bluuebunny/crossref_metadata_2025 huggingface dataset:
Vertical Structure of Ice Clouds and Vertical Air Motion from Vertically Pointing Cloud Radar Measurements
Authors: Ye Bo-Young , Lee GyuWon
Published: November 2021

The vertical structure of ice clouds and vertical air motion (Vair) were investigated using vertically pointing Ka-band cloud radar. The distributions of reflectivity (Z), Doppler velocity (VD), and spectrum width (SW) were analyzed for three ice cloud types, namely, cirrus, anvil, and stratiform clouds. The radar parameters of the cirrus clouds showed narrower distributions than those of the stratiform and anvil clouds. In the vertical structures, the rapid growth of Z and VD occurred in the layer between 8 and 12 km (roughly a layer of −40 °C to −20 °C) for all ice clouds. The prominent feature in the stratiform clouds was an elongated “S” shape in the VD near 7–7.5 km (at approximately −16 °C to −13 °C) due to a significant decrease in an absolute value of VD. The mean terminal fall velocity (Vt) and Vair in the ice clouds were estimated using pre-determined Vt–Z relationships (Vt = aZb) and the observed VD. Although the cirrus clouds demonstrated wide distributions in coefficients a and exponents b depending on cloud heights, they showed a smaller change in Z and Vt values compared to that of the other cloud types. The anvil clouds had a larger exponent than that of the stratiform clouds, indicating that the ice particle density of anvil clouds increases at a faster rate compared with the density of stratiform clouds for the same Z increment. The significant positive Vair appeared at the top of all ice clouds in range up to 0.5 m s−1, and the anvil clouds showed the deepest layer of upward motion. The stratiform and anvil clouds showed a dramatic increase in vertical air motion in the layer of 6–8 km as shown by the rapid decrease of VD. This likely caused increase of supersaturation above. A periodic positive Vair linked with a significant reduction in VD appeared at the height of 7–8 km (approximately −15 °C) dominantly in the stratiform clouds. This layer exhibited a bi-modal power spectrum produced by pre-existing larger ice particles and newly formed numerous smaller ice particles. This result raised a question on the origins of smaller ice particles such as new nucleation due to increased supersaturation by upward motion below or the seeder-feeder effect. In addition, the retrieved Vair with high-resolution data well represented a Kelvin-Helmholtz wave development.
ICESat-2 Marine Bathymetry: Extraction, Refraction Adjustment and Vertical Accuracy as a Function of Depth in Mid-Latitude Temperate Contexts
Authors: Coveney Seamus , Monteys Xavier , Hedley John D. , Castillo-Campo Yeray , Kelleher Brian
Published: November 2021

Nearshore bathymetric data are used in many coastal monitoring applications, but acquisition conditions can be challenging. Shipborne surveys are prone to the risk of grounding in shallow waters, and scheduled airborne surveys often fail to coincide with optimal atmospheric and water conditions. As an alternative, since its launch in 2018, ICESat-2 satellite laser profile altimetry data provide free and readily available data on a 91-day repeat cycle, which may contain incidental bathymetric returns when suitable environmental conditions prevail. In this paper, the vertical accuracy of extracted, refraction-adjusted ICESat-2 nearshore marine bathymetric data is evaluated at four test sites in a Northern hemisphere, temperate latitude location. Multiple ICEsat-2 bathymetric values that occurred in close horizontal proximity to one another were averaged at a spatial scale of 1 m and compared with Multibeam Echosounder bathymetric survey data and Global Navigation Satellite System reference data. Mean absolute errors of less than 0.15 m were observed up to depths of 5 m, with errors of less than 0.24 m (to 6 m), 0.39 m (to 7 m) and 0.52 m (to 10 m). The occurrence of larger bathymetric errors with depth, which increase to 0.54 m at maximum photon depths of 11 m, appears to be primarily related to reduced numbers of geolocated photons with depth. The accuracies achieved up to 6 m suggest that the manual extraction, refraction adjustment and bathymetric filtering steps were effective. Overall, the results suggest that ICESat-2 bathymetric data accuracy may be sufficient to be considered for use in nearshore coastal monitoring applications where shipborne and airborne bathymetric data might otherwise be applied.
Onboard Digital Beamformer with Multi-Frequency and Multi-Group Time Delays for High-Resolution Wide-Swath SAR
Authors: Xu Wei , Yu Qi , Fang Chonghua , Huang Pingping , Tan Weixian , Qi Yaolong
Published: November 2021

Scan-on-receive (SCORE) digital beamforming (DBF) in elevation can significantly improve the signal-to-noise ratio (SNR) and suppress range ambiguities in spaceborne synthetic aperture radar (SAR). It has been identified as one of the important methods to obtain high-resolution wide-swath (HRWS) SAR images. However, with the improvement of geometric resolution and swath width, the residual pulse extension loss (PEL) due to the long pulse duration in the conventional spaceborne onboard DBF processor must be considered and reduced. In this paper, according to the imaging geometry of the spaceborne DBF SAR system, the reason for the large attenuation of the receiving gain at the edge of the wide swath is analyzed, and two improved onboard DBF methods to mitigate the receive gain loss are given and analyzed. Taking account of both the advantages and drawbacks of the two improved DBF methods presented, a novel onboard DBF processor with multi-frequency and multi-group time delays in HRWS SAR is proposed. Compared with the DBF processor only with multi-group time delays, the downlink data rate was clearly reduced, while focusing performance degradation due to phase and amplitude errors between different frequency bands could be mitigated compared with the DBF processor only with multi-frequency time delays. The simulation results of both point and distributed targets validate the proposed DBF processor.
Integrating EfficientNet into an HAFNet Structure for Building Mapping in High-Resolution Optical Earth Observation Data
Authors: Ferrari Luca , Dell’Acqua Fabio , Zhang Peng , Du Peijun
Published: November 2021

