ABSTRACT. In this paper, a parametric study of a new compact planner patch antenna for ultra-wideband (UWB) applications is presented. As a starting point in this study, we have achieved to design a primal antenna model that has been illustrated with practical realization, but which corresponds to a low band performance (2.7–8.2 GHz) compared to our optimized final antenna model. Whereas, the optimized antenna configuration consists of a slotted rectangular and a graduated design for the patch and a small ground plane. The different parameters are investigated for the improvement of the antenna’s proprieties and for the achievement of the preferred UWB band (3.4-15 GHz),. Our optimized antenna, which dimensions are 50×60×1.58 mm3, was fed by an SMA female connector, with a characteristic impedance of 50Ω; in order to measure the return loss and VSWR and to compare them with the simulation results. The bandwidth got measurement ranges from 3.5 GHz to 15 GHz for VSWR < 2. The radiation pattern is directional on the greater part of the working band. Both softwares CST Microwave studio and High-Frequency Structure Simulator (HFSS) are used for simulation which results are in great agreement with the measured parameters.

ABSTRACT. In this paper, we study the performance of a multi-source multi-relay decode and-Forward (DF) cooperative wireless network. We evaluate different scenarios in multi-source multi-relay which allow to analyse the trade-off between the coding gain and the diversity order. Simulation results show that the cooperative diversity order in these scenarios can be designed to maximize the network coding gain in terms of Bit Error Rate (BER) versus signal-to-noise ratio (SNR).

ABSTRACT. In this paper, a compact quad-band bandpass filter based on complement square split-ring resonator (CSRR) defected ground structure (DGS) is proposed. The CSRR as DGS can generate two passbands. Two transmission zeros are created to improve the selectivity of the proposed passband filter and isolation between the generated passbands. Design is simulated using ADS/Momentum and EM Simulation software (CST). The simulated results show that the proposed quad-band BPF has four passbands centered at 1.96, 3.19, 4.69 and 6.43 GHz with the fractional bandwidth of 17.85, 9.4, 11.96 and 23.29%, respectively. The simulated ILs at CF of each passband are 1.23, 2.25, 1.74, and 1.54 dB, respectively.

ABSTRACT. This paper introduces and studies a new approach is investigated to enhance the gain of Microstrip Antenna Array using the substrate integrated waveguide (SIW). The integration of SIW is first implemented in a single microstrip antenna and then in a 1x2 array. This development is based on two designs, one for the 1x2 antenna array with microstrip feed-line with SIW and the other for the inset feed-line with SIW. All these structures are designed using the Rogers Duroïd TM5880 substrate (εr = 2.2 and h = 0.5mm). The proposed design improves on the shortcomings of a conventional microstrip patch antenna (MPA). The proposed array is analyzed for work in (Ku/K) band applications, improved gain and sufficient bandwidth. Therefore, the maximum bandwidth and gain obtained from the 1x2 SIW sawtooth antenna array is 410 MHz and 9.27dBi respectively.

ABSTRACT. In this article, a novel H-shaped stacked ultra wideband (UWB) Dielectric Resonator Antenna (DRA) With Dual Band-Notched Characteristics for WLAN/ITU Bands is presented and studied. The proposed DRA structure consists of H-shaped stacked dielectric resonator fed by a beveled patch, adhered to the back side surface of the stacked resonators and connected to the microstrip line, partial ground plane. To realize dual band-notched characteristics of WLAN (5.1–5.9 GHz) and ITU8.0 (8.025–8.4 GHz), a C-shaped slot is embedding on the beveled patch and U-shaped band stop filter is placed near the microstrip line. A comprehensive parametric study is carried out using HFSS software to achieve the optimum antenna performance and optimize the bandwidth of the proposed antenna. From the simulation results, it is found that the proposed antenna structure operates over a frequency range of 3.59 GHz to 12.23 GHz with a fractional bandwidth of 109.23%, with two band rejections in the frequency bands of 5.20–5.68 GHz and 8.10–8.43 GHz, which Indicating that the antenna is a good candidate for UWB applications, and having better gain and radiation characteristics. The center frequencies and bandwidths of the two notched bands can be adjusted by altering the parameters of the two slots.

