Real Position Research Articles

An efficient improved singular spectrum analysis for processing GNSS position time series with missing data

Summary The improved SSA (ISSA) method is widely recognized for directly extracting signals from gappy time series without requiring prior interpolation. However, it is rather time consuming, particularly for long time series with large window sizes, such as Global Navigation Satellite System (GNSS) position time series. This study proposes an efficient ISSA method that yields equivalent results to the ISSA method while significantly reducing computation time. Both methods aim to minimize the quadratic norm of principal components, while our method has fewer unknown parameters in the principal component computation than those of the ISSA method. We evaluate the performance of the proposed method using real GNSS position time series from 27 permanent stations located in mainland China. Results show that the proposed method can effectively reduce computation time than the ISSA method and the improvement depends on the chosen window size, the time series length, and the percentage of missing data. This efficient approach can be naturally extended to principal component analysis (PCA) and multi-channel SSA (MSSA) for processing multiple incomplete time series, improving computational efficiencies compared to the modified PCA and the improved MSSA while maintaining unchanged results. We also compare the ISSA method with the modified SSA (SSAM) and the iterative SSA methods using both real and synthetic time series data. Results indicate that the ISSA method outperforms the SSAM method, and when conducted iteratively, also surpasses the iterative SSA method.

Polar perspectives: a deep dive into geo-referencing historical Antarctic photos

ABSTRACT The utility of historical image repositories is often limited due to the lack of geo-referencing. A good example is the TriMetrogon Aerial (TMA) archive, a collection of historical aerial images of Antarctica between 1940 and 2000. These images are difficult to use, as their geolocation is only approximately, with location errors in the order of tens of km. This study addresses this challenge by developing an automated geo-referencing workflow that leverages recent advancements in machine-learning-based tie-point matching. We use the algorithm LightGlue, to establish tie-points between geo-referenced Sentinel-2 satellite imagery and historical non-geo-referenced aerial images. To aid the process, we use already known approximate positions of the historical images. Due to the sub-optimal and inhomogeneous quality of the aerial images, only a portion of the images can be geo-referenced directly by matching. For the remaining images, we employed alternative means of geo-referencing, again based on tie-point matching. Out of a subset of 4,459 images located inside the research area, 3,393 images could be geo-referenced, a percentage of 76%. Reasons for the geo-referencing failing are insufficient contrast or an approximate position too far away from the real position. The workflow can easily be applied to historical images from other archives, to enhance the usability of historical image repositories for scientific research.

Data driven surrogate signal extraction for dynamic PET using selective PCA: time windows versus the combination of components

Objective. Respiratory motion correction is beneficial in positron emission tomography (PET), as it can reduce artefacts caused by motion and improve quantitative accuracy. Methods of motion correction are commonly based on a respiratory trace obtained through an external device (like the real time position management system) or a data driven method, such as those based on dimensionality reduction techniques (for instance principal component analysis (PCA)). PCA itself being a linear transformation to the axis of greatest variation. Data driven methods have the advantage of being non-invasive, and can be performed post-acquisition. However, their main downside being that they are adversely affected by the tracer kinetics of the dynamic PET acquisition. Therefore, they are mostly limited to static PET acquisitions. This work seeks to extend on existing PCA-based data-driven motion correction methods, to allow for their applicability to dynamic PET imaging. Approach. The methods explored in this work include; a moving window approach (similar to the Kinetic Respiratory Gating method from Schleyer et al (2014)), extrapolation of the principal component from later time points to earlier time points, and a method to score, select, and combine multiple respiratory components. The resulting respiratory traces were evaluated on 22 data sets from a dynamic [18F]-FDG study on patients with idiopathic pulmonary fibrosis. This was achieved by calculating their correlation with a surrogate signal acquired using a real time position management system. Main results. The results indicate that all methods produce better surrogate signals than when applying conventional PCA to dynamic data (for instance, a higher correlation with a gold standard respiratory trace). Extrapolating a late time point principal component produced more promising results than using a moving window. Scoring, selecting, and combining components held benefits over all other methods. Significance. This work allows for the extraction of a surrogate signal from dynamic PET data earlier in the acquisition and with a greater accuracy than previous work. This potentially allows for numerous other methods (for instance, respiratory motion correction) to be applied to this data (when they otherwise could not be previously used).

