bluuebunny/crossref_metadata_2025 huggingface dataset:
Integrate the Canopy SIF and Its Derived Structural and Physiological Components for Wheat Stripe Rust Stress Monitoring
Authors: Jing Xia , Li Bingyu , Ye Qixing , Zou Qin , Yan Jumei , Du Kaiqi
Published: July 2022

Solar-induced chlorophyll fluorescence (SIF) has great advantages in the remote sensing detection of crop stress. However, under stripe rust stress, the effects of canopy structure and leaf physiology on the variations in canopy SIF are unclear, and these influencing factors are entangled during the development of disease, resulting in an unclear coupling relationship between SIFcanopy and the severity level (SL) of disease, which affects the remote sensing detection accuracy of wheat stripe rust. In this study, the observed canopy SIF was decomposed into NIRVP, which can characterize the canopy structure, and SIFtot, which can sensitively reflect the physiological status of crops. Additionally, the main factors driving the variations in canopy SIF under different disease severities were analyzed, and the response characteristics of SIFcanopy, NIRVP, and SIFtot to SL under stripe rust stress were studied. The results showed that when the severity level (SL) of disease was lower than 20%, NIRVP was more sensitive to variation in SIFcanopy than SIFtot, and the correlation between SIFtot and SL was 6.6% higher than that of SIFcanopy. Using the decomposed SIFtot component allows one to detect the stress state of plants before variations in vegetation canopy structure and leaf area index and can realize the early diagnosis of crop diseases. When the severity level (SL) of disease was in the state of moderate incidence (20% < SL ≤ 45%), the variation in SIFcanopy was affected by both NIRVP and SIFtot, and the detection accuracy of SIFcanopy for wheat stripe rust was better than that of the NIRVP and SIFtot components. When the severity level (SL) of disease reached a severe level (SL > 45%), SIFtot was more sensitive to the variation in SIFcanopy, and NIRVP reached a highly significant level with SL, which could better realize the remote sensing detection of wheat stripe rust disease severity. The research results showed that analyzing variations in SIFcanopy by using the decomposed canopy structure and physiological response signals can effectively capture additional information about plant physiology, detect crop pathological variations caused by disease stress earlier and more accurately, and promote crop disease monitoring and research progress.
Wide Area Detection and Distribution Characteristics of Landslides along Sichuan Expressways
Authors: Chen Bo , Li Zhenhong , Zhang Chenglong , Ding Mingtao , Zhu Wu , Zhang Shuangcheng , Han Bingquan , Du Jiantao , Cao Yanbo , Zhang Chi , Liao Zhiyong , Zhou Shuanke , Wang Jianwei , Peng Jianbing
Published: July 2022

Wide area landslide detection is a major international research hotspot in the field of geological hazards, and the integration of multi-temporal optical satellite images and spaceborne interferometric synthetic aperture radar (InSAR) appears to be an effective way to realize this. In this paper, a technical framework is presented for wide area landslide detection: (i) multi-temporal satellite optical images are used to detect landslides with distinguishable geomorphological features; (ii) Generic Atmospheric Correction Online Service (GACOS) assisted InSAR stacking is employed to generate annual surface displacement rate maps in radar line of sight using satellite SAR images from both ascending and descending tracks, which are in turn utilized to automatically detect active landslides from ground motion using hotspot analysis, and (iii) the distribution characteristics of the detected landslides are investigated by examining their relationships with topographic and hydrological factors. Three expressways in Sichuan Province, China—namely the Yakang (Ya’an-Kangding), Yaxi (Ya’an-Xichang), and Lushi (Luding-Shimian) expressways—and their surrounding regions (a total area of approximately 20,000 square kilometers) were chosen as the study area. A total of 413 landslides were detected, among which 320 were detected using multi-temporal satellite optical images, and 109 were detected using GACOS-assisted InSAR stacking. It should be noted that only 16 landslides were detected by both approaches; these landslides all exhibited not only obvious geomorphological features but also ground motion. A statistical analysis of the topographic and hydrological factors shows that of the detected landslides: 81% are distributed at elevations of 1000–2500 m, over 60% lie within the elevation range of 100~400 m, and 90% present with medium and steep slopes (20°~45°), and 80% are located within areas seeing an annual rainfall of 950~1050 mm. Nine landslides were found to pose potential safety hazards to the expressways. The research findings in this paper have directly benefitted the Sichuan expressways; equally important, it is believed that the technical framework presented in this paper will provide guidance for hazard mitigation and the prevention of transportation hazards in the future.
Lidar-Based Aboveground Biomass Estimations for the Maya Archaeological Site of Yaxnohcah, Campeche, Mexico
Authors: Vázquez-Alonso Mariana , Lentz David L. , Dunning Nicholas P. , Carr Christopher , Anaya Hernández Armando , Reese-Taylor Kathryn
Published: July 2022

