bluuebunny/crossref_metadata_2025 huggingface dataset:
Recent Crustal Deformation Based on Interpolation of GNSS Velocity in Continental China
Authors: Bian Weiwei , Wu Jicang , Wu Weiwei
Published: November 2020

We used the interpolation method of two-dimensional vector velocity field data based on Green’s function to conduct coupled interpolation with a Poisson’s ratio of 0.5 for 1966 horizontal velocity field data from 1999 to 2017 and obtained the uniform velocity field and strain rate field with a grid of 1°. The main results are as follows: the eastern Himalayan structure as the center, the eastern Lhasa block, the eastern Qiangtang block, the Sichuan-Yunnan block, and the Burma block form a strong deformation rate zone of continuous deformation in the fan-shaped region, which has been a strong deformation rate zone for earthquakes of magnitude 7 or higher in continental China since 1963. Besides, the eastward movement of crustal material in the Tibetan Plateau is blocked by the stable South China block. Therefore, the direction of crustal material movement is deflected, which gradually forms a clockwise rotation motion system centered on the eastern Himalayan structure. Finally, our research shows that the influencing factors of strong earthquakes include velocity change, non-uniform strain distribution, accumulation of larger strain, and the difference of the second strain rate invariant. Strong earthquakes are closely related to the difference in energy accumulation in space.
Sediment Classification of Acoustic Backscatter Image Based on Stacked Denoising Autoencoder and Modified Extreme Learning Machine
Authors: Zhou Ping , Chen Gang , Wang Mingwei , Chen Jifa , Li Yizhe
Published: November 2020

Acoustic backscatter data are widely applied to study the distribution characteristics of seabed sediments. However, the ghosting and mosaic errors in backscatter images lead to interference information being introduced into the feature extraction process, which is conducted with a convolutional neural network or auto encoder. In addition, the performance of the existing classifiers is limited by such incorrect information, meaning it is difficult to achieve fine classification in survey areas. Therefore, we propose a sediment classification method based on the acoustic backscatter image by combining a stacked denoising auto encoder (SDAE) and a modified extreme learning machine (MELM). The SDAE is used to extract the deep-seated sediment features, so that the training network can automatically learn to remove the residual errors from the original image. The MELM model, which integrates weighted estimation, a Parzen window and particle swarm optimization, is applied to weaken the interference of mislabeled samples on the training network and to optimize the random expression of input layer parameters. The experimental results show that the SDAE-MELM method greatly reduces mutual interference between sediment types, while the sediment boundaries are clear and continuous. The reliability and robustness of the proposed method are better than with other approaches, as assessed by the overall classification effect and comprehensive indexes.
Mud Volcanism at the Taman Peninsula: Multiscale Analysis of Remote Sensing and Morphometric Data
Authors: Skrypitsyna Tatyana N. , Florinsky Igor V. , Beloborodov Denis E. , Gaydalenok Olga V.
Published: November 2020

Mud volcanism is observed in many tectonically active regions worldwide. One of the typical areas of mud volcanic activity is the Taman Peninsula, Russia. In this article, we examine the possibilities of multiscale analysis of remote sensing and morphometric data of different origins, years, scales, and resolutions for studying mud volcanic landscapes. The research is exemplified by the central-northern margin of the Taman Peninsula, where mud volcanism has only been little studied. The data set included one arc-second gridded Advanced Land Observing Satellite World three-dimensional (3D) digital surface model (AW3D30 DSM); a Corona historical declassified satellite photography; high-resolution imagery from an unmanned aerial survey (UAS) conducted with a multi-copter drone DJI Phantom 4 Pro, as well as a series of 1-m gridded morphometric models, including 12 curvatures (minimal, maximal, mean, Gaussian, unsphericity, horizontal, vertical, difference, vertical excess, horizontal excess, accumulation, and ring one) derived from UAS-based images. The data analysis allowed us to clarify the conditions of neotectonic development in the central-northern margin of the Taman Peninsula, as well as to specify manifestations of the mud volcanism in this region. In particular, we were able to detect minor and weakly topographically expressed mud volcanic features (probably, inactive gryphons, and salses), which are hidden by long-term farming practice (e.g., ploughed and covered by soil).
Hydrological Model Calibration with Streamflow and Remote Sensing Based Evapotranspiration Data in a Data Poor Basin
Authors: Sirisena T. A. Jeewanthi G. , Maskey Shreedhar , Ranasinghe Roshanka
Published: November 2020