A three-level nested portfolio optimization model with position allocation

Existing portfolio optimization models cannot well capture the real position allocation requirement, leading to limited impact in practice. To overcome this challenge, we propose a three-level nested portfolio optimization model with position allocation. Within this model, the inner and middle levels collaboratively determine the optimal portfolio, while the outer level focuses on optimizing the holding proportion of each stock in the optimal portfolio. Compared with existing models with portfolio weights, the proposed model imposes the position allocation constraint that precisely characterizes the limitations on holding each stock. This constraint is crucial for investors to obey securities trading regulations involving position limitations and to mitigate the potential impact of market risks. To address the nonlinear and nonconvex nature of the novel model, we develop an intelligent optimization algorithm by effectively hybridizing the support vector regression and the enhanced grey wolf optimizer. We comprehensively evaluate its performance using eight metrics, including accumulative return, annual return, Sharpe ratio, maximum drawdown, absolute and relative win ratios, predictive precision and accuracy. The experimental results indicate that (i) the proposed model can achieve more excess returns than those stock selection models not considering position allocation, especially for the large-cap stocks; (ii) compared with other state-of-the-art meta-heuristics, the enhanced grey wolf optimizer can yield better portfolio in conjunction with the support vector regression; (iii) in the context of the Chinese A-share stock market, specific financial indicators such as return on equity, inventory turnover rate, net income growth rate, and debt-to-equity ratio should be given greater consideration compared to other financial metrics.

Analyzing RPM and RGSC Infrared Camera Sensitivity: A Comparative Study of Breathing Cycle Replication Verifications and Limitations.

This study addresses challenges in delivering high radiation doses and managing organ motion in Stereotactic Body Radiation Therapy (SBRT) for thoracic and abdominal cancer. It evaluates Varian's Real Time Position Management (RPM) system's infrared camera sensitivity during crucial Four-Dimensional computed tomography (4D-CT) scans for planning and treatment. The analysis includes CT simulator, LINAC (Novalis Tx and TrueBeam STx). This research enhances SBRT precision by offering insights into RPM and RGSC system performance across machines, impacting treatment planning and delivery optimization. The QUASAR™ Respiratory Motion Assembly phantom is aligned with precision using lasers. It is configured with either six-dot reflective or four-dot lens marker blocks featuring a retroreflective marker placed on the phantom's surface. Motion is induced by adjusting the amplitude, and the camera position is finely tuned to monitor the marker's movements. This investigation entails variations in seconds per breath (SPB) within the Quasar breath platform, specifically at intervals of 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, and 5.0 seconds while maintaining a 1cm amplitude camera setting. For TrueBeam-STx: Ensure SPB values are kept above 1.8 seconds for accurate replication. For Novalis-Tx: Stay within an SPB range of up to 2.0 seconds for reliable reproducibility. For CT Simulator: Optimal replication up to an SPB of 2.2 seconds; avoid SPB values below 1.8 seconds for reliable detection. Data for TrueBeam-STx, Novalis-Tx, and the CT simulator shows discrepancies in replicating the breathing cycle as Seconds Per Breath (SPB) decreases. Effective Infrared (IR) sensitivity is observed until SPB thresholds: 1.8s (TrueBeam-STx), 2.2s (Novalis-Tx), and 2.2s (CT simulator). We should consider values equal to or greater than the mentioned breathing periods. Variations in replicating breathing cycles signal challenges in planning and delivering treatments, especially with lower SPB values. These insights guide clinicians to adapt treatments based on machine-specific capabilities for accurate and reproducible outcomes.

