bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Plant Growth Regulators on Osmotic Regulatory Substances and Antioxidant Enzyme Activity of Nitraria tangutorum
Authors: Didi Dom Alizet , Su Shiping , Sam Faisal Eudes , Tiika Richard John , Zhang Xu
Published: September 2022

Plant growth regulators (PGRs) are natural hormones and synthetic hormone analogues. At low concentrations, PGRs have the ability to influence cell division, cell expansion, and cell structure and function, in addition to mediating environmental stress. In this study, experiments were conducted to determine how exogenous PGRs indole acetic acid (IAA), abscisic acid (ABA), and gibberellic acid (GA) influenced osmotic regulatory substances and activity of antioxidant enzymes in Nitraria tangutorum. Using a completely randomized design, IAA, ABA, and GA3 were applied as foliar spray at concentrations of 50 mg/L, 100 mg/L, 150 mg/L, and 200 mg/L to N. tangutorum shrubs. Some selected shrubs did not receive any treatment and served as the control (Ck). The results showed that the foliar spray of IAA, ABA, and GA3 significantly increased the content of osmotic regulatory substances (soluble sugar, soluble protein, and proline) and antioxidant enzymes (SOD and POD) at most concentrations. In addition, the malondialdehyde (MDA) content significantly reduced after treatment, but after regrowth of coppiced shrubs, lipid peroxidation increased and was still lower than Ck. Our study provides evidence that 100 mg/L 150 mg/L, and 200 mg/L concentrations of IAA, ABA, and GA3 treatments are effective for enhancing osmotic regulatory substances and the activity of antioxidant enzymes in N. tangutorum, which offers an effective strategy not only for increasing tolerance to abiotic and biotic stresses, but also improving the adaptability of N. tangutorum shrubs to the environment.
Bio-Based Polymer Developments from Tall Oil Fatty Acids by Exploiting Michael Addition
Authors: Pomilovskis Ralfs , Mierina Inese , Fridrihsone Anda , Kirpluks Mikelis
Published: September 2022

In this study, previously developed acetoacetates of two tall-oil-based and two commercial polyols were used to obtain polymers by the Michael reaction. The development of polymer formulations with varying cross-link density was enabled by different bio-based monomers in combination with different acrylates—bisphenol A ethoxylate diacrylate, trimethylolpropane triacrylate, and pentaerythritol tetraacrylate. New polymer materials are based on the same polyols that are suitable for polyurethanes. The new polymers have qualities comparable to polyurethanes and are obtained without the drawbacks that come with polyurethane extractions, such as the use of hazardous isocyanates or reactions under harsh conditions in the case of non-isocyanate polyurethanes. Dynamic mechanical analysis, differential scanning calorimetry, thermal gravimetric analysis, and universal strength testing equipment were used to investigate the physical and thermal characteristics of the created polymers. Polymers with a wide range of thermal and mechanical properties were obtained (glass transition temperature from 21 to 63 °C; tensile modulus (Young’s) from 8 MPa to 2710 MPa and tensile strength from 4 to 52 MPa). The synthesized polymers are thermally stable up to 300 °C. The suggested method may be used to make two-component polymer foams, coatings, resins, and composite matrices.
A 3D Collagen-Based Bioprinted Model to Study Osteosarcoma Invasiveness and Drug Response
Authors: Pellegrini Evelin , Desando Giovanna , Petretta Mauro , Cellamare Antonella , Cristalli Camilla , Pasello Michela , Manara Maria Cristina , Grigolo Brunella , Scotlandi Katia
Published: September 2022

The biological and therapeutic limits of traditional 2D culture models, which only partially mimic the complexity of cancer, have recently emerged. In this study, we used a 3D bioprinting platform to process a collagen-based hydrogel with embedded osteosarcoma (OS) cells. The human OS U-2 OS cell line and its resistant variant (U-2OS/CDDP 1 μg) were considered. The fabrication parameters were optimized to obtain 3D printed constructs with overall morphology and internal microarchitecture that accurately match the theoretical design, in a reproducible and stable process. The biocompatibility of the 3D bioprinting process and the chosen collagen bioink in supporting OS cell viability and metabolism was confirmed through multiple assays at short- (day 3) and long- (day 10) term follow-ups. In addition, we tested how the 3D collagen-based bioink affects the tumor cell invasive capabilities and chemosensitivity to cisplatin (CDDP). Overall, we developed a new 3D culture model of OS cells that is easy to set up, allows reproducible results, and better mirrors malignant features of OS than flat conditions, thus representing a promising tool for drug screening and OS cell biology research.
Click Chemistry: A Promising Tool for Building Hierarchical Structures
Authors: Badria Adel
Published: September 2022

