bluuebunny/crossref_metadata_2025 huggingface dataset:
A Hybrid Fault Diagnosis Approach Using FEM Optimized Sensor Positioning and Machine Learning
Authors: Jung Sang Jin , Shifat Tanvir Alam , Hur Jang-Wook
Published: September 2022

Sensor acquired signal has been a fundamental measure in rotary machinery condition monitoring (CM) to enhance system reliability and stability. Inappropriate sensor mounting can lead to loss of fault-related information and generate false alarms in industrial systems. To ensure reliable system operation, in this paper we investigate a system’s multiple degrees-of-freedom (DOF) using the finite element method (FEM) to find the optimum sensor mounting position. An appropriate sensor position is obtained by the highest degree of deformation in FEM modal analysis. The effectiveness of the proper sensor mounting position was compared with two other sensor mounting points, which were selected arbitrarily. To validate the effectiveness of this method we considered a gear-actuator test bench, where the sensors were mounted in the same place as the FEM simulation. Vibration data were acquired through these sensors for different health states of the system and failure patterns were recognized using an artificial neural network (ANN) model. An ANN model shows that the optimum sensor mounting point found in FEM has the highest accuracy, compared to other mounting points. A hybrid CM framework, combining the physics-based and data-driven approaches, provides robust fault detection and identification analysis of the gear-actuator system.
A Novel Approach to Guided Implant Surgery: A Technical Note
Authors: Franchini Federico , Fuzzi Massimo , Leone Renato , Sorrentino Roberto
Published: September 2022

Computer-guided software and kits have significantly improved the clinical applications of implant surgery. Nonetheless, some technical problems are still in evidence during clinical procedures because of cumbersome surgical tools that can limit access to implant sites, mainly in posterior areas of the mouth in the presence of bulky anatomical structures and in patients with reduced mouth-opening capacity. The present paper aimed to present a novel approach to guided implant surgery, describing the technical characteristics of an innovative guided surgical kit made up of modified sleeves and modular surgical drills. The proposed guided surgical kit is based on a novel patented system of sleeves and modular burs, with an increased length of the metal sleeves and a reduced height of the drills. The innovative design of the proposed system would allow the clinician to position guided fixtures in all clinical situations; the reduced encumbrance would be particularly helpful to gain access to the posterior areas of both maxilla and mandible, which have limited inter-arch space, with an easy and user-friendly approach. The modular system could overcome anatomical limitations, such as reduced mouth-opening capacity, and permit clinicians to maintain the stability and integrity of the surgical templates, even in cases where there is very limited intermaxillary space.
Model-Free Deep Recurrent Q-Network Reinforcement Learning for Quantum Circuit Architectures Design
Authors: Sogabe Tomah , Kimura Tomoaki , Chen Chih-Chieh , Shiba Kodai , Kasahara Nobuhiro , Sogabe Masaru , Sakamoto Katsuyoshi
Published: September 2022

Artificial intelligence (AI) technology leads to new insights into the manipulation of quantum systems in the Noisy Intermediate-Scale Quantum (NISQ) era. Classical agent-based artificial intelligence algorithms provide a framework for the design or control of quantum systems. Traditional reinforcement learning methods are designed for the Markov Decision Process (MDP) and, hence, have difficulty in dealing with partially observable or quantum observable decision processes. Due to the difficulty of building or inferring a model of a specified quantum system, a model-free-based control approach is more practical and feasible than its counterpart of a model-based approach. In this work, we apply a model-free deep recurrent Q-network (DRQN) reinforcement learning method for qubit-based quantum circuit architecture design problems. This paper is the first attempt to solve the quantum circuit design problem from the recurrent reinforcement learning algorithm, while using discrete policy. Simulation results suggest that our long short-term memory (LSTM)-based DRQN method is able to learn quantum circuits for entangled Bell–Greenberger–Horne–Zeilinger (Bell–GHZ) states. However, since we also observe unstable learning curves in experiments, suggesting that the DRQN could be a promising method for AI-based quantum circuit design application, more investigation on the stability issue would be required.
A Posthuman Dharma: Enthiran 2.0
Authors: Cohen Signe
Published: September 2022

S. Shankar’s 2018 Tamil language science fiction film 2.0, the stand-alone sequel to his 2010 blockbuster Enthiran, presents a bleak vision of a near-present time when obsession with technology has led to deteriorating human relationships as well as destruction of the natural world. The film articulates a posthuman dharma founded on the understanding that humans have an ethical obligation towards all living things, not merely other humans. The film posits the individual as fractured and unstable but valorizes the interconnectivity of humans and non-humans, which is underscored by the film’s innovative evocation of the rasas of classical Indian aesthetics in the context of non-human agents. This essay argues that 2.0 presents a Hindu-inflected ecological posthumanism as the only viable alternative to a dystopian future.
One or Two Roots? Yi Zhi and the Dilemma of Practical Reason
Authors: Xie Xiaodong
Published: September 2022

