The quantity of scientific images associated with patient care has increased markedly in recent years due to the rapid development of hospitals and research facilities. Every hospital generates more medical photographs, resulting in more than 10 GB of data per day being produced by a single image appliance. Software is used extensively to scan and locate diagnostic photographs to identify patient's precise information, which can be valuable for medical science research and advancement. An image recovery system is used to meet this need. This paper suggests an optimized classifier framework focused on a hybrid adaptive neuro-fuzzy approach to accomplish this goal. In the user query, similarity measurement, and the image content, fuzzy sets represent the vagueness that occurs in such data sets. The optimized classifying method 'hybrid adaptive neuro-fuzzy is enhanced with the improved cuckoo search optimization. Score values are determined by utilizing the linear discriminant analysis (LDA) of such classified images. The preliminary findings indicate that the proposed approach can be more reliable and effective at estimation than can existing approaches.
Citation: Janarthanan R, Eshrag A. Refaee, Selvakumar K, Mohammad Alamgir Hossain, Rajkumar Soundrapandiyan, Marimuthu Karuppiah. Biomedical image retrieval using adaptive neuro-fuzzy optimized classifier system[J]. Mathematical Biosciences and Engineering, 2022, 19(8): 8132-8151. doi: 10.3934/mbe.2022380
[1] | Yanyan Cui, Chaojun Wang . Dirichlet and Neumann boundary value problems for bi-polyanalytic functions on the bicylinder. AIMS Mathematics, 2025, 10(3): 4792-4818. doi: 10.3934/math.2025220 |
[2] | Yanyan Cui, Chaojun Wang . The inhomogeneous complex partial differential equations for bi-polyanalytic functions. AIMS Mathematics, 2024, 9(6): 16526-16543. doi: 10.3934/math.2024801 |
[3] | Naveed Iqbal, Azmat Ullah Khan Niazi, Ikram Ullah Khan, Rasool Shah, Thongchai Botmart . Cauchy problem for non-autonomous fractional evolution equations with nonlocal conditions of order (1,2). AIMS Mathematics, 2022, 7(5): 8891-8913. doi: 10.3934/math.2022496 |
[4] | Emad Salah, Ahmad Qazza, Rania Saadeh, Ahmad El-Ajou . A hybrid analytical technique for solving multi-dimensional time-fractional Navier-Stokes system. AIMS Mathematics, 2023, 8(1): 1713-1736. doi: 10.3934/math.2023088 |
[5] | Saulo Orizaga, Ogochukwu Ifeacho, Sampson Owusu . On an efficient numerical procedure for the Functionalized Cahn-Hilliard equation. AIMS Mathematics, 2024, 9(8): 20773-20792. doi: 10.3934/math.20241010 |
[6] | Ikram Ullah, Muhammad Bilal, Aditi Sharma, Hasim Khan, Shivam Bhardwaj, Sunil Kumar Sharma . A novel approach is proposed for obtaining exact travelling wave solutions to the space-time fractional Phi-4 equation. AIMS Mathematics, 2024, 9(11): 32674-32695. doi: 10.3934/math.20241564 |
[7] | Andrey Muravnik . Wiener Tauberian theorem and half-space problems for parabolic and elliptic equations. AIMS Mathematics, 2024, 9(4): 8174-8191. doi: 10.3934/math.2024397 |
[8] | Bin He . Developing a leap-frog meshless methods with radial basis functions for modeling of electromagnetic concentrator. AIMS Mathematics, 2022, 7(9): 17133-17149. doi: 10.3934/math.2022943 |
[9] | Andrey Muravnik . Nonclassical dynamical behavior of solutions of partial differential-difference equations. AIMS Mathematics, 2025, 10(1): 1842-1858. doi: 10.3934/math.2025085 |
[10] | Hong Li, Keyu Zhang, Hongyan Xu . Solutions for systems of complex Fermat type partial differential-difference equations with two complex variables. AIMS Mathematics, 2021, 6(11): 11796-11814. doi: 10.3934/math.2021685 |
The quantity of scientific images associated with patient care has increased markedly in recent years due to the rapid development of hospitals and research facilities. Every hospital generates more medical photographs, resulting in more than 10 GB of data per day being produced by a single image appliance. Software is used extensively to scan and locate diagnostic photographs to identify patient's precise information, which can be valuable for medical science research and advancement. An image recovery system is used to meet this need. This paper suggests an optimized classifier framework focused on a hybrid adaptive neuro-fuzzy approach to accomplish this goal. In the user query, similarity measurement, and the image content, fuzzy sets represent the vagueness that occurs in such data sets. The optimized classifying method 'hybrid adaptive neuro-fuzzy is enhanced with the improved cuckoo search optimization. Score values are determined by utilizing the linear discriminant analysis (LDA) of such classified images. The preliminary findings indicate that the proposed approach can be more reliable and effective at estimation than can existing approaches.
Partial differential equations are closely related to many physical problems in real life. For example, the ninth-order linear or non-linear boundary value problems are related to the laminar viscous flow in a semi-porous channel or the hydro-magnetic stability, and the telegraph equations are related to the vibrations within objects or the propagation of waves. There are also many different types of partial differential equations in engineering and other applied sciences. Zhang et al. [1] discussed cubic spline solutions of ninth-order linear and non-linear boundary value problems using a cubic B-spline. Shah et al. [2] proposed a new and efficient operational matrix method for solving time-fractional telegraph equations with Dirichlet boundary conditions. Nisar et al. [3] proposed a hybrid mesh free framework based on Padˊe approximation in order to solve the numerical solutions of nonlinear partial differential equations. There are also many different types of partial differential equations in chemistry, engineering, and other applied sciences. There have been many successful conclusions about these partial differential equations.
Complex partial differential equations of analytic functions also have a wide range of applications. Bi-analytic functions, the generalizations of analytic functions, have important applications in elasticity. In 1961, Sander [4] studied the properties of pairs of functions {u(x,y),v(x,y)} with binary real variables (x,y), which satisfy the system of partial differential equations:
{∂u∂x−∂v∂y=θ,∂u∂y+∂v∂x=ω,(k+1)∂θ∂x+∂ω∂y=0,(k+1)∂θ∂y−∂ω∂x=0, |
for the real constant k(k≠−1), where θ(x,y) and ω(x,y) are continuously differentiable functions of x and y. Sander provided the definition of bi-analytic functions of type k, which are of great significance for studying some physical problems for k>0, and extended some properties of analytic functions to bi-analytic functions.
In 1965, Lin and Wu [5] introduced the function class that is more extensive than Sander's function class, i.e., bi-analytic functions of the type (λ,k) which are defined by the system of equations:
{1k∂u∂x−∂v∂y=θ,∂u∂y+1k∂v∂x=ω,k∂θ∂x+λ∂ω∂y=0,k∂θ∂y−λ∂ω∂x=0, |
where θ(x,y) and ω(x,y) are continuously differentiable functions of x and y, and λ,k are real constants with λ≠0,1,k2, and 0<k<1. The complex form of the system is
k+12∂f∂ˉz−k−12∂f∂z=λ−k4λφ(z)+λ+k4λ¯φ(z), |
in which φ(z)=kϑ−iλω is analytic and is called the associate function of f(z)=u+iv. In [5], the general expression and the properties of bi-analytic functions of the type (λ,k) were researched in detail.
Hua et al. [6] introduced a mechanical interpretation for bi-analytic functions and promoted the corresponding function theory. Thereafter, bi-analytic functions aroused widespread attention from many scholars [7,8,9]. In 1994, Kumar [10] discussed a broader class of functions, i.e., bi-polyanalytic functions, and investigated several Riemann-Hilbert problems for systems of n-order partial differential equations applying polyanalytic functions [11] and bi-polyanalytic functions on the unit disk. In 2005, Kumar and Prakash [12] investigated Dirichlet problems for the Poisson equation and some boundary value problems for bi-polyanalytic functions on the unit disk. They obtained the explicit representations of the solutions and the corresponding solvable conditions. In 2006, Begehr and Kumar [13] discussed some complex partial differential equations of higher order. Some boundary value problems for bi-polyanalytic functions were solved on different conditions on the unit disk.
In recent years, some other boundary value problems for bi-analytic functions were solved [14,15,16,17]. With the gradual improvements of the theory for bi-analytic functions and polyanalytic functions [18,19,20,21] in the complex plane, some scholars attempted to generalize the relevant achievements to spaces of several complex variables [22,23].
In this paper, based on the work of the former researchers, we study a class of Schwarz problems for polyanalytic functions on the bicylinder. Then, from the perspective of series and applying the particular solution to the Schwarz problem for polyanalytic functions, we discuss a Dirichlet problem for bi-polyanalytic functions on the bicylinder.
In the following, let the bicylinder D2=D1×D2={(z1,z2):|z1|<1,|z2|<1}, and let ∂0D2 denote the characteristic boundary of D2. Let C(G) represent the set of continuous functions within G.
To get the main results, we need to discuss the following Schwarz problem.
Theorem 2.1. Let gμν∈C(∂0D2;R) for 1≤μ,ν≤m−1 (m≥2), and let
˜ϕ(z)=+∞∑m1,m2=0m−1∑˜v1=μm−1∑˜v2=νˉz˜v11ˉz˜v22˜v1!˜v2!um−~v1,m−~v2m1,m2zm11zm22, | (2.1) |
where
um−~v1,m−~v2m1,m2={1(2πi)2∫∂0D2m−μ−1∑l1=0m−ν−1∑l2=0g(μ+l1)(ν+l2)(ζ)l1!l2!˜Adζ1dζ2ζ1ζ2,{˜v1=μ˜v2=ν,1(2πi)2∫∂0D2m−μ−1∑l1=0m−1−˜v2∑l2=0g(μ+l1)(˜v2+l2)(ζ)l1!l2!˜B|v2=˜v2−νdζ1dζ2ζ1ζ2,{˜v1=μν<˜v2≤m−1,1(2πi)2∫∂0D2m−1−˜v1∑l1=0m−ν−1∑l2=0g(˜v1+l1)(ν+l2)(ζ)l1!l2!˜C|v1=˜v1−μdζ1dζ2ζ1ζ2,{μ<˜v1≤m−1˜v2=ν,1(2πi)2∫∂0D2m−1−˜v1∑l1=0m−1−˜v2∑l2=0g(˜v1+l1)(˜v2+l2)(ζ)l1!l2!˜D|v1=˜v1−μv2=˜v2−νdζ1dζ2ζ1ζ2,{μ<˜v1≤m−1ν<˜v2≤m−1, | (2.2) |
and
{˜A=2[m1∑j1=0m2∑j2=0Cm1−j1l1(−ζ1−ˉζ1)l1−m1+j1ˉζj11Cm2−j2l2(−ζ2−ˉζ2)l2−m2+j2ˉζj22+D21|v1=0m2∑j2=0Cm2−j2l2(−ζ2−ˉζ2)l2−m2+j2ˉζj22+D31|v2=0m1∑j1=0Cm1−j1l1(−ζ1−ˉζ1)l1−m1+j1ˉζj11]−(D11D12+D22D23+D32D33)|v1=v2=0+2(−ζ1−ˉζ1)l1[m2∑j2=0Cm2−j2l2(−ζ2−ˉζ2)l2−m2+j2ˉζj22ˉζm11+B21D31−D23+B222B21]|v2=0+2(−ζ2−ˉζ2)l2[m1∑j1=0Cm1−j1l1(−ζ1−ˉζ1)l1−m1+j1ˉζj11ˉζm22+D21C21−D11+C222C21]|v1=0+2(¯ζ1m1C21+B21¯ζ2m2−B21C21)(−ζ1−ˉζ1)l1(−ζ2−ˉζ2)l2,˜B=2{[m1∑j1=0Cm1−j1l1(−ζ1−ˉζ1)l1−m1+j1ˉζj11−(−ζ1−ˉζ1)l1ˉζm11]m2∑j2=0Cm2−j2l2(−ζ2−ˉζ2)l2−m2+j2ˉζj22+D21|v1=0m2∑j2=0Cm2−j2l2(−ζ2−ˉζ2)l2−m2+j2ˉζj22+D31m1∑j1=0Cm1−j1l1(−ζ1−ˉζ1)l1−m1+j1ˉζj11−B21D31}−(D11D12+D22|v1=0D23+D32D33−B21D23−B21B22), |
{˜C=2{m1∑j1=0Cm1−j1l1(−ζ1−ˉζ1)l1−m1+j1ˉζj11[m2∑j2=0Cm2−j2l2(−ζ2−ˉζ2)l2−m2+j2ˉζj22−(−ζ2−ˉζ2)l2ˉζm22]+D21m2∑j2=0Cm2−j2l2(−ζ2−ˉζ2)l2−m2+j2ˉζj22+D31|v2=0m1∑j1=0Cm1−j1l1(−ζ1−ˉζ1)l1−m1+j1ˉζj11−D21C21}−(D11D12+D22D23+D32D33|v2=0−D11C21−C22C21),˜D=2[m1∑j1=0m2∑j2=0Cm1−j1l1(−ζ1−ˉζ1)l1−m1+j1ˉζj11Cm2−j2l2(−ζ2−ˉζ2)l2−m2+j2ˉζj22+D21m2∑j2=0Cm2−j2l2(−ζ2−ˉζ2)l2−m2+j2ˉζj22+D31m1∑j1=0Cm1−j1l1(−ζ1−ˉζ1)l1−m1+j1ˉζj11]−(D11D12+D22D23+D32D33), |
in which
D11={Cm1l1(−ζ1−ˉζ1)l1−m1,0≤m1≤l1,0,m1>l1,D12={Cm2l2(−ζ2−ˉζ2)l2−m2,0≤m2≤l2,0,m2>l2,D21={0,0≤m1<l1,(−1)l1+v1ˉζm1−l11,m1≥l1,D22={(−1)m1+v1,m1=l1,0,m1≠l1,D23={Cm2l2(−ζ2−ˉζ2)l2−m2,0≤m2≤l2,0,m2>l2,D31={0,0≤m2<l2,(−1)l2+v2ˉζm2−l22,m2≥l2,D32={Cm1l1(−ζ1−ˉζ1)l1−m1,0≤m1≤l1,0,m1>l1,D33={(−1)m2+v2,m2=l2,0,m2≠l2, |
and
C21={0,m2≥1,1,m2=0,C22={(−1)m1+v1,m1=l1,0,m1≠l1, |
B21={0,m1≥1,1,m1=0,B22={(−1)m2+v2,m2=l2,0,m2≠l2. |
Then, ˜ϕ(z) satisfies
ℜ∂μˉz1∂νˉz2˜ϕ(z)=gμν(z)(z∈∂0D2),ℑ∂μˉz1∂νˉz2˜ϕ(0,z2)=0=ℑ∂μˉz1∂νˉz2˜ϕ(z1,0)(z1∈D1,z2∈D2). |
Proof: 1) Let
˜ϕ(z)=m−1∑˜v1=μm−1∑˜v2=νˉz˜v11ˉz˜v22˜v1!˜v2!u˜v1˜v2(z), | (2.3) |
in which
u˜v1˜v2(z)={1(2πi)2∫∂0D2m−μ−1∑l1=0m−ν−1∑l2=0g(μ+l1)(ν+l2)(ζ)l1!l2!(A1−A2−A3+A4)dζ1dζ2ζ1ζ2,{˜v1=μ˜v2=ν,1(2πi)2∫∂0D2m−μ−1∑l1=0m−1−˜v2∑l2=0g(μ+l1)(˜v2+l2)(ζ)l1!l2!(B1−B2)|v2=˜v2−νdζ1dζ2ζ1ζ2,{˜v1=μν<˜v2≤m−1,1(2πi)2∫∂0D2m−1−˜v1∑l1=0m−ν−1∑l2=0g(˜v1+l1)(ν+l2)(ζ)l1!l2!(C1−C2)|v1=˜v1−μdζ1dζ2ζ1ζ2,{μ<˜v1≤m−1˜v2=ν,1(2πi)2∫∂0D2m−1−˜v1∑l1=0m−1−˜v2∑l2=0g(˜v1+l1)(˜v2+l2)(ζ)l1!l2!D|v1=˜v1−μv2=˜v2−νdζ1dζ2ζ1ζ2,{μ<˜v1≤m−1ν<˜v2≤m−1, | (2.4) |
is analytic on D2, and
{A1=[(z1−ζ1−ˉζ1)l1(z2−ζ2−ˉζ2)l2+(−z1)l1(z2−ζ2−ˉζ2)l2+(z1−ζ1−ˉζ1)l1(−z2)l2][2ζ1ζ2(ζ1−z1)(ζ2−z2)−1],A2=(−ζ1−ˉζ1)l1{(z2−ζ2−ˉζ2)l2[2ζ1ζ2(ζ1−z1)(ζ2−z2)−1]+(−z2)l2[2ζ2ζ2−z2−1]},A3=(−ζ2−ˉζ2)l2{(z1−ζ1−ˉζ1)l2[2ζ1ζ2(ζ1−z1)(ζ2−z2)−1]+(−z1)l1[2ζ1ζ1−z1−1]},A4=(−ζ1−ˉζ1)l1(−ζ2−ˉζ2)l2[2ζ1ζ1−z1+2ζ2ζ2−z2−2],B1=[(z1−ζ1−ˉζ1)l1(z2−ζ2−ˉζ2)l2+(−z1)l1(z2−ζ2−ˉζ2)l2+(z1−ζ1−ˉζ1)l1(−z2)l2(−1)v2]⋅[2ζ1ζ2(ζ1−z1)(ζ2−z2)−1],B2=(−ζ1−ˉζ1)l1{(z2−ζ2−ˉζ2)l2[2ζ1ζ2(ζ1−z1)(ζ2−z2)−1]+(−z2)l2(−1)v2[2ζ2ζ2−z2−1]},C1=[(z1−ζ1−ˉζ1)l1(z2−ζ2−ˉζ2)l2+(−z1)l1(−1)v1(z2−ζ2−ˉζ2)l2+(z1−ζ1−ˉζ1)l1(−z2)l2]⋅[2ζ1ζ2(ζ1−z1)(ζ2−z2)−1],C2=(−ζ2−ˉζ2)l2{(z1−ζ1−ˉζ1)l2[2ζ1ζ2(ζ1−z1)(ζ2−z2)−1]+(−z1)l1(−1)v1[2ζ1ζ1−z1−1]},D=[(z1−ζ1−ˉζ1)l1(z2−ζ2−ˉζ2)l2+(−z1)l1(−1)v1(z2−ζ2−ˉζ2)l2+(z1−ζ1−ˉζ1)l1(−z2)l2(−1)v2]⋅[2ζ1ζ2(ζ1−z1)(ζ2−z2)−1]. |
In the following, we will show that ˜ϕ(z) satisfies
ℜ∂μˉz1∂νˉz2˜ϕ(z)=gμν(z)(z∈∂0D2),ℑ∂μˉz1∂νˉz2˜ϕ(0,z2)=0=ℑ∂μˉz1∂νˉz2˜ϕ(z1,0)(z1∈D1,z2∈D2). |
By (2.3), we get that
∂μˉz1∂νˉz2˜ϕ(z)=m−1∑˜v1=μm−1∑˜v2=νˉz˜v1−μ1ˉz˜v2−ν2(˜v1−μ)!(˜v2−ν)!u˜v1˜v2(z)=m−1−μ∑v1=0m−1−ν∑v2=0ˉzv11ˉzv22v1!v2!u(v1+μ)(v2+ν)(z), | (2.5) |
where
u(v1+μ)(v2+ν)={1(2πi)2∫∂0D2m−μ−1∑l1=0m−ν−1∑l2=0g(μ+l1)(ν+l2)(ζ)l1!l2!(A1−A2−A3+A4)dζ1dζ2ζ1ζ2,{v1=0v2=0,1(2πi)2∫∂0D2m−μ−1∑l1=0m−1−v2−ν∑l2=0g(μ+l1)(v2+ν+l2)(ζ)l1!l2!(B1−B2)dζ1dζ2ζ1ζ2,{v1=00<v2≤m−1−ν,1(2πi)2∫∂0D2m−1−v1−μ∑l1=0m−ν−1∑l2=0g(v1+μ+l1)(ν+l2)(ζ)l1!l2!(C1−C2)dζ1dζ2ζ1ζ2,{0<v1≤m−1−μv2=0,1(2πi)2∫∂0D2m−1−v1−μ∑l1=0m−1−v2−ν∑l2=0g(v1+μ+l1)(v2+ν+l2)(ζ)l1!l2!Ddζ1dζ2ζ1ζ2,{0<v1≤m−1−μ0<v2≤m−1−ν. |
Therefore, for 1≤k1,k2≤m−1,
∂m−k1ˉz1∂m−k2ˉz2˜ϕ(z)=k1−1∑v1=0k2−1∑v2=0ˉzv11ˉzv22v1!v2!u(v1+m−k1)(v2+m−k2)(z), | (2.6) |
in which
u(v1+m−k1)(v2+m−k2)={∫∂0D2k1−1∑l1=0k2−1∑l2=0g(m−k1+l1)(m−k1+l2)(ζ)(2πi)2l1!l2!(A1−A2−A3+A4)dζ1dζ2ζ1ζ2,{v1=0v2=0,∫∂0D2k1−1∑l1=0k2−1−v2∑l2=0g(m−k1+l1)(m−k2+v2+l2)(ζ)(2πi)2l1!l2!(B1−B2)dζ1dζ2ζ1ζ2,{v1=00<v2≤k2−1,∫∂0D2k1−1−v1∑l1=0k2−1∑l2=0g(m−k1+v1+l1)(m−k2+l2)(ζ)(2πi)2l1!l2!(C1−C2)dζ1dζ2ζ1ζ2,{0<v1≤k1−1v2=0,∫∂0D2k1−1−v1∑l1=0k2−1−v2∑l2=0g(m−k1+v1+l1)(m−k2+v2+l2)(ζ)(2πi)2l1!l2!Ddζ1dζ2ζ1ζ2,{0<v1≤k1−10<v2≤k2−1. |
Let
ϕ1(z)=ˉzv11ˉzv22v1!v2!1(2πi)2∫∂0D2k1−1−v1∑l1=0k2−1−v2∑l2=0g(m−k1+v1+l1)(m−k2+v2+l2)(ζ)l1!l2!dζ1dζ2ζ1ζ2. |
For 1≤k1,k2≤m−1, (2.6) follows that
∂m−k1ˉz1∂m−k2ˉz2˜ϕ(z)=0∑v1=00∑v2=0ϕ1(z)(A1−A2−A3+A4)+0∑v1=0k2−1∑v2=1ϕ1(z)(B1−B2)+k1−1∑v1=10∑v2=0ϕ1(z)(C1−C2)+k1−1∑v1=1k2−1∑v2=1ϕ1(z)D=k1−1∑v1=0k2−1∑v2=0ϕ1(z)D−0∑v1=0k2−1∑v2=0ϕ1(z)B2−k1−1∑v1=00∑v2=0ϕ1(z)C2+0∑v1,v2=0ϕ1(z)A4=1(2πi)2∫∂0D2k1−1∑v1=0k1−1−v1∑l1=0k2−1∑v2=0k2−1−v2∑l2=0ˉzv11ˉzv22v1!v2!g(m−k1+v1+l1)(m−k2+v2+l2)(ζ)l1!l2![(z1−ζ1−ˉζ1)l1(z2−ζ2−ˉζ2)l2+(−z1)l1(−1)v1(z2−ζ2−ˉζ2)l2+(z1−ζ1−ˉζ1)l1(−z2)l2(−1)v2][2ζ1ζ2(ζ1−z1)(ζ2−z2)−1]dζζ−1(2πi)2∫∂0D2k1−1∑λ1=0k2−1∑v2=0k2−1−v2∑l2=0ˉzv22v2!g(m−k1+λ1)(m−k2+v2+l2)(ζ)λ1!l2!(−ζ1−ˉζ1)λ1⋅{(z2−ζ2−ˉζ2)l2[2ζ1ζ2(ζ1−z1)(ζ2−z2)−1]+(−z2)l2(−1)v2[2ζ2ζ2−z2−1]}dζ1dζ2ζ1ζ2−1(2πi)2∫∂0D2k1−1∑v1=0k1−1−v1∑l1=0k2−1∑λ2=0ˉzv11v1!g(m−k1+v1+l1)(m−k2+λ2)(ζ)l1!λ2!(−ζ2−ˉζ2)λ2⋅{(z1−ζ1−ˉζ1)l1[2ζ1ζ2(ζ1−z1)(ζ2−z2)−1]+(−z1)l1(−1)v1[2ζ1ζ1−z1−1]}dζ1dζ2ζ1ζ2+∫∂0D2k1−1∑λ1=0k2−1∑λ2=0g(m−k1+λ1)(m−k2+λ2)(ζ)(2πi)2λ1!λ2!(−ζ1−ˉζ1)λ1(−ζ2−ˉζ2)λ2[2ζ1ζ1−z1+2ζ2ζ2−z2−2]dζζ=1(2πi)2∫∂0D2k1−1∑λ1=0k2−1∑λ2=0λ1∑v1=0λ2∑v2=0ˉzv11ˉzv22λ1!λ2!Cv1λ1Cv2λ2g(m−k1+λ1)(m−k2+λ2)[(z1−ζ1−ˉζ1)λ1−v1(z2−ζ2−ˉζ2)λ2−v2+(−z1)λ1−v1(−1)v1(z2−ζ2−ˉζ2)λ2−v2+(z1−ζ1−ˉζ1)λ1−v1(−z2)λ2−v2(−1)v2][2ζ1ζ2(ζ1−z1)(ζ2−z2)−1]dζζ−1(2πi)2∫∂0D2k1−1∑λ1=0k2−1∑λ2=0(−ζ1−ˉζ1)λ1λ1!λ2!λ2∑v2=0Cv2λ2ˉzv22g(m−k1+λ1)(m−k2+λ2)(ζ)⋅{(z2−ζ2−ˉζ2)λ2−v2[2ζ1ζ2(ζ1−z1)(ζ2−z2)−1]+(−z2)λ2−v2(−1)v2[2ζ2ζ2−z2−1]}dζ1dζ2ζ1ζ2−1(2πi)2∫∂0D2k1−1∑λ1=0k2−1∑λ2=0(−ζ2−ˉζ2)λ2λ1!λ2!λ1∑v1=0Cv1λ1ˉzv11g(m−k1+λ1)(m−k2+λ2)(ζ)⋅{(z1−ζ1−ˉζ1)λ1−v1[2ζ1ζ2(ζ1−z1)(ζ2−z2)−1]+(−z1)λ1−v1(−1)v1[2ζ1ζ1−z1−1]}dζ1dζ2ζ1ζ2+∫∂0D2k1−1∑λ1=0k2−1∑λ2=0g(m−k1+λ1)(m−k2+λ2)(ζ)(2πi)2λ1!λ2!(−ζ1−ˉζ1)λ1(−ζ2−ˉζ2)λ2[2ζ1ζ1−z1+2ζ2ζ2−z2−2]dζ1dζ2ζ1ζ2=1(2πi)2∫∂0D2k1−1∑λ1=0k2−1∑λ2=01λ1!λ2!{λ1∑v1=0Cv1λ1(z1−ζ1−ˉζ1)λ1−v1ˉzv11λ2∑v2=0Cv2λ2(z2−ζ2−ˉζ2)λ2−v2ˉzv22−(−ζ1−ˉζ1)λ1λ2∑v2=0Cv2λ2(z2−ζ2−ˉζ2)λ2−v2ˉzv22+λ1∑v1=0Cv1λ1(−z1)λ1−v1(−ˉz1)v1λ2∑v2=0Cv2λ2(z2−ζ2−ˉζ2)λ2−v2ˉzv22−λ1∑v1=0Cv1λ1(z1−ζ1−ˉζ1)λ1−v1ˉzv11(−ζ2−ˉζ2)λ2+λ1∑v1=0Cv1λ1(z1−ζ1−ˉζ1)λ1−v1ˉzv11λ2∑v2=0Cv2λ2(−z2)λ2−v2(−ˉz2)v2}⋅g(m−k1+λ1)(m−k2+λ2)(ζ)[2ζ1ζ2(ζ1−z1)(ζ2−z2)−1]dζ1dζ2ζ1ζ2−1(2πi)2∫∂0D2k1−1∑λ1=0k2−1∑λ2=0g(m−k1+λ1)(m−k2+λ2)(ζ)λ1!λ2!{(−ζ1−ˉζ1)λ1λ2∑v2=0Cv2λ2(−z2)λ2−v2(−ˉz2)v2[2ζ2ζ2−z2−1]+λ1∑v1=0Cv1λ1(−z1)λ1−v1(−ˉz1)v1(−ζ2−ˉζ2)λ2[2ζ1ζ1−z1−1]−(−ζ1−ˉζ1)λ1(−ζ2−ˉζ2)λ2[2ζ1ζ1−z1+2ζ2ζ2−z2−2]}dζζ=∫∂0D2k1−1∑λ1=0k2−1∑λ2=0g(m−k1+λ1)(m−k2+λ2)(2πi)2λ1!λ2!{(z1+ˉz1−ζ1−ˉζ1)λ1(z2+ˉz2−ζ2−ˉζ2)λ2−[(−ζ1−ˉζ1)λ1−(−z1−ˉz1)λ1]⋅(z2+ˉz2−ζ2−ˉζ2)λ2−(z1+ˉz1−ζ1−ˉζ1)λ1[(−ζ2−ˉζ2)λ2−(−z2−ˉz2)λ2]}[2ζ1ζ2(ζ1−z1)(ζ2−z2)−1]dζ1dζ2ζ1ζ2+∫∂0D2k1−1∑λ1=0k2−1∑λ2=0g(m−k1+λ1)(m−k2+λ2)(2πi)2λ1!λ2!(−ζ1−ˉζ1)λ1[(−ζ2−ˉζ2)λ2−(−z2−ˉz2)λ2][2ζ2ζ2−z2−1]dζ1dζ2ζ1ζ2+∫∂0D2k1−1∑λ1=0k2−1∑λ2=0g(m−k1+λ1)(m−k2+λ2)(2πi)2λ1!λ2λ2[2ζ1ζ1−z1−1]dζ1dζ2ζ1ζ2. | (2.7) |
Applying the properties of the Cauchy kernels on D2 and D, for z∈∂0D2, (2.7) leads to
ℜ∂m−k1ˉz1∂m−k2ˉz2˜ϕ(z)=k1−1∑λ1=0k2−1∑λ2=0{g(m−k1+λ1)(m−k2+λ2)(ζ)λ1!λ2λ2−(z1+ˉz1−ζ1−ˉζ1)λ1[(−ζ2−ˉζ2)λ2−(−z2−ˉz2)λ2]]}|ζ1=z1ζ2=z2+∫∂D1k1−1∑λ1=0k2−1∑λ2=0(−ζ1−ˉζ1)λ12πλ1!λ2!{g(m−k1+λ1)(m−k2+λ2)(ζ)[(−ζ2−ˉζ2)λ2−(−z2−ˉz2)λ2]}|ζ2=z2dζ1iζ1+∫∂D2k1−1∑λ1=0k2−1∑λ2=0(−ζ2−ˉζ2)λ22πλ1!λ2!{g(m−k1+λ1)(m−k2+λ2)(ζ)[(−ζ1−ˉζ1)λ1−(−z1−ˉz1)λ1]}|ζ1=z1dζ2iζ2=g(m−k1)(m−k2)(z), |
which means ℜ∂μˉz1∂νˉz2˜ϕ(z)=gμν(z) for z∈∂0D2.
In addition, by (2.7), we get that
ℑ∂m−k1ˉz1∂m−k2ˉz2˜ϕ(0,z2)=ℑ{∫∂0D2k1−1∑λ1=0k2−1∑λ2=0g(m−k1+λ1)(m−k2+λ2)(ζ)(2πi)2λ1!λ2![(−ζ1−ˉζ1)λ1(z2+ˉz2−ζ2−ˉζ2)λ2−(−ζ1−ˉζ1)λ1⋅(z2+ˉz2−ζ2−ˉζ2)λ2−(−ζ1−ˉζ1)λ1[(−ζ2−ˉζ2)λ2−(−z2−ˉz2)λ2]](2ζ2ζ2−z2−1)dζ1dζ2ζ1ζ2+∫∂0D2k1−1∑λ1=0k2−1∑λ2=0g(m−k1+λ1)(m−k2+λ2)(ζ)(2πi)2λ1!λ2!(−ζ1−ˉζ1)λ1[(−ζ2−ˉζ2)λ2−(−z2−ˉz2)λ2][2ζ2ζ2−z2−1]dζ1dζ2ζ1ζ2+∫∂0D2k1−1∑λ1=0k2−1∑λ2=0g(m−k1+λ1)(m−k2+λ2)(ζ)(2πi)2λ1!λ2!(−ζ1−ˉζ1)λ1(−ζ2−ˉζ2)λ2dζ1dζ2ζ1ζ2}=ℑ{∫∂0D2k1−1∑λ1=0k2−1∑λ2=0g(m−k1+λ1)(m−k2+λ2)(ζ)(2πi)2λ1!λ2!(−ζ1−ˉζ1)λ1(−ζ2−ˉζ2)λ2dζ1dζ2ζ1ζ2}=0. |
Similarly, we have that
ℑ∂m−k1ˉz1∂m−k2ˉz2˜ϕ(z1,0)=ℑ{∫∂0D2k1−1∑λ1=0k2−1∑λ2=0g(m−k1+λ1)(m−k2+λ2)(ζ)(2πi)2λ1!λ2λ2−(z1+ˉz1−ζ1−ˉζ1)λ1(−ζ2−ˉζ2)λ2](2ζ1ζ1−z1−1)dζ1dζ2ζ1ζ2+∫∂0D2k1−1∑λ1=0k2−1∑λ2=0g(m−k1+λ1)(m−k2+λ2)(ζ)(2πi)2λ1!λ2!(−ζ1−ˉζ1)λ1(−ζ2−ˉζ2)λ2dζ1dζ2ζ1ζ2+∫∂0D2k1−1∑λ1=0k2−1∑λ2=0g(m−k1+λ1)(m−k2+λ2)(ζ)(2πi)2λ1!λ2λ2[2ζ1ζ1−z1−1]dζζ}=ℑ{∫∂0D2k1−1∑λ1=0k2−1∑λ2=0g(m−k1+λ1)(m−k2+λ2)(ζ)(2πi)2λ1!λ2!(−ζ1−ˉζ1)λ1(−ζ2−ˉζ2)λ2dζ1dζ2ζ1ζ2}=0. |
Therefore,
ℑ∂μˉz1∂νˉz2˜ϕ(0,z2)=0=ℑ∂μˉz1∂νˉz2˜ϕ(z1,0). |
2) In the expression of u~v1~v2(z) determined by (2.4),
D=[(z1−ζ1−ˉζ1)l1(z2−ζ2−ˉζ2)l2+(−z1)l1(−1)v1(z2−ζ2−ˉζ2)l2+(z1−ζ1−ˉζ1)l1(−z2)l2(−1)v2]⋅[2ζ1ζ2(ζ1−z1)(ζ2−z2)−1]=[l1∑p1=0Cp1l1zp11(−ζ1−ˉζ1)l1−p1l2∑q1=0Cq1l2zq12(−ζ2−ˉζ2)l2−q1+(−z1)l1(−1)v1l2∑q1=0Cq1l2zq12(−ζ2−ˉζ2)l2−q1+l1∑p1=0Cp1l1zp11(−ζ1−ˉζ1)l1−p1(−z2)l2(−1)v2][2∞∑j1=0zj11ζj11∞∑j2=0zj22ζj22−1]. | (2.8) |
Moreover, we have that
l1∑p1=0Cp1l1zp11(−ζ1−ˉζ1)l1−p1l2∑q1=0Cq1l2zq12(−ζ2−ˉζ2)l2−q1[2+∞∑j1=0zj11ζj11∞∑j2=0zj22ζj22−1]=2l1∑p1=0+∞∑j1=0Cp1l1(−ζ1−ˉζ1)l1−p1ˉζj11zp1+j11l2∑q1=0+∞∑j2=0Cq1l2(−ζ2−ˉζ2)l2−q1ˉζj22zq1+j22−l1∑p1=0l2∑q1=0Cp1l1Cq1l2(−ζ1−ˉζ1)l1−p1(−ζ2−ˉζ2)l2−q1zp11zq12=2+∞∑j1=0l1+j1∑m1=j1Cm1−j1l1(−ζ1−ˉζ1)l1−m1+j1ˉζj11zm11+∞∑j2=0l2+j2∑m2=j2Cm2−j2l2(−ζ2−ˉζ2)l2−m2+j2ˉζj22zm22−l1∑m1=0l2∑m2=0Cm1l1Cm2l2(−ζ1−ˉζ1)l1−m1(−ζ2−ˉζ2)l2−m2zm11zm22=2+∞∑m1=0m1∑j1=0Cm1−j1l1(−ζ1−ˉζ1)l1−m1+j1ˉζj11zm11+∞∑m2=0m2∑j2=0Cm2−j2l2(−ζ2−ˉζ2)l2−m2+j2ˉζj22zm22−l1∑m1=0l2∑m2=0Cm1l1Cm2l2(−ζ1−ˉζ1)l1−m1(−ζ2−ˉζ2)l2−m2zm11zm22=+∞∑m1,m2=0[2m1∑j1=0m2∑j2=0Cm1−j1l1(−ζ1−ˉζ1)l1−m1+j1ˉζj11Cm2−j2l2(−ζ2−ˉζ2)l2−m2+j2ˉζj22−D11D12]zm11zm22, | (2.9) |
in which
D11={Cm1l1(−ζ1−ˉζ1)l1−m1,0≤m1≤l1,0,m1>l1,D12={Cm2l2(−ζ2−ˉζ2)l2−m2,0≤m2≤l2,0,m2>l2, |
and
(−z1)l1(−1)v1l2∑q1=0Cq1l2zq12(−ζ2−ˉζ2)l2−q1[2∞∑j1=0zj11ζj11+∞∑j2=0zj22ζj22−1]=2+∞∑j1=0(−1)l1+v1ˉζj11zl1+j11+∞∑j2=0l2∑q1=0Cq1l2(−ζ2−ˉζ2)l2−q1ˉζj22zq1+j22−zl11(−1)l1+v1l2∑q1=0Cq1l2(−ζ2−ˉζ2)l2−q1zq12=2+∞∑m1=l1(−1)l1+v1ˉζm1−l11zm11+∞∑m2=0m2∑j2=0Cm2−j2l2(−ζ2−ˉζ2)l2−m2+j2ˉζj22zm22−l2∑m2=0(−1)l1+v1Cm2l2(−ζ2−ˉζ2)l2−m2zl11zm22=+∞∑m1,m2=0[2D21m2∑j2=0Cm2−j2l2(−ζ2−ˉζ2)l2−m2+j2ˉζj22−D22D23]zm11zm22, | (2.10) |
where
D21={0,0≤m1<l1,(−1)l1+v1ˉζm1−l11,m1≥l1,D22={(−1)m1+v1,m1=l1,0,m1≠l1,D23={Cm2l2(−ζ2−ˉζ2)l2−m2,0≤m2≤l2,0,m2>l2. |
Similarly, we get that
l1∑p1=0Cp1l1zp11(−ζ1−ˉζ1)l1−p1(−z2)l2(−1)v2[2∞∑j1=0zj11ζj11+∞∑j2=0zj22ζj22−1]=+∞∑m1,m2=0[2D31m1∑j1=0Cm1−j1l1(−ζ1−ˉζ1)l1−m1+j1ˉζj11−D32D33]zm11zm22, | (2.11) |
in which
D31={0,0≤m2<l2,(−1)l2+v2ˉζm2−l22,m2≥l2,D32={Cm1l1(−ζ1−ˉζ1)l1−m1,0≤m1≤l1,0,m1>l1,D33={(−1)m2+v2,m2=l2,0,m2≠l2. |
Plugging (2.9)–(2.11) into (2.8) gives that
D=2+∞∑m1,m2=0{[m1∑j1=0m2∑j2=0Cm1−j1l1(−ζ1−ˉζ1)l1−m1+j1ˉζj11Cm2−j2l2(−ζ2−ˉζ2)l2−m2+j2ˉζj22+D21m2∑j2=0Cm2−j2l2(−ζ2−ˉζ2)l2−m2+j2ˉζj22+D31m1∑j1=0Cm1−j1l1(−ζ1−ˉζ1)l1−m1+j1ˉζj11]−12(D11D12+D22D23+D32D33)}zm11zm22. | (2.12) |
In addition, as the result of
(z1−ζ1−ˉζ1)l2[2ζ1ζ2(ζ1−z1)(ζ2−z2)−1]+(−z1)l1(−1)v1[2ζ1ζ1−z1−1]=l1∑p1=0Cp1l1zp11(−ζ1−ˉζ1)l1−p1[2+∞∑j1=0zj11ζj11+∞∑j2=0zj22ζj22−1]+zl11(−1)l1+v1(2+∞∑j1=0zj11ζj11−1)=2+∞∑m1=0m1∑j1=0Cm1−j1l1(−ζ1−ˉζ1)l1−m1+j1ˉζj11zm11+∞∑m2=0ˉζm22zm22−l1∑p1=0Cp1l1(−ζ1−ˉζ1)l1−p1zp11+2+∞∑m1=l1(−1)l1+v1ˉζm1−l11zm11−(−1)l1+v1zl11=2+∞∑m1,m2=0[m1∑j1=0Cm1−j1l1(−ζ1−ˉζ1)l1−m1+j1ˉζj11ˉζm22+D21C21−12(D11+C22)C21]zm11zm22, |
in which
C21={0,m2≥1,1,m2=0,C22={(−1)m1+v1,m1=l1,0,m1≠l1, |
we have that
C2=(−ζ2−ˉζ2)l2{(z1−ζ1−ˉζ1)l2[2ζ1ζ2(ζ1−z1)(ζ2−z2)−1]+(−z1)l1(−1)v1[2ζ1ζ1−z1−1]}=2(−ζ2−ˉζ2)l2+∞∑m1,m2=0[m1∑j1=0Cm1−j1l1(−ζ1−ˉζ1)l1−m1+j1ˉζj11ˉζm22+D21C21−D11+C222C21]zm11zm22, | (2.13) |
which follows that
C1−C2=D|v2=0−C2=2+∞∑m1,m2=0{m1∑j1=0Cm1−j1l1(−ζ1−ˉζ1)l1−m1+j1ˉζj11[m2∑j2=0Cm2−j2l2(−ζ2−ˉζ2)l2−m2+j2ˉζj22−(−ζ2−ˉζ2)l2ˉζm22]+D21m2∑j2=0Cm2−j2l2(−ζ2−ˉζ2)l2−m2+j2ˉζj22+D31|v2=0m1∑j1=0Cm1−j1l1(−ζ1−ˉζ1)l1−m1+j1ˉζj11−D21C21−12(D11D12+D22D23+D32D33|v2=0−D11C21−C22C21)}zm11zm22. | (2.14) |
Similarly, we have that
B2=(−ζ1−ˉζ1)l1{(z2−ζ2−ˉζ2)l2[2ζ1ζ2(ζ1−z1)(ζ2−z2)−1]+(−z2)l2(−1)v2[2ζ2ζ2−z2−1]}=2(−ζ1−ˉζ1)l1+∞∑m1,m2=0[m2∑j2=0Cm2−j2l2(−ζ2−ˉζ2)l2−m2+j2ˉζj22ˉζm11+B21D31−D23+B222B21]zm11zm22, | (2.15) |
and
B1−B2=D|v1=0−B2=2+∞∑m1,m2=0{[m1∑j1=0Cm1−j1l1(−ζ1−ˉζ1)l1−m1+j1ˉζj11−(−ζ1−ˉζ1)l1ˉζm11]m2∑j2=0Cm2−j2l2(−ζ2−ˉζ2)l2−m2+j2ˉζj22+D21|v1=0m2∑j2=0Cm2−j2l2(−ζ2−ˉζ2)l2−m2+j2ˉζj22+D31m1∑j1=0Cm1−j1l1(−ζ1−ˉζ1)l1−m1+j1ˉζj11−B21D31−12(D11D12+D22|v1=0D23+D32D33−B21D23−B21B22)}zm11zm22, | (2.16) |
in which
B21={0,m1≥1,1,m1=0,B22={(−1)m2+v2,m2=l2,0,m2≠l2. |
Therefore,
A1−A2−A3+A4=D|v1=v2=0−B2|v2=0−C2|v1=0+A4=D|v1=v2=0−B2|v2=0−C2|v1=0+2+∞∑m1,m2=0(¯ζ1m1C21+B21¯ζ2m2−B21C21)(−ζ1−ˉζ1)l1(−ζ2−ˉζ2)l2zm11zm22 | (2.17) |
as the result of
A4=(−ζ1−ˉζ1)l1(−ζ2−ˉζ2)l2[2ζ1ζ1−z1+2ζ2ζ2−z2−2]=2(−ζ1−ˉζ1)l1(−ζ2−ˉζ2)l2[+∞∑j1=0zj11ζj11++∞∑j2=0zj22ζj22−1]=2(−ζ1−ˉζ1)l1(−ζ2−ˉζ2)l2[+∞∑m1=0¯ζ1m1zm11+∞∑m2=0C21zm22++∞∑m1=0B21zm11+∞∑m2=0¯ζ2m2zm22−+∞∑m1=0B21zm11+∞∑m2=0C21zm22]=2(−ζ1−ˉζ1)l1(−ζ2−ˉζ2)l2+∞∑m1,m2=0(¯ζ1m1C21+B21¯ζ2m2−B21C21)zm11zm22. |
On the other hand, due to the analyticity of the function u~v1~v2(z), it can be expressed as
u~v1~v2(z)=+∞∑m1,m2=0um−~v1,m−~v2m1,m2zm11zm22,μ≤~v1≤m−1,ν≤~v2≤m−1. | (2.18) |
Plugging (2.12), (2.14), (2.16), and (2.17) into (2.4), and considering the Eq (2.18), we get (2.2). Moreover, (2.18) and (2.3) lead to (2.1). Therefore, from the result in the first part (1), it can be concluded that ˜ϕ(z) determined by (2.1) satisfies
ℜ∂μˉz1∂νˉz2˜ϕ(z)=gμν(z)(z∈∂0D2),ℑ∂μˉz1∂νˉz2˜ϕ(0,z2)=0=ℑ∂μˉz1∂νˉz2˜ϕ(z1,0)(z1∈D1,z2∈D2). |
Theorem 3.1. Let φ∈C(∂0D2;C), λ∈R∖{−1,0,1}, and let gμν∈C(∂0D2;R) for 1≤μ,ν≤m−1 (m≥2). Then, the problem
∂ˉz1∂ˉz2f(z)=λ−14λϕ(z)+λ+14λˉϕ(z),∂mˉz1∂mˉz2ϕ(z)=0(z∈D2) |
with the conditions
f(z)=φ(z),ℜ∂μˉz1∂νˉz2ϕ(z)=gμν(z)(z∈∂0D2),ℑ∂μˉz1∂νˉz2ϕ(0,z2)=0=ℑ∂μˉz1∂νˉz2ϕ(z1,0) |
is solvable and the solution is
f(z)=λ−14λ[+∞∑m1,m2=0u0,0m1,m2zm11zm22ˉz1ˉz2++∞∑m1,m2=0m−1∑v1=μm−1∑v2=νum−v1,m−v2m1,m2zm11zm22⋅ˉzv1+11ˉzv2+12(v1+1)!(v2+1)!]+λ+14λ[+∞∑m1,m2=0¯u0,0m1,m2ˉzm1+11m1+1ˉzm2+12m2+1++∞∑m1,m2=0m−1∑v1=μm−1∑v2=νzv11zv22v1!v2!⋅¯um−v1,m−v2m1,m2ˉzm1+11m1+1ˉzm2+12m2+1]++∞∑m1,m2=0bm1,m2zm11zm22, |
where um−v1,m−v2m1,m2 is determined by (2.2) (in which ~v1 and ~v2 are replaced by v1 and v2, respectively), and u0,0m1,m2, bm1,m2 are determined by the following:
(ⅰ) for 1≤m1≤m−1 and 1≤m2≤m−1,
u0,0m1,m2=(m1+1)(m2+1){λλ+11π2∫2π0e−it1(m1+1)∫2π0e−it2(m2+1)¯φ(eit1,eit2)dt2dt1−λ−1λ+1m−μ∑v1=1m−ν∑v2=1¯uv1,v2(m−m1−v1),(m−m2−v2)(m−v1+1)!(m−v2+1)!−m−μ∑v1=1m−ν∑v2=11(m−v1)!(m−v2)!uv1,v2(m+m1−v1),(m+m2−v2)(m+m1−v1+1)(m+m2−v2+1)}; | (3.1) |
(ⅱ) for m1≥1 and m2≥1,
u0,00,0=λ+1(2π)2∫2π0e−it1∫2π0e−it2¯φ(eit1,eit2)dt2dt1−λ−1(2π)2∫2π0eit1∫2π0eit2φ(eit1,eit2)dt2dt1−m−μ∑v1=1m−ν∑v2=1uv1,v2(m−v1),(m−v2)(m−v1+1)!(m−v2+1)!, | (3.2) |
u0,00,m2=4λλ−11(2π)2∫2π0eit1∫2π0eit2(1−m2)φ(eit1,eit2)dt2dt1−m−μ∑v1=1m−ν∑v2=1uv1,v2(m−v1),(m−v2+m2)(m−v1+1)!(m−v2+1)!−λ+1λ−1{m−μ∑v1=1m−ν∑v2=11(m−v1+1)!(m−v2)!¯uv1,v2(m−v1),(m−v2−m2)m−v2−m2+1,1≤m2≤ν,m−μ∑v1=1m−m2∑v2=11(m−v1+1)!(m−v2)!¯uv1,v2(m−v1),(m−v2−m2)m−v2−m2+1,ν<m2≤m−1,0,m2>m−1, | (3.3) |
u0,0m1,0=4λλ−11(2π)2∫2π0ei(1−m1)t1∫2π0eit2φ(eit1,eit2)dt2dt1−m−μ∑v1=1m−ν∑v2=1uv1,v2(m−v1+m1),(m−v2)(m−v1+1)!(m−v2+1)!−λ+1λ−1{m−μ∑v1=1m−ν∑v2=11(m−v1)!(m−v2+1)!¯uv1,v2(m−v1−m1,(m−v2))m−v1−m1+1,1≤m1≤μ,m−m1∑v1=1m−ν∑v2=11(m−v1)!(m−v2+1)!¯uv1,v2(m−v1−m1,(m−v2))m−v1−m1+1,μ<m1≤m−1,0,m1>m−1; | (3.4) |
(ⅲ) with u0,01,1 being determined by (3.1),
b0,0=1(2π)2∫2π0∫2π0φ(eit1,eit2)dt2dt1−λ−14λ[u0,01,1+m−μ∑v1=1m−ν∑v2=1uv1,v2(m−v1+1),(m−v2+1)(m−v1+1)!(m−v2+1)!]−λ+14λm−μ∑v1=1m−ν∑v2=11(m−v1)!(m−v2)!¯uv1,v2(m−v1−1),(m−v2−1)(m−v1)(m−v2); | (3.5) |
(ⅳ) for {m1≥mm2≥m or {1≤m1≤m−1m2≥m or {m1≥m1≤m2≤m−1,
u0,0m1,m2=4λλ+1(m1+1)(m2+1)(2π)2∫2π0e−i(m1+1)t1∫2π0e−i(m2+1)t2¯φ(eit1,eit2)dt2dt1−m−μ∑v1=1m−ν∑v2=1(m1+1)(m2+1)(m−v1)!(m−v2)!uv1,v2(m−v1+m1),(m−v2+m2)(m−v1+m1+1)(m−v2+m2+1); | (3.6) |
(ⅴ) for m1,m2≥1,
bm1,m2=1(2π)2∫2π0e−im1t1∫2π0e−im2t2φ(eit1,eit2)dt2dt1−λ−14λ[u0,0(m1+1),(m2+1)+m−μ∑v1=1m−ν∑v2=1uv1,v2(m1+m−v1+1),(m2+m−v2+1)(m−v1+1)!(m−v2+1)!]−λ+14λ{m−1−m1∑v1=1m−1−m2∑v2=11(m−v1)!(m−v2)!¯uv1,v2(m−1−m1−v1),(m−1−m2−v2)(m−v1−m1)(m−v2−m2),1≤m1,m2<m−1,0,m1,m2≥m−1, | (3.7) |
bm1,0=1(2π)2∫2π0∫2π0e−im1t1φ(eit1,eit2)dt2dt1−λ−14λ[u0,0(m1+1),1+m−μ∑v1=1m−ν∑v2=1uv1,v2(m1+m−v1+1),(m−v2+1)(m−v1+1)!(m−v2+1)!]−λ+14λ{m−1−m1∑v1=1m−ν∑v2=11(m−v1)!(m−v2)!¯uv1,v2(m−1−m1−v1),(m−1−v2)(m−v1−m1)(m−v2),1≤m1<m−1,0,m1≥m−1, | (3.8) |
b0,m2=1(2π)2∫2π0∫2π0e−im2t2φ(eit1,eit2)dt2dt1−λ−14λ[u0,01,(m2+1)+m−μ∑v1=1m−ν∑v2=1uv1,v2(m−v1+1),(m2+m−v2+1)(m−v1+1)!(m−v2+1)!]−λ+14λ{m−μ∑v1=1m−1−m2∑v2=11(m−v1)!(m−v2)!¯uv1,v2(m−1−v1),(m−1−m2−v2)(m−v1)(m−v2−m2),1≤m2<m−1,0,m2≥m−1, | (3.9) |
in which u0,0(m1+1),(m2+1), u0,0(m1+1),1, and u0,01,(m2+1) are determined by (3.6).
Proof: 1) By Theorem 2.1,
ϕ(z)=+∞∑m1,m2=0m−1∑v1=μm−1∑v2=νˉzv11ˉzv22v1!v2!um−v1,m−v2m1,m2zm11zm22+u0(z), |
where um−v1,m−v2m1,m2 is determined by (2.2) (in which ~v1 and ~v2 are replaced by v1 and v2, respectively), and u0(z) is analytic on D2. Thus, u0(z) can be represented as
u0(z)=+∞∑m1,m2=0u0,0m1,m2zm11zm22, |
in which u0,0m1,m2 is to be determined.
Let
ϕ1(z)=u0(z)ˉz1ˉz2+m−1∑v1=μm−1∑v2=νuv1v2(z)(v1+1)!(v2+1)!ˉzv1+11ˉzv2+12, |
where
uv1,v2(z)=+∞∑m1,m2=0um−v1,m−v2m1,m2zm11zm22,μ≤v1≤m−1,ν≤v2≤m−1. |
Thus, ∂ˉz1∂ˉz2ϕ1(z)=ϕ(z). Let
~u0(z)=+∞∑m1,m2=0u0,0m1,m2zm1+11m1+1zm2+12m2+1, |
and let
˜uv1,v2(z)=+∞∑m1,m2=0um−v1,m−v2m1,m2zm1+11m1+1zm2+12m2+1,μ≤v1≤m−1,ν≤v2≤m−1. |
Then, we get that ∂z1∂z2~u0(z)=u0(z) and ∂z1∂z2˜uv1,v2(z)=uv1,v2(z). Let
ϕ2(z)=¯~u0(z)+m−1∑v1=μm−1∑v2=νˉzv11ˉzv22v1!v2!˜uv1v2(z), |
which follows that
∂ˉz1∂ˉz2ϕ2(z)=¯∂z1∂z2¯ϕ2=¯∂z1∂z2~u0(z)+m−1∑v1=μm−1∑v2=νˉzv11ˉzv22v1!v2!∂z1∂z2˜uv1v2(z)=¯u0(z)+m−1∑v1=μm−1∑v2=νˉzv11ˉzv22v1!v2!uv1v2(z)=¯ϕ(z). |
Therefore,
∂ˉz1∂ˉz2[λ−14λϕ1(z)+λ+14λϕ2(z)]=λ−14λϕ(z)+λ+14λˉϕ(z), |
which means that
λ−14λϕ1(z)+λ+14λϕ2(z) |
is a special solution to
∂ˉz1∂ˉz2f(z)=λ−14λϕ(z)+λ+14λˉϕ(z). |
So, the solution of the problem is
f(z)=[λ−14λϕ1(z)+λ+14λϕ2(z)]+ψ(z)=λ−14λ[u0(z)ˉz1ˉz2+m−1∑v1=μm−1∑v2=νuv1v2(z)(v1+1)!(v2+1)!ˉzv1+11ˉzv2+12]+λ+14λ[¯~u0(z)+m−1∑v1=μm−1∑v2=νzv11zv22v1!v2!¯˜uv1v2(z)]+ψ(z)=λ−14λ[+∞∑m1,m2=0u0,0m1,m2zm11zm22ˉz1ˉz2++∞∑m1,m2=0m−1∑v1=μm−1∑v2=νum−v1,m−v2m1,m2zm11zm22⋅ˉzv1+11ˉzv2+12(v1+1)!(v2+1)!]+λ+14λ[+∞∑m1,m2=0¯u0,0m1,m2ˉzm1+11m1+1ˉzm2+12m2+1++∞∑m1,m2=0m−1∑v1=μm−1∑v2=νzv11zv22v1!v2!⋅¯um−v1,m−v2m1,m2ˉzm1+11m1+1ˉzm2+12m2+1]++∞∑m1,m2=0bm1,m2zm11zm22, | (3.10) |
where ψ(z)=∑+∞m1,m2=0bm1,m2zm11zm22 is analytic on D2, and u0,0m1,m2 and bm1,m2 are to be determined.
2) In this part, we seek the expressions of u0,0m1,m2 and bm1,m2.
For z∈∂0D2, let z1=eit1 and z2=eit2 (t1,t2∈[0,2π]). Then, we get that
φ(eit1,eit2)=f(eit1,eit2)=λ−14λ[+∞∑m1,m2=0u0,0m1,m2ei(m1−1)t1ei(m2−1)t2++∞∑m1,m2=0m−μ∑~v1=1m−ν∑~v2=1u~v1,~v2m1,m2⋅eit1(m1−m+~v1−1)eit2(m2−m+~v2−1)(m−~v1+1)!(m−~v2+1)!]+λ+14λ[+∞∑m1,m2=0¯u0,0m1,m2e−it1(m1+1)m1+1e−it2(m2+1)m2+1++∞∑m1,m2=0m−μ∑~v1=1m−ν∑~v2=1e−it1(m1+1−m+~v1)e−it2(m2+1−m+~v2)(m−~v1)!(m−~v2)!¯u~v1,~v2m1,m2(m1+1)(m2+1)]++∞∑m1,m2=0bm1,m2eim1t1eim2t2=λ−14λ[0∑m1=0+∞∑m2=0u0,00,m2e−it1ei(m2−1)t2++∞∑m1=10∑m2=0u0,0m1,0ei(m1−1)t1e−it2++∞∑m1,m2=1u0,0m1,m2ei(m1−1)t1ei(m2−1)t2+m−μ∑~v1=1m−ν∑~v2=1m−~v1∑m1=0m−~v2∑m2=0u~v1,~v2m1,m2eit1(m1−(m−~v1+1))eit2(m2−(m−~v2+1))(m−~v1+1)!(m−~v2+1)!+m−μ∑~v1=1m−ν∑~v2=1+∞∑m1,m2=0u~v1,~v2(m1+(m−~v1+1)),(m2+(m−~v2+1))eim1t1eim2t2(m−~v1+1)!(m−~v2+1)!+m−μ∑~v1=1m−ν∑~v2=1m−~v1∑m1=0+∞∑m2=0u~v1,~v2m1,(m2+(m−~v2+1))eit1(m1−(m−~v1+1))eim2t2(m−~v1+1)!(m−~v2+1)!+m−μ∑~v1=1m−ν∑~v2=1+∞∑m1=0m−~v2∑m2=0u~v1,~v2(m1+(m−~v1+1)),m2eim1t1eit2(m2−(m−~v2+1))(m−~v1+1)!(m−~v2+1)!]+λ+14λ[+∞∑m1,m2=1¯u0,0(m1−1),(m2−1)e−it1m1m1e−it2m2m2++∞∑m1,m2=0m−μ∑~v1=1m−ν∑~v2=1e−it1(m1+1−m+~v1)e−it2(m2+1−m+~v2)(m−~v1)!(m−~v2)!¯u~v1,~v2m1,m2(m1+1)(m2+1)]++∞∑m1,m2=0bm1,m2eim1t1eim2t2. | (3.11) |
(ⅰ) In the case of 0≤r1,r2≤m−2, multiplying both sides of the Eq (3.11) by eit1(m−r1)eit2(m−r2), and then integrating with respect to t1,t2∈[0,2π] yields that
1(2π)2∫2π0eit1(m−r1)[∫2π0eit2(m−r2)φ(eit1,eit2)dt2]dt1=λ−14λ[+∞∑m2=0u0,00,m212π∫2π0eit1(m−r1−1)dt1⋅12π∫2π0eit2(m2−1+m−r2)dt2++∞∑m1=1u0,0m1,012π∫2π0eit1(m1−1+m−r1)dt1⋅12π∫2π0eit2(−1+m−r2)dt2++∞∑m1,m2=1u0,0m1,m212π∫2π0eit1(m1−1+m−r1)dt1⋅12π∫2π0eit2(m2−1+m−r2)dt2+m−μ∑v1=1m−ν∑v2=1m−v1∑m1=0m−v2∑m2=0uv1,v2m1,m212π∫2π0eit1(m1+v1−1−r1)dt1⋅12π∫2π0eit2(m2+v2−1−r2)dt2(m−v1+1)!(m−v2+1)!+m−μ∑v1=1m−ν∑v2=1+∞∑m1,m2=0uv1,v2(m1+(m−v1+1)),(m2+(m−v2+1))12π∫2π0eit1(m1+m−r1)dt1⋅12π∫2π0eit2(m2+m−r2)dt2(m−v1+1)!(m−v2+1)!+m−μ∑v1=1m−ν∑v2=1m−v1∑m1=0+∞∑m2=0uv1,v2m1,(m2+(m−v2+1))12π∫2π0eit1(m1+v1−1−r1)dt1⋅12π∫2π0eit2(m2+m−r2)dt2(m−v1+1)!(m−v2+1)!+m−μ∑v1=1m−ν∑v2=1+∞∑m1=0m−v2∑m2=0uv1,v2(m1+(m−v1+1)),m212π∫2π0eit1(m1+m−r1)dt1⋅12π∫2π0eit2(m2+v2−1−r2)dt2(m−v1+1)!(m−v2+1)!]+λ+14λ[+∞∑m1,m2=1¯u0,0(m1−1),(m2−1)m1m212π∫2π0eit1(m−r1−m1)dt1⋅12π∫2π0eit2(m−r2−m2)dt2++∞∑m1,m2=0m−μ∑v1=1m−ν∑v2=112π∫2π0eit1(m−r1−(m1+1−m+v1))dt1⋅12π∫2π0eit2(m−r2−(m2+1−m+v2))dt2(m−v1)!(m−v2)!⋅¯uv1,v2m1,m2(m1+1)(m2+1)]++∞∑m1,m2=0bm1,m212π∫2π0eit1(m1+m−r1)dt1⋅12π∫2π0eit2(m2+m−r2)dt2=λ−14λ[m−μ∑v1=1m−ν∑v2=1uv1,v2(1+r1−v1),(1+r2−v2)(m−v1+1)!(m−v2+1)!]+λ+14λ[¯u0,0(m−r1−1),(m−r2−1)(m−r1)(m−r2)+m−μ∑v1=1m−ν∑v2=11(m−v1)!(m−v2)!¯uv1,v2(2m−r1−1−v1),(2m−r2−1−v2)(2m−r1−v1)(2m−r2−v2)], |
and the last equation is due to
12π∫2π0eimtdt={0,m≠0(m∈Z),1,m=0. |
Therefore,
u0,0(m−r1−1),(m−r2−1)=(m−r1)(m−r2){4λλ+11(2π)2∫2π0e−it1(m−r1)∫2π0e−it2(m−r2)¯φ(eit1,eit2)dt2dt1−λ−1λ+1[m−μ∑v1=1m−ν∑v2=1¯uv1,v2(1+r1−v1),(1+r2−v2)(m−v1+1)!(m−v2+1)!]−m−μ∑v1=1m−ν∑v2=11(m−v1)!(m−v2)!uv1,v2(2m−r1−1−v1),(2m−r2−1−v2)(2m−r1−v1)(2m−r2−v2)}, |
which leads to (3.1) for 1\leq m_1, \; m_2\leq m-1 .
(ⅱ) Multiplying both sides of the Eq (3.11) by e^{it_1}e^{it_2} , and then integrating with respect to t_1, t_2\in[0, 2\pi] yields that
\begin{aligned} &\frac{1}{(2\pi)^2}\int^{2\pi}_0e^{it_1}\Big[\int^{2\pi}_0e^{it_2}\varphi(e^{it_1}, e^{it_2})dt_2\Big]dt_1\\ & = \!\frac{\lambda-1}{4\lambda} \Big[\sum\limits_{m_2 = 0}^{+\infty}u_{0, m_2}^{0, 0}\frac{1}{2\pi}\int^{2\pi}_0e^{im_2t_2}dt_2 +\sum\limits_{m_1 = 1}^{+\infty}u_{m_1, 0}^{0, 0}\frac{1}{2\pi}\int^{2\pi}_0e^{im_1t_1}dt_1\\ &\; \; +\!\sum\limits_{m_1, m_2 = 1}^{+\infty}\!\!\!u_{m_1, m_2}^{0, 0}\frac{1}{2\pi}\int^{2\pi}_0e^{im_1t_1}dt_1\cdot \frac{1}{2\pi}\int^{2\pi}_0e^{im_2t_2}dt_2\\ &\; \; +\sum\limits^{m-\mu}_{v_1 = 1}\sum\limits^{m-\nu}_{v_2 = 1} \sum\limits_{m_1 = 0}^{m-v_1}\sum\limits_{m_2 = 0}^{m-v_2} \!\frac{u_{m_1, m_2}^{v_1, v_2}\frac{1}{2\pi}\int^{2\pi}_0 e^{it_1(m_1-m+v_1)}dt_1\cdot \frac{1}{2\pi}\int^{2\pi}_0e^{it_2(m_2-m+v_2)}dt_2} {(m-v_1+1)!(m-v_2+1)!}\\ &\; \; +\!\!\sum\limits^{m-\mu}_{v_1 = 1}\sum\limits^{m-\nu}_{v_2 = 1} \!\sum\limits_{m_1, m_2 = 0}^{+\infty} \!\!\!\!\!\frac{u_{(m_1+(m-v_1+1)), (m_2+(m-v_2+1))}^{v_1, v_2} \frac{1}{2\pi}\!\!\int^{2\pi}_0 \!\!\!e^{it_1(m_1+1)}dt_1\!\cdot\! \frac{1}{2\pi}\!\!\int^{2\pi}_0\!\!\!e^{it_2(m_2+1)}dt_2} {(m-v_1+1)!(m-v_2+1)!}\\ &\; \; +\sum\limits^{m-\mu}_{v_1 = 1}\sum\limits^{m-\nu}_{v_2 = 1} \sum\limits_{m_1 = 0}^{m-v_1}\sum\limits_{m_2 = 0}^{+\infty} \!\frac{u_{m_1, (m_2+(m-v_2+1))}^{v_1, v_2} \frac{1}{2\pi}\!\int^{2\pi}_0 \!e^{it_1(m_1-m+v_1)}dt_1\cdot \frac{1}{2\pi}\!\int^{2\pi}_0\!e^{it_2(m_2+1)}dt_2} {(m-v_1+1)!(m-v_2+1)!}\\ &\; \; +\sum\limits^{m-\mu}_{v_1 = 1}\sum\limits^{m-\nu}_{v_2 = 1} \sum\limits_{m_1 = 0}^{+\infty}\sum\limits_{m_2 = 0}^{m-v_2} \!\frac{u_{(m_1+(m-v_1+1)), m_2}^{v_1, v_2} \frac{1}{2\pi}\int^{2\pi}_0 e^{it_1(m_1+1)}dt_1\cdot \frac{1}{2\pi}\int^{2\pi}_0e^{it_2(m_2-m+v_2)}dt_2} {(m-v_1+1)!(m-v_2+1)!}\Big]\\ &\; \; +\frac{\lambda+1}{4\lambda} \Big[\sum\limits_{m_1, m_2 = 1}^{+\infty}\frac{\overline{u_{(m_1-1), (m_2-1)}^{0, 0}}}{m_1m_2} \frac{1}{2\pi}\!\int^{2\pi}_0 \!e^{it_1(1-m_1)}dt_1\cdot \frac{1}{2\pi}\!\int^{2\pi}_0\!e^{it_2(1-m_2)}dt_2\\ &\; \; +\sum\limits_{m_1, m_2 = 0}^{+\infty}\sum\limits^{m-\mu}_{v_1 = 1}\sum\limits^{m-\nu}_{v_2 = 1} \frac{\frac{1}{2\pi}\!\int^{2\pi}_0 \!e^{-it_1(m_1-m+v_1)}dt_1\cdot \frac{1}{2\pi}\!\int^{2\pi}_0\!e^{-it_2(m_2-m+v_2)}dt_2} {(m-v_1)!(m-v_2)!}\\ &\; \; \cdot\frac{\overline{u_{m_1, m_2}^{v_1, v_2}}}{(m_1+1)(m_2+1)} \Big]+ \sum\limits_{m_1, m_2 = 0}^{+\infty}b_{m_1, m_2}\frac{1}{2\pi}\!\int^{2\pi}_0 \!e^{it_1(m_1+1)}dt_1\cdot \frac{1}{2\pi}\!\int^{2\pi}_0\!e^{it_2(m_2+1)}dt_2\\ & = \!\frac{\lambda-1}{4\lambda} \Big[u_{0, 0}^{0, 0}+\sum\limits^{m-\mu}_{v_1 = 1}\sum\limits^{m-\nu}_{v_2 = 1} \!\frac{u_{(m-v_1), (m-v_2)}^{v_1, v_2}} {(m-v_1+1)!(m-v_2+1)!}\Big]\\ &\; \; +\frac{\lambda+1}{4\lambda} \Big[\overline{u_{0, 0}^{0, 0}} +\sum\limits^{m-\mu}_{v_1 = 1}\sum\limits^{m-\nu}_{v_2 = 1} \frac{\overline{u_{(m-v_1), (m-v_2)}^{v_1, v_2}}}{(m-v_1+1)!(m-v_2+1)!} \Big], \end{aligned} |
which leads to (3.2).
Additionally, for r_2\geq 1 , multiplying both sides of the Eq (3.11) by e^{it_1}e^{i(1-r_2)t_2} , and then integrating with respect to t_1, t_2\in[0, 2\pi] yields that
\begin{aligned} &\frac{1}{(2\pi)^2}\int^{2\pi}_0e^{it_1}\Big[\int^{2\pi}_0e^{it_2(1-r_2)}\varphi(e^{it_1}, e^{it_2})dt_2\Big]dt_1\\ & = \!\frac{\lambda-1}{4\lambda} \Big[\!\sum\limits_{m_2 = 0}^{+\infty}u_{0, m_2}^{0, 0} \frac{1}{2\pi}\int^{2\pi}_0e^{it_2(m_2-r_2)}dt_2 +\!\sum\limits_{m_1 = 1}^{+\infty}u_{m_1, 0}^{0, 0}\frac{1}{2\pi}\int^{2\pi}_0e^{im_1t_1}dt_1\cdot \frac{1}{2\pi}\int^{2\pi}_0e^{-ir_2t_2}dt_2\\ &\; \; +\!\sum\limits_{m_1, m_2 = 1}^{+\infty}\!\!\!u_{m_1, m_2}^{0, 0}\frac{1}{2\pi}\int^{2\pi}_0e^{im_1t_1}dt_1\cdot \frac{1}{2\pi}\int^{2\pi}_0e^{it_2(m_2-r_2)}dt_2\\ &\; \; +\sum\limits^{m-\mu}_{v_1 = 1}\sum\limits^{m-\nu}_{v_2 = 1} \sum\limits_{m_1 = 0}^{m-v_1}\sum\limits_{m_2 = 0}^{m-v_2} \!\frac{u_{m_1, m_2}^{v_1, v_2}\frac{1}{2\pi}\int^{2\pi}_0 e^{it_1(m_1-m+v_1)}dt_1\cdot \frac{1}{2\pi}\int^{2\pi}_0e^{it_2(m_2-m+v_2-r_2)}dt_2} {(m-v_1+1)!(m-v_2+1)!}\\ &\; \; +\sum\limits^{m-\mu}_{v_1 = 1}\sum\limits^{m-\nu}_{v_2 = 1} \!\sum\limits_{m_1, m_2 = 0}^{+\infty} \!\!\!\!\!\frac{u_{(m_1+(m-v_1+1)), (m_2+(m-v_2+1))}^{v_1, v_2} \frac{1}{2\pi}\!\!\int^{2\pi}_0 \!\!\!e^{it_1(m_1+1)}dt_1\!\cdot\! \frac{1}{2\pi}\!\!\int^{2\pi}_0\!\!\!e^{it_2(m_2+1-r_2)}dt_2} {(m-v_1+1)!(m-v_2+1)!}\\ &\; \; +\sum\limits^{m-\mu}_{v_1 = 1}\sum\limits^{m-\nu}_{v_2 = 1} \sum\limits_{m_1 = 0}^{m-v_1}\sum\limits_{m_2 = 0}^{+\infty} \!\frac{u_{m_1, (m_2+(m-v_2+1))}^{v_1, v_2} \frac{1}{2\pi}\!\int^{2\pi}_0 \!e^{it_1(m_1-m+v_1)}dt_1\cdot \frac{1}{2\pi}\!\int^{2\pi}_0\!e^{it_2(m_2+1-r_2)}dt_2} {(m-v_1+1)!(m-v_2+1)!}\\ &\; \; +\sum\limits^{m-\mu}_{v_1 = 1}\sum\limits^{m-\nu}_{v_2 = 1} \sum\limits_{m_1 = 0}^{+\infty}\sum\limits_{m_2 = 0}^{m-v_2} \!\frac{u_{(m_1+(m-v_1+1)), m_2}^{v_1, v_2} \frac{1}{2\pi}\int^{2\pi}_0 e^{it_1(m_1+1)}dt_1\cdot \frac{1}{2\pi}\int^{2\pi}_0e^{it_2(m_2-m+v_2-r_2)}dt_2} {(m-v_1+1)!(m-v_2+1)!}\Big]\\ &\; \; +\frac{\lambda+1}{4\lambda} \Big[\sum\limits_{m_1, m_2 = 1}^{+\infty}\frac{\overline{u_{(m_1-1), (m_2-1)}^{0, 0}}}{m_1m_2} \frac{1}{2\pi}\!\int^{2\pi}_0 \!e^{it_1(1-m_1)}dt_1\cdot \frac{1}{2\pi}\!\int^{2\pi}_0\!e^{it_2(1-r_2-m_2)}dt_2\\ &\; \; +\sum\limits_{m_1, m_2 = 0}^{+\infty}\sum\limits^{m-\mu}_{v_1 = 1}\sum\limits^{m-\nu}_{v_2 = 1} \frac{\frac{1}{2\pi}\!\int^{2\pi}_0 \!e^{-it_1(m_1-m+v_1)}dt_1\cdot \frac{1}{2\pi}\!\int^{2\pi}_0\!e^{-it_2(m_2-m+v_2+r_2)}dt_2} {(m-v_1)!(m-v_2)!}\\ &\; \; \cdot\frac{\overline{u_{m_1, m_2}^{v_1, v_2}}}{(m_1+1)(m_2+1)} \Big]+ \sum\limits_{m_1, m_2 = 0}^{+\infty}b_{m_1, m_2}\frac{1}{2\pi}\!\int^{2\pi}_0 \!e^{it_1(m_1+1)}dt_1\cdot \frac{1}{2\pi}\!\int^{2\pi}_0\!e^{it_2(m_2+1-r_2)}dt_2\\ & = \!\frac{\lambda-1}{4\lambda} \Big[u^{0, 0}_{0, r_2}+\sum\limits^{m-\mu}_{v_1 = 1}\sum\limits^{m-\nu}_{v_2 = 1} \!\frac{u_{(m-v_1), (m-v_2+r_2)}^{v_1, v_2}} {(m-v_1+1)!(m-v_2+1)!}\Big]\\ &\; \; +\frac{\lambda+1}{4\lambda}\left\{ \begin{array}{ll} \sum\limits^{m-\mu}_{v_1 = 1}\sum\limits^{m-\nu}_{v_2 = 1} \frac{1}{(m-v_1+1)!(m-v_2)!} \frac{\overline{u_{(m-v_1), (m-v_2-r_2)}^{v_1, v_2}}}{m-v_2-r_2+1}, \quad 1\leq r_2\leq \nu, \\ \sum\limits^{m-\mu}_{v_1 = 1}\sum\limits^{m-r_2}_{v_2 = 1} \frac{1}{(m-v_1+1)!(m-v_2)!} \frac{\overline{u_{(m-v_1), (m-v_2-r_2)}^{v_1, v_2}}}{m-v_2-r_2+1}, \quad \nu < r_2\leq m-1, \\ 0, \quad r_2 > m-1, \end{array} \right. \end{aligned} |
and the last equation is due to
\begin{aligned} &\sum\limits_{m_1, m_2 = 0}^{+\infty}\sum\limits^{m-\mu}_{v_1 = 1}\sum\limits^{m-\nu}_{v_2 = 1} \frac{\frac{1}{2\pi}\!\int^{2\pi}_0 \!e^{-it_1(m_1-m+v_1)}dt_1\cdot \frac{1}{2\pi}\!\int^{2\pi}_0\!e^{-it_2(m_2-m+v_2+r_2)}dt_2} {(m-v_1)!(m-v_2)!} \frac{\overline{u_{m_1, m_2}^{v_1, v_2}}}{(m_1+1)(m_2+1)}\\ & = \left\{ \begin{array}{ll} \sum\limits^{m-\mu}_{v_1 = 1}\sum\limits^{m-\nu}_{v_2 = 1} \frac{1}{(m-v_1+1)!(m-v_2)!} \frac{\overline{u_{(m-v_1), (m-v_2-r_2)}^{v_1, v_2}}}{m-v_2-r_2+1}, \quad 1\leq r_2\leq \nu, \\ \sum\limits^{m-\mu}_{v_1 = 1}\sum\limits^{m-r_2}_{v_2 = 1} \frac{1}{(m-v_1+1)!(m-v_2)!} \frac{\overline{u_{(m-v_1), (m-v_2-r_2)}^{v_1, v_2}}}{m-v_2-r_2+1}, \quad \nu < r_2\leq m-1, \\ 0, \quad r_2 > m-1. \end{array} \right. \end{aligned} |
Therefore, for r_2\geq 1 ,
\begin{equation} \begin{aligned} u^{0, 0}_{0, r_2}& = \!\frac{4\lambda}{\lambda-1} \frac{1}{(2\pi)^2}\!\!\int^{2\pi}_0\!\!e^{it_1}\!\!\int^{2\pi}_0\!\!e^{it_2(1-r_2)}\varphi(e^{it_1}, e^{it_2})dt_2dt_1 \!-\!\!\sum\limits^{m-\mu}_{v_1 = 1}\sum\limits^{m-\nu}_{v_2 = 1} \!\frac{u_{(m-v_1), (m-v_2+r_2)}^{v_1, v_2}} {(m\!-\!v_1\!+\!1)!(m\!-\!v_2\!+\!1)!}\\ &\; \; -\frac{\lambda+1}{\lambda-1}\left\{ \begin{array}{ll} \sum\limits^{m-\mu}_{v_1 = 1}\sum\limits^{m-\nu}_{v_2 = 1} \frac{1}{(m\!-\!v_1\!+\!1)!(m\!-\!v_2)!} \frac{\overline{u_{(m-v_1), (m-v_2-r_2)}^{v_1, v_2}}}{m\!-\!v_2\!-\!r_2\!+\!1}, \quad 1\leq r_2\leq \nu, \\ \sum\limits^{m-\mu}_{v_1 = 1}\sum\limits^{m-r_2}_{v_2 = 1} \frac{1}{(m\!-\!v_1\!+\!1)!(m\!-\!v_2)!} \frac{\overline{u_{(m-v_1), (m-v_2-r_2)}^{v_1, v_2}}}{m-v_2-r_2+1}, \quad \nu < r_2\leq m-1, \\ 0, \quad r_2 > m-1. \end{array} \right. \end{aligned} \end{equation} | (3.12) |
For r_1\geq 1 , multiplying both sides of the equation (3.11) by e^{i(1-r_1)t_1}e^{it_2} and then integrating with respect to t_1, t_2\in[0, 2\pi] , similar to (3.12), yields that
\begin{equation} \begin{aligned} u^{0, 0}_{r_1, 0}& = \!\frac{4\lambda}{\lambda-1} \frac{1}{(2\pi)^2}\!\!\int^{2\pi}_0\!\!e^{i(1-r_1)t_1}\!\!\int^{2\pi}_0\!\!e^{it_2}\varphi(e^{it_1}, e^{it_2})dt_2dt_1 \!-\!\!\sum\limits^{m-\mu}_{v_1 = 1}\sum\limits^{m-\nu}_{v_2 = 1} \!\frac{u_{(m-v_1+r_1), (m-v_2)}^{v_1, v_2}} {(m\!-\!v_1\!+\!1)!(m\!-\!v_2\!+\!1)!}\\ &\; \; -\frac{\lambda+1}{\lambda-1}\left\{ \begin{array}{ll} \!\! \sum\limits^{m-\mu}_{v_1 = 1}\sum\limits^{m-\nu}_{v_2 = 1} \frac{1}{(m\!-\!v_1)!(m\!-\!v_2\!+\!1)!} \frac{\overline{u_{(m-v_1-r_1, (m-v_2))}^{v_1, v_2}}}{m-v_1-r_1+1}, \quad 1\!\leq\! r_1\!\leq\! \mu, \\ \!\! \sum\limits^{m-r_1}_{v_1 = 1}\sum\limits^{m-\nu}_{v_2 = 1} \frac{1}{(m\!-\!v_1)!(m\!-\!v_2\!+\!1)!} \frac{\overline{u_{(m-v_1-r_1, (m-v_2))}^{v_1, v_2}}}{m-v_1-r_1+1}, \quad \mu\! < \! r_1\!\leq\! m\!-\!1, \\ \!0, \quad r_1 > m-1. \end{array} \right. \end{aligned} \end{equation} | (3.13) |
(ⅲ) In addition, integrating both sides of the Eq (3.11) with respect to t_1, t_2\in[0, 2\pi] yields that
\begin{aligned} &\frac{1}{(2\pi)^2}\int^{2\pi}_0\int^{2\pi}_0\varphi(e^{it_1}, e^{it_2})dt_2dt_1\\ & = \!\frac{\lambda-1}{4\lambda} \Big[\!\sum\limits_{m_2 = 0}^{+\infty}u_{0, m_2}^{0, 0}\frac{1}{2\pi}\int^{2\pi}_0e^{it_1}dt_1\cdot \frac{1}{2\pi}\int^{2\pi}_0e^{it_2(m_2-1)}dt_2\\ &\; \; +\!\sum\limits_{m_1 = 1}^{+\infty}u_{m_1, 0}^{0, 0}\frac{1}{2\pi}\int^{2\pi}_0e^{it_1(m_1-1)}dt_1\cdot \frac{1}{2\pi}\int^{2\pi}_0e^{-it_2}dt_2\\ &\; \; +\!\sum\limits_{m_1, m_2 = 1}^{+\infty}u_{m_1, m_2}^{0, 0}\frac{1}{2\pi}\int^{2\pi}_0e^{it_1(m_1-1)}dt_1\cdot \frac{1}{2\pi}\int^{2\pi}_0e^{it_2(m_2-1)}dt_2\\ &\; \; +\sum\limits^{m-\mu}_{v_1 = 1}\sum\limits^{m-\nu}_{v_2 = 1} \sum\limits_{m_1 = 0}^{m-v_1}\sum\limits_{m_2 = 0}^{m-v_2} \!\frac{u_{m_1, m_2}^{v_1, v_2}\frac{1}{2\pi}\int^{2\pi}_0 e^{it_1(m_1+v_1-1-m)}dt_1\cdot \frac{1}{2\pi}\int^{2\pi}_0e^{it_2(m_2+v_2-1-m)}dt_2} {(m-v_1+1)!(m-v_2+1)!}\\ &\; \; +\sum\limits^{m-\mu}_{v_1 = 1}\sum\limits^{m-\nu}_{v_2 = 1} \!\sum\limits_{m_1, m_2 = 0}^{+\infty} \frac{u_{(m_1+(m-v_1+1)), (m_2+(m-v_2+1))}^{v_1, v_2} \frac{1}{2\pi}\!\!\int^{2\pi}_0 \!\!\!e^{im_1t_1}dt_1\!\cdot\! \frac{1}{2\pi}\!\!\int^{2\pi}_0\!\!\!e^{im_2t_2}dt_2} {(m-v_1+1)!(m-v_2+1)!}\\ &\; \; +\sum\limits^{m-\mu}_{v_1 = 1}\sum\limits^{m-\nu}_{v_2 = 1} \sum\limits_{m_1 = 0}^{m-v_1}\sum\limits_{m_2 = 0}^{+\infty} \frac{u_{m_1, (m_2+(m-v_2+1))}^{v_1, v_2} \frac{1}{2\pi}\!\int^{2\pi}_0 \!e^{it_1(m_1+v_1-1-m)}dt_1\cdot \frac{1}{2\pi}\!\int^{2\pi}_0\!e^{im_2t_2}dt_2} {(m-v_1+1)!(m-v_2+1)!}\\ &\; \; +\sum\limits^{m-\mu}_{v_1 = 1}\sum\limits^{m-\nu}_{v_2 = 1} \sum\limits_{m_1 = 0}^{+\infty}\sum\limits_{m_2 = 0}^{m-v_2} \frac{u_{(m_1+(m-v_1+1)), m_2}^{v_1, v_2} \frac{1}{2\pi}\int^{2\pi}_0 e^{im_1t_1}dt_1\cdot \frac{1}{2\pi}\int^{2\pi}_0e^{it_2(m_2+v_2-1-m)}dt_2} {(m-v_1+1)!(m-v_2+1)!}\Big]\\ &\; \; +\frac{\lambda+1}{4\lambda} \Big[\sum\limits_{m_1, m_2 = 1}^{+\infty}\frac{\overline{u_{(m_1-1), (m_2-1)}^{0, 0}}}{m_1m_2} \frac{1}{2\pi}\!\int^{2\pi}_0 \!e^{-im_1t_1}dt_1\cdot \frac{1}{2\pi}\!\int^{2\pi}_0\!e^{-im_2t_2}dt_2\\ &\; \; +\sum\limits_{m_1, m_2 = 0}^{+\infty}\sum\limits^{m-\mu}_{v_1 = 1}\sum\limits^{m-\nu}_{v_2 = 1} \frac{\frac{1}{2\pi}\!\int^{2\pi}_0 \!e^{-it_1(m_1+1-m+v_1)}dt_1\cdot \frac{1}{2\pi}\!\int^{2\pi}_0\!e^{-it_2(m_2+1-m+v_2)}dt_2} {(m-v_1)!(m-v_2)!}\\ &\; \; \cdot\frac{\overline{u_{m_1, m_2}^{v_1, v_2}}}{(m_1+1)(m_2+1)} \Big]+ \sum\limits_{m_1, m_2 = 0}^{+\infty}b_{m_1, m_2}\frac{1}{2\pi}\!\int^{2\pi}_0 \!e^{im_1t_1}dt_1\cdot \frac{1}{2\pi}\!\int^{2\pi}_0\!e^{im_2t_2}dt_2\\ & = \!\frac{\lambda-1}{4\lambda} \Big[u^{0, 0}_{1, 1}+\sum\limits^{m-\mu}_{v_1 = 1}\sum\limits^{m-\nu}_{v_2 = 1} \!\frac{u_{(m-v_1+1), (m-v_2+1)}^{v_1, v_2}} {(m-v_1+1)!(m-v_2+1)!}\Big]\\ &\; \; +\frac{\lambda+1}{4\lambda} \sum\limits^{m-\mu}_{v_1 = 1}\sum\limits^{m-\nu}_{v_2 = 1} \frac{1}{(m-v_1)!(m-v_2)!} \frac{\overline{u_{(m-v_1-1), (m-v_2-1)}^{v_1, v_2}}}{(m-v_1)(m-v_2)}+b_{0, 0}, \end{aligned} |
which follows (3.5), where u^{0, 0}_{1, 1} is determined by (3.1).
(ⅳ) In the case of r_1, r_2\geq m+1 , multiplying both sides of the Eq (3.11) by e^{ir_1t_1}e^{ir_2t_2} , and then integrating with respect to t_1, t_2\in[0, 2\pi] yields that
\begin{aligned} &\frac{1}{(2\pi)^2}\int^{2\pi}_0e^{ir_1t_1}\Big[\int^{2\pi}_0e^{ir_2t_2}\varphi(e^{it_1}, e^{it_2})dt_2\Big]dt_1\\ & = \!\frac{\lambda-1}{4\lambda} \Big[\!\sum\limits_{m_2 = 0}^{+\infty}u_{0, m_2}^{0, 0}\frac{1}{2\pi}\int^{2\pi}_0e^{it_1(r_1-1)}dt_1\cdot \frac{1}{2\pi}\int^{2\pi}_0e^{it_2(m_2-1+r_2)}dt_2\\ &\; \; +\!\sum\limits_{m_1 = 1}^{+\infty}u_{m_1, 0}^{0, 0}\frac{1}{2\pi}\int^{2\pi}_0e^{it_1(m_1-1+r_1)}dt_1\cdot \frac{1}{2\pi}\int^{2\pi}_0e^{it_2(-1+r_2)}dt_2\\ &\; \; +\!\sum\limits_{m_1, m_2 = 1}^{+\infty}\!\!\!u_{m_1, m_2}^{0, 0}\frac{1}{2\pi}\int^{2\pi}_0e^{it_1(m_1-1+r_1)}dt_1\cdot \frac{1}{2\pi}\int^{2\pi}_0e^{it_2(m_2-1+r_2)}dt_2\\ &\; \; +\sum\limits^{m-\mu}_{v_1 = 1}\sum\limits^{m-\nu}_{v_2 = 1} \sum\limits_{m_1 = 0}^{m-v_1}\sum\limits_{m_2 = 0}^{m-v_2} \frac{u_{m_1, m_2}^{v_1, v_2}\frac{1}{2\pi}\int^{2\pi}_0 e^{it_1(m_1-(m-v_1+1)+r_1)}dt_1\cdot \frac{1}{2\pi}\int^{2\pi}_0e^{it_2(m_2-(m-v_2+1)+r_2)}dt_2} {(m-v_1+1)!(m-v_2+1)!}\\ &\; \; +\sum\limits^{m-\mu}_{v_1 = 1}\sum\limits^{m-\nu}_{v_2 = 1} \!\sum\limits_{m_1, m_2 = 0}^{+\infty} \frac{u_{(m_1+(m-v_1+1)), (m_2+(m-v_2+1))}^{v_1, v_2} \frac{1}{2\pi}\!\!\int^{2\pi}_0 \!\!\!e^{it_1(m_1+r_1)}dt_1\!\cdot\! \frac{1}{2\pi}\!\!\int^{2\pi}_0\!\!\!e^{it_2(m_2+r_2)}dt_2} {(m-v_1+1)!(m-v_2+1)!}\\ &\; \; +\sum\limits^{m-\mu}_{v_1 = 1}\sum\limits^{m-\nu}_{v_2 = 1} \sum\limits_{m_1 = 0}^{m-v_1}\sum\limits_{m_2 = 0}^{+\infty} \frac{u_{m_1, (m_2+(m-v_2+1))}^{v_1, v_2} \frac{1}{2\pi}\!\int^{2\pi}_0 \!e^{it_1(m_1-(m-v_1+1)+r_1)}dt_1\cdot \frac{1}{2\pi}\!\int^{2\pi}_0\!e^{it_2(m_2+r_2)}dt_2} {(m-v_1+1)!(m-v_2+1)!}\\ &\; \; +\sum\limits^{m-\mu}_{v_1 = 1}\sum\limits^{m-\nu}_{v_2 = 1} \sum\limits_{m_1 = 0}^{+\infty}\sum\limits_{m_2 = 0}^{m-v_2} \frac{u_{(m_1+(m-v_1+1)), m_2}^{v_1, v_2} \frac{1}{2\pi}\!\!\int^{2\pi}_0 \!\!e^{it_1(m_1+r_1)}dt_1\cdot \frac{1}{2\pi}\!\!\int^{2\pi}_0\!\!e^{it_2(m_2-(m-v_2+1)+r_2)}dt_2} {(m-v_1+1)!(m-v_2+1)!}\Big]\\ &\; \; +\frac{\lambda+1}{4\lambda} \Big[\sum\limits_{m_1, m_2 = 1}^{+\infty}\frac{\overline{u_{(m_1-1), (m_2-1)}^{0, 0}}}{m_1m_2} \frac{1}{2\pi}\!\int^{2\pi}_0 \!e^{it_1(-m_1+r_1)}dt_1\cdot \frac{1}{2\pi}\!\int^{2\pi}_0\!e^{it_2(-m_2+r_2)}dt_2\\ &\; \; +\sum\limits_{m_1, m_2 = 0}^{+\infty}\sum\limits^{m-\mu}_{v_1 = 1}\sum\limits^{m-\nu}_{v_2 = 1} \frac{\frac{1}{2\pi}\!\int^{2\pi}_0 \!e^{-it_1(m_1+1-m+v_1-r_1)}dt_1\cdot \frac{1}{2\pi}\!\int^{2\pi}_0\!e^{-it_2(m_2+1-m+v_2-r_2)}dt_2} {(m-v_1)!(m-v_2)!}\\ &\; \; \cdot\frac{\overline{u_{m_1, m_2}^{v_1, v_2}}}{(m_1+1)(m_2+1)} \Big]+ \sum\limits_{m_1, m_2 = 0}^{+\infty}b_{m_1, m_2}\frac{1}{2\pi}\!\int^{2\pi}_0 \!e^{it_1(m_1+r_1)}dt_1\cdot \frac{1}{2\pi}\!\int^{2\pi}_0\!e^{it_2(m_2+r_2)}dt_2\\ & = \frac{\lambda+1}{4\lambda} \Big[\frac{\overline{u_{(r_1-1), (r_2-1)}^{0, 0}}}{r_1r_2} +\sum\limits^{m-\mu}_{v_1 = 1}\sum\limits^{m-\nu}_{v_2 = 1} \frac{1}{(m-v_1)!(m-v_2)!} \frac{\overline{u_{(m-v_1+r_1-1), (m-v_2+r_2-1)}^{v_1, v_2}}}{(m-v_1+r_1)(m-v_2+r_2)} \Big]. \end{aligned} |
Therefore,
\begin{equation} \begin{aligned} u_{r_1, r_2}^{0, 0}& = \frac{4\lambda}{\lambda+1} \frac{(r_1+1)(r_2+1)}{(2\pi)^2}\int^{2\pi}_0e^{-i(r_1+1)t_1}\int^{2\pi}_0e^{-i(r_2+1)t_2} \overline{\varphi(e^{it_1}, e^{it_2})}dt_2dt_1\\ &\; \; -\sum\limits^{m-\mu}_{v_1 = 1}\sum\limits^{m-\nu}_{v_2 = 1} \frac{(r_1+1)(r_2+1)}{(m-v_1)!(m-v_2)!} \frac{u_{(m-v_1+r_1), (m-v_2+r_2)}^{v_1, v_2}}{(m-v_1+r_1+1)(m-v_2+r_2+1)}, \end{aligned} \end{equation} | (3.14) |
for r_1, r_2\geq m .
Similarly, in the case of r_1\geq m+1 and 0\leq r_2\leq m-2 , multiplying both sides of the Eq (3.11) by e^{ir_1t_1}e^{it_2(m-r_2)} , and then integrating with respect to t_1, t_2\in[0, 2\pi] yields that u_{r_1, r_2}^{0, 0} ( r_1\geq m, \; 1\leq r_2\leq m-1 ) has the same representation as (3.14). In the case of 0\leq r_1\leq m-2 and r_2\geq m+1 , multiplying both sides of the Eq (3.11) by e^{it_1(m-r_1)}e^{ir_2t_2} , and then integrating with respect to t_1, t_2\in[0, 2\pi] yields that u_{r_1, r_2}^{0, 0} ( 1\leq r_1\leq m-1, \; r_2\geq m ) has the same representation as (3.14).
(ⅴ) Similarly, for r_1, r_2\geq 1 , multiplying both sides of the Eq (3.11) by e^{-ir_1t_1}e^{-ir_2t_2} , and then integrating with respect to t_1, t_2\in[0, 2\pi] , we can get the expression of b_{r_1r_2} which leads to (3.7). For r_1\geq 1 , multiplying both sides of the Eq (3.11) by e^{-ir_1t_1} , and then integrating with respect to t_1, t_2\in[0, 2\pi] yields the expression of b_{r_10} which leads to (3.8). For r_2\geq 1 , we can get b_{0r_2} which leads to (3.9).
Remark 3.2. The results obtained in this article extend the existing conclusions about ployanalytic functions and bi-ployanalytic functions. On this basis, we can study other partial differential equation problems. For example, it would be interesting to discuss whether bi-polyanalytic or even ployanalytical function solutions exist for some nonlocal integrable partial differential equations (see, e.g., [24]), which need to explore the solutions to the corresponding Riemann-Hilbert problems. Additionally, the non-existence of solutions to Cauchy problems on the real line for first-order nonlocal differential equations (see, e.g., [25]) indicates that we can attempt to discuss the generalizations of analytical solutions for partial differential equation problems.
With the help of the series expansion of polyanalytic functions, and applying the properties of Cauchy kernels on the bicylinder and the unit disk, we first discuss a class of Schwarz problems with the conditions concerning the real and imaginary parts of high-order partial differentiation for polyanalytic functions on the bicylinder. On this basis, we investigate a type of boundary value problem for bi-polyanalytic functions with Dirichlet boundary conditions on the bicylinder. From the perspective of series, we obtain the specific representation of the solution to the Dirichlet problem. The method used in this article, with the help of series expansion, is different from the previous methods for solving boundary value problems. It is a very effective method and can be used to solve other types of problems regarding complex partial differential equations of bi-polyanalytic functions in high-dimensional complex spaces. The conclusions of this article also lay a necessary foundation for further research on polyanalytic and bi-polyanalytic functions.
Yanyan Cui: conceptualization, project administration, writing original, writing-review and editing; Chaojun Wang: investigation, writing original, writing-review and editing. All authors have read and approved the final version of the manuscript for publication.
The authors are grateful to the anonymous referees for their valuable comments and suggestions which improved the quality of this article. This work was supported by the NSF of China (No. 11601543), the NSF of Henan Province (Nos. 222300420397 and 242300421394), and the Science and Technology Research Projects of Henan Provincial Education Department (No. 19B110016).
The authors declare no conflict of interest.
[1] |
A. Baazaoui, W. Barhoumi, A. Ahmed, E. Zagrouba, Modeling clinician medical-knowledge in terms of med-level features for semantic content-based mammogram retrieval, Expert Syst. Appl., 94 (2018), 11–20. https://doi.org/10.1016/j.eswa.2017.10.034 doi: 10.1016/j.eswa.2017.10.034
![]() |
[2] |
B. J. Campana, E. J. Keogh, A compression‐based distance measure for texture, Stat. Anal. Data Min., 3 (2010), 381–398. https://doi.org/10.1002/sam.10093 doi: 10.1002/sam.10093
![]() |
[3] |
S. R. Dubey, S. K. Singh, S. K. Singh, Local bit-plane decoded pattern: a novel feature descriptor for biomedical image retrieval, IEEE J. Biomed. Health. Inf., 20 (2015), 1139–1147. https://doi.org/10.1109/JBHI.2015.2437396 doi: 10.1109/JBHI.2015.2437396
![]() |
[4] |
Y. Kumar, A. Aggarwal, S. Tiwari, K. Singh, An efficient and robust approach for biomedical image retrieval using Zernike moments, Biomed. Signal Process. Control, 39 (2018), 459–473. https://doi.org/10.1016/j.bspc.2017.08.018 doi: 10.1016/j.bspc.2017.08.018
![]() |
[5] |
M. Lavanya, P. M. Kannan, Lung lesion detection in CT scan images using the fuzzy local information cluster means (FLICM) automatic segmentation algorithm and back propagation network classification, Asian Pac. J. Cancer Prev., 18 (2017), 3395–3399. https://doi.org/10.22034/APJCP.2017.18.12.3395 doi: 10.22034/APJCP.2017.18.12.3395
![]() |
[6] |
M. Lazaridis, A. Axenopoulos, D. Rafailidis, P. Daras, Multimedia search and retrieval using multimodal annotation propagation and indexing techniques, Signal Process. Image Commun., 28 (2013), 351–367. https://doi.org/10.1016/j.image.2012.04.001 doi: 10.1016/j.image.2012.04.001
![]() |
[7] |
R. Manickavasagam, S. Selvan, Automatic detection and classification of lung nodules in CT image using optimized neuro fuzzy classifier with cuckoo search algorithm, J. Med. Syst., 43 (2019), 1–9. https://doi.org/10.1007/s10916-019-1177-9 doi: 10.1007/s10916-019-1177-9
![]() |
[8] |
S. Murala, Q. J. Wu, Local ternary co-occurrence patterns: a new feature descriptor for MRI and CT image retrieval, Neurocomputing, 119 (2013), 399–412. https://doi.org/10.1016/j.neucom.2013.03.018 doi: 10.1016/j.neucom.2013.03.018
![]() |
[9] |
S. Murala, Q. J. Wu, Spherical symmetric 3D local ternary patterns for natural, texture and biomedical image indexing and retrieval, Neurocomputing, 149 (2015), 1502–1514. https://doi.org/10.1016/j.neucom.2014.08.042 doi: 10.1016/j.neucom.2014.08.042
![]() |
[10] |
S. H. Peng, D. H. Kim, S. L. Lee, M. K. Lim, Texture feature extraction based on a uniformity estimation method for local brightness and structure in chest CT images, Comput. Biol. Med., 40 (2010), 931–942. https://doi.org/10.1016/j.compbiomed.2010.10.005 doi: 10.1016/j.compbiomed.2010.10.005
![]() |
[11] |
M. M. Rahman, S. K. Antani, G. R. Thoma, A learning-based similarity fusion and filtering approach for biomedical image retrieval using SVM classification and relevance feedback, IEEE Trans. Inf. Technol. Biomed., 15 (2011), 640–646. https://doi.org/10.1109/TITB.2011.2151258 doi: 10.1109/TITB.2011.2151258
![]() |
[12] |
J. Song, Y. Guo, L. Gao, X. Li, A. Hanjalic, H. T. Shen, From deterministic to generative: Multimodal stochastic RNNs for video captioning, IEEE Trans. Neural Networks Learn. Syst., 30 (2018), 3047–3058. https://doi.org/10.1109/TNNLS.2018.2851077 doi: 10.1109/TNNLS.2018.2851077
![]() |
[13] |
J. Song, H. Zhang, X. Li, L. Gao, M. Wang, R. Hong, Self-supervised video hashing with hierarchical binary auto-encoder, IEEE Trans. Image Process., 27 (2018), 3210–3221. https://doi.org/10.1109/TIP.2018.2814344 doi: 10.1109/TIP.2018.2814344
![]() |
[14] |
D. Unay, A. Ekin, R. S. Jasinschi, Local structure-based region-of-interest retrieval in brain MR images, IEEE Trans. Inf. Technol. Biomed., 14 (2010), 897–903. https://doi.org/10.1109/TITB.2009.2038152 doi: 10.1109/TITB.2009.2038152
![]() |
[15] |
S. K. Vipparthi, S. Murala, A. B. Gonde, Q. J. Wu, Local directional mask maximum edge patterns for image retrieval and face recognition, IET Comput. Vision, 10 (2016), 182–192. https://doi.org/10.1049/iet-cvi.2015.0035 doi: 10.1049/iet-cvi.2015.0035
![]() |
[16] | R. Janarthanan, A. Chakraborty, A. Konar, A. K. Nagar, Ad hoc reasoning in chained fuzzy systems realized with Diens-Rescher implication, in 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1 (2013), 1–6. https://doi.org/10.1109/FUZZ-IEEE.2013.6622561 |
[17] |
R. Janarthanan, A. Konar, A. Chakraborty, Propositional syntax and semantics induced knowledge re-structuring in a fuzzy logic network for ad hoc reasoning, Int. J. Approximate Reasoning, 82 (2017), 138–160. https://doi.org/10.1016/j.ijar.2016.12.009 doi: 10.1016/j.ijar.2016.12.009
![]() |
[18] |
R. Janarthanan, S. Doss, R. Balamurali, Robotic-based nonlinear device fault detection with sensor fault and limited capacity for communication, J. Ambient Intell. Hum. Comput., 11 (2020), 6373–6385. https://doi.org/10.1007/s12652-020-01946-8 doi: 10.1007/s12652-020-01946-8
![]() |
[19] |
R. Janarthanan, S. Doss, S. Baskar, Optimized unsupervised Deep learning assisted reconstructed coder in the on-nodule wearable sensor for Human Activity Recognition, Neurocomputing, 164 (2020), 1–10. https://doi.org/10.1016/j.measurement.2020.108050 doi: 10.1016/j.measurement.2020.108050
![]() |
[20] |
C. A. Hussain, D. V. Rao, S. A. Mastani, RetrieveNet: a novel deep network for medical image retrieval, Evol. Intell., 14 (2020), 1449–1458. https://doi.org/10.1007/s12065-020-00401-z doi: 10.1007/s12065-020-00401-z
![]() |
[21] |
R. Hatibaruah, V. K. Nath, D. Hazarika, Local bit plane adjacent neighborhood dissimilarity pattern for medical CT image retrieval, Procedia Comput. Sci., 165 (2019), 83–89. https://doi.org/10.1016/j.procs.2020.01.073 doi: 10.1016/j.procs.2020.01.073
![]() |
[22] | Y. D. Mistry. Textural and color descriptor fusion for efficient content-based image retrieval algorithm, Iran J. Comput. Sci., 3 (2020), 169–183. https://doi.org/10.1007/s42044-020-00056-0 |
[23] |
G. S. Kumar, P. K. Mohan, Local mean differential excitation pattern for content based image retrieval, SN Appl. Sci., 1 (2019), 1–10. https://doi.org/10.1007/S42452-018-0047-2 doi: 10.1007/S42452-018-0047-2
![]() |
[24] |
G. Raghuraman, J. P. Ananth, K. L. Shunmuganathan, L. Sairamesh, Local structure-based region-of-interest retrieval in brain MR images, J. Comput. Theor. Nanosci., 12 (2015), 5562–5565. https://doi.org/10.1166/jctn.2015.4684 doi: 10.1166/jctn.2015.4684
![]() |
[25] | S. Sabena, P. Yogesh, L. SaiRamesh, Image retrieval using canopy and improved K mean clustering, in International conference on emerging technology trends, 1 (2011), 15–19. |
[26] | C. A. Vinodhini, S. Sabena, L. S. Ramesh, A Robust and Fast Fundus Image Enhancement by Dehazing, in International Conference On Computational Vision and Bio Inspired Computing, 1 (2018), 1111–1119. https://doi.org/10.1007/978-3-030-41862-5_113 |
[27] |
S. Gupta, P. P. Roy, D. P. Dogra, B. Kim, Retrieval of colour and texture images using local directional peak valley binary pattern, Pattern Anal. Appl., 23 (2020), 1569–1585. https://doi.org/10.1007/s10044-020-00879-4 doi: 10.1007/s10044-020-00879-4
![]() |
[28] |
A. Manickam, R. Soundrapandiyan, S. C. Satapathy, R. D. J. Samuel, S. Krishnamoorthy, U. Kiruthika, et al., Local directional extrema number pattern: A new feature descriptor for computed tomography image retrieval, Arabian J. Sci. Eng., 1 (2021), 1–23. https://doi.org/10.1007/s13369-021-06024-5 doi: 10.1007/s13369-021-06024-5
![]() |
[29] |
S. Basu, M. Karuppiah, M. Nasipuri, A. Halder, N. Radhakrishnan, Bio-inspired cryptosystem with DNA cryptography and neural networks, J. Syst. Archit., 94 (2019), 24–31. https://doi.org/10.1016/j.sysarc.2019.02.005 doi: 10.1016/j.sysarc.2019.02.005
![]() |
[30] |
R. Selvanambi, J. Natarajan, M. Karuppiah, S. H. Islam, M. Hassan, G. Fortino, Lung cancer prediction using higher-order recurrent neural network based on glowworm swarm optimization, Neural Comput. Appl., 32 (2020), 4373–4386. https://doi.org/10.1007/s00521-018-3824-3 doi: 10.1007/s00521-018-3824-3
![]() |
[31] |
S. Basu, M. Karuppiah, K. Selvakumar, K. C. Li, S. H. Islam, M. M. Hassan, et al., An intelligent/cognitive model of task scheduling for IoT applications in cloud computing environment, Future Gener. Comput. Syst., 88 (2018), 254–261. https://doi.org/10.1016/j.future.2018.05.056 doi: 10.1016/j.future.2018.05.056
![]() |
[32] |
R. Elakkiya, P. Vijayakumar, M. Karuppiah, COVID_SCREENET: COVID-19 screening in chest radiography images using deep transfer stacking, Inf. Syst. Front., 23 (2021), 1369–1383. https://doi.org/10.1007/s10796-021-10123-x doi: 10.1007/s10796-021-10123-x
![]() |
[33] |
F. Wu, X. Li, L. Xu, S. Kumari, M. Karuppiah, J. Shen, A lightweight and privacy-preserving mutual authentication scheme for wearable devices assisted by cloud server, Comput. Electri. Eng., 63 (2017), 168–181. https://doi.org/10.1016/j.compeleceng.2017.04.012 doi: 10.1016/j.compeleceng.2017.04.012
![]() |
[34] |
S. Kumari, M. Karuppiah, A. K. Das, X. Li, F. Wu, N. Kumar, A secure authentication scheme based on elliptic curve cryptography for IoT and cloud servers, J. Supercomput., 74 (2018), 6428–6453. https://doi.org/10.1007/s11227-017-2048-0 doi: 10.1007/s11227-017-2048-0
![]() |
[35] |
S. Basu, M. Karuppiah, S. Rajkumar, R. Niranchana, Modification of AES using genetic algorithms for high-definition image encryption, Int. J. Intell. Syst. Technol. Appl., 17 (2018), 452–466. https://doi.org/10.1504/IJISTA.2018.095106 doi: 10.1504/IJISTA.2018.095106
![]() |
[36] |
A. R. Sanjay, R. Soundrapandiyan, M. Karuppiah, R. Ganapathy, CT and MRI image fusion based on discrete wavelet transform and Type-2 fuzzy logic, Int. J. Intell. Eng. Syst., 10 (2017), 355–362. https://doi.org/10.22266/ijies2017.0630.40 doi: 10.22266/ijies2017.0630.40
![]() |
1. | Yanyan Cui, Chaojun Wang, Dirichlet and Neumann boundary value problems for bi-polyanalytic functions on the bicylinder, 2025, 10, 2473-6988, 4792, 10.3934/math.2025220 |