Testing cosmic anisotropy with Padé approximations and the latest Pantheon+ sample

Cosmography can be used to constrain the kinematics of the Universe in a model-independent way. In this work, we attempt to combine the Pad$ e $ approximations with the latest Pantheon+ sample to test the cosmological principle. Based on the Pad$ e $ approximations, we first applied cosmographic constraints to different-order polynomials including third-order (Pad$ e $), fourth-order (Pad$ e $), and fifth-order (Pad$ e $) ones. The statistical analyses show that the Pad$ e $ polynomial has the best performance. Its best fits are $H_ $ = 72.53pm 0.28 km s$^ $ Mpc$^ $, $q_ $, and $j_ $. By further fixing $j_ $ = 1.00, it can be found that the Pad$ e $ polynomial can describe the Pantheon+ sample better than the regular Pad$ e $ polynomial and the usual cosmological models (including the Lambda CDM, $w$CDM, CPL, and $R_h$ = ct models). Based on the Pad$ e $ ($j_ $ = 1) polynomial and the hemisphere comparison method, we tested the cosmological principle and found the preferred directions of cosmic anisotropy, such as (l, b) = (304.6$^ circ circ $) and (311.1$^ circ circ $) for $q_ $ and $H_ $, respectively. These two directions are consistent with each other at a $1 confidence level, but the corresponding results of statistical isotropy analyses including isotropy and isotropy with real positions are quite different. The statistical significance of $ is stronger than that of $q_ $; that is, 4.75sigma and 4.39sigma for isotropy and isotropy with real positions, respectively. Reanalysis with fixed $q_ = -0.55$ (corresponds to $ m $ = 0.30) gives similar results. Overall, our model-independent results provide clear indications of a possible cosmic anisotropy, which must be taken seriously. Further testing is needed to better understand this signal.

Features for Evaluating Source Localization Effectiveness in Sound Maps from Acoustic Cameras.

Acoustic cameras (ACs) have become very popular in the last decade as an increasing number of applications in environmental acoustics are observed, which are mainly used to display the points of greatest noise emission of one or more sound sources. The results obtained are not yet certifiable because the beamforming algorithms or hardware behave differently under different measurement conditions, but at present, not enough studies have been dedicated to clarify the issues. The present study aims to provide a methodology to extract analytical features from sound maps obtained with ACs, which are generally only visual information. Based on the inputs obtained through a specific measurement campaign carried out with an AC and a known sound source in free field conditions, the present work elaborated a methodology for gathering the coordinates of the maximum emission point on screen, its distance from the real position of the source and the uncertainty associated with this position. The results obtained with the proposed method can be compared, thus acting as a basis for future comparison studies among calculations made with different beamforming algorithms or data gathered with different ACs in all real case scenarios. The method can be applicable to any other sector interested in gathering data from intensity maps not related to sound.

Performance of steel protective structure for historical masonry aqueducts against rockfall: Numerical and field studies

This study investigates the design and impact performance of novel steel protection system intended to safeguard historical masonry aqueducts from damage caused by hazardous rocks. The historical aqueduct under consideration is located on the Sumela Monastery campus. To achieve this, we developed a three-dimensional (3D) photogrammetric model of the study area and identified all risky blocks along with their real positions. Subsequently, we conducted 3200 probabilistic rockfall analyses in eight critical trajectories with four different rock sizes to calculate the velocity, kinetic energy, and run-out distance of fallen blocks. In the second part of the study, finite element models of the steel protective structure were created using Abaqus software. A nonlinear dynamic impact analysis method was applied to analyze nine rockfall scenarios with two different geometries and four different sizes. Based on the rockfall and impact analyses, sections of the protection structure were designed using plastic design criteria. The steel protection structure for aqueducts demonstrated high effectiveness against rockfall, with a risk index (Ir) ranging from 0.93 to 1.00 (except for the upper end of the column–beam connection impacts). Even in these scenarios, the structure provided protection by reducing rock energy and altering its trajectory. In the third part, a steel structure was constructed in the field, and a rock cleaning process was carried out. During rock cleaning, punctures occurred while breaking sharp rocks. To mitigate this risk, a parametric study was conducted using a steel angle stopper. The proposed simple steel angle stopper effectively reduced rockfall damage to historical structures by blocking rock passages and rapidly reducing kinetic energy. Ultimately, during the removal of 5000 m³ of hazardous rocks, no damage was inflicted on the historical structure. The protection system showed a near 100 % success rate in safeguarding the aqueduct. This temporary structure can be employed to protect historical structures from rockfall hazards.