Mohism has two versions of ethics, attributed to Mozi and Yi Zhi 夷之, respectively. Mozi introduced an ethics usually described as utilitarian, emphasizing universal love as the basis of impartiality. However, the problem with this emphasis is that it leads to neglecting the development of rational self-interest. Accordingly, Yi Zhi’s remarks are a clarification or modification of Mozi’s thoughts. First, Yi Zhi alluded to the concept of undifferentiated love to explain universal love as the basis of impartiality. Second, as he understood the concept of undifferentiated love in relation to the idea that “bestowing love begins with one’s parents”, Yi Zhi incorporated rational self-interest. Moreover, Mencius criticized Yi Zhi and disparaged his remarks as two roots (二本 er ben), contrasting it to Confucian ethics, which he said was one root. This division between one root (一本 yi ben) and two roots has garnered significant attention. On the one hand, Zhu Xi believed that the essence of two roots is undifferentiated love, wherein he concluded that it is applicable to both Mozi and Yi Zhi. On the other hand, most later scholars interpreted two roots from an ethical perspective, arguing that Yi Zhi faced the dilemma of two conflicting moral theories. Considering the basic principles of moral philosophy, the ethics of Mozi and Mencius are one root, and only that of Yi Zhi is two roots. This article shows that Yi Zhi and Henry Sidgwick, the founder of classical utilitarianism, face the same dilemma of practical reason: the conflict between utilitarianism and the self-interest of egoism.
The Distinctiveness of Christian Gratitude: A Theological Survey
Authors: Dunnington Kent
Published: September 2022

The positive psychology movement has increased and deepened our understanding of gratitude and its contribution to human well-being. Most of the literature to date has focused on gratitude to human benefactors, and the same has been true of philosophical analyses of gratitude. More recently, scholars of gratitude have turned their attention to gratitude to God, but relatively little work has been done on the relationship between particular theologies and spiritualities on the one hand and the phenomenology and structure of gratitude on the other. This essay makes a contribution to that strand of investigation by surveying the work of six Christian theologians, each of whom make bold, sometimes cryptic, claims about the distinctiveness of Christian gratitude and gratitude to God. The essay challenges universalist assumptions about the structure and phenomenology of gratitude, including gratitude to God.
Hinduism, Belief and the Colonial Invention of Religion: A before and after Comparison
Authors: Ranganathan Shyam
Published: September 2022

As known from the academic literature on Hinduism, the foreign, Persian word, “Hindu” (meaning “Indian”), was used by the British to name everything indigenously South Asian, which was not Islam, as a religion. If we adopt explication as our research methodology, which consists in the application of the criterion of logical validity to organize various propositions of perspectives we encounter in research in terms of a disagreement, we discover: (a) what the British identified as “Hinduism” was not characterizable by a shared set of beliefs or shared outlook, but a disagreement or debate about basic topics of philosophy with a discourse on tenets of moral philosophy anchoring the debate; and (b), the Western tradition’s historical commitment to language as the vehicle of thought not only leads to the conflation of propositions with beliefs, but to interpreting (explaining by way of belief) on the basis of the Eurocentric tradition rooted exclusively in ancient Greek philosophy. Interpretation on the basis of the Western tradition leads to the Western tradition vindicating itself as the non-traditional, non-religious, rational platform—the secular—for explaining everything—the residua are what get called religions on a global scale. This serves the political function of insulating Western colonialism from indigenous moral and political criticism. Given that Western colonialism is the pivotal event, before which South Asians just had philosophy, and after which they had religion (the explanatory residua of Eurocentric interpretation), we can ask about Hindu religious belief. This only pertains to the period after colonialism, when Hindus adopted a Westcentric frame for understanding their tradition as religious because of colonization. Prior to this, the tradition the British identified as “Hindu” had a wide variety of philosophical approaches to justification, which often criticized propositional attitudes, like belief, as irrational.
Learning-Based Shared Control Using Gaussian Processes for Obstacle Avoidance in Teleoperated Robots
Authors: Teodorescu Catalin Stefan , Groves Keir , Lennox Barry
Published: September 2022

Physically inspired models of the stochastic nature of the human-robot-environment interaction are generally difficult to derive from first principles, thus alternative data-driven approaches are an attractive option. In this article, Gaussian process regression is used to model a safe stop maneuver for a teleoperated robot. In the proposed approach, a limited number of discrete experimental training data points are acquired to fit (or learn) a Gaussian process model, which is then used to predict the evolution of the process over a desired continuous range (or domain). A confidence measure for those predictions is used as a tuning parameter in a shared control algorithm, and it is demonstrated that it can be used to assist a human operator by providing (low-level) obstacle avoidance when they utilize the robot to carry out safety-critical tasks that involve remote navigation using the robot. The algorithm is personalized in the sense that it can be tuned to match the specific driving style of the person that is teleoperating the robot over a specific terrain. Experimental results demonstrate that with the proposed shared controller enabled, the human operator is able to more easily maneuver the robot in environments with (potentially dangerous) static obstacles, thus keeping the robot safe and preserving the original state of the surroundings. The future evolution of this work will be to apply this shared controller to mobile robots that are being deployed to inspect hazardous nuclear environments, ensuring that they operate with increased safety.
Enhanced Dual Filter for Floating Wind Lidar Motion Correction: The Impact of Wind and Initial Scan Phase Models
Authors: Salcedo-Bosch Andreu , Rocadenbosch Francesc , Sospedra Joaquim
Published: September 2022

