bluuebunny/crossref_metadata_2025 huggingface dataset:
Developing and Testing Models for Sea Surface Wind Speed Estimation with GNSS-R Delay Doppler Maps and Delay Waveforms
Authors: Bu Jinwei , Yu Kegen , Zhu Yongchao , Qian Nijia , Chang Jun
Published: November 2020

This paper focuses on sea surface wind speed estimation based on cyclone global navigation satellite system reflectometry (GNSS-R) data. In order to extract useful information from delay-Doppler map (DDM) data, three delay waveforms are presented for wind speed estimation. The delay waveform without Doppler shift is defined as central delay waveform (CDW), and the integral of the delay waveforms with different Doppler shift values is defined as integral delay waveform (IDW), while the difference between normalized IDW (NIDW) and normalized CDW (NCDW) is defined as differential delay waveform (DDW). We first propose a data filtering method based on threshold setting for data quality control. This method can select good-quality DDM data by adjusting the root mean square (RMS) threshold of cleaned DDW. Then, the normalized bistatic radar scattering cross section (NBRCS) and the leading edge slope (LES) of IDW are calculated using clean DDM data. Wind speed estimation models based on NBRCS and LES observations are then developed, respectively, and on this basis, a combination wind speed estimation model based on determination coefficient is further proposed. The CYGNSS data and ECMWF reanalysis data collected from 12 May 2020 to 12 August 2020 are used, excluding data collected on land, to evaluate the proposed models. The evaluation results show that the wind speed estimation accuracy of the piecewise function model based on NBRCS is 2.3 m/s in terms of root mean square error (RMSE), while that of the double-parameter and triple-parameter models is 2.6 and 2.7 m/s, respectively. The wind speed estimation accuracy of the double-parameter and triple-parameter models based on LES is 3.3 and 2.5 m/s. The results also demonstrate that the RMSE of the combination method is 2.1 m/s, and the coefficient of determination is 0.906, achieving a considerable performance gain compared with the individual NBRCS- and LES-based methods.
Two-Dimensional Simulation of Barley Growth and Yield Using a Model Integrated with Remote-Controlled Aerial Imagery
Authors: Shawon Ashifur Rahman , Ko Jonghan , Jeong Seungtaek , Shin Taehwan , Lee Kyung Do , Shim Sang In
Published: November 2020

It is important to be able to predict the yield and monitor the growth conditions of crops in the field to increase productivity. One way to assess field-based geospatial crop productivity is by integrating a crop model with a remote-controlled aerial system (RAS). The objective of this study was to simulate spatiotemporal barley growth and yield based on the development of a crop-modeling system integrated with RAS-based remote sensing images. We performed field experiments to obtain ground truth data and RAS images of crop growth conditions and yields at Chonnam National University (CNU), Gwangju, South Korea in 2018, and at Gyeongsang National University (GNU), Jinju, South Gyeongsang, South Korea in 2018 and 2019. In model calibration, there was no significant difference (p = 0.12) between the simulated barley yields and measured yields, based on a two-sample t-test at CNU in 2018. In model validation, there was no significant difference between simulated yields and measured yields at p = 0.98 and 0.76, according to two-sample t-tests at GNU in 2018 and 2019, respectively. The remote sensing-integrated crop model accurately reproduced geospatial variations in barley yield and growth variables. The results demonstrate that the crop modeling approach is useful for monitoring at-field barley conditions.
Closed-Loop Temperature Control Based on Fiber Bragg Grating Sensors for Laser Ablation of Hepatic Tissue
Authors: Korganbayev Sanzhar , Orrico Annalisa , Bianchi Leonardo , De Landro Martina , Wolf Alexey , Dostovalov Alexander , Saccomandi Paola
Published: November 2020

Laser ablation (LA) of cancer is a minimally invasive technique based on targeted heat release. Controlling tissue temperature during LA is crucial to achieve the desired therapeutic effect in the organs while preserving the healthy tissue around. Here, we report the design and implementation of a real-time monitoring system performing closed-loop temperature control, based on fiber Bragg grating (FBG) spatial measurements. Highly dense FBG arrays (1.19 mm length, 0.01 mm edge-to-edge distance) were inscribed in polyimide-coated fibers using the femtosecond point-by-point writing technology to obtain the spatial resolution needed for accurate reconstruction of high-gradient temperature profiles during LA. The zone control strategy was implemented such that the temperature in the laser-irradiated area was maintained at specific set values (43 and 55 °C), in correspondence to specific radii (2 and 6 mm) of the targeted zone. The developed control system was assessed in terms of measured temperature maps during an ex vivo liver LA. Results suggest that the temperature-feedback system provides several advantages, including controlling the margins of the ablated zone and keeping the maximum temperature below the critical values. Our strategy and resulting analysis go beyond the state-of-the-art LA regulation techniques, encouraging further investigation in the identification of the optimal control-loop.
Identification of Building Damage from UAV-Based Photogrammetric Point Clouds Using Supervoxel Segmentation and Latent Dirichlet Allocation Model
Authors: Liu Chaoxian , Sui Haigang , Huang Lihong
Published: November 2020