Automated extraction of buildings from Earth observation (EO) data is important for various applications, including updating of maps, risk assessment, urban planning, and policy-making. Combining data from different sensors, such as high-resolution multispectral images (HRI) and light detection and ranging (LiDAR) data, has shown great potential in building extraction. Deep learning (DL) is increasingly used in multi-modal data fusion and urban object extraction. However, DL-based multi-modal fusion networks may under-perform due to insufficient learning of “joint features” from multiple sources and oversimplified approaches to fusing multi-modal features. Recently, a hybrid attention-aware fusion network (HAFNet) has been proposed for building extraction from a dataset, including co-located Very-High-Resolution (VHR) optical images and light detection and ranging (LiDAR) joint data. The system reported good performances thanks to the adaptivity of the attention mechanism to the features of the information content of the three streams but suffered from model over-parametrization, which inevitably leads to long training times and heavy computational load. In this paper, the authors propose a restructuring of the scheme, which involved replacing VGG-16-like encoders with the recently proposed EfficientNet, whose advantages counteract exactly the issues found with the HAFNet scheme. The novel configuration was tested on multiple benchmark datasets, reporting great improvements in terms of processing times, and also in terms of accuracy. The new scheme, called HAFNetE (HAFNet with EfficientNet integration), appears indeed capable of achieving good results with less parameters, translating into better computational efficiency. Based on these findings, we can conclude that, given the current advancements in single-thread schemes, the classical multi-thread HAFNet scheme could be effectively transformed by the HAFNetE scheme by replacing VGG-16 with EfficientNet blocks on each single thread. The remarkable reduction achieved in computational requirements moves the system one step closer to on-board implementation in a possible, future “urban mapping” satellite constellation.
Spatial–Temporal Evolution of Vegetation NDVI in Association with Climatic, Environmental and Anthropogenic Factors in the Loess Plateau, China during 2000–2015: Quantitative Analysis Based on Geographical Detector Model
Authors: Dong Yi , Yin Dongqin , Li Xiang , Huang Jianxi , Su Wei , Li Xuecao , Wang Hongshuo
Published: November 2021

In the Loess Plateau (LP) of China, the vegetation degradation and soil erosion problems have been shown to be curbed after the implementation of the Grain for Green program. In this study, the LP is divided into the northwestern semi-arid area and the southeastern semi-humid area using the 400 mm isohyet. The spatial–temporal evolution of the vegetation NDVI during 2000–2015 are analyzed, and the driving forces (including factors of climate, environment, and human activities) of the evolution are quantitatively identified using the geographical detector model (GDM). The results showed that the annual mean NDVI in the entire LP was 0.529, and it decreased from the semi-humid area (0.619) to the semi-arid area (0.346). The mean value of the coefficient of variation of the NDVI was 0.1406, and it increased from the semi-humid area (0.1165) to the semi-arid area (0.1926). The annual NDVI growth rate in the entire LP was 0.0079, with the NDVI growing faster in the semi-humid area (0.0093) than in the semi-arid area (0.0049). The largest increments of the NDVI were from grassland, farmland, and woodland. The GDM results revealed that changes in the spatial distribution of the NDVI could be primarily explained by the climatic and environmental factors in the semi-arid area, such as precipitation, soil type, and vegetation type, while the changes were mainly explained by the anthropogenic factors in the semi-humid area, such as the GDP density, land-use type, and population density. The interactive analysis showed that interactions between factors strengthened the impacts on the vegetation change compared with an individual factor. Furthermore, the ranges/types of factors suitable for vegetation growth were determined. The conclusions of this study have important implications for the formulation and implementation of ecological conservation and restoration strategies in different regions of the LP.
Zero-Shot Pipeline Detection for Sub-Bottom Profiler Data Based on Imaging Principles
Authors: Zheng Gen , Zhao Jianhu , Li Shaobo , Feng Jie
Published: November 2021

With the increasing number of underwater pipeline investigation activities, the research on automatic pipeline detection is of great significance. At this stage, object detection algorithms based on Deep Learning (DL) are widely used due to their abilities to deal with various complex scenarios. However, DL algorithms require massive representative samples, which are difficult to obtain for pipeline detection with sub-bottom profiler (SBP) data. In this paper, a zero-shot pipeline detection method is proposed. First, an efficient sample synthesis method based on SBP imaging principles is proposed to generate samples. Then, the generated samples are used to train the YOLOv5s network and a pipeline detection strategy is developed to meet the real-time requirements. Finally, the trained model is tested with the measured data. In the experiment, the trained model achieved a mAP@0.5 of 0.962, and the mean deviation of the predicted pipeline position is 0.23 pixels with a standard deviation of 1.94 pixels in the horizontal direction and 0.34 pixels with a standard deviation of 2.69 pixels in the vertical direction. In addition, the object detection speed also met the real-time requirements. The above results show that the proposed method has the potential to completely replace the manual interpretation and has very high application value.
Retrieval of Snow Albedo and Total Ozone Column from Single-View MSI/S-2 Spectral Reflectance Measurements over Antarctica
Authors: Kokhanovsky Alexander , Gascoin Simon , Arnaud Laurent , Picard Ghislain
Published: November 2021

