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Edge Computing Architecture

Edge Computing Architecture refers to a distributed computing paradigm that brings computation and data storage closer to the location where it is needed, rather than relying on a central data center. This approach significantly reduces latency, improves response times, and optimizes bandwidth usage by processing data locally on devices or edge servers. Key components of edge computing include:

  • Devices: IoT sensors, smart devices, and mobile phones that generate data.
  • Edge Nodes: Local servers or gateways that aggregate, process, and analyze the data from devices before sending it to the cloud.
  • Cloud Services: Centralized storage and processing capabilities that handle complex computations and long-term data analytics.

By implementing an edge computing architecture, organizations can enhance real-time decision-making capabilities while ensuring efficient data management and reduced operational costs.

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Beta Function Integral

The Beta function integral is a special function in mathematics, defined for two positive real numbers xxx and yyy as follows:

B(x,y)=∫01tx−1(1−t)y−1 dtB(x, y) = \int_0^1 t^{x-1} (1-t)^{y-1} \, dtB(x,y)=∫01​tx−1(1−t)y−1dt

This integral converges for x>0x > 0x>0 and y>0y > 0y>0. The Beta function is closely related to the Gamma function, with the relationship given by:

B(x,y)=Γ(x)Γ(y)Γ(x+y)B(x, y) = \frac{\Gamma(x) \Gamma(y)}{\Gamma(x+y)}B(x,y)=Γ(x+y)Γ(x)Γ(y)​

where Γ(n)\Gamma(n)Γ(n) is defined as:

Γ(n)=∫0∞tn−1e−t dt\Gamma(n) = \int_0^\infty t^{n-1} e^{-t} \, dtΓ(n)=∫0∞​tn−1e−tdt

The Beta function often appears in probability and statistics, particularly in the context of the Beta distribution. Its properties make it useful in various applications, including combinatorial problems and the evaluation of integrals.

General Equilibrium

General Equilibrium refers to a state in economic theory where supply and demand are balanced across all markets in an economy simultaneously. In this framework, the prices of goods and services adjust so that the quantity supplied equals the quantity demanded in every market. This concept is essential for understanding how various sectors of the economy interact with each other.

One of the key models used to analyze general equilibrium is the Arrow-Debreu model, which demonstrates how competitive equilibrium can exist under certain assumptions, such as perfect information and complete markets. Mathematically, we can express the equilibrium conditions as:

∑i=1nDi(p)=∑i=1nSi(p)\sum_{i=1}^{n} D_i(p) = \sum_{i=1}^{n} S_i(p)i=1∑n​Di​(p)=i=1∑n​Si​(p)

where Di(p)D_i(p)Di​(p) represents the demand for good iii at price ppp and Si(p)S_i(p)Si​(p) represents the supply of good iii at price ppp. General equilibrium analysis helps economists understand the interdependencies within an economy and the effects of policy changes or external shocks on overall economic stability.

Graphene Bandgap Engineering

Graphene, a single layer of carbon atoms arranged in a two-dimensional honeycomb lattice, is renowned for its exceptional electrical and thermal conductivity. However, it inherently exhibits a zero bandgap, which limits its application in semiconductor devices. Bandgap engineering refers to the techniques used to modify the electronic properties of graphene, thereby enabling the creation of a bandgap. This can be achieved through various methods, including:

  • Chemical Doping: Introducing foreign atoms into the graphene lattice to alter its electronic structure.
  • Strain Engineering: Applying mechanical strain to the material, which can induce changes in its electronic properties.
  • Quantum Dot Integration: Incorporating quantum dots into graphene to create localized states that can open a bandgap.

By effectively creating a bandgap, researchers can enhance graphene's suitability for applications in transistors, photodetectors, and other electronic devices, enabling the development of next-generation technologies.

Lead-Lag Compensator

A Lead-Lag Compensator is a control system component that combines both lead and lag compensation strategies to improve the performance of a system. The lead part of the compensator helps to increase the system's phase margin, thereby enhancing its stability and transient response by introducing a positive phase shift at higher frequencies. Conversely, the lag part provides negative phase shift at lower frequencies, which can help to reduce steady-state errors and improve tracking of reference inputs.

Mathematically, a lead-lag compensator can be represented by the transfer function:

C(s)=K(s+z)(s+p)⋅(s+z1)(s+p1)C(s) = K \frac{(s + z)}{(s + p)} \cdot \frac{(s + z_1)}{(s + p_1)}C(s)=K(s+p)(s+z)​⋅(s+p1​)(s+z1​)​

where:

  • KKK is the gain,
  • zzz and ppp are the zero and pole of the lead part, respectively,
  • z1z_1z1​ and p1p_1p1​ are the zero and pole of the lag part, respectively.

By carefully selecting these parameters, engineers can tailor the compensator to meet specific performance criteria, such as improving rise time, settling time, and reducing overshoot in the system response.

Weierstrass Preparation Theorem

The Weierstrass Preparation Theorem is a fundamental result in complex analysis and algebraic geometry that provides a way to study holomorphic functions near a point where they have a zero. Specifically, it states that for a holomorphic function f(z)f(z)f(z) defined in a neighborhood of a point z0z_0z0​ where f(z0)=0f(z_0) = 0f(z0​)=0, we can write f(z)f(z)f(z) in the form:

f(z)=(z−z0)kg(z)f(z) = (z - z_0)^k g(z)f(z)=(z−z0​)kg(z)

where kkk is the order of the zero at z0z_0z0​ and g(z)g(z)g(z) is a holomorphic function that does not vanish at z0z_0z0​. This decomposition is particularly useful because it allows us to isolate the behavior of f(z)f(z)f(z) around its zeros and analyze it more easily. Moreover, g(z)g(z)g(z) can be expressed as a power series, ensuring that we can study the local properties of the function without losing generality. The theorem is instrumental in various areas, including the study of singularities, local rings, and deformation theory.

Green’S Function

A Green's function is a powerful mathematical tool used to solve inhomogeneous differential equations subject to specific boundary conditions. It acts as the response of a linear system to a point source, effectively allowing us to express the solution of a differential equation as an integral involving the Green's function and the source term. Mathematically, if we consider a linear differential operator LLL, the Green's function G(x,s)G(x, s)G(x,s) satisfies the equation:

LG(x,s)=δ(x−s)L G(x, s) = \delta(x - s)LG(x,s)=δ(x−s)

where δ\deltaδ is the Dirac delta function. The solution u(x)u(x)u(x) to the inhomogeneous equation Lu(x)=f(x)L u(x) = f(x)Lu(x)=f(x) can then be expressed as:

u(x)=∫G(x,s)f(s) dsu(x) = \int G(x, s) f(s) \, dsu(x)=∫G(x,s)f(s)ds

This framework is widely utilized in fields such as physics, engineering, and applied mathematics, particularly in the analysis of wave propagation, heat conduction, and potential theory. The versatility of Green's functions lies in their ability to simplify complex problems into more manageable forms by leveraging the properties of linearity and superposition.