Accurate assessment of building damage is very important for disaster response and rescue. Traditional damage detection techniques using 2D features at a single observing angle cannot objectively and accurately reflect the structural damage conditions. With the development of unmanned aerial vehicle photogrammetric techniques and 3D point processing, automatic and accurate damage detection for building roof and facade has become a research hotspot in recent work. In this paper, we propose a building damage detection framework based on the boundary refined supervoxel segmentation and random forest–latent Dirichlet allocation classification. First, the traditional supervoxel segmentation method is improved to segment the point clouds into good boundary refined supervoxels. Then, non-building points such as ground and vegetation are removed from the generated supervoxels. Next, latent Dirichlet allocation (LDA) model is used to construct the high-level feature representation for each building supervoxel based on the selected 2D image and 3D point features. Finally, LDA model and random forest algorithm are employed to identify the damaged building regions. This method is applied to oblique photogrammetric point clouds collected from the Beichuan Country Earthquake Site. The research achieves the 3D damage assessment for building facade and roof. The result demonstrates that the proposed framework is capable of achieving around 94% accuracy for building point extraction and around 90% accuracy for damage identification. Moreover, both of the precision and recall for building damage detection reached around 89%. Concluded from comparison analysis, the proposed method improved the damage detection accuracy and the highest improvement ratio is over 8%.
Growth Analysis of Wheat Using Machine Vision: Opportunities and Challenges
Authors: Ajlouni Mohammad , Kruse Audrey , Condori-Apfata Jorge A. , Valderrama Valencia Maria , Hoagland Chris , Yang Yang , Mohammadi Mohsen
Published: November 2020

Crop growth analysis is used for the assessment of crop yield potential and stress tolerance. Capturing continuous plant growth has been a goal since the early 20th century; however, this requires a large number of replicates and multiple destructive measurements. The use of machine vision techniques holds promise as a fast, reliable, and non-destructive method to analyze crop growth based on surrogates for plant traits and growth parameters. We used machine vision to infer plant size along with destructive measurements at multiple time points to analyze growth parameters of spring wheat genotypes. We measured side-projected area by machine vision and RGB imaging. Three traits, i.e., biomass (BIO), leaf dry weight (LDW), and leaf area (LA), were measured using low-throughput techniques. However, RGB imaging was used to produce side projected area (SPA) as the high throughput trait. Significant effects of time point and genotype on BIO, LDW, LA, and SPA were observed. SPA was a robust predictor of leaf area, leaf dry weight, and biomass. Relative growth rate estimated using SPA was a robust predictor of the relative growth rate measured using biomass and leaf dry weight. Large numbers of entries can be assessed by this method for genetic mapping projects to produce a continuous growth curve with fewer replicates.
IoT Adaptive Dynamic Blockchain Networking Method Based on Discrete Heartbeat Signals
Authors: Hu Xueyang , Zheng Yili , Su Yu , Guo Rui
Published: November 2020

The combination of blockchain technology and Internet of Things (IoT) technology has brought many significant advantages and new development directions. With the development of embedded technology and 5G communication technology, the performance limitations and network limitations that are traditionally believed to restrict the application of blockchain technology to IoT devices have been broken. The development of “blockchain + 5G + IoT” provides reliable data from the source for the blockchain, linking the credible mapping of physical assets and digital assets. However, at the beginning of the blockchain design, the application of the IoT was not fully considered, so there have been some obvious defects in applying the blockchain technology in the IoT. In the Byzantine fault tolerance (BFT) consensus algorithm of traditional blockchain, the entire blockchain network will become paralyzed when more than 1/3 of the nodes in the network are offline. However, in IoT applications, this situation is likely to occur and greatly limits the security and stability of the application of blockchain technology in the IoT. In order to solve this problem, we proposed an IoT adaptive dynamic blockchain networking method based on discrete heartbeat signals. The feature of the method is to set a different monitoring time for each group of nodes, that is, discrete heartbeat signals monitoring. When the number of nodes gradually decreases, the IoT adaptive dynamic blockchain network can dynamically adapt to this process. Even when more than 1/3 of the IoT are offline, the adaptive dynamic IoT blockchain network can maintain stable running. This method also has the advantages of a short network expectation recovery time and avoids instantaneous system paralysis caused by the thundering herd effect. This research improves the security and stability of the application of blockchain technology in the IoT, and provides the necessary technical foundation for the better combination of blockchain technology and IoT technology.
Experimental Study of a Signal Modulation Method to Improve eLORAN Data Channel Communications
Authors: Yang Chaozhong , Wang Yulin , Li Shifeng , Yan Wenhe
Published: November 2020

