bluuebunny/crossref_metadata_2025 huggingface dataset:
Bistatic High-Frequency Radar Cross-Section of the Ocean Surface with Arbitrary Wave Heights
Authors: Silva Murilo Teixeira , Huang Weimin , Gill Eric W.
Published: February 2020

The scattering theory developed in the past decades for high-frequency radio oceanography has been restricted to surfaces with small heights and small slopes. In the present work, the scattering theory for bistatic high-frequency radars is extended to ocean surfaces with arbitrary wave heights. Based on recent theoretical developments in the scattering theory for ocean surfaces with arbitrary heights for monostatic radars, the electric field equations for bistatic high-frequency radars in high sea states are developed. This results in an additional term related to the first-order electric field, which is only present when the small-height approximation is removed. Then, the radar cross-section for the additional term is derived and simulated, and its impact on the total radar cross-section at different radar configurations, dominant wave directions, and sea states is assessed. The proposed term is shown to impact the total radar cross-section at high sea states, dependent on radar configuration and dominant wave direction. The present work can contribute to the remote sensing of targets on the ocean surface, as well as the determination of the dominant wave direction of the ocean surface at high sea states.
Building Shadow Detection on Ghost Images
Authors: Zhou Guoqing , Sha Hongjun
Published: February 2020

Although many efforts have been made on building shadow detection from aerial images, little research on simultaneous shadows detection on both building roofs and grounds has been presented. Hence, this paper proposes a new method for simultaneous shadow detection on ghost image. In the proposed method, a corner point on shadow boundary is selected and its 3D approximate coordinate is calculated through photogrammetric collinear equation on the basis of assumption of average elevation within the aerial image. The 3D coordinates of the shadow corner point on shadow boundary is used to calculate the solar zenith angle and the solar altitude angle. The shadow areas on the ground, at the moment of aerial photograph shooting are determined by the solar zenith angle and the solar altitude angle with the prior information of the digital building model (DBM). Using the relationship between the shadows of each building and the height difference of buildings, whether there exists a shadow on the building roof is determined, and the shadow area on the building roof on the ghost image is detected on the basis of the DBM. High-resolution aerial images located in the City of Denver, Colorado, USA are used to verify the proposed method. The experimental results demonstrate that the shadows of the 120 buildings in the study area are completely detected, and the success rate is 15% higher than the traditional shadow detection method based on shadow features. Especially, when the shadows occur on the ground and on the buildings roofs, the successful rate of shadow detection can be improved by 9.42% and 33.33% respectively.
Chiroptical Sensing: A Conceptual Introduction
Authors: Ozcelik Ani , Pereira-Cameselle Raquel , Poklar Ulrih Natasa , Petrovic Ana G. , Alonso-Gómez José Lorenzo
Published: February 2020

Chiroptical responses have been an essential tool over the last decades for chemical structural elucidation due to their exceptional sensitivity to geometry and intermolecular interactions. In recent times, there has been an increasing interest in the search for more efficient sensing by the rational design of tailored chiroptical systems. In this review article, advances made in chiroptical systems towards their implementation in sensing applications are summarized. Strategies to generate chiroptical responses are illustrated. Theoretical approaches to assist in the design of these systems are discussed. The development of efficient chiroptical reporters in different states of matter, essential for the implementation in sensing devises, is reviewed. In the last part, remarkable examples of chiroptical sensing applications are highlighted.
Heterogeneous Sensing Data Analysis for Commercial Waste Collection
Authors: Melakessou Foued , Kugener Paul , Alnaffakh Neamah , Faye Sébastien , Khadraoui Djamel
Published: February 2020

Waste collection has become a major issue all over the world, especially when it is offered as a service for businesses; unlike public waste collection where the parameters are relatively homogeneous. This industry can greatly benefit from new sensing technologies and advances in artificial intelligence that have been achieved over the last few years. However, in most situations waste management systems are based on obsolete technologies, with a low level of interoperability and thus offering static processes. The most advanced solutions are generally limited to statistical, non-predictive approaches and have a limited view of reality, making them weakly effective in dealing with day-to-day business issues (overflowing containers, poor quality of service, etc.). This paper presents a case study currently being developed in Luxembourg with a company offering a business waste collection service, which has a significant amount of constraints to consider. Our main objective is to investigate the use of multiple waste data sources to derive useful indicators for improving collection processes. We start with company-owned historical data and then investigate GPS information from tracking devices positioned on collection trucks. Furthermore, we analyze data collected from ultrasonic sensors deployed on almost 50 different containers to measure fill levels. We describe the deployment steps and show that this approach, combined with anomaly detection and prediction techniques, has the potential to change the way this business operates. We also discuss the interest of the different datasets presented and multi-objective optimization issues. To the best of our knowledge, this article is the first major work dedicated to the world of professional waste collection.
Statistical Analysis of Noise Propagation Effect for Mixed RF/FSO AF Relaying Application in Wireless Sensor Networks
Authors: Jeong Dae-Kyo , Park Cheol-Sun , Kim Dongwoo
Published: February 2020

