bluuebunny/crossref_metadata_2025 huggingface dataset:
Protective Effect of Processed Polygoni multiflori Radix and Its Major Substance during Scopolamine-Induced Cognitive Dysfunction
Authors: Kim Ji-Hyun , Kim Ji Hyun , He Mei Tong , Kim Su Cheol , Hwa Kyung Pan , Cho Kye Man , Cho Eun Ju
Published: February 2021

Alzheimer’s disease (AD) is the most common cognitive disorder in the elderly population. However, effective pharmacological agents targeting AD have not been developed. The processed Polygoni multiflori Radix (PPM) and its main active substance, 2,3,5,4′-tetrahydroxystilbene-2-O-β-glucoside (TSG), has received considerable attention, majorly due to its neuroprotective activities against multiple biological activities within the human body. In this study, we provide new evidence on the therapeutic effect of PPM and TSG during cognitive impairment by evaluating the ameliorative potential of PPM and TSG in scopolamine-induced amnesia in ICR mice. PPM (100 or 200 mg/kg) was orally administered during the experimental period (days 1–15), and scopolamine was intraperitoneally injected to induce cognitive deficits during the behavioural test periods (days 8–15). The administration of PPM and TSG significantly improved memory loss and cognitive dysfunction in behavioural tests and regulated the cholinergic function, brain-derived neurotrophic factor, and neural apoptosis. The present study suggests that PPM and TSG improved scopolamine-induced cognitive dysfunction, but further study has to be supported for the clinical application of PPM and TSG for AD prevention and treatment.
Parallel Multiset Rewriting Systems with Distorted Rules
Authors: Sburlan Cristina , Sburlan Dragoş-Florin
Published: February 2021

Most of the parallel rewriting systems which model (or which are inspired by) natural/artificial phenomena consider fixed, a priori defined sets of string/multiset rewriting rules whose definitions do not change during the computation. Here we modify this paradigm by defining level-t distorted rules—rules for which during their applications one does not know the exact multiplicities of at most t∈N species of objects in their output (although one knows that such objects will appear at least once in the output upon the execution of this type of rules). Subsequently, we define parallel multiset rewriting systems with t-distorted computations and we study their computational capabilities when level-1 distorted catalytic promoted rules are used. We construct robust systems able to cope with the level-1 distortions and prove the computational universality of the model.
Additive Manufacturing of Astragaloside-Containing Polyurethane Nerve Conduits Influenced Schwann Cell Inflammation and Regeneration
Authors: Chen Yueh-Sheng , Chang Shih-Sheng , Ng Hooi Yee , Huang Yu-Xuan , Chen Chien-Chang , Shie Ming-You
Published: February 2021

The peripheral nervous system is the bridge of communication between the central nervous system and other body systems. Autologous nerve grafting is the mainstream method for repair of nerve lesions greater than 20 mm. However, there are several disadvantages and limitations of autologous nerve grafting, thus prompting the need for fabrication of nerve conduits for clinical use. In this study, we successfully fabricated astragaloside (Ast)-containing polyurethane (PU) nerve guidance conduits via digital light processing, and it was noted that the addition of Ast improved the hydrophilicity of traditional PU conduits by at least 23%. The improved hydrophilicity not only led to enhanced cellular proliferation of rat Schwann cells, we also noted that levels of inflammatory markers tumor necrosis factor-alpha (TNF-α) and cyclooxygenase-2 (COX-2) significantly decreased with increasing concentrations of Ast. Furthermore, the levels of neural regeneration markers were significantly enhanced with the addition of Ast. This study demonstrated that Ast-containing PU nerve conduits can be potentially used as an alternative solution to regenerate peripheral nerve injuries.
Sensitivity of Performance Indexes to Disaster Risk
Authors: Hodoshima Jiro , Yamawake Toshiyuki
Published: February 2021

We examine how sensitive the new performance indexes incorporating high moments and disaster risk are to disaster risk. The new performance indexes incorporating high moments and disaster risk are the Aumann-Serrano performance index and Foster-Hart performance index proposed by Kadan and Liu. These performance indexes provide evaluations sensitive to the underlying risk. We show, by numerical examples and empirical examples, how sensitive these indexes are to disaster risk. Although these indexes are known to be either quite sensitive or excessively sensitive to disaster risk or maximum loss in the literature, we show by the regression analysis of the index and summary statistics these indexes are in fact not excessively sensitive to maximum loss in representative stock data, which contain disastrous observations. The numerical estimate of the Foster-Hart performance index is found to be effective in showing the performance index. Our analysis suggests these indexes can handle various empirical data containing quite disastrous observations.
Time Coordination and Collision Avoidance Using Leader-Follower Strategies in Multi-Vehicle Missions
Authors: Tabasso Camilla , Cichella Venanzio , Mehdi Syed Bilal , Marinho Thiago , Hovakimyan Naira
Published: February 2021