There are mainly two types of data modulation methods used for enhanced LOng-RAnge Navigation (eLORAN) systems: pulse position modulation (PPM) and supernumerary interpulse modulation (SIM). The typical application for PPM is tri-state PPM (3S-PPM), also known as Eurofix. The typical application for SIM is ninth pulse modulation. Both of these methods are phase modulation methods. Phase modulation coding, a very mature technology, is used at present. To achieve a better demodulation success rate of eLORAN digital modulation signals at longer distances, a method of using the transmitting station duplex mode to transmit a digital modulation pulse group after LORAN-C transmitting a pulse group is proposed to realize modulation pulse on–off modulation. In this method, a broadcasting experiment was performed on the BPL (The call sign of eLORAN time service system in China) broadcaster station. After monitoring, a good receiving demodulation effect was initially obtained.
An Investigation of the Accuracy of EC5 and 5TE Capacitance Sensors for Soil Moisture Monitoring in Urban Soils-Laboratory and Field Calibration
Authors: Kanso Tala , Gromaire Marie-Christine , Ramier David , Dubois Philippe , Chebbo Ghassan
Published: November 2020

Recently, emphasis has been placed on finding a reliable estimation of soil water content. In this study, two capacitance sensors EC5 and 5TE (METER Group) were utilized. These sensors provide many benefits relative to other sensors in that they are cost-effective and very economical regarding energy use, operate at a high measurement frequency of 70 MHz, and are dedicated to measuring at a small volume because of their small size. This makes them suitable for the context of use in this research, which consists of multiple sustainable drainage systems SuDS. Several studies have evaluated these two types of sensor but not for urban soils with specific characteristics. In addition, results from the literature are divergent and the published calibration data are limited. Therefore, an in-depth investigation of their accuracy is assessed in this paper. At first, the literature’s existing procedures and methods were examined. The sensor-to-sensor variability, as well as repeatability, were tested in soil and solutions. Additionally, a field calibration method was conducted to estimate the effects of soil texture on sensors readings. Two laboratory calibration methods having different principles were also applied, compared to each other and to the field calibration as well. Results revealed weak sensor-to-sensor variability (coefficient of variation up to 15% in soil) and also good repeatability (0.1%), for both sensors. A soil-specific calibration equation has improved the estimation of the volumetric water content. In case of soil having high field bulk density, the undisturbed soil calibration method described and proposed in this paper gives promising results. The latter method yields a volumetric water content (VWC) prediction accuracy of 0.025 m3∙m−3 on a sandy loam soil. This paper presents a large knowledge of capacitance sensors measurement technique as well as their calibration procedures and methods. Limitations of existing procedures have been identified and key elements for selecting the appropriate one are suggested. Derived calibration equations have also been provided for three urban soils with different particle size distribution, ranging from sandy loam to silt loam. Accurate monitoring of soil moisture content in urban soils is thus achievable.
Sensitivity Analysis of Acoustic Emission Detection Using Fiber Bragg Gratings with Different Optical Fiber Diameters
Authors: Violakis Georgios , Le-Quang Tri , Shevchik Sergey A. , Wasmer Kilian
Published: November 2020

Acoustic Emission (AE) detection and, in particular, ultrasound detection are excellent tools for structural health monitoring or medical diagnosis. Despite the technological maturity of the well-received piezoelectric transducer, optical fiber AE detection sensors are attracting increasing attention due to their small size, and electromagnetic and chemical immunity as well as the broad frequency response of Fiber Bragg Grating (FBG) sensors in these fibers. Due to the merits of their small size, FBGs were inscribed in optical fibers with diameters of 50 and 80 μm in this work. The manufactured FBGs were used for the detection of reproducible acoustic waves using the edge filter detection method. The acquired acoustic signals were compared to the ones captured by a standard 125 μm-diameter optical fiber FBG. Result analysis was performed by utilizing fast Fourier and wavelet decompositions. Both analyses reveal a higher sensitivity and dynamic range for the 50 μm-diameter optical fiber, despite it being more prone to noise than the other two, due to non-standard splicing methods and mode field mismatch losses. Consequently, the use of smaller-diameter optical fibers for AE detection is favorable for both the sensor sensitivity as well as physical footprint.
Study on the Vibration Characteristics of the Telescope T80 in the Javalambre Astrophysical Observatory (JAO) Aimed at Detecting Invalid Images
Authors: Arranz Martínez Fernando , Martín Ferrer Raúl , Palacios-Navarro Guillermo , Ramos Lorente Pedro
Published: November 2020

The location of large telescopes, generally far from the data processing centers, represents a logistical problem for the supervision of the capture of images. In this work, we carried out a preliminary study of the vibration signature of the T80 telescope at the Javalambre Astrophysical Observatory (JAO). The study analyzed the process of calculating the displacement that occurs because of the vibration in each of the frequencies in the range of interest. We analyzed the problems associated with very low frequencies by means of simulation, finding the most critical vibrations below 20 Hz, since they are the ones that generate greater displacements. The work also relates previous studies based on simulation with the real measurements of the vibration of the telescope taken remotely when it is subjected to different positioning movements (right ascension and/or declination) or when it performs movement actions such as those related to filter trays or mirror cover. The obtained results allow us to design a remote alarm system to detect invalid images (taken with excess vibration).
A High-Voltage and Low-Noise Power Amplifier for Driving Piezoelectric Stack Actuators
Authors: Xu Lisong , Li Hongwen , Li Pengzhi , Ge Chuan
Published: November 2020