An enhanced filter for floating Doppler wind lidar motion correction is presented. The filter relies on an unscented Kalman filter prototype for floating-lidar motion correction without access to the internal line-of-sight measurements of the lidar. In the present work, we implement a new architecture based on two cooperative estimation filters and study the impact of different wind and initial scan phase models on the filter performance in the coastal environment of Barcelona. Two model combinations are considered: (i) a basic random walk model for both the wind turbulence and the initial scan phase and (ii) an auto-regressive model for wind turbulence along with a uniform circular motion model for the scan phase. The filter motion-correction performance using each of the above models was evaluated with reference to a fixed lidar in different wind and motion scenarios (low- and high-frequency turbulence cases) recorded during a 25-day campaign at “Pont del Petroli”, Barcelona, by clustered statistical analysis. The auto-regressive wind model and the uniform circular motion phase model permitted the filter to overcome divergence in all wind and motion scenarios. The statistical indicators comparing both instruments showed overall improvement. The mean deviation increased from 1.62% (without motion correction) to −0.07% (with motion correction), while the root-mean-square error decreased from 1.87% to 0.58%, and the determination coefficient (R2) improved from 0.90 to 0.96.
Application of Time-Domain Airborne Electromagnetic Method to the Study of Qingchengzi Ore Concentration Area in China
Authors: Fu Changmin , Di Qingyun , Lei Da , Wang Xiaotian , Zhang Yilang
Published: September 2022

Qingchengzi is an important polymetallic mineral concentration zone in China. However, long-term mining has led to the depletion of proven reserves in the Qingchengzi ore concentration zone. Therefore, it is necessary to carry out prospecting in the periphery of the existing ore bodies. Taking into account the complex surface conditions of the Qingchengzi mining area, we carried out an airborne time-domain electromagnetic survey in this region and obtained a high-precision underground resistivity model which covers the whole area. The quasi-3D inversion result shows a good agreement with the surface geological information. After analyzing the relationship between the underground geological structures and the resistivity model, we constructed a geological model with the distribution of the main strata, the rock masses and the faults. The reconstructed resistivity distribution of the entire region added new knowledge to understand the geology of the Qingchengzi survey area.
Hierarchical Superpixel Segmentation for PolSAR Images Based on the Boruvka Algorithm
Authors: Deng Jie , Wang Wei , Quan Sinong , Zhan Ronghui , Zhang Jun
Published: September 2022

Superpixel segmentation for polarimetric synthetic aperture radar (PolSAR) images plays a key role in remote-sensing tasks, such as ship detection and land-cover classification. However, the existing methods cannot directly generate multi-scale superpixels in a hierarchical style and they will take a long time when multi-scale segmentation is executed separately. In this article, we propose an effective and accurate hierarchical superpixel segmentation method, by introducing a minimum spanning tree (MST) algorithm called the Boruvka algorithm. To accurately measure the difference between neighboring pixels, we obtain the scattering mechanism information derived from the model-based refined 5-component decomposition (RFCD) and construct a comprehensive dissimilarity measure. In addition, the edge strength map and homogeneity measurement are considered to make use of the structural and spatial distribution information in the PolSAR image. On this basis, we can generate superpixels using the distance metric along with the MST framework. The proposed method can maintain good segmentation accuracy at multiple scales, and it generates superpixels in real time. According to the experimental results on the ESAR and AIRSAR datasets, our method is faster than the current state-of-the-art algorithms and preserves somewhat more image details in different segmentation scales.
On the Use of Sentinel-2 NDVI Time Series and Google Earth Engine to Detect Land-Use/Land-Cover Changes in Fire-Affected Areas
Authors: Lasaponara Rosa , Abate Nicodemo , Fattore Carmen , Aromando Angelo , Cardettini Gianfranco , Di Fonzo Marco
Published: September 2022

This study aims to assess the potential of Sentinel-2 NDVI time series and Google Earth Engine to detect small land-use/land-cover changes (at the pixel level) in fire-disturbed environs. To capture both slow and fast changes, the investigations focused on the analysis of trends in NDVI time series, selected because they are extensively used for the assessment of post-fire dynamics mainly linked to the monitoring of vegetation recovery and fire resilience. The area considered for this study is the central–southern part of the Italian peninsula, in particular the regions of (i) Campania, (ii) Basilicata, (iii) Calabria, (iv) Toscana, (v) Umbria, and (vi) Lazio. For each fire considered, the study covered the period from the year after the event to the present. The multi-temporal analysis was performed using two main data processing steps (i) linear regression to extract NDVI trends and enhance changes over time and (ii) random forest classification to capture and categorize the various changes. The analysis allowed us to identify changes occurred in the selected case study areas and to understand and evaluate the trend indicators that mark a change in land use/land cover. In particular, different types of changes were identified: (i) woodland felling, (ii) remaking of paths and roads, and (ii) transition from wooded area to cultivated field. The reliability of the changes identified was assessed and confirmed by the high multi-temporal resolution offered by Google Earth. Results of this comparison highlighted that the overall accuracy of the classification was higher than 0.86.
Landslide Detection and Mapping Based on SBAS-InSAR and PS-InSAR: A Case Study in Gongjue County, Tibet, China
Authors: Yao Jiaming , Yao Xin , Liu Xinghong
Published: September 2022

