Hichem Maaref

Morphology and photoluminescence studies of electrochemically etched heavily doped p-type GaAs in HF solution

Abstract Porous GaAs layers have been produced by electrochemical anodic etching of (100) heavily doped p-type GaAs substrate in HF solution. Scanning electron microscopy revealed the presence of etch pits ranging in size from 0.01 to 2 μm and they were strongly dependent on the electrochemical etching conditions. The etch pits chemical composition consists of O, Ga and As whereas the porous structure consists predominantly of GaAs as performed by energy dispersive X-ray analyzer. Typical porous structure with pores diameter ranging from 15 to 50 nm has been obtained. Room temperature photoluminescence (PL) investigations reveal the presence of two and in one case three PL bands besides the PL band of the started GaAs. Peaks wavelengths positions were approximately located in 600–900 nm range. The PL bands peaks wavelengths positions depend on the electrochemical etching conditions and they were approximately unchanged with increasing temperature. However, their PL intensity increased slowly with increasing temperature and tend to saturate. The observed PL bands were explained by the quantum confinement effects in GaAs nanocrystallites.

Controlling a drone: Comparison between a based model method and a fuzzy inference system

The work describes an automatically on-line self-tunable fuzzy inference system (STFIS) of a new configuration of mini-flying called XSF (X4 Stationnary Flyer) drone. A fuzzy controller based on on-line optimization of a zero order Takagi-Sugeno fuzzy inference system (FIS) by a back propagation-like algorithm is successfully applied. It is used to minimize a cost function that is made up of a quadratic error term and a weight decay term that prevents an excessive growth of parameters. Thus, we carried out control for the continuation of simple trajectories such as the follow-up of straight lines, and complex (half circle, corner, and helicoidal) by using the STFIS technique. This permits to prove the effectiveness of the proposed control law. Simulation results and a comparison with a static feedback linearization controller (SFL) are presented and discussed. We studied the robustness of the two controllers used in the presence of disturbances. We presented two types of disturbances, the case of a breakdown of an engine as well as a gust of wind.

Self-organized growth, ripening, and optical properties of uncapped InP/GaP (100) islands

In this article, we study the formation of self-assembling uncapped InP islands grown by metal organic vapor phase epitaxy on (100) GaP. Statistical analysis of the AFM data for this fresh sample show three-modal distribution of vertical height InP islands with elliptical base shape. At low temperature, linearity of islands (wetting layer (WL)) photoluminescence (PL) integrated emission with excitation power indicated low defect density on island (WL) interfaces. Uncapped InP islands are unstable with time, their size increases as their surface density decrease and their height distribution change with time. However, a broadening in PL spectra is observed as a function of time. This broadening can be correlated to the rearrangement versus time of the island height distribution as seen by AFM analysis.

Self-organized InAs/GaAs quantum dots multilayers with growth interruption emitting at 1.3 μm

We have grown single, 10 and 20 InAs/GaAs quantum dots (QDs) multilayers by molecular beam epitaxy in Stranski-Krastanov growth mode with and without growth interruption. Multilayer structures of InAs QDs have been studied by photoluminescence (PL) and atomic force microscopy (AFM) techniques. Between 1 and 10 layers of QDs, 10 K PL shows a shift energy, and a PL linewidth reduction. Moreover, AFM image of the 10 layers sample shows that the InAs QDs size remains constant and almost uniform when the growth is without interruption. These effects are attributed to electronic coupling between QDs in the the columns. However, we show the possibility of extending the spectral range of luminescence due to InAs QDs up to 1.3 μm. Realisation of such a wavelength emission is related to formation of lateral associations or coupling of QDs (LAQDs or LCQDs) during InAs deposition when growth interruption (20 s) is used after each InAs QDs layer deposition. The growth interruption applied after the deposition of the InAs layer allows the formation of well-developed InAs dots (large dot size).

Multi-model classification method in heterogeneous image databases

Automatic heterogeneous image recognition is a challenging research topic in computer vision. In fact, a general purpose images require multiple descriptors to cover their diverse category contents. However, not all extracted features are always relevant. Furthermore, simply concatenating multiple features may not be efficient for recognizing images in heterogeneous databases. In this context, we propose a new heterogeneous image recognition system, which aims to enhance the recognition results while decreasing the computational complexity. In particular, the proposed system is based on two complementary methods: adaptive relevant feature selection and multi-model classification method (MM-CM). Since it employs hierarchically selected features, the MM-CM ensures better classification accuracy and significantly less computation time than existing classification methods. The performance of the proposed image recognition system is assessed through two image databases and a large number of features. A comparison with competing algorithms from the literature is also provided. Our extensive experimental results show that an adaptive feature selection based MM-CM outperforms existing methods and improves the classification results in heterogeneous image databases.

Capacitance-voltage analysis of InAs quantum dots grown on InAlAs/InP(001)

The electronic properties of InAs quantum dots (QDs) grown on InAlAs/InP(001) were studied by using capacitance-voltage (C-V) analysis and photoluminescence (PL) measurements. The level positions of electrons and holes could be studied separately by using n- and p-type InAlAs matrices, respectively. The holes are found to be more confined than electrons in these kinds of dots.

Protuberance Selection descriptor for breast cancer diagnosis

In breast cancer field, researchers aim to automatically discriminate between benign and malignant masses in order to assist radiologists. In general, benign masses have smoothed contours, whereas, malignant tumors have spiculated boundaries. In this context, finding the adequate description remains a real challenge due to the complexity of mass boundaries. In this paper, we propose a novel shape descriptor named the Protuberance Selection (PS) based on depression and protuberance detection. This descriptor allows a good characterization of lobulations and spiculations in mass boundaries. Furthermore, it ensures invariance to geometric transformations. Experimental results show that the specified descriptor provides a promising classification performance. Also, results confirm that the new PS descriptor outperforms several shape features commonly used in breast cancer domain.

Alloy broadening effect on optical properties of InGaAs grown by MOCVD with TMAs precursor

Abstract We investigate the MOCVD growth characteristic of the In 0.47 Ga 0.53 As layers lattice matched to InP using TMAs as an alternative source of arsine. Improvement of the InGaAs quality was studied by means of PL lines, the origin of photoluminescence (PL), atomic force microscopy. Low temperature PL spectra exhibit a broad this broadening was analyzed using quantitative models for the linewidth of band exciton based on compositional fluctuations within the crystal volume. This statistical fluctuation of the composition affects not only the PL line width but also structural properties of the InGaAs epilayer. Furthermore, by increasing V/III ratio, a degradation of the InGaAs optical and structural quality was observed.

Characterisation of mammographic masses using a new Spiculated Mass Descriptor in computer aided diagnosis systems

Automatic breast mass recognition as malignant or benign is addressed in order to assist radiologists to make decision. Several researches was based on roughness measures to characterise breast mass boundaries. However, used features generally do not consider specific characteristics of mass spiculations. In this context, we propose a new shape descriptor allowing to characterise simultaneously the number, the depth and the width of spicules. Consequently, the proposed Spiculated Mass Descriptor (SMD) allows to distinguish between ambiguous cases such as lobulated benign masses and microlobulated malignant masses which are hard to classify using common shape analysis methods. In addition, it ensures invariance to geometrical transformations which preserve a robust descriptor insensitive to shifts, orientations and scalings. SMD efficiency is evaluated on the known Digital Database for Screening Mammography (DDSM) using the area under the Receiver Operating Characteristic (ROC) curve analysis. Experimental results show that the new descriptor outperforms several shape features and provides satisfying classification results of benign and malignant masses.

Intelligent control for a drone by self-tunable Fuzzy Inference System

The work describes an automatically on-line Self-Tunable Fuzzy Inference System (STFIS) of a new configuration of mini-flying called XSF (X4 Stationnary Flyer) drone. A Fuzzy controller based on on-line optimization of a zero order Takagi-Sugeno fuzzy inference system (FIS) by a back propagation-like algorithm is successfully applied. It is used to minimize a cost function that is made up of a quadratic error term and a weight decay term that prevents an excessive growth of parameters. Thus, we carried out control for the continuation of simple trajectories such as the follow-up of straight lines, and complex (half circle, corner) by using the STFIS technique. This permits to prove the effectiveness of the proposed control law. We studied the robustness of the two controllers used in the presence of disturbances. We presented two types of disturbances, the case of a gust of wind and taking into account white noise disturbances. A comparison between the Self-Tunable Fuzzy Inference System (STFIS) and Adaptive Network based Fuzzy Inference System (ANFIS) is given.