Localization Speed Research Articles

Enabling real-time reconstruction for large field-of-view single-molecule localization microscopy using discrete field-dependent point-spread function

Single-molecule localization microscopy (SMLM) is a powerful super-resolution imaging technique that offers resolution far beyond the optical diffraction limit. The commonly used high numerical-aperture (NA) objective lenses in SMLM can only provide a nearly ideal point-spread function (PSF) at the center of the field-of-view (FOV), whereas the off-axis PSF is often distorted due to optical aberrations. Since precision and accuracy of three-dimensional (3D) spatial localization of single molecules heavily depend on the system’s PSF, the FOV of 3D SMLM is often restricted to about 50 µm × 50 µm limiting its applications in visualizing intra-/intercellular interactions and high-throughput single-molecule analysis. Here we present a systematic study to show the influence of optical aberrations on large FOV 3D SMLM using unmodified, astigmatic, and double-helix PSFs. Our results show that optical aberrations introduce significant localization errors during image reconstruction and thereby produce unreliable imaging results at the corner of the FOV. To maximize SMLM’s FOV, we proposed and verified the potential of using discrete field-dependent PSFs to retain precise and accurate single-molecule localization and compare their reconstruction results using simulated resolution test patterns and biological structures. Moreover, GPU acceleration empowers a discrete PSF calibration model with high localization speed, which can provide real-time SMLM image reconstruction. We envision these results will further guide the development of strategies that can provide real-time and reliable image reconstruction in large FOV 3D SMLM.

Joint Source Localization and Propagation Speed Estimation Using TDOA with Hypothesized Propagation Speed

Joint Source Localization and Propagation Speed Estimation Using TDOA with Hypothesized Propagation Speed

Research on Target Hybrid Recognition and Localization Methods Based on an Industrial Camera and a Depth Camera in Complex Scenes

In order to improve the target visual recognition and localization accuracy of robotic arms in complex scenes with similar targets, hybrid recognition and localization methods based on an industrial camera and depth camera are proposed. First, according to the speed and accuracy requirements of target recognition and localization, YOLOv5s is introduced as the basic algorithm model for target hybrid recognition and localization. Then, in order to improve the accuracy of target recognition and coarse localization based on an industrial camera (eye-to-hand), the AFPN feature fusion module, simple and parameter-free attention module (SimAM), and soft non-maximum suppression (Soft NMS) are introduced. In order to improve the accuracy of target recognition and fine localization based on a depth camera (eye-in-hand), the SENetV2 backbone network structure, dynamic head module, deformable attention mechanism, and chain-of-thought prompted adaptive enhancer network are introduced. After that, on the basis of constructing a dual camera platform for target hybrid recognition and localization, the hand–eye calibration, collection and production of image datasets required for model training are completed. Finally, for the docking of the oil filling port, the hybrid recognition and localization experimental tests are completed in sequence. The test results show that in target recognition and coarse localization based on the industrial camera, the recognition accuracy of the designed model reaches 99%, and the average localization errors in the horizontal and vertical directions are 2.22 mm and 3.66 mm, respectively. In target recognition and fine localization based on the depth camera, the recognition accuracy of the designed model reaches 98%, and the average errors in depth, horizontal, and vertical directions are 0.12 mm, 0.28 mm, and 0.16 mm, respectively. These not only verify the effectiveness of the target hybrid recognition and localization methods based on dual cameras, but also demonstrate that they meet the high-precision recognition and localization requirements in complex scenes.

Enhanced Indoor Positioning Using RSSI and Time-Distributed Auto Encoder-Gated Recurrent Unit Model.

This study presents a novel approach to indoor positioning leveraging radio frequency identification (RFID) technology based on received signal strength indication (RSSI). The proposed methodology integrates Gaussian Kalman filtering for effective signal preprocessing and a time-distributed auto encoder-gated recurrent unit (TAE-GRU) model for precise location prediction. Addressing the prevalent challenges of low accuracy and extended localization times in current systems, the proposed method significantly enhances the preprocessing of RSSI data and effectively captures the temporal relationships inherent in the data. Experimental validation demonstrates that the proposed approach achieves a 75.9% improvement in localization accuracy over simple neural network methods and markedly enhances the speed of localization, thereby proving its practical applicability in real-world indoor localization scenarios.

