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Bearings are considered as indispensable and critical components of mechanical equipment, which support the basic forces and dynamic loads. Across different condition monitoring (CM) techniques, infrared thermography (IRT) has gained the limelight due to its noncontact nature, high accuracy, and reliability. This article presents the use of IRT for the bearing fault diagnosis. A two-dimensional discrete wavelet transform (2D-DWT) has been applied for the decomposition of the thermal image. Principal component analysis (PCA) has been used for the reduction of dimensionality of extracted features, and thereafter the most relevant features are accomplished. Furthermore, support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighbor (KNN) as the classifiers were considered for classification of faults and performance assessment. The results reveal that the SVM outperformed LDA as well as KNN. Noncontact condition monitoring shows a great potential to be implemented in determining the health of machine. The utilization of noncontact thermal imaging-based instruments has enormous potential in anticipating the maintenance and increased machine availability.

Irregularity is the main characteristic of electroencephalographic signals (EEG), which needs a specific analysis method for neurological disease diagnosis. An efficient tool for signal irregularity analysis is Sample Entropy (SampEn). In this context, our paper was elaborated. We used SampEn to design a Machine Learning model for brain state detection based on EEG signals, which allows to differentiate between healthy (H) subjects, epileptic subjects during seizures free intervals (E) and epileptic subjects during seizures (S). Two main novelties are presented in our paper. The first one is related to the outline of the designed machine learning model, signal derivatives are determined as preprocessing step, then extracted features are SampEn and Standard Deviation (STD) from EEG signals and its first and second derivatives. These features are firstly used to train a K-Nearest Neighbor classifier (KNN) and yield high accuracy. After that, we select the most relevant features and we design our proposed classifier that provides better accuracy. The second one is related to the performance of our model to overcome some crucial purposes. In addition to the highest achieved accuracy, 100% for seizure detection, 99.2% for epilepsy detection and 99.86% for three class classification cases, our model used few features and simple classifier which involves fast running time. That is why we can consider our model as a suitable tool for real time applications.

The compass-type bipedal robot is an impulsive hybrid mechanical system that can produce complex behaviors, chaos and bifurcations. In this work, we analyze the passive dynamic walking of the compass robot to further explore its complexity and these nonlinear phenomena while descending inclined surfaces. The bifurcation diagrams are mainly adopted to investigate the complex walking behaviors by varying the slope angle and the length of the lower-leg segment. We show exhibition of the period-doubling bifurcation, the Neimark-Sacker bifurcation, the period-remerging phenomenon, bubbles and chaos. The Poincare map and the state space are also used to further analyze these complex behaviors.

The compass-gait biped robot is a two-DoF legged mechanical system that has been known by its passive dynamic walking. This kind of passive biped robot is modeled by an impulsive hybrid nonlinear system that exhibits complex behaviors. This paper is concerned with the stabilization of the passive dynamic walking of the compass-gait biped robot by designing an analytical expression of the controlled Poincare map. The suggested analytical method starts with the time-piecewise linearization of the impulsive system augmented with the controller, around a desired one-periodic passive hybrid limit cycle. By virtue of the first-order Taylor series, we design an explicit expression of the controlled Poincare map. We present also a simplified expression of the controlled Poincare map having a reduced dimension. In order to accomplish our goal concerned with the stabilization of the passive walking dynamics of the compass-gait biped robot, we develop first the linearized Poincare map around the period-1 fixed point of the Poincare map and we adopt a state-feedback control law to stabilize it. Furthermore, in order to enhance the effectiveness and robustness of the stabilization process, we adopt an LMI-based optimization approach by considering the controlled Poincare map to design the feedback gain. In the end of this work, we provide some numerical and graphical simulation results, to show the validity of the designed control law by means of the controlled Poincare map in the stabilization of the passive dynamic walking of the compass-gait biped robot.

This paper addresses the problem of Static Output Feedback (SOF) stabilization for continuous-time linear systems subject to norm-bounded parameter uncertainties. Usually this issue leads to the feasibility of a Bilinear Matrix Inequality (BMI), which is difficult to linearize to get non conservative Linear matrix inequality (LMI) conditions. In this paper, by means of some technical lemmas, we transform the BMI into a new LMI with a line search over two scalar variables. The obtained LMI conditions are less conservative than those existing in the literature. Numerical evaluations are presented to show the superiority of the proposed method.