In this paper, we investigate the so-called noise propagation effect in a mixed radio-frequency/ free-space optical (RF/FSO) amplifying-and-forwarding (AF) relaying system that is applied for data transmission in wireless sensor networks. The noise propagation could be essentially severe when battery-charged sensor nodes have very limited transmit power. We provide an exact expression on the cumulative distribution function (CDF) of end-to-end signal-to-noise power ratio (SNR) for a dual-hop mixed RF/FSO AF relaying system. We assume a tightly power-constrained amplifying gain at the relay, which has been usually ignored in existing performance studies for the mixed RF/FSO AF system. It however should be considered to properly evaluate the noise propagation effect especially if the relaying power is not infinite or the sensor has a poor budget in transmit power. We apply the derived exact CDF to evaluate the system performances such as outage probability, average bit-error rate, and ergodic capacity. Numerical investigation is used to justify that the proposed analysis is exactly matched with the simulation and shows that the performance gap caused by the inclusion of the noise propagation effect is significant (about 2–12%) especially when the SNR per hop is in the medium- or the low-SNR ranges (i.e., at 10–20 dB).
A Deep-Learning-based 3D Defect Quantitative Inspection System in CC Products Surface
Authors: Zhao Liming , Li Fangfang , Zhang Yi , Xu Xiaodong , Xiao Hong , Feng Yang
Published: February 2020

To create an intelligent surface region of interests (ROI) 3D quantitative inspection strategy a reality in the continuous casting (CC) production line, an improved 3D laser image scanning system (3D-LDS) was established based on binocular imaging and deep-learning techniques. In 3D-LDS, firstly, to meet the requirements of the industrial application, the CCD laser image scanning method was optimized in high-temperature experiments and secondly, we proposed a novel region proposal method based on 3D ROI initial depth location for effectively suppressing redundant candidate bounding boxes generated by pseudo-defects in a real-time inspection process. Thirdly, a novel two-step defects inspection strategy was presented by devising a fusion deep CNN model which combined fully connected networks (for defects classification/recognition) and fully convolutional networks (for defects delineation). The 3D-LDS’ dichotomous inspection method of defects classification and delineation processes are helpful in understanding and addressing challenges for defects inspection in CC product surfaces. The applicability of the presented methods is mainly tied to the surface quality inspection for slab, strip and billet products.
Fluorescence-Sensor Mapping for the in Vineyard Non-Destructive Assessment of Crimson Seedless Table Grape Quality
Authors: Tuccio Lorenza , Cavigli Lucia , Rossi Francesca , Dichala Olga , Katsogiannos Fotis , Kalfas Ilias , Agati Giovanni
Published: February 2020

Non-destructive tools for the in situ evaluation of vine fruit quality and vineyard management can improve the market value of table grape. We proposed a new approach based on a portable fluorescence sensor to map the ripening level of Crimson Seedless table grape in five different plots in the East, Central-North and South of the Macedonia Region of Greece. The sensor provided indices of ripening and color such as SFRR and ANTHRG correlated to the chlorophyll and anthocyanin berry contents, respectively. The mean ANTHRG index was significantly different among all the plots examined due to the occurrence of different environmental conditions and/or asynchronous ripening processes. The indices presented moderate, poor in some cases, spatial variability, probably due to a significant vine-to-vine, intra-vine and intra-bunch variability. The cluster analysis was applied to the plot with the most evident spatial structure (at Kilkis). Krigged maps of the SFRR, ANTHRG and yield were classified by k-means clustering in two-zones that differed significantly in their mean values. ANTHRG and SFRR were inversely correlated over 64% of the plot. SFRR appeared to be a potential useful proxy of yield since it was directly correlated to yield over 66% of the plot. The grape color (ANTHRG) was slightly higher over the low-yield zones with respect to the high-yield zones. Our study showed that the combination of anthocyanins and chlorophyll indices detected in the field on Crimson Seedless table grape by a portable fluorescence sensor can help in defining the best harvest time and the best areas for harvesting.
The Application of Flexible Areas of Interest to Pilot Mobile Eye Tracking
Authors: Gomolka Zbigniew , Kordos Damian , Zeslawska Ewa
Published: February 2020

Recent progress in the development of mobile Eye Tracking (ET) systems shows that there is a demand for modern flexible solutions that would allow for dynamic tracking of objects in the video stream. The paper describes a newly developed tool for work with ET glasses, and its advantages are outlined with the example of a pilot study. A flight task is performed on the FNTP II MCC simulator, and the pilots are equipped with the Mobile Tobii Glasses. The proposed Smart Trainer tool performs dynamic object tracking in a registered video stream, allowing for an interactive definition of Area of Interest (AOI) with blurred contours for the individual cockpit instruments and for the construction of corresponding histograms of pilot attention. The studies are carried out on a group of experienced pilots with a professional pilot CPL(A) license with instrumental flight (Instrument Rating (IR)) certification and a group of pilots without instrumental training. The experimental section shows the differences in the perception of the flight process between two distinct groups of pilots with varying levels in flight training for the ATPL(A) line pilot license. The proposed Smart Trainer tool might be exploited in order to assess and improve the process of training operators of advanced systems with human machine interfaces.
Prediction of Individual User’s Dynamic Ranges of EEG Features from Resting-State EEG Data for Evaluating Their Suitability for Passive Brain–Computer Interface Applications
Authors: Cha Ho-Seung , Han Chang-Hee , Im Chang-Hwan
Published: February 2020