The rock mass along the Jinsha River is relatively broken under complex geological action. Many ancient landslides were distributed along the Jinsha River in Gongjue County, which is very dangerous under the action of gravity, tectonic stress and river erosion. Efficient and accurate identification and monitoring of landslides is important for disaster monitoring and early warning. Interferometric synthetic aperture radar (InSAR) technology has been proved to be an effective technology for landslide hazard identification and mapping. However, great uncertainty inevitably exists due to the single deformation observation method, resulting in wrong judgment during the process of landslide detection. Therefore, to address the uncertainties arising from single observations, a cross-comparison method is put forward using SBAS-InSAR (small baseline subset InSAR) and PS-InSAR (permanent scatterers InSAR) technology. Comparative analysis of the spatial complementarity of interference points and temporal deformation refined the deformation characteristics and verified the reliability of the InSAR results, aiding in the comprehensive identification and further mapping of landslides. Landslides along the Jinsha River in Gongjue County were studied in this paper. Firstly, 14 landslides with a total area of 20 km2 were identified by using two time-series InSAR methods. Then, the deformation characteristics of these landslides were validated by UAV (unmanned aerial vehicle) images, multiresource remote sensing data and field investigation. Further, the precipitation data were introduced to analyze the temporal deformation characteristics of two large landslides. Lastly, the influence of fault activity on landslide formation is further discussed. Our results demonstrate that the cross-comparison of the time-series InSAR method can effectively verify the accuracy of landslide identification.
Integrating Remote Sensing and Spatiotemporal Analysis to Characterize Artificial Vegetation Restoration Suitability in Desert Areas: A Case Study of Mu Us Sandy Land
Authors: Chen Zhanzhuo , Huang Min , Xiao Changjiang , Qi Shuhua , Du Wenying , Zhu Daoye , Altan Orhan
Published: September 2022

One of the major barriers to hindering the sustainable development of the terrestrial environment is the desertification process, and revegetation is one of the most significant duties in anti-desertification. Desertification deteriorates land ecosystems through species decline, and remote sensing is becoming the most effective way to monitor desertification. Mu Us Sandy Land is the fifth largest desert and the representative area under manmade vegetation restorations in China. Therefore, it is essential to understand the spatiotemporal characteristics of artificial desert transformation for seeking the optimal revegetation location for future restoration planning. However, there are no previous studies focusing on exploring regular patterns between the spatial distribution of vegetation restoration and human-related geographical features. In this study, we use Landsat satellite data from 1986 to 2020 to achieve annual monitoring of vegetation change by a threshold segmentation method, and then use spatiotemporal analysis with Open Street Map (OSM) data to explore the spatiotemporal distribution pattern between vegetation occurrence and human-related features. We construct an artificial vegetation restoration suitability index (AVRSI) by considering human-related features and topographical factors, and we assess artificial suitability for vegetation restoration by mapping methods based on that index and the vegetation distribution pattern. The AVRSI can be commonly used for evaluating restoration suitability in Sandy areas and it is tested acceptable in Mu Us Sandy Land. Our results show during this period, the segmentation threshold and vegetation area of Mu Us Sandy Land increased at rates of 0.005/year and 264.11 km2/year, respectively. Typically, we found the artificial restoration vegetation suitability in Mu Us area spatially declines from southeast to northwest, but eventually increases in the most northwest region. This study reveals the revegetation process in Mu Us Sandy Land by figuring out its spatiotemporal vegetation change with human-related features and maps the artificial revegetation suitability.
Prediction of Sea Surface Temperature by Combining Interdimensional and Self-Attention with Neural Networks
Authors: Guo Xing , He Jianghai , Wang Biao , Wu Jiaji
Published: September 2022

Sea surface temperature (SST) is one of the most important and widely used physical parameters for oceanography and meteorology. To obtain SST, in addition to direct measurement, remote sensing, and numerical models, a variety of data-driven models have been developed with a wealth of SST data being accumulated. As oceans are comprehensive and complex dynamic systems, the distribution and variation of SST are affected by various factors. To overcome this challenge and improve the prediction accuracy, a multi-variable long short-term memory (LSTM) model is proposed which takes wind speed and air pressure at sea level together with SST as inputs. Furthermore, two attention mechanisms are introduced to optimize the model. An interdimensional attention strategy, which is similar to the positional encoding matrix, is utilized to focus on important historical moments of multi-dimensional input; a self-attention strategy is adopted to smooth the data during the training process. Forty-three-year monthly mean SST and meteorological data from the fifth-generation ECMWF (European Centre for Medium-Range Weather Forecasts) reanalysis (ERA5) are collected to train and test the model for the sea areas around China. The performance of the model is evaluated in terms of different statistical parameters, namely the coefficient of determination, root mean squared error, mean absolute error and mean average percentage error, with a range of 0.9138–0.991, 0.3928–0.8789, 0.3213–0.6803, and 0.1067–0.2336, respectively. The prediction results indicate that it is superior to the LSTM-only model and models taking SST only as input, and confirm that our model is promising for oceanography and meteorology investigation.
Latest Altimetry-Based Sea Ice Freeboard and Volume Inter-Annual Variability in the Antarctic over 2003–2020
Authors: Garnier Florent , Bocquet Marion , Fleury Sara , Bouffard Jérôme , Tsamados Michel , Remy Frédérique , Garric Gilles , Chenal Aliette
Published: September 2022

