scholarly journals Optical alignment algorithm using Hadamard transformation

2007 ◽  
Vol 4 (16) ◽  
pp. 504-509
Author(s):  
Ki Seok Kwak ◽  
Jin Bae Park ◽  
Tae Sung Yoon ◽  
Jae Won Kho
Keyword(s):  
Alignment Algorithm ◽  
Hadamard Transformation ◽  
Optical Alignment

scholarly journals Precise Motion Control of a Piezoelectric Microgripper for Microspectrometer Assembly

2009 ◽  
Author(s):  
Kanty Rabenorosoa ◽  
Ce´dric Cle´vy ◽  
Philippe Lutz ◽  
Aditya N. Das ◽  
Rakesh Murthy ◽  
...  
Keyword(s):  
Material Science ◽  
Degrees Of Freedom ◽  
Alignment Algorithm ◽  
Modeling And Control ◽  
Rotational Degrees Of Freedom ◽  
Optical Alignment ◽  
Hybrid Silicon ◽  
The Fourier Transform ◽  
Wavelength Detection ◽  
And Control

The Fourier Transform (FTIR) microspectrometer discussed in this paper is an example of a complex Micro-Opto-Electro-Mechanical System (MOEMS) configured as an optical bench on a chip. It is an important benchmark application for microtechnology due to increased demands for the use of miniature wavelength detection instruments in bio, nano and material science. This device can be manufactured using automated microassembly and precision alignment of hybrid silicon and glass components, and in particular, of a micro-beamsplitter cube along 3 rotational degrees of freedom. In this paper, a piezoelectric microgripper with four degrees of freedom was attached to a precision robot in order to enhance its dexterity and align the beamsplitter to arcsecond angular tolerance. The modeling and control of the microgripper, and the alignment algorithm utilizing a novel spot-Jacobian servoing technique are discussed. Experimental results obtained during joint on-going work in Texas and in France are presented, demonstrating the advantage of using the microgripper for optical alignment of the microspectrometer.

scholarly journals A high-resolution solar spectrum 2000 Å–2200 Å

1970 ◽  
Vol 36 ◽  
pp. 271-273
Author(s):  
B. B. Jones ◽  
B. C. Boland ◽  
R. Wilson ◽  
S. T. F. Engstrom
Keyword(s):  
High Resolution ◽  
Solar Spectrum ◽  
Solar Image ◽  
Roll Control ◽  
North East ◽  
The North ◽  
Alignment System ◽  
Optical Alignment ◽  
South Axis ◽  
System Operating

A high-resolution solar spectrum in the range 2000–2200 Å was obtained in a recent flight of a sunpointing Skylark rocket. This was launched at 04.21 hr UT on April 22, 1969 from Woomera and reached an apogee of 178 km. An optical alignment system operating on the main vehicle pointing system gave a net stabilisation of ±3 arc sec in the position of the solar image relative to the spectrograph slit. The slit, of length 1.0 mm, was set in the north-east quadrant parallel to and 5 arc min from the north/south axis, its lower edge being 1 arc min from the equator. The roll control of ±2.5° was provided entirely by the standard Elliott Bros. type of vehicle stabilisation.

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.

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