Watermarking techniques have been considered as one of the best efficacious methods 

to solve the issues of protecting copyright due to the proliferation of digital multimedia 

information on the internet. The digital multimedia information is such as image, 

audio, and video which can today be easily copied, distributed, and modified without 

loss in fidelity. As a result, it is necessary to utilize some kind of copyright protection 

techniques to avoid illegal copying, distribution, and digital copyright modification. 

The digital watermarking techniques have engaged the researchers’ attention as a 

technique for protecting copyright in the digital multimedia information. This 

technique includes a subcategory of information-hiding technologies in which the 

ownership information is hidden into the original multimedia information without 

impacting the original multimedia information quality. Despite the advances in digital 

watermarking techniques, the main objective of many types of research in digital 

watermarking topic is to improve transparency and resistance to attacks and the present 

digital watermarking schemes are not sufficiently stable or strong against various 

attacks. This study aims to develop state-of-the-art digital watermarking techniques 

for different multimedia information through improving the imperceptibility and 

robustness to geometrical attacks and signal processing operations. Four novel robust 

blind digital watermarking schemes are introduced in this thesis for copyright 

protection of multimedia information that have not been previously introduced. The 

first robust blind digital watermarking scheme is applied for colour images utilizing 

discrete wavelet transform in combination with Walsh Hadamard transform and 

Hessenberg decomposition. The second robust blind digital watermarking scheme is 

applied for grayscale images utilizing discrete wavelet transform in combination with 

Walsh Hadamard transform and Hessenberg decomposition. The third robust blind 

digital watermarking scheme is applied for colour videos utilizing discrete wavelet 

transform in combination with Walsh Hadamard transform and Hessenberg 

decomposition. Forth robust blind digital watermarking scheme is applied for audio 

signals utilizing discrete wavelet transform in combination with Walsh Hadamard 

transform and Hessenberg decomposition. Several experiments are performed in order 

to present the effectiveness of the proposed digital watermarking schemes in terms of 

imperceptibility and robustness against attacks related to each scheme. The proposed 

digital watermarking schemes achieve good imperceptibility and robustness against 

geometrical attacks and signal processing operations. The proposed digital 

watermarking schemes have a superior performance in terms of imperceptibility and 

robustness than other reported schemes in the literature.

Keywords: Watermarking, Copyright Protection, Discrete Wavelet Transform, Walsh 

Hadamard Transform, Hessenberg Decomposition.

Optical Coherence Tomography (OCT) is one of the most vital tools for diagnosing and tracking progress of medication of various retinal disorders. Many methods have been proposed to aid with the analysis of retinal images due to the intricacy of retinal structures, the tediousness of manual segmentation and variation from different specialists. However image artifacts, in addition to inhomogeneity in pathological structures, remain a challenge, with negative influence on the performance of segmentation algorithms. In this paper we present an automatic retinal layer segmentation method, which comprises of fuzzy histogram hyperbolization and graph cut methods. We impose hard constraints to limit search region to sequentially segment 8 boundaries and 7 layers of the retina on 150 OCT B-Sans images, 50 each from the temporal, nasal and center of foveal regions. Our method shows positive results, with additional tolerance and adaptability to contour variance and pathological inconsistence of the retinal structures in all regions.

A new record for Abutilon theophrasti is recorded for the first time in the flora of Libya. This widespread agricultural weed was collected from El-Hamra region (El-Qabel) in the Nafusa Mountain south of Tripoli. To facilitate further identification and future detection, a full description, habitat information, distribution map and distribution data are provided. A brief discussion about the most important threats posed by this species is presented.

Understanding diversity is important in our inclusive society to hedge against ignorance and accommodate plural perspectives. Diversity nowadays can be observed in online social spaces. People from different backgrounds (e.g. gender, age, culture, expertise) are interacting every day around online digital objects (e.g. videos, images and web articles) leaving their social content in different format, commonly as textual comments and profiles. The social clouds around digital objects (i.e. user comments, user profiles and other metadata of digital objects) offer rich source of information about the users and their perspectives on different domains. Although, researchers from disparate disciplines have been working on understanding and measuring diversity from different perspectives, little has been done to automatically measure diversity in social clouds. This is the main objective of this research. This research proposes a semantic driven computational model to systematically represent and automatically measure diversity in a social cloud. Definitions from a prominent diversity framework and Semantic Web techniques underpin the proposed model. Diversity is measured based on four diversity indices - variety, balance, coverage and (within and across) disparity with regards to two perspectives – (a) domain, which is captured in user comments and represented by domain ontologies, and (b) user, which is captured in profiles of users who made the comments and represented by a proposed User Diversity Ontology. The proposed model is operationalised resulting in a Semantic Driven Diversity Analytics Tool (SeDDAT), which is responsible for diversity profiling based on the diversity indices. The proposed approach of applying the model is illustrated on social clouds from two social spaces - open (YouTube) and closed (Active Video Watching (AVW-Space)). The open social cloud shows the applicability of the model to generate diversity profiles of a large pool of videos (600) with thousands of users and comments. Closed social clouds of two user groups around same set of videos illustrate transferability and further utility of the model. A list of possible diversity patterns within social clouds is provided, which in turn deepen the understanding of diversity and open doors for further utilities of the diversity profiles. The proposed model is applicable in similar scenarios, such as in the social clouds around MOOCs and news articles.