The security of multimedia documents becomes an urgent need, especially with the increasing image falsifications provided by the easy access and use of image manipulation tools. Hence, usage of image authentication techniques fulfills this need. In this paper, we propose an effective self-embedding fragile watermarking scheme for color images tamper detection and restoration. To decrease the capacity of insertion, a Bayer pattern is used to reduce the color host image into a gray-level watermark, to further improve the security Torus Automorphism permutation is used to scramble the gray-level watermark. In our algorithm, three copies of the watermark are inserted over three components (R, G, and B channels) of the color host image, providing a high probability of detection accuracy and recovery if one copy is destroyed. In the tamper detection process, a majority voting technique is used to determine the legitimacy of the image and recover the tampered regions after interpolating the extracted gray-level watermark. Using our proposed method, tampering rate can achieve 25% with a high visual quality of recovered image and PSNR values greater than 34 (dB). Experimental results demonstrate that the proposed method affords three major properties: the high quality of watermarked image, the sensitive tamper detection and high localization accuracy besides the high-quality of recovered image.

The high suppleness of Bayesian networks has led to their wide application in a variety of dependability modeling and analysis problems. The main objective of this paper is to extend the use of such powerful tool to estimate the occurrence frequency of failures and consequences in a straightforward way. Such extension is based on the employment of a transformation operator to substitute the original terms with matrices that hold the full dependability description of the corresponding element. Two simple case studies in reliability and safety contexts are treated using the suggested method whose results are validated through their comparison to the corresponding results of other classical dependability techniques.

In this study, an optimized extended Kalman filter (EKF), and an interval type-2 fuzzy sliding mode control (IT2FSMC) in presence of uncertainties and disturbances are presented for robotic manipulators. The main contribution is the proposal of a novel application of Biogeography-Based Optimization (BBO) to optimize the EKF in order to achieve high performance estimation of states. The parameters to be optimized are the covariance matrices Q and R, which play an important role in the performances of EKF. The interval type-2 fuzzy logic system is used to avoid chattering phenomenon in the sliding mode control (SMC). Lyapunov theorem is used to prove the stability of control system. The suggested control approach is demonstrated using a computer simulation of two-link manipulator. Finally, simulations results show the robustness and effectiveness of the proposed scheme, and exhibit a more superior performance than its conventional counterpart.

Automatic Speech Recognition can be considered as a transcription of spoken utterances into text which can be used to monitor/command a specific system. In this paper, we propose a general end-to-end approach to sequence learning that uses Long Short-Term Memory (LSTM) to deal with the non-uniform sequence length of the speech utterances. The neural architecture can recognize the Arabic spoken digit spelling of an isolated Arabic word using a classification methodology, with the aim to enable natural human-machine interaction. The proposed system consists to, first, extract the relevant features from the input speech signal using Mel Frequency Cepstral Coefficients (MFCC) and then these features are processed by a deep neural network able to deal with the non uniformity of the sequences length. A recurrent LSTM or GRU architecture is used to encode sequences of MFCC features as a fixed size vector that will feed a multilayer perceptron network to perform the classification. The whole neural network classifier is trained in an end-to-end manner. The proposed system outperforms by a large gap the previous published results on the same database.

The emergence of new data handling technologies and analytics enabled the organization of big data in processes as an innovative aspect in wireless sensor networks (WSNs). Big data paradigm, combined with WSN technology, involves new challenges that are necessary to resolve in parallel. Data aggregation is a rapidly emerging research area. It represents one of the processing challenges of big sensor networks. This paper introduces the big data paradigm, its main dimensions that represent one of the most challenging concepts, and its principle analytic tools which are more and more introduced in the WSNs technology. The paper also presents the big data challenges that must be overcome to efficiently manipulate the voluminous data, and proposes a new classification of these challenges based on the necessities and the challenges of WSNs. As the big data aggregation challenge represents the center of our interest, this paper surveys its proposed strategies in WSNs.