In recent years, the increasing popularity of multi-vehicle missions has been accompanied by a growing interest in the development of control strategies to ensure safety in these scenarios. In this work, we propose a control framework for coordination and collision avoidance in cooperative multi-vehicle missions based on a speed adjustment approach. The overall problem is decoupled in a coordination problem, in order to ensure coordination and inter-vehicle safety among the agents, and a collision-avoidance problem to guarantee the avoidance of non-cooperative moving obstacles. We model the network over which the cooperative vehicles communicate using tools from graph theory, and take communication losses and time delays into account. Finally, through a rigorous Lyapunov analysis, we provide performance bounds and demonstrate the efficacy of the algorithms with numerical and experimental results.
A Review of GPR Application on Transport Infrastructures: Troubleshooting and Best Practices
Authors: Solla Mercedes , Pérez-Gracia Vega , Fontul Simona
Published: February 2021

The non-destructive testing and diagnosis of transport infrastructures is essential because of the need to protect these facilities for mobility, and for economic and social development. The effective and timely assessment of structural health conditions becomes crucial in order to assure the safety of the transportation system and time saver protocols, as well as to reduce excessive repair and maintenance costs. Ground penetrating radar (GPR) is one of the most recommended non-destructive methods for routine subsurface inspections. This paper focuses on the on-site use of GPR applied to transport infrastructures, namely pavements, railways, retaining walls, bridges and tunnels. The methodologies, advantages and disadvantages, along with up-to-date research results on GPR in infrastructure inspection are presented herein. Hence, through the review of the published literature, the potential of using GPR is demonstrated, while the main limitations of the method are discussed and some practical recommendations are made.
High Accuracy Interpolation of DEM Using Generative Adversarial Network
Authors: Yan Li , Tang Xingfen , Zhang Yi
Published: February 2021

Digital elevation model (DEM) interpolation is aimed at predicting the elevation values of unobserved locations, given a series of collected points. Over the years, the traditional interpolation methods have been widely used but can easily lead to accuracy degradation. In recent years, generative adversarial networks (GANs) have been proven to be more efficient than the traditional methods. However, the interpolation accuracy is not guaranteed. In this paper, we propose a GAN-based network named gated and symmetric-dilated U-net GAN (GSUGAN) for improved DEM interpolation, which performs visibly and quantitatively better than the traditional methods and the conditional encoder-decoder GAN (CEDGAN). We also discuss combinations of new techniques in the generator. This shows that the gated convolution and symmetric dilated convolution structure perform slightly better. Furthermore, based on the performance of the different methods, it was concluded that the Convolutional Neural Network (CNN)-based method has an advantage in the quantitative accuracy but the GAN-based method can obtain a better visual quality, especially in complex terrains. In summary, in this paper, we propose a GAN-based network for improved DEM interpolation and we further illustrate the GAN-based method’s performance compared to that of the CNN-based method.
Analysis of the Influence of DTM Source Data on the LS Factors of the Soil Water Erosion Model Values with the Use of GIS Technology
Authors: Fijałkowska Anna
Published: February 2021

Counteracting soil degradation is one of the strategic priorities for sustainable development. One of the most important current challenges is effective management of available resources. Multiple studies in various aspects of soil water erosion are conducted in many research institutions in the world. They concern, among others, the development of risk estimation models and the use of new data for modelling. The aim of the presented research was a discussion on the impact of the accuracy and detail of elevation data sources on the results of soil water erosion topographic factors modelling. Elevation data for this research were chosen to reflect various technologies of data acquisition, differences in the accuracy and detail of field forms mapping and, consequently, the spatial resolution of the digital terrain models (DTMs). The methodology of the universal soil loss equation USLE/RUSLE was used for the L and S factors modelling and calculation. The research was carried out in three study areas located in different types of geographical regions in Poland: uplands, highlands and lake districts. The proposed methodology consisted of conducting detailed comparative elevation and slope value assessments, calculating the values of topographical factors of the universal soil loss equation: slope length (L) and slope (S) and a detailed analysis of the total LS factors values. An approach to assess LS factors values within homogeneous areas such as agricultural plots has also been proposed. The studies draw the conclusion that the values of topographical factors obtained from various DTM sources were significantly different. It was shown that the choice of the right modelling data has a significant impact on the L and S factors values and, thus, also, on the decision-making process. The conducted research has definitely shown that data of the highest accuracy and detail should be used to study local phenomena (inter alia erosion), even analysing a large area.
Sea State from Single Optical Images: A Methodology to Derive Wind-Generated Ocean Waves from Cameras, Drones and Satellites
Authors: Almar Rafael , Bergsma Erwin , Catalan Patricio , Cienfuegos Rodrigo , Suarez Leandro , Lucero Felipe , Lerma Alexandre , Desmazes Franck , Perugini Eleonora , Palmsten Margaret , Chickadel Chris
Published: February 2021

