Adaptive removable visible watermarking technique using dual watermarking for digital color images

This paper proposes a removable visible watermarking system based on a dual watermark technique and blind removal. A visible watermark pattern is embedded in the cosine discrete transform (DCT) domain, taking into consideration the texture and luminance features of the watermark and host images to create a visible watermarked image. To prevent illegal visible watermark removal, the original watermark is embedded in an invisible manner in the visible watermarked image by employing the Quantization Index Modulation-Dither Modulation (QIM-DM) technique, thus ensuring that the original watermark cannot be obtained by malicious attacks. The visible watermark removal process is carried out using only the correct user’s keys, without the need for additional information, such as the original watermark or the original host image, which allows a high-quality image to be obtained; however, if the user’s keys used in the removal process are wrong, the visible watermarked image suffers higher distortion in its content, even in non-visible watermarked regions. The experimental results show that the proposed system outperforms previous related works in terms of blind removal, preservation of the quality of the unmarked recovered image, and higher visual degradation of the content in the recovered image if an illegal removal attempt is performed.     

Chattering-Free Sliding Mode Altitude Control for a Quad-Rotor Aircraft: Real-Time Application

Nowadays, the chattering problem in sliding mode control is one of the most important points to consider in real-time applications. To address this problem, a real-time robust altitude control scheme is proposed for the efficient performance of a Quad-rotor aircraft system using a continuous sliding mode control. The sensing of altitude measurement sensing is performed by a pressure sensor in order to obtain a robust altitude control of the vehicle in hovering mode both indoor and outdoor. The altitude measurement has the advantage of introducing this state information directly in the closed loop control which should be very useful for achieving robust stabilization of the altitude control. Accordingly, we propose a sliding mode control strategy without chattering. The sliding mode control proposed removes the chattering phenomenon by replacing a sign function with a high-slope saturation function. The control algorithm is derived from the Lyapunov stability theorem. Moreover, we have assumed that the actuators are able to respond quickly and accurately and we have not enforced limits on the control signals for a real-time application. Finally, to verify the satisfactory performance of proposed nonlinear control law, several simulations and experimental results of the Chattering-free sliding mode control for the Quad-rotor aircraft in the presence of bounded disturbances are presented.     

A color image watermarking framework for copyright protection of stereo images based on binocular just noticeable difference and LU decomposition

Multimedia Tools and Applications - The copyright protection of three-dimensional (3D) content is a matter of interest in artistic and creative works due to the rights of the holder for the...     

Bayesian networks for enhancement of requirements engineering: a literature review

Requirements analysis is the software engineering stage that is closest to the users’ world. It also involves tasks that are knowledge intensive. Thus, the use of Bayesian networks (BNs) to model this knowledge would be a valuable aid. These probabilistic models could manage the imprecision and ambiguities usually present in requirements engineering (RE). In this work, we conduct a literature review focusing on where and how BNs are applied on subareas of RE in order to identify which gaps remain uncovered and which methods might engineers employ to incorporate this intelligent technique into their own requirements processes. The scarcity of identified studies (there are only 20) suggests that not all RE areas have been properly investigated in the literature. The evidence available for adopting BNs into RE is sufficiently mature yet the methods applied are not easily translatable to other topics. Nonetheless, there are enough studies supporting the applicability of synergistic cooperation between RE and BNs. This work provides a background for understanding the current state of research encompassing RE and BNs. Functional, non-functional and -ilities requirements artifacts are enhanced by the use of BNs. These models were obtained by interacting with experts or by learning from databases. The most common criticism from the point of view of BN experts is that the models lack validation, whereas requirements engineers point to the lack of a clear application method for BNs and the lack of tools for incorporating them as built-in help functions.     

Convergence analysis of some multiobjective evolutionary algorithms when discovering motifs

An important issue in multiobjective optimization is the study of the convergence speed of algorithms. An optimization problem must be defined as simple as possible to minimize the computational cost required to solve it. In this work, we study the convergence speed of seven multiobjective evolutionary algorithms: DEPT, MO-VNS, MOABC, MO-GSA, MO-FA, NSGA-II, and SPEA2; when solving an important biological problem: the motif discovery problem. We have used twelve instances of four different organisms as benchmark, analyzing the number of fitness function evaluations required by each algorithm to achieve reasonable quality solutions. We have used the hypervolume indicator to evaluate the solutions discovered by each algorithm, measuring its quality every 100 evaluations. This methodology also allows us to study the hit rates of the algorithms over 30 independent runs. Moreover, we have made a deeper study in the more complex instance of each organism. In this study, we observe the increase of the archive (number of non-dominated solutions) and the spread of the Pareto fronts obtained by the algorithm in the median execution. As we will see, our study reveals that DEPT, MOABC, and MO-FA provide the best convergence speeds and the highest hit rates.     

