Three-dimensional Localization Research Articles

Repetitive transcranial magnetic stimulation enhanced by neuronavigation in the treatment of depressive disorder and schizophrenia

This editorial assesses the advancements in neuronavigation enhanced repetitive transcranial magnetic stimulation for depressive disorder and schizophrenia treatment. Conventional repetitive transcranial magnetic stimulation faces challenges due to the intricacies of brain anatomy and patient variability. Neuronavigation offers innovative solutions by integrating neuroimaging with three-dimensional localization to pinpoint brain regions and refine therapeutic targeting. This systematic review of recent literature underscores the enhanced efficacy of neuronavigation in improving treatment outcomes for these disorders. This editorial highlights the pivotal role of neuronavigation in advancing psychiatric care.

Three-dimensional localization and tracking of chromosomal loci throughout the Escherichia coli cell cycle.

The intracellular position of genes may impact their expression, but it has not been possible to accurately measure the 3D position of chromosomal loci. In 2D, loci can be tracked using arrays of DNA-binding sites for transcription factors (TFs) fused with fluorescent proteins. However, the same 2D data can result from different 3D trajectories. Here, we have developed a deep learning method for super-resolved astigmatism-based 3D localization of chromosomal loci in live E. coli cells which enables a precision better than 61 nm at a signal-to-background ratio of ~4 on a heterogeneous cell background. Determining the spatial localization of chromosomal loci, we find that some loci are at the periphery of the nucleoid for large parts of the cell cycle. Analyses of individual trajectories reveal that these loci are subdiffusive both longitudinally (x) and radially (r), but that individual loci explore the full radial width on a minute time scale.

Sensitive imaging of actinide materials in shielded radioactive waste

This paper reports on the development of a method for enhanced non-destructive assay (NDA) of radioactive waste using the novel technique neutron-gamma emission tomography (NGET). The technique relies on the detection of correlated fast neutrons and gamma rays emitted in spontaneous or induced fission. It is based on fast organic scintillators and enables sensitive detection and three-dimensional (3D) localization of the fission events. The technique is passive and does not require moving components. In this work, we apply the NGET technique to the category of radioactive waste which is often referred to as historic or legacy waste. This can include mixed wastes encased in shielded containers many decades ago, before the advent of detailed waste description criteria. These low or intermediate level wastes are often associated with lacking, limited or conflicting documentation. This poses a challenge when assigning the waste to the proper disposal route as well as in deciding whether the waste needs to undergo sorting and conditioning to fulfil waste acceptance criteria both with regards to safe interim storage and to its ultimate disposal. Actinides, such as isotopes of uranium and plutonium, with their typically long half-lives and decay chains are of special interest in this regard since they may challenge the long-term safety assessment in repositories predicated on mainly shorter half-life radionuclides if undetected. Accurate identification and localisation of actinides is also important from a safeguards perspective, especially since they are generally difficult to detect and localise by established passive means due to their relatively weak radiation emissions, in particular in shielded containments and in the presence of strong radiation fields from other radioactive materials. In this paper we present findings of measurements on shielded containments of long-lived radioactive waste performed at the Studsvik site in Sweden, as well as measurements on a laboratory assembly simulating a grouted waste drum. Similarities and differences between the novel NGET technique and a commercially available gamma imaging system are also briefly discussed.

Dual-biomimetic curved compound-eye camera system for multi-target distance measurement in a large field of view

Biomimetic curved compound-eye cameras (BCCECs) have attracted great attention for their potential applications in a variety of fields such as target recognition, monitor and three-dimensional localization in military due to their unique optical properties such as large field of view (FOV) and small size. In this work, we proposed a multi-target distance measurement method based on a dual-BCCEC system in a large FOV. To guarantee the precise measurement of the distance of multiple targets, a feature point searching and matching algorithm is developed for the dual-BCCEC system to improve the localizing efficiency of common feature points. In addition, a CALibration Tag (CALTag) self-recognition calibration method is also developed to calibrate ommatidia of the BCCEC with a high efficiency. Based on these two methods, the coordinates of multiple targets with clear feature points can be obtained after the distortion correction in sub-images and thus the distances of multiple targets with clear feature points can be achieved simultaneously with a single compound-eye raw image. The experiment results show that the dual-BCCEC system has a high distant measurement accuracy with an error of less than 6.80% for at least ten different targets in the a working distance ranging from 400 to 600 m in a quite large FOV of 98°×98°. The method demonstrated in this work can pave the way for multi-targets tracking in those related areas with high security monitoring requirements.

