Miracle-dz/UniFAR-FTUs huggingface dataset:
Recovering Missing Trajectory Data for Mobile Laser Scanning Systems

Trajectory data are often used as important auxiliary information in preprocessing and extracting the target from mobile laser scanning data. However, the trajectory data stored independently may be lost and destroyed for various reasons, making the data unavailable for the relevant models. This study proposes recovering the trajectory of the scanner from point cloud data following the scanning principles of a rotating mirror. Two approaches are proposed from different input conditions: Ordered three-dimensional coordinates of point cloud data, with and without acquisition time. We recovered the scanner’s ground track through road point density analysis and restored the position of the center of emission of the laser based on plane reconstruction on a single scanning line. The validity and reliability of the proposed approaches were verified in the four typical urban, rural, winding, and viaduct road environments using two systems from different manufacturers. The result deviations of the ground track and scanner trajectory from their actual position were a few centimeters and less than 1 decimeter, respectively. Such an error is sufficiently small for the trajectory data to be used in the relevant algorithms.
Model-based Analysis of Maintenance-induced Availability of Aircraft in an Airline Network

Optimized aircraft maintenance concepts usually aim at increasing the aircraft availability and reducing costs. A model is presented that quantifies the effect of the increased aircraft availability when applying different maintenance concepts on an entire airline fleet. An agent-based approach is suggested: The aircraft with their maintenance tasks are dynamically assigned to the flight schedule. One major advantage of the presented methodology is that constraints for specific aircraft tails can be added easily. This is potentially of interest when testing new maintenance concept that are more focused on optimizing maintenance for single aircraft, by e.g. a stronger dependency on aircraft usage and findings.
An Approach to a Self-organizing Production in Comparison to a Centrally Planned Production

Industry 4.0, a part of the German high tech strategy, prefers self-organization in production over central production planning for the sake of greater flexibility, faster response to disruptions and to deviations, and less effort. Current planning systems usually plan centrally. We developed a universal self-organizing production and empirically compared its performance to a centrally planned production. Compared to most other approaches, the self-organizing production does not just schedule already existing operations, but starts with material requirements, explodes the bills of materials, creates production orders, and schedules backwards. The self-organizing production is based upon agents. For better comprehensibility, we additionally implemented central planning. The results of self-organization in production are promising in relation to central planning; especially when disruptions and deviations occur. Furthermore, the results suggest as best solution a combination of rough central planning and self-organization.
Vertical Distribution of Arctic Methane in 2009-2018 Using Ground-Based Remote Sensing

We analyzed the vertical distribution of atmospheric methane (CH 4 ) retrieved from measurements by ground-based Fourier Transform Spectrometer (FTS) instrument in Sodankylä, Northern Finland. The retrieved dataset covers 2009–2018. We used a dimension reduction retrieval method to extract the profile information, since each measurement contains around three pieces of information about the profile shape between 0 and 40 km. We compared the retrieved profiles against Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) satellite measurements and AirCore balloon-borne profile measurements. Additional comparison at the lowest tropospheric layer was done against in-situ measurements from a 50-m-high mast. In general, the ground-based FTS and ACE-FTS profiles agreed within 10% below 20 km and within 30% in the stratosphere between 20 and 40 km. Our method was able to accurately capture reduced methane concentrations inside the polar vortex in the Arctic stratosphere. The method produced similar trend characteristics as the reference instruments even when a static prior profile was used. Finally, we analyzed the time series of the CH 4 profile datasets and estimated the trend using the dynamic linear model (DLM).
An Efficient Algorithm for Kinematics Estimation with Application to Dynamic Gait Stability using a Contact-less Skeleton Tracking System

This paper presents an optimized algorithm for estimating static and dynamic gait parameters. We use a markerand contact-less motion capture system that identifies 20 joints of a person walking along a corridor. Based on the proposed gait cycle detection basic metrics as walking frequency, step/stride length, and support phases are estimated automatically. Applying a rigid body model, we are capable to calculate static and dynamic gait stability metrics. We conclude with initial results of a clinical study evaluating orthopaedic technical support.
Motor Rehabilitation and Biotelemetry Data Acquisition with Kinect

: Accessibility and inclusiveness of people with disabilities is a recurring theme that is already perceived as an issue in the ﬁeld of human rights. Ramps, elevators, among other devices aim at the inclusion of these individuals with limited mobility. Various types of motor limitations, specially partial limitations, are linked to corresponding physical-motor rehabilitation process, with the purpose of reducing or eliminating the patient’s dependence on a caregiver or devices for adaptation. Patients with motor disabilities must practice physio-therapeutical exercises along a physician in order to perform body and muscle analysis to ensure the patient’s well-being. To reach a more accurate analysis, physiotherapists use a range of devices to acquire patient data, such as the spirometer, to acquire the patient’s breath intensity and lung capacity. Similarly, there are other technologies capable of acquiring motion data and quantifying them. This work aims to develop a system that, paired together with an exercise game project (exergame), can acquire and transmit the motion data acquired in-game for an easier and faster analysis of the patient’s growth, relying on graphs, tables, and other visual indicators to improve the evaluation of physiotherapeutic treatments. The usage together with an exergame also has beneﬁts such as increased patient compliance with the treatment and improvements in well-being.
Cell Segmentation by Image-to-Image Translation using Multiple Different Discriminators

