Electrical engineering in the context of "Cryptography"

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👉 Electrical engineering in the context of Cryptography

Cryptography, or cryptology (from Ancient Greek: κρυπτός, romanizedkryptós "hidden, secret"; and γράφειν graphein, "to write", or -λογία -logia, "study", respectively), is the practice and study of techniques for secure communication in the presence of adversarial behavior. More generally, cryptography is about constructing and analyzing protocols that prevent third parties or the public from reading private messages. Modern cryptography exists at the intersection of the disciplines of mathematics, computer science, information security, electrical engineering, digital signal processing, physics, and others. Core concepts related to information security (data confidentiality, data integrity, authentication and non-repudiation) are also central to cryptography. Practical applications of cryptography include electronic commerce, chip-based payment cards, digital currencies, computer passwords and military communications.

Cryptography prior to the modern age was effectively synonymous with encryption, converting readable information (plaintext) to unintelligible nonsense text (ciphertext), which can only be read by reversing the process (decryption). The sender of an encrypted (coded) message shares the decryption (decoding) technique only with the intended recipients to preclude access from adversaries. The cryptography literature often uses the names "Alice" (or "A") for the sender, "Bob" (or "B") for the intended recipient, and "Eve" (or "E") for the eavesdropping adversary. Since the development of rotor cipher machines in World War I and the advent of computers in World War II, cryptography methods have become increasingly complex and their applications more varied.

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Electrical engineering in the context of Engineering

Engineering is the practice of using natural science, mathematics, and the engineering design process to solve problems within technology, increase efficiency and productivity, and improve systems. The traditional disciplines of engineering are civil, mechanical, electrical, and chemical. The academic discipline of engineering encompasses a broad range of more specialized subfields, and each can have a more specific emphasis for applications of mathematics and science. In turn, modern engineering practice spans multiple fields of engineering, which include designing and improving infrastructure, machinery, vehicles, electronics, materials, and energy systems. For related terms, see glossary of engineering.

As a human endeavor, engineering has existed since ancient times, starting with the six classic simple machines. Examples of large-scale engineering projects from antiquity include impressive structures like the pyramids, elegant temples such as the Parthenon, and water conveyances like hulled watercraft, canals, and the Roman aqueduct. Early machines were powered by humans and animals, then later by wind. Machines of war were invented for siegecraft. In Europe, the scientific and industrial revolutions advanced engineering into a scientific profession and resulted in continuing technological improvements. The steam engine provided much greater power than animals, leading to mechanical propulsion for ships and railways. Further scientific advances resulted in the application of engineering to electrical, chemical, and aerospace requirements, plus the use of new materials for greater efficiencies.

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Electrical engineering in the context of Complex analysis

Complex analysis, traditionally known as the theory of functions of a complex variable, is the branch of mathematical analysis that investigates functions of a complex variable of complex numbers. It is helpful in many branches of mathematics, including real analysis, algebraic geometry, number theory, analytic combinatorics, and applied mathematics, as well as in physics, including the branches of hydrodynamics, thermodynamics, quantum mechanics, and twistor theory. By extension, use of complex analysis also has applications in engineering fields such as nuclear, aerospace, mechanical and electrical engineering.

At first glance, complex analysis is the study of holomorphic functions that are the differentiable functions of a complex variable. By contrast with the real case, a holomorphic functions is always infinitely differentiable and equal to the sum of its Taylor series in some neighborhood of each point of its domain.This makes methods and results of complex analysis significantly different from that of real analysis. In particular, contrarily, with the real case, the domain of every holomorphic function can be uniquely extended to almost the whole complex plane. This implies that the study of real analytic functions needs often the power of complex analysis. This is, in particular, the case in analytic combinatorics.

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Electrical engineering in the context of Engineering physics

Engineering physics (EP), sometimes engineering science, is the field of study combining pure science disciplines (such as physics, mathematics, chemistry) and engineering disciplines (computer, nuclear, electrical, aerospace, medical, materials, mechanical, etc.).

In many languages, the term technical physics is also used.It has been used since 1861, after being introduced by the German physics teacher J. Frick [de] in his publications.

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Electrical engineering in the context of Electronics

Electronics is a scientific and engineering discipline that studies and applies the principles of physics to design, create, and operate devices that manipulate electrons and other electrically charged particles. It is a subfield of physics and electrical engineering which uses active devices such as transistors, diodes, and integrated circuits to control and amplify the flow of electric current and to convert it from one form to another, such as from alternating current (AC) to direct current (DC) or from analog signals to digital signals. Electronics is often contrasted with electrical power engineering, which focuses on generation, transmission, and distribution of electric power rather than signal processing or device level control.

Electronic devices have significantly influenced the development of many aspects of modern society, such as telecommunications, entertainment, education, health care, industry, and security. The main driving force behind the advancement of electronics is the semiconductor industry, which continually produces ever-more sophisticated electronic devices and circuits in response to global demand. The semiconductor industry is one of the global economy's largest and most profitable industries, with annual revenues exceeding $481 billion in 2018. The electronics industry also encompasses other branches that rely on electronic devices and systems, such as e-commerce, which generated over $29 trillion in online sales in 2017. Practical electronic systems commonly combine analog and digital techniques, using analog front ends with digital processing.

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Electrical engineering in the context of Signal processing

Signal processing is an electrical engineering subfield that focuses on analyzing, modifying and synthesizing signals, such as sound, images, potential fields, seismic signals, altimetry processing, and scientific measurements. Signal processing techniques are used to optimize transmissions, digital storage efficiency, correcting distorted signals, improve subjective video quality, and to detect or pinpoint components of interest in a measured signal.

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Electrical engineering in the context of Information theory

Information theory is the mathematical study of the quantification, storage, and communication of information. The field was established and formalized by Claude Shannon in the 1940s, though early contributions were made in the 1920s through the works of Harry Nyquist and Ralph Hartley. It is at the intersection of electronic engineering, mathematics, statistics, computer science, neurobiology, physics, and electrical engineering.

A key measure in information theory is entropy. Entropy quantifies the amount of uncertainty involved in the value of a random variable or the outcome of a random process. For example, identifying the outcome of a fair coin flip (which has two equally likely outcomes) provides less information (lower entropy, less uncertainty) than identifying the outcome from a roll of a die (which has six equally likely outcomes). Some other important measures in information theory are mutual information, channel capacity, error exponents, and relative entropy. Important sub-fields of information theory include source coding, algorithmic complexity theory, algorithmic information theory and information-theoretic security.

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Electrical engineering in the context of Channel capacity

Channel capacity, in electrical engineering, computer science, and information theory, is the theoretical maximum rate at which information can be reliably transmitted over a communication channel.

Following the terms of the noisy-channel coding theorem, the channel capacity of a given channel is the highest information rate (in units of information per unit time) that can be achieved with arbitrarily small error probability.

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