Non-linear radio frequency model identification using a hybrid genetic optimiser for minimal user intervention

2011 ◽  
Vol 5 (15) ◽  
pp. 1880
Author(s):  
J. Hu ◽  
J.Q. Lowry ◽  
K.G. Gard ◽  
M.B. Steer
Keyword(s):  
Radio Frequency ◽  
Model Identification ◽  
Frequency Model ◽  
Non Linear ◽  
User Intervention ◽  
Minimal User Intervention

3DBVSMAPPER: a program for automatically generating bond-valence sum landscapes

2012 ◽  
Vol 45 (5) ◽  
pp. 1054-1056 ◽  
Author(s):  
Matthew Sale ◽  
Maxim Avdeev
Keyword(s):  
Computer Program ◽  
Three Dimensional ◽  
Bond Valence ◽  
Spatial Distributions ◽  
Energy Landscapes ◽  
Bond Valence Sum ◽  
Scripting Language ◽  
Materials Studio ◽  
User Intervention ◽  
Minimal User Intervention

A computer program,3DBVSMAPPER, was developed to generate bond-valence sum maps and bond-valence energy landscapes with minimal user intervention. The program is designed to calculate the spatial distributions of bond-valence values on three-dimensional grids, and to identify infinitely connected isosurfaces in these spatial distributions for a given bond-valence mismatch or energy threshold and extract their volume and surface area characteristics. It is implemented in the Perl scripting language embedded in AccelrysMaterials Studioand has the capacity to process automatically an unlimited number of materials using crystallographic information files as input.

Rotorcraft control response using linearised and non-linear flight dynamic models with different inflow models

2017 ◽  
Vol 121 (1238) ◽  
pp. 553-575 ◽  
Author(s):  
T. Sakthivel ◽  
C. Venkatesan
Keyword(s):  
Dynamic Model ◽  
Dynamic Models ◽  
Model Identification ◽  
Flight Test ◽  
Step Size ◽  
Response Characteristics ◽  
Wide Range ◽  
Non Linear ◽  
Steady Level ◽  
Control Response

ABSTRACTThe aim of the present study is to develop a relatively simple flight dynamic model which should have the ability to analyse trim, stability and response characteristics of a rotorcraft under various manoeuvring conditions. This study further addresses the influence of numerical aspects of perturbation step size in linearised model identification and integration timestep on non-linear model response. In addition, the effects of inflow models on the non-linear response are analysed. A new updated Drees inflow model is proposed in this study and the applicability of this model in rotorcraft flight dynamics is studied. It is noted that the updated Drees inflow model predicts the control response characteristics fairly close to control response characteristics obtained using dynamic inflow for a wide range of flight conditions such as hover, forward flight and recovery from steady level turn. A comparison is shown between flight test data, the control response obtained from the simple flight dynamic model, and the response obtained using a more detailed aeroelastic and flight dynamic model.

scholarly journals A Practical Guide to Resonance Frequency Assessment for Heart Rate Variability Biofeedback

2020 ◽  
Vol 14 ◽  
Author(s):  
Fred Shaffer ◽  
Zachary M. Meehan
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Resonance Frequency ◽  
Muscle Tension ◽  
Initial Assessment ◽  
Cardiorespiratory System ◽  
Frequency Model ◽  
Heart Rate Variability Biofeedback ◽  
Resonance Frequencies ◽  
Non Linear

Heart rate variability (HRV) represents fluctuations in the time intervals between successive heartbeats, which are termed interbeat intervals. HRV is an emergent property of complex cardiac-brain interactions and non-linear autonomic nervous system (ANS) processes. A healthy heart is not a metronome because it exhibits complex non-linear oscillations characterized by mathematical chaos. HRV biofeedback displays both heart rate and frequently, respiration, to individuals who can then adjust their physiology to improve affective, cognitive, and cardiovascular functioning. The central premise of the HRV biofeedback resonance frequency model is that the adult cardiorespiratory system has a fixed resonance frequency. Stimulation at rates near the resonance frequency produces large-amplitude blood pressure oscillations that can increase baroreflex sensitivity over time. The authors explain the rationale for the resonance frequency model and provide detailed instructions on how to monitor and assess the resonance frequency. They caution that patterns of physiological change must be compared across several breathing rates to evaluate candidate resonance frequencies. They describe how to fine-tune the resonance frequency following an initial assessment. Furthermore, the authors critically assess the minimum epochs required to measure key HRV indices, resonance frequency test-retest reliability, and whether rhythmic skeletal muscle tension can replace slow paced breathing in resonance frequency assessment.

A smartphone fluorescence imaging-based mobile biosensing system integrated with a passive fluidic control cartridge for minimal user intervention and high accuracy

Lab on a Chip ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1502-1511 ◽  
Author(s):  
Won-Il Lee ◽  
Younghyeon Park ◽  
Jaemin Park ◽  
Sajal Shrivastava ◽  
Young-Min Son ◽  
...  
Keyword(s):  
Fluorescence Imaging ◽  
High Accuracy ◽  
Fluidic Control ◽  
Biosensing System ◽  
User Intervention ◽  
Minimal User Intervention

A biosensor with minimal user interventions and high accuracy.

Sign in / Sign up

Export Citation Format

Share Document