The relatively stable conditions of the sea ice cover in the Antarctic, observed for almost 40 years, seem to be changing recently. Therefore, it is essential to provide sea ice thickness (SIT) and volume (SIV) estimates in order to anticipate potential multi-scale changes in the Antarctic sea ice. For that purpose, the main objectives of this work are: (1) to assess a new sea ice freeboard, thickness and volume altimetry dataset over 2003–2020 and (2) to identify first order impacts of the sea ice recent conditions. To produce these series, we use a neuronal network to calibrate Envisat radar freeboards onto CryoSat-2 (CS2). This method addresses the impacts of surface roughness on Low Resolution Mode (LRM) measurements. During the 2011 common flight period, we found a mean deviation between Envisat and CryoSat-2 radar freeboards by about 0.5 cm. Using the Advanced Microwave Scanning Radiometer (AMSR) and the dual-frequency Altimetric Snow Depth (ASD) data, our solutions are compared with the Upward looking sonar (ULS) draft data, some in-situ measurement of the SIMBA campaign, the total freeboards of 6 Operation Ice Bridge (OIB) missions and ICESat-2 total freeboards. Over 2003–2020, the global mean radar freeboard decreased by about −14% per decade and the SIT and SIV by about −10% per decade (considering a snow depth climatology). This is marked by a slight increase through 2015, which is directly followed by a strong decrease in 2016. Thereafter, freeboards generally remained low and even continued to decrease in some regions such as the Weddell sea. Considering the 2013–2020 period, for which the ASD data are available, radar freeboards and SIT decreased by about −40% per decade. The SIV decreased by about −60% per decade. After 2016, the low SIT values contrast with the sea ice extent that has rather increased again, reaching near-average values in winter 2020. The regional analysis underlines that such thinning (from 2016) occurs in all regions except the Amundsen-Bellingshausen sea sector. Meanwhile, we observed a reversal of the main regional trends from 2016, which may be the signature of significant ongoing changes in the Antarctic sea ice.
Retrieving Water Quality Parameters from Noisy-Label Data Based on Instance Selection
Authors: Liu Yuyang , Liu Jiacheng , Zhao Yubo , Wang Xueji , Song Shuyao , Liu Hong , Yu Tao
Published: September 2022

As an important part of the "air–ground" integrated water quality monitoring system, the inversion of water quality from unmanned airborne hyperspectral image has attracted more and more attention. Meanwhile, unmanned aerial vehicles (UAVs) have the characteristics of small size, flexibility and quick response, and can complete the task of water environment detection in a large area, thus avoiding the difficulty in obtaining satellite data and the limitation of single-point monitoring by ground stations. Most researchers use UAV for water quality monitoring, they take water samples back to library or directly use portable sensors for measurement while flying drones at the same time. Due to the UAV speed and route planning, the actual sampling time and the UAV passing time cannot be guaranteed to be completely synchronized, and there will be a difference of a few minutes. For water quality parameters such as chromaticity (chroma), chlorophyll-a (chl-a), chemical oxygen demand (COD), etc., the changes in a few minutes are small and negligible. However, for the turbidity, especially in flowing water body, this value of it will change within a certain range. This phenomenon will lead to noise error in the measured suspended matter or turbidity, which will affect the performance of regression model and retrieval accuracy. In this study, to solve the quality problem of label data in a flowing water body, an unmanned airborne hyperspectral water quality retrieval experiment was carried out in the Xiao River in Xi’an, China, which verified the rationality and effectiveness of label denoising analysis of different water quality parameters. To identify noisy label instances efficiently, we proposed an instance selection scheme. Furthermore, considering the limitation of the dataset samples and the characteristic of regression task, we build a 1DCNN model combining a self attention mechanism (SAM) and the network achieves the best retrieving performance on turbidity and chroma data. The experiment results show that, for flowing water body, the noisy-label instance selection method can improve retrieval performance slightly on the COD parameter, but improve greatly on turbidity and chroma data.
A New Strategy for Forest Height Estimation Using Airborne X-Band PolInSAR Data
Authors: Xie Jinwei , Li Lei , Zhuang Long , Zheng Yu
Published: September 2022

