickness change between genders. In addition, fatigue slopes were not associated with demography or anthropometry. Our findings showed fair convergent associations between these methods, providing synergistic muscle fatigue information.
A Combined Physical and Mathematical Calibration Method for Low-Cost Cameras in the Air and Underwater Environment
Authors: Ma Zhenling , Zhong Xu , Xie Hong , Zhou Yongjun , Chen Yuan , Wang Jiali
Published: February 2023

Low-cost camera calibration is vital in air and underwater photogrammetric applications. However, various lens distortions and the underwater environment influence are difficult to be covered by a universal distortion compensation model, and the residual distortions may still remain after conventional calibration. In this paper, we propose a combined physical and mathematical camera calibration method for low-cost cameras, which can adapt to both in-air and underwater environments. The commonly used physical distortion models are integrated to describe the image distortions. The combination is a high-order polynomial, which can be considered as basis functions to successively approximate the image deformation from the point of view of mathematical approximation. The calibration process is repeated until certain criteria are met and the distortions are reduced to a minimum. At the end, several sets of distortion parameters and stable camera interior orientation (IO) parameters act as the final camera calibration results. The Canon and GoPro in-air calibration experiments show that GoPro owns distortions seven times larger than Canon. Most Canon distortions have been described with the Australis model, while most decentering distortions for GoPro still exist. Using the proposed method, all the Canon and GoPro distortions are decreased to close to 0 after four calibrations. Meanwhile, the stable camera IO parameters are obtained. The GoPro Hero 5 Black underwater calibration indicates that four sets of distortion parameters and stable camera IO parameters are obtained after four calibrations. The camera calibration results show a difference between the underwater environment and air owing to the refractive and asymmetric environment effects. In summary, the proposed method improves the accuracy compared with the conventional method, which could be a flexible way to calibrate low-cost cameras for high accurate in-air and underwater measurement and 3D modeling applications.
The Effect of Soil-Structure Interaction on the Seismic Response of Structures Using Machine Learning, Finite Element Modeling and ASCE 7-16 Methods
Authors: Ali Tabish , Eldin Mohamed Nour , Haider Waseem
Published: February 2023

Seismic design of structures taking into account the soil-structure interaction (SSI) methods is considered to be more efficient, cost effective, and safer then fixed-base designs, in most cases. Finite element methods that use direct equations to solve SSI problems are very popular, but the prices of the software are very high, and the analysis time is very long. Even though some low-cost and efficient software are available, the structures are mostly analyzed for the superstructure only, without using the geotechnical properties of the ground and its interaction effects. The reason is that a limited number of researchers have the knowledge of both geotechnical and structural engineering to model accurately the coupled soil-structure system. However, a cost-effective, less time-consuming and easy-to-implement technique is to analyze the structure along with ground properties using machine learning methods. The database techniques using machine learning are robust and provide reliable results. Thus, in this study, machine learning techniques, such as artificial neural networks and support vector machines are used to investigate the effect of soil-structure interactions on the seismic response of structures for different earthquake scenarios. Four frame structures are investigated by varying the soil and seismic properties. In addition, varying sample sizes and different optimization algorithms are used to obtain the best machine learning framework. The input parameters contain both soil and seismic properties, while the outputs consist of three engineering demand parameters. The network is trained using three and five-story buildings and tested on a three-story building with mass irregularity and a four-story building. Furthermore, the proposed method is compared with the dynamic responses obtained using fixed-base and ASCE 7-16 SSI methods. The proposed machine learning method showed better results compared with fixed-base and ASCE 7-16 methods with the nonlinear time history analysis results as a reference.
Design and Control of Monolithic Compliant Gripper Using Shape Memory Alloy Wires
Authors: Then Mozhi Ganapathy , Dhanalakshmi Kaliaperumal , Choi Seung-Bok
Published: February 2023

