Reflection Symmetry Detection in Earth Observation Data.

The paper presents a new algorithm for reflection symmetry detection, which is specialized to detect maximal symmetric patterns in an Earth observation (EO) dataset. First, we stress the particularities that make symmetry detection in EO data different from detection in other geometric sets. The EO data acquisition cannot provide exact pairs of symmetric elements and, therefore, the approximate symmetry must be addressed, which is accomplished by voxelization. Besides this, the EO data symmetric patterns in the top view usually contain the most useful information for further processing and, thus, it suffices to detect symmetries with vertical symmetry planes. The algorithm first extracts the so-called interesting voxels and then finds symmetric pairs of line segments, separately for each horizontal voxel slice. The results with the same symmetry plane are then merged, first in individual slices and then through all the slices. The detected maximal symmetric patterns represent the so-called partial symmetries, which can be further processed to identify global and local symmetries. LiDAR datasets of six urban and natural attractions in Slovenia of different scales and in different voxel resolutions were analyzed in this paper, demonstrating high detection speed and quality of solutions.

Graph Neural Network-Based Method of Spatiotemporal Land Cover Mapping Using Satellite Imagery.

Multispectral satellite imagery offers a new perspective for spatial modelling, change detection and land cover classification. The increased demand for accurate classification of geographically diverse regions led to advances in object-based methods. A novel spatiotemporal method is presented for object-based land cover classification of satellite imagery using a Graph Neural Network. This paper introduces innovative representation of sequential satellite images as a directed graph by connecting segmented land region through time. The method's novel modular node classification pipeline utilises the Convolutional Neural Network as a multispectral image feature extraction network, and the Graph Neural Network as a node classification model. To evaluate the performance of the proposed method, we utilised EfficientNetV2-S for feature extraction and the GraphSAGE algorithm with Long Short-Term Memory aggregation for node classification. This innovative application on Sentinel-2 L2A imagery produced complete 4-year intermonthly land cover classification maps for two regions: Graz in Austria, and the region of Portoroz, Izola and Koper in Slovenia. The regions were classified with Corine Land Cover classes. In the level 2 classification of the Graz region, the method outperformed the state-of-the-art UNet model, achieving an average F1-score of 0.841 and an accuracy of 0.831, as opposed to UNet's 0.824 and 0.818, respectively. Similarly, the method demonstrated superior performance over UNet in both regions under the level 1 classification, which contains fewer classes. Individual classes have been classified with accuracies up to 99.17%.

LiDAR-Based Maintenance of a Safe Distance between a Human and a Robot Arm.

This paper demonstrates the capabilities of three-dimensional (3D) LiDAR scanners in supporting a safe distance maintenance functionality in human-robot collaborative applications. The use of such sensors is severely under-utilised in collaborative work with heavy-duty robots. However, even with a relatively modest proprietary 3D sensor prototype, a respectable level of safety has been achieved, which should encourage the development of such applications in the future. Its associated intelligent control system (ICS) is presented, as well as the sensor's technical characteristics. It acquires the positions of the robot and the human periodically, predicts their positions in the near future optionally, and adjusts the robot's speed to keep its distance from the human above the protective separation distance. The main novelty is the possibility to load an instance of the robot programme into the ICS, which then precomputes the future position and pose of the robot. Higher accuracy and safety are provided, in comparison to traditional predictions from known real-time and near-past positions and poses. The use of a 3D LiDAR scanner in a speed and separation monitoring application and, particularly, its specific placing, are also innovative and advantageous. The system was validated by analysing videos taken by the reference validation camera visually, which confirmed its safe operation in reasonably limited ranges of robot and human speeds.

FLoCIC: A Few Lines of Code for Raster Image Compression.

A new approach is proposed for lossless raster image compression employing interpolative coding. A new multifunction prediction scheme is presented first. Then, interpolative coding, which has not been applied frequently for image compression, is explained briefly. Its simplification is introduced in regard to the original approach. It is determined that the JPEG LS predictor reduces the information entropy slightly better than the multi-functional approach. Furthermore, the interpolative coding was moderately more efficient than the most frequently used arithmetic coding. Finally, our compression pipeline is compared against JPEG LS, JPEG 2000 in the lossless mode, and PNG using 24 standard grayscale benchmark images. JPEG LS turned out to be the most efficient, followed by JPEG 2000, while our approach using simplified interpolative coding was moderately better than PNG. The implementation of the proposed encoder is extremely simple and can be performed in less than 60 lines of programming code for the coder and 60 lines for the decoder, which is demonstrated in the given pseudocodes.

A New Transformation Technique for Reducing Information Entropy: A Case Study on Greyscale Raster Images.