This study aims to provide a technique applied to archaeology to estimate lidar-based aboveground biomass (AGB) in contemporary tropical forests surrounding archaeological sites. Accurate AGB estimations are important to serve as a baseline to evaluate the wood resources that the ancient Maya could have used for the development of their cities. A lidar processing model is proposed to study the contemporary forest surrounding the Yaxnohcah archaeological site. As tropical forests are highly diverse environments where species are not uniformly distributed, it was necessary to consider the variation within the forest to obtain accurate AGB. Four vegetation communities were defined from a supervised classification of a Sentinel-2 satellite image. A stratified sample was then selected for the field survey that comprised 73 transects of 500 m2 each. To estimate the transect AGB, we used an allometric equation that requires diameter, height, and wood density measurements for identified species. Linear-derived models provided the relationship between field data with lidar statistics for each vegetation type. Predicted average AGB values agreed with those obtained in the field. However, they significantly differed between vegetation types, averaging 83 Mg/ha for lowland forest, 178 for transition forest, and 215 for upland forest communities. From those results, we created a map with wall-to-wall AGB estimates following the distribution of vegetation classes that could complement archaeological research of past land use. Vegetation classification also helped determine that there is a spatial relationship between vegetation communities and the distribution of archaeological settlement features for the ancient city of Yaxnohcah.
Accuracy Evaluation and Analysis of GNSS Tropospheric Delay Inversion from Meteorological Reanalysis Data
Authors: Liu Guolin , Huang Guanwen , Xu Ying , Ta Liangyu , Jing Ce , Cao Yu , Wang Ziwei
Published: July 2022

Accurate estimation of tropospheric delay is significant for global navigation satellite system’s (GNSS) high-precision navigation and positioning. However, due to the random and contingent changes in weather conditions and water vapor factors, the classical tropospheric delay model cannot accurately reflect changes in tropospheric delay. In recent years, with the development of meteorological observation/detection and numerical weather prediction (NWP) technology, the accuracy and resolution of meteorological reanalysis data have been effectively improved, providing a new solution for the inversion and modeling of regional or global tropospheric delays. Here, we evaluate the consistency and accuracy of three different types of reanalysis data (i.e., ERA5, MERRA2, and CRA40) used to invert the zenith tropospheric delay (ZTD) from 436 international GNSS service (IGS) stations in 2020, based on the integral method. The results show that the ZTD inversion of the three types of reanalysis data was consistent with the IGS ZTD, even in heavy rain conditions. Furthermore, the average precision of the ZTD inversion of the ERA5 reanalysis data was higher, where the mean deviation (bias), mean absolute error (MAE), and root mean square (RMS) were –3.39, 9.69, and 12.55 mm, respectively. The ZTD average precisions of the MERRA2 and CRA40 inversions were comparable, showing slightly worse performance than the ERA5. In addition, we further analyzed the global distribution characteristics of the ZTD errors inverted from the reanalysis data. The results show that ZTD errors inverted from the reanalysis data were highly correlated with station latitude and climate type, and they were mainly concentrated in the tropical climate zone at low latitudes. Compared to dividing error areas by latitude, dividing error areas by climatic category could better reflect the global distribution of errors and would also provide a data reference for the establishment of tropospheric delay models considering climate type.
SAR Tomography Based on Atomic Norm Minimization in Urban Areas
Authors: Liu Ning , Li Xinwu , Peng Xing , Hong Wen
Published: July 2022

Synthetic aperture radar (SAR) tomography (TomoSAR) is a powerful tool for the three-dimensional (3D) reconstruction of buildings in urban areas. At present, the compressed sensing (CS) technique has been widely used in the TomoSAR inversion of urban areas because of the sparsity of the backscattering power of buildings along the elevation direction. However, this algorithm discretizes the elevation and assumes that the scatterers are located on predetermined finite grids. In fact, scatterers can lie anywhere in the elevation direction, regardless of grid point constraints. The phenomenon of scatterer positioning errors due to elevation discretization is called the off-grid effect, which will affect the height estimation accuracy of TomoSAR. To overcome this problem, we proposed a TomoSAR reconstruction algorithm based on atomic norm minimization (Tomo-ANM) in this paper. Tomo-ANM employs ANM, a continuous compressed sensing technique, to obtain scatterer positions on the continuous dictionary, thus eliminating the off-grid effect. Baseline compensation is necessary to obtain the data of virtual uniform baselines or the samples of uniform data during preprocessing. A fast realization of ANM, IVDST, is utilized to accelerate the process. Tomo-ANM was tested through simulation experiments, and the results confirmed the validity of eliminating the influence of off-grid effects and exhibited an improved location accuracy and detection rate in less time compared with the on-grid TomoSAR algorithm SL1MMER. Real data experiments based on eight staring spotlight TerraSAR-X images showed that Tomo-ANM can improve the accuracy of building height estimation by 4.83% relative to its real height.
Depth Estimation for Integral Imaging Microscopy Using a 3D–2D CNN with a Weighted Median Filter
Authors: Imtiaz Shariar Md , Kwon Ki-Chul , Hossain Md. Biddut , Alam Md. Shahinur , Jeon Seok-Hee , Kim Nam
Published: July 2022

