bluuebunny/crossref_metadata_2025 huggingface dataset:
Reliable Estimates of Merchantable Timber Volume from Terrestrial Laser Scanning
Authors: Panagiotidis Dimitrios , Abdollahnejad Azadeh
Published: September 2021

Simple and accurate determination of merchantable tree height is needed for accurate estimations of merchantable volume. Conventional field methods of forest inventory can lead to biased estimates of tree height and diameter, especially in complex forest structures. Terrestrial laser scanner (TLS) data can be used to determine merchantable height and diameter at different heights with high accuracy and detail. This study focuses on the use of the random sampling consensus method (RANSAC) for generating the length and diameter of logs to estimate merchantable volume at the tree level using Huber’s formula. For this study, we used two plots; plot A contained deciduous trees and plot B consisted of conifers. Our results demonstrated that the TLS-based outputs for stem modelling using the RANSAC method performed very well with low bias (0.02 for deciduous and 0.01 for conifers) and a high degree of accuracy (97.73% for deciduous and 96.14% for conifers). We also found a high correlation between the proposed method and log length (−0.814 for plot A and −0.698 for plot B), which is an important finding because this information can be used to determine the optimum log properties required for analyzing stem curvature changes at different heights. Furthermore, the results of this study provide insight into the applicability and ergonomics during data collection from forest inventories solely from terrestrial laser scanning, thus reducing the need for field reference data.
A Deep Fusion uNet for Mapping Forests at Tree Species Levels with Multi-Temporal High Spatial Resolution Satellite Imagery
Authors: Guo Ying , Li Zengyuan , Chen Erxue , Zhang Xu , Zhao Lei , Xu Enen , Hou Yanan , Liu Lizhi
Published: September 2021

It is critical to acquire the information of forest type at the tree species level due to its strong links with various quantitative and qualitative indicators in forest inventories. The efficiency of deep-learning classification models for high spatial resolution (HSR) remote sensing image has been demonstrated with the ongoing development of artificial intelligence technology. However, due to limited statistical separability and complicated circumstances, completely automatic and highly accurate forest type mapping at the tree species level remains a challenge. To deal with the problem, a novel deep fusion uNet model was developed to improve the performance of forest classification refined at the dominant tree species level by combining the beneficial phenological characteristics of the multi-temporal imagery and the powerful features of the deep uNet model. The proposed model was built on a two-branch deep fusion architecture with the deep Res-uNet model functioning as its backbone. Quantitative assessments of China’s Gaofen-2 (GF-2) HSR satellite data revealed that the suggested model delivered a competitive performance in the Wangyedian forest farm, with an overall classification accuracy (OA) of 93.30% and a Kappa coefficient of 0.9229. The studies also yielded good results in the mapping of plantation species such as the Chinese pine and the Larix principis.
Multiple Images Improve Lake CDOM Estimation: Building Better Landsat 8 Empirical Algorithms across Southern Canada
Authors: Koll-Egyed Talia , Cardille Jeffrey A. , Deutsch Eliza
Published: September 2021

Coloured dissolved organic matter (CDOM) is an important water property for lake management. Remote sensing using empirical algorithms has been used to estimate CDOM, with previous studies relying on coordinated field campaigns that coincided with satellite overpass. However, this requirement reduces the maximum possible sample size for model calibration. New satellites and advances in cloud computing platforms offer opportunities to revisit assumptions about methods used for empirical algorithm calibration. Here, we explore the opportunities and limits of using median values of Landsat 8 satellite images across southern Canada to estimate CDOM. We compare models created using an expansive view of satellite image availability with those emphasizing a tight timing between the date of field sampling and the date of satellite overpass. Models trained on median band values from across multiple summer seasons performed better (adjusted R2 = 0.70, N = 233) than models for which imagery was constrained to a 30-day time window (adjusted R2 = 0.45). Model fit improved rapidly when incorporating more images, producing a model at a national scale that performed comparably to others found in more limited spatial extents. This research indicated that dense satellite imagery holds new promise for understanding relationships between in situ CDOM and satellite reflectance data across large areas.
Light-Weight Cloud Detection Network for Optical Remote Sensing Images with Attention-Based DeeplabV3+ Architecture
Authors: Yao Xudong , Guo Qing , Li An
Published: September 2021