This paper proposes a new string transformation technique called Move with Interleaving (MwI). Four possible ways of rearranging 2D raster images into 1D sequences of values are applied, including scan-line, left-right, strip-based, and Hilbert arrangements. Experiments on 32 benchmark greyscale raster images of various resolutions demonstrated that the proposed transformation reduces information entropy to a similar extent as the combination of the Burrows-Wheeler transform followed by the Move-To-Front or the Inversion Frequencies. The proposed transformation MwI yields the best result among all the considered transformations when the Hilbert arrangement is applied.

IoT and Satellite Sensor Data Integration for Assessment of Environmental Variables: A Case Study on NO2.

This paper introduces a novel approach to increase the spatiotemporal resolution of an arbitrary environmental variable. This is achieved by utilizing machine learning algorithms to construct a satellite-like image at any given time moment, based on the measurements from IoT sensors. The target variables are calculated by an ensemble of regression models. The observed area is gridded, and partitioned into Voronoi cells based on the IoT sensors, whose measurements are available at the considered time. The pixels in each cell have a separate regression model, and take into account the measurements of the central and neighboring IoT sensors. The proposed approach was used to assess NO2 data, which were obtained from the Sentinel-5 Precursor satellite and IoT ground sensors. The approach was tested with three different machine learning algorithms: 1-nearest neighbor, linear regression and a feed-forward neural network. The highest accuracy yield was from the prediction models built with the feed-forward neural network, with an RMSE of 15.49 ×10-6 mol/m2.

3D Convex Hull-Based Registration Method for Point Cloud Watermark Extraction.

Most 3D point cloud watermarking techniques apply Principal Component Analysis (PCA) to protect the watermark against affine transformation attacks. Unfortunately, they fail in the case of cropping and random point removal attacks. In this work, an alternative approach is proposed that solves these issues efficiently. A point cloud registration technique is developed, based on a 3D convex hull. The scale and the initial rigid affine transformation between the watermarked and the original point cloud can be estimated in this way to obtain a coarse point cloud registration. An iterative closest point algorithm is performed after that to align the attacked watermarked point cloud to the original one completely. The watermark can then be extracted from the watermarked point cloud easily. The extensive experiments confirmed that the proposed approach resists the affine transformation, cropping, random point removal, and various combinations of these attacks. The most dangerous is an attack with noise that can be handled only to some extent. However, this issue is common to the other state-of-the-art approaches.

Data Trustworthiness Evaluation in Mobile Crowdsensing Systems with Users' Trust Dispositions' Consideration.

Mobile crowdsensing is a powerful paradigm that exploits the advanced sensing capabilities and ubiquity of smartphones in order to collect and analyze data on a scale that is impossible with fixed sensor networks. Mobile crowdsensing systems incorporate people and rely on their participation and willingness to contribute up-to-date and accurate information, meaning that such systems are prone to malicious and erroneous data. Therefore, trust and reputation are key factors that need to be addressed in order to ensure sustainability of mobile crowdsensing systems. The objective of this work is to define the conceptual trust framework that considers human involvement in mobile crowdsensing systems and takes into account that users contribute their opinions and other subjective data besides the raw sensing data generated by their smart devices. We propose a novel method to evaluate the trustworthiness of data contributed by users that also considers the subjectivity in the contributed data. The method is based on a comparison of users' trust attitudes and applies nonparametric statistic methods. We have evaluated the performance of our method with extensive simulations and compared it to the method proposed by Huang that adopts Gompertz function for rating the contributions. The simulation results showed that our method outperforms Huang's method by 28.6% on average and the method without data trustworthiness calculation by 33.6% on average in different simulation settings.

Membrane Potential and Calcium Dynamics in Beta Cells from Mouse Pancreas Tissue Slices: Theory, Experimentation, and Analysis.

Beta cells in the pancreatic islets of Langerhans are precise biological sensors for glucose and play a central role in balancing the organism between catabolic and anabolic needs. A hallmark of the beta cell response to glucose are oscillatory changes of membrane potential that are tightly coupled with oscillatory changes in intracellular calcium concentration which, in turn, elicit oscillations of insulin secretion. Both membrane potential and calcium changes spread from one beta cell to the other in a wave-like manner. In order to assess the properties of the abovementioned responses to physiological and pathological stimuli, the main challenge remains how to effectively measure membrane potential and calcium changes at the same time with high spatial and temporal resolution, and also in as many cells as possible. To date, the most wide-spread approach has employed the electrophysiological patch-clamp method to monitor membrane potential changes. Inherently, this technique has many advantages, such as a direct contact with the cell and a high temporal resolution. However, it allows one to assess information from a single cell only. In some instances, this technique has been used in conjunction with CCD camera-based imaging, offering the opportunity to simultaneously monitor membrane potential and calcium changes, but not in the same cells and not with a reliable cellular or subcellular spatial resolution. Recently, a novel family of highly-sensitive membrane potential reporter dyes in combination with high temporal and spatial confocal calcium imaging allows for simultaneously detecting membrane potential and calcium changes in many cells at a time. Since the signals yielded from both types of reporter dyes are inherently noisy, we have developed complex methods of data denoising that permit for visualization and pixel-wise analysis of signals. Combining the experimental approach of high-resolution imaging with the advanced analysis of noisy data enables novel physiological insights and reassessment of current concepts in unprecedented detail.

