High-precision Localization Research Articles

Super‐Resolved Protein Imaging Using Bifunctional Light‐Up Aptamers

AbstractEfficient labeling methods for protein visualization with minimal tag size and appropriate photophysical properties are required for single‐molecule localization microscopy (SMLM), providing insights into the organization and interactions of biomolecules in cells at the molecular level. Among the fluorescent light‐up aptamers (FLAPs) originally developed for RNA imaging, RhoBAST stands out due to its remarkable brightness, photostability, fluorogenicity, and rapid exchange kinetics, enabling super‐resolved imaging with high localization precision. Here, we expand the applicability of RhoBAST to protein imaging by fusing it to protein‐binding aptamers. The versatility of such bifunctional aptamers is demonstrated by employing a variety of protein‐binding aptamers and different FLAPs. Moreover, fusing RhoBAST with the GFP‐binding aptamer AP3 facilitates high‐ and super‐resolution imaging of GFP‐tagged proteins, which is particularly valuable in view of the widespread availability of plasmids and stable cell lines expressing proteins fused to GFP. The bifunctional aptamers compare favorably with standard antibody‐based immunofluorescence protocols, as they are 7‐fold smaller than antibody conjugates and exhibit higher bleaching‐resistance. We demonstrate the effectiveness of our approach in super‐resolution microscopy in secondary mammalian cell lines and primary neurons by RhoBAST‐PAINT, an SMLM protein imaging technique that leverages the transient binding of the fluorogenic rhodamine dye SpyRho to RhoBAST.

Probabilistic diagnostic imaging method based on the difference between the arrival moments of ultrasonic guided wave signals

Ultrasonic guided wave (UGW), using the probabilistic diagnostic imaging (PDI) method is widely used in structural health monitoring. The PDI method, which incorporates signal time-of-flight (TOF), has significant advantages in high-precision localization and multi-damage recognition. However, the TOF acquisition process introduces errors. This paper proposes a modified PDI method based on the difference between the arrival moments of guided wave (GW) signals. This method eliminates the need to acquire excitation to obtain TOF, reducing the requirements for GW detector, and minimizing the impact of TOF errors on damage localization. An experimental system for composite laminates was constructed, and the experimental results showed that the method had an average localization error of 4.73 mm for single damages. The average localization error for both multiple damages and damage at edge locations was less than 20.0 mm. The localization error is satisfactory with reference to the size of the monitored area and damages.

Characterizing Rotational Dynamics and Heterogeneity via Single-Molecule Intensity Measurements.

Dynamic heterogeneity in glassy systems has typically been characterized at the single-molecule level by extracting rotational relaxation time scales from linear dichroism (LD) collected via two orthogonally polarized channels. However, in such measurements, localization precision is diminished due to photons lost relative to collection in a single detection channel. This poses challenges in characterizing rotational and translational dynamics simultaneously, as translational measurements require high localization precision. In this paper, we present a method for extracting rotational dynamics of glassy systems at the single-molecule level from intensity fluctuations of fluorescent probe molecules in a wide-field configuration without the use of a polarizing optical component. Through numerical analysis, we show that LD and intensity measurements probing rotational dynamics report similar, approximately second-order rotational correlation decays even at low signal to noise. Thus, within the assumptions of small, isotropic rotations, LD and intensity autocorrelation analysis should provide identical information on the time scale and heterogeneity of rotational dynamics. We then present experimental results that validate this numerical result, with direct comparison of LD and intensity-based approaches across probe molecules in both polymeric and small-molecule glass formers as well as across optical configurations. Our results demonstrate moderate correlation on a per-probe basis between rotational time scales obtained from both approaches, with deviations consistent with those expected in a dynamically heterogeneous system. We envision that this easily accessible strategy will be of use across disciplines for characterizing single-molecule rotational dynamics in limited signal situations and/or when high-precision localization is required.

