Results The lab experiment was done in two parts, one with the NAND, NOR, XOR and Hex Inverters and the other with a 7483 full adder gate, both will verify the truth table when two input bits and a carry are added together. The circuits were built by examining the 1 bits through a K-Map to create a Boolean expression for the sum and carry. The Boolean expression for the sum was A⊕B⊕C and the carry as AB+BC_in+AC_in. From these two expressions, we notice that we must use two exclusive-ORs gates in the sum inputs for A, B, and C. For the sum, we have to use NOR and NAND (the only available gates from the lab manual). Both sum and carry are added in the same circuit and we will use the pin diagrams to make the proper connections. The circuit diagram …show more content…
This will allow us to add two binary number together once it is built. There was an issue with the carry output LED turning on or off. The original schematics were modified by adding hex inverters after the output of the two NAND gate and last NOR gate. After fixing the issue, we successfully proven the truth table to the corresponding inputs and outputs. If the input were 1, 0, and 1 (A, B, and Cin), then the A and B input are added together along with the Cin.: 1 + 0 + 1 (carry) = 10. For this binary addition, the carry out will be 1 and sum out will be 0. As proven in the pictures for both circuit experiments, the sum LED light is OFF and the carry out LED is ON. Aer example, if the input were 1, 1, and 1 (A, B, and Cin), the result will be 1 + 1+ 1 (carry) = 11, the sum out will be 1 and carry is 1; therefore the sum and carry LED light are both ON. This proves that we prove our theoretical results of the truth table with our experimental …show more content…
In the demonstration, we only add a 2-bit (a0,b0) with another 2-bit binary (a1,b1) together. In the first picture on the last section of the Figures/Graphs, we added 01 and 00 together and the result was 01; the first LED on the right turns ON and the second LED is stilled turned OFF. In the last two pictures, 01 + 00 = 10 (the second LED turned on and the first one was OFF) and 11 + 11 = 110; the LED indicate this long results, the carry out was 1 and the sum (s1, s0) was 1 and 0. This circuit allowed us to understand the idea of full adders ICs which can be used in electronics like computers, tablets, etc. to add binaries together to perform specific functions and desired
Such as, 2 2 2 , , r s s r r r s r r r L L R L R M L L M L PM L R Where rd s i u , , and r : are respectively, the stator voltage, stator current, rotor flux and rotor speed. The indices d, q indicates a direct and quadrate index according to the usual d-axis and q-axis components in the synchronous rotating frame. M L L R R r s r s , , , , and : are respectively, stator and rotor resistance, stator and rotor inductance, mutual inductance and total leakage factor. P, J, TL and f: are respectively, the number of pole pairs, the rotor inertia, the load torque and the friction coefficient.
III SYNTHESIS AND SIMULATIONS RESULTS The simulation and synthesis work is finally done by the xilinix and modelsim respectively. Figure 5:synthesis results of Fault FFT. The figures intimate the fault injected FFT,which is checked by the manual error injected via all diferent possibilities by using RTL scripting. Eventhough the soft error is added in the FFT the error detector code 100% detect the errors and corrector correct the errors.
(a) 3Mbps / 150Kbpa =3 X 1024 / 150 = 3072 / 150 =20.48 20 Users can be supported 150Kbps dedicated. (b)
Figure shows the intersection of line joining the camera center and image points ${\bf x}$ and ${\bf x'}$ which will be the 3D point ${\bf X}$.\\ \end{figure} The ‘gold standard’ reconstruction algorithm minimizes the sum of squared errors between the measured and predicted image positions of the 3D point in all views in which it is visible, i.e.\\ \begin{equation} {\bf X=\textrm{arg min} \sum_{i} ||x_i-\hat{x_i}(P_i,X)||^2} \end{equation} Where ${\bf x_i}$ and ${\bf \hat{x_i}(P_i,X)}$ are the measured and predicted image positions in view $i$ under the assumption that image coordinate measurement noise is Gaussian-distributed, this approach gives the maximum likelihood solution for ${\bf X}$. Hartley and Sturm [3] describe a non-iterative
determine each pixel belongs to background or foreground. Wis the weights between the pattern and summationneurons, which are used to point out with which a pattern belongs to the background or foreground. They areupdated when each new value of a pixel at a certain position received by implementing the following function:Wt+1ib =fc(1−βNpn)Wib+MAtβ!(37)Wt+1i f=(1−Wt+1ib)(38)whereWtibis the weight between theith pattern neuron and the background summation neuron at timet,βisthe learning rate,Npnis the number of the pattern neurons of BNN,fcis the following function:fc(x)1,x>1x,x≤1(39)MAtindicates the neuron with the maximum response (activation potential) at frame t, according to:MAt1,f or neuron with maximum response0,otherwise(40)Function
a). Based on the observation, we assume that the distance between two stations is 0.375 KM Mean time to send = propogation time + transmission time = 375m. + 1000bits 200 x 106 m/sec. 10 000 000 bps. = 102 μsec. b).
When it concerns the security of your home or business, sound locks are the first line of defense. However, it is an unfortunate fact that there are many people that are not particularly well-educated about locks. As a result, they may not be aware of the answers to a couple of common questions concerning these components of their security systems. After learning these two answers, you will be better able to minimize some of the problems that your locks might encounter.
Qus1 (A)- 1 2 3 5 4 6 F F T F F T F T F T T T T F T F T T T T F F F T Tautology of statement "Changed in the table so as not to get similarity" (B)- 1 2 3 4 5 Tautology Qus−2: It is clear that, P(x) is true for all values as, P(1) is True, P(2) is true. Thus, the truth value is (((True)))). It is clear that P(0.5) is True, but P(x) is False for x =
This technique is called the Riemann Sum. It is named after German Mathematician Bernhard Riemann. Let us take first these summation notations: The following summation formulas are very useful in evaluating area of a region: E (1) ∑_(i=1)^n▒〖 i= (n(n+1))/2〗 E (2) ∑_(i=1)^n▒〖i²= (n(n+1)(2n+1))/6〗 E (3) ∑_(i=1)^n▒〖i³= (n² (n+1)²)/4〗 E (4) ∑_(i=1)^n▒〖k=nk〗 k = constant E (5) ∑_(i=1)^n▒〖ka〗_i = k∑_(i=1)^n▒a_i E (6) ∑_(i=1)^n▒〖(a_i+ b_i)〗=
Add (+) # 2) subtract (-) # 3) Multiply (*) # 4) Divide (/) def getFirstNumber(self): #Gets first number from user self.number1 = self.__checkNumber("Please enter the first number: ") def getSecondNumber(self): #Gets second number from user self.number2 =
Sharing identical truth values does not necessarily make the expressions identical in-themselves/in form.
This contradiction to Boolean logic spawned Lotfi A.
Multiplication: multiplied by, times, increased by a factor of etc. Division: per, out
the second section we give basic definition of Fuzzy Associative memories. In the third section introduction of FAM is given. In the fourth section the problem we analyse the problem with