Volume 296, Issue 2 pp. 588-609
ORIGINAL ARTICLE
Open Access

On the convergence properties of sampling Durrmeyer-type operators in Orlicz spaces

Danilo Costarelli

Corresponding Author

Danilo Costarelli

Department of Mathematics and Computer Science, University of Perugia, Perugia, Italy

Correspondence

Danilo Costarelli, Department of Mathematics and Computer Science, University of Perugia, 1, Via Vanvitelli, 06123 Perugia, Italy.

Email: [email protected]

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Michele Piconi

Michele Piconi

Department of Mathematics and Computer Science, University of Perugia, Perugia, Italy

Department of Mathematics and Computer Science ‘Ulisse Dini’ University of Florence, Firenze, Italy

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Gianluca Vinti

Gianluca Vinti

Department of Mathematics and Computer Science, University of Perugia, Perugia, Italy

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First published: 21 December 2022
Citations: 3
Present address Danilo Costarelli, Via Vanvitelli, 06123 Perugia, Italy.

Abstract

Here, we provide a unifying treatment of the convergence of a general form of sampling-type operators, given by the so-called sampling Durrmeyer-type series. The main result consists of the study of a modular convergence theorem in the general setting of Orlicz spaces L φ ( R ) $L^\varphi (\mathbb {R})$ . From the latter theorem, the convergence in L p ( R ) $L^p(\mathbb {R})$ , in L α log β L $L^\alpha \log ^\beta L$ , and in the exponential spaces can be obtained as particular cases. For the completeness of the theory, we provide a pointwise and uniform convergence theorem on R $\mathbb {R}$ , in case of bounded continuous and bounded uniformly continuous functions; in this context, we also furnish a quantitative estimate for the order of approximation, using the modulus of continuity of the function to be approximated. Finally, applications and examples with graphical representations are given for several sampling series with special kernels.

1 INTRODUCTION

The theory of sampling series is one of the most challenging topics in Approximation Theory, in view of its many applications, especially in signal and image processing.

Sampling-type operators (series) have been introduced in order to study approximate versions of the well-known Wittaker–Kotel'nikov–Shannon sampling theorem (see, e.g., [26, 35, 46]). Among the most studied families of sampling operators, we can find the celebrated family of the generalized (see, e.g., [1, 11, 12, 14, 34, 43, 47, 48]) and Kantorovich-type series (see, e.g., [8, 40]) that have been introduced in the 80s and in 2007, respectively, thanks to the crucial contribution of the German mathematician P. L. Butzer and his coauthors.

The aim of this work is to study the main approximation properties, including convergence results and quantitative estimates, for the so-called sampling Durrmeyer-type series. In particular, we deal with the convergence of the above family of operators in the general context of Orlicz spaces, which represents the main goal of this paper. For the sake of completeness of the theory, we also provide a pointwise and a uniform convergence theorem on R $\mathbb {R}$ , for both bounded continuous and bounded uniformly continuous functions, respectively. Furthermore, we also prove a quantitative estimate for the order of approximation, in terms of the modulus of continuity of the involved function and a qualitative rate of convergence is also deduced.

From the literature, it is well known that the classical Bernstein polynomials
B n f ( x ) : = k = 0 n p n , k ( x ) f k n , p n , k = n k x k ( 1 x ) n k , x [ 0 , 1 ] , $$\begin{equation*} {\left(B_nf\right)}(x):=\sum _{k=0}^{n}p_{n,k}(x)f{\left(\frac{k}{n}\right)},\,\,\,p_{n,k}=\binom{n}{k}x^k(1-x)^{n-k},\,\,\,x\in [0, 1], \end{equation*}$$
are known since 1912 and they have been used in order to give one of the most elegant proof of the Weierstrass approximation theorem by algebraic polynomials in the space of the continuous functions over the interval [0, 1]. The Durrmeyer method applied to Bernstein polynomials is obtained replacing the sample value f k n $f\left(\frac{k}{n}\right)$ by an integral in which the same generating kernel p n , k $p_{n,k}$ appears, that is,
D n f ( x ) : = ( n + 1 ) k = 0 n p n , k ( x ) 0 1 p n , k ( u ) f ( u ) d u , x [ 0 , 1 ] . $$\begin{equation*} {\left(D_nf\right)}(x):=(n+1)\sum _{k=0}^{n}p_{n,k}(x)\int _{0}^{1}p_{n,k}(u)f(u)du,\,\,\,x\in [0, 1]. \end{equation*}$$
The literature about this family of operators and its generalizations is very wide; we quote here, for example, [25, 27-31]. In this paper, we apply the Durrmeyer method to the sampling series in a generalized form, considering operators of the following type,
( S w φ , ψ f ) ( x ) : = k Z φ ( w x k ) w R ψ ( w u k ) f ( u ) d u , x R , $$\begin{eqnarray} (S_w^{\varphi ,\psi }f)(x):= \sum _{k\in \mathbb {Z}} \varphi (wx-k) w \int _{\mathbb {R}}\psi (wu-k)f(u)du, \,\, x\in \mathbb {R}, \end{eqnarray}$$ (*)
for w > 0 $w>0$ , in which we replace the integral means by a general convolution integral. In fact, in the sampling Durrmeyer-type series, the kernel functions φ and ψ satisfy certain moments conditions, together with suitable singularity assumptions, in both continuous and discrete form. As it is well known, sampling Durrmeyer operators (*) represent a generalization of the generalized and of the Kantorovich sampling series, as showed in Section 7.

Orlicz spaces have been introduced in the 30s thanks to the Polish mathematician W. Orlicz, as a natural extension of L p $L ^ p$ spaces, and other useful spaces very used in functional analysis and its applications, such as Zygmund and exponential spaces. Thus, in this paper, we provide a unifying theory, not only in the sense that sampling Durrmeyer-type series (*) represent a generalization of the above-mentioned sampling-type operators, but also since the main convergence results will be given in the general setting of Orlicz spaces. Here, we consider the most natural notion of convergence, that is the so-called “modular convergence,” introduced by the modular functional defined on the space. One of the main advantage in studying approximation theorems in Orlicz spaces is the possibility to approximate not-necessarily continuous signals. This is what usually occurs in real-world applications (see, e.g., [3, 18]), in which signals are not very regular (as happens, e.g., for images).

In the context of Orlicz spaces, we first prove a modular inequality for the operators S w φ , ψ $S_w^{\varphi ,\psi }$ , and then we establish a modular convergence theorem. At the end of the paper, several examples of kernels φ and ψ have been provided together with numerical examples and graphical representations.

Moreover, we remark that the theory of the above discrete sampling-type series has been studied also in a more general form and in a different setting in some papers and, in this context, they are known with the name of quasi-interpolation or quasi-projection operators. Contributions to this theory have been given by several authors, see, for example, [9, 10, 24, 32, 33, 35], especially for what concerns the study of approximation results in shift invariant spaces, L p $L^p$ -spaces, Sobolev spaces, and Besov spaces, under suitable Strang– Fix– type conditions. Finally, let us mention that recently, asymptotic expansion and Voronovskaja-type formulas have been proved in [4, 6].

2 PRELIMINARIES AND NOTATIONS

We denote by C ( R ) $C(\mathbb {R})$  the space of all uniformly continuous and bounded functions f : R R $f: \mathbb {R} \rightarrow \mathbb {R}$ , by C c ( R ) $C_c(\mathbb {R})$  the subspace of C ( R ) $C(\mathbb {R})$  whose elements have compact support. Moreover by M ( R ) $M(\mathbb {R})$ , we denote the space of all (Lebesgue) measurable real functions over R $\mathbb {R}$ .

Let φ : R 0 + R 0 + $\varphi : \mathbb {R}^+_0 \rightarrow \mathbb {R}^+_0$  be a convex φ-function, that is, φ satisfies the following assumptions:
  1. φ is convex in R 0 + $\mathbb {R}^+_0$ ;
  2. φ ( 0 ) = 0 $\varphi (0) = 0$ and φ ( u ) > 0 $\varphi (u) > 0$ , for every u > 0 $u >0$ .
Let us consider the functional
I φ [ f ] : = I R φ ( | f ( x ) | ) d x , f M ( R ) . $$\begin{equation*} I^{\varphi }[f] := \int _{I\!\!R}\varphi (|f(x)|)dx, \nobreakspace \nobreakspace f \in M(\mathbb {R}). \end{equation*}$$
As it is well known (see, e.g., [7, 38, 42]), I φ $I^{\varphi }$  is a convex modular functional on M ( R ) $M(\mathbb {R})$  and the Orlicz space generated by φ is defined by
L φ ( R ) = { f M ( R ) : I φ [ λ f ] < + , for some λ > 0 } . $$\begin{equation*} L^{\varphi }(\mathbb {R}) = \lbrace f \in M(\mathbb {R}): I^{\varphi }[\lambda f] &lt; + \infty , \mbox{for some}\nobreakspace \lambda &gt;0\rbrace . \end{equation*}$$
The Orlicz space L φ ( R ) $L^{\varphi }(\mathbb {R})$  is a vector space and the vector subspace
E φ ( R ) = { f M ( R ) : I φ [ λ f ] < + , for every λ > 0 } , $$\begin{equation*} E^{\varphi }(\mathbb {R}) = \lbrace f \in M(\mathbb {R}): I^{\varphi }[\lambda f] &lt; + \infty , \mbox{for every}\nobreakspace \lambda &gt;0\rbrace , \end{equation*}$$
is called the space of all finite elements of L φ ( R ) $L^{\varphi }(\mathbb {R})$ . In general, E φ ( R ) $E^{\varphi }(\mathbb {R})$  is a proper subspace of L φ ( R ) $L^{\varphi }(\mathbb {R})$  and they coincide if φ satisfies the well-known Δ2-condition, that is, if there exists a constant M > 0 $M&gt;0$ such that
φ ( 2 u ) M φ ( u ) ( u R 0 + ) . $$\begin{equation*} \varphi (2u)\le M\varphi (u)\,\,\,\,(u\in \mathbb {R}^+_0). \end{equation*}$$
Examples of functions φ satisfying the Δ2-condition are φ ( u ) = u p $\varphi (u)=u^p$ , 1 p < + $1\le p &lt;+\infty$ , or φ α , β ( u ) = u α log β ( e + u ) $\varphi _{\alpha ,\beta }(u)=u^\alpha \log ^\beta (e+u)$ , for α 1 $\alpha \ge 1$ and β > 0 $\beta &gt;0$ , which generate respectively, the L p $L^p$ -spaces and the Zygmund spaces L α log β L $L^\alpha \log ^\beta L$ . On the other hand, the φ-function φ α ( t ) = e t α 1 $\varphi _\alpha (t)=e^{t^\alpha }-1$ , α > 0 $\alpha &gt;0$ , generates the so-called exponential spaces, which are examples of Orlicz spaces for which E φ α ( R ) L φ α ( R ) $E^{\varphi _\alpha }(\mathbb {R})\subset L^{\varphi _\alpha }(\mathbb {R})$ .
In L φ ( R ) $L^{\varphi }(\mathbb {R})$ , we work with a notion of convergence called modular convergence: We will say that a net of functions ( f w ) w > 0 L φ ( R ) $(f_w)_{w &gt;0} \subset L^{\varphi }(\mathbb {R})$   is modularly convergent to a function f L φ ( R ) $f \in L^{\varphi }(\mathbb {R})$  if
lim w + I φ [ λ ( f w f ) ] = 0 , $$\begin{equation*} \lim _{w \rightarrow +\infty }I^{\varphi }[\lambda (f_w -f)] = 0, \end{equation*}$$
for some λ > 0 $\lambda &gt;0$ . This notion induces a topology in L φ ( R ) $L^{\varphi }(\mathbb {R})$ ,  called modular topology.
In the space L φ ( R ) $L^{\varphi }(\mathbb {R})$ , we can also introduce a norm (the Luxemburg norm), defined by
f φ : = inf { λ > 0 : I φ [ f / λ ] 1 } . $$\begin{equation*} \Vert f\Vert _{\varphi } := \inf \lbrace \lambda &gt;0 : I^{\varphi }[f/\lambda ] \le 1\rbrace . \end{equation*}$$
Thus, we also have a stronger notion of convergence in L φ ( R ) $L^{\varphi }(\mathbb {R})$ , namely, the norm convergence. It is well known that f w f φ 0 $\Vert f_w - f\Vert _{\varphi } \rightarrow 0$ , as w + $w\rightarrow +\infty$ , if and only if I φ [ λ ( f w f ) ] 0 $I^{\varphi }[\lambda (f_w-f)] \rightarrow 0$ , as w + $w\rightarrow +\infty$ , for every λ > 0 $\lambda &gt;0$ . The two notions of convergence are equivalent if and only if the function φ satisfies the Δ2-condition. For further details, in the matter, see, for example, [7, 36, 39, 41, 42].