This study proposes a robust depth map framework based on a convolutional neural network (CNN) to calculate disparities using multi-direction epipolar plane images (EPIs). A combination of three-dimensional (3D) and two-dimensional (2D) CNN-based deep learning networks is used to extract the features from each input stream separately. The 3D convolutional blocks are adapted according to the disparity of different directions of epipolar images, and 2D-CNNs are employed to minimize data loss. Finally, the multi-stream networks are merged to restore the depth information. A fully convolutional approach is scalable, which can handle any size of input and is less prone to overfitting. However, there is some noise in the direction of the edge. A weighted median filtering (WMF) is used to acquire the boundary information and improve the accuracy of the results to overcome this issue. Experimental results indicate that the suggested deep learning network architecture outperforms other architectures in terms of depth estimation accuracy.
Application of Multi-Electrode Resistivity Method on Semi-Diagenesis Rocks in Freezing Area and Its’ Implications
Authors: Kan Wenguang , Yang Zailin , Sun Menghan
Published: July 2022

Recently, with the development of geophysical exploration technology, geophysical engineering instruments and methods have also improved. The multi-electrode resistivity method is widely used in engineering exploration. In this paper, multi-electrode resistivity tests were carried out in a seasonal frozen soil area in Heilongjiang Province, to provide an optimized multi-electrode resistivity method under the conditions of frozen soil. Combined with shear wave velocity tests and standard penetration tests, multi-electrode resistivity tests were used to comprehensively analyze and evaluate the physical and mechanical properties of Tertiary semi-diagenesis rocks. The results show that the high resistivity due to the frozen surface layer acting as a shield can be eliminated by technical means. It is feasible to test the resistivity through the frozen surface layer. The multi-electrode resistivity method can visually reflect the interface between saturated sand and semi-diagenetic rocks. Dividing the interface between saturated sand and semi-diagenetic rocks is advantageous as the morphology of the resistivity curve has a significant curvature change. There is a strong correlation between the resistance and shear wave velocity of a strata in which the Pearson correlation coefficient is as high as 0.99. The multi-electrode resistivity method test used in combination with the shear wave velocity test and the standard penetration test could give the bearing capacity and frictional resistance of semi-diagenetic rocks, which saves a lot of time and material costs in engineering exploration.
A Feasibility Study on Deep Learning Based Brain Tumor Segmentation Using 2D Ellipse Box Areas
Authors: Ali Muhaddisa Barat , Bai Xiaohan , Gu Irene Yu-Hua , Berger Mitchel S. , Jakola Asgeir Store
Published: July 2022

In most deep learning-based brain tumor segmentation methods, training the deep network requires annotated tumor areas. However, accurate tumor annotation puts high demands on medical personnel. The aim of this study is to train a deep network for segmentation by using ellipse box areas surrounding the tumors. In the proposed method, the deep network is trained by using a large number of unannotated tumor images with foreground (FG) and background (BG) ellipse box areas surrounding the tumor and background, and a small number of patients (<20) with annotated tumors. The training is conducted by initial training on two ellipse boxes on unannotated MRIs, followed by refined training on a small number of annotated MRIs. We use a multi-stream U-Net for conducting our experiments, which is an extension of the conventional U-Net. This enables the use of complementary information from multi-modality (e.g., T1, T1ce, T2, and FLAIR) MRIs. To test the feasibility of the proposed approach, experiments and evaluation were conducted on two datasets for glioma segmentation. Segmentation performance on the test sets is then compared with those used on the same network but trained entirely by annotated MRIs. Our experiments show that the proposed method has obtained good tumor segmentation results on the test sets, wherein the dice score on tumor areas is (0.8407, 0.9104), and segmentation accuracy on tumor areas is (83.88%, 88.47%) for the MICCAI BraTS’17 and US datasets, respectively. Comparing the segmented results by using the network trained by all annotated tumors, the drop in the segmentation performance from the proposed approach is (0.0594, 0.0159) in the dice score, and (8.78%, 2.61%) in segmented tumor accuracy for MICCAI and US test sets, which is relatively small. Our case studies have demonstrated that training the network for segmentation by using ellipse box areas in place of all annotated tumors is feasible, and can be considered as an alternative, which is a trade-off between saving medical experts’ time annotating tumors and a small drop in segmentation performance.
Characteristics of Channel Eigenvalues and Mutual Coupling Effects for Holographic Reconfigurable Intelligent Surfaces
Authors: Sun Shu , Tao Meixia
Published: July 2022