The present study deals with the biostratigraphic and geochemical analysis of the Cenomanian-Turonian deposits of the Thénièt El Manchar district in the Bellezma-Batna Range, and their vertical fluctuations. A 180 m-thick profile is described in this area. This reference section encompasses the ‘Marnes de Smail’ Formation, subdivided into four lithostratigraphic units (IA, IB, IC, and ID respectively) and dated of the Cenomanian (ammonites and coral), and the base of the ‘Dolomies de l’Oued Skhoun’ Formation (unit IIA), dated of the Lower Turonian (planktonic foraminifers). Both qualitative and quantitative analysis of foraminifers (planktonic and/or benthonic), ostracodes and of geochemical indices, lead us to reconstitute the evolution of both palaeoenvironmental and palaeobathymetrical changes during this time interval. Subsequently units IA, IB, IC and the lower part of unit ID (all Cenomanian) are displaying agglutinated benthonic foraminifers and complete ostracode shells, as evidence of a shelfal environment and weak hydrodynamism. Within these deposits microfaunal assemblages display a low-to-medium species diversity and a relatively high specimen abundance, supporting evidence of normal trophic conditions and water oxygenation. The upper part of unit ID (topmost Cenomanian), and unit IIA (basal Turonian) are documenting an environmental setting into deeper conditions, supported by the standard event succession already recorded in northern Africa, namely: the abundance of planktonic foraminifers, the occurrence of ‘filaments’ and the sudden reduction of ostracofauna. Furthermore the total carbonate isotope fluctuations (δ¹³C and δ¹⁸O) are documenting isotopic anomalies related to palaeoenvironmental changes. The δ¹³C results, coupled with those of TOC, are evidencing a low primary productivity while the δ¹⁸O data are supporting a temperature rise as the main potential drive of the onset of OAE2. These paleoenvironmental assertions are consistent with the regional paleogeographic context and are highlighting tethysian features.

BLEVE is one of major accidents observed in gas industry causing severe damage to people and environment. Its effects are manifested in three ways: shock wave propagation, fireball radiation and fragments projection. To assess these effects, risk decision-makers often use Quantitative Risk Analysis (QRA). In most cases, QRA data are obtained from empirical correlations. However, these correlations are not very satisfactory because they generally overestimate BLEVE effects and do not take into account geometry effects. In order to overcome the limitations of these empirical approaches, CFD modeling appears as a powerful tool able to provide more accurate data to better realize QRA. In this paper, the objective is to develop a CFD methodology in order to predict BLEVE thermal effects. Numerical simulations are carried out using the CFD code FDS. A sensitivity analysis of numerical models is performed in order to choose the right parameters allowing to model the fireball dynamics. The models retained are based on a single-step combustion using EDC model coupled with a LES turbulence model. Predictions show good agreement in comparison with results issued from three large-scale experiments. Furthermore, a case study on a propane accumulator in an Algerian gas processing unit is carried out.

In this paper, a new optically controlled tunneling field effect transistor (OC-TFET) based on SiGe/Si/Ge hetero-channel is proposed to improve optical commutation speed and reduce power consumption. An exhaustive study of the device switching behavior associated with different hetero-channel structures has been carried out using an accurate numerical simulation. Moreover, a new figure of Merit (FoM) parameter called optical swing factor that describes the phototransistor optical commutation speed is proposed. We demonstrate that the band-to-band tunneling effect can be beneficial for improving the device optical commutation speed. The impact of the Ge mole fraction of the SiGe source region on the device FoMs is investigated. It is found that the optimized design with 40% of Ge content offers the opportunity to overcome the trade-off between ultrafast and very sensitive photoreceiver performance, where it yields 48 mV/dec of optical swing factor and 155 dB of I ON /I OFF ratio. An overall performance comparison between the proposed OC-TFET device and the conventional designs is performed, where the proposed structure ensures high optical detectivity for very low optical powers (sub-1pW) as compared to that of the conventional counterparts. Therefore, the proposed OC-TFET provides the possibility for bridging the gap between improved optical commutation speed and reduced power consumption, which makes it a potential alternative for high-performance inter-chip data communication applications.

The article introduces the benefits and application features of Silvaco Technology Computer Aided Design‘TCAD’ tool to predict the performance of electrical components and their reliability. In this work, in order to improve the electrical parameters of MOS transistor such as, threshold voltage and flat band voltage, we have simulated Phosphorus and Arsenic diffusion profiles in three dimensions before and after thermal annealing in a highly doped polysilicon film using the simulator Silvaco TCAD based on Pearson type IV models. The model takes into account the distribution of vacancy mechanisms and effects related to high concentrations, such as the formation of clusters to study solid solubility limit. The results have been analyzed and discussed in order to extract depth of doping (Phosphorus and Arsenic) and they have been able to optimize the silicon oxide thickness, to reduce the penetration of doping. Based on earlier studies a study of the effect of solubility on these profiles was performed