: This paper presents a cell image segmentation method by improving the pix2pix. Pix2pix improves the accuracy by competing a generator and a discriminator. The relationship of generator and discriminator is likened as follows. A generator is a fraudster who creates a fake image to fool the discriminator. A discriminator is a police officer who checks the fake image created by the generator. If we increase the number of police officers and different police officers are used, they have different roles and various viewpoints are used to check the fake image. In experiments, we evaluate our method on segmentation problem of cell images. We compared our method with conventional pix2pix using one discriminator. As a result, the accuracy will be improved. Thus, we propose to use multiple different discriminators to improve the segmentation accuracy of pix2pix. We confirmed that our proposed method outperformed conventional pix2pix and pix2pix using multiple same discriminators.
Object-Based Change Detection of Very High Resolution Images by Fusing Pixel-Based Change Detection Results Using Weighted Dempster-Shafer Theory

Change detection (CD), one of the primary applications of multi-temporal satellite images, is the process of identifying changes in the Earth’s surface occurring over a period of time using images of the same geographic area on different dates. CD is divided into pixel-based change detection (PBCD) and object-based change detection (OBCD). Although PBCD is more popular due to its simple algorithms and relatively easy quantitative analysis, applying this method in very high resolution (VHR) images often results in misdetection or noise. Because of this, researchers have focused on extending the PBCD results to the OBCD map in VHR images. In this paper, we present a proposed weighted Dempster-Shafer theory (wDST) fusion method to generate the OBCD by combining multiple PBCD results. The proposed wDST approach automatically calculates and assigns a certainty weight for each object of the PBCD result while considering the stability of the object. Moreover, the proposed wDST method can minimize the tendency of the number of changed objects to decrease or increase based on the ratio of changed pixels to the total pixels in the image when the PBCD result is extended to the OBCD result. First, we performed co-registration between the VHR multitemporal images to minimize the geometric dissimilarity. Then, we conducted the image segmentation of the co-registered pair of multitemporal VHR imagery. Three change intensity images were generated using change vector analysis (CVA), iteratively reweighted-multivariate alteration detection (IRMAD), and principal component analysis (PCA). These three intensity images were exploited to generate different binary PBCD maps, after which the maps were fused with the segmented image using the wDST to generate the OBCD map. Finally, the accuracy of the proposed CD technique was assessed by using a manually digitized map. Two VHR multitemporal datasets were used to test the proposed approach. Experimental results confirmed the superiority of the proposed method by comparing the existing PBCD methods and the OBCD method using the majority voting technique.
Metrics and Trends in Assessing the Scientific Impact

The economy of science has been traditionally shaped around the design of metrics that attempt to capture several diﬀerent facets of the impact of scientiﬁc works. Analytics and mining around (co-)citation and co-authorship graphs, taking into account also parameters such as time, scientiﬁc output per ﬁeld, and active years, are often the fundamental pieces of information that are considered in most of the well adopted metrics. There are, however, many other aspects that can contribute further to the assessment of scientiﬁc impact, as well as to the evaluation of the performance of individuals, and organisations, e.g., university de-partments and research centers. Such facets may cover for example the measurement of research funding raised, the impact of scientiﬁc works in patented ideas, or even the extent to which a scientiﬁc work constituted the basis for the birth of a new discipline or a new scientiﬁc (sub)area. In this work we are going to present an overview of the most recent trends in novel metrics for assessing scientiﬁc impact and performance, as well as the technical challenges faced by integrating a plethora of heterogeneous data sources in order to be able to shape the necessary views for these metrics, and the novel information extraction techniques employed to facilitate the process.
Hops—A Millennium Review