Conventional calibration methods adopted in hydrological modelling are based on streamflow data measured at certain river sections. However, streamflow measurements are usually sparse and, in such instances, remote-sensing-based products may be used as an additional dataset(s) in hydrological model calibration. This study compares two main calibration approaches: (a) single variable calibration with streamflow and evapotranspiration separately, and (b) multi-variable calibration with both variables together. Here, we used remote sensing-based evapotranspiration data from Global Land Evaporation: the Amsterdam Model (GLEAM ET), and measured streamflow at four stations to calibrate a Soil and Water Assessment Tool (SWAT) and evaluate the performances for Chindwin Basin, Myanmar. Our results showed that when one variable (either streamflow or evapotranspiration) is used for calibration, it led to good performance with respect to the calibration variable but resulted in reduced performance in the other variable. In the multi-variable calibration using both streamflow and evapotranspiration, reasonable results were obtained for both variables. For example, at the basin outlet, the best NSEs (Nash-Sutcliffe Efficiencies) of streamflow and evapotranspiration on monthly time series are, respectively, 0.98 and 0.59 in the calibration with streamflow alone, and 0.69 and 0.73 in the calibration with evapotranspiration alone. Whereas, in the multi-variable calibration, the NSEs at the basin outlet are 0.97 and 0.64 for streamflow and evapotranspiration, respectively. The results suggest that the GLEAM ET data, together with streamflow data, can be used for model calibration in the study region as the simulation results show reasonable performance for streamflow with an NSE > 0.85. Results also show that many different sets of parameter values (‘good parameter sets’) can produce results comparable to the best parameter set.
Assessment of Extreme Diurnal Warming in Operational Geosynchronous Satellite Sea Surface Temperature Products
Authors: Wick Gary A. , Castro Sandra L.
Published: November 2020

We evaluate the reliability and basic characteristics of observations of extreme DW events from current operational geostationary satellite sea surface temperature (SST) products through examination of three months of diurnal warming (DW) estimates derived by different methodologies from the Spinning Enhanced Visible and Infrared Imager on Meteosat-11, Advanced Himawari Imager on Himawari-8, and Advanced Baseline Imager on the Geostationary Operational Environmental Satellite (GOES)-16. This work primarily focuses on the following research questions: (1) Can these operational SST products accurately characterize extreme DW events? (2) What are the amplitudes and frequencies of these events? To answer these, we compute distributions of DW and DW exceedance and compare them amongst the different methods and geostationary sensors. Examination of the DW estimates demonstrates several challenges in accurately deriving distributions of the DW amplitude, particularly associated with estimating the “foundation” temperature and uncertainties in cloud screening. Overall, the results suggest that current geostationary sensors can reliably assess extreme DW, but the estimates are sensitive to the computational methods applied. We thus suggest careful processing/screening of the SST retrievals. We find a value of 3 K, corresponding to the 99th percentile, provides a potential practical threshold for extreme warming, but events of at least 6 K were reliably observed. Warming in excess of 6 K occurred somewhere an average of 47% of the time, and its probability at a given location was of O(10−6).
Spatial Sharpening of KOMPSAT-3A MIR Images Using Optimal Scaling Factor
Authors: Oh Kwan-Young , Jung Hyung-Sup , Park Sung-Hwan , Lee Kwang-Jae
Published: November 2020

This paper present efficient methods for merging KOMPSAT-3A (Korea Multi-Purpose Satellite) medium wave Infrared (MIR) and panchromatic (PAN) images. Spatial sharpening techniques have been developed to create an image with both high spatial and high spectral resolution by combining the desired qualities of a PAN image with high spatial and low spectral resolution and an MS/MIR image with low spatial and high spectral resolution. The proposed methods can extract an optimal scaling factor, and uses the tactics of appropriately controlling the balance between the spatial and spectral resolutions. KOMPSAT-3A PAN and MIR images were used to test and evaluate the performance of the proposed methods. A qualitative assessment were performed using the image quality index (Q4), the cross correlation index (CC) and the relative global dimensional synthesis error (Spectral/Spatial ERGAS). These tests indicate that the proposed methods preserve the spectral and spatial characteristics of the original MIR and PAN images. Visual analysis reveals that the spectral and spatial information derived from the proposed methods were well retained in the test images. A comparison of the results of the proposed methods with those obtained from applying existing ones such as the Multi Sensor Fusion (MSF) technique or the Guide Filter Based Fusion (GF) show the efficiency of the new fusion process to be superior to the one of the others. The results showed a significant improvement in fusion capability for KOMPSAT-3A MIR imagery.
Analyzing Daily Estimation of Forest Gross Primary Production Based on Harmonized Landsat-8 and Sentinel-2 Product Using SCOPE Process-Based Model
Authors: Raj Rahul , Bayat Bagher , Lukeš Petr , Šigut Ladislav , Homolová Lucie
Published: November 2020