Experiments of Ultrasonic Positioning System with Symmetrical Array Used in Jiangmen Underground Neutrino Observatory

The experimental environment of the JUNO is a spherical container filled with a liquid scintillator (alkylbenzene) with a diameter of about 35 m. To observe neutrino interactions in alkylbenzene with photomultiplier tubes (PMTs) and to precisely measure the neutrino properties in this environment, it is necessary to design a high-precision localization system for the PMT device. In this paper, we report the design of an ultrasonic localization system with a symmetrical receiving array, based on the construction of an experimental setup that reproduces the configuration of JUNO’s environment. We show through positioning consistency and accuracy measurements that the ultrasonic localization system has a high localization accuracy and can perform effective localization in an alkylbenzene solution with 98% purity.

Research on high precision localization of space target with multi-sensor association

In response to the challenges of acquiring spatial target position information and achieving high precision in existing methods, this paper proposes a multi-dimensional high-precision positioning method for spatial targets through multi-sensor fusion. Utilizing optical detection technology, the method extracts two-dimensional positional information of spatial targets on the observation plane. By deriving a fusion positioning formula for visible light and infrared based on the Gaussian mixture TPHD, the proposed method enhances positioning accuracy by 0.2 m compared to using visible light or infrared alone. Additionally, by integrating laser ranging for distance dimension information, precise target positioning in the world coordinate system is achieved. Outdoor experiments for spatial target positioning validate the method's effectiveness, utilizing visible light and infrared cameras along with laser ranging. Comparative analysis with a binary star angular measurement-only method demonstrates 17.9 % improvement in positioning accuracy, with the proposed method achieving 0.12 m accuracy for 5 cm spatial targets at 5 km distance.

Structural Health Monitoring of Glass Fiber-Reinforced Polymer Laminates with Carbon Nanotube-Coated Glass Fiber Sensing Layer after Low-Velocity Impact Using Electrical Resistance Tomography

Structural health monitoring (SHM) of composite materials is of great significance in various practical applications. However, it is a challenge to accurately monitor the damage of composites without affecting their mechanical properties. In this paper, an embedded sensing layer based on carbon nanotube-coated glass fiber is designed, combined with electrical resistance tomography (ERT) for in situ damage monitoring. Multi-wall carbon nanotube-coated glass fiber (MWCNT-GF) is prepared and embedded into laminates as an in situ sensing layer. Low-velocity impact experiments demonstrate that the embedded sensing layer has high compatibility with the composite laminates and has no adverse effect on its impact response; although, the energy absorption behavior of glass fiber-reinforced polymer (GFRP) laminates containing MWCNT-GF occurs about 10% earlier than that of GFRP laminates overall. ERT technology is used to analyze the laminates after a low-velocity impact test. The results show that the in situ monitoring method with the embedded MWCNT-GF sensing layer can achieve high precision in imaging localization of impact damage, and the error of the detected damage area is only 4.5%.

Localization algorithm of soil moisture monitoring in irrigation area based on weighted correction of distance measurement

At present, Wireless Sensor Network technology are widely used in the fields of agricultural environment monitoring. Whether we use ground network or ground-air coordination, the position coordinates of unknown nodes are an important feature of sensor information collection. To achieve the goal of a wireless monitoring system for field soil irrigation and its node positioning, we proposed an RSSI (Received Signal Intensity Indication)-based agricultural environment irrigation monitoring system. The algorithm has three stages: RSSI ranging, error correction, and localization. In the RSSI ranging phase, we obtain inter-node measurement distance by wireless channel modeling. In the distance-weighted correction phase, considering the difference between the measured distance of the beacon nodes and the actual distance, we analyze and select the relative error coefficient for the beacon nodes to correct the measured distance between the unknown nodes and the beacon nodes within their communication range. In the positioning stage for the nodes to be located, we estimated the required node coordinates using a weighted centroid localization method. In addition, by analyzing the monitoring data, we know the changes in soil moisture in real-time, which provides new ideas for irrigation automation and the efficient utilization of water resources. When designing the algorithm, we thoroughly considered the influence of RSSI ranging inaccuracy and the quantity of beacon nodes on the localization precision. The test demonstrated that the method presented in this paper has higher localization precision and lower computation cost.