Discriminative models have been shown to be more advantageous for pattern recognition problem in machine learning. For this study, the main focus is developing a new hybrid model that combines the advantages of a discriminative technique namely the support vector machines (SVM) with the full efficiency offered through covariance multivariate generalized Gaussian mixture models (MGGMM). This new hybrid MGGMM applies the Fisher and Kullback–Leibler kernels derived from MGGMM to improve the kernel function of SVM. This approach is based on two different learning techniques explicitly: the Fisher scoring algorithm and the Bayes inference technique based on Markov Chain Monte Carlo and Metropolis–Hastings algorithm. These learning methods work with two model selection approaches (minimum message length and marginal likelihood) to determine the number of clusters. The effectiveness of the framework is demonstrated through extensive experiments including synthetic datasets, facial expression recognition and human activity recognition.

Motivation: Upper-limb motor impairment is one of the most common consequences after Stroke. Limited capability for performing reaching and grasping movements hinders the execution of most activities of daily living. Consequently, the quality lives of the affected individuals are severely compromised. Due to these facts, the recovery of the upper limb functional capabilities is currently one of the keystones of the rehabilitation therapy. Background: Researchers are developing new methods and technologies to boost the outcomes of rehabilitation therapy. A hybrid robotic system has been proposed as a promising rehabilitation technology that combines a passive device (Armeo Spring exoskeleton) to support the arm weight against gravity with a Functional Electrical Stimulation (FES) system to execute the reaching task. This system provides to patients the possibility of training specifically and intensive exercises. Objective: The main objective of this paper is to investigate the performance and robustness of a Feedback Error learning (FEL) scheme mixed with sliding mode control (SMC) to control the FES. Methods: We implemented a nonlinear model describing the muscle response to FES and the dynamic behavior of the elbow joint. Using this model we carried out a simulation study to compare four control strategies: computed torque control (CTC), sliding mode Control (SMC), and adaptive feedback control using FEL: ANN+ CTC and FEL: ANN+SMC. We tested these controllers in two different simulation conditions: In the absence and presence of fatigue. To check the performance of the controllers, we compared the root means square (RMSE) of tracking error and the Normalized RMS of muscle stimulation for various range of movement (ROM). Results: All four controllers achieved good tracking performance in the absence of perturbations. When introducing muscle fatigue, good tracking performance is given essentially by the adaptive control ANN+SMC. Conclusion: Among the proposed approaches, we conclude that the adaptive control (FEL: ANN + SMC) is the most efficient and robust controller, which has been proven by calculating RMSE.

In this work, we propose a new adaptive chaotic steganographic method based on the Discrete Cosine Transform (DCT) and a reversible mapping function. The mapping function is used to map the...

We propose in this paper to recognize human activities through an unsupervised learning of finite multivariate generalized Gaussian mixture model. We address an important cue in finite mixture model which is the estimation of the mixture model’s parameters for a full covariance matrix. We have developed a novel learning algorithm based on Fixed-point covariance matrix estimator combined with the Expectation-Maximization algorithm. Furthermore, we have proposed an appropriate minimum message length (MML) criterion to deal with model selection problem. We evaluated our proposed method on synthetic datasets and a challenging application namely : Human activity recognition from images and videos. The obtained resutls show clearly the merits of our proposed framework which has better capabilities with full covariance matrix when modeling correlated data.

This paper proposes a robust feedback controller using Linear Matrix Inequalities (LMIs) formulation for the stabilization of an underactuated mechanical system, namely the Inertia Wheel Inverted Pendulum (IWIP), in its upright position. Such mechatronic system is subject to state constraints, external disturbances and norm-bounded parametric uncertainties. The main idea to solve the stabilization problem lies in the use of the S-procedure Lemma. Such problem is then transformed into a solving problem of Bilinear Matrix Inequalities (BMIs). Through the Schur complement Lemma and the Matrix Inversion Lemma, a linearization procedure is employed to transform the BMIs into LMIs. Some improvements and comparisons with other LMI-based design techniques without state constraints are developed and discussed. An extensive portfolio of numerical studies is presented. The effectiveness and robustness of the proposed feedback controller toward uncertainties in the friction parameters and external disturbances are illustrated through simulation results.

