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Basis function

In functional analysis, a basis function refers to a member of a set of functions that spans a vector space of functions, enabling any element in that space to be expressed uniquely as a linear combination of the basis functions.^[1] These sets are fundamental in infinite-dimensional spaces, such as Hilbert or Banach spaces, where they facilitate the decomposition and analysis of functions analogous to finite-dimensional vector bases.^[2] Key types of bases distinguish themselves by their topological properties and convergence requirements. A Schauder basis for a Banach space X is a sequence \{e_n\}_{n=1}^\infty \subset X such that every x \in X admits a unique representation x = \sum_{n=1}^\infty c_n e_n, where the series converges in the norm of X.^[2] In contrast, a Hamel basis (or algebraic basis) consists of a linearly independent set that spans the space via finite linear combinations only, often uncountable and less practical for infinite-dimensional settings due to non-constructive existence via the axiom of choice.^[1] For Hilbert spaces equipped with an inner product, orthonormal bases—complete sets of functions \{\alpha_j\} satisfying (\alpha_j, \alpha_k) = \delta_{jk}—allow Parseval's identity, where the coefficients are inner products, simplifying expansions like Fourier series.^[1] Basis functions play a central role in approximation theory, where they enable the representation of arbitrary functions by linear combinations of simpler, predefined forms, such as polynomials or trigonometric functions, to achieve uniform or least-squares approximations.^[3] This is crucial for numerical methods, including spectral approximations in partial differential equations, where the choice of basis (e.g., global vs. local support) affects convergence rates and computational efficiency.^[3] In broader applications, such as signal processing and quantum mechanics, orthonormal bases underpin transforms like the Fourier or wavelet expansions, providing tools for decomposition, compression, and analysis of complex data.^[1]

Mathematical Foundations

Vector Spaces and Linear Independence

A vector space over a field, such as the real numbers \mathbb{R} or complex numbers \mathbb{C}, is a nonempty set V equipped with operations of addition and scalar multiplication that satisfy specific axioms. These include closure under addition and scalar multiplication, associativity and commutativity of addition, existence of a zero vector, existence of additive inverses, distributivity of scalar multiplication over vector addition and field addition, compatibility of scalar multiplication with field multiplication, and the existence of a multiplicative identity in the field.^[4]/06:_Vector_Spaces/6.01:_Examples_and_Basic_Properties) A set of vectors \{v_1, \dots, v_n\} in a vector space V is linearly independent if the only solution to the equation a_1 v_1 + \dots + a_n v_n = 0, where a_1, \dots, a_n are scalars from the field, is a_1 = \dots = a_n = 0./02:_Vectors_matrices_and_linear_combinations/2.04:_Linear_independence) This condition ensures that no vector in the set can be expressed as a nontrivial linear combination of the others. A spanning set S for V is a subset such that every vector in V can be written as a finite linear combination of elements from S./09:_Vector_Spaces/9.02:_Spanning_Sets) A basis for a vector space V is a set that is both linearly independent and spans V, allowing every vector in V to be uniquely expressed as a linear combination of basis elements. The dimension of V, denoted \dim V, is the number of vectors in any basis for V, which is well-defined and finite for finite-dimensional spaces. In finite-dimensional examples, such as \mathbb{R}^n, the standard basis consists of the vectors e_1 = (1, 0, \dots, 0), e_2 = (0, 1, \dots, 0), up to e_n = (0, \dots, 0, 1), which are linearly independent and span \mathbb{R}^n./02:_Systems_of_Linear_Equations-_Geometry/2.07:_Basis_and_Dimension)/11:_Basis_and_Dimension/11.01:Bases_in(Ren)) In infinite-dimensional vector spaces, such as certain function spaces, a Hamel basis (also called an algebraic basis) exists but is typically non-constructive and requires the axiom of choice for its existence; every vector is a finite linear combination of basis elements, though such bases are rarely used in practice due to their pathological properties.^[5]^[6]