Three-dimensional Anderson localization of light in materials with fluctuating electric and magnetic properties

Anderson localization of electromagnetic waves, caused by the disorder-induced arrest of wave diffusion, has been experimentally observed in systems with spatially fluctuating permeability, but only in lower dimensions, not in three dimensions. This paper introduces what we believe to be a novel theoretical approach to the Maxwell equations considering both electric and magnetic disorder. It demonstrates that when both the dielectric constant and magnetic permeability fluctuate in space, the spectral range for three-dimensional Anderson localization significantly increases.

Enhancing the data processing speed of a deep-learning-based three-dimensional single molecule localization algorithm (FD-DeepLoc) with a combination of feature compression and pipeline programming

Three-dimensional (3D) single molecule localization microscopy (SMLM) plays an important role in biomedical applications, but its data processing is very complicated. Deep learning is a potential tool to solve this problem. As the state of art 3D super-resolution localization algorithm based on deep learning, FD-DeepLoc algorithm reported recently still has a gap with the expected goal of online image processing, even though it has greatly improved the data processing throughput. In this paper, a new algorithm Lite-FD-DeepLoc is developed on the basis of FD-DeepLoc algorithm to meet the online image processing requirements of 3D SMLM. This new algorithm uses the feature compression method to reduce the parameters of the model, and combines it with pipeline programming to accelerate the inference process of the deep learning model. The simulated data processing results show that the image processing speed of Lite-FD-DeepLoc is about twice as fast as that of FD-DeepLoc with a slight decrease in localization accuracy, which can realize real-time processing of [Formula: see text] pixels size images. The results of biological experimental data processing imply that Lite-FD-DeepLoc can successfully analyze the data based on astigmatism and saddle point engineering, and the global resolution of the reconstructed image is equivalent to or even better than FD-DeepLoc algorithm.

Experimental three-dimensional location and size distribution of rising bubbles in a cylindrical column through light field imaging

Experimental three-dimensional location and size distribution of rising bubbles in a cylindrical column through light field imaging

Combined Acoustic Emission and Ultrasonic Measurements in Cross Laminated Timber-Steel Composite Beam

Combined acoustic emission and ultrasonic measurements were carried out before and during a four-point bending test on a composite floor beam made of cross-laminated timber with length of 11.6 m, width of 1.5 m, and thickness of 0.28 m. One key aspect of the composite design is the formation of the shear-resistant connection between the panel and steel girder. For the measurements a 16-channel system with combined acoustic emission and transmission measurements was used. The signals were digitized with a very high frequency of 10 MHz with amplitude resolution of 16 bits. To monitor acoustic emission activity a network of 16 AE sensors were attached to the surface of the specimen in the central part of the specimen where the highest deflection and load were expected. For this purpose, broadband AE sensors with measurement frequency of up to 200 kHz were used. The ultimate failure of the specimen occurred at a maximum test load of approximately 200 kN. The deflection at this force was about 200 mm. During the tests which lasted about 50 minutes approximately 8,500 AE events were detected. More than 2,600 events were recorded by at least six channels which makes them well suited for three-dimensional localization. Because of the inhomogeneous cross-laminated timber conventional localization using an unique velocity model are not possible. Therefore, a velocity model was experimentally determined by measure the velocity of the elastic waves in all directions. The greatest value was measured in the longitudinal direction along the beam axis. The average value is approximately 4000 m/s. While in the transverse direction the velocity drops down to approximately 2800 m/s. The lowest value was measured in thickness direction of about 1800 m/s.

An efficient three-dimensional node localization using recurrent neural networks in unmanned aerial vehicle-assisted wireless networks: an optimization perspective

PurposeUnlike many existing methods that are primarily focused on two-dimensional localization, this research paper extended the scope to three-dimensional localization. This enhancement is particularly significant for unmanned aerial vehicle (UAV) applications that demand precise altitude information, such as infrastructure inspection and aerial surveillance, thereby broadening the applicability of UAV-assisted wireless networks.Design/methodology/approachThe paper introduced a novel method that employs recurrent neural networks (RNNs) for node localization in three-dimensional space within UAV-assisted wireless networks. It presented an optimization perspective to the node localization problem, aiming to balance localization accuracy with computational efficiency. By formulating the localization task as an optimization challenge, the study proposed strategies to minimize errors while ensuring manageable computational overhead, which are crucial for real-time deployment in dynamic UAV environments.FindingsSimulation results demonstrated significant improvements, including a channel capacity of 99.95%, energy savings of 89.42%, reduced latency by 99.88% and notable data rates for UAV-based communication with an average localization error of 0.8462. Hence, the proposed model can be used to enhance the capacity of UAVs to work effectively in diverse environmental conditions, offering a reliable solution for maintaining connectivity during critical scenarios such as terrestrial environmental crises when traditional infrastructure is unavailable.Originality/valueConventional localization methods in wireless sensor networks (WSNs), such as received signal strength (RSS), often entail manual configuration and are beset by limitations in terms of capacity, scalability and efficiency. It is not considered for 3-D localization. In this paper, machine learning such as multi-layer perceptrons (MLP) and RNN are employed to facilitate the capture of intricate spatial relationships and patterns (3-D), resulting in enhanced localization precision and also improved in channel capacity, energy savings and reduced latency of UAVs for wireless communication.

