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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Han, Kyung Min Kim, Young J. Ewha Womans Univ Dept Comp Sci & Engn Seoul South Korea

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

We propose a fully autonomous system for mobile robot exploration in unknown environments. Our system employs a novel frontier detection algorithm based on the fast front propagation (FFP) technique and uses parallel path planning to reach the detected front regions. Given an occupancy grid map in 2D, possibly updated online, our algorithm can find all the frontier points that can allow mobile robots to visit unexplored regions to maximize the exploratory coverage. Our FFP method is six similar to seven times faster than the state-of-theart wavefront frontier detection algorithm in terms of finding frontier points without compromising the detection accuracy. The speedup can be further accelerated by simplifying the map without degrading the detection accuracy. To expedite locating the optimal frontier point, We also eliminate spurious points by the obstacle filter and the novel boundary filter. In addition, we parallelize the global planning phase using the branch-andbound A*, where the search space of each thread is confined by its best knowledge discovered during the parallel search. As a result, our parallel path-planning algorithm operating on 20 threads is about 30 times faster than the vanilla exploration system that operates on a single thread. Our method is validated through extensive experiments, including autonomous robot exploration in both synthetic and real-world scenarios. In the real-world experiment, we show that an autonomous navigation system using a human-sized mobile manipulator robot equipped with a low-end embedded processor that fully integrates our FFP and parallel path-planning algorithms.

关键词： Buildings filtering algorithms Manipulators Path planning Planning Mobile robots Detection algorithms

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A Study of Endpoint-Constrained Nonlinear Tracking Filters

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2021年第6期57卷 3952-3961页

作者： Ford, Kevin R. Haug, Anton J. Johns Hopkins Univ Appl Phys Lab Laurel MD 20723 USA Johns Hopkins Univ Appl Phys Lab Ellicott City MD 20723 USA

The development and performance of an endpoint-constrained (EPC) extended Kalman filter (EKF) were published by Haug and Ford, where the EPC EKF was shown to outperform similar non-EPC EKFs against highly maneuverable antiship cruise missile (ASCM) trajectories. The purpose of this article is to resolve two issues identified during the original study. The first issue is that EKFs may not offer superior performance when the target's motion is highly nonlinear. The second issue is that the EPC EKF performs poorly against the high diver trajectory, where the target does not satisfy the EPC until the dive begins. This article presents a new filter that uses a modified version of the EPC and a multiple-model algorithm to achieve superior performance against a larger set of ASCM trajectories than the EPC EKF.

关键词： Target tracking Mathematical model Trajectory filtering algorithms Kalman filters Aerodynamics Predictive models Constrained Kalman filter endpoint constraint extended Kalman filter (EKF) interacting multiple-model (IMM) particle filtering target tracking unscented Kalman filter (UKF)

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Metric and Accuracy Ranked Feature Inclusion: Hybrids of Filter and Wrapper Feature Selection Approaches

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IEEE ACCESS 2021年 9卷 128687-128701页

作者： Thejas, G. S. Garg, Rameshwar Iyengar, S. S. Sunitha, N. R. Badrinath, Prajwal Chennupati, Shasank Tarleton State Univ Texas A&M Univ Syst Dept Comp Sci & Elect Engn Stephenville TX 76401 USA RV Coll Engn Dept Comp Sci & Engn Bengaluru 560059 Karnataka India Florida Int Univ Knight Fdn Sch Comp & Informat Sci Miami FL 33199 USA Siddaganga Inst Technol Dept Comp Sci & Engn Tumakuru 572103 India Univ N Carolina Global Womens Hlth Chapel Hill NC 27599 USA

Feature selection has emerged as a craft, using which we boost the performance of our learning model. Feature or Attribute Selection is a data preprocessing technique, where only the most informative features are considered and given to the predictor. This reduces the computational overhead and improves the correctness of the classifier. Attribute Selection is commonly carried out by applying some filter or by using the performance of the learning model to gauge the quality of the attribute subset. Metric Ranked Feature Inclusion and Accuracy Ranked Feature Inclusion are the two novel hybrid feature selection methods we introduce in this paper. These algorithms follow a two-stage procedure, the first of which is feature ranking, followed by feature subset selection. They differ in the way they rank the features but follow the same subset selection technique. Multiple experiments have been conducted to assess our models. We compare our results with numerous works of the past and validate our models using 12 datasets. From the results, we infer that our algorithms perform better than many existent models.