Vegetation top-of-canopy reflectance contains valuable information for estimating vegetation biochemical and structural properties, and canopy photosynthesis (gross primary production (GPP)). Satellite images allow studying temporal variations in vegetation properties and photosynthesis. The National Aeronautics and Space Administration (NASA) has produced a harmonized Landsat-8 and Sentinel-2 (HLS) data set to improve temporal coverage. In this study, we aimed to explore the potential and investigate the information content of the HLS data set using the Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) model to retrieve the temporal variations in vegetation properties, followed by the GPP simulations during the 2016 growing season of an evergreen Norway spruce dominated forest stand. We optimized the optical radiative transfer routine of the SCOPE model to retrieve vegetation properties such as leaf area index and leaf chlorophyll, water, and dry matter contents. The results indicated percentage differences less than 30% between the retrieved and measured vegetation properties. Additionally, we compared the retrievals from HLS data with those from hyperspectral airborne data for the same site, showing that HLS data preserve a considerable amount of information about the vegetation properties. Time series of vegetation properties, retrieved from HLS data, served as the SCOPE inputs for the time series of GPP simulations. The SCOPE model reproduced the temporal cycle of local flux tower measurements of GPP, as indicated by the high Nash–Sutcliffe efficiency value (>0.5). However, GPP simulations did not significantly change when we ran the SCOPE model with constant vegetation properties during the growing season. This might be attributed to the low variability in the vegetation properties of the evergreen forest stand within a vegetation season. We further observed that the temporal variation in maximum carboxylation capacity had a pronounced effect on GPP simulations. We focused on an evergreen forest stand. Further studies should investigate the potential of HLS data across different forest types, such as deciduous stand.
Multi-TALK: Multi-Microphone Cross-Tower Network for Jointly Suppressing Acoustic Echo and Background Noise
Authors: Park Song-Kyu , Chang Joon-Hyuk
Published: November 2020

In this paper, we propose a multi-channel cross-tower with attention mechanisms in latent domain network (Multi-TALK) that suppresses both the acoustic echo and background noise. The proposed approach consists of the cross-tower network, a parallel encoder with an auxiliary encoder, and a decoder. For the multi-channel processing, a parallel encoder is used to extract latent features of each microphone, and the latent features including the spatial information are compressed by a 1D convolution operation. In addition, the latent features of the far-end are extracted by the auxiliary encoder, and they are effectively provided to the cross-tower network by using the attention mechanism. The cross tower network iteratively estimates the latent features of acoustic echo and background noise in each tower. To improve the performance at each iteration, the outputs of each tower are transmitted as the input for the next iteration of the neighboring tower. Before passing through the decoder, to estimate the near-end speech, attention mechanisms are further applied to remove the estimated acoustic echo and background noise from the compressed mixture to prevent speech distortion by over-suppression. Compared to the conventional algorithms, the proposed algorithm effectively suppresses the acoustic echo and background noise and significantly lowers the speech distortion.
An Objective Methodology for the Selection of a Device for Continuous Mobility Assessment
Authors: Bonci Tecla , Keogh Alison , Del Din Silvia , Scott Kirsty , Mazzà Claudia ,   on behalf of the Mobilise-D consortium
Published: November 2020

Continuous monitoring by wearable technology is ideal for quantifying mobility outcomes in “real-world” conditions. Concurrent factors such as validity, usability, and acceptability of such technology need to be accounted for when choosing a monitoring device. This study proposes a bespoke methodology focused on defining a decision matrix to allow for effective decision making. A weighting system based on responses (n = 69) from a purpose-built questionnaire circulated within the IMI Mobilise-D consortium and its external collaborators was established, accounting for respondents’ background and level of expertise in using wearables in clinical practice. Four domains (concurrent validity, CV; human factors, HF; wearability and usability, WU; and data capture process, CP), associated evaluation criteria, and scores were established through literature research and group discussions. While the CV was perceived as the most relevant domain (37%), the others were also considered highly relevant (WU: 30%, HF: 17%, CP: 16%). Respondents (~90%) preferred a hidden fixation and identified the lower back as an ideal sensor location for mobility outcomes. Overall, this study provides a novel, holistic, objective, as well as a standardized approach accounting for complementary aspects that should be considered by professionals and researchers when selecting a solution for continuous mobility monitoring.
Deep Q-Network-Based Cooperative Transmission Joint Strategy Optimization Algorithm for Energy Harvesting-Powered Underwater Acoustic Sensor Networks
Authors: Han Song , Li Luo , Li Xinbin
Published: November 2020

Cooperative transmission is a promising technology for underwater acoustic sensor networks (UASNs) to ensure the effective collection of underwater information. In this paper, we study the joint relay selection and power allocation problem to maximize the cumulative quality of information transmission in energy harvesting-powered UASNs (EH-UASNs). First, we formulate the process of cooperative transmission with joint strategy optimization as a Markov decision process model. In the proposed model, an effective state expression is presented to better reveal interactive relationship between learning and environment, thereby improving the learning ability. Then, we further propose a novel reward function which can guide nodes to adjust power strategy adaptively to balance instantaneous capacity and long-term quality of service (QoS) under the dynamic unpredictable energy harvesting. More specifically, we propose a deep Q-network-based resource allocation algorithm for EH-UASNs to solve the complex coupled strategy optimization problem without any prior underwater environment information. Finally, simulation results verify the superior performance of the proposed algorithm in improving the cumulative network capacity and reducing outages.
Probabilistic Load Forecasting for Building Energy Models
Authors: Lucas Segarra Eva , Ramos Ruiz Germán , Fernández Bandera Carlos
Published: November 2020