In this paper, we are interested in the analysis of the passive walking gaits of the compass-type bipedal robot while descending sloped surfaces. This type of biped robot is a two-degree-freedom mechanical system characterized by an impulsive hybrid dynamics that brings out attractive nonlinear phenomena like chaos and different types of bifurcations. In this work, we investigate by means of bifurcation diagrams, the influence of certain parameters on the passive motion of bipedal robot. As a result, we show the exhibition of new complex walking behaviors emerged from different types of bifurcations such as the period-doubling bifurcation and the Neimark-Sacker bifurcation, and also emerged via the period-remerging scenario. These results further confirm the complexity of the biped/human walk.

Modeling a biped robot with a complicated structure and descending an inclined plane is a challenge due to the complexity of its dynamic walking model. In this work, we develop the mathematical model of a kneed robot, with rigid segments and with punctual contact with the ground. We reformulate such complicated model as an impulsive hybrid nonlinear system. Moreover, using chiefly bifurcation diagrams, we study the influence of certain inertial and geometrical parameters on the passive gaits of the kneed bipedal robot. We show via simulations the exhibition of the Neimark-Sacker bifurcation emanated from the period-1 gaits, and hence the appearance of complex quasi-periodic gaits.

In this paper, we introduce a control approach using a quadratic polynomial expression of the controlled Poincare Map to actively stabilize the passive walking motion of the two-degree-of-freedom compass-gait biped walker. The passive gait cycle of the bipedal walker is depicted for a given fixed point. The control of the passive gaits involves firstly the reconstruction of the nonlinear complex dynamics describing the passive bipedal walking into an amendable linear system around the period-1 limit cycle. It involves secondly the determination of the quadratic polynomial expression of the nonlinear controlled Poincare Map, and finally the identification of its period-1 fixed point. Successively, to stabilize such fixed point, we develop the linearized Poincare Map, which will be explored to design the feedback gain of the control law. The control problem is cast then into a convex optimization involving a linear matrix inequality (LMI) by maximizing the bound on the nonlinear term in the Poincare map. Simulation outputs illustrate the efficiency of the adopted LMI-based optimization method in the control of the passive motion of the compass-gait walker.

This paper deals with a stability study and a robustness analysis of an exoskeleton- upper limb system, used for rehabilitation. The considered system is a robot with two degrees of freedom controlled by a Model Free Terminal Sliding Mode algorithm. The objective is to control the flexion/extension movement of the shoulder and the elbow in presence of uncertainties and disturbances. The dynamic model of the system was presented. An Input-to-State Stability study is realized to demonstrate the stability of the system. A robustness analysis using Monte Carlo method is developed. Simulation results are provided to prove the performances and the effectiveness of the Model Free Terminal Sliding mode algorithm and the stability of the system face to disturbances and uncertainties.

International audience; In this paper, we consider chaotic sequences as an alternative to other sequences in the literature for the design of radar waveforms in multiple-input multiple-output (MIMO) radar. For this aim we here adopt a statistical approach; by considering the codes defining the transmitted waveform as real-izations of a random variable we show that a suitable distribution of the random variable can give good codes. As an example we show how the chaotic skew tent map allows to generate deterministic codes having the desired statistical properties and thus makes it possible to obtain a good ambiguity function. These results are confirmed by simulations and compared to those using Gold codes and optimized codes.

Machine learning is an expanding research area. Its main application is in the medical field and particularly the detection of epilepsy and epileptic seizures through electroencephalographic signals (EEG). It aims to design an intelligent framework that enables an immediate diagnosis of this disease without neurological consultation and thus saves the lives of the epileptic patients by detecting seizures and warning them before it happens. However, as a real-time application, this kind of framework faces several challenges such as accuracy, fast responses, and optimal memory usage. Within this context, our work was carried out. We propose a new machine learning framework based on chaos and fractal theories. Two main novelties are presented in this paper. Firstly, we propose a new method for signal preprocessing, and we reconstruct new versions of studied EEG signals using derivative determination and chaotic injection. Secondly, we suggest a new method for fractal analysis using Higuchi fractal dimension (HFD). In fact, HFDs extracted from EEG derivatives lead to detect epilepsy, whereas HFDs extracted from EEG with a chaotic signal injection lead to seizure detection. In addition, feature fusion helped to linearize all classification problems. An experimental study using the Bonn EEG database proves the efficiency of our contributions in comparison to published research. An accuracy of 100% was achieved in different classification cases using few features and a simple linear classifier.