3 THE GENERALIZED SAMPLING DURRMEYER SERIES

Here, we recall the definition of the family of the generalized sampling Durrmeyer operators. Such operators have been first introduced in [6] in order to study asymptotic expansion and Voronovskaja-type theorems in case of sufficiently regular functions. Let us consider two functions φ , ψ L 1 ( R ) $\varphi , \psi \in L^1(\mathbb {R})$ , such that φ is bounded in a neighborhood of the origin, and satisfying
k Z φ ( u k ) = 1 , for every u R , and R ψ ( u ) d u = 1 . $$\begin{equation} \sum _{k\in \mathbb {Z}} \varphi (u-k)=1,\text{ for every $u\in \mathbb {R}$, and} \int _{\mathbb {R}} \psi (u)du=1. \end{equation}$$ (3.1)
Note that, ψ defines an approximate identity (see, e.g., [5, 13, 37, 44]) by the formula ψ w ( u ) : = w ψ ( w u ) $\psi _w(u):=w\psi (wu)$ , u R $u\in \mathbb {R}$ and w > 0 $w&gt;0$ . For any ν N 0 $\nu \in \mathbb {N}_0$ , let us define the discrete and continuous algebraic moments of φ and ψ, respectively, as follows:
m ν ( φ , u ) : = k Z φ ( u k ) ( k u ) ν , u R , $$\begin{equation*} m_{\nu }(\varphi ,u):=\sum _{k\in \mathbb {Z}} \varphi (u-k)(k-u)^\nu ,\quad u\in \mathbb {R}, \end{equation*}$$
and
m ν ψ : = R t ν ψ ( t ) d t $$\begin{equation*} \tilde{m}_\nu {\left(\psi \right)}:= \int _{\mathbb {R}}t^\nu \psi (t)dt \end{equation*}$$
while the discrete and continuous absolute moments of order ν 0 $\nu \ge 0$ are
M ν ( φ ) : = sup u R k Z φ ( u k ) u k ν $$\begin{equation} M_{\nu }(\varphi ):=\sup _{u\in \mathbb {R} }\sum _{k\in \mathbb {Z}}{\left| \varphi (u-k)\right|}{\left|u-k\right|}^\nu \end{equation}$$ (3.2)
and
M ν ( ψ ) : = R t ν ψ ( t ) d t , $$\begin{equation*} \tilde{M}_\nu (\psi ):= \int _{\mathbb {R}}{\left|t\right|}^\nu {\left| \psi (t)\right|}dt, \end{equation*}$$
respectively. Note that, for a function f : R R $f:\mathbb {R}\rightarrow \mathbb {R}$ , the definition of the moments M ν ( f ) , M ν ( f ) $M_\nu (f), \tilde{M}_\nu (f)$ can also be given for any ν 0 $\nu \ge 0$ . Now, we will call kernels a pair of functions φ and ψ belonging to L 1 ( R ) $L^1(\mathbb {R})$ , satisfying (3.1), and such that, there exists r > 0 $r&gt;0$ for which M r ( φ ) < + $M_r(\varphi )&lt;+\infty$ . For w > 0 $w&gt;0$ and for kernels φ and ψ, we define a family of operators S w φ , ψ w > 0 $\left(S_w^{\varphi ,\psi }\right)_{w&gt;0}$ by
S w φ , ψ f ( x ) = k Z φ ( w x k ) w R ψ ( w u k ) f ( u ) d u , x R , $$\begin{equation} {\left(S_w^{\varphi ,\psi }f\right)}(x)= \sum _{k\in \mathbb {Z}} \varphi (wx-k) w\int _{\mathbb {R}}\psi (wu-k)f(u)du,\text{$\qquad $} x\in \mathbb {R}, \end{equation}$$ (3.3)
for any given function f such that the above series is convergent, for every x R $x\in \mathbb {R}$ . S w φ , ψ $S_w^{\varphi ,\psi }$ are called the sampling Durrmeyer operators based on φ and ψ.

Remark 3.1.Note that, the assumption that the function φ belongs to L 1 ( R ) $L^1(\mathbb {R})$ is required in order to study the above sampling Durrmeyer operators in the general context of Orlicz spaces.

Now, we recall the following lemma that will be useful in the next sections.

Lemma 3.2.Under the above assumptions on the kernel φ, we have

  • (i)

    M 0 ( φ ) : = sup u R k Z φ ( u k ) < + ; $M_{0}(\varphi ):=\sup _{u\in \mathbb {R} }\sum _{k\in \mathbb {Z}}\left| \varphi (u-k)\right| &lt;+\infty ;$

  • (ii)

    For every γ > 0 $\gamma &gt;0$ ,

    lim w + w x k > γ w φ ( w x k ) = 0 , $$\begin{equation*} \lim _{w\rightarrow +\infty }\sum _{{\left|wx-k\right|}&gt;\gamma w}{\left| \varphi (wx-k)\right|}=0, \end{equation*}$$
    uniformly with respect to x R $x\in \mathbb {R}$ .

For a proof of Lemma 3.2, see, for example, [8].

Remark 3.3.We note the following:

  • (a)

    For μ , ν > 0 $\mu ,\nu &gt;0$ with μ ν $\mu \le \nu$ , then M ν ( φ ) < + $M_\nu (\varphi )&lt;+\infty$ implies M μ ( φ ) < + $M_\mu (\varphi )&lt;+\infty$ , see, for example, [22]. Moreover, if φ has compact support, we immediately have that M ν ( φ ) < + $M_\nu (\varphi )&lt;+\infty$ , for every ν 0 $\nu \ge 0$ . Finally, in an analogous way, for μ , ν > 0 $\mu , \nu &gt;0$ with μ ν $\mu \le \nu$ , M ν ( ψ ) < + $\tilde{M}_\nu (\psi )&lt;+\infty$ implies M μ ( ψ ) < + $\tilde{M}_\mu (\psi )&lt;+\infty$ .

  • (b)

    From Lemma 3.2, S w φ , ψ $S_w^{\varphi ,\psi }$ are well defined for every f L ( R ) $f \in L^\infty (\mathbb {R})$ . Indeed,

    S w φ , ψ f ( x ) M 0 ( φ ) M 0 ( ψ ) f , x R . $$\begin{equation*} {\left|{\left(S_w^{\varphi ,\psi }f\right)}(x)\right|} \le M_{0}(\varphi )\tilde{M_{0}}(\psi )\Vert f \Vert _\infty ,\ x \in \mathbb {R}. \end{equation*}$$
    Thus, sampling Durrmeyer operators are bounded linear operators mapping L ( R ) $L^\infty (\mathbb {R})$ into itself.

  • (c)

    If a function g is bounded and g ( u ) = O ( u α ) $g(u)=\mathcal {O}(\left|u\right|^{-\alpha })$ , as u + $\left|u\right|\rightarrow +\infty$ , with α > ν + 1 $\alpha &gt;\nu +1$ , ν > 0 $\nu &gt;0$ , then

    M μ ( g ) , M μ ( g ) < + , for every 0 μ ν , $$\begin{equation*} \widetilde{M}_\mu (g),\ M_\mu (g)&lt;+\infty ,\text{ for every }0\le \mu \le \nu , \end{equation*}$$
    see, for example, [22].

4 POINTWISE AND UNIFORM CONVERGENCE

From now on, in the whole paper, we will always consider kernels φ and ψ satisfying the assumptions introduced in Section 3. Note that, with the name kernels, we refer to both the functions φ and ψ, even if they satisfy different assumptions. Now, we prove the following pointwise and uniform convergence theorem.

Theorem 4.1.Let f L ( R ) $f\in L^\infty (\mathbb {R})$ . Then,

lim w + ( S w φ , ψ f ) ( x ) = f ( x ) $$\begin{equation*} \lim _{w\rightarrow +\infty }(S_w^{\varphi ,\psi }f)(x)=f(x) \end{equation*}$$
at any point x of continuity of f. Moreover, if f C ( R ) $f\in C(\mathbb {R})$ , then
lim w + S w φ , ψ f f = 0 . $$\begin{equation*} \lim _{w\rightarrow +\infty }\Vert S_w^{\varphi ,\psi }f-f\Vert _\infty =0. \end{equation*}$$

Proof.We only prove the second part of the theorem, since the first part can be obtained by similar methods. Let ε > 0 $\varepsilon &gt;0$ be fixed. Then, there exists δ > 0 $\delta &gt;0$ such that f ( x ) f ( y ) < ε $\left|f(x)-f(y)\right|&lt;\varepsilon$ when x y < δ $\left|x-y\right|&lt;\delta$ . Let x R $x \in \mathbb {R}$ be fixed. Using (3.1), we have

( S w φ , ψ f ) ( x ) f ( x ) = k Z φ ( w x k ) w R ψ ( w u k ) f ( u ) d u f ( x ) = k Z φ ( w x k ) w R ψ ( w u k ) f ( u ) f ( x ) d u k Z φ ( w x k ) w R ψ ( w u k ) f ( u ) f ( x ) d u = w x k δ 2 w + w x k > δ 2 w φ ( w x k ) w R ψ ( w u k ) f ( u ) f ( x ) d u $$\begin{eqnarray*} {\left|(S_w^{\varphi ,\psi }f)(x)-f(x)\right|} &=& {\left| \sum _{k\in \mathbb {Z}} \varphi (wx-k) w \int _{\mathbb {R}}\psi (wu-k)f(u)du-f(x) \right|}\\ &=& {\left| \sum _{k\in \mathbb {Z}} \varphi (wx-k) w \int _{\mathbb {R}}\psi (wu-k) {\left[ f(u)-f(x) \right]}du \right|}\\ &\le& \sum _{k\in \mathbb {Z}}{\left| \varphi (wx-k) \right|} w \int _{\mathbb {R}} {\left|\psi (wu-k)\right|} {\left| f(u)-f(x) \right|} du\\ &=& {\left\lbrace \sum _{{\left| wx-k \right|} \le \frac{\delta }{2}w}\ +\ \sum _{{\left| wx-k \right|} &gt; \frac{\delta }{2}w} \right\rbrace} {\left| \varphi (wx-k) \right|} w \int _{\mathbb {R}} {\left|\psi (wu-k)\right|} {\left| f(u)-f(x) \right|} du \end{eqnarray*}$$
= I 1 + I 2 $=I_1 + I_2$ . The first term can be further divided into
I 1 = w x k δ 2 w φ ( w x k ) w w u k < δ 2 w + w u k δ 2 w ψ ( w u k ) f ( u ) f ( x ) d u $$\begin{equation*} I_1 = \sum _{{\left| wx-k \right|} \le \frac{\delta }{2}w} {\left| \varphi (wx-k) \right|} w{\left\lbrace \int _{{\left| wu-k \right|} &lt; \frac{\delta }{2}w}\ + \ \int _{{\left| wu-k \right|} \ge \frac{\delta }{2}w} \right\rbrace} {\left|\psi (wu-k)\right|} {\left| f(u)-f(x) \right|} du \end{equation*}$$
= I 1 , 1 + I 1 , 2 $=I_{1,1}+I_{1,2}$ . For u R $u \in \mathbb {R}$ such that w u k < δ 2 w $\left|wu-k \right| &lt; \frac{\delta }{2}w$ , if w x k δ 2 w $\left| wx-k \right| \le \frac{\delta }{2}w$ , we have
u x u k w + k w x < δ 2 + δ 2 = δ . $$\begin{equation*} {\left| u-x \right|} \le {\left|u- \frac{k}{w} \right|}+{\left| \frac{k}{w}-x \right|} &lt; \frac{\delta }{2}+ \frac{\delta }{2}=\delta . \end{equation*}$$
Thus,
I 1 , 1 < ε w x k δ 2 w φ ( w x k ) w w u k < δ 2 w ψ ( w u k ) d u . $$\begin{equation*} I_{1,1}&lt;\varepsilon \sum _{{\left| wx-k \right|} \le \frac{\delta }{2}w} {\left| \varphi (wx-k) \right|} w \int _{{\left| wu-k \right|} &lt; \frac{\delta }{2}w}\ {\left|\psi (wu-k)\right|} du. \end{equation*}$$
Now, by the change of variable w u k = y $wu-k=y$ , and recalling that ψ L 1 ( R ) $\psi \in L^1(\mathbb {R})$ , we have
w w u k < δ 2 w ψ ( w u k ) d u R ψ ( u ) d u = ψ 1 , $$\begin{equation*} w\int _{{\left| wu-k \right|} &lt; \frac{\delta }{2}w}\ {\left|\psi (wu-k)\right|} du\le \int _{\mathbb {R}} {\left|\psi (u)\right|} du=\Vert \psi \Vert _1, \end{equation*}$$
for every w 0 $w\ge 0$ . Thus,
I 1 , 1 < M 0 ( φ ) ψ 1 ε , for every w 0 . $$\begin{equation*} I_{1,1}&lt;M_{0}(\varphi )\Vert \psi \Vert _1\varepsilon ,\text{ for every $w\ge 0$}. \end{equation*}$$
Moreover,
I 1 , 2 2 f w x k δ 2 w φ ( w x k ) w w u k > δ 2 w ψ ( w u k ) d u , $$\begin{equation*} I_{1,2} \le 2\Vert f \Vert _\infty \sum _{{\left| wx-k \right|} \le \frac{\delta }{2}w} {\left| \varphi (wx-k) \right|} w \int _{{\left| wu-k \right|} &gt; \frac{\delta }{2}w}\ {\left|\psi (wu-k)\right|}du, \end{equation*}$$
with
w u k > δ 2 w w ψ ( w u k ) d u = y > δ 2 w ψ ( y ) d y 0 , as w + , $$\begin{equation*} \int _{{\left| wu-k \right|} &gt; \frac{\delta }{2}w}\ w{\left|\psi (wu-k)\right|} du=\int _{{\left| y \right|} &gt; \frac{\delta }{2}w}{\left| \psi (y) \right|} \,dy\rightarrow 0,\,\,\text{as $w\rightarrow +\infty $}, \end{equation*}$$
since ψ L 1 ( R ) $\psi \in L^1({\mathbb {R}})$ . Then, there exists a sufficiently large w 1 ¯ R $\bar{w_1}\in \mathbb {R}$ such that
I 1 , 2 2 f M 0 ( φ ) ε , for every w w 1 ¯ . $$\begin{equation*} I_{1,2} \le 2\Vert f \Vert _\infty M_{0}(\varphi )\varepsilon ,\text{ for every $w\ge \bar{w_1}$}. \end{equation*}$$
By similar reasoning, we obtain the following inequality:
I 2 2 f ψ 1 w x k > δ 2 w φ ( w x k ) . $$\begin{equation*} I_2 \le 2\Vert f \Vert _\infty \Vert \psi \Vert _1\sum _{{\left| wx-k \right|} &gt; \frac{\delta }{2}w} {\left| \varphi (wx-k) \right|}. \end{equation*}$$
From property (ii) of Lemma 3.2, there exists a sufficiently large w 2 ¯ R $\bar{w_2}\in \mathbb {R}$ such that
I 2 2 f ψ 1 ε , for every w w 2 ¯ . $$\begin{equation*} I_2\le 2\Vert f \Vert _\infty \Vert \psi \Vert _1\varepsilon ,\text{ for every $w \ge \bar{w_2}$}. \end{equation*}$$
Setting K : = M 0 ( φ ) ψ 1 + 2 f ( M 0 ( φ ) + ψ 1 ) $K:=M_{0}(\varphi )\Vert \psi \Vert _1+2\Vert f \Vert _\infty (M_{0}(\varphi )+\Vert \psi \Vert _1)$ and w ¯ : = max w 1 ¯ , w 2 ¯ $\bar{w}:=\max \left\lbrace \bar{w_1},\bar{w_2}\right\rbrace$ , we have
( S w φ , ψ f ) ( x ) f ( x ) K ε , for every w w ¯ . $$\begin{equation*} {\left|(S_w^{\varphi ,\psi }f)(x)-f(x)\right|} \le K\varepsilon ,\text{ for every $w\ge \bar{w}$}. \end{equation*}$$
Finally, observing that the above estimate does not depend on x R $x\in \mathbb {R}$ , we easily obtain
sup x R ( S w φ , ψ f ) ( x ) f ( x ) = S w φ , ψ f f K ε , for every w w ¯ $$\begin{equation*} \sup _{x\in \mathbb {R}}{\left|(S_w^{\varphi ,\psi }f)(x)-f(x)\right|}=\Vert S_w^{\varphi ,\psi }f-f\Vert _\infty \le K\varepsilon ,\text{ for every $w\ge \bar{w}$} \end{equation*}$$
and thus the proof follows by the arbitrariness of ε > 0 $\varepsilon &gt;0$ . $\Box$

