bluuebunny/crossref_metadata_2025 huggingface dataset:
In Vivo Dosimetry for Superficial High Dose Rate Brachytherapy with Optically Stimulated Luminescence Dosimeters: A Comparison Study with Metal-Oxide-Semiconductor Field-Effect Transistors
Authors: Lopes Alana , Sabondjian Eric , Baltazar Alejandra Rangel
Published: October 2022

The purpose of the study was to calibrate and commission optically-stimulated luminescence dosimeters (OSLDs) for in vivo measurements in contact-based 192Ir treatments for superficial high dose rate (HDR) brachytherapy in place of metal-oxide-semiconductor field-effect transistors (MOSFETs). Dose linearity and dose rate dependence were tested by varying source-to-OSLD distance and dwell time. Angular dependence was measured using a solid water phantom setup for OSLD rotation. A group of OSLDs were readout 34 consecutive times to test readout depletion while OSLDs were optically annealed using a mercury lamp for 34.7 h. End-to-end tests were performed using a Freiburg flap and Valencia applicator. OSLD measurements were compared to MOSFETs and treatment planning system (TPS) doses. OSLD response was supralinear for doses above 275 cGy. They were found to be independent of dose rate and dependent on the incident angle in edge-on scenarios. OSLDs exhibited minimal readout depletion and were successfully annealed after 24 h of illumination. Freiburg flap measurements agreed well with the TPS. For the Valencia, OSLDs showed to be the more accurate system over MOSFETs, with a maximum disagreement with the TPS being 0.09%. As such, OSLDs can successfully be used in place of MOSFETs for in vivo dosimetry for superficial HDR brachytherapy.
Recycling of Cement–Wood Board Production Waste into a Low-Strength Cementitious Binder
Authors: Argalis Pauls P. , Sinka Maris , Bajare Diana
Published: October 2022

Cement is a widely used building material, with more than 4.4 billion metric tons produced in 2021. Unfortunately, the excessive use of cement raises several environmental issues, one of which is the massive amounts of CO2e produced as a by-product. Using recycled materials in the concrete mix is widely employed to solve this problem. A method for minimizing the use of natural cement by substituting it with secondary cementitious material that consists of wood–cement board manufacturing waste has been studied in this paper. The cement in the waste stream was reactivated by a mechanical treatment method—the use of a planetary mill, allowing it to regain its cementitious properties and be used as a binder. Physical and mineralogical analysis of the binder material was performed using X-ray diffraction (XRD), thermogravimetry/differential thermal analysis (TG/DTA) and Brunauer–Emmett–Teller analysis; granulometry and compressive strength tests were also carried out. The results show that the grinding process did not significantly change the mineralogical composition and the specific surface area; it did, however, affect the compressive strength of the samples prepared by using the reactivated binding material; also, the addition of plasticizer to the mix increased compressive strength by 2.5 times. Samples were cured in high-humidity conditions. The optimal water-to-binder (W/B) ratio was found to be 0.7 because of the wood particles that absorb water in their structure. Compressive strength increased as the grinding time increased.
Does Mystical Experience Give Access to Reality?
Authors: Marshall Paul
Published: October 2022

Mystical experiences can bring an overwhelming sense that deeper realities have been contacted or that the everyday world has been apprehended as it truly is. Philosophical study of the experiences has not given much attention to their metaphysical significance, especially to the insights they may offer on fundamental issues such as the nature of reality, self, consciousness, and time. There are reasons for the neglect, and in the present article I consider two major theoretical obstacles to finding metaphysical significance in the experiences: a radical form of contextualism and a reductionist approach to neuroscience. With these obstacles addressed, there is room to consider how mystical experience and metaphysics can be brought into dialogue, a task facilitated by the contemporary resurgence of interest in alternatives to materialist metaphysics and a renewed interest in mystical experience encouraged by psychedelic research.
Conceptual Art, Theology, and Re-Presentation
Authors: Anderson Jonathan A.
Published: October 2022

Within the vast and varied scholarship of contemporary art, the relations between conceptual art and religion generally have not received careful investigation. There are, however, potentially quite subtle and complicated interrelations in play here that warrant closer study. This article develops and expands such study, first, by clarifying how procedural and re-presentational ways of thinking function in conceptual art, and, second, by showing how these help us to identify six general “logics” within which the interrelations of conceptual art and religion might be reexamined in the histories of contemporary art, both critically and constructively. These six categories are helpful heuristic guides, but each must be substantiated through fine-grained investigations of particular artists and artworks, and each involves “religion” in ways that open into and require particular theological modes of questioning. Therefore, third, this article then turns to a case study of contemporary Belgian artist Kris Martin, focusing especially on For Whom (2012), a work featuring a readymade two-ton church bell that swings on the hour but without a clapper (and thus without sound). Martin’s work consistently re-presents Christian forms and artifacts in compromised states—vacant altarpieces, broken statuary, etc.—invoking histories of European secularization while also retrieving and reactivating theological questions and grammars within those histories. By clarifying these various points of reference, particularly in dialogue with John Donne (from whom Martin borrows his title), this study attends to one instance of a significant interfacing of conceptual art, religion, and theology.
Improvement of Typhoon Intensity Forecasting by Using a Novel Spatio-Temporal Deep Learning Model
Authors: Jiang Shuailong , Fan Hanjie , Wang Chunzai
Published: October 2022

