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Applying orthogonal space time block coding (OSTBC) to multiple-input multiple-output (MIMO) systems helps reduce receiver complexity. However, this approach has been applied only to flat fading channels, as when the channel is a frequency selective fading MIMO channel, OSTBC cannot be used directly since its orthogonal propriety may be lost. Furthermore, the MIMO channel is not always known. To deal with this problem, many techniques were proposed to estimate the channel using a training sequence. Unfortunately, these techniques reduce the useful spectral bandwidth. This paper proposes OSTBC for blind channel estimation and data detection in the case of a MIMO frequency selective channel. The aim of this new OSTBC is twofold: to solve the ambiguity of channel estimation and to reduce the complexity of the detector. By exploiting the well-known technique of orthogonal frequency division multiplexing (OFDM), the frequency selective fading MIMO channel is split into a set of flat fading subchannels. Moreover, to accommodate the fact that a MIMO channel can be time varying, the steady state Kalman channel estimator (SS-KCE) is extended to track the channel’s fast variations. The performance of the proposed blind algorithm is related by the adequate choice of the number of subcarriers and it is compared with other existing approaches by means of Monte Carlo simulations.

Reliable road traffic state identification systems should be designed to provide accurate traffic state information anywhere and anytime. In this paper we propose a road traffic classification system, based on traffic variables estimated using the second order Divided Difference Kalman Filter (DDKF2). This filter is compared with the Extended Kalman Filter (EKF) using both simulated and real-world dataset of highway traffic. Monte-Carlo simulations indicate that the DDKF2 outperforms the EKF filter in terms of parameters estimation error. The real-word evaluation of the DDKF2 filter in terms of classification rate confirms that this filter is promising for real-world traffic state identification systems.

In WiFi-based indoor positioning, the received signal strength (RSS) measurements are commonly used to estimate the mobile user location. However, these measurements significantly fluctuate over time and are susceptible to human movement, multipath and Non-Line-of-Sight (NLOS) propagation, which reduce the location accuracy. In this paper, an enhancement positioning method based on the nearest neighbor algorithm is proposed. The distribution of the RSS samples recorded from several Access Points (APs) are used rather than their average, for reducing the location errors introduced by the RSS variations and the multipath problem. The proposed algorithm, named the Nearest K th Nearest Neighbor (NK-NN) is experimentally evaluated and compared to other powerful methods. The results show that the proposed method outperforms these methods.

Security is a major concern in Electronic Payment (E-Payment) systems. Usually, these systems are protected against illegal users, so-called hackers, by different means, such as Personal identification numbers (PINs), passwords, cards, etc. However, these hackers may manage to bypass this protection by having recourse to different strategies. Many techniques have been proposed to counter hacking attempts; however, there are still situations where an illegal user may succeed to access the E-payment system easily by stealing from a legal user its payment card. The use of Artificial Intelligence methods for face authentication, like deep learning, has made facial biometry a highly developing and accurate technology, especially in the past decade. In this paper, we propose the joint use of deep learning-based facial biometry and RFID cards to reinforce the security of an E-Payment system. By doing so, we ensure that a user should be physically present carrying his RFID card to be able to access the E-Payment system. We have tested three deep learning-based face authentication models and validated them on MUCT and CASIA Face-V5 datasets, to choose the most suitable one for our proposed secured E-Payment system, obtaining top verification rates of 99.90% and 99.26%, respectively. Two versions of this system are proposed; in the first version, which is based on a Personnel Computer (PC) and a Raspberry card, face authentication is implemented in a PC and the control of the RFID reader is performed by a Raspberry Pi 3, whereas in the second version, which may be considered as an embedded system, all the job is accomplished by the Raspberry Pi.

Speed-density relationships also referred as fundamental diagram (FD), play a central role in traffic flow theory and transportation engineering. However, the main problem in developing the FDs reside in the way their parameters are selected. In this paper we use the whale optimization algorithm (WOA) to calibrate the unknown parameters of the FD, this algorithm is compared with Complex-Box algorithm that is well used in the literature. The obtained results confirm that the traffic model developed using WOA is more accurate and thus, the traffic classification rate obtained using the proposed system including the WOA was significantly increased.

Gender classification is a demographic attribute that found an increasing amount of applications particularly in human-computer interaction, security access control and biometrics. The purpose of this paper is to investigate the feasibility of using time series for gender classification. After transforming input depth images of faces into one-dimensional vector using Peano-Hilbert space filling curve, a dimensionality reduction step is performed to convert this vector to a sequence of alphabets using Symbolic Aggregate approXimation (SAX). With FRGCv2 dataset, standard 10-fold cross-validation experiments are performed using quadratic kernel for SVM classifier. The results have shown that the proposed approach can reach a correct classification rate higher than 96%.