Function Spaces and Norms

Function spaces are infinite-dimensional vector spaces consisting of functions satisfying certain properties, equipped with algebraic operations of pointwise addition and scalar multiplication. A prominent example is the space C[0,1], which comprises all continuous real-valued functions on the closed interval [0,1].^[7] Another fundamental class is the L^p spaces for $1 \leq p < \infty, defined as equivalence classes of measurable functions f on a measure space (such as [0,1] with Lebesgue measure) where \int |f|^p \, d\mu < \infty, with functions considered equivalent if they differ only on a set of measure zero.^[8] Hilbert spaces form a special category of function spaces that are complete inner product spaces, enabling the study of orthogonality and projections. The space L^2[0,1] exemplifies a Hilbert space, consisting of square-integrable functions with the inner product \langle f, g \rangle = \int_0^1 f(x) \overline{g(x)} \, dx, which induces the norm \|f\|_2 = \sqrt{\int_0^1 |f(x)|^2 \, dx}.^[9] Completeness in these spaces ensures that Cauchy sequences converge to an element within the space, a property essential for the convergence of infinite series expansions, such as those involving basis functions.^[9] Norms on function spaces quantify the size of functions and define convergence topologies, distinguishing between pointwise and integral behaviors. In C[0,1], the uniform norm \|f\|_\infty = \sup_{x \in [0,1]} |f(x)| measures the maximum deviation, promoting uniform (pointwise) convergence, whereas the L^p norm \|f\|_p = \left( \int_0^1 |f(x)|^p \, dx \right)^{1/p} emphasizes integral averages, leading to convergence in mean.^[7]^[8] This distinction is critical, as sequences converging in L^p may not converge uniformly, affecting the approximation properties of bases. Banach spaces are complete normed linear spaces, providing a framework for rigorous analysis in infinite dimensions; C[0,1] under the uniform norm is a canonical Banach space, where every Cauchy sequence of continuous functions converges uniformly to a continuous limit.^[10] Unlike finite-dimensional spaces like \mathbb{R}^n, which admit finite bases spanning the entire space via linear combinations, infinite-dimensional Banach spaces lack finite bases and require more sophisticated constructs like Schauder bases—countable sequences allowing unique infinite series representations with norm convergence—to span the space effectively.^[11] This shift addresses the limitations of algebraic (Hamel) bases, which rely on finite combinations and become unmanageable in infinite dimensions.^[11]

Definition and Properties

Formal Definition

In the context of function spaces, which are vector spaces consisting of functions equipped with a suitable topology, a basis function refers to a member of a family \{\phi_n\}_{n \in I} that forms a basis for the space. Specifically, every function f in the space can be uniquely represented as f = \sum_{n \in I} c_n \phi_n, where the sum converges in the topology of the space, and the coefficients c_n are scalars determined uniquely by f.^[12] This representation generalizes the notion of a basis from finite-dimensional vector spaces to infinite-dimensional settings like those encountered in analysis. In finite-dimensional function spaces, such as the space of polynomials of degree at most d, the expansion is a finite sum yielding exact equality f = \sum_{n=1}^d c_n \phi_n. However, in infinite-dimensional spaces, the sum is typically infinite, requiring convergence in a norm or other topology; for instance, the partial sums satisfy \|f - \sum_{n=1}^N c_n \phi_n\| \to 0 as N \to \infty, where \|\cdot\| denotes the norm of the space. The uniqueness of the coefficients c_n follows from the linear independence of the \{\phi_n\}, ensuring that no nontrivial linear combination vanishes.^[13] A more precise framework for infinite-dimensional cases is provided by the concept of a Schauder basis in a Banach space, which applies directly to many function spaces like C[0,1] or L^p spaces. A Schauder basis \{\phi_n\} for a Banach space X is a sequence such that every f \in X has a unique expansion f = \sum_{n=1}^\infty c_n \phi_n with \|f - \sum_{n=1}^N c_n \phi_n\| \to 0 as N \to \infty. This was introduced by Juliusz Schauder in 1927 to handle topological aspects absent in algebraic bases.^[14] In Hilbert spaces such as L^2, an orthogonal Schauder basis further satisfies Parseval's identity: \sum_{n=1}^\infty |c_n|^2 = \|f\|^2, preserving the norm through the expansion. Unlike Schauder bases, which require unique coefficients, frames or overcomplete systems in function spaces allow multiple representations of the same f, providing redundancy but sacrificing uniqueness for robustness in applications like signal processing.^[12]

