Loading [MathJax]/jax/output/SVG/jax.js
Research article Special Issues

Histogram tomography

  • Received: 29 September 2019 Accepted: 22 October 2019 Published: 04 November 2019
  • In many tomographic imaging problems the data consist of integrals along lines or curves. Increasingly we encounter "rich tomography" problems where the quantity imaged is higher dimensional than a scalar per voxel, including vectors tensors and functions. The data can also be higher dimensional and in many cases consists of a one or two dimensional spectrum for each ray. In many such cases the data contain not just integrals along rays but the distribution of values along the ray. If this is discretized into bins we can think of this as a histogram. In this paper we introduce the concept of "histogram tomography". For scalar problems with histogram data this holds the possibility of reconstruction with fewer rays. In vector and tensor problems it holds the promise of reconstruction of images that are in the null space of related integral transforms. For scalar histogram tomography problems we show how bins in the histogram correspond to reconstructing level sets of function, while moments of the distribution are the x-ray transform of powers of the unknown function. In the vector case we suggest a reconstruction procedure for potential components of the field. We demonstrate how the histogram longitudinal ray transform data can be extracted from Bragg edge neutron spectral data and hence, using moments, a non-linear system of partial differential equations derived for the strain tensor. In x-ray diffraction tomography of strain the transverse ray transform can be deduced from the diffraction pattern the full histogram transverse ray transform cannot. We give an explicit example of distributions of strain along a line that produce the same diffraction pattern, and characterize the null space of the relevant transform.

    Citation: William R. B. Lionheart. Histogram tomography[J]. Mathematics in Engineering, 2020, 2(1): 55-74. doi: 10.3934/mine.2020004

    Related Papers:

    [1] Md Hasanuzzaman, Mohammad Imdad . Relation theoretic metrical fixed point results for Suzuki type ZR-contraction with an application. AIMS Mathematics, 2020, 5(3): 2071-2087. doi: 10.3934/math.2020137
    [2] Mustafa Mudhesh, Hasanen A. Hammad, Eskandar Ameer, Muhammad Arshad, Fahd Jarad . Novel results on fixed-point methodologies for hybrid contraction mappings in Mb-metric spaces with an application. AIMS Mathematics, 2023, 8(1): 1530-1549. doi: 10.3934/math.2023077
    [3] Nehad Abduallah Alhajaji, Afrah Ahmad Noman Abdou, Jamshaid Ahmad . Application of fixed point theory to synaptic delay differential equations in neural networks. AIMS Mathematics, 2024, 9(11): 30989-31009. doi: 10.3934/math.20241495
    [4] Tahair Rasham, Muhammad Nazam, Hassen Aydi, Abdullah Shoaib, Choonkil Park, Jung Rye Lee . Hybrid pair of multivalued mappings in modular-like metric spaces and applications. AIMS Mathematics, 2022, 7(6): 10582-10595. doi: 10.3934/math.2022590
    [5] Fatima M. Azmi . New fixed point results in double controlled metric type spaces with applications. AIMS Mathematics, 2023, 8(1): 1592-1609. doi: 10.3934/math.2023080
    [6] Amjad Ali, Muhammad Arshad, Awais Asif, Ekrem Savas, Choonkil Park, Dong Yun Shin . On multivalued maps for φ-contractions involving orbits with application. AIMS Mathematics, 2021, 6(7): 7532-7554. doi: 10.3934/math.2021440
    [7] Yan Sun, Xiao-lan Liu, Jia Deng, Mi Zhou . Some fixed point results for α-admissible extended Z-contraction mappings in extended rectangular b-metric spaces. AIMS Mathematics, 2022, 7(3): 3701-3718. doi: 10.3934/math.2022205
    [8] Umar Ishtiaq, Fahad Jahangeer, Doha A. Kattan, Manuel De la Sen . Generalized common best proximity point results in fuzzy multiplicative metric spaces. AIMS Mathematics, 2023, 8(11): 25454-25476. doi: 10.3934/math.20231299
    [9] Shaoyuan Xu, Yan Han, Suzana Aleksić, Stojan Radenović . Fixed point results for nonlinear contractions of Perov type in abstract metric spaces with applications. AIMS Mathematics, 2022, 7(8): 14895-14921. doi: 10.3934/math.2022817
    [10] Hanadi Zahed, Zhenhua Ma, Jamshaid Ahmad . On fixed point results in F-metric spaces with applications. AIMS Mathematics, 2023, 8(7): 16887-16905. doi: 10.3934/math.2023863
  • In many tomographic imaging problems the data consist of integrals along lines or curves. Increasingly we encounter "rich tomography" problems where the quantity imaged is higher dimensional than a scalar per voxel, including vectors tensors and functions. The data can also be higher dimensional and in many cases consists of a one or two dimensional spectrum for each ray. In many such cases the data contain not just integrals along rays but the distribution of values along the ray. If this is discretized into bins we can think of this as a histogram. In this paper we introduce the concept of "histogram tomography". For scalar problems with histogram data this holds the possibility of reconstruction with fewer rays. In vector and tensor problems it holds the promise of reconstruction of images that are in the null space of related integral transforms. For scalar histogram tomography problems we show how bins in the histogram correspond to reconstructing level sets of function, while moments of the distribution are the x-ray transform of powers of the unknown function. In the vector case we suggest a reconstruction procedure for potential components of the field. We demonstrate how the histogram longitudinal ray transform data can be extracted from Bragg edge neutron spectral data and hence, using moments, a non-linear system of partial differential equations derived for the strain tensor. In x-ray diffraction tomography of strain the transverse ray transform can be deduced from the diffraction pattern the full histogram transverse ray transform cannot. We give an explicit example of distributions of strain along a line that produce the same diffraction pattern, and characterize the null space of the relevant transform.


    The theory of set-valued analysis plays a key role in various branches of mathematics because of its applications in areas such as control theory, game theory, biomathematics, qualitative physics, viability theory, and so on. In particular, the idea of multivalued mappings in fixed point theory was initiated by von Neumann in the study of game theory. On the other hand, the notion of multivalued mappings in metric fixed point theory was brought up by Nadler [28] who used the concept of Hausdorff metric to obtain a generalization of Banach contraction principle. Banach fixed point theorem (see [7]) is the earliest, simple and versatile classical result for single-valued mappings in fixed point theory with metric space structure. More than a handful of literature embrace applications and extensions of this principle from different perspectives, for example, by weakening the hypotheses, employing different mappings and various forms of quasi and pseudo-metric spaces. Meanwhile, a number of generalizations in diverse frames of Nadler's fixed point result have also been investigated by several authors; see, for example, [1,5,17,19,27] and references therein.