With the recent development of low-cost wearable electroencephalogram (EEG) recording systems, passive brain–computer interface (pBCI) applications are being actively studied for a variety of application areas, such as education, entertainment, and healthcare. Various EEG features have been employed for the implementation of pBCI applications; however, it is frequently reported that some individuals have difficulty fully enjoying the pBCI applications because the dynamic ranges of their EEG features (i.e., its amplitude variability over time) were too small to be used in the practical applications. Conducting preliminary experiments to search for the individualized EEG features associated with different mental states can partly circumvent this issue; however, these time-consuming experiments were not necessary for the majority of users whose dynamic ranges of EEG features are large enough to be used for pBCI applications. In this study, we tried to predict an individual user’s dynamic ranges of the EEG features that are most widely employed for pBCI applications from resting-state EEG (RS-EEG), with the ultimate goal of identifying individuals who might need additional calibration to become suitable for the pBCI applications. We employed a machine learning-based regression model to predict the dynamic ranges of three widely used EEG features known to be associated with the brain states of valence, relaxation, and concentration. Our results showed that the dynamic ranges of EEG features could be predicted with normalized root mean squared errors of 0.2323, 0.1820, and 0.1562, respectively, demonstrating the possibility of predicting the dynamic ranges of the EEG features for pBCI applications using short resting EEG data.
An Adaptive Genetic Algorithm of Adjusting Sensor Acquisition Frequency
Authors: Chen Feng , Xu Shouzhi , Zhao Ying , Zhang Hui
Published: February 2020

Portable meteorological stations are widely applied in environment monitoring systems, but they are always limited in power-supplying due to no cable power, especially in long-term monitoring scenarios. Reducing power consumption by adjusting a suitable frequency of sensor acquisition is very important for wireless sensor nodes. The regularity of historical environment data from a monitoring system is analyzed, and then an optimization model of an adaptive genetic algorithm for environment monitoring data acquisition strategies is proposed to lessen sampling frequency. According to the historical characteristics, the algorithm dynamically changes the recent data acquisition frequency so as to collect data with a smaller acquisition frequency, which will reduce the energy consumption of the sensor. Experiment results in a practical environment show that the algorithm can greatly reduce the acquisition frequency, and can obtain the environment monitoring data changing curve with less error compared with the high-frequency acquisition of fixed frequency.
DOA Estimation Using Fourth-Order Cumulants in Nested Arrays with Structured Imperfections
Authors: Wang Baoping , Zheng Junhao
Published: February 2020

Recently developed super nested array families have drawn much attention owing to their merits on keeping the benefits of the standard nested arrays while further mitigating coupling in dense subarray portions. In this communication, a new mutual coupling model for nested arrays is constructed. Analyzing the structure of the newly formed mutual coupling matrix, a transformation of the distorted steering vector to separate angular information from the mutual coupling coefficients is revealed. By this property, direction of arrival (DOA) estimates can be determined via a grid search for the minimum of a determinant function of DOA, which is induced by the rank reduction property. We also extend the robust DOA estimation method to accommodate the unknown mutual coupling and gain-phase mismatches in the nested array. Compared with the schemes of super nested array families on reducing the mutual coupling effects, the solutions presented in this paper has two advantages: (a) It is applicable to the standard nested arrays without rearranging the configuration to increase the inter-element spacing, alleviating the cross talk in dense uniform linear arrays (ULAs) as well as gain-phase errors in sparse ULA parts; (b) Perturbations in nested arrays are estimated in colored noise, which is significant but rarely discussed before. Simulations results corroborate the superiority of the proposed methods using fourth-order cumulants.
Preparation of “Ion-Imprinting” Difunctional Magnetic Fluorescent Nanohybrid and Its Application to Detect Cadmium Ions
Authors: Chen Lina , Lu Yue , Qin Minshu , Liu Fa , Huang Liang , Wang Jing , Xu Hui , Li Na , Huang Guobao , Luo Zhihui , Zheng Baodong
Published: February 2020