VALUE OF OIL FIELD INTEGRATED MODEL DEVELOPMENT

Today, there is practically no doubt about the need to use geological and dynamic models for the development of oil fields. Most of the world's companies actively use them and are guided by the results obtained as a result of their construction and calculations on them. At the same time, the value of creating integrated models of oil fields at a late stage of development among specialists in various fields raises many questions and doubts. In the work performed, an integrated model is understood as a combination of three models into a single calculation system: a geological and dynamic model of the field, models of all wells and a model of the surface development network. The latter includes both a production and transportation system and a system for injecting formation stimulation agents. Opinions differ on the need and feasibility of creating integrated models, since there are no statistics on their widespread use and the benefits obtained from this in the form of additional oil production or cost savings.This article shows the results of creating an integrated model of a large N oil field, which has been in operation for more than 60 years and is at a late stage of development. The work was carried out on the domestic software complex, during which the parameters of the initial information and factors affecting the quality of creating an integrated model were determined. When creating an integrated model, information about the real position of pipelines, their dimensional characteristics, as well as the reliability of measurements of wellhead pressures and volumes of pumped fluid are important. As a result of the construction and adaptation, the possibilities of practical application of the integrated model were determined, such as determining the state of pipelines and identifying bottlenecks in them. In the process of work, the database of various services is synchronized. In practice, the narrow section of the oil pipeline identified on the model was replaced, and additional oil production from the section was obtained. Thus, the value of creating an integrated model in the form of the actual result of its application in order to increase oil production is shown.

Design of an SMA-Based Actuator for Replicating Normal Gait Patterns in Pediatric Patients with Cerebral Palsy.

Cerebral Palsy refers to a group of incurable motor disorders affecting 0.22% of the global population. Symptoms are managed by physiotherapists, often using rehabilitation robotics. Exoskeletons, offering advantages over conventional therapies, are evolving to be more wearable and biomimetic, requiring new flexible actuators that mimic human tissue. The main objective behind this article is the design of a flexible exosuit based on shape-memory-alloy-based artificial muscles for pediatric patients that replicate the walking cycle pattern in the ankle joint. Thus, four shape-memory-alloy-based actuators were sewn to an exosuit at the desired actuation points and controlled by a two-level controller. The loop is closed through six inertial sensors that estimate the real angular position of both ankles. Different frequencies of actuation have been tested, along with the response of the actuators to different walking cycle patterns. These tests have been performed over long periods of time, comparing the reference created by a reference generator based on pediatric walking patterns and the response measured by the inertial sensors. The results provide important measurements concerning errors, working frequencies and cooling times, proving that this technology could be used in this and similar applications and highlighting its limitations.

Detection of Plant Location Based on Spectral Analysis Using GIS, GPS and Remote Sensing

In this study, plant position determination was made based on multispectral remote sensing for high-precision agricultural applications targeting the plant itself. In order to determine the plant location, silage corn was planted. Ground control points were fixed for geo-referencing purposes on the subjects determined for the purpose of taking images. Multispectral images were taken with a UAV and plants were detected by image processing after spectral analysis. The plant positions in the images were determined with GIS and the difference between the real plant positions measured with RTK-GPS was determined by calculating rmse values. 96% of the plants in all images taken could be identified. The average rmse value calculated using the coordinates of the plants detected in the images and the real plant coordinates determined by RTK-GPS was found to be 87.99 mm.