    The analysis of new spaces and their properties have been an interesting topic among current mathematical research. In this direction, the notion of b-metric spaces is presently thriving. The idea commenced with the work of Bakhtin [6] and Bourbaki [9]. Thereafter, Czerwik [13] gave a postulate which is weaker than the classical triangle inequality and formally established a b-metric space with a view of improving the Banach fixed point theorem. Meanwhile, the notion of b-metric spaces is gaining fast generalizations. For a recent short survey on basic concepts and results in fixed point theory in the framework of b-metric spaces, we refer the interested reader to Karapinar [25]. On similar development, one of the active branches of fixed point theory that is also currently drawing attentions of researchers is the study of hybrid contractions. The concept has been viewed in two directions, viz, first, hybrid contraction deals with contractions involving both single-valued and multivalued mappings and the second merges linear and nonlinear contractions. Recently, Karapinar and Fulga [23] inaugurated a novel notion of b-hybrid contraction in the framework of b-metric space and studied the existence and uniqueness of fixed points for such contractions. Their ideas merged several existing results in the corresponding literature. Similarly, by using the concept of α-admissible mapping due to Samet [33], Chifu and Karapinar [12] improved the main result in [23] by combining the idea of simulation functions of Khojasteh et al. [26]. Interestingly, hybrid fixed point theory has potential applications in functional inclusions, optimization theory, fractal graphics, discrete dynamics for set-valued operators and other areas of nonlinear functional analysis.

    In this work, we introduce two notions, admissible multivalued hybrid Z-contractions and multivalued hybrid Z-contractions in the framework of b-metric spaces and establish sufficient conditions for existence of fixed points for such contractions. A few consequences of our main theorem are pointed out by using variants of simulation functions. Overall, the ideas presented herein unify and complement several significant fixed point theorems in the setting of both single-valued and set-valued mappings involving either linear or nonlinear contractions. Nontrivial illustrative examples are provided to authenticate the hypotheses of our main result. From application perspective, some fixed point theorems of b-metric spaces endowed with partial ordering and graph are derived and solvability conditions of nonlinear matrix equations are investigated. In particular, this paper complements the main results of Branciari [11], Chifu and Karapinar [12], Czerwik [13], Jachymski [22], Karapinar and Agarwal [23], Karapinar and Fulga [24], Khojasteh [26], Nadler [28], Rhoades [31], Samet [33] and a few others in the comparable literature.

    In this section, we collect important notation, useful definitions and basic results coherent with the literature. Hereafter, we denote by N, R+ and R the sets of natural numbers, non-negative reals and real numbers, respectively.

    Czerwik [13] formally defined the notion of a b-metric space as follows: Let X be a nonempty set and η1 be a constant. Suppose that the mapping μ:X×XR+ satisfies the following conditions for all x,y,zX:

    (i) μ(x,y)=0 if and only if x=y (self-distancy);

    (ii) μ(x,y)=μ(y,x) (symmetry);

    (iii) μ(x,y)η[μ(x,z)+μ(z,y)] (weighted triangle inequality).

    Then, the tripled (X,μ,η) is called a b-metric space. It is noteworthy that every metric is a b-metric with the parameter η=1. Also, in general, a b-metric is not a continuous functional. Hence, the class of b-metric is larger than the class of classical metric.

    Example 2.1. [8] Let X=lp(R) with 0<p<1, where

    lp(R)={{xn}nNR:n=1|xn|p<}.

    Define μ:X×XR+ as

    μ(x,y)=(n=1|xnyn|p)1p,

    where x={xn}nN and y={yn}nN. Then, μ is a b-metric with parameter η=21p and hence (X,μ,21p) is a b-metric space.

    Example 2.2. [18] Let X=N{} and μ:X×XR+ be defined by

    μ(x,y)={0,if x=y|1x1y|,if x,yareevenorxy=5,if x,yareoddandxy2,otherwise.

    Then, (X,μ,η) is a b-metric space with parameter η=3, but μ is not a continuous functional.

    Definition 2.3. [10] Let (X,μ,η) be a b-metric space. A sequence {xn}nN is said to be:

    (i) convergent if and only if there exists xX such that xnx as n, and we write this as limnμ(xn,x)=0.

    (ii) Cauchy if and only if μ(xn,xm)0 as n,m.

    (iii) complete if every Cauchy sequence in X is convergent.

    In a b-metric space, the limit of a sequence is not always unique. However, if a b-metric is continuous, then every convergent sequence has a unique limit.

    Definition 2.4. [10] Let (X,μ,η) be a b-metric space. Then, a subset A of X is called:

    (i) compact if and only if for every sequence of elements of A, there exists a subsequence that converges to an element of A.

    (ii) closed if and only if for every sequence {xn}nN of elements of A that converges to an element x, we have xA.

    Definition 2.5. A nonempty subset A of X is called proximal if, for each xX, there exists aA such that μ(x,a)=μ(x,A).

    Throughput this paper, we denote by N(X), CB(X), Pr(X), and K(X), the family of nonempty subsets of X, the set of all nonempty closed and bounded subsets of X, the family of all nonempty proximal subsets of X, and the class of nonempty closed and compact subsets of X, respectively.

    Let (X,μ,η) be a b-metric space. For A,BK(X), the function b:K(X)×K(X)R+, defined by

    b(A,B)={max{supxAμ(x,B),supxBμ(x,A)},if it exists ,otherwise,

    is called generalized Hausdorff b-metric on K(X) induced by the b-metric μ, where

    μ(x,A)=infyAμ(x,y).

    Remark 2.6. Since every compact set is proximal and every proximal set is closed, we have the inclusions:

    K(X)Pr(X)CB(X)N(X).

    Definition 2.7. Let (X,μ,η) be a metric space. A set-valued mapping T:XN(X) is called a multi-valued mapping. A point uX is said to be a fixed point of T if uTu.

    Definition 2.8. Let X be a nonempty. A multivalued mapping T:XN(X) is said to be α-admissible with respect to a function α:X×XR+, if for each xX and yTx with α(x,y)1, we have α(y,z)1 for all zTy.

    Not long ago, a family of auxiliary functions under the name simulation functions was introduced by Khojasteh et al. [26] to unify various types of contractions.

    Definition 2.9. [26] A simulation function is a mapping ξ:R+×R+R satisfying the following axioms:

    (i) ξ(0,0)=0;

    (ii) ξ(t,s)<ts for all t,s>0;

    (iii) if {tn}n1 and {sn}n1 are sequences in (0,) such that limntn=limnsn>0, then limnsupξ(tn,sn)<0.

    We denote the family of simulation functions by Z.

    Example 2.10. [26] Let ξi:R+×R+R(i=i,2,3) be defined by

    (i) ξ1(t,s)=τ(s)ϕ(t) for all t,sR+, where τ,ϕ:R+R+ are continuous functions such that τ(t)=ϕ(t)=0 if and only if t=0 and τ(t)<tϕ(t) for all t>0.

