bluuebunny/crossref_metadata_2025 huggingface dataset:
Time-Frequency Reversion-Based Spectrum Analysis Method and Its Applications in Radar Imaging
Authors: Fu Jixiang , Xing Mengdao , Sun Guangcai
Published: February 2021

Spectrum analysis (SA) plays an important role in radar signal processing, especially in radar imaging algorithm design. Because it is usually hard to obtain the analytical expression of spectrum by the Fourier integral directly, principle of stationary phase (POSP)-based SA is applied to approximate this integral. However, POSP requires the phase of the signal to vary rapidly, which is not the case in circular synthetic aperture radar (SAR) and turntable inverse SAR (ISAR). To solve this problem, a new SA method based on time-frequency reversion (TFRSA) is proposed, which utilizes the relationship of the Fourier transform pairs and their corresponding signal phases. In addition, the connection between the imaging geometry and time-frequency relationship is also analyzed and utilized to help solve the time-frequency reversion. When the TFRSA is applied to the linear trajectory SAR, the obtained spectrum expression is the same as the result of POSP. When it is applied to ISAR, the spectrum expressions of near-field and far-field are derived and their difference is found to be position-independent. Based on this finding, an extended polar format algorithm (EPFA) for near-field ISAR imaging is proposed, which can solve the distortion and defocusing problems caused by traditional ISAR imaging algorithms. When it is applied to the circular SAR (CSAR), a new and efficient imaging method based on EPFA is proposed, which can solve the low efficiency problem of conventional BP-based CSAR imaging algorithms. The simulations and real data processing results are provided to validate the effectiveness of proposed method.
Earth Environmental Monitoring Using Multi-Temporal Synthetic Aperture Radar: A Critical Review of Selected Applications
Authors: Amitrano Donato , Di Martino Gerardo , Guida Raffaella , Iervolino Pasquale , Iodice Antonio , Papa Maria Nicolina , Riccio Daniele , Ruello Giuseppe
Published: February 2021

Microwave remote sensing has widely demonstrated its potential in the continuous monitoring of our rapidly changing planet. This review provides an overview of state-of-the-art methodologies for multi-temporal synthetic aperture radar change detection and its applications to biosphere and hydrosphere monitoring, with special focus on topics like forestry, water resources management in semi-arid environments and floods. The analyzed literature is categorized on the base of the approach adopted and the data exploited and discussed in light of the downstream remote sensing market. The purpose is to highlight the main issues and limitations preventing the diffusion of synthetic aperture radar data in both industrial and multidisciplinary research contexts and the possible solutions for boosting their usage among end-users.
A Modeling Approach for Predicting the Resolution Capability in Terrestrial Laser Scanning
Authors: Chaudhry Sukant , Salido-Monzú David , Wieser Andreas
Published: February 2021

The minimum size of objects or geometrical features that can be distinguished within a laser scanning point cloud is called the resolution capability (RC). Herein, we develop a simple analytical expression for predicting the RC in angular direction for phase-based laser scanners. We start from a numerical approximation of the mixed-pixel bias which occurs when the laser beam simultaneously hits surfaces at grossly different distances. In correspondence with previous literature, we view the RC as the minimum angular distance between points on the foreground and points on the background which are not (severely) affected by a mixed-pixel bias. We use an elliptical Gaussian beam for quantifying the effect. We show that the surface reflectivities and the distance step between foreground and background have generally little impact. Subsequently, we derive an approximation of the RC and extend it to include the selected scanning resolution, that is, angular increment. We verify our model by comparison to the resolution capabilities empirically determined by others. Our model requires parameters that can be taken from the data sheet of the scanner or approximated using a simple experiment. We describe this experiment herein and provide the required software on GitHub. Our approach is thus easily accessible, enables the prediction of the resolution capability with little effort and supports assessing the suitability of a specific scanner or of specific scanning parameters for a given application.
Analysis of the Snow Water Equivalent at the AEMet-Formigal Field Laboratory (Spanish Pyrenees) During the 2019/2020 Winter Season Using a Stepped-Frequency Continuous Wave Radar (SFCW)
Authors: Alonso Rafael , Pozo José María García del , Buisán Samuel T. , Álvarez José Adolfo
Published: February 2021

Snow makes a great contribution to the hydrological cycle in cold regions. The parameter to characterize available the water from the snow cover is the well-known snow water equivalent (SWE). This paper presents a near-surface-based radar for determining the SWE from the measured complex spectral reflectance of the snowpack. The method is based in a stepped-frequency continuous wave radar (SFCW), implemented in a coherent software defined radio (SDR), in the range from 150 MHz to 6 GHz. An electromagnetic model to solve the electromagnetic reflectance of a snowpack, including the frequency and wetness dependence of the complex relative dielectric permittivity of snow layers, is shown. Using the previous model, an approximated method to calculate the SWE is proposed. The results are presented and compared with those provided by a cosmic-ray neutron SWE gauge over the 2019–2020 winter in the experimental AEMet Formigal-Sarrios test site. This experimental field is located in the Spanish Pyrenees at an elevation of 1800 m a.s.l. The results suggest the viability of the approximate method. Finally, the feasibility of an auxiliary snow height measurement sensor based on a 120 GHz frequency modulated continuous wave (FMCW) radar sensor, is shown.
Combined Geometric Positioning and Performance Analysis of Multi-Resolution Optical Imageries from Satellite and Aerial Platforms Based on Weighted RFM Bundle Adjustment
Authors: Song Wenping , Liu Shijie , Tong Xiaohua , Niu Changling , Ye Zhen , Jin Yanmin
Published: February 2021