Typhoons can cause massive casualties and economic damage, and accurately predicting typhoon intensity has always been a hot topic both in theory and practice. In consideration with the spatial and temporal complexity of typhoons, machine learning methods have recently been applied in typhoon forecasting. In this paper, we attempt to improve typhoon intensity forecasting by treating it as a spatio-temporal problem in the deep learning field. In particular, we propose a novel typhoon intensity forecasting model named the Typhoon Intensity Spatio-temporal Prediction Network (TITP-Net). The proposed model takes multidimensional environmental variables and physical factors of typhoons into account and fully extracts the information from the datasets by capturing spatio-temporal dependencies with a spatial attention module, which includes two-dimensional and three-dimensional convolutional operations. A series of experiments with a comprehensive framework by using TITP-Net are conducted. The MAEs of the forecasts with 18, 24, 36 and 48 h lead time obtain a significant improvement by 7.02%, 6.53%, 6.25% and 5.37% compared with some existing deep learning models and dynamical models from official agencies.
Banana Mapping in Heterogenous Smallholder Farming Systems Using High-Resolution Remote Sensing Imagery and Machine Learning Models with Implications for Banana Bunchy Top Disease Surveillance
Authors: Alabi Tunrayo R. , Adewopo Julius , Duke Ojo Patrick , Kumar P. Lava
Published: October 2022

Banana (and plantain, Musa spp.), in sub-Saharan Africa (SSA), is predominantly grown as a mixed crop by smallholder farmers in backyards and small farmlands, typically ranging from 0.2 ha to 3 ha. The crop is affected by several pests and diseases, including the invasive banana bunchy top virus (BBTV, genus Babuvirus), which is emerging as a major threat to banana production in SSA. The BBTV outbreak in West Africa was first recorded in the Benin Republic in 2010 and has spread to the adjoining territories of Nigeria and Togo. Regular surveillance, conducted as part of the containment efforts, requires the identification of banana fields for disease assessment. However, small and fragmented production spread across large areas poses complications for identifying all banana farms using conventional field survey methods, which is also time-consuming and expensive. In this study, we developed a remote sensing approach and machine learning (ML) models that can be used to identify banana fields for targeted BBTV surveillance. We used medium-resolution synthetic aperture radar (SAR), Sentinel 2A satellite imagery, and high-resolution RGB and multispectral aerial imagery from an unmanned aerial vehicle (UAV) to develop an operational banana mapping framework by combining the UAV, SAR, and Sentinel 2A data with the Support Vector Machine (SVM) and Random Forest (RF) machine learning algorithms. The ML algorithms performed comparatively well in classifying the land cover, with a mean overall accuracy (OA) of about 93% and a Kappa coefficient (KC) of 0.89 for the UAV data. The model using fused SAR and Sentinel 2A data gave an OA of 90% and KC of 0.86. The user accuracy (UA) and producer accuracy (PA) for the banana class were 83% and 78%, respectively. The BBTV surveillance teams used the banana mapping framework to identify banana fields in the BBTV-affected southwest Ogun state of Nigeria, which helped in detecting 17 sites with BBTV infection. These findings suggest that the prediction of banana and other crops in the heterogeneous smallholder farming systems is feasible, with the precision necessary to guide BBTV surveillance in large areas in SSA.
Mapping the Spatiotemporal Pattern of Sandy Island Ecosystem Health during the Last Decades Based on Remote Sensing
Authors: Chi Yuan , Liu Dahai
Published: October 2022

Sandy islands suffer multiple external disturbances and thus experience drastic temporal ecological changes, and the two parts, that is, the states of essential components (Part 1) and the resilience under multiple disturbances (Part 2), are both indispensable to the sandy island ecosystem health. In this study, a model for the sandy island ecosystem health was established by integrating the two parts. In Part 1, the states were measured following the framework of vegetation, soil, and landscape, and a total of 12 factors in the three components were adopted. In Part 2, three typical disturbances, namely, geomorphological change, soil salinization, and human influence, were identified, and the resilience across different time intervals was measured by clarifying the intrinsic correlations between the components and disturbances. A sandy island ecosystem health index (SIEHI) was proposed based on the two parts, and Chongming Island was selected as the study area to demonstrate the model. The results indicated that the SIEHI continuously increased from 1988 to 2017, denoting the good effects of “Eco-Island construction” on Chongming Island. In different components and factors, the vegetation and soil components contributed more than the landscape component to the sandy island ecosystem health, and vegetation quality and soil carbon/nitrogen were the factors that made the most contributions. In different disturbances, the human influence played a major role in driving the spatiotemporal variations of the sandy island ecosystem health. Farming and building construction contributed the most and accounted for 37.12% and 35.59% of the total human influence, respectively, while traffic development exerted the highest influence per area. Then, influence coefficients of different human activities on the sandy island were determined, and measures for different functional zones were proposed for balancing the protection and development and achieving the sandy island ecosystem-based management.
Detecting Maritime Infrared Targets in Harsh Environment by Improved Visual Attention Model Preselector and Anti-Jitter Spatiotemporal Filter Discriminator
Authors: Ma Dongdong , Dong Lili , Xu Wenhai
Published: October 2022