Clouds in optical remote sensing images cause spectral information change or loss, that affects image analysis and application. Therefore, cloud detection is of great significance. However, there are some shortcomings in current methods, such as the insufficient extendibility due to using the information of multiple bands, the intense extendibility due to relying on some manually determined thresholds, and the limited accuracy, especially for thin clouds or complex scenes caused by low-level manual features. Combining the above shortcomings and the requirements for efficiency in practical applications, we propose a light-weight deep learning cloud detection network based on DeeplabV3+ architecture and channel attention module (CD-AttDLV3+), only using the most common red–green–blue and near-infrared bands. In the CD-AttDLV3+ architecture, an optimized backbone network-MobileNetV2 is used to reduce the number of parameters and calculations. Atrous spatial pyramid pooling effectively reduces the information loss caused by multiple down-samplings while extracting multi-scale features. CD-AttDLV3+ concatenates more low-level features than DeeplabV3+ to improve the cloud boundary quality. The channel attention module is introduced to strengthen the learning of important channels and improve the training efficiency. Moreover, the loss function is improved to alleviate the imbalance of samples. For the Landsat-8 Biome set, CD-AttDLV3+ achieves the highest accuracy in comparison with other methods, including Fmask, SVM, and SegNet, especially for distinguishing clouds from bright surfaces and detecting light-transmitting thin clouds. It can also perform well on other Landsat-8 and Sentinel-2 images. Experimental results indicate that CD-AttDLV3+ is robust, with a high accuracy and extendibility.
Potential and Challenges of Harmonizing 40 Years of AVHRR Data: The TIMELINE Experience
Authors: Dech Stefan , Holzwarth Stefanie , Asam Sarah , Andresen Thorsten , Bachmann Martin , Boettcher Martin , Dietz Andreas , Eisfelder Christina , Frey Corinne , Gesell Gerhard , Gessner Ursula , Hirner Andreas , Hofmann Matthias , Kirches Grit , Klein Doris , Klein Igor , Kraus Tanja , Krause Detmar , Plank Simon , Popp Thomas , Reinermann Sophie , Reiners Philipp , Roessler Sebastian , Ruppert Thomas , Scherbachenko Alexander , Vignesh Ranjitha , Wolfmueller Meinhard , Zwenzner Hendrik , Kuenzer Claudia
Published: September 2021

Earth Observation satellite data allows for the monitoring of the surface of our planet at predefined intervals covering large areas. However, there is only one medium resolution sensor family in orbit that enables an observation time span of 40 and more years at a daily repeat interval. This is the AVHRR sensor family. If we want to investigate the long-term impacts of climate change on our environment, we can only do so based on data that remains available for several decades. If we then want to investigate processes with respect to climate change, we need very high temporal resolution enabling the generation of long-term time series and the derivation of related statistical parameters such as mean, variability, anomalies, and trends. The challenges to generating a well calibrated and harmonized 40-year-long time series based on AVHRR sensor data flown on 14 different platforms are enormous. However, only extremely thorough pre-processing and harmonization ensures that trends found in the data are real trends and not sensor-related (or other) artefacts. The generation of European-wide time series as a basis for the derivation of a multitude of parameters is therefore an extremely challenging task, the details of which are presented in this paper.
Exploiting Structured CNNs for Semantic Segmentation of Unstructured Point Clouds from LiDAR Sensor
Authors: Ibrahim Muhammad , Akhtar Naveed , Ullah Khalil , Mian Ajmal
Published: September 2021

Accurate semantic segmentation of 3D point clouds is a long-standing problem in remote sensing and computer vision. Due to the unstructured nature of point clouds, designing deep neural architectures for point cloud semantic segmentation is often not straightforward. In this work, we circumvent this problem by devising a technique to exploit structured neural architectures for unstructured data. In particular, we employ the popular convolutional neural network (CNN) architectures to perform semantic segmentation of LiDAR data. We propose a projection-based scheme that performs an angle-wise slicing of large 3D point clouds and transforms those slices into 2D grids. Accounting for intensity and reflectivity of the LiDAR input, the 2D grid allows us to construct a pseudo image for the point cloud slice. We enhance this image with low-level image processing techniques of normalization, histogram equalization, and decorrelation stretch to suit our ultimate object of semantic segmentation. A large number of images thus generated are used to induce an encoder-decoder CNN model that learns to compute a segmented 2D projection of the scene, which we finally back project to the 3D point cloud. In addition to a novel method, this article also makes a second major contribution of introducing the enhanced version of our large-scale public PC-Urban outdoor dataset which is captured in a civic setup with an Ouster LiDAR sensor. The updated dataset (PC-Urban_V2) provides nearly 8 billion points including over 100 million points labeled for 25 classes of interest. We provide a thorough evaluation of our technique on PC-Urban_V2 and three other public datasets.
A Deep Learning-Based Framework for Automated Extraction of Building Footprint Polygons from Very High-Resolution Aerial Imagery
Authors: Li Ziming , Xin Qinchuan , Sun Ying , Cao Mengying
Published: September 2021