Of all the herbs that have been used to flavor and preserve beer over the ages, only the hop (Humulus lupulus L.) is now regarded as an essential raw material in brewing throughout the world. Although hops were cultivated in Babylon as far back as 200 A.D., there is no record of their use to make beer until about 1079. The value of hops for flavor and preservation of alcoholic beverages appears to have been recognized by the twelfth century, and, in the thirteenth century, the hop started to threaten traditional herbs, such as rosemary, yarrow, coriander, and bog myrtle. The Bavarian Purity Law of 1516 decreed that only hops could be used for bittering beers in Germany. For a long time, sweet and strong ales were preferred in England, and the hop was condemned as a “wicked and pernicious weed. ” Good sense eventually prevailed, however, and hops were first grown in England in 1524, 100 years before the first gardens were established in North America. Although selection of improved varieties must have been a continuous process for most of the second millennium, the breeding of hops as an organized, scientific operation dates from the start of the twentieth century with a program initiated at Wye College, England, in 1904. In the same year, the α-acid humulone was first isolated as a crystalline solid, and during the next 60 years our knowledge of hop chemistry and understanding of the isomerization process was developed, leading to great benefits for breeders, growers, and brewers alike. Over the last 40 years, there has been a rapid proliferation of types of processed hops, or hop products, which offer the brewer several economic and quality advantages over the use of whole hops, and the contribution of hops to beer aroma, bitterness, foam, and light stability can now be controlled more effectively than ever before.
An Atmospheric Correction Using High Resolution Numerical Weather Prediction Models for Satellite-Borne Single-Channel Mid-Wavelength and Thermal Infrared Imaging Sensors

This paper presents a single-channel atmospheric correction method for remotely sensed infrared (wavelength of 3–15 μm) images with various observation angles. The method is based on basic radiative transfer equations with a simple absorption-focused regression model to calculate the optical thickness of each atmospheric layer. By employing a simple regression model and re-organization of atmospheric profiles by considering viewing geometry, the proposed method conducts atmospheric correction at every pixel of a numerical weather prediction model in a single step calculation. The Visible Infrared Imaging Radiometer Suite (VIIRS) imaging channel (375 m) I4 (3.55~3.93 μm) and I5 (10.50~12.40 μm) bands were used as mid-wavelength and thermal infrared images to demonstrate the effectiveness of the proposed single-channel atmospheric correction method. The estimated sea surface temperatures (SSTs) obtained by the proposed method with high resolution numerical weather prediction models were compared with sea-truth temperature data from ocean buoys, multichannel-based SST products from VIIRS/MODIS, and results from MODerate resolution atmospheric TRANsmission 5 (MODTRAN 5), for validation. High resolution (1.5 km and 12 km) numerical weather prediction (NWP) models distributed by the Korea Meteorological Administration (KMA) were employed as input atmospheric data. Nighttime SST estimations with the I4 band showed a root mean squared error (RMSE) of 0.95 °C, similar to that of the VIIRS product (RMSE: 0.92 °C) and lower than that of the MODIS product (RMSE: 1.74 °C), while estimations with the I5 band showed an RMSE of 1.81 °C. RMSEs from MODTRAN simulations were similar (within 0.2 °C) to those of the proposed method (I4: 0.81 °C, I5: 1.67 °C). These results demonstrated the competitive performance of a regression-based method using high-resolution numerical weather prediction (NWP) models for atmospheric correction of single-channel infrared imaging sensors.
Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities

Currently, climate change poses a global threat, which may compromise the sustainability of agriculture, forestry and other land surface systems. In a changing world scenario, the economic importance of Remote Sensing (RS) to monitor forests and agricultural resources is imperative to the development of agroforestry systems. Traditional RS technologies encompass satellite and manned aircraft platforms. These platforms are continuously improving in terms of spatial, spectral, and temporal resolutions. The high spatial and temporal resolutions, flexibility and lower operational costs make Unmanned Aerial Vehicles (UAVs) a good alternative to traditional RS platforms. In the management process of forests resources, UAVs are one of the most suitable options to consider, mainly due to: (1) low operational costs and high-intensity data collection; (2) its capacity to host a wide range of sensors that could be adapted to be task-oriented; (3) its ability to plan data acquisition campaigns, avoiding inadequate weather conditions and providing data availability on-demand; and (4) the possibility to be used in real-time operations. This review aims to present the most significant UAV applications in forestry, identifying the appropriate sensors to be used in each situation as well as the data processing techniques commonly implemented.
A Review of Lithium-Ion Battery Fault Diagnostic Algorithms: Current Progress and Future Challenges

The usage of Lithium-ion (Li-ion) batteries has increased significantly in recent years due to their long lifespan, high energy density, high power density, and environmental benefits. However, various internal and external faults can occur during the battery operation, leading to performance issues and potentially serious consequences, such as thermal runaway, fires, or explosion. Fault diagnosis, hence, is an important function in the battery management system (BMS) and is responsible for detecting faults early and providing control actions to minimize fault effects, to ensure the safe and reliable operation of the battery system. This paper provides a comprehensive review of various fault diagnostic algorithms, including model-based and non-model-based methods. The advantages and disadvantages of the reviewed algorithms, as well as some future challenges for Li-ion battery fault diagnosis, are also discussed in this paper.
Visualizing and Modifying Difficult Pixels in Cell Image Segmentation