    (ii) ξ2(t,s)=sΛ(t,s)Γ(t,s)t for all t,sR+, where Λ,Γ:R+(0,) are two continuous functions with respect to each variable such that Λ(t,s)>Γ(t,s) for all t,s>0.

    (iii) ξ3(t,s)=sψ(s)t for all t,sR+, where ψ:R+R+ is a continuous function such that ψ(0)=0 if and only if t=0.

    Then, ξi (i=1,2,3) are simulation functions.

    For more examples of simulation functions, see [2,4,20].

    Definition 2.11. [26] Let (X,μ) be a metric space. A mapping T:XX is called a Z-contraction with respect to ξZ, if

    ξ(μ(Tx,Ty),μ(x,y))0forallx,yX. (2.1)

    The following is the main result in [26].

    Theorem 2.12. [26] Every Z-contraction on a complete metric space has a unique fixed point.

    An example of a Z-contraction is the Banach contraction which can be obtained by setting ξ(t,s)=ρst, where ρ[0,1) in (2.1).

    The idea of comparison function was initiated by Rus [32] and it has been studied by several researchers in order to obtain more general forms of contraction mappings.

    Definition 2.13. [32] A mapping φ:R+R+ is said to be a comparison function if it is nondecreasing and φn(t)0 as n for all t0.

    Example 2.14. The following functions φ:R+R+ are comparison functions:

    (i) φ(t)=ςt for all t0, where ς(0,1).

    (ii) φ(t)=tt+1 for each t0.

    Definition 2.15. [23,32] A nondecreasing function φ:R+R+ is called:

    (i) a c-comparison function if φn(t)0 as n for every tR+;

    (ii) a b-comparison function if there exist k0N, λ(0,1) and a convergent non-negative series n=1xn such that ηk+1φk+1(t)ληkφk(t)+xk, for η1,kk0 and any t0, where φn denotes the nth iterate of φ.

    Denote by Ωb, the family of functions φ:R+R+ satisfying the following conditions:

    (i) φ is a b-comparison function;

    (ii) φ(t)=0 if and only if t=0.

    (iii) φ is continuous.

    Remark 2.16. A b-comparison function is a c-comparison function when η=1. Moreover, it can be shown that a c-comparison function is a comparison function, but the converse is not always true. Notice that in Example 2.13, (i) is a c-comparison function. But, (ii) is not a c-comparison function.

    Lemma 2.17. [32] For a comparison function φ:R+R+, the following properties hold:

    (i) each iterate φn,nN is also a comparison function;

    (iii) φ(t)<t for all t>0.

    Lemma 2.18. [32] Let φ:R+R+ be a b-comparison function. Then, the series k=0ηkφk(t) converges for every tR+.

    Remark 2.19. [23] In Lemma 2.18, every b-comparison function is a comparison function and thus, in Lemma 2.17, every b-comparison function satisfies φ(t)<t.

    Lemma 2.20. ([34]) Let (X,σ,η) be a b-metric space. For A,BK(X) and x,yX, the following conditions hold:

    (i) μ(x,B)b(A,B) for any xA.

    (ii) μ(x,B)μ(x,b) for any bB.

    (iii) μ(x,A)η[μ(x,y)+μ(y,A)].

    (i v) μ(x,A)=0xA.

    (v) b(A,B)=0A=B.

    (vi) b(A,B)=b(B,A).

    (vii) b(A,B)η[b(A,C)+b(C,B)].

    The concepts of admissible multivalued hybrid Z-contractions and multivalued hybrid Z-contractions are introduced as follows.

    Definition 3.1. Let (X,μ,η) be a b-metric space. A set-valued map T:XK(X) is called an admissible multivalued hybrid Z-contraction with respect to ξZ, if there exists a function α:X×XR+ and a b-comparison function φ:R+R+ such that

    ξ(α(x,y)b(Tx,Ty),φ(MrT(x,y)))0, (3.1)

    for all x,yX, where

    MrT(x,y)={[A(x,y)]1r,for r>0,x,yXB(x,y),forr=0,x,yX,
    A(x,y)=a1(μ(x,y))r+a2(μ(x,Tx))r+a3(μ(y,Ty))r+a4(μ(y,Ty)(1+μ(x,Tx))1+μ(x,y))r+a5(μ(y,Tx)(1+μ(x,Ty))1+μ(x,y))r,

    and

    B(x,y)=(μ(x,y))a1(μ(x,Tx))a2(μ(y,Ty))a3(μ(y,Ty)(1+μ(x,Tx))1+μ(x,y))a4(μ(x,Ty)+μ(y,Tx)2η)a5,

    with r0 and ai0(i=1,2,3,4,5) such that 5i=1ai=1.

    Remark 3.2.

    (i) In Definition 3.1, if α(x,y)=1, then T is called a multivalued hybrid Z-contraction with respect ξZ.

    (ii) If T is an admissible multivalued hybrid Z-contraction with respect to ξZ, then for all x,yX,

    α(x,y)b(Tx,Ty)<φ(MrT(x,y)). (3.2)

    To prove the assertion (ii), suppose xy, then, μ(x,y)>0. If Tx=Ty, we have α(x,y)b(Tx,Ty)=0<φ(MrT(x,y)). Otherwise, b(Tx,Ty)>0. If α(x,y)=0, then (3.2) holds trivially. So, assume that α(x,y)>0, an using (ii) in Definition 2.9, we obtain

    0ξ(α(x,y)b(x,y),φ(MrT(x,y)))<φ(MrT(x,y))α(x,y)b(Tx,Ty).

    The following definition is very significant in the proof of our results.

    Definition 3.3. Let (X,μ,η) be a b-metric space. A multivalued mapping T:XK(X) is said to be -continuous at uX, if for any sequence {xn}n1 in X,

    limnμ(xn,u)=0limnb(Txn,Tu)=0.

    We say that T is -continuous if it is continuous at each point of X.

    Definition 3.3 can be reformulated as follows:

    T is said to be -continuous at a point u, if for every ϵ>0, there exists a δ>0 such that

    μ(xn,u)<δb(Txn,Tu)<ϵ.

    Example 3.4. Let X=R and μ(x,y)=|xy|2 for all x,yX. Then, (X,μ,η=2) is a b-metric space. Define T:XK(X) by Tx=[x,x+5] for all xX. Then, b(Tx,Ty)=|xy|2. For any ϵ>0, take δ=ϵ7. Then, μ(x,y)<δ implies b(Tx,Ty)<ϵ. Consequently, T is -continuous.

    Theorem 3.5. Let (X,μ,η) be a complete b-metric space and T:XK(X) be an admissible multivalued hybrid Z-contraction with respect to ξZ. Suppose also that the following conditions are satisfied:

    (i) T is an α-admissible multivalued mapping;

    (ii) there exists x0X and x1Tx0 such that α(x0,x1)1;

    (iii) T is -continuous;

    (i v) Tx is proximal for each xX.