We proposed a simple algorithm to retrieve the total ozone column and snow properties (spectral albedo and effective light absorption path) using the high spatial resolution single–view MSI/S-2 measurements over Antarctica. In addition, the algorithm allows the retrieval of the snow grain size on a scale of 10–20 m. This algorithm should be useful for the understanding of intra-pixel total ozone and snow albedo variability in complement to satellite observations performed on a much coarser spatial resolution scale (0.3–1 km and even larger spatial scales).
Understanding UX Better: A New Technique to Go beyond Emotion Assessment
Authors: Marques Leonardo , Matsubara Patrícia Gomes , Nakamura Walter Takashi , Ferreira Bruna Moraes , Wiese Igor Scaliante , Gadelha Bruno Freitas , Zaina Luciana Martinez , Redmiles David , Conte Tayana Uchôa
Published: November 2021

User experience (UX) is a quality aspect that considers the emotions evoked by the system, extending the usability concept beyond effectiveness, efficiency, and satisfaction. Practitioners and researchers are aware of the importance of evaluating UX. Thus, UX evaluation is a growing field with diverse approaches. Despite various approaches, most of them produce a general indication of the experience as a result and do not seek to capture the problem that gave rise to the bad UX. This information makes it difficult to obtain relevant results to improve the application, making it challenging to identify what caused a negative user experience. To address this gap, we developed a UX evaluation technique called UX-Tips. This paper presents UX-Tips and reports two empirical studies performed in an academic and an industrial setting to evaluate it. Our results show that UX-Tips had good performance in terms of efficiency and effectiveness, making it possible to identify the causes that led to a negative user experience, and it was easy to use. In this sense, we present a new technique suitable for use in both academic and industrial settings, allowing UX evaluation and finding the problems that may lead to a negative experience.
An Ultrasonic Laminated Transducer for Viscoelastic Media Detection
Authors: Yang Shunmin , Song Wenai , Chen Yifang , Yang Lu , Wang Mingquan , Lian Yongjian , Liu Kangchi
Published: November 2021

Based on the principle of underwater transducers, an ultrasonic four-laminated transducer with a frequency of 1 MHz was proposed to solve the problem of large energy attenuation when ultrasonic waves propagate in viscoelastic media. First, this study targeted solid rocket propellant as the research object, and the energy attenuation characteristics of ultrasonic waves propagating in viscoelastic media were analyzed through the derivation of the wave equation. Second, the structure of a four-laminated transducer with a frequency of 1 MHz was designed, and the resonance frequency was obtained by a graphical method. The sound field simulation and experimental results showed that the gain of the four-laminated transducer was 15 dB higher than that of the single-wafer transducer. An ultrasonic feature scanning system was built to complete the qualitative and quantitative detection of the smallest artificial hole (ϕ2 mm × 10 mm). Finally, two different natural defects were scanned, and the results were compared with those obtained using an industrial computed tomography detection system. The results showed that the ultrasonic method was more accurate in characterizing two natural defects. The primary cause was that the industrial CT was not sensitive to defects parallel to the incident direction of the ray. Therefore, this study not only achieved the qualitative and quantitative nondestructive testing of solid rocket propellants, but also provides an important reference for other viscoelastic components.
An Open-Source Wireless Electrophysiological Complex for In Vivo Recording Neuronal Activity in the Rodent’s Brain
Authors: Erofeev Alexander , Kazakov Dmitriy , Makarevich Nikita , Bolshakova Anastasia , Gerasimov Evgenii , Nekrasov Arseniy , Kazakin Alexey , Komarevtsev Ivan , Bolsunovskaja Marina , Bezprozvanny Ilya , Vlasova Olga
Published: November 2021

Multi-electrode arrays (MEAs) are a widely used tool for recording neuronal activity both in vitro/ex vivo and in vivo experiments. In the last decade, researchers have increasingly used MEAs on rodents in vivo. To increase the availability and usability of MEAs, we have created an open-source wireless electrophysiological complex. The complex is scalable, recording the activity of neurons in the brain of rodents during their behavior. Schematic diagrams and a list of necessary components for the fabrication of a wireless electrophysiological complex, consisting of a base charging station and wireless wearable modules, are presented.
Demand-Driven Data Acquisition for Large Scale Fleets
Authors: Matesanz Philip , Graen Timo , Fiege Andrea , Nolting Michael , Nejdl Wolfgang
Published: November 2021