Combined geometric positioning using images with different resolutions and imaging sensors is being increasingly widely utilized in practical engineering applications. In this work, we attempt to perform the combined geometric positioning and performance analysis of multi-resolution optical images from satellite and aerial platforms based on weighted rational function model (RFM) bundle adjustment without using ground control points (GCPs). Firstly, we introduced an integrated image matching method combining least squares and phase correlation. Next, for bundle adjustment, a combined model of the geometric positioning based on weighted RFM bundle adjustment was derived, and a method for weight determination was given to make the weights of all image points variable. Finally, we conducted experiments using a case study in Shanghai with ZiYuan-3 (ZY-3) satellite imagery, GeoEye-1 satellite imagery, and Digital Mapping Camera (DMC) aerial imagery to validate the effectiveness of the proposed weighted method, and to investigate the positioning accuracy by using different combination scenarios of multi-resolution heterogeneous images. The experimental results indicate that the proposed weighted method is effective, and the positioning accuracy of different combination scenarios can give a good reference for the combined geometric positioning of multi-stereo heterogeneous images in future practical engineering applications.
Evaluation of IMERG Level-3 Products in Depicting the July to October Rainfall over Taiwan: Typhoon Versus Non-Typhoon
Authors: Huang Wan-Ru , Liu Pin-Yi , Chang Ya-Hui , Lee Cheng-An
Published: February 2021

This study assesses the performance of satellite precipitation products (SPPs) from the latest version, V06B, Integrated Multi-satellitE Retrievals for Global Precipitation Mission (IMERG) Level-3 (including early, late, and final runs), in depicting the characteristics of typhoon season (July to October) rainfall over Taiwan within the period of 2000–2018. The early and late runs are near-real-time SPPs, while final run is post-real-time SPP adjusted by monthly rain gauge data. The latency of early, late, and final runs is approximately 4 h, 14 h, and 3.5 months, respectively, after the observation. Analyses focus on the seasonal mean, daily variation, and interannual variation of typhoon-related (TC) and non-typhoon-related (non-TC) rainfall. Using local rain-gauge observations as a reference for evaluation, our results show that all IMERG products capture the spatio-temporal variations of TC rainfall better than those of non-TC rainfall. Among SPPs, the final run performs better than the late run, which is slightly better than the early run for most of the features assessed for both TC and non-TC rainfall. Despite these differences, all IMERG products outperform the frequently used Tropical Rainfall Measuring Mission 3B42 v7 (TRMM7) for the illustration of the spatio-temporal characteristics of TC rainfall in Taiwan. In contrast, for the non-TC rainfall, the final run performs notably better relative to TRMM7, while the early and late runs showed only slight improvement. These findings highlight the advantages and disadvantages of using IMERG products for studying or monitoring typhoon season rainfall in Taiwan.
The Regenerative Potential of Managed Calluna Heathlands—Revealing Optical and Structural Traits for Predicting Recovery Dynamics
Authors: Neumann Carsten , Schindhelm Anne , Müller Jörg , Weiss Gabriele , Liu Anna , Itzerott Sibylle
Published: February 2021

The potential of vegetation recovery through resprouting of plant tissue from buds after the removal of aboveground biomass is a key resilience strategy for populations under abrupt environmental change. Resprouting leads to fast regeneration, particularly after the implementation of mechanical mowing as part of active management for promoting open habitats. We investigated whether recovery dynamics of resprouting and the threat of habitat conversion can be predicted by optical and structural stand traits derived from drone imagery in a protected heathland area. We conducted multivariate regression for variable selection and random forest regression for predictive modeling using 50 spectral predictors, textural features and height parameters to quantify Calluna resprouting and grass invasion in before-mowing images that were related to vegetation recovery in after-mowing imagery. The study reveals that Calluna resprouting can be explained by significant optical predictors of mainly green reflectance in parental individuals. In contrast, grass encroachment is identified by structural canopy properties that indicate before-mowing grass interpenetration as starting points for after-mowing dispersal. We prove the concept of trait propagation through time providing significant derivates for a low-cost drone system. It can be utilized to build drone-based decision support systems for evaluating consequences and requirements of habitat management practice.
Online 3-Dimensional Path Planning with Kinematic Constraints in Unknown Environments Using Hybrid A* with Tree Pruning
Authors: Scharff Willners Jonatan , Gonzalez-Adell Daniel , Hernández Juan David , Pairet Èric , Petillot Yvan
Published: February 2021