In this paper, based on the principles of general operational amplifiers, a high-voltage operational amplifier is developed. Considering the influences of piezoelectric stack actuators on the circuit, a novel structure using the high-voltage operational amplifier as a noninverting amplifier is proposed. Because of the simple circuit principles and the voltage feedback control structure, the proposed power amplifier has the advantages of low noise and small size, and it can be realized by discrete electric elements easily. In the application of precision positioning, a power amplifier using the proposed circuit principles for driving piezoelectric stack actuators is designed, simulated, and tested. The simulated results show that the proposed power amplifier could conform to the theory of the circuit. The experimental results show that the designed power amplifier conforms to the simulation, the bandwidth of the power amplifier is about 57 kHz, and the ripple of the power amplifier is less than 2 mV. Furthermore, the output of the proposed power amplifier maintains the same type of wave within in a large range of frequency, while the input is the sinusoidal or square wave, and the resolution of the mechanism which the power amplifier is applied in is about 4.5 nm. By selecting the critical electronic elements and using feedback control, the proposed circuit structure is able to realize a low-cost and high-performance power amplifier to drive piezoelectric stack actuators flexibly, which is the novel work of the paper.
In Situ Shear Test for Revealing the Mechanical Properties of the Gravelly Slip Zone Soil
Authors: Zou Zongxing , Zhang Qi , Xiong Chengren , Tang Huiming , Fan Lei , Xie Fang , Yan Junbiao , Luo Yinfeng
Published: November 2020

Slip zone soil is usually composed of clay or silty clay; in some special geological environments, it contains gravels, which make the properties of the slip zone soil more complex. Unfortunately, in many indoor shear tests, gravels are removed to meet the demands of apparatus size, and the in situ mechanical property of the gravelly slip zone soil is rarely studied. In this study, the shear mechanical property of the gravelly slip zone soil of Huangtupo landslide in the Three Gorges Reservoir area of China was investigated by the in situ shear test. The test results show that the shear deformation process of the gravelly slip zone soil includes an elastic deformation stage, elastic–plastic deformation stage, and plastic deformation stage. Four functions were introduced to express the shear constitutive model of the gravelly slip zone soil, and the asymmetric sigmoid function was demonstrated to be the optimum one to describe the relationship of the shear stress and shear displacement with a correlation coefficient of 0.986. The comparison between the in situ test and indoor direct shear test indicates that gravels increase the strength of the slip zone soil. Therefore, the shear strength parameters of the gravelly slip zone soil obtained by the in situ test are more preferable for evaluating the stability of the landslide and designing the anti-slide structures.
Bicycling Phase Recognition for Lower Limb Amputees Using Support Vector Machine Optimized by Particle Swarm Optimization
Authors: Li Xinxin , Liu Zuojun , Gao Xinzhi , Zhang Jie
Published: November 2020

A novel method for recognizing the phases in bicycling of lower limb amputees using support vector machine (SVM) optimized by particle swarm optimization (PSO) is proposed in this paper. The method is essential for enhanced prosthetic knee joint control for lower limb amputees in carrying out bicycling activity. Some wireless wearable accelerometers and a knee joint angle sensor are installed in the prosthesis to obtain data on the knee joint and ankle joint horizontal, vertical acceleration signal and knee joint angle. In order to overcome the problem of high noise content in the collected data, a soft-hard threshold filter was used to remove the noise caused by the vibration. The filtered information is then used to extract the multi-dimensional feature vector for the training of SVM for performing bicycling phase recognition. The SVM is optimized by PSO to enhance its classification accuracy. The recognition accuracy of the PSO-SVM classification model on testing data is 93%, which is much higher than those of BP, SVM and PSO-BP classification models.
Performance Evaluation of an Integrated Fuzzy-Based Driving-Support System for Real-Time Risk Management in VANETs
Authors: Bylykbashi Kevin , Qafzezi Ermioni , Ampririt Phudit , Ikeda Makoto , Matsuo Keita , Barolli Leonard
Published: November 2020