Automakers manage vast fleets of connected vehicles and face an ever-increasing demand for their sensor readings. This demand originates from many stakeholders, each potentially requiring different sensors from different vehicles. Currently, this demand remains largely unfulfilled due to a lack of systems that can handle such diverse demands efficiently. Vehicles are usually passive participants in data acquisition, each continuously reading and transmitting the same static set of sensors. However, in a multi-tenant setup with diverse data demands, each vehicle potentially needs to provide different data instead. We present a system that performs such vehicle-specific minimization of data acquisition by mapping individual data demands to individual vehicles. We collect personal data only after prior consent and fulfill the requirements of the GDPR. Non-personal data can be collected by directly addressing individual vehicles. The system consists of a software component natively integrated with a major automaker’s vehicle platform and a cloud platform brokering access to acquired data. Sensor readings are either provided via near real-time streaming or as recorded trip files that provide specific consistency guarantees. A performance evaluation with over 200,000 simulated vehicles has shown that our system can increase server capacity on-demand and process streaming data within 269 ms on average during peak load. The resulting architecture can be used by other automakers or operators of large sensor networks. Native vehicle integration is not mandatory; the architecture can also be used with retrofitted hardware such as OBD readers.
Experimental Results of Underwater Acoustic Communication with Nonlinear Frequency Modulation Waveform
Authors: An Jeongha , Ra Hyungin , Youn Changhyun , Kim Kiman
Published: November 2021

In this paper, we propose underwater acoustic (UWA) communications using a generalized sinusoidal frequency modulation (GSFM) waveform, which has a distinct ambiguity function (AF) and correlation function characteristic. For these reasons, it is more robust in multipath channels than the conventional chirp spread spectrum (CSS) with a linear frequency modulation (LFM) waveform. Four types of GSFM waveforms that are orthogonal to each other are applied for each symbol in the proposed method. To evaluate the performance of the proposed method, we compared the performances of the proposed method and conventional method by conducting diverse experiments: simulations, lake trials and sea trials. In the simulation results, the proposed method shows better performance than the conventional method. The lake trial was conducted with a distance of 300~400 m between the transmitter and receiver. As a result of the experiment, the average bit error rate (BER) of the proposed method is 3.52×10−2 and that of the conventional method is 3.52×10−1, which shows that the proposed method is superior to the conventional method. The sea trial was conducted at a distance of approximately 20 km between the transmitter and receiver at a depth of 1500 m, and the receiver was composed of 16 vertical line arrays (VLAs) with a hydrophone. The proposed method had a BER of 0.3×10−2 in one channel and was error free in the other.
Surface Albedo and Temperature Models for Surface Energy Balance Fluxes and Evapotranspiration Using SEBAL and Landsat 8 over Cerrado-Pantanal, Brazil
Authors: Angelini Lucas Peres , Biudes Marcelo Sacardi , Machado Nadja Gomes , Geli Hatim M. E. , Vourlitis George Louis , Ruhoff Anderson , Nogueira José de Souza
Published: November 2021

The determination of the surface energy balance fluxes (SEBFs) and evapotranspiration (ET) is fundamental in environmental studies involving the effects of land use change on the water requirement of crops. SEBFs and ET have been estimated by remote sensing techniques, but with the operation of new sensors, some variables need to be parameterized to improve their accuracy. Thus, the objective of this study is to evaluate the performance of algorithms used to calculate surface albedo and surface temperature on the estimation of SEBFs and ET in the Cerrado-Pantanal transition region of Mato Grosso, Brazil. Surface reflectance images of the Operational Land Imager (OLI) and brightness temperature (Tb) of the Thermal Infrared Sensor (TIRS) of the Landsat 8, and surface reflectance images of the MODIS MOD09A1 product from 2013 to 2016 were combined to estimate SEBF and ET by the surface energy balance algorithm for land (SEBAL), which were validated with measurements from two flux towers. The surface temperature (Ts) was recovered by different models from the Tb and by parameters calculated in the atmospheric correction parameter calculator (ATMCORR). A model of surface albedo (asup) with surface reflectance OLI Landsat 8 developed in this study performed better than the conventional model (acon) SEBFs and ET in the Cerrado-Pantanal transition region estimated with asup combined with Ts and Tb performed better than estimates with acon. Among all the evaluated combinations, SEBAL performed better when combining asup with the model developed in this study and the surface temperature recovered by the Barsi model (Tsbarsi). This demonstrates the importance of an asup model based on surface reflectance and atmospheric surface temperature correction in estimating SEBFs and ET by SEBAL.
Research on Joint-Angle Prediction Based on Artificial Neural Network for Above-Knee Amputees
Authors: Yang Jianyu , Li Guanchao , Zhao Xiaofei , Xie Hualong
Published: November 2021

In the current study, our research group proposed an asymmetric lower extremity exoskeleton to enable above-knee amputees to walk with a load. Due to the absence of shank and foot, the knee and ankle joint at the amputation side of the exoskeleton lack tracking targets, so it is difficult to realize the function of assisted walking when going up and downstairs. Currently, the use of lower-limb electromyography to predict the angles of lower limb joints has achieved remarkable results. However, the prediction effect was poor when only using electromyography from the thigh. Therefore, this paper introduces hip-angle and plantar pressure signals for improving prediction effect and puts forward a joint prediction method of knee- and ankle-joint angles by electromyography of the thigh, hip-joint angle, and plantar pressure signals. The generalized regression neural network optimized by the golden section method is used to predict the joint angles. Finally, the parameters (the maximum error, the Root-Mean-Square error (RMSE), and correlation coefficient (γ)) were calculated to verify the feasibility of the prediction method.
A Novel Method for the Accurate Measurement of Soil Infiltration Line by Portable Vector Network Analyzer
Authors: Li Xiaobin , Liu Zhengguang , Lin Lei , Fan Hao , Liang Xingyu , Xu Jinghui
Published: November 2021