In this work, we have fabricated a novel difunctional magnetic fluorescent nanohybrid (DMFN) for the determination of cadmium ions (Cd2+) in water samples, where the “off-on” model and “ion-imprinting” technique were incorporated simultaneously. The DMFN were composed of CdTe/CdS core-shell quantum dots (QD) covalently linked onto the surface of polystyrene magnetic microspheres (PMM) and characterized using ultraviolet-visible spectroscopy (UV-Vis), fluorescence spectroscopy, and transmission electron microscopy (TEM). Based on the favorable magnetic and fluorescent properties of the DMFN, the chemical etching of ethylene diamine tetraacetic acid (EDTA) at the surface produced specific Cd2+ recognition sites and quenched the red fluorescence of outer CdTe/CdS QD. Under optimal determination conditions, such as EDTA concentration, pH, and interfering ions, the working curve of determining Cd2+ was obtained; the equation was obtained Y = 34,759X + 254,894 (R = 0.9863) with a line range 0.05–8 μM, and the detection limit was 0.01 μM. Results showed that synthesized magnetic fluorescent microspheres had high sensitivity, selectivity, and reusability in detection. Moreover, they have significant potential value in fields such as biomedicine, analytical chemistry, ion detection, and fluorescence labeling.
Molecularly Imprinted Polymers and Surface Imprinted Polymers Based Electrochemical Biosensor for Infectious Diseases
Authors: Cui Feiyun , Zhou Zhiru , Zhou H. Susan
Published: February 2020

Owing to their merits of simple, fast, sensitive, and low cost, electrochemical biosensors have been widely used for the diagnosis of infectious diseases. As a critical element, the receptor determines the selectivity, stability, and accuracy of the electrochemical biosensors. Molecularly imprinted polymers (MIPs) and surface imprinted polymers (SIPs) have great potential to be robust artificial receptors. Therefore, extensive studies have been reported to develop MIPs/SIPs for the detection of infectious diseases with high selectivity and reliability. In this review, we discuss mechanisms of recognition events between imprinted polymers with different biomarkers, such as signaling molecules, microbial toxins, viruses, and bacterial and fungal cells. Then, various preparation methods of MIPs/SIPs for electrochemical biosensors are summarized. Especially, the methods of electropolymerization and micro-contact imprinting are emphasized. Furthermore, applications of MIPs/SIPs based electrochemical biosensors for infectious disease detection are highlighted. At last, challenges and perspectives are discussed.
MoreAir: A Low-Cost Urban Air Pollution Monitoring System
Authors: Gryech Ihsane , Ben-Aboud Yassine , Guermah Bassma , Sbihi Nada , Ghogho Mounir , Kobbane Abdellatif
Published: February 2020

MoreAir is a low-cost and agile urban air pollution monitoring system. This paper describes the methodology used in the development of this system along with some preliminary data analysis results. A key feature of MoreAir is its innovative sensor deployment strategy which is based on mobile and nomadic sensors as well as on medical data collected at a children’s hospital, used to identify urban areas of high prevalence of respiratory diseases. Another key feature is the use of machine learning to perform prediction. In this paper, Moroccan cities are taken as case studies. Using the agile deployment strategy of MoreAir, it is shown that in many Moroccan neighborhoods, road traffic has a smaller impact on the concentrations of particulate matters (PM) than other sources, such as public baths, public ovens, open-air street food vendors and thrift shops. A geographical information system has been developed to provide real-time information to the citizens about the air quality in different neighborhoods and thus raise awareness about urban pollution.
A Secure Enhanced Non-Cooperative Cognitive Division Multiple Access for Vehicle-to-Vehicle Communication
Authors: Al-Absi Mohammed Abdulhakim , Al-Absi Ahmed Abdulhakim , Lee Hoon Jae
Published: February 2020

The growth of the Internet has led to the increasing usage of smart infotainment applications on the vehicular ad-hoc network (VANET). Preserving privacy and security regarding the provision of smart infotainment applications while on the go is most desired. Thus, a secure authentication scheme is required. Many privacy-preserving security schemes have been developed in recent times using cryptography approaches. However, these incur key management and communication overhead. The usage of third-party servers incurs the overhead of key computation, storage and distribution. Post completion of the initialization phase, the message is secured using cryptography and is shared among vehicles. The design of the proposed secure enhanced non-cooperative cognitive division multiple access ( S − ENCCMA ) aims to eliminate the need for the local message available with the parties to be released for provisioning secure safety-related applications. To overcome the research challenges, this work presents a novel security scheme, namely secure non-cooperative cognitive medium access ( S − ENCCMA ). The experiment is conducted to evaluate the overhead incurred in provisioning security to ENCCMA . The outcome shows that the overhead incurred by S − ENCCMA over ENCCMA was negligible to provide the real-time security requirements of smart infotainment applications, which is experimentally shown in this paper in terms of throughput, collision and successful packet transmission considering varied environmental models such as cities, highways and rural areas.
A Novel Electrochemical Sensor Based on Electropolymerized Ion Imprinted PoPD/ERGO Composite for Trace Cd(II) Determination in Water
Authors: Wang Jiyang , Hu Jingfang , Hu Shiwei , Gao Guowei , Song Yu
Published: February 2020