Key Properties and Schauder Bases

Basis functions in Hilbert spaces often exhibit orthogonality, a property that simplifies the representation of elements. For an orthonormal basis \{\phi_n\} in a Hilbert space, the inner product satisfies \langle \phi_m, \phi_n \rangle = \delta_{mn}, where \delta_{mn} is the Kronecker delta. This orthogonality allows coefficients in the expansion f = \sum c_n \phi_n to be directly computed as c_n = \langle f, \phi_n \rangle, enhancing computational efficiency and stability in approximations.^[12] In more general Banach spaces, Schauder bases lack this orthogonality but possess biorthogonality. A Schauder basis \{\phi_n\} admits a dual (biorthogonal) sequence \{\psi_n\} in the dual space such that \langle \phi_m, \psi_n \rangle = \delta_{mn}. Coefficients are then extracted via c_n = \langle f, \psi_n \rangle, ensuring unique representations despite the absence of inner product structure. This duality is fundamental to the theory, as it guarantees the basis spans the space densely.^[12] The stability of a Schauder basis is quantified by its basis constant, defined as \Lambda = \sup_N \|P_N\|, where P_N is the bounded projection onto the span of the first N basis elements. A smaller basis constant indicates greater stability, with \Lambda \geq 1 always holding, and equality to 1 for monotone bases where partial sums are contractive. This measure is crucial for assessing how perturbations affect expansions.^[12] Unconditional bases represent a stronger variant of Schauder bases, where series convergence holds independently of the ordering of terms, provided the coefficients are square-summable in Hilbert settings. For instance, the Fourier basis in L^2 forms an unconditional basis, allowing rearrangements without altering convergence. Such bases are permutation-invariant and imply democratic properties in the space.^[12] Riesz bases extend orthonormal concepts to general Hilbert spaces, defined as the image of an orthonormal basis under a bounded invertible linear operator. They preserve essential properties like unconditional convergence and boundedness of projections, with frame bounds A, B > 0 satisfying A \|f\|^2 \leq \sum | \langle f, \phi_n \rangle |^2 \leq B \|f\|^2 for all f. This makes Riesz bases topologically equivalent to orthonormal ones.^[12] Regarding existence, Stefan Banach conjectured that every separable Banach space admits a Schauder basis, but Per Enflo constructed a counterexample in 1973: a separable Banach space without such a basis, also lacking the approximation property. While many classical separable spaces like \ell^p and L^p possess bases, this result highlights the non-universality of Schauder bases in separable settings.

Types of Basis Functions

Polynomial Bases

Polynomial bases are fundamental in approximation theory, particularly for representing functions on finite intervals. The monomial basis, consisting of the set {1, x, x^2, \dots, x^n}, spans the vector space P_n of all polynomials of degree at most n. This basis is complete within P_n, allowing any polynomial in this space to be uniquely expressed as a finite linear combination \sum_{k=0}^n c_k x^k.^[15] In the space of continuous functions C[0,1] equipped with the uniform norm, the monomials do not form a Schauder basis, as not every continuous function admits a unique uniform-convergent series expansion in this basis; such expansions converge only for analytic functions. However, the linear span of the monomials is dense in C[0,1], as established by the Weierstrass approximation theorem, which asserts that for any continuous function f on a compact interval [a,b] and any \epsilon > 0, there exists a polynomial p such that \|f - p\|_\infty < \epsilon. This density justifies the use of monomials for approximating continuous functions, though the basis is ill-conditioned, exhibiting large basis constants that lead to numerical instability in computations, particularly evident in the poor conditioning of the associated Vandermonde matrix.^[16]^[17]^[15] For analytic functions on [0,1], the monomials do form a Schauder basis under the uniform norm, with expansions given by Taylor series that converge uniformly to the function. A representative example is the Taylor series expansion of an analytic function f around x=0:

f(x) = \sum_{n=0}^\infty \frac{f^{(n)}(0)}{n!} x^n,

where the series converges to f(x) in a neighborhood of 0, and the coefficients are uniquely determined by the derivatives.^[18] To mitigate the ill-conditioning of monomials, orthogonal polynomial bases are often preferred. The Legendre polynomials \{P_n(x)\} form an orthogonal basis for L^2[-1,1] with respect to the weight function 1, satisfying \int_{-1}^1 P_m(x) P_n(x) \, dx = \frac{2}{2n+1} \delta_{mn}. They are generated by the recursion relations P_0(x) = 1, P_1(x) = x, and for n \geq 1,