Accurate measurement of soil infiltration lines is very important for agricultural irrigation systems. It can help monitor the irrigation of soil to control irrigation amounts and promote crop growth. The soil infiltration line is a complex dynamic boundary and is difficult to model accurately, leading to estimation deviation. A traditional TDR (time domain reflectometry) method is used in soil infiltration line measurement, but it lacks good applicability and accuracy. In this paper, we proposed a method—VFTT (The vector network analyzer’s frequency domain signals are converted to the time domain)—by the time domain to frequency domain conversion principle to improve the accuracy of soil infiltration line measurement. The experiment results show that the measurement method of soil infiltration line based on VFTT has high accuracy and robustness. After fitting the measured value with the actual one, R2 reaching more than 0.98 can effectively measure the position of the soil infiltration line.
Smart Tree: An Architectural, Greening and ICT Multidisciplinary Approach to Smart Campus Environments
Authors: Fortes Sergio , Hidalgo-Triana Noelia , Sánchez-la-Chica Juan-Manuel , García-Ceballos María-Luz , Cantizani-Estepa Juan , Pérez-Latorre Andrés-Vicente , Baena Eduardo , Pineda Andrés , Barrios-Corpa Jorge , García-Marín Alberto
Published: November 2021

At present, climate change, pollution, and uncontrolled urbanism threaten not only natural ecosystems, but also the urban environment. Approaches to mitigate these challenges and able to provide an alternative for the use of the space are deemed to be multidisciplinary, combining architecture, vegetation integration, circular economy and information and communications technologies (ICT). University campuses are a key scenario to evaluate such solutions as their student and research community is intrinsically willing to support these experiences and provide a wide knowledge on the fields necessary for their design and implementation. However, the creation of areas combining usability and sustainability is commonly lacking a multidisciplinary approach combining all these different perspectives. Hence, the present work aims to overcome this limitation by the development of a novel integrated approach for campus spaces for co-working and leisure, namely a “Smart Tree”, where novel architecture, furniture design, flora integration, environmental sensoring and communications join together. To this end, a survey of the literature is provided, covering related approaches as well as general principles behind them. From this, the general requirements and constraints for the development of the Smart Tree area are identified, establishing the main interactions between the architecture, greening and ICT perspectives. Such requirements guide the proposed system design and implementation, whose impact on the environment is analyzed. Finally, the research challenges and lessons learned for their development are identified in order to support future works.
Performance and Deployment of Low-Cost Particle Sensor Units to Monitor Biomass Burning Events and Their Application in an Educational Initiative
Authors: Reisen Fabienne , Cooper Jacinta , Powell Jennifer C. , Roulston Christopher , Wheeler Amanda J.
Published: November 2021

Biomass burning smoke is often a significant source of airborne fine particles in regional areas where air quality monitoring is scarce. Emerging sensor technology provides opportunities to monitor air quality on a much larger geographical scale with much finer spatial resolution. It can also engage communities in the conversation around local pollution sources. The SMoke Observation Gadget (SMOG), a unit with a Plantower dust sensor PMS3003, was designed as part of a school-based Science, Technology, Engineering and Mathematics (STEM) project looking at smoke impacts in regional areas of Victoria, Australia. A smoke-specific calibration curve between the SMOG units and a standard regulatory instrument was developed using an hourly data set collected during a peat fire. The calibration curve was applied to the SMOG units during all field-based validation measurements at several locations and during different seasons. The results showed strong associations between individual SMOG units for PM2.5 concentrations (r2 = 0.93–0.99) and good accuracy (mean absolute error (MAE) < 2 μg m−3). Correlations of the SMOG units to reference instruments also demonstrated strong associations (r2 = 0.87–95) and good accuracy (MAE of 2.5–3.0 μg m−3). The PM2.5 concentrations tracked by the SMOG units had a similar response time as those measured by collocated reference instruments. Overall, the study has shown that the SMOG units provide relevant information about ambient PM2.5 concentrations in an airshed impacted predominantly by biomass burning, provided that an adequate adjustment factor is applied.
A Smart Capacitive Sensor Skin with Embedded Data Quality Indication for Enhanced Safety in Human–Robot Interaction
Authors: Scholl Christoph , Tobola Andreas , Ludwig Klaus , Zanca Dario , Eskofier Bjoern M.
Published: November 2021