This paper presents the design, fabrication and testing of a shape memory alloy (SMA)-actuated monolithic compliant gripping mechanism that enables translational motion of the gripper tips for grasping operation suitable for micromanipulation and microassembly. The design is validated using a finite element analysis (FEA), and a prototype is created for experimental testing. The reported gripping structure is simple and easy to build and design. The gripper is demonstrated to have a displacement amplification gain of 3.7 that allows maximum tip displacement up to 1.2 cm to possess good handling range and geometric advantage which cannot be accomplished by conventional grippers. The position of the gripper tip is predicted from the variation in the electrical resistance of the SMA wire based on the self-sensing phenomena. Self-sensing actuation of the SMA allows the design of a compact and lightweight structure; moreover, it supports the control loop/scheme to use the same SMA element both as an actuator and sensor for position control. The geometrical dimensions of the SMA wire-actuated monolithic compliant gripper is 0.09 m × 0.04 m and can be operated to handle objects with a maximum size of 0.012 m weighing up to 35 g.
Improving Speech Recognition Performance in Noisy Environments by Enhancing Lip Reading Accuracy
Authors: Li Dengshi , Gao Yu , Zhu Chenyi , Wang Qianrui , Wang Ruoxi
Published: February 2023

The current accuracy of speech recognition can reach over 97% on different datasets, but in noisy environments, it is greatly reduced. Improving speech recognition performance in noisy environments is a challenging task. Due to the fact that visual information is not affected by noise, researchers often use lip information to help to improve speech recognition performance. This is where the performance of lip recognition and the effect of cross-modal fusion are particularly important. In this paper, we try to improve the accuracy of speech recognition in noisy environments by improving the lip reading performance and the cross-modal fusion effect. First, due to the same lip possibly containing multiple meanings, we constructed a one-to-many mapping relationship model between lips and speech allowing for the lip reading model to consider which articulations are represented from the input lip movements. Audio representations are also preserved by modeling the inter-relationships between paired audiovisual representations. At the inference stage, the preserved audio representations could be extracted from memory by the learned inter-relationships using only video input. Second, a joint cross-fusion model using the attention mechanism could effectively exploit complementary intermodal relationships, and the model calculates cross-attention weights on the basis of the correlations between joint feature representations and individual modalities. Lastly, our proposed model achieved a 4.0% reduction in WER in a −15 dB SNR environment compared to the baseline method, and a 10.1% reduction in WER compared to speech recognition. The experimental results show that our method could achieve a significant improvement over speech recognition models in different noise environments.
Model Updating Concept Using Bridge Weigh-in-Motion Data
Authors: Hekič Doron , Anžlin Andrej , Kreslin Maja , Žnidarič Aleš , Češarek Peter
Published: February 2023

Finite element (FE) model updating of bridges is based on the measured modal parameters and less frequently on the measured structural response under a known load. Until recently, the FE model updating did not consider strain measurements from sensors installed for weighing vehicles with bridge weigh-in-motion (B-WIM) systems. A 50-year-old multi-span concrete highway viaduct, renovated between 2017 and 2019, was equipped with continuous monitoring system with over 200 sensors, and a B-WIM system. In the most heavily instrumented span, the maximum measured longitudinal strains induced by the full-speed calibration vehicle passages were compared with the modelled strains. Based on the sensitivity study results, three variables that affected its overall stiffness were updated: Young’s modulus adjustment factor of all structural elements, and two anchorage reduction factors that considered the interaction between the superstructure and non-structural elements. The analysis confirmed the importance of the initial manual FE model updating to correctly reflect the non-structural elements during the automatic nonlinear optimisation. It also demonstrated a successful use of pseudo-static B-WIM loading data during the model updating process and the potential to extend the proposed approach to using random B-WIM-weighed vehicles for FE model updating and long-term monitoring of structural parameters and load-dependent phenomena.
Real-Time Dense Reconstruction with Binocular Endoscopy Based on StereoNet and ORB-SLAM
Authors: Huo Jiayi , Zhou Changjiang , Yuan Bo , Yang Qing , Wang Liqiang
Published: February 2023

Binocular endoscopy is gradually becoming the future of minimally invasive surgery (MIS) thanks to the development of stereo vision. However, some problems still exist, such as the low reconstruction accuracy, small surgical field, and low computational efficiency. To solve these problems, we designed a framework for real-time dense reconstruction in binocular endoscopy scenes. First, we obtained the initial disparity map using an SGBM algorithm and proposed the disparity confidence map as a dataset to provide StereoNet training. Then, based on the depth map predicted by StereoNet, the corresponding left image of each depth map was input into the Oriented Fast and Brief-Simultaneous Localization and Mapping (ORB-SLAM) framework using an RGB-D camera to realize the real-time dense reconstruction of the binocular endoscopy scene. The proposed algorithm was verified in the stomach phantom and a real pig stomach. Compared with the ground truth, the proposed algorithm’s RMSE is 1.620 mm, and the number of effective points in the point cloud is 834,650, which is a significant improvement in the mapping ability compared with binocular SLAM and ensures the real-time performance of the algorithm while performing dense reconstruction. The effectiveness of the proposed algorithm is verified.
Contactless Sensing of Water Properties for Smart Monitoring of Pipelines
Authors: Riboldi Christian , Castillo Danilo A. Carnevale , Crafa Daniele M. , Carminati Marco
Published: February 2023