Efficient control of high-precision three-dimensional atom localization via probe absorption in a five-level phase-coherent atomic system

We propose a new scheme for high-precision three-dimensional (3D) atom localization by observing the spatially modulated absorption of a weak probe field operating in a partially closed-loop dependent five-level atomic system. Different spatial structures of localization patterns are presented by controlling the Rabi frequency, detuning, and field-induced collective phase-coherence with a variety of superposed standing wave field configurations. Our results highlight that 100% detection probability of atom is possible in the present model in many ways with high precision measurement of spatial absorption. It has been shown that, in the presence of standing wave fields, position information of the atom with maximum detection probability can be efficiently controlled by employing the travelling-wave field in the system. In the present work, we note that the maximum detection probability of the atom is attainable with the limit of spatial resolution better than λ/50. The efficacy of the present model is to find its application in atom nanolithography and atom-imaging having importance in quantum information processing.

Three-dimensional grid-free sound source localization method based on deep learning

Sound source localization (SSL) technology is a popular method for identifying the locations of noise sources, which serves as a prerequisite for noise control. Deep learning, as a data-driven tool, shows broad perspectives in the field of SSL with its powerful nonlinear fitting ability. The existing deep learning-based SSL methods only provide a two-dimensional (2D) representation of the sound source location and cannot obtain the specific coordinates of the sound source in three-dimensional (3D) space. Although traditional beamforming methods can be directly generalized to 3D scenes in principle, they suffer from the limitations of insufficient vertical resolution and high computational cost. Therefore, a 3D grid-free SSL method (3DGF) informed by deep learning is suggested in this study to enhance the accuracy and computational efficiency of 3D localization. First, the number of data channels is compressed to respect limited memory resources during the training process. Subsequently, a dense convolutional neural network (DenseNet) model is utilized to obtain the 3D spatial coordinates of the sound source using the processed 3D beamforming map as input. Since the coordinates are continuous and are not constrained by the grid of the beamforming map, the grid-free strategy presents more accurate localization results. Then, the effects of the volume of training data and the compression ratio are analyzed, respectively, in simulation, and the localization performance with different signal-to-noise ratios (SNRs) is also tested. Finally, by comparing 3DGF with DAMAS, both simulation and experimental results demonstrate that 3DGF improves the accuracy and efficacy of 3D localization. Meanwhile, its satisfactory generalization ability and robustness against noise highlight its potential for practical applications.

A modified sampling method for the precise detection of prostate cancer tissues using a three-dimensional stereotaxic location technique.

The rapid and accurate acquisition of prostate cancer pathological tissue is critical to prostate cancer research but has traditionally proven challenging. However, the gradual application of three-dimensional (3D) modeling in medical practice has overcome many of the related limitations. This cohort study aimed to compare the difference between a 3D stereotaxic sampling method and traditional cognitive sampling method to clarify the factors affecting sampling. An analysis of 111 men who received radical prostatectomy for prostate cancer at The First Affiliated Hospital of Soochow University between November 2020 and April 2022 was conducted. The positive rate of the cognitive sampling method and the 3D stereotaxic sampling method and their respective influencing factors, such as age, body mass index (BMI), prostate-specific antigen (PSA), PSA density (PSAD), International Society of Urological Pathology (ISUP) grade, tumor volume, number of positive needles from perineal puncture, clinical T stage, and tumor image location, were compared and analyzed, and a cohort study was conducted. Among the 111 patients, there were 57 cases of cognitive sampling and 54 cases of 3D stereotaxic sampling. In this study, the positive rate of cognitive sampling was 29.82% (17/57,), and the positive rate of 3D stereotaxic sampling was 61.11% (33/54), with the positive rate of 3D stereotaxic sampling being significantly higher than that of cognitive sampling (P=0.001). In cognitive sampling, tumor volume [odds ratio (OR) =1.10; 95% confidence interval (CI): 1.02-1.20], number of positive biopsy cores (OR =1.30; 95% CI: 1.06-1.60), Prostate Imaging Report and Data System (PI-RADS) score (OR =5.54; 95% CI: 1.60-19.12), and clinical T stage (OR =2.36; 95% CI: 1.31-4.25) were identified as influencing factors; in 3D stereotaxic sampling, these influencing factors were eliminated, with ORs of 1.22 (95% CI: 0.78-1.90), 0.88 (95% CI: 0.72-1.09), 1.09 (95% CI: 0.62-1.92), and 1.51 (95% CI: 0.86-2.65), respectively, representing a statistically significant difference (P<0.05). The 3D stereotaxic sampling method can accurately obtain the required prostate cancer tissue from the prostate in vitro within a short time, and the factors affecting the positive rate of sampling can be eliminated.