Smart sensors are an integral part of the Fourth Industrial Revolution and are widely used to add safety measures to human–robot interaction applications. With the advancement of machine learning methods in resource-constrained environments, smart sensor systems have become increasingly powerful. As more data-driven approaches are deployed on the sensors, it is of growing importance to monitor data quality at all times of system operation. We introduce a smart capacitive sensor system with an embedded data quality monitoring algorithm to enhance the safety of human–robot interaction scenarios. The smart capacitive skin sensor is capable of detecting the distance and angle of objects nearby by utilizing consumer-grade sensor electronics. To further acknowledge the safety aspect of the sensor, a dedicated layer to monitor data quality in real-time is added to the embedded software of the sensor. Two learning algorithms are used to implement the sensor functionality: (1) a fully connected neural network to infer the position and angle of objects nearby and (2) a one-class SVM to account for the data quality assessment based on out-of-distribution detection. We show that the sensor performs well under normal operating conditions within a range of 200 mm and also detects abnormal operating conditions in terms of poor data quality successfully. A mean absolute distance error of 11.6mm was achieved without data quality indication. The overall performance of the sensor system could be further improved to 7.5mm by monitoring the data quality, adding an additional layer of safety for human–robot interaction.
Intelligence-Aware Batch Processing for TMA with Bearings-Only Measurements
Authors: Oliva Gabriele , Farina Alfonso , Setola Roberto
Published: November 2021

This paper develops a framework to track the trajectory of a target in 2D by considering a moving ownship able to measure bearing measurements. Notably, the framework allows one to incorporate additional information (e.g., obtained via intelligence) such as knowledge on the fact the target’s trajectory is contained in the intersection of some sets or the fact it lies outside the union of other sets. The approach is formally characterized by providing a constrained maximum likelihood estimation (MLE) formulation and by extending the definition of the Cramér–Rao lower bound (CRLB) matrix to the case of MLE problems with inequality constraints, relying on the concept of generalized Jacobian matrix. Moreover, based on the additional information, the ownship motion is chosen by mimicking the Artificial Potential Fields technique that is typically used by mobile robots to aim at a goal (in this case, the region where the target is assumed to be) while avoiding obstacles (i.e., the region that is assumed not to intersect the target’s trajectory). In order to show the effectiveness of the proposed approach, the paper is complemented by a simulation campaign where the MLE computations are carried out via an evolutionary ant colony optimization software, namely, mixed-integer distributed ant colony optimization solver (MIDACO-SOLVER). As a result, the proposed framework exhibits remarkably better performance, and in particular, we observe that the solution is less likely to remain stuck in unsatisfactory local minima during the MLE computation.
Simulation of the Transmission Spectrum of Long-Period Fiber Gratings Structures with a Propagating Acoustic Shock Front
Authors: Ivanov Oleg V. , Caldas Paulo , Rego Gaspar
Published: November 2021

In this paper, we investigate modification of transmission spectra of long-period fiber grating structures with an acoustic shock front propagating along the fiber. We simulate transmission through inhomogeneous long-period fiber gratings, π-shift and reflective π-shift gratings deformed by an acoustic shock front. Coupled mode equations describing interaction of co-propagating modes in a long-period fiber grating structures with inhomogeneous deformation are used for the simulation. Two types of apodization are considered for the grating modulation amplitude, such as uniform and raised-cosine. We demonstrate how the transmission spectrum is produced by interference between the core and cladding modes coupled at several parts of the gratings having different periods. For the π-shift long-period fiber grating having split spectral notch, the gap between the two dips becomes several times wider in the grating with the acoustic wave front than the gap in the unstrained grating. The behavior of reflective long-period fiber gratings depends on the magnitude of the phase shift near the reflective surface: an additional dip is formed in the 0-shift grating and the short-wavelength dip disappears in the π-shift grating.
A Method for Parameter Identification of Composite Beam Piezoelectric Energy Harvester
Authors: Zhang Xuhui , Zhang Chao , Wang Lin , Chen Luyang , Chen Xiaoyu , Xu Dongmei , Fan Hongwei , Zhu Fulin
Published: November 2021

This paper proposes a parameter identification method for the multiparameter identification study of the linear–arch composite beam piezoelectric energy harvester. According to the voltage response characteristics of the system under short-circuit conditions, the mechanical equation is solved by transient excitation, combined with the backbone curve theory and logarithmic attenuation method, to obtain the system’s linear damping, linear stiffness, and nonlinear stiffness. According to the voltage response characteristics of the system under open-circuit conditions, combined with the electrical equations, the system electromechanical coupling coefficient and equivalent capacitance coefficient are obtained; numerical simulation results show that the identification parameters have good accuracy. Finally, an experimental platform was built for verification, and the results show that the method has high accuracy and practicability.
Assessment of Stress in the Soil Surrounding the Axially Loaded Model Pile by Thin, Flexible Sensors
Authors: Żarkiewicz Krzysztof , Qatrameez Waleed
Published: November 2021