The hierarchical structures are utilized at different levels in nature. Moreover, a wide spectrum of nature’s properties (e.g., mechanical, physical and biological properties) has been attributed to this hierarchy. Different reviews have been published to cover the use of click chemistry in building hierarchical structures. However, each one of those reviews focused on a narrow area on this topic, i.e., specific chemical reaction, such as in thiol-ene chemistry, or a specific molecule or compound such as polyhedral oligomeric silsesquioxane, or a certain range of hierarchical structures between the nano to micro range, e.g., nanocrystals. In this review, a frame to connect the dots between the different published works has been demonstrated. This article will not attempt to give an exhaustive review of all the published work in the field, instead the potential of click chemistry to build hierarchical structures of different levels using building blocks of different length scales has been shown through two main approaches. The first is a one-step direct formation of 3D micro/macrometer dimensions structures from Pico dimensions structures (molecules, monomers, etc.). The second approach includes several steps Pico ➔ 0D nano ➔ 1D nano ➔ 2D nano ➔ 3D nano/micro/macro dimensions structures. Another purpose of this review article is to connect between (a) the atomic theory, which covers the atoms and molecules in the picometer dimensions (picoscopic chemistry set); (b) “nano-periodic system” model, which covers different nanobuilding blocks in the nanometers range such as nanoparticles, dendrimers, buckyball, etc. which was developed by Tomalia; and (c) the micro/macrometer dimensions level.
Cytotoxicity Assessment of a New Design for a Biodegradable Ureteral Mitomycin Drug-Eluting Stent in Urothelial Carcinoma Cell Culture
Authors: Soria Federico , Martínez-Pla Luna , Aznar-Cervantes Salvador D. , de la Cruz Julia E. , Fernández Tomás , Pérez-Fentes Daniel , Llanes Luis , Sánchez-Margallo Francisco Miguel
Published: September 2022

Urothelial tumour of the upper urinary tract is a rare neoplasm, but unfortunately, it has a high recurrence rate. The reduction of these tumour recurrences could be achieved by the intracavitary instillation of adjuvant chemotherapy after nephron-sparing treatment in selected patients, but current instillation methods are ineffective. Therefore, the aim of this in vitro study is to evaluate the cytotoxic capacity of a new instillation technology through a biodegradable ureteral stent/scaffold coated with a silk fibroin matrix for the controlled release of mitomycin C as an anti-cancer drug. Through a comparative study, we assessed, in urothelial carcinoma cells in a human cancer T24 cell culture for 3 and 6 h, the cytotoxic capacity of mitomycin C by viability assay using the CCK-8 test (Cell counting Kit-8). Cell viability studies in the urothelial carcinoma cell line confirm that mitomycin C embedded in the polymeric matrix does not alter its cytotoxic properties and causes a significant decrease in cell viability at 6 h versus in the control groups. These findings have a clear biomedical application and could be of great use to decrease the recurrence rate in patients with upper tract urothelial carcinomas by increasing the dwell time of anti-cancer drugs.
Effect of Various Organic Fertilisers on Phosphorus Mineralisation, Use Efficiency and Maize Yield
Authors: Mnthambala Frank , Tilley Elizabeth , Tyrrel Sean , Sakrabani Ruben
Published: September 2022

When soils are phosphorus (P) deficient, external sources in the form of fertilisers have to be applied to increase crop yields. The world depends on mined sources for P fertilisers, and recent reports indicate that an increase in the human population has led to rising demand for P fertilisers, making its future supply uncertain. A low supply of chemical P fertilisers may lead to food insecurity. Although the efficacy of organic sources of P is unclear, organic waste materials containing P can potentially replace inorganic P sources. Previously, organic fertilisers have been used to supply N and even P, but the application rates were mostly N based, resulting in inconsistent and comparable results. This research was conducted to understand P mineralisation and the availability of the P-based organic fertilisers. The results showed that available P in the soil at 3 weeks accounted for 50%, 6 weeks accounted for 49%, and 9 weeks counted for 46% of the maize yield. The organic P sources maintained soil available P above the threshold available P value in Malawi. The P sources did not affect the maize P use efficiency (PUE). The results indicate that organic P sources could be used as an alternative fertiliser for maize production in Malawi.
Quantifying the Influence of Climate Change and Anthropogenic Activities on the Net Primary Productivity of China’s Grasslands
Authors: Zhou Xiafei , Peng Binbin , Zhou Ying , Yu Fang , Wang Xue-Chao
Published: September 2022