ABSTRACT. This paper presents a new architectural development of an impulse ultra-wide-band Radar for through-the-wall applications. The proposed architecture is based on FPGA (\textit{Field Programmable Gate Array}) that can provides an extremely narrow pulse duration. These pulses are then up-converted as the transmitting signal. At the receiver side, high sampling oscilloscope is applied for acquisition the transmitted pulses. The different signal processing steps before image formation are analysed and presented. Experiment results are performed for single and multiple human objects. These results demonstrate a high image resolution, that can observing persons behind the wall using a back-projection algorithm. Therefore, the proposed experiment can be applied in a real embedded system of through-the-wall imaging.

ABSTRACT. A simple four-dimensional chaotic circuit based on Colpitts configuration is presented, it generates chaotic oscillations at Sub-1GHz frequencies with almost flat spectrum. The chaotic dynamics are illustrated by waveforms of the signals, bifurcation diagrams, phase portraits of the attractors and spectra of the oscillations, using both numerical and circuit simulation. The Circuit simulation is carried out using Advanced Design System (ADS) tool, with the PSpice model of the used transistor to support the theoretical analyses. The spectral characteristic of the proposed generator makes it promising for many communication applications such as spread spectrum communication, direct chaotic communication and so on.

ABSTRACT. Diabetic retinopathy is a retinal disease that affects diabetes patients and the major cause of blindness for age population. It is an asymptomatic disease, which involves changes to blood vessels that can cause them to bleed or leak fluid, causing distortion of vision. Therefore, the blood vessels extraction is very important to help ophthalmologists to recognise this disease at the first stage in order to prevent an eventual loss of vision. Consequently, in this paper, we propose an automatic system for diabetic retinopathy detection from color fundus images. The proposed approach is based on the segmentation of blood vessels and extracts the geometric features, which are used in the early detection of diabetic retinopathy. The Hessian matrix, ISODATA algorithm and active contour are used for the segmentation of the blood vessels, we have used. Finally, we have applied the decision tree CART algorithm to classify images into normal (NO-DR) or DR. The proposed system was tested on the DRIVE and Messidor databases and achieved an average sensitivity, specificity and accuracy of 89%, 99% and 96%, respectively for the segmentation of retinal vessels and 91%, 100% and 93%, respectively for the classification of diabetic retinopathy. Finally, the obtained results indicate that our approach is effective in diabetic retinopathy detection with better accuracy over existing methods, which can help ophthalmologists in early diagnosis.

ABSTRACT. Facial recognition techniques are considered the most important fields of research in biometric technology. In this research paper, we present a Face Recognition (FR) system divided into three steps: The Viola-Jones face detection algorithm, facial image enhancement using Adaptive Histogram Equalization algorithm (AHE), and feature learning for classification. For learning the features followed by classification we used VGG16, ResNet50 Convolutional Neural Networks (CNN) architectures for the proposed system. Our experimental work was performed on the Extended Yale B database and CMU PIE face database. Finally, the comparison with the other methods on both databases shows the robustness and effectiveness of the proposed approach. Where the Resnet50 architecture has achieved a rate of 97, 23% and 98, 38% respectively.

ABSTRACT. Recently, total variation (TV) based minimization algorithms have obtained a considerable success in compressed sensing (CS) recovery for images, but the use of total variation is not able to recover the fine details and texture of images. In this paper, we propose an improved recovery algorithm by incorporating the local smoothness and nonlocal self-similarity constraints regularization in compressed sensing optimization problem, which help to preserve image properties. Furthermore, an efficient augmented Lagrangian algorithm is used to solve the above problem and optimize the solution. The proposed algorithm called total variation by augmented Lagrangian method (TV.ALM) is compared against Nesterov’s algorithm (NESTA) and Two-step Iterative shrinkage/thresholding algorithm (TwIST) to evaluate their performance. Experimental results based on quality assessment such as peak signal-to-noise ratio, mean square error and structural similarity indicate that our algorithm TV.ALM achieves significant performance improvements in image recovery.