Foundation piles transfer the applied vertical load to the surrounding soil by skin friction and base resistance. These two components induce stress in the soil. The load transfer is still not fully recognized, and some pile load tests analyses have raised many doubts. The present paper aimed to measure the stress levels during pile load tests in laboratory conditions. This research examined the possibilities of using thin, flexible sensors in measuring the stress in soil. Two sensors were used: tactile pressure sensor with mapping system and color film pressure sensors with digital analyzing. Calibration and preliminary tests of the sensors have been described. This calibration proved that this kind of sensor could measure the stress in the soil in laboratory conditions. The results of stress distribution in the soil, shown as pressure maps, have been presented. Significant stress changes were observed in pile load tests. Rough and smooth piles were compared in the analyses. Stress distribution was the result of simultaneous interaction of pile skin and base. The knowledge about stresses surrounding the pile allows us to carry out a deeper analysis of the pile–soil interaction.
Adaptive Multi-Channel Clustering in IEEE 802.11s Wireless Mesh Networks
Authors: Rethfeldt Michael , Brockmann Tim , Beichler Benjamin , Haubelt Christian , Timmermann Dirk
Published: November 2021

WLAN mesh networks are one of the key technologies for upcoming smart city applications and are characterized by a flexible and low-cost deployment. The standard amendment IEEE 802.11s introduces low-level mesh interoperability at the WLAN MAC layer. However, scalability limitations imposed by management traffic overhead, routing delays, medium contention, and interference are common issues in wireless mesh networks and also apply to IEEE 802.11s networks. Possible solutions proposed in the literature recommend a divide-and-conquer scheme that partitions the network into clusters and forms smaller collision and broadcast domains by assigning orthogonal channels. We present CHaChA (Clustering Heuristic and Channel Assignment), a distributed cross-layer approach for cluster formation and channel assignment that directly integrates the default IEEE 802.11s mesh protocol information and operating modes, retaining unrestricted compliance to the WLAN standard. Our concept proposes further mechanisms for dynamic cluster adaptation, including subsequent cluster joining, isolation and fault detection, and node roaming for cluster balancing. The practical performance of CHaChA is demonstrated in a real-world 802.11s testbed. We first investigate clustering reproducibility, duration, and communication overhead in static network scenarios of different sizes. We then validate our concepts for dynamic cluster adaptation, considering topology changes that are likely to occur during long-term network operation and maintenance.
A Range-Based Algorithm for Autonomous Navigation of an Aerial Drone to Approach and Follow a Herd of Cattle
Authors: Gnanasekera Manaram , Katupitiya Jay , Savkin Andrey V. , De Silva A.H.T. Eranga
Published: November 2021

This paper proposes an algorithm that will allow an autonomous aerial drone to approach and follow a steady or moving herd of cattle using only range measurements. The algorithm is also insensitive to the complexity of the herd’s movement and the measurement noise. Once arrived at the herd of cattle, the aerial drone can follow it to a desired destination. The primary motivation for the development of this algorithm is to use simple, inexpensive and robust sensing hence range sensors. The algorithm does not depend on the accuracy of the range measurements, rather the rate of change of range measurements. The proposed method is based on sliding mode control which provides robustness. A mathematical analysis, simulations and experimental results with a real aerial drone are presented to demonstrate the effectiveness of the proposed method.
New Stimulation Device to Drive Multiple Transverse Intrafascicular Electrodes and Achieve Highly Selective and Rich Neural Responses
Authors: Guiho Thomas , López-Álvarez Victor Manuel , Čvančara Paul , Hiairrassary Arthur , Andreu David , Stieglitz Thomas , Navarro Xavier , Guiraud David
Published: November 2021

Peripheral Nerve Stimulation (PNS) is a promising approach in functional restoration following neural impairments. Although it proves to be advantageous in the number of implantation sites provided compared with intramuscular or epimysial stimulation and the fact that it does not require daily placement, as is the case with surface electrodes, the further advancement of PNS paradigms is hampered by the limitation of spatial selectivity due to the current spread and variations of nerve physiology. New electrode designs such as the Transverse Intrafascicular Multichannel Electrode (TIME) were proposed to resolve this issue, but their use was limited by a lack of innovative multichannel stimulation devices. In this study, we introduce a new portable multichannel stimulator—called STIMEP—and implement different stimulation protocols in rats to test its versatility and unveil the potential of its combined use with TIME electrodes in rehabilitation protocols. We developed and tested various stimulation paradigms in a single fascicle and thereafter implanted two TIMEs. We also tested its stimulation using two different waveforms. The results highlighted the versatility of this new stimulation device and advocated for the parameterizing of a hyperpolarizing phase before depolarization as well as the use of small pulse widths when stimulating with multiple electrodes.
Differences in Physiological Signals Due to Age and Exercise Habits of Subjects during Cycling Exercise
Authors: Lin Szu-Yu , Jao Chi-Wen , Wang Po-Shan , Liou Michelle , Wu Jun-Liang , Chun Hsiao , Tseng Ching-Ting , Wu Yu-Te
Published: November 2021

Numerous studies indicated the physical benefits of regular exercise, but the neurophysiological mechanisms of regular exercise in elders were less investigated. We aimed to compare changes in brain activity during exercise in elderly people and in young adults with and without regular exercise habits. A total of 36 healthy young adults (M/F:18/18) and 35 healthy elderly adults (M/F:20/15) participated in this study. According to exercise habits, each age group were classified into regular and occasional exerciser groups. ECG, EEG, and EMG signals were recorded using V-AMP with a 1-kHz sampling rate. The participants were instructed to perform three 5-min bicycle rides with different exercise loads. The EEG spectral power of elders who exercised regularly revealed the strongest positive correlation with their exercise intensity by using Pearson correlation analysis. The results demonstrate that exercise-induced significant cortical activation in the elderly participants who exercised regularly, and most of the p-values are less than 0.001. No significant correlation was observed between spectral power and exercise intensity in the elders who exercised occasionally. The young participants who exercised regularly had greater cardiac and neurobiological efficiency. Our results may provide a new exercise therapy reference for adult groups with different exercise habits, especially for the elders.
Integrated Nanophotonic Waveguide-Based Devices for IR and Raman Gas Spectroscopy
Authors: Alberti Sebastián , Datta Anurup , Jágerská Jana
Published: November 2021