The highly competitive and rapidly advancing autonomous vehicle race has been on for several years now, and it has made the driver-assistance systems a shadow of their former self. Nevertheless, automated vehicles have many obstacles on the way, and until we have them on the roads, promising solutions that can be achievable in the near future should be sought-after. Driving-support technologies have proven themselves to be effective in the battle against car crashes, and with Vehicular Ad hoc Networks (VANETs) supporting them, their efficiency is expected to rise steeply. In this work, we propose and implement a driving-support system which, on the one hand, could immensely benefit from major advancement of VANETs, but on the other hand can effectively be implemented as a stand-alone system. The proposed system consists of a non-intrusive integrated fuzzy-based system able to detect a risky situation in real time and alert the driver about the danger. It makes use of the information acquired from various in-car sensors as well as from communications with other vehicles and infrastructure to evaluate the condition of the considered parameters. The parameters include factors that affect the driver’s ability to drive, such as his/her current health condition and the inside environment in which he/she is driving, the vehicle speed, and factors related to the outside environment such as the weather and road condition. We show the effect of these parameters on the determination of the driving risk level through simulations and experiments and explain how these risk levels are translated into actions that can help the driver to manage certain risky situations, thus improving the driving safety.
PatientDataChain: A Blockchain-Based Approach to Integrate Personal Health Records
Authors: Cernian Alexandra , Tiganoaia Bogdan , Sacala Ioan , Pavel Adrian , Iftemi Alin
Published: November 2020

Currently there is not a single trusted infrastructure used for the exchange and storage of medical data along the healthcare value chain and, thus, there is no platform used for monitoring patients’ traceability within the entire healthcare chain. This situation leads to difficult communication and increased procedural costs, and thus it limits healthcare players from developing a better understanding and know-how of patients’ traceability that could further boost innovation and development of the best-fitted health services. PatientDataChain blockchain-based technology is a novel approach, based on a decentralized healthcare infrastructure that incorporates a trust layer in the healthcare value chain. Our aim was to provide an integrated vision based on interoperability principles, that relies on the usage of specific sensors from various wearable devices, allowing us to collect specific data from patients’ medical records. Interconnecting different healthcare providers, the collected data is integrated into a unitary personal health records (PHR) system, where the patient is the owner of his/her data. The decentralized nature of PatientDataChain, based on blockchain technology, leveraged the proper context to create a novel and improved data-sharing and exchange system, which is secure, flexible, and reliable. This approach brings increased benefits to data confidentiality and privacy, while providing secure access to patient medical records. This paper presents the design, implementation, and experimental validation of our proposed system, called PatientDataChain. The original contributions of our paper include the definition of the concept of unifying the entire healthcare value chain, the design of the architectural model of the system, the development of the system components, as well as the validation through a proof of concept (PoC) conducted with a medical clinic from Bucharest, using a dataset of 100 patients and over 1000 transactions. The proof of concept demonstrated the feasibility of the model in integrating the personal health records from heterogeneous sources (healthcare systems and sensors) in a unified, decentralized PHR system, with enhanced data exchange among healthcare players.
A Novel GAN-Based Synthesis Method for In-Air Handwritten Words
Authors: Zhang Xin , Xue Yang
Published: November 2020

In recent years, with the miniaturization and high energy efficiency of MEMS (micro-electro-mechanical systems), in-air handwriting technology based on inertial sensors has come to the fore. Most of the previous works have focused on character-level in-air handwriting recognition. In contrast, few works focus on word-level in-air handwriting tasks. In the field of word-level recognition, researchers have to face the problems of insufficient data and poor generalization performance of recognition methods. On one hand, the training of deep neural learning networks usually requires a particularly large dataset, but collecting data will take a lot of time and money. On the other hand, a deep recognition network trained on a small dataset can hardly recognize samples whose labels do not appear in the training set. To address these problems, we propose a two-stage synthesis method of in-air handwritten words. The proposed method includes a splicing module guided by an additional corpus and a generating module trained by adversarial learning. We carefully design the proposed network so that it can handle word sample inputs of arbitrary length and pay more attention to the details of the samples. We design multiple sets of experiments on a public dataset. The experimental results demonstrate the success of the proposed method. What is impressive is that with the help of the air-writing word synthesizer, the recognition model learns the context information (combination information of characters) of the word. In this way, it can recognize words that have never appeared in the training process. In this paper, the recognition model trained on synthetic data achieves a word-level recognition accuracy of 62.3% on the public dataset. Compared with the model trained using only the public dataset, the word-level accuracy is improved by 62%. Furthermore, the proposed method can synthesize realistic samples under the condition of limited of in-air handwritten character samples and word samples. It largely solves the problem of insufficient data. In the future, mathematically modeling the strokes between characters in words may help us find a better way to splice character samples. In addition, we will apply our method to various datasets and improve the splicing module and generating module for different tasks.
Distributed Algorithm for Base Station Assignment in 4G/5G Machine-Type Communication Scenarios with Backhaul Limited Conditions
Authors: Esquivel-Mendiola Edgar A. , Galeana-Zapién Hiram , Covarrubias David H. , Aldana-Bobadilla Edwin
Published: November 2020