As a prospective key technology for the next-generation wireless communications, reconfigurable intelligent surfaces (RISs) have gained tremendous research interest in both the academia and industry in recent years. Only limited knowledge, however, has been obtained about the channel eigenvalue characteristics and spatial degrees of freedom (DoF) of systems containing RISs, especially when mutual coupling (MC) is present between the array elements. In this paper, we focus on the small-scale spatial correlation and eigenvalue properties excluding and including MC effects, for RISs with a quasi-continuous aperture (i.e., holographic RISs). Specifically, asymptotic behaviors of far-field and near-field eigenvalues of the spatial correlation matrix of holographic RISs without MC are first investigated, where the counter-intuitive observation of a lower DoF with more elements is explained by leveraging the power spectrum of the spatial correlation function. Second, a novel metric is proposed to quantify the inter-element correlation or coupling strength in RISs and ordinary antenna arrays. Furthermore, in-depth analysis is performed regarding the MC effects on array gain, effective spatial correlation, and eigenvalue architectures for a variety of element intervals when a holographic RIS works in the radiation and reception mode, respectively. The analysis and numerical results demonstrate that a considerable amount of the eigenvalues of the spatial correlation matrix correspond to evanescent waves that are promising for near-field communication and sensing. More importantly, holographic RISs can potentially reach an array gain conspicuously larger than conventional arrays by exploiting MC, and MC has discrepant impacts on the effective spatial correlation and eigenvalue structures at the transmitter and receiver.
Machine-Learning-Based Fine Tuning of Input Signals for Mechano-Tactile Display
Authors: Yamanaka Shuto , Nagatomo Tatsuho , Hiraki Takefumi , Ishizuka Hiroki , Miki Norihisa
Published: July 2022

Deducing the input signal for a tactile display to present the target surface (i.e., solving the inverse problem for tactile displays) is challenging. We proposed the encoding and presentation (EP) method in our prior work, where we encoded the target surface by scanning it using an array of piezoelectric devices (encoding) and then drove the piezoelectric devices using the obtained signals to display the surface (presentation). The EP method reproduced the target texture with an accuracy of over 80% for the five samples tested, which we refer to as replicability. Machine learning is a promising method for solving inverse problems. In this study, we designed a neural network to connect the subjective evaluation of tactile sensation and the input signals to a display; these signals are described as time-domain waveforms. First, participants were asked to touch the surface presented by the mechano-tactile display based on the encoded data from the EP method. Then, the participants recorded the similarity of the surface compared to five material samples, which were used as the input. The encoded data for the material samples were used as the output to create a dataset of 500 vectors. By training a multilayer perceptron with the dataset, we deduced new inputs for the display. The results indicate that using machine learning for fine tuning leads to significantly better accuracy in deducing the input compared to that achieved using the EP method alone. The proposed method is therefore considered a good solution for the inverse problem for tactile displays.
Leakages in District Heating Networks—Model-Based Data Set Quality Assessment and Localization
Authors: Vahldiek Kai , Rüger Bernd , Klawonn Frank
Published: July 2022

Large spontaneous leakages in district heating networks (DHNs) require a separation of the affected network part, as interruption of the heat supply is imminent. Measurement data of 22 real events was analyzed for localization, but suitable results were not always achieved. In this paper, the reasons are investigated and a model for data evaluation (MoFoDatEv) is developed for further insights. This contains prior knowledge and a simplified physical model for the reaction of the DHN in the case of a large spontaneous leakage. A model like this does not exist so far. It determines the time point and the duration of the pressure drop of the pressure wave which is caused by such leakages. Both parameters and the evaluation time frame are optimized for each event separately. The quality assessment leads to a categorization of the events based on several parameters, and correlations between the pressure and the refill mass flow are found. A minimum leakage size is deduced for successful evaluation. Furthermore, MoFoDatEv can also be used for leakage localization directly, combining two steps from previous publications. Therefore, more data contribute to the result. The application is conducted with artificial data to prove the model concept, and also with real measurement data.
Single VDGA-Based Mixed-Mode Universal Filter and Dual-Mode Quadrature Oscillator
Authors: Roongmuanpha Natchanai , Tangsrirat Worapong , Pukkalanun Tattaya
Published: July 2022

This article presents the circuit designs for a mixed-mode universal biquadratic filter and a dual-mode quadrature oscillator, both of which use a single voltage differencing gain amplifier (VDGA), one resistor, and two capacitors. The proposed circuit has the following performance characteristics: (i) simultaneous implementation of standard biquadratic filter functions with three inputs and two outputs in all four possible modes, namely, voltage-mode (VM), current-mode (CM), trans-admittance-mode (TAM), and trans-impedance-mode (TIM); (ii) electronic adjustment of the natural angular frequency and independently single-resistance controllable high-quality factor; (iii) performing a dual-mode quadrature oscillator with simultaneous voltage and current output responses; (iv) orthogonal resistive and/or electronic control of the oscillation condition and frequency; (v) employing all grounded passive components in the quadrature oscillator function; and (vi) simpler topology due to the use of a single VDGA. VDGA non-idealities and parasitic elements are also investigated and analyzed in terms of their influence on circuit performance. To prove the study hypotheses, computer simulations with TSMC 0.18 μm CMOS technology and experimental confirmatory testing with off-the-shelf integrated circuits LM13600 have been performed.
Construction and Evaluation of an Optical Medium Voltage Transducer Module Aimed at a 132 kV Optical Voltage Sensor for WAMPAC Systems
Authors: Fusiek Grzegorz , Niewczas Pawel
Published: July 2022