Accurate building footprint polygons provide essential data for a wide range of urban applications. While deep learning models have been proposed to extract pixel-based building areas from remote sensing imagery, the direct vectorization of pixel-based building maps often leads to building footprint polygons with irregular shapes that are inconsistent with real building boundaries, making it difficult to use them in geospatial analysis. In this study, we proposed a novel deep learning-based framework for automated extraction of building footprint polygons (DLEBFP) from very high-resolution aerial imagery by combining deep learning models for different tasks. Our approach uses the U-Net, Cascade R-CNN, and Cascade CNN deep learning models to obtain building segmentation maps, building bounding boxes, and building corners, respectively, from very high-resolution remote sensing images. We used Delaunay triangulation to construct building footprint polygons based on the detected building corners with the constraints of building bounding boxes and building segmentation maps. Experiments on the Wuhan University building dataset and ISPRS Vaihingen dataset indicate that DLEBFP can perform well in extracting high-quality building footprint polygons. Compared with the other semantic segmentation models and the vector map generalization method, DLEBFP is able to achieve comparable mapping accuracies with semantic segmentation models on a pixel basis and generate building footprint polygons with concise edges and vertices with regular shapes that are close to the reference data. The promising performance indicates that our method has the potential to extract accurate building footprint polygons from remote sensing images for applications in geospatial analysis.
Application of Copernicus Data for Climate-Relevant Urban Planning Using the Example of Water, Heat, and Vegetation
Authors: Bühler Michael Max , Sebald Christoph , Rechid Diana , Baier Eberhard , Michalski Alexander , Rothstein Benno , Nübel Konrad , Metzner Martin , Schwieger Volker , Harrs Jan-Albrecht , Jacob Daniela , Köhler Lothar , in het Panhuis Gunnar , Tejeda Raymundo C. Rodríguez , Herrmann Michael , Buziek Gerd
Published: September 2021

Specific climate adaptation and resilience measures can be efficiently designed and implemented at regional and local levels. Climate and environmental databases are critical for achieving the sustainable development goals (SDGs) and for efficiently planning and implementing appropriate adaptation measures. Available federated and distributed databases can serve as necessary starting points for municipalities to identify needs, prioritize resources, and allocate investments, taking into account often tight budget constraints. High-quality geospatial, climate, and environmental data are now broadly available and remote sensing data, e.g., Copernicus services, will be critical. There are forward-looking approaches to use these datasets to derive forecasts for optimizing urban planning processes for local governments. On the municipal level, however, the existing data have only been used to a limited extent. There are no adequate tools for urban planning with which remote sensing data can be merged and meaningfully combined with local data and further processed and applied in municipal planning and decision-making. Therefore, our project CoKLIMAx aims at the development of new digital products, advanced urban services, and procedures, such as the development of practical technical tools that capture different remote sensing and in-situ data sets for validation and further processing. CoKLIMAx will be used to develop a scalable toolbox for urban planning to increase climate resilience. Focus areas of the project will be water (e.g., soil sealing, stormwater drainage, retention, and flood protection), urban (micro)climate (e.g., heat islands and air flows), and vegetation (e.g., greening strategy, vegetation monitoring/vitality). To this end, new digital process structures will be embedded in local government to enable better policy decisions for the future.
Evaluation of Polycentric Spatial Structure in the Urban Agglomeration of the Pearl River Delta (PRD) Based on Multi-Source Big Data Fusion
Authors: He Xiong , Cao Yongwang , Zhou Chunshan
Published: September 2021

The rapid development of the urban city has led to great changes in the urban spatial structure. Thus, analyses of polycentric urban spatial structures are important for understanding these kinds of structures. In order to accurately evaluate the polycentric spatial structure of urban agglomerations and judge the differences between the actual development situation and overall planning of urban agglomerations, this study proposes a new method to identify the polycentric spatial structure of urban agglomerations in the Pearl River Delta based on the fusion of nighttime light (NTL) data, point of interest (POI) data, and Tencent migration data (TMG). In the first step, the NTL, POI, and TMG data are fused via wavelet transform; in the second step, Anselin local Moran’s I (LMI) and geographically weighted regression (GWR) were used to identify the main centers and subcenters, respectively. In the third step, the accuracy of the results of this study was further verified and discussed in the context of overall planning. The results show that the accuracy of urban polycenter identification via LMI and GWR after data fusion was 92.84%, and the Kappa value was 0.8971, which was higher than the results of polycenter identification via the traditional relative threshold. After comparing the identification results with the overall planning, firstly, we see that the fusion of multi-source big data can help to accurately evaluate the polycentric spatial structure within the urban agglomeration. Secondly, the fusion of dynamic data and static data can help identify the polycentric spatial structure of urban space more accurately. Therefore, this study can provide a new design for urban polycentric spatial structures, and further provide a reliable reference for the spatial optimization of urban agglomeration and the formulation of regional spatial development policies.
Simultaneous Radio Occultation Predictions for Inter-Satellite Comparison of Bending Angle Profiles from COSMIC-2 and GeoOptics
Authors: Chen Yong , Shao Xi , Cao Changyong , Ho Shu-peng
Published: September 2021