A key milestone for the pervasive diffusion of wireless sensing nodes for smart monitoring of water quality and quantity in distribution networks is the simplification of the installation of sensors. To address this aspect, we demonstrate how two basic contactless sensors, such as piezoelectric transducers and strip electrodes (in a longitudinal interdigitated configuration to sense impedance inside and outside of the pipe with potential for impedimetric leak detection), can be easily clamped on plastic pipes to enable the measurement of multiple parameters without contact with the fluid and, thus, preserving the integrity of the pipe. Here we report the measurement of water flow rate (up to 24 m3/s) and temperature with ultrasounds and of the pipe filling fraction (capacitance at 1 MHz with ~cm3 resolution) and ionic conductivity (resistance at 20 MHz from 700 to 1400 μS/cm) by means of impedance. The equivalent impedance model of the sensor is discussed in detail. Numerical finite-element simulations, carried out to optimize the sensing parameters such as the sensing frequency, confirm the lumped models and are matched by experimental results. In fact, a 6 m long, 30 L demonstration hydraulic loop was built to validate the sensors in realistic conditions (water speed of 1 m/s) monitoring a pipe segment of 0.45 m length and 90 mm diameter (one of the largest ever reported in the literature). Tradeoffs in sensors accuracy, deployment, and fabrication, for instance, adopting single-sided flexible PCBs as electrodes protected by Kapton on the external side and experimentally validated, are discussed as well.
Hybrid Beamforming and Relay Selection for End-to-End SNR Maximization in Single-User Multi-Relay MIMO Systems
Authors: Mustafa Hafiz Muhammad Tahir , Baik Jung-In , You Young-Hwan , Song Hyoung-Kyu , Abbasi Zunira
Published: February 2023

This paper proposes a novel hybrid beamforming and relay selection scheme for spectral efficiency maximization in a non-regenerative multi-relay multi-input multi-output (MIMO) system. The analog beamforming component in the radio-frequency (RF) domain must follow an element-wise constant modulus constraint, which makes the underlying design problem mathematically intractable and therefore, it is quite challenging to obtain the global optimal solution. To address this problem, phase-only precoding/combining matrices are derived by maximizing the end-to-end received signal-to-noise ratio (SNR) under transmit power constraint at the source and each relay node. This task is achieved by decomposing the original complicated optimization problem into two independent components. The first component designs the RF precoder/combiner at source and relay nodes by maximizing the received SNR at relay nodes. While the second component attempts to derive the analog precoder/combiner at relay nodes and destination by maximizing the received SNR at the destination. Digital baseband processing matrices are obtained by deriving the closed-form expression, which minimizes interference among different sub-channels. Finally, the relay selection is made by maximizing the overall SNR from the source to the destination. Computer simulations reveal that the performance of the proposed algorithm is close to its fully digital counterpart and approximately 6% higher than the specified relay-assisted hybrid beamforming techniques. Moreover, the proposed method achieves more than 15% higher performance in a sparse scattering environment when compared with the given relay selection techniques.
Phononic Crystal Made of Silicon Ridges on a Membrane for Liquid Sensing
Authors: Gueddida Abdellatif , Zhang Victor , Carpentier Laurent , Bonhomme Jérémy , Bonello Bernard , Pennec Yan , Djafari-Rouhani Bahram
Published: February 2023

We propose the design of a phononic crystal to sense the acoustic properties of a liquid that is constituted by an array of silicon ridges on a membrane. In contrast to other concepts, the ridges are immersed in the liquid. The introduction of a suitable cavity in the periodic array gives rise to a confined defect mode with high localization in the cavity region and strong solid–liquid interaction, which make it sensitive to the acoustic properties of the liquid. By using a finite element method simulation, we theoretically study the transmission and cavity excitation of an incident flexural wave of the membrane. The observation of the vibrations of this mode can be achieved either outside the area of the phononic crystal or just above the cavity. We discuss the existence of the resonant modes, as well as its quality factor and sensitivity to liquid properties as a function of the geometrical parameters. The performance of the proposed sensor has then been tested to detect the variation in NaI concentration in a NaI–water mixture.
Covert Channel Communication as an Emerging Security Threat in 2.5D/3D Integrated Systems
Authors: Miketic Ivan , Dhananjay Krithika , Salman Emre
Published: February 2023

