Digital image watermarking: its formal model, fundamental properties and possible attacks

While formal definitions and security proofs are well established in some fields like cryptography and steganography, they are not as evident in digital watermarking research. A systematic development of watermarking schemes is desirable, but at present their development is usually informal, ad hoc, and omits the complete realization of application scenarios. This practice not only hinders the choice and use of a suitable scheme for a watermarking application, but also leads to debate about the state-of-the-art for different watermarking applications. With a view to the systematic development of watermarking schemes, we present a formal generic model for digital image watermarking. Considering possible inputs, outputs, and component functions, the initial construction of a basic watermarking model is developed further to incorporate the use of keys. On the basis of our proposed model, fundamental watermarking properties are defined and their importance exemplified for different image applications. We also define a set of possible attacks using our model showing different winning scenarios depending on the adversary capabilities. It is envisaged that with a proper consideration of watermarking properties and adversary actions in different image applications, use of the proposed model would allow a unified treatment of all practically meaningful variants of watermarking schemes.

Attracting and Retaining Staff : The Role of Branding and Industry Image

In increasingly competitive labour markets, attracting and retaining talent has become a prime concern of organisations. Employers need to understand the range of factors that influence career decision making and the role of employer branding in attracting human capital that best fits and contributes to the strategic aims of an organisation. This chapter identifies the changing factors that attract people to certain employment and industries and discusses the importance of aligning employer branding with employee branding to create a strong, genuine and lasting employer brand. Whilst organisations have long used marketing and branding practices to engender loyalty in customers, they are increasingly expanding this activity to differentiate organisations and make them attractive from an employee perspective. This chapter discusses employer branding and industry image as two important components of attraction strategies and describes ways companies can maximise their brand awareness in the employment market to both current and future employees.

Prediction of thermal expansion properties of carbon nanotubes using molecular dynamics simulations

The axial coefficients of thermal expansion (CTE) of various carbon nanotubes (CNTs), i.e., single-wall carbon nanotubes (SWCNTs), and some multi-wall carbon nanotubes (MWCNTs), were predicted using molecular dynamics (MDs) simulations. The effects of two parameters, i.e., temperature and the CNT diameter, on CTE were investigated extensively. For all SWCNTs and MWCNTs, the obtained results clearly revealed that within a wide low temperature range, their axial CTEs are negative. As the diameter of CNTs decreases, this temperature range for negative axial CTEs becomes narrow, and positive axial CTEs appear in high temperature range. It was found that the axial CTEs vary nonlinearly with the temperature, however, they decrease linearly as the CNT diameter increases. Moreover, within a wide temperature range, a set of empirical formulations was proposed for evaluating the axial CTEs of armchair and zigzag SWCNTs using the above two parameters. Finally, it was found that the absolute value of the negative axial CTE of any MWCNT is much smaller than those of its constituent SWCNTs, and the average value of the CTEs of its constituent SWCNTs. The present fundamental study is very important for understanding the thermal behaviors of CNTs in such as nanocomposite temperature sensors, or nanoelectronics devices using CNTs.

Insight into the thermal decomposition of kaolinite intercalated with potassium acetate : an evolved gas analysis

The thermal decomposition process of kaolinite–potassium acetate intercalation complex has been studied using simultaneous thermogravimetry coupled with Fourier-transform infrared spectroscopy and mass spectrometry (TG-FTIR-MS). The results showed that the thermal decomposition of the complex took place in four temperature ranges, namely 50–100, 260–320, 320–550, and 650–780 °C. The maximal mass losses rate for the thermal decomposition of the kaolinite–potassium acetate intercalation complex was observed at 81, 296, 378, 411, 486, and 733 °C, which was attributed to (a) loss of the adsorbed water, (b) thermal decomposition of surface-adsorbed potassium acetate (KAc), (c) the loss of the water coordinated to potassium acetate in the intercalated kaolinite, (d) the thermal decomposition of intercalated KAc in the interlayer of kaolinite and the removal of inner surface hydroxyls, (e) the loss of the inner hydroxyls, and (f) the thermal decomposition of carbonate derived from the decomposition of KAc. The thermal decomposition of intercalated potassium acetate started in the range 320–550 °C accompanied by the release of water, acetone, carbon dioxide, and acetic acid. The identification of pyrolysis fragment ions provided insight into the thermal decomposition mechanism. The results showed that the main decomposition fragment ions of the kaolinite–KAc intercalation complex were water, acetone, carbon dioxide, and acetic acid. TG-FTIR-MS was demonstrated to be a powerful tool for the investigation of kaolinite intercalation complexes. It delivers a detailed insight into the thermal decomposition processes of the kaolinite intercalation complexes characterized by mass loss and the evolved gases.

Human identification from video using advanced gait recognition techniques

The solutions proposed in this thesis contribute to improve gait recognition performance in practical scenarios that further enable the adoption of gait recognition into real world security and forensic applications that require identifying humans at a distance. Pioneering work has been conducted on frontal gait recognition using depth images to allow gait to be integrated with biometric walkthrough portals. The effects of gait challenging conditions including clothing, carrying goods, and viewpoint have been explored. Enhanced approaches are proposed on segmentation, feature extraction, feature optimisation and classification elements, and state-of-the-art recognition performance has been achieved. A frontal depth gait database has been developed and made available to the research community for further investigation. Solutions are explored in 2D and 3D domains using multiple images sources, and both domain-specific and independent modality gait features are proposed.