关键词： Feature extraction Prediction algorithms Support vector machines Optimization Correlation filtering algorithms Computer science Feature selection filter wrapper hybrid K-means V-measure random forest

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Poisson-Gaussian Noise Reduction for X-Ray Images Based on Local Linear Minimum Mean Square Error Shrinkage in Nonsubsampled Contourlet Transform Domain

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IEEE ACCESS 2021年 9卷 100637-100651页

作者： Lee, Sangyoon Kang, Moon Gi Yonsei Univ Sch Elect & Elect Engn Seoul 03722 South Korea

Noise reduction is important for X-ray images because it can reduce radiation exposure to patients. X-ray image noise has a Poisson-Gaussian distribution, and recently, noise analysis and removal in multiscale transformations have been widely implemented. The nonsubsampled contourlet transform (NSCT) is a multiscale transformation suitable for medical images that separates the scale and direction. This study proposes a Poisson-Gaussian noise-removal method using NSCT shrinkage that is based on the characteristics of Poisson-Gaussian noise in NSCT domain. It has the structure of a block-matching 3D filtering algorithm in the form of basic estimation and noise removal process;however, the main processes are modified to consider Poisson-Gaussian noise characteristics. In the basic estimation process, an NSCT shrinkage method that is suitable for Poisson-Gaussian noise characteristics is developed by optimizing the local linear minimum mean square error estimator in the NSCT domain. In the denoising step, the noise term of the Wiener filter is determined using the result of the NSCT shrinkage, and finally, the denoised image is obtained. The proposed method is applied to simulated and real X-ray images and is compared with other state-of-the-art Poisson-Gaussian noise removal methods;it exhibits excellent results in both quantitative and qualitative aspects.

关键词： Wiener filters Three-dimensional displays Smoothing methods Estimation Transforms Mean square error methods filtering algorithms Poisson-Gaussian noise noise removal nonsubsampled contourlet transform (NSCT) X-ray image local linear minimum mean square error (LLMMSE) filtering block-matching and 3D filtering (BM3D)

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Efficient Bayesian Detection of Faint Curved Edges in Noisy Images

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IEEE ACCESS 2024年 12卷 186343-186361页

作者： Ofir, Nati Weizmann Inst Sci IL-7610001 Rehovot Israel

Detecting edges in images is a fundamental problem in computer vision with many applications. Many edge detection algorithms have been proposed over the past several decades. These algorithms can deal effectively with the problem, but often face difficulties when applied to images taken under poor visual conditions of faint edges and noisy backgrounds. Such conditions occur frequently in various imaging domains including biomedical, satellite, and high shutter speed, and may even occur in natural images. In this work, the proposed method introduces an efficient method to detect faint edges in noisy images. The first question addressed is how to detect curved edges efficiently. Previous work showed that faint edges can be detected by applying a search over the space of possible curves. While this search space is exponentially large in the number of image pixels, the proposed algorithm novel multiscale algorithm carries a search through a large subset of the space in practical polynomial time. The introduced algorithm is based on a novel hierarchical partitioning of the image into triangular or rectangular tiles. In addition, the second question addressed is how to decide if a curve in the image indeed corresponds to a (possibly faint) edge. To that end, the paper introduces a Bayesian approach that incorporates the intensity and shape features of an edge. The proposed method utilizes relevant statistical priors on edge contrast and shape. Finally, the algorithm utilizes natural images to derive a prior on-edge contrast. As the manuscript experiments demonstrate, in comparison to previous works the proposed algorithm is efficient and obtains higher quality of edge detection.