Because the penetration depth of electromagnetic waves in forests is large in the longer wavelength band, most traditional forest height estimation methods are carried out using polarimetric interferometry synthetic aperture radar (PolInSAR) data of the L or P band, and the estimation method is a three-stage method based on the random volume over ground (RVoG) model. For X-band electromagnetic waves, the penetration depth of radar waves in forests is limited, so the traditional forest height estimation method is no longer applicable. In view of the above problems, in this paper we propose a new forest height estimation strategy for airborne X-band PolInSAR data. Firstly, the sub-view interferometric SAR pairs obtained via frequency segmentation (FS) in the Doppler domain are used to extend the polarimetric interferometry coherence coefficient (PolInCC) range of the original SAR image under different polarization states, so as to obtain the accurate ground phase. For the determination of the effective volume coherence coefficient (VCC), part of the fitting line of the extended-range PolInCC distribution that is intercepted by the fixed extinction coherence coefficient curve (FECCC) of the fixed range is averaged to obtain the accurate effective VCC. Finally, the high-precision forest canopy height in the X-band is estimated using the effective VCC with the ground phase removed in the look-up table (LUT). The effectiveness of the proposed method was verified using airborne-measured data obtained in Shaanxi Province, China. The comparison was carried out using different strategies, in which we substituted one step of the process with the conventional method. The results indicated that our new strategy could reduce the root mean square error (RMSE) of the predicted canopy height vastly to 1.02 m, with a lower estimation height error of 12.86%.
A Multiscale and Multitask Deep Learning Framework for Automatic Building Extraction
Authors: Yin Jichong , Wu Fang , Qiu Yue , Li Anping , Liu Chengyi , Gong Xianyong
Published: September 2022

Detecting buildings, segmenting building footprints, and extracting building edges from high-resolution remote sensing images are vital in applications such as urban planning, change detection, smart cities, and map-making and updating. The tasks of building detection, footprint segmentation, and edge extraction affect each other to a certain extent. However, most previous works have focused on one of these three tasks and have lacked a multitask learning framework that can simultaneously solve the tasks of building detection, footprint segmentation and edge extraction, making it difficult to obtain smooth and complete buildings. This study proposes a novel multiscale and multitask deep learning framework to consider the dependencies among building detection, footprint segmentation, and edge extraction while completing all three tasks. In addition, a multitask feature fusion module is introduced into the deep learning framework to increase the robustness of feature extraction. A multitask loss function is also introduced to balance the training losses among the various tasks to obtain the best training results. Finally, the proposed method is applied to open-source building datasets and large-scale high-resolution remote sensing images and compared with other advanced building extraction methods. To verify the effectiveness of multitask learning, the performance of multitask learning and single-task training is compared in ablation experiments. The experimental results show that the proposed method has certain advantages over other methods and that multitask learning can effectively improve single-task performance.
Timely and Low-Cost Remote Sensing Practices for the Assessment of Landslide Activity in the Service of Hazard Management
Authors: Kyriou Aggeliki , Nikolakopoulos Konstantinos G. , Koukouvelas Ioannis K.
Published: September 2022

Landslides are among the most dangerous and catastrophic events in the world. The increasing progress in remote sensing technology made landslide observations timely, systematic and less costly. In this context, we collected multi-dated data obtained by Unmanned Aerial Vehicle (UAV) campaigns and Terrestrial Laser Scanning (TLS) surveys for the accurate and immediate monitoring of a landslide located in a steep and v-shaped valley, in order to provide operational information concerning the stability of the area to the local authorities. The derived data were processed appropriately, and UAV-based as well as TLS point clouds were generated. The monitoring and assessment of the evolution of the landslide were based on the identification of instability phenomena between the multi-dated UAV and TLS point clouds using the direct cloud-to-cloud comparison and the estimation of the deviation between surface sections. The overall evaluation of the results revealed that the landslide remains active for three years but is progressing particularly slowly. Moreover, point clouds arising from a UAV or a TLS sensor can be effectively utilized for landslide monitoring with comparable accuracies. Nevertheless, TLS point clouds proved to be denser and more appropriate in terms of enhancing the accuracy of the monitoring process. The outcomes were validated using measurements, acquired by the Global Navigation Satellite System (GNSS).
A Dual-Encoder-Condensed Convolution Method for High-Precision Indoor Positioning
Authors: Meng Xiangxu , Li Wei , Zlatanova Sisi , Zhao Zheng , Wang Xiao
Published: September 2022

We study the problem of indoor positioning, which is a fundamental service in managing and analyzing objects in indoor environments. Unpredictable signal interference sources increase the degeneration of the accuracy and robustness of existing solutions. Deep learning approaches have recently been widely studied to overcome these challenges and attain better performance. In this paper, we aim to develop efficient algorithms, such as the dual-encoder-condensed convolution (DECC) method, which can achieve high-precision positioning for indoor services. In particular, firstly, we develop a convolutional module to add the original channel state information to the location information. Secondly, to explore channel differences between different antennas, we adopt a dual-encoder stacking mechanism for parallel calculation. Thirdly, we develop two different convolution kernels to speed up convergence. Performance studies on the indoor scenario and the urban canyon scenario datasets demonstrate the efficiency and effectiveness of our new approach.
A Novel Freeze-Thaw State Detection Algorithm Based on L-Band Passive Microwave Remote Sensing
Authors: Lv Shaoning , Wen Jun , Simmer Clemens , Zeng Yijian , Guo Yuanyuan , Su Zhongbo
Published: September 2022