The Global Navigation Satellite System (GNSS) radio occultation (RO) is a remote sensing technique that uses International System of Units (SI) traceable GNSS signals for atmospheric limb soundings. The RO bending angle/sounding profiles are needed for assimilation in Numerical Weather Prediction (NWP) models, weather, climate, and space weather applications. Evaluating these RO data to ensure the high data quality for these applications is becoming more and more critical. This study presents a method for predicting radio occultation events, from which simultaneous radio occultation (SRO) for a pair of low-Earth-orbit (LEO) satellites on the limb to the same GNSS satellite can be obtained. The SRO method complements the Simultaneous Nadir Overpass (SNO) method (for nadir viewing satellite instruments), which has been widely used to inter-calibrate LEO to LEO and LEO to geosynchronous-equatorial-orbit (GEO) satellites. Unlike the SNO method, the SRO method involves three satellites: a GNSS and two LEO satellites with RO receivers. The SRO method allows for the direct comparison of bending angles when the simultaneous RO measurements for two LEO satellites receiving the same GNSS signal pass through approximately the same atmosphere within minutes in time and within less than 200 km of distance from each other. The prediction method can also be applied to radiosonde overpass prediction, and coordinate radiosonde launches for inter-comparisons between RO and radiosonde profiles. The main advantage of the SRO comparisons of bending angles is the significantly reduced uncertainties due to the much shorter time and smaller atmospheric path differences than traditional RO comparisons. To demonstrate the usefulness of this method, we present a comparison of the Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2) and GeoOpitcs RO profiles using SRO data for two time periods: Commercial Weather Data (CWD) data delivery order-1 (DO-1): 15 December 2020–15 January 2021 and CWD DO-2: 17 March 2021–31 August 2021. The results show good agreement in the bending angles between the COSMIC-2 RO measurements and those from GeoOptics, although systematic biases are also found in the inter-comparisons. Instrument and processing algorithm performances for the signal-to-noise ratio (SNR), penetration height, and bending angle retrieval uncertainty are also characterized. Given the efficiency of this method and the many RO measurements that are publicly and commercially available as well as the expansion of receiver capabilities to all GNSS systems, it is expected that this method can be used to validate/inter-calibrate GNSS RO measurements from different missions.
Development and Evaluation of a New Method for AMSU-A Cloud Detection over Land
Authors: Wu Zhiwen , Li Juan , Qin Zhengkun
Published: September 2021

Satellite data are the main source of information for operational data assimilation systems, and Advanced Microwave Sounding Unit-A (AMSU-A) data are one of the types of satellite data that contribute most to the reduction of numerical forecast errors. However, the assimilation of AMSU-A data over land lags behind that over the ocean. In this respect, the accuracy of cloud detection over land is one of the factors affecting the assimilation of AMSU-A data, especially for the window and low-peaking channel (23–53.59 GHz and 89 GHz) data. Strong surface emissivity and high spatial and temporal variability make it difficult to distinguish between the radiative contributions of clouds and the atmosphere. Based on the differences in the response characteristics of different channels to clouds, five AMSU-A window and low-peaking channels (channels 1–4 and 15) were selected to develop a new index for cloud detection over land. Case studies showed that the AMSU-A cloud index can detect most of the convective clouds; additionally, by further matching the MHS (Microwave Humidity Sounder) cloud detection index, we can effectively distinguish between cloudy and clear-sky observations. Batch test results also verified the accuracy and stability of the new cloud detection method. By referring to the MODIS (Moderate Resolution Imaging Spectroradiometer) cloud product, the POD (probability of detection) of the cloud fields of view with the new method was nearly 84%. By using the new cloud detection method to remove the cloudy data, the bias and standard deviation of the observation-minus-simulated brightness temperature (O−B) were significantly reduced, with the bias of O−B for channels 2–4 being below 1.0 K and the standard deviation of channels 5 and 6 being nearly 1.0 K.
Design of SiC-Doped Piezoresistive Pressure Sensor for High-Temperature Applications
Authors: Wejrzanowski Tomasz , Tymicki Emil , Plocinski Tomasz , Bucki Janusz Józef , Tan Teck Leong
Published: September 2021

Within these studies the piezoresistive effect was analyzed for 6H-SiC and 4H-SiC material doped with various elements: N, B, and Sc. Bulk SiC crystals with a specific concentration of dopants were fabricated by the Physical Vapor Transport (PVT) technique. For such materials, the structures and properties were analyzed using X-ray diffraction, SEM, and Hall measurements. The samples in the form of a beam were also prepared and strained (bent) to measure the resistance change (Gauge Factor). Based on the results obtained for bulk materials, piezoresistive thin films on 6H-SiC and 4H-SiC substrate were fabricated by Chemical Vapor Deposition (CVD). Such materials were shaped by Focus Ion Beam (FIB) into pressure sensors with a specific geometry. The characteristics of the sensors made from different materials under a range of pressures and temperatures were obtained and are presented herewith.
Carrier Diversity Incorporation to Low-Complexity Near-ML Detection for Multicarrier Systems over V2V Radio Channel
Authors: Del Puerto-Flores Jose Alberto , Peña-Campos Fernando , Parra-Michel Ramón , Del-Valle-Soto Carolina
Published: September 2021

Inter-carrier interference (ICI) in vehicle to vehicle (V2V) orthogonal frequency division multiplexing (OFDM) systems is a common problem that makes the process of detecting data a demanding task. Mitigation of the ICI in V2V systems has been addressed with linear and non-linear iterative receivers in the past; however, the former requires a high number of iterations to achieve good performance, while the latter does not exploit the channel’s frequency diversity. In this paper, a transmission and reception scheme for low complexity data detection in doubly selective highly time varying channels is proposed. The technique couples the discrete Fourier transform spreading with non-linear detection in order to collect the available channel frequency diversity and successfully achieving performance close to the optimal maximum likelihood (ML) detector. When compared with the iterative LMMSE detection, the proposed system achieves a higher performance in terms of bit error rate (BER), reducing the computational cost by a third-part when using 48 subcarriers, while in an OFDM system with 512 subcarriers, the computational cost is reduced by two orders of magnitude.
A Tension/Pressure Integrated Resistive Sensor Comprising of a PDMS-LC-MWCNT Composite
Authors: Luo Miao , Zhang Yumeng , Luo Yuxiang , Lu Jiangang
Published: September 2021