Electrooculogram (EOG), while being a source of useful information. Many researches and applications such as Human computer interaction (HCI) and physiological state monitoring rely match more on it then other resources like video surveillance. In our work we’re interested in awareness level classification for driver drowsiness detection, in the purpose of improving road security. However, EOG acquisition requires placing electrodes around eye’s subject all the time which is not comfortable and convenient for many applications. Therefore, we propose a generation of a Bio-inspired EOG signal (pseudo EOG) using video camera. As for EOG signal, the bio-inspired one contains useful information of eye state and movements in time. Critical features were extracted or calculated from the bio-inspired EOG signal and used as inputs of a fuzzy logic classification of the awareness level in time.

Biometric technologies have been widely used in recent years for authentication and security purposes. Face recognition is one of the important techniques for its simple way to obtain the subject samples without being intrusive. However, this technology has been employed mostly using the visible spectrum only, which suffers from some limitations such as illumination change, facial expression and pose variations. The infrared spectrum offers some advantages over the visible spectrum, mainly the robustness to light change. In this paper, we propose a new multispectral framework that uses both infrared and visible spectra with an optimization based on a new proposed feature selection algorithm and the PSO-SVM. Experimental tests were conducted on IRIS OTCBVS Thermal/Visible and CSIST Lab 2 Databases. The obtained results clearly demonstrate the effectiveness of our new framework compared to a mono-spectral face recognition system.

In this paper, a new fuzzy alpha-beta filter for tracking a highly maneuvering target is presented. The target may maneuver at uniform angle rates by executing sharp turns within a short time. A set of fuzzy if–then rules is used to estimate the index of maneuverability (tracking index λ). This index λ is used to estimate the optimal αβ filter coefficients. The proposed filter is referred to as the Improved Fuzzy alpha-beta filter (IFαβ filter). It is shown through numerical simulations that the proposed filter does not only achieve a good tracking accuracy, but can also be used to detect the start and the end of target maneuvers. Compared to the standard fuzzy alpha-beta filter, the proposed fuzzy algorithm is better, in terms of both accuracy and computational complexity.

In both military and civilian surveillance systems such as Air Traffic Control (ATC) systems, tracking targets in clutter using radar involves dealing with a number of challenges, all related to real time decision and data fusion theories. A major challenge is to detect the real targets through the received measurements in real time and to activate the tracking process.This paper deals with real time automatic initiation of tracks in clutter. Among the proposed solutions in the literature to handle this problem, the conventional Hough transforms (HT) and the Modified Hough Transforms (MHT) have been shown to be effective as track initiator techniques in dense clutter. However, these techniques are computationally intensive. Inasmuch as the tracking of targets is a real time application, we propose in this work, to modify both the Hough transforms (HT) and the Modified Hough Transforms (MHT) so that they can work in real time in adverse environments. The resulting techniques are called Real Time HT (RTHT) and Real Time MHT (RTMHT). Monte Carlo simulations are used to assess the performance of the proposed techniques.

In this paper, we address the problem of gender classification based on facial images. The Speeded Up Robust Feature (SURF) algorithm descriptors are used as features to built dictionaries and a multi-task Sparse Representation Classification (SRC) is used as classifier to determine the gender of an individual face. Our approach uses smaller and compact dictionaries by removing the redundant atoms from the constructed ones. The feasibility of using the SURF on the shape index map for gender classification is demonstrated through experimental investigation conducted on FRGCv2 dataset. The proposed approach achieves 91.04±1.19% of correct gender classification rate using only 5% of the size of the dictionary and 97.83 ± 0.76% is obtained using 23% of the size of the dictionary.

A commonly encountered problem for tracking community is: target tracking in Cartesian plane while the measurements are delivered by radar in polar coordinates. In this paper we consider a maneuvering target tracking problem by using the Fast Interacting Multiple Model (FastIMM) algorithm. Based on the theoretical framework of the Debiased Converted Measurements Kalman Filter (DCMKF), we propose the use of the pseudo-static versions based on the well known αβ and αβγ filters. The performances of these nonlinear filters, have been assessed by means of Monte Carlo simulations and compared to that of the standard filter. The proposed filter is also suitable for real time implementation, which makes it a potential candidate for applications on embedded systems.

Vehicular Ad hoc Networks is pro mising research area to imp rove traffic safety by developing an intelligent inter-vehicle co mmunicat ion system. Routing of data in a vehicular ad hoc network is a challenging task due to the high dynamics of such a network. This paper presents a performance study of routing protocols in various network environment (Urban, Highway) based on metrics such as throughput, packet delivery ratio and average end-to-end delay. For the imp lementation purpose we have used network simulator-2 (NS-2) and VanetMobisim.