    Then, T has at least one fixed point in X.

    Proof. By Condition (ii), there exists x0X and x1Tx0 such that α(x0,x1)1. If x0=x1 (or x0=x=y=x1), then from (3.1), we have

    0ξ(α(x0,x1)b(Tx0,Tx1),φ(MrT(x0,x1)))<φ(MrT(x0,x1))α(x0,x1)b(Tx0,Tx1),

    which is equivalent to

    α(x0,x1)b(Tx0,Tx1)φ(MrT(x0,x1)). (3.3)

    Then, for r>0, using the proximality of T, we get

    MrT(x0,x1)=[A(x0,x1)]1r=[a1(μ(x0,x1))r+a2(μ(x0,Tx0))r+a3(μ(x1,Tx1))ra4(μ(x1,Tx1)(1+μ(x0,Tx0))1+μ(x0,x1))r+a5(μ(x1,Tx0)(1+μ(x0,Tx1))1+μ(x0,x1))r]1r=[a1(μ(x0,x1))r+a2(μ(x0,x1))r+a3(μ(x1,Tx1))r+a4(μ(x1,Tx1)(1+μ(x0,x1))1+μ(x0,x1))r+a5(μ(x1,x1)(1+μ(x0,Tx0))1+μ(x0,x1))r]1r=[a1(μ(x0,x1))r+a2(μ(x0,x1))r+a3(μ(x1,Tx1))r+a4(μ(x1,Tx1))r]1r=[(a1+a2)(μ(x0,x1))r+(a3+a4)(μ(x1,Tx1))r]1r=[(a1+a2)(μ(x1,x1))r+(a3+a4)(μ(x1,Tx0))r]1r(x1=x0)=[(a1+a2)(0)r+(a3+a4)(μ(x1,x1))r]1r=0.

    Similarly, B(x0,x1)=0. Hence, (3.3) becomes α(x0,x1)b(Tx0,Tx1)φ(0)=0, which implies that Tx0=Tx1. It follows directly that x1Tx0=Tx1; that is, x1 is a fixed point of T. So, hereafter, we assume that x0x1 and x1Tx1 so that μ(x1,Tx1)>0. Since Tx1K(X) and x1Tx0, there exists x2Tx1 with x1x2 such that

    μ(x1,x2)b(Tx0,Tx1)α(x0,x1)b(Tx0,Tx1). (3.4)

    Setting x=x0 and y=x1 in (3.1), gives

    0ξ(α(x0,x1)b(Tx0,Tx1),φ(MrT(x0,x1)))<φ(MrT(x0,x1))α(x0,x1)b(Tx0,Tx1),

    which can also be written as

    α(x0,x1)b(Tx0,Tx1)φ(MrT(x0,x1)). (3.5)

    Combining (3.4) and (3.5), yields

    μ(x1,x2)φ(MrT(x0,x1)). (3.6)

    Given that T is α-admissible and x2Tx1, we have α(x1,x2)1. If x2Tx2, then taking x1=x2 (or x1=x=y=x2), as in previous steps, we find directly that x2 is a fixed point of T. So, suppose that x2Tx2 so that μ(x2,Tx2)>0. Since Tx1,Tx2K(X) and x2Tx1, there exists a point x3Tx2 with x2x3 such that

    μ(x2,x3)b(Tx1,Tx2)α(x1,x2)b(Tx1,Tx2). (3.7)

    Putting x=x1 and y=x2 in (3.1), we get

    0ξ(α(x1,x2)b(Tx1,Tx2),φ(MrT(x1,x2)))<φ(MrT(x1,x2))α(x1,x1)b(Tx1,Tx2),

    which is equivalent to

    α(x1,x2)b(Tx1,Tx2)φ(MrT(x1,x2)). (3.8)

    From (3.7) and (3.8), we have

    μ(x2,x3)φ(MrT(x1,x2)). (3.9)

    Continuing with this iteration, we generate a sequence {xn}n1 in X with xnTxn,xn+1Txn, α(xn,xn+1)1 such that

    μ(xn,xn+1)φ(MrT(xn1,xn)). (3.10)

    Now, we investigate (3.10) under the following cases:

    Case 1: r>0. In this case, from (3.1), using the proximality of T, we have

    MrT(xn1,xn)=[A(xn1,xn)]1r=[a1(μ(xn1,xn))r+a2(μ(xn1,Txn1))r+a3(μ(xn,Txn))r+a4(μ(xn,Txn)(1+μ(xn1,Txn1))1+μ(xn1,xn))r+a5(μ(xn,Txn1)(1+μ(xn1,Txn))1+μ(xn1,xn))r]1r=[a1(μ(xn1,xn))r+a2(μ(xn1,xn))r+a3(μ(xn,xn+1))r+a4(μ(xn,xn+1)(1+μ(xn1,xn))1+μ(xn1,xn))r+a5(μ(xn,xn)(1+μ(xn1,xn+1))1+μ(xn1,xn))r]1r=[(a1+a2)(μ(xn1,xn))r+(a3+a4)(μ(xn,xn+1))r]1r. (3.11)

    From (3.10) and (3.11), we get

    μ(xn,xn+1)φ([(a1+a2)(μ(xn1,xn))r+(a3+a4)(μ(xn,xn+1))r]1r). (3.12)

    Assume that μ(xn1,xn)μ(xn,xn+1), then, since φ is nondecreasing and noting that a1+a2+a3+a41, from (3.12), we have

    μ(xn,xn+1)φ([μ(xn,xn+1)r]1r)=φ(μ(xn,xn+1))<μ(xn,xn+1),

    a contradiction. Consequently, (3.12) becomes

    μ(xn,xn+1)φ(μ(xn1,xn))φ2(μ(xn2,xn1))φn(μ(x0,x1)). (3.13)

    Now, let m,nN with m>n. Then, by triangular inequality in (X,μ,η), we have

    μ(xn,xm)ημ(xn,xn+1)+η2μ(xn+1,xn+2)++ηmnμ(xm1,xm)ηφn(μ(x0,x1))+η2φn+1(μ(x0,x1))++ηmn+1φm(μ(x0,x1))=1ηn1(ηnφn(μ(x0,x1))+ηn+1φn+1(μ(x0,x1))++ηmφm(μ(x0,x1)))=1ηn1mi=nηiφi(μ(x0,x1))1ηn1i=0ηiφi(μ(x0,x1)). (3.14)

    Since φ is a b-comparison function, it follows from Lemma 2.18 that the series i=0φi(μ(x0,x1)) is convergent. Setting Sk=ki=1φi(μ(x0,x1)), (3.14) can be written as

    μ(xn,xm)1ηn1(Sm1Sn1). (3.15)

    Letting n,m in (3.15), we obtain μ(xn,xm)0, which proves that {xn}n1 is a Cauchy sequence in X. Completeness of this space implies that there exists uX such that

    limnμ(xn,u)=0. (3.16)

    Now, we show that uTu. Using the triangle inequality in X, we have

    μ(u,Tu)η[μ(u,xn)+μ(xn,Tu)]ημ(u,xn)+ηb(Txn1,Tu). (3.17)

    Since T is -continuous, passing to limit as n in (3.17), we have μ(u,Tu)=0, which implies that uTu.