ABSTRACT. Surveillance videos are considered as a very important mechanism in a smart city project. In recent years, a method called deep learning was combined with reinforcement learning techniques to learn useful representations for the problems with high dimensional raw data input. The deep reinforcement obtained astonishing results in AI fields by providing great performance to problems that were previously intractable such as video games and robotics applications. In this paper, we develop a Deep Q Learning Network (DQN) to localize anomalies in videos by enabling the agent to learn how to detect and recognize the abnormalities in videos. Our idea is inspired by multiple instance learning (MIL) techniques based on common share features with reinforcement learning. We consider normal and abnormal videos as bags and the selection of videos clips as actions. In our DQN architecture we will use the new 3D Convolutional Neural Network that perform 3D convolutions over the spatiotemporal video volume specially developed for video analysis and action recognition followed by a fully connected layer, which compute probability for each video segment in both positive(anomalous) and negative(normal) bags indicating how likely a clip is containing an anomaly. The next step will be to generate a special state by taking the two segments with the highest anomaly score(Q value) from positive and negative videos so that the agent will be able to avoid a false alarm in anomaly detection. Our method is applied to a new large-scale dataset of 128 hours of videos called UCF-Anomaly-Detection-Dataset, it is about of 1900 long and untrimmed real-world surveillance videos, with 13 cases of realistic anomalies.

ABSTRACT. The objective of this paper is to analyze radiographic images of patients and to discriminate between them using nine morphological and clinical parameters. Four models were constructed and trained using three Bayes classifiers: Bayesian logistic regression, Byes Net, and Naive Bayes. The purpose was to find the best configuration combining selected features and best classifier providing the highest rate of classification. The validation was done using the '10-fold cross-validation' technique. A total of 100 images were collected from patients, divided into two groups, 50 healthy subjects and 50 osteoporotic patients. The results obtained reveal that the selected features model combined to the Bayesian logistic regression classifier provided accurate discrimination between the two populations, with ACC = 87% demonstrating the performance of this configuration.

ABSTRACT. Billions of suitcases and other belongings are checked every year in airport X-ray systems around the world. This process is very important because it involves the detection of potentially dangerous objects such as firearms and explosives. However, the image quality of x-ray screening devices needed to be improved. This article attempts to make a contribution to the field of improving the radiographic image of luggage by proposing a complete procedure for improving the image quality based on discrete wavelet transform (DWT) image fusion followed by a noise suppression operation to obtain a good improvement of the fusion result. This article also presents a comparative analysis of five well-known image enhancement algorithm models, the evaluation is done using the signal-to-noise ratio (PSNR). The results show that the proposed system provides promising results compared to the other techniques tested, which proves that the proposed approach is very appropriate and recommended for easy/fast identification of threatening objects in a manual or automatic manner.

ABSTRACT. This paper presents a fractal and multifractal analysis for detection and segmentation of breast anomalies using box counting method. The first step is the detection of the pathological breast by comparing parameters extracted from the multifractal spectrum of pairs of mammograms. The second consists to find the suspect region by decomposing the pathological mammogram into four blocks. The suspect region is characterized by a low value of Holder exponent, large values of asymmetry, lacunarity and fractal dimension. Finally, segmentation of some anomalies is made on α-image and compared with k-means method. The whole process was applied on images of the Mini-Mias database. The obtained results are promising.

ABSTRACT. Local Binary Pattern (LBP) is a gray scale-invariant local operator, it is encoded with each pixel by neighbourhood thresholding which is considered as powerful and simple in the case of real-time implementation. The latter is efficient in characterizing the texture and can be adapted to different problems. Different descriptors have been developed that are based on LBP. In our case, we chose Local Phase Quantization (LPQ), Local Ternary Patterns (LTP), Binarized Statistical Image Features (BSIF), Multiblock Local Binary Pattern (MB−LBP) to characterize meteorological images and make a comparative analysis between these different methods. Since the objective is to distinguish between precipitation and non-precipitation pixels, we inject the characteristic at the input of a SVM classifier. The approaches were performed on two databases taken from Setif (Algeria) and Bordeaux (France). The experiments show that the best results are obtained by combining LBP to the variance for Setif while the addition of BSIF and LPQ gives the best results when considering the Bordeaux region.

ABSTRACT. This paper deals with the design of a 60 GHz microstrip patch antenna using transmitarray structures. The influence on the gain and bandwidth was investigated in terms of size and shape. Simulated results showed that the antenna performances can be significantly improved by using rectangular slots instead of circular slots.

ABSTRACT. Breast cancer is the most feared disease in the female population. Early detection plays an important role to improve prognosis. Mammography is the best examination for the detection of breast cancer. However, in some cases, reading mammograms is difficult for radiologists. For this reason, several researches have been conducted to develop Computer Aided Diagnosis tools (CAD) for this disease which aims to interpret mammography images. This paper investigates a new CAD system based on Transductive scheme and Mutual Information for breast abnormalities diagnosis. In the proposed method, a feature vector contains a combination of two features extraction method: Grey Level Co-occurrence Matrix and local Binary Pattern. In the next step, a novel scheme combining Mutual Information and Correlation-based feature selection was applied for selecting the most relevant features. Finally, the classification was achieved using a Transductive Support Vector Machine classifier. The effectiveness of the proposed CAD is examined on the DDSM dataset using classification accuracy, recall and precision. Experimental results demonstrate that the proposed CAD system is clinically significant and can be used to classify the abnormalities of the breast.

