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There are several drawbacks in the existing localization (active contour) models. Some models have poor capability of handling uneven illuminations and low contrasts, others are sensitive to initial condition, and there are still some models which can not converge to the object boundary stably. In this paper, following the routes of Localizing Region-Based Active Contours (LRBAC) which is an important localization method, we propose a new localized active contour model. By altering the underlying construction logic, our proposed algorithm overcomes the problem of LRBAC with respect to poor convergence stability. Compared with some state-of-the-art localization models, our new algorithm is more similar to an edge-based one and therefore performs better when handling uneven illuminations and low contrasts. Moreover, combining the features of the region-based and the edge-based active contours, we propose, for our algorithm, a simple approach to dynamically control the localization size. This dynamical method makes our algorithm more robust to the initial condition. Detailed theoretical analysis and comparison are presented to clarify the features of our proposed algorithm. Experimental results on real-image segmentation underline the effectiveness of our proposed algorithm.

A self-localization method that can seamlessly switch positions and attitudes estimated using normal distributions transform (NDT) scan matching and a real-time kinematic global navigation satellite system (GNSS) is successfully developed. One of the issues encountered in this method is the sharing of global coordinates among the different estimation methods. Therefore, the three-dimensional environmental maps utilized in the NDT scan matching are created based on the planar Cartesian coordinate system used in the GNSS to obtain accurate information regarding the location, shape, and size of the actual terrain and geographic features. Consequently, seamlessly switching between different methods enables mobile robots to stably obtain accurate estimated positions and attitudes. An autonomous driving experiment is conducted using this self-localization method in the Tsukuba Challenge 2022, and the mobile robot completed a designated course involving more than 2 km in an urban area.

A method for the direct outdoor localization of multiple impulse acoustic sources by a distributed microphone array is proposed. This localization problem is of great interest for gunshot, firecracker and explosion detection localization in a civil environment, as well as for gun, mortar, small arms, artillery, sniper detection localization in military battlefield monitoring systems. Such a kind of localization is a complicated technical problem in many aspects. In such a scenario, the permutation of impulse arrivals on distributed microphones occurs, so the application of classical two-step localization methods, such as time-of-arrival (TOA), time-difference-of-arrival (TDOA), angle-of-arrival (AOA), fingerprint methods, etc., is faced with the so-called association problem, which is difficult to solve. The association problem does not exist in the proposed method for direct (one-step) localization, so the proposed method is more suitable for localization in a given acoustic scenario than the mentioned two-step localization methods. Furthermore, in the proposed method, direct localization is performed impulse by impulse. The observation interval used for the localization could not be arbitrarily chosen; it is limited by the duration of impulses. In the mathematical model formulated in the paper, atmospheric factors in acoustic signal propagation (temperature, pressure, etc.) are included. The results of simulations show that by using the proposed method, centimeter localization accuracy can be achieved.

Damage monitoring systems based on Lamb wave health monitoring technology have attracted considerable attention for scientific research and industrial applications. In this article, two types of single-mode Lamb waves are obtained using symmetric and anti-symmetric methods, respectively, to determine a crack identification signal. A numerical simulation of a welded steel plate model was conducted using the ABAQUS/EXPLICIT module, which is a dynamic solver. The propagation process and the corresponding effect of the Lamb waves over the complete and damaged models are simulated. According to the propagation characteristics and with the assistance of the ellipse localization method with MATLAB, the location of crack damage is simulated by the amplitude addition method and the crack damage location is determined. The results show that the simulation results are in good agreement with the actual crack damage. Furthermore, the received signals are compared and analyzed from an energy perspective. Two types of single-mode Lamb wave monitoring methods are also compared. In addition, it is demonstrated that a symmetric excitation can simplify the received waves and recognize crack damage in plates in welded steel structures from an experimental perspective of this work.