Without any prior knowledge, it has always been a serious challenge to accurately detect infrared targets under a maritime harsh environment (MHE). To solve this problem, the main contribution of this paper is to use the improved visual attention model (VAM) preselector and anti-jitter spatiotemporal filter (ASF) discriminator to detect infrared targets in the MHE. The proposed method consists of image preprocessing, the single frame detection of suspected targets and a multi-frame judgment of real targets. First, in the process of single-frame image processing, a combination of the Gaussian difference filtering and local minimum filtering is applied to overcome the uneven background brightness distribution and improve the saliency of the target. Second, an intensity standard deviation method is designed to determine the unevenness of the background. According to the difference in background smoothness, an appropriate “center-surround difference” operation is selected to suppress sea wave interference, and the single frame suspected target candidate region is obtained. Third, in order to “align” the multi-frame image sequence, a method for correcting the position of the inter-frame jitter is proposed. The inter-frame jitter is measured and compensated by the inter-frame block matching results. Finally, according to the three assumptions of multi-frame spatiotemporal filtering, single-frame false targets are filtered out and combined with the OTSU method to segment the real target area. To evaluate the performance index of the proposed method by comparing it with the four other state-of-the-art methods for dealing with the MHE. The experimental results show that the algorithm achieves the maximum detection rate (DR) on the premise of being far lower than the false alarm rate (FAR) of the comparison method. The final experimental results also confirmed that the proposed algorithm is more suitable for infrared target detection in diverse MHEs.
The Bureya Landslide Recent Evolution According to Spaceborne SAR Interferometry Data
Authors: Zakharov Alexander , Zakharova Liudmila
Published: October 2022

The study presents the results of interferometric processing and analysis of ALOS-1/2 and Sentinel-1 SAR data acquired over the landslide territory of the Bureya river bank in 2006–2018. The novelty of the study is that, for the first time, the joint analysis of interferometric measurements of the landslide movement with meteorological information and time series of SRTM-X, ALOS-1, and TanDEM-X digital elevation models were performed. The results allowed us to make a conclusion that the Bureya landslide was evolving within the frame of the depression formed by a relict landslide which was stable at least at the beginning of the 21st century. The Bureya reservoir filling in 2003–2009 with a 60 m rise of the water and subsequent seasonal water level oscillations supposedly triggered the landslide activity. The landslide movement in the first decade may be characterized by a stable rate of movement all year around. A steady increase in precipitation level in the second decade, with heavy rainfalls in 2013, 2016, and 2018 led to a prominent acceleration of the landslide rate of movement, especially in warm seasons, with a final landslide in December 2018.
A Calculation Model for Ground Surface Temperature in High-Altitude Regions of the Qinghai-Tibet Plateau, China
Authors: Chai Mingtang , Li Nan , Liu Furong , Gao Yu , Mu Yanhu , Ma Wei
Published: October 2022

As a major parameter in the energy balance of the ground surface, temperature represents the level of exchange of energy and moisture between the ground and air. The Qinghai-Tibet Plateau (QTP) has the permafrost region with the highest altitude and the largest area in low–middle latitude of the world. The variation in ground surface temperature has an impact on the existence and development of the permafrost. Therefore, the analysis of the ground surface temperature in the QTP is significant to reflect the energy exchange in permafrost regions. This paper collected solar radiation data and calculated the conversion coefficient from total solar radiation to long-wave radiation of the ground surface on different underlying surfaces. The ground surface temperature was inversely calculated and modified based on the reception of solar radiation on different underlying surfaces. A simplified calculation model of ground surface temperature was built to reflect the ground surface temperature on different underlying surfaces of the QTP. The calculation results were compared with MODIS and showed good fitness, providing a systematic and reliable method for calculating the ground surface temperature on the QTP. The above model plays a significant role in the estimation of soil moisture, ground surface energy and water balance.
Experimenting Agriculture 4.0 with Sensors: A Data Fusion Approach between Remote Sensing, UAVs and Self-Driving Tractors
Authors: Barrile Vincenzo , Simonetti Silvia , Citroni Rocco , Fotia Antonino , Bilotta Giuliana
Published: October 2022