Enhanced Network Security Protection through Data Analysis and Machine Learning: An Application of GraphSAGE for Anomaly Detection and Operational Intelligence

With the Internet's rapid expansion, network security challenges have become increasingly complex and prominent. Traditional protection methods, largely dependent on predefined rules and patterns, demonstrate limited effectiveness against sophisticated and unknown network attacks, failing to harness the full potential of extensive network data. This study addresses the challenges faced by modern cybersecurity, particularly the limitations of traditional defense methods in countering unknown and complex attacks, by proposing a solution that integrates data analysis and machine learning technologies. The focus of this research is placed on network security anomaly detection as well as on intelligent network operations and maintenance exception management based on graph network algorithms, aiming to enhance security defense capabilities and operational efficiency. Specifically, the main contributions and innovations of this paper include: 1. Innovations in sampling, aggregation, and loss functions within the Graph Sample and Aggregation (GraphSAGE) model to improve the accuracy and robustness of the model for network anomaly detection; 2. The introduction of a novel network anomaly root cause analysis and localization model, which, combined with an optimized root cause likelihood assessment method and search scheme, significantly enhances the speed and accuracy of anomaly localization; 3. The design of an integrated decision support system that can automatically adjust protection strategies as network conditions change, achieving a high level of automation and intelligence in cybersecurity management. This work not only provides effective technical support for network security protection but also opens new avenues for future cybersecurity research.

High-performance subpixel edge location based on FPGA for horizon sensors

Horizon edge localization accuracy and speed are key factors in the performance of horizon sensors. This paper proposes a high-performance sub-pixel edge localization algorithm base on Field Programmable Gate Array (FPGA) for horizon sensors. The algorithm is carefully designed and simplified according to the computational capabilities and limitations of FPGAs. By making full use of the parallel computing capability of FPGA and carrying out multi-stage pipeline design, the algorithm can complete image acquisition, rough edge localization, sub-pixel edge localization, and projections from pixel points to unit vectors at the same time, which greatly reduces the delay caused by image processing. The experimental results show that FPGA-based image processing shows a significant superiority in terms of speed compared to traditional embedded processors.

Inertial Algorithm with Dry Fraction and Convolutional Sparse Coding for 3D Localization with Light Field Microscopy

Light field microscopy is a high-speed 3D imaging technique that records the light field from multiple angles by the microlens array(MLA), thus allowing us to obtain information about the light source from a single image only. For the fundamental problem of neuron localization, we improve the method of combining depth-dependent dictionary with sparse coding in this paper. In order to obtain higher localization accuracy and good noise immunity, we propose an inertial proximal gradient acceleration algorithm with dry friction, Fast-IPGDF. By preventing falling into a local minimum, our algorithm achieves better convergence and converges quite fast, which improves the speed and accuracy of obtaining the locolization of the light source based on the matching depth of epipolar plane images (EPI). We demonstrate the effectiveness of the algorithm for localizing non-scattered fluorescent beads in both noisy and non-noisy environments. The experimental results show that our method can achieve simultaneous localization of multiple point sources and effective localization in noisy environments. Compared to existing studies, our method shows significant improvements in both localization accuracy and speed.

Implementation and analysis of a parallel kalman filter algorithm for lidar localization based on CUDA technology.

Introduction: Navigation satellite systems can fail to work or work incorrectly in a number of conditions: signal shadowing, electromagnetic interference, atmospheric conditions, and technical problems. All of these factors can significantly affect the localization accuracy of autonomous driving systems. This emphasizes the need for other localization technologies, such as Lidar. Methods: The use of the Kalman filter in combination with Lidar can be very effective in various applications due to the synergy of their capabilities. The Kalman filter can improve the accuracy of lidar measurements by taking into account the noise and inaccuracies present in the measurements. Results: In this paper, we propose a parallel Kalman algorithm in three-dimensional space to speed up the computational speed of Lidar localization. At the same time, the initial localization accuracy of the latter is preserved. A distinctive feature of the proposed approach is that the Kalman localization algorithm itself is parallelized, rather than the process of building a map for navigation. The proposed algorithm allows us to obtain the result 3.8 times faster without compromising the localization accuracy, which was 3% for both cases, making it effective for real-time decision-making. Discussion: The reliability of this result is confirmed by a preliminary theoretical estimate of the acceleration rate based on Ambdahl's law. Accelerating the Kalman filter with CUDA for Lidar localization can be of significant practical value, especially in real-time and in conditions where large amounts of data from Lidar sensors need to be processed.