As one of China’s most common vegetation types, grasslands comprise about 27.5% of its terrestrial area and 41% of its carbon storage. Since climate change (CC) and human activities (HA) have a great effect on grasslands, quantifying the contributions of CC and HA on grassland net primary productivity (NPP) is crucial in understanding the mechanisms of grassland regional carbon balances. However, current approaches, including residual trend, biophysical model and environmental background-based methods, have limitations on different scales, especially on the national scale of China. To improve assessment accuracy, modifications to the environmental background-based method were introduced in calculating the CC and HA contributions to the actual NPP (ANPP). In this study, the grassland ANPP in national nature reserves was defined as the environmental background value (PNPP), which was only affected by CC and without HA. The pixel PNPP outside the nature reserves could be replaced by the pixel PNPP in the nature reserve with the most similar habitat in the same natural ecological geographical division. The impact of HA on grassland ANPP (HNPP) could be identified by calculating the difference between PNPP and ANPP. Finally, the contributions of CC and HA to ANPP changes were assessed by the trends of ANPP, PNPP, and HNPP. The results showed that the average grassland ANPP significantly increased from 2001 to 2020. CC contributed 71.0% to ANPP change, whereas HA contributed 29.0%. Precipitation was the main contributor to grassland growth among arid and semi-arid regions, while temperature inhibited productivity in these areas. HA was the major cause of degradation in China’s grasslands, although the effects have declined over time. The research could provide support support for government decisions. It could also provide a new and feasible research method for quantitatively evaluating grasslands and other ecosystems.
Intrinsic Calibration of Multi-Beam LiDARs for Agricultural Robots
Authors: Sun Na , Qiu Quan , Fan Zhengqiang , Li Tao , Ji Chao , Feng Qingchun , Zhao Chunjiang
Published: September 2022

With the advantages of high measurement accuracy and wide detection range, LiDARs have been widely used in information perception research to develop agricultural robots. However, the internal configuration of the laser transmitter layout changes with increasing sensor working duration, which makes it difficult to obtain accurate measurement with calibration files based on factory settings. To solve this problem, we investigate the intrinsic calibration of multi-beam laser sensors. Specifically, we calibrate the five intrinsic parameters of LiDAR with a nonlinear optimization strategy based on static planar models, which include measured distance, rotation angle, pitch angle, horizontal distance, and vertical distance. Firstly, we establish a mathematical model based on the physical structure of LiDAR. Secondly, we calibrate the internal parameters according to the mathematical model and evaluate the measurement accuracy after calibration. Here, we illustrate the parameter calibration with three steps: planar model estimation, objective function construction, and nonlinear optimization. We also introduce the ranging accuracy evaluation metrics, including the standard deviation of the distance from the laser scanning points to the planar models and the 3σ criterion. Finally, the experimental results show that the ranging error of calibrated sensors can be maintained within 3 cm, which verifies the effectiveness of the laser intrinsic calibration.
Analysis of the Anomalous Environmental Response to the 2022 Tonga Volcanic Eruption Based on GNSS
Authors: Zhou Maosheng , Gao Hao , Yu Dingfeng , Guo Jinyun , Zhu Lin , Yang Lei , Pan Shunqi
Published: September 2022

On 15 January 2022, a violent eruption and tsunami of the Hunga Tonga-Hunga Ha’apai (HTHH) volcano in Tonga, South Pacific, caused widespread international concern. In order to detect the anomalous environmental response caused by the HTHH volcanic eruption based on GNSS ionospheric data, GNSS tropospheric data and GNSS coordinate time series, a new method combining the zenith non-hydrostatic delay difference method and the extreme-point symmetric mode decomposition (ESMD) method, was proposed to detect tropospheric anomalies. The moving interquartile range method and the ESMD method were introduced to detect ionospheric anomalous and coordinate time series anomalies, respectively. The results showed that 9–10 h before the eruption of the Tonga volcano and 11–12 h after the eruption of the Tonga volcano, obvious total electron content (TEC) anomalies occurred in the volcanic eruption center and its northeast and southeast, with the maximum abnormal value of 15 TECU. Significant tropospheric anomalies were observed on the day of the HTHH volcano eruption as well as 1–3 days and 16–17 days after the eruption, and the abnormal intensity was more than 10 times that of normal. The coordinate time series in direction E showed very significant anomalies at approximately 2:45 p.m. on 14 January, at approximately 4:30 a.m.–5:40 a.m. on 15 January, and at approximately 3:45 a.m. on 16 January, with anomalies reaching a maximum of 7–8 times daily. The abnormality in the direction north (N) is not obvious. Very prominent anomalies can be observed in the direction up (U) at approximately 4:30 a.m.–5:40 a.m., with the intensity of the anomalies exceeding the normal by more than 10 times. In this study, GNSS was successfully used to detect the anomalous environmental response during this HTHH volcano eruption.
Particulate Matter Concentrations over South Korea: Impact of Meteorology and Other Pollutants
Authors: Allabakash Shaik , Lim Sanghun , Chong Kyu-Soo , Yamada Tomohito
Published: September 2022