Geomatics is important for agriculture 4.0; in fact, it uses different types of data (remote sensing from satellites, Unmanned Aerial Vehicles-UAVs, GNSS, photogrammetry, laser scanners and other types of data) and therefore it uses data fusion techniques depending on the different applications to be carried out. This work aims to present on a study area concerning the integration of data acquired (using data fusion techniques) from remote sensing techniques, UAVs, autonomous driving machines and data fusion, all reprocessed and visualised in terms of results obtained through GIS (Geographic Information System). In this work we emphasize the importance of the integration of different methodologies and data fusion techniques, managing data of a different nature acquired with different methodologies to optimise vineyard cultivation and production. In particular, in this note we applied (focusing on a vineyard) geomatics-type methodologies developed in other works and integrated here to be used and optimised in order to make a contribution to agriculture 4.0. More specifically, we used the NDVI (Normalized Difference Vegetation Index) applied to multispectral satellite images and drone images (suitably combined) to identify the vigour of the plants. We then used an autonomous guided vehicle (equipped with sensors and monitoring systems) which, by estimating the optimal path, allows us to optimise fertilisation, irrigation, etc., by data fusion techniques using various types of sensors. Everything is visualised on a GIS to improve the management of the field according to its potential, also using historical data on the environmental, climatic and socioeconomic characteristics of the area. For this purpose, experiments of different types of Geomatics carried out individually on other application cases have been integrated into this work and are coordinated and integrated here in order to provide research/application cues for Agriculture 4.0.
Ultrasonic Imaging and Sensors
Authors: Camacho Jorge , Svilainis Linas , Álvarez-Arenas Tomás Gómez
Published: October 2022

Ultrasound imaging is a wide research field, covering areas from wave propagation physics, sensors and front-end electronics to image reconstruction algorithms and software [...]
Deep Learning-Based Energy Expenditure Estimation in Assisted and Non-Assisted Gait Using Inertial, EMG, and Heart Rate Wearable Sensors
Authors: Lopes João M. , Figueiredo Joana , Fonseca Pedro , Cerqueira João J. , Vilas-Boas João P. , Santos Cristina P.
Published: October 2022

Energy expenditure is a key rehabilitation outcome and is starting to be used in robotics-based rehabilitation through human-in-the-loop control to tailor robot assistance towards reducing patients’ energy effort. However, it is usually assessed by indirect calorimetry which entails a certain degree of invasiveness and provides delayed data, which is not suitable for controlling robotic devices. This work proposes a deep learning-based tool for steady-state energy expenditure estimation based on more ergonomic sensors than indirect calorimetry. The study innovates by estimating the energy expenditure in assisted and non-assisted conditions and in slow gait speeds similarly to impaired subjects. This work explores and benchmarks the long short-term memory (LSTM) and convolutional neural network (CNN) as deep learning regressors. As inputs, we fused inertial data, electromyography, and heart rate signals measured by on-body sensors from eight healthy volunteers walking with and without assistance from an ankle-foot exoskeleton at 0.22, 0.33, and 0.44 m/s. LSTM and CNN were compared against indirect calorimetry using a leave-one-subject-out cross-validation technique. Results showed the suitability of this tool, especially CNN, that demonstrated root-mean-squared errors of 0.36 W/kg and high correlation (ρ > 0.85) between target and estimation (R¯2 = 0.79). CNN was able to discriminate the energy expenditure between assisted and non-assisted gait, basal, and walking energy expenditure, throughout three slow gait speeds. CNN regressor driven by kinematic and physiological data was shown to be a more ergonomic technique for estimating the energy expenditure, contributing to the clinical assessment in slow and robotic-assisted gait and future research concerning human-in-the-loop control.
An Energy-Efficient and Blockchain-Integrated Software Defined Network for the Industrial Internet of Things
Authors: Asaithambi Sasikumar , Ravi Logesh , Kotb Hossam , Milyani Ahmad H. , Azhari Abdullah Ahmed , Nallusamy Senthilkumar , Varadarajan Vijayakumar , Vairavasundaram Subramaniyaswamy
Published: October 2022

The number of unsecured and portable Internet of Things (IoT) devices in the smart industry is growing exponentially. A diversity of centralized and distributed platforms have been implemented to defend against security attacks; however, these platforms are insecure because of their low storage capacities, high power utilization, single node failure, underutilized resources, and high end-to-end delay. Blockchain and Software-Defined Networking (SDN) are growing technologies to create a secure system and to ensure safe network connectivity. Blockchain technology offers a strong and trustworthy foundation to deal with threats and problems, including safety, privacy, adaptability, scalability, and security. However, the integration of blockchain with SDN is still in the implementation phase, which provides an efficient resource allocation and reduced latency that can overcome the issues of industrial IoT networks. We propose an energy-efficient blockchain-integrated software-defined networking architecture for Industrial IoT (IIoT) to overcome these challenges. We present a framework for implementing decentralized blockchain integrated with SDN for IIoT applications to achieve efficient energy utilization and cluster-head selection. Additionally, the blockchain-enabled distributed ledger ensures data consistency throughout the SDN controller network and keeps a record of the nodes enforced in the controller. The simulation result shows that the proposed model provides the best energy consumption, end-to-end latency, and overall throughput compared to the existing works.
Switchgear Digitalization—Research Path, Status, and Future Work
Authors: Kaštelan Nediljko , Vujović Igor , Krčum Maja , Assani Nur
Published: October 2022