    Case 2: r=0. For this, take x=xn1Txn and y=xnTxn+1 in (3.1), then, by proximality of T, we have

    MrT(xn1,xn)=B(xn1,xn)=(μ(xn1,xn))a1(μ(xn1,Txn1))a2(μ(xn,Txn))a3(μ(xn,Txn)(1+μ(xn1,Txn1))1+μ(xn1,xn))a4(μ(xn1,Txn)+(xn,Txn1)2η)a5=(μ(xn1,xn))a1(μ(xn1,xn))a2(μ(xn,xn+1))a3(μ(xn,xn+1)(1+μ(xn1,xn))1+μ(xn1,xn))a4(μ(xn1,xn+1)+(xn,xn)2η)a5=(μ(xn1,xn))a1+a2(μ(xn,xn+1))a3+a4(μ(xn1,xn)+μ(xn,xn+1)2)a5. (3.18)

    It is well-known that for any p,q,l>0,

    (p+q2)lpl+ql2. (3.19)

    Applying (3.19) to (3.18), gives

    MrT(xn1,xn)(μ(xn1,xn))a1+a2(μ(xn,xn+1))a3+a4((μ(xn1,xn))a52+(μ(xn,xn+1))a52). (3.20)

    Recall that from (3.1), we get

    0ξ(α(xn1,xn)b(Txn1,Txn),φ(MrT(xn1,xn)))<φ(MrT(xn1,xn))α(xn1,xn)b(Txn1,Txn),

    which is equivalent to

    μ(xn,xn+1)α(xn1,xn)b(Txn1,Txn)φ(MrT(xn1,xn)). (3.21)

    Assume that μ(xn1,xn)μ(xn,xn+1), then (3.20) becomes

    MrT(xn1,xn)(μ(xn,xn+1))a1+a2+a3+a4+a5=μ(xn,xn+1). (3.22)

    Since φ is nondecreasing, from (3.22) and (3.21), we have

    μ(xn,xn+1)φ(μ(xn,xn+1))<μ(xn,xn+1),

    a contradiction. Therefore, (3.21) yields

    μ(xn,xn+1)φ(μ(xn1,xn))φ2(μ(xn2,xn1))φn(μ(x0,x1)). (3.23)

    Following the same procedures as in the Case r>0, it follows from (3.23) that {xn}n1 is a Cauchy sequence in X, and the completeness of this space implies that there exists uX such that

    limnμ(xn,u)=0. (3.24)

    To show that uTu, consider:

    μ(u,Tu)η[μ(u,xn)+μ(xn,Tu)]ημ(xn,u)+ηb(Txn1,Tu). (3.25)

    Since T is -continuous, letting n in (3.25), and using (3.24), we obtain μ(u,Tu)=0, which implies that uTu.

    Theorem 3.6. Let (X,μ,η) be a complete b-metric space and T:XK(X) be a multivalued mapping satisfying the following conditions:

    (i) T is an α-admissible multivalued mapping;

    (ii) there exists x0X and x1Tx0 such that α(x0,x1)1;

    (iii) T is -continuous;

    (i v) Tx is proximal for each xX.

    Assume further that there exist ξZ, φΩb and a function α:X×XR+ such that for all x,yX,

    ξ(α(x,y)b(Tx,Ty),φ([A(x,y)]1r))0, (3.26)

    where A(x,y) is as given in Definition 3.1.

    Then, T has at least one fixed point in X.

    The proof follows from Case 1 of Theorem 3.5.

    Remark 3.7. It is necessary to remind the reader that Theorems 3.5 and 3.6 are invalid if we take CB(X) instead of K(X) (for example, see [28,Theorem 5]).

    Example 3.8. Let X=[1,) and μ(x,y)=|xy|2 for all x,yX. Then, (X,μ,η=2) is a complete b-metric space. Note that (X,μ,η=2) is not a metric space, since for x=1,y=4 and z=3, we have

    μ(x,y)=9>5=μ(x,z)+μ(z,y).

    Define T:XK(X) by

    Tx={{4},if x[1,3)[1,4x],ifx[3,),

    and the functions α:X×XR+, φ:R+R+ by

    α(x,y)={5,if  x,y[1,3)1600,if x=4,y=50,otherwise,

    φ(t)=t2 for all t0. Also, take ξ(t,s)=18st for all s,tR+. Clearly, ξZ and φΩb. Now, we show that (3.26) is satisfied under the following cases:

    Case 1: x,y[1,3). For this, Tx=Ty={4} and so, b(Tx,Ty) = 0 for all x,yX. Hence,

    ξ(α(x,y)b(Tx,Ty),φ([A(x,y)]1r))=ξ(0,φ([A(x,y)]1r))=18φ([A(x,y)]1r)0.

    Case 2: x=4 and y=5. For this, we have

    ξ(α(4,5)b(T4,T5),φ([A(4,5)]1r))=18φ([A(4,5)]1r)α(4,5)b(T4,T5), (3.27)

    b(T4,T5)=16, and

    [A(4,5)]1r=[a1(μ(4,5))r+a2(μ(4,T4))r+a3(μ(5,T5))r+a4(μ(5,T5)(1+μ(4,T4))1+μ(4,5))r+a5(μ(5,T4)(1+μ(4,T5))1+μ(4,5))r]1r. (3.28)

    By taking a1=a2=12 and a3=a4=a5=0 in (3.28), we get

    [A(4,5)]1r=[12(1)r+12(0)r]1r=[12]1r1asr.

    Therefore, (3.27) becomes

    ξ(α(4,5)b(T4,T5),φ([A(4,5)]1r))=18φ(1)16600=18(121675)0.

    Moreover, it is obvious that T is α-admissible, Tx is proximal and -continuous for each xX. Consequently, all the assumptions of Theorem 3.6 are satisfied. We can see that T has many fixed points in X.

    In this section, we show that several interesting fixed point results can be deduced from our main theorem, especially by availing variants of simulation functions.

    Corollary 4.1. Let (X,μ,η) be a complete b-metric space and T:XK(X) be a multivalued mapping satisfying the condition:

    α(x,y)b(Tx,Ty)φ(MrT(x,y)), (4.1)

    for all x,yX, where φΩb and α:X×XR+ is a function. Assume also that the following conditions hold:

    (i) T is an α-admissible multivalued mapping;

    (ii) there exists x0X and x1Tx0 such that α(x0,x1)1;

    (iii) T is -continuous;

    (i v) Tx is proximal for each xX.