Application of accuracy improvement algorithms for extraction of topographic information and drainage network from DEM using GIS

The extraction of drainage network and watershed information is prerequisite for the study of watershed characteristics like morphometric analysis, which provides a basis for hydrological planning and modeling. The advanced tools of algorithms, Shuttle Radar Topographic Mission (SRTM) Digital Elevation Model (DEM) data and Geographical Information System (GIS) software are used to extract drainage networks and their watershed boundaries. These tools are complicated to use or produce more errors in the extraction of elevation and drainage networks when applied to flat areas. For removal of errors and to improve the accuracy in preparation of DEM and extraction of drainage network, Burada Kalava River Basin, Andhra Pradesh, India has been taken for application of accuracy improvement algorithms. An automatic generation of drainage network and watershed using digital elevation model results in positional errors due to variations in slope and topography. This study aimed to generate a catchment area and stream network that closely represent the natural stream network and the streams’ real positions. The step-by-step methodology using GRASS-interfaced Quantum GIS algorithms are given for pre-processing of DEM data to improve the positional accuracy before automatic extraction of the stream network and catchment area to resemble the real situation of the watershed. Secondly, efforts are made to analyze the DEM during automatic generation of the stream network and catchment area by assigning various area threshold values, including the application of pour point coordinates in improving the stream network and watershed characteristics. The results are verified and validated with the field information in order to improve the accuracy levels of DEM quality in generation of drainage network and catchment area.

Correction for geometric distortion in the flattened representation of pipeline external surface

Conventional cameras often produce external pipeline surface images with significant perspective-projection distortions, making determining the type and size of defects on the pipeline surface difficult. To address this issue, we present a novel approach for correcting the geometric distortion of a constant diameter pipeline (CDP) external surface image based on apparent contour pairs (ACPs). First, the camera is calibrated and the real projection position of ACPs accurately extracted. Second, a single-view perspective projection model of the CDP is adopted to realize a three-dimensional reconstruction of the pipeline. Subsequently, the flattened image of the CDP’s outer surface is obtained by performing equidistant sampling of cross sections, determining the visibility of discrete points, and mapping projection points onto a flat surface. Simulations and real experiments demonstrated the high accurate method in the correction of CDP images. Experiments conducted under different outdoor scenarios confirmed the robustness of the proposed method.

Transfer accuracy of 3D printed versus CAD/CAM milled surgical guides for temporary orthodontic implants: A preclinical micro CT study

ObjectivesTemporary anchorage devices (TADs) have become an integral part of comprehensive orthodontic treatments. This study evaluated the transfer accuracy of three-dimensional (3D) printed and computer-aided design/computer-aided manufacturing (CAD/CAM) milled surgical guides for orthodontic TADs using micro-computed tomography (CT) imaging in a preclinical trial. MethodsOverall, 30 surgical guides were used to place TADs into typodonts; 3D printing and CAD/CAM milling were used to produce the guides. The virtual target positions of the TADs were compared to the real positions in terms of spatial and angular deviations using digital superimposition. Micro-CT imaging was used to detect the positions. To evaluate reliability, two investigators collected the measurements twice. Intra-rater and inter-rater correlations were tested. ResultsIn total, 60 palatal TADs were evaluated. The mean coronal deviations in the print group ranged from 0.15 ± 0.20 mm to 0.71 ± 0.22 mm, whereas in the mill group, they ranged from 0.09 ± 0.15 mm to 0.83 ± 0.23 mm. At the apical tip, the overall deviations in the print group ranged from 0.14 ± 0.56 mm to 1.27 ± 0.66 mm, whereas in the mill group, they ranged from 0.15 ± 0.57 mm to 1.09 ± 0.44 mm. The mean intra-class and inter-class correlation coefficients ranged from 0.904 to 0.987. No statistically significant differences were found between the groups. ConclusionsCAD/CAM milled guides yielded spatial and angular accuracies comparable to those of 3D printed guides with notable deviations in the vertical positioning of TADs. Clinical significanceDigital planning of orthodontic temporary implants combines clinical predictability and the safety of surrounding tissue. Therefore, the transfer accuracy of the guides is crucial. This preclinical study was the first to evaluate CAD/CAM milling for orthodontic guides and found its accuracy comparable to that of the current "gold standard".