A progressive paradigm shift from centralized to distributed network architectures has been consolidated since the 4G communication standard, calling for novel decision-making mechanisms with distributed control to operate at the network edge. This situation implies that each base station (BS) must manage resources independently to meet the quality of service (QoS) of existing human-type communication devices (HTC), as well as the emerging machine type communication (MTC) devices from the internet of things (IoT). In this paper, we address the BS assignment problem, whose aim is to determine the most appropriate serving BS to each mobile device. This problem is formulated as an optimization problem for maximizing the system throughput and imposing constraints on the air interface and backhaul resources. The assignment problem is challenging to solve, so we present a simple yet valid reformulation of the original problem while using dual decomposition theory. Subsequently, we propose a distributed price-based BS assignment algorithm that performs at each BS the assignment process, where a novel pricing update scheme is presented. The simulation results show that our proposed solution outperforms traditional maximum signal to interference plus noise ratio (Max-SINR) and minimum path-loss (Min-PL) approaches in terms of system throughput.
EEG Fingerprints under Naturalistic Viewing Using a Portable Device
Authors: Fraschini Matteo , Meli Miro , Demuru Matteo , Didaci Luca , Barberini Luigi
Published: November 2020

The electroencephalogram (EEG) has been proven to be a promising technique for personal identification and verification. Recently, the aperiodic component of the power spectrum was shown to outperform other commonly used EEG features. Beyond that, EEG characteristics may capture relevant features related to emotional states. In this work, we aim to understand if the aperiodic component of the power spectrum, as shown for resting-state experimental paradigms, is able to capture EEG-based subject-specific features in a naturalistic stimuli scenario. In order to answer this question, we performed an analysis using two freely available datasets containing EEG recordings from participants during viewing of film clips that aim to trigger different emotional states. Our study confirms that the aperiodic components of the power spectrum, as evaluated in terms of offset and exponent parameters, are able to detect subject-specific features extracted from the scalp EEG. In particular, our results show that the performance of the system was significantly higher for the film clip scenario if compared with resting-state, thus suggesting that under naturalistic stimuli it is even easier to identify a subject. As a consequence, we suggest a paradigm shift, from task-based or resting-state to naturalistic stimuli, when assessing the performance of EEG-based biometric systems.
Optical and Mass Flow Sensors for Aiding Vehicle Navigation in GNSS Denied Environment
Authors: Moussa Mohamed , Zahran Shady , Mostafa Mostafa , Moussa Adel , El-Sheimy Naser , Elhabiby Mohamed
Published: November 2020

Nowadays, autonomous vehicles have achieved a lot of research interest regarding the navigation, the surrounding environmental perception, and control. Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) is one of the significant components of any vehicle navigation system. However, GNSS has limitations in some operating scenarios such as urban regions and indoor environments where the GNSS signal suffers from multipath or outage. On the other hand, INS standalone navigation solution degrades over time due to the INS errors. Therefore, a modern vehicle navigation system depends on integration between different sensors to aid INS for mitigating its drift during GNSS signal outage. However, there are some challenges for the aiding sensors related to their high price, high computational costs, and environmental and weather effects. This paper proposes an integrated aiding navigation system for vehicles in an indoor environment (e.g., underground parking). This proposed system is based on optical flow and multiple mass flow sensors integrations to aid the low-cost INS by providing the navigation extended Kalman filter (EKF) with forward velocity and change of heading updates to enhance the vehicle navigation. The optical flow is computed for frames taken using a consumer portable device (CPD) camera mounted in the upward-looking direction to avoid moving objects in front of the camera and to exploit the typical features of the underground parking or tunnels such as ducts and pipes. On the other hand, the multiple mass flow sensors measurements are modeled to provide forward velocity information. Moreover, a mass flow differential odometry is proposed where the vehicle change of heading is estimated from the multiple mass flow sensors measurements. This integrated aiding system can be used for unmanned aerial vehicles (UAV) and land vehicle navigations. However, the experimental results are implemented for land vehicles through the integration of CPD with mass flow sensors to aid the navigation system.
Dense-RefineDet for Traffic Sign Detection and Classification
Authors: Sun Chang , Ai Yibo , Wang Sheng , Zhang Weidong
Published: November 2020

Detecting and classifying real-life small traffic signs from large input images is difficult due to their occupying fewer pixels relative to larger targets. To address this challenge, we proposed a deep-learning-based model (Dense-RefineDet) that applies a single-shot, object-detection framework (RefineDet) to maintain a suitable accuracy–speed trade-off. We constructed a dense connection-related transfer-connection block to combine high-level feature layers with low-level feature layers to optimize the use of the higher layers to obtain additional contextual information. Additionally, we presented an anchor-design method to provide suitable anchors for detecting small traffic signs. Experiments using the Tsinghua-Tencent 100K dataset demonstrated that Dense-RefineDet achieved competitive accuracy at high-speed detection (0.13 s/frame) of small-, medium-, and large-scale traffic signs (recall: 84.3%, 95.2%, and 92.6%; precision: 83.9%, 95.6%, and 94.0%). Moreover, experiments using the Caltech pedestrian dataset indicated that the miss rate of Dense-RefineDet was 54.03% (pedestrian height > 20 pixels), which outperformed other state-of-the-art methods.
Recognition of Impulse of Love at First Sight Based On Photoplethysmography Signal
Authors: Lu Huan , Yuan Guangjie , Zhang Jin , Liu Guangyuan
Published: November 2020