5 QUANTITATIVE ESTIMATES IN C ( R ) $C(\mathbb {R})$

Here, we provide a quantitative estimate for the rate of convergence of the sampling Durrmeyer operators for f C ( R ) $f\in C(\mathbb {R})$ , in terms of the modulus of continuity, defined by
ω ( f , δ ) : = sup f ( x ) f ( y ) : x y < δ , x , y R , $$\begin{equation*} \omega (f,\delta ):=\sup {\left\lbrace {\left|f(x)-f(y)\right|}: {\left|x-y\right|}&lt;\delta ,\,x, y\in \mathbb {R} \right\rbrace} , \end{equation*}$$
δ > 0 $\delta &gt;0$ . We recall the following well-known inequality
ω ( f , λ δ ) ( λ + 1 ) ω ( f , δ ) , for every δ , λ > 0 . $$\begin{equation} \omega (f,\lambda \delta )\le (\lambda +1)\omega (f,\delta ),\,\,\,\text{for every $\delta ,\lambda &gt;0$.}\end{equation}$$ (5.1)

We can prove what follows.

Theorem 5.1.Suppose that φ and ψ are such that M 1 ( φ ) + M 1 ( ψ ) < + $M_1(\varphi )+\tilde{M}_1(\psi )&lt;+\infty$ and let f C ( R ) $f\in C(\mathbb {R})$ . Then, we have

S w φ , ψ f f C φ , ψ ω f , 1 w , $$\begin{equation*} \Vert S_w^{\varphi ,\psi }f-f\Vert _\infty \le C^{\varphi ,\psi }\omega {\left(f,\frac{1}{w}\right)}, \end{equation*}$$
for every w > 0 $w&gt;0$ , where C φ , ψ = M 0 ( φ ) M 0 ( ψ ) + M 1 ( ψ ) + M 1 ( φ ) M 0 ( ψ ) $C^{\varphi ,\psi }=M_0(\varphi )\left(\tilde{M}_0(\psi )+\tilde{M}_1(\psi )\right)+M_1(\varphi )\tilde{M}_0(\psi )$ .

Proof.Let x R $x\in \mathbb {R}$ be fixed. We have

( S w φ , ψ f ) ( x ) f ( x ) k Z φ ( w x k ) w R ψ ( w u k ) f ( u ) f ( x ) d u k Z φ ( w x k ) w R ψ ( w u k ) ω f , u x d u k Z φ ( w x k ) w R ψ ( w u k ) ω f , 1 w ( 1 + w u x ) d u = ω f , 1 w k Z φ ( w x k ) w R ψ ( w u k ) ( 1 + w u x ) d u = ω f , 1 w k Z φ ( w x k ) w R ψ ( w u k ) d u + w R ψ ( w u k ) w u x d u , $$\begin{eqnarray*} {\left|(S_w^{\varphi ,\psi }f)(x)-f(x)\right|} &\le& \sum _{k\in \mathbb {Z}}{\left| \varphi (wx-k) \right|} w \int _{\mathbb {R}} {\left|\psi (wu-k)\right|} {\left| f(u)-f(x) \right|} du\\ &\le& \sum _{k\in \mathbb {Z}}{\left| \varphi (wx-k) \right|} w \int _{\mathbb {R}} {\left|\psi (wu-k)\right|} \omega {\left(f,{\left|u-x\right|}\right)}du\\ &\le& \sum _{k\in \mathbb {Z}}{\left| \varphi (wx-k) \right|} w \int _{\mathbb {R}} {\left|\psi (wu-k)\right|}\omega {\left(f,\frac{1}{w}\right)}(1+w{\left|u-x\right|}) du\\ &=& \omega {\left(f,\frac{1}{w}\right)}\sum _{k\in \mathbb {Z}}{\left| \varphi (wx-k) \right|} w \int _{\mathbb {R}} {\left|\psi (wu-k)\right|}(1+w{\left|u-x\right|}) du\\ &=& \omega {\left(f,\frac{1}{w}\right)}\sum _{k\in \mathbb {Z}}{\left| \varphi (wx-k) \right|} {\left\lbrace w \int _{\mathbb {R}} {\left|\psi (wu-k)\right|}du +w \int _{\mathbb {R}} {\left|\psi (wu-k)\right|} w {\left|u-x\right|}du\right\rbrace} , \end{eqnarray*}$$
for every w > 0 $w&gt;0$ , where the previous estimate is a consequence of the definition of ω ( f , 1 w ) $\omega (f,\frac{1}{w})$ and of (5.1) with λ = w u x > 0 $\lambda = w\left|u-x\right|&gt;0$ and δ = 1 w $\delta =\frac{1}{w}$ . Now we estimate the following term:
w R ψ ( w u k ) w u w x d u R w ψ ( w u k ) w u k d u + R w ψ ( w u k ) k w x d u = M 1 ( ψ ) + k w x M 0 ( ψ ) . $$\begin{eqnarray*} && w \int _{\mathbb {R}} {\left|\psi (wu-k)\right|} {\left|wu-wx\right|}du\\ &&\quad \le \int _{\mathbb {R}} w {\left|\psi (wu-k)\right|} {\left|wu-k\right|}du+\int _{\mathbb {R}} w {\left|\psi (wu-k)\right|} {\left|k-wx\right|}du\\ &&\quad =\tilde{M}_1(\psi )+{\left|k-wx\right|}\tilde{M}_0(\psi ). \end{eqnarray*}$$
So we obtain
( S w φ , ψ f ) ( x ) f ( x ) ω f , 1 w k Z φ ( w x k ) M 0 ( ψ ) + M 1 ( ψ ) + k w x M 0 ( ψ ) = ω f , 1 w k Z φ ( w x k ) M 0 ( ψ ) + k Z φ ( w x k ) M 1 ( ψ ) + k Z φ ( w x k ) k w x M 0 ( ψ ) = ω f , 1 w M 0 ( φ ) M 0 ( ψ ) + M 1 ( ψ ) + M 1 ( φ ) M 0 ( ψ ) . $$\begin{eqnarray*} {\left|(S_w^{\varphi ,\psi }f)(x)-f(x)\right|} &\le& \omega {\left(f,\frac{1}{w}\right)}\sum _{k\in \mathbb {Z}}{\left| \varphi (wx-k) \right|} {\left\lbrace \tilde{M}_0(\psi )+\tilde{M}_1(\psi )+{\left|k-wx\right|}\tilde{M}_0(\psi )\right\rbrace}\\ &=& \omega {\left(f,\frac{1}{w}\right)} {\left\lbrace \sum _{k\in \mathbb {Z}}{\left|\varphi (wx-k)\right|}\tilde{M}_0(\psi )+\sum _{k\in \mathbb {Z}}{\left|\varphi (wx-k)\right|}\tilde{M}_1(\psi ) +\sum _{k\in \mathbb {Z}}{\left|\varphi (wx-k)\right|} {\left|k-wx\right|}\tilde{M}_0(\psi )\right\rbrace}\\ &=& \omega {\left(f,\frac{1}{w}\right)}{\left\lbrace M_0(\varphi ){\left(\tilde{M}_0(\psi )+\tilde{M}_1(\psi )\right)}+M_1(\varphi )\tilde{M}_0(\psi )\right\rbrace} . \end{eqnarray*}$$
Now, setting C φ , ψ : = M 0 ( φ ) M 0 ( ψ ) + M 1 ( ψ ) + M 1 ( φ ) M 0 ( ψ ) < + $C^{\varphi ,\psi }:=M_0(\varphi )\left(\tilde{M}_0(\psi )+\tilde{M}_1(\psi )\right)+M_1(\varphi )\tilde{M}_0(\psi )&lt;+\infty$ , we finally obtain
S w φ , ψ f f C φ , ψ ω f , 1 w , $$\begin{equation*} \Vert S_w^{\varphi ,\psi }f-f\Vert _\infty \le C^{\varphi ,\psi }\omega {\left(f,\frac{1}{w}\right)}, \end{equation*}$$
for every w > 0 $w&gt;0$ . This completes the proof. $\Box$

Recalling the definition of Lipschitz classes L i p α $Lip\,\alpha$ , namely,
L i p α : = f C ( R ) : ω ( f , δ ) = O ( δ α ) , as δ 0 + , $$\begin{equation*} Lip\,\alpha :={\left\lbrace f\in C(\mathbb {R}):\omega (f,\delta )=\mathcal {O}(\delta ^\alpha ),\text{ as }\delta \rightarrow 0^+ \right\rbrace} , \end{equation*}$$
with 0 < α 1 $0&lt;\alpha \le 1$ , by Theorem 5.1, we can immediately obtain the following corollary.

Corollary 5.2.Under the assumptions of Theorem 5.1, and assuming in addition that f L i p α $f\in Lip\,\alpha$ , 0 < α 1 $0&lt;\alpha \le 1$ , then

S w φ , ψ f f C w α , $$\begin{equation*} \Vert S_w^{\varphi ,\psi }f-f\Vert _\infty \le Cw^{-\alpha }, \end{equation*}$$
for every w > 0 $w&gt;0$ , and for a suitable positive constant C > 0 $C&gt;0$ .

6 MODULAR CONVERGENCE IN ORLICZ SPACES

In this section, we study the convergence properties of the sampling Durrmeyer operators ( S w φ , ψ ) w > 0 $(S_w^{\varphi ,\psi })_{w&gt;0}$ in the general setting of Orlicz spaces.

In order to obtain a modular convergence theorem in L η ( R ) $L^{\eta }(\mathbb {R})$ , we first prove a modular continuity property for the family ( S w φ , ψ ) w > 0 $(S_w^{\varphi ,\psi })_{w&gt;0}$ .

