(2FAOC)16 is equivalent to
- (195 084)10
- (001011111010 0000 1100)2
- Both both above
- None of these
I’m sure many of you have seen or faced this question on either competitive exams or during interviews of Computer Science, Information Technology (IT), Distributed Systems or other similar domains. The correct term should be (2FA0C)16 and NOT (2FAOC)16 as O (zero) carries no meaning in Hex values.
The full form of FAOC is Formal Aspect Of Computing in computer organization; and FAOC is generally used to represent the fixed number ie a constant or static number. Or you can relate it as 2FA0C of the hex digits (replace 0 and O) as asked during exams.
BTW, the correct equivalent of hex (2FAOC)16 is both
- (001011111010 0000 1100)2 in binary number, and
- (195084)10 in decimal number.
Binary number (001011111010 0000 1100)2 = Decimal number (195084)10 = Hexadecimal number (2FAOC)16.
| Decimal calculation steps for FAOC: (00101111101000001100)₂ = (0 × 2¹⁹) + (0 × 2¹⁸) + (1 × 2¹⁷) + (0 × 2¹⁶) + (1 × 2¹⁵) + (1 × 2¹⁴) + (1 × 2¹³) + (1 × 2¹²) + (1 × 2¹¹) + (0 × 2¹⁰) + (1 × 2⁹) + (0 × 2⁸) + (0 × 2⁷) + (0 × 2⁶) + (0 × 2⁵) + (0 × 2⁴) + (1 × 2³) + (1 × 2²) + (0 × 2¹) + (0 × 2⁰) = (195084)₁₀ |
For those interested ones, FAOC’s involvement can be found here in an IEEE Journal (Reliable Distributed Systems): Rigorous Performance Evaluation of Self-Stabilization Using Probabilistic Model Checking.
Extract from the IEEE Journal that states about FAOC includes (taken from IEEE)
Self-stabilization is a versatile category of fault-tolerance that guarantees system recovery to normal behavior within a finite number of steps, when the state of the system is perturbed by transient faults (or equally, the initial state of the system can be some arbitrary state). The performance of self-stabilizing algorithms is conventionally characterized in the literature by asymptotic computation complexity.
We argue that such characterization of performance is too abstract and does not reflect accurately the realities of deploying a distributed algorithm in practice. Our new metric for characterizing the performance of self-stabilizing algorithms is the expected mean value of recovery time. Our metric has several crucial features.
Firstly, it encodes accurate average case speed of recovery. Secondly, we show that our evaluation method can effectively incorporate several other parameters that are of importance in practice and have no place in asymptotic computation complexity. Examples include the type of distributed scheduler, likelihood of occurrence of faults, the impact of faults on speed of recovery, and network topology. We utilize a deep analysis technique, namely, probabilistic model checking to rigorously compute our proposed metric.
You saved my life. Most of the websites are claiming the binary option as correct, but you have given great explanation.
Thanks