(BaTiO3)1-x + (Co0.5Ni0.5Nb0.06Fe1.94O4)x nanocomposites: Structure,morphology, magnetic and dielectric properties

Two phase-based nanocomposites consisting of dielectric barium titanate (BaTiO₃ or BTO) and magnetic spinel ferrite Co_0.5Ni_0.5Nb_0.06Fe_1.94O₄ (CNNFO) have been synthesized through solid state route. Series of (BaTiO₃)_1-x + (Co_0.5Ni_0.5Nb_0.06Fe_1.94O₄)_x nanocomposites with x content of 0.00, 0.25, 0.50, 0.75, and 1.00 were considered. The structure has been examined via X-rays diffraction (XRD) and indicated the occurrence of both perovskite BTO and spinel CNNFO phases in various nanocomposites. A phase transition from tetragonal BTO structure to cubic structure occurs with inclusion of CNNFO phase. The average crystallites size of BTO phase decreases, whereas that for the CNNFO phase increases with increasing x in various nanocomposites. The morphological observations revealed that the porosity is highly reduced, and the connectivity between grains is enhanced with increasing x content. The optical properties have been investigated by UV−vis diffuse reflectance spectroscopy. The deduced band gap energy (E_g) value is found to reduce with increasing the content of spinel ferrite phase. The magnetic as well as the dielectric properties were also investigated. The analysis showed that CNNFO ferrite phase greatly affects the magnetic properties and dielectric response of BTO material. The obtained findings can be useful to enhance the performances of magneto-dielectric composite-based systems.     

Stability and robustness of fuzzy adaptive control of nonlinear systems

In this paper, we propose a new approach that guarantees the stability and robustness of an adaptive control law of a nonlinear system.The control diagram proposed contains a Takagi–Sugeno–Kang fuzzy controller (TSK-FC) and a training block allowing the online adaptation of the FC parameters. The adaptation algorithm used is based on the gradient with minimization of the quadratic error between the system output and that desired by using the direct method of Lyapunov. However, our approach considers the gradient step of each adaptive FC parameter to be bound. This approach was applied to the control of an inverted pendulum. The results obtained confirm well the validity of such an adaptation especially the guarantee of the pendulum stability and the robustness of its control with respect to the disturbances introduced on the FC parameters and the pendulum itself.     

Automated Proofs of Block Cipher Modes of Operation

We present a Hoare logic for proving semantic security and determining exact security bounds of a block cipher mode of operation. We propose a simple yet expressive programming language to specify encryption modes, semantic functions for each command (statement) in the language, an assertion language that allows to state predicates and axioms, and rules to propagate the predicates through the commands of a program. We also provide heuristics for finding loop invariants that are necessary for the application of our rule on for-loops. This enables us to prove the security of protocols that take arbitrary length messages as input. We implemented a prototype that uses this logic to automatically prove the security of block cipher modes of operation. This prototype can prove the security of many standard modes of operation, such as Cipher Block Chaining (CBC), Cipher FeedBack mode (CFB), Output FeedBack (OFB), and CounTeR mode (CTR).     

Deep multilayer multiple kernel learning

Multiple kernel learning (MKL) approach has been proposed for kernel methods and has shown high performance for solving some real-world applications. It consists on learning the optimal kernel from one layer of multiple predefined kernels. Unfortunately, this approach is not rich enough to solve relatively complex problems. With the emergence and the success of the deep learning concept, multilayer of multiple kernel learning (MLMKL) methods were inspired by the idea of deep architecture. They are introduced in order to improve the conventional MKL methods. Such architectures tend to learn deep kernel machines by exploring the combinations of multiple kernels in a multilayer structure. However, existing MLMKL methods often have trouble with the optimization of the network for two or more layers. Additionally, they do not always outperform the simplest method of combining multiple kernels (i.e., MKL). In order to improve the effectiveness of MKL approaches, we introduce, in this paper, a novel backpropagation MLMKL framework. Specifically, we propose to optimize the network over an adaptive backpropagation algorithm. We use the gradient ascent method instead of dual objective function, or the estimation of the leave-one-out error. We test our proposed method through a large set of experiments on a variety of benchmark data sets. We have successfully optimized the system over many layers. Empirical results over an extensive set of experiments show that our algorithm achieves high performance compared to the traditional MKL approach and existing MLMKL methods.     

Automatic Generation of Invariants

When proving invariance properties of programs, one is faced with two problems. The first problem is related to the necessity of proving tautologies of the considered assertion language, whereas the second manifests itself in the need of finding sufficiently strong invariants. This paper focuses on the second problem and describes techniques for the automatic generation of invariants. The first set of these techniques is applicable to sequential transition systems and allows deriving so-called local invariants, i.e., predicates which are invariant at some control location. The second is applicable on networks of transition systems and allows combining local invariants of the sequential components to obtain local invariants of the global system.     

