Perfect secret sharing scheme based on vertex domination set

Due to the fast development in data communication systems and computer networks in recent years, the necessity to protect the secret data has become extremely imperative. Several methods have been proposed to protect the secret data; one of them is the secret sharing scheme. It is a method of distributing a secret K among a finite set of participants, in such a way that only predefined subset of participant is enabled to reconstruct a secret from their shares. A secret sharing scheme realizing uniform access structure described by a graph has received a considerable attention. In this scheme, each vertex represents a participant and each edge represents a minimum authorized subset. In this paper, an independent dominating set of vertices in a graph G is introduced and applied as a novel idea to construct a secret sharing scheme such that the vertices of the graph represent the participants and the dominating set of vertices in G represents the minimal authorized set. While most of the previous schemes were based on the principle of adjacent vertices, the proposed scheme is based upon the principle of non-adjacent vertices. We prove that the scheme is perfect, and the lower bound of the information rate of this new construction is improved when compared to some well-known previous constructions. We include an experiment involving security threats to demonstrate the effectiveness of the proposed scheme.     

Stabilization of Fractional‐Order Linear Systems with State and Input Delay

This paper describes a variable structure control for fractional‐order systems with delay in both the input and state variables. The proposed method includes a fractional‐order state predictor to eliminate the input delay. The resulting state‐delay system is controlled through a sliding mode approach where the controller uses a sliding surface defined by fractional order integral. Then, the proposed control law ensures that the state trajectories reach the sliding surface in finite time. Based on recent results of Lyapunov stability theory for fractional‐order systems, the stability of the closed loop is studied. Finally, an illustrative example is given to show the interest of the proposed approach.     

Design optimization of the quantization and a pipelined 2D-DCT for real-time applications

The Discrete Cosine Transform (DCT) is one of the most widely used techniques for image compression. Several algorithms are proposed to implement the DCT-2D. The scaled SDCT algorithm is an optimization of the DCT-1D, which consists in gathering all the multiplications at the end. In this paper, in addition to the hardware implementation on an FPGA, an extended optimization has been performed by merging the multiplications in the quantization block without having an impact on the image quality. A simplified quantization has been performed also to keep higher the performances of the all chain. Tests using MATLAB environment have shown that our proposed approach produces images with nearly the same quality of the ones obtained using the JPEG standard. FPGA-based implementations of this proposed approach is presented and compared to other state of the art techniques. The target is an an Altera Cyclone II FPGA using the Quartus synthesis tool. Results show that our approach outperforms the other ones in terms of processing-speed, used resources and power consumption. A comparison has been done between this architecture and a distributed arithmetic based architecture.     

Measurement of the optical damage threshold in fused quartz

The damage thresholds of five different types of quartz glass used for the production of spectroscopic cuvettes for liquids were determined with single temporal and spatial mode nanosecond pulses at 532 nm. One of the glasses had a damage threshold of ?420 J/cm(2), which was more than twice that of the other glasses.     

A New Adaptive Beamforming of Multiband Fractal Antenna Array in Strong-Jamming Environment

This paper proposes, for the first time, a new radiation pattern synthesis for fractal antenna array that combines the unique multi-band characteristics of fractal arrays with the adaptive beamforming requirements in wireless environment with high-jamming power. In this work, a new adaptive beamforming method based on discrete cbKalman filter is proposed for linear Cantor fractal array with high performance and low computational requirements. The proposed Kalman filter-based beamformer is compared with the Least Mean Squares (LMS) and the Recursive Least Squares (RLS) techniques under various parameter regimes, and the results reveal the superior performance of the proposed approach in terms of beamforming stability, Half-Power Beam Width (HPBW), maximum Side-Lobe Level (SLL), null depth at the direction of interference signals, and convergence rate for different Signal to Interference (SIR) values. Also, the results demonstrate that the suggested approach not only achieves perfect adaptation of the radiation pattern synthesis at high jamming power, but also keep the same SLL at different operating frequencies. This shows the usefulness of the proposed approach in multi-band smart antenna technology for mobile communications and other wireless systems.

     

Numerical and experimental investigation of the mesoscale fracture behaviour of quenched steels

During heat treatment processes, especially during quenching, cracks may form because of the presence of high thermal and mechanical stresses and strains. Notwithstanding the fact that increasingly detailed modelling for heat treatment is being performed (considering, i.a., grain size, creep and transformation plasticity), homogeneous microstructures are still normally assumed. Chemical and hence structural inhomogeneities are not commonly explicitly considered, which is especially accentuated in the case of real parts simulation because of the resulting numerical problem's size. Intensive quenching on a cylindrical specimen of 100Cr6 (SAE) steel is proposed here to experimentally investigate the microcrack generation. A finite element based model is proposed to numerically evaluate the fracture behaviour in a two‐step simulation. First, by solving the quenching problem in direct correspondance with the experimental test performed, and second, by studying the mesoscale response taking into account the influence of second phase particles in a representative volume element based approach. The maximum principal stress criterion is used to trigger the fracture by means of the extended finite element method at the mesoscale. The trend to form cracks in the surface region, experimentally observed, has been well captured by the model. The influence of carbides sizes and content on the mesoscale fracture response has been numerically analysed as well. A good agreement has been reached between the simulations and the experimental results, exhibiting the potential of the introduced approach to be used as a failure prediction methodology.     