A flexible strain sensor which integrates both pressure sensing and tension sensing functions is demonstrated with an active layer comprising of polydimethy-lsiloxane (PDMS) elastomer, liquid crystal (LC), and multi-walled carbon nanotubes (MWCNTs). The introduction of LC improves the agglomeration of MWCNTs in PDMS and decreases Young’s modulus of flexible resistive sensors. The tension/pressure integrated resistive sensor not only shows a broad tensile sensing range of 140% strain but also shows a good sensitivity of the gauge factor, 40, with tensile force. Besides, the tension/pressure integrated resistive sensor also shows good linearity and sensitivity under pressure. The resistance of the pressure sensor increases as the applied pressure increases because of the decrease in the cross-sectional area of the path. The sensor also shows good hydrophobic properties which may help it to work under complex environment. The tension/pressure integrated sensor shows great promising applications in electronic skins and wearable devices.
On the Design of Soret Zone Plates Based on Binary Sequences Using Directional Transducers
Authors: Candelas Pilar , Pérez-López Sergio , Fuster José Miguel
Published: September 2021

In this work, we analyze the effect of the distribution of transparent Fresnel regions over the focusing profile of Soret Zone Plates (SZP) based on binary sequences. It is shown that this effect becomes very significant in those fields where directional transducers are employed, such as microwaves or acoustics. A thorough analysis of both the SZP transmission efficiency and the focusing enhancement factor is presented. Moreover, experimental measurements are also carried out for a particular type of binary sequence, the Cantor ternary set, validating the theoretical model and demonstrating that the distribution of transparent Fresnel regions becomes a critical parameter in applications requiring directional emitters.
Three-Dimensional Microscopic Image Reconstruction Based on Structured Light Illumination
Authors: Shi Taichu , Qi Yang , Zhu Cheng , Tang Ying , Wu Ben
Published: September 2021

In this paper, we propose and experimentally demonstrate a three-dimensional (3D) microscopic system that reconstructs a 3D image based on structured light illumination. The spatial pattern of the structured light changes according to the profile of the object, and by measuring the change, a 3D image of the object is reconstructed. The structured light is generated with a digital micro-mirror device (DMD), which controls the structured light pattern to change in a kHz rate and enables the system to record the 3D information in real time. The working distance of the imaging system is 9 cm at a resolution of 20 μm. The resolution, working distance, and real-time 3D imaging enable the system to be applied in bridge and road crack examinations, and structure fault detection of transportation infrastructures.
Estimation and Error Analysis for Optomechanical Inertial Sensors
Authors: Kelly Patrick , Majji Manoranjan , Guzmán Felipe
Published: September 2021

A sensor model and methodology to estimate the forcing accelerations measured using a novel optomechanical inertial sensor with the inclusion of stochastic bias and measurement noise processes is presented. A Kalman filter for the estimation of instantaneous sensor bias is developed; the outputs from this calibration step are then employed in two different approaches for the estimation of external accelerations applied to the sensor. The performance of the system is demonstrated using simulated measurements and representative values corresponding to a bench-tested 3.76 Hz oscillator. It is shown that the developed methods produce accurate estimates of the bias over a short calibration step. This information enables precise estimates of acceleration over an extended operation period. These results establish the feasibility of reliably precise acceleration estimates using the presented methods in conjunction with state of the art optomechanical sensing technology.
Low Velocity Impact Localization of Variable Thickness Composite Laminates
Authors: Lu Guan , Zhou Yuchen , Xu Yiming
Published: September 2021

Variable thickness composite laminates (VTCL) are susceptible to impact during use and may result in irreparable internal damage. In order to locate the internal impact damage of complex composite structures and monitor the impact signals of VTCL at the same time, a low velocity impact (LVI) monitoring system based on an optical fiber sensing network was constructed. Fiber Bragg grating (FBG) sensors are suitable for monitoring strain characteristics. By arranging FBG sensors on the laminate, we studied the spectrum analysis and localization of the impact signal collected by a FBG demodulator at constant temperature. The prior knowledge of variable thickness composite structures is difficult to obtain, and the multi-sensor dynamic monitoring is complex and difficult to realize. In order to locate the LVI of composite structures without prior knowledge, based on empirical mode decomposition (EMD), we proposed an impact localization method with zero-mean normalized cross-correlation (ZNCC) and thickness correction. The experimental results of LVI localization verification show that the ZNCC algorithm can effectively remove the temperature cross-sensitivity and impact energy influencing factors, and the thickness correction can reduce the interference of variable thickness characteristics on localization performance. The maximum localization error is 24.41 mm and the average error is 15.67 mm, which meets engineering application requirements. The method of variable-thickness normalization significantly improves impact localization performance for VTCL.
Access-Point Centered Window-Based Radio-Map Generation Network
Authors: Kim Won-Yeol , Tae Soo-Ho , Seo Dong-Hoan
Published: September 2021