Knowing the freeze-thaw (FT) state of the land surface is essential for many aspects of weather forecasting, climate, hydrology, and agriculture. Microwave L-band emission contains rather direct information about the FT-state because of its impact on the soil dielectric constant, which determines microwave emissivity and the optical depth profile. However, current L-band-based FT algorithms need reference values to distinguish between frozen and thawed soil, which are often not well known. We present a new FT-state-detection algorithm based on the daily variation of the H-polarized brightness temperature of the SMAP L3c FT global product for the northern hemisphere, which is available from 2015 to 2021. Exploiting the daily variation signal allows for a more reliable state detection, particularly during the transition periods, when the near-surface soil layer may freeze and thaw on sub-daily time scales. The new algorithm requires no reference values; its results agree with the SMAP FT state product by up to 98% in summer and up to 75% in winter. Compared to the FT state inferred indirectly from the 2-m air temperature and collocated soil temperature at 0–7 cm of the ERA5-land reanalysis, the new FT algorithm has a similar performance to the SMAP FT product. The most significant differences occur over the midlatitudes, including the Tibetan plateau and its downstream area. Here, daytime surface heating may lead to daily FT transitions, which are not considered by the SMAP FT state product but are correctly identified by the new algorithm. The new FT algorithm suggests a 15 days earlier start of the frozen-soil period than the ERA5-land’s estimate. This study is expected to extend the L-band microwave remote sensing data for improved FT detection.
Oriented Ship Detection Based on Intersecting Circle and Deformable RoI in Remote Sensing Images
Authors: Zhang Jun , Huang Ruofei , Li Yan , Pan Bin
Published: September 2022

Ship detection is an important topic in the task of understanding remote sensing images. One of the challenges for ship detection is the large length–width ratio of ships, which may weaken the feature extraction ability. Simultaneously, ships inclining in any direction is also a challenge for ship detection in remote sensing images. In this paper, a novel Oriented Ship detection method is proposed based on an intersecting Circle and Deformable region of interest (OSCD-Net), which aims at describing the characteristics of a large length–width ratio and arbitrary direction. OSCD-Net is composed of two modules: an intersecting circle rotated detection head (ICR-head) and a deformable region of interest (DRoI). The ICR-head detects a horizontal bounding box and an intersecting circle to obtain an oriented bounding box. DRoI performs three RoIAlign with different pooled sizes for each feature candidate region. In addition, the DRoI module uses transformation and deformation operations to pay attention to ship feature information and align feature shapes. OSCD-Net shows promising performance on public remote sensing image datasets.
Laser Ranging Bathymetry Using a Photon-Number-Resolving Detector
Authors: Cohen Lior , Istrati Daniel , Sher Yoni , Brand Zev , Eisenberg Hagai S.
Published: September 2022

The sensitivity and robustness against background noise of optical measurements, and specifically range-finding, can be improved by detecting the light with photon-number-resolving detectors (PNRD). We use a PNRD to detect single pulse reflections from the seabed level in the presence of high attenuation of the sea water. Measurements are performed from above the sea level, overcoming broad daylight conditions. We demonstrate continuous measurement of the seabed depth up to around 24 m, using laser pulse energies of 10 μJ, while sailing at speed of 2.2 knots. Additionally, we use these data to extract values of the refractive index and optical attenuation in coastal seawater. The method could be used as a novel and optically-accurate bathymetry tool for coastal research and underwater sensing applications.
Real-Time Scene Monitoring for Deaf-Blind People
Authors: Kassem Khaled , Caramazza Piergiorgio , Mitchell Kevin J. , Miller Mitch , Emadi Azadeh , Faccio Daniele
Published: September 2022

It is estimated that at least 15 million people worldwide live with severe deaf-blindness, with many more experiencing varying degrees of deaf-blindness. The existing options of assistance are mostly limited to walking canes, guide dogs and human care. We propose a wearable device which harnesses a multi-antenna mmWave radar transceiver and a haptic feedback array for real time detection of a person moving within a scene. We present our findings from a series of workshops with participants classed with multi-sensory impairments (MSI), to demonstrate the relative success of this approach and its potential for integration into existing assistance for the MSI of the future.
Development and Validation of an Algorithm for the Digitization of ECG Paper Images
Authors: Randazzo Vincenzo , Puleo Edoardo , Paviglianiti Annunziata , Vallan Alberto , Pasero Eros
Published: September 2022