Research on an Autonomous Localization Method for Trains Based on Pulse Observation in a Tunnel Environment.

China's rail transit system is developing rapidly, but achieving seamless high-precision localization of trains throughout the entire route in closed environments such as tunnels and culverts still faces significant challenges. Traditional localization technologies cannot meet current demands, and the present paper proposes an autonomous localization method for trains based on pulse observation in a tunnel environment. First, the Letts criterion is used to eliminate abnormal gyro data, the CEEMDAN method is employed for signal decomposition, and the decomposed signals are classified using the continuous mean square error and norm method. Noise reduction is performed using forward linear filtering and dynamic threshold filtering, respectively, maximizing the retention of its effective signal components. A SINS/OD integrated localization model is established, and an observation equation is constructed based on velocity matching, resulting in an 18-dimensional complex state space model. Finally, the EM algorithm is used to address Non-Line-Of-Sight and multipath effect errors. The optimized model is then applied in the Kalman filter to better adapt to the system's observation conditions. By dynamically adjusting the noise covariance, the localization system can continue to maintain continuous high-precision position information output in a tunnel environment.

A high-precision localization method for underwater targets incorporating direct path recognition and sound rays bending compensation

A high-precision localization method for underwater targets incorporating direct path recognition and sound rays bending compensation

Super-Resolved Protein Imaging Using Bifunctional Light-Up Aptamers.

Efficient labeling methods for protein visualization with minimal tag size and appropriate photophysical properties are required for single-molecule localization microscopy (SMLM), providing insights into the organization and interactions of biomolecules in cells at the molecular level. Among the fluorescent light-up aptamers (FLAPs) originally developed for RNA imaging, RhoBAST stands out due to its remarkable brightness, photostability, fluorogenicity, and rapid exchange kinetics, enabling super-resolved imaging with high localization precision. Here, we expand the applicability of RhoBAST to protein imaging by fusing it to protein-binding aptamers. The versatility of such bifunctional aptamers is demonstrated by employing a variety of protein-binding aptamers and different FLAPs. Moreover, fusing RhoBAST with the GFP-binding aptamer AP3 facilitates high- and super-resolution imaging of GFP-tagged proteins, which is particularly valuable in view of the widespread availability of plasmids and stable cell lines expressing proteins fused to GFP. The bifunctional aptamers compare favorably with standard antibody-based immunofluorescence protocols, as they are 7-fold smaller than antibody conjugates and exhibit higher bleaching-resistance. We demonstrate the effectiveness of our approach in super-resolution microscopy in secondary mammalian cell lines and primary neurons by RhoBAST-PAINT, an SMLM protein imaging technique that leverages the transient binding of the fluorogenic rhodamine dye SpyRho to RhoBAST.

An accurate tidal peak localization method in radial arterial pulse signals based on hybrid neural networks

Background: cardiovascular diseases (CVDs) have become the leading causes of death worldwide. Arterial stiffness and elasticity are important indicators of cardiovascular health. Pulse wave analysis (PWA) is essential for analyzing arterial stiffness and elasticity, which are highly dependent on the tidal peak (P 2). P 2 is one of the four key physiological points, which also include percussion peaks (P 1), diastolic notches (P 3), and diastolic peaks (P 4). P 1, P 3, and P 4 are often local maxima or minima, facilitating their identification via the second derivatives method, a classic localization method for key physiological points. Classic methods such as the second derivative method, Empirical Mode Decomposition (EMD), and Wavelet Transform (WT), have been employed for the extraction and analysis of the P 2. Due to individual variation and arterial stiffness, locating the P 2 using classic methods is particularly challenging. Methods: we propose a hybrid neural network based on Residual Networks (ResNet) and bidirectional Long Short-Term Memory Networks (Bi-LSTM), successfully achieving high-precision localization of the P 2 in radial artery pulse signals. Meanwhile, we compared our method with the second derivative method, EMD, WT, Convolutional Neural Networks (CNN) and the hybrid model with ResNet and LSTM. Results: the results indicate that our proposed model exhibits significantly higher accuracy compared to other algorithms. Overall, MAEs and RMSEs for our proposed method are 62.60% and 58.84% on average less than those for other algorithms. The average R Adj 2 is 29.20% higher. The outcomes of the efficiency evaluation suggest that the hybrid model performs more balancedly without any significant shortcomings, which indicates that the Bi-LSTM structure upgrades the performances of LSTM. Significance: our hybrid model can provide the medical field with improved diagnostic tools and promote the development of clinical practice and research.