This paper reports on the construction and characterization of an optical voltage transducer module for applications in the field of wide-area monitoring, protection, and control (WAMPAC). The optical medium voltage transducer (MVT) module was designed to be combined with a capacitive voltage divider (CVD) to form a voltage sensor intended for 132 kV high voltage (HV) networks. The MVT module comprises a combination of a piezoelectric transducer (PZT) and a fiber Bragg grating (FBG) as a core optical sensing element. Changes in the input voltage across the PZT translate into strain being detected by the FBG. The resultant FBG peak wavelength can be calibrated in terms of the input voltage to obtain a precise voltage measurement. The module was experimentally evaluated in the laboratory, and its performance was assessed based on the requirements specified by the IEC standards for electronic voltage transformers and low power voltage transformers. The results of accuracy tests demonstrate that the MVT module is free from hysteresis, within the experimental error, and is capable of simultaneously meeting the requirements for 0.1 metering and 1P protection classes specified by the IEC 60044-7 and IEC 61869-11 standards.
LTC-Mapping, Enhancing Long-Term Consistency of Object-Oriented Semantic Maps in Robotics
Authors: Matez-Bandera Jose-Luis , Fernandez-Chaves David , Ruiz-Sarmiento Jose-Raul , Monroy Javier , Petkov Nicolai , Gonzalez-Jimenez Javier
Published: July 2022

This paper proposes LTC-Mapping, a method for building object-oriented semantic maps that remain consistent in the long-term operation of mobile robots. Among the different challenges that compromise this aim, LTC-Mapping focuses on two of the more relevant ones: preventing duplicate instances of objects (instance duplication) and handling dynamic scenes. The former refers to creating multiple instances of the same physical object in the map, usually as a consequence of partial views or occlusions. The latter deals with the typical assumption made by object-oriented mapping methods that the world is static, resulting in outdated representations when the objects change their positions. To face these issues, we model the detected objects with 3D bounding boxes, and analyze the visibility of their vertices to detect occlusions and partial views. Besides this geometric modeling, the boxes are augmented with semantic information regarding the categories of the objects they represent. Both the geometric entities (bounding boxes) and their semantic content are propagated over time through data association and a fusion technique. In addition, in order to keep the map curated, the non-detection of objects in the areas where they should appear is also considered, proposing a mechanism that removes them from the map once there is evidence that they have been moved (i.e., multiple non-detections occur). To validate our proposal, a number of experiments have been carried out using the Robot@VirtualHome ecosystem, comparing its performance with a state-of-the-art alternative. The results report a superior performance of LTC-Mapping when modeling both geometric and semantic information of objects, and also support its online execution.
Adaptive Lag Smoother for State Estimation
Authors: Poddar Shashi , Crassidis John L.
Published: July 2022

Fixed-lag smoothing has been used across different disciplines for offline analysis in many applications. With rising computational power and parallel processing architectures, fixed-lag smoothers are increasingly integrated into online processing system with small delays. This delay is directly related to the lag-length used in system design, which needs to be chosen appropriately. In this work, an adaptive approach is devised to choose an appropriate lag-length that provides a good trade-off between accuracy and computational requirements. The analysis shown in this paper for the error dynamics of the fixed-lag smoother over the lags helps in understanding its saturation over increasing lags. In order to provide the empirical results, simulations are carried out over a second-order Newtonian system, single-axis attitude estimation, Van der Pol’s oscillator, and three-axis attitude estimation. The simulation results demonstrate the performance achieved with an adaptive-lag smoother as compared to a fixed-lag smoother with very high lag-length.
InjectMeAI—Software Module of an Autonomous Injection Humanoid
Authors: Ampadu Kwame Owusu , Rokohl Florian , Mahmood Safdar , Reichenbach Marc , Huebner Michael
Published: July 2022

The recent pandemic outbreak proved social distancing effective in helping curb the spread of SARS-CoV-2 variants along with the wearing of masks and hand gloves in hospitals and assisted living environments. Health delivery personnel having undergone training regarding the handling of patients suffering from Corona infection have been stretched. Administering injections involves unavoidable person to person contact. In this circumstance, the spread of bodily fluids and consequently the Coronavirus become eminent, leading to an upsurge of infection rates among nurses and doctors. This makes enforced home office practices and telepresence through humanoid robots a viable alternative. In providing assistance to further reduce contact with patients during vaccinations, a software module has been designed, developed, and implemented on a Pepper robot that estimates the pose of a patient, identifies an injection spot, and raises an arm to deliver the vaccine dose on a bare shoulder. Implementation was done using the QiSDK in an android integrated development environment with a custom Python wrapper. Tests carried out yielded positive results in under 60 s with an 80% success rate, and exposed some ambient lighting discrepancies. These discrepancies can be solved in the near future, paving a new way for humans to get vaccinated.
A Novel Path Planning Strategy for a Cleaning Audit Robot Using Geometrical Features and Swarm Algorithms
Authors: Pathmakumar Thejus , Muthugala M. A. Viraj J. , Samarakoon S. M. Bhagya P. , Gómez Braulio Félix , Elara Mohan Rajesh
Published: July 2022