Love at first sight is a well-known and interesting phenomenon, and denotes the strong attraction to a person of the opposite sex when first meeting. As far as we know, there are no studies on the changes in physiological signals between the opposite sexes when this phenomenon occurs. Although privacy is involved, knowing how attractive a partner is may be beneficial to building a future relationship in an open society where both men and women accept each other. Therefore, this study adopts the photoplethysmography (PPG) signal acquisition method (already applied in wearable devices) to collect signals that are beneficial for utilizing the results of the analysis. In particular, this study proposes a love pulse signal recognition algorithm based on a PPG signal. First, given the high correlation between the impulse signals of love at first sight and those for physical attractiveness, photos of people with different levels of attractiveness are used to induce real emotions. Then, the PPG signal is analyzed in the time, frequency, and nonlinear domains, respectively, in order to extract its physiological characteristics. Finally, we propose the use of a variety of machine learning techniques (support vector machine (SVM), random forest (RF), linear discriminant analysis (LDA), and extreme gradient enhancement (XGBoost)) for identifying the impulsive states of love, with or without feature selection. The results show that the XGBoost classifier has the highest classification accuracy (71.09%) when using the feature selection.
Multi-Criteria Ranking of Green Materials According to the Goals of Sustainable Development
Authors: Balali Amirhossein , Valipour Alireza , Zavadskas Edmundas Kazimieras , Turskis Zenonas
Published: November 2020

Modern, well-educated and experienced policy-makers support and promote the use of environmentally friendly materials and resources. The use of green resources is an exceptional and inevitable strategy to meet the needs of a rapidly growing Earth population. The growing population raises the need for new housing construction and urban infrastructure development. Such substances in construction refer to green building materials (GBMs). The environmental impact is lower if GBMs replace non-GBMs. Here, ranking among GBMs can facilitate and support the selection process. This study aimed to contribute to the body of knowledge to introduce a method for identifying and prioritizing GBMs in the construction industry to use in green building. The required data were collected using existing literature, interviews and questionnaires. Relevant Sustainable Development Goals (SDGs) are the first criteria for assessing GBM selection criteria. Critical weighted GBM selection criteria are the second criteria for prioritizing GBMs. The results show that “Natural, Plentiful and Renewable”, “Affordability from cradle to gate” and “Affordability during operation” are the top three GBM selection criteria. The real case study helped select “Stramit Strawboard”, “Aluminium Composite Panels (ACPs)” and “Solar Roof Tiles” as the most suitable GBMs for use in the context of the study. The model and results presented in this study will help actors of the construction industry to select and use GBMs more quickly and thus achieve a better level of construction sustainability, as well as environmental friendliness, than before.
Planning of Safety of Cities and Territory from the Point of View of Population Protection in the Czech Republic
Authors: Pokorny Jiri , Machalova Barbora , Slivkova Simona , Brumarova Lenka , Vlcek Vladimir
Published: November 2020

Ensuring territorial safety is one of the state’s main tasks, and the public administration plays a primary role in fulfilling it. The safety and sustainability of a territory is ensured by, inter alia, safety planning, including spatial planning. Spatial planning contains safety requirements for population protection. The requirements for ensuring the safety of the population included in the spatial plan vary significantly between countries. The existing population protection requirements contained in the spatial plan have been unsatisfactory for a long time. The main issue is that they are out of date and difficult to apply. The article presents a new proposed method for determining requirements for the protection of the population, which is based on evaluating the risks in the cities and territory. The specific population protection requirements are determined based on the resulting risks and their scaling. The requirements are classified as general when the territory is not faced with external risks or specific when there are such risks. The method is applied to the conditions of the Czech Republic. In terms of national standard specifics, there are requirements in areas of public infrastructure, public utility buildings, and public benefit measures. The method for determining population protection requirements can be considered applicable in a general form by various countries if the national standards specifics or other aspects are taken into account.
Visitors’ Preferences on the Characteristics of Bogor Botanical Gardens
Authors: Affandi Sigit Dian , Halimatussadiah Alin , Asrofani Farha Widya
Published: November 2020

This study aims to estimate the economic benefits of the Bogor Botanical Gardens (BBG) in terms of willingness to pay (WTP) and, using the estimation, to make a scenario of the entrance fee. Data were collected through a survey among 414 local visitors using the travel cost method (TCM) and choice modeling method (CM). The result from the TCM reveals that travel cost, gender, and education significantly affect the number of visits to the BBG. The value of consumer surplus amounts to IDR 582,072 (USD 40.90) and the total value of benefits obtained is IDR 728.8 billion (USD 51.21 million) per year. Meanwhile, the CM result shows that compensating surplus is estimated at IDR 19,704 (USD 1.38) and the total value of benefits is IDR 24.67 billion (USD 1.73 million) per year. The CM shows that the attributes of service cars as well as information and environmental educational tools and infrastructure have positive and significant values to the visitors. Thus, there is a potential to increase the entrance fee for tourists to become IDR 20,629 (USD 1.45) per visit. Through the increase in entrance fee, the BBG could generate a potential revenue of IDR 25.83 billion (USD 1.81 million) per year, which can improve the number of plant species, information and environmental educational tools and infrastructures, and service cars inside the BBG.
Knowledge and Perceptions of Environmental Issues by African Americans/Blacks in Washington, DC, USA: Giving Voice to the Voiceless
Authors: Harrison Elgloria , Milton Ashley D. , Richardson Matthew L.
Published: November 2020