In the current energy context of intelligent buildings and smart grids, the use of load forecasting to predict future building energy performance is becoming increasingly relevant. The prediction accuracy is directly influenced by input uncertainties such as the weather forecast, and its impact must be considered. Traditional load forecasting provides a single expected value for the predicted load and cannot properly incorporate the effect of these uncertainties. This research presents a methodology that calculates the probabilistic load forecast while accounting for the inherent uncertainty in forecast weather data. In the recent years, the probabilistic load forecasting approach has increased in importance in the literature but it is mostly focused on black-box models which do not allow performance evaluation of specific components of envelope, HVAC systems, etc. This research fills this gap using a white-box model, a building energy model (BEM) developed in EnergyPlus, to provide the probabilistic load forecast. Through a Gaussian kernel density estimation (KDE), the procedure converts the point load forecast provided by the BEM into a probabilistic load forecast based on historical data, which is provided by the building’s indoor and outdoor monitoring system. An hourly map of the uncertainty of the load forecast due to the weather forecast is generated with different prediction intervals. The map provides an overview of different prediction intervals for each hour, along with the probability that the load forecast error is less than a certain value. This map can then be applied to the forecast load that is provided by the BEM by applying the prediction intervals with their associated probabilities to its outputs. The methodology was implemented and evaluated in a real school building in Denmark. The results show that the percentage of the real values that are covered by the prediction intervals for the testing month is greater than the confidence level (80%), even when a small amount of data are used for the creation of the uncertainty map; therefore, the proposed method is appropriate for predicting the probabilistic expected error in load forecasting due to the use of weather forecast data.
Ka Band Holographic Imaging System Based on Linear Frequency Modulation Radar
Authors: Meng Yang , Lin Chuan , Zang Jiefeng , Qing Anyong , Nikolova Natalia K.
Published: November 2020

Millimeter wave (MMW) technology is expanding rapidly into security screening for dangerous items concealed under clothing. It uses safe non-ionizing radiation and penetrates clothing well. We present a new planar system at Ka band for the three-dimensional simultaneous imaging of both sides of an inspected person where the images are produced in real time by a recently proposed generalized holographic reconstruction algorithm. Low-cost linear frequency modulation (LFM) radar technology is used along with a simple but efficient method for system calibration. Experimental characterization of the spatial resolution and the sensitivity of the system prototype has been carried out. It is established that the achieved spatial resolution is 6 mm or better if the item is not obscured by clothing and it may deteriorate to 7 mm depending on the clothing hiding the item. The spatial sensitivity is confirmed to be at least 2 mm.
AF-SSD: An Accurate and Fast Single Shot Detector for High Spatial Remote Sensing Imagery
Authors: Yin Ruihong , Zhao Wei , Fan Xudong , Yin Yongfeng
Published: November 2020

There are a large number of studies on geospatial object detection. However, many existing methods only focus on either accuracy or speed. Methods with both fast speed and high accuracy are of great importance in some scenes, like search and rescue, and military information acquisition. In remote sensing images, there are some targets that are small and have few textures and low contrast compared with the background, which impose challenges on object detection. In this paper, we propose an accurate and fast single shot detector (AF-SSD) for high spatial remote sensing imagery to solve these problems. Firstly, we design a lightweight backbone to reduce the number of trainable parameters of the network. In this lightweight backbone, we also use some wide and deep convolutional blocks to extract more semantic information and keep the high detection precision. Secondly, a novel encoding–decoding module is employed to detect small targets accurately. With up-sampling and summation operations, the encoding–decoding module can add strong high-level semantic information to low-level features. Thirdly, we design a cascade structure with spatial and channel attention modules for targets with low contrast (named low-contrast targets) and few textures (named few-texture targets). The spatial attention module can extract long-range features for few-texture targets. By weighting each channel of a feature map, the channel attention module can guide the network to concentrate on easily identifiable features for low-contrast and few-texture targets. The experimental results on the NWPU VHR-10 dataset show that our proposed AF-SSD achieves superior detection performance: parameters 5.7 M, mAP 88.7%, and 0.035 s per image on average on an NVIDIA GTX-1080Ti GPU.
Motion Capture Data Analysis in the Instantaneous Frequency-Domain Using Hilbert-Huang Transform
Authors: Dong Ran , Cai Dongsheng , Ikuno Soichiro
Published: November 2020

Motion capture data are widely used in different research fields such as medical, entertainment, and industry. However, most motion researches using motion capture data are carried out in the time-domain. To understand human motion complexities, it is necessary to analyze motion data in the frequency-domain. In this paper, to analyze human motions, we present a framework to transform motions into the instantaneous frequency-domain using the Hilbert-Huang transform (HHT). The empirical mode decomposition (EMD) that is a part of HHT decomposes nonstationary and nonlinear signals captured from the real-world experiments into pseudo monochromatic signals, so-called intrinsic mode function (IMF). Our research reveals that the multivariate EMD can decompose complicated human motions into a finite number of nonlinear modes (IMFs) corresponding to distinct motion primitives. Analyzing these decomposed motions in Hilbert spectrum, motion characteristics can be extracted and visualized in instantaneous frequency-domain. For example, we apply our framework to (1) a jump motion, (2) a foot-injured gait, and (3) a golf swing motion.
Hollow-Core Negative Curvature Fiber with High Birefringence for Low Refractive Index Sensing Based on Surface Plasmon Resonance Effect
Authors: Qiu Shi , Yuan Jinhui , Zhou Xian , Li Feng , Wang Qiwei , Qu Yuwei , Yan Binbin , Wu Qiang , Wang Kuiru , Sang Xinzhu , Long Keping , Yu Chongxiu
Published: November 2020