To keep pace with global energy efficiency trends and, in particular, emission reduction targets in the maritime sector, both onshore and maritime power distribution systems need to be adapted to the relevant new technologies and concepts. As an important link in the distribution chain, medium-voltage switchgear (MV) is expected to be stable and reliable while operating as efficiently as possible. Failures of MV equipment, while rare because the equipment must be safe to handle and use, have far-reaching consequences. The consequences of such failures, whether to the shore or marine power system, present risks to the entire power plant, so an accurate assessment of equipment condition is required to identify potential failures early. The solution is an emerging concept of digital switchgear, where the implementation of sensor technology and communication protocols enables effective condition monitoring, and the creation of a database that, when combined with machine learning algorithms, enables predictive maintenance and/or fault detection. This paper presents, step by step, the previous challenges, the current research (divided into predictive maintenance, condition monitoring, and fault detection categories), and the future directions in this field. The use of artificial intelligence is discussed to eliminate the disadvantage of manually interpreting operational data, and recommendations for future work are formulated, such as the need to standardize test procedures and data sets to train and compare different algorithms before they are used in practice.
The ATI-ET Triangle Model: A Novel Approach to Estimate Soil Moisture Applied to MODIS Data
Authors: Luo Dayou , Wen Xingping , Li Shuling , Cao Jiaju
Published: October 2022

A simple soil moisture (SM) estimation method is proposed using apparent thermal inertia (ATI) and evapotranspiration (ET) data. Among the methods of estimating SM by using thermal infrared (TIR) remote sensing, the ATI method is widely used in bare soil and low vegetation areas. However, large surface ET will cause ATI error, resulting in lower accuracy of SM estimation. To overcome this problem, the potential of ATI-ET space for estimating the SM of bare and vegetated farmland in the dry season (no irrigation) is studied. ATI and ET data were used to construct triangle feature space, and six distance parameters are extracted from the positions of random pixels in the triangle. Some correlation estimates were made to derive those parameters that were useful for SM estimation, which were three in total. The SM estimation model consisting of these three parameters was built. Compared with the ATI model, the ATI-ET triangle model can not only be applied to areas with high ET, but also has higher accuracy in estimating SM. The ATI-ET triangle model is more suitable for application in bare soil and low vegetation areas. As the Normalized Difference Vegetation Index increases, the accuracy of the model estimates decreases. To show the high portability of the proposed model for SM estimation, we chose another set of in situ SM data acquired in Tibetan Plateau. The results proved the effectiveness of the model in other similar study regions.
Correction of Range-Variant Motion Error and Residual RCM in Sparse Regularization SAR Imaging
Authors: Zhang Jingyi , Ni Jiacheng
Published: October 2022

Lq (0 < q ≤ 1) regularization has been confirmed effective when applied to sparse SAR imaging. However, the inaccuracies caused by motion errors in the observation model will lead to various degradations and defocus in the reconstructed image. For high-resolution and light-small SAR systems, the range-variant motion errors will decrease the accuracy of range cell migration correction (RCMC), and residual range cell migration (RCM) will exceed multiple range resolution cells and degrade the image quality substantially. Aiming at this problem, in this paper, a novel azimuth-range decoupled sparse SAR imaging method with coarse-to-fine range-variant motion errors and residual RCM correction method is proposed. First, a one-step motion compensation (MOCO) operator is proposed using the inertial navigation systems (INS)/global positioning systems (GPS) information, which can significantly reduce the residual RCM and improve the reconstruction accuracy. Second, a fine high-order phase-error correction method is performed to correct the range and cross-range-varying phase errors using a joint imaging and phase-error estimation scheme, which will further improve the image focusing quality. Experimental results indicate the effectiveness of the proposed method.
Cost-Driven Design of Printed Wideband Antennas with Reduced Silver Ink Consumption for the Internet of Things
Authors: Claus Nicolas , Verhaevert Jo , Rogier Hendrik
Published: October 2022

The Internet of Things (IoT) accelerates the need for compact, lightweight and low-cost antennas combining wideband operation with a high integration potential. Although screen printing is excellently suited for manufacturing conformal antennas on a flexible substrate, its application is typically limited due to the expensive nature of conductive inks. This paper investigates how the production cost of a flexible coplanar waveguide (CPW)-fed planar monopole antenna can be reduced by exploiting a mesh-based method for limiting ink consumption. Prototypes with mesh grids of different line widths and densities were screen-printed on a polyethylene terephthalate (PET) foil using silver-based nanoparticle ink. Smaller line widths decrease antenna gain and efficiency, while denser mesh grids better approximate unmeshed antenna behavior, albeit at the expense of greater ink consumption. A meshed prototype of 34.76×58.03mm with almost 80% ink reduction compared to an unmeshed counterpart is presented. It is capable of providing wideband coverage in the IMT/LTE-1/n1 (1.92–2.17 GHz), LTE-40/n40 (2.3–2.4 GHz), 2.45 GHz ISM (2.4–2.4835 GHz), IMT-E/LTE-7/n7 (2.5–2.69 GHz), and n78 5G (3.3–3.8 GHz) frequency bands. It exhibits a peak radiation efficiency above 90% and a metallized surface area of 2.46 cm2 (yielding an ink-to-total-surface ratio of 12.2%).
Monitoring Subsidence Area with the Use of Satellite Radar Images and Deep Transfer Learning
Authors: Franczyk Anna , Bała Justyna , Dwornik Maciej
Published: October 2022

