Najoua Essoukri Ben Amara

Najoua Essoukri Ben Amara


Research Focus & Metrics

Academic Career Length

19Years (2008-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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31 May 2017 in Energies
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IBInes Baccouche
Ines Baccouche
SJSabeur Jemmali
Sabeur Jemmali
BMBilal Manai
Bilal Manai
NONoshin Omar
Noshin Omar
NANajoua Essoukri Ben Amara
Najoua Essoukri Ben Amara
Accurate modeling of the nonlinear relationship between the open circuit voltage (OCV) and the state of charge (SOC) is required for adaptive SOC estimation during the lithium-ion (Li-ion) battery operation. Online SOC estimation should meet several constraints, such as the computational cost, the number of parameters, as well as the accuracy of the model. In this paper, these challenges are considered by proposing an improved simplified and accurate OCV model of a nickel manganese cobalt (NMC) Li-ion battery, based on an empirical analytical characterization approach. In fact, composed of double exponential and simple quadratic functions containing only five parameters, the proposed model accurately follows the experimental curve with a minor fitting error of 1 mV. The model is also valid at a wide temperature range and takes into account the voltage hysteresis of the OCV. Using this model in SOC estimation by the extended Kalman filter (EKF) contributes to minimizing the execution time and to reducing the SOC estimation error to only 3% compared to other existing models where the estimation error is about 5%. Experiments are also performed to prove that the proposed OCV model incorporated in the EKF estimator exhibits good reliability and precision under various loading profiles and temperatures.

27 March 2020 in Electronics
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AMamira mimouna
amira mimouna
Ihsen AlouaniIhsen Alouani
Ihsen Alouani
Anouar BEN KHALIFAAnouar BEN KHALIFA
Anouar BEN KHALIFA
YHYassin El HILLALI
Yassin El HILLALI
abdelmalik taleb-ahmedabdelmalik taleb-ahmed
abdelmalik taleb-ahmed
NANajoua Essoukri Ben Amara
Najoua Essoukri Ben Amara
A reliable environment perception is a crucial task for autonomous driving, especially in dense traffic areas. Recent improvements and breakthroughs in scene understanding for intelligent transportation systems are mainly based on deep learning and the fusion of different modalities. In this context, we introduce OLIMP: A heterOgeneous Multimodal Dataset for Advanced EnvIronMent Perception. This is the first public, multimodal and synchronized dataset that includes UWB radar data, acoustic data, narrow-band radar data and images. OLIMP comprises 407 scenes and 47,354 synchronized frames, presenting four categories: pedestrian, cyclist, car and tram. The dataset includes various challenges related to dense urban traffic such as cluttered environment and different weather conditions. To demonstrate the usefulness of the introduced dataset, we propose a fusion framework that combines the four modalities for multi object detection. The obtained results are promising and spur for future research.

26 August 2024 in Materials
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HDHanen Douiri
Hanen Douiri
MAMarwa Abid
Marwa Abid
LHLamia Rzouga Haddada
Lamia Rzouga Haddada
layla brinilayla brini
layla brini
Alessandra ToncelliAlessandra Toncelli
Alessandra Toncelli
Ramzi MaalejRamzi Maalej
Ramzi Maalej
Blue-emitting bismuth-doped lanthanum oxide (La2O3: Bi3+) with various concentrations of Bi was synthesized using the sol–gel combustion method and used for visualization of latent fingerprints (LFPs). An X-ray diffraction (XRD) study revealed the hexagonal structure of the phosphors and total incorporation of the bismuth in the La2O3 matrix. Field Emission Scanning Electron Microscopy (FE-SEM) and Fourier Transform Infrared Spectroscopy (FTIR) were used to study the morphology and the relative vibrations of the synthesized samples. Photoluminescence (PL) studies showed strong blue emission around 460 nm due to the 3P1 → 1S0 transition. Clear bright-blue fingerprint images were obtained with the powder dusting method on various surfaces like aluminum, compact discs, glass, wood and marble. A first evaluation of these images indicated a clear visualization of all three levels of details and a very high contrast ranging from 0.41 on marble to 0.90 on aluminum. As a further step, we used an algorithm for extracting fingerprint minutiae with which we succeeded in detecting all three levels of fingerprint details and even the most difficult ones, like open and closed pores. According to these analyses, La2O3: Bi phosphor is demonstrated to be an effective blue fluorescent powder for excellent visualization of latent fingerprints.

31 January 2018 in Journal of Imaging
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OZOussama Zayene
Oussama Zayene
STSameh Masmoudi Touj
Sameh Masmoudi Touj
JHJean Hennebert
Jean Hennebert
RIRolf Ingold
Rolf Ingold
NANajoua Essoukri Ben Amara
Najoua Essoukri Ben Amara
Recognizing texts in video is more complex than in other environments such as scanned documents. Video texts appear in various colors, unknown fonts and sizes, often affected by compression artifacts and low quality. In contrast to Latin texts, there are no publicly available datasets which cover all aspects of the Arabic Video OCR domain. This paper describes a new well-defined and annotated Arabic-Text-in-Video dataset called AcTiV 2.0. The dataset is dedicated especially to building and evaluating Arabic video text detection and recognition systems. AcTiV 2.0 contains 189 video clips serving as a raw material for creating 4063 key frames for the detection task and 10,415 cropped text images for the recognition task. AcTiV 2.0 is also distributed with its annotation and evaluation tools that are made open-source for standardization and validation purposes. This paper also reports on the evaluation of several systems tested under the proposed detection and recognition protocols.

11 June 2021 in International Journal on Document Analysis and Recognition (IJDAR)
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OMOlfa Mechi
Olfa Mechi
MMMaroua MEHRI
Maroua MEHRI
RIRolf Ingold
Rolf Ingold
NANajoua Essoukri Ben Amara
Najoua Essoukri Ben Amara

18 August 2017 in IET Computer Vision
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BSBassem Seddik
Bassem Seddik
SGSami Gazzah
Sami Gazzah
NANajoua Essoukri Ben Amara
Najoua Essoukri Ben Amara
This study addresses the problem of efficiently combining the joint, RGB and depth modalities of the Kinect sensor in order to recognise human actions. For this purpose, a multi‐layered fusion scheme concatenates different specific features, builds specialised local and global SVM models and then iteratively fuses their different scores. The authors essentially contribute in two levels: (i) they combine the performance of local descriptors with the strength of global bags‐of‐visual‐words representations. They are able then to generate improved local decisions that allow noisy frames handling. (ii) They also study the performance of multiple fusion schemes guided by different features concatenations, Fisher vectors representations concatenation and later iterative scores fusion. To prove the efficiency of their approach, they have evaluated their experiments on two challenging public datasets: CAD‐60 and CGC‐2014. Competitive results are obtained for both benchmarks.