In this paper, a hollow-core negative curvature fiber (HC-NCF) with high birefringence is proposed for low refractive index (RI) sensing based on surface plasmon resonance effect. In the design, the cladding region of the HC-NCF is composed of only one ring of eight silica tubes, and two of them are selectively filled with the gold wires. The influences of the gold wires-filled HC-NCF structure parameters on the propagation characteristic are investigated by the finite element method. Moreover, the sensing performances in the low RI range of 1.20–1.34 are evaluated by the traditional confinement loss method and novel birefringence analysis method, respectively. The simulation results show that for the confinement loss method, the obtained maximum sensitivity, resolution, and figure of merit of the gold wires-filled HC-NCF-based sensor are −5700 nm/RIU, 2.63 × 10−5 RIU, and 317 RIU−1, respectively. For the birefringence analysis method, the obtained maximum sensitivity, resolution, and birefringence of the gold wires-filled HC-NCF-based sensor are −6100 nm/RIU, 2.56 × 10−5 RIU, and 1.72 × 10−3, respectively. It is believed that the proposed gold wires-filled HC-NCF-based low RI sensor has important applications in the fields of biochemistry and medicine.
Structural Design and Crawling Pattern Generator of a Planar Quadruped Robot for High-Payload Locomotion
Authors: Kang Ru , Meng Fei , Chen Xuechao , Yu Zhangguo , Fan Xuxiao , Ming Aiguo , Huang Qiang
Published: November 2020

Load capacity is an important index to reflect the practicability of legged robots. Existing research into quadruped robots has not analyzed their load performance in terms of their structural design and control method from a systematic point of view. This paper proposes a structural design method and crawling pattern generator for a planar quadruped robot that can realize high-payload locomotion. First, the functions required to evaluate the leg’s load capacity are established, and quantitative comparative analyses of the candidates are performed to select the leg structure with the best load capacity. We also propose a highly integrated design method for a driver module to improve the robot’s load capacity. Second, in order to realize stable load locomotion, a novel crawling pattern generator based on trunk swaying is proposed which can realize lateral center of mass (CoM) movement by adjusting the leg lengths on both sides to change the CoM projection in the trunk width direction. Finally, loaded crawling simulations and experiments performed with our self-developed quadruped robot show that stable crawling with load ratios exceeding 66% can be realized, thus verifying the effectiveness and superiority of the proposed method.
Study on Optimal Broadcast Ephemeris Parameters for GEO/IGSO Navigation Satellites
Authors: Choi Jin Haeng , Kim Gimin , Lim Deok Won , Park Chandeok
Published: November 2020

This paper proposes new sets of suitable broadcast ephemeris parameters for geosynchronous (GEO) and inclined geosynchronous (IGSO) navigation satellites (NSs). Despite the increasing number of GEO and IGSO NSs, global positioning system (GPS)-type ephemeris parameters are still widely used for them. In an effort to provide higher fit accuracy, we analyze a variety of broadcast ephemeris parameters for GEO and IGSO satellites along with their orbital characteristics and propose optimal sets of parameters. Nonsingular elements and orbital plane rotation are adopted for alleviating/avoiding the singularity issues of GEO satellites. On the basis of 16 parameters of GPS LNAV, we add one to four parameters out of 28 correction ones to determine optimal sets of ephemeris parameters providing higher accuracy. All possible parameter sets are tested with the least-square curve fit for four BeiDou GEOs and six BeiDou IGSOs. Their fit accuracies are compared to determine the optimal broadcast ephemeris parameters that provide minimum fit errors. The set of optimal ephemeris parameters depends on the type of orbit. User range error (URE) accuracies of the proposed optimal ephemeris parameters ensure results within 2.4 cm for IGSO and 3.8 cm for GEO NSs. Moreover, the experimental results present common parameter sets for both IGSO and GEO for compatibility and uniformity. Compared with four conventional/well-known sets of ephemeris parameters for BeiDou, our proposed parameters can enhance accuracies of up to 34.5% in terms of URE. We also apply the proposed optimal parameter sets to one GEO and three IGSO satellites of QZSS. The effects of fitting intervals, number of parameters, total bits, and orbit types on the fit accuracy are addressed in detail.
Resource Allocation for Edge Computing without Using Cloud Center in Smart Home Environment: A Pricing Approach
Authors: Liu Huan , Li Shiyong , Sun Wei
Published: November 2020