Robot-aided cleaning auditing is pioneering research that uses autonomous robots to assess a region’s cleanliness level by analyzing the dirt samples collected from various locations. Since the dirt sample gathering process is more challenging, adapting a coverage planning strategy from a similar domain for cleaning is non-viable. Alternatively, a path planning approach to gathering dirt samples selectively at locations with a high likelihood of dirt accumulation is more feasible. This work presents a first-of-its-kind dirt sample gathering strategy for the cleaning auditing robots by combining the geometrical feature extraction and swarm algorithms. This combined approach generates an efficient optimal path covering all the identified dirt locations for efficient cleaning auditing. Besides being the foundational effort for cleaning audit, a path planning approach considering the geometric signatures that contribute to the dirt accumulation of a region has not been device so far. The proposed approach is validated systematically through experiment trials. The geometrical feature extraction-based dirt location identification method successfully identified dirt accumulated locations in our post-cleaning analysis as part of the experiment trials. The path generation strategies are validated in a real-world environment using an in-house developed cleaning auditing robot BELUGA. From the experiments conducted, the ant colony optimization algorithm generated the best cleaning auditing path with less travel distance, exploration time, and energy usage.
A RUL Estimation System from Clustered Run-to-Failure Degradation Signals
Authors: Cho Anthony D. , Carrasco Rodrigo A. , Ruz Gonzalo A.
Published: July 2022

The prognostics and health management disciplines provide an efficient solution to improve a system’s durability, taking advantage of its lifespan in functionality before a failure appears. Prognostics are performed to estimate the system or subsystem’s remaining useful life (RUL). This estimation can be used as a supply in decision-making within maintenance plans and procedures. This work focuses on prognostics by developing a recurrent neural network and a forecasting method called Prophet to measure the performance quality in RUL estimation. We apply this approach to degradation signals, which do not need to be monotonical. Finally, we test our system using data from new generation telescopes in real-world applications.
PCO-Based BLE Mesh Accelerator
Authors: Bukreyev Ivan , Yüksel Hazal , Ho Ken , Apsel Alyssa
Published: July 2022

Bluetooth Low Energy (BLE) mesh networks enable diverse communication for the Internet of Things (IoT). However, existing BLE mesh implementations cannot simultaneously achieve low-power operation, symmetrical communication, and scalability. A major limitation of mesh networks is the inability of the BLE stack to handle network-scalable time synchronization. Pulse-coupled oscillators (PCOs) have been studied extensively and are able to achieve fast and reliable synchronization across a range of applications and network topologies. This paper presents a lightweight physical (PHY) layer accelerator to the BLE stack that enables scalable synchronization command with a PCO. The accelerator is a fully digital solution that can be synthesized with only the standard cells available in any silicon technology. This paper provides a detailed analysis of PCO-based BLE mesh networks and explores per-node system-level requirements. Finally, the analytical results are validated with measurements of a custom radio node based on the ubiquitous AD9364 transceiver.
A Novel Low-Latency and Energy-Efficient Task Scheduling Framework for Internet of Medical Things in an Edge Fog Cloud System
Authors: Alatoun Kholoud , Matrouk Khaled , Mohammed Mazin Abed , Nedoma Jan , Martinek Radek , Zmij Petr
Published: July 2022

In healthcare, there are rapid emergency response systems that necessitate real-time actions where speed and efficiency are critical; this may suffer as a result of cloud latency because of the delay caused by the cloud. Therefore, fog computing is utilized in real-time healthcare applications. There are still limitations in response time, latency, and energy consumption. Thus, a proper fog computing architecture and good task scheduling algorithms should be developed to minimize these limitations. In this study, an Energy-Efficient Internet of Medical Things to Fog Interoperability of Task Scheduling (EEIoMT) framework is proposed. This framework schedules tasks in an efficient way by ensuring that critical tasks are executed in the shortest possible time within their deadline while balancing energy consumption when processing other tasks. In our architecture, Electrocardiogram (ECG) sensors are used to monitor heart health at home in a smart city. ECG sensors send the sensed data continuously to the ESP32 microcontroller through Bluetooth (BLE) for analysis. ESP32 is also linked to the fog scheduler via Wi-Fi to send the results data of the analysis (tasks). The appropriate fog node is carefully selected to execute the task by giving each node a special weight, which is formulated on the basis of the expected amount of energy consumed and latency in executing this task and choosing the node with the lowest weight. Simulations were performed in iFogSim2. The simulation outcomes show that the suggested framework has a superior performance in reducing the usage of energy, latency, and network utilization when weighed against CHTM, LBS, and FNPA models.
Microstrip-Fed 3D-Printed H-Sectorial Horn Phased Array
Authors: Zhou Ivan , Pradell Lluís , Villegas José Maria , Vidal Neus , Albert Miquel , Jofre Lluís , Romeu Jordi
Published: July 2022