An innovative approach to near-infrared spectroscopy using a standard mobile device and its clinical application in the real-time visualization of peripheral veins.

BACKGROUND: Excessive venipunctures are a significant problem both in emergency rooms and during hospital stays. Near-infrared (NIR) illumination devices improve venipuncture success rate but their usage is limited by their availability and economic cost. The objectives of this study were to develop a low-cost NIR spectroscopy prototype from a standard mobile device, to evaluate its efficacy and acceptance as an educational tool, and in a clinical setting. METHODS: Through a user-centric design process a prototype device was developed. Its educational efficacy was evaluated through a non-invasive, observational study (20 student clinicians, 25 subjects) and its acceptance was assessed using quantitative and qualitative analysis. A smaller clinical trial was performed by a group of 4 medical professionals over a period of 6 weeks that involved 64 patients. RESULTS: The prototype enables real-time visualization of peripheral veins on a variety of Android-based devices. The prototype was 35.2% more successful in visualizing and locating veins (n = 500 attempts) than the nursing students. The acceptance assessment revealed high perception of usefulness, satisfaction, and ease of use. In the clinical trial, 1.6 (SD 1.3) additional veins per patient were identified compared with the traditional visualization methods. CONCLUSIONS: To the best of our knowledge this is the first study that describes the design, feasibility and application of an NIR spectroscopy prototype developed on a standard mobile device.

Towards a low-cost mobile subcutaneous vein detection solution using near-infrared spectroscopy.

Excessive venipunctures are both time- and resource-consuming events, which cause anxiety, pain, and distress in patients, or can lead to severe harmful injuries. We propose a low-cost mobile health solution for subcutaneous vein detection using near-infrared spectroscopy, along with an assessment of the current state of the art in this field. The first objective of this study was to get a deeper overview of the research topic, through the initial team discussions and a detailed literature review (using both academic and grey literature). The second objective, that is, identifying the commercial systems employing near-infrared spectroscopy, was conducted using the PubMed database. The goal of the third objective was to identify and evaluate (using the IEEE Xplore database) the research efforts in the field of low-cost near-infrared imaging in general, as a basis for the conceptual model of the upcoming prototype. Although the reviewed commercial devices have demonstrated usefulness and value for peripheral veins visualization, other evaluated clinical outcomes are less conclusive. Previous studies regarding low-cost near-infrared systems demonstrated the general feasibility of developing cost-effective vein detection systems; however, their limitations are restricting their applicability to clinical practice. Finally, based on the current findings, we outline the future research direction.

Data on annotated approximate bilaterally symmetric leaf-off trees based on particle flow simulation and predefined tree crown shape.

Trees are natural objects, where deviations through the branches amplify geometric data for 3D representation and bring challenges to various applications dealing with 3D models, such as compression, visualization, symmetry detection, and radiative transfer simulation. This data article describes dataset of approximately symmetric 3D tree models with manually identified predominant symmetry plane in each tree model. Parameters for procedural tree synthesis were manually adjusted to produce approximately bilaterally symmetric trees which are grouped into species with distinct features. In the last step, each tree was manually annotated with approximate symmetry plane. This dataset contains geometric data of branches, manually defined parameters for tree synthesis method, point clouds, and a division plane with a score of bilateral symmetry strength. The generated trees can be used as benchmark data for verification of approximate reflectional symmetry detection methods. Additionally, generated 3D tree models can be used for other applications requiring pregenerated trees, such as compression of tree models, instancing, decimation methods, and radiative transfer simulation and modeling.

Efficient Representation of Geometric Tree Models with Level-of-Detail Using Compressed 3D Chain Code.

In the paper, we present a method for space-efficient representation of geometric tree models, which are provided as skeletons with radii attached to individual branch segments. The proposed approach uses a new differential 3D chain code to encode orientation changes of consecutive branch segments, which allows optimizing chain code generation for increased compressibility while maintaining control over the model reconstruction error. The presented method is the first to encode the complete branching geometry including the branch radii and provides level-of-detail construction directly from the chain code. It is demonstrated that by using interpolative encoding of the resulting tree descriptors and radii sequences, the storage requirements for geometric description of a mixed all-aged forest can be reduced to less than 15 percent of its raw size while preserving the structural fidelity of tree models.

Planar cell polarity genes frizzled4 and frizzled6 exert patterning influence on arterial vessel morphogenesis.