    Then, there exists uX such that uTu.

    Proof. Set ξ:=ξ(t,s)=φ(s)t for all t,sR+ in Theorem 3.5. Then, (4.1) follows easily. Note that φ(s)tZ. Consequently, Theorem 3.5 can be applied to find uX such that uTu.

    Corollary 4.2. (Rhoades type [31]) Let (X,μ,η) be a complete b-metric space and T:XK(X) be a multivalued mapping satisfying the following:

    b(Tx,Ty)φ(MrT(x,y))φ2(MrT(x,y)), (4.2)

    for all x,yX, where φ:R+R+ is lower semicontinuous and φ1(0)={0}. Also, assume that the following conditions hold:

    (i) T is -continuous;

    (ii) Tx is proximal for each xX.

    Then, there exists uX such that uTu.

    Proof. In Theorem 3.5, take ξ:=ξ(t,s)=sφ(s)t for all t,sR+ and α(x,y)=1 for all x,yX. Then (4.2) follows. Notice that sφ(s)tZ. Hence, by Theorem 3.5, T has a fixed point in X.

    Corollary 4.3. (Nadler's type ([28])) Let (X,μ,η) be a complete b-metric space and T:XK(X) be a multivalued mapping satisfying:

    b(Tx,Ty)λμ(x,y), (4.3)

    for all x,yX, where λ(0,1). Assume also that the following assertion hold:

    (i) T is -continuous;

    (ii) Tx is proximal for each xX.

    Then, T has a fixed point in X.

    Proof. Take α(x,y)=1, ξ:=ξ(t,s)=λst for all s,tR+ and put φ(t)=λt for all t0, with λ(0,1) in Theorem 3.5. Then (4.3) is obtainable. Notice that λstZ. Hence, it follows from Theorem 3.5 that there exists uX such that uTu.

    Corollary 4.4. (Branciari's type [11]) Let (X,μ,η) be a complete b-metric space and T:XK(X) be a multivalued mapping satisfying:

    b(Tx,Ty)0ω(t)μtφ(MrT(x,y)), (4.4)

    for all x,yX, where ω:R+R+ is a function such that γ0ω(t)μt>0 for each γ>0. Also, assume that the following conditions hold:

    (i) T is -continuous;

    (ii) Tx is proximal for each xX.

    Then, there exists uX such that uTu.

    Proof. Put α(x,y)=1 and ξ:=ξ(t,s)=st0ω(u)μu for all s,tR+. Then (4.4) follows easily. Note that ξZ. Thus, by Theorem 3.5, T has a fixed point in X.

    Corollary 4.5. Let (X,μ,η) be a complete b-metric space and T:XK(X) be a multivalued mapping satisfying:

    Λ(b(Tx,Ty),φ(MrT(x,y)))φ(MrT(x,y))Γ(b(Tx,Ty),φ(MrT(x,y)))b(Tx,Ty), (4.5)

    for all x,yX with b(Tx,Ty)>0, where Λ,Γ:R+R+ are continuous functions with respect to each arguments such that Λ(t,s)>Γ(t,s) for all t,s>0. Assume further that the following axioms hold:

    (i) T is -continuous;

    (ii) Tx is proximal for each xX.

    Then, T has a fixed point in X.

    Proof. Setting ξ:=ξ(t,s)=sΛ(t,s)Γ(t,s)t for all t,sR+ and α(x,y)=1 in Theorem 3.5, we obtain (4.5). Observe that ξZ. Consequently, Theorem 3.5 can be employed to locate uX such that uTu.

    Corollary 4.6. Let (X,μ,η) be a complete b-metric space and Θ:XX be a single-valued mapping satisfying the condition:

    μ(Θx,Θy)φ2([A(x,y)]1r), (4.6)

    for all x,yX, where φΩb and [A(x,y)]1r is as given in Definition 3.1. Then, there exists uX such that Θu=u.

    Proof. Take ξ:=ξ(t,s)=φ(s)t in Theorem 3.5, then ξZ and (4.6) follows directly. Then, consider a multivalued mapping T:XK(X) defined by Tx={Θx} for all xX. Clearly, {Θx}K(X) for each xX. Hence, Theorem 3.5 can be applied with α(x,y)=1 for all x,yX, to find uX such that uTu={Θu}; which further implies that u=Θu.

    Remark 4.7. It is obvious that we can obtain more consequences of our results by considering other variants of simulation functions. Also, by taking the parameter η=1, all the established results herein reduce to their classical metric space equivalence. In particular, by following the idea of Corollary 4.6, single-valued versions of the rest results presented here can also be pointed out.

    Fixed point theory in partially ordered sets is one of the highly useful branches of fixed point theory with enormous applications in areas such as matrix equations, boundary value problems, and many more. For some articles in this direction, see [3,29,30]. In this section, our main result is applied to deduce its analogue in the framework of ordered b-metric space. Indeed, a b-metric space can be equipped with a partial ordering; that is, if (X,) is a partially ordered set, then (X,μ,η,) is known as an ordered b-metric space. Accordingly, we say that x,yX are comparable if either xy or yx holds. Let L,MX, then LM if for each lL, there exists mM such that lm.

    Theorem 5.1. Let (X,μ,η,) be a complete ordered b-metric space and T:XK(X) be a multivalued mapping. Suppose that there exist ξZ, φΩb and a function α:X×XR+ such that

    ξ(α(x,y)b(Tx,Ty),φ(MrT(x,y)))0, (5.1)

    for all x,yX with TxTy. Also, assume that the following conditions hold:

    (i) there exists x0X and x1Tx0 such that TxTy;

    (ii) for each xX and yTx with TxTy, we have TyTz for all zTy;

    (iii) T is -continuous;

    (i v) Tx is proximal for each xX.

    Then, there exists uX such that uTu.

    Proof. Let the function α:X×XR+ be defined by

    α(x,y)={1,if TxTy0,otherwise.

    Obviously, T is an α-admissible multivalued mapping. In fact, take xX and yTx with α(x,y)1, then TxTy; and by hypothesis (ii), TyTz for each zTy. It follows that α(y,z)1 for all zTy. Moreover, by inequality (5.1), we find that T is an admissible multivalued hybrid Z-contraction with respect to zZ. Consequently, all the axioms of Theorem 3.5 are satisfied. Hence, T has a fixed point in X.