关键词： Image edge detection Noise measurement Bayes methods filtering algorithms Matched filters Vegetation Bayesian detection edge detection noisy images

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A New Nonlocal Iterative Trilateral Filter for SAR Images Despeckling

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Xu, Li Liu, Peng Jin, Ya-Qiu Fudan Univ Key Lab Informat Sci Electromagnet Waves Shanghai 200433 Peoples R China

Speckle noise, a common artifact inherent in synthetic aperture radar (SAR) imagery, significantly degrades image quality. This deterioration in quality impedes critical tasks, such as segmentation, object recognition, and other related applications. This article introduces an innovative iterative trilateral filtering approach for SAR imagery enhancement, which is distinct in its use of Haar wavelet coefficients combined with pixel-domain distance confidence levels. By incorporating nonlocal (NL) information, the proposed approach aims to augment conventional wavelet transforms, effectively utilizing imprecise pixel data through confidence-weighted aggregations. The method uniquely integrates transform-domain, spatial-domain, and statistical properties of SAR images, which significantly enhance image clarity by producing high-fidelity despeckled outputs. Compared with traditional wavelet transforms, the proposed approach focuses on the discreteness analysis of inter-patch wavelet coefficients, which allow for deeper insights and better preservation of SAR image structures. The algorithm computes single-level Haar wavelet coefficients for patch pairs and determines wavelet weights based on inter-coefficient distances. Then, the algorithm calculates guided image pixel distances and reduces patchwise Bhattacharyya distances to encode pixel distance confidence. These three weights are then consolidated for effective despeckling. To address directional blurring due to SAR image anisotropy, the conventional NL method's square pixel vector is replaced with circular and annular vectors. The proposed approach demonstrates significant improvements in image integrity preservation, reducing speckle noise, as evidenced in our experiments with both simulated and real datasets.

关键词： Optical filters Radar polarimetry filtering algorithms filtering theory Wavelet transforms Noise Wavelet coefficients Despeckle iterative trilateral filter synthetic aperture radar (SAR)

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Improved initial alignment algorithm of SINS on shaking base based on Kalman filter

Improved initial alignment algorithm of SINS on shaking base...

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Conference on Sensor Data Fusion - Trends, Solutions, Applications (SDF)

作者： Shi, Ling-Feng Hou, Zhi-Yong Shi, Yifan Liu, Lushi Xidian Univ Inst Elect CAD Xian 710071 Shaanxi Peoples R China Queens Univ Dept Mech & Mat Engn Kingston ON K7L 2N8 Canada Univ Stuttgart Dept Aerosp Engn D-70569 Stuttgart Germany

ISBN: (数字)9781665486729

ISBN: (纸本)9781665486729

In the initial alignment of shaking base, Kalman filter is easy to diverge, and the alignment result is poor. Many scholars use various nonlinear filtering methods to solve this problem. However, these filtering methods not only require heavy calculation, but also suffer from the low accuracy problem. To solve these problems, an improved adaptive Kalman filter (IAKF) algorithm is proposed to complete the initial alignment of the shaking base. The experimental results show that the improved algorithm has high anti-interference and anti divergence ability. Within the MATLAB simulation, pitch, roll and heading errors can be stabilized at about 10 degrees. The real-world experiment with Redmi K30Pro mobile phone shows that the alignment result of the improved algorithm is more accurate. The difference of multiple alignment results is within 5 degrees when IAKF is used, while the difference of traditional filter is more than 20 degrees.

关键词： Micromechanical devices filtering filtering algorithms Market research Stability analysis Mobile handsets Mathematical models

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A generalized framework for pacing artifact removal using a Hampel filter 44

A generalized framework for pacing artifact removal using a ...