Enhanced Berth Mapping and Clothoid Trajectory Prediction Aided Intelligent Underground Localization

In response to the widespread absence of global navigation satellite system (GNSS) signals in underground parking scenes, we propose a multimodal localization method that integrates enhanced berth mapping with Clothoid trajectory prediction, enabling high-precision localization for intelligent vehicles in underground parking environments. This method began by constructing a lightweight map based on the key berths. The map consisted of a series of discrete nodes, each encompassing three elements: holistic and local scene features extracted from an around-view image, and the global pose of the mapping vehicle calculated using the positions of the key berth’s corner points. An adaptive localization strategy was employed during the localization phase based on the trajectory prediction result. A progressive localization strategy, relying on multi-scale feature matching, was applied to the nodes within the map coverage range. Additionally, a compensation localization strategy that combined odometry with the prior pose was utilized for the nodes outside the map coverage range. The experiments conducted in two typical underground parking scenes demonstrated that the proposed method achieved a trajectory prediction accuracy of 40 cm, a nearest map search accuracy exceeding 92%, and a metric localization accuracy meeting the 30 cm standard. These results indicate that the proposed approach satisfies the high-precision, robust, real-time localization requirements for intelligent vehicles in underground parking scenes, while effectively reducing the map memory requirements.

High-precision apple recognition and localization method based on RGB-D and improved SOLOv2 instance segmentation

Intelligent apple-picking robots can significantly improve the efficiency of apple picking, and the realization of fast and accurate recognition and localization of apples is the prerequisite and foundation for the operation of picking robots. Existing apple recognition and localization methods primarily focus on object detection and semantic segmentation techniques. However, these methods often suffer from localization errors when facing occlusion and overlapping issues. Furthermore, the few instance segmentation methods are also inefficient and heavily dependent on detection results. Therefore, this paper proposes an apple recognition and localization method based on RGB-D and an improved SOLOv2 instance segmentation approach. To improve the efficiency of the instance segmentation network, the EfficientNetV2 is employed as the feature extraction network, known for its high parameter efficiency. To enhance segmentation accuracy when apples are occluded or overlapping, a lightweight spatial attention module is proposed. This module improves the model position sensitivity so that positional features can differentiate between overlapping objects when their semantic features are similar. To accurately determine the apple-picking points, an RGB-D-based apple localization method is introduced. Through comparative experimental analysis, the improved SOLOv2 instance segmentation method has demonstrated remarkable performance. Compared to SOLOv2, the F1 score, mAP, and mIoU on the apple instance segmentation dataset have increased by 2.4, 3.6, and 3.8%, respectively. Additionally, the model’s Params and FLOPs have decreased by 1.94M and 31 GFLOPs, respectively. A total of 60 samples were gathered for the analysis of localization errors. The findings indicate that the proposed method achieves high precision in localization, with errors in the X, Y, and Z axes ranging from 0 to 3.95 mm, 0 to 5.16 mm, and 0 to 1 mm, respectively.