Air pollution is a serious challenge in South Korea and worldwide, and negatively impacts human health and mortality rates. To assess air quality and the spatiotemporal characteristics of atmospheric particulate matter (PM), PM concentrations were compared with meteorological conditions and the concentrations of other airborne pollutants over South Korea from 2015 to 2020, using different linear and non-linear models such as linear regression, generalized additive, and multivariable linear regression models. The results showed that meteorological conditions played a significant role in the formation, transportation, and deposition of air pollutants. PM2.5 levels peaked in January, while PM10 levels peaked in April. Both were at their lowest levels in July. Further, PM2.5 was the highest during winter, followed by spring, autumn, and summer, whereas PM10 was the highest in spring followed by winter, autumn, and summer. PM concentrations were negatively correlated with temperature, relative humidity, and precipitation. Wind speed had an inverse relationship with air quality; zonal and vertical wind components were positively and negatively correlated with PM, respectively. Furthermore, CO, black carbon, SO2, and SO4 had a positive relationship with PM. The impact of transboundary air pollution on PM concentration in South Korea was also elucidated using air mass trajectories.
RGB-ICP Method to Calculate Ground Three-Dimensional Deformation Based on Point Cloud from Airborne LiDAR
Authors: Sang Mengting , Wang Wei , Pan Yani
Published: September 2022

With the rapid development of LiDAR technology in recent years, high-resolution LiDAR data possess a great capability to describe fine surface morphology in detail; thus, differencing multi-temporal datasets becomes a powerful tool to explain the surface deformation process. Compared with other differencing methods, ICP algorithms can directly estimate 3D displacements and rotations; thus, surface deformation parameters can be obtained by aligning window point clouds. However, the traditional ICP algorithm usually requires a good initial pose of the point cloud and relies on calculating the spatial distance to match the corresponding points, which can easily lead the algorithm to the local optimum. To address the above problems, we introduced the color information of the point cloud and proposed an improved ICP method that fuses RGB (RGB-ICP) to reduce the probability of matching errors by filtering color-associated point pairs, thus improving the alignment accuracy. Through simulated experiments, the ability of the two algorithms to estimate 3D deformation was compared, and the RGB-ICP algorithm could significantly reduce the deformation deviation (30–95%) in the three-dimensional direction. In addition, the RGB-ICP algorithm was applicable to different terrain structures, especially for smooth terrain, where the improvement was the most effective in the horizontal direction. Finally, it is worth believing that the RGB-ICP algorithm can play a unique role in surface change detection and provide a reliable basis for explaining the surface motion process.
BSC-Net: Background Suppression Algorithm for Stray Lights in Star Images
Authors: Li Yabo , Niu Zhaodong , Sun Quan , Xiao Huaitie , Li Hui
Published: September 2022

Most background suppression algorithms are weakly robust due to the complexity and fluctuation of the star image’s background. In this paper, a background suppression algorithm for stray lights in star images is proposed, which is named BSC-Net (Background Suppression Convolutional Network) and consist of two parts: “Background Suppression Part” and “Foreground Retention Part”. The former part achieves background suppression by extracting features from various receptive fields, while the latter part achieves foreground retention by merging multi-scale features. Through this two-part design, BSC-Net can compensate for blurring and distortion of the foreground caused by background suppression, which is not achievable in other methods. At the same time, a blended loss function of smooth_L1&Structure Similarity Index Measure (SSIM) is introduced to hasten the network convergence and avoid image distortion. Based on the BSC-Net and the loss function, a dataset consisting of real images will be used for training and testing. Finally, experiments show that BSC-Net achieves the best results and the largest Signal-to-Noise Ratio (SNR) improvement in different backgrounds, which is fast, practical and efficient, and can tackle the shortcomings of existing methods.
Analysis of Global Sea Level Change Based on Multi-Source Data
Authors: Jia Yongjun , Xiao Kailin , Lin Mingsen , Zhang Xi
Published: September 2022

Global sea level rise is both a major indicator and consequence of global warming. At present, global warming is causing sea level rise in two main ways: one is the thermal expansion of sea water, and the other is the injection of large amounts of fresh water into the ocean after glaciers and ice sheets melt. In this paper, satellite altimeter data are used to study the total changes of global sea level from 2002 to 2020. Different from most previous studies, this study proposes a calculation method of sea level anomaly using only the along track altimetry data, which is similar to considering the satellite points as tide gauges, in order to avoid the error caused by interpolation in the map data. In addition, GRACE satellite data are used to calculate the changes of global sea level caused by water increase; temperature and salinity data are used to calculate the changes from ocean thermal expansion. Next, using satellite altimetry data, the calculation results show that the global sea level rise rate in the period of 2002–2020 is 3.3 mm/a. During this period, the sea level change caused by the increase of sea water calculated with GRACE satellite data is 2.07 mm/a, and that caused by the thermal expansion of seawater is 0.62 mm/a. The sea level rise caused by the increase of water volume accounts for 62.7% of the total sea level rise.
Implementation of a Distributed Framework for Permissioned Blockchain-Based Secure Automotive Supply Chain Management
Authors: Zafar Saima , Hassan Syed Faseeh Ul , Mohammad AlSharef , Al-Ahmadi Ahmad Aziz , Ullah Nasim
Published: September 2022