Recent works on crowd counting mainly leverage Convolutional Neural Networks (CNNs) to count by regressing density maps, and have achieved great progress. In the density map, each person is represented by a Gaussian blob, and the final count is obtained from the integration of the whole map. However, it is difficult to accurately predict the density map on dense regions. A major issue is that the density map on dense regions usually accumulates density values from a number of nearby Gaussian blobs, yielding different large density values on a small set of pixels. This makes the density map present variant patterns with significant pattern shifts and brings a long-tailed distribution of pixel-wise density values. In this paper, we aim to address such issue in the density map. Specifically, we propose a simple and effective Learning to Scale (L2S) module, which automatically scales dense regions into reasonable closeness levels (reflecting image-plane distance between neighboring people). L2S directly normalizes the closeness in different patches such that it dynamically separates the overlapped blobs, decomposes the accumulated values in the ground-truth density map, and thus alleviates the pattern shifts and long-tailed distribution of density values. This helps the model to better learn the density map. We also explore the effectiveness of L2S in localizing people by finding the local minima of the quantized distance (w.r.t. person location map), which has a similar issue as density map regression. To the best of our knowledge, such localization method is also novel in localization-based crowd counting. We further introduce a customized dynamic cross-entropy loss, significantly improving the localization-based model optimization. Extensive experiments demonstrate that the proposed framework termed AutoScale improves upon some state-of-the-art methods in both regression and localization benchmarks on three crowded datasets and achieves very competitive performance on two sparse datasets. An implementation of our method is available at https://github.com/dk-liang/AutoScale.git.

This study focuses on the localization of multiple radiation sources and introduces a direct localization method based on carrier frequency search, called the Carrier Frequency Estimation Direct Position Determination (DPD-CFE) method. The proposed method effectively addresses the localization challenges posed by radiation sources with varying carrier frequencies while concurrently providing a coarse estimation of the carrier frequency parameters for each source. This method only provides initial position estimates for emitters with different carrier frequencies and does not involve the study of emitter position estimation accuracy.

Burrowing animals threaten the structural integrity of dams, necessitating accurate localization within complex underground tunnels. Traditional sound source localization (SSL) methods struggle in such reverberant environments. To address this, we propose DSACNN22, a Direction of Sound Arrival (DSA) estimation network that leverages log-magnitude and phase-difference features with spatial attention to enhance localization accuracy. Experimental results using Pyroomacoustics-simulated data demonstrate DSACNN22’s robust spatial cue extraction capabilities in challenging acoustics, advancing burrow-detection capacities for tunnel-exploration robots.

The complexity and variability of the underwater environment have presented a significant technical challenge in the detection and localization of underwater object. In this paper, an underwater object detection and localization method based on the BEVFormer model is presented and experimentally verified for effectiveness. The BEVFormer model incorporates spatial cross-attention and temporal self-attention, leveraging information from both temporal and spatial scales to enhance robustness. The Generative Adversarial Network CycleGAN was employed to generate the underwater dataset U-nuScenes, based on the nuScenes dataset. Results show that the method presented in this paper achieved 24% mAP and 0.35 NDS on U-nuScenes under the condition of utilizing only visual information.

We introduce an interferometric single-molecule localization method for super-resolution fluorescence microscopy. Fluorescence molecules are located by the intensities of multiple excitation patterns of an interference fringe, providing around a twofold improvement in the localization precision compared with the conventional imaging with the same photon budget. We demonstrate this technique by resolving nanostructures down to 5 nm in size over a large 25 × 25 μm2 field of view.

With the increasingly prominent problem of faults at the end of distribution network feeders, a fault area localization method based on simulated annealing Tianniu swarm optimization (SABSO) algorithm is proposed to address the shortcomings of traditional fault localization methods. This method achieves rapid and accurate location of fault areas by collecting and analyzing fault information at the end of the distribution network feeder. The experimental results show that this method has higher accuracy and can accurately locate the fault area at the end of the distribution network feeder, providing strong support for the safe and stable operation of the distribution network.