Correction Technique Research Articles

A fundus vessel segmentation method based on double skip connections combined with deep supervision.

Fundus vessel segmentation is vital for diagnosing ophthalmic diseases like central serous chorioretinopathy (CSC), diabetic retinopathy, and glaucoma. Accurate segmentation provides crucial vessel morphology details, aiding the early detection and intervention of ophthalmic diseases. However, current algorithms struggle with fine vessel segmentation and maintaining sensitivity in complex regions. Challenges also stem from imaging variability and poor generalization across multimodal datasets, highlighting the need for more advanced algorithms in clinical practice. This paper aims to explore a new vessel segmentation method to alleviate the above problems. We propose a fundus vessel segmentation model based on a combination of double skip connections, deep supervision, and TransUNet, namely DS2TUNet. Initially, the original fundus images are improved through grayscale conversion, normalization, histogram equalization, gamma correction, and other preprocessing techniques. Subsequently, by utilizing the U-Net architecture, the preprocessed fundus images are segmented to obtain the final vessel information. Specifically, the encoder firstly incorporates the ResNetV1 downsampling, dilated convolution downsampling, and Transformer to capture both local and global features, which upgrades its vessel feature extraction ability. Then, the decoder introduces the double skip connections to facilitate upsampling and refine segmentation outcomes. Finally, the deep supervision module introduces multiple upsampling vessel features from the decoder into the loss function, so that the model can learn vessel feature representations more effectively and alleviate gradient vanishing during the training phase. Extensive experiments on publicly available multimodal fundus datasets such as DRIVE, CHASE_DB1, and ROSE-1 demonstrate that the DS2TUNet model attains F1-scores of 0.8195, 0.8362, and 0.8425, with Accuracy of 0.9664, 0.9741, and 0.9557, Sensitivity of 0.8071, 0.8101, and 0.8586, and Specificity of 0.9823, 0.9869, and 0.9713, respectively. Additionally, the model also exhibits excellent test performance on the clinical fundus dataset CSC, with F1-score of 0.7757, Accuracy of 0.9688, Sensitivity of 0.8141, and Specificity of 0.9801 based on the weight trained on the CHASE_DB1 dataset. These results comprehensively validate that the proposed method obtains good performance in fundus vessel segmentation, thereby aiding clinicians in the further diagnosis and treatment of fundus diseases in terms of effectiveness and feasibility.

Automated image transcription for perinatal blood pressure monitoring using mobile health technology.

This paper introduces a novel approach to address the challenges associated with transferring blood pressure (BP) data obtained from oscillometric devices used in self-measured BP monitoring systems to integrate this data into medical health records or a proxy database accessible by clinicians, particularly in low literacy populations. To this end, we developed an automated image transcription technique to effectively transcribe readings from BP devices, ultimately enhancing the accessibility and usability of BP data for monitoring and managing BP during pregnancy and the postpartum period, particularly in low-resource settings and low-literate populations. In the designed study, the photos of the BP devices were captured as part of perinatal mobile health (mHealth) monitoring programs, conducted in four studies across two countries. The Guatemala Set 1 and Guatemala Set 2 datasets include the data captured by a cohort of 49 lay midwives from 1697 and 584 pregnant women carrying singletons in the second and third trimesters in rural Guatemala during routine screening. Additionally, we designed an mHealth system in Georgia for postpartum women to monitor and report their BP at home with 23 and 49 African American participants contributing to the Georgia I3 and Georgia IMPROVE projects, respectively. We developed a deep learning-based model which operates in two steps: LCD localization using the You Only Look Once (YOLO) object detection model and digit recognition using a convolutional neural network-based model capable of recognizing multiple digits. We applied color correction and thresholding techniques to minimize the impact of reflection and artifacts. Three experiments were conducted based on the devices used for training the digit recognition model. Overall, our results demonstrate that the device-specific model with transfer learning and the device independent model outperformed the device-specific model without transfer learning. The mean absolute error (MAE) of image transcription on held-out test datasets using the device-independent digit recognition were 1.2 and 0.8 mmHg for systolic and diastolic BP in the Georgia IMPROVE and 0.9 and 0.5 mmHg in Guatemala Set 2 datasets. The MAE, far below the FDA recommendation of 5 mmHg, makes the proposed automatic image transcription model suitable for general use when used with appropriate low-error BP devices.

Medial Tibial Condylar Valgus Osteotomy improve clinical outcomes and effectively corrects significant Tibial varus deformities without compromising joint line obliquity or patellar heigh

BackgroundTibial condylar valgus osteotomy (TCVO), or Chiba osteotomy, is a recognized procedure for treating advanced knee osteoarthritis in middle-aged individuals. Although its effectiveness is established, limited literature exists on its outcomes for specific conditions such as post-traumatic deformities, Blount disease (BD), and Pagoda-like proximal tibia varus deformities. HypothesisWe hypothesized that TCVO could improve both clinical and radiographic outcomes in patients with severe varus deformities, correcting lower-limb variances while preserving joint line obliquity (JLO) and patellar height in substantial varus deformities. Patients and methodsThis study included 33 patients who underwent TCVO, with etiologies including post-traumatic (26 patients, 78.8%), Blount disease (4 patients, 12.1%), and Pagoda deformities (3 patients, 9.1%). The average age was 32.5 years (standard deviation = 12.8 years). Techniques for deformity correction varied, including Patient Specific Instrumentation (PSI) for 10 patients (30.3%) and locking plate fixation for 13 patients (39.4%). We assessed several radiological parameters such as hip-knee-ankle angle (HKA), medial proximal tibial angle (MPTA), joint line convergence angle (JLCA), Caton-Deschamps index (CDI), and proximal posterior tibial angle (PPTA). Patient-reported outcomes, including the Knee injury and Osteoarthritis Outcome Score (KOOS) and simple knee value (SKV), were recorded both pre-operatively and two years post-operatively. ResultsStatistically significant improvements were observed in clinical scores and alignment changes two years post-operatively, including increases in HKA (from 166.1 to 178.4, p < .0001) and MPTA (from 72.4 to 85.1, p < .0001), and a decrease in JLO (from 9.8 to 3.2, p < .0001). No significant changes were noted in JLCA, PPTA, and CDI, with the patellar height remaining stable post-operatively. The achieved corrections closely matched the pre-operative plans. ConclusionTCVO offers significant improvements in radiological and clinical parameters for patients with substantial tibial varus deformities, effectively maintaining JLO and patellar height. Optimal outcomes from TCVO are dependent on careful patient selection and precise execution of surgical techniques. Level of evidenceIII.

A deep convolutional neural network for Blind element Error Correction of Spatial Heterodyne Spectrometer using line selective convolutional blocks

The "GF Special Project" is a massive remote sensing technology initiative including a number of satellites and various observation platforms. GF-5 is the satellite with the most payloads, the highest spectral resolution, and the most difficulty in development, and it can monitor a variety of environmental elements using spatial heterodyne spectroscopy (SHS) technology, including atmospheric aerosols, carbon dioxide, methane, terrestrial vegetation, straw burning, and urban heat islands. In this study, a novel blind element error correction technique based on deep learning network is investigated and developed for spatial heterodyne interferograms, as well as the formation mechanism and distribution characteristics of the SHS interferometric data. LSConv-Net, a new CNN model, was created and trained to denoise in the presence of high-density and ultra-high-density blind element errors. We do this by introducing a new line-selective convolutional (LSConv) block. Simultaneously, experimental validation of blind element error correction utilizing laboratory water vapor interferometric data and atmospheric CO2 absorption interferometric data from GF-5, and the change in FWHM before and after the experiment was tested using potassium lamp interferograms. Experiments show that the Deep neural networks trained with this model may successfully suppress the effect of blind element noise on spectra, recover spectra that have been overwhelmed by high-density blind element noise without any effect on other non-blind pixels, and surpass all similar techniques in terms of spectral recovery.

Deep Learning-Based Classification of Powerlifting Movements Using Mediapipe

The analysis of sports movements is of great importance for optimizing sports performance, minimizing injury risks, and ensuring that athletes work with correct techniques. Powerlifting is a power sport consisting of fundamental movements such as bench press, deadlift, and squat. These movements are inherently complex and challenging to execute. Therefore, it is of great importance to perform these movements with the correct technique and safely. The aim of this study was to classify these movements using deep learning methods to ensure that the basic movements in powerlifting sports (bench press, deadlift, and squat) are applied with correct techniques and to minimize the risk of injury. In this study, feature extraction was performed on powerlifting movements using the deep learning-based SqueezeNet model, followed by classification using machine learning methods. The dataset was compiled from 876 images of bench press, deadlift, and squat movements sourced from various online platforms. Additionally, the dataset was expanded through data augmentation techniques, and key points of posture estimation were added to the images using the Mediapipe library. The obtained datasets were classified using Neural Network, Logistic Regression, Support Vector Machine and Random Forest algorithms and model performances were evaluated using various metrics. The findings revealed that the Neural Network model demonstrated superior performance, achieving the highest accuracy (0.989). Additionally, the integration of pose estimation and data augmentation techniques significantly enhanced classification accuracy and overall model performance. The findings of this study show that deep learning methods are powerful tools in sports movement analysis and can make significant contributions to the evaluation of athletes' performance.

Experimental identification of the corrective capabilities of the spring technique in addressing multiapical femoral deformities

Background. The technique of multiapical deformities correction with several orthopedic hexapods (one for each apex of deformity) is accepted as a standard one. However, usage of two or more hexapods on one segment is uncomfortable for the patient. Besides, software calculation for each of them is difficult and laborious for an orthopedic surgeon. Application of only one orthopedic hexapod with one software calculation is the advantage of the spring technique (ST) of multiapical deformities correction. However, its application is hindered by the fact that the corrective capabilities of this technique have not been studied yet. The aim of the study was to identify by the bench test the corrective capabilities of the spring technique and compare them with the capabilities of the standard one. Methods. The bench test was performed using plastic models of the femur. One-ring modules were used to fix each of the bone fragments. Foam rubber discs were used to imitate soft tissues. The mobile ring was moved relative to the base one in translation, angulation, distraction and rotation. The movement was stopped if one of the struts reached its minimum or maximum length, as well as if one of them touched a frame, transosseous elements or “soft tissues”. The Mann-Whitney U-test was used for statistical analysis. Results. When using all the six struts equipped with standard threaded rods, the corrective capabilities of the spring technique are 58-97% (on average 72%) lower than of the standard one. When replacing 2-6 (depending on the type of motion) threaded rods with longer ones, the capabilities of ST increases by 36-466% (on average 257%). This provides better result for ST in translation (in three directions), varus and recurvation angulation. Conclusions. Corrective capabilities of the spring technique in 5 out of 11 types of motions are better than the capabilities of the standard technic if struts of the orthopedic hexapod are equipped with threaded rods of greater length.

High-Precision Analysis Using μPMU Data for Smart Substations

This paper proposes a correction technique for bad data and high-precision analysis based on micro-phasor measurement unit (μPMU) data for a stable and reliable smart substation. First, a high-precision wide-area monitoring system (WAMS) with 35 μPMUs installed at Korea’s Yeonggwang substation, which is connected to renewable energy sources (RESs), is introduced. Time-synchronized μPMU data are collected through the phasor data concentrator (PDC). A pre-processing program is implemented and utilized to integrate the raw data of each μPMU into a single comma-separated values (CSV) snapshot file based on the Timetag. After presenting the technique for identification and correction of event, duplicate, and spike bad data of μPMU, causal relationships are confirmed through the voltage and current fluctuations for a total of five states, such as T/L fault, tap-up, tap-down, generation, and generation shutdown. Additionally, the difference in active power between the T/L and the secondary side of the M.Tr is compared, and the fault ride through (FRT) regulations, when the fault in wind power generation (WP), etc., occurred, is analyzed. Finally, a statistical analysis, such as boxplot and kernel density, based on the instantaneous voltage fluctuation rate (IVFR) is conducted. As a result of the simulation evaluation, the proposed correction technique and precise analysis can accurately identify various phenomena in substations and reliably estimate causal relationships.

Enthusiastic Study of Children of Indonesia Migrant Workers in Selangor, Malaysia Who Do Not Have Access to Formal Education

The purpose of this study is to provide a description of the results of observations on the children of Malaysian migrant workers, especially about the enthusiasm or enthusiasm for learning children not only learning about understanding social sciences but one of them is learning religious science through religious science lessons and forms of application in the form of daily prayers, prayer readings, recitations and da'wah practices in the form of lectures. This material not only conveys the knowledge but also in practice and application as well as the way of delivery and good and correct lecture techniques, besides that also with lectures children can learn public speaking, speaking in public with good and directed conduct. Indonesia's children are future leaders and future generations of the Indonesia nation for the next 10 years, therefore the position of the young generation of Indonesia's children is very much needed. The enthusiasm for learning children in the Sungai Buloh area of Selangor has a very extraordinary spirit of learning. Most of the residents of the community in the Sungai Buloh Selangor area are Indonesia citizens who have lived in Malaysia, so there are quite a lot of children aged 7-13 years who are in elementary school. The Sungai Buloh Learning Studio is one of the main avenues for children of migrant workers to learn social and religious sciences, most of the people in the Sungai Buloh area of Selangor lack access to education due to their incomplete documents so that it is difficult for children to continue to public schools in Kuala Lumpur, this is one of the things that is worrisome because of the lack of access to education for the children of Indonesia migrant workers who are domiciled in the Sungai Buloh region of Selangor, Malaysia. With the enthusiasm of good children to learn but hindered by their incomplete documents, access to education for migrant workers living in the Sungai Buloh Selanggor area, Malaysia is quite difficult.

INVOLVEMENT OF THE LOCATION OF INTRAPARENCHYMAL EDEMA AFTER A CEREBROVASCULAR EVENT IN THE APPROACH OF A DECOMPRESSIVE CRANIECTOMY

Introduction: Decompressive craniectomy, introduced by Kocher in 1901, reduces intracranial pressure by removing part of the skull. It is used in severe neurological conditions such as brain injury and stroke, allowing the swollen brain to expand, thereby reducing mortality and improving function. Objective: To evaluate the effect of the location of intraparenchymal edema on the efficacy of decompressive craniectomy and postoperative clinical outcomes. Methods: A systematic search was performed in PubMed, Cochrane Library and Scopus. This search was focused on studies related to intracranial hypertension and its neurosurgical approach. Results: In this study, the following risk factors for mortality after decompressive craniotomy were identified: male sex, age over 50 years and late surgery. Operating between 12-24 hours improves results, especially in the non-dominant hemisphere. Correct surgical technique minimizes complications and improves the healing process of patients. Discussions: This study demonstrates that the location of intra-abdominal edema affects the results of craniectomy, with better results in the non-dominant hemisphere. A better understanding of brain anatomy and individualization of surgical techniques are essential to reduce complications and improve surgical outcomes after stroke. Conclusions: In conclusion, patient survival and functionality can be improved as seen from a modified Rankin scale by precise knowledge of the location of intraparenchymal edema and an approach with reduced damage to collateral structures.

초등학교 체육수업에서 인공지능 자세추정 앱을 적용한 교육적 효과 탐색 - 네트형 경쟁 활동 배구 언더핸드 동작을 중심으로

Purpose: The purpose of this study is to explore the educational effects and assess the improvement of movements by applying an AI-based app that estimates posture and provides feedback on underhand volleyball skills to elementary school students. Methods: The research participants consisted of 177 students (age: 12 years old, height: 152.8±7.01 cm, weight: 46.72±11.2 kg, BMI: 19.90±3.68 kg/㎡) attending Elementary School located in A City. First, the volleyball underhand pass motion was analyzed to confirm the order and method for teaching the correct posture, and based on this, feedback to be used in the app was constructed. Next, through previous research on the types and operating principles of artificial intelligence posture estimation models Learned how to create an app to run on a web page. Based on this, a plan was established and an app to be used in class was developed directly. Results: The results that the experimental group showed an increase of approximately 7 minutes in practice time, whereas the comparison group showed a decrease of about 6 minutes (F = 20.873, p &lt; .001). In terms of students' perceptions and degree of change after the app-based class, the observability domain increased after the app-based class (F = 49.216, p &lt; .001). The testability domain also increased after the app-based class (F = 71.017, p &lt; .001). Conclusion: Through the use of an AI posture estimation app in net-type competitive activities, students were able to perform the correct underhand volleyball technique, and their learning effects during the lessons also improved.

Macroeconomic stability and economic growth: Evidence from Bangladesh

This paper investigates the link and causal relationship between macroeconomic stability and economic growth in Bangladesh for the period 1971 through 2023. Cointegration, vector error correction technique, and the Granger causality test are applied for this purpose. The paper finds sufficient evidence that macroeconomic stability and economic growth are positively related with each other in the long run. Like the long-run relationship, government consumption and investment proxy are significant in the short-run among all the factors of stability while only government consumption and inflation show some causal relationship with growth. Thus, the results are suggestive of the fact that it is better to comment about factors of stability separately rather than macroeconomic stability as a whole. The results also emphasize evaluation of both the long-run and the short-run effects of macroeconomic stability separately for effective policymaking.

버추얼 프로덕션을 위한 IMU 모션캡쳐 정확도와 활용에 대한 고찰

IMU-based motion capture systems are gaining attention as a low-cost, easy-to-install alternative to Optical Motion Capture (OMC) due to their wide capture volume. This study analyzes the technical features of IMU systems and compares their accuracy with OMC, reviewing correction techniques to address magnetic interference and sensor drift. Six prior studies showed that post-correction accuracy had an RMSE of 0.3-7.2, with sports movements achieving 79.32-100% accuracy. IMU systems are advantageous for real-time processing and live performance, making them valuable in virtual production. However, they have limitations in precise motion capture, such as facial capture, requiring hybrid system support and further technological advancements.

Controlling the Apex in Early Onset Scoliosis Through Active Apex Correction (APC) Non Fusion Growth Modulating Technique, Is It a Myth?

A multicenter retrospective study. To determine the rate of Apex facet fusion in children with Early Onset Scoliosis treated surgically with the Active Apex Correction (APC) technique. Seventeen patients were treated with the APC technique as index surgery for Early Onset Scoliosis with more than 1year of follow-up. A 3D CT scan was done to determine the facet fusion rate in the deformity's apex controlled with posterior tethering. The average follow-up time was 26.4months (12-56), Average age at index surgery was 81.2months (30-132), and average number of surgeries done 1.3. Apical vertebrae studied for facet fusion were the 3 vertebrae in the apex in each patient that were subjected to posterior tethering according to the APC technique. In total they were 86 apical vertebrae (172 Facets studied with 86 convex side, 86 concave side). Our observations showed that 29 facet joints were fused (16% of the total facets studied),15 were on the convex, 14 on the concave side (no statistically significant difference). Regarding the facet joint distance in the non-fused facets was 0.99mm on the convex side and 1.08mm on the concave side with no statistical significance difference. APC for Early Onset Scoliosis achieves apical growth modulation and control utilizing posterior tethering without inducing fusion. This study demonstrated that APC is an effective non-fusion technique through the low incidence of facet fusion levels at the Apex, limiting the crankshaft phenomena seen in cases with apex control through arthrodesis.

Design of CCM boost converter utilizing fractional-order PID and Lyapunov-based PID techniques for PF correction

Design of CCM boost converter utilizing fractional-order PID and Lyapunov-based PID techniques for PF correction

Validation of the Lift-Off Screw Technique in Patients Undergoing Corrective Osteotomy for Malunited Distal Radius Fractures

The purpose of this study was to validate the clinical accuracy of the lift-off screw (LOS) technique for volar tilt correction (VTC) in patients undergoing corrective osteotomy for dorsally angulated distal radius fracture malunions. We conducted a retrospective review of 23 patients with dorsally angulated distal radius fracture malunions treated with corrective osteotomy using the LOS technique. The LOS equation Ls= [tan(Tc) ∗ Lp+ C]/[cos(⍬s)] and standardized intraoperative fluoroscopic images were used to determine and compare the calculated and clinical VTC and final volar tilt. Correlations between the LOS length and the clinical VTC were calculated, as well as between the desired VTC and the correction accuracy. Preoperative volar tilt ranged from-6° to-50° (mean=-22.9° ± 10.6°). The calculated VTC was 32.7° ± 9.4°, and the clinical VTC achieved was 25.8° ± 9.3°. The difference between the clinical and calculated correction was-6.9°, with an average postoperative clinical volar tilt of 2.8° ± 5.7°, compared with a calculated volar tilt of 9.7° ± 4.4°. There was a moderately strong positive correlation between LOS screw length and clinical VTC achieved, and a moderately weak negative correlation between the desired amount of correction and the accuracy of the correction. The LOS technique is a reproducible method to plan the amount of sagittal plane correction during corrective osteotomy surgery for dorsally angulated distal radius fracture malunions. We demonstrate that this technique underestimates the clinical correction achieved by an average of 7°, with larger deformities experiencing greater undercorrection. Undercorrection of volar tilt during corrective osteotomy should be anticipated by surgeons and considered in future implant and cutting guide designs. Therapeutic IV.

Baseline offset correction technique for terahertz signals based on improved wavelet multiresolution analysis

During the terahertz nondestructive testing of bonded structures, the incomplete discharge of the capacitance in the photoconductive antenna within the terahertz time-domain spectroscopy system results in a shift of the terahertz baseline produced by the antenna. This baseline shift causes variations in the amplitude information of the detected signals. Consequently, when feature imaging of the detection waveforms is performed, the baseline shift can lead to erroneous detection results. In this study, an improved wavelet multiresolution analysis method was used to eliminate high-frequency noise and baseline offset in terahertz detection. The method is based on the frequency characteristics of the detection waveforms, setting thresholds and using similarity as a measurement standard to determine the number of decomposition layers. Ultimately, this achieves the correction of the baseline offset in terahertz signals. Compared with other baseline correction methods, the method presented in this paper achieves the lowest root mean square error of 0.57%, the highest signal-to-noise ratio of 12.64%, and a defect identification accuracy of 96.27% in two-dimensional visualization results.

Assessing clustering methods using Shannon's entropy

Unsupervised clustering techniques are crucial for effectively partitioning datasets into meaningful subgroups. In this paper, we introduce a novel clustering fuzziness metric based on Shannon's entropy, which quantifies the level of uncertainty in clustering assignments. This metric is employed to develop a statistical test for evaluating clustering algorithms and to propose parametric corrections to enhance their performance. We provide empirical evidence showing that our approach significantly improves clustering quality compared to well-known algorithms such as Fuzzy K-Means (FKM) and Fanny. For instance, applying our correction method led to a notable decrease in Jensen-Shannon Divergence (JSD) values, indicating enhanced clustering accuracy. Our methodology is demonstrated through comprehensive experiments on both simulated and real-world datasets. Additionally, our approach proves effective in determining the optimal number of clusters, showing superiority over traditional methods. These results highlight the practical benefits of our metric and correction techniques for improving clustering performance.

Enhancing wind turbine load response tool through geometric non-linearities correction: Case study on DEFlex and design load comparison

This paper introduces a rapid correction technique applied in DEFlex, Ørsted’s in-house aero-servo-hydro-elastic solver for offshore wind turbines. The technique addresses geometric non-linearities from large wind turbine blade deflections, focusing specifically on correcting the angle of attack for torsional deflections. To assess the implementation, comparisons were made with the literature and HAWC2, using the IEA 15-MW wind turbine blade subjected to static and simplified dynamic loading. Additionally, simulations of normal operation scenarios, both with and without turbulence, were performed on a real wind turbine exceeding 10 MW rated power. The results were compared with Siemens Gamesa Renewable Energys tool BHawC, which utilizes a co-rotational formulation for capturing large blade deflections. The findings reveal that coupling DEFlex with the correction method accurately captures the angle of attack dynamics and significantly improves the agreement of fatigue loads.

Three-Dimensional Probe Mispositioning Errors Compensation: A Feasibility Study in the Non-Redundant Helicoidal Near to Far-Field Transformation Case

A feasibility study on the compensation of 3D mispositioning errors of the probe occurring in the characterization of a long antenna, via a non-redundant (NR) near to far-field (NTFF) transformation with helicoidal scan, is conducted in this article. Such types of errors can result from imperfections in the rail driving the linear motion of the probe and from an imprecise synchronization of the linear and rotational movements of the probe and the antenna when drawing the scan helix. To correct them, an approach, which proceeds through two steps, is proposed. The former step uses a technique called cylindo rical wave (CW) correction for compensating the phase of the near-field (NF) samples, which, owing to the rail imperfections, result in not being acquired over the measurement cylinder surface. The latter exploits an iterative scheme to restore the samples at the sampling points required by the adopted NR representation along the scan helix from those obtained by applying the CW correction technique and impaired by 2D mispositioning errors. The so compensated NF samples are then effectively recovered via a 2D optimal sampling interpolation (OSI) scheme to accurately obtain the input data required to carry out the standard cylindrical NTFF transformation. The OSI representation is determined here by assuming a long antenna under test as enclosed in a prolate ellipsoid or cylinder ending into two hemispheres (cigar) in order to make, depending on the particular geometry of the considered antenna, the representation effectively non-redundant. The reported numerical simulation results show the capability of the proposed approach to compensate even severe 3D mispositioning errors, thus enabling its usage in a real measurement scenario.

Destination and time-series inference of moving objects via conditionally Markov process

AbstractThis paper presents a destination and time-series inference algorithm for tracking moving targets. The destination of the object is considered the intent, and inference and state estimation are performed in the Bayesian framework. To describe the destination-aware target motion, we construct the state transition model using a conditionally Markov process. We introduce a multiple model to achieve simultaneous intent and time-series inferences. Given finite destination candidates, the maximum a posteriori hypothesis is chosen as the destination. For time-series inference, local estimates obtained from Kalman filters are fused to yield target state estimates. To address unspecified terminal conditions, the proposed algorithm incorporates parameter correction techniques based on relative geometry. Numerical simulations are performed to validate the proposed inference algorithm.