Fractional Order Nonlinear Bone Remodeling Dynamics Using the Supervised Neural Network

This study aims to solve the nonlinear fractional-order mathematical model (FOMM) by using the normal and dysregulated bone remodeling of the myeloma bone disease (MBD). For the more precise performance of the model, fractional-order derivatives have been used to solve the disease model numerically. The FOMM is preliminarily designed to focus on the critical interactions between bone resorption or osteoclasts (OC) and bone formation or osteoblasts (OB). The connections of OC and OB are represented by a nonlinear differential system based on the cellular components, which depict stable fluctuation in the usual bone case and unstable fluctuation through the MBD. Untreated myeloma causes by increasing the OC and reducing the osteoblasts, resulting in net bone waste the tumor growth. The solutions of the FOMM will be provided by using the stochastic framework based on the Levenberg-Marquardt backpropagation (LVMBP) neural networks (NN), i.e., LVMBPNN. The mathematical performances of three variations of the fractional-order derivative based on the nonlinear disease model using the LVMPNN. The static structural performances are 82% for investigation and 9% for both learning and certification. The performances of the LVMBPNN are authenticated by using the results of the Adams-Bashforth-Moulton mechanism. To accomplish the capability, steadiness, accuracy, and ability of the LVMBPNN, the performances of the error histograms (EHs), mean square error (MSE), recurrence, and state transitions (STs) will be provided.     

Type 2 Diabetes Risk Prediction Using Deep Convolutional Neural Network Based-Bayesian Optimization

Diabetes mellitus is a long-term condition characterized by hyperglycemia. It could lead to plenty of difficulties. According to rising morbidity in recent years, the world’s diabetic patients will exceed 642 million by 2040, implying that one out of every ten persons will be diabetic. There is no doubt that this startling figure requires immediate attention from industry and academia to promote innovation and growth in diabetes risk prediction to save individuals’ lives. Due to its rapid development, deep learning (DL) was used to predict numerous diseases. However, DL methods still suffer from their limited prediction performance due to the hyperparameters selection and parameters optimization. Therefore, the selection of hyper-parameters is critical in improving classification performance. This study presents Convolutional Neural Network (CNN) that has achieved remarkable results in many medical domains where the Bayesian optimization algorithm (BOA) has been employed for hyperparameters selection and parameters optimization. Two issues have been investigated and solved during the experiment to enhance the results. The first is the dataset class imbalance, which is solved using Synthetic Minority Oversampling Technique (SMOTE) technique. The second issue is the model's poor performance, which has been solved using the Bayesian optimization algorithm. The findings indicate that the Bayesian based-CNN model superbases all the state-of-the-art models in the literature with an accuracy of 89.36%, F1-score of 0.88.6, and Matthews Correlation Coefficient (MCC) of 0.88.6.     

AI-12.5 wt % Si ribbons prepared by melt spinning

Aluminium-silicon alloy with composition 12.5 wt% Si was rapidly quenched from the melt at a cooling rate of about 10⁶ Ks^–1 using the melt-spinning technique. The resulting ribbons were investigated by resistivity measurements and X-ray diffraction. The resistivity of the 20 m thick ribbons was 26.6 cm. This is 4.4 times its bulk resistivity value. The activation energy calculated from isothermal ageing was 1.14 ± 0.1 eV. The limit of primary solid solubility is extended almost to the eutectic composition and the large supersaturation is relieved by thermal annealing.     

Détermination du facteur de multiplication M pour un fonctionnement optimal des photodétecteurs à avalanche

The purpose of this paper is relative to the determination of the multiplication factor Mfor an optimum behaviour of the avalanche photodetector. Two types of avalanche photodetector s are compared: a silicon structure n ⁺ πpπp ⁺ Read diode and a GaAlAs heterostructure. Calculating the Noise Equivalent Power (nep)of the whole system, the law S_i(f) =2q I_phoM_x is used, where the parameter x is determined experimentally in both avalanche photodetectors. The exponent x (between 2 and 4) dominates in the nep expression and the ideal nep for the avalanche photodetector system is obtained when x equals 2.