Subsidence, especially in populated areas, is becoming a threat to human life and property. Monitoring and analyzing the effects of subsidence over large areas using in situ measurements is difficult and depends on the size of the subsidence area and its location. It is also time-consuming and costly. A far better solution that has been used in recent years is Differential Interferometry Synthetic Aperture Radar (DInSAR) monitoring. It allows the monitoring of land deformations in large areas with high accuracy and very good spatial and temporal resolution. However, the analysis of SAR images is time-consuming and involves an expert who can easily overlook certain details. Therefore, it is essential, especially in the case of early warning systems, to prepare tools capable of identifying and monitoring subsidence in interferograms. This article presents a study on automated detection and monitoring of subsidence troughs using deep-transfer learning. The area studied is the Upper Silesian Coal Basin (southern Poland). Marked by intensive coal mining, it is particularly prone to subsidence of various types. Additionally, the results of trough detection obtained with the use of convolutional neural networks were compared with the results obtained with the Hough transform and the circlet transform.
ACM: Accuracy-Aware Collaborative Monitoring for Software-Defined Network-Wide Measurement
Authors: Gu Jiqing , Song Chao , Dai Haipeng , Shi Lei , Wu Jinqiu , Lu Li
Published: October 2022

Software-defined measurement (SDM) is a simple and efficient way to deploy measurement tasks and collect measurement data. With SDM, it is convenient for operators to implement fine-grained network-wide measurements at the flow level, from which many important functions can benefit. The prior work provides mechanisms to distribute flows to monitors, such that each monitor can identify its non-overlapped subset of flows to measure, and a certain global performance criterion is optimized, such as load balance or flow coverage. Many applications of network management can benefit from a function that can find large flows efficiently, such as congestion control by dynamically scheduling large flows, caching of forwarding table entries, and network capacity planning. However, the current network-wide measurements neglect the diversity of different flows as they treat large flows and small flows equally. In this paper, we present a mechanism of accuracy-aware collaborative monitoring (ACM) to improve the measurement accuracies of large flows in network-wide measurements at the flow level. The structure of the sketch is an approximate counting algorithm, and a high-measurement accuracy can be achieved by merging the results from multiple monitors with sketches, which is termed as collaborative monitoring. The core idea of our method is to allocate more monitors to large flows and achieve the load balance to provide accuracy-aware monitoring. We modeled our problem as an integer–linear programming problem, which is NP-hard. Thus, we propose an approximation algorithm, named the improved longest processing time algorithm (iLPTA); we proved that its approximation ratio is (12+nl). We propose a two-stage online distribution algorithm (TODA). Moreover, we proved that its approximation ratio is (1+nl−1). The iLPTA is an offline approximation algorithm used to assign monitors for each flow, which prove the validity and feasibility of the core idea. The TODA is an online algorithm that attempts to achieve the load balance by selecting the monitor with the smallest load to a large flow. Our extensional experiment results verify the effectiveness of our proposed algorithms.
Design and Implementation of a New Training Flight Simulator System
Authors: Wei Ming-Yen , Fang Shen-An , Liu Ji-Wei
Published: October 2022

Aircraft flight simulators have good cost efficiency, high reusability, and high flight safety. All airlines and aircraft manufacturing companies choose it as sophisticated training equipment for ground simulation, effectively reducing pilot training costs, ensuring personnel safety and aircraft wear and tear. The new simulator proposed in this paper combines a digital motion-cueing algorithm, flight software and motion platform to make pilots feel as if they are in the real world. By using EtherCAT technology to drive the motion-cueing platform, it can improve the data transmission speed of the simulator as well as the strong anti-interference ability of communication and the control operation efficiency. Therefore, the simulated flight subjects can perform long-distance and large-angle training. Next, a set of measurement systems was established to provide monitoring items including attitude, velocity and acceleration, which can be displayed on the screen and recorded on the computer in real time and dynamically. Finally, seven training subjects were implemented to demonstrate the feasibility and correctness of the proposed method.
An Indoor-Monitoring LiDAR Sensor for Patients with Alzheimer Disease Residing in Long-Term Care Facilities
Authors: Joo Ji-Eun , Hu Yu , Kim Sujin , Kim Hyunji , Park Sunyoung , Kim Ji-Hoon , Kim Younghyun , Park Sung-Min
Published: October 2022