A novel electrochemical sensor based on electropolymerized ion imprinted poly (o-phenylenediamine) PoPD/electrochemical reduced graphene (ERGO) composite on glass carbon electrode (GCE) was fabricated for selective and sensitive determination of trace Cd(II) in water. ERGO was first deposited on the surface of GCE by electrochemical cyclic voltammetry (CV) scanning to enhance the electron transport activity at electrode surface. The ion imprinted polymer (IIP) of imprinted PoPD was then in situ electropolymerized on ERGO via CV scanning with oPD as functional monomer and Cd(II) ions as template, following removal of the template using electrochemical peroxidation method. The obtained imprinted PoPD/RERGO composites were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray energy spectroscopy (EDS) for the observation of their morphologies and components. The electrochemical behavior of the imprinted PoPD/ERGO/GCE was performed by CV and SWASV. The fabricated sensor of the imprinted PoPD/ERGO/GCE showed a good selectivity toward target Cd(II) ions in the presence of other heavy metal ions. Under the optimized experimental conditions, the sensor exhibited a good linear relationship between SWASV stripping peak values and Cd(II) concentration in the range of 1 to 50 ng/mL, with the limit of detection as 0.13 ng/mL (S/N = 3). The proposed electrochemical sensor of imprinted PoPD/ERGO/GCE was successfully applied for trace Cd(II) determination in real water samples.
Detection of Early Signs of Diabetic Retinopathy Based on Textural and Morphological Information in Fundus Images
Authors: Colomer Adrián , Igual Jorge , Naranjo Valery
Published: February 2020

Estimated blind people in the world will exceed 40 million by 2025. To develop novel algorithms based on fundus image descriptors that allow the automatic classification of retinal tissue into healthy and pathological in early stages is necessary. In this paper, we focus on one of the most common pathologies in the current society: diabetic retinopathy. The proposed method avoids the necessity of lesion segmentation or candidate map generation before the classification stage. Local binary patterns and granulometric profiles are locally computed to extract texture and morphological information from retinal images. Different combinations of this information feed classification algorithms to optimally discriminate bright and dark lesions from healthy tissues. Through several experiments, the ability of the proposed system to identify diabetic retinopathy signs is validated using different public databases with a large degree of variability and without image exclusion.
Cross-Section Deformation Analysis and Visualization of Shield Tunnel Based on Mobile Tunnel Monitoring System
Authors: Sun Haili , Liu Shuang , Zhong Ruofei , Du Liming
Published: February 2020

With the ongoing developments in laser scanning technology, applications for describing tunnel deformation using rich point cloud data have become a significant topic of investigation. This study describes the independently developed a mobile tunnel monitoring system called the second version of Tunnel Scan developed by Capital Normal University (CNU-TS-2) for data acquisition, which has an electric system to control its forward speed and is compatible with various laser scanners such as the Faro and Leica models. A comparison with corresponding data acquired by total station data demonstrates that the data collected by CNU-TS-2 is accurate. Following data acquisition, the overall and local deformation of the tunnel is determined by denoising and 360° deformation analysis of the point cloud data. To enhance the expression of the analysis results, this study proposes an expansion of the tunnel point cloud data into a two-dimensional image via cylindrical projection, followed by an expression of the tunnel deformation through color difference to visualize the deformation. Compared with the three-dimensional modeling method of visualization, this method is easier to implement and facilitates storage. In addition, it is conducive to the performance of comprehensive analysis of problems such as water leakage in the tunnel, thereby achieving the effect of multiple uses for a single image.
Theoretical Investigation of an Alcohol-Filled Tellurite Photonic Crystal Fiber Temperature Sensor Based on Four-Wave Mixing
Authors: Sun Yue , Yan Xin , Wang Fang , Zhang Xuenan , Li Shuguang , Suzuki Takenobu , Ohishi Yasutake , Cheng Tonglei
Published: February 2020

For this study, a temperature sensor utilizing a novel tellurite photonic crystal fiber (PCF) is designed. In order to improve the sensor sensitivity, alcohol is filled in the air holes of the tellurite PCF. Based on the degenerate four-wave mixing theory, temperature sensing in the mid-infrared region (MIR) can be achieved by detecting the wavelength shift of signal waves and idler waves during variations in temperature. Simulation results show that at a pump wavelength of 3550 nm, the temperature sensitivity of this proposed sensor can be as high as 0.70 nm/°C. To the best of our knowledge, this is the first study to propose temperature sensing in the MIR by drawing on four-wave mixing (FWM) in a non-silica PCF.
Mask-Refined R-CNN: A Network for Refining Object Details in Instance Segmentation
Authors: Zhang Yiqing , Chu Jun , Leng Lu , Miao Jun
Published: February 2020