ABSTRACT. The loss of a limb or a fraction of it, which is either due to a congenital malformation or a surgical amputation over an accident or a disease, puts an enormous amount of strain on the person both emotionally and financially speaking. The overall aim of this work is to contribute in the development of a low-cost hand prosthesis. In this paper, a system for identification of two gestures (wrist flexion and wrist extension) by decoding surface electromyography (EMG) signals of amputees is proposed. The acquisition circuit developed has 2 simultaneous channels and provides good quality resulting signals on output. A Database has been created and filled with signals acquired from 20 healthy subjects. For analysis of the signals, five level DWT decomposition was used, and for classification ANN was employed. After the analysis of data, we were able to identify and detect the movements. The method demonstrated have shown an overall suitable accuracy around 80% and can further be used for the prosthesis.

ABSTRACT. Nowadays, fault detection in induction motors has gained importance. Motor health monitoring is performed to diagnose their operating condition using vibration signals. These signals are processed using different signal processing methods to extract the characteristic parameters permitting localization of the fault. In this paper, we propose a diagnostic method based on Hilbert and Discrete Wavelet Transforms for the detection of bearing faults in asynchronous machines. The discrete wavelet transform (DWT) is intended to provide the detail coefficients while the Hilbert transform (HT) is used to obtain the temporal envelope then the envelope spectrum of the detail. The kurtosis value indicates the optimum decomposition wavelet level containing the significant frequencies corresponding to faults for early detection. The result obtained by HT-DWT is more suitable for the analysis of emergency signals. This technique is effective for either stationary or non-stationary signals. Healthy case is compared to faulty case in order to extract frequencies characterizing different faults. The validation of this approach is evaluated by comparing theoretical with experimental results.

ABSTRACT. In this paper, a novel rectangular shaped patch antenna is designed for Ultra-Wide Band applications. The studied antenna is compact and has a small size (20 mm x 21.5 mm) with a 50-Ω feed line and a partial ground plane which offer a very simple geometry. The antenna is printed on FR4 material which has a depth of 1.60 mm and a relative permittivity equal to 4.3.The different results are obtained using the CST Microwave Studio software. The antenna operates over a 3.12 GHz to 17.37 GHz range with a return loss less than -10 dB and voltage standing wave ratio (VSWR) less than 2.The reported antenna achieves a maximum gain of 5 dB.

ABSTRACT. As flow rates increase, chromatic dispersion becomes a major disadvantage in long distance links. In this paper, we present simulation results using the beam propagating method (BPM) of a hexagonal microstructured fiber infiltrated with water designed for broadband chromatic dispersion compensation using temperature change

ABSTRACT. A number of modulation techniques have been proposed and studied for the successful of the operation Ground-Ground wireless optical communication link. Each modulation scheme has its advantages and drawbacks. The PPM (Pulse Position Modulation) had shown to be desirable for its power efficiency. In this paper, we discuss the use of RZ-PPM (Return-To-Zero PPM) and Non-Return-To-Zero (NRZ PPM) modulation techniques for free space optical communications systems. The results show that RZ-PPM shows more power efficiency at expense of a de gradation in term of spectral efficiency.

ABSTRACT. 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.

ABSTRACT. In this work,we study the effect of Marijuana binaural beats on the brain. For this aim three groups are allocated: before, during, and after 10 min from hearing binaural beats. The electroencephalogram (EEG) signal was detected using Bitalino sensor and analyzed using Matlab software. The synchronization degree parameter is calculated using bispectrale analysis. The obtained results are compared to six others parameters which also extracted from the same analysis, such as entropies, the average of bispectrale amplitude, and weighted center of the bi-spectral. The obtained results are very satisfactory and show that the synchronization is high in normal cases, low after hearing to Marijuana binaural beats, and zero during hearing to digital drugs.