This paper introduces an indoor-monitoring LiDAR sensor for patients with Alzheimer disease residing in long-term care facilities (LTCFs), and this sensor exploits an optoelectronic analog front-end (AFE) to detect light signals from targets by utilizing on-chip avalanche photodiodes (APDs) realized in a 180 nm CMOS process and a neural processing unit (NPU) used for motion detection and decisions, especially for incidents of falls occurring in LTCFs. The AFE consists of an on-chip CMOS P+/N-well APD, a linear-mode transimpedance amplifier, a post-amplifier, and a time-to-digital converter, whereas the NPU exploits network sparsity and approximate processing elements for low-power operation. This work provides a potential solution of low-cost, low-power, indoor-monitoring LiDAR sensors for patients with Alzheimer disease in LTCFs.
Review of Learning-Based Robotic Manipulation in Cluttered Environments
Authors: Mohammed Marwan Qaid , Kwek Lee Chung , Chua Shing Chyi , Al-Dhaqm Arafat , Nahavandi Saeid , Eisa Taiseer Abdalla Elfadil , Miskon Muhammad Fahmi , Al-Mhiqani Mohammed Nasser , Ali Abdulalem , Abaker Mohammed , Alandoli Esmail Ali
Published: October 2022

Robotic manipulation refers to how robots intelligently interact with the objects in their surroundings, such as grasping and carrying an object from one place to another. Dexterous manipulating skills enable robots to assist humans in accomplishing various tasks that might be too dangerous or difficult to do. This requires robots to intelligently plan and control the actions of their hands and arms. Object manipulation is a vital skill in several robotic tasks. However, it poses a challenge to robotics. The motivation behind this review paper is to review and analyze the most relevant studies on learning-based object manipulation in clutter. Unlike other reviews, this review paper provides valuable insights into the manipulation of objects using deep reinforcement learning (deep RL) in dense clutter. Various studies are examined by surveying existing literature and investigating various aspects, namely, the intended applications, the techniques applied, the challenges faced by researchers, and the recommendations adopted to overcome these obstacles. In this review, we divide deep RL-based robotic manipulation tasks in cluttered environments into three categories, namely, object removal, assembly and rearrangement, and object retrieval and singulation tasks. We then discuss the challenges and potential prospects of object manipulation in clutter. The findings of this review are intended to assist in establishing important guidelines and directions for academics and researchers in the future.
Enhanced Authenticated Key Agreement for Surgical Applications in a Tactile Internet Environment
Authors: Lee Tian-Fu , Ye Xiucai , Chen Wei-Yu , Chang Chi-Chang
Published: October 2022

The Tactile Internet enables physical touch to be transmitted over the Internet. In the context of electronic medicine, an authenticated key agreement for the Tactile Internet allows surgeons to perform operations via robotic systems and receive tactile feedback from remote patients. The fifth generation of networks has completely changed the network space and has increased the efficiency of the Tactile Internet with its ultra-low latency, high data rates, and reliable connectivity. However, inappropriate and insecure authentication key agreements for the Tactile Internet may cause misjudgment and improper operation by medical staff, endangering the life of patients. In 2021, Kamil et al. developed a novel and lightweight authenticated key agreement scheme that is suitable for remote surgery applications in the Tactile Internet environment. However, their scheme directly encrypts communication messages with constant secret keys and directly stores secret keys in the verifier table, making the scheme vulnerable to possible attacks. Therefore, in this investigation, we discuss the limitations of the scheme proposed by Kamil scheme and present an enhanced scheme. The enhanced scheme is developed using a one-time key to protect communication messages, whereas the verifier table is protected with a secret gateway key to mitigate the mentioned limitations. The enhanced scheme is proven secure against possible attacks, providing more security functionalities than similar schemes and retaining a lightweight computational cost.
Embedding Biometric Information in Interpolated Medical Images with a Reversible and Adaptive Strategy
Authors: Chi Heng-Xiao , Horng Ji-Hwei , Chang Chin-Chen , Li Yung-Hui
Published: October 2022

How to hide messages in digital images so that messages cannot be discovered and tampered with is a compelling topic in the research area of cybersecurity. The interpolation-based reversible data hiding (RDH) scheme is especially useful for the application of medical image management. The biometric information of patients acquired by biosensors is embedded into an interpolated medical image for the purpose of authentication. The proposed scheme classifies pixel blocks into complex and smooth ones according to each block’s dynamic range of pixel values. For a complex block, the minimum-neighbor (MN) interpolation followed by DIM embedding is applied, where DIM denotes the difference between the block’s interpolated pixel values and the maximum pixel values. For a smooth block, the block mean (BM) interpolation is followed by a prediction error histogram (PEH) embedding and a difference expansion (DE) embedding is applied. Compared with previous methods, this adaptive strategy ensures low distortion due to embedding for smooth blocks while it provides a good payload for complex blocks. Our scheme is suitable for both medical and general images. Experimental results confirm the effectiveness of the proposed scheme. Performance comparisons with state-of-the-art schemes are also given. The peak signal to noise ratio (PSNR) of the proposed scheme is 10.32 dB higher than the relevant works in the best case.
Recognizing Significant Components of Electrical Waveforms of Actuators Operated by Vehicle Controllers
Authors: Więcławski Krzysztof , Antkowiak Maja , Figlus Tomasz
Published: October 2022