A Cloud-IoT Architecture for Latency-Aware Localization in Earthquake Early Warning.

An effective earthquake early warning system requires rapid and reliable earthquake source detection. Despite the numerous proposed epicenter localization solutions in recent years, their utilization within the Internet of Things (IoT) framework and integration with IoT-oriented cloud platforms remain underexplored. This paper proposes a complete IoT architecture for earthquake detection, localization, and event notification. The architecture, which has been designed, deployed, and tested on a standard cloud platform, introduces an innovative approach by implementing P-wave "picking" directly on IoT devices, deviating from traditional regional earthquake early warning (EEW) approaches. Pick association, source localization, event declaration, and user notification functionalities are also deployed on the cloud. The cloud integration simplifies the integration of other services in the architecture, such as data storage and device management. Moreover, a localization algorithm based on the hyperbola method is proposed, but here, the time difference of arrival multilateration is applied that is often used in wireless sensor network applications. The results show that the proposed end-to-end architecture is able to provide a quick estimate of the earthquake epicenter location with acceptable errors for an EEW system scenario. Rigorous testing against the standard of reference in Italy for regional EEW showed an overall 3.39 s gain in the system localization speed, thus offering a tangible metric of the efficiency and potential proposed system as an EEW solution.

Eye detection and coarse localization of pupil for video-based eye tracking systems

A video-based eye tracking system generally captures NIR images, each of which contains one or two eyes of a subject. The subject’s point of gaze is then determined using 3D eye model and pupil centre corneal reflection technique. Eye detection and pupil localization play significant roles in video-based eye tracking systems. However, face rotation, wearing glasses, eye-shape variation and illumination variation make it difficult to detect an eye and localize a pupil accurately in the images captured by video-based eye tracking systems. In this paper, we proposed an eye detector that adopts a coarse-to-fine strategy. The eye detector consists of three classifiers: an ATLBP-THACs feature-based cascade classifier, a branch CNN and a multi-task CNN. We also proposed a method for coarse pupil localization. Coarse localization is an important step for pupil localization since it can provide initial pupil coordinates for a fine pupil localization method. Given a downscaled eye image, a shallow CNN is used to estimate the location of seven landmarks. On this basis, pupil center and radius are estimated. A method for small dim target enhancement is used to increase the contrast between pupil and background. The main goal of pupil enhancement is to make it easier to binarize an eye image. At last, component filtering is made by utilizing the estimated pupil center and radius. We collected a dataset named neepuEYE dataset that consists of 5500 NIR eye images from 109 people. The images can be used to generate augmented samples for training an eye detector since they contain eyes with different shape, orientation and pupil localization. Experimental results show that our eye detector is a fast and robust detector. Furthermore, our method for coarse pupil localization can obtain not only high detection rate but also high localization speed.

Comprehensive fault reporting for three-terminal transmission line using adaptive estimation of line parameters

In this study, the post-fault synchronized phasors acquired from the local phasor measurement units (PMUs) are used to provide comprehensive fault reporting in Three terminal transmission line system (TTTL). The reporting data incorporates the classification, section identification, and localization of the fault based on the post-fault measurements of positive, negative, and zero sequence quantities. For accurate reporting, a proposed algorithm computes transmission line parameters, on hourly basis, to account for fluctuations in characteristics caused by temperature influences arising from either the loading factor or the climatic vagaries. Upon fault occurrence, the algorithm firstly determines fault classification and faulty section. Thereafter, localization of symmetrical faults is achieved via the positive sequence quantities to solve the positive sequence fault loop equation while that of asymmetrical faults is obtained through the negative sequence quantities to solve the negative sequence fault loop equation. The fault localization speed of the reporting proposed algorithm is attributed to the fact that synchronized data are obtained after one and half cycles from the fault onset. PSCAD/EMTDC platform is dedicated for time-domain investigations, while the proposed algorithm is carried out in MATLAB. The proposed algorithm was tested against 1537 fault characteristics at different locations along the TTTL, including varying fault resistances, loading conditions, and network configurations, in addition to assessing its sensitivity to changes in the TTTL parameters. These tests confirmed the algorithm’s reliability to be used with confidence in a variety of scenarios as the only 13 tests were observed with an error of estimation greater than 1%.