Quantitative analysis of the vascular network anatomy is critical for the understanding of the vasculature structure and function. In this study, we have combined microcomputed tomography (microCT) and computational analysis to provide quantitative three-dimensional geometrical and topological characterization of the normal kidney vasculature, and to investigate how 2 core genes of the Wnt/planar cell polarity, Frizzled4 and Frizzled6, affect vascular network morphogenesis. Experiments were performed on frizzled4 (Fzd4-/-) and frizzled6 (Fzd6-/-) deleted mice and littermate controls (WT) perfused with a contrast medium after euthanasia and exsanguination. The kidneys were scanned with a high-resolution (16 µm) microCT imaging system, followed by 3D reconstruction of the arterial vasculature. Computational treatment includes decomposition of 3D networks based on Diameter-Defined Strahler Order (DDSO). We have calculated quantitative (i) Global scale parameters, such as the volume of the vasculature and its fractal dimension (ii) Structural parameters depending on the DDSO hierarchical levels such as hierarchical ordering, diameter, length and branching angles of the vessel segments, and (iii) Functional parameters such as estimated resistance to blood flow alongside the vascular tree and average density of terminal arterioles. In normal kidneys, fractal dimension was 2.07±0.11 (n = 7), and was significantly lower in Fzd4-/- (1.71±0.04; n = 4), and Fzd6-/- (1.54±0.09; n = 3) kidneys. The DDSO number was 5 in WT and Fzd4-/-, and only 4 in Fzd6-/-. Scaling characteristics such as diameter and length of vessel segments were altered in mutants, whereas bifurcation angles were not different from WT. Fzd4 and Fzd6 deletion increased vessel resistance, calculated using the Hagen-Poiseuille equation, for each DDSO, and decreased the density and the homogeneity of the distal vessel segments. Our results show that our methodology is suitable for 3D quantitative characterization of vascular networks, and that Fzd4 and Fzd6 genes have a deep patterning effect on arterial vessel morphogenesis that may determine its functional efficiency.

Progressive lossless compression of volumetric data using small memory load.

Nowadays, applications dealing with volumetric datasets, Medical applications being a typical representative, have become possible even on low cost computers due to a rapid increase of computer memory and processing power. However, even today, dealing with volumetric datasets creates two considerable problems: slow visualization and large file sizes. While recently, due to significant progress in graphics hardware, real-time or near real-time volume visualization has become possible, volume compression still remains a problematic issue. This paper introduces a new method for lossless compression of volumetric datasets. It is based on quadtree encoding. The method consists of three steps: during initialization, so-called division quadtree is built. The smallest unit of the division quadtree is called basic macro-block. During the processing phase, Boolean intersection is built on pairs of quadtrees, and the differences are stored. In the last phase, the variable length encoding is applied to reduce the entropy among the differences. Proposed method supports progressive visualization, what is especially important when a transfer trough the internet is needed. To test the efficiency of this method it was compared to popular octree encoding scheme. The results proved that data coherence is exploited more sufficiently using proposed quadtree approach. Additional advantage of this approach is that the algorithm does not need a lot of memory space. Only two quadtrees of two consecutive slices need be loaded in the memory at the same time. This feature makes this algorithm extremely attractive for possible hardware implementation. This paper introduces a new method for the compression of volumetric datasets. It is based on quadtree encoding. This method consists of three steps: during initialization, a so-called division quadtree is built. The smallest, unit of the division quadtree is called a basic macro-block. A Boolean intersection is built on pairs of quadtrees during the processing phase and the differences are stored. In the last phase, variable length encoding is applied to reduce entropy among the differences. This method has been compared with the popular octree-based method and gives, in general, better compression results. In addition, this method can be realized using small on-board memory.

Lossless compression of threshold-segmented medical images.

This paper introduces a new algorithm for losslessly compressing voxel-based 3D CT medical images. Firstly, medical data is segmented according to selected ranges of the Hounsfield scale. The data is then arranged into two data streams. The first stream identifies the positions of the remaining data after segmentation. This information is compressed by the JBIG standard. The second stream contains the exact data values and it is losslessly compressed by our algorithm. The efficiency of this approach has been evaluated by a prototype application. This approach represents an interesting alternative for the long-term storage of medical 2D and 3D images, and for applications in telemedicine. The compression method can be used either for 2D or 3D medical data.

Progressive visualization of losslessly compressed DICOM files over the Internet.

Presented in this paper is a method for progressive transfer and visualization of losslessly compressed DICOM files. The files from the same study are considered as a volumetric object, which is progressively compressed using a quadtree-based method. The textual information of the DICOM file is compressed using a predicting scheme. Decompression and visualization are implemented with Java applet technology. Users can display and interact with the object even during the data transfer. Tests have shown that an object can be recognized up to 70% faster than with the classical approaches.