In this paper we present an extension to the hybrid A* (HA*) path planner. This extension allows autonomous underwater vehicle (AUVs) to plan paths in 3-dimensional (3D) environments. The proposed approach enables the robot to operate in a safe manner by accounting for the vehicle’s motion constraints, thus avoiding collisions and ensuring that the calculated paths are feasible. Secondly, we propose an improvement for operations in unexplored or partially known environments by endowing the planner with a tree pruning procedure, which maintains a valid and feasible search-tree during operation. When the robot senses new obstacles in the environment that invalidate its current path, the planner prunes the tree of branches which collides with the environment. The path planning algorithm is then initialised with the pruned tree, enabling it to find a solution in a lower time than replanning from scratch. We present results obtained through simulation which show that HA* performs better in known underwater environments than compared algorithms in regards to planning time, path length and success rate. For unknown environments, we show that the tree pruning procedure reduces the total planning time needed in a variety of environments compared to running the full planning algorithm during replanning.
Machine Learning and Simulation-Optimization Coupling for Water Distribution Network Contamination Source Detection
Authors: Grbčić Luka , Kranjčević Lado , Družeta Siniša
Published: February 2021

This paper presents and explores a novel methodology for solving the problem of a water distribution network contamination event, which includes determining the exact source of contamination, the contamination start and end times and the injected contaminant concentration. The methodology is based on coupling a machine learning algorithm for predicting the most probable contamination sources in a water distribution network with an optimization algorithm for determining the values of contamination start time, end time and injected contaminant concentration for each predicted node separately. Two slightly different algorithmic frameworks were constructed which are based on the mentioned methodology. Both algorithmic frameworks utilize the Random Forest algorithm for classification of top source contamination node candidates, with one of the frameworks directly using the stochastic fireworks optimization algorithm to determine the contamination start time, end time and injected contaminant concentration for each predicted node separately. The second framework uses the Random Forest algorithm for an additional regression prediction of each top node’s start time, end time and contaminant concentration and is then coupled with the deterministic global search optimization algorithm MADS. Both a small sized (92 potential sources) network with perfect sensor measurements and a medium sized (865 potential sources) benchmark network with fuzzy sensor measurements were used to explore the proposed frameworks. Both algorithmic frameworks perform well and show robustness in determining the true source node, start and end times and contaminant concentration, with the second framework being extremely efficient on the fuzzy sensor measurement benchmark network.
Training with Agency-Inspired Feedback from an Instrumented Glove to Improve Functional Grasp Performance
Authors: Liu Mingxiao , Wilder Samuel , Sanford Sean , Saleh Soha , Harel Noam Y. , Nataraj Raviraj
Published: February 2021

Sensory feedback from wearables can be effective to learn better movement through enhanced information and engagement. Facilitating greater user cognition during movement practice is critical to accelerate gains in motor function during rehabilitation following brain or spinal cord trauma. This preliminary study presents an approach using an instrumented glove to leverage sense of agency, or perception of control, to provide training feedback for functional grasp. Seventeen able-bodied subjects underwent training and testing with a custom-built sensor glove prototype from our laboratory. The glove utilizes onboard force and flex sensors to provide inputs to an artificial neural network that predicts achievement of “secure” grasp. Onboard visual and audio feedback was provided during training with progressively shorter time delay to induce greater agency by intentional binding, or perceived compression in time between an action (grasp) and sensory consequence (feedback). After training, subjects demonstrated a significant reduction (p < 0.05) in movement pathlength and completion time for a functional task involving grasp-move-place of a small object. Future work will include a model-based algorithm to compute secure grasp, virtual reality immersion, and testing with clinical populations.
The New Hyperspectral Satellite PRISMA: Imagery for Forest Types Discrimination
Authors: Vangi Elia , D’Amico Giovanni , Francini Saverio , Giannetti Francesca , Lasserre Bruno , Marchetti Marco , Chirici Gherardo
Published: February 2021

Different forest types based on different tree species composition may have similar spectral signatures if observed with traditional multispectral satellite sensors. Hyperspectral imagery, with a more continuous representation of their spectral behavior may instead be used for their classification. The new hyperspectral Precursore IperSpettrale della Missione Applicativa (PRISMA) sensor, developed by the Italian Space Agency, is able to capture images in a continuum of 240 spectral bands ranging between 400 and 2500 nm, with a spectral resolution smaller than 12 nm. The new sensor can be employed for a large number of remote sensing applications, including forest types discrimination. In this study, we compared the capabilities of the new PRISMA sensor against the well-known Sentinel-2 Multi-Spectral Instrument (MSI) in recognition of different forest types through a pairwise separability analysis carried out in two study areas in Italy, using two different nomenclature systems and four separability metrics. The PRISMA hyperspectral sensor, compared to Sentinel-2 MSI, allowed for a better discrimination in all forest types, increasing the performance when the complexity of the nomenclature system also increased. PRISMA achieved an average improvement of 40% for the discrimination between two forest categories (coniferous vs. broadleaves) and of 102% in the discrimination between five forest types based on main tree species groups.
Suitability and Comparison of Questionnaires Assessing Virtual Reality-Induced Symptoms and Effects and User Experience in Virtual Environments
Authors: Somrak Andrej , Pogačnik Matevž , Guna Jože
Published: February 2021