Sea state is a key variable in ocean and coastal dynamics. The sea state is either sparsely measured by wave buoys and satellites or modelled over large scales. Only a few attempts have been devoted to sea state measurements covering a large domain; in particular its estimation from optical images. With optical technologies becoming omnipresent, optical images offer incomparable spatial resolution from diverse sensors such as shore-based cameras, airborne drones (unmanned aerial vehicles/UAVs), or satellites. Here, we present a standalone methodology to derive the water surface elevation anomaly induced by wind-generated ocean waves from optical imagery. The methodology was tested on drone and satellite images and compared against ground truth. The results show a clear dependence on the relative azimuth view angle in relation to the wave crest. A simple correction is proposed to overcome this bias. Overall, the presented methodology offers a practical way of estimating ocean waves for a wide range of applications.
Using Long-Term Earth Observation Data to Reveal the Factors Contributing to the Early 2020 Desert Locust Upsurge and the Resulting Vegetation Loss
Authors: Wang Lei , Zhuo Wen , Pei Zhifang , Tong Xingyuan , Han Wei , Fang Shibo
Published: February 2021

Massive desert locust swarms have been threatening and devouring natural vegetation and agricultural crops in East Africa and West Asia since 2019, and the event developed into a rare and globally concerning locust upsurge in early 2020. The breeding, maturation, concentration and migration of locusts rely on appropriate environmental factors, mainly precipitation, temperature, vegetation coverage and land-surface soil moisture. Remotely sensed images and long-term meteorological observations across the desert locust invasion area were analyzed to explore the complex drivers, vegetation losses and growing trends during the locust upsurge in this study. The results revealed that (1) the intense precipitation events in the Arabian Peninsula during 2018 provided suitable soil moisture and lush vegetation, thus promoting locust breeding, multiplication and gregarization; (2) the regions affected by the heavy rainfall in 2019 shifted from the Arabian Peninsula to West Asia and Northeast Africa, thus driving the vast locust swarms migrating into those regions and causing enormous vegetation loss; (3) the soil moisture and NDVI anomalies corresponded well with the locust swarm movements; and (4) there was a low chance the eastwardly migrating locust swarms would fly into the Indochina Peninsula and Southwest China.
Quantifying Optically Derived Two-Dimensional Wave-Averaged Currents in the Surf Zone
Authors: Anderson Dylan , Bak A. Spicer , Brodie Katherine L. , Cohn Nicholas , Holman Rob A. , Stanley John
Published: February 2021

Complex two-dimensional nearshore current patterns are generated by feedbacks between sub-aqueous morphology and momentum imparted on the water column by breaking waves, winds, and tides. These non-stationary features, such as rip currents and circulation cells, respond to changing environmental conditions and underlying morphology. However, using fixed instruments to observe nearshore currents is limiting due to the high costs and logistics necessary to achieve adequate spatial sampling resolution. A new technique for processing surf-zone imagery, WAMFlow, quantifies fluid velocities to reveal complex, multi-scale (10 s–1000 s meters) nearshore surface circulation patterns. We apply the concept of a wave-averaged movie (WAM) to measure surf-zone circulation patterns on spatial scales of kilometers in the alongshore and 100 s of meters in the cross-shore. The approach uses a rolling average of 2 Hz optical imagery, removing the dominant optical clutter of incident waves, to leave the residual foam or water turbidity features carried by the flow. These residual features are tracked as quasi-passive tracers in space and time using optical flow, which solves for u and v as a function of image intensity gradients in x, y, and t. Surf zone drifters were deployed over multiple days with varying nearshore circulations to validate the optically derived flow patterns. Root mean square error are reduced to 0.1 m per second after filtering based on image attributes. The optically derived patterns captured longshore currents, rip currents, and gyres within the surf zone. Quantifying nearshore circulation patterns using low-cost image platforms and open-source computer vision algorithms presents the potential to further our understanding of fundamental surf zone dynamics.
Multi-Scale Attentive Aggregation for LiDAR Point Cloud Segmentation
Authors: Geng Xiaoxiao , Ji Shunping , Lu Meng , Zhao Lingli
Published: February 2021

Semantic segmentation of LiDAR point clouds has implications in self-driving, robots, and augmented reality, among others. In this paper, we propose a Multi-Scale Attentive Aggregation Network (MSAAN) to achieve the global consistency of point cloud feature representation and super segmentation performance. First, upon a baseline encoder-decoder architecture for point cloud segmentation, namely, RandLA-Net, an attentive skip connection was proposed to replace the commonly used concatenation to balance the encoder and decoder features of the same scales. Second, a channel attentive enhancement module was introduced to the local attention enhancement module to boost the local feature discriminability and aggregate the local channel structure information. Third, we developed a multi-scale feature aggregation method to capture the global structure of a point cloud from both the encoder and the decoder. The experimental results reported that our MSAAN significantly outperformed state-of-the-art methods, i.e., at least 15.3% mIoU improvement for scene-2 of CSPC dataset, 5.2% for scene-5 of CSPC dataset, and 6.6% for Toronto3D dataset.
Quantitative Precipitation Estimates Using Machine Learning Approaches with Operational Dual-Polarization Radar Data
Authors: Shin Kyuhee , Song Joon Jin , Bang Wonbae , Lee GyuWon
Published: February 2021