ABSTRACT. The advent of non-invasive imaging in sections, has allowed the development of a new morphological exploration and semiology based on the 3D examination of organs. The extraction of myocardial pathologies in cardiac cine-MRI often requires a prior segmentation of the left ventricle (LV). In this paper, a new semi-automatic computer system for left ventricle analysis from short-axis cardiac cine MRI is presented. For this, we have applied a set of image processing and mathematical morphology operators in order to segment the LV. In the next, the parameters such as end-diastolic volume (EDV), end-systolic volume (ESV) and ejection fraction (EF) are calculated for characterization. The proposed method was tested on the Heart database containing 18 patients. The obtained results are satisfactory and give good correlation between our segmentation method and that of experts which shows the accuracy of the proposed method which can be used to aid diagnosis.

ABSTRACT. In remotely sensed data, two commonly held ways are used to obtain a relevant partitioning. The first one consists to apply several clustering algorithms individually, then to choose the best partition. The second commonly held way consists to apply the same clustering algorithm with several initializations and to choose the best partition. It is worth noting that the best partition is chosen according to a given evaluation criterion. Conversely, the Clustering ensembles can provide the best partition with high accuracy and consequently overcome limitations of traditional approaches based on single classifiers. Clustering ensembles usually involve two stages. First, multiple partitions are obtained through several runs of initial clustering analysis. Subsequently, the specific consensus function is used in order to find a final consensus partition from multiple input partitions. In this paper, we investigate this technique in the unsupervised classification by using Synthetic data and composite data image. The first stage is assumed by four clustering algorithms, the well-known k-means algorithm, the k-harmonic means algorithm, Fuzzy c-means and the self-organizing map. The best clustering is obtained according to WB index. The relabeling and the voting methods are used in the second stage. Experimental results obtained outperform the results of the individual clustering.

ABSTRACT. The transmission of medical images via a communication system makes it possible to make several medical diagnoses and reduce the distance traveled by the patients. The good quality of the images transmitted is more than necessary to have the right medical opinion. The MC-CDMA system combined with the SPIHT coder allows a good transmission of medical images with a very acceptable quality of the images received.

ABSTRACT. This paper introduces a new method to construct zero cross correlation (ZCC) code for spectral amplitude coding-optical code division multiple access (SAC-OCDMA) systems. Its construction is based on an identity matrix which characterized by: flexibility in code weight and simple construction steps. According to numerical results, SAC-OCDMA system has better performance using our proposed ZCC code comparing with dynamic cyclic shift (DCS) and random diagonal (RD) codes in term of BER thanks to zero cross correlation property. Where, it can enhance the system capacity up to 3.26 and 1.76 times, and data rate is also enhanced about 5.75 and 3.02 times comparing with DCS and RD codes respectively.

ABSTRACT. In this paper, the long range target tracking problem is presented. Two versions of nonlinear Kalman Filters are used to bring out their performances, the first one is the Extended Kalman Filter EKF and the second one is the Quadrature Kalman Filter (QKF). The problem addressed is the target tracking with high cross-range that moves in plan. The position measurements are provided by radar in polar coordinates. The simulations results show that, in terms of Root Mean Square Errors (RMSE), the QKF filter performs better than the classical EKF.

ABSTRACT. We address with this paper some real-life healthy and epileptic EEG signals classification. Our proposed method is based on the use of the discrete wavelet transform (DWT) and Support Vector Machine (SVM). For each EEG signal, five wavelet decomposition level is applied which allow obtaining five spectral sub-bands correspond to five rhythms (Delta, Theta, Alpha, Beta and gamma). After the extraction of some features on each sub-band (energy, standard deviation, and entropy) a moving average (MA) is applied to the resulting features vectors and then used as inputs to SVM to train and test. We test the method on EEG signals during two datasets: normal and epileptics, without and with using MA to compare results. Three parameters are evaluated such as sensitivity, specificity, and accuracy to test the performances of the used methods.

ABSTRACT. In this article, the authors aim to study, present and compare in an optimal way the functions of coding and decoding of a channel of transmission through the deferent channel coders (Turbo-Code, Convolution Code, Code en Bloc, Code LDPC). Whatever the quality of the communication media and transmission techniques used, disturbances will occur causing errors. In its conditions, the received binary sequence will not be identical to the following emitted. To eliminate these problems, techniques of protection against transmission errors can be used. To eliminate the noise introduced by the channel and to improve the reception of the communication system it is necessary to add an Error Correcting Encoder (ECC) in this chain. As a choice of the Code, it is possible to use turbo codes because these codes make it possible to approach the theoretical limit of correction presented by Shannon. It is for this reason that turbo codes are used in most modern communications systems. The results of simulations obtained under conditions compatible with a hardware realization of the encoder and the decoder show the performances very close to the theoretical limits of the channel coding