An automotive supply chain includes a range of activities from the concept of the product to its final transfer to a customer and subsequent vehicle maintenance. The three distinct stages of this chain are production, sales, and maintenance. In many countries, automobile records are not available to the public and anyone who has access to the central database or government systems can tamper with these records. In addition, used vehicle maintenance and transfer histories remain unavailable or inaccessible. These issues can be overcome by incorporating state-of-the-art blockchain technology into automotive supply chain management. Blockchain technology uses a chain of blocks for distributed transfer and storage of information, creating a decentralized data register that makes records of any digital asset tamper-proof and transparent. In this paper, we implement a permissioned blockchain-based framework for secure and efficient supply chain management of the automobile industry. We employed Hyperledger Fabric; an enterprise-grade distributed ledger platform for developing solutions. In our solution, the blockchain is customized and private in order to ensure system security. We evaluated our system in terms of memory cost, monetary cost, and speed of execution. Our results demonstrate that only 346 MB of extra memory space is required for storing the automotive data of 1 million users, thus rendering the memory cost negligible. The monetary cost is insignificant as all open source blockchain resources are employed, and the speed of record update is also fast. Our results also show that the decentralization of the automotive supply chain using blockchain can implement system security with minor modifications in the established configuration of the web application database.
ConcentrateNet: Multi-Scale Object Detection Model for Advanced Driving Assistance System Using Real-Time Distant Region Locating Technique
Authors: Wu Bo-Xun , Shivanna Vinay M. , Hung Hsiang-Hsuan , Guo Jiun-In
Published: September 2022

This paper proposes a deep learning based object detection method to locate a distant region in an image in real-time. It concentrates on distant objects from a vehicular front camcorder perspective, trying to solve one of the common problems in Advanced Driver Assistance Systems (ADAS) applications, which is, to detect the smaller and faraway objects with the same confidence as those with the bigger and closer objects. This paper presents an efficient multi-scale object detection network, termed as ConcentrateNet to detect a vanishing point and concentrate on the near-distant region. Initially, the object detection model inferencing will produce a larger scale of receptive field detection results and predict a potentially vanishing point location, that is, the farthest location in the frame. Then, the image is cropped near the vanishing point location and processed with the object detection model for second inferencing to obtain distant object detection results. Finally, the two-inferencing results are merged with a specific Non-Maximum Suppression (NMS) method. The proposed network architecture can be employed in most of the object detection models as the proposed model is implemented in some of the state-of-the-art object detection models to check feasibility. Compared with original models using higher resolution input size, ConcentrateNet architecture models use lower resolution input size, with less model complexity, achieving significant precision and recall improvements. Moreover, the proposed ConcentrateNet architecture model is successfully ported onto a low-powered embedded system, NVIDIA Jetson AGX Xavier, suiting the real-time autonomous machines.
A Novel Reconstruction Algorithm with High Performance for Compressed Ultrafast Imaging
Authors: Shen Qian , Tian Jinshou , Pei Chengquan
Published: September 2022

Compressed ultrafast photography (CUP) is a type of two-dimensional (2D) imaging technique to observe ultrafast processes. Intelligence reconstruction methods that influence the imaging quality are an essential part of a CUP system. However, existing reconstruction algorithms mostly rely on image priors and complex parameter spaces. Therefore, it usually takes a lot of time to obtain acceptable reconstruction results, which limits the practical application of the CUP. In this paper, we proposed a novel reconstruction algorithm named PnP-FFDNet, which can provide a high quality and high efficiency compared to previous methods. First, we built a forward model of the CUP and three sub-optimization problems were obtained using the alternating direction multiplier method (ADMM), and the closed-form solution of the first sub-optimization problem was derived. Secondly, inspired by the PnP-ADMM framework, we used an advanced denoising algorithm based on a neural network named FFDNet to solve the second sub-optimization problem. On the real CUP data, PSNR and SSIM are improved by an average of 3 dB and 0.06, respectively, compared with traditional algorithms. Both on the benchmark dataset and on the real CUP data, the proposed method reduces the running time by an average of about 96% over state-of-the-art algorithms, and show comparable visual results, but in a much shorter running time.
Advances in Indoor Positioning and Indoor Navigation
Authors: Perez-Navarro Antoni , Montoliu Raúl , Torres-Sospedra Joaquín
Published: September 2022

Locating devices in indoor environments has become a key issue for many emerging location-based applications and intelligent spaces in different fields [...]
A Novel System for the Measurement of an Evaporation Duct Using the Magnetic Coupling Principle for Power Feeding and Data Transmission
Authors: Wang Qiang , Li Xingfei , Li Hongyu , Yang Shaobo , Yang Shizhong , Ma Linlin , Zhao Jingbo
Published: September 2022

