I designed basic clipper circuit using single 3.3v Zener.When i simulate this on proteus and while checking output by keeping knob on AC of respective chanel and output shown is 2v positive and 2v negative from refrence ground(obviously wrong).When i change knob to DC it shows correct output of 3.3 positive and 0.7 negative.I dont know why it is giving correct vaalue on DC as my Input is AC.
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I have a program in matlab which uses power and delay profile for modelling the channel. For example
power profile =[0 -8 -10 -15]
delay profile =[0 117 183 333]
However I have to use another pathloss equation which is
38.7 +16.7 log10(d)+18.2log_10(fc)+Xs+Ad
where d is distance between trans-recviever, fc is carrier frequency and Xs is the shadowing loss, Ad is some attenuation factor that I want to add. Should I multiple this expression with the power profile
I am new to MP programming and I am working on a project with codevision program, which requires to:
Connect potentiometer at A0.
Generate a PWM signal with an equivalent average DC voltage to the measured value of the voltage of the potentiometer.
The measured voltage from the DAC is displayed on line 1 of lcd (16*2).
Choose a suitable RC circuit to get a pure DC voltage.
Connect the generated PWM signal to the ICP1 and calculate its duty cycle and display it on line 2 off the screen.
So the screen should be like:
Pot1 = 0.000 V
Duty Cycle = ..%
I made the first line but I don't know how to implement timer1 into this requirement.
Also timer 1 has ICRH and ICRL and I am confused which of them should be used.
At first blush this presumably means -
(1) looking only at lower IR frequencies,
(2) select a IR frequency cut-off for low frequency buckets of the u/v FFT grid
(3) Once we have that, derive the power distribution - squares of amplitudes - for that IR range of frequency buckets the camera supports.
(4) Fit that distribution against the Rayleigh-Jones classical Black Box radiation formula:
(https://en.wikipedia.org/wiki/Rayleigh%E2%80%93Jeans_law#Other_forms_of_Rayleigh%E2%80%93Jeans_law)
(5) Assign a Temperature of 'best fit'.
The units for B(ν,T) are Power per unit frequency per unit surface area at equilibrium Temperature
Of course, this leaves many details out, such as (6) cancelling background, etc, but one could perhaps use the opposite facing camera to assist in that. Where buckets do not straddle the temperature of interest, (7) use a one-sided distribution to derive an inferred Gaussian curve to fit the Rayleigh-Jeans curve at that derived central frequency ν, for measured temperature T.
Finally (8) check if this procedure can consistently detect a high vs low surface temperature (9) check if it can consistently identify a 'fever' temperature (say, 101 Fahrenheit / 38 Celcius) pointing at a forehead.
If all that can be done, (10) Voila! a body fever detector
So those who are capable can fill us in on whether this is possible to do so for eventual posting at an app store as a free Covid19 safe body temperature app? I have a strong sense there's quite a few out there who can verify this in a week or two!
It appears that the analog signal assumed in (1) and (2) are not available in the Android digital Camera2 interface.
Android RAW image stream, that is uncompressed YUV, is already encoded Y green monochrome, and U,V are blue and red shifts from zero for converting green monochrome to color.
The original analog frequency / energy signal is not immediately accessible. So adaptation is not possible (yet).
I have the task to simulate a camera with a full well capacity of 10.000 Photons per sensor element
in numpy. My first Idea was to do it like that:
camera = np.random.normal(0.0,1/10000,np.shape(img))
Imgwithnoise= img+camera
but it hardly shows an effect.
Has someone an idea how to do it?
From what I interpret from your question, if each physical pixel of the sensor has a 10,000 photon limit, this points to the brightest a digital pixel can be on your image. Similarly, 0 incident photons make the darkest pixels of the image.
You have to create a map from the physical sensor to the digital image. For the sake of simplicity, let's say we work with a grayscale image.
Your first task is to fix the colour bit-depth of the image. That is to say, is your image an 8-bit colour image? (Which usually is the case) If so, the brightest pixel has a brightness value = 255 (= 28 - 1, for 8 bits.) The darkest pixel is always chosen to have a value 0.
So you'd have to map from the range 0 --> 10,000 (sensor) to 0 --> 255 (image). The most natural idea would be to do a linear map (i.e. every pixel of the image is obtained by the same multiplicative factor from every pixel of the sensor), but to correctly interpret (according to the human eye) the brightness produced by n incident photons, often different transfer functions are used.
A transfer function in a simplified version is just a mathematical function doing this map - logarithmic TFs are quite common.
Also, since it seems like you're generating noise, it is unwise and conceptually wrong to add camera itself to the image img. What you should do, is fix a noise threshold first - this can correspond to the maximum number of photons that can affect a pixel reading as the maximum noise value. Then you generate random numbers (according to some distribution, if so required) in the range 0 --> noise_threshold. Finally, you use the map created earlier to add this noise to the image array.
Hope this helps and is in tune with what you wish to do. Cheers!
I have finite set of points in simple 2-dimensional Euclidean space ( I know coordinates of these points).
Let's say I pick point A(x1,y1) and B(x2,y2) in 2-dimensional Euclidean space. So I have a line AB. I need to find coordinates of such point C (actually I need to find if point C is in my set of points) , that length of AB = AC and lines AB and AC form right angle. (Actually two points should satisfy these conditions: on one side of the line AB and on other side)
This should be done in constant time.
You basically just want to rotate point B around point A by 90 degrees, right? If so, then first you translate A to the origin, then rotate, then translate back.
C = [-(y2-y1)+x1,x2-x1+y1]; // rotate +90 deg
C = [y2-y1+x1,-(x2-x1)+y1]; // rotate -90 deg