(n+1) P_{n+1}(x) = (2n+1) x P_n(x) - n P_{n-1}(x).

This orthogonality facilitates efficient projections and expansions in function spaces. Another important orthogonal family is the Chebyshev polynomials of the first kind \{T_n(x)\}, which possess the minimax property: among all monic polynomials of degree n on [-1,1], the scaled Chebyshev polynomial T_n(x)/2^{n-1} has the smallest maximum norm, equaling $1/2^{n-1}. Defined by T_n(\cos \theta) = \cos(n \theta) for \theta \in [0, \pi], they are particularly useful for interpolation due to their equioscillation, which minimizes the maximum approximation error.^[19]

Trigonometric and Fourier Bases

Trigonometric bases form a fundamental class of orthonormal bases in the space of square-integrable functions on periodic intervals, enabling frequency-based decompositions of functions. The standard trigonometric system on the interval [0,1] consists of the constant function 1 together with the functions \cos(2\pi n x) and \sin(2\pi n x) for n = 1, 2, \dots. These functions are orthogonal with respect to the L^2[0,1] inner product \langle f, g \rangle = \int_0^1 f(x) g(x) \, dx, where \langle 1, 1 \rangle = 1, \langle \cos(2\pi n x), \cos(2\pi m x) \rangle = \frac{1}{2} \delta_{nm}, and similarly for sines, with cross terms vanishing for n \neq m. To achieve orthonormality, the constant is already normalized, while the cosine and sine functions are scaled by \sqrt{2}, yielding the orthonormal set \left\{1, \sqrt{2} \cos(2\pi n x), \sqrt{2} \sin(2\pi n x) \mid n \in \mathbb{N}\right\}.^[20] Any function f \in L^2[0,1] can be expanded in this basis via its Fourier series:

f(x) = \frac{a_0}{2} + \sum_{n=1}^\infty \left( a_n \cos(2\pi n x) + b_n \sin(2\pi n x) \right),

where the coefficients are given by

a_0 = 2 \int_0^1 f(x) \, dx, \quad a_n = 2 \int_0^1 f(x) \cos(2\pi n x) \, dx, \quad b_n = 2 \int_0^1 f(x) \sin(2\pi n x) \, dx

for n \geq 1. These formulas arise from the orthogonality relations and ensure that the partial sums converge to f in the L^2 norm. The trigonometric polynomials—finite linear combinations of these basis functions—are dense in L^2[0,1], establishing the system's completeness as an orthonormal basis; this follows from the Riesz-Fischer theorem applied to the closure of the span.^[20] An equivalent formulation uses complex exponentials, particularly on the interval [-\pi, \pi]. The set \left\{ \frac{e^{i n x}}{\sqrt{2\pi}} \mid n \in \mathbb{Z} \right\} forms an orthonormal basis for L^2[-\pi, \pi], with inner product \langle f, g \rangle = \int_{-\pi}^\pi f(x) \overline{g(x)} \, dx. The corresponding Fourier series is f(x) = \sum_{n=-\infty}^\infty c_n e^{i n x}, where c_n = \frac{1}{2\pi} \int_{-\pi}^\pi f(x) e^{-i n x} \, dx, and convergence holds in L^2. This exponential basis connects directly to the Fourier transform on the real line, where the transform decomposes non-periodic functions into continuous superpositions of complex exponentials, extending the periodic frequency analysis.^[21] Despite pointwise convergence issues, partial sums of Fourier series exhibit the Gibbs phenomenon near discontinuities of f, manifesting as persistent overshoots and undershoots that do not diminish with increasing terms; the overshoot amplitude approaches approximately 8.95% of the jump size. This arises from the slow decay of Fourier coefficients for non-smooth functions, leading to ringing artifacts in the approximation. A classic example is the square wave function f(x) = -1 for -\pi < x < 0 and f(x) = 1 for $0 < x < \pi, extended periodically. Its Fourier series is \frac{4}{\pi} \sum_{k=1,3,5,\dots}^\infty \frac{\sin(k x)}{k}, which converges slowly near the jumps at x = 0, \pm \pi, with Gibbs overshoots clearly visible even in high-order partial sums, illustrating the limitations for discontinuous data.^[22]