The electrocardiogram (ECG) signal describes the heart’s electrical activity, allowing it to detect several health conditions, including cardiac system abnormalities and dysfunctions. Nowadays, most patient medical records are still paper-based, especially those made in past decades. The importance of collecting digitized ECGs is twofold: firstly, all medical applications can be easily implemented with an engineering approach if the ECGs are treated as signals; secondly, paper ECGs can deteriorate over time, therefore a correct evaluation of the patient’s clinical evolution is not always guaranteed. The goal of this paper is the realization of an automatic conversion algorithm from paper-based ECGs (images) to digital ECG signals. The algorithm involves a digitization process tested on an image set of 16 subjects, also with pathologies. The quantitative analysis of the digitization method is carried out by evaluating the repeatability and reproducibility of the algorithm. The digitization accuracy is evaluated both on the entire signal and on six ECG time parameters (R-R peak distance, QRS complex duration, QT interval, PQ interval, P-wave duration, and heart rate). Results demonstrate the algorithm efficiency has an average Pearson correlation coefficient of 0.94 and measurement errors of the ECG time parameters are always less than 1 mm. Due to the promising experimental results, the algorithm could be embedded into a graphical interface, becoming a measurement and collection tool for cardiologists.
Gait Recognition with Self-Supervised Learning of Gait Features Based on Vision Transformers
Authors: Pinčić Domagoj , Sušanj Diego , Lenac Kristijan
Published: September 2022

Gait is a unique biometric trait with several useful properties. It can be recognized remotely and without the cooperation of the individual, with low-resolution cameras, and it is difficult to obscure. Therefore, it is suitable for crime investigation, surveillance, and access control. Existing approaches for gait recognition generally belong to the supervised learning domain, where all samples in the dataset are annotated. In the real world, annotation is often expensive and time-consuming. Moreover, convolutional neural networks (CNNs) have dominated the field of gait recognition for many years and have been extensively researched, while other recent methods such as vision transformer (ViT) remain unexplored. In this manuscript, we propose a self-supervised learning (SSL) approach for pretraining the feature extractor using the DINO model to automatically learn useful gait features with the vision transformer architecture. The feature extractor is then used for extracting gait features on which the fully connected neural network classifier is trained using the supervised approach. Experiments on CASIA-B and OU-MVLP gait datasets show the effectiveness of the proposed approach.
Performance of Contactless Respiratory Rate Monitoring by Albus HomeTM, an Automated System for Nocturnal Monitoring at Home: A Validation Study
Authors: Do William , Russell Richard , Wheeler Christopher , Lockwood Megan , De Vos Maarten , Pavord Ian , Bafadhel Mona
Published: September 2022

Respiratory rate (RR) is a clinically important predictor of cardio-respiratory deteriorations. The mainstay of clinical measurement comprises the manual counting of chest movements, which is variable between clinicians and limited to sporadic readings. Emerging solutions are limited by poor adherence and acceptability or are not clinically validated. Albus HomeTM is a contactless and automated bedside system for nocturnal respiratory monitoring that overcomes these limitations. This study aimed to validate the accuracy of Albus Home compared to gold standards in real-world sleeping environments. Participants undertook overnight monitoring simultaneously using Albus Home and gold-standard polygraphy with thoraco-abdominal respiratory effort belts (SomnomedicsEU). Reference RR readings were obtained by clinician-count of polygraphy data. For both the Albus system and reference, RRs were measured in 30-s segments, reported as breaths/minute, and compared. Accuracy was defined as the percentage of RRs from the Albus system within ±2 breaths/minute of reference counts. Across a diverse validation set of 32 participants, the mean accuracy exceeded 98% and was maintained across different participant characteristics. In a Bland–Altman analysis, Albus RRs had strong agreement with reference mean differences and the limits of agreement of −0.4 and ±1.2 breaths/minute, respectively. Albus Home is a contactless yet accurate system for automated respiratory monitoring. Validated against gold –standard methods, it enables long-term, reliable nocturnal monitoring without patient burden.
Compact Quad Band MIMO Antenna Design with Enhanced Gain for Wireless Communications
Authors: Nej Sanjukta , Ghosh Anumoy , Ahmad Sarosh , Ghaffar Adnan , Hussein Mousa
Published: September 2022

In this paper, a novel microstrip line-fed meander-line-based four-elements quad band Multiple Input and Multiple Output (MIMO) antenna is proposed with a gain enhancement technique. The proposed structure resonates at four bands simultaneously, that is, 1.23, 2.45, 3.5 and 4.9 GHz, which resemble GPS L2, Wi-Fi, Wi-MAX and WLAN wireless application bands, respectively. The unit element is extended to four elements MIMO antenna structure exhibiting isolation of more than 22 dB between the adjacent elements without disturbing the resonant frequencies. In order to enhance the gain, two orthogonal microstrip lines are incorporated between the antenna elements which result in significant gain improvement over all the four resonances. Furthermore, the diversity performance of the MIMO structure is analyzed. The Envelope Co-Relation Coefficient (ECC), Diversity Gain (DG), Channel Capacity Loss (CCL), Mean Effective Gain (MEG) and Multiplexing Efficiency are obtained as 0.003, 10 dB, 0.0025 bps/Hz, −3 dB (almost) and 0.64 (min.), respectively, which are competent and compatible with practical wireless applications. The Total Active Reflection Coefficient (TARC) resembles the characteristic of the individual antenna elements. The layout area of the overall MIMO antenna is 0.33 λ × 0.29 λ, where λ is the free-space wavelength corresponding to the lowest resonance. The advantage of the proposed structure has been assessed by comparing it with previously reported MIMO structures based on number of antenna elements, isolation, gain, CCL and compactness. A prototype of the proposed MIMO structure is fabricated, and the measured results are found to be aligned with the simulated results.