3D Shape Estimation Of Tendon-Driven Catheters Using Ultrasound Imaging

Active cable/tendon-driven catheters are becoming an established part of the minimally invasive surgical procedures. Therefore, there has been growing interest in literature in estimating the shape of their distal end especially using clinical ultrasound (US) imaging systems. The purpose of this thesis is to use a B-mode US imaging system to design time-efficient, accurate and robust algorithm for 3D shape estimation of tendon-driven catheters. Kalman filter (KF), Adaptive Kalman filter (AKF) and Particle filter (PF) algorithms were developed for this purpose. First, they were applied to a series of simulated US B-mode images where AKF provided the best estimate (error: 0.2 ± 0.1 mm). Second, they were applied to a series of experimentally obtained US B-mode images. Calibration procedures were carried out to calibrate these US images in the experiment’s workspace. The PF was shown to provide the best 3D shape estimate (error: 8.6 ± 0.1 mm). However, since almost the same accuracy could be achieved with AKF in ten times less computational time, AKF was concluded to be the best method, in terms of accuracy and efficiency, to estimate the 3D shape of tendon-driven catheters.

3D Shape Estimation Of Tendon-Driven Catheters Using Ultrasound Imaging

Active cable/tendon-driven catheters are becoming an established part of the minimally invasive surgical procedures. Therefore, there has been growing interest in literature in estimating the shape of their distal end especially using clinical ultrasound (US) imaging systems. The purpose of this thesis is to use a B-mode US imaging system to design time-efficient, accurate and robust algorithm for 3D shape estimation of tendon-driven catheters. Kalman filter (KF), Adaptive Kalman filter (AKF) and Particle filter (PF) algorithms were developed for this purpose. First, they were applied to a series of simulated US B-mode images where AKF provided the best estimate (error: 0.2 ± 0.1 mm). Second, they were applied to a series of experimentally obtained US B-mode images. Calibration procedures were carried out to calibrate these US images in the experiment’s workspace. The PF was shown to provide the best 3D shape estimate (error: 8.6 ± 0.1 mm). However, since almost the same accuracy could be achieved with AKF in ten times less computational time, AKF was concluded to be the best method, in terms of accuracy and efficiency, to estimate the 3D shape of tendon-driven catheters.

PERBANDINGAN ALGORITMA SPECKLE FILTERING PADA CITRA SYNTHETIC APERTURE RADAR UNTUK ESTIMASI STOK KARBON VEGETASI TEGAKAN RUANG TERBUKA HIJAU (STUDI KASUS: KECAMATAN SUNGAI PINANG, KOTA SAMARINDA)

Vegetasi tegakan pada RTH menyimpan stok karbon yang lebih banyak dibandingkan dengan vegetasi non tegakan sehingga dibutuhkan cara dan teknologi yang efektif dan efisien untuk melakukan monitoring dan perhitungan stok karbon. Penginderaan jauh sistem aktif menggunakan citra SAR mampu berperan dalam deteksi, monitoring, dan perhitungan stok karbon tanpa terhalang oleh tutupan awan dan pengaruh atmosfer dengan cakupan area yang luas, waktu yang cepat, biaya yang relatif lebih murah dengan hasil yang baik. Salah satu kendala dalam pemanfaatan citra SAR adalah adanya noise atau speckle, sehingga memerlukan speckle filter yang tepat untuk meminimalisir noise. Penelitian ini bertujuan untuk membandingkan performa algoritma speckle filtering pada Citra Sentinel 1A SAR dalam mengestimasi biomassa dan stok karbon tegakan RTH dan mengetahui besarnya nilai kandungan biomassa dan stok karbon pada vegetasi tegakan RTH. Metode yang digunakan dalam penelitian ini meliputi analisis statistik inferensial parametrik berupa analisis korelasi Pearson Product Moment dan analisis regresi linear sederhana antara nilai backscatter SAR pada berbagai algoritma speckle filtering dengan nilai biomassa tegakan berdasarkan persamaan alometrik. Uji akurasi model dilakukan menggunakan metode Standard Estimate Error (SEE) terhadap model estimasi yang dibangun. Hasil menunjukkan bahwa algoritma Median pada polarisasi VH memiliki akurasi maksimum tertinggi sebesar 67,35% dan estimasi kesalahan terkecil sebesar 0,11825 ton/piksel. Total biomassa tegakan RTH menggunakan algoritma Median VH di Kecamatan Sungai Pinang sebesar 37.969,4163 ton, sementara total stok karbon tegakan RTH menggunakan algoritma Median VH sebesar 17.845,62566 ton.

A non-local macroscopic model for traffic flow

In this work we propose a non-local Hamilton-Jacobi model for traffic flow and we prove the existence and uniqueness of the solution of this model. This model is justified as the limit of a rescaled microscopic model. We also propose a numerical scheme and we prove an estimate error between the continuous solution of this problem and the numerical one. Finally, we provide some numerical illustrations.