Although virtual reality (VR) has already achieved technological maturity, there are still some significant drawbacks for technology acceptance and broader user adoption, presenting research challenges. Thus, there is a need for standard, reliable, and quick assessment tools for Virtual Reality-Induced Symptoms and Effects (VRISE) and user experience in VR Assessing VRISE and user experience could be time consuming, especially when using objective physiological measures. In this study, we have reviewed, compared, and performed a suitability assessment of existing standard measures for evaluating VRISE and user experience in VR We have developed a first-person VR game with different scenes and different conditions. For assessing VRISE symptoms, we have used the Simulator Sickness Questionnaire (SSQ) and Fast Motion Sickness Score (FMS). For assessing user experience, we have used the short version of the User Experience Questionnaire (UEQ-S). We have also used a novel Virtual Reality Neuroscience Questionnaire (VRNQ) for assessing VRISE and user experience aspects. The result has shown that FMS and VRNQ (VRISE section) are suitable for quick assessment of VRISE and that VRNQ (User experience section) is suitable for assessing user experience. The advantage of FMS and VRNQ questionnaires is that they are shorter to fulfill and easier to understand. FMS also enables to record the VRISE levels during the virtual experience and thus capturing its trend over time. Another advantage of the VRNQ is that it also provides the minimum and parsimonious cut-offs to appraise the suitability of VR software, which we have confirmed in our study to be adequate.
Detail-Preserving Shape Unfolding
Authors: Liu Bin , Wang Weiming , Zhou Jun , Li Bo , Liu Xiuping
Published: February 2021

Canonical extrinsic representations for non-rigid shapes with different poses are preferable in many computer graphics applications, such as shape correspondence and retrieval. The main reason for this is that they give a pose invariant signature for those jobs, which significantly decreases the difficulty caused by various poses. Existing methods based on multidimentional scaling (MDS) always result in significant geometric distortions. In this paper, we present a novel shape unfolding algorithm, which deforms any given 3D shape into a canonical pose that is invariant to non-rigid transformations. The proposed method can effectively preserve the local structure of a given 3D model with the regularization of local rigid transform energy based on the shape deformation technique, and largely reduce geometric distortion. Our algorithm is quite simple and only needs to solve two linear systems during alternate iteration processes. The computational efficiency of our method can be improved with parallel computation and the robustness is guaranteed with a cascade strategy. Experimental results demonstrate the enhanced efficacy of our algorithm compared with the state-of-the-art methods on 3D shape unfolding.
An Infrared-Visible Image Registration Method Based on the Constrained Point Feature
Authors: Li Qingqing , Han Guangliang , Liu Peixun , Yang Hang , Luo Huiyuan , Wu Jiajia
Published: February 2021

It is difficult to find correct correspondences for infrared and visible image registration because of different imaging principles. Traditional registration methods based on the point feature require designing the complicated feature descriptor and eliminate mismatched points, which results in unsatisfactory precision and much calculation time. To tackle these problems, this paper presents an artful method based on constrained point features to align infrared and visible images. The proposed method principally contains three steps. First, constrained point features are extracted by employing an object detection algorithm, which avoids constructing the complex feature descriptor and introduces the senior semantic information to improve the registration accuracy. Then, the left value rule (LV-rule) is designed to match constrained points strictly without the deletion of mismatched and redundant points. Finally, the affine transformation matrix is calculated according to matched point pairs. Moreover, this paper presents an evaluation method to automatically estimate registration accuracy. The proposed method is tested on a public dataset. Among all tested infrared-visible image pairs, registration results demonstrate that the proposed framework outperforms five state-of-the-art registration algorithms in terms of accuracy, speed, and robustness.
A Low-Cost NDIR-Based N2O Gas Detection Device for Agricultural Soils: Assembly, Calibration Model Validation, and Laboratory Testing
Authors: Bandara K.M.T.S. , Sakai Kazuhito , Nakandakari Tamotsu , Yuge Kozue
Published: February 2021

This research presents a low-cost, easy-to-assemble nondispersive infrared (NDIR) device for monitoring N2O gas concentration in agricultural soils during field and laboratory experiments. The study aimed to develop a cost-effective instrument with a simple optic structure suitable for detecting a wide range of soil N2O gas concentrations with a submerged silicone diffusion cell. A commercially available, 59 cm path-length gas cell, microelectromechanical systems (MEMS)-based infrared emitter, pyroelectric detector, two anti-reflective (AR) coated optical windows, and one convex lens were assembled into a simple instrument with secure preciseness and responsivity. Control of the IR emitter and data recording processes was achieved through a microcontroller unit (MCU). Tests on humidity tolerance and the saturation rate of the diffusion cell were carried out to test the instrument function with the soil atmosphere. The developed calibration model was validated by repeatability tests and accuracy tests. The soil N2O gas concentration was monitored at the laboratory level by a specific experimental setup. The coefficient of determination (R2) of the repeatability tests was more than 0.9995 with a 1–2000 ppm measurability range and no impact of air humidity on the device output. The new device achieved continuous measuring of soil N2O gas through a submerged diffusion cell.
Two-Color Infrared Sensor on the PbTe: In p-n Junction
Authors: Gradauskas Jonas , Dzundza Bohdan , Chernyak Leonid , Dashevsky Zinovy
Published: February 2021