An Underwater Source Location Privacy Protection Scheme Based on Game Theory in a Multi-Attacker Cooperation Scenario.

Ensuring source location privacy is crucial for the security of underwater acoustic sensor networks amid the growing use of marine environmental monitoring. However, the traditional source location privacy scheme overlooks multi-attacker cooperation strategies and also has the problem of high communication overhead. This paper addresses the aforementioned limitations by proposing an underwater source location privacy protection scheme based on game theory under the scenario of multiple cooperating attackers (SLP-MACGT). First, a transformation method of a virtual coordinate system is proposed to conceal the real position of nodes to a certain extent. Second, through using the relay node selection strategy, the diversity of transmission paths is increased, passive attacks by adversaries are resisted, and the privacy of source nodes is protected. Additionally, a secure data transmission technique utilizing fountain codes is employed to resist active attacks by adversaries, ensuring data integrity and enhancing data transmission stability. Finally, Nash equilibrium could be achieved after the multi-round evolutionary game theory of source node and multiple attackers adopting their respective strategies. Simulation experiments and performance evaluation verify the effectiveness and reliability of SLP-MACGT regarding aspects of the packet forwarding success rate, security time, delay and energy consumption: the packet delivery rate average increases by 30%, security time is extended by at least 85%, and the delay is reduced by at least 90% compared with SSLP, PP-LSPP, and MRGSLP.

CPAK classification detect the real knee joint apex position in less than half of the knees.

Accurate assessment of the knee joint line is essential for surgeries involving the knee. The knee joint line obliquity (KJLO) is a radiological measurement that evaluates the position of the knee joint relative to the ground and is frequently used in preoperative planning and clinical follow-up. On the other hand, coronal plane alignment of the knee (CPAK) classifications assesses the joint line as the summation of the medial proximal tibial angle (MPTA) and mechanical lateral distal femoral angle (mLDFA). The purpose of this study is to determine the concordance of these two measurement techniques. This cross-sectional study evaluated the long-leg standing radiographs (LSRs) of 164 healthy knees. The extremity KJLO and CPAK classification JLO were measured twice by twoobservers at 8-week intervals. The knee joint line apex positions (proximal, neutral and distal) of the two measurement techniques are compared (concordance or discordant). The intraobserver and interobserver reliability were examined using the intraclass correlation coefficient (ICC). Possible causes of the discordant were evaluated with univariate and multivariate logistic regression analysis. CPAK classification detected the KJLO apex position in 70 extremities (42.7%) only. Subgroups CPAK JLO detected 13.6% of the proximal apex, 20.4% of the neutral, and 90.7% of the distal apex (p < 0.01). Upon multivariate logistic regression analysis, the variable KJLO apex position (proximal, neutral vs. distal, odds ratio (OR) = 10.291, 95% confidence interval [CI] = 2.225-25.656, and (p < 0.01) was determined as a risk factor for discordant. The CPAK JLO measurement technique can be misleading in defining the KJLO apex position and the concordance between them is less than 50%. It has a high tendency to misleadingly predict proximal and neutral apex positions, which can potentially have negative implications for assessing the joint line. Level I.

Vibration displacement measurement method based on vision Gaussian fitting and edge optimisation for rotating shafts

The vibrations of rotating machinery contain crucial information for monitoring the operational status of machines and diagnosing potential faults. Measuring vibration displacement using machine vision is currently a promising approach for machine monitoring in industries. However, the existing vision-based vibration displacement measurement methods cannot satisfy the high-accuracy requirements for detecting micrometre-level displacements generated by low-amplitude vibrations of rotating machinery. Therefore, this study proposes an improved noncontact-type vibration displacement measurement method based on Gaussian fitting and edge optimisation for rotating shafts. The real number-type edge positions of the rotating shafts within the selected region of interest (ROI) are accurately extracted via Gaussian edge fitting. Subsequently, edge-linking schemes, double-threshold detection and edge fitting are preformed to optimise the real number-type position of the edges. Finally, continuous displacement extraction of rotating shafts is achieved by monitoring the edge positions in the ROI. The coefficient of determination is used to evaluate a series of simulated and actual experiments quantitatively. The experimental results verify the feasibility, effectiveness, and robustness of the proposed method. An eddy current sensor is used in the actual experiments to verify the accuracy of the proposed method. The result shows that the coefficient of determination reaches 0.9792 when the amplitude of the measured displacement signals is only 22.7 μm.