From now on, we denote by η a convex φ-function. Now, we can prove the following.

Theorem 6.1.Let ψ be a kernel such that M 0 ( ψ ) < + $M_0(\psi )&lt;+\infty$ , and f L η ( R ) $f\in L^{\eta }(\mathbb {R})$ be fixed. Then, there exists λ > 0 $\lambda &gt;0$ such that

I η [ λ S w φ , ψ f ] M 0 ( ψ ) φ 1 M 0 ( φ ) M 0 ( ψ ) I η [ λ M 0 ( φ ) M 0 ( ψ ) f ] , w > 0 . $$\begin{equation*} I^{\eta }[\lambda S_w^{\varphi ,\psi }f ]\le \frac{M_0(\psi )\Vert \varphi \Vert _1}{M_0(\varphi )\tilde{M_0}(\psi )} I^{\eta }[\lambda M_0(\varphi )\tilde{M_0}(\psi )f],\,\,w&gt;0. \end{equation*}$$
In particular, S w φ , ψ $S_w^{\varphi ,\psi }$ are well defined and belong to L η ( R ) $L^{\eta }(\mathbb {R})$ , for every w > 0 $w&gt;0$ .

Proof.Since f L η ( R ) $f\in L^{\eta }(\mathbb {R})$ , there exists λ ¯ > 0 $\overline{\lambda }&gt;0$ such that I η [ λ ¯ f ] < + $I^{\eta }[\overline{\lambda }f]&lt;+\infty$ . We consider now λ > 0 $\lambda &gt;0$ such that

λ M 0 ( φ ) M 0 ( ψ ) λ ¯ . $$\begin{equation*} \lambda M_0(\varphi )\tilde{M_0}(\psi )\le \overline{\lambda }. \end{equation*}$$
Then, we have I η [ λ M 0 ( φ ) M 0 ( ψ ) f ] < + $I^{\eta }[\lambda M_0(\varphi )\tilde{M_0}(\psi )f]&lt;+\infty$ . Applying Jensen inequality twice, the change of variable w u k = t $wu-k=t$ , and Fubini–Tonelli theorem, we obtain
I η λ S w φ , ψ f = R η λ S w φ , ψ f ( x ) d x = R η λ k Z φ ( w x k ) w R ψ ( w u k ) f ( u ) d u d x R η λ k Z φ ( w x k ) w R ψ ( w u k ) f ( u ) d u d x 1 M 0 ( φ ) R k Z η λ M 0 ( φ ) ψ 1 w R ψ ( w u k ) ψ 1 f ( u ) d u φ ( w x k ) d x = 1 M 0 ( φ ) R k Z η λ M 0 ( φ ) ψ 1 R ψ ( t ) ψ 1 f t + k w d t φ ( w x k ) d x 1 M 0 ( φ ) ψ 1 R k Z φ ( w x k ) d x R ψ ( t ) η λ M 0 ( φ ) ψ 1 f t + k w d t = 1 M 0 ( φ ) ψ 1 R k Z φ ( w x k ) d x w R ψ ( w u k ) η λ M 0 ( φ ) ψ 1 f u d u = 1 M 0 ( φ ) ψ 1 R φ ( y ) d y R k Z ψ ( w u k ) η λ M 0 ( φ ) ψ 1 f ( u ) d u M 0 ( ψ ) M 0 ( φ ) ψ 1 R φ ( y ) d y R η λ M 0 ( φ ) ψ 1 f ( u ) d u = M 0 ( ψ ) M 0 ( φ ) ψ 1 φ 1 I η [ λ M 0 ( φ ) ψ 1 f ] = M 0 ( ψ ) M 0 ( φ ) M 0 ( ψ ) φ 1 I η [ λ M 0 ( φ ) M 0 ( ψ ) f ] < + , $$\begin{eqnarray*} I^{\eta }\left[\lambda {\left(S_w^{\varphi ,\psi }f\right)}\right] &=& \int _{\mathbb {R}}\eta {\left(\lambda {\left| {\left(S_w^{\varphi ,\psi }f\right)}(x) \right|} \right)}dx\\ &=& \int _{\mathbb {R}}\eta {\left(\lambda {\left| \sum _{k\in \mathbb {Z}}\varphi (wx-k){\left[ w\int _{\mathbb {R}}\psi (wu-k)f(u)du \right]} \right|} \right)}dx\\ &\le& \int _{\mathbb {R}}\eta {\left(\lambda \sum _{k\in \mathbb {Z}}{\left|\varphi (wx-k)\right|}{\left[ w\int _{\mathbb {R}}{\left|\psi (wu-k)\right|}{\left|f(u)\right|}du \right]} \right)}dx\\ &\le& \frac{1}{M_0(\varphi )}\int _{\mathbb {R}}\sum _{k\in \mathbb {Z}}\eta {\left(\lambda M_0(\varphi ) \Vert \psi \Vert _1 w \int _{\mathbb {R}}\frac{{\left|\psi (wu-k)\right|}}{ \Vert \psi \Vert _1}{\left|f(u)\right|}du \right)}{\left| \varphi (wx-k) \right|} dx\\ &=& \frac{1}{M_0(\varphi )}\int _{\mathbb {R}}\sum _{k\in \mathbb {Z}}\eta {\left(\lambda M_0(\varphi ) \Vert \psi \Vert _1 \int _{\mathbb {R}}\frac{{\left|\psi (t)\right|}}{ \Vert \psi \Vert _1}{\left|f{\left(\frac{t+k}{w}\right)}\right|}dt \right)}{\left| \varphi (wx-k) \right|} dx\\ &\le& \frac{1}{M_0(\varphi )\Vert \psi \Vert _1}\int _{\mathbb {R}}\sum _{k\in \mathbb {Z}}{\left| \varphi (wx-k) \right|} dx \, \int _{\mathbb {R}}{\left|\psi (t)\right|} \eta {\left(\lambda M_0(\varphi )\Vert \psi \Vert _1 {\left|f{\left(\frac{t+k}{w}\right)}\right|}\right)}dt\\ &=& \frac{1}{M_0(\varphi )\Vert \psi \Vert _1}\int _{\mathbb {R}}\sum _{k\in \mathbb {Z}}{\left| \varphi (wx-k) \right|} dx \, w\int _{\mathbb {R}}{\left|\psi (wu-k)\right|} \eta {\left(\lambda M_0(\varphi )\Vert \psi \Vert _1 {\left|f{\left(u\right)}\right|}\right)}du\\ &=& \frac{1}{M_0(\varphi )\Vert \psi \Vert _1}\int _{\mathbb {R}}{\left| \varphi (y) \right|} dy \, \int _{\mathbb {R}}\sum _{k\in \mathbb {Z}}{\left|\psi (wu-k)\right|} \eta {\left(\lambda M_0(\varphi ) \Vert \psi \Vert _1{\left|f(u)\right|}\right)}du\\ &\le& \frac{M_0(\psi )}{M_0(\varphi )\Vert \psi \Vert _1}\int _{\mathbb {R}}{\left| \varphi (y) \right|} dy \, \int _{\mathbb {R}} \eta {\left(\lambda M_0(\varphi )\Vert \psi \Vert _1 {\left|f(u)\right|}\right)}du\\ &=& \frac{M_0(\psi )}{M_0(\varphi )\Vert \psi \Vert _1}\Vert \varphi \Vert _1 I^{\eta }[\lambda M_0(\varphi )\Vert \psi \Vert _1f]\\ &=& \frac{M_0(\psi )}{M_0(\varphi )\tilde{M_0}(\psi )}\Vert \varphi \Vert _1 I^{\eta }[\lambda M_0(\varphi )\tilde{M_0}(\psi )f]&lt;+\infty , \end{eqnarray*}$$
with the change of variable w x k = y $wx-k=y$ . $\Box$

As a consequence of the previous theorem, it turns out that the operators S w φ , ψ $S_w^{\varphi ,\psi }$ are well defined in L η ( R ) $ L^{\eta }(\mathbb {R})$ and map L η ( R ) $ L^{\eta }(\mathbb {R})$ into itself. Moreover, we also have that S w φ , ψ $S_w^{\varphi ,\psi }$ is modularly continuous, that is, for any modularly convergent sequence f k k L η ( R ) $\left(f_k\right)_k\subset L^{\eta }(\mathbb {R})$ , with f k f L η ( R ) $f_k\rightarrow f\in L^{\eta }(\mathbb {R})$ , it turns out that I η [ λ S w φ , ψ f S w φ , ψ f k ] 0 $I^{\eta }[\lambda \left(S_w^{\varphi ,\psi }f-S_w^{\varphi ,\psi }f_k\right)]\rightarrow 0$ , as k + $k\rightarrow +\infty$ . Indeed, it is well known that there exists λ ¯ > 0 $\overline{\lambda }&gt;0$ such that I η [ λ ¯ ( f f k ) ] 0 $I^{\eta }[\overline{\lambda }(f-f_k)]\rightarrow 0$ , as k + $k\rightarrow +\infty$ , and so, choosing λ > 0 $\lambda &gt;0$ such that λ M 0 ( φ ) M 0 ( ψ ) λ ¯ $\lambda M_0(\varphi )\tilde{M_0}(\psi )\le \overline{\lambda }$ , we have:
I η [ λ S w φ , ψ f S w φ , ψ f k ] = I η [ λ S w φ , ψ ( f f k ) ] M 0 ( ψ ) φ 1 M 0 ( φ ) M 0 ( ψ ) I η [ λ M 0 ( φ ) M 0 ( ψ ) ( f f k ) ] M 0 ( ψ ) φ 1 M 0 ( φ ) M 0 ( ψ ) I η [ λ ¯ ( f f k ) ] 0 , $$\begin{eqnarray*} I^{\eta }[\lambda {\left(S_w^{\varphi ,\psi }f-S_w^{\varphi ,\psi }f_k\right)}] &=& I^{\eta }[\lambda S_w^{\varphi ,\psi }(f-f_k)]\\ &\le& \frac{M_0(\psi )\Vert \varphi \Vert _1}{M_0(\varphi )\tilde{M_0}(\psi )} I^{\eta }[\lambda M_0(\varphi )\tilde{M_0}(\psi )(f-f_k)]\\ &\le& \frac{M_0(\psi )\Vert \varphi \Vert _1}{M_0(\varphi )\tilde{M_0}(\psi )} I^{\eta }[\overline{\lambda }(f-f_k)]\rightarrow 0, \end{eqnarray*}$$
as k + $k\rightarrow +\infty$ .

Now, we are able to prove the main theorem of this section.

Theorem 6.2.Let ψ be a kernel such that M 0 ( ψ ) < + $M_0(\psi )&lt;+\infty$ , and let f L η ( R ) $f\in L^{\eta }(\mathbb {R})$ be fixed. Then, there exists λ > 0 $\lambda &gt;0$ such that

lim w I η [ λ S w φ , ψ f f ] = 0 . $$\begin{equation*} \lim _{w\rightarrow \infty }I^{\eta }[\lambda {\left(S_w^{\varphi ,\psi }f-f\right)}]=0. \end{equation*}$$

Proof.First of all, since f L η ( R ) $f\in L^\eta (\mathbb {R})$ , we have that there exist λ 1 , λ 2 > 0 $\lambda _1, \lambda _2&gt;0$ such that I η [ λ 1 f ] < + $I^\eta [\lambda _1f]&lt;+\infty$ , and