A 3D-printed phased array consisting of four H-Sectorial horn antennas of 200 g weight with an ultra-wideband rectangular-waveguide-to-microstrip-line transition operating over the whole LMDS and K bands (24.25–29.5 GHz) is presented. The transition is based on exciting three overlapped transversal patches that radiate into the waveguide. The transition provides very low insertion losses, ranging from 0.30 dB to 0.67 dB over the whole band of operation (23.5–30.4 GHz). The measured fractional bandwidth of the phased array including the transition was 20.8% (24.75–30.3 GHz). The antenna was measured for six different scanning angles corresponding to six different progressive phases α, ranging from 0° to 140° at the central frequency band of operation of 26.5 GHz. The maximum gain was found in the broadside direction α = 0°, with 15.2 dB and efficiency η = 78.5%, while the minimum was found for α = 140°, with 13.7 dB and η = 91.2%.
Reconstructing Superquadrics from Intensity and Color Images
Authors: Tomašević Darian , Peer Peter , Solina Franc , Jaklič Aleš , Štruc Vitomir
Published: July 2022

The task of reconstructing 3D scenes based on visual data represents a longstanding problem in computer vision. Common reconstruction approaches rely on the use of multiple volumetric primitives to describe complex objects. Superquadrics (a class of volumetric primitives) have shown great promise due to their ability to describe various shapes with only a few parameters. Recent research has shown that deep learning methods can be used to accurately reconstruct random superquadrics from both 3D point cloud data and simple depth images. In this paper, we extended these reconstruction methods to intensity and color images. Specifically, we used a dedicated convolutional neural network (CNN) model to reconstruct a single superquadric from the given input image. We analyzed the results in a qualitative and quantitative manner, by visualizing reconstructed superquadrics as well as observing error and accuracy distributions of predictions. We showed that a CNN model designed around a simple ResNet backbone can be used to accurately reconstruct superquadrics from images containing one object, but only if one of the spatial parameters is fixed or if it can be determined from other image characteristics, e.g., shadows. Furthermore, we experimented with images of increasing complexity, for example, by adding textures, and observed that the results degraded only slightly. In addition, we show that our model outperforms the current state-of-the-art method on the studied task. Our final result is a highly accurate superquadric reconstruction model, which can also reconstruct superquadrics from real images of simple objects, without additional training.
A Method for Detection of Corn Kernel Mildew Based on Co-Clustering Algorithm with Hyperspectral Image Technology
Authors: Kang Zhen , Huang Tianchen , Zeng Shan , Li Hao , Dong Lei , Zhang Chaofan
Published: July 2022

Hyperspectral imaging can simultaneously acquire spectral and spatial information of the samples and is, therefore, widely applied in the non-destructive detection of grain quality. Supervised learning is the mainstream method of hyperspectral imaging for pixel-level detection of mildew in corn kernels, which requires a large number of training samples to establish the prediction or classification models. This paper presents an unsupervised redundant co-clustering algorithm (FCM-SC) based on multi-center fuzzy c-means (FCM) clustering and spectral clustering (SC), which can effectively detect non-uniformly distributed mildew in corn kernels. This algorithm first carries out fuzzy c-means clustering of sample features, extracts redundant cluster centers, merges the cluster centers by spectral clustering, and finally finds the category of corresponding cluster centers for each sample. It effectively solves the problems of the poor ability of the traditional fuzzy c-means clustering algorithm to classify the data with complex structure distribution and the complex calculation of the traditional spectral clustering algorithm. The experimental results demonstrated that the proposed algorithm could describe the complex structure of mildew distribution in corn kernels and exhibits higher stability, better anti-interference ability, generalization ability, and accuracy than the supervised classification model.
Bridging 3D Slicer and ROS2 for Image-Guided Robotic Interventions
Authors: Connolly Laura , Deguet Anton , Leonard Simon , Tokuda Junichi , Ungi Tamas , Krieger Axel , Kazanzides Peter , Mousavi Parvin , Fichtinger Gabor , Taylor Russell H.
Published: July 2022

Developing image-guided robotic systems requires access to flexible, open-source software. For image guidance, the open-source medical imaging platform 3D Slicer is one of the most adopted tools that can be used for research and prototyping. Similarly, for robotics, the open-source middleware suite robot operating system (ROS) is the standard development framework. In the past, there have been several “ad hoc” attempts made to bridge both tools; however, they are all reliant on middleware and custom interfaces. Additionally, none of these attempts have been successful in bridging access to the full suite of tools provided by ROS or 3D Slicer. Therefore, in this paper, we present the SlicerROS2 module, which was designed for the direct use of ROS2 packages and libraries within 3D Slicer. The module was developed to enable real-time visualization of robots, accommodate different robot configurations, and facilitate data transfer in both directions (between ROS and Slicer). We demonstrate the system on multiple robots with different configurations, evaluate the system performance and discuss an image-guided robotic intervention that can be prototyped with this module. This module can serve as a starting point for clinical system development that reduces the need for custom interfaces and time-intensive platform setup.
Multi-Feature Matching GM-PHD Filter for Radar Multi-Target Tracking
Authors: Tao Jin , Jiang Defu , Yang Jialin , Zhang Chao , Wang Song , Han Yan
Published: July 2022