Since the evaporation duct height (EDH) only covers the antenna height of most shipborne microwave radars, mastering the EDH in advance has great significance in achieving long-range target detection. In this paper, a set of hydrological and meteorological sensors based on the gradient meteorological instrument (GMI) were built to monitor the evaporation duct of the South China Sea (SCS). However, the monitoring needed to be interrupted during the battery replacement of the sensor, which could result in the loss of some important data collection. On the basis of the inductively coupled power transfer (ICPT) technology, the resonance principle was used to compensate the inductive reactance on the closed steel ring (CSR), and the energy stored in the super capacitor was introduced for data collection and return. A novel measuring system for the detection of an evaporation duct was proposed. To avoid iterative calculation by setting the initial value of the current evaporation duct models in large-scale and multi time evaporation duct prediction and diagnosis, on the basis of the non-iterative air–sea flux (NAF) model, the EDH was obtained by introducing the K theoretical flux observation method into the atmospheric refractive index equation. Finally, preliminary experimental results are presented for the detection of evaporation duct to demonstrate the feasibility and effectiveness of the proposed system. The communication accuracy rate of the proposed system was 99.7%. The system transmission power reached 22.8 W. The research results of the NAF model adaptability showed that the mean value of the EDH was 8.7 m, which was lower than the mean EDH of the SCS. The EDH calculated by the NAF model in the unstable air–sea stratification state was slightly lower than that calculated by the NPS model. The diagnosis of the EDH by the NAF model was similar to that of the NPS model, but the calculation stability of the NAF model was better.
An Experimental Assessment of Depth Estimation in Transparent and Translucent Scenes for Intel RealSense D415, SR305 and L515
Authors: Curto Eva , Araujo Helder
Published: September 2022

RGB-D cameras have become common in many research fields since these inexpensive devices provide dense 3D information from the observed scene. Over the past few years, the RealSense™ range from Intel® has introduced new, cost-effective RGB-D sensors with different technologies, more sophisticated in both hardware and software. Models D415, SR305, and L515 are examples of successful cameras launched by Intel® RealSense™ between 2018 and 2020. These three cameras are different since they have distinct operating principles. Then, their behavior concerning depth estimation while in the presence of many error sources will also be specific. For instance, semi-transparent and scattering media are expected error sources for an RGB-D sensor. The main new contribution of this paper is a full evaluation and comparison between the three Intel RealSense cameras in scenarios with transparency and translucency. We propose an experimental setup involving an aquarium and liquids. The evaluation, based on repeatability/precision and statistical distribution of the acquired depth, allows us to compare the three cameras and conclude that Intel RealSense D415 has overall the best behavior namely in what concerns the statistical variability (also known as precision or repeatability) and also in what concerns valid measurements.
A Modulated Wideband Converter Model Based on Linear Algebra and Its Application to Fast Calibration
Authors: Burel Gilles , Fiche Anthony , Gautier Roland
Published: September 2022

In the context of cognitive radio, smart cities and Internet-of-Things, the need for advanced radio spectrum monitoring becomes crucial. However, surveillance of a wide frequency band without using extremely expensive high sampling rate devices is a challenging task. The recent development of compressed sampling approaches offers a promising solution to these problems. In this context, the Modulated Wideband Converter (MWC), a blind sub-Nyquist sampling system, is probably the most realistic approach and was successfully validated in real-world conditions. The MWC can be realized with existing analog components, and there exist calibration methods that are able to integrate the imperfections of the mixers, filters and ADCs, hence allowing its use in the real world. The MWC underlying model is based on signal processing concepts such as filtering, modulation, Fourier series decomposition, oversampling and undersampling, spectrum aliasing, and so on, as well as in-flow data processing. In this paper, we develop an MWC model that is entirely based on linear algebra, matrix theory and block processing. We show that this approach has many interests: straightforward translation of mathematical equations into simple and efficient software programming, suppression of some constraints of the initial model, and providing a basis for the development of an extremely fast system calibration method. With a typical MWC acquisition device, we obtained a speed-up of the calibration computation time by a factor greater than 20 compared with a previous implementation.
Improved Monitoring of Wildlife Invasion through Data Augmentation by Extract–Append of a Segmented Entity
Authors: Lee Jaekwang , Lim Kangmin , Cho Jeongho
Published: September 2022

Owing to the continuous increase in the damage to farms due to wild animals’ destruction of crops in South Korea, various methods have been proposed to resolve these issues, such as installing electric fences and using warning lamps or ultrasonic waves. Recently, new methods have been attempted by applying deep learning-based object-detection techniques to a robot. However, for effective training of a deep learning-based object-detection model, overfitting or biased training should be avoided; furthermore, a huge number of datasets are required. In particular, establishing a training dataset for specific wild animals requires considerable time and labor. Therefore, this study proposes an Extract–Append data augmentation method where specific objects are extracted from a limited number of images via semantic segmentation and corresponding objects are appended to numerous arbitrary background images. Thus, the study aimed to improve the model’s detection performance by generating a rich dataset on wild animals with various background images, particularly images of water deer and wild boar, which are currently causing the most problematic social issues. The comparison between the object detector trained using the proposed Extract–Append technique and that trained using the existing data augmentation techniques showed that the mean Average Precision (mAP) improved by ≥2.2%. Moreover, further improvement in detection performance of the deep learning-based object-detection model can be expected as the proposed technique can solve the issue of the lack of specific data that are difficult to obtain.
A Review on Multiscale-Deep-Learning Applications
Authors: Elizar Elizar , Zulkifley Mohd Asyraf , Muharar Rusdha , Zaman Mohd Hairi Mohd , Mustaza Seri Mastura
Published: September 2022