I η [ λ 2 f ( · ) f ( · + h ) ] 0 , as h 0 , $$\begin{equation*} I^\eta [\lambda _2{\left(f(\cdot )-f(\cdot +h)\right)}]\rightarrow 0,\text{ as $h\rightarrow 0$}, \end{equation*}$$
that is, for every fixed ε > 0 $\varepsilon &gt;0$ , there exists δ > 0 $\delta &gt;0$ such that
R η λ 2 f ( u + h ) f ( u ) d u < ε , $$\begin{equation} \int _{\mathbb {R}}\eta {\left(\lambda _2{\left|f(u+h)-f(u)\right|}\right)}du&lt;\varepsilon ,\end{equation}$$ (6.1)
for every h R $h\in \mathbb {R}$ such that h δ $\left| h\right|\le \delta$ (see, e.g., [7]). Now, we fix λ > 0 $\lambda &gt;0$ such that
λ min λ 2 2 M 0 ( φ ) ψ 1 , λ 1 4 M 0 ( φ ) ψ 1 . $$\begin{equation*} \lambda \le \min {\left\lbrace \frac{\lambda _2}{2M_0(\varphi )\Vert \psi \Vert _1},\frac{\lambda _1}{4M_0(\varphi )\Vert \psi \Vert _1}\right\rbrace} . \end{equation*}$$
Thus, by the properties of the convex modular functional I η $I^\eta$ , we can write what follows:
I η λ S w φ , ψ f f = R η λ S w φ , ψ f ( x ) f ( x ) d x = R η λ S w φ , ψ f ( x ) k Z φ ( w x k ) w R ψ ( w u k ) f u + x k w d u + k Z φ ( w x k ) w R ψ ( w u k ) f u + x k w d u f ( x ) d x 1 2 R η 2 λ S w φ , ψ f ( x ) k Z φ ( w x k ) w R ψ ( w u k ) f u + x k w d u d x + R η 2 λ k Z φ ( w x k ) w R ψ ( w u k ) f u + x k w d u f ( x ) d x = : 1 2 J 1 + J 2 . $$\begin{eqnarray*} I^\eta {\left[\lambda {\left(S_w^{\varphi ,\psi }f-f\right)}\right]} &=& \int _{\mathbb {R}}\eta {\left(\lambda {\left|{\left(S_w^{\varphi ,\psi }f\right)}(x)-f(x)\right|}\right)}dx\\ &=& \int _{\mathbb {R}}\eta {\left(\lambda {\left|{\left(S_w^{\varphi ,\psi }f\right)}(x)-\sum _{k\in \mathbb {Z}}\varphi (wx-k)w\int _{\mathbb {R}}\psi (wu-k)f{\left(u+x-\frac{k}{w}\right)}du\right.}\right.}\\ && {\left.{\left.+\sum _{k\in \mathbb {Z}}\varphi (wx-k)w\int _{\mathbb {R}}\psi (wu-k)f{\left(u+x-\frac{k}{w}\right)}du-f(x)\right|}\right)}dx\\ &\le& \frac{1}{2}{\left\lbrace \int _{\mathbb {R}}\eta {\left(2\lambda {\left|{\left(S_w^{\varphi ,\psi }f\right)}(x)-\sum _{k\in \mathbb {Z}}\varphi (wx-k)w\int _{\mathbb {R}}\psi (wu-k)f{\left(u+x-\frac{k}{w}\right)}du\right|}\right)}dx\right.}\\ && {\left.+\int _{\mathbb {R}}\eta {\left(2\lambda {\left|\sum _{k\in \mathbb {Z}}\varphi (wx-k)w\int _{\mathbb {R}}\psi (wu-k)f{\left(u+x-\frac{k}{w}\right)}du-f(x)\right|}\right)}dx\right\rbrace}\\ &=:& \frac{1}{2}{\left\lbrace J_1+J_2\right\rbrace} . \end{eqnarray*}$$
First, we estimate J1. Applying Jensen inequality twice similarly to the proof of Theorem 6.1, the change of variable w x k = t $wx-k=t$ , and Fubini–Tonelli theorem, we obtain
J 1 R η 2 λ k Z φ ( w x k ) w R ψ ( w u k ) f u + x k w f ( u ) d u d x 1 M 0 ( φ ) w R φ ( t ) k Z η 2 λ M 0 ( φ ) w R ψ ( w u k ) f u + t w f ( u ) d u d t 1 M 0 ( φ ) ψ 1 R φ ( t ) k Z R ψ ( w u k ) η 2 λ M 0 ( φ ) ψ 1 f u + t w f ( u ) d u d t 1 M 0 ( φ ) ψ 1 R φ ( t ) R M 0 ( ψ ) η 2 λ M 0 ( φ ) ψ 1 f u + t w f ( u ) d u d t . $$\begin{eqnarray*} {\left|J_1\right|} &\le & \int _{\mathbb {R}}\eta {\left(2\lambda \sum _{k\in \mathbb {Z}}{\left|\varphi (wx-k)\right|}w\int _{\mathbb {R}}{\left|\psi (wu-k)\right|}{\left|f{\left(u+x-\frac{k}{w}\right)}-f(u)\right|}du\,\right)}dx\\ &\le& \frac{1}{M_0(\varphi )w}\int _{\mathbb {R}}{\left|\varphi (t)\right|}{\left[\sum _{k\in \mathbb {Z}}\eta {\left(2\lambda M_0(\varphi )w\int _{\mathbb {R}}{\left|\psi (wu-k)\right|}{\left|f{\left(u+\frac{t}{w}\right)}-f(u)\right|}du\right)}\right]}dt\\ &\le& \frac{1}{M_0(\varphi )\Vert \psi \Vert _1}\int _{\mathbb {R}}{\left|\varphi (t)\right|}{\left[\sum _{k\in \mathbb {Z}}\int _{\mathbb {R}}{\left|\psi (wu-k)\right|}\eta {\left(2\lambda M_0(\varphi )\Vert \psi \Vert _1{\left|f{\left(u+\frac{t}{w}\right)}-f(u)\right|}\right)}du\right]}dt\\ &\le& \frac{1}{M_0(\varphi )\Vert \psi \Vert _1}\int _{\mathbb {R}}{\left|\varphi (t)\right|}{\left[\int _{\mathbb {R}}M_0(\psi ) \eta {\left(2\lambda M_0(\varphi )\Vert \psi \Vert _1{\left|f{\left(u+\frac{t}{w}\right)}-f(u)\right|}\right)}du\right]}dt. \end{eqnarray*}$$
Now, using δ given in (6.1), we can split the above integral as follows:
J 1 M 0 ( ψ ) M 0 ( φ ) ψ 1 t δ w + t > δ w φ ( t ) R η 2 λ M 0 ( φ ) ψ 1 f u + t w f ( u ) d u d t = : J 1 , 1 + J 1 , 2 . $$\begin{eqnarray*} {\left|J_1\right|} &\le& \frac{M_0(\psi )}{M_0(\varphi )\Vert \psi \Vert _1}{\left\lbrace \int _{{\left|t\right|}\le \delta w}+\int _{{\left|t\right|}&gt;\delta w} \right\rbrace} {\left|\varphi (t)\right|}{\left[\int _{\mathbb {R}}\eta {\left(2\lambda M_0(\varphi )\Vert \psi \Vert _1{\left|f{\left(u+\frac{t}{w}\right)}-f(u)\right|}\right)}du\right]}dt\\ &=:& J_{1,1}+J_{1,2}. \end{eqnarray*}$$
Now, using the inequality in (6.1) with h = t w $h=\frac{t}{w}$ , we have
J 1 , 1 = M 0 ( ψ ) M 0 ( φ ) ψ 1 t δ w φ ( t ) R η 2 λ M 0 ( φ ) ψ 1 f u + t w f ( u ) d u d t M 0 ( ψ ) M 0 ( φ ) ψ 1 t δ w φ ( t ) R η λ 2 f u + t w f ( u ) d u d t M 0 ( ψ ) φ 1 M 0 ( φ ) ψ 1 ε , $$\begin{eqnarray*} {\left|J_{1,1}\right|} &=& \frac{M_0(\psi )}{M_0(\varphi )\Vert \psi \Vert _1}\int _{{\left|t\right|}\le \delta w}{\left|\varphi (t)\right|}{\left[\int _{\mathbb {R}}\eta {\left(2\lambda M_0(\varphi )\Vert \psi \Vert _1{\left|f{\left(u+\frac{t}{w}\right)}-f(u)\right|}\right)}du\right]}dt\\ &\le& \frac{M_0(\psi )}{M_0(\varphi )\Vert \psi \Vert _1}\int _{{\left|t\right|}\le \delta w}{\left|\varphi (t)\right|}{\left[\int _{\mathbb {R}}\eta {\left(\lambda _2{\left|f{\left(u+\frac{t}{w}\right)}-f(u)\right|}\right)}du\right]}dt\le \frac{M_0(\psi )\Vert \varphi \Vert _1}{M_0(\varphi )\Vert \psi \Vert _1}\varepsilon , \end{eqnarray*}$$
for every w > 0 $w&gt;0$ . For what concerns J1, 2, by the convexity of η, we have
J 1 , 2 M 0 ( ψ ) M 0 ( φ ) ψ 1 t > δ w φ ( t ) 1 2 R η 4 λ M 0 ( φ ) ψ 1 f u + t w d u + R η 4 λ M 0 ( φ ) ψ 1 f u d u d t . $$\begin{eqnarray*} {\left|J_{1,2}\right|} &\le& \frac{M_0(\psi )}{M_0(\varphi )\Vert \psi \Vert _1}\int _{{\left|t\right|}&gt;\delta w}{\left|\varphi (t)\right|}\frac{1}{2}{\left[\int _{\mathbb {R}}\eta {\left(4\lambda M_0(\varphi )\Vert \psi \Vert _1{\left|f{\left(u+\frac{t}{w}\right)}\right|}\right)}du +\int _{\mathbb {R}}\eta {\left(4\lambda M_0(\varphi )\Vert \psi \Vert _1{\left|f{\left(u\right)}\right|}\right)}du\right]}dt. \end{eqnarray*}$$
Now we can observe that, since φ L 1 ( R ) $\varphi \in L^1(\mathbb {R})$ , there exists w 1 ¯ > 0 $\overline{w_1}&gt;0$ such that
t > δ w φ ( t ) d t < ε , $$\begin{equation*} \int _{{\left|t\right|}&gt;\delta w}{\left|\varphi (t)\right|}dt&lt;\varepsilon , \end{equation*}$$
for every w w 1 ¯ $w\ge \overline{w_1}$ . Moreover, noting that
R η 4 λ M 0 ( φ ) ψ 1 f u + t w d u = R η 4 λ M 0 ( φ ) ψ 1 f u d u , $$\begin{equation*} \int _{\mathbb {R}}\eta {\left(4\lambda M_0(\varphi )\Vert \psi \Vert _1{\left|f{\left(u+\frac{t}{w}\right)}\right|}\right)}du=\int _{\mathbb {R}}\eta {\left(4\lambda M_0(\varphi )\Vert \psi \Vert _1{\left|f{\left(u\right)}\right|}\right)}du, \end{equation*}$$
for every t R $t\in \mathbb {R}$ and w > 0 $w&gt;0$ , we have
J 1 , 2 M 0 ( ψ ) M 0 ( φ ) ψ 1 t > δ w φ ( t ) d t R η 4 λ M 0 ( φ ) ψ 1 f u d u M 0 ( ψ ) M 0 ( φ ) ψ 1 I η λ 1 f t > δ w φ ( t ) d t < M 0 ( ψ ) M 0 ( φ ) ψ 1 I η λ 1 f ε , $$\begin{eqnarray*} {\left|J_{1,2}\right|} &\le& \frac{M_0(\psi )}{M_0(\varphi )\Vert \psi \Vert _1}\int _{{\left|t\right|}&gt;\delta w}{\left|\varphi (t)\right|}dt\int _{\mathbb {R}}\eta {\left(4\lambda M_0(\varphi )\Vert \psi \Vert _1{\left|f{\left(u\right)}\right|}\right)}du\\ &\le& \frac{M_0(\psi )}{M_0(\varphi )\Vert \psi \Vert _1} I^\eta {\left[\lambda _1f\right]}\int _{{\left|t\right|}&gt;\delta w}{\left|\varphi (t)\right|}dt&lt;\frac{M_0(\psi )}{M_0(\varphi )\Vert \psi \Vert _1}I^\eta {\left[\lambda _1f\right]}\varepsilon , \end{eqnarray*}$$
for every w w 1 ¯ $w\ge \overline{w_1}$ . Now, we estimate J2. By the change of variable t = u k w $t = u-\frac{k}{w}$ , applying Jensen inequality twice, and Fubini–Tonelli theorem, we have
J 2 = R η 2 λ k Z φ ( w x k ) w R ψ ( w u k ) f u + x k w f ( x ) d u d x = R η 2 λ k Z φ ( w x k ) w R ψ ( w t ) f t + x f ( x ) d t d x R η 2 λ k Z φ ( w x k ) w R ψ ( w t ) f t + x f ( x ) d t d x 1 M 0 ( φ ) R k Z φ ( w x k ) η 2 λ M 0 ( φ ) w R ψ ( w t ) f t + x f ( x ) d t d x 1 M 0 ( φ ) R M 0 ( φ ) η 2 λ M 0 ( φ ) R ψ ( y ) f y w + x f ( x ) d y d x 1 ψ 1 R R ψ ( y ) η 2 λ M 0 ( φ ) ψ 1 f y w + x f ( x ) d y d x = 1 ψ 1 R ψ ( y ) R η 2 λ M 0 ( φ ) ψ 1 f y w + x f ( x ) d x d y , $$\begin{eqnarray*} {\left|J_2\right|} &=& \int _{\mathbb {R}}\eta {\left(2\lambda {\left|\sum _{k\in \mathbb {Z}}\varphi (wx-k)w\int _{\mathbb {R}}\psi (wu-k){\left[f{\left(u+x-\frac{k}{w}\right)}-f(x)\right]}du\right|}\right)}dx\\ &=& \int _{\mathbb {R}}\eta {\left(2\lambda {\left|\sum _{k\in \mathbb {Z}}\varphi (wx-k)w\int _{\mathbb {R}}\psi (wt){\left[f{\left(t+x\right)}-f(x)\right]}dt\right|}\right)}dx\\ &\le& \int _{\mathbb {R}}\eta {\left(2\lambda \sum _{k\in \mathbb {Z}}{\left|\varphi (wx-k)\right|}w\int _{\mathbb {R}}{\left|\psi (wt)\right|}{\left|f{\left(t+x\right)}-f(x)\right|}dt\right)}dx\\ &\le& \frac{1}{M_0(\varphi )}\int _{\mathbb {R}}\sum _{k\in \mathbb {Z}}{\left|\varphi (wx-k)\right|}\eta {\left(2\lambda M_0(\varphi ) w\int _{\mathbb {R}}{\left|\psi (wt)\right|}{\left|f{\left(t+x\right)}-f(x)\right|}dt\right)}dx\\ &\le& \frac{1}{M_0(\varphi )}\int _{\mathbb {R}}M_0(\varphi )\eta {\left(2\lambda M_0(\varphi ) \int _{\mathbb {R}}{\left|\psi (y)\right|}{\left|f{\left(\frac{y}{w}+x\right)}-f(x)\right|}dy\right)}dx\\ &\le& \frac{1}{\Vert \psi \Vert _1}\int _{\mathbb {R}}{\left[ \int _{\mathbb {R}}{\left|\psi (y)\right|}\eta {\left(2\lambda M_0(\varphi )\Vert \psi \Vert _1{\left|f{\left(\frac{y}{w}+x\right)}-f(x)\right|}\right)}dy\right]}dx\\ &=&\frac{1}{\Vert \psi \Vert _1}\int _{\mathbb {R}}{\left|\psi (y)\right|}{\left[\int _{\mathbb {R}}\eta {\left(2\lambda M_0(\varphi )\Vert \psi \Vert _1{\left|f{\left(\frac{y}{w}+x\right)}-f(x)\right|}\right)}dx\right]}dy, \end{eqnarray*}$$
where we have used the change of variable y = w t $y=wt$ . Then, using again δ given in (6.1), we can rewrite the above integral as follows:
J 2 1 ψ 1 y δ w + y > δ w ψ ( y ) R η 2 λ M 0 ( φ ) ψ 1 f y w + x f ( x ) d x d y = : J 2 , 1 + J 2 , 2 . $$\begin{eqnarray*} {\left|J_2\right|} &\le& \frac{1}{\Vert \psi \Vert _1}{\left\lbrace \int _{{\left|y\right|}\le \delta w}+\int _{{\left|y\right|}&gt;\delta w} \right\rbrace} {\left|\psi (y)\right|}{\left[ \int _{\mathbb {R}}\eta {\left(2\lambda M_0(\varphi )\Vert \psi \Vert _1{\left|f{\left(\frac{y}{w}+x\right)}-f(x)\right|}\right)}dx\right]}dy\\ &=:& J_{2,1}+J_{2,2}. \end{eqnarray*}$$
Thus, similarly to before, using the inequality in (6.1) with h = y w $h=\frac{y}{w}$ , we obtain
J 2 , 1 = 1 ψ 1 y δ w ψ ( y ) R η 2 λ M 0 ( φ ) ψ 1 f y w + x f ( x ) d x d y 1 ψ 1 y δ w ψ ( y ) R η λ 2 f y w + x f ( x ) d x d y < 1 ψ 1 ψ 1 ε = ε , $$\begin{eqnarray*} {\left|J_{2,1}\right|} &=& \frac{1}{\Vert \psi \Vert _1}\int _{{\left|y\right|}\le \delta w}{\left|\psi (y)\right|}{\left[\int _{\mathbb {R}}\eta {\left(2\lambda M_0(\varphi ) \Vert \psi \Vert _1{\left|f{\left(\frac{y}{w}+x\right)}-f(x)\right|}\right)}dx\right]}dy\\ &\le& \frac{1}{\Vert \psi \Vert _1}\int _{{\left|y\right|}\le \delta w}{\left|\psi (y)\right|}{\left[\int _{\mathbb {R}}\eta {\left(\lambda _2{\left|f{\left(\frac{y}{w}+x\right)}-f(x)\right|}\right)}dx\right]}dy&lt;\frac{1}{\Vert \psi \Vert _1}\Vert \psi \Vert _1\varepsilon =\varepsilon , \end{eqnarray*}$$
for every w > 0 $w&gt;0$ . Now, for what concerns the last term J2, 2, by the convexity of η, we have
J 2 , 2 1 ψ 1 y > δ w ψ ( y ) 1 2 R η 4 λ M 0 ( φ ) ψ 1 f x + y w d x + R η 4 λ M 0 ( φ ) ψ 1 f x d x d y . $$\begin{eqnarray*} {\left|J_{2,2}\right|} &\le& \frac{1}{\Vert \psi \Vert _1}\int _{{\left|y\right|}&gt;\delta w}{\left|\psi (y)\right|}\frac{1}{2}{\left[\int _{\mathbb {R}}\eta {\left(4\lambda M_0(\varphi )\Vert \psi \Vert _1{\left|f{\left(x+\frac{y}{w}\right)}\right|}\right)}dx\right.}\\ && {\left.+\int _{\mathbb {R}}\eta {\left(4\lambda M_0(\varphi )\Vert \psi \Vert _1{\left|f{\left(x\right)}\right|}\right)}dx\right]}dy. \end{eqnarray*}$$
Now, we can observe that, since ψ L 1 ( R ) $\psi \in L^1(\mathbb {R})$ , there exists w 2 ¯ > 0 $\overline{w_2}&gt;0$ such that
y > δ w ψ ( y ) d y < ε , $$\begin{equation*} \int _{{\left|y\right|}&gt;\delta w}{\left|\psi (y)\right|}dy&lt;\varepsilon , \end{equation*}$$
for every w w 2 ¯ $w\ge \overline{w_2}$ and, similarly to before, we have
J 2 , 2 1 ψ 1 R η 4 λ M 0 ( φ ) ψ 1 f ( x ) d x y > δ w ψ ( y ) d y 1 ψ 1 y > δ w ψ ( y ) d y I η [ λ 1 f ] < ε ψ 1 I η [ λ 1 f ] , $$\begin{eqnarray*} {\left|J_{2,2}\right|} &\le& \frac{1}{\Vert \psi \Vert _1}\int _{\mathbb {R}}\eta {\left(4\lambda M_0(\varphi ) \Vert \psi \Vert _1{\left|f(x)\right|}\right)}dx\int _{{\left|y\right|}&gt;\delta w}{\left|\psi (y)\right|}dy\\ &\le & \frac{1}{\Vert \psi \Vert _1}\int _{{\left|y\right|}&gt;\delta w}{\left|\psi (y)\right|}dy\, I^\eta [\lambda _1f]&lt;\frac{\varepsilon }{\Vert \psi \Vert _1}I^\eta [\lambda _1f], \end{eqnarray*}$$
for every w w 2 ¯ $w\ge \overline{w_2}$ . Finally, setting w ¯ : = max w 1 ¯ , w 2 ¯ $\overline{w}:=\max \left\lbrace \overline{w_1},\overline{w_2}\right\rbrace$ and
K : = M 0 ( ψ ) 2 ψ 1 φ 1 M 0 ( φ ) + ψ 1 M 0 ( ψ ) + I η [ λ 1 f ] 1 M 0 ( φ ) + 1 M 0 ( ψ ) , $$\begin{equation*} K:=\frac{M_0(\psi )}{2\Vert \psi \Vert _1}{\left[\frac{\Vert \varphi \Vert _1}{M_0(\varphi )}+\frac{\Vert \psi \Vert _1}{M_0(\psi )}+I^\eta [\lambda _1f]{\left(\frac{1}{M_0(\varphi )}+\frac{1}{M_0(\psi )}\right)}\right]}, \end{equation*}$$
we have
I η λ S w φ , ψ f f K ε , $$\begin{equation*} I^\eta {\left[\lambda {\left(S_w^{\varphi ,\psi }f-f\right)}\right]}\le K\varepsilon , \end{equation*}$$
for every w w ¯ $w\ge \overline{w}$ . Thus, the proof follows by the arbitrariness of ε. $\Box$