Recently, more and more smart homes have become one of important parts of home infrastructure. However, most of the smart home applications are not interconnected and remain isolated. They use the cloud center as the control platform, which increases the risk of link congestion and data security. Thus, in the future, smart homes based on edge computing without using cloud center become an important research area. In this paper, we assume that all applications in a smart home environment are composed of edge nodes and users. In order to maximize the utility of users, we assume that all users and edge nodes are placed in a market and formulate a pricing resource allocation model with utility maximization. We apply the Lagrangian method to analyze the model, so an edge node (provider in the market) allocates its resources to a user (customer in the market) based on the prices of resources and the utility related to the preference of users. To obtain the optimal resource allocation, we propose a pricing-based resource allocation algorithm by using low-pass filtering scheme and conform that the proposed algorithm can achieve an optimum within reasonable convergence times through some numerical examples.
Effective Fundus Image Decomposition for the Detection of Red Lesions and Hard Exudates to Aid in the Diagnosis of Diabetic Retinopathy
Authors: Romero-Oraá Roberto , García María , Oraá-Pérez Javier , López-Gálvez María I. , Hornero Roberto
Published: November 2020

Diabetic retinopathy (DR) is characterized by the presence of red lesions (RLs), such as microaneurysms and hemorrhages, and bright lesions, such as exudates (EXs). Early DR diagnosis is paramount to prevent serious sight damage. Computer-assisted diagnostic systems are based on the detection of those lesions through the analysis of fundus images. In this paper, a novel method is proposed for the automatic detection of RLs and EXs. As the main contribution, the fundus image was decomposed into various layers, including the lesion candidates, the reflective features of the retina, and the choroidal vasculature visible in tigroid retinas. We used a proprietary database containing 564 images, randomly divided into a training set and a test set, and the public database DiaretDB1 to verify the robustness of the algorithm. Lesion detection results were computed per pixel and per image. Using the proprietary database, 88.34% per-image accuracy (ACCi), 91.07% per-pixel positive predictive value (PPVp), and 85.25% per-pixel sensitivity (SEp) were reached for the detection of RLs. Using the public database, 90.16% ACCi, 96.26% PPV_p, and 84.79% SEp were obtained. As for the detection of EXs, 95.41% ACCi, 96.01% PPV_p, and 89.42% SE_p were reached with the proprietary database. Using the public database, 91.80% ACCi, 98.59% PPVp, and 91.65% SEp were obtained. The proposed method could be useful to aid in the diagnosis of DR, reducing the workload of specialists and improving the attention to diabetic patients.
Eliminating the Effect of Reflectance Properties on Reconstruction in Stripe Structured Light System
Authors: Song Zhao , Song Zhan , Ye Yuping
Published: November 2020

The acquisition of the geometry of general scenes is related to the interplay of surface geometry, material properties and illumination characteristics. Surface texture and non-Lambertian reflectance properties degrade the reconstruction results by structured light technique. Existing structured light techniques focus on different coding strategy and light sources to improve reconstruction accuracy. The hybrid system consisting of a structured light technique and photometric stereo combines the depth value with normal information to refine the reconstruction results. In this paper, we propose a novel hybrid system consisting of stripe-based structured light and photometric stereo. The effect of surface texture and non-Lambertian reflection on stripe detection is first concluded. Contrary to existing fusion strategy, we propose an improved method for stripe detection to reduce the above factor’s effects on accuracy. The reconstruction problem for general scene comes down to using reflectance properties to improve the accuracy of stripe detection. Several objects, including checkerboard, metal-flat plane and free-form objects with complex reflectance properties, were reconstructed to validate our proposed method, which illustrates the effectiveness on improving the reconstruction accuracy of complex objects. The three-step phase-shifting algorithm was implemented and the reconstruction results were given and also compared with ours. In addition, our proposed framework provides a new feasible scheme for solving the ongoing problem of the reconstruction of complex objects with variant reflectance. The problem can be solved by subtracting the non-Lambertian components from the original grey values of stripe to improve the accuracy of stripe detection. In the future, based on stripe structured light technique, more general reflection models can be used to model different types of reflection properties of complex objects.
An Optimal Flow Admission and Routing Control Policy for Resource Constrained Networks
Authors: Hamouda Essia
Published: November 2020

Overloaded network devices are becoming an increasing problem especially in resource limited networks with the continuous and rapid increase of wireless devices and the huge volume of data generated. Admission and routing control policy at a network device can be used to balance the goals of maximizing throughput and ensuring sufficient resources for high priority flows. In this paper we formulate the admission and routing control problem of two types of flows where one has a higher priority than the other as a Markov decision problem. We characterize the optimal admission and routing policy, and show that it is a state-dependent threshold type policy. Furthermore, we conduct extensive numerical experiments to gain more insight into the behavior of the optimal policy under different systems’ parameters. While dynamic programming can be used to solve such problems, the large size of the state space makes it untractable and too resource intensive to run on wireless devices. Therefore, we propose a fast heuristic that exploits the structure of the optimal policy. We empirically show that the heuristic performs very well with an average reward deviation of 1.4% from the optimal while being orders of magnitude faster than the optimal policy. We further generalize the heuristic for the general case of a system with n (n>2) types of flows.
A Textile Sleeve for Monitoring Oxygen Saturation Using Multichannel Optical Fibre Photoplethysmography
Authors: Ballaji Hattan K. , Correia Ricardo , Korposh Serhiy , Hayes-Gill Barrie R. , Hernandez Francisco U. , Salisbury Byron , Morgan Stephen P.
Published: November 2020

