scholarly journals Localization precision in chromatic multifocal imaging

2021 ◽  
Author(s):  
Muhammad Junaid Amin ◽  
Sabine Petry ◽  
Joshua Shaevitz ◽  
Haw Yang
Keyword(s):  
Localization Precision

Improvement on localization precision and positioning rate in Grid-Scan algorithm

2013 ◽  
Vol 32 (12) ◽  
pp. 3521-3524
Author(s):  
Mu-dong LI ◽  
Wei XIONG ◽  
Qing LIANG
Keyword(s):  
Localization Precision

scholarly journals MINSTED fluorescence localization and nanoscopy

2021 ◽  
Author(s):  
Michael Weber ◽  
Marcel Leutenegger ◽  
Stefan Stoldt ◽  
Stefan Jakobs ◽  
Tiberiu S. Mihaila ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Super Resolution ◽  
Diffraction Limit ◽  
Stimulated Emission ◽  
Far Field ◽  
Inner Membrane ◽  
Mitochondrial Inner Membrane ◽  
Molecular Scale ◽  
Localization Precision ◽  
Super Resolution Microscopy

AbstractWe introduce MINSTED, a fluorophore localization and super-resolution microscopy concept based on stimulated emission depletion (STED) that provides spatial precision and resolution down to the molecular scale. In MINSTED, the intensity minimum of the STED doughnut, and hence the point of minimal STED, serves as a movable reference coordinate for fluorophore localization. As the STED rate, the background and the required number of fluorescence detections are low compared with most other STED microscopy and localization methods, MINSTED entails substantially less fluorophore bleaching. In our implementation, 200–1,000 detections per fluorophore provide a localization precision of 1–3 nm in standard deviation, which in conjunction with independent single fluorophore switching translates to a ~100-fold improvement in far-field microscopy resolution over the diffraction limit. The performance of MINSTED nanoscopy is demonstrated by imaging the distribution of Mic60 proteins in the mitochondrial inner membrane of human cells.

scholarly journals Three-dimensional total-internal reflection fluorescence nanoscopy with nanometric axial resolution by photometric localization of single molecules

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Alan M. Szalai ◽  
Bruno Siarry ◽  
Jerónimo Lukin ◽  
David J. Williamson ◽  
Nicolás Unsain ◽  
...  
Keyword(s):  
Single Molecule ◽  
Total Internal Reflection ◽  
Single Molecules ◽  
Fluorescence Microscope ◽  
Internal Reflection ◽  
Total Internal Reflection Fluorescence ◽  
Axial Position ◽  
Localization Microscopy ◽  
Localization Precision ◽  
Single Molecule Localization Microscopy

AbstractSingle-molecule localization microscopy enables far-field imaging with lateral resolution in the range of 10 to 20 nanometres, exploiting the fact that the centre position of a single-molecule’s image can be determined with much higher accuracy than the size of that image itself. However, attaining the same level of resolution in the axial (third) dimension remains challenging. Here, we present Supercritical Illumination Microscopy Photometric z-Localization with Enhanced Resolution (SIMPLER), a photometric method to decode the axial position of single molecules in a total internal reflection fluorescence microscope. SIMPLER requires no hardware modification whatsoever to a conventional total internal reflection fluorescence microscope and complements any 2D single-molecule localization microscopy method to deliver 3D images with nearly isotropic nanometric resolution. Performance examples include SIMPLER-direct stochastic optical reconstruction microscopy images of the nuclear pore complex with sub-20 nm axial localization precision and visualization of microtubule cross-sections through SIMPLER-DNA points accumulation for imaging in nanoscale topography with sub-10 nm axial localization precision.

scholarly journals Localization Algorithm Based on Iterative Centroid Estimation for Wireless Sensor Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Rui Jiang ◽  
Xin Wang ◽  
Li Zhang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Anchor Node ◽  
Centroid Estimation ◽  
Anchor Nodes ◽  
Localization Precision ◽  
Unknown Node ◽  
Received Signal Strength Indication

According to the application of range-free localization technology for wireless sensor networks (WSNs), an improved localization algorithm based on iterative centroid estimation is proposed in this paper. With this methodology, the centroid coordinate of the space enclosed by connected anchor nodes and the received signal strength indication (RSSI) between the unknown node and the centroid are calculated. Then, the centroid is used as a virtual anchor node. It is proven that there is at least one connected anchor node whose distance from the unknown node must be farther than the virtual anchor node. Hence, in order to reduce the space enclosed by connected anchor nodes and improve the location precision, the anchor node with the weakest RSSI is replaced by this virtual anchor node. By applying this procedure repeatedly, the localization algorithm can achieve a good accuracy. Observing from the simulation results, the proposed algorithm has strong robustness and can achieve an ideal performance of localization precision and coverage.

scholarly journals Model Free Localization with Deep Neural Architectures by Means of an Underwater WSN

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3530
Author(s):  
Juan Parras ◽  
Santiago Zazo ◽  
Iván A. Pérez-Álvarez ◽  
José Luis Sanz González
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Acoustic Signals ◽  
Model Free ◽  
Underwater Environment ◽  
The Neural Network ◽  
Anchor Nodes ◽  
Localization Precision ◽  
Significant Effort ◽  
Localization Systems

In recent years, there has been a significant effort towards developing localization systems in the underwater medium, with current methods relying on anchor nodes, explicitly modeling the underwater channel or cooperation from the target. Lately, there has also been some work on using the approximation capabilities of Deep Neural Networks in order to address this problem. In this work, we study how the localization precision of using Deep Neural Networks is affected by the variability of the channel, the noise level at the receiver, the number of neurons of the neural network and the utilization of the power or the covariance of the received acoustic signals. Our study shows that using deep neural networks is a valid approach when the channel variability is low, which opens the door to further research in such localization methods for the underwater environment.

scholarly journals A Two-Branch Network for Weakly Supervised Object Localization

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 955
Author(s):  
Chang Sun ◽  
Yibo Ai ◽  
Sheng Wang ◽  
Weidong Zhang
Keyword(s):  
Ground Truth ◽  
Object Localization ◽  
Feature Extracting ◽  
Multi Scale ◽  
Main Challenge ◽  
Branch Model ◽  
Localization Precision ◽  
Object Parts ◽  
Weakly Supervised ◽  
Scale Detection

Weakly supervised object localization (WSOL) has attracted intense interest in computer vision for instance level annotations. As a hot research topic, a number of existing works concentrated on utilizing convolutional neural network (CNN)-based methods, which are powerful in extracting and representing features. The main challenge in CNN-based WSOL methods is to obtain features covering the entire target objects, not only the most discriminative object parts. To overcome this challenge and to improve the detection performance of feature extracting related WSOL methods, a CNN-based two-branch model was presented in this paper to locate objects using supervised learning. Our method contained two branches, including a detection branch and a self-attention branch. During the training process, the two branches interacted with each other by regarding the segmentation mask from the other branch as the pseudo ground truth labels of itself. Our model was able to focus on capturing the information of all the object parts due to the self-attention mechanism. Additionally, we embedded multi-scale detection into our two-branch method to output two-scale features. We evaluated our two-branch network on the CUB-200-2011 and VOC2007 datasets. The pointing localization, intersection over union (IoU) localization, and correct localization precision (CorLoc) results demonstrated competitive performance with other state-of-the-art methods in WSOL.

Sign in / Sign up

Export Citation Format

Share Document