scholarly journals Direct Signal Detection Without Data‐Aided: A MIMO Functional Network Approach

10.5772/63213 ◽  
2016 ◽  
Author(s):  
Xiukai Ruan ◽  
Yanhua Tan ◽  
Yuxing Dai ◽  
Guihua Cui ◽  
Xiaojing Shi ◽  
...  
Keyword(s):  
Signal Detection ◽  
Functional Network ◽  
Network Approach ◽  
Direct Signal

A Concept of a Passive Radar with Quadrature Microwave Phase Discriminators

2012 ◽  
Vol 19 (1) ◽  
pp. 95-104
Author(s):  
Adam Rutkowski
Keyword(s):  
Signal Detection ◽  
Reference Signal ◽  
Digital Signal ◽  
Ambiguity Function ◽  
Function Evaluation ◽  
Passive Radar ◽  
Direct Signal ◽  
Signal Processors ◽  
Signals Of Opportunity ◽  
Cross Ambiguity Function

A Concept of a Passive Radar with Quadrature Microwave Phase Discriminators Passive radar does not have its own emitter. It uses so-called signals of opportunity emitted by non-cooperative illuminators. During the detection of reflected signals, a direct signal from a non-cooperative emitter is used as the reference signal. Detection of electromagnetic echoes is, in present day radars, performed by finding the maximum of the cross ambiguity function. This function is based on the multiplication of the received signal and the reference signal. Detection of echoes by means of a quadrature microwave phase discriminator QMPD was proposed in the work as an alternative solution for ambiguity function evaluation. This discriminator carries out vectorial summing of the received and the reference signals. The summing operations in QMPD are carried out with the aid of microwave elements and without the use of expensive digital signal processors. Definitions of the phase and phase difference of the so-called simple signals and noise signals were described. A proposal of a passive radar equipped with several independent quadrature microwave phase discriminators was presented. Ideas of algorithms of object detection and of the distance-to-object estimation designed for this radar have been also sketched.

Direct sequence estimation: a functional network approach

2016 ◽  
Vol 30 (3) ◽  
pp. 977-985
Author(s):  
Xiukai Ruan ◽  
Yanhua Tan ◽  
Guihua Cui ◽  
Wenbin Liu ◽  
Xiaojing Shi ◽  
...  
Keyword(s):  
Functional Network ◽  
Direct Sequence ◽  
Network Approach ◽  
Sequence Estimation

The Collaborative Network Approach: a model for advancing patient-centric research for Castleman disease and other rare diseases

2019 ◽  
Vol 3 (1) ◽  
pp. 97-105
Author(s):  
Mary Zuccato ◽  
Dustin Shilling ◽  
David C. Fajgenbaum
Keyword(s):  
Rare Diseases ◽  
Treatment Options ◽  
Castleman Disease ◽  
High Impact ◽  
Collaborative Network ◽  
Network Approach ◽  
Grant Applications ◽  
Research Questions ◽  
Novel Strategy ◽  
Patient Centric

Abstract There are ∼7000 rare diseases affecting 30 000 000 individuals in the U.S.A. 95% of these rare diseases do not have a single Food and Drug Administration-approved therapy. Relatively, limited progress has been made to develop new or repurpose existing therapies for these disorders, in part because traditional funding models are not as effective when applied to rare diseases. Due to the suboptimal research infrastructure and treatment options for Castleman disease, the Castleman Disease Collaborative Network (CDCN), founded in 2012, spearheaded a novel strategy for advancing biomedical research, the ‘Collaborative Network Approach’. At its heart, the Collaborative Network Approach leverages and integrates the entire community of stakeholders — patients, physicians and researchers — to identify and prioritize high-impact research questions. It then recruits the most qualified researchers to conduct these studies. In parallel, patients are empowered to fight back by supporting research through fundraising and providing their biospecimens and clinical data. This approach democratizes research, allowing the entire community to identify the most clinically relevant and pressing questions; any idea can be translated into a study rather than limiting research to the ideas proposed by researchers in grant applications. Preliminary results from the CDCN and other organizations that have followed its Collaborative Network Approach suggest that this model is generalizable across rare diseases.

Can patients with obsessive-compulsive disorder discriminate between percepts and mental images? A signal detection analysis.

1994 ◽  
Vol 103 (3) ◽  
pp. 445-454 ◽  
Author(s):  
Halle D. Brown ◽  
Stephen M. Kosslyn ◽  
Hans C. Breiter ◽  
Lee Baer ◽  
Michael A. Jenike
Keyword(s):  
Signal Detection ◽  
Obsessive Compulsive Disorder ◽  
Mental Images ◽  
Obsessive Compulsive ◽  
Compulsive Disorder ◽  
Detection Analysis ◽  
Signal Detection Analysis

Sample Size and the Detection of Correlation--A Signal Detection Account: Comment on Kareev (2000) and Juslin and Olsson (2005).

2005 ◽  
Vol 112 (1) ◽  
pp. 268-279 ◽  
Author(s):  
Richard B. Anderson ◽  
Michael E. Doherty ◽  
Neil D. Berg ◽  
Jeff C. Friedrich
Keyword(s):  
Signal Detection ◽  
Sample Size

So, Whatever Happened to Signal Detection Theory?

1995 ◽  
Vol 40 (10) ◽  
pp. 972-972
Author(s):  
Jerome R. Busemeyer
Keyword(s):  
Signal Detection ◽  
Signal Detection Theory ◽  
Detection Theory

Do logistic regression and signal detection identify different subgroups at risk? Implications for the design of tailored interventions.

2001 ◽  
Vol 6 (1) ◽  
pp. 35-48 ◽  
Author(s):  
Michaela Kiernan ◽  
Helena C. Kraemer ◽  
Marilyn A. Winkleby ◽  
Abby C. King ◽  
C. Barr Taylor
Keyword(s):  
At Risk ◽  
Logistic Regression ◽  
Signal Detection ◽  
Tailored Interventions
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