Textile-based systems are an attractive prospect for wearable technology as they can provide monitoring of key physiological parameters in a comfortable and unobtrusive form. A novel system based on multichannel optical fibre sensor probes integrated into a textile sleeve is described. The system measures the photoplethysmogram (PPG) at two wavelengths (660 and 830 nm), which is then used to calculate oxygen saturation (SpO2). In order to achieve reliable measurement without adjusting the position of the garment, four plastic optical fibre (POF) probes are utilised to increase the likelihood that a high-quality PPG is obtained due to at least one of the probes being positioned over a blood vessel. Each probe transmits and receives light into the skin to measure the PPG and SpO2. All POFs are integrated in a stretchable textile sleeve with a circumference of 15 cm to keep the sensor in contact with the subject’s wrist and to minimise motion artefacts. Tests on healthy volunteers show that the multichannel PPG sensor faithfully provides an SpO2 reading in at least one of the four sensor channels in all cases with no need for adjusting the position of the sleeve. This could not be achieved using a single sensor alone. The multichannel sensor is used to monitor the SpO2 of 10 participants with an average wrist circumference of 16.0 ± 0.6 cm. Comparing the developed sensor’s SpO2 readings to a reference commercial oximeter (reflectance Masimo Radical-7) illustrates that the mean difference between the two sensors’ readings is −0.03%, the upper limit of agreement (LOA) is 0.52% and the lower LOA is −0.58%. This multichannel sensor has the potential to achieve reliable, unobtrusive and comfortable textile-based monitoring of both heart rate and SpO2 during everyday life.
Spectral Verification of the Mechanisms behind FBG-Based Ultrasonic Guided Wave Detection
Authors: Goossens Sidney , Berghmans Francis , Geernaert Thomas
Published: November 2020

Ultrasonic guided wave (UGW) detection with fiber Bragg grating (FBG)-based sensors has received increasing attention in the last decades due to the ability to perform non-destructive inspection (NDI) of large plate-like surfaces with a network of lightweight and multiplexed sensors. For accurate UGW measurements, several studies concluded that the ratio between the wavelength of the UGW and the length of the FBG should be above 7. However, shorter FBGs suffer from a lower FBG reflectivity and less steep slopes in the reflection spectrum. In this work we experimentally verified the effect of a passing UGW on the Bragg peak of FBG sensors of different lengths. By performing edge-filtering interrogation throughout the FBG’s reflection spectrum, we were able to reconstruct the FBG’s spectral response to a UGW in function of time. Our experimental findings are partially in line with those in the literature considering the UGW wavelength to FBG length ratio and the corresponding Bragg peak changes. We experimentally show for the first time that for shorter FBG sensors, the strain modulation is translated mostly into Bragg peak shifting, while for longer FBG sensors, Bragg peak deformation takes over as main mechanism. Despite the different mechanism for the latter, the UGW can still be detected by edge-filtering on the steepest slope, and with a much higher sensitivity.
Explainable Artificial Intelligence for Developing Smart Cities Solutions
Authors: Thakker Dhavalkumar , Mishra Bhupesh Kumar , Abdullatif Amr , Mazumdar Suvodeep , Simpson Sydney
Published: November 2020

Traditional Artificial Intelligence (AI) technologies used in developing smart cities solutions, Machine Learning (ML) and recently Deep Learning (DL), rely more on utilising best representative training datasets and features engineering and less on the available domain expertise. We argue that such an approach to solution development makes the outcome of solutions less explainable, i.e., it is often not possible to explain the results of the model. There is a growing concern among policymakers in cities with this lack of explainability of AI solutions, and this is considered a major hindrance in the wider acceptability and trust in such AI-based solutions. In this work, we survey the concept of ‘explainable deep learning’ as a subset of the ‘explainable AI’ problem and propose a new solution using Semantic Web technologies, demonstrated with a smart cities flood monitoring application in the context of a European Commission-funded project. Monitoring of gullies and drainage in crucial geographical areas susceptible to flooding issues is an important aspect of any flood monitoring solution. Typical solutions for this problem involve the use of cameras to capture images showing the affected areas in real-time with different objects such as leaves, plastic bottles etc., and building a DL-based classifier to detect such objects and classify blockages based on the presence and coverage of these objects in the images. In this work, we uniquely propose an Explainable AI solution using DL and Semantic Web technologies to build a hybrid classifier. In this hybrid classifier, the DL component detects object presence and coverage level and semantic rules designed with close consultation with experts carry out the classification. By using the expert knowledge in the flooding context, our hybrid classifier provides the flexibility on categorising the image using objects and their coverage relationships. The experimental results demonstrated with a real-world use case showed that this hybrid approach of image classification has on average 11% improvement (F-Measure) in image classification performance compared to DL-only classifier. It also has the distinct advantage of integrating experts’ knowledge on defining the decision-making rules to represent the complex circumstances and using such knowledge to explain the results.
Energy Saving of a University Building Using a Motion Detection Sensor and Room Management System
Authors: Lee Jong-Won , Kim Young Il
Published: November 2020