A lead telluride sensor was fabricated on the base of a p-n PbTe junction created on a PbTe single crystal grown by the Czochralski technique, followed by the diffusion of an indium donor impurity into a crystal. The capacitance-voltage and current-voltage characteristics of the sensor were measured over the temperature range from 80 K to 150 K. A prototype of a high-temperature mid-IR sensor, a PbTe diode, with a cut-off wavelength of 4 μm, operating at temperatures up to 150 K, was demonstrated for the first time. The advantage of the sensor is that its operating temperature is high enough to be reached by a solid-state thermoelectric cooler. The sensor showed a specific detectivity value of 1010 cm Hz1/2/W at a temperature of 150 K and a wavelength of 4.2 μm. The possibility to sense pulses of long-IR radiation by means of the PbTe diode was also demonstrated over the 100–180 K temperature range. For the first time, a two-photon absorption-caused photovoltaic effect was observed in PbTe at a wavelength of 9.5 μm at 150 K.
Evaluation of Optical and Radar Based Motion Capturing Technologies for Characterizing Hand Movement in Rheumatoid Arthritis—A Pilot Study
Authors: Phutane Uday , Liphardt Anna-Maria , Bräunig Johanna , Penner Johann , Klebl Michael , Tascilar Koray , Vossiek Martin , Kleyer Arnd , Schett Georg , Leyendecker Sigrid
Published: February 2021

In light of the state-of-the-art treatment options for patients with rheumatoid arthritis (RA), a detailed and early quantification and detection of impaired hand function is desirable to allow personalized treatment regiments and amend currently used subjective patient reported outcome measures. This is the motivation to apply and adapt modern measurement technologies to quantify, assess and analyze human hand movement using a marker-based optoelectronic measurement system (OMS), which has been widely used to measure human motion. We complement these recordings with data from markerless (Doppler radar) sensors and data from both sensor technologies are integrated with clinical outcomes of hand function. The technologies are leveraged to identify hand movement characteristics in RA affected patients in comparison to healthy control subjects, while performing functional tests, such as the Moberg-Picking-Up Test. The results presented discuss the experimental framework and present the limiting factors imposed by the use of marker-based measurements on hand function. The comparison of simple finger motion data, collected by the OMS, to data recorded by a simple continuous wave radar suggests that radar is a promising option for the objective assessment of hand function. Overall, the broad scope of integrating two measurement technologies with traditional clinical tests shows promising potential for developing new pathways in understanding of the role of functional outcomes for the RA pathology.
Natural Frequencies Identification by FEM Applied to a 2-DOF Planar Robot and Its Validation Using MUSIC Algorithm
Authors: Martínez-Cruz Salvador , Amézquita-Sánchez Juan P. , Pérez-Soto Gerardo I. , Rivera-Guillén Jesús R. , Morales-Hernández Luis A. , Camarillo-Gómez Karla A.
Published: February 2021

In this paper, the natural frequencies (NFs) identification by finite element method (FEM) is applied to a two degrees-of-freedom (2-DOF) planar robot, and its validation through a novel experimental methodology, the Multiple Signal Classification (MUSIC) algorithm, is presented. The experimental platforms are two different 2-DOF planar robots with different materials for the links and different types of actuators. The FEM is carried out using ANSYS™ software for the experiments, with vibration signal analysis by MUSIC algorithm. The advantages of the MUSIC algorithm against the commonly used fast Fourier transform (FFT) method are also presented for a synthetic signal contaminated by three different noise levels. The analytical and experimental results show that the proposed methodology identifies the NFs of a high-resolution robot even when they are very closed and when the signal is embedded in high-level noise. Furthermore, the results show that the proposed methodology can obtain a high-frequency resolution with a short sample data set. Identifying the NFs of robots is useful for avoiding such frequencies in the path planning and in the selection of controller gains that establish the bandwidth.
Calculating the Effect of Ribs on the Focus Quality of a Therapeutic Spherical Random Phased Array
Authors: Zubair Muhammad , Dickinson Robert
Published: February 2021