This work discusses the proposition of the identification of electrical waveforms resulting from the executive systems’ operation, controlled by the vehicle electronic modules. This proposition results from the fact that the electric current powering the actuator has two functions: to supply and to control. Observation of such waveforms enables the ongoing control as well as the diagnostics of the state of the executive elements. This work focused on the fundamentals of the method implementation in the vehicle controller. The algorithm for detecting the model values of the waveform has been developed and described, allowing for an efficient control of the system. The algorithm, after being used in the memory of the control module and having the measuring subassemblies (gauging the voltage and electric current) added, will enable the automatic detection of the essential values. The developed code, after the optimization, can support the control performed by the ECU, which is damage-orientated. The paper presents examples of the operation of a computational program developed on the basis of the adopted algorithm. Tests were performed on an electromagnetic valve—fuel injector—of a spark-ignition engine for different cases of its operation. The effectiveness of the program was demonstrated when detecting changes occurring in the current signals of fuel injectors corresponding to different engine speeds (time of 4 and 8 ms) and different loads (pressure of 0.2 and 0.4 MPa).
Optical Gas-Cell Dynamic Adsorption in a Photoacoustic Spectroscopy-Based SOF2 and SO2F2 Gas Sensor
Authors: Zhang Ying , Wang Mingwei , Yu Pengcheng , Liu Zhe
Published: October 2022

SO2F2 and SOF2 are the main components from the decomposition of insulation gas SF6. Photoacoustic spectroscopy (PAS) has been acknowledged as an accurate sensing technique. Polar material adsorption for SO2F2 and SOF2 in the photoacoustic gas cell of PAS may affect detection efficiency. In this paper, the optical gas-cell dynamic adsorptions of four different materials and the detection effects on SO2F2 and SOF2 are theoretically analyzed and experimentally demonstrated. The materials, including grade 304 stainless steel (SUS304), grade 6061 aluminum alloy (Al6061), polyvinylidene difluoride (PVDC), and polytetrafluoroethylene (PTFE), were applied inside the optical gas cell. The results show that, compared with metallic SUS304 and Al6061, plastic PVDC and PTFE would reduce the gas adsorption of SO2F2 and SOF2 by 10 to 20% and shorten the response time during gas exchange. The complete gas defusing period in the experiment was about 30 s. The maximum variations of the 90% rising time between the different adsorption materials were approximately 3 s for SO2F2 and 6 s for SOF2, while the generated photoacoustic magnitudes were identical. This paper explored the material selection for PAS-based gas sensing in practical applications.
An Order-Preserving Encryption Scheme Based on Weighted Random Interval Division for Ciphertext Comparison in Wearable Systems
Authors: Gui Ruowei , Yang Liu , Gui Xiaolin
Published: October 2022

With the rapid development of wearable devices with various sensors, massive sensing data for health management have been generated. This causes a potential revolution in medical treatments, diagnosis, and prediction. However, due to the privacy risks of health data aggregation, data comparative analysis under privacy protection faces challenges. Order-preserving encryption is an effective scheme to achieve private data retrieval and comparison, but the existing order-preserving encryption algorithms are mainly aimed at either integer data or single characters. It is urgent to build a lightweight order-preserving encryption scheme that supports multiple types of data such as integer, floating number, and string. In view of the above problems, this paper proposes an order-preserving encryption scheme (WRID-OPES) based on weighted random interval division (WRID). WRID-OPES converts all kinds of data into hexadecimal number strings and calculates the frequency and weight of each hexadecimal number. The plaintext digital string is blocked and recombined, and each block is encrypted using WRID algorithm according to the weight of each hexadecimal digit. Our schemes can realize the order-preserving encryption of multiple types of data and achieve indistinguishability under ordered selection plaintext attack (IND-OCPA) security in static data sets. Security analysis and experiments show that our scheme can resist attacks using exhaustive methods and statistical methods and has linear encryption time and small ciphertext expansion rate.
Real-Time Oil Leakage Detection on Aftermarket Motorcycle Damping System with Convolutional Neural Networks
Authors: Bianchi Federico , Speziali Stefano , Marini Andrea , Proietti Massimiliano , Menculini Lorenzo , Garinei Alberto , Bellani Gabriele , Marconi Marcello
Published: October 2022

In this work, we describe in detail how Deep Learning and Computer Vision can help to detect fault events of the AirTender system, an aftermarket motorcycle damping system component. One of the most effective ways to monitor the AirTender functioning is to look for oil stains on its surface. Starting from real-time images, AirTender is first detected in the motorbike suspension system, simulated indoor, and then, a binary classifier determines whether AirTender is spilling oil or not. The detection is made with the help of the Yolo5 architecture, whereas the classification is carried out with the help of a suitably designed Convolutional Neural Network, OilNet40. In order to detect oil leaks more clearly, we dilute the oil in AirTender with a fluorescent dye with an excitation wavelength peak of approximately 390 nm. AirTender is then illuminated with suitable UV LEDs. The whole system is an attempt to design a low-cost detection setup. An on-board device, such as a mini-computer, is placed near the suspension system and connected to a full hd camera framing AirTender. The on-board device, through our Neural Network algorithm, is then able to localize and classify AirTender as normally functioning (non-leak image) or anomaly (leak image).