Self-supervised monocular depth estimation on water scenes via specular reflection prior

Monocular depth estimation from a single image is an ill-posed problem for computer vision due to insufficient reliable cues as the prior knowledge. Besides the inter-frame supervision, namely stereo and adjacent frames, extensive prior information is available in the same frame. Reflections from specular surfaces, informative intra-frame priors, enable us to reformulate the ill-posed depth estimation task as a multi-view synthesis. This paper proposes the first self-supervision for deep-learning depth estimation on water scenes via intra-frame priors, known as reflection supervision and geometrical constraints. In the first stage, a water segmentation network is performed to separate the reflection components from the entire image. Next, we construct a self-supervised framework to predict the target appearance from reflections, perceived as other perspectives. The photometric re-projection error, incorporating SmoothL1 and a novel photometric adaptive SSIM, is formulated to optimize pose and depth estimation by aligning the transformed virtual depths and source ones. As a supplement, the water surface is determined from real and virtual camera positions, which complement the depth of the water area. Furthermore, to alleviate these laborious ground truth annotations, we introduce a large-scale water reflection scene (WRS) dataset rendered from Unreal Engine 4. Extensive experiments on the WRS dataset prove the feasibility of the proposed method compared to state-of-the-art depth estimation techniques.

Low-cost parallel delta robot for a pick-and-place application with the support of the vision system

The main topic of this paper is how to develop pick and place delta robot with high efficiency mechanical parts and prosaically assemble it, additionally built electrical wiring diagram all of this supported by vision computer system. The system has a low-cost part and solves many problems to build this robot. Electrical section has two main components master and slave the master is raspberry pi and slave is Arduino, master is used to vision detect object and Arduino is used to invers kinematics equation, move motors to target position, control in hydraulic system to pull the object with suction cup. The process of vison system is passing through some steps, detect the object by color range, contouring box then send the real position to an Arduino. The experimental process proved the effectiveness and accuracy of the delta robot vision system in the sorting process from conveyor system. The target of system that function is applied with a low cost.

Automatic Dependent Surveillance-Broadcast Deceptive Jamming Detection Method Based on Track Data

The characteristics of automatic dependent surveillance-broadcast (ADS-B) technology, such as unencrypted channels and open data protocols, make it extremely vulnerable to various intentional or unintentional spoofing and jamming, which seriously threatens air traffic safety. Traditional approaches do not update their high-bit data for a long time, which causes the situations of the same encoding results to have different aircraft positions and it is difficult to determine the real position of the aircraft and effectively detect ADS-B jamming. Therefore, it is urgent to propose more effective detection methods for ADS-B spoofing and jamming. First, the methods and principles of frequency shift calculation and automatic monitoring algorithm are introduced. Then, signal consistency detection and error robustness are analyzed, and a track-data-based detection model for abnormal ADS-B data are proposed. The track-data-based detection methods can use multi-point sampling judgment algorithm to realize the reception and processing of data bits. This method makes full use of the information of multiple sampling values of each data bit and the reference power value obtained in the preamble detection, and determines the data value and confidence value through the relationship between the two. The detection methods for ADS-B spoofing and jamming distinguishes the true and false signals and the authenticity of target positions by comparing the arrival time difference of each signal at the ground station. Finally, the research results show that, with the increase of signal to noise ratio, no matter the estimation based on virtual array translation or the estimation based on circular stationary signal, their estimation accuracies are improved with gradually increasing trends.