Thermal conductivity of configurable two-dimensional carbon nanotube architecture and strain modulation

We reported the thermal conductivity of the two-dimensional carbon nanotube (CNT)-based architecture, which can be constructed through welding of single-wall CNTs by electron beam. Using large-scale nonequilibrium molecular dynamics simulations, the thermal conductivity is found to vary with different junction types due to their different phonon scatterings at the junction. The strong length and strain dependence of the thermal conductivity suggests an effective avenue to tune the thermal transport properties of the CNT-based architecture, benefiting the design of nanoscale thermal rectifiers or phonon engineering.

SAIVT-ADMRG @ MediaEval 2014 social event detection

This paper outlines the approach taken by the Speech, Audio, Image and Video Technologies laboratory, and the Applied Data Mining Research Group (SAIVT-ADMRG) in the 2014 MediaEval Social Event Detection (SED) task. We participated in the event based clustering subtask (subtask 1), and focused on investigating the incorporation of image features as another source of data to aid clustering. In particular, we developed a descriptor based around the use of super-pixel segmentation, that allows a low dimensional feature that incorporates both colour and texture information to be extracted and used within the popular bag-of-visual-words (BoVW) approach.

Flammability and thermal properties of novel semi aromatic polyamide/organoclay nanocomposite

The development of semi aromatic polyamide/organoclays nanocomposites (PANC) is reported in this communication. New polyamide (PA) was successfully synthesized through direct polycondensation reaction between bio-based diacid and aromatic diamine. PA exhibited strong UV vis absorption band at 412 nm. Its photoluminescence spectrum showed maximum band at 511 nm in the green region. The surface modification of montmorillonite was carried out through ion-exchange reaction using 1,4-bis[4-aminophenoxy]butane (APB) as a modifier. Then PANCs containing 3 and 6 wt.% of the modified montmorillonite (MMT-APB) were prepared. Flammability and thermal properties of PA and the nanocomposites were studied by microscale combustion calorimeter (MCC), thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC). TGA results in both air and nitrogen atmospheres indicated improving in thermal properties of PANCs compared to the neat PA. According to MCC analysis, a 31.6% reduction in pHRR value has been achieved by introducing 6 wt.% of the organoclay in PA matrix.

Passenger segmentation using smart card data

Transit passenger market segmentation enables transit operators to target different classes of transit users for targeted surveys and various operational and strategic planning improvements. However, the existing market segmentation studies in the literature have been generally done using passenger surveys, which have various limitations. The smart card (SC) data from an automated fare collection system facilitate the understanding of the multiday travel pattern of transit passengers and can be used to segment them into identifiable types of similar behaviors and needs. This paper proposes a comprehensive methodology for passenger segmentation solely using SC data. After reconstructing the travel itineraries from SC transactions, this paper adopts the density-based spatial clustering of application with noise (DBSCAN) algorithm to mine the travel pattern of each SC user. An a priori market segmentation approach then segments transit passengers into four identifiable types. The methodology proposed in this paper assists transit operators to understand their passengers and provides them oriented information and services.

Pre-processing for approximate Bayesian computation in image analysis

Most of the existing algorithms for approximate Bayesian computation (ABC) assume that it is feasible to simulate pseudo-data from the model at each iteration. However, the computational cost of these simulations can be prohibitive for high dimensional data. An important example is the Potts model, which is commonly used in image analysis. Images encountered in real world applications can have millions of pixels, therefore scalability is a major concern. We apply ABC with a synthetic likelihood to the hidden Potts model with additive Gaussian noise. Using a pre-processing step, we fit a binding function to model the relationship between the model parameters and the synthetic likelihood parameters. Our numerical experiments demonstrate that the precomputed binding function dramatically improves the scalability of ABC, reducing the average runtime required for model fitting from 71 hours to only 7 minutes. We also illustrate the method by estimating the smoothing parameter for remotely sensed satellite imagery. Without precomputation, Bayesian inference is impractical for datasets of that scale.

The use of a Riesz fractional differential-based approach for texture enhancement in image processing

Texture enhancement is an important component of image processing that finds extensive application in science and engineering. The quality of medical images, quantified using the imaging texture, plays a significant role in the routine diagnosis performed by medical practitioners. Most image texture enhancement is performed using classical integral order differential mask operators. Recently, first order fractional differential operators were used to enhance images. Experimentation with these methods led to the conclusion that fractional differential operators not only maintain the low frequency contour features in the smooth areas of the image, but they also nonlinearly enhance edges and textures corresponding to high frequency image components. However, whilst these methods perform well in particular cases, they are not routinely useful across all applications. To this end, we apply the second order Riesz fractional differential operator to improve upon existing approaches of texture enhancement. Compared with the classical integral order differential mask operators and other first order fractional differential operators, we find that our new algorithms provide higher signal to noise values and superior image quality.

Energetic and thermal properties of tilt grain boundaries in graphene/hexagonal boron nitride heterostructures

Graphene/hexagonal boron nitride (G/h-BN) heterostructure has attracted tremendous research efforts owing to its great potential for applications in nano-scale electronic devices. In such hybrid materials, tilt grain boundaries (GBs) between graphene and h-BN grains may have unique physical properties, which have not been well understood. Here we have conducted non-equilibrium molecular dynamics simulations to study the energetic and thermal properties of tilt GBs in G/h-BN heterostructures. The effect of misorientation angles of tilt GBs on both GB energy and interfacial thermal conductance are investigated.