7 APPLICATIONS TO PARTICULAR CASES

In this section, we want to show how the sampling Durrmeyer-type series generalize some other well-known families of sampling-type series. Moreover, we will also consider applications to some special instances of Orlicz spaces.

In order to show that the generalized sampling-type series, introduced by Butzer in the 80s (see, e.g., [1, 2, 11, 12, 14, 43, 47]), are particular cases of the sampling Durrmeyer-type series, we need to give a distributional interpretation of the above operators, choosing, for example, as kernel ψ the Dirac delta distribution δ. Indeed, using the scaling and convolution property of the Dirac delta distribution, and recalling that δ is even, the generalized sampling operators
( G w φ f ) ( x ) : = k Z f k w φ ( w x k ) , x R , $$\begin{equation*} (G_w^\varphi f)(x):=\sum _{k\in \mathbb {Z}}f{\left(\frac{k}{w}\right)}\varphi (wx-k), \qquad x\in \mathbb {R}, \end{equation*}$$
can be obtained as follows
S w φ , δ f ( x ) = k Z φ ( w x k ) w R δ ( w u k ) f ( u ) d u = k Z φ ( w x k ) w R δ w u k w f ( u ) d u = k Z φ ( w x k ) R δ u k w f ( u ) d u = k Z φ ( w x k ) ( δ f ) k w = k Z φ ( w x k ) f k w = ( G w φ f ) ( x ) , $$\begin{eqnarray*} {\left(S_w^{\varphi ,\delta }f\right)}(x) &=& \sum _{k\in \mathbb {Z}} \varphi (wx-k) w \int _{\mathbb {R}}\delta (wu-k)f(u)du\\ &=& \sum _{k\in \mathbb {Z}} \varphi (wx-k) w \int _{\mathbb {R}}\delta {\left[w{\left(u-\frac{k}{w}\right)}\right]}f(u)du\\ &=& \sum _{k\in \mathbb {Z}} \varphi (wx-k)\int _{\mathbb {R}}\delta {\left(u-\frac{k}{w}\right)}f(u)du\\ &=& \sum _{k\in \mathbb {Z}} \varphi (wx-k)(\delta *f){\left(\frac{k}{w}\right)}\\ &=& \sum _{k\in \mathbb {Z}} \varphi (wx-k)f{\left(\frac{k}{w}\right)}= (G_w^\varphi f)(x), \end{eqnarray*}$$
for any f C ( R ) $f\in C(\mathbb {R})$ . Thus, S w φ , δ f ( x ) = ( G w φ f ) ( x ) $\left(S_w^{\varphi ,\delta }f\right)(x)=(G_w^\varphi f)(x)$ , for every x R $x\in \mathbb {R}$ and w > 0 $w&gt;0$ .
Similarly to what has been made for the generalized sampling operators, also the sampling Kantorovich operators (see, e.g., [8])
( K w χ f ) ( x ) = k Z χ ( w x k ) w k w k + 1 w f ( u ) d u , x R , $$\begin{equation*} (K_w^\chi f)(x)=\sum _{k\in \mathbb {Z}}\chi (wx-k){\left[w\int _{\frac{k}{w}}^{\frac{k+1}{w}}f(u)du\right]},\text{$\qquad x\in \mathbb {R}$,} \end{equation*}$$
can be viewed as sampling Durrmeyer-type operators. Indeed, for f L ( R ) $f\in L^\infty (\mathbb {R})$ and ψ ( t ) = χ [ 0 , 1 ] ( t ) $\psi (t)=\chi _{[0,1]}(t)$ , t R $t\in \mathbb {R}$ , where χ is the characteristic function of the set [ 0 , 1 ] R $[0,1]\subset \mathbb {R}$ , we have
S w φ , χ [ 0 , 1 ] f ( x ) = k Z φ ( w x k ) w R χ [ 0 , 1 ] ( w u k ) f ( u ) d u = k Z φ ( w x k ) w k w k + 1 w f ( u ) d u = ( K w φ f ) ( x ) . $$\begin{eqnarray*} {\left(S_w^{\varphi ,\chi _{[0,1]}}f\right)}(x) &=& \sum _{k\in \mathbb {Z}} \varphi (wx-k) w \int _{\mathbb {R}}\chi _{[0,1]}(wu-k)f(u)du\\ &=& \sum _{k\in \mathbb {Z}} \varphi (wx-k) w \int _{\frac{k}{w}}^{\frac{k+1}{w}}f(u)du=(K_w^\varphi f)(x). \end{eqnarray*}$$
Thus, S w φ , χ [ 0 , 1 ] f ( x ) = ( K w φ f ) ( x ) $\left(S_w^{\varphi ,\chi _{[0,1]}}f\right)(x)=(K_w^\varphi f)(x)$ , for every x R $x\in \mathbb {R}$ and w > 0 $w&gt;0$ . Finally, we observe that, in this case, ψ satisfies the condition of Remark 3.3 (c). Hence, all the modular convergence results hold. For further theoretical results concerning sampling Kantorovich operators, the readers can see references [16, 19-23, 40]; moreover, for applications to image reconstruction and enhancement, see, for example, [17, 18].

Finally, we will apply the previous convergence results in some useful cases of Orlicz spaces. First, we consider the particular case when η ( u ) = u p $\eta (u)=u^p$ for u 0 $u\ge 0$ and 1 p < + $1\le p&lt;+\infty$ . Here, L η ( R ) = E η ( R ) = L p ( R ) $L^\eta (\mathbb {R})=E^\eta (\mathbb {R})=L^p(\mathbb {R})$ , 1 p < + $1\le p&lt;+\infty$ , and in this frame, the modular convergence and the usual Luxemburg norm-convergence are equivalent. From the theory developed in the previous sections, we have the following corollaries.