Research on an Insulator Defect Detection Method Based on Improved YOLOv5

Insulators are widely used in various aspects of the power system and play a crucial role in ensuring the safety and stability of power transmission. Insulator detection is an important measure to guarantee the safety and stability of the transmission system, and accurate localization of insulators is a prerequisite for detection. In this paper, we propose an improved method based on the YOLOv5s model to address the issues of slow localization speed and low accuracy in insulator detection in power systems. In our approach, we first re-cluster the insulator image samples using the k-means algorithm to obtain different sizes of anchor box parameters. Then, we add the non-local attention module (NAM) to the feature extraction module of the YOLOv5s algorithm. The NAM improves the attention mechanism using the weights’ contribution factors and scaling factors. Finally, we recursively replace the ordinary convolution module in the neck network of the YOLOv5 model with the gated normalized convolution (gnConv). Through these improvements, the feature extraction capability of the network is enhanced, and the detection performance of YOLOv5s is improved, resulting in increased accuracy and speed in insulator defect localization. In this paper, we conducted training and evaluation on a publicly available dataset of insulator defects. Experimental results show that the proposed improved YOLOv5s model achieves a 1% improvement in localization accuracy compared to YOLOv5. The proposed method balances accuracy and speed, meeting the requirements of online insulator localization in power system inspection.

DB-Net: Detecting Vehicle Smoke with Deep Block Networks

Vision-based vehicle smoke detection aims to locate the regions of vehicle smoke in video frames, which plays a vital role in intelligent surveillance. Existing methods mainly consider vehicle smoke detection as a problem of bounding-box-based detection or pixel-level semantic segmentation in the deep learning era, which struggle to address the trade-off of localization accuracy and speed. In addition, although various studies have been reported, there is no open benchmark available for real vehicle smoke detection. To address these issues, we made three contributions as follows: (i) We built a real-world vehicle smoke semantic segmentation dataset with 3962 polygon-based annotated vehicle smoke images, which will be released to the community. (ii) We regard vehicle smoke detection as a block-wise prediction problem and propose a conceptually new, yet simple deep block network model (DB-Net). It provides more accurate localization information than bounding-box-based ones and has a lower computational cost than semantic segmentation methods. (iii) We introduce a coarse-to-fine training strategy, where we first pre-train a model on bounding-box annotated data and then fine-tune it on pixel-wise labeled data. We compare our DB-Net to several advanced methods and evaluate them in several metrics. Extensive experiments demonstrate that our method is significantly superior to other methods.

Accelerate the Time-Reversal Computing of Lightning Location Using GPU and Phase-Difference Filter

Time-reversal (TR) technique has been proved to be an effective method to locate the lightning sources, which could get much more accurate results and is capable of finding the weak radiation sources. However, the huge amount of calculation makes it not meet the fast localization requirement of the lightning map array. To accelerate the localization speed of TR, this article proposes to use graphics processing unit (GPU) parallel computing and shared memory to calculate the radiation source directions. Then, the phase difference is combined with TR to filter out the less relevant direction points thus to further speed up the computation. Finally, as for the real lightning data received by the antennas, not every sliding window contains the radiation signals during the TR calculation, we use the signal-interference energy ratio to judge whether a sliding window has the radiation signal, thus, we only need to calculate the windows of which the ratios are above the threshold. Experiments show that, in simulation, by using the first two skills, the computing speed is greatly improved by more than 1700 times than a single CPU serial computing; in 500 ms real lightning data localization, our method is 263 times faster than the original CPU methods, meanwhile, the location result is as good as the latter.

Accelerated MINFLUX Nanoscopy, through Spontaneously Fast-Blinking Fluorophores.