This paper illustrates a stabilization approach of the passive bipedal locomotion of the compass-gait biped model based on an exclusively developed enhanced design of the closed form of the Controlled Poincare Map (CPM). The followed technique relies on transforming the impulsive hybrid nonlinear dynamics of the passive motion into a linear form around a period-1 limit cycle. Forward, we simplify the complicated resulted expression using the second order of the Taylor Series. This technique, enables us to design a closed form of the CPM. The control of the passive bipedal locomotion starts with the identification of the period-1 fixed point of the non-CPM and continues with the determination of the linearized PM around such fixed point. Next, a feedback controller is adopted to stabilize this identified fixed point. Some simulation results are provided at the end to illustrate the efficiency of the control process of the passive walking motion of the compass-gait robot model.

In this paper, we propose a new method for the automatic diagnosis of epilepsy via encephalographic signals (EEG). Our objective is the detection of epilepsy and epileptic seizures through EEG of healthy subjects (H), epileptic subject (E) and epileptic subject during seizures (S). Two novelties are deliberated in this paper. In the first method, we have exploited EEG and its derivatives, which gives significant results from calculations of just three features, the variances of the signals and its first and second derivatives. In the second one, we have used a kernel trick that allows an implicit redescription of the extracted features, by the conversion of the nonlinear problem to linear space, which ultimately facilitates the classification step and gives reliable result in fast running time. The experimental test via the Bonn EEG dataset proves the efficiency of the proposed method, an accuracy of 100 % is achieved in seizures detection problem and of 99.8 % in epilepsy detection problem, moreover for the differentiation of three cases 99.85 % of accuracy was achieved.

This paper deals with a new adaptive terminal sliding mode control of an exoskeleton-upper limb system. The considered system is a 2 DoF robot in interaction with an upper limb used for rehabilitation. The objective is to control the flexion/ extension movement of the shoulder and the elbow in presence of disturbances. First, the modeling and the control of the system using different laws were made. Next, a stability study is realized. Then, a comparison study between these different algorithms was done based on Monte Carlo robustness analysis method. Finally, the performance and the effectiveness of the adaptive terminal sliding mode algorithm in presence of uncertainties are provided by simulation results.

This paper discusses a stabilization method for the passive dynamic walking (PDW) of the compass-gait biped robot using an explicit analytical classical expression of the controlled Poincare map in order to control its passive gaits. A linearization method is used to cast the problem from the impulsive hybrid nonlinear dynamics into the linear dynamics around a desired one-periodic hybrid limit cycle of the PDW. Thus, using the first-order Taylor approximation, we design an explicit expression of the controlled Poincare map. In order to control the passive gaits, we develop first the linearized Poincare map obtained around the period-one fixed point. We adopt a state-feedback control law using the LMI approach to stabilize this fixed point. We present finally some simulation results to reveal the efficiency of the designed control law in the control of the passive-dynamic locomotion of the compass-gait biped robot.

This work presents a new model free terminal sliding mode control of an exoskeleton-upper limb system. The considered system is a 2 DoF robot in interaction with an upper limb used for rehabilitation. The objective is to control the flexion/ extension movement of the shoulder and the elbow joints in presence of disturbances. First, the modeling and the control of the system using the terminal sliding mode and the model free algorithms were made. Then, a combination between these two controllers was done to get the new model free terminal sliding mode approach. A stability study is realized. To prove the performance of the tested controller, a robustness analysis was needed. The performance and the robustness of the proposed controller in presence of matched uncertainties are provided by simulation results.

this paper presents one of our contributions in our palm vein recognition system, this contribution consists on region of interest extraction (ROI)in images obtained by near infrared. The proposed ROI extraction is find using a new process to find the candidate key points then a square based on perpendicular lines algorithme is used to detect the region of interest, our method is performed at 99% of correct segmentation rate.