Distributed control and speed sensorless for the synchronisation of multi-robot systems

This paper investigates a synchronization approach to trajectory tracking of networked robotic systems while maintaining time-varying formations. The objective is to control networked robots to track a desired trajectory while synchronizing their behaviors. Combining trajectory tracking and synchronization algorithms, the developed approach uses a cross-coupling technical to create interconnections for mutual synchronization of robots. The main objective of distributed approach is to generate an emerging behavior using only local information interactions. First, a distributed scheme is developed to achieve the networked robots synchronization on undirected graph. Then, the leaderless synchronized tracking problem in the case when only position measurements are available, will be presented. For both cases: In the presence of the velocity feedback or in its absence, the controller, designed by incorporating the cross-coupling technical into a sliding mode control architecture, successfully guarantees asymptotic convergence to zero of both position tracking and synchronization errors simultaneously. The Lyapunov-based approach has been used to establish the multi-robot systems asymptotic stability. A real-time software simulator is developed to visualize the synchronized behaviors. Based on LabVIEW integrated development environment (IDE), a developed human-machine-interface (HMI) allows its user to control, in real time, the networked robots. Simulation and experimental results are provided to demonstrate performances of the proposed control schemes.     

Bacteriological effects of functionalized cotton dressings

This study aims to achieve effects of improved hydrophilicity and microorganism inhibition, which are rarely simultaneously present in wound dressings. Cotton gauzes were modified using the grafting of a polymer-based β-cyclodextrin. After optimizing the grafting conditions, the untreated and modified cellulosic samples were principally characterized using FT-IR spectroscopy, TGA/DTA analysis, in vitro drug release, and wettability measurements. In light of desired characteristics of wound dressings, the effectiveness of procedures was evaluated. It was found that contact angles for cotton gauzes decreased after functionalization, which means that hydrophilicity was proven to become excellent. A successful Methylene Blue complexation was confirmed through measure of the dyebath exhaustion using UV-spectrophotometry. Then, during Methylen Blue release test, we reported an initial burst release of active ingredient over 7?h, followed by zero-order release. The treatment effect on antimicrobial activity was investigated by growth inhibition, which was proven against Staphylococcus aureus and Escherichia coli.     

Efficient 3D point clouds classification for face detection using linear programming and data mining

Most of the applications related to security and biometric rely on skin region detection such as face detection, adult 3D objects filtering, and gesture recognition. In this paper, we propose a robust method for skin detection on 3D coloured point clouds. Then, we extend this method to solve the problem of 3D face detection. To do so, we construct a weighted graph from initial coloured 3D point clouds. Then, we present a linear programming algorithm using a predictive model based on a data mining approach to classify and label graph vertices as skin and non-skin regions. Moreover, we apply some refinement rules on skin regions to confirm the presence of a face. Furthermore, we demonstrate the robustness of our method by showing and analysing some experimental results. Finally, we show that our method deals with many data that can be represented by a weighted graph such as 2D images and 3D models.     

Simulation of driver fatigue monitoring via blink rate detection,using 65 nm CMOS technology

This paper proposes a system to detect and measure blink rate to determine fatigue levels. The method involved analysing specific frames to determine that a blink occurred, and then monitoring the time between successive blinks. The program was simulated in python using a Raspberry Pi Zero and a standard USB camera. For the blink rate detection block, a gate level schematic was implemented in Cadence software using 65 nm CMOS technology. The design was based around an asynchronous 6-bit based edge counter which was designed using D-flip-flops. The simulation calculated the average blink rate and compared this to the most recent blink rate. The outcome would determine if an alarm signal should be sent to the alarm. The system consumed 130 μA from a 1.2 V power supply.     

Solar electricity prospects in Oman using GIS-based solar radiation maps

This paper discusses solar power prospects in Oman. First, the geographic and topographic information about Oman are presented. The methodology of producing solar radiation maps using GIS tools is then discussed. The results obtained show very high potential of solar radiation over all the lands of Oman during the whole year. A slope analysis has allowed calculating the yearly electricity generation potential for different Concentrated Solar Power (CSP) technologies such as the parabolic trough, parabolic dish, tower, and concentrated PV. For instance if only 10% of the land of Oman with a slope less than 1% is considered an exploitable land for the parabolic trough CSP technology, then the total calculated potential of yearly electricity generation would be about 7.6 million GWh, which is many multiples of (680 times) the current generation supply in Oman which was about 11,189 GWh in 2007.