Dimensional Gap Research Articles

PRSCS-Net: Progressive 3D/2D rigid Registration network with the guidance of Single-view Cycle Synthesis

The 3D/2D registration for 3D pre-operative images (computed tomography, CT) and 2D intra-operative images (X-ray) plays an important role in image-guided spine surgeries. Conventional iterative-based approaches suffer from time-consuming processes. Existing learning-based approaches require high computational costs and face poor performance on large misalignment because of projection-induced losses or ill-posed reconstruction. In this paper, we propose a Progressive 3D/2D rigid Registration network with the guidance of Single-view Cycle Synthesis, named PRSCS-Net. Specifically, we first introduce the differentiable backward/forward projection operator into the single-view cycle synthesis network, which reconstructs corresponding 3D geometry features from two 2D intra-operative view images (one from the input, and the other from the synthesis). In this way, the problem of limited views during reconstruction can be solved. Subsequently, we employ a self-reconstruction path to extract latent representation from pre-operative 3D CT images. The following pose estimation process will be performed in the 3D geometry feature space, which can solve the dimensional gap, greatly reduce the computational complexity, and ensure that the features extracted from pre-operative and intra-operative images are as relevant as possible to pose estimation. Furthermore, to enhance the ability of our model for handling large misalignment, we develop a progressive registration path, including two sub-registration networks, aiming to estimate the pose parameters via two-step warping volume features. Finally, our proposed method has been evaluated on a public dataset CTSpine1k and an in-house dataset C-ArmLSpine for 3D/2D registration. Results demonstrate that PRSCS-Net achieves state-of-the-art registration performance in terms of registration accuracy, robustness, and generalizability compared with existing methods. Thus, PRSCS-Net has potential for clinical spinal disease surgical planning and surgical navigation systems.

Video Coding for Machines: Compact Visual Representation Compression for Intelligent Collaborative Analytics.

As an emerging research practice leveraging recent advanced AI techniques, e.g. deep models based prediction and generation, Video Coding for Machines (VCM) is committed to bridging to an extent separate research tracks of video/image compression and feature compression, and attempts to optimize compactness and efficiency jointly from a unified perspective of high accuracy machine vision and full fidelity human vision. With the rapid advances of deep feature representation and visual data compression in mind, in this paper, we summarize VCM methodology and philosophy based on existing academia and industrial efforts. The development of VCM follows a general rate-distortion optimization, and the categorization of key modules or techniques is established including feature-assisted coding, scalable coding, intermediate feature compression/optimization, and machine vision targeted codec, from broader perspectives of vision tasks, analytics resources, etc. From previous works, it is demonstrated that, although existing works attempt to reveal the nature of scalable representation in bits when dealing with machine and human vision tasks, there remains a rare study in the generality of low bit rate representation, and accordingly how to support a variety of visual analytic tasks. Therefore, we investigate a novel visual information compression for the analytics taxonomy problem to strengthen the capability of compact visual representations extracted from multiple tasks for visual analytics. A new perspective of task relationships versus compression is revisited. By keeping in mind the transferability among different machine vision tasks (e.g. high-level semantic and mid-level geometry-related), we aim to support multiple tasks jointly at low bit rates. In particular, to narrow the dimensionality gap between neural network generated features extracted from pixels and a variety of machine vision features/labels (e.g. scene class, segmentation labels), a codebook hyperprior is designed to compress the neural network-generated features. As demonstrated in our experiments, this new hyperprior model is expected to improve feature compression efficiency by estimating the signal entropy more accurately, which enables further investigation of the granularity of abstracting compact features among different tasks.

CO2 to solar fuel: design and reactivity of inorganic perovskites

Carbon dioxide release by human activity is the major cause of global warming. Decreasing the concentration of CO2 in the atmosphere is a challenge that needs to be addressed. In addition to their negative impact on the environment, the availability of petroleum-based fuel is decreasing. The photoconversion of CO2 into so-called green solar fuel is a possible alternative to reduce the quantity of carbon dioxide in the atmosphere aiming the limitation of greenhouse effect. Among the photocatalyst studied for these reactions, the perovskite-based appeared as one of the most promising class of materials. These materials possess unique optoelectronic properties and exhibit significant variability in terms of their dimensionality, structure, morphology, grain size, and tunable band gap, as well as the position of their valence band and conduction band. This review discusses both the classics and innovative perovskite synthesis methods such as solid-state reaction, hydrothermal and solvothermal synthesis, hot injection or chemical precipitation. Then, the use of these materials for the photoreduction of CO2 into fuel such as formic acid, methanol and methane is detailed.

Wall effect on the wake characteristics of a transversely rotating sphere

In the present work, the flow over a transversely rotating sphere placed at varying separation from a plane wall at a Reynolds number Re=U∞Dν of 300 is numerically investigated using Open Source Field Operation and Manipulation, where Re is defined based on the free stream velocity (U∞) and the diameter (D) of the sphere. Three values of the non-dimensional rotational speed ω*=ωD2U∞, viz., −1, 0 and 1, have been chosen with ω being the dimensional rotation rate with anticlockwise rotation being positive. The non-dimensional separation gap G=gD between the sphere and the wall is varied from 0.25 to 3.0. Here, g is the dimensional gap between the sphere and the wall. At ω*=0 and G = 0.25, the wall completely suppresses vortex shedding from the sphere, whereas flow is found to be unsteady for other values of ω* and G. As compared to the case in the absence of the wall, the presence of the wall causes an increase in vortex shedding frequency for ω*=0 and 1 and decrease for ω*=−1. Hilbert spectrum reveals that the wake nonlinearity remains unchanged with an increase in G for ω*=0. On the other hand, it increases for ω*=−1 and decreases for ω*=1. Similar to the observation made for vortex shedding, the presence of wall increases drag force on the sphere for ω*=0 and 1 and decreases for ω*=−1. In order to reveal the spatial and temporal behavior of the coherent structures in the unsteady wake, dynamic mode decomposition (DMD) has been performed. For all the values of G, DMD mode 1 is found to be the primary vortex shedding mode.

Coupled discriminative manifold alignment for low-resolution face recognition

In practical applications, due to a long distance between the monitored population and monitoring equipment, the face images or human pose captured by the cameras often incur low-resolution (LR), small size, and poor quality, which leads to extreme difficulty in directly matching an LR face with the high-resolution (HR) ones in the gallery. In this paper, we propose a novel coupled discriminative manifold alignment (CDMA) method for LR face recognition. Specifically, in the training stage the principal component analysis (PCA) is first used to reduce the dimensional gap between LR and HR facial features. Next the LR face images and the corresponding HR face images are converted into a common shared feature subspace by learning two linear mappings in a supervised manner, where the neighborhood samples within the same class and from different classes are jointly exploited to align the manifold structures of LR and HR faces. In the test stage, for a given LR face in the probe set, two learned coupled mappings (CMs) are applied to match the HR images in the gallery set through the correlative metric. Thorough experimental results on three representative face databases verify the effectiveness of the proposed method in comparing with other state-of-the-art competitors. In particular, the proposed method is capable of yielding more competitive recognition performance than other predecessors when lower dimensional feature subspaces are applied to match the expected HR faces.

Ultrasound Frame-to-Volume Registration via Deep Learning for Interventional Guidance.

Fusing intraoperative 2-D ultrasound (US) frames with preoperative 3-D magnetic resonance (MR) images for guiding interventions has become the clinical gold standard in image-guided prostate cancer biopsy. However, developing an automatic image registration system for this application is challenging because of the modality gap between US/MR and the dimensionality gap between 2-D/3-D data. To overcome these challenges, we propose a novel US frame-to-volume registration (FVReg) pipeline to bridge the dimensionality gap between 2-D US frames and 3-D US volume. The developed pipeline is implemented using deep neural networks, which are fully automatic without requiring external tracking devices. The framework consists of three major components, including one) a frame-to-frame registration network (Frame2Frame) that estimates the current frame's 3-D spatial position based on previous video context, two) a frame-to-slice correction network (Frame2Slice) adjusting the estimated frame position using the 3-D US volumetric information, and three) a similarity filtering (SF) mechanism selecting the frame with the highest image similarity with the query frame. We validated our method on a clinical dataset with 618 subjects and tested its potential on real-time 2-D-US to 3-D-MR fusion navigation tasks. The proposed FVReg achieved an average target navigation error of 1.93 mm at 5-14 fps. Our source code is publicly available at https://github.com/DIAL-RPI/Frame-to-Volume-Registration.

Cross-Domain Image-Object Retrieval Based on Weighted Optimal Transport

Given a 2D image query and a pool of 3D objects, the goal of image-object retrieval is to rank the 3D objects according to how well their content fits the query. Previous methods usually project 2D images and 3D objects into a joint embedding space and minimize the distance metric to complete the retrieval task. Since 2D images and 3D objects come from two different domains with large discrepancy, even when 3D objects and 2D images are mapped to a shared space, the gap in feature distribution remains significant, which always leads to domain misalignment. In this work, we propose a novel image-object retrieval method by leveraging optimal transport theory. Specifically, to tackle the dimensionality gap between 2D images and 3D objects, we first represent a 3D object via a sequence of its 2D projections. We then design a Cross-Domain View Attention module (CDVA) to automatically compute the optimal combination of 3D object projections given a 2D query image. Next, we exploit Weighted Optimal Transport (WOT)-based distance to depict the discrepancy between 2D images and 3D objects, and reduce the discrepancy to achieve instance-level alignment. Through this scheme, the transported 2D images and 3D objects with the same label are enforced to follow similar distributions. Finally, we design an explicit Category Centroid Alignment module (CCA) to achieve class-level alignment to improve the retrieval performance. Extensive experiments show that our method can achieve competitive performance on the MI3DOR and MI3DOR-2 benchmarks.

Calculation method of hosiery knitting parameters based on three-dimensional mesh sampling

Custom socks, such as medical compression socks and hiking socks, are precisely designed and knitted according to the user's foot shape. However, developing styles of custom socks with commercial computer-aided design software requires workers to manually calculate the knitting parameters, and repeatedly adjust the knitting parameters through sample-making to meet the design requirements. We present a method to optimize the manual calculation and adjustment process of knitting parameters by using graphics technology. Our method converts a sock mesh model created by conventional modeling software into knitting parameters. Hosiery machines are able to knit socks with the same dimensions as the mesh based on the output parameters. Specifically, given user-defined top and toe markers on an input mesh, our system runs a sampling procedure over this mesh to gradually generate a knitting path that contains shape and dimension features of the mesh, and transforms the knitting path into knitting parameters for machine knitting. We demonstrated the feasibility of our method by knitting on a computerized hosiery machine. Filling the knitted socks with a soft foam model, we found that the knitted socks fit roughly well with the mesh surface, and the dimensional gap between the input mesh and the knitted socks (relaxed state) in several positions was below 4.90%.

A New Sparse-Learning Model for Maximum Gap Reduction of Composite Fuselage Assembly

Natural dimensional variabilities of incoming fuselages affect the assembly speed and quality of fuselage joins in composite fuselage assembly processes. Shape control is critical to ensure the quality of composite fuselage assembly. In current practice, the structures are adjusted to the design shape in terms of the loss for further assembly without considering the existing dimensional gap between two structures. Such practice has two limitations: (a) controlling each fuselage to the design shape may not be the optimal shape control strategy in terms of a pair of incoming fuselages with different incoming dimensions; (b) the maximum gap is the key concern during the fuselage assembly process, so the loss of gap after control ought to be considered. This article proposes an optimal shape control methodology via the loss for the composite fuselage assembly process by considering the existing dimensional gap between the incoming pair of fuselages. On the other hand, due to the limitation on the number of available actuators in practice, we face an important problem of finding the best locations for the actuators among many potential locations, which makes the problem a sparse estimation problem. We are the first to solve the optimal shape control in the fuselage assembly process using the model under the framework of sparse estimation, where we use the penalty to control the sparsity of the resulting estimator. From the statistical point of view, this can be formulated as the loss based linear regression, and under some standard assumptions, such as the restricted eigenvalue (RE) conditions, and the light-tailed noise, the nonasymptotic estimation error of the regularized linear model is derived, which meets the upper-bound in the existing literature. Compared to the current practice, the case study shows that our proposed method significantly reduces the maximum gap between two fuselages after shape adjustments.

A Study on Dimensional and Marginal Gap Changes Through Application of Different Sintering Methods and Veneer Firings on Zirconia Crowns

Background: The marginal fit of dental restorations is a vital factor for long-term success. The veneering process of zirconia, which involves multiple firings at high temperatures, has shown to affect the marginal fit of zirconia cores. The aim of this study is to observe dimensional and marginal gap changes during veneer firing of zirconia frameworks sintered with different procedures.
 Methods: 30 Y-TZP ((inCoris ZI, Sirona, Bensheim, Germany) crown frameworks sintered with three different durations of 8 hours, 2 hours and 10 minutes. Occlusal and buccal dimensional measurements based on reference points measured with toolmaker’s microscope. Frameworks subjected to veneer firings without application of veneering material to observe the effect of firing process only. All dimensional and marginal gap measurements were repeated and after veneer firings. Data were analysed with one-way ANOVA and paired t test.
 Results: No significant differences observed between the sintering shrinkage of three groups. Marginal gaps decreased after veneer firings for all groups and the decrease was significant for 2 hour and 10-minute sintering groups (p

Fast and Accurate Lane Detection via Graph Structure and Disentangled Representation Learning.

It is desirable to maintain high accuracy and runtime efficiency at the same time in lane detection. However, due to the long and thin properties of lanes, extracting features with both strong discrimination and perception abilities needs a huge amount of calculation, which seriously slows down the running speed. Therefore, we design a more efficient way to extract the features of lanes, including two phases: (1) Local feature extraction, which sets a series of predefined anchor lines, and extracts the local features through their locations. (2) Global feature aggregation, which treats local features as the nodes of the graph, and builds a fully connected graph by adaptively learning the distance between nodes, the global feature can be aggregated through weighted summing finally. Another problem that limits the performance is the information loss in feature compression, mainly due to the huge dimensional gap, e.g., from 512 to 8. To handle this issue, we propose a feature compression module based on decoupling representation learning. This module can effectively learn the statistical information and spatial relationships between features. After that, redundancy is greatly reduced and more critical information is retained. Extensional experimental results show that our proposed method is both fast and accurate. On the Tusimple and CULane benchmarks, with a running speed of 248 FPS, F1 values of 96.81% and 75.49% were achieved, respectively.

Cesium manganese chloride: Stable lead-free perovskite from bulk to single layer

Motivated by the recent advances in perovskite-based solar cells, here we investigate stability, electronic properties and vibrational characteristics of lead-free perovskite, CsMnCl3, and its low dimensional forms by means of first-principles calculations. Structural optimizations reveal that, regardless of whether it is bulk or ultra-thin single layer cubic perovskite structure, CsMnCl3 crystal exhibit robust antiferromagnetism in its ground state due to oppositely aligned magnetic moments of Mn atoms. In addition to total energy calculations, phonon band dispersions indicate that CsMnCl3 structure sustains its dynamical stability down to its thinnest single layer crystal structures. The calculated Raman spectrums state that while the first-order Raman scattering is forbidden for bulk CsMnCl3 due to the cubic symmetry; dimensional-reduction-driven symmetry breaking leads to emergence of experimentally-observable distinctive Raman active modes in bilayer and single-layer crystal structures. Moreover, the electronic band dispersions reveal that from its bulk to ultra-thin single layer structures CsMnCl3 crystals are robust antiferromagnetic insulators. Multiple valid features like controllable dimensionality, robust antiferromagnetism and wide electronic band gap make cubic CsMnCl3 crystal as a potential candidate for nano-scale optoelectronic applications.

Bidirectional loss function for Label Enhancement and distribution learning

Label distribution learning (LDL) is an interpretable and general learning paradigm that has been applied in many real-world applications. In contrast to the simple logical vector in single-label learning (SLL) and multi-label learning (MLL), LDL assigns labels with a description degree to each instance. In practice, two challenges exist in LDL, namely, how to address the dimensional gap problem during the learning process of LDL and how to exactly recover label distributions from existing logical labels, i.e., Label Enhancement (LE). For most existing LDL and LE algorithms, the fact that the dimension of the input matrix is much higher than that of the output one is always ignored and it typically leads to the dimensional reduction owing to the unidirectional projection. The valuable information hidden in the feature space is lost during the mapping process. To this end, this study considers bidirectional projections function which can be applied in LE and LDL problems simultaneously. More specifically, this novel loss function not only considers the mapping errors generated from the projection of the input space into the output one but also accounts for the reconstruction errors generated from the projection of the output space back to the input one. This loss function aims to potentially reconstruct the input data from the output data. Therefore, it is expected to obtain more accurate results. Experiments on several real-world datasets are carried out to demonstrate the superiority of the proposed method for both LE and LDL. Specifically, BD-LE achieves optimal performance in 85.71% cases and renders sub-optimal in 13.09% cases. BD-LDL ranks 1st in 90.48% cases across six evaluation measurements. Compared with the baseline algorithms, the bidirectional projection methods can outperform the best baselines over 7.38% and 9.98% on average for LE and LDL, respectively.

Optimal Design of Fixture Layout for Compliant Part With Application in Ship Curved Panel Assembly

AbstractIn a ship assembly process, a large number of compliant parts are involved. The ratio of the part thickness to the length or the width is typically 0.001–0.012. Fixture design is a critical task in the ship assembly process due to its impact on the deformation and dimensional variation of the compliant parts. In current practice, fixtures are typically uniformly distributed under the part to be assembled, which is non-optimal, and large dimensional gaps may occur during assembly. This paper proposed a methodology for the optimal design of the fixture layout in the ship assembly process by systematically integrating direct stiffness method and simulated annealing algorithm, which aims to minimize dimensional gaps along the assembly interface to further improve the quality and efficiency of seam welding. The case study shows that the proposed method significantly reduced the dimensional gaps of the compliant curved panel parts in a ship assembly process.

Defect physics and doping engineering in semiconductor optoelectronic materials

Semiconductor materials play a central role in the integrated circuits, photovoltaics, information and communication, microelectronic devices, lighting, and so on. However, the performance of semiconductor optoelectronic devices depends critically on the dopability and defect engineering of semiconductor materials. In this paper, firstly, we introduce the doping properties in semiconductors and the theoretical progress of charged defect calculations over the past decades, including the traditional jellium model. To overcome the disadvantages and limitations of the traditional jellium model, recently, we propose a straightforward and universal theory, i.e., transfer real state model (TRSM), which can calculate directly the charged defect properties in both bulk and low-dimensional semiconductors. In the jellium model for a finite supercell size calculation, it suffers a serious problem to determine the defect properties in low-dimensional semiconductors due to charge distribution in the vacuum region. However, our TRSM method by putting the ionized electrons or holes on a real host band edge states naturally keeps the supercell neutral and provides clear physical meaning. For three-dimensional bulk materials, the defect formation energy and transition energy level calculated by our TRSM method are almost the same as the results obtained by using the traditional jellium model. For low dimensional semiconductors, however, the TRSM method cures the divergence issue that occurred in the jellium model due to long-range electrostatic Coulomb interactions. Secondly, we discuss the wide band gap semiconductors. By analyzing the doping limit law, we elucidate the effective methods for defect engineering in oxide wide band gap semiconductors, and then how their p-type conductivity can be improved. Those methods are based on two rules: (1) Reducing the ionization energy of acceptors; (2) suppressing the formation of compensating donors. Further, the physical mechanism of the difference in conductivity between ionic and covalent compounds in the amorphous wide band gap semiconductors is introduced. A band coupling model is employed to clarify the difference between pseudo-hydrogen passivated and real-hydrogen passivated low dimensional wide band gap semiconductors. Thirdly, we discuss the semiconductor alloys with high doping concentration. In the nonisovalent semiconductor alloys, due to strong wave function localization of the band edge states, the physical properties of nonisovalent alloys do not meet the statistical average law, distinct from the isovalent alloys. The band crossing phenomenon in large mismatched isovalent semiconductor alloys is often misinterpreted by a phenomenological two-level band anti-crossing model. Fortunately, this phenomenon has now been properly explained by the band broadening picture. When doping small concentration of Bi or N impurities into GaAs, the defect levels are localized due to the weak interactions between impurities. The band edges of GaAs1− x Bi x and GaAs1− x N x consist of the host atoms. The impurity level gradually broadens out with the increase of the impurity concentration, and thus the band edges of defective GaAs are dominated by impurities. Finally, we focus on the diffusion of metal impurities in semiconductors. The fundamental reason for the differences in the diffusion of Ag and Cu atoms between ionic and covalent semiconductors is clarified. The s-d coupling between the d orbitals of the diffusors and the s orbitals of the host materials are responsible for the behavior of diffusive atoms. The deep understanding of metal impurities in semiconductor materials offers effective theoretical guidance for controlling the diffusion properties of impurities in different types of semiconductor materials.

Synergistic strengthening effect of in-situ synthesized WC1-x nanoparticles and graphene nanosheets in copper matrix composites

In metal matrix composites, it is commonly a hard task to achieve synergistic strengthening and toughening effects between two-dimensional graphene and zero-dimensional nanoparticles (NPs) reinforcements due to their huge dimensional gap. Herein, we developed an in-situ solid-state reaction method to incorporate graphene nanosheets (GNSs) and WC1-x NPs into copper matrix. It was proved for the first time by thermodynamics and crystallography calculations that the unusual γ-WC1-x phase can be generated from the solid reaction and maintain stable at room temperature. Thanks to the unique distribution types of WC1-x, i.e. intergranular or intragranular distribution while other parts anchored on the surface of the GNSs, Orowan strengthening and load transfer strengthening were simultaneously achieved in the composites. Meanwhile, the thermal conductivity was also measured to be equal to that of pure Cu, exhibiting a promising prospect in applications as advanced multifunctional structural materials.

Derivation and analysis of coupled PDEs on manifolds with high dimensionality gap arising from topological model reduction

Multiscale methods based on coupled partial differential equations defined on bulk and embedded manifolds are still poorly explored from the theoretical standpoint, although they are successfully used in applications, such as microcirculation and flow in perforated subsurface reservoirs. This work aims at shedding light on some theoretical aspects of a multiscale method consisting of coupled partial differential equations defined on one-dimensional domains embedded into three-dimensional ones. Mathematical issues arise because the dimensionality gap between the bulk and the inclusions is larger than one, that is the high dimensionality gap case. First, we show that such model derives from a system of fully three-dimensional equations, by the application of a topological model reduction approach. Secondly, we rigorously analyze the problem, showing that the averaging operators applied for the model reduction introduce a regularization effect that resolves the issues due to the singularity of solutions and to the ill-posedness of restriction operators. Then, we exploit the structure of the model reduction technique to analyze the modeling error. This study confirms that for infinitesimally small inclusions, the modeling error vanishes. Finally, we discretize the problem by means of the finite element method and we analyze the approximation and the model error by means of numerical experiments.

Invariant Measures and Lower Ricci Curvature Bounds

Given a metric measure space $(X,d,\mathfrak {m})$ that satisfies the Riemannian Curvature Dimension condition, RCD∗(K,N), and a compact subgroup of isometries G ≤ Iso(X) we prove that there exists a G −invariant measure, $\mathfrak {m}_G$ equivalent to $\mathfrak {m}$ such that $(X,d,\mathfrak {m}_G)$ is still a RCD∗(K,N) space. We also obtain applications to Lie group actions on RCD∗(K,N) spaces. We look at homogeneous spaces, symmetric spaces and obtain dimensional gaps for closed subgroups of isometries.

Use of a Modified DANP-mV Model to Improve Quality of Life in Rural Residents: The Empirical Case of Xingshisi Village, China

Climate change-related anomalies have increased public concern regarding environmental protection. This has opened newer rural development avenues. In this regard, livability of villages is crucial; it can be evaluated based on the villagers’ quality of life (QoL). The WHOQOL-BREF, a comprehensive cross-cultural and cross-disciplinary scale proposed by the World Health Organization to assess QoL, has aided in assessing and improving QoL in different regions. However, the factors of this instrument are mutually influential, necessitating an improvement strategy considering the entire system. This problem may be resolved using the DANP-mV model. However, the traditional DANP-mV model includes many items and responding to all of them is difficult for experts. Therefore, by using the case of Xingshisi Village in China, this study proposed a modified DANP-mV model to provide additional suggestions for systematic improvement of the QoL and livability in the village. Xingshisi is a model village built according to an aspirational benchmark; however, different from the traditional definition of a benchmark, this village exhibits room for improvement. Although the modified model reduces the number of questions from 650 to 168, its effect remains similar to that of the traditional model. Moreover, in the modified model, physical capacity (D1) presented the largest dimensional gap. The interaction among the factors indicated that considering the effect of the environment (D4) and developing a systematic improvement strategy are necessary to improve the livability of villages facing limited resources.

Optical characteristics of one dimensional metal-dielectric photonic band gap material

This paper describes the optical transmittance and reflection of one dimensional metal-dielectric photonic-band gap material (1D M-D PBG), which is made of different thicknesses ITO and Ag layers. It is found that structures with a unit size below 80 nm and a smaller metal fraction leads to improvement of optical transmittance. For unit sizes larger than 80 nm, the reflection at the shorter and longer wavelengths increases. This is due to the generation of a structural and plasmonic band gap. In addition, the reflection in both ranges increases and broadens by increasing Ag films thicknesses. The reflection spectrum induced by structure shifts towards longer wavelength as a result of unit size increasing and the reflection due to plasmonic band gap piles beyond to optical range. The results are very useful for optical filter of 1D M-D PBG design.