ABSTRACT. Carbon monoxide brings great harms to people dailys life. In addition, it’s a very toxic gas undetectable by humans, it occurs when a device burns a fuel such as gas, wood, oil and its derivatives, etc. and spreads very quickly inside the homes. In this work, is presented an IoT prototype for detection of a dangerous gas based on the WeMos D1 mini card programmed by Arduino IDE software to prevent asphyxiation accidents. IoT technology gives us great capabilities for the development of Web applications that connect the real world to the digital world. In this prototype, we used the MQ-2 gas sensor for detection. When the gas concentration exceeds a certain threshold, the system triggers the fan and if the detected value exceeds a very large threshold, it immediately alerts the users by sending alerts in the form of notifications via the Pushbullet platform.

ABSTRACT. The field of medical image segmentation is incredibly broad. Several works has been conducted on the study of such a field, especially Magnetic Resonance Imaging (MRI). This article aims to perform brain tumor segmentation on MRI images using an improved clustering method based on K-Means (KM) algorithm. The proposed approach deals with some limitations of the standard KM algorithm such as, the random initialization of clusters centroids, and the noise sensitivity. The main idea consists to combine the Darwinian Particle Swarm Optimization (DPSO) technique, the KM algorithm and the Morphological Reconstruction (MR) operation. The DPSO technique is applied to the MRI medical images for initialization of cluster centroids. Moreover, the MR filter is used to eliminate the noise and generate more compact and well separated clusters. The performance of the proposed method was evaluated by comparing it to some state of the art segmentation algorithms such as, standard KM clustering algorithm and DPSO-based multilevel thresholding. The results show the effectiveness of the proposed method.

ABSTRACT. This article investigates a Modified Search Equation of Artificial Bee Colony called ModSABC. To ensure that ABC does not stuck in local optima, this study modified search equations for employed and onlooker bees managing more parameters for each food source (more features). ModSABC increases the performance and reduces the size of feature subsets. Experimental results validate that the ModSABC method outperforms on UCI data standard ABC, Zhu ABC, Wang and al ABC, particle swarm (PSO). Conducted studies confirm the effectiveness of the proposed FS approach.

ABSTRACT. Although the induction motor (IM) is known for its robustness and low cost of construction, it can happen that the it presents electrical, magnetic or mechanical faults. In this paper, a Hilbert-Huang Transform (HHT) spectral envelope of the stator current signature is proposed for the diagnosis of an IM rotor broken bar (RBB) fault. Firstly, the classical well-known spectral analysis Fast Fourier Transform (FFT) technique is applied to the stator current signal to obtain the harmonic frequency characterizing the RBB fault. Secondly, the HHT technique based on the ensemble empirical mode decomposition (EEMD) algorithm is proposed and applied to the stator current signal to obtain a function called the intrinsic mode function (IMF) containing the frequency that is related to the harmonic frequency characterizing the RBB fault. A comparative study is then carried out to illustrate the HHT technique merits and its superiority with respect to the FFT classical one. To test the effectiveness of the proposed HHT technique and validate the simulation results obtained, several experimental tests are also conducted using a test bench.

ABSTRACT. Just few years ago, a new branch in microwave engineering developed with the emergence of Metamaterials (MTMs). The implementation of the first artificial medium with negative effective dielectric permittivity and magnetic permeability opened the door to the experimental study of a new kind of media: left-handed media (LHM). In this paper, the study and design of a printed monopole antenna operating in L band based on High Impedance Surface (HIS) substrate is proposed. The aim is to simultaneously reduce the antenna size and maintain the operating frequency but also to satisfy the desired antenna performance. The EM simulations results of directivity, gain and reflection coefficient are of great importance and affirm the employing of metamaterials in such applications

