Heart Disease and its true medicine

Less than a century ago heart disease was an extremely rare disease. However, today it is the cause of death of more people in the world than all other deadly diseases taken together. The most encompassing researches and studies on heart health have indicated that lack of happiness and gratification is by far the biggest risk factor resulting in heart problems. Since happiness is amongst the principal expressions of love, only those medicines that are love-based can truly and completely heal the heart and protect one from disease and ageing. If fear is the motivating factor that compels someone to go for a particular treatment or initiate major changes in life style or diet the chances of disease prevention or recovery are minimized. The current approaches for achieving heart’s health, free from any life threatening condition, are primarily symptom-orientated and do not deal with the underlying causes.

Cited from [http://www.lifepositive.com/body/body-holistic/heart/heart.asp]

Some lesser-known truths about programming

Some lesser-known truths about programming

Introduction of a paper

[Background and the focus domain]

[Literature review and evaluation]

[In this article, what you are proposing]

[Paper structure]

The domain should be introduced in a reverse triangle way. That is, from bigger domain to smaller one, and finally point you are working on.

This section discusses the results and conclusions of previously published studies, to help explain why the current study is of scientific interest[cited from http://classweb.gmu.edu/biologyresources/writingguide/Introduction.htm]

WiMax and LTE

coming from Father tech

WiMax Computer guys WiFi

LTE Cellphone guys GPRS

Parameter Estimation in System Identification

1. System Identification = Model structure selection or define + Model parameter estimation

2. Parameter estimation is a mapping: Z----> sigma*

3. Essence of Model is its prediction ability.

4. Two criteria:

Scalar norm or function of errors

Data are not related to Predictive error.

pem and lsqcurvefit

they are really sensitive to initial condition and parameter values.

this really haunting on me several days.

Something Help Kalman

Hinfinity Filter is a minmax filter, which minimise worsts estimation. Meanwhile, Kalman minimise average estimation error.

Kalman Smoother is useful, when you want to estimate the state trajectory after obtaining the whole signal serials.

Steady State Kalman filter have constant K and P.

State Space Realization of IIR

cited from [https://ccrma.stanford.edu/~jos/fp/State_Space_Realization.html]

IIR filters have an extensively used matrix representation called state space form (or ``state space realizations''). They are especially convenient for representing filters with multiple inputs andmultiple outputs (MIMO filters). An order digital filter with inputs and outputs can be written in state-space form as follows:

			(F.4)

where is the length state vector at discrete time , is a vector of inputs, and the output vector. is the state transition matrix, and it determines thedynamics of the system (its poles, or resonant modes).

aij

I saw "Introduction to linear dynamical systems" on YouTube, It is much better than what my teacher taught me, that is a little bothered me.

matrix is about linear algebra.

y=Ax

1) y is measurement, x is something we do not know.

2) y is output, x is input.

3) x is signal, y is transformation output.

aij is is weight on xi to produce yj, ith row concerns with ith output, jth column concerns with jth input.

1) aij = 0, then yj does not depend on xi

2) some aij is bigger in i row, y is more depend on xi

3) some aij is bigger in j column, xi more affect yj

4) lower triangular are zeros, yi only depends on x1....xi

5) diagnal, yi only depends on xi.

6) sparsity means relationship between x and y is not so complex.

System Identification

yeah, i am here, jumped too fast!

some commands of matlab:

lsqnonlin--------non-linear least square optimization

lsqcurvefit------use lsqnonlin to fit a curve

pem------------prediction error model

idgrey----------grey box model

idnlgrey--------grey box for non linear model.

ss --------------state space model

sim------------model simulation

bode-----------bode graph

step------------step response

impulse---------impulse response

c2d-------------convert continuous model to discrete model

system identification method:

state space

poles and zeros

frequency method i.e., bode graph

PID control

optimization method:

newton

gauss-newton

steepest gradient

levenberg marquardt