Robust distributed detection using low power acoustic sensors

2005 ◽  
Author(s):  
Brian P. Flanagan ◽  
Kenneth W. Parker
Keyword(s):  
Low Power ◽  
Distributed Detection ◽  
Acoustic Sensors

Less Power, Greater Conflict

2018 ◽  
Vol 49 (1) ◽  
pp. 47-62 ◽  
Author(s):  
Petra C. Schmid
Keyword(s):  
Low Power ◽  
High Power ◽  
Goal Pursuit ◽  
Goal Conflict ◽  
Personal Goals ◽  
Multiple Goals ◽  
Objective Performance ◽  
The Future ◽  
The Way

Abstract. Power facilitates goal pursuit, but how does power affect the way people respond to conflict between their multiple goals? Our results showed that higher trait power was associated with reduced experience of conflict in scenarios describing multiple goals (Study 1) and between personal goals (Study 2). Moreover, manipulated low power increased individuals’ experience of goal conflict relative to high power and a control condition (Studies 3 and 4), with the consequence that they planned to invest less into the pursuit of their goals in the future. With its focus on multiple goals and individuals’ experiences during goal pursuit rather than objective performance, the present research uses new angles to examine power effects on goal pursuit.

An interdependence account of sexism and power: Men’s hostile sexism, biased perceptions of low power, and relationship aggression.

2019 ◽  
Vol 117 (2) ◽  
pp. 338-363 ◽  
Author(s):  
Emily J. Cross ◽  
Nickola C. Overall ◽  
Rachel S. T. Low ◽  
James K. McNulty
Keyword(s):  
Low Power ◽  
Hostile Sexism ◽  
Relationship Aggression

Power mad [low-power design]

2004 ◽  
Vol 18 (3) ◽  
pp. 37
Author(s):  
J. Frenkil
Keyword(s):  
Low Power ◽  
Low Power Design

A Strategy for Analysing Multiple Risk Factors with Application to Cervical Pain Syndrome

1989 ◽  
Vol 28 (01) ◽  
pp. 14-19 ◽  
Author(s):  
J. F. Dartigues ◽  
Ph. Peytour ◽  
E. Puymirat ◽  
P. Henry ◽  
M. Gagnon ◽  
...  
Keyword(s):  
Risk Factors ◽  
Low Power ◽  
Head Trauma ◽  
Pain Syndrome ◽  
Psychological Status ◽  
Confounding Factors ◽  
Cervical Pain ◽  
Extreme Form ◽  
Explanatory Variables ◽  
Multiple Risk Factors

Abstract:When studying the possible effects of several factors in a given disease, two major problems arise: (1) confounding, and (2) multiplicity of tests. Frequently, in order to cope with the problem of confounding factors, models with multiple explanatory variables are used. However, the correlation structure of the variables may be such that the corresponding tests have low power: in its extreme form this situation is coined by the term “multicollinearity”. As the problem of multiplicity is still relevant in these models, the interpretation of results is, in most cases, very hazardous. We propose a strategy - based on a tree structure of the variables - which provides a guide to the interpretation and controls the risk of erroneously rejecting null hypotheses. The strategy was applied to a study of cervical pain syndrome involving 990 subjects and 17 variables. Age, sex, head trauma, posture at work and psychological status were all found to be important risk factors.

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