In general, most of the existing convolutional neural network (CNN)-based deep-learning models suffer from spatial-information loss and inadequate feature-representation issues. This is due to their inability to capture multiscale-context information and the exclusion of semantic information throughout the pooling operations. In the early layers of a CNN, the network encodes simple semantic representations, such as edges and corners, while, in the latter part of the CNN, the network encodes more complex semantic features, such as complex geometric shapes. Theoretically, it is better for a CNN to extract features from different levels of semantic representation because tasks such as classification and segmentation work better when both simple and complex feature maps are utilized. Hence, it is also crucial to embed multiscale capability throughout the network so that the various scales of the features can be optimally captured to represent the intended task. Multiscale representation enables the network to fuse low-level and high-level features from a restricted receptive field to enhance the deep-model performance. The main novelty of this review is the comprehensive novel taxonomy of multiscale-deep-learning methods, which includes details of several architectures and their strengths that have been implemented in the existing works. Predominantly, multiscale approaches in deep-learning networks can be classed into two categories: multiscale feature learning and multiscale feature fusion. Multiscale feature learning refers to the method of deriving feature maps by examining kernels over several sizes to collect a larger range of relevant features and predict the input images’ spatial mapping. Multiscale feature fusion uses features with different resolutions to find patterns over short and long distances, without a deep network. Additionally, several examples of the techniques are also discussed according to their applications in satellite imagery, medical imaging, agriculture, and industrial and manufacturing systems.
An Improved Kinect Recognition Method for Identifying Unsafe Behaviors of Metro Passengers
Authors: Lu Ying , Yu Yifan , Lv Xifan , Tao Tingting
Published: September 2022

In order to solve the problem of the low action recognition accuracy of passengers’ unsafe behaviors caused by redundant joints, this study proposes an efficient recognition method based on a Kinect sensor. The method uses the pelvis as the starting point of the vector and high-frequency bone joints as the end point to construct the recognition feature vector. The joint angle difference between actions is obtained by using the cosine law, and the initial test result is converted into action similarity combined with the DTW similarity algorithm. Considering the combination of 3 angle features and 4 joint feature selection methods, 12 combined recognition models are formed. A comparative experiment was carried out to identify five types of unsafe behaviors of metro passengers—punch, one-armed call for help, arms for help, trip forward and trip backwards. The results show that the overall selection of joints has a poor similarity effect and cannot achieve the purpose of recognition. The overall recognition model effect of the local “pelvis divergence method” is higher than that of the local “adjacent joint method”, and the local “pelvis divergence method” has the highest recognition result of the maximum angle difference model, and the recognition results of the five unsafe behaviors are 86.9%, 89.2%, 85.5%, 86.7%, and 88.3%, respectively, and the recognition accuracy of this method is 95.7%, indicating the feasibility of the model. The recognition results are more concentrated and more stable, which significantly improves the recognition rate of metro passengers’ unsafe behavior.
A New Double-Layer Decentralized Consistency Algorithm for the Multi-Satellite Autonomous Mission Allocation Based on a Block-Chain
Authors: Cheng Fei , Ning Xin , Dong Yunfeng
Published: September 2022

The traditional on-board centralized-distributed mission negotiation architecture has poor security and reliability. It can easily give rise to the collapse of the whole system when the master node is attacked by malicious nodes. To address this issue, the decentralized consistency algorithms commonly used in the internet world are referred to in this paper. Firstly, four typical consistency algorithms suitable for the Internet and which are named RAFT, PBFT, RIPPLE and DPOS are selected and modified for a multi-satellite autonomous mission negotiation. Additionally, based on the above modified consistency algorithms, a new double-layer decentralized consistency algorithm named DDPOS is proposed. It is well known that the above four common consistency algorithms cannot have both a low resource occupation and high security. The DDPOS algorithm can integrate the advantages of four common consistency algorithms due to its freedom of choice attribute, which can enable satellite clusters to flexibly adopt different appropriate consistency algorithms and the number of decentralized network layers. The DDPOS algorithm not only greatly improves the security and reliability of the whole satellite cluster, but also effectively reduces the computing and communication resources occupation of the satellite cluster. Without the presence of a malicious node attack, the resource occupation of the DDPOS algorithm is almost the same as that of the RAFT algorithm. However, in the case of a malicious node attack, compared with the RAFT algorithm, the total computation and total bandwidth occupation of the DDPOS algorithm have decreased by 67% and 75%, respectively. Moreover, it is surprising that although the DDPOS algorithm is more complex, its code size is only about 8% more than the RAFT algorithm. Finally, the effectiveness and feasibility of the DDPOS algorithm in the on-board practical application are analyzed and verified via simulation experiments.
A Highly Sensitive and Flexible Capacitive Pressure Sensor Based on Alignment Airgap Dielectric
Authors: Kim Soo-Wan , Oh Geum-Yoon , Lee Kang-In , Yang Young-Jin , Ko Jeong-Beom , Kim Young-Woo , Hong Young-Sun
Published: September 2022