Our study elucidated knowledge and perceptions of natural resources and climate change by African Americans/Blacks in Washington, DC since they are a traditionally marginalized population and to see if we could dispel the perception that they have low knowledge and interest in environmental issues. Secondarily, we wanted to determine if knowledge and perceptions vary across age groups. We conducted a survey of 491 Blacks in the District and asked 26 questions/statements related to natural resources, climate change, economics, and health. Participants were categorized into four age groups 18–25, 26–40, 41–65, and 66 and older for analysis. We found that the level of environmental knowledge across the age groups was relatively high and largely similar. Our results suggest that Blacks care about the environment, see the environment as beneficial to their health, and are knowledgeable about natural resources. However, younger Blacks (18–25) were the least likely to think of the natural world as a community to which they belong, report feelings of connection to it, and recognize that it impacts their personal welfare. In conclusion, we find that Blacks in the District possess environmental awareness and use local environmental language, depicting the uniqueness of their community. The mainstream environmental movement may fail to recognize this local language, leading to exclusion of vulnerable populations based on a faulty premise that these populations lack knowledge or interest in environmental issues.
How Are the Smart Readiness Indicators Expected to Affect the Energy Performance of Buildings: First Evidence and Perspectives
Authors: Fokaides Paris A. , Panteli Christiana , Panayidou Andri
Published: November 2020

In 2018, the European Commission adopted the Smart Readiness Indicator (SRI) concept in the recast of the directive on the energy efficiency of buildings. The set of SRIs is a measure of the intelligence of buildings systems, and its promotion is expected to contribute to the energy savings of the building sector. These indicators are relatively new and were developed only at the beginning of last decade, within European standards. This study introduces and elaborates on these indicators, as delivered in the final report of the European Commission. Some first results, which are obtained using a tool developed by the European Commission, are also presented. The work identifies gaps and perspectives for improvement of this system, as well as predicting the evolution of its implementation in the coming years, through specific numerical scenarios.
Impacts of Neighboring Buildings on the Cold Island Effect of Central Parks: A Case Study of Beijing, China
Authors: Han Dongrui , Yang Xiaohuan , Cai Hongyan , Xu Xinliang
Published: November 2020

Urban parks have been considered as an effective measure to mitigate the urban heat island (UHI) effects. Many studies have investigated the impacts of shape, size and interior components on the cold island effect (CIE) of parks, while little attention has been given to the impact of neighboring buildings. Thus, taking twenty-two parks in Beijing as samples, this study investigated the impacts of the neighboring building on the CIE of central parks. The results showed that the average land surface temperature (LST) of parks are 30.98 °C in summer and −1.10 °C in winter. Parks have a strong CIE in summer, and average cold island footprint (CIF) and LST difference are 0.15 km2 and 2.01 °C higher than that in winter. The components of the building in the CIF of parks are dominated by middle-rise building (MRB), followed by low-rise building (LRB), and high-rise building (HRB) is the least dominant. The percentage of landscape (PLAND) and landscape shape index (LSI) of MRB, and perimeter area fractal dimension (PAFRAC) of LRB are significantly related to CIF in summer and winter. This study could extend scientific understanding of the impacts of neighboring buildings on the CIE of central parks, and could guide urban planners in mitigating the UHI effects through the rational allocation of buildings.
An Innovative Framework for Teaching/Learning Technical Courses in Architectural Education
Authors: Shareef Sardar S. , Farivarsadri Guita
Published: November 2020

This study presents a teaching/learning framework based on parallels between problem-based learning (PBL), constructivist pedagogy, and design, which enables students to learn technical courses in a way that promotes sustainable and self-directed learning. The study used qualitative content analysis of literature surveyed from scientific databases to determine thematic codes and find the relations. The theoretical framework was implemented in a case study conducted in a second-year course in building construction technology at Tishk International University, Sulaimaniya, Iraq. The results indicate that solving ill-defined problems increased student enjoyment in learning various subjects through several teaching methods including self-directed learning. The instructor’s role is to facilitate learning rather than to provide knowledge by showing the solutions. This stimulates the students’ curiosity toward understanding problems and approaching solutions through a game-based scheme. The suggested framework can be a guide for instructors teaching technical courses of any kind. This method equips students with technical knowledge that benefits them in their studies and their professional lives after graduation, as they can integrate both their design and technical knowledge.