The introduction of MINFLUX nanoscopy allows single molecules to be localized with one nanometer precision in as little as one millisecond. However, current applications have so far focused on increasing this precision by optimizing photon collection, rather than minimizing the localization time. Concurrently, commonly used fluorescent switches are specifically designed for stochastic methods (e.g., STORM), optimized for a high photon yield and rather long on-times (tens of milliseconds). Here, accelerated MINFLUX nanoscopy with up to a 30-fold gain in localization speed is presented. The improvement is attained by designing spontaneously blinking fluorescent markers with remarkably fast on-times, down to 1-3ms, matching the iterative localization process used in a MINFLUX microscope. This design utilizes a silicon rhodamine amide core, shifting the spirocyclization equilibrium toward an uncharged closed form at physiological conditions and imparting intact live cell permeability, modified with a fused (benzo)thiophene spirolactam fragment. The best candidate for MINFLUX microscopy (also suitable for STORM imaging) is selected through detailed characterization of the blinking behavior of single fluorophores, bound to different protein tags. Finally, optimization of the localization routines, customized to the fast blinking times, renders a significant speed improvement on a commercial MINFLUX microscope.

A Novel Detection and Localization Approach of Open-Circuit Switch Fault for the Grid-Connected Modular Multilevel Converter

The open-circuit fault detection and localization (FDL) technique can improve the reliability of the modular multilevel converter (MMC). However, the conventional software-based FDL methods usually have a heavy computation burden or a limited localization speed. This article proposes a simplified and fast software-based FDL approach for the grid-connected MMC. First, the errors between the measured state variables (the output current and the circulating current) and their estimated values are calculated. By comparing these errors with their threshold values, the switch fault can not only be detected but also be localized to the specific arm. Then, the capacitor voltages in this faulty arm are collected, and the submodule (SM) with the highest capacitor voltage is selected. To confirm the switch fault in this SM, a modified <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Pauta</i> criterion is presented to check the abnormal voltage data. As a result, the computation burden of the proposed software-based FDL approach is significantly reduced, and the faulty SM can be localized in a short period. Simulation and experimental results verify that the proposed approach can effectively detect and localize different open-circuit faults, and it is immune to the step of power references.

FRL: Fast and Reconfigurable Accelerator for Distributed Sound Source Localization

Sound source localization (SSL) has been widely applied in industrial and civil fields. And with the development of wearable devices and the Internet of Things (IoT), it is attractive to deploy the SSL system onto embedded and portable devices. However, the software-based SSL system causes excessive response delay and is often affected by environmental noise. To overcome this obstacle, we propose the fast and precise localization (FPL) algorithm for distributed SSL systems. It combines the benefits of both time difference of arrival (TDOA) and steered response power (SRP) methods, and thus it is able to localize sound sources fast and precisely. To further improve the localization speed, we propose the fast and reconfigurable localization (FRL) accelerator, which is an algorithm-hardware co-designed SSL accelerator. It adopts multiple distributed localization nodes for higher localization precision and higher robustness to environmental interference, and it can be configured into either the fast or precise mode to adapt to various environments. Experimental evaluations show that our proposed FPL algorithm can achieve high localization speed and precision, and the field-programmable gate array (FPGA)-based FRL accelerator outperforms the software implementation by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$48.6\times $ </tex-math></inline-formula> and outperforms the prior FPGA-based SSL accelerators by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$20\times \sim 838.2\times $ </tex-math></inline-formula> .

Integration of somatosensory and motor-related information in the auditory system.

