发帖
雷达卡
RCC Institute of Information Technology India
Textbook:Intelligent Multi-Modal Data Processing
Author(s): Soham Sarkar
Description:
Chapter 1 introduces the concepts of multimodal data processing, with an emphasis on issues and challenges in this domain. The chapter also elucidates the different application areas of multimodal data processing.
Digital information science has emerged to seek a copyright protection solution for digital content disseminated through communication channels. A review of the related literatured suggests that most of the domain methods have poor capacity control and are vulnerable to attacks. Chapter 2 presents a casting analogy and performance investigation of the proposed transform domain representative data-hiding system using a digital modulation technique.
A watermark is constructed using a Boolean operation on the author signature data with an adaptive classifier that approximates the frequency masking characteristics of the visual system.
In Chapter 3, the authors present a digital image watermarking technique based on bio-
metrics and implement it in hardware using a field-programmable gate array (FPGA). This scheme is focused on the covariance saliency method. For extreme security and individual authentication, biometrics such as the iris are introduced. This technique hides biometric information in a cover image so efficiently that the robustness and imperceptibility of thecover image are less likely to be affected and the image is not distorted (as proven duringseveral attacks). A hardware implementation of this algorithm is also provided for the sake of self-sufficiency.
In Chapter 4, an invisible, spatial domain-based image watermarking scheme is demonstrated. One of the most traditional spatial techniques is simple least significant bits (LSB) replacement, which offers high data transparency for embedded information.
However, only a small amount of data can be hidden in the case of single-bit (preferably LSB) replacement; consequently, the payload capacity is much less. Additionally, the data sustainability or robustness of the watermark is decreased, as most attacks affect the LsB plane. Thus, the proposed logic follows an adaptive LSB replacement technique where multiple bits are replaced from each pixel to implant the watermark. This adaptive bit replacement is performed in such a way that both the payload and the signal-to-noise ratio can be increased up to a certain level. Intelligent image clustering is utilized to obtain an optimized result.
Video content summarization is a popular research area. Everyday storage of video data is becoming increasingly important and popular. In the process, it is growing into big data.
Summarization is an effective technique to obtain video content from large video data.
In addition, indexing and browsing are required for large video data. One of the effective techniques for video summarization is based on keyframes: important video frames. In Chapter 5, the authors propose a keyframe-based video summarization technique using a dense captioning model. Initially, video data is taken as input to the model. The modelgenerates region captioning as output, which is converted into a chunk of sentences after applying the clustering technique. This chunk of sentences is summarized to obtain video summary output.
Inthemodernera,self-drivingcarsarethemostattention-grabbingdevelopmentinthe autonomousvehicleindustry.Untilnow,GoogleandTeslahavebeenthemosttheencour-aging participants in this industry. However, no one has yet achieved fully autonomous driving.Chapter6isbasedonautonomousdrivinginself-drivingcarsandfocusesonfully autonomousdrivinginanysituation.Thisdrivingachievementispossibleduetotheuse ofreinforcementlearningandmodernalgorithmscreatedforautonomousdriving.
The Internet of Things (IoT) is an important means of connecting smart devices called sensors throughaphysicalorcloudnetwork.Itamassesalargeamountofdatafromthese devices. However, an interoperability problem occurs when integrating data from different sensors or devices because the sensors’ data sets are not compatible with each other. The process of data fusion in the IoT network has to be homogeneous and consistent, so thecontrolofdataisanimportantfeatureinthisfield.TheIoTprovidesopportunitiesfor datafusionincomputer-basedsystemstoimproveoperationalperformance,increasecom-mondimensionality,andreduceambiguity.Chapter7introducesanevolutionarystudyof multimodaldatafusioninthesmartenvironment.Itexaminesdatafusionmotivationsfor theIoTwithaspecificfocusonusingalgorithms(suchasprobabilistictechniques,artifi-cialintelligencealgorithms,andtheoryofbeliefmethods)andparticularIoTenvironments (centralized,distributed,hybrid,orblockchainsystems).
Chapter 8 illustrates new, fast, adaptive, optimized blind channel estimation for a cyclic prefix–aided, space-time block-coded multiple input-multiple output orthogonal frequency division multiplexing (STBC-MIMO-OFDM) system. The bottleneck of earlier blind channel estimation techniques was due to high complexity and low convergence. Also, accurate transmission of multimodal data such as hyperspectral images, medical images in the healthcare sector, massive data in social media, and audiovisual signals is still under research. To overcome this problem, a modified flower pollination algorithm (MFPA) has been implemented to optimize data. The optimized MFPA provides good bit error rate (BER) and symbol error rate (SER) performance compared to the traditional flowerpollinationalgorithm(FPA).
Chapter11concludesthebookwithafocusonupcomingtrendsinmultimodaldatapro-cessing.
Intelligent Multi-Modal Data Processing.pdf
(9.81 MB, 需要: RMB 29 元)
京公网安备 11010802022788号