Fingerprinting is the term used to describe a common indoor radio-mapping positioning technology that tracks moving objects in real time. To use this, a substantial number of measurement processes and workflows are needed to generate a radio-map. Accordingly, to minimize costs and increase the usability of such radio-maps, this study proposes an access-point (AP)-centered window (APCW) radio-map generation network (RGN). The proposed technique extracts parts of a radio-map in the form of a window based on AP floor plan coordinates to shorten the training time while enhancing radio-map prediction accuracy. To provide robustness against changes in the location of the APs and to enhance the utilization of similar structures, the proposed RGN, which employs an adversarial learning method and uses the APCW as input, learns the indoor space in partitions and combines the radio-maps of each AP to generate a complete map. By comparing four learning models that use different data structures as input based on an actual building, the proposed radio-map learning model (i.e., APCW-based RGN) obtains the highest accuracy among all models tested, yielding a root-mean-square error value of 4.01 dBm.
Progressive Deep Learning Framework for Recognizing 3D Orientations and Object Class Based on Point Cloud Representation
Authors: Lee Sukhan , Yang Yongjun
Published: September 2021

Deep learning approaches to estimating full 3D orientations of objects, in addition to object classes, are limited in their accuracies, due to the difficulty in learning the continuous nature of three-axis orientation variations by regression or classification with sufficient generalization. This paper presents a novel progressive deep learning framework, herein referred to as 3D POCO Net, that offers high accuracy in estimating orientations about three rotational axes yet with efficiency in network complexity. The proposed 3D POCO Net is configured, using four PointNet-based networks for independently representing the object class and three individual axes of rotations. The four independent networks are linked by in-between association subnetworks that are trained to progressively map the global features learned by individual networks one after another for fine-tuning the independent networks. In 3D POCO Net, high accuracy is achieved by combining a high precision classification based on a large number of orientation classes with a regression based on a weighted sum of classification outputs, while high efficiency is maintained by a progressive framework by which a large number of orientation classes are grouped into independent networks linked by association subnetworks. We implemented 3D POCO Net for full three-axis orientation variations and trained it with about 146 million orientation variations augmented from the ModelNet10 dataset. The testing results show that we can achieve an orientation regression error of about 2.5° with about 90% accuracy in object classification for general three-axis orientation estimation and object classification. Furthermore, we demonstrate that a pre-trained 3D POCO Net can serve as an orientation representation platform based on which orientations as well as object classes of partial point clouds from occluded objects are learned in the form of transfer learning.
Design and Analysis of a Novel 24 GHz Up-Conversion Mixer with Improved Derivative Super-Position Linearizer Technique for 5G Applications
Authors: Siddique Abrar , Delwar Tahesin Samira , Behera Prangyadarsini , Biswal Manas Ranjan , Haider Amir , Ryu Jee-Youl
Published: September 2021

A 24 GHz high linear, high-gain up-conversion mixer is realized for fifth-generation (5G) applications in the 65 nm CMOS process. The mixer’s linearity is increased by applying an Improved Derivative Super-Position (I-DS) technique cascaded between the mixer’s transconductance and switching stage. The high gain and stability of amplifiers in the transconductance stage of the mixer are achieved using novel tunable capacitive cross-coupled common source (TCC-CS) transistors. Using the I-DS, the third-order non-linear coefficient of current is closed to zero, enhancing the linearity. Additionally, a TCC-CS, which is realized by varactors, neutralizes the gate-to-drain parasitic capacitance (Cgd) of transistors in the transconductance stage of the mixer and contributes to the improvement of the gain and stability of the mixer. The measured 1 dB compression point OP1dB of the designed mixer is 4.1 dBm and IP1dB is 0.67 dBm at 24 GHz. The conversion gain of 4.1 dB at 24 GHz and 3.2 ± 0.9 dB, from 20 to 30 GHz is achieved in the designed mixer. Furthermore, a noise figure of 3.8 dB is noted at 24 GHz. The power consumption of the mixer is 4.9 mW at 1.2 V, while the chip area of the designed mixer is 0.4 mm2.
Tungsten Disulfide Nanotube-Modified Conductive Paper-Based Chemiresistive Sensor for the Application in Volatile Organic Compounds’ Detection
Authors: Huang Song-Jeng , Immanuel Philip Nathaniel , Yen Yi-Kuang , Yen Ching-Lung , Tseng Chi-En , Lin Guan-Ting , Lin Che-Kuan , Huang Zhong-Xuan
Published: September 2021