Traditional radar-based rainfall estimation is typically done by known functional relationships between the rainfall intensity (R) and radar measurables, such as R–Zh, R–(Zh, ZDR), etc. One of the biggest advantages of machine learning algorithms is the applicability to a non-linear relationship between a dependent variable and independent variables without any predefined relationships. We explored the potential use of two supervised machine learning methods (regression tree and random forest) in rainfall estimation using dual-polarization radar variables. The regression tree does not require normalization and scaling of data; however, this method is quite unstable since each split depends on the parent split. Since the random forest is an ensemble method of regression trees, it has less variability in prediction compared with regression trees, but consumes more computer resources. We considered several different configurations for machine learning algorithms with different sets of dependent and independent variables. The random forest model was appropriately tuned. In the test of variable importance, the specific differential phase (differential reflectivity) was the most important variable to predict the rainfall rate (residual that is the difference between the true rainfall rate and the one estimated from the R–Z relationship). The models were evaluated by 10-fold cross-validation. The best model was the random forest model using a residual with the non-classified training set. The results indicated that the machine learning algorithms outperformed the traditional R–Z relationship. Then, we applied the best machine learning model to an S-band dual-polarization radar (Mt. Myeonbong) and validated the result with ground rain gauges. The results of the application to radar data showed that the estimates of the residuals had spatial variability. The stratiform and weak rain areas had positive residuals while convective areas had negative residuals, indicating that the spatial error structure driven by the R–Z relationship was well captured by the model. The rainfall rates of all pixels over the study area were adjusted with the estimated residuals. The rainfall rates adjusted by residual showed excellent agreement with the rain gauge, especially at high rainfall rates.
Landslide-Induced Mass Transport of Radionuclides along Transboundary Mailuu-Suu River Networks in Central Asia
Authors: Li Fengqing , Torgoev Isakbek , Zaredinov Damir , Li Marina , Talipov Bekhzod , Belousova Anna , Kunze Christian , Schneider Petra
Published: February 2021

Seismically triggered landslides are a major hazard and have caused severe secondary losses. This problem is especially important in the seismic prone Mailuu-Suu catchment in Kyrgyzstan, as it hosts disproportionately sensitive active or legacy uranium sites with deposited radioactive extractive wastes. These sites show a quasi-continuous release of radioactive contamination into surface waters, and especially after natural hazards, a sudden and massive input of pollutants into the surface waters is expected. However, landslides of contaminated sediments into surface waters represent a substantial exposure pathway that has not been properly addressed in the existing river basin management to date. To fill this gap, satellite imagery was massively employed to extract topography and geometric information, and the seismic Scoops3D and the one-dimensional numerical model, Hydrologic Engineering Centre, River Analysis System (HEC-RAS), were chosen to simulate the landslide-induced mass transport of total suspended solids (TSS) and natural radionuclides (Pb-210 as a proxy for modeling purposes) within the Mailuu-Suu river networks under two earthquake and two hydrological scenarios. The results show that the seismically vulnerable areas dominated in the upstream areas, and the mass of landslides increased dramatically with the increase of earthquake levels. After the landslides, the concentrations of radionuclides increased suddenly and dramatically. The peak values decreased along the longitudinal gradient of river networks, with the concentration curves becoming flat and wide in the downstream sections, and the transport speed of radionuclides decreased along the river networks. The conclusions of this study are that landslides commonly release a significant amount of pollutants with a relatively fast transport along river networks. Improved quantitative understanding of waterborne pollution dispersion across national borders will contribute to better co-ordination between governments and regulatory authorities of riparian states and, consequently, to future prevention of transnational political conflicts that have flared up in the last two decades over alleged pollution of transboundary water bodies.
Shadow Detection and Compensation from Remote Sensing Images under Complex Urban Conditions
Authors: Zhou Tingting , Fu Haoyang , Sun Chenglin , Wang Shenghan
Published: February 2021

Due to the block of high-rise objects and the influence of the sun’s altitude and azimuth, shadows are inevitably formed in remote sensing images particularly in urban areas, which causes missing information in the shadow region. In this paper, we propose a new method for shadow detection and compensation through objected-based strategy. For shadow detection, the shadow was highlighted by an improved shadow index (ISI) combined color space with an NIR band, then ISI was reconstructed by the objects acquired from the mean-shift algorithm to weaken noise interference and improve integrity. Finally, threshold segmentation was applied to obtain the shadow mask. For shadow compensation, the objects from segmentation were treated as a minimum processing unit. The adjacent objects are likely to have the same ambient light intensity, based on which we put forward a shadow compensation method which always compensates shadow objects with their adjacent non-shadow objects. Furthermore, we presented a dynamic penumbra compensation method (DPCM) to define the penumbra scope and accurately remove the penumbra. Finally, the proposed methods were compared with the stated-of-art shadow indexes, shadow compensation method and penumbra compensation methods. The experiments show that the proposed method can accurately detect shadow from urban high-resolution remote sensing images with a complex background and can effectively compensate the information in the shadow region.
Measurement Matrix Optimization for Compressed Sensing System with Constructed Dictionary via Takenaka–Malmquist Functions
Authors: Xu Qiangrong , Sheng Zhichao , Fang Yong , Zhang Liming
Published: February 2021

