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Alona [7]
4 years ago
8

Design a 10-to-4 encoder with inputs in the l-out-of-10 code and outputs in a code like normal BCD except that input lines 8 and

9 are encoded into "E" and " F", respectively.

Engineering
1 answer:
zvonat [6]4 years ago
6 0

Answer:

See image attached.

Explanation:

This device features priority encoding of the inputs

to ensure that only the highest order data line is en-

coded. Nine input lines are encoded to a four line

BCD output. The implied decimal zero condition re-

quires no input condition as zero is encoded when

all nine datalinesare athigh logic level. Alldata input

and outputs are active at the low logic level. All in-

puts are equipped with protection circuits against

static discharge and transient excess voltage.

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1. How many pieces of 12-1/2" long wood can be cut from a piece of
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B. If you do 39-3/4 divided by 12-1/2 you get 3.18
8 0
3 years ago
A biotechnology company produced 225 doses of somatropin, including 11 which were defective. Quality control test 15 samples at
Radda [10]

Answer:

  • <u>0.59</u>

Explanation:

The <em>batch</em> is <em>rejected</em> if any of the <em>random samples are found defective</em>, or, what is the same, it will be accepted only if all 15 samples are good.

The probability that none be defective is the same probability that all the samples are good. Thus, start to calculate the probability that the batch is accepted.

The probability that the first sample is good is 214 /225, because there are 225 - 11 = 214 good samples in 225 doses.

The probability that the second samples is good too is 213/224, because there is 1 less good sample, in the 224 remaining samples.

By the same process, you conclude that the consecutive probabilities of selecting a good sample are: 212/223, 211/222, 210/221, . . . up to 199/211.

The joint probability of all the samples are good is the product of each probability:

\frac{214}{225}\cdot\frac{213}{224}\cdot\frac{212}{223}\cdot\frac{211}{222}\cdot\frac{210}{221}\cdot\frac{209}{220}\cdot\frac{208}{219}\cdot\frac{207}{218}\cdot\frac{206}{217}\cdot\frac{205}{216}\cdot\frac{204}{215}\cdot\frac{203}{214}\cdot\frac{202}{213}\cdot\frac{201}{212}\cdot\frac{200}{211}\cdot\frac{199}{210}

The result is: 0.41278 ≈ 0.41

The conclusion is that the probability that all the samples are good and the batch is accepted is 0.41.

Therefore, <em>the probability that the batch is rejected</em> is 1 - 0.41 = 0.59.

4 0
4 years ago
CO is the abbreviation for
garri49 [273]

Answer:

CO is usually the abbreviation for company

Explanation:

8 0
3 years ago
Read 2 more answers
For laminar flow over a hot flat plate, the local heat transfer coefficient decreases with distance because (select all that are
kotegsom [21]

Answer:

B. The thickness of the heated region near the plate is increasing.

Explanation:

First we know that, a boundary layer is the layer of fluid in the immediate vicinity of a bounding surface where the effects of viscosity are significant. The fluid is often slower due to the effects of viscosity. Advection i.e the transfer of heat by the flow of liquid becomes less since the flow is slower, thereby the local heat transfer coefficient decreases.

From law of conduction, we observe that heat transfer rate will decrease based on a smaller rate of temperature, the thickness therefore increases while the local heat transfer coefficient decreases with distance.

3 0
4 years ago
The term supervised learning refers to the fact that we give the learning algorithm a data set, in which the "right answers" wer
kenny6666 [7]

Answer: output value

Explanation: Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs.

Supervised learning infers a function from labeled training data consisting of a set of training examples.

In supervised learning, each example consists of a pair of an input object (a vector) and a desired output value (the supervisory signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. An optimal scenario will allow for the algorithm to correctly determine the class labels for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a "reasonable" way

A wide range of supervised learning algorithms are available, each having its own strengths and weaknesses.

You should now that, there is no single learning algorithm that works better than the other on all supervised learning problems

5 0
3 years ago
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