On-chip devices for absorption spectroscopy and Raman spectroscopy have been developing rapidly in the last few years, triggered by the growing availability of compact and affordable tunable lasers, detectors, and on-chip spectrometers. Material processing that is compatible with mass production has been proven to be capable of long low-loss waveguides of sophisticated designs, which are indispensable for high-light–analyte interactions. Sensitivity and selectivity have been further improved by the development of sorbent cladding. In this review, we discuss the latest advances and challenges in the field of waveguide-enhanced Raman spectroscopy (WERS) and waveguide infrared absorption spectroscopy (WIRAS). The development of integrated light sources and detectors toward miniaturization will be presented, together with the recent advances on waveguides and cladding to improve sensitivity. The latest reports on gas-sensing applications and main configurations for WERS and WIRAS will be described, and the most relevant figures of merit and limitations of different sensor realizations summarized.
Processing Complex Events in Fog-Based Internet of Things Systems for Smart Agriculture
Authors: da Costa Bezerra Sandy F. , Filho Airton S. M. , Delicato Flavia C. , da Rocha Atslands R.
Published: November 2021

The recent growth of the Internet of Things’ services and applications has increased data processing and storage requirements. The Edge computing concept aims to leverage the processing capabilities of the IoT and other devices placed at the edge of the network. One embodiment of this paradigm is Fog computing, which provides an intermediate and often hierarchical processing tier between the data sources and the remote Cloud. Among the major benefits of this concept, the end-to-end latency can be decreased, thus favoring time-sensitive applications. Moreover, the data traffic at the network core and the Cloud computing workload can be reduced. Combining the Fog computing paradigm with Complex Event Processing (CEP) and data fusion techniques has excellent potential for generating valuable knowledge and aiding decision-making processes in the Internet of Things’ systems. In this context, we propose a multi-tier complex event processing approach (sensor node, Fog, and Cloud) that promotes fast decision making and is based on information with 98% accuracy. The experiments show a reduction of 77% in the average time of sending messages in the network. In addition, we achieved a reduction of 82% in data traffic.
Efficient Strike Artifact Reduction Based on 3D-Morphological Structure Operators from Filtered Back-Projection PET Images
Authors: Chiu Chun-Yi , Huang Yung-Hui , Du Wei-Chang , Wang Chi-Yuan , Chen Huei-Yong , Shiu Yun-Shiuan , Lu Nan-Han , Chen Tai-Been
Published: November 2021

Positron emission tomography (PET) can provide functional images and identify abnormal metabolic regions of the whole-body to effectively detect tumor presence and distribution. The filtered back-projection (FBP) algorithm is one of the most common images reconstruction methods. However, it will generate strike artifacts on the reconstructed image and affect the clinical diagnosis of lesions. Past studies have shown reduction in strike artifacts and improvement in quality of images by two-dimensional morphological structure operators (2D-MSO). The morphological structure method merely processes the noise distribution of 2D space and never considers the noise distribution of 3D space. This study was designed to develop three-dimensional-morphological structure operators (3D MSO) for nuclear medicine imaging and effectively eliminating strike artifacts without reducing image quality. A parallel operation was also used to calculate the minimum background standard deviation of the images for three-dimensional morphological structure operators with the optimal response curve (3D-MSO/ORC). As a result of Jaszczak phantom and rat verification, 3D-MSO/ORC showed better denoising performance and image quality than the 2D-MSO method. Thus, 3D MSO/ORC with a 3 × 3 × 3 mask can reduce noise efficiently and provide stability in FBP images.
Automatic Detection of K-Complexes Using the Cohen Class Recursiveness and Reallocation Method and Deep Neural Networks with EEG Signals
Authors: Dumitrescu Catalin , Costea Ilona-Madalina , Cormos Angel-Ciprian , Semenescu Augustin
Published: November 2021

Evoked and spontaneous K-complexes are thought to be involved in sleep protection, but their role as biomarkers is still under debate. K-complexes have two major functions: first, they suppress cortical arousal in response to stimuli that the sleeping brain evaluates to avoid signaling danger; and second, they help strengthen memory. K-complexes also play an important role in the analysis of sleep quality, in the detection of diseases associated with sleep disorders, and as biomarkers for the detection of Alzheimer’s and Parkinson’s diseases. Detecting K-complexes is relatively difficult, as reliable methods of identifying this complex cannot be found in the literature. In this paper, we propose a new method for the automatic detection of K-complexes combining the method of recursion and reallocation of the Cohen class and the deep neural networks, obtaining a recursive strategy aimed at increasing the percentage of classification and reducing the computation time required to detect K-complexes by applying the proposed methods.