Compressed sensing (CS) has been proposed to improve the efficiency of signal processing by simultaneously sampling and compressing the signal of interest under the assumption that the signal is sparse in a certain domain. This paper aims to improve the CS system performance by constructing a novel sparsifying dictionary and optimizing the measurement matrix. Owing to the adaptability and robustness of the Takenaka–Malmquist (TM) functions in system identification, the use of it as the basis function of a sparsifying dictionary makes the represented signal exhibit a sparser structure than the existing sparsifying dictionaries. To reduce the mutual coherence between the dictionary and the measurement matrix, an equiangular tight frame (ETF) based iterative minimization algorithm is proposed. In our approach, we modify the singular values without changing the properties of the corresponding Gram matrix of the sensing matrix to enhance the independence between the column vectors of the Gram matrix. Simulation results demonstrate the promising performance of the proposed algorithm as well as the superiority of the CS system, designed with the constructed sparsifying dictionary and the optimized measurement matrix, over existing ones in terms of signal recovery accuracy.
Emotion Detection for Social Robots Based on NLP Transformers and an Emotion Ontology
Authors: Graterol Wilfredo , Diaz-Amado Jose , Cardinale Yudith , Dongo Irvin , Lopes-Silva Edmundo , Santos-Libarino Cleia
Published: February 2021

For social robots, knowledge regarding human emotional states is an essential part of adapting their behavior or associating emotions to other entities. Robots gather the information from which emotion detection is processed via different media, such as text, speech, images, or videos. The multimedia content is then properly processed to recognize emotions/sentiments, for example, by analyzing faces and postures in images/videos based on machine learning techniques or by converting speech into text to perform emotion detection with natural language processing (NLP) techniques. Keeping this information in semantic repositories offers a wide range of possibilities for implementing smart applications. We propose a framework to allow social robots to detect emotions and to store this information in a semantic repository, based on EMONTO (an EMotion ONTOlogy), and in the first figure or table caption. Please define if appropriate. an ontology to represent emotions. As a proof-of-concept, we develop a first version of this framework focused on emotion detection in text, which can be obtained directly as text or by converting speech to text. We tested the implementation with a case study of tour-guide robots for museums that rely on a speech-to-text converter based on the Google Application Programming Interface (API) and a Python library, a neural network to label the emotions in texts based on NLP transformers, and EMONTO integrated with an ontology for museums; thus, it is possible to register the emotions that artworks produce in visitors. We evaluate the classification model, obtaining equivalent results compared with a state-of-the-art transformer-based model and with a clear roadmap for improvement.
SICD: Novel Single-Access-Point Indoor Localization Based on CSI-MIMO with Dimensionality Reduction
Authors: Zhang Yunwei , Wang Weigang , Xu Chendong , Qin Jie , Yu Shujuan , Zhang Yun
Published: February 2021

With the rise of location-based services and the rapidly growing requirements related to their applications, indoor localization based on channel state information–multiple-input multiple-output (CSI-MIMO) has become an important research topic. However, indoor localization based on CSI-MIMO has some disadvantages, including noise and high data dimensions. To overcome the above drawbacks, we proposed a novel method of indoor localization based on CSI-MIMO, named SICD. For SICD, a novel localization fingerprint was first designed which can reflect the time–frequency and space–frequency characteristics of CSI-MIMO under a single access point (AP). To reduce the redundancy in the data of CSI-MIMO amplitude, we developed a data dimensionality reduction algorithm. Moreover, by leveraging a log-normal distribution, we calculated the conditional probability of the naive Bayes classifier, which was used to predict the moving object’s location. Compared with other state-of-the-art methods, the results of the experiment confirm that the SICD effectively improves localization accuracy.
Ultrasonic-Based Environmental Perception for Mobile 5G-Oriented XR Applications
Authors: Davoli Luca , Paraskevopoulos Ioannis , Campanella Cinzia , Bauro Stefano , Vio Tommaso , Abrardo Andrea , Ferrari Gianluigi
Published: February 2021

One of the sectors that is expected to significantly benefit from 5G network deployment is eXtended Reality (XR). Besides the very high bandwidth, reliability, and Quality of Service (QoS) to be delivered to end users, XR also requires accurate environmental perception for safety reasons: this is fundamental when a user, wearing XR equipment, is immersed in a “virtual” world, but moves in a “real” environment. To overcome this limitation (especially when using low-cost XR equipments, such as cardboards worn by the end user), it is possible to exploit the potentialities offered by Internet of Things (IoT) nodes with sensing/actuating capabilities. In this paper, we rely on ultrasonic sensor-based IoT systems to perceive the surrounding environment and to provide “side information” to XR systems, then performing a preliminary experimental characterization campaign with different ultrasonic IoT system configurations worn by the end user. The combination of the information flows associated with XR and IoT components is enabled by 5G technology. An illustrative experimental scenario, relative to a “Tourism 4.0” IoT-aided VR application deployed by Vodafone in Milan, Italy, is presented.
Fully Automatic Fall Risk Assessment Based on a Fast Mobility Test
Authors: Tylman Wojciech , Kotas Rafał , Kamiński Marek , Marciniak Paweł , Woźniak Sebastian , Napieralski Jan , Sakowicz Bartosz , Janc Magdalena , Józefowicz-Korczyńska Magdalena , Zamysłowska-Szmytke Ewa
Published: February 2021