An ability to integrate information provided by different sensory modalities is a fundamental feature of neurons in many brain areas. Because visual and auditory inputs often originate from the same external object, which may be located some distance away from the observer, the synthesis of these cues can improve localization accuracy and speed up behavioral responses. By contrast, multisensory interactions occurring close to the body typically involve a combination of tactile stimuli with other sensory modalities. Moreover, most activities involving active touch generate sound, indicating that stimuli in these modalities are frequently experienced together. In this review, we examine the basis for determining sound-source distance and the contribution of auditory inputs to the neural encoding of space around the body. We then consider the perceptual consequences of combining auditory and tactile inputs in humans and discuss recent evidence from animal studies demonstrating how cortical and subcortical areas work together to mediate communication between these senses. This research has shown that somatosensory inputs interface with and modulate sound processing at multiple levels of the auditory pathway, from the cochlear nucleus in the brainstem to the cortex. Circuits involving inputs from the primary somatosensory cortex to the auditory midbrain have been identified that mediate suppressive effects of whisker stimulation on auditory thalamocortical processing, providing a possible basis for prioritizing the processing of tactile cues from nearby objects. Close links also exist between audition and movement, and auditory responses are typically suppressed by locomotion and other actions. These movement-related signals are thought to cancel out self-generated sounds, but they may also affect auditory responses via the associated somatosensory stimulation or as a result of changes in brain state. Together, these studies highlight the importance of considering both multisensory context and movement-related activity in order to understand how the auditory cortex operates during natural behaviors, paving the way for future work to investigate auditory-somatosensory interactions in more ecological situations.

Real-time terahertz characterization for composite delamination using a lightweight CPU adaptive network

Delamination defects, as common damages occurring in fiber reinforced composite laminates, are often difficult to be detected due to their invisibility and concealment. Terahertz (THz) technique, as a promising alternative to conventional nondestructive testing (NDT) approaches, has emerged great potentials in quantitative characterization of delamination defects in composites. However, since THz signals are susceptible to confounding interference during THz inspection, such as the noise, interlayer reflection, dispersion and overlap, the accuracy and speed of defect localization and identification are usually contradictory. Generally, to improve the accuracy, many complex manual signal processing methods can be applied, which however will inevitably decrease the inference speed for delamination inspection and cannot fulfill the automatic and real-time analysis requirements in practical THz applications. Here, an intelligent THz 3D characterization system based on the lightweight deep learning (DL) model is proposed to realize the automatic and real-time characterization of hidden delamination defects in composites with high accuracy and low CPU latency. The core aims to specifically design a lightweight and end-to-end CPU adaptive network to obtain the optimal balance between the accuracy and speed. A series of experiments are implemented to validate the superior comprehensive performance of the system in terms of classification accuracy and inference speed on low-cost CPU-based platform. Compared with other methods, the proposed system can be not only able to obtain the superior accuracy, but also to obtain low CPU latency. Therefore, our work provides a novel and general paradigm for DL-based THz intelligent 3D characterization, which will promote the deployment of the system for the automatic and real-time THz characterization in industrial applications.

Rapid localization of radioactive leaks based on hybrid adaptive grey wolf algorithm

Radioactive source localization algorithms have been widely used in the detection of nuclear accident areas. But some shortcomings, such as complex algorithm structure, slow localization speed and poor accuracy, were obviously performed to affect mobile robot locating autonomously. In this paper, a potential alternative method was investigated to be a new usage of locating leaks, just via specifying the change of exposure rate. In this model, several key factors, such as gamma ray attenuation, scattering factor, travel angle guide, spatial discretization, etc., were taken into consideration, to demonstrate the effectiveness of the algorithm, which is appropriated in unknown areas of the radioactive waste repository. Since there are three factors with different contribution, such as position, quantity of the source and gamma ray energy, which considered to demonstrate its impact on success. So, a hybrid adaptive grey wolf algorithm (HAGWO) has been adopted and implemented to develop a novel rapid method of radioactive leak location. Three aspects, including the good point set initialization in population size, balanced convergence function, and self-adaptive greedy strategy for population update, were optimized and merged into the locating model. To investigate the effectiveness of the algorithm, results of HAGWO are compared with grey wolf algorithm (GWO), good point set initialization strategy GWO(GGWO) and adaptive head wolf strategy GWO (ALGWO) in convergence speed, accuracy, stability and positioning error of single and double leak points. It is observed that convergence speed is increased by 37.93 ± 2% at the highest; the convergence accuracy is increased by 92.42 ± 2% at the most; the stability is improved by 30% ∼ 50%. The positioning error of single leak point is within 1.08%, and the positioning error of double leak point is less than 8.90%. Besides, compared with GWO, GGWO and ALGWO, the single-point accuracy is improved by 1.36 percentage points (to GWO), and the double-point accuracy is improved by 40.35 percentage points (to ALGWO) at most. It is observed that HAGWO performs the best in locating leaks, with a faster convergence, stronger stability and more accuracy.