Wavelet and Other Orthogonal Bases

Wavelet bases represent a class of orthogonal functions that provide localized representations in both time and frequency domains, offering advantages over global supports in Fourier or polynomial bases by capturing non-stationary features efficiently. These bases are generated from a mother wavelet \psi(x) through dilations and translations, forming the family \psi_{j,k}(x) = 2^{j/2} \psi(2^j x - k) for j, k \in \mathbb{Z}, which constitutes an orthonormal basis for L^2(\mathbb{R}) under appropriate conditions. Central to their construction is multiresolution analysis (MRA), a framework that decomposes L^2(\mathbb{R}) into nested subspaces V_j spanned by dilates of a scaling function \phi, with the wavelet \psi spanning the orthogonal complement W_j = V_{j+1}^\perp. The Haar basis serves as the simplest example of a wavelet system, defined by the mother wavelet \psi(x) = 1 for x \in [0, 0.5) and \psi(x) = -1 for x \in [0.5, 1), and zero elsewhere; this forms an orthogonal basis for L^2(\mathbb{R}).^[23] Despite its piecewise constant nature, which limits smoothness, the Haar system is computationally efficient and provides perfect reconstruction in discrete settings. For applications requiring higher regularity, Daubechies wavelets extend this by constructing compactly supported orthogonal wavelets with vanishing moments, allowing better approximation of smooth functions; for instance, the D_4 wavelet has two vanishing moments and support width 4. Beyond wavelets, other orthogonal bases include Hermite functions, defined as \psi_n(x) = \frac{1}{\sqrt{2^n n! \sqrt{\pi}}} H_n(x) e^{-x^2/2} where H_n(x) are Hermite polynomials, forming an orthonormal basis for L^2(\mathbb{R}).^[24] These functions are eigenfunctions of the Fourier transform and are particularly suited for problems involving quadratic potentials or quantum harmonic oscillators. In general, wavelet bases, including those like Haar and Daubechies, form Riesz bases in L^2(\mathbb{R}), meaning they are boundedly equivalent to orthonormal bases with frame bounds A and B satisfying $0 < A \leq \sum | \langle f, \psi_{j,k} \rangle |^2 \leq B \|f\|^2 < \infty for all f \in L^2(\mathbb{R}). This property ensures stable expansions and reconstructions, highlighting their utility for localized analysis compared to the delocalized nature of Fourier bases.

Applications

Approximation and Interpolation

Basis functions play a central role in approximation theory by enabling the representation of functions within finite-dimensional subspaces. The best approximation of a function f in a normed linear space V by elements from a subspace W spanned by the first N basis functions is defined as the infimum \inf_{g \in W} \|f - g\|, where \|\cdot\| denotes a norm such as the L^2 or L^\infty norm.^[25] For finite-dimensional W, a best approximation w^* exists for any f \in V, and in inner product spaces, w^* is characterized by the orthogonality condition (f - w^*, w) = 0 for all w \in W.^[25] This projection onto the span of the basis functions minimizes the error and is unique in strictly convex spaces.^[25] Interpolation using basis functions seeks an exact match of f at specified nodes, constructing a function that passes through given points. In the case of polynomial bases, Lagrange interpolation provides such an approximant: for distinct nodes x_1, \dots, x_n and values y_i = f(x_i), the interpolating polynomial is