: In this paper, we visualize and modify difficult pixels to recognize for deep learning. In general, an image includes pixels that are easy or difficult to recognize. At the final layer, many deep learning methods use a softmax function to convert the outputs of network to probabilities. Pixels with small maximum probability are often difficult to recognize. We visualize those difficult pixels in a test image using the relationship between confidence and pixel-wise difficulty. By visualizing difficult pixels, we confirm the connection of cell membrane that could not be recognized by conventional method. We can connect the cell membrane by modifying difficult pixels. In experiments, we use cell image of mouse liver dataset including three classes; “cell membrane”, “cell nucleus” and “cytoplasm”. Our proposed method shows high recall score for “cell membrane”. We also confirmed the connection of cell membrane in qualitative evaluation.
An End-to-End and Localized Post-Processing Method for Correcting High-Resolution Remote Sensing Classification Result Images

Since the result images obtained by deep semantic segmentation neural networks are usually not perfect, especially at object borders, the conditional random field (CRF) method is frequently utilized in the result post-processing stage to obtain the corrected classification result image. The CRF method has achieved many successes in the field of computer vision, but when it is applied to remote sensing images, overcorrection phenomena may occur. This paper proposes an end-to-end and localized post-processing method (ELP) to correct the result images of high-resolution remote sensing image classification methods. ELP has two advantages. (1) End-to-end evaluation: ELP can identify which locations of the result image are highly suspected of having errors without requiring samples. This characteristic allows ELP to be adapted to an end-to-end classification process. (2) Localization: Based on the suspect areas, ELP limits the CRF analysis and update area to a small range and controls the iteration termination condition. This characteristic avoids the overcorrections caused by the global processing of the CRF. In the experiments, ELP is used to correct the classification results obtained by various deep semantic segmentation neural networks. Compared with traditional methods, the proposed method more effectively corrects the classification result and improves classification accuracy.
Synthetic Aperture Radar Flood Detection under Multiple Modes and Multiple Orbit Conditions: A Case Study in Japan on Typhoon Hagibis, 2019

Flood detection using a spaceborne synthetic aperture radar (SAR) has become a powerful tool for organizing disaster responses. The detection accuracy is increased by accumulating pre-event observations, whereas applying multiple observation modes results in an inadequate number of observations with the same mode from the same orbit. Recent flood detection studies take advantage of the large number of pre-event observations taken from an identical orbit and observation mode. On the other hand, those studies do not take account of the use of multiple orbits and modes. In this study, we examined how the analysis results suffered when pre-event observations were only taken from a different orbit or mode to that of the post-event observation. Experimental results showed that inundation areas were overlooked under such non-ideal conditions. On the other hand, the detection accuracy could be recovered by combining analysis results from possible alternate datasets and became compatible with ideal cases.
Object Tracking using CSRT Tracker and RCNN

: Nowadays, Object tracking is one of the trendy and under investigation topic of Computer Vision that challenges with several issues that should be considered while creating tracking systems, such as, visual appearance, occlusions, camera motion, and so on. In several tracking algorithms Convolutional Neural Network (CNN) has been applied to take advantage of its powerfulness in feature extraction that convolutional layers can characterize the object from different perspectives and treat tracking process from misclassification. To overcome these problems, we integrated the Region based CNN (Faster RCNN) pre-trained object detection model that the OpenCV based CSRT (Channel and Spatial Reliability Tracking) tracker has a high chance to identifying objects features, classes and locations as well. Basically, CSRT tracker is C++ implementation of the CSR-DCF (Channel and Spatial Reliability of Discriminative Correlation Filter) tracking algorithm in OpenCV library. Experimental results demonstrated that CSRT tracker presents better tracking outcomes with integration of object detection model, rather than using tracking algorithm or filter itself.
Energy Efficient Routing in Wireless Sensor Networks: A Comprehensive Survey

Wireless Sensor Networks (WSNs) are among the most emerging technologies, thanks to their great capabilities and their ever growing range of applications. However, the lifetime of WSNs is extremely restricted due to the delimited energy capacity of their sensor nodes. This is why energy conservation is considered as the most important research concern for WSNs. Radio communication is the utmost energy consuming function in a WSN. Thus, energy efficient routing is necessitated to save energy and thus prolong the lifetime of WSNs. For this reason, numerous protocols for energy efficient routing in WSNs have been proposed. This article offers an analytical and up to date survey on the protocols of this kind. The classic and modern protocols presented are categorized, depending on i) how the network is structured, ii) how data are exchanged, iii) whether location information is or not used, and iv) whether Quality of Service (QoS) or multiple paths are or not supported. In each distinct category, protocols are both described and compared in terms of specific performance metrics, while their advantages and disadvantages are discussed. Finally, the study findings are discussed, concluding remarks are drawn, and open research issues are indicated.