With the rapid development of flexible vision sensors and visual sensor networks, computer vision tasks, such as object detection and tracking, are entering a new phase. Accordingly, the more challenging comprehensive task, including instance segmentation, can develop rapidly. Most state-of-the-art network frameworks, for instance, segmentation, are based on Mask R-CNN (mask region-convolutional neural network). However, the experimental results confirm that Mask R-CNN does not always successfully predict instance details. The scale-invariant fully convolutional network structure of Mask R-CNN ignores the difference in spatial information between receptive fields of different sizes. A large-scale receptive field focuses more on detailed information, whereas a small-scale receptive field focuses more on semantic information. So the network cannot consider the relationship between the pixels at the object edge, and these pixels will be misclassified. To overcome this problem, Mask-Refined R-CNN (MR R-CNN) is proposed, in which the stride of ROIAlign (region of interest align) is adjusted. In addition, the original fully convolutional layer is replaced with a new semantic segmentation layer that realizes feature fusion by constructing a feature pyramid network and summing the forward and backward transmissions of feature maps of the same resolution. The segmentation accuracy is substantially improved by combining the feature layers that focus on the global and detailed information. The experimental results on the COCO (Common Objects in Context) and Cityscapes datasets demonstrate that the segmentation accuracy of MR R-CNN is about 2% higher than that of Mask R-CNN using the same backbone. The average precision of large instances reaches 56.6%, which is higher than those of all state-of-the-art methods. In addition, the proposed method requires low time cost and is easily implemented. The experiments on the Cityscapes dataset also prove that the proposed method has great generalization ability.
Error-Aware Data Clustering for In-Network Data Reduction in Wireless Sensor Networks
Authors: Alam M. K. , Aziz Azrina Abd , Latif S. A. , Awang Azlan
Published: February 2020

A wireless sensor network (WSN) deploys hundreds or thousands of nodes that may introduce large-scale data over time. Dealing with such an amount of collected data is a real challenge for energy-constraint sensor nodes. Therefore, numerous research works have been carried out to design efficient data clustering techniques in WSNs to eliminate the amount of redundant data before transmitting them to the sink while preserving their fundamental properties. This paper develops a new error-aware data clustering (EDC) technique at the cluster-heads (CHs) for in-network data reduction. The proposed EDC consists of three adaptive modules that allow users to choose the module that suits their requirements and the quality of the data. The histogram-based data clustering (HDC) module groups temporal correlated data into clusters and eliminates correlated data from each cluster. Recursive outlier detection and smoothing (RODS) with HDC module provides error-aware data clustering, which detects random outliers using temporal correlation of data to maintain data reduction errors within a predefined threshold. Verification of RODS (V-RODS) with HDC module detects not only random outliers but also frequent outliers simultaneously based on both the temporal and spatial correlations of the data. The simulation results show that the proposed EDC is computationally cheap, able to reduce a significant amount of redundant data with minimum error, and provides efficient error-aware data clustering solutions for remote monitoring environmental applications.
Shadow Detection in Still Road Images Using Chrominance Properties of Shadows and Spectral Power Distribution of the Illumination
Authors: Ibarra-Arenado Manuel José , Tjahjadi Tardi , Pérez-Oria Juan
Published: February 2020

A well-known challenge in vision-based driver assistance systems is cast shadows on the road, which makes fundamental tasks such as road and lane detections difficult. In as much as shadow detection relies on shadow features, in this paper, we propose a set of new chrominance properties of shadows based on the skylight and sunlight contributions to the road surface chromaticity. Six constraints on shadow and non-shadowed regions are derived from these properties. The chrominance properties and the associated constraints are used as shadow features in an effective shadow detection method intended to be integrated on an onboard road detection system where the identification of cast shadows on the road is a determinant stage. Onboard systems deal with still outdoor images; thus, the approach focuses on distinguishing shadow boundaries from material changes by considering two illumination sources: sky and sun. A non-shadowed road region is illuminated by both skylight and sunlight, whereas a shadowed one is illuminated by skylight only; thus, their chromaticity varies. The shadow edge detection strategy consists of the identification of image edges separating shadowed and non-shadowed road regions. The classification is achieved by verifying whether the pixel chrominance values of regions on both sides of the image edges satisfy the six constraints. Experiments on real traffic scenes demonstrated the effectiveness of our shadow detection system in detecting shadow edges on the road and material-change edges, outperforming previous shadow detection methods based on physical features, and showing the high potential of the new chrominance properties.
Temperature-Modulated Micromechanical Thermal Analysis with Microstring Resonators Detects Multiple Coherent Features of Small Molecule Glass Transition
Authors: Karl Maximilian , Thamdrup Lasse H.E. , Rantanen Jukka , Boisen Anja , Rades Thomas
Published: February 2020

Micromechanical Thermal Analysis utilizes microstring resonators to analyze a minimum amount of sample to obtain both the thermal and mechanical responses of the sample during a heating ramp. We introduce a modulated setup by superimposing a sinusoidal heating on the linear heating and implementing a post-measurement data deconvolution process. This setup is utilized to take a closer look at the glass transition as an important fundamental feature of amorphous matter with relations to the processing and physical stability of small molecule drugs. With an additionally developed image and qualitative mode shape analysis, we are able to separate distinct features of the glass transition process and explain a previously observed two-fold change in resonance frequency. The results from this setup indicate the detection of initial relaxation to viscous flow onset as well as differences in mode responsivity and possible changes in the primary resonance mode of the string resonators. The modulated setup is helpful to distinguish these processes during the glass transition with varying responses in the frequency and quality factor domain and offers a more robust way to detect the glass transition compared to previously developed methods. Furthermore, practical and theoretical considerations are discussed when performing measurements on string resonators (and comparable emerging analytical techniques) for physicochemical characterization.
Inline Reticulorumen pH as an Indicator of Cows Reproduction and Health Status
Authors: Antanaitis Ramūnas , Juozaitienė Vida , Malašauskienė Dovilė , Televičius Mindaugas
Published: February 2020