To save electricity consumption in university buildings, we measured and compared the amount of electricity use with and without motion detection sensors and room management systems in underground parking lots, lecture rooms, and dormitories of a university building. The underground parking lots and lecture rooms were measured as sensors were applied and then removed during the semester. University classes are held weekly, so it can be assumed that the number of cars and people’s entering and using conditions are the same. In the university’s underground parking lots, a daily electricity savings of 39.5 Wh/(m2 day) of lights was achieved, with a savings rate of 77.6%. In the lecture rooms, these values were 25.0 Wh/(m2 day) and 32.4%, respectively. Savings in the use of air conditioning were 55.0 Wh/(m2 day), with a savings rate of 27.9%. Dormitories use electrical energy for lighting, heating, and socket outlets. As a reference group, 120 rooms were selected and the room management system was applied to 10 samples. For dormitories, daily electricity savings of 142.4 Wh/(m2 day) were achieved, with a savings rate of 28.2%. Thus, this study demonstrated that applying motion detection sensors and room management systems saved significant electrical energy in university underground parking lots, lecture rooms, and dormitories.
Experimental Testing of Masonry Subjected to Concentrated Load in Direction of Bed Joints
Authors: Kozielova Marie , Mynarzova Lucie , Mynarcik Petr
Published: November 2020

One of the options for rehabilitating existing masonry buildings is a post-tensioning of masonry. The deformation properties of the masonry in the direction parallel to bed joints are essential for the design of a suitable value of the applied force. However, these values are usually not available or are defined only roughly. The aim of this study was to determine, as accurately as possible, the deformation characteristics of the masonry in the direction parallel to the bed joints. Experimental measuring of masonry samples was carried out in laboratory facilities designed specifically for testing the triaxial state of stress of the masonry. The obtained deformations were used to determine the modulus of elasticity of the masonry in the direction of bed joints. Within these experimental tests, laboratory measuring of mechanical masonry properties was carried out, and deformation characteristics were defined on the basis of calculations. The strengths of masonry units and mortar were tested in the laboratory and, according to the tests results, the modulus of elasticity in the direction perpendicular to bed joints was determined. The modulus of elasticity in the direction parallel to bed joints was verified through a simple numerical model. The calculated deformations were compared with the measured values. On the basis of the determination of the deformation characteristics of masonry in the direction of bed joints, the ratios of the modulus of elasticity in the direction perpendicular and parallel to bed joints were concluded.
An Explorative Study of How Visceral States Influence the Relationship between Social Proof Heuristics and Donation Behavior When Consumers Are Using Self-Service Kiosks
Authors: Pawar Sanchit , Fagerstrøm Asle , Sigurdsson Valdimar
Published: November 2020

Self-service kiosks are increasingly being used in situations where a person is out to buy food and/or drinks. Several cause-related marketing initiatives have capitalized on using self-service technology to include small donation requests at the point of purchase. In this context, it is highly likely that during such purchase situations, a person is under the influence of a visceral state like hunger or thirst. This study investigated how a simulated visceral state of thirst could influence donation behavior. More specifically, how donation social proof indicators presented on self-service kiosk screens can impact likelihood to buy. Results of a conjoint study (n = 83) demonstrate that, in a visceral state situation, only a high level of social proof related to donation has a positive impact on likelihood to buy. Any other level of social proof (medium, low and not mentioned), decrease the likelihood to buy in such situations. A scenario simulation analysis shows that cases which included a high level of social proof have relatively higher preference. Consequently, antecedent situational variables like visceral states must be taken into consideration when cause-related marketing activities such as social proof (related to donation) are used in self-service kiosks.
The Microsimulation Model for Assessing the Impact of Inbound Traffic Flows for Container Terminals Located near City Centers
Authors: Grubisic Neven , Krljan Tomislav , Maglić Livia , Vilke Siniša
Published: November 2020

The growth of container transport places increasing demand on traffic, especially in situations where container terminals are located near the city centers. The main problem is traffic congestion on networks caused by the integration of Heavy-Duty Vehicles and urban traffic flows. The main objective is to identify the critical traffic parameters which cause negative organizational and environmental impacts on the existing and future traffic demand. A micro-level traffic simulation model was implemented for the testing of the proposed framework-based supply, demand, and control layers. The model was generated and calibrated based on the example of a mid-size Container Terminal “Brajdica” and the City of Rijeka, Croatia. The results indicate that the critical parameters are Queue Length on the approach road to the Container Terminal and the Stop Delay on the main city corridor. High values of these parameters cause negative effects on the environment because of increased fuel consumption and the generation of extra pollution. Due to this problem, a sensitivity analysis of the traffic system performance has been conducted, with a decrement of Terminal Gate Time distribution by 10%. After re-running simulations, the results indicate the impact of subsequent variation in Terminal Gate Time on the decrease of critical parameters, fuel consumption, and vehicle pollution.