P(x) = \sum_{j=1}^n y_j \prod_{\substack{k=1 \\ k \neq j}}^n \frac{x - x_k}{x_j - x_k},

which is of degree at most n-1 and satisfies P(x_i) = y_i for each i.^[26] This method leverages the polynomial basis to ensure uniqueness within the space of polynomials of that degree.^[26] Theoretical guarantees on approximation errors are provided by results such as Jackson's theorem, which bounds the error for the best uniform approximation. For trigonometric (Fourier) bases, the theorem states that if f is k-times continuously differentiable on [-\pi, \pi], the error in approximating f by the best trigonometric polynomial of degree N is O(1/N^k), depending on the modulus of continuity of the k-th derivative.^[27] This establishes the convergence rate for smooth functions in L^\infty norm using partial sums of Fourier series as near-optimal approximants.^[27] A constructive approach to the Weierstrass approximation theorem, which asserts that continuous functions on [0,1] can be uniformly approximated by polynomials, is given by Bernstein polynomials. These are defined as

B_n(f; x) = \sum_{k=0}^n f\left(\frac{k}{n}\right) \binom{n}{k} x^k (1-x)^{n-k},

where \binom{n}{k} is the binomial coefficient.^[28] As n \to \infty, B_n(f; x) \to f(x) uniformly for continuous f, with the proof relying on uniform continuity and the probabilistic interpretation of the binomial terms as a Bernstein distribution concentrating around x.^[28] This provides an explicit sequence of polynomial approximants using the monomial basis.^[28] However, polynomial interpolation can exhibit instabilities, as illustrated by the Runge phenomenon. When interpolating on equidistant nodes in [-1,1] with high-degree polynomials, large oscillations occur near the endpoints, even for smooth functions like f(x) = 1/(1 + 25x^2).^[29] For a degree-10 interpolant at 11 equidistant points, the error diverges significantly at the boundaries due to the ill-conditioning of the Vandermonde matrix underlying the polynomial basis expansion.^[29] This highlights the need for non-equidistant nodes, such as Chebyshev points, to mitigate such artifacts in practice.^[29] Basis function expansions also underpin collocation methods for approximate solutions to partial differential equations (PDEs). In these methods, the solution is sought as an expansion u(x) \approx \sum_{i=1}^N c_i \phi_i(x) in a basis \{\phi_i\}, where coefficients c_i are chosen to satisfy the PDE exactly at selected collocation points.^[30] For elliptic PDEs with random coefficients, combining reduced basis techniques with sparse grid collocation reduces dimensionality while preserving accuracy, as the greedy selection of basis functions captures dominant solution manifolds.^[30] This approach yields efficient approximations, with error bounds tied to the subspace dimension and collocation grid density.^[30]

Signal Processing and Analysis

In signal processing, basis functions enable the decomposition of signals into constituent components, facilitating analysis, feature extraction, and manipulation in both time and frequency domains. Orthogonal bases, such as those derived from trigonometric functions, form the foundation for many techniques, allowing signals to be represented as linear combinations of these basis elements for efficient processing.^[31] The Fourier transform is a cornerstone for frequency-domain analysis, expressing a signal as a sum of complex exponentials, which are basis functions of the form e^{i \omega x}. The continuous Fourier transform of a function f(x) is given by

\hat{f}(\omega) = \int_{-\infty}^{\infty} f(x) e^{-i \omega x} \, dx,

revealing the frequency content of continuous-time signals like audio or electromagnetic waves. For digital signals, the discrete Fourier transform (DFT) adapts this to finite sequences, enabling practical implementation via algorithms like the fast Fourier transform (FFT) for spectrum estimation and filtering.^[32]^[31] To address non-stationary signals where frequency content varies over time, the short-time Fourier transform (STFT) applies a window to localize the Fourier analysis. The STFT uses basis functions e^{i \omega (t - \tau)} modulated by a time window centered at \tau, producing a time-frequency representation that balances resolution trade-offs governed by the uncertainty principle. This approach is widely used in speech processing and radar for tracking evolving spectral features.^[33] For signals with transient or localized features, such as shocks or bursts, the continuous wavelet transform (CWT) provides superior time-frequency localization using scalable, shifted basis functions called wavelets. The CWT of a signal f(t) with respect to a mother wavelet \psi is defined as