Multi-target tracking (MTT) is one of the most important functions of radar systems. Traditional multi-target tracking methods based on data association convert multi-target tracking problems into single-target tracking problems. When the number of targets is large, the amount of computation increases exponentially. The Gaussian mixture probability hypothesis density (GM-PHD) filtering based on a random finite set (RFS) provides an effective method to solve multi-target tracking problems without the requirement of explicit data association. However, it is difficult to track targets accurately in real-time with dense clutter and low detection probability. To solve this problem, this paper proposes a multi-feature matching GM-PHD (MFGM-PHD) filter for radar multi-target tracking. Using Doppler and amplitude information contained in radar echo to modify the weights of Gaussian components, the weight of the clutter can be greatly reduced and the target can be distinguished from clutter. Simulations show that the proposed MFGM-PHD filter can improve the accuracy of multi-target tracking as well as the real-time performance with high clutter density and low detection probability.
“From Beautification to Ennobling”: The Exterior Mural Mosaics from Suceava of the Socialist Era
Authors: Purici Ştefan , Mareci Sabol Harieta
Published: July 2022

The mosaic is one of the most durable monumental artworks, hence the belief in its permanence. It is resilient to shocks, abrasion, moisture, and frost, protecting, in turn, the walls it covers. These qualities made the mosaic one of interest to the communist authorities, who later considered it suitable for beautifying the exterior walls of various buildings. The article addresses the issue of the symbolic and identity aspect of art in the urban space. The authors discuss the exterior mural mosaics from Suceava, during the communist period, as an expression of the compromise between the ideological commands of the period and the neo-traditionalist and neo-folklorizing direction professed by the artists.
The Gender Gap in Income and the COVID-19 Pandemic in Ireland
Authors: Doorley Karina , O’Donoghue Cathal , Sologon Denisa M.
Published: July 2022

The gender income gap is large and well documented in many countries. Recent research shows that it is mainly driven by differences in working patterns between men and women but also by wage differences. The tax–benefit system cushions the gender income gap by redistributing it between men and women. The COVID-19 pandemic has resulted in unprecedented levels of unemployment in 2020 in many countries, with some suggestions that men and women have been differently affected. This research investigated the effect of the COVID-19 pandemic on the gender gap in income in Ireland. By using nowcasting techniques and microsimulation, we modeled the effect of pandemic-induced employment and wage changes on the market and disposable income. We showed how the pandemic and the associated tax–benefit support could be expected to change the income gap between men and women. Policy conclusions were drawn about future redistribution between men and women.
Urban-Rural Dichotomy of Quality of Life
Authors: Petrovič František , Maturkanič Patrik
Published: July 2022

This paper is focused on the urban-rural dichotomy as one of the dichotomies of quality of life (QOL). The first objective is to find out whether the quality of urban life, or the quality of rural life, is higher in Czechia. The hypothesis assumes that the quality of urban life is higher. The second objective is to identify predictors of QOL and compare values for the residents of cities and villages. In the Theoretical Background section, we discuss cities and urban life, plus villages and rural life. In the QOL section, we explore the quality of urban and rural life in Czechia, and give examples of QOL measurements in Czech municipalities. In the Measurement and Findings section we measure QOL with a subjective indicator on the Cantril scale, and derive findings from these measurements. The correlations between QOL and other variables yield very strong predictors of both urban and rural quality of life. The measurements also yielded two “by products”: The first was the finding that women’s measured values are, except for women’s quality of city life, higher than the measured values of men. The second was the finding that the combination of quality of place and quality of the environment, which we named ‘geographical matter’, was rated higher by the respondents than the combination of QOL and happiness, which we named ‘psychological matter’.
Influence of Residential Photovoltaic Promotion Policy on Installation Intention in Typical Regions of China
Authors: Wang Shali , Wu Jiaxi , Peng Yunan , Xu Jane , Leinonen Lisa , Wang Yuyu , Meng Zheng
Published: July 2022

Increasing the popularity of distributed photovoltaic technology among Chinese residents is of great significance to achieve the dual carbon goal (emission peak and carbon neutrality). In this study, we collected 1424 questionnaire samples and used PLS-SEM for group modeling and comparative analysis of bungalow and building residents. The results show that living conditions, costs, risks of installation, maintenance, and economic efficiency are the five significant influencing factors for residents to decide whether to install a distributed residential photovoltaic equipment. Compared with building residents, bungalow residents tend to feel more concerned about the cost and risk of residential photovoltaic equipment during installation, maintenance, and use. On the other hand, bungalow residents show greater sensitivity to the corresponding photovoltaic promotion policies. By contrast, building residents pay more attention to the direct benefit created by the installation of residential photovoltaic equipment. Therefore, adopting the strategy from bungalow residents to building residents could help promote the distributed photovoltaic system progressively. As for the subsidy decline, more attention shall be paid to the progress of a gradual reduction of the subsidies. It is advisable to pay attention to integrating the upstream and downstream industry chains of the distributed photovoltaic systems, thus reducing the concern of residents about the difficulty in installing, maintaining, and protecting the distributed photovoltaic equipment. By clarifying the different impacts of promotion policies on the demand side, this study provides a practical reference for the further adjustment made to distributed photovoltaic promotion policies.