The overlaying rib cage is a major hindrance in treating liver tumors with high intensity focused ultrasound (HIFU). The problems caused are overheating of the ribs due to its high ultrasonic absorption capability and degradation of the ultrasound intensity distribution in the target plane. In this work, a correction method based on binarized apodization and geometric ray tracing approach was employed to avoid heating the ribs. A detailed calculation of the intensity distribution in the focus plane was undertaken to quantify and avoid the effect on HIFU beam generated by a 1-MHz 256-element random phased array after the ultrasonic beam passes through the rib cage. Focusing through the ribs was simulated for 18 different idealized ribs-array configurations and 10 anatomically correct ribs-array configurations, to show the effect of width of the ribs, intercostal spacing and the relative position of ribs and array on the quality of focus, and to identify the positions that are more effective for HIFU applications in the presence of ribs. Acoustic simulations showed that for a single focus without beam steering and for the same total acoustic power, the peak intensity at the target varies from a minimum of 211 W/cm2 to a maximum of 293 W/cm2 for a nominal acoustic input power of 15 W, whereas the side lobe level varies from 0.07 Ipeak to 0.28 Ipeak and the separation between the main lobe and side lobes varies from 2.5 mm to 6.3 mm, depending on the relative positioning of the array and ribs and the beam alignment. An increase in the side lobe level was observed by increasing the distance between the array and the ribs. The parameters of focus splitting and the deterioration of focus quality caused by the ultrasonic propagation through the ribs were quantified in various possible different clinical scenarios. In addition to idealized rib topology, anatomical realistic ribs were used to determine the focus quality of the HIFU beam when the beam is steered both in axial and transverse directions and when the transducer is positioned at different depths from the rib cage.
Machine Learning Algorithms for Activity-Intensity Recognition Using Accelerometer Data
Authors: Gomes Eduardo , Bertini Luciano , Campos Wagner Rangel , Sobral Ana Paula , Mocaiber Izabela , Copetti Alessandro
Published: February 2021

In pervasive healthcare monitoring, activity recognition is critical information for adequate management of the patient. Despite the great number of studies on this topic, a contextually relevant parameter that has received less attention is intensity recognition. In the present study, we investigated the potential advantage of coupling activity and intensity, namely, Activity-Intensity, in accelerometer data to improve the description of daily activities of individuals. We further tested two alternatives for supervised classification. In the first alternative, the activity and intensity are inferred together by applying a single classifier algorithm. In the other alternative, the activity and intensity are classified separately. In both cases, the algorithms used for classification are k-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF). The results showed the viability of the classification with good accuracy for Activity-Intensity recognition. The best approach was KNN implemented in the single classifier alternative, which resulted in 79% of accuracy. Using two classifiers, the result was 97% accuracy for activity recognition (Random Forest), and 80% for intensity recognition (KNN), which resulted in 78% for activity-intensity coupled. These findings have potential applications to improve the contextualized evaluation of movement by health professionals in the form of a decision system with expert rules.
Transrectal Ultrasound and Photoacoustic Imaging Probe for Diagnosis of Prostate Cancer
Authors: Jang Jihun , Kim Jinwoo , Lee Hak Jong , Chang Jin Ho
Published: February 2021

A combined transrectal ultrasound and photoacoustic (TRUS–PA) imaging probe was developed for the clear visualization of morphological changes and microvasculature distribution in the prostate, as this is required for accurate diagnosis and biopsy. The probe consisted of a miniaturized 128-element 7 MHz convex array transducer with 134.5° field-of-view (FOV), a bifurcated optical fiber bundle, and two optical lenses. The design goal was to make the size of the TRUS–PA probe similar to that of general TRUS probes (i.e., about 20 mm), for the convenience of the patients. New flexible printed circuit board (FPCB), acoustic structure, and optical lens were developed to meet the requirement of the probe size, as well as to realize a high-performance TRUS–PA probe. In visual assessment, the PA signals obtained with the optical lens were 2.98 times higher than those without the lens. Moreover, the in vivo experiment with the xenograft BALB/c (Albino, Immunodeficient Inbred Strain) mouse model showed that TRUS–PA probe was able to acquire the entire PA image of the mouse tight behind the porcine intestine about 25 mm depth. From the ex vivo and in vivo experimental results, it can be concluded that the developed TRUS–PA probe is capable of improving PA image quality, even though the TRUS–PA probe has a cross-section size and an FOV comparable to those of general TRUS probes.
An Optimal Routing Algorithm for Unmanned Aerial Vehicles
Authors: Kim Sooyeon , Kwak Jae Hyun , Oh Byoungryul , Lee Da-Han , Lee Duehee
Published: February 2021

A delivery service using unmanned aerial vehicles (UAVs) has potential as a future business opportunity, due to its speed, safety and low-environmental impact. To operate a UAV delivery network, a management system is required to optimize UAV delivery routes. Therefore, we create a routing algorithm to find optimal round-trip routes for UAVs, which deliver goods from depots to customers. Optimal routes per UAV are determined by minimizing delivery distances considering the maximum range and loading capacity of the UAV. In order to accomplish this, we propose an algorithm with four steps. First, we build a virtual network to describe the realistic environment that UAVs would encounter during operation. Second, we determine the optimal number of in-service UAVs per depot. Third, we eliminate subtours, which are infeasible routes, using flow variables part of the constraints. Fourth, we allocate UAVs to customers minimizing delivery distances from depots to customers. In this process, we allow multiple UAVs to deliver goods to one customer at the same time. Finally, we verify that our algorithm can determine the number of UAVs in service per depot, round-trip routes for UAVs, and allocate UAVs to customers to deliver at the minimum cost.
Intertwined Carbon Nanotubes and Ag Nanowires Constructed by Simple Solution Blending as Sensitive and Stable Chloramphenicol Sensors
Authors: Zhu Yangguang , Li Xiufen , Xu Yuting , Wu Lidong , Yu Aimin , Lai Guosong , Wei Qiuping , Chi Hai , Jiang Nan , Fu Li , Ye Chen , Lin Cheng-Te
Published: February 2021