Our study hypothesis is that the interline registered pH of the cow reticulum can be used as an indicator of health and reproductive status. The main objective of this study was to examine the relationship of pH, using the indicators of the automatic milking system (AMS), with some parameters of cow blood components. The following four main groups were used to classify cow health status: 15–30 d postpartum, 1–34 d after insemination, 35 d after insemination (not pregnant), and 35 d (pregnant). Using the reticulum pH assay, the animals were categorized as pH < 6.22 (5.3% of cows), pH 6.22–6.42 (42.1% of cows), pH 2.6–6.62 (21.1% of cows), and pH > 6.62 (10.5% of cows). Using milking robots, milk yield, fat protein, lactose level, somatic cell count, and electron conductivity were registered. Other parameters assessed included the temperature and pH of the contents of reticulorumens. Assessment of the aforementioned parameters was done using specific smaX-tec boluses. Blood gas parameters were assessed using a blood gas analyzer (EPOC (Siemens Healthcare GmbH, Erlangen, Germany). The study findings indicated that pregnant cows have a higher pH during insemination than that of non-pregnant ones. It was also noted that cows with a low fat/protein ratio, lactose level, and high SCC had low reticulorumen pH. They also had the lowest blood pH. It was also noted that, with the increase of reticulorumen pH, there was an increased level of blood potassium, a high hematocrit, and low sodium and carbon dioxide saturation.
Classification of VLF/LF Lightning Signals Using Sensors and Deep Learning Methods
Authors: Wang Jiaquan , Huang Qijun , Ma Qiming , Chang Sheng , He Jin , Wang Hao , Zhou Xiao , Xiao Fang , Gao Chao
Published: February 2020

Lightning waveform plays an important role in lightning observation, location, and lightning disaster investigation. Based on a large amount of lightning waveform data provided by existing real-time very low frequency/low frequency (VLF/LF) lightning waveform acquisition equipment, an automatic and accurate lightning waveform classification method becomes extremely important. With the widespread application of deep learning in image and speech recognition, it becomes possible to use deep learning to classify lightning waveforms. In this study, 50,000 lightning waveform samples were collected. The data was divided into the following categories: positive cloud ground flash, negative cloud ground flash, cloud ground flash with ionosphere reflection signal, positive narrow bipolar event, negative narrow bipolar event, positive pre-breakdown process, negative pre-breakdown process, continuous multi-pulse cloud flash, bipolar pulse, skywave. A multi-layer one-dimensional convolutional neural network (1D-CNN) was designed to automatically extract VLF/LF lightning waveform features and distinguish lightning waveforms. The model achieved an overall accuracy of 99.11% in the lightning dataset and overall accuracy of 97.55% in a thunderstorm process. Considering its excellent performance, this model could be used in lightning sensors to assist in lightning monitoring and positioning.
Motion Artifact Suppression for Insulated EMG to Control Myoelectric Prostheses
Authors: Roland Theresa
Published: February 2020

Myoelectric prostheses help amputees to regain independence and a higher quality of life. These prostheses are controlled by electromyography, which measures an electrical signal at the skin surface during muscle contractions. In this contribution, the electromyography is measured with innovative flexible insulated sensors, which separate the skin and the sensor area by a dielectric layer. Electromyography sensors, and biosignal sensors in general, are striving for higher robustness against motion artifacts, which are a major obstacle in real-world environment. The motion artifact suppression algorithms presented in this article, prevent the activation of the prosthesis drive during artifacts, thereby achieving a substantial performance boost. These algorithms classify the signal into muscle contractions and artifacts. Therefore, new time domain features, such as Mean Crossing Rate are introduced and well-established time domain features (e.g., Zero-Crossing Rate, Slope Sign Change) are modified and implemented. Various artificial intelligence models, which require low calculation resources for an application in a wearable device, were investigated. These models are neural networks, recurrent neural networks, decision trees and logistic regressions. Although these models are designed for a low-power real-time embedded system, high accuracies in discriminating artifacts to contractions of up to 99.9% are achieved. The models were implemented and trained for fast response leading to a high performance in real-world environment. For highest accuracies, recurrent neural networks are suggested and for minimum runtime (0.99–1.15 μ s), decision trees are preferred.
Influence of Temperature Variation on the Vibrational Characteristics of Fused Silica Cylindrical Resonators for Coriolis Vibratory Gyroscopes
Authors: Xiao Pengbo , Qiu Zhinan , Luo Yiming , Pan Yao , Qu Tianliang , Yang Kaiyong , Luo Hui , Qin Shiqiao
Published: February 2020