This paper presents a fall risk assessment approach based on a fast mobility test, automatically evaluated using a low-cost, scalable system for the recording and analysis of body movement. This mobility test has never before been investigated as a sole source of data for fall risk assessment. It can be performed in a very limited space and needs only minimal additional equipment, yet provides large amounts of information, as the presented system can obtain much more data than traditional observation by capturing minute details regarding body movement. The readings are provided wirelessly by one to seven low-cost micro-electro-mechanical inertial measurement units attached to the subject’s body segments. Combined with a body model, these allow segment rotations and translations to be computed and for body movements to be recreated in software. The subject can then be automatically classified by an artificial neural network based on selected values in the test, and those with an elevated risk of falls can be identified. Results obtained from a group of 40 subjects of various ages, both healthy volunteers and patients with vestibular system impairment, are presented to demonstrate the combined capabilities of the test and system. Labelling of subjects as fallers and non-fallers was performed using an objective and precise sensory organization test; it is an important novelty as this approach to subject labelling has never before been used in the design and evaluation of fall risk assessment systems. The findings show a true-positive ratio of 85% and true-negative ratio of 63% for classifying subjects as fallers or non-fallers using the introduced fast mobility test, which are noticeably better than those obtained for the long-established Timed Up and Go test.
Robustifying the Deployment of tinyML Models for Autonomous Mini-Vehicles
Authors: de Prado Miguel , Rusci Manuele , Capotondi Alessandro , Donze Romain , Benini Luca , Pazos Nuria
Published: February 2021

Standard-sized autonomous vehicles have rapidly improved thanks to the breakthroughs of deep learning. However, scaling autonomous driving to mini-vehicles poses several challenges due to their limited on-board storage and computing capabilities. Moreover, autonomous systems lack robustness when deployed in dynamic environments where the underlying distribution is different from the distribution learned during training. To address these challenges, we propose a closed-loop learning flow for autonomous driving mini-vehicles that includes the target deployment environment in-the-loop. We leverage a family of compact and high-throughput tinyCNNs to control the mini-vehicle that learn by imitating a computer vision algorithm, i.e., the expert, in the target environment. Thus, the tinyCNNs, having only access to an on-board fast-rate linear camera, gain robustness to lighting conditions and improve over time. Moreover, we introduce an online predictor that can choose between different tinyCNN models at runtime—trading accuracy and latency—which minimises the inference’s energy consumption by up to 3.2×. Finally, we leverage GAP8, a parallel ultra-low-power RISC-V-based micro-controller unit (MCU), to meet the real-time inference requirements. When running the family of tinyCNNs, our solution running on GAP8 outperforms any other implementation on the STM32L4 and NXP k64f (traditional single-core MCUs), reducing the latency by over 13× and the energy consumption by 92%.
Low Complexity Robust Data Demodulation for GNSS
Authors: Ortega Lorenzo , Poulliat Charly , Boucheret Marie Laure , Aubault Roudier Marion , Al-Bitar Hanaa , Closas Pau
Published: February 2021

In this article, we provide closed-form approximations of log-likelihood ratio (LLR) values for direct sequence spread spectrum (DS-SS) systems over three particular scenarios, which are commonly found in the Global Navigation Satellite System (GNSS) environment. Those scenarios are the open sky with smooth variation of the signal-to-noise ratio (SNR), the additive Gaussian interference, and pulsed jamming. In most of the current communications systems, block-wise estimators are considered. However, for some applications such as GNSSs, symbol-wise estimators are available due to the low data rate. Usually, the noise variance is considered either perfectly known or available through symbol-wise estimators, leading to possible mismatched demodulation, which could induce errors in the decoding process. In this contribution, we first derive two closed-form expressions for LLRs in additive white Gaussian and Laplacian noise channels, under noise uncertainty, based on conjugate priors. Then, assuming those cases where the statistical knowledge about the estimation error is characterized by a noise variance following an inverse log-normal distribution, we derive the corresponding closed-form LLR approximations. The relevance of the proposed expressions is investigated in the context of the GPS L1C signal where the clock and ephemeris data (CED) are encoded with low-density parity-check (LDPC) codes. Then, the CED is iteratively decoded based on the belief propagation (BP) algorithm. Simulation results show significant frame error rate (FER) improvement compared to classical approaches not accounting for such uncertainty.
Using Aerial and Vehicular NFV Infrastructures to Agilely Create Vertical Services
Authors: Nogales Borja , Silva Miguel , Vidal Ivan , Luís Miguel , Valera Francisco , Sargento Susana , Azcorra Arturo
Published: February 2021