Single-molecule localization microscopy at 2.4-fold resolution improvement with optical lattice pattern illumination

Traditional camera-based single-molecule localization microscopy (SMLM), with its high imaging resolution and localization throughput, has made significant advancements in biological and chemical researches. However, due to the limitation of the fluorescence signal-to-noise ratio (SNR) of a single molecule, its resolution is difficult to reach to 5 nm. Optical lattice produces a nondiffracting beam pattern that holds the potential to enhance microscope performance through its high contrast and penetration depth. Here, we propose a new method named LatticeFLUX which utilizes the wide-field optical lattice pattern illumination for individual molecule excitation and localization. We calculated the Cramér-Rao lower bound of LatticeFLUX resolution and proved that our method can improve the single molecule localization precision by 2.4 times compared with the traditional SMLM. We propose a scheme using 9-frame localization, which solves the problem of uneven lattice light illumination. Based on the experimental single-molecule fluorescence SNR, we coded the image reconstruction software to further verify the resolution enhancement capability of LatticeFLUX on simulated punctate DNA origami, line pairs, and cytoskeleton. LatticeFLUX confirms the feasibility of using 2D structured light illumination to obtain high single-molecule localization precision under high localization throughput. It paves the way for further implementation of ultra-high resolution full 3D structured-light-illuminated SMLM.

Predicted position-driven deep learning channel estimation for massive MIMO systems

Multiple-input multiple-output (MIMO) can significantly improve the energy efficiency and spectral efficiency of wireless communication systems, and obtaining accurate channel state information is a key prerequisite. However, traditional channel estimation techniques often have high computational complexity, poor channel estimation performance, and do not fully utilize the information in the communication scene. In this paper, a channel estimator (PrePD-CNN) with convolutional neural network (CNN) driven by predicted position information is proposed based on high-precision localization technology for 6G. The channel estimation performance is improved by rationally transforming the sensing information and accurately reconstructing the quasi-channel state as auxiliary information. Simulation results show that the BER performance and MSE performance of PrePD-CNN are significantly lower than that of traditional channel estimation algorithms. Therefore, the proposed scheme combines stronger robustness and good generalization, which not only reduces the complexity of the algorithm but also improves the performance.

Determine the location of acoustic emission source on wood surface by using a wavelet-energy attenuation method

ABSTRACT To determine the location of wood damage and fracture, an acoustic emission source localization method based on wavelet-energy attenuation law was proposed. High-speed acquisition equipment and LabVIEW software were used to design a three-channel AE signal acquisition system. The wavelet transform is used to decompose the original signals into five frequency levels, and the second, third, and fourth level were selected for reconstruction of the acoustic emission signal. Finally, the reconstructed signals was used to determine the location of the acoustic emission source in accordance with the energy attenuation law. The results indicated that when the distance between the three sensors were 100 and 200 mm, the average location error obtained using the original signals in accordance with the energy attenuation law was 12.6 and 18.4 mm, respectively. However, the average location error obtained using the reconstructed signals in accordance with the energy attenuation law was 2.2 and 6.9 mm, respectively. These results reveal that the proposed method can achieve high-precision localization of acoustic emission source. This research enrich and expand the content of AE technology and wooden building research, provide new ideas and methods for non-destructive testing of similar structures, and promote research progress in related fields..

Heterogeneous heatmap distillation framework based on unbiased alignment for lightweight human pose estimation

The growing demand for mobile devices has generated interest in lightweight human pose estimation. Currently, lightweight estimation generally uses heatmap-based methods, which has demonstrated exceptional performance. However, their use of non-differentiable post-processing imposes considerable inference latencies. Conversely, integral-based approaches expedite the inference process by employing a soft-argmax operation but compromise in accuracy. Integrating explicit heatmap knowledge learned using the heatmap-based method into the implicit heatmap generated by the integral-based method, thereby combining the best of both worlds, offers a promising avenue. However, owing to the disparities in supervision and inference processes, the explicit and implicit heatmaps are heterogeneous. Consequently, direct transfer of knowledge presents difficulties in ensuring consistencies in heat value and location. In this paper, we propose a novel Heterogeneous Heatmap Distillation (HHD) framework that effectively tackles these challenges. The framework seamlessly integrates explicit heatmap knowledge that contains high-precision localization information into implicit heatmaps. The framework revolves around an unbiased heatmap alignment scheme encompassing two steps: heterogeneous heatmap normalization and unbiased cropping. Heterogeneous heatmap normalization separately normalizes the output feature maps of both the teacher and student models, alleviating potential heat value bias during the knowledge transfer. Unbiased cropping applies closed-form computation on the normalized teacher and student heatmap to eliminate location bias. Additionally, mirror expansion is implemented to handle potential cases wherein the cropped region extends beyond the image boundary. Extensive experiments demonstrate the efficiency and effectiveness of our methods on the MSCOCO and MPII datasets compared to other integral-based lightweight networks. Our source codes and pre-trained models are available at https://github.com/ducongju/HHD.