The fused silica cylindrical resonator is a type of axisymmetric resonator that can be used for Coriolis vibratory gyroscopes. Although the resonant frequency, frequency mismatch, and Q factor are natural properties of the resonator, they can change with temperature. Therefore, the temperature drift severely limits the detection accuracy and bias stability of the gyroscope. In this paper, the influence of temperature variation on the vibrational characteristics of fused silica cylindrical resonators was investigated. Experiments were performed on a fused silica cylindrical resonator coated with Cr/Au films. It was shown that at the temperature range from 253.15 K to 353.15 K, the resonant frequency linearly increased with temperature, the frequency mismatch remained unchanged, and the Q factor gradually increased till about 333.15 K, when it began to decrease. Meanwhile, the change of thermoelastic damping with temperature may dominate the variation of Q factor at the temperature range from 253.15 K to 353.15 K. This phenomenon was theoretically analyzed and the variation trends of results were consistent with the theoretical analysis. This study indicates that, for the fused silica cylindrical resonator, to discover the influence of temperature variation on the resonant frequency, frequency mismatch, and Q factor, there are certain rules to follow and repeat. The relationship between temperature and frequency can be established, which provides the feasibility of using self-calibration based on temperature characteristics of the resonator for temperature drift compensations. Additionally, there is an optimum temperature that may improve the performance of the Coriolis vibratory gyroscope with the fused silica cylindrical resonator.
Vehicle Make and Model Recognition using Bag of Expressions
Authors: Jamil Adeel Ahmad , Hussain Fawad , Yousaf Muhammad Haroon , Butt Ammar Mohsin , Velastin Sergio A.
Published: February 2020

Vehicle make and model recognition (VMMR) is a key task for automated vehicular surveillance (AVS) and various intelligent transport system (ITS) applications. In this paper, we propose and study the suitability of the bag of expressions (BoE) approach for VMMR-based applications. The method includes neighborhood information in addition to visual words. BoE improves the existing power of a bag of words (BOW) approach, including occlusion handling, scale invariance and view independence. The proposed approach extracts features using a combination of different keypoint detectors and a Histogram of Oriented Gradients (HOG) descriptor. An optimized dictionary of expressions is formed using visual words acquired through k-means clustering. The histogram of expressions is created by computing the occurrences of each expression in the image. For classification, multiclass linear support vector machines (SVM) are trained over the BoE-based features representation. The approach has been evaluated by applying cross-validation tests on the publicly available National Taiwan Ocean University-Make and Model Recognition (NTOU-MMR) dataset, and experimental results show that it outperforms recent approaches for VMMR. With multiclass linear SVM classification, promising average accuracy and processing speed are obtained using a combination of keypoint detectors with HOG-based BoE description, making it applicable to real-time VMMR systems.
A Novel Experiment-Free Site-Specific TDoA Localization Performance-Evaluation Approach
Authors: Ye Xiaokang , Rodríguez-Piñeiro José , Liu Yuan , Yin Xuefeng , Pérez Yuste Antonio
Published: February 2020

Time difference of arrival (TDoA) technology is widely utilized for source localization, which stimulates many studies on performance-evaluation approaches for TDoA localization systems. Some approaches using simulations are designed merely for a simple Line-of-Sight (LoS) scenario while some other ones using experiments show high cost and inefficiency. This paper proposes an integrated approach to evaluate a TDoA localization system in an area with a complicated environment. Radio propagation graph is applied through a simulation to obtain channel impulse responses (CIRs) between a source to be located and the TDoA sensors for the area. Realistic signals received by the sensors in baseband are emulated combining the source transmitted signal and the CIRs. A hardware unit takes charge of sending the radio emulated received signals to the system under test, which is consistent with real experimental measurements. Statistical analysis of the system is allowed based on localization errors obtained comparing the system’s estimates with the ground truth of the source location. Verified results for LoS and non-LoS scenarios with variable transmitted signal bandwidths and signal-to-noise ratios, as well as for three variations of the sensor locations in an automobile circuit, show the usability of the proposed experiment-free performance-evaluation approach.
Large-Scale Marsh Loss Reconstructed from Satellite Data in the Small Sanjiang Plain since 1965: Process, Pattern and Driving Force
Authors: Yan Fengqin
Published: February 2020

Monitoring wetland dynamics and related land-use changes over long-time periods is essential to understanding wetland evolution and supporting knowledge-based conservation policies. Combining multi-source remote sensing images, this study identifies the dynamics of marshes, a core part of wetlands, in the Small Sanjiang Plain (SSP), from 1965 to 2015. The influence of human activities on marsh patterns is estimated quantitatively by the trajectory analysis method. The results indicate that the marsh area decreased drastically by 53.17% of the total SSP area during the study period, which covered the last five decades. The marsh mostly transformed to paddy field and dry farmland in the SSP from 1965 to 2015, indicating that agricultural encroachment was the dominant contributor to marsh degradation in the area. Analysis of the landscape indexes indicates that marsh fragmentation was aggravated during the past five decades in the SSP. Trajectory analysis also indicated that human activities have acted as the primary driving force of marsh changes in the SSP since 1965. This study provides scientific information to better understand the evolution of the wetland and to implement ecological conservation and sustainable management of the wetlands in the future.