Tomographic alignment algorithm for an extremely large three-mirror telescope: invisible modes

2010 ◽  
Vol 49 (33) ◽  
pp. 6395 ◽  
Author(s):  
Piotr Piatrou ◽  
Gary Chanan
Keyword(s):  
Alignment Algorithm ◽  
Mirror Telescope

Investigation of the Deformation of Mirror Telescope Surface under Symmetric Location of Controlled Supporting Rods

1999 ◽  
Vol 31 (7-9) ◽  
pp. 22-27
Author(s):  
Yuriy P. Ladikov-Roev ◽  
Victor I. Panchuk ◽  
Georgiy A. Cheshko ◽  
Ludmila I. Samoilenko
Keyword(s):  
Mirror Telescope

scholarly journals Liquid-Mirror Telescope Surveys

1998 ◽  
Vol 11 (1) ◽  
pp. 464-467
Author(s):  
P. Hickson
Keyword(s):  
Integration Time ◽  
Spectral Energy ◽  
Energy Distributions ◽  
Ccd Cameras ◽  
Photometric Redshifts ◽  
Rotating Liquid ◽  
Liquid Mirrors ◽  
Under Construction ◽  
Optical Telescopes ◽  
Mirror Telescope

Abstract Recent advances in the technology of rotating liquid-mirrors now make feasible the construction of large optical telescopes for dedicated survey programs. Two three-metre-class astronomical telescopes have been built and asix-metre telescope is under construction. These instruments observe in zenith-pointing mode, using drift-scanning CCD cameras to record continuous imaging of a strip of sky typically 20 arcmin wide. This enables them to observe of order 100 square degrees of sky with an integration time of a few minutes per night. Data can be co-added from night to night in order to increase the depth of the survey. Liquid-mirror telescopes are particularly wellsuited to surveys using broad or intermediate bandwidth filters to obtain photometric redshifts and spectral energy distributions for faint galaxies and quasars.

scholarly journals GIANA allows computationally-efficient TCR clustering and multi-disease repertoire classification by isometric transformation

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Hongyi Zhang ◽  
Xiaowei Zhan ◽  
Bo Li
Keyword(s):  
T Cell ◽  
T Cell Receptor ◽  
Large Scale ◽  
Cell Receptor ◽  
Alignment Algorithm ◽  
Computationally Efficient ◽  
Antigen Specificity ◽  
Non Invasive ◽  
Isometric Transformation ◽  
Specific Receptors

AbstractSimilarity in T-cell receptor (TCR) sequences implies shared antigen specificity between receptors, and could be used to discover novel therapeutic targets. However, existing methods that cluster T-cell receptor sequences by similarity are computationally inefficient, making them impractical to use on the ever-expanding datasets of the immune repertoire. Here, we developed GIANA (Geometric Isometry-based TCR AligNment Algorithm) a computationally efficient tool for this task that provides the same level of clustering specificity as TCRdist at 600 times its speed, and without sacrificing accuracy. GIANA also allows the rapid query of large reference cohorts within minutes. Using GIANA to cluster large-scale TCR datasets provides candidate disease-specific receptors, and provides a new solution to repertoire classification. Querying unseen TCR-seq samples against an existing reference differentiates samples from patients across various cohorts associated with cancer, infectious and autoimmune disease. Our results demonstrate how GIANA could be used as the basis for a TCR-based non-invasive multi-disease diagnostic platform.

scholarly journals Strong-Weak Feature Alignment for 3D Object Detection

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1205
Author(s):  
Zhiyu Wang ◽  
Li Wang ◽  
Bin Dai
Keyword(s):  
Object Detection ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Feature Representation ◽  
Alignment Algorithm ◽  
3D Object ◽  
3D Point Clouds ◽  
Object Feature ◽  
3D Object Detection ◽  
Feature Alignment

Object detection in 3D point clouds is still a challenging task in autonomous driving. Due to the inherent occlusion and density changes of the point cloud, the data distribution of the same object will change dramatically. Especially, the incomplete data with sparsity or occlusion can not represent the complete characteristics of the object. In this paper, we proposed a novel strong–weak feature alignment algorithm between complete and incomplete objects for 3D object detection, which explores the correlations within the data. It is an end-to-end adaptive network that does not require additional data and can be easily applied to other object detection networks. Through a complete object feature extractor, we achieve a robust feature representation of the object. It serves as a guarding feature to help the incomplete object feature generator to generate effective features. The strong–weak feature alignment algorithm reduces the gap between different states of the same object and enhances the ability to represent the incomplete object. The proposed adaptation framework is validated on the KITTI object benchmark and gets about 6% improvement in detection average precision on 3D moderate difficulty compared to the basic model. The results show that our adaptation method improves the detection performance of incomplete 3D objects.

scholarly journals Traffic Type Recognition Method for Unknown Protocol—Applying Fuzzy Inference

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 36
Author(s):  
Sang-Won Kim ◽  
Kee-Cheon Kim
Keyword(s):  
Fuzzy Inference ◽  
Static Field ◽  
Theoretical Background ◽  
Alignment Algorithm ◽  
Traffic Classification ◽  
Recognition Method ◽  
Inference System ◽  
Traffic Characteristics ◽  
Sequence Alignment Algorithm ◽  
Low Performance

In this paper, we propose a system that can recognize traffic types without prior knowledge of static features such as protocol header information by combining protocol analysis based on an ecological sequence alignment algorithm in a bioinformatics and fuzzy inference system. The algorithm proposed in this paper obtained up to a 91% level of performance at a similar level to several existing algorithms in experiments using datasets containing various types of traffic. In addition, it showed an excellent accuracy of 82.5% or more even under severe conditions that lowered the amount of data to a level of at least 40% or only included data in the middle of the traffic. This shows that the problem of dependence on initial data that frequently occurs in existing machine learning and deep learning-based traffic classification algorithms does not appear in the proposed algorithm. Furthermore, based on the ability to directly extract traffic characteristics without being dependent on static field values, it has secured the ability to respond with a small number of data by taking advantage of the flexibility of the membership function of the fuzzy inference engine. Through this, the applicability to low-power and low-performance environments such as IoT networks was confirmed. In this paper, we describe in detail the theoretical background for constructing such an algorithm and relevant experiments and considerations for actual verification.

An Improved Initial Alignment Method of Strap-Down Inertial Navigation System on a Swaying Base

2013 ◽  
Vol 415 ◽  
pp. 143-148
Author(s):  
Li Hua Zhu ◽  
Xiang Hong Cheng
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Alignment Algorithm ◽  
Alignment Method ◽  
Initial Alignment ◽  
Vehicle Simulation ◽  
Robust Property ◽  
Simulation Results ◽  
Lever Arm Effect ◽  
The Impact

The design of an improved alignment method of SINS on a swaying base is presented in this paper. FIR filter is taken to decrease the impact caused by the lever arm effect. And the system also encompasses the online estimation of gyroscopes’ drift with Kalman filter in order to do the compensation, and the inertial freezing alignment algorithm which helps to resolve the attitude matrix with respect to its fast and robust property to provide the mathematical platform for the vehicle. Simulation results show that the proposed method is efficient for the initial alignment of the swaying base navigation system.

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