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Ltd., Atsugi, Japan, Researcher/Senior Researcher, Researches on Semiconductor Quantum Dots for Quantum Information, Semiconductor Optoelectronic Materials and Devices. \nApril, 2012 – March 2014: University of Tokyo, Tokyo, Japan, Senior Researcher, Researches on Quantum Information Processing Devices. \nApril, 2014 – now: Southwest Institute of Technical Physics, Chengdu, China, Professor, Researches on Semiconductor Optoelectronic Materials and Devices. \nJune, 2015 – now: University of Electronic Science and Technology, Chengdu, China, Professor, Researches on Nanoscaled Semiconductors and Quantum Information Processing Devices.\n \nAchievements\nSystematically studied the property of porous silicon materials and verified their mechanism; found green and ultraviolet luminescence, and clarified the multiple luminescence mechanisms of nanocrystalline-silicon embedded in SiO2, which is valuable to silicon-based optoelectronic integration; realized enhanced hole mobility in amorphous silicon, verified the existence of deep trap states in amorphous selenium, providing ways to improve amorphous optoelectronic materials. \nDiscovered lateral coupling between self-assembled quantum dots (QDs) and their tuning effect to 2D electron gas; illustrated and deeply explained the metal-insulator transition in 2D ordered QD arrays, all of which are worth in optoelectronic application of semiconductor QDs. \nDeveloped Sb-free technique to double the InAs/GaAs QD density and suppress the atomic interdiffusion, helped producing 1.3 um QD lasers, which won Japanese national prizes and had been merchandized; developed 1.06 um quantum-well lasers, which have been used to produce pure-green lasers robust against high temperature. \nFound a way to access buried QDs by scanning tunneling microscope; achieved a way to prepare diluted QDs by post-annealing and clarified its mechanisms; invented a technique to control the size and site of QDs by atomic-force microscopy lithography, and an apparatus to detect single electron spin states by optically-detected magnetic resonance; designed a few types of micropillar cavities applicable to realize 1.55 um highly-efficient, even coherent (strongly coupled) InAs/InP QD single photon sources; produced fiber-integrated photon-entangled sources, all of which are very useful to the applications of QDs in quantum information processing. \nDeveloped focal-plane single-photon avalanche detectors, providing central devices for 3D laser detecting and ranging system; explored antimonide middle- and long-wavelength infrared detectors and the surface plasmon enhancement effect in such detectors; advanced the acetone-sensing function of Eu-doped SnO2 nano-belt; found Nickle Phosphide serving as a good catalyst in hydrogen-producing. 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Appl”\nMember of APS (American Physics Society)\nMember of OSA (Optical Society of America)\nPermanent Member of China Physical Science and Technology\nPermanent Member of the Chinese Optical Society\nTechnical committee member of PIERS, organizing a series of “quantum information processing and devices” sessions\nTechnical committee member of ICICM",institutionString:"Southwest University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Southwest University",institutionURL:null,country:{name:"China"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"20",title:"Physics",slug:"physics"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"453623",firstName:"Silvia",lastName:"Sabo",middleName:null,title:"Mrs.",imageUrl:"https://mts.intechopen.com/storage/users/453623/images/20396_n.jpg",email:"silvia@intechopen.com",biography:null}},relatedBooks:[{type:"book",id:"8356",title:"Metastable, Spintronics Materials and Mechanics of Deformable Bodies",subtitle:"Recent Progress",isOpenForSubmission:!1,hash:"1550f1986ce9bcc0db87d407a8b47078",slug:"solid-state-physics-metastable-spintronics-materials-and-mechanics-of-deformable-bodies-recent-progress",bookSignature:"Subbarayan Sivasankaran, Pramoda Kumar Nayak and Ezgi Günay",coverURL:"https://cdn.intechopen.com/books/images_new/8356.jpg",editedByType:"Edited by",editors:[{id:"190989",title:"Dr.",name:"Subbarayan",surname:"Sivasankaran",slug:"subbarayan-sivasankaran",fullName:"Subbarayan Sivasankaran"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophile",surname:"Theophanides",slug:"theophile-theophanides",fullName:"Theophile Theophanides"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. 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1. Introduction
Photography has a vast history as it is used to preserve our lives’ most important memories. As such, it tries to conserve a scene as realistically as possible. During the years, it evolved from the camera obscura [1, 2] where scenes were captured only for a brief moment, to black and white photography, requiring to stay still in front of the camera for long hours, to nowadays imaging devices, where the picture is captured instantaneously, digitalized, and the colors are close to what our eyes perceive [3].
Though it preserves the content of the scene, the immersion is lost, as well as the depth information, since the camera projects the scene from 3D to 2D.
To increase the immersion, the next step is to recreate the parallax of the scene, giving the opportunity to the viewer to move freely and see different perspectives, exactly as if the subject was miniaturized in front of our eyes, or the environment virtually rendered around us. Despite this desire, no device capable of acquiring the scene in its entirety directly in 3D has been designed so far.
Creating the parallax effect assumes capturing the scene from all the possible viewpoints and selecting the viewpoint to display on demand for the user’s viewing position. This is physically impossible; instead, we may synthesize any viewpoint from only a couple of captured viewpoints generating all missing information following some basic assumptions [4, 5].
There exist many approaches to generate novel viewpoints from input views. Early methods were based on 3D reconstruction [6, 7] to render the obtained 3D model. More recently, neural radiance fields (NeRF) [8] used machine learning methods to recreate a volumetric representation of the scene. Other methods avoid the explicit 3D information reconstruction, such as depth image-based rendering (DIBR) [9] that will be described in this chapter, or multiplane images [10, 11]. Finally, novel viewpoints can be synthesized by an intelligent interpolation using physical invariants (the epipolar plane image), rather than interpolating directly the image’s colors. Representatives of this last category are the shearlet transform [12] and techniques using deep learning [13].
This chapter provides comprehensive elements to bring photographs of a natural scene to the third dimension, for example, making the captured scene immersive, through holography or virtual reality (VR). The presented 3D rendering technique differs from traditional computer graphics by its input—instead of modeling 3D objects with their geometry and materials that interact with light sources, we use photographs that are warped to follow the viewer gaze direction using the reference view synthesis (RVS) [14, 15, 16] software that follows the view synthesis process of Figure 1.
Figure 1.
DIBR brings photographs to 3D by using depth information to create new viewpoints. It preserves photorealism and allows the user to experience motion parallax.
RVS has been developed during the exploration and standardization activities of MPEG-I – where”I″ refers to Immersive – focusing on developing new compression and file formats for immersive video.
The chapter is structured as follows—the first part explains the principles of depth image-based rendering, gives an overview of the possible artifacts that can be encountered when creating a DIBR implementation, and finally, implementation details of RVS are described. The second part provides practical advice for using RVS on some example datasets.
2. Principles of depth image-based rendering
To recreate the parallax effect, we use the depth image-based rendering [9] method. It warps or distorts the input color image as a function of its associated depth map, which itself stores, for every pixel, the distance between the camera and the projected point along the camera optical axis. This method is based on the observation that a stereoscopic pair of images, for example, taken with a few centimeters shift between each other, carry the depth information of the photographed subject. As shown in Figure 2, the relative shift d, aka. the disparity, of foreground objects is larger than for background objects.
Figure 2.
Disparity d=d2−d1 between two pixels representing the same projected point.
2.1 Projection equation and disparity
Let us consider two pinhole cameras facing an object at distance D (see Figure 2). The projection of this object on each image will have a disparity d. Using the similar triangles ratios of fD and d1+d2B, we obtain:
d=B×fDE1
where B is the baseline, i.e., the distance between the two camera centers, and f their focal length.
This implies that, given two images and their depth maps, we can create a virtual view in the middle, between the inputs, by shifting the pixels over half their disparity.
Eq. 1 can be generalized to any camera settings using the pose (translation and rotation – extrinsic parameters, Eq. 2) and internal camera parameters (focal length fx and fy expressed in pixels in the x and y directions, and principal point ppxppy – intrinsic parameters, Eq. (3).
We call an input image and its camera parameters an input viewpoint. We aim to recreate a new virtual view with given new parameters, called target viewpoint. For this, we deproject (i.e., from 2D to 3D) the pixels of the input image to 3D, and reproject (i.e., from 3D to 2D) them to the target image using the projection equation.
Let P=Rt be the inverse (i.e., world to camera) 3×4 pose matrix of a camera with R the rotation matrix and t the translation:
P=Rt=r11r12r13txr21r22r23tyr31r32r33tz,E2
and K its 3×3 intrinsic matrix:
K=fx0ppx0fyppy001.E3
In homogeneous coordinates, a point X=xyz1t at depth D from the input camera pin=uv1t is projected to a pixel pin=uv1t following the projection Equation [6]:
Dpin=KinPinXE4
Hence, given the input image and the depth value of the pixel, we can deproject X:
X=DRin−Rin−1tinTKin−1pinE5
Eventually, this allows to reproject X in the new camera, using Eq. 4 with Pout and Kout:
pout∝DKoutPoutRin−Rin−1tinTKin−1pinE6
To obtain the pixel value, we divide the obtained vector by the third coordinate (i.e., the depth of the point in the new camera).
Applying this operation to every pixel of the input image creates a novel view.
The core principle of DIBR is to apply this deprojection and reprojection to all the pixels of the input images, using a depth map (i.e., a single-channel image encoding the depth value of each pixel). RVS uses this basic principle, but of course, there are many pitfalls one should handle correctly. This is further explained in the following sections.
2.2 Frequent artifacts
We now know the basic principles of DIBR. Unfortunately, simply shifting the pixels of an input image in the function of their depth does not create a photo-realistic result.
The first problem is occlusion handling. When an object is visible in the input image but hidden by an object lying more in the foreground in the target, it is occluded and its pixels should not appear in the rendered image. This can be solved by choosing, among all the pixels from various objects ending up in the same pixel on the screen, the pixel with the minimal depth. A more critical problem is the one of disocclusions, for example, when an object should be visible in the target image but does not appear in the input image because it is hidden (Figure 3a). In that case, a hole is created in the rendered image. One solution is to add more input images in the hope to obtain this missing information [15, 17]. Another approach is to inpaint the empty pixels [18, 19]. In RVS, it is possible to choose any number of viewpoints and a basic inpainting fills the remaining disocclusions.
Figure 3.
(a) Disocclusion artifact (classroom dataset), (b) crack artifacts (Toystable dataset), (c) Artifacts due to inconsistency in color among the input images. (dataset fencing, courtesy of Poznan University of Technology), (d) ghosting artifacts (dataset Toystable).
Cracks and dilation are other frequent DIBR artifacts. We can observe them in Figure 3b. They are created as the user moves forward (step-in), increases the resolution (zoom), or observes slanted objects. Those cracks correspond to pixels in the target that do not have a preimage in the input view (i.e., no input pixel is mapped to them). However, as their neighboring pixels have a preimage, their color can be interpolated. In other words, the input pixels should be mapped to more than one pixel to compensate for this effect. This can be done using superpixels [20], adapting the pixels size to the camera movement [21], or linking neighboring pixels for rasterization [15, 16] (chosen solution in RVS: adjacent pixels are grouped into triangles that are colorized).
Even if increasing the number of input images can reduce the number of disocclusions, it brings new challenges, as those views need to be consistent in color, in estimated geometry, and in estimated pose. Notably, the depth estimation and the blending of multiple views together rely on consistent colors between the images. As not all camera sensors are equal, small differences in color rapidly generate incoherent depth estimations or nonhomogeneous color patches during view blending (Figure 3c). Color correction is usually needed prior to the view synthesis [22, 23] or during the blending step [24, 25].
Moreover, as DIBR relies on the depth information, errors in the depth estimation, a misalignment between the color image and the depth map, or errors in the camera pose estimation lead to ghosting artifacts. When several views are blended together, these artifacts make the objects or their borders appear doubled (Figure 3d). A depth map refinement [26, 27] is one way to solve this problem. Another is to choose weighted blending coefficients based on the reliability of each input image [11, 16] (chosen solution in RVS).
Finally, DIBR is structurally limited to the rendering of diffuse objects. Indeed shifting the pixels in the function of their depth assumes that they do not present view-dependent aspects, such as transparency or specularity. When such objects, so-called non-Lambertian, are present in the scene, the linear hypothesis in pixel displacement in the function of the camera displacement is not valid anymore. Adapting the DIBR principles to non-Lambertian objects is nevertheless possible by exploiting additional information, such as structure, normal, and indexes of refraction [28], or a more accurate approximation of the pixel displacement [29–31] (chosen solution in RVS).
2.3 RVS in practice
The DIBR software RVS is designed to render novel viewpoints from any number of input images and depth maps and their camera parameters, without suffering from the above limitations. In order to create a novel view, the input images are warped sequentially. The obtained result is then blended into an image accumulating the outputs of each reprojected input image. This pipeline is shown in Figure 4. The warping and blending operations are performed alternatively for each input image using OpenGL [32] or on the CPU [15].
Figure 4.
Overview of the processing pipeline. (1) view selection (optional), (2) warping, (3) blending, (4) Inpainting (optional).
To obtain high-quality results, it is recommended to select candidate input views properly. Therefore, the first step in RVS is an optional view selection. The n views the closest to the target image are selected in order to reduce the computation time. Otherwise, all the input images are used to create a new viewpoint.
The second step in RVS is the warping phase. Each input image is divided into a grid of triangles whose vertices are adjacent pixels (Figure 5a). Each of these vertices is reprojected to fit to the new camera pose and parameters (Figure 5b) and rasterized to avoid the cracks artifacts of Figure 3b. Then, each new triangle is given a score that will be used in the blending phase. This score describes the quality of a warped triangle—if the pixels lie on a disocclusion area, their triangle will be stretched and should hence be discarded from the final result (black areas in Figure 4-warping and Figure 5c). The remaining triangles are then rasterized according to their vertex color in the input image (Figure 5c). A depth test prioritizes the pixels with the lowest depth.
Figure 5.
Adjacent pixels of an input image (a) are grouped into triangles independently of their depth before being reprojected to their new image location (b). Triangles detected as lying on a disocclusion are discarded, resulting in a new warped image (c).
When the input images are warped to the target viewpoint, the results need to be blended into one single image. For a given pixel, the final output color c is the weighted mean of the color ci of each warped input:
c=1∑iwi∑iwiciE7
where wi is a weight representing the quality of a triangle [16], prioritizing foreground objects and highest quality triangles.
Finally, as shown in the inpainting of Figure 4, when multiple views are blended together, several occluded regions remain; if the occlusions are small enough, a basic inpainting process can be applied to remove them. Of course, the quality of the inpainting can compromise the overall image quality, hence, inpainting is not recommended. In RVS, the inpainting is not automatic but can be activated. In that case, the empty pixels take the color of the nearest non-empty pixel.
2.3.1 Non-Lambertian case
In the general case, DIBR uses depth maps to predict pixel displacement. However, a point on a non-Lambertian surface does not have a proper color (its color can rapidly change with a change in viewing direction); its appearance is a function of the surrounding scene, the normal at that point, and the index of refraction for refractive surfaces (see Figure 6). This not only makes depth estimation through stereo matching impossible but also implies that even with a correct depth map, the object cannot be rendered by a simple pixel shifting.
Figure 6.
The aspect of non-Lambertian objects is view dependent—Their surface does not appear the same color in each viewing direction.
Alternatively, to model the non-Lambertian surface in itself, it is possible to track its feature movements on the surface [29, 33, 34]. DIBR can be generalized to non-Lambertian objects by replacing the usual depth maps with the coefficients of a polynomial approximating the non-Lambertian features displacement [30, 31]. To clearly understand what this means, let us start with what happens for diffuse objects, where for a lateral camera movement xy, the new position uv of a pixel u0v0 is given by:
uv=u0v0+fDxy.E8
We extend this equation for non-Lambertian objects using polynomials:
uv=u0v0+PuxyPvxy,E9
with Puxy=∑i∑jaijxiyj and Pvxy=∑i∑jbijxiyj. Clearly, the diffuse case corresponds to a1,0=b0,1=fD and all other coefficients aij,bij set to zero.
Consequently, Eq. 9 approximates by a polynomial the nonlinear displacement of a refracted or reflected feature moving on non-Lambertian objects.
However, the polynomial expression rapidly diverges in extrapolation (e.g., when synthesizing a target view that is outside of the input images’ hull). The computed feature displacement becomes greater than the inverse of the non-Lambertian object’s depth, making the feature to be rendered outside of the non-Lambertian surface. This approximation is hence designed for interpolation and small extrapolation only.
Furthermore, these polynomials are not directly related to the physical reality of the non-Lambertian object. Hence, contrary to the simple relation linking the depth to the disparity of a diffuse object cf. Figure 2 and Eq. (1), the polynomials of Eq. (9) do not give the object geometry or the index of refraction.
The polynomial is rather designed to “track” non-Lambertian features that move nonlinearly across the input images. It nevertheless encounters the following limitations. For content with semi-transparent objects, the maps should be divided into several layers before applying the polynomial or depth image-based rendering. Scenes with glints and glossiness make it difficult to track features on their surfaces, often leading to a failure case of the proposed method.
3. Reference view synthesis (RVS) software
This section provides practical recommendations for the use of the reference view synthesizer (RVS) [14, 15, 16, 32, 35] (https://gitlab.com/mpeg-i-visual/rvs) developed as a DIBR-based view synthesizer for the MPEG immersive video (MIV) standard (https://mpeg-miv.org). Without further details on the compression and storage of immersive content [36], we give a comprehensive method to practically use the software on some test sequences (also provided to the MPEG community while developing RVS).
The following paragraphs give documentation on the image format, the axis system, and the data structure to synthesize new viewpoints from available ready-to-use datasets and/or new content users may provide.
3.1 Input images
RVS can accept any number of input images with depth maps, the only limitation being the computer memory. Each input color image must be provided along with its corresponding depth map.
3.1.1 Color images
The color images can be encoded on three RGB color channels, with 8-bit integers each, in any image format readable by OpenCV, for example, PNG or JPEG format.
Additionally, raw images in YUV can be used, with a bit depth of 8, 10, or 16 bits. In this case, multi-frame raw video can be used, applying the view synthesis on all specified frames.
3.1.2 Depth maps
The depth maps represent the depth coordinate of every point in the image following the forward axis of the camera. Similar to color images, they can be provided in different formats. They have to match the resolution of the input images, but they use only one channel.
The first option is to use the OpenEXR format. In that case, the software reads the depth value in float and uses it directly for reprojection.
In the case of integer coded formats, such as YUV or PNG, the precision can be set to 8, 10, or 16 bit per depth value—the bit depth. YUV files can be encoded in YUV420 or YUV400 format, only the Y channel being used. However, the quantization does not allow to directly use the integer as a depth value. Indeed, it would be impossible to use a depth map in meter units for objects in the range of a few meters or centimeters from the cameras.
To overcome this problem, the depth value is encoded into MPEG’s disparity format, mapping the closest object to 2bitdepth−1, and the farthest to 1. To obtain the actual depth value, first we divide the encoded depth map value by 2bitdepth−1 to obtain a value d in the range of 01, then remap the value in the range nf using:
d′=f×nn+d×f−n.E10
With n and f the near and far values of the scene and d′ the depth value lying in nf.
For very far objects, this equation is simplified (f ≥1000) to
d′=ndE11
The value 0 in the encoded depth maps is considered as invalid depth. It corresponds, for example, to disocclusions in a depth-sensing device-acquired map. Figure 7 shows an encoded depth map with invalid pixels and objects at different depths. Clearly, the foreground has high values, which corresponds to being a disparity value, that is, the inverse of a depth, cf. Eq. (11).
Figure 7.
Encoded depth map on integer values. Due to the shift between the color sensor and the depth sensor, the depth map reprojected to the color image misses some information, leaving invalid pixels, encoded on 0. The foreground objects are encoded on high disparities, while the background objects are encoded on low disparities.
In the case of polynomial maps for non-Lambertian objects, it is possible to encode up to degree 3 polynomials, hence 18 coefficients, and pass an additional depth map and mask for the non-Lambertian objects. Those coefficients are encoded similarly to the depth maps, using EXR (directly the float value) or YUV (normalized) format. The polynomial maps are numbered from 0 to 19 as follows.
with ai corresponding to the map i and bi to the map 10+i. The remaining map 9 is used to encode the depth map for Lambertian objects and the map 19 is used as a mask representing non-Lambertian objects (0 for Lambertian, 1 for non-Lambertian). The coefficients not used are left to 0. If the coefficients are encoded in YUV format, the depth (map n∘9) is normalized using Eq. 10, the mask (map n∘19) has 0 and 1 values and the other coefficients are linearly normalized between minimal m and maximal M values: ai′=M−mai+m.
3.2 Camera parameters
Additionally to the input images, the camera parameters must be known to create a novel view with DIBR and RVS. The extrinsic parameters describe the position and the rotation of the camera (Eq. 2), while the intrinsic parameters describe the projection matrix (Eq. 3). Perspective and equirectangular projections are also supported, requiring a slightly different description, as explained hereafter.
3.2.1 Extrinsic parameters
Common graphics processing software and APIs, such as Blender [37], COLMAP [38], OpenGL [39], Vulkan [40], specify their own coordinate system, often admitting different axes and directions, and different image coordinates. Transferring data from one application to the other requires then several coordinate transformation steps, which will be summarized here. We use the Omnidirectional Media Format (OMAF) [41] coordinate system of MPEG-I, combined with yaw-pitch-roll angles.
OMAF is the first industry standard for VR. It specifies the coordinate system used in VR applications, the projection and rectangular region-wise packing methods, the metadata storage, encapsulation, signaling, and streaming of omnidirectional data, and finally the media profiles and presentation profiles. For these reasons, it has been adopted in the camera configuration files of RVS.
The OMAF coordinate system is described in Figure 8. The axes are defined as follows:
X: Back-to-front, forward
Y: Lateral, left
Z: Vertical, up
Figure 8.
The omnidirectional media format coordinate system.
The rotations in degrees are defined with the Yaw-pitch-roll:
Yaw: Around the vertical axis
Pitch: Around the lateral axis
Roll: Around the back-to-front axis
A camera facing forward has all its rotation angles set to 0. The rotation matrix of the camera (world to camera) in our axis coordinate system is then given by:
R=RzyawRypitchRxrollE13
In order to transform a coordinate system from an application to OMAF, one needs to define the coordinate change matrix that matches the three axes, for example:
P=00−11000−10E14
This matrix sets x′y′z′(OMAF) = −zx−y(application), that is, it represents a coordinate system with the axes (left, down, backward). To transfer from this system to OMAF, we apply it to the rotation and position as follow:
R′=P.R.PTp′=P.pE15
where R′ and p′ are the rotation and position of the OMAF system, while R and p the rotation and position in the old coordinates.
The unit of the coordinate system does not have any prerequisite but must correspond to one of the depth maps.
RVS handles any number of input and target cameras, each of them can have its own parameters and projection types. In the case of a stereoscopic head-mounted display for VR, two target views – one for each eye – need to be synthesized with a relative position (interpupillary distance) corresponding to the eye distance, usually given by the headset’s framework along with the intrinsic parameters.
3.2.2 Intrinsic parameters
The intrinsic parameters can be defined for perspective or equirectangular projections. In both cases, the resolution needs to be specified.
For perspective projection, the input images need to be undistorted. In that way, only the focal length and the principal point need to be specified. Those values are in pixel units, the sensor size corresponding to the image resolution. The focals are given by fxfy, corresponding to the horizontal and vertical axis. The principal point ppxppy is defined from the top-left corner of the image as described in Figure 9(a). A principal point at the center of the image has a value of half the resolution. In the case of equirectangular projection (Figure 9b), the horizontal and vertical viewing range must be specified in degrees. For a full 360∘ panoramic image, the horizontal range is −180,180 and the vertical range is −9090. For a 180∘ image, the horizontal and vertical ranges are −9090.
Figure 9.
Intrinsic parameters of the camera for (a) a pinhole projection, (b) an equirectangular projection.
3.2.3 Camera file
The image specifications and camera parameters are specified in a json file with informative headers. An example with a perspective and an equirectangular camera is given here.
An optional parameter, DisplacementMethod, can be set to Polynomial instead of default parameter Depth to specify that, instead of a depth map (Eq. 10), RVS reads a displacement map (Eq. 12). In that case, similarly to the Depth_range, a Multi_depth_range can be specified for the polynomial coefficients in YUV format.
3.3 View synthesis file
In order to perform the view synthesis, an experiment setup file is created. It gives camera views specifications (which views to synthesize given the input viewpoints) in an easy to use json format. The file contains:
Input and target camera parameters file paths—path to the camera file described in the previous subsection. The same file can be used twice if all the input and target cameras are in the same file;
Input and target camera names matched with the camera names contained in the camera files. Any number of inputs and outputs can be specified;
Input images, output images, and depth maps file paths. In the case of polynomial maps, numbered from 0 to 19, the number is replaced by a *;
Number of output frames. Useful for uncompressed YUV video files. The synthesized number of frames can exceed the number of frames in the input videos by specifying an optional NumberOfOutputFrames. In that case, the video will be played back and forth until the desired number of frames is reached;
Precision: super-resolution factor to reach sub-pixel accuracy;
Colorspace: internal working color space, can be YUV or RGB. Following the color space used, the result may present small color variations;
Blending specifications: the method can be Simple (for CPU and GPU usage) or Multispectral (for CPU). Multispectral blending detects the borders in the images, to blend them with a hard threshold and therefore avoids ghosting. The factor represents the power on the weights of Eq. 7.
To test the view synthesis, we provide references to datasets that are provided with their cameras configuration json files. Publicly available datasets are available at the following addresses, while others have been provided as test scenes for MPEG-I immersive video exploration and standardization activities.
Overview of the open-source datasets. (a) Toystable consists of two camera arrays at 25 cm (5×5 cameras) and 55 cm (3×5 cameras) from the scene. (b) Magritte is a 21×21 camera array. (c) Rabbit is a 3×7 array of 5×5 subaperture images of a plenoptic camera. (d) Bear (4×8 cameras) is a dataset captured by a Lidar camera with estimated and sensed depth maps.
4. Displays
We provide in Figure 11 results obtained with the RVS software on various display types—autostereoscopic or light field screen (Courtesy ETRO-VUB, Belgium), holographic stereograms [56], and head-mounted displays. Additional videos can be found at the following links: https://youtu.be/ikJb9JaaE54 (holographic stereogram) and https://youtu.be/vavw-TcbHf4 (head-mounted-display).
Figure 11.
Instead of acquiring the 100 of views needed for the different kinds of display, RVS recreates them using four input images. (a) Autostereoscopic screen, (b) holographic stereogram, (c) head-mounted display.
Displaying a dynamic scene in VR requires real-time view synthesis, preferably at 90 frames per second and at a minimum of 30 frames per second for each eye. However, the processing time depends on the number of input images and their resolution – since their pixels form the mesh – resulting in different frame rates [16] (see Table 1). Using a NVIDIA GTX 1080TI GPU, around four input images at a full HD resolution can be processed to obtain a high visual quality while reaching real-time navigation.
The frame rate for view synthesis in VR depends on the number of input images and their resolution. The output images all have the resolution of oculus rift (i.e., 1080×1200 pixels). Those results have been obtained on a windows PC with Intel Xeon E5–2680@2.7GHz CPU and NVIDIA GTX 1080TI GPU.
In the case of the currently developed light field head-mounted display [57], the constraint is double—in addition to the real-time requirement, all the light rays reaching the user’s pupils need to be displayed to make the eye accommodation possible on the close objects, that is, not only one image per eye but all the micro-parallax views around the eye position are rendered.
4.1 Additional tools
In this section, we provide references for additional tools, which are not directly involved in the view synthesis but are nevertheless useful to prepare a dataset.
4.1.1 Camera calibration
The first step prior to DIBR is finding the camera parameters. Accurate intrinsic parameters, including distortion parameters, can be found using a calibration checkerboard-pattern, if the scene has a large enough baseline, or directly during the scene reconstruction (structure-from-motion (SfM) with the retrieval of intrinsic parameters). Using a pattern gives more accurate results but requires a supplementary preprocessing step. There exists open-source software such as Kalibr [58] and OpenCV [59] for camera calibration.
Extrinsic parameters of a set of cameras are retrieved using SfM, with or without the intrinsic parameters known as prior [60]. There exist many open-source software such as COLMAP [38] or AliceVision [61]. Those softwares calibrate the camera and automatically undistort the images.
4.1.2 Depth estimation
Besides parameters estimation, DIBR requires corresponding depth maps for each input view. If they are not acquired with a depth-sensing device, they can be computed using stereo-matching algorithms. Among many algorithms, Depth Estimation Reference Software (DERS) [62] and Immersive Video Depth Estimation (IVDE) [63, 64] are recognized by the MPEG-I community.
5. Conclusions
In this chapter, an overview of the main steps and frequent problems of view synthesis are described. By starting from sparse input pictures, we showed a DIBR method that renders the parallax effect on a multitude of displays, allowing a user to experience new aspects of multimedia immersion. In the second part, a description of how one can start experimenting with the state-of-the-art RVS software is thoroughly explained to avoid common pitfalls.
As research progresses, novel methods to create view synthesis emerge, such as NeRF, however, recent research results demonstrate that DIBR methods will still reach high-quality performances [16], in real time, that will be highly applicable in immersive applications, for example, in the context of MPEG immersive video.
Acknowledgments
This work was supported in part by the Fonds de la Recherche Scientifique – FNRS, Belgium, under Grant n·33679514, ColibriH; and in part by the HoviTron project that received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement N·951989 (https://www.hovitron.eu/).
Conflict of interest
The authors declare no conflict of interest.
Abbreviations
CPU
Central Processing Unit
DERS
Depth Estimation Reference Software
DIBR
Depth image-based rendering
GPU
Graphics Processing Unit
IVDE
Immersive Video Depth Estimation
MIV
MPEG Immersive Video
MPEG-I
Moving Picture Experts Group-Immersive
NeRF
Neural Radiance Field
OMAF
Omnidirectional Media Format
RGB
Red-Green-Blue format
RVS
Reference View Synthesizer
SfM
Structure-from-Motion
VR
Virtual Reality
YUV
Luminance-Chrominance format
\n',keywords:"DIBR, RVS, view synthesis, depth map, virtual reality, rendering, 3D geometry, light field, non-Lambertian",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/80515.pdf",chapterXML:"https://mts.intechopen.com/source/xml/80515.xml",downloadPdfUrl:"/chapter/pdf-download/80515",previewPdfUrl:"/chapter/pdf-preview/80515",totalDownloads:129,totalViews:0,totalCrossrefCites:0,dateSubmitted:null,dateReviewed:"December 23rd 2021",datePrePublished:"February 18th 2022",datePublished:null,dateFinished:"February 18th 2022",readingETA:"0",abstract:"This chapter addresses the view synthesis of natural scenes in virtual reality (VR) using depth image-based rendering (DIBR). This method reaches photorealistic results as it directly warps photos to obtain the output, avoiding the need to photograph every possible viewpoint or to make a 3D reconstruction of a scene followed by a ray-tracing rendering. An overview of the DIBR approach and frequently encountered challenges (disocclusion and ghosting artifacts, multi-view blending, handling of non-Lambertian objects) are described. Such technology finds applications in VR immersive displays and holography. Finally, a comprehensive manual of the Reference View Synthesis software (RVS), an open-source tool tested on open datasets and recognized by the MPEG-I standardization activities (where”I″ refers to”immersive”) is described for hands-on practicing.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/80515",risUrl:"/chapter/ris/80515",signatures:"Sarah Fachada, Daniele Bonatto, Mehrdad Teratani and Gauthier Lafruit",book:{id:"11191",type:"book",title:"3D Computer Graphics",subtitle:null,fullTitle:"3D Computer Graphics",slug:null,publishedDate:null,bookSignature:"Dr. Branislav Sobota",coverURL:"https://cdn.intechopen.com/books/images_new/11191.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:null,printIsbn:"979-953-307-X-X",pdfIsbn:null,isAvailableForWebshopOrdering:!0,editors:[{id:"109378",title:"Dr.",name:"Branislav",middleName:null,surname:"Sobota",slug:"branislav-sobota",fullName:"Branislav Sobota"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Principles of depth image-based rendering",level:"1"},{id:"sec_2_2",title:"2.1 Projection equation and disparity",level:"2"},{id:"sec_3_2",title:"2.2 Frequent artifacts",level:"2"},{id:"sec_4_2",title:"2.3 RVS in practice",level:"2"},{id:"sec_4_3",title:"2.3.1 Non-Lambertian case",level:"3"},{id:"sec_7",title:"3. Reference view synthesis (RVS) software",level:"1"},{id:"sec_7_2",title:"3.1 Input images",level:"2"},{id:"sec_7_3",title:"3.1.1 Color images",level:"3"},{id:"sec_8_3",title:"3.1.2 Depth maps",level:"3"},{id:"sec_10_2",title:"3.2 Camera parameters",level:"2"},{id:"sec_10_3",title:"3.2.1 Extrinsic parameters",level:"3"},{id:"sec_11_3",title:"3.2.2 Intrinsic parameters",level:"3"},{id:"sec_12_3",title:"3.2.3 Camera file",level:"3"},{id:"sec_14_2",title:"3.3 View synthesis file",level:"2"},{id:"sec_15_2",title:"3.4 Datasets",level:"2"},{id:"sec_17",title:"4. Displays",level:"1"},{id:"sec_17_2",title:"4.1 Additional tools",level:"2"},{id:"sec_17_3",title:"4.1.1 Camera calibration",level:"3"},{id:"sec_18_3",title:"4.1.2 Depth estimation",level:"3"},{id:"sec_21",title:"5. Conclusions",level:"1"},{id:"sec_22",title:"Acknowledgments",level:"1"},{id:"sec_25",title:"Conflict of interest",level:"1"},{id:"sec_24",title:"Abbreviations",level:"1"}],chapterReferences:[{id:"B1",body:'Euclid of Alexandria. Optics; 300 BC.'},{id:"B2",body:'Al-Haytham HI. Book of Optics. Vol. I-VII circa 1027'},{id:"B3",body:'Renner E. Pinhole Photography: From Historic Technique to Digital Application. 4th ed. Amsterdam, Boston: Focal Press; 2009'},{id:"B4",body:'Maeda T, Suenaga R, Suzuki K, Panahpour Tehrani M, Takahashi K, Fujii T. Free Viewpoint Video for Sports Events Using Multi-Resolution Visual Hull and Micro-Facet Billboarding. Proc. 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High-quality holographic stereogram generation using four RGBD images. Applied Optics. 2021;60(4):A250–A259. Publisher: Optical Society of America.'},{id:"B57",body:'Bonatto D, Hirt G, Kvasov A, Fachada S, Lafruit G. MPEG Immersive Video tools for Light-Field Head Mounted Displays. Munich, Germany: IEEE International Conference on Visual Communications and Image Processing; 2021. p. 2'},{id:"B58",body:'Furgale P, Rehder J, Siegwart R. Unified temporal and spatial calibration for multi-sensor systems. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. Tokyo: IEEE; 2013. pp. 1280-1286'},{id:"B59",body:'Bradski G. The open CV library. Dr Dobb’s Journal: Software Tools for the Professional Programmer. 2000;25(11):120–123;Publisher: Miller Freeman Inc.'},{id:"B60",body:'Quan L, Lan Z. Linear N-point camera pose determination. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1999;21(8):774-780'},{id:"B61",body:'Griwodz C, Gasparini S, Calvet L, Gurdjos P, Castan F, Maujean B, et al. AliceVision Meshroom: An open-source 3D reconstruction pipeline. Proceedings of the 12th ACM Multimedia Systems Conference - MMSys ‘21. ACM Press; 2021'},{id:"B62",body:'Rogge S, Bonatto D, Sancho J, Salvador R, Juarez E, Munteanu A, et al. MPEG-I Depth Estimation Reference Software. In: 2019 International Conference on 3D Immersion (IC3D). Brussels, Belgium: IEEE; 2019. pp. 1-6'},{id:"B63",body:'Mieloch D, Stankiewicz O, Domański M. Depth Map Estimation for Free-Viewpoint Television and Virtual Navigation. IEEE Access, Conference Name: IEEE Access. 2020;8:5760-5776'},{id:"B64",body:'Mieloch D, Dziembowski A. Proposal of IVDE 3.0 [m55751]. ISO/IEC JTC1/SC29/WG11; 2020'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Sarah Fachada",address:null,affiliation:'
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"Open access contributes to scientific excellence and integrity. It opens up research results to wider analysis. It allows research results to be reused for new discoveries. And it enables the multi-disciplinary research that is needed to solve global 21st century problems. Open access connects science with society. It allows the public to engage with research. To go behind the headlines. And look at the scientific evidence. And it enables policy makers to draw on innovative solutions to societal challenges".
\n\n
Carlos Moedas, the European Commissioner for Research Science and Innovation at the STM Annual Frankfurt Conference, October 2016.
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Open Access background
\\n\\n
The Open Access publishing movement started in the early 2000s when academic leaders from around the world participated in the formation of the Budapest Initiative. They developed recommendations for an Open Access publishing process, “which has worked for the past decade to provide the public with unrestricted, free access to scholarly research—much of which is publicly funded. Making the research publicly available to everyone—free of charge and without most copyright and licensing restrictions—will accelerate scientific research efforts and allow authors to reach a larger number of readers” (reference: http://www.budapestopenaccessinitiative.org)
\\n\\n
IntechOpen’s co-founders, both scientists themselves, created the company while undertaking research in robotics at Vienna University. Their goal was to spread research freely “for scientists, by scientists’ to the rest of the world via the Open Access publishing model. The company soon became a signatory of the Budapest Initiative, which currently has more than 1000 supporting organizations worldwide, ranging from universities to funders.
\\n\\n
At IntechOpen today, we are still as committed to working with organizations and people who care about scientific discovery, to putting the academic needs of the scientific community first, and to providing an Open Access environment where scientists can maximize their contribution to scientific advancement. By opening up access to the world’s scientific research articles and book chapters, we aim to facilitate greater opportunity for collaboration, scientific discovery and progress. We subscribe wholeheartedly to the Open Access definition:
\\n\\n
“By “open access” to [peer-reviewed research literature], we mean its free availability on the public internet, permitting any users to read, download, copy, distribute, print, search, or link to the full texts of these articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose, without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself. The only constraint on reproduction and distribution, and the only role for copyright in this domain, should be to give authors control over the integrity of their work and the right to be properly acknowledged and cited” (reference: http://www.budapestopenaccessinitiative.org)
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Open Access Standards followed by IntechOpen
\\n\\n
OAI-PMH
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As a firm believer in the wider dissemination of knowledge, IntechOpen supports the Open Access Initiative Protocol for Metadata Harvesting (OAI-PMH Version 2.0). Read more
\\n\\n
License
\\n\\n
Book chapters published in edited volumes are distributed under the Creative Commons Attribution 3.0 Unported License (CC BY 3.0). IntechOpen upholds a very flexible Copyright Policy. There is no copyright transfer to the publisher and Authors retain exclusive copyright to their work. All Monographs/Compacts are distributed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). Read more
\\n\\n
Peer Review Policies
\\n\\n
All scientific works are Peer Reviewed prior to publishing. Read more
\\n\\n
OA Publishing Fees
\\n\\n
The Open Access publishing model employed by IntechOpen eliminates subscription charges and pay-per-view fees, enabling readers to access research at no cost. In order to sustain operations and keep our publications freely accessible we levy an Open Access Publishing Fee for manuscripts, which helps us cover the costs of editorial work and the production of books. Read more
\\n\\n
Digital Archiving Policy
\\n\\n
IntechOpen is committed to ensuring the long-term preservation and the availability of all scholarly research we publish. We employ a variety of means to enable us to deliver on our commitments to the scientific community. Apart from preservation by the Croatian National Library (for publications prior to April 18, 2018) and the British Library (for publications after April 18, 2018), our entire catalogue is preserved in the CLOCKSS archive.
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Open Science
\\n\\n
Open Science is transparent and accessible knowledge that is shared and developed through collaborative networks.
\\n\\n
Open Science is about increased rigour, accountability, and reproducibility for research. It is based on the principles of inclusion, fairness, equity, and sharing, and ultimately seeks to change the way research is done, who is involved and how it is valued. It aims to make research more open to participation, review/refutation, improvement and (re)use for the world to benefit.
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Open Science refers to doing traditional science with more transparency involved at various stages, for example by openly sharing code and data. It implies a growing set of practices - within different disciplines - aiming at:
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\\n\\t
Promoting open and publicly accessible education tools
\\n\\t
Transparency in experimental methodology, observation, and collection of data
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Reproducible research data and re-analysis
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Public availability and re-usability of scientific data
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Public accessibility and transparency of scientific communication
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Transparent peer-review and publishing practices
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Using web-based tools to facilitate scientific collaboration
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Supporting exchange of knowledge and research materials between disciplines
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Supporting exchange of knowledge and research materials between scientific communities and industry.
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We aim at improving the quality and availability of scholarly communication by promoting and practicing:
The Open Access publishing movement started in the early 2000s when academic leaders from around the world participated in the formation of the Budapest Initiative. They developed recommendations for an Open Access publishing process, “which has worked for the past decade to provide the public with unrestricted, free access to scholarly research—much of which is publicly funded. Making the research publicly available to everyone—free of charge and without most copyright and licensing restrictions—will accelerate scientific research efforts and allow authors to reach a larger number of readers” (reference: http://www.budapestopenaccessinitiative.org)
\n\n
IntechOpen’s co-founders, both scientists themselves, created the company while undertaking research in robotics at Vienna University. Their goal was to spread research freely “for scientists, by scientists’ to the rest of the world via the Open Access publishing model. The company soon became a signatory of the Budapest Initiative, which currently has more than 1000 supporting organizations worldwide, ranging from universities to funders.
\n\n
At IntechOpen today, we are still as committed to working with organizations and people who care about scientific discovery, to putting the academic needs of the scientific community first, and to providing an Open Access environment where scientists can maximize their contribution to scientific advancement. By opening up access to the world’s scientific research articles and book chapters, we aim to facilitate greater opportunity for collaboration, scientific discovery and progress. We subscribe wholeheartedly to the Open Access definition:
\n\n
“By “open access” to [peer-reviewed research literature], we mean its free availability on the public internet, permitting any users to read, download, copy, distribute, print, search, or link to the full texts of these articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose, without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself. The only constraint on reproduction and distribution, and the only role for copyright in this domain, should be to give authors control over the integrity of their work and the right to be properly acknowledged and cited” (reference: http://www.budapestopenaccessinitiative.org)
\n\n
Open Access Standards followed by IntechOpen
\n\n
OAI-PMH
\n\n
As a firm believer in the wider dissemination of knowledge, IntechOpen supports the Open Access Initiative Protocol for Metadata Harvesting (OAI-PMH Version 2.0). Read more
\n\n
License
\n\n
Book chapters published in edited volumes are distributed under the Creative Commons Attribution 3.0 Unported License (CC BY 3.0). IntechOpen upholds a very flexible Copyright Policy. There is no copyright transfer to the publisher and Authors retain exclusive copyright to their work. All Monographs/Compacts are distributed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). Read more
\n\n
Peer Review Policies
\n\n
All scientific works are Peer Reviewed prior to publishing. Read more
\n\n
OA Publishing Fees
\n\n
The Open Access publishing model employed by IntechOpen eliminates subscription charges and pay-per-view fees, enabling readers to access research at no cost. In order to sustain operations and keep our publications freely accessible we levy an Open Access Publishing Fee for manuscripts, which helps us cover the costs of editorial work and the production of books. Read more
\n\n
Digital Archiving Policy
\n\n
IntechOpen is committed to ensuring the long-term preservation and the availability of all scholarly research we publish. We employ a variety of means to enable us to deliver on our commitments to the scientific community. Apart from preservation by the Croatian National Library (for publications prior to April 18, 2018) and the British Library (for publications after April 18, 2018), our entire catalogue is preserved in the CLOCKSS archive.
\n\n
Open Science
\n\n
Open Science is transparent and accessible knowledge that is shared and developed through collaborative networks.
\n\n
Open Science is about increased rigour, accountability, and reproducibility for research. It is based on the principles of inclusion, fairness, equity, and sharing, and ultimately seeks to change the way research is done, who is involved and how it is valued. It aims to make research more open to participation, review/refutation, improvement and (re)use for the world to benefit.
\n\n
Open Science refers to doing traditional science with more transparency involved at various stages, for example by openly sharing code and data. It implies a growing set of practices - within different disciplines - aiming at:
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Promoting open and publicly accessible education tools
\n\t
Transparency in experimental methodology, observation, and collection of data
\n\t
Reproducible research data and re-analysis
\n\t
Public availability and re-usability of scientific data
\n\t
Public accessibility and transparency of scientific communication
\n\t
Transparent peer-review and publishing practices
\n\t
Using web-based tools to facilitate scientific collaboration
\n\t
Supporting exchange of knowledge and research materials between disciplines
\n\t
Supporting exchange of knowledge and research materials between scientific communities and industry.
\n
\n\n
We aim at improving the quality and availability of scholarly communication by promoting and practicing:
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\n\t
Open Access
\n\t
Open Data
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Open Metrics and Impact
\n\t
Open Source
\n
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He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:null},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. 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The Proteomics topic aims to attract contributions on all aspects of MS-based proteomics that, by pushing the boundaries of MS capabilities, may address biological problems that have not been resolved yet.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",keywords:"Mono- and Two-Dimensional Gel Electrophoresis (1-and 2-DE), Liquid Chromatography (LC), Mass Spectrometry/Tandem Mass Spectrometry (MS; MS/MS), Proteins"}],annualVolumeBook:{},thematicCollection:[],selectedSeries:null,selectedSubseries:null},seriesLanding:{item:{id:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983",scope:"Biochemistry, the study of chemical transformations occurring within living organisms, impacts all areas of life sciences, from molecular crystallography and genetics to ecology, medicine, and population biology. Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. This Biochemistry Series will address the current research on biomolecules and the emerging trends with great promise.",coverUrl:"https://cdn.intechopen.com/series/covers/11.jpg",latestPublicationDate:"July 5th, 2022",hasOnlineFirst:!0,numberOfOpenTopics:4,numberOfPublishedChapters:320,numberOfPublishedBooks:32,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},subseries:[{id:"14",title:"Cell and Molecular Biology",keywords:"Omics (Transcriptomics; Proteomics; Metabolomics), Molecular Biology, Cell Biology, Signal Transduction and Regulation, Cell Growth and Differentiation, Apoptosis, Necroptosis, Ferroptosis, Autophagy, Cell Cycle, Macromolecules and Complexes, Gene Expression",scope:"The Cell and Molecular Biology topic within the IntechOpen Biochemistry Series aims to rapidly publish contributions on all aspects of cell and molecular biology, including aspects related to biochemical and genetic research (not only in humans but all living beings). We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics include, but are not limited to: Advanced techniques of cellular and molecular biology (Molecular methodologies, imaging techniques, and bioinformatics); Biological activities at the molecular level; Biological processes of cell functions, cell division, senescence, maintenance, and cell death; Biomolecules interactions; Cancer; Cell biology; Chemical biology; Computational biology; Cytochemistry; Developmental biology; Disease mechanisms and therapeutics; DNA, and RNA metabolism; Gene functions, genetics, and genomics; Genetics; Immunology; Medical microbiology; Molecular biology; Molecular genetics; Molecular processes of cell and organelle dynamics; Neuroscience; Protein biosynthesis, degradation, and functions; Regulation of molecular interactions in a cell; Signalling networks and system biology; Structural biology; Virology and microbiology.",annualVolume:11410,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"79367",title:"Dr.",name:"Ana Isabel",middleName:null,surname:"Flores",fullName:"Ana Isabel Flores",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRpIOQA0/Profile_Picture_1632418099564",institutionString:null,institution:{name:"Hospital Universitario 12 De Octubre",institutionURL:null,country:{name:"Spain"}}},{id:"328234",title:"Ph.D.",name:"Christian",middleName:null,surname:"Palavecino",fullName:"Christian Palavecino",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000030DhEhQAK/Profile_Picture_1628835318625",institutionString:null,institution:{name:"Central University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"186585",title:"Dr.",name:"Francisco Javier",middleName:null,surname:"Martin-Romero",fullName:"Francisco Javier Martin-Romero",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSB3HQAW/Profile_Picture_1631258137641",institutionString:null,institution:{name:"University of Extremadura",institutionURL:null,country:{name:"Spain"}}}]},{id:"15",title:"Chemical Biology",keywords:"Phenolic Compounds, Essential Oils, Modification of Biomolecules, Glycobiology, Combinatorial Chemistry, Therapeutic peptides, Enzyme Inhibitors",scope:"Chemical biology spans the fields of chemistry and biology involving the application of biological and chemical molecules and techniques. In recent years, the application of chemistry to biological molecules has gained significant interest in medicinal and pharmacological studies. This topic will be devoted to understanding the interplay between biomolecules and chemical compounds, their structure and function, and their potential applications in related fields. Being a part of the biochemistry discipline, the ideas and concepts that have emerged from Chemical Biology have affected other related areas. This topic will closely deal with all emerging trends in this discipline.",annualVolume:11411,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null,editorialBoard:[{id:"219081",title:"Dr.",name:"Abdulsamed",middleName:null,surname:"Kükürt",fullName:"Abdulsamed Kükürt",profilePictureURL:"https://mts.intechopen.com/storage/users/219081/images/system/219081.png",institutionString:null,institution:{name:"Kafkas University",institutionURL:null,country:{name:"Turkey"}}},{id:"241413",title:"Dr.",name:"Azhar",middleName:null,surname:"Rasul",fullName:"Azhar Rasul",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRT1oQAG/Profile_Picture_1635251978933",institutionString:null,institution:{name:"Government College University, Faisalabad",institutionURL:null,country:{name:"Pakistan"}}},{id:"178316",title:"Ph.D.",name:"Sergey",middleName:null,surname:"Sedykh",fullName:"Sergey Sedykh",profilePictureURL:"https://mts.intechopen.com/storage/users/178316/images/system/178316.jfif",institutionString:null,institution:{name:"Novosibirsk State University",institutionURL:null,country:{name:"Russia"}}}]},{id:"17",title:"Metabolism",keywords:"Biomolecules Metabolism, Energy Metabolism, Metabolic Pathways, Key Metabolic Enzymes, Metabolic Adaptation",scope:"Metabolism is frequently defined in biochemistry textbooks as the overall process that allows living systems to acquire and use the free energy they need for their vital functions or the chemical processes that occur within a living organism to maintain life. Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. Thus all studies on metabolism will be considered for publication.",annualVolume:11413,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"243049",title:"Dr.",name:"Anca",middleName:null,surname:"Pantea Stoian",fullName:"Anca Pantea Stoian",profilePictureURL:"https://mts.intechopen.com/storage/users/243049/images/system/243049.jpg",institutionString:null,institution:{name:"Carol Davila University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"203824",title:"Dr.",name:"Attilio",middleName:null,surname:"Rigotti",fullName:"Attilio Rigotti",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institutionString:null,institution:{name:"Pontifical Catholic University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"300470",title:"Dr.",name:"Yanfei (Jacob)",middleName:null,surname:"Qi",fullName:"Yanfei (Jacob) Qi",profilePictureURL:"https://mts.intechopen.com/storage/users/300470/images/system/300470.jpg",institutionString:null,institution:{name:"Centenary Institute of Cancer Medicine and Cell Biology",institutionURL:null,country:{name:"Australia"}}}]},{id:"18",title:"Proteomics",keywords:"Mono- and Two-Dimensional Gel Electrophoresis (1-and 2-DE), Liquid Chromatography (LC), Mass Spectrometry/Tandem Mass Spectrometry (MS; MS/MS), Proteins",scope:"With the recognition that the human genome cannot provide answers to the etiology of a disorder, changes in the proteins expressed by a genome became a focus in research. Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. 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