نتایج جستجو برای: تخمین جهت ورود doa

تعداد نتایج: 194716  

Journal: :Vision Research 2011
P. A. Williams J. E. Morgan M. Votruba

Dominant optic atrophy (DOA) is the most common inherited optic neuropathy affecting one in every 12,000 people. It presents with bilateral visual loss, central visual fields defects, colour vision disturbance and optic disc pallor. OPA1 has been identified as the responsible gene and its locus mapped to chromosome 3q28-q29. Mutations in this gene are responsible for the clinical phenotype in o...

2016
Theddeus O.H. Prasetyono Puri A. Lestari

BACKGROUND One-per-mil tumescent solution, which contains 0.2% lidocaine with 1:1,000,000 epinephrine, has been reported to be clinically effective for hand surgery under local anesthesia. However, it was lacking in its basic pharmacokinetics profile in regard to the onset of action (OOA) and duration of action (DOA). METHODS A randomized, double-blind study was conducted on 12 volunteers who...

2013
Weijian Si Liangtian Wan Lutao Liu Zuoxi Tian

The pattern of each element in conformal array has a different direction for the curvature of conformal carrier, which results in polarization diversity of conformal array antenna. Polarization parameters of incident signals are considered in snapshot data model in order to describe the polarization diversity of conformal array antenna. It is required that the polarization parameters and direct...

Journal: :JCM 2013
Xuejun Mao Hanhuai Pan

In array signal processing, direction of arrival (DOA) estimation has typically play a key role. Its task is to find the directions impinging on an array antenna to increase the performance of the received signal. How to let DOA estimation methods are applicable to most environments, has become the key of technique implementation. The previous traditional algorithms can get superior performance...

2002
Thushara D. Abhayapala Hemant Bhatta

A novel method for coherent broadband direction of arrival (DOA) estimation is introduced based on physics of signal propagation. This technique does not require any preliminary knowledge of DOA angles nor the number of sources to be estimated. As an illustration, two simulation examples covering single and multi-group scenarios are presented.

1997
Kay Iversen Mike Wolf Detlef Mämpel H. Schubert Christian Schmidt

In this paper we present a novel infrared communication system for large cells and low user mobility. The proposed transmitter technique allows nearly perfect direction-of-arrival (DoA) estimation in time division multiple access (TDMA) environments only with three photodiodes. Using a photodiode array we show that known algorithms for DoA-estimation can be applied in spatial division multiple ...

2016
Mohamed M. M. Omar Darwish A. E. Mohamed Soha M. Haikel

The smart antenna systems combine antenna arrays with digital signal processing (DSP) algorithms. In a smart antenna system a specialized signal processor computes the direction of arrival (DOA) of a user and also adds the strength of the signals from each antenna element together to form a beam towards the direction as computed by DOA. If additional users join in

2005
Yasuo Kokubun Ryuji Kohno Koichi Ichige Minseok Kim

Smart antennas basically attempt to enhance the desired signal power and suppress the interferers by beamforming toward the DOA (direction-of-arrival) of the desired signal and nullsteering in the case of the interferences’ DOAs in line-of-sight (LOS) situation. This paper considers a smart antenna system for cellular base station application. The signals at the base station are received throug...

2017
Ashish Patwari

Array signal processing has attracted the interest of the scientific community for the past several decades. An Array of sensor elements (be it microphones, hydrophones, antenna elements, piezoelectric sensors) achieves better performance than a single element would. Antenna arrays are made up of antenna elements which can be arranged in a variety of configurations (with respect to the geometry...

Journal: :CoRR 2016
Zai Yang Jian Li Petre Stoica Lihua Xie

3 Sparse Representation and DOA estimation 7 3.1 Sparse Representation and Compressed Sensing . . . . . . . . . . . . . . . . . . . . . . . . 7 3.1.1 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3.1.2 Convex Relaxation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3.1.3 `q Optimization . . . . . . . . . . . . . . . . . . . ....

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