In this paper, a novel method for locating multiple moving objects in a video sequences captured by a stationary camera is proposed. In order to determine the precise location of the objects in an image, a new local regions based level set model is carried out. The whole process consists of two main parts: the global detection and the fine localization. During the global detection, the presence or absence of an object in an image is determined by the Mixture of Gaussians method. For the fine localization, we propose to reformulate global energies by utilizing little squared windows centered on each point over a thin band surrounding the zero level set, hence the object contour can be reshaped into small local interior and exterior regions that lead to compute a family of adaptive local energies, which enables us to well localize the moving objects. Moreover, we propose to adapt the smoothness of the contours, and the accuracy of the objects’ perimeter of different shapes with an automatic stopping criterion. The proposed method has been tested on different real urban traffic videos, and the experiment results demonstrate that our algorithm can locate effectively and accurately the moving objects; optimize the results of the localized objects and also decrease the computations load.

The paper addresses the problem of adaptive manoeuvring targets tracking in clutter with a phased array radar. The tracking algorithm is based on the combination of the interacting multiple models (IMM) algorithm and the joint probabilistic data association filter (JPDAF), the resulting algorithm is named IMMJPDAF algorithm. Moreover, the phased array radar is a multifunction radar with the capability to select adaptively the sampling time interval; consequently, the tracking performance is improved. First, a complete comparative study between the IMMJPDAF algorithm and the multirate IMMJPDAF (MRIMMJPDAF) algorithm for tracking close manoeuvring targets with varying amounts of clutter density is presented. Then a description is made of the integration of a new fast method into the IMMJPDAF algorithm to adaptively select the next update time according to the targets motions. We call the resulting algorithm, the fast adaptive IMMJPDAF (FAIMMJPDAF) algorithm. Furthermore, an enhancement of the tracking accuracy in the FAIMMJPDAF algorithm is made by also taking into account the separation distance between targets in the selection of the next update time. The performance of the proposed algorithm, named improved fast adaptive IMMJPDAF (IFAIMMJPDAF) algorithm is assessed via Monte Carlo Simulations and compared with that of four algorithms that use an adaptive selection of the update time: FAIMMJPDAF algorithm, the adaptive IMMJPDAF that uses a modified version of the van Keuk criterion (MAIMMJPDAF), the adaptive IMMJPDAF that uses the original van Keuk method (AIMMJPDAF), and the IMMJPDAF (CIMMJPDAF) that uses a constant update time.

We propose and evaluate a three-dimensional (3D) face recognition approach that applies the speeded up robust feature (SURF) algorithm to the depth representation of shape index map, under real-world conditions, using only a single gallery sample for each subject. First, the 3D scans are preprocessed, then SURF is applied on the shape index map to find interest points and their descriptors. Each 3D face scan is represented by keypoints descriptors, and a large dictionary is built from all the gallery descriptors. At the recognition step, descriptors of a probe face scan are sparsely represented by the dictionary. A multitask sparse representation classification is used to determine the identity of each probe face. The feasibility of the approach that uses the SURF algorithm on the shape index map for face identification/authentication is checked through an experimental investigation conducted on Bosphorus, University of Milano Bicocca, and CASIA 3D datasets. It achieves an overall rank one recognition rate of 97.75%, 80.85%, and 95.12%, respectively, on these datasets.

Nose tip localization is an important step for registration, preprocessing and recognition of 3D face data. In this paper, we propose a new approach for the nose tip detection that is robust to pose and expression variations and in presence of occlusions. From a rotated 3D face, we extract facial curves that are matched to a profile curve model. An optimal matching using the Riemannian geometry, based on the Elastic Shape Analysis is performed to obtain the accurate nose tip. The proposed method requires no training and can locate the nose tip in less than 6 seconds. Experiments are performed on the Bosphorus database. Quantitative analysis and comparison with the ground truth locations are provided. The results confirm that our approach achieves 97.68% with error no larger than 12 mm and 98.19% within 20 mm.

Different location determination methods using wireless signal strength have been proposed to improve the location accuracy and mitigate the multipath problem in indoor environment. In this paper, a fingerprinting-probabilistic approach for indoor localization using wireless technology is proposed. The method is based on the use of the Gaussian Mixture Model (GMM) to approximate the probability distribution of the strength of the signal received by a mobile from Access Points (AP). This probability distribution is then used to infer the mobile location. The performance of the proposed method is compared experimentally to that of another powerful method. The comparison shows the effectiveness of the GMM method.

Nighttime traffic flow congestion estimation is a challenging problem, the use of a model for describing the traffic parameters is a good solutions but it is necessary to have an accurate information about the traffic's state before constructing the model. In this paper we propose a new method for developing a road traffic flow model in this challenging situation using accurately measured traffic parameters.

The capacity to resolve closely spaced sources is one of the important issues of direction of arrival estimation algorithms. For this issue, we present a reduced subspace method based on invariance of noise subspace to the power of radiating sources. Unlike the conventional subspace methods, we use Eigen-values of reduced noise subspace to estimate direction of arrival of narrowband sources with a large array. Our approach is based on the Nystrom method. The main advantage of this method is its low computational complexity. Simulations show that the performances of the proposed method are superior to those of MUSIC when the sources are close and with power level differences between them.