Multi-scale Single Image Super-Resolution with Remote-Sensing Application Using Transferred Wide Residual Network

In the integrated circuit (IC) designing floorplanning is an important phase in the process of obtaining the layout of the circuit to be designed. The floorplanning determines the performance, size, yield, and reliability of VLSI ICs. The obtained results in this step are necessary for the other consecutive process of the chip designing. VLSI floorplanning from the computational point of view is a non-polynomial hard (NP-hard problem), and hence cannot be efficiently solved by the classical optimization techniques. In this paper, we have proposed a metaheuristic approach to address the problem by using the parallel particle swarm optimization (P-PSO) technique. The P-PSO uses a new greedy operation on the sequence pair (SP) to explore the search space to find an optimal solution. Experimental results on the Microelectronic Centre of North Carolina and Gigascale Systems Research Center benchmark shows that the applied parallel PSO (P-PSO) may be used to produce an optimal solution.

     

A mortar finite element approach for point,line, and surface contact

An approach for investigating finite deformation contact problems with frictional effects with a special emphasis on nonsmooth geometries such as sharp corners and edges is proposed in this contribution. The contact conditions are separately enforced for point contact, line contact, and surface contact by employing 3 different sets of Lagrange multipliers and, as far as possible, a variationally consistent discretization approach based on mortar finite element methods. The discrete unknowns due to the Lagrange multiplier approach are eliminated from the system of equations by employing so‐called dual or biorthogonal shape functions. For the combined algorithm, no transition parameters are required, and the decision between point contact, line contact, and surface contact is implicitly made by the variationally consistent framework. The algorithm is supported by a penalty regularization for the special scenario of nonparallel edge‐to‐edge contact. The robustness and applicability of the proposed algorithms are demonstrated with several numerical examples.     

Emotional speaker recognition in real life conditions using multiple descriptors and i-vector speaker modeling technique

Multimedia Tools and Applications - Emotional speaker recognition under real life conditions becomes an urgent need for several applications. This paper proposes a novel approach using multiple...     

Fe<Subscript>3</Subscript>O<Subscript>4</Subscript> nanoparticles coated by new functionalized tetraaza-2,3 dialdehyde micro-crystalline cellulose: synthesis,characterization, and catalytic application for degradation of Acid Yellow 17

In this study, we developed an original approach for preparing cellulose-coated magnetite nanoparticles (NPs). Two novel Schiff bases (PDA-g-DAC) and [Bz-(PDA-g-DAC)] were synthesized via condensation reactions of periodate oxidized micro-crystalline cellulose (DAC) with o-phenylene diamine (PDA) to obtain its azomethine derivative with 85% yield. Subsequently, the functionalization of (PDA-g-DAC) with benzil (Bz) yields the tetraaza macrocycle [Bz-(PDA-g-DAC)]. The physicochemical characterization of the condensation products was performed using ¹³CNMR, FTIR, ATG, DSC, and X-ray diffraction techniques. Magnetic nanomaterial-based Schiff base cellulose was successfully prepared using in situ chemical co-precipitation of coordinated ferric and ferrous ions in cellulose Schiff base matrix under optimized conditions, and then, its magnetic properties were characterized. The results demonstrated that the Fe₃O₄ NPs coated with [Bz-(PDA-g-DAC)] were homogeneously coated in the matrix under ultrasonic irradiation with the saturation magnetization of 69.50 emu g^?1. In addition, XRD line broadening analysis showed that the average particle size of the NPs was 37.3 nm. Furthermore, FTIR spectra demonstrated that [Bz-(PDA-g-DAC)] concavity was anchored to magnetite Fe₃O₄ NPs through azomethine groups. Vibrating sample magnetometry (VSM) of [Bz-(PDA-g-DAC)@Fe₃O₄] magnetic nanocomposite samples showed the typical behavior of ferromagnetism. This study provided a green and facile method to inhibit magnetic nanoparticle aggregation. Activity results revealed that the prepared [Bz-(PDA-g-DAC)@Fe₃O₄] catalyst shows the maximum activity for degradation of Acid Yellow 17 (AY17) compared to other prepared catalysts. After degradation reaction, the [Bz-(PDA-g-DAC)@Fe₃O₄] catalyst was recovered from the reaction mixture via an external magnet and used for further five consecutive cycles with excellent catalytic activity, successively, which was comparable to the fresh catalyst. The catalyst degradation efficiency and its easy separation exhibited that [Bz-(PDA-g-DAC)@Fe₃O₄] catalyst is a promising material for the removal of AY17 from aqueous solutions in green chemistry perspectives.     

Anomaly Detection in ICS Datasets with Machine Learning Algorithms

An Intrusion Detection System (IDS) provides a front-line defense mechanism for the Industrial Control System (ICS) dedicated to keeping the process operations running continuously for 24 hours in a day and 7 days in a week. A well-known ICS is the Supervisory Control and Data Acquisition (SCADA) system. It supervises the physical process from sensor data and performs remote monitoring control and diagnostic functions in critical infrastructures. The ICS cyber threats are growing at an alarming rate on industrial automation applications. Detection techniques with machine learning algorithms on public datasets, suitable for intrusion detection of cyber-attacks in SCADA systems, as the first line of defense