Applying intelligent agents and semantic web services in eGovernment environments

The increasing volume of eGovernment‐related services is demanding new approaches for service integration and interoperability in this domain. Semantic web (SW) technologies and applications can leverage the potential of eGovernment service integration and discovery, thus tackling the problems of semantic heterogeneity characterizing eGovernment information sources and the different levels of interoperability. eGovernment services will therefore be semantically described in the foreseeable future. In an environment with semantically annotated services, software agents are essential as the entities responsible for exploiting the semantic content in order to automate some tasks, and so enhance the user's experience. In this paper, we present a framework that provides a seamless integration of semantic web services and intelligent agents technologies by making use of ontologies to facilitate their interoperation. The proposed framework can assist in the development of powerful and flexible distributed systems in complex, dynamic, heterogeneous, unpredictable and open environments. Our approach is backed up by a proof‐of‐concept implementation, where the breakthrough of integrating disparate eGovernment services has been tested.     

Fault Estimation for a Quad-Rotor MAV Using a Polynomial Observer

This work addresses the problem of fault detection and diagnosis (FDD) for a quad-rotor mini air vehicle (MAV). Actuator faults are considered on this paper. The basic idea behind the proposed method is to estimate the faults signals using the extended state observers theory. To estimate the faults, a polynomial observer (Aguilar et al. 2011; Mata-Machuca et al., Commun Nonlinear Sci Numer Simul 15(12):4114–4130, 2010, BioSystems 100(1):65–69, 2010) is presented by using the available measurements and know inputs of the system. In order to investigate the diagnosability properties of the system, a differential algebra approach is proposed (Cruz-Victoria et al., J Frankl Inst 345(2):102–118, 2008; and Martinez-Guerra and Diop, IEE P-Contr Theor Ap 151(1):130–135, 2004). The effectiveness of the methodology is illustrated by means of numerical simulations.     

Learning Photometric Invariance for Object Detection

Color is a powerful visual cue in many computer vision applications such as image segmentation and object recognition. However, most of the existing color models depend on the imaging conditions that negatively affect the performance of the task at hand. Often, a reflection model (e.g., Lambertian or dichromatic reflectance) is used to derive color invariant models. However, this approach may be too restricted to model real-world scenes in which different reflectance mechanisms can hold simultaneously.     

Adaptive quaternion control of a 3-DOF inertial stabilised platforms

ABSTRACT

Inertial stabilised platforms are increasingly popular with a large range of products available mainstream. Most items are controlled using popular algorithms that sometimes do not offer best achievable performances. Present paper proposes an advanced control which aims at improving these latter. The exposed solution is based on quaternion representation and self-adapts to the characteristics of the payload it tries to stabilise. Proposed control law ensures the stability of the system whatever the required orientation path is. Although only simulation has been performed to check the performances of such control, results look very promising compared to non-adaptive controls and may help to construct more polyvalent and efficient gimbals which would further facilitate their expansion. Proposed control law can also be applied, as is, to every system that shares the same quaternion-based rotational dynamics.     

Early diagnosis of Alzheimer’s disease based on Partial Least Squares and Support Vector Machine

An accurate and early diagnosis of the Alzheimer’s disease (AD) is of fundamental importance for the patient medical treatment. It has been shown that pathological manifestations of AD may be detected thought functional images even before that the patients becomes symptomatic. This fact has led researchers to propose new ways for analyzing functional data in order to get more accurate Computer-Aided Diagnosis (CAD) systems for this disorder. In this paper we show an effective approach for Single Photon Emission Computed Tomography feature extraction that improves the accuracy of CAD systems for AD. The proposed methodology uses a Partial Least Squares algorithm for extracting score vectors and the Out-Of-Bag error for selecting a number of scores that are used as features. Subsequently, a Support Vector Machine (SVM) based classifier determines the underlying class of the images, thus performing diagnostics. In order to test this approach we have used an image database for AD with 97 SPECT images from controls and AD patients. The images were visually labeled by experienced clinicians after the properly normalization. Several experiments have been developed to compare the proposed methodology and previous approaches. The results show that our method yields accuracy rates over 90%, outperforming several recently reported CAD systems for AD diagnosis.