MONIA ALOUANE

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Papers by MONIA ALOUANE

A QoS Aware Cluster Head Selection and Hybrid Cryptography Routing Protocol for Enhancing Efficiency and Security of VANETs

IEEE Access

Nowadays, VANET (Vehicular Ad hoc Network) is one of the key aspects of developing advanced intel... more Nowadays, VANET (Vehicular Ad hoc Network) is one of the key aspects of developing advanced intelligent transportation systems. Due to its huge mobility and rapid topology alteration, the network exposes to link failure that affects the firmness of the network and causes delay and congestion. Additionally, the dynamic change in the network routing affects the network's security, making it vulnerable to various attacks, and results data loss. An efficient and highly secured routing protocol is needed to overcome these drawbacks. Subsequently, this research proposes a new routing protocol that combines the Quality of Service (QoS)-aware Cluster Head (CH) selection and hybrid cryptography named QoS+. The QoS+ protocol is mainly divided into QoS-based CH selection and hybrid cryptography modules. The CH selection module based on QoS parameters attempts to provide reliable and stable clusters and improve the firmness and connectivity during the communication process of the network. The hybrid cryptography module contains Advanced Encryption Standard (AES) and Elliptic Curve Cryptosystems (ECC) algorithms. It attempts to improve the security and privacy of the network. The QoS+ protocol is evaluated by a developed VANET simulator using NS2 software. The simulator consists of a network model, a load model, and an attack model. Various speed and transmission ranges and gray hole and wormhole attacks are used in the simulator. The outcome calculated from the performance analysis shows that the proposed QoS+ protocol has a 7% to 24% higher message success rate, 500 to 800 higher packets normalized routing load, 350 to 550 Kbps higher throughput, 5% to 17% higher efficiency, and 50ms to 12ms lower end-to-end delay when compared with the earlier works of ECHS and KMSUNET. The proposed QoS+ protocol also achieves superior performance in terms of CH efficiency, cluster member efficiency, and average cluster number with various speeds and transmission ranges. INDEX TERMS Vehicular ad hoc network (VANET), quality of service (QoS), network security, cluster head (CH).

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Speech Emotion Recognition based on Long Short Term Memory network

2022 IEEE Information Technologies & Smart Industrial Systems (ITSIS)

An Intelligent Diabetic Patient Tracking System Based on Machine Learning for E-Health Applications

Sensors

Background: Continuous surveillance helps people with diabetes live better lives. A wide range of... more Background: Continuous surveillance helps people with diabetes live better lives. A wide range of technologies, including the Internet of Things (IoT), modern communications, and artificial intelligence (AI), can assist in lowering the expense of health services. Due to numerous communication systems, it is now possible to provide customized and distant healthcare. Main problem: Healthcare data grows daily, making storage and processing challenging. We provide intelligent healthcare structures for smart e-health apps to solve the aforesaid problem. The 5G network must offer advanced healthcare services to meet important requirements like large bandwidth and excellent energy efficacy. Methodology: This research suggested an intelligent system for diabetic patient tracking based on machine learning (ML). The architectural components comprised smartphones, sensors, and smart devices, to gather body dimensions. Then, the preprocessed data is normalized using the normalization procedure....

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A New Approach for Sparse Phase Retrieval Based on Support Lifting and Least Square Estimation

HAL (Le Centre pour la Communication Scientifique Directe), May 19, 2017

To recover a signal x from the magnitude of a possible linear transform of it, problem known as P... more To recover a signal x from the magnitude of a possible linear transform of it, problem known as Phase Retrieval (PR), signal sparsity property has been used to guide the uniqueness of the solution. This paper presents herein a new method for sparse phase retrieval (SPR). Based on a lifting operation, we reduce the problem of SPR to solving a linear system with regards to a vectorized version of xx T . We then use the structured sparsity property of this vectorized form to interpret this operation rather as a lifting operation of the signal support. The signal support is identified iteratively using the gradient pursuit principle in conjunction with subsequent refinements aiming to control the stability of the updated solution. A simple least square estimation on the lifted support is then brought out, iteratively and if required, to determine the lifted solution; from which a rank-1 decomposition is achieved to recover the signal of interest. Simulation results confirm the efficiency of the so-called Greedy Support-Lifting Based algorithm (GSuLA) with acceptable complexity. Robustness of the algorithm is also assured for noisy measurements.

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Performance Analysis of Deep Learning Algorithms in Diagnosis of Malaria Disease

Diagnostics

Malaria is predominant in many subtropical nations with little health-monitoring infrastructure. ... more Malaria is predominant in many subtropical nations with little health-monitoring infrastructure. To forecast malaria and condense the disease’s impact on the population, time series prediction models are necessary. The conventional technique of detecting malaria disease is for certified technicians to examine blood smears visually for parasite-infected RBC (red blood cells) underneath a microscope. This procedure is ineffective, and the diagnosis depends on the individual performing the test and his/her experience. Automatic image identification systems based on machine learning have previously been used to diagnose malaria blood smears. However, so far, the practical performance has been insufficient. In this paper, we have made a performance analysis of deep learning algorithms in the diagnosis of malaria disease. We have used Neural Network models like CNN, MobileNetV2, and ResNet50 to perform this analysis. The dataset was extracted from the National Institutes of Health (NIH) w...

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AAL: Intelligible City for All Evaluation of Ecological Intelligibility Test for Normal Hearing and Hearing Impaired

HAL (Le Centre pour la Communication Scientifique Directe), 2014

International audienc

Nonstationary Cma For Decision Feedback Equalization Of Markovian Time Varying Channels

In this paper, we propose a modified version of the Constant Modulus Algorithm (CMA) tailored for... more In this paper, we propose a modified version of the Constant Modulus Algorithm (CMA) tailored for blind Decision Feedback Equalizer (DFE) of first order Markovian time varying channels. The proposed NonStationary CMA (NSCMA) is designed so that it explicitly takes into account the Markovian structure of the channel nonstationarity. Hence, unlike the classical CMA, the NSCMA is not blind with respect to the channel time variations. This greatly helps the equalizer in the case of realistic channels, and avoids frequent transmissions of training sequences. This paper develops a theoretical analysis of the steady state performance of the CMA and the NSCMA for DFEs within a time varying context. Therefore, approximate expressions of the mean square errors are derived. We prove that in the steady state, the NSCMA exhibits better performance than the classical CMA. These new results are confirmed by simulation. Through an experimental study, we demonstrate that the Bit Error Rate (BER) is ...

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Audio encoding based on the Empirical Mode Decomposition

2009 17th European Signal Processing Conference, 2009

This paper deals with a new approach for perceptual audio encoding, based on the Empirical Mode D... more This paper deals with a new approach for perceptual audio encoding, based on the Empirical Mode Decomposition (EMD). The audio signal is decomposed adaptively into intrinsic oscillatory components by EMD called Intrinsic Mode Functions (IMFs), which can be fully described by their extrema. These extrema are encoded after an appropriate thresholding scheme controlled by a psycho-acoustic model. The decoder recovers the original signal after IMFs reconstruction by means of spline interpolation and their summation. The proposed approach is applied to different audio signals and results are compared to wavelets and to MPEG1-layer3 (MP3) approaches. Relying on exhaustive simulations, the obtained results show that the proposed compression scheme performs better than the MP3 and the wavelet approach in terms of bit rate and audio quality.

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Intrinsic Mode Functions

In this paper a new method for voiced speech enhancement combining the Empirical Mode Decompositi... more In this paper a new method for voiced speech enhancement combining the Empirical Mode Decomposition (EMD) and the Adaptive Center Weighted Average (ACWA) filter is introduced. Noisy signal is decomposed adaptively into intrinsic oscillatory components called Intrinsic Mode Functions (IMFs). Since voiced speech structure is mostly distributed on both medium and low frequencies, the shorter scale IMFs of the noisy signal are beneath noise, however the longer scale ones are less noisy. Therefore, the main idea of the proposed approach is to only filter the shorter scale IMFs, and to keep the longer scale ones unchanged. In fact, the filtering of longer scale IMFs will introduce distortion rather than reducing noise. The denoising method is applied to several voiced speech signals with different noise levels and the results are compared with wavelet approach, ACWA filter and EMD-ACWA (filtering of all IMFs using ACWA filter). Relying on exhaustive simulations, we show the efficiency of the proposed method for reducing noise and its superiority over other denoising methods, i.e., to improve Signal-to-Noise Ratio (SNR), and to offer better listening quality based on a Perceptual Evaluation of Speech Quality (PESQ). The present study is limited to signals corrupted by additive white Gaussian noise.

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Classification of Compressed Audio Signals via Compressive Sensing in a Tele-Monitoring Context

Home tele-monitoring was proposed in recent years as a patient management approach aimed at reduc... more Home tele-monitoring was proposed in recent years as a patient management approach aimed at reducing the steadily increasing healthcare costs. It consists on profiting from artificial intelligence and technologies to make sure that patients are safe in their homes. Wireless sensor networks (WSNs), for example, provide an infrastructure capable of supporting this type of applications. The sensors collect information about what is happening in their surroundings and transmit it to the analysis center where data is interpreted and identified. This analysis phase is crucial as it provides accurate information about the safety level of the patient. Dealing with sensor networks, Compressive Sensing(CS) [1] is a quite recent attractive paradigm adapted to the low complexity requirement on the sensor side. In this work we intend to use CS to optimize the sensing and analysis process in an audio WSN.

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Watermark-Driven Acoustic Echo Cancellation

IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2018

The performance of adaptive acoustic echo cancelers (AEC) is sensitive to the non-stationarity an... more The performance of adaptive acoustic echo cancelers (AEC) is sensitive to the non-stationarity and correlation of speech signals. In this article, we explore a new approach based on an adaptive AEC driven by data hidden in speech, to enhance the AEC robustness. We propose a two-stage AEC, where the first stage is a classical NLMS-based AEC driven by the farend speech. In the signal, we embed -in an extended conception of data hiding-an imperceptible white and stationary signal, i.e. a watermark. The goal of the second stage AEC is to identify the misalignment of the first stage. It is driven by the watermark solely, and takes advantage of its appropriate properties (stationary and white) to improve the robustness of the two-stage AEC to the non-stationarity and correlation of speech, and thus reduce the overall system misadjustment. We test two kinds of implementations: in the first implementation, referred to as A-WdAEC (Adaptive Watermark driven AEC), the watermark is a white stationary Gaussian noise. Driven by this signal, the second stage converges faster than the classical AEC and provides better performance in steady state. In the second implementation, referred to as MLS-WdAEC, the watermark is built from maximum length sequences (MLS). Thus, the second stage performs a block identification of the first stage misalignment, given by the circular correlation watermark/preprocessed version of the first stage residual echo. The advantage of this implementation lies in its robustness against noise and under-modeling. Simulation results show the relevance of the "watermark-driven AEC" approach, compared to the classical "error driven AEC".

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Congruent auditory display and confusion in sound localization: Case of elderly drivers

Transportation Research Part F: Traffic Psychology and Behaviour, 2017

Elderly people may suffer from age-related hearing loss. They often report difficulties to percei... more Elderly people may suffer from age-related hearing loss. They often report difficulties to perceive and localize sound sources in noisy environment. How this can be a driving safety issue? This study investigates the effect of hearing impairment, on the driver behavior to localize external sound sources such as emergency vehicles sirens. Subjective tests show that localization confusion appears to be a common problem. Thus, we focus on how to assist the driver, taking into consideration the age-related hearing impairment, to better localize emergency vehicle siren. The hypothesis was based on the stimulus-response compatibility using an effective congruent auditory display, for attentional guidance toward the direction of arrival (DOA) of the external alarm. The proposed approach aims to reduce front-back confusion and enhance the sound localization accuracy of the driver, which is very important for elderly driver, subject to older age cognitive decline. A localization test was performed in lab and in vivo on a test track, where a group of drivers was asked to identify the DOA of an emergency vehicle siren, with and without a dedicated embedded system set up to assist them on sound localization task.

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Watermark-Aided Pre-Echo Reduction in Low Bit-Rate Audio Coding

Journal of the Audio Engineering Society, Jul 9, 2012

A non stationary/infinite precision system analogy for fixed-point digital adaptive filter analysis

2005 12th IEEE International Conference on Electronics, Circuits and Systems, 2005

Qualitative and quantitative performances of adaptive filters can significantly vary when they ar... more Qualitative and quantitative performances of adaptive filters can significantly vary when they are implemented in finite precision arithmetic. Analyzing theoretically such performances is very complex. In this paper, we present a new approach which simplifies both transient and steady state analysis of implemented adaptive filters. Indeed, the finite precision adaptive identification scheme in a stationary context is replaced by an

Blind decision feedback equalizer with tracking ability in severe propagation conditions

Vehicular Technology Conference Fall 2000. IEEE VTS Fall VTC2000. 52nd Vehicular Technology Conference (Cat. No.00CH37152), 2000

The non-stationary RLS algorithm is used for decision feedback equalization of time varying chann... more The non-stationary RLS algorithm is used for decision feedback equalization of time varying channels. We consider Markovian time variations of the impulse response of the channel as in radio mobile communications. The non-stationary RLS algorithm has the capacity to learn in real time about time varying model of the channel, so it presents a better tracking capacity than the classical

Boosting the EKF for distributed estimation in binary WSN through unscented transformation and iterative processing

2012 5th International Symposium on Communications, Control and Signal Processing, 2012

This paper deals with the problem of Distributed Estimation (DE) in binary Wireless Sensor Networ... more This paper deals with the problem of Distributed Estimation (DE) in binary Wireless Sensor Networks (WSN). Specifically, we propose to enhance the well-known Sign Of Innovation (SOI) based Extended Kalman Filter (SOI-EKF) [1] using the Unscented Transformation (UT) and iterative processing. The unscented transformation is especially used here to boost the quality of the predicted observation, already used in the SOI, and thus improve its pertinency; and most of all, improve the Kalman gain computation in the correction step. The so-developed filter, referenced here by the SOI Unscented Kalman Like Filter (SOI-UKLF) exhibit stable convergence behavior, compared to the SOI-EKF, when used in a target tracking application. Besides, an Iterated version of the SOI-EKF (I-SOI-EKF) is also proposed to enhance the tracking performance by stabilizing the filter output.

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Distribution discontinuities detection using algebraic technique for spectrum sensing in cognitive radio

Proceedings of the 5th International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications, 2010

In this paper 1 , we propose a new Spectrum Sensing technique for spectrum distribution discontin... more In this paper 1 , we propose a new Spectrum Sensing technique for spectrum distribution discontinuities detection using algebraic detection (AD). The presented mathematical background leading to such technique is based on operational calculus and differential algebra which offers a new stand point in sensing theory. Even if this background is quite heavy, the sensing algorithm related to this technique is nevertheless very simple and remains with extremely low complexity, comparable even to the Energy Detection technique (ED). Simulations results show good performance of the proposed technique in terms of reliability and accuracy with low complexity and good robustness in noisy context.

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Doping and witness watermarking for audio processing

International Workshop on Systems, Signal Processing and their Applications, WOSSPA, 2011

... S. Djaziri Larbi (1) , G. Mahé (2) , I. Marrakchi (1,2) , M. Turki (1) , M. Jaıdane (1 ... st... more ... S. Djaziri Larbi (1) , G. Mahé (2) , I. Marrakchi (1,2) , M. Turki (1) , M. Jaıdane (1 ... statistical characteristics of signals in order to fulfill particular con-ditions requested by algorithms or systems: it may sta-tionarize, gaussianize a signal or make its characteristic function band-limited. ...

Hybrid embedding strategy for a blind audio watermarking system using EMD and ISA techniques

2010 4th International Symposium on Communications, Control and Signal Processing (ISCCSP), 2010

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A new EMD denoising approach dedicated to voiced speech signals

2008 2nd International Conference on Signals, Circuits and Systems, 2008

This paper introduces a new voiced speech denoising approach based on the empirical mode decompos... more This paper introduces a new voiced speech denoising approach based on the empirical mode decomposition (EMD) associated to an appropriate sifting process. Noisy signal is decomposed adaptively into intrinsic oscillatory components called (IMFs). Since, the energy of a voiced speech signal is distributed over low and medium frequencies, the obtained lower order IMFs (high frequency components) are expected to be

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