A GGP approach to solve non convex min-max predictive controller for a class of constrained MIMO systems described by state-space models

This paper proposes a new method to solve non convex min-max predictive controller for a class of constrained linear Multi Input Multi Output (MIMO) systems. A parametric uncertainty state space model is adopted to describe the dynamic behavior of the real process. Moreover, the output deviation method is used to design the j-step ahead output predictor. The control law is obtained by the resolution of a non convex min-max optimization problem under input constraints. The key idea is to transform the initial non convex optimization problem to a convex one by means of variable transformations. To this end, the Generalized Geometric Programming (GGP) which is a global deterministic optimization method is used. An efficient implementation of this approach will lead to an algorithm with a low computational burden. Simulation results performed on Multi Input Multi Output (MIMO) system show successful set point tracking, constraints satisfaction and good non-zero disturbance rejection.     

An efficient VLSI architecture and FPGA implementation of the Finite Ridgelet Transform

In this paper, an efficient architecture for the Finite Ridgelet Transform (FRIT) suitable for VLSI implementation based on a parallel, systolic Finite Radon Transform (FRAT) and a Haar Discrete Wavelet Transform (DWT) sub-block, respectively is presented. The FRAT sub-block is a novel parametrisable, scalable and high performance core with a time complexity of O(p ²), where p is the block size. Field Programmable Gate Array (FPGA) and Application Specific Integrated Circuit (ASIC) implementations are carried out to analyse the performance of the FRIT core developed.

Compositional characteristics and aromatic profile of date palm seeds from seven varieties grown in Tunisia

This paper reports a discrimination study based on the physico‐chemical characteristics, fatty acids and profile of volatile compounds of the seeds from seven date palm varieties (Phoenix dactylifera L.) grown in Tunisia. Date seeds contained 10.49–14.76% moisture; 6.28–11.2% fat (on a dry weight basis); 2.67–12.85% protein; 0.91–6.06% reducing sugar; 0.61–2.98% sucrose and 0.97–1.17% ash. Gas liquid chromatography revealed that the oil fraction of the date palm seeds contained eighteen fatty acids, with oleic acid (30.77–42.50%) and lauric acid (18.51–27.48%) as the main unsaturated and saturated ones. Volatile profile showed differences among varieties. In total, forty‐five compounds were identified, mainly alcohols, aldehydes and unsaturated hydrocarbons ones. This study provides evidence that the seeds of date may be a potential source of valuable nutrients with interesting functionality.     

Core–Shell Nanoarchitectonics of CoFe2O4 Encapsulated La2Fe2O6 Nanoparticles for Their Use in Various Applications

It would be helpful to achieve appropriate synthetic routes to attain larger-scale production at industrial levels of nanocomposites at low costs. In the present work, diphasic composites with core–shell nanostructures formed by La₂Fe₂O₆/CoFe₂O₄ are investigated. The core–shell structure is fabricated via different preparation methods. The advantages and the demerits of the synthesis techniques are discussed. The presence of both the spinel CoFe₂O₄ nano ferrite and orthorhombic La₂Fe₂O₆ perovskite phases is revealed by X-ray diffraction. XPS spectroscopy is utilized to investigate the chemical composition of the prepared samples. The hysteresis loops of the prepared samples exhibit a smooth loop that is resulted from the existence of two homogeneous magnetic phases. For the first time, it has been found that the preparation conditions have the advantage of reducing the switching field distribution value for the core–shell nanoparticles. Exchange coupled core–shell nanoparticles present a high potential to regulate the magnetic properties for numerous applications such as heavy metal removal and/or data storage devices. The maximum adsorption capacity (qm) of Cr III on the core–shell (S3) is higher compared to other adsorbents previously testified in the literature. The cost-effective and eco-friendly prepared core–shell samples with good metal removal capacity have great potential for commercialization.