Corollary 7.1.Let ψ be such that M 0 ( ψ ) < + $M_0(\psi )&lt;+\infty$ . Then, for every f L p ( R ) $f\in L^p(\mathbb {R})$ , 1 p < + $1\le p&lt;+\infty$ , we have

S w φ , ψ f p M 0 ( ψ ) 1 p M 0 ( φ ) p 1 p φ 1 1 / p M 0 ( ψ ) p 1 p f p , w > 0 . $$\begin{equation*} \Vert S_w^{\varphi ,\psi }f\Vert _p\le M_0(\psi )^{\frac{1}{p}}M_0(\varphi )^{\frac{p-1}{p}}\Vert \varphi \Vert _1^{1/p}\tilde{M_0}(\psi )^{\frac{p-1}{p}}\Vert f\Vert _p,\,\,\,w&gt;0. \end{equation*}$$
In particular, S w φ , ψ f $S_w^{\varphi ,\psi }f$ is well defined in L p ( R ) $L^p(\mathbb {R})$ and S w φ , ψ f L p ( R ) $S_w^{\varphi ,\psi }f\in L^p(\mathbb {R})$ whenever f L p ( R ) $f\in L^p(\mathbb {R})$ .

Proof.A direct application of Theorem 6.1 with η ( u ) = u p $\eta (u)=u^p$ , yields

S w φ , ψ f p p M 0 ( ψ ) φ 1 M 0 ( φ ) M 0 ( ψ ) M 0 ( φ ) p M 0 ( ψ ) p f p p , $$\begin{equation*} \Vert S_w^{\varphi ,\psi }f\Vert _p^p\le \frac{M_0(\psi )\Vert \varphi \Vert _1}{M_0(\varphi )\tilde{M_0}(\psi )}M_0(\varphi )^p\tilde{M_0}(\psi )^p\Vert f\Vert _p^p, \end{equation*}$$
from which the assertion follows. $\Box$

Moreover we immediately obtain the following convergence result.

Corollary 7.2.Let ψ be such that M 0 ( ψ ) < + $M_0(\psi )&lt;+\infty$ . For every f L p ( R ) $f\in L^p(\mathbb {R})$ , 1 p < + $1\le p&lt;+\infty$ , we have

lim w + S w φ , ψ f f p = 0 . $$\begin{equation*} \lim _{w\rightarrow +\infty }\Vert S_w^{\varphi ,\psi }f-f\Vert _p=0. \end{equation*}$$

As another important case, we can consider the function η α , β ( u ) = u α log β ( e + u ) $\eta _{\alpha ,\beta }(u)=u^\alpha \log ^\beta (e+u)$ , u 0 $u\ge 0$ , for α 1 $\alpha \ge 1$ and β > 0 $\beta &gt;0$ . The corresponding Orlicz spaces are the so-called interpolation spaces and are given by the set of functions f M ( R ) $f\in \mathcal {M}(\mathbb {R})$ for which
I η α , β [ λ f ] = R λ f ( x ) α log β ( e + λ f ( x ) ) d x < + , $$\begin{equation*} I^{\eta _{\alpha ,\beta }}[\lambda f]=\int _{\mathbb {R}}{\left(\lambda {\left|f(x)\right|} \right)}^\alpha \log ^\beta (e+\lambda {\left|f(x)\right|} )dx&lt;+\infty , \end{equation*}$$
for some λ > 0 $\lambda &gt;0$ , and they are denoted by L α log β L ( R ) $L^\alpha \log ^\beta L(\mathbb {R})$ . Note that the function η α , β $\eta _{\alpha ,\beta }$ satisfies the Δ2-property, which means that L α log β L ( R ) $L^\alpha \log ^\beta L(\mathbb {R})$ coincides with the space of its finite elements E η α , β ( R ) $E^{\eta _{\alpha ,\beta }}(\mathbb {R})$ . As a consequence of the Theorem 6.1, we can obtain the following corollary, for example, for the case α = β = 1 $\alpha =\beta =1$ .

Corollary 7.3.Let ψ be such that M 0 ( ψ ) < + $M_0(\psi )&lt;+\infty$ . For every f L log L $f\in L\log L$ , there holds

R S w φ , ψ f ( x ) log ( e + λ S w φ , ψ f ( x ) ) d x M 0 ( ψ ) φ 1 R f ( u ) log ( e + λ M 0 ( φ ) M 0 ( ψ ) f ( u ) ) d u , $$\begin{eqnarray*} &&\int _{\mathbb {R}}{\left|S_w^{\varphi ,\psi }f(x)\right|}\log (e+\lambda {\left|S_w^{\varphi ,\psi }f(x)\right|})dx\\ &&\quad \le M_0(\psi )\Vert \varphi \Vert _1\int _{\mathbb {R}}{\left|f(u)\right|}\log (e+\lambda M_0(\varphi )\tilde{M_0}(\psi ){\left|f(u)\right|})du, \end{eqnarray*}$$
λ > 0 $\lambda &gt;0$ . In particular, S w φ , ψ f $S_w^{\varphi ,\psi }f$ is well defined in L log L $ L\log L$ and S w φ , ψ f L log L $S_w^{\varphi ,\psi }f\in L\log L$ whenever f L log L $f\in L\log L$ .

Since in the above case of Orlicz spaces the Δ2-condition is fulfilled, the modular convergence and the norm convergence are equivalent and we immediately obtain the following convergence theorem.

Corollary 7.4.Let ψ be such that M 0 ( ψ ) < + $M_0(\psi )&lt;+\infty$ . For every f L log L $f\in L\log L$ and for every λ > 0 $\lambda &gt;0$ , we have

lim w + R S w φ , ψ f ( x ) f ( x ) log ( e + λ S w φ , ψ f ( x ) f ( x ) ) d x = 0 , $$\begin{equation*} \lim _{w\rightarrow +\infty }\int _{\mathbb {R}}{\left|S_w^{\varphi ,\psi }f(x)-f(x)\right|}\log (e+\lambda {\left|S_w^{\varphi ,\psi }f(x)-f(x)\right|})dx=0, \end{equation*}$$
or, equivalently,
lim w + S w φ , ψ f f L log L = 0 , $$\begin{equation*} \lim _{w\rightarrow +\infty }\Vert S_w^{\varphi ,\psi }f-f\Vert _{L\log L}=0, \end{equation*}$$
where · L log L $\Vert \cdot \Vert _{L\log L}$ is the Luxemburg norm associated to I η 1 , 1 $I^{\eta _{1,1}}$ .

As the last particular case, we consider the exponential spaces generated by the φ-function η α ( u ) = e u α 1 $\eta _\alpha (u)=e^{u^\alpha }-1$ , u 0 $u\ge 0$ for some α > 0 $\alpha &gt;0$ . Here, the Orlicz space L η α ( R ) $L^{\eta _\alpha }(\mathbb {R})$ consists of those functions f M ( R ) $f\in \mathcal {M}(\mathbb {R})$ for which
I η α [ λ f ] = R exp λ f ( x ) α 1 d x < + $$\begin{equation*} I^{\eta _{\alpha }}[\lambda f]=\int _{\mathbb {R}}{\left(\exp {{\left(\lambda {\left| f(x)\right|}\right)}^\alpha }-1\right)}dx&lt;+\infty \end{equation*}$$
for some λ > 0 $\lambda &gt;0$ . Since η α $\eta _\alpha$ does not satisfy the Δ2-property, the space L η α ( R ) $L^{\eta _\alpha }(\mathbb {R})$ does not coincide with the space of its finite elements E η α ( R ) $E^{\eta _\alpha }(\mathbb {R})$ . As a consequence, modular convergence is no more equivalent to norm convergence. By Theorem 6.1, we can obtain the following.

Corollary 7.5.Let ψ be such that M 0 ( ψ ) < + $M_0(\psi )&lt;+\infty$ . For every f L η α ( R ) $f\in L^{\eta _\alpha }(\mathbb {R})$ , there holds

R exp λ S w φ , ψ ( x ) α 1 d x M 0 ( ψ ) φ 1 M 0 ( φ ) M 0 ( ψ ) R exp λ M 0 ( φ ) M 0 ( ψ ) f ( x ) α 1 d x , $$\begin{eqnarray*} &&\int _{\mathbb {R}}{\left(\exp {{\left(\lambda {\left| S_w^{\varphi ,\psi }(x)\right|}\right)}^\alpha }-1\right)}dx\\ &&\quad \le \frac{M_0(\psi )\Vert \varphi \Vert _1}{M_0(\varphi )\tilde{M_0}(\psi )}\int _{\mathbb {R}}{\left(\exp {{\left(\lambda M_0(\varphi )\tilde{M_0}(\psi ){\left| f(x)\right|}\right)}^\alpha }-1\right)}dx, \end{eqnarray*}$$
λ > 0 $\lambda &gt;0$ . In particular, S w φ , ψ f $S_w^{\varphi ,\psi }f$ is well defined in L η α ( R ) $L^{\eta _\alpha }(\mathbb {R})$ and S w φ , ψ f L η α ( R ) $S_w^{\varphi ,\psi }f\in L^{\eta _\alpha }(\mathbb {R})$ whenever f L η α ( R ) $f\in L^{\eta _\alpha }(\mathbb {R})$ .

Since in this case Δ2-property is not fulfilled, we can only state a result on modular convergence rather than on norm convergence. The next corollary follows immediately from Theorem 6.2.

Corollary 7.6.Let ψ be such that M 0 ( ψ ) < + $M_0(\psi )&lt;+\infty$ . For every f L η α ( R ) $f\in L^{\eta _\alpha }(\mathbb {R})$ , there exists λ > 0 $\lambda &gt;0$ such that

lim w R exp λ S w φ , ψ f ( x ) f ( x ) α 1 d x = 0 . $$\begin{equation*} \lim _{w\rightarrow \infty }\int _{\mathbb {R}}{\left(\exp {{\left(\lambda {\left| S_w^{\varphi ,\psi }f(x)-f(x)\right|}\right)}^\alpha }-1\right)}dx=0. \end{equation*}$$

8 EXAMPLES WITH GRAPHICAL REPRESENTATIONS

In this last section, we want to show specific examples of kernel functions φ and ψ, for which the results proved in this paper hold, together with some graphical examples.
  1. We put φ ( t ) = σ 3 ( t ) $\varphi (t)=\sigma _3(t)$ , where σ3 is the central B-spline of order 3, that is,

    σ 3 ( t ) : = 1 2 j = 0 3 3 j 3 2 + t j + 2 , t R , $$\begin{equation} \sigma _3(t):=\frac{1}{2}\sum _{j=0}^{3}\binom{3}{j}{\left(\frac{3}{2}+t-j\right)}^2_+, \,\,\,t\in \mathbb {R}, \end{equation}$$ (8.1)
    (see Figure 1). In general, we define the central B-spline of order n N $n\in \mathbb {N}$ as
    σ n ( t ) : = 1 ( n 1 ) ! j = 0 n ( 1 ) j n j n 2 + t j + n 1 , t R , $$\begin{equation*} \sigma _n(t):=\frac{1}{(n-1)!}\sum _{j=0}^{n}(-1)^j\binom{n}{j}{\left(\frac{n}{2}+t-j \right)}_+^{n-1},\,\, \,t\in \mathbb {R}, \end{equation*}$$
    where ( · ) + $(\cdot )_+$ denotes the positive part, that is, ( t ) + : = max { t , 0 } , t R $(t)_+:=\max \lbrace t,0\rbrace , \,t\in \mathbb {R}$ .

    The Fourier transform of σ n $\sigma _n$ is given by

    σ n ̂ ( v ) = sinc n v 2 π , v R , $$\begin{equation*} \widehat{\sigma _n}(v)=\text{sinc}^n{\left(\frac{v}{2\pi }\right)},\text{ }v\in \mathbb {R}, \end{equation*}$$
    (see, e.g., [14, 15, 45]), where the sinc-function is defined by
    sinc ( v ) : = sin π v π v , v R 0 , 1 , v = 0 . $$\begin{equation*} \text{sinc}(v):= {\begin{cases} \frac{\sin \pi v}{\pi v}, & v\in \mathbb {R}\setminus 0, \\ 1, & v=0. \end{cases}} \end{equation*}$$
    The functions σ n $\sigma _n$ are bounded on R $\mathbb {R}$ for all n N $n\in \mathbb {N}$ with compact support [ n / 2 , n / 2 ] $[-n/2,n/2]$ . This implies that σ n L 1 ( R ) $\sigma _n\in L^1(\mathbb {R})$ and the moment condition M r ( φ ) < + $M_r(\varphi )&lt;+\infty$ is satisfied for all r > 0 $r&gt;0$ . It is well known that the singularity assumption (3.1) on φ is equivalent to prove the following condition expressed in terms of σ n ̂ $\widehat{\sigma _n}$ :
    σ n ̂ ( 2 k π ) = 1 , k = 0 , 0 , k Z 0 . $$\begin{equation} \widehat{\sigma _n}(2k\pi )={\begin{cases} 1, & k=0, \\ 0, & k\in \mathbb {Z}\setminus 0. \end{cases}}\end{equation}$$ (8.2)
    The equivalence between the two conditions is a direct consequence of the Poisson summation formula (see, e.g., [13]).