ABSTRACT. In this paper, a simple mathematical model has been evaluated for an optical link constitute with a single mode fiber (SMF), a dispersion compensating fiber (DCF) and Erbium doped fiber amplifier (EDFA). The model allows us to perspecte the tolerance by which the channel signal power level passing through a cascade of (SMF+DCF+EDFA) may be driven into unacceptable regime of bit errors which means a distorted signal shape at the output of the link. The propagation of the multiplexed signal into the optical link was described by two differential equations. The first one describes the propagation on the (SMF+DCF) portion using the nonlinear Schrödinger equation (NLS) [1] which takes into account the linear effects such as the attenuation and dispersion effects, and the nonlinear effects such as the Kerr effect, Raman and Brillouin scattering. The second is the ordinary differential equation (ODE) proposed by Bononi et al [2] which describes the propagation of the signal in the (EDFA). The performance of the optical system under this mathematical model was investigate. Where we varied certain parameters such as the length of the link, the input signal power, the EDFA pump power, the doped fiber amplifier length and the modulation format (RZ/NRZ). The analysis justifies the accuracy that each component must have to guarantee a good signal reception at the end of the link.

ABSTRACT. Both mutual coupling and footprint area impose the major design constraints in planar antenna array. In most recent studies, artificially structured electromagnetic materials, termed metamaterials (MTMs) are commonly investigated for their highly desirable electromagnetic proprieties, which makes them potential candidate for this purpose. In this paper, the design of an array of two closely spaced rectangular patches based on spiral metamaterial-artificial transmission lines (MTM-ATLs) is presented and examined. In addition, the one-dimensional (1-D) composite right-left handed (CRLH) equivalent circuit is used and the corresponding parameters are extracted to characterize the proposed structure. The corresponding full-wave simulations indicating that more than 15dB mutual coupling reduction is achieved, accompanying improved gain, efficiency, and VSWR. The possibility of exploiting this design for wireless personal communications is suggested

ABSTRACT. In this paper, we propose a construction of two sets of ternary zero correlation zone (ZCZ) codes for quasi-synchronous (QS) code-division multiple-access (CDMA) systems. The proposed spreading codes almost satisfy the theoretical limit of code size for a given code length and ZCZ. Codes from different sets are mutually orthogonal. Furthermore, odd number-indexed codes in a set with even number-indexed codes in the other set have also ZCZ. The proposed construction is based on mutually orthogonal complementary sets and perfect codes.

ABSTRACT. In Voice over Internet Protocol (VoIP) applications, packets loss is major source of speech impairment. This work consists on using packet loss concealment methods based on Multiple Description Coding (MDC) and Forward Error Correction (FEC) to improve the speech quality deterioration caused by packets loss for Code-Excited Linear Prediction(CELP) based coders in packet network. We applied our approach to the standard ITU-T G.722.2 standard speech coder to evaluate its performance. The Perceptual Evaluation of Speech Quality (PESQ) and Enhanced Modified Bark Spectral Distortion (EMBSD) tests under various packet loss conditions confirm that the proposed algorithm, namely MDC is superior to the concealment algorithm embedded in the G.722.2. The performance measures demonstrate that the concealment method based FEC is better than the interleaving and MDC methods at the expense of extra delay.

ABSTRACT. Isotropicallity and high gain are two highly desired features in the design of rectennas for high and efficient energy harvesting capability. Therefore, in this paper, an efficient design of antenna array is proposed for energy harvesting application at 2.45 GHz frequency band. The array includes eight microstrip antennas (MSAs) elements which are designed based on air superstrate and reflector_ground plane. This has allowed the gain improvement of the structure which is more than 8.20 dB at all the preferred directions. The MSAs elements are vertically positioned to form a pentagon which has helped to achieve full 360° radiation at the azimuth with nearly 54° of 3 dB beamwidth at the elevation

ABSTRACT. The study done in this paper focuses on the detection of breast cancer by Support vector Regression, by rotating the transmitting antenna from 15◦, 30◦, 45◦, 60◦, 75◦, to 90◦ relative to its initial position which is of 0◦ (i.e., to the opposite of the receiving antenna). We have generated our database by using a CST electromagnetic simulator for each antenna location. A very optimistic results have been observed with the Support Vector Machine method. Hence, the proposed algorithm is very potential for early tumor detection to save human lives in the future.

ABSTRACT. In this paper, we propose a hyperchaos-based speech encryption algorithm for AMR-WB Codec. The algorithm adopts a 5D hyperchaotic system, and the generated key stream is employed for confusion and diffusion processes. In order to strengthen the security of the cryptosystem, a chaotic shift keying mechanism is employed and keys are assigned for confusion and diffusion processes. Whereas the keys Xn are employed for the confusion of the frame's parameters using permutation, the keys Yn, Zn, Wn and Un are used for diffusion that is performed by the Xor operation with the parameters of the 4 codec's sub-frames. Simulation and statistical analysis through the Enhanced Modified Bark Spectral Distortion (EMBSD) and Mean Opinion Score (MOS) prove that this approach works well and confirm the efficiency of our presented schemes.