In this paper, first, a broad overview of existing covert channel communication-based security attacks is provided. Such covert channels establish a communication link between two entities that are not authorized to share data. The secret data is encoded into different forms of signals, such as delay, temperature, or hard drive location. These signals and information are then decoded by the receiver to retrieve the secret data, thereby mitigating some of the existing security measures. The important steps of covert channel attacks are described, such as data encoding, communication protocol, data decoding, and models to estimate communication bandwidth and bit error rate. Countermeasures against covert channels and existing covert channel detection techniques are also summarized. In the second part of the paper, the implications of such attacks for emerging packaging technologies, such as 2.5D/3D integration are discussed. Several covert channel threat models for 2.5D/3D ICs are also proposed.
GridHTM: Grid-Based Hierarchical Temporal Memory for Anomaly Detection in Videos
Authors: Monakhov Vladimir , Thambawita Vajira , Halvorsen Pål , Riegler Michael A.
Published: February 2023

The interest in video anomaly detection systems that can detect different types of anomalies, such as violent behaviours in surveillance videos, has gained traction in recent years. The current approaches employ deep learning to perform anomaly detection in videos, but this approach has multiple problems. For example, deep learning in general has issues with noise, concept drift, explainability, and training data volumes. Additionally, anomaly detection in itself is a complex task and faces challenges such as unknownness, heterogeneity, and class imbalance. Anomaly detection using deep learning is therefore mainly constrained to generative models such as generative adversarial networks and autoencoders due to their unsupervised nature; however, even they suffer from general deep learning issues and are hard to properly train. In this paper, we explore the capabilities of the Hierarchical Temporal Memory (HTM) algorithm to perform anomaly detection in videos, as it has favorable properties such as noise tolerance and online learning which combats concept drift. We introduce a novel version of HTM, named GridHTM, which is a grid-based HTM architecture specifically for anomaly detection in complex videos such as surveillance footage. We have tested GridHTM using the VIRAT video surveillance dataset, and the subsequent evaluation results and online learning capabilities prove the great potential of using our system for real-time unsupervised anomaly detection in complex videos.
Impact of Anatomical Variability on Sensitivity Profile in fNIRS–MRI Integration
Authors: Bonilauri Augusto , Sangiuliano Intra Francesca , Baglio Francesca , Baselli Giuseppe
Published: February 2023

Functional near-infrared spectroscopy (fNIRS) is an important non-invasive technique used to monitor cortical activity. However, a varying sensitivity of surface channels vs. cortical structures may suggest integrating the fNIRS with the subject-specific anatomy (SSA) obtained from routine MRI. Actual processing tools permit the computation of the SSA forward problem (i.e., cortex to channel sensitivity) and next, a regularized solution of the inverse problem to map the fNIRS signals onto the cortex. The focus of this study is on the analysis of the forward problem to quantify the effect of inter-subject variability. Thirteen young adults (six males, seven females, age 29.3 ± 4.3) underwent both an MRI scan and a motor grasping task with a continuous wave fNIRS system of 102 measurement channels with optodes placed according to a 10/5 system. The fNIRS sensitivity profile was estimated using Monte Carlo simulations on each SSA and on three major atlases (i.e., Colin27, ICBM152 and FSAverage) for comparison. In each SSA, the average sensitivity curves were obtained by aligning the 102 channels and segmenting them by depth quartiles. The first quartile (depth < 11.8 (0.7) mm, median (IQR)) covered 0.391 (0.087)% of the total sensitivity profile, while the second one (depth < 13.6 (0.7) mm) covered 0.292 (0.009)%, hence indicating that about 70% of the signal was from the gyri. The sensitivity bell-shape was broad in the source–detector direction (20.953 (5.379) mm FWHM, first depth quartile) and steeper in the transversal one (6.082 (2.086) mm). The sensitivity of channels vs. different cortical areas based on SSA were analyzed finding high dispersions among subjects and large differences with atlas-based evaluations. Moreover, the inverse cortical mapping for the grasping task showed differences between SSA and atlas based solutions. In conclusion, integration with MRI SSA can significantly improve fNIRS interpretation.
Assessment of Accuracy in Unmanned Aerial Vehicle (UAV) Pose Estimation with the REAL-Time Kinematic (RTK) Method on the Example of DJI Matrice 300 RTK
Authors: Czyża Szymon , Szuniewicz Karol , Kowalczyk Kamil , Dumalski Andrzej , Ogrodniczak Michał , Zieleniewicz Łukasz
Published: February 2023