5G communications have become an enabler for the creation of new and more complex networking scenarios, bringing together different vertical ecosystems. Such behavior has been fostered by the network function virtualization (NFV) concept, where the orchestration and virtualization capabilities allow the possibility of dynamically supplying network resources according to its needs. Nevertheless, the integration and performance of heterogeneous network environments, each one supported by a different provider, and with specific characteristics and requirements, in a single NFV framework is not straightforward. In this work we propose an NFV-based framework capable of supporting the flexible, cost-effective deployment of vertical services, through the integration of two distinguished mobile environments and their networks: small sized unmanned aerial vehicles (SUAVs), supporting a flying ad hoc network (FANET) and vehicles, promoting a vehicular ad hoc network (VANET). In this context, a use case involving the public safety vertical will be used as an illustrative example to showcase the potential of this framework. This work also includes the technical implementation details of the framework proposed, allowing to analyse and discuss the delays on the network services deployment process. The results show that the deployment times can be significantly reduced through a distributed VNF configuration function based on the publish–subscribe model.
Validation of a Laser Ranged Scanner-Based Detection of Spatio-Temporal Gait Parameters Using the aTUG Chair
Authors: Fudickar Sebastian , Kiselev Jörn , Stolle Christian , Frenken Thomas , Steinhagen-Thiessen Elisabeth , Wegel Sandra , Hein Andreas
Published: February 2021

This article covers the suitability to measure gait-parameters via a Laser Range Scanner (LRS) that was placed below a chair during the walking phase of the Timed Up&Go Test in a cohort of 92 older adults (mean age 73.5). The results of our study demonstrated a high concordance of gait measurements using a LRS in comparison to the reference GAITRite walkway. Most of aTUG’s gait parameters demonstrate a strong correlation coefficient with the GAITRite, indicating high measurement accuracy for the spatial gait parameters. Measurements of velocity had a correlation coefficient of 99%, which can be interpreted as an excellent measurement accuracy. Cadence showed a slightly lower correlation coefficient of 96%, which is still an exceptionally good result, while step length demonstrated a correlation coefficient of 98% per leg and stride length with an accuracy of 99% per leg. In addition to confirming the technical validation of the aTUG regarding its ability to measure gait parameters, we compared results from the GAITRite and the aTUG for several parameters (cadence, velocity, and step length) with results from the Berg Balance Scale (BBS) and the Activities-Specific Balance Confidence-(ABC)-Scale assessments. With confidence coefficients for BBS and velocity, cadence and step length ranging from 0.595 to 0.798 and for ABC ranging from 0.395 to 0.541, both scales demonstrated only a medium-sized correlation. Thus, we found an association of better walking ability (represented by the measured gait parameters) with better balance (BBC) and balance confidence (ABC) overall scores via linear regression. This results from the fact that the BBS incorporates both static and dynamic balance measures and thus, only partly reflects functional requirements for walking. For the ABC score, this effect was even more pronounced. As this is to our best knowledge the first evaluation of the association between gait parameters and these balance scores, we will further investigate this phenomenon and aim to integrate further measures into the aTUG to achieve an increased sensitivity for balance ability.
DSSM: Distributed Streaming Data Sharing Manager
Authors: Fukuda Hiroaki , Gunji Ryota , Hasegawa Tadahiro , Leger Paul , Figueroa Ismael
Published: February 2021

Developing robot control software systems is difficult because of a wide variety of requirements, including hardware systems and sensors, even though robots are demanding nowadays. Middleware systems, such as Robot Operating System (ROS), are being developed and widely used to tackle this difficulty. Streaming data Sharing Manager (SSM) is one of such middleware systems that allow developers to write and read sensor data with timestamps using a Personal Computer (PC). The timestamp feature is essential for the robot control system because it usually uses multiple sensors with their own measurement cycles, meaning that measured sensor values with different timestamps become useless for the robot control. Using SSM allows developers to use measured sensor values with the same timestamps; however, SSM assumes that only one PC is used. Thereby, if one process consumes CPU resources intensively, other processes cannot finish their assumed deadlines, leading to the unexpected behavior of a robot. This paper proposes an SSM middleware, named Distributed Streaming data Sharing Manager (DSSM), that enables distributing processes on SSM to different PCs. We have developed a prototype of DSSM and confirmed its behavior so far. In addition, we apply DSSM to an existing real SSM based robot control system that autonomously controls an unmanned vehicle robot. We then reveal its advantages and disadvantages via several experiments by measuring resource usages.
Analysis of Whole-Body Coordination Patterning in Successful and Faulty Spikes Using Self-Organising Map-Based Cluster Analysis: A Secondary Analysis
Authors: Sarvestan Javad , Svoboda Zdeněk , Alaei Fatemeh , Mulloy Franky
Published: February 2021