ABSTRACT. This paper addresses the simultaneous localization and mapping (SLAM) of a robot in an unknown environment. Several techniques were proposed in the researches to solve this problem employ the different representations of the environment either by points or lines with their information on the extreme geometric positions. The objective of this paper has proposed a solution based on Extended Kalman Filter (EKF) for a single robot. The algorithm developed in our work is the EKF-SLAM which is implemented experimentally using a Pioneer 3-AT mobile robot equipped with a 2D laser telemeter. The obtained results using EKF-SLAM based lines shows the improvements in terms of accuracy and smoothness compared to the EKF-SLAM based points and odometer.

ABSTRACT. We describe in this paper, an algorithm for automatic diffracted order(s) detection and location of a digital multiplexed off-axis hologram. In noiseless composite holograms, the centroid of each diffracted order corresponds to one of these maximum values of power spectra, but in real situation, this schema is not reproduced. The adequate suppression of the zero order as the preliminary step has for effect to suppress the maximum values corresponding to this term, and also to drastically enhance the power spectra of the diffracted orders and thus improve their detection. The proposed method is integrated as an automatic filtering procedure. We demonstrate from the experimental multiplexed microparticules holograms the effectiveness of the proposed method to discriminate between different diffracted orders and to give the exact location of highest value representing the diffracted order of interest.

ABSTRACT. In this paper, we deal with newborn’s EEG seizure detection. The presented approach is combined with previously proposed methods in literature in order to enhance detection performance. The features extracted from Doppler-lag domain are comparatively assessed using receiver operator characteristic (ROC) analysis measure. On the other hand, we combine further features that are obtained using time-frequency signal analysis tools. In particular, the recently proposed time-frequency distributions (TFDs) based on kernels with compact support (KCS) are employed and compared to the best-known quadratic representations. The obtained results show detection performance improvement by up to 1.5% compared to the other investigated methods.

ABSTRACT. Abstract— Data management used in a cloud environment is always challenging. Data replication is a well-known data management technique that has been commonly adopted by cloud systems as an effective solution to achieve good performance in terms of response time, load balancing and most importantly, high data availability and reliability. To maximize the benefit of data replication, replicas placement and replacement in the system is critical. Several replica placement strategies have been proposed in the literature; some of them were based on criteria such as: mean service time, failure probability, load variance, latency, storage usage and other. For each criterion is assigned a weight by the user which reflects the importance of each criterion. We propose in this paper an original approach based on a multi-criteria decision analysis model called ELECTRE-I (ELimination and Choice Expressing REality). This modeling allows us to improve the selection of the candidate site for replica placement, which have a good impact on the simulation results, and improve performances

ABSTRACT. Compressive Sensing (CS) is a new approach in signal processing that aims acquiring and compressing signal simultaneously. CS suggests that if a signal is sparse, the original signal can be reconstructed by exploiting a few random measurements using reconstruction algorithms. In this paper, we investigate the performance of Smoothed- L0 norm (SL0) algorithm to reconstruct an audio signal and compare its performance to the two most used algorithms in audio CS: -magic and Orthogonal Matching Pursuit (OMP). This study adopts the Modified Discrete Cosine Transform (MDCT) for sparse representation and random Gaussian matrix for measurement matrix. These Algorithms are evaluated using Signal-to-noise ratio (SNR) and computational complexity for different number of measurements. Results show that SL0 algorithm performs better in both reconstruction quality and computational complexity.

ABSTRACT. Recognizing human emotion in speech signal has attracted much attention and plays an important role in affect computing, artificial intelligence and signal processing areas. The aim of speech emotion recognition system is to extract the information from the speech signal and identify the emotional state of a human being. In this work emotional speech corpus in Algerian Dialect was created for parameters extraction to analyze the emotions of fear, anger and neutral. The selected parameters in our study are the prosodic (pitch, intensity and duration), the unvoiced frames, jitter, shimmer and cepstral parameters MFCCs (Mel-Frequency Cepstral Coefficients). The system of recognition is based on the method of classification KNN (K-Nearest Neighbor). The obtained results lead us to observe that combined prosodies, jitter, shimmer, unvoiced frames and MFCCs parameters give recognition rate important (84.02%).