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44th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Nagahawatte, Nipuni D. Cheng, Leo K. Avci, Recep Bear, Laura R. Paskaranandavadivel, Niranchan Univ Auckland Auckland Bioengn Inst Auckland New Zealand Fdn Bordeaux Univ IHU Liryc CRCTB U1045 INSERM Bordeaux France Univ Bordeaux Bordeaux France

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

Cardiac pacing is a clinical therapy widely used for treating irregular heart rhythms. Equivalent techniques for the treatment of gastric functional motility disorders hold great potential. Accurate analysis of pacing studies is often hindered by the stimulus artifacts which are superimposed on the recorded signals. This paper presents a semi-automated artifact detection method using a Hampel filter accompanied by 2 separate artifact reconstruction methods: (i) an auto-regressive model, and (ii) weighted mean to estimate the underlying signal. The developed framework was validated on synthetic and experimental signals containing large periodic pacing artifacts alongside evoked bioelectrical events. The performance of the proposed algorithms was quantified for gastric and cardiac pacing data collected in vivo. A lower mean RMS difference was achieved by the artifact segment reconstructed using the auto-regression (0.37 +/- 0.49 mV) method compared to the weighted mean (0.69 +/- 0.69 mV) method. Therefore, a more accurate artifact reconstruction was provided by the auto-regression approach.

关键词： In vivo Heart beat Scholarships Biological system modeling Medical treatment Reconstruction algorithms filtering algorithms

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Distributed Event-Triggered Localization for High Latency Communication

Distributed Event-Triggered Localization for High Latency Co...

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American Control Conference (ACC)

作者： Barbier, Luke Morrissey, Luke Ahmed, Nisar Frew, Eric Martinez, Sonia Center, Kenneth Univ Colorado Smead Aerosp Engn Sci Boulder CO 80309 USA Univ Calif San Diego Dept Mech & Aerosp Engn La Jolla CA USA OrbitLog Inc Greenbelt MD USA

ISBN: (数字)9781665451963

ISBN: (纸本)9781665451963

This paper presents a Kalman Filter-based solution to the problem of decentralized cooperative robot localization and tracking with high latency and low bandwidth communication using an event-triggered fusion algorithm. Event-triggering is a compression technique for measurement sharing whereby only 'surprising' measurements are exchanged between agents. For unsurprising measurements, the lack of a measurement transmission is itself information about the value of a measurement that can be fused by other agents. This relies on consistency between 'common' estimates of agents, but, in high latency communication, consistency is not guaranteed and is difficult to achieve with bandwidth restrictions. We present a novel 'Delta-Tier' algorithm to address these issues, using token-passing to preserve consistency and auto-selection of event trigger thresholds delta to fit the available bandwidth. We demonstrate the success of our approach in achieving decentralized performance that is on par with idealized centralized fusion for an autonomous underwater robotics application in a simulated nonlinear environment, where robots must localize each other and track other non-cooperative agents.

关键词： Location awareness Bandwidth filtering algorithms Robot localization Kalman filters Robots

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The JM-Filter to Detect Specific Frequency in Monitored Signal

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2021年 69卷 1468-1476页

作者： Jaber, Marwan A. Massicotte, Daniel Univ Quebec Trois Rivieres Elect & Comp Engn Dept Trois Rivieres PQ G9A 5H7 Canada

The Discrete Fourier Transform (DFT) is a mathematical procedure that stands at the center of the processing inside a digital signal processor. It has been widely known and argued in relevant literature that the Fast Fourier Transform (FFT) is useless in detecting specific frequencies in a monitored signal of length N because most of the computed results are ignored. In this paper, we present an efficient FFT-based method to detect specific frequencies in a monitored signal, which will then be compared to the most frequently used method which is the recursive Goertzel algorithm that detects and analyses one selectable frequency component from a discrete signal. The proposed JM-Filter algorithm presents a reduction of iterations compared to the first and second order Goertzel algorithm by a factor of r, where r represents the radix of the JM-Filter. The obtained results are significant in terms of computational reduction and accuracy in fixed-point implementation. Gains of 15 dB and 19 dB in signal to quantization noise ratio (SQNR) were respectively observed for the proposed first and second order radix-8 JM-Filter in comparison to Goertzel algorithm.

关键词： Signal processing algorithms Discrete Fourier transforms Monitoring Frequency-domain analysis filtering algorithms OFDM Radiofrequency integrated circuits Discrete Fourier transform Goertzel algorithm one frequency detection low complexity fixed-point precision

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