This study investigated the whole-body coordination patterning in successful and faulty spikes using self-organising map-based cluster analysis. Ten young, elite volleyball players (aged 15.5 ± 0.7 years) performed 60 volleyball spikes in a real-game environment. Adopting the cluster analysis, based on a self-organising map, whole-body coordination patterning was explored between successful and faulty spikes of individual players. The self-organising maps (SOMs) portrayed whole body, lower and upper limb coordination dissimilarities during the jump phase and the ball impact phases between the successful and faulty spikes. The cluster analysis illustrated that the whole body, upper limb and lower limb coordination patterning of each individual’s successful spikes were similar to their faulty spikes. Range of motion patterning also demonstrated no differences in kinematics between spike outcomes. Further, the upper limb angular velocity patterning of the players’ successful/faulty spikes were similar. The SPM analysis portrayed significant differences between the normalized upper limb angular velocities from 35% to 45% and from 76% to 100% of the spike movement. Although the lower limb angular velocities are vital for achieving higher jumps in volleyball spikes, the results of this study portrayed that the upper limb angular velocities distinguish the differences between successful and faulty spikes among the attackers. This confirms the fact that volleyball coaches should shift their focus toward the upper limb velocity and coordination training for higher success rates in spiking for volleyball attackers.
Ambulatory Human Gait Phase Detection Using Wearable Inertial Sensors and Hidden Markov Model
Authors: Liu Long , Wang Huihui , Li Haorui , Liu Jiayi , Qiu Sen , Zhao Hongyu , Guo Xiangyang
Published: February 2021

Gait analysis, as a common inspection method for human gait, can provide a series of kinematics, dynamics and other parameters through instrumental measurement. In recent years, gait analysis has been gradually applied to the diagnosis of diseases, the evaluation of orthopedic surgery and rehabilitation progress, especially, gait phase abnormality can be used as a clinical diagnostic indicator of Alzheimer Disease and Parkinson Disease, which usually show varying degrees of gait phase abnormality. This research proposed an inertial sensor based gait analysis method. Smoothed and filtered angular velocity signal was chosen as the input data of the 15-dimensional temporal characteristic feature. Hidden Markov Model and parameter adaptive model are used to segment gait phases. Experimental results show that the proposed model based on HMM and parameter adaptation achieves good recognition rate in gait phases segmentation compared to other classification models, and the recognition results of gait phase are consistent with ground truth. The proposed wearable device used for data collection can be embedded on the shoe, which can not only collect patients’ gait data stably and reliably, ensuring the integrity and objectivity of gait data, but also collect data in daily scene and ambulatory outdoor environment.
Coal and Gangue Separating Robot System Based on Computer Vision
Authors: Sun Zhiyuan , Huang Linlin , Jia Ruiqing
Published: February 2021

In coal production, the raw coal contains a large amount of gangue, which affects the quality of coal and pollutes the environment. Separating coal and gangue can improve coal quality, save energy, and reduce consumption and make rational use of resources. The separated gangue can also be reused. Robots with computer vision technology have become current research hotspots due to simple equipment, are efficient, and create no pollution to the environment. However, the difficulty in identifying coal and gangue is that the difference between coal and gangue is small, and the background and prospects are similar. In addition, due to the irregular shape of gangue, real-time grasping requirements make robot control difficult. This paper presents a coal and gangue separating robot system based on computer vision, proposes a convolutional neural network to extract the classification and location information, and designs a robot multi-objective motion planning algorithm. Through simulation and experimental verification, the accuracy of coal gangue identification reaches 98% under the condition of ensuring real-time performance. The average separating rate reaches 75% on low-, medium-, and high-speed moving conveyor belts, which meets the needs of actual projects. This method has important guiding significance in detection and separation of objects in complex scenes.
Innovative Mini Ultralight Radioprobes to Track Lagrangian Turbulence Fluctuations within Warm Clouds: Electronic Design
Authors: Paredes Quintanilla Miryam E. , Abdunabiev Shahbozbek , Allegretti Marco , Merlone Andrea , Musacchio Chiara , Pasero Eros G. A. , Tordella Daniela , Canavero Flavio
Published: February 2021

Characterization of dynamics inside clouds remains a challenging task for weather forecasting and climate modeling as cloud properties depend on interdependent natural processes at micro- and macro-scales. Turbulence plays an important role in particle dynamics inside clouds; however, turbulence mechanisms are not yet fully understood partly due to the difficulty of measuring clouds at the smallest scales. To address these knowledge gaps, an experimental method for measuring the influence of small-scale turbulence in cloud formation in situ and producing an in-field cloud Lagrangian dataset is being developed by means of innovative ultralight radioprobes. This paper presents the electronic system design along with the obtained results from laboratory and field experiments regarding these compact (diameter ≈30 cm), lightweight (≈20 g), and expendable devices designed to passively float and track small-scale turbulence fluctuations inside warm clouds. The fully customized mini-radioprobe board (5 cm × 5 cm) embeds sensors to measure local fluctuations and transmit data to the ground in near real time. The tests confirm that the newly developed probes perform well, providing accurate information about atmospheric turbulence as referenced in space. The integration of multiple radioprobes allows for a systematic and accurate monitoring of atmospheric turbulence and its impact on cloud formation.