Toxic and nontoxic volatile organic compound (VOC) gases are emitted into the atmosphere from certain solids and liquids as a consequence of wastage and some common daily activities. Inhalation of toxic VOCs has an adverse effect on human health, so it is necessary to monitor their concentration in the atmosphere. In this work, we report on the fabrication of inorganic nanotube (INT)-tungsten disulfide, paper-based graphene–PEDOT:PSS sheet and WS2 nanotube-modified conductive paper-based chemiresistors for VOC gas sensing. The WS2 nanotubes were fabricated by a two-step reaction, that is oxide reduction and sulfurization, carried out at 900 °C. The synthesized nanotubes were characterized by FE-SEM, EDS, XRD, Raman spectroscopy, and TEM. The synthesized nanotubes were 206–267 nm in diameter. The FE-SEM results show the length of the nanotubes to be 4.5–8 µm. The graphene–PEDOT:PSS hybrid conductive paper sheet was fabricated by a continuous coating process. Then, WS2 nanotubes were drop-cast onto conductive paper for fabrication of the chemiresistors. The feasibility and sensitivity of the WS2 nanotube-modified paper-based chemiresistor were tested in four VOC gases at different concentrations at room temperature (RT). Experimental results show the proposed sensor to be more sensitive to butanol gas when the concentration ranges from 50 to 1000 ppm. The limit of detection (LOD) of this chemiresistor for butanol gas was 44.92 ppm. The WS2 nanotube-modified paper-based chemiresistor exhibits good potential as a VOC sensor with the advantages of flexibility, easy fabrication, and low fabrication cost.
Analysis of Impact of Natural Ventilation Strategies in Ventilation Rates and Indoor Environmental Acoustics Using Sensor Measurement Data in Educational Buildings
Authors: de la Hoz-Torres María L. , Aguilar Antonio J. , Ruiz Diego P. , Martínez-Aires María Dolores
Published: September 2021

Indoor environmental conditions can significantly affect occupants’ health and comfort. These conditions are especially important in educational buildings, where students, teachers and staff spend long periods of the day and are vulnerable to these factors. Recently, indoor air quality has been a focus of attention to ensure that disease transmission in these spaces is minimised. In order to increase the knowledge in this field, experimental tests have been carried out to characterise the impact of natural ventilation strategies on indoor air quality and the acoustic environment. This study has evaluated three ventilation scenarios in four different classrooms in buildings of the University of Granada, considering different window and door opening configurations. Ventilation rates were estimated using the CO2 Decay Method, and background noise recordings were made in each classroom for acoustic tests. Results show that specific natural ventilation strategies have a relevant impact that is worth considering on the background noise in indoor spaces. In this sense ventilation rates provided by the different configurations varied between 3.7 and 39.8 air changes per hour (ACH) and the acoustic tests show a background noise ranging from 43 to 54 dBA in these scenarios. Consequently, managers and teachers should take into account not only the ACH, but also other collateral impacts on the indoor environmental conditions such as the thermal comfort or the acoustic environment.
Significant Wave Height Estimation from Joint CYGNSS DDMA and LES Observations
Authors: Yang Shuai , Jin Shuanggen , Jia Yan , Ye Mingda
Published: September 2021

The significant wave height (SWH) of oceans is the main parameter in describing the sea state, which has been widely used in the establishment of ocean process models and the field of navigation and transportation. However, traditional methods such as satellite radar altimeters and buoys cannot achieve SWH estimations with high spatial and temporal resolution. Recently, the spaceborne Global Navigation Satellite System reflectometry (GNSS-R) has provided an opportunity to estimate SWH with a rapid global coverage and high temporal resolution observations, particularly with the Cyclone Global Navigation Satellite System (CYGNSS) mission. In this paper, SWH was estimated using the polynomial function relationship between SWH from ERA5 and Delay-Doppler Map Average (DDMA) as well as Leading Edge Slope (LES) from CYGNSS data. Then, the SWH estimated from CYGNSS data was validated by ERA-Interim data, AVISO data, and buoy data. The results showed that the average correlation coefficient of CYGNSS SWH was 0.945, and the average RMSE was 0.257 m when compared to the ERA-Interim SWH data. The RMSE was 0.423 m and the correlation coefficient was 0.849 when compared with the AVISO SWH. The correlation coefficient with the buoy data was 0.907, and the RMSE was 0.247 m. This method can provide suitable SWH estimation data for ocean dynamics research and ocean environment prediction.
Bridges over Troubled Waters? The Political Economy of Public-Private Partnerships in the Water Sector
Authors: Shambaugh George , Joshi Shareen
Published: September 2021

Global concerns about water security and water scarcity are motivating local governments, investors, and international financial institutions to prioritize investments in the water sector. Over the past thirty years, public–private partnerships (PPPs) have been popular mechanisms for encouraging private sector investment and helping local governments overcome economic, political, and technical challenges associated with large infrastructure projects in the water, electricity, and transportation sectors. We argue that the political economy factors that affect the prevalence of PPPs in the water sector—which must serve broad populations of people at low cost—are different than other types of infrastructure projects. We use the World Bank’s Private Participation in Infrastructure (PPI) database to explore factors that affect the likelihood that PPPs will be initiated in water relative to other sectors, and in water treatment relative to water utilities. We demonstrate that the likelihoods of PPPs in the water sector and water treatment are positively correlated with levels of output from industries that are water-intensive and pollution-intensive when the host country relies heavily on fossil fuels to generate electricity. Furthermore, when corruption levels are high, projects are more likely to be initiated in water than in other sectors, but those investments are more likely to be in water utilities than water treatment.
Microwave Induction Heating of Polymer-Modified Asphalt Materials for Self-Healing and Deicing
Authors: Fakhri Mansour , Javadi Sajad , Sedghi Reza , Sassani Alireza , Arabzadeh Ali , Baveli Bahmai Behnam
Published: September 2021