     

FPGA implementations of fast Fourier transforms for real-time signal and image processing

Applications based on the fast Fourier transform (FFT), such as signal and image processing, require high computational power, plus the ability to experiment with algorithms. Reconfigurable hardware devices in the form of field programmable gate arrays (FPGAs) have been proposed as a way of obtaining high performance at an economical price. However, users must program FPGAs at a very low level and have a detailed knowledge of the architecture of the device being used. They do not therefore facilitate easy development of, or experimentation with, signal/image processing algorithms. To try to reconcile the dual requirements of high performance and ease of development, the paper reports on the design and realisation of a high level framework for the implementation of 1D and 2D FFTs for real-time applications. A wide range of FFT algorithms, including radix-2, radix-4, split-radix and fast Hartley transform (FHT) have been implemented under a common framework in order to enable system designers to meet different system requirements. Results show that the parallel implementation of 2D FFT achieves linear speed-up and real-time performance for large matrix sizes. Finally, an FPGA-based parametrisable environment based on 2D FFT is presented as a solution for frequency-domain image filtering application.     

Tuning the crystal structure and optical properties of selective area grown InGaAs nanowires

Catalyst-free InGaAs nanowires grown by selective area epitaxy are promising building blocks for future optoelectronic devices in the infrared spectral region.D...     

Morphological segmenting and neighborhood pixel-based locality preserving projection on brain fMRI dataset for semantic feature extraction: an affective computing study

Two specific chemical receptive fields of brain, namely the amygdala and the orbital-frontal cortex, are related to valence and arousal in medical experiments. Functional magnetic resonance imaging (fMRI), which is a noninvasive, repeatable, and atomical tool for medical imaging in clinic system, was widely used in affective computing; however, it faces its dataset processing difficulty for dimensional reduction as well as for decreasing the computational complexity. In addition, features extraction from those de-dimensionality datasets is a challenging issue. The current work solved the de-dimensionality issue by using some preprocessing algorithms including clustering, morphological segmenting, and locality preserving projection. In order to keep useful information in fMRI dataset for reduction process, improved neighborhood pixel-based locality preserving projection (NP-LPP) algorithm was addressed and continuously for feature extraction operating using Otsu weighted sum of histogram. Furthermore, a modified covariance power spectral density (MC-PSD) separately in an fMRI Valence–Arousal experiments was measured. The results were analyzed and compared with affective norms English words system. The experiments established that the proposed methods of NP-LPP effectively simplified high complexity of fMRI, and Otsu weighted sum of histogram exhibited superior performance for features extraction compared to the MC-PSD through the calculation root mean standard error. The current proposed method provided a potential application and promising research direction on human semantic retrieval through medical imaging dataset.

     

Texture anisotropy technique in brain degenerative diseases

Structural alterations anisotropy-based measured for different areas for the most common types of dementia diseases could be a biomarker of brain impairment. The current work aims to assess whether texture anisotropy can discriminate both healthy versus Alzheimer’s and Pick’s patients based on regional evaluation while maintaining high predictive power. The investigated area is reduced from the whole-brain surface to three major lobes (i.e., frontal, temporal and parietal). A predictive model was proposed to associate a disease with a specific area in the brain based on the anisotropy values. Simultaneous analysis of 1680 measurements from 105 brain magnetic resonance images acquired as T2w and PD sequences was performed to establish the significance of the model. The cerebral calcinosis disease has been used as artificial ground truth. The association based on textural anisotropy between targeted diseases and control patients was performed by using Pearson’s correlation coefficients. A new proposed consistency index investigated the texture anisotropy relevance for all image’s types and all analyzed classes and regions. The validation study is based on area under the receiver-operating characteristic curve that depicted the overall diagnostic performance of the texture anisotropy in each region. The proposed model demonstrated that texture anisotropy is accurate solution in diagnosis of Alzheimer’s and Pick’s diseases when the investigated area is reduced to major lobes, with sensitivity >90% and specificity >80%.