Assessment of the relationship between the three-dimensional precise location of the mandibular third molar and the volume ratio of the impacted mandibular third molar to the mandibular angle, and the patterns of mandibular angle fracture: A retrospective study

This study aimed to evaluate the relationship between the precise three-dimensional location of the third molar (M3) and mandibular angle fracture (MAF) patterns and to assess the effect of the volume ratio occupied by M3 in the mandibular angle on fracture patterns. The location of M3 was assessed in 218 patients with MAF using computed tomography reconstruction. The bone volume of the mandibular angle and the bone volume occupied by M3 were measured to calculate the volume ratio of M3 to the mandibular angle (M3/MA). MAF patterns were categorized into simple fracture (Type I), displaced fracture (Type II), and comminuted fracture (Type III) based on fracture severity. The results showed that the location of M3 significantly influenced MAF patterns (vertical position: P = .001; horizontal position: P = .002; angulation: P = .027, respectively) and the volume ratio of M3/MA was significantly higher for Type III fracture than Types I and II (P < .001). Regression analysis showed that the horizontal position and angulation of M3 and the volume ratio of M3/MA were the main predictors for comminuted MAF. A larger volume ratio (odds ratio [OR], 1.201; 95% confidence interval [CI], 1.037–1.391; P < .014), Class III position (OR, 7.978; 95% CI, 1.275–49.910; P < .026), and horizontal angulation (OR, 7.212; 95% CI, 1.028–50.581; P < .047) of the M3 were more prone to comminuted MAF than simple fracture. Our findings indicate that the location of M3 significantly affects MAF patterns, and that M3 may weaken the mandibular angle by occupying more bone space, thereby increasing the risk of a comminuted fracture.

Optimization of indoor vehicle ultra-wideband 3D localization using graph attention networks

Ultra-wideband technology is recognized as a low-cost and interference-resistant method for indoor vehicle localization, crucial for advancing intelligent transportation systems. Within this domain, the time difference of arrival algorithm finds extensive application. However, the accuracy of indoor three-dimensional (3D) localization is significantly affected by multipath propagation and non-line-of-sight deviations. To improve localization accuracy, this study proposes an innovative method utilizing the graph attention network framework. This method extracts relevant features and employs attention mechanisms to differentially weight individual nodes; it adeptly captures crucial spatial relationships among nodes, thereby mitigating the effects of multipath propagation and non-line-of-sight deviations, ultimately achieving finer indoor vehicle localization. Experimental results from tests on line-of-sight and non-line-of-sight data sets illustrate the significant improvement in indoor vehicle 3D localization accuracy afforded by the proposed graph attention network–based method. Compared to the commonly used built-in error-state Kalman filter algorithm in commercial ultra-wideband applications and alternative deep learning algorithms, the proposed method achieves error reductions ranging from 60.58% to 84.16% in line-of-sight environments and from 65.59% to 95.21% in non-line-of-sight environments.

Weak localization and magnetoresistance phenomena of hydrogen-annealed WO3 films

We explore the electronic transport properties of hydrogenated epitaxial WO3 films. Ionized hydrogen not only generates an electron carrier but also acts as an impurity center, providing a useful approach to investigate a macroscopic quantum transport phenomenon of weak localization. Temperature dependent-resistivity and anomalous magnetoresistance results are analyzed based on Mott's variable range hopping and weak localization. From the model studies, the phase coherence length is estimated to have a temperature dependence of T −1.6 and is determined to vary from ∼22 nm at 80 K to ∼180 nm at 20 K. The length at 20 K is of a similar order of magnitude of film thickness, which requires a dimensional crossover between three-dimensional and two-dimensional localization.