Flexible capacitive pressure sensors with a simple structure and low power consumption are attracting attention, owing to their wide range of applications in wearable electronic devices. However, it is difficult to manufacture pressure sensors with high sensitivity, wide detection range, and low detection limits. We developed a highly sensitive and flexible capacitive pressure sensor based on the porous Ecoflex, which has an aligned airgap structure and can be manufactured by simply using a mold and a micro-needle. The existence of precisely aligned airgap structures significantly improved the sensor sensitivity compared to other dielectric structures without airgaps. The proposed capacitive pressure sensor with an alignment airgap structure supports a wide range of working pressures (20–100 kPa), quick response time (≈100 ms), high operational stability, and low-pressure detection limit (20 Pa). Moreover, we also studied the application of pulse wave monitoring in wearable sensors, exhibiting excellent performance in wearable devices that detect pulse waves before and after exercise. The proposed pressure sensor is applicable in electronic skin and wearable medical assistive devices owing to its excellent functional features.
Flexible and Efficient Security Framework for Many-to-Many Communication in a Publish/Subscribe Architecture
Authors: Van Glabbeek Roald , Deac Diana , Perale Thomas , Steenhaut Kris , Braeken An
Published: September 2022

Message Queuing Telemetry Transport (MQTT) is a lightweight publish/subscribe protocol, which is currently one of the most popular application protocols in Internet of Things (IoT) thanks to its simplicity in use and its scalability. The secured version, MQTTS, which combines MQTT with the Transport Layer Security (TLS) protocol, has several shortcomings. It only offers one-to-one security, supports a limited number of security features and has high computation and communication costs. In this paper, we propose a flexible and lightweight security solution to be integrated in MQTT, addressing many-to-many communication, which reduces the communication overhead by 80% and the computational overhead by 40% for the setup of a secure connection on the client side.
The Accuracy of Commercially Available Fitness Trackers in Patients after Stroke
Authors: Holubová Anna , Malá Eliška , Hoidekrová Kristýna , Pětioký Jakub , Ďuriš Andrea , Mužík Jan
Published: September 2022

Background: Fitness trackers could represent an easy-to-use and cheap tool for continuous tracking of physical activity of stroke survivors during the period of their recovery at home. The aim of the study was to examine the accuracy of the Fitbit activity tracker in locomotor activity monitoring of stroke survivors with respect to gait disorders, walking speed, walking aid, and placement of the tracker on body. Methods: Twenty-four ambulatory stroke survivors (15 men and 9 women) with locomotion/gait disorder were involved in the study. Patients underwent two walking tests with the Fitbit Alta HR trackers attached on 5 different places on body. The accuracy of the trackers has been analyzed on 3 groups of patients—those walking without any walking aid, those using a single-point stick and those using a rolling walker. Results: For no-aid patients, the most accurate place was the waist. Patients with a single-point stick revealed the smallest deviations for a tracker attached to a healthy lower limb, and patients with a rolling walker revealed the smallest deviations for a tracker attached on the paretic lower limb. Conclusions: An accuracy comparable with the healthy population can be reached for all of the three groups of patients, while fulfilling the conditions for minimum speed of 2 km/h and optimal placement of the trackers with respect to a walking aid and aspect to impairment.
Lateral Flow Immunoassay Reader Technologies for Quantitative Point-of-Care Testing
Authors: Park Jongwon
Published: September 2022

Due to the recent pandemic caused by coronavirus disease 2019 (COVID-19), the lateral flow immunoassay used for its rapid antigen test is more popular than ever before. However, the history of the lateral flow immunoassay is about 60 years old, and its original purpose of use, such as a COVID-19 rapid antigen test or a pregnancy test, was the qualitative detection of a target analyte. Recently, the demand for quantitative analysis of lateral flow immunoassays is increasing in various fields. Lateral flow immunoassays for quantitative detection using various materials and sensor technologies are being introduced, and readers for analyzing them are being developed. Quantitative analysis readers are highly anticipated for their future development in line with technological advancements such as optical, magnetic field, photothermal, and electrochemical sensors and trends such as weight reduction, miniaturization, and cost reduction of systems. In addition, the sensing, processing, and communication functions of portable personal devices such as smartphones can be used as tools for the quantitative analysis of lateral flow immunoassays. As a result, lateral flow immunoassays can efficiently achieve the goal of rapid diagnosis by point-of-care testing. Readers used for the quantification of lateral flow immunoassays were classified according to the adopted sensor technology, and the research trends in each were reviewed in this paper. The development of a quantitative analysis system was often carried out in the assay aspect, so not only the readers but also the assay development cases were reviewed if necessary. In addition, systems for quantitative analysis of COVID-19, which have recently been gaining importance, were introduced as a separate section.