This paper presents a continuous work of designing a classical analytical expression of the Poincare map to improve our research related to the passive dynamic walking (PDW) of the planar compass-gait biped robot. The demonstration illustrated in this paper provides the steps and simulation results allowing to consistently modeling the expression of the Poincare map. The design method we followed is based on the linearization of the flow of the impulsive hybrid nonlinear dynamics of the passive biped robot around a desired one-periodic hybrid limit cycle. Thus, via the second-order Taylor approximation, we design an explicit expression of the Poincare map. We develop also an equation providing the step period of the biped locomotion. In the end, we present some numerical and graphical simulation results to compare between the features exhibited with the impulsive hybrid nonlinear dynamics and the developed Poincare map and also the previously designed Poincare map using the first-order Taylor approximation. These results show the efficiency and the validity of the designed Poincare map in the analysis of the PDW of the compass-gait biped robot.

This paper is concerned with the investigation of the nonlinear dynamic behavior of a one-degree-of-freedom (1-DoF) impact mechanical oscillator subject to a single rigid constraint and under an OGY-based state-feedback control law. Our analysis is mainly carried out through bifurcation diagrams. Several cases of the raised behaviors are also illustrated through time-traces, phase portraits and Poincare sections. To prevent some problems in the computation of unstable solutions and their continuation to locate and characterize local bifurcations, we develop an analytical expression of a stroboscopic controlled hybrid Poincare map. Moreover, we present conditions for determining the fixed point of such Poincare map and for studying its stability. Through the hybrid Poincare map, we analyze the displayed nonlinear phenomena in the controlled impacting oscillator. We show that several interesting behaviors are revealed including the period-doubling route to chaos, the period-adding cascade, interior and boundary crisis, the complete and incomplete chaotic chattering, the cyclic-fold bifurcation, the saddle-saddle bifurcation, the Neimark–Sacker bifurcation, the sub-critical period-doubling bifurcation, the grazing bifurcation, among others. Furthermore, we show also that the 1-DoF impacting mechanical oscillator displays, for the first time, the border-collision bifurcation, which is exhibited due to the OGY-based state-feedback control. In addition, we establish conditions for the localization of the border-collision bifurcation. Its occurrence is investigated via a two-parameter bifurcation diagram.

This paper continues our research work on analysis of passive dynamic walking of the two-degree-of-freedom planar compass-gait biped robot under the OGY-based state-feedback control. We showed, in our previous works, that the walking dynamics of the compass-gait model under control exhibits chaos and periodic-doubling and cyclic-fold bifurcations. In a first part, our analysis of the walking behavior was achieved using the impulsive hybrid continuous-time nonlinear dynamics of the compass-gait model under the OGY-based control. In a second part, our study of the controlled gait and the displayed local bifurcations was realized via the controlled hybrid Poincare map. In the present work, we demonstrate, for the first time, the appearance of the Neimark–Sacker bifurcation in the controlled dynamic walking of the compass-gait model. Our investigation is achieved via the controlled hybrid Poincare map instead of the impulsive continuous dynamics model of the bipedal walking. For such study, we mainly use bifurcation diagrams and 2D phase portraits of the discrete Poincare map. We show that such Neimark–Sacker bifurcation is generated from a period-1 gait and is localized in a small range of the bifurcation parameter, the slope angle. We introduce a two-parameter bifurcation diagram to study occurrence of the Neimark–Sacker bifurcation.

In recent years, AES is known as the excellent choice between the existing symmetric cryptosystems to encrypt binary or text data. However, this standard encryption algorithm is unsuitable for images regarding the particular features of these kinds of data such as high correlation, high redundancy, and voluminosity. Based on this fact, we have designed a new cryptosystem taking into account the aforementioned characteristics which provides high performance and security level. Actually, the cryposystem adopts a new structure named Outer-Inner structure. This new structure consists of two phases: Outer phase and Inner phase. In the former phase, the image is treated as a single block. The purpose of this phase is to address the correlation and the redundancy issue of images. In order to ensure the efficiency and to avoid the problem of treating an image as a single block where several image encryption algorithms suffer from it, this phase only involves lightweight operations. In the later phase the image is treated as a set of fixed blocks. The aim of this phase is to address the voluminosity issue. In fact, during this phase each block can be encrypted independently through an iterative function. The experiment results show that 7 rounds are sufficient to reach an acceptable security level.