This study evaluates the influence of polymer-modification on the induction heating capability of asphalt mastic in a microwave field, and investigates how effectively this approach can be utilized for ice melting and self-healing purposes. To this end, different asphalt mastic mixtures with different polymer-modification and mixing procedures were tested under microwave field exposure for induction heating capability, ice-melting ability, and self-healing capacity. The mixtures were made through warm-mix and hot-mix procedures with four bituminous binders, including virgin (unmodified) asphalt and the same binder modified with three types of polymers. The results showed the effectiveness of microwave induction heating of asphalt mastic for both crack-healing and deicing purposes. The binder type was found to influence the ice melting and crack healing rates, such that using a warm-mix asphalt binder resulted in a more efficient heat generation and conduction than using a virgin asphalt binder. While polymer-modification undermined induction-heating, ice-melting, and self-healing performances, SBS-modified asphalt binders exhibited better performance than the other polymer-modified binders.
The Approach of Value Innovation towards Superior Performance, Competitive Advantage, and Sustainable Growth: A Systematic Literature Review
Authors: Hajar Mohammed A. , Alkahtani Ammar Ahmed , Ibrahim Daing Nasir , Darun Mohd Ridzuan , Al-Sharafi Mohammed A. , Tiong Sieh Kiong
Published: September 2021

The value innovation strategy of pursuing differentiation and low cost has diverged and correlated with various notions and perspectives, which adds complexity and ambiguity to the current knowledge of value innovation. Thus, this study uses a systematic literature review methodology to identify key scientific contributions to the field of value innovation by providing a structured reliable overview of the current knowledge. This study aims to integrate the findings of previous research on value innovation to identify where conclusions converge and diverge and highlight emerging trends and gaps in the literature. This study seeks to answer the research question, “How can value innovation be an approach for superior performance, competitive advantage, or sustainable growth?” In this context, results are achieved through analyzing and synthesizing 73 empirical articles on value innovation literature published from 1997 to January 2021. Particularly, this study contributes to the extant literature by providing an integrative framework that summarizes the literature findings and addressing thematic classifications of the value innovation process. This study also helps further improve research on value innovation by identifying gaps and suggesting a conceptual model to mitigate those gaps.
A Cooperation Project in Lesotho: Renewable Energy Potential Maps Embedded in a WebGIS Tool
Authors: Pasanisi Francesco , Righini Gaia , D’Isidoro Massimo , Vitali Lina , Briganti Gino , Grauso Sergio , Moretti Lorenzo , Tebano Carlo , Zanini Gabriele , Mahahabisa Mabafokeng , Letuma Mosuoe , Raliselo Muso , Seitlheko Mokhethi
Published: September 2021

In this paper the background, activities undertaken, and main outcomes of the cooperation project “Renewable Energy Potential Maps for Lesotho” are presented. The project was launched in 2018 in fulfilment of the Paris Agreement by the Italian Ministry for the Environment and the Lesotho Ministry of Energy and Meteorology, with the aim to facilitate the local Government in the future planning and development of renewable energy in the country. A user-oriented WebGIS platform was utilised to share and analyse the outcomes of the project: a hydrological map to recognize potential areas for power generation; a wind atlas to identify specific sites with the most potential for wind energy generation; a solar radiation map, defining the different levels of radiation intensity, useful to localise sites for photovoltaic production. Human capacity building and technology transfer were carried out to strengthen the local expertise and ability to manage and plan renewable energy sources exploitation. The implementation of the project was based on a fruitful collaboration between scientists and stakeholders at the same time giving the local authorities a useful dataset and tool for renewable energy growth in Lesotho.
Examining Self-Regulated Learning Strategy Model: A Measurement Invariance Analysis of MSLQ-CAL among College Students in China
Authors: Guo Haitao , Tong Fuhui , Wang Zhuoying , Tang Shifang , Yoon Myeongsun , Ying Ming , Yu Xiaofeng
Published: September 2021

This study examined psychometric properties and measurement invariance of the Motivated Strategies for Learning Questionnaire for Chinese adult learners, learning strategy scale (MSLQ-CAL-LS). Data were collected from 2499 college students from 15 Chinese universities. Results from factor analysis suggested satisfactory psychometric properties of MSLQ-CAL-LS. We further identified strong evidence to support the configural, metric, scalar and strict invariance across the gender groups, confirming the appropriate use of MSLQ-CAL-LS that can accurately capture the construct of self-regulated learning (SRL) strategies among both female and male Chinese adult learners. This study provides one step forward to measure SRL outside the Western context. Recommendations for future research are discussed.