W_f(a, b) = \frac{1}{\sqrt{|a|}} \int_{-\infty}^{\infty} f(t) \overline{\psi\left( \frac{t - b}{a} \right)} \, dt,

where a controls scale (inversely related to frequency) and b handles translation, making it ideal for detecting abrupt changes in non-stationary processes like seismic events.^[34] In data compression, basis functions allow energy compaction by representing signals with fewer coefficients, as seen in the JPEG standard, which employs the discrete cosine transform (DCT) based on cosine functions. The DCT transforms image blocks into coefficients where low-frequency components dominate, enabling quantization and retention of only large coefficients for lossy compression ratios up to 100:1 while preserving perceptual quality.^[35] Signal denoising leverages orthogonal basis expansions by thresholding small coefficients, which often capture noise rather than signal structure, followed by reconstruction. In wavelet or Fourier bases, soft or hard thresholding—setting coefficients below a noise-estimated threshold to zero—achieves near-optimal mean-squared error reduction, particularly for signals sparse in the chosen basis, as demonstrated in seminal nonlinear estimation theory.^[36]^[37] A practical example is electrocardiogram (ECG) analysis, where wavelet bases decompose heart signals to isolate QRS complexes and detect anomalies like arrhythmias. By applying multi-resolution wavelet transforms, subtle deviations in P-waves or T-waves indicative of ischemia can be extracted with high sensitivity, outperforming traditional filters in noisy ambulatory recordings.^[38]^[39]

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[25]
Jackson's Theorem -- from Wolfram MathWorld
Jackson's theorem is a statement about the error E_n(f) of the best uniform approximation to a real function f(x) on [-1,1] by real polynomials of degree at ...
[26]
[PDF] Weierstrass approximation theorem by S. Bernstein
There is a lovely proof of the Weierstrass approximation theorem by S. Bernstein. We shall show that any function, continuous on the closed interval [0,1] ...
[27]
[PDF] The Runge Phenomenon and Piecewise Polynomial Interpolation
Aug 16, 2017 · The Runge phenomenon shows high-degree polynomial interpolation can cause spurious oscillations, especially near singularities, making it ...
[28]
Reduced Basis Collocation Methods for Partial Differential ...
The sparse grid stochastic collocation method is a new method for solving partial differential equations with random coefficients.
[29]
[PDF] EE 261 - The Fourier Transform and its Applications
1 Fourier Series. 1. 1.1 Introduction and Choices to Make . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. 1.2 Periodic Phenomena .
[30]
[PDF] Chapter 4: Frequency Domain and Fourier Transforms
Frequency domain analysis and Fourier transforms are key for signal and system analysis, breaking down time signals into sinusoids.
[31]
The Short-Time Fourier Transform - Stanford CCRMA
The Short-Time Fourier Transform (STFT) (or short-term Fourier transform) is a powerful general-purpose tool for audio signal processing.
[32]
[PDF] Wavelet Signal Processing for Transient Feature Extraction - DTIC
Wavelet transform techniques were developed to extract low dimensional feature data that allowed a simple classification scheme to easily separate the various ...
[33]
[PDF] Image Compression Using the Discrete Cosine Transform
It is widely used in image compression. Here we develop some simple functions to compute the DCT and to compress images.
[34]
[PDF] DE-NOISING BY SOFT-THRESHOLDING - Stanford University
Our proof of these results develops new facts about abstract statistical inference and its connection with an optimal recovery model. Key Words and Phrases.
[35]
[PDF] On denoising and best signal representation
As shown in Fig. 1, a signal reconstruction is obtained by thresholding a set of coefficients in a given basis and then applying an inverse transformation. ...
[36]
Robust and Accurate Anomaly Detection in ECG Artifacts ... - NIH
Therefore, this work proposes a novel anomaly detection technique that is highly robust and accurate in the presence of ECG artifacts which can effectively ...
[37]
A Survey of Heart Anomaly Detection Using Ambulatory ... - MDPI
Wavelet transform is a powerful method for analyzing non-stationary signals, such as ECGs [37]. The DWT noise removal method is used in [38,39,40]. This method ...