    The notion of contraction mapping on a metric space with a graph was introduced by Jachymski [22]. In this subsection, we deduce a fixed point result in the setting of b-metric space endowed with a graph. Following [22], let (X,μ,η) be a metric space and D denotes the diagonal of the Cartesian product X×X. Consider a directed graph G such that the set VG of its vertices coincides with X, and the set EG of its edges contains all loops, that is, (x,x)EG for every xVG (or DEG). We presume that G has no parallel edges so that it can be identified with the pair (VG,EG). Furthermore, G is taken as a weighted graph (for details, see [22]) by allocating to each edge the distance between its vertices. If x and y are vertices in a graph G, then a path from x to y of length l(lN) is a sequence {xi}li=0 of l+1 vertices such that x=x0,xl=y and (xn1,xn)EG for i=1,,l. A graph G is connected if there is a path between any two of its vertices. For some fixed point results with graphic contractions, we refer [14,15,22]. Hereafter, a nonempty set X endowed with a graph G shall be written as (X,G). First, we introduce the following auxiliary concepts.

    Definition 5.2. Let (X,μ,η,G) be a b-metric space. A multivalued mapping T:XK(X) is said to be G-continuous at uX, if given uX and a sequence {xn}n1 such that μ(xn,u)0 as n and (xn1,xn)EG for all nN, we have b(Txn,Tu)0 as n, for all (Txn1,Tu)EG, with xnTxn1. T is G-continuous if it is G-continuous at each point of X.

    Definition 5.3. Let (X,G) be a nonempty set. We say that a multivalued mapping T:XK(X) is edge-preserving, if for all x,yX, (x,y)EG implies (Tx,Ty)EG.

    Definition 5.4. Let (X,μ,η,G) be a metric space. A subset Δ of X is called proximal, if for each xX, there exists κΔ with (x,κ)EG such that μ(x,κ)=μ(x,Δ).

    Now, we present the main result of this subsection as follows.

    Theorem 5.5. Let (X,μ,η,G) be a complete b-metric space and T:XK(X) be a multivalued mapping. Assume that there exist ξZ, φΩb and a function α:X×XR+ such that

    ξ(α(x,y)b(Tx,Ty),φ(MrT(x,y)))0, (5.2)

    for all x,yX. Moreover, suppose also that the following assertions hold:

    (i) for all x,yX, (x,y)EG implies (Tx,Ty)EG;

    (ii) there exists x0X and x1Tx0 with (x0,x1)EG;

    (iii) for each xX and yTx with (Tx,Ty)EG, we have (Ty,Tz)EG for all zTy;

    (i v) T is G-continuous;

    (v) Tx is proximal for each xX.

    Then, there exists uX such that uTu.

    Proof. Define the function α:X×XR+ by

    α(x,y)={1,if (x,y)EG0,otherwise.

    First, we show that T is an admissible multivalued mapping. Let xX and yTx with α(x,y)1, then (x,y)EG and, by Condition (i), (Tx,Ty)EG; hence, from (iii), (Tx,Tz)EG for each zTy. Therefore, α(y,z)1, which implies that (y,z)EG. So, combining the inequality (5.2), we have that T is an admissible multivalued hybrid Z-contraction with respect to ξZ. Finally, it is easy to see that conditions (iv) and (v) imply the hypotheses (iii) and (iv), respectively of Theorem 3.5. Consequently, all the assertions of Theorem 3.5 are satisfied and hence T has a fixed point in X.

    It has always been a worthwhile research to investigate an adequate technique to solve matrix equations because the existence of solutions of matrix equations arises in various applications such as in stochastic filtering, system theory, dynamic programming, control theory, statistics, ladder networks, and a host of other branches of sciences. For some results on this line, we refer [16,21].

    Let ϝn,Υn and Ξn denote the set of all n×n Hermitian, positive definite and positive semi-definite matrices, respectively. >0 (respectively, 0) means that Υn (respectively, Ξn). Let the spectral norm of a matrix be defined by

    1=ϱ+(),

    where ϱ() represents the greatest eigenvalue of the matrix . The Ky Fan norm is defined as

    1=ni=1Ξi(),

    where {Ξ1(),Ξ2(),,Ξn()} is the set of singular values of .

    Consider the nonlinear matrix equation:

    =θ+mi=1iβ()i, (6.1)

    where θΥn, i(i=1,2,,m) are n×n matrices and β is a single-valued mapping.

    Theorem 6.1. Let β:ϝnϝn maps Υn into Υn and θΥn. Suppose also that the following conditions are satisfied:

    (i) there exists a constant (0,12) such that mi=1ii2In, where In is an n×n matrix;

    (ii) mi=1iβ(θ)>0;

    (iii) for all ,ϝn,

    β()β()2112μ(,), (6.2)

    where

    μ(,)=[a1(μ(,))r+a2(μ(,β()))r+a3(μ(,β()))r+a4(μ(,β())(1+μ(,β()))1+μ(,))r+a5(μ(,β())(1+μ(,β()))1+μ(ı,))r]1r,

    where r>0 and ai0(i=1,2,3,4,5) such that 5i=1ai=1.

    Then, (6.1) has a solution in Υn.

    Proof. Let μ:ϝn×ϝnR be defined by

    μ(,)=21=(tr())2,

    for all ,ϝn. Then, (ϝn,μ,η=2) is a complete b-metric space. Consider the mappings σ:ϝnϝn and φ:R+R+, respectively defined by

    σ()=θ+mi=1iβ()i (6.3)

    and φ(t)=2t for all t0 and (0,12). Then, φΩb. Also, notice that the solution of (6.1) is a fixed point of (6.3).

    Let ,ϝn with so that μ(,)>0. Then, we have

    σ()σ()1=tr(σ()σ())=mi=1tr(ii(β()β()))=tr((mi=1ii(β()β())))mi=1iiβ()β()1μ(,). (6.4)

    From (6.4), we have σ()σ()212μ(,), which implies that

    μ(σ(),σ())φ2(μ(,)).

    Hence, all the assertions of Corollary 4.6 are satisfied. Consequently, (6.1) has a solution.

    Example 6.2. Consider the nonlinear matrix equation:

    =θ+2i=1iβ()i, (6.5)

    where θ,1 and 2 are respectively given by

    θ=[0.020.0020.0020.0020.020.0020.0020.0020.02],1=[0.150.0020.0020.020.150.0020.020.0020.15]
    and2=[0.250.0020.0020.0020.250.0020.0020.0020.25]

    Also, let the mappings β,σ:ϝ3ϝ3 be respectively defined by

    β()=3andσ()=θ+2i=1iβ()i.

    Then, by taking =25, we find that all the hypotheses of Theorem 6.2 hold.

    It is well-known that in some abstract spaces, the triangle inequality does not hold. But, by multiplying the constant η1 on the right-hand side of the triangle inequality, one can obtain a more useful abstract structure, now called a b-metric space in the literature. Following this orientation, in this work, two new ideas, admissible multivalued hybrid Z-contractions and multivalued hybrid Z-contractions in the framework of b-metric spaces using generalized Hausdorff metric are initiated. The established concept herein unifies several results in one theorem. A few of these special cases are pointed out. Thereafter, to indicate some applications of our results, a few fixed point theorems in the setting of fixed point results of b-metric spaces endowed with partial ordering and graph are deduced and solvability conditions of nonlinear matrix equations are investigated.