    Rewriting explicitly the expression in (8.1), we have

    σ 3 ( t ) : = 3 4 t 2 , t 1 2 , 1 2 3 2 t 2 , 1 2 < t 3 2 , 0 , t 3 2 , t R . $$\begin{equation*} \sigma _3(t):= {\begin{cases} \frac{3}{4}-t^2, & {\left| t \right|}\le \frac{1}{2}, \\[5pt] \frac{1}{2}{\left(\frac{3}{2}-{\left| t\right|}\right)}^2, & \frac{1}{2}&lt;{\left| t \right|}\le \frac{3}{2}, \\[7pt] 0, & {\left| t \right|}\ge \frac{3}{2}, \end{cases}} \, \,t\in \mathbb {R}. \end{equation*}$$

    Now, we put ψ ( t ) = χ [ 0 , 1 ] ( t ) $\psi (t)=\chi _{[0,1]}(t)$ , t R $t\in \mathbb {R}$ . In this case, sampling Durrmeyer-type series becomes

    S w σ 3 , χ [ 0 , 1 ] f ( x ) = k Z σ 3 ( w x k ) w R χ [ 0 , 1 ] ( w u k ) f ( u ) d u = k Z σ 3 ( w x k ) w k w k + 1 w f ( u ) d u = K w σ 3 f ( x ) , x R . $$\begin{eqnarray*} {\left(S_w^{\sigma _3,\chi _{[0,1]}}f\right)}(x) &=& \sum _{k\in \mathbb {Z}}\sigma _3(wx-k) w \int _{\mathbb {R}}\chi _{[0,1]}(wu-k)f(u)du\\ &=&\sum _{k\in \mathbb {Z}}\sigma _3(wx-k)w\int _{\frac{k}{w}}^{\frac{k+1}{w}}f(u)du={\left(K_w^{\sigma _3}f\right)}(x),\,\, \,x\in \mathbb {R}. \end{eqnarray*}$$
    Next, we apply the sampling Durrmeyer operator previously obtained, S w σ 3 , χ [ 0 , 1 ] $S_w^{\sigma _3,\chi _{[0,1]}}$ , to a particular regular function in C ( R ) $C(\mathbb {R})$ , namely, f ( x ) : = 1 x 2 + 1 , x R $f(x):=\frac{1}{x^2+1},\,x\in \mathbb {R}$ . The sampling Durrmeyer series S w σ 3 , χ [ 0 , 1 ] f $S_w^{\sigma _3,\chi _{[0,1]}}f$ of this function for w = 5 $w=5$ and w = 10 $w=10$ are given in Figure 2. The red dotted line is the graph of the operator S w σ 3 , χ [ 0 , 1 ] f $S_w^{\sigma _3,\chi _{[0,1]}}f$ , while the black line is the graph of f.

    In this case, according to the quantitative estimate given in Theorem 5.1, the order of uniform convergence of S w σ 3 , χ [ 0 , 1 ] f $S_w^{\sigma _3,\chi _{[0,1]}}f$ to f is at least O ( 1 w ) $\mathcal {O}(\frac{1}{w})$ , as w + $w\rightarrow +\infty$ .

    Moreover, according to the Voronovskaja formula for the sampling Durrmeyer operator established in [4], it is possible to reach a better order of approximation, in case of sufficiently regular functions and under additional assumptions on the moments. This happens, for example, choosing ψ ( t ) = 1 2 χ [ 1 , 1 ] ( t ) $\psi (t)=\frac{1}{2}\chi _{[-1,1]}(t)$ , t R $t\in \mathbb {R}$ . In this case, the sampling Durrmeyer-type series becomes

    S w σ 3 , 1 2 χ [ 1 , 1 ] f ( x ) = k Z σ 3 ( w x k ) w R 1 2 χ [ 1 , 1 ] ( w u k ) f ( u ) d u = k Z σ 3 ( w x k ) w 2 k 1 w k + 1 w f ( u ) d u , x R . $$\begin{eqnarray*} {\left(S_w^{\sigma _3,\frac{1}{2}\chi _{[-1,1]}}f \right)}(x) &=& \sum _{k\in \mathbb {Z}}\sigma _3(wx-k) w \int _{\mathbb {R}}\frac{1}{2}\chi _{[-1,1]}(wu-k)f(u)du\\ &=& \sum _{k\in \mathbb {Z}}\sigma _3(wx-k)\frac{w}{2}\int _{\frac{k-1}{w}}^{\frac{k+1}{w}}f(u)du,\,\, \,x\in \mathbb {R}. \end{eqnarray*}$$
    Thus, since f C 2 ( R ) $f\in C^2(\mathbb {R})$ and applying the Voronovskaja formula, we obtain that the order of approximation of S w σ 3 , 1 2 χ [ 1 , 1 ] f $S_w^{\sigma _3,\frac{1}{2}\chi _{[-1,1]}}f$ to f is at least O 1 w 2 $\mathcal {O}\left(\frac{1}{w^2}\right)$ , as w + $w\rightarrow +\infty$ . Below are the graphs of the sampling Durrmeyer series S w σ 3 , 1 2 χ [ 1 , 1 ] $S_w^{\sigma _3,\frac{1}{2}\chi _{[-1,1]}}$ of f for w = 5 $w=5$ and w = 10 $w=10$ (see Figure 3).

  2. Now, in order to show an application of Theorem 6.2, a useful example can be given considering functions that are not necessarily continuous and belonging to some L p $L^p$ -space. Hence, choosing

    φ ( t ) = σ 2 ( t ) = ( 1 t ) χ [ 1 , 1 ] ( t ) , t R , $$\begin{equation*} \varphi (t)=\sigma _2(t)=(1-{\left|t\right|})\chi _{[-1,1]}(t),\qquad t\in \mathbb {R}, \end{equation*}$$
    where σ2 is the central B-spline of order 2 (see Figure 4) and ψ ( t ) = χ [ 0 , 1 ] ( t ) $\psi (t)=\chi _{[0,1]}(t)$ , t R $t\in \mathbb {R}$ , we want to apply the sampling Durrmeyer series S w σ 2 , χ [ 0 , 1 ] $S_w^{\sigma _2,\chi _{[0,1]}}$ to two different discontinuous functions (see Figure 5), namely,
    f 1 ( x ) : = 1 , x 1 , 0 , x > 1 , $$\begin{equation*} f_1(x):= {\begin{cases} 1, & {\left| x \right|}\le 1, \\ 0, & {\left| x \right|}&gt; 1, \end{cases}} \end{equation*}$$
    as well as f2, defined by
    f 2 ( x ) : = 9 x 2 , x < 1 , 2 , 1 x < 0 , 1 , 0 x < 1 , 50 x 4 , x 1 . $$\begin{equation*} f_2(x):= {\begin{cases} \displaystyle \frac{9}{x^2}, & x &lt; -1, \\[3pt] 2, & -1\le x &lt;0, \\ 1, & 0\le x &lt;1,\\[3pt] \displaystyle \frac{-50}{x^4}, & x\ge 1. \end{cases}} \end{equation*}$$

    In general, we want to underline that from the properties of the kernel φ = σ n $\varphi =\sigma _n$ and since ψ = χ [ 0 , 1 ] $\psi =\chi _{[0,1]}$ (which has compact support) satisfies trivially the condition (c) of Remark 3.3, Corollary 7.2, Corollary 7.4, and Corollary 7.6 hold.

    The sampling Durrmeyer series with w = 5 $w=5$ and w = 10 $w=10$ of the functions f1 and f2 are given in Figure 6 and Figure 7, respectively. As before, the red dotted lines represent the graphs of the operators S w σ 2 , χ [ 0 , 1 ] f 1 $S_w^{\sigma _2,\chi _{[0,1]}}f_1$ and S w σ 2 , χ [ 0 , 1 ] f 2 $S_w^{\sigma _2,\chi _{[0,1]}}f_2$ , while the black lines denote the graphs of the functions f1 and f2. Finally, it should also be noted that, since both the kernels φ and ψ have compact support, for the evaluation of S w σ 2 , χ [ 0 , 1 ] f ( x ) $S_w^{\sigma _2,\chi _{[0,1]}}f(x)$ for a specific x R $x\in \mathbb {R}$ , only a finite number of mean values are needed, both in the case of functions with compact support, like f1, as in the case of functions with unbounded support, like f2.

Details are in the caption following the image
The spline function σ3
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The sampling Durrmeyer series S w σ 3 , χ [ 0 , 1 ] f $S_w^{\sigma _3,\chi _{[0,1]}}f$ with w = 5 $w=5$ (on left) and w = 10 $w=10$ (on right)
Details are in the caption following the image
The sampling Durrmeyer series S w σ 3 , 1 2 χ [ 1 , 1 ] f $S_w^{\sigma _3,\frac{1}{2}\chi _{[-1,1]}}f$ with w = 5 $w=5$ (on left) and w = 10 $w=10$ (on right)
Details are in the caption following the image
The spline function σ2
Details are in the caption following the image
The graphs of the functions f1 and f2
Details are in the caption following the image
The sampling Durrmeyer series S w σ 2 , χ [ 0 , 1 ] f 1 $S_w^{\sigma _2,\chi _{[0,1]}}f_1$ with w = 5 $w=5$ (on left) and w = 10 $w=10$ (on right)
Details are in the caption following the image
The sampling Durrmeyer series S w σ 2 , χ [ 0 , 1 ] f 2 $S_w^{\sigma _2,\chi _{[0,1]}}f_2$ with w = 5 $w=5$ (on left) and w = 10 $w=10$ (on right)
In conclusion, in order to underline that the convergence results proved in this paper hold for a large class of kernels φ and ψ, we observe that it is possible to provide examples of sampling Durrmeyer operators based on a more general kernel ψ, also with unbounded support. For example, we can choose as ψ the Fejér kernel (see Figure 8), defined by
F ( t ) : = 1 2 sinc 2 t 2 , t R . $$\begin{equation*} F(t):=\frac{1}{2}\text{sinc}^2{\left(\frac{t}{2}\right)},\,\,t\in \mathbb {R}. \end{equation*}$$
Details are in the caption following the image
The Fejér kernel F

Obviously, F is bounded and nonnegative on R $\mathbb {R}$ , belongs to L 1 ( R ) $L^1(\mathbb {R})$ , and satisfies R F ( t ) d t = 1 $\int _{\mathbb {R}}F(t)dt=1$ . Moreover, the moment condition M 0 ( ψ ) < + $M_0(\psi )&lt;+\infty$ is trivially fulfilled in view of Remark 3.3 (c) with 0 < ν < 1 $0&lt;\nu &lt;1$ .

Finally, it is interesting to observe that the Fejér kernel can be chosen also as the kernel φ. Indeed, since its Fourier transform is given by
F ̂ ( v ) : = 1 v π , v π , 0 , v > π , $$\begin{equation*} \widehat{F}(v):= {\begin{cases} \displaystyle 1-{\left|\frac{v}{\pi }\right|}, & v\le \pi , \\ 0, & v&gt;\pi , \end{cases}} \end{equation*}$$
(see, e.g., [13]), it follows, by the equivalent condition (8.2) (applied to F in place of σ n $\sigma _n$ ), that F satisfies the discrete singularity assumption (3.1) on φ.

ACKNOWLEDGMENTS

The authors are members of the Gruppo Nazionale per l'Analisi Matematica, la Probabilità e le loro Applicazioni (GNAMPA) of the Istituto Nazionale di Alta Matematica (INdAM), of the Gruppo UMI (Unione Matematica Italiana) T.A.A. (Teoria dell'Approssimazione e Applicazioni), and of the network RITA (Research ITalian network on Approximation). Fundings: The first and the third authors have been partially supported within the (1) 2022 GNAMPA-INdAM Project “Enhancement e segmentazione di immagini mediante operatori di tipo campionamento e metodi variazionali per lo studio di applicazioni biomediche” and (2) “Metodiche di Imaging non invasivo mediante angiografia OCT sequenziale per lo studio delle Retinopatie degenerative dell'Anziano (M.I.R.A.)”, funded by the Fondazione Cassa di Risparmio di Perugia (FCRP), 2019, while the third author within the projects: (1) Ricerca di Base 2019 dell'Università degli Studi di Perugia - “Integrazione, Approssimazione, Analisi Nonlineare e loro Applicazioni”, (2) “Metodi e processi innovativi per lo sviluppo di una banca di immagini mediche per fini diagnostici” (B.I.M.) funded by FCRP, 2018, (3) “CARE: A regional information system for Heart Failure and Vascular Disorder”, PRJ Project - 1507 Action 2.3.1 POR FESR 2014–2020, 2020,

    CONFLICT OF INTEREST

    The authors declare no potential conflict of interests.

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