A novel meta‐learning network for partial discharge source localization in gas‐insulated switchgear via digital twin

AbstractDue to the requirement for highly precise synchronous sampling and the substantial reliance on time difference calculations, the current partial discharge (PD) localization based on the time difference of arrival is only applicable in certain situations. As digital twin technology has advanced, it is possible to employ virtual models to support gas‐insulated switchgear (GIS) PD localization. To do this, we propose a meta‐learning (ML) network with the aid of digital twin for actual GIS PD localization. Firstly, a GIS digital twin model was established to acquire an auxiliary simulated sample library. Then, a temporal convolutional network is established to extract the discriminable features, effectively obtain the time dependence between features, and improve the accuracy of localization. Next, ML is adopted to quickly learn meta‐knowledge that can be applied across tasks, and the model's sensitivity to task changes is improved. Finally, the model is fine‐tuned through a limited number of samples from the target task, and high precise PD localization is achieved. The experimental results demonstrate that the ML has an average localization error of only 9.25 cm and a probability density rose to 93% within 20 cm, which is clearly superior to previous methods.

Accurate Localization in LOS/NLOS Channel Coexistence Scenarios Based on Heterogeneous Knowledge Graph Inference

Accurate localization is one of the basic requirements for smart cities and smart factories. In wireless cellular network localization, the straight-line propagation of electromagnetic waves between base stations and users is called line-of-sight (LOS) wireless propagation. In some cases, electromagnetic wave signals cannot propagate in a straight line due to obstruction by buildings or trees, and these scenarios are usually called non-LOS (NLOS) wireless propagation. Traditional localization algorithms such as TDOA, AOA, etc. , are based on LOS channels, which are no longer applicable in environments where NLOS propagation is dominant, and in most scenarios, the number of base stations with LOS channels containing users is often small, resulting in traditional localization algorithms being unable to satisfy the accuracy demand of high-precision localization. In addition, some nonideal factors may be included in the actual system, all of which can lead to localization accuracy degradation. Therefore, the approach developed in this paper uses knowledge graph and graph neural network (GNN) technology to model communication data as knowledge graphs, and it adopts the knowledge graph inference technique based on a heterogeneous graph attention mechanism to infer unknown data representations in complex scenarios based on the known data and the relationships between the data to achieve high-precision localization in scenarios with LOS/NLOS channel coexistence. We experimentally demonstrate a spatial 2D localization accuracy level of approximately 10 meters on multiple datasets and find that our proposed algorithm has higher accuracy and stronger robustness than the state-of-the-art algorithms.

High-Precision Localization of Passive Intermodulation Source in Radio Frequency Transmission Lines Based on Dual-Frequency Signals

With the development of communication technology, the interference from passive intermodulation to the communication system has become increasingly severe. Locating the source of passive intermodulation and then repairing or replacing the device experiencing passive intermodulation is a reliable method. Therefore, this paper proposes a dual-frequency signal localization method based on the principle of phase ranging to accurately locate the passive intermodulation sources in RF cables. This method switches the test signal frequency to obtain the phase of passive intermodulation signals at different frequencies to localize the passive intermodulation source. This paper analyzes the principle of the passive intermodulation localization method based on dual-frequency signals, establishes a localization system for experiments, and finally, analyzes the experimental error to propose an optimization strategy. Through experimental verification, this method can stabilize the positioning error of a passive intermodulation source in a transmission line less than 0.06 m. Meanwhile, this method has high positioning accuracy and the advantages of slight computational complexity, fast positioning speed, and only a small number of requirements on hardware resources. It can work in communication scenarios like satellite and base stations, providing a new technical solution for localizing passive intermodulation interference sources in 5G and 6G communication systems.