    This project was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, Saudi Arabia under grant no. (KEP-66-130-38). The authors, therefore, acknowledge with thanks DSR technical and financial support.

    The authors declare that they have no competing interests.



    [1] Natterer F (2001) The Mathematics of Computerized Tomography, Philadelphia: Society for Industrial and Applied Mathematics.
    [2] Sales M, Strobl M, Shinohara T, et al. (2018) Three dimensional polarimetric neutron tomography of magnetic fields. Sci Rep 8: 2214.
    [3] Desai NM, Lionheart WRB, Sales M, et al. (2019) Polarimetric neutron tomography of magnetic fields: Uniqueness of solution and reconstruction. Inverse Probl DOI: https://doi.org/10.1088/1361-6420/ab44e0.
    [4] An X, Kraetschmer T, Takami K, et al. (2011) Validation of temperature imaging by H2O absorption spectroscopy using hyperspectral tomography in controlled experiments. Appl Opt 50: A29-A37.
    [5] Ma L, Li X, Sanders S, et al. (2013) 50-kHz-rate 2D imaging of temperature and H2O concentration at the exhaust plane of a J85 engine using hyperspectral tomography. Opt Express 21: 1152-1162. doi: 10.1364/OE.21.001152
    [6] Andersson F (2005) The Doppler moment transform in Doppler tomography. Inverse Probl 21: 1249.
    [7] Sharafutdinov VA (1994) Integral Geometry of Tensor Fields, Walter de Gruyter.
    [8] Lionheart WRB, Withers PJ (2015) Diffraction tomography of strain. Inverse Probl 31: 045005.
    [9] Boman J, Sharafutdinov V (2018) Stability estimates in tensor tomography. Inverse Probl Imaging 12: 1245-1262. doi: 10.3934/ipi.2018052
    [10] Bogachev VI (2007) Measure Theory, Berlin: Springer Science & Business Media.
    [11] Akhiezer NI (1965) The Classical Moment Problem: And Some Related Questions in Analysis, Edinburgh: Oliver & Boyd.
    [12] Gardner RJ, McMullen P (1980) On Hammer's X-Ray Problem. J Lond Math Soc 2: 171-175.
    [13] Gardner RJ, Gritzmann P (1997) Discrete tomography: Determination of finite sets by X-rays. T Am Math Soc 349: 2271-2295.
    [14] Gardner RJ, Kiderlen M (2007) A solution to Hammer's X-ray reconstruction problem. Adv Math 214: 323-343. doi: 10.1016/j.aim.2007.02.005
    [15] Gardner RJ (2006) Geometric Tomography, 2 Eds., Cambridge University Press.
    [16] Faridani A, Ritman EL, Smith KT (1992) Local tomography. SIAM J Appl Math 52: 459-484.
    [17] Herman GT, Kuba A (2012) Discrete Tomography: Foundations, Algorithms, and Applications, Springer Science & Business Media.
    [18] Batenburg KJ, Sijbers J (2011) DART: A practical reconstruction algorithm for discrete tomography. IEEE T Image Process 20: 2542-2553.
    [19] Bentz C, Costa MC, De Werra D, et al. (2008) On a graph coloring problem arising from discrete tomography. Networks 51: 256-267.
    [20] Schuster T (2008) 20 years of imaging in vector field tomography: A review, In: Math. Methods in Biomedical Imaging and Intensity-Modulated Radiation Therapy (IMRT). Ser. Publications of the Scuola Normale Superiore, 7: 389-424.
    [21] Sparr G, Strahlen K, Lindstrom K, et al. (1995) Doppler tomography for vector fields. Inverse Probl 11: 1051.
    [22] Kravtsov YA (1968) "Quasi-isotropic" approximation of geometrical optics. Dokl Akad Nauk SSSR 183: 74-76.
    [23] Aben H, Errapart A, Ainola L, et al. (2005) Photoelastic tomography for residual stress measurement in glass. Opt Eng 44: 093601.
    [24] Tomlinson RA, Yang H, Szotten D, et al. (2006) The design and commissioning of a novel tomographic polariscope, In: SEM Annual Conf. and Exposition on Experimental and Applied Mechanics, 1141-1147.
    [25] Lionheart W, Sharafutdinov V (2009) Reconstruction algorithm for the linearized polarization tomography problem with incomplete data. Contemp Math 14: 137.
    [26] Johnstone D, van Helvoort A, Midgley P (2017) Nanoscale strain tomography by scanning precession electron diffraction. Microsc Microanal 23: 1710-1711.
    [27] Woracek R, Santisteban J, Fedrigo A, et al. (2018) Diffraction in neutron imaging-A review, In: Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 878: 141-158.
    [28] Georgievskii D (2016) Generalized compatibility equations for tensors of high ranks in multidimensional continuum mechanics. Russ J Math Phys 23: 475-483.
    [29] Sklar M (1959) Fonctions de répartition à n dimensions et leurs marges. Publ Inst Statist Univ Paris 8: 229-231.
    [30] Nelsen RB (2007) An Introduction to Copulas, Springer Science & Business Media.
    [31] Santisteban JR, Edwards L, Fitzpatrick ME, et al. (2002) Strain imaging by Bragg edge neutron transmission, In: Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 481: 765-768.
    [32] Abbey B, Zhang SY, Vorster WJJ, et al. (2009) Feasibility study of neutron strain tomography. Procedia Eng 1: 185-188.
    [33] Knops RJ, Payne LE (1971) Modern Uniqueness Theorems in Three-Dimensional Elastostatics, In: Uniqueness Theorems in Linear Elasticity, Berlin: Springer, 32-60.
    [34] Gregg AWT, Hendriks JN, Wensrich CM, et al. (2017) Tomographic reconstruction of residual strain in axisymmetric systems from Bragg-edge neutron imaging. Mech Res Commun 85: 96-103.
    [35] Wensrich CM, Hendriks JN, Gregg A, et al. (2016) Bragg-edge neutron transmission strain tomography for in situ loadings, In: Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, 383: 52-58.
    [36] Hendriks JN, Gregg AWT, Wensrich CM, et al. (2017) Bragg-edge elastic strain tomography for in situ systems from energy-resolved neutron transmission imaging. Phys Rev Mater 1: 053802.
    [37] Desai NM, Lionheart WRB (2016) An explicit reconstruction algorithm for the transverse ray transform of a second rank tensor field from three axis data. Inverse Probl 32: 115009.
  • Reader Comments
  • © 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(5028) PDF downloads(472) Cited by(6)

Figures and Tables

Figures(5)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog