Novel entropy coding and its application of the compression of 3D image and video signals

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe broadcast industry is moving future Digital Television towards Super high resolution TV (4k or 8k) and/or 3D TV. This ultimately will increase the demand on data rate and subsequently the demand for highly efficient codecs. One of the techniques that researchers found it one of the promising technologies in the industry in the next few years is 3D Integral Image and Video due to its simplicity and mimics the reality, independently on viewer aid, one of the challenges of the 3D Integral technology is to improve the compression algorithms to adequate the high resolution and exploit the advantages of the characteristics of this technology. The research scope of this thesis includes designing a novel coding for the 3D Integral image and video compression. Firstly to address the compression of 3D Integral imaging the research proposes novel entropy coding which will be implemented first on 2D traditional images content in order to compare it with the other traditional common standards then will be applied on 3D Integra image and video. This approach seeks to achieve high performance represented by high image quality and low bit rate in association with low computational complexity. Secondly, new algorithm will be proposed in an attempt to improve and develop the transform techniques performance, initially by using a new adaptive 3D-DCT algorithm then by proposing a new hybrid 3D DWT-DCT algorithm via exploiting the advantages of each technique and get rid of the artifact that each technique of them suffers from. Finally, the proposed entropy coding will be further implemented to the 3D integral video in association with another proposed algorithm that based on calculating the motion vector on the average viewpoint for each frame. This approach seeks to minimize the complexity and reduce the speed without affecting the Human Visual System (HVS) performance. Number of block matching techniques will be used to investigate the best block matching technique that is adequate for the new proposed 3D integral video algorithm

Live immerse video-audio interactive multimedia

The aim of the paper is to provide an insight of the progress made in 3D Holoscopic video technology. The paper will show an example of a coding technique based on 3D discrete cosine transform (DCT) which take full advantage of the data structure of 3D Holoscopic video. Various grouping of micro-images in a single 3D DCT computation is discussed

Hybrid bioactive hydroxyapatite/polycaprolactone nanoparticles for enhanced osteogenesis

Hydroxyapatite nanoparticles (HApN) are largely employed as osteogenic inorganic material. Inorganic/polymeric hybrid nanostructures can provide versatile bioactivity for superior osteogenicity, particularly as nanoparticles. Herein, we present hybrid biomaterial-based hydroxyapatite/polycaprolactone nanoparticles (HAp/PCL NPs) realized using simple preparation techniques to augment HApN osteogenicity. Using wet chemical precipitation, we optimized HApN crystalline properties utilizing a 23-factorial design. Optimized HApN exhibited typical Ca/P elemental ratio with high reaction yield. Surface area analysis revealed their mesoporous nature and high surface area. Hybrid HAp/PCL NPs prepared using direct emulsification-solvent evaporation maintained HApN crystallinity with no observed chemical interactions. To the best of our knowledge, we are the first to elaborate the biocompatibility and osteogenicity of nanoparticulate hybrid HAp/PCL. Hybrid HAp/PCL NPs outperformed HApN regarding mesenchymal cell proliferation and osteodifferentiation with reduction of possible cytotoxicity. Unlike HApN, hybrid HAp/PCL NPs presented moderate expression of early osteogenic markers, Runx-2 and osteopontin and significantly elevated expression of the late osteogenic marker, bone sialoprotein after 10-day culture. Our results indicate that hybrid bioactive HAp/PCL NPs could offer a more prominent osteogenic potential than plain HApN for bone regenerative applications as a standalone nanoplatform or as part of complex engineered systems

An Improved Oral Nutraceutical-Based Intervention for Management of Obesity: Pterostilbene Loaded Chitosan Nanoparticles - Supplementary tables.docx

       Aim: To formulate and assess the oral   anti-obesity effect of polymeric-based Pterostilbene(PS)-nanoparticles. Methods: Pterostilbene-hydroxypropyl   beta-cyclodextrin inclusion complex-loaded in chitosan-nanoparticles (PS/HPβCD-NPs) were   prepared and characterized in-vitro. Cytotoxicity, pharmacokinetics   and anti-obesity effects were assessed on Caco-2 cell line and high fat   diet-induced obesity. In-vivo   assessment included histological examination, protein and gene expression of   obesity biomarkers in adipose tissues. Results: Safe PS/HPβCD-NPs were successfully prepared with improved   bioavailability compared to free PS. PS/HPβCD-NPs showed improved anti-obesity   effect supported by histological examination, lipid profile, UCP1 gene   expression and protein expression of SIRT-1, COX-2, IL-6 and leptin.  Conclusion: Orally administered PS   nanoparticles is a new and promising anti-obesity strategy owing to its   sustainable weight loss and minimal side effects which is of great   socio-economic impact.        Supplementary figure 1:  Transmission   electron microscope of a) Unloaded NP and b) Pt/HPβCD loaded NP at magnification 25,000x. The scale bar   represents 200 nm. Supplementary   figure 2: a) Cellular uptake of free   C6, C6/HPßCD complex and C6/HPßCD-loaded NP (100 ng/mL C-6) in caco-2 cells using confocal laser microscopy and b)   Quantitative analysis of fluorescence intensity using Image J. Statistical   significance was shown at ***p≤0.001 and ****p≤0.0001 when compared to free   C-6. Supplementary figure 3: a) Serum   lipid profile in rats of different experimental groups where b) VLDL-C , c)   LDL/HDL ratio and d) Total cholesterol were calculated and plotted against   time (4, 8 and 16 weeks). Each point represents the   mean ± SD of 8   samples. Statistical significance is shown where ****p≤0.0001,   ***p≤0.001, **p≤0.01 and *p≤0.05 when samples when compared to untreated HFD   groups.</p

An Improved Oral Nutraceutical-Based Intervention for Management of Obesity: Pterostilbene Loaded Chitosan Nanoparticles - Fig S3.tif

       Aim: To formulate and assess the oral   anti-obesity effect of polymeric-based Pterostilbene(PS)-nanoparticles. Methods: Pterostilbene-hydroxypropyl   beta-cyclodextrin inclusion complex-loaded in chitosan-nanoparticles (PS/HPβCD-NPs) were   prepared and characterized in-vitro. Cytotoxicity, pharmacokinetics   and anti-obesity effects were assessed on Caco-2 cell line and high fat   diet-induced obesity. In-vivo   assessment included histological examination, protein and gene expression of   obesity biomarkers in adipose tissues. Results: Safe PS/HPβCD-NPs were successfully prepared with improved   bioavailability compared to free PS. PS/HPβCD-NPs showed improved anti-obesity   effect supported by histological examination, lipid profile, UCP1 gene   expression and protein expression of SIRT-1, COX-2, IL-6 and leptin.  Conclusion: Orally administered PS   nanoparticles is a new and promising anti-obesity strategy owing to its   sustainable weight loss and minimal side effects which is of great   socio-economic impact.        Supplementary figure 1:  Transmission   electron microscope of a) Unloaded NP and b) Pt/HPβCD loaded NP at magnification 25,000x. The scale bar   represents 200 nm. Supplementary   figure 2: a) Cellular uptake of free   C6, C6/HPßCD complex and C6/HPßCD-loaded NP (100 ng/mL C-6) in caco-2 cells using confocal laser microscopy and b)   Quantitative analysis of fluorescence intensity using Image J. Statistical   significance was shown at ***p≤0.001 and ****p≤0.0001 when compared to free   C-6. Supplementary figure 3: a) Serum   lipid profile in rats of different experimental groups where b) VLDL-C , c)   LDL/HDL ratio and d) Total cholesterol were calculated and plotted against   time (4, 8 and 16 weeks). Each point represents the   mean ± SD of 8   samples. Statistical significance is shown where ****p≤0.0001,   ***p≤0.001, **p≤0.01 and *p≤0.05 when samples when compared to untreated HFD   groups.</p

Deep Transfer Learning Enabled Intelligent Object Detection for Crowd Density Analysis on Video Surveillance Systems

Object detection is a computer vision based technique which is used to detect instances of semantic objects of a particular class in digital images and videos. Crowd density analysis is one of the commonly utilized applications of object detection. Since crowd density classification techniques face challenges like non-uniform density, occlusion, inter-scene, and intra-scene deviations, convolutional neural network (CNN) models are useful. This paper presents a Metaheuristics with Deep Transfer Learning Enabled Intelligent Crowd Density Detection and Classification (MDTL-ICDDC) model for video surveillance systems. The proposed MDTL-ICDDC technique mostly concentrates on the effective identification and classification of crowd density on video surveillance systems. In order to achieve this, the MDTL-ICDDC model primarily leverages a Salp Swarm Algorithm (SSA) with NASNetLarge model as a feature extraction in which the hyperparameter tuning process is performed by the SSA. Furthermore, a weighted extreme learning machine (WELM) method was utilized for crowd density and classification process. Finally, the krill swarm algorithm (KSA) is applied for an effective parameter optimization process and thereby improves the classification results. The experimental validation of the MDTL-ICDDC approach was carried out with a benchmark dataset, and the outcomes are examined under several aspects. The experimental values indicated that the MDTL-ICDDC system has accomplished enhanced performance over other models such as Gabor, BoW-SRP, Bow-LBP, GLCM-SVM, GoogleNet, and VGGNet

An Improved Oral Nutraceutical-Based Intervention for Management of Obesity: Pterostilbene Loaded Chitosan Nanoparticles - Fig S2.tif

       Aim: To formulate and assess the oral   anti-obesity effect of polymeric-based Pterostilbene(PS)-nanoparticles. Methods: Pterostilbene-hydroxypropyl   beta-cyclodextrin inclusion complex-loaded in chitosan-nanoparticles (PS/HPβCD-NPs) were   prepared and characterized in-vitro. Cytotoxicity, pharmacokinetics   and anti-obesity effects were assessed on Caco-2 cell line and high fat   diet-induced obesity. In-vivo   assessment included histological examination, protein and gene expression of   obesity biomarkers in adipose tissues. Results: Safe PS/HPβCD-NPs were successfully prepared with improved   bioavailability compared to free PS. PS/HPβCD-NPs showed improved anti-obesity   effect supported by histological examination, lipid profile, UCP1 gene   expression and protein expression of SIRT-1, COX-2, IL-6 and leptin.  Conclusion: Orally administered PS   nanoparticles is a new and promising anti-obesity strategy owing to its   sustainable weight loss and minimal side effects which is of great   socio-economic impact.        Supplementary figure 1:  Transmission   electron microscope of a) Unloaded NP and b) Pt/HPβCD loaded NP at magnification 25,000x. The scale bar   represents 200 nm. Supplementary   figure 2: a) Cellular uptake of free   C6, C6/HPßCD complex and C6/HPßCD-loaded NP (100 ng/mL C-6) in caco-2 cells using confocal laser microscopy and b)   Quantitative analysis of fluorescence intensity using Image J. Statistical   significance was shown at ***p≤0.001 and ****p≤0.0001 when compared to free   C-6. Supplementary figure 3: a) Serum   lipid profile in rats of different experimental groups where b) VLDL-C , c)   LDL/HDL ratio and d) Total cholesterol were calculated and plotted against   time (4, 8 and 16 weeks). Each point represents the   mean ± SD of 8   samples. Statistical significance is shown where ****p≤0.0001,   ***p≤0.001, **p≤0.01 and *p≤0.05 when samples when compared to untreated HFD   groups.</p

Enhanced Search-and-Rescue Optimization-Enabled Secure Route Planning Scheme for Internet of Drones Environment

The Internet of Drones (IoD) is greatly developed and promotes many civil applications. However, it can still be prone to several security problems which threaten public safety. The issue of security poses further problems upon linking the IoD to the Internet, as its data stream is exposed to attack. For secure communication between drones, an effective route planning scheme with a major intention of accomplishing security is needed. With this aim, this study develops an enhanced search-and-rescue optimization-enabled secure route planning (ESRO-SRP) scheme for the IoD environment. The presented ESRO-SRP technique mainly aims to derive a set of optimal routes to the destination. In addition, the ESRO-SRP algorithm is derived by the integration of the quasi-oppositional-based learning (QOBL) concept with the conventional SRO algorithm. Moreover, the presented ESRO-SRP technique derived a fitness function encompassing different input parameters such as residual energy, distance, and degree of trust. The experimental validation of the ESRO-SRP technique is carried out under several aspects, and the results demonstrated the enhancements of the ESRO-SRP model over recent approaches. The ESRO-SRP model has provided an increased packet delivery ratio (PDR) of 86%, whereas the BRUe-IoE, ORP-FANET, UAVe-WSN, and TR-UAV Swarm approaches have accomplished a minimal PDR of 79.60%, 73.60%, 67.60%, and 63.20%, respectively

Artificial Hummingbird Algorithm with Transfer-Learning-Based Mitotic Nuclei Classification on Histopathologic Breast Cancer Images

Recently, artificial intelligence (AI) is an extremely revolutionized domain of medical image processing. Specifically, image segmentation is a task that generally aids in such an improvement. This boost performs great developments in the conversion of AI approaches in the research lab to real medical applications, particularly for computer-aided diagnosis (CAD) and image-guided operation. Mitotic nuclei estimates in breast cancer instances have a prognostic impact on diagnosis of cancer aggressiveness and grading methods. The automated analysis of mitotic nuclei is difficult due to its high similarity with nonmitotic nuclei and heteromorphic form. This study designs an artificial hummingbird algorithm with transfer-learning-based mitotic nuclei classification (AHBATL-MNC) on histopathologic breast cancer images. The goal of the AHBATL-MNC technique lies in the identification of mitotic and nonmitotic nuclei on histopathology images (HIs). For HI segmentation process, the PSPNet model is utilized to identify the candidate mitotic patches. Next, the residual network (ResNet) model is employed as feature extractor, and extreme gradient boosting (XGBoost) model is applied as a classifier. To enhance the classification performance, the parameter tuning of the XGBoost model takes place by making use of the AHBA approach. The simulation values of the AHBATL-MNC system are tested on medical imaging datasets and the outcomes are investigated in distinct measures. The simulation values demonstrate the enhanced outcomes of the AHBATL-MNC method compared to other current approaches