The growing possibilities offered by unmanned aerial vehicles (UAV) in many areas of life, in particular in automatic data acquisition, spur the search for new methods to improve the accuracy and effectiveness of the acquired information. This study was undertaken on the assumption that modern navigation receivers equipped with real-time kinematic positioning software and integrated with UAVs can considerably improve the accuracy of photogrammetric measurements. The research hypothesis was verified during field measurements with the use of a popular Enterprise series drone. The problems associated with accurate UAV pose estimation were identified. The main aim of the study was to perform a qualitative assessment of the pose estimation accuracy of a UAV equipped with a GNSS RTK receiver. A test procedure comprising three field experiments was designed to achieve the above research goal: an analysis of the stability of absolute pose estimation when the UAV is hovering over a point, and analyses of UAV pose estimation during flight along a predefined trajectory and during continuous flight without waypoints. The tests were conducted in a designated research area. The results were verified based on direct tachometric measurements. The qualitative assessment was performed with the use of statistical methods. The study demonstrated that in a state of apparent stability, horizontal deviations of around 0.02 m occurred at low altitudes and increased with a rise in altitude. Mission type significantly influences pose estimation accuracy over waypoints. The results were used to verify the accuracy of the UAV’s pose estimation and to identify factors that affect the pose estimation accuracy of an UAV equipped with a GNSS RTK receiver. The present findings provide valuable input for developing a new method to improve the accuracy of measurements performed with the use of UAVs.
Comparative Study of Cis- and Trans-Priming Effect of PEG and BABA in Cowpea Seedlings on Exposure to PEG-Induced Osmotic Stress
Authors: Aswathi K. P. Raj , Sen Akhila , Puthur Jos T.
Published: February 2023

The growth and performance of cowpea seedlings are negatively impacted by climate change and the subsequent occurrence of drought stress. Osmotic stress leads to the formation of reactive oxygen species, causing membrane breakdown, and impairs metabolic activities. The harmful effects of osmotic stress can be reduced by using seed priming techniques. Seeds of cowpea var. Anaswara were treated with polyethylene glycol (PEG) and β-amino butyric acid (BABA) as priming agents. The seedlings emerged from the primed seeds have been found to reduce the lipid peroxidation rates and improve plant water status by accumulating osmolytes such as proline, total free aminoacids, and total soluble sugars, and also enhanced the production of non-enzymatic antioxidants such as total phenolics, ascorbate, and glutathione, as well as increased the activities of enzymatic antioxidants such as catalase, peroxidase, and superoxide dismutase, which effectively scavenge ROS and maintain the homeostasis of the cell. PEG priming (cis-priming) and BABA priming (trans-priming) exhibited differential physiochemical responses in cowpea subjected to PEG stress. The current work investigates the extent of stress tolerance acquired through seed priming, and it will help to make a sensitive variety to a more tolerant one. Physiochemical responses of seedlings emerged from BABA-primed seeds towards PEG stress were better regulated to encounter the PEG-induced osmotic stress than the seedlings emerged from PEG-primed seeds.
Research Progress on Extraction and Separation of Active Components from Loquat Leaves
Authors: Xiao Siqiu , Wang Wei , Liu Ying
Published: February 2023

Loquat is an evergreen tree belonging to the genus Loquat in Rosaceae. It is widely used in the processing of food and medicine. Based on the literature findings, the anti-tumor, antibacterial, anti-inflammatory and anti-oxidation activities of the extracted loquat leaves are related to its active components. The extracted loquat leaves, in addition, demonstrated remarkable, and privileged medicinal and commercial values, and recently attracted the interest of researchers. The current review aimed to summarize several important bioactive components in loquat leaves, their extraction and separation techniques, pharmacological activities, and research progress. In addition, the application prospect of bioactive components from loquat leaves was prospected, which provided a theoretical basis for its further development and utilization.