The Three-Dimensional Relationship of the Axes of the Capitate and Third Metacarpal

We quantified the morphology and angulation of the third metacarpal (MC3) relative to the capitate using three-dimensional computed tomography data to inform surgical procedures such as total wrist arthroplasty and wrist arthrodesis. Specifically, we report the three-dimensional location of the intersections of the long axis of MC3 axis with the capitate cortical surface, the sagittal and coronal angles between the MC3 and capitate axes, and the MC3 shaft angle in the sagittal plane. We tested the hypothesis that these metrics did not differ between women and men. Three-dimensional bone models of the capitate and MC3 were analyzed in 130 subjects (61M and 69F). Long axes of the MC3 and capitate were computed. The intersection of the metacarpal long axis with the cortical surface of the capitate, the angle between the metacarpal-capitate axes, and metacarpal shaft angle were calculated and compared between men and women. The long axis of the MC3 intersected the capitate at two locations on the outer cortical surface of the capitate. The proximal intersection was located near the midportion of the capitate, whereas the distal intersection was typically located within the capitate-MC3 articulation. The angle between the axes of the capitate and MC3 in the sagittal plane was a mean of 15°, ranging from 5° to 23°. The mean sagittal MC3 shaft angle was 166° and ranged from 158° to 173°.There were only subtle differences in these metrics between the sexes. The long axis of the MC3 penetrates the dorsal surface of the capitate about its midportion, but there is notable variation in this location as well as in the angular relationships. Three-dimensional measurements of the relationships between the third metacarpal and the capitate may serve as an important reference for the placement of intramedullary wires, plates, devices, and prosthetics.

Spatial Localization of Transformer Inspection Robot Based on Adaptive Denoising and SCOT-β Generalized Cross-Correlation.

In the detection process of the internal defects of large oil-immersed transformers, due to the huge size of large transformers and metal-enclosed structures, the positional localization of miniature inspection robots inside the transformer faces great difficulties. To address this problem, this paper proposes a three-dimensional positional localization method based on adaptive denoising and the SCOT weighting function with the addition of the exponent β (SCOT-β) generalized cross-correlation for L-type ultrasonic arrays of transformer internal inspection robots. Aiming at the strong noise interference in the field, the original signal is decomposed by an improved Empirical Mode Decomposition (EMD) method, and the optimal center frequency and bandwidth of each mode are adaptively searched. By extracting the modes in the frequency band of the positional localization signal, suppressing the modes in the noise frequency band, and reconstructing the Intrinsic Mode Function (IMF) of the independently selected superior modal components, a signal with a high signal-to-noise ratio is obtained. In addition, for the traditional mutual correlation algorithm with a large delay estimation error at a low signal-to-noise ratio, this paper adopts an improved generalized joint weighting function, SCOT-β, which improves the anti-jamming ability of the generalized mutual correlation method at a low signal-to-noise ratio by adding an exponential function to the denominator term of the SCOT weighting function's generalized cross-correlation. Finally, the accurate positional localization of the transformer internal inspection robot is realized based on the quadratic L-array and search-based maximum likelihood estimation method. Simulation and experimental results show the following: the improved EMD denoising method better improves the signal-to-noise ratio of the positional localization signal with a lower distortion rate; in the transformer test tank, which is 120 cm in length, 100 cm in width, and 100 cm in height, based on the positional localization method in this paper, the average relative positional localization error of the transformer internal inspection robot in three-dimensional space is 2.27%, and the maximum positional localization error is less than 2 cm, which meets the requirements of engineering positional localization.

An explainable variational autoencoder model for three-dimensional acoustic emission source localization in hollow cylindrical structures

An explainable variational autoencoder model for three-dimensional acoustic emission source localization in hollow cylindrical structures

Efficient control of three-dimensional atom localization via probe absorption in a phase-coherent atomic medium

Efficient control of three-dimensional atom localization via probe absorption in a phase-coherent atomic medium

Research on precise identification and localization methods for static small targets based on multimodal data fusion

This investigation introduces a multimodal fusion-based methodology for the precise detection and accurate localization of static small targets against uniform backgrounds. Small targets often present fewer pixels in imagery, which significantly reduces the detectable features and complicates accurate identification and localization. Our approach integrates visual data from CMOS cameras, distance readings from laser rangefinders, and orientation data from angle sensors. The image processing involves non-local means denoising and Canny edge detection to enhance small target detection. We map two-dimensional images to three-dimensional spaces, combining depth and orientation to precisely determine the target’s coordinates, aiding in three-dimensional localization. Empirical results confirm the method’s high accuracy, with a localization error under 2.5%. Such conclusive evidence highlights the approach’s efficacy, presenting a cost-efficient alternative for the nuanced detection and localization of small entities.