In our previous work, we have analyzed the passive dynamic walking of the compass-gait biped model under the OGY-based state-feedback control using the impulsive hybrid nonlinear dynamics. Such study was carried out through bifurcation diagrams. It was shown that the controlled bipedal gait exhibits attractive nonlinear phenomena such as the cyclic-fold (saddle-node) bifurcation, the period-doubling (flip) bifurcation and chaos. Moreover, we revealed that, using the controlled continuous-time dynamics, we encountered a problem in finding, identifying and hence following branches of (un)stable solutions in order to characterize local bifurcations. The present paper solves such problem and then provides a further investigation of the controlled bipedal walking dynamics using the developed analytical expression of the controlled hybrid Poincare map. Thus, we show that analysis via such Poincare map allows to follow branches of both stable and unstable fixed points in bifurcation diagrams and hence to explore the complete dynamics of the controlled compass-gait biped model. We demonstrate the generation, other than the conventional local bifurcations in bipedal walking, i.e. the flip bifurcation and the saddle-node bifurcation, of a saddle-saddle bifurcation, a subcritical flip bifurcation and a new type of a local bifurcation, the saddle-flip bifurcation. In addition, to further understand the occurrence of the local bifurcations, we present an analysis with a two-parameter bifurcation diagram. Some new hidden walking dynamics are identified.

In this paper, we propose a partial compression-encryption of images using chaotic system and Discrete Wavelet Transform (DWT) combined with Set Partitioning in Hierarchical Trees (SPIHT). The proposed cryptosystem adopts three stages for confusion and diffusion which are well integrated into the compression chain. The chaotic system used in this approach is Chirikov Standard Map. Some cryptographic tests and metrics are applied to measure the degree of security and analyze the performance of the partial compression-encryption scheme. The evaluation and simulation analysis indicate that our proposal possesses excellent statistical and cryptographic properties; it is extremely sensitive to the small change in secret key, resists against common cryptanalytic attacks, has a high speed and provides high picture quality of the reconstructed image. A compressionencryption system of image based on SPIHT and Chirikov map is proposed.Encryption scheme is designed to operate with SPIHT without degrade its performance.Our work is supported on many mathematical tools and chaotic systems.The proposal scheme can resist against exhaustive key and cryptanalytic attacks.A high speed and very secure system with very low computational complexity.

In this paper, a new chaos-based partial image encryption scheme based on Substitution-boxes (S-box) constructed by chaotic system and Linear Fractional Transform (LFT) is proposed. It encrypts only the requisite parts of the sensitive information in Lifting-Wavelet Transform (LWT) frequency domain based on hybrid of chaotic maps and a new S-box. In the proposed encryption scheme, the characteristics of confusion and diffusion are accomplished in three phases: block permutation, substitution, and diffusion. Then, we used dynamic keys instead of fixed keys used in other approaches, to control the encryption process and make any attack impossible. The new S-box was constructed by mixing of chaotic map and LFT to insure the high confidentiality in the inner encryption of the proposed approach. In addition, the hybrid compound of S-box and chaotic systems strengthened the whole encryption performance and enlarged the key space required to resist the brute force attacks. Extensive experiments were conducted to evaluate the security and efficiency of the proposed approach. In comparison with previous schemes, the proposed cryptosystem scheme showed high performances and great potential for prominent prevalence in cryptographic applications.

A new method for obtaining strong S-boxes based on chaotic map and Teaching–Learning-Based Optimization (TLBO) is presented in this paper. Our method presents eight rounds; each round contains two transformations: row left shifting and columnwise rotation. The vectors for the transformations are different from one round to another, and they are controlled by two keys to the logistic map. These two keys are optimized by using TLBO which aims to construct a strong S-box that satisfies to the criteria set in advance. Test for the following criteria such as bijectivity, nonlinearity, strict avalanche criteria, equiprobable inputs/outputs XOR distribution is analyzed. Additionally, we will provide many comparisons with other S-boxes and test of the sensitivity to keys. The results of performance test show that the proposed design S-boxes presents good cryptography proprieties and can resist to several attacks.