Chloramphenicol (CAP) is a harmful compound associated with human hematopathy and neuritis, which was widely used as a broad-spectrum antibacterial agent in agriculture and aquaculture. Therefore, it is significant to detect CAP in aquatic environments. In this work, carbon nanotubes/silver nanowires (CNTs/AgNWs) composite electrodes were fabricated as the CAP sensor. Distinguished from in situ growing or chemical bonding noble metal nanomaterials on carbon, this CNTs/AgNWs composite was formed by simple solution blending. It was demonstrated that CNTs and AgNWs both contributed to the redox reaction of CAP in dynamics, and AgNWs was beneficial in thermodynamics as well. The proposed electrochemical sensor displayed a low detection limit of up to 0.08 μM and broad linear range of 0.1–100 μM for CAP. In addition, the CNTs/AgNWs electrodes exhibited good performance characteristics of repeatability and reproducibility, and proved suitable for CAP analysis in real water samples.
Depolarization Characteristics of Different Reflective Interfaces Indicated by Indices of Polarimetric Purity (IPPs)
Authors: Li Dekui , Guo Kai , Sun Yongxuan , Bi Xiang , Gao Jun , Guo Zhongyi
Published: February 2021

Compared with the standard depolarization index, indices of polarimetric purity (IPPs) have better performances to describe depolarization characteristics of targets with different roughnesses of interfaces under different incident angles, which allow us a further analysis of the depolarizing properties of samples. Here, we use IPPs obtained from different reflective interfaces as a criterion of depolarization property to characterize and classify targets covered by organic paint layers with different roughness. We select point-light source as radiation source with wavelength as 632.8 nm, and four samples, including Cu, Au, Al and Al2O3, covered by an organic paint layer with refractive index of n = 1.46 and Gaussian roughness of α = 0.05~0.25. Under different incident angles, the values of P1, P2, P3 at divided 90 × 360 grid points and their mean values in upper hemisphere have been obtained and discussed in the IPPs space. The results show that the depolarization performances of the different reflective interfaces (materials, incident angles and surface roughness) are unique in IPPs space, providing us with a new avenue to analyze and characterize different targets.
Recent Advances in Quenchbody, a Fluorescent Immunosensor
Authors: Dong Jinhua , Ueda Hiroshi
Published: February 2021

The detection of viruses, disease biomarkers, physiologically active substances, drugs, and chemicals is of great significance in many areas of our lives. Immunodetection technology is based on the specificity and affinity of antigen–antibody reactions. Compared with other analytical methods such as liquid chromatography coupled with mass spectrometry, which requires a large and expensive instrument, immunodetection has the advantages of simplicity and good selectivity and is thus widely used in disease diagnosis and food/environmental monitoring. Quenchbody (Q-body), a new type of fluorescent immunosensor, is an antibody fragment labeled with fluorescent dyes. When the Q-body binds to its antigen, the fluorescence intensity increases. The detection of antigens by changes in fluorescence intensity is simple, easy to operate, and highly sensitive. This review comprehensively discusses the principle, construction, application, and current progress related to Q-bodies.
Mobile Deep Learning System That Calculates UVI Using Illuminance Value of User’s Location
Authors: Oh Seung-Taek , Ga Deog-Hyeon , Lim Jae-Hyun
Published: February 2021

Ultraviolet rays are closely related with human health and, recently, optimum exposure to the UV rays has been recommended, with growing importance being placed on correct UV information. However, many countries provide UV information services at a local level, which makes it impossible for individuals to acquire user-based, accurate UV information unless individuals operate UV measurement devices with expertise on the relevant field for interpretation of the measurement results. There is a limit in measuring ultraviolet rays’ information by the users at their respective locations. Research about how to utilize mobile devices such as smartphones to overcome such limitation is also lacking. This paper proposes a mobile deep learning system that calculates UVI based on the illuminance values at the user’s location obtained with mobile devices’ help. The proposed method analyzed the correlation between illuminance and UVI based on the natural light DB collected through the actual measurements, and the deep learning model’s data set was extracted. After the selection of the input variables to calculate the correct UVI, the deep learning model based on the TensorFlow set with the optimum number of layers and number of nodes was designed and implemented, and learning was executed via the data set. After the data set was converted to the mobile deep learning model to operate under the mobile environment, the converted data were loaded on the mobile device. The proposed method enabled providing UV information at the user’s location through a mobile device on which the illuminance sensors were loaded even in the environment without UVI measuring equipment. The comparison of the experiment results with the reference device (spectrometer) proved that the proposed method could provide UV information with an accuracy of 90–95% in the summers, as well as in winters.
Text Detection Using Multi-Stage Region Proposal Network Sensitive to Text Scale
Authors: Nagaoka Yoshito , Miyazaki Tomo , Sugaya Yoshihiro , Omachi Shinichiro
Published: February 2021

Recently, attention has surged concerning intelligent sensors using text detection. However, there are challenges in detecting small texts. To solve this problem, we propose a novel text detection CNN (convolutional neural network) architecture sensitive to text scale. We extract multi-resolution feature maps in multi-stage convolution layers that have been employed to prevent losing information and maintain the feature size. In addition, we developed the CNN considering the receptive field size to generate proposal stages. The experimental results show the importance of the receptive field size.
An IoT-Based Life Cycle Assessment Platform of Wind Turbines
Authors: An Jinjing , Zou Zhuo , Chen Guoping , Sun Yaojie , Liu Ran , Zheng Lirong
Published: February 2021

Life cycle assessment (LCA) is conducive to the change in the wind power industry management model and is beneficial to the green design of products. Nowadays, none of the LCA systems are for wind turbines and the concept of Internet of Things (IoT) in LCA is quite a new idea. In this paper, a four-layer LCA platform of wind turbines based on IoT architecture is designed and discussed. In the data transmission layer, intelligent sensing of wind turbines can be achieved and their status and location can be monitored. In the data transmission layer, the LCA platform can be effectively integrated with enterprise information systems through the object name service (ONS) and directory service (DS). In the platform layer, a model based on IMPACT 2002+ is developed, and four management modules are designed. In the application layer, different from other systems, energy payback time (EPBT) is selected as an important evaluation index for wind turbines. Compared with the existing LCA systems, the proposed system is specifically for wind turbines and can collect data in real-time, leading to improved accuracy and response time.
Anti-Trafficking in the Time of FOSTA/SESTA: Networked Moral Gentrification and Sexual Humanitarian Creep
Authors: Musto Jennifer , Fehrenbacher Anne E. , Hoefinger Heidi , Mai Nicola , Macioti P. G. , Bennachie Calum , Giametta Calogero , D’Adamo Kate
Published: February 2021

Globally, sex workers have highlighted the harms that accompany anti-prostitution efforts advanced via anti-trafficking policy, and there is a growing body of social science research that has emerged documenting how anti-trafficking efforts contribute to carceral and sexual humanitarian interventions. Yet mounting evidence on the harms of anti-trafficking policies has done little to quell the passage of more laws, including policies aimed at stopping sexual exploitation facilitated by technology. The 2018 passage of the Allow States and Victims to Fight Online Sex Trafficking Act (FOSTA) in the U.S. House of Representatives, and the corresponding Senate bill, the Stop Enabling Sex Traffickers Act (SESTA), is a case study in how efforts to curb sexual exploitation online actually heighten vulnerabilities for the people they purport to protect. Drawing on 34 months of ethnographic fieldwork and interviews with sex workers and trafficked persons (n = 58) and key informants (n = 20) in New York and Los Angeles, we analyze FOSTA/SESTA and its harmful effects as a launchpad to more broadly explore how technology, criminalization, shifting governance arrangements, and conservative moralities cohere to exacerbate sex workers’ vulnerability.
Training Elites and Structuring the Medico-Social Sector in Guadeloupe (1967–1980). The Role of the 1967 Generation
Authors: Ferez Sylvain , Ruffié Sébastien , Villoing Gaël
Published: February 2021

Guadeloupe left its status as a colony to become a French department with the “assimilation” law of 19 March 1946. Twenty years later, the promise of republican equality associated with this change is largely disappointed. Affected by the events of “May ‘67”, when the French state violently repressed demonstrations in Pointe-à-Pitre, the generation at the origin of the medico-social sector left to study in France in a tense political context. An analysis of the educational and professionalization paths of this generation, in connection with its political-union commitment, sheds light on the social and identity issues involved in the structuring of this sector.
Managers’ Competences in Private Hospitals for Investment Decisions during the COVID-19 Pandemic
Authors: Marques Isabel , Serrasqueiro Zélia , Nogueira Fernanda
Published: February 2021

The COVID-19 pandemic has posed an unprecedented challenge for health systems worldwide. The increased demand for investment in hospitals has become one of the greatest financial vulnerabilities, and in this context, the manager’s involvement in decision-making is associated with better analysis in order to achieve better results. This article aims to define a model to outline the manager profile in private hospitals, as well as the process and the relationship with investment decision-making, so as to guide future work to improve institutions’ performance and ensure the sustainability of patient care processes and the use of resources. Semi-structured interviews were held with an administrative (or financial) director in Brazil, Canada and Portugal and analyzed by the conventional content analysis method and coded, using NVivo 11, identifying the main topics. A model for investment decision-making is proposed to improve resource allocation and performance. The results indicate, for multidisciplinary training, where managers contribute to an efficient use of resources and contribute to the maintenance of quality of care, including about investment and financing of hospitals, where performance analysis reflects on decision-making.