Performance Analysis of Ice-Relative Upward-Looking Doppler Navigation of Underwater Vehicles Beneath Moving Sea Ice

This paper addresses the problem of ice-relative underwater robotic vehicle navigation relative to moving or stationary contiguous sea ice. A review of previously-reported under-ice navigation methods is given, as well as motivation for the use of under-ice robotic vehicles with precision navigation capabilities. We then describe our proposed approach, which employs two or more satellite navigation beacons atop the sea ice along with other precision vehicle and ship mounted navigation sensors to estimate vehicle, ice, and ship states by means of an Extended Kalman Filter. A performances sensitivity analysis for a simulated 7.7 km under ice survey is reported. The number and the location of ice deployed satellite beacons, rotational and translational ice velocity, and separation of ship-based acoustic range sensors are varied, and their effects on estimate error and uncertainty are examined. Results suggest that increasing the number and/or separation of ice-deployed satellite beacons reduces estimate uncertainty, whereas increasing separation of ship-based acoustic range sensors has little impact on estimate uncertainty. Decreasing ice velocity is also correlated with reduced estimate uncertainty. Our analysis suggests that the proposed method is feasible and can offer scientifically useful navigation accuracy over a range of operating conditions.

Vehicular Localization Enhancement via Consensus

This paper presents a strategy to cooperatively enhance the vehicular localization in vehicle-to-everything (V2X) networks by exchanges and updates of local data in a consensus-based manner. Where each vehicle in the network can obtain its location estimate despite its possible inaccuracy, the proposed strategy takes advantage of the abundance of the local estimates to improve the overall accuracy. During the execution of the strategy, vehicles exchange each other’s inter-vehicular relationship pertaining to measured distances and angles in order to update their own estimates. The iteration of the update rules leads to averaging out the measurement errors within the network, resulting in all vehicles’ localization error to retain similar magnitudes and orientations with respect to the ground truth locations. Furthermore, the estimate error of the anchor—the vehicle with the most reliable localization performance—is temporarily aggravated through the iteration. Such circumstances are exploited to simultaneously counteract the estimate errors and effectively improve the localization performance. Simulated experiments are conducted in order to observe the nature and its effects of the operations. The outcomes of the experiments and analysis of the protocol suggest that the presented technique successfully enhances the localization performances, while making additional insights regarding performance according to environmental changes and different implementation techniques.

Implementation of the Kalman Filter for a Geostatistical Bivariate Spatiotemporal Estimation of Hydraulic Conductivity in Aquifers

The estimation of the hydraulic parameters of an aquifer such as the hydraulic conductivity is somehow complicated due to its heterogeneity, on the other hand field and laboratory tests are both time consuming and costly. The use of geostatistical-based techniques for data assimilation could represent an alternative tool that allows the use of space-time aquifer behaviour to characterize hydraulic conductivity heterogeneity. In this paper, a spatiotemporal bivariate methodology was implemented combining historical hydraulic head data with hydraulic conductivity sparse data in order to obtain an estimate of the spatial distribution of the latter variable. This approach takes advantage of the correlation between the hydraulic conductivity (K) and the hydraulic head (H) behaviour through time. In order to evaluate this approach, a synthetic experiment was constructed through a transitory numerical flow-model that simulates hydraulic head values in a horizontally-heterogeneous aquifer. Geostatistical tools were used to describe the correlation between simulated spatiotemporal data of hydraulic head and the spatial distribution of the hydraulic conductivity in a group of model nodes. Subsequently, the Kalman filter was used to estimate the hydraulic conductivity values at nonsampled sites. The results showed acceptable differences between estimated and synthetic hydraulic conductivity data, with low estimate error variances (predominating the 1 m2/day2 value for K for all the cases, however, the smallest number of cells with values above 2 m2/day2 correspond to the bivariate spatiotemporal case) and the best agreement between the estimated errors and the selected model variance (SMSE values of 0.574 and 0.469) were found for the bivariate cases, which suggests that the implemented methodology could be used for reducing calibration efforts, particularly when the hydraulic parameters data are scarce.

Ship Security Relative Integrated Navigation with Injected Fault Measurement Attack and Unknown Statistical Property Noises

In this work, the ship relative integrated navigation approaches are studied for the navigation scenarios with the measurements disturbed by unknown statistical property noises and with the injected fault measurement attacks. On the basis of the limited energy property of system noises, the navigation states are estimated by the local finite horizon H∞ filter to satisfy the performance index function. Then, the local estimates are fused in the relative integrated navigation system with the weight fusion parameters obtained by using the local estimate error measurements. Further, the injected fault measurement attacks are considered in the relative integrated navigation systems. Due to the system noises and the measurement noises having unknown statistical property, the classical Chi-square test can hardly be utilized to detect the injected fault measurements. Therefore, a secure relative integrated navigation method is proposed with a distance-based clustering detector. The finial simulation results illustrate the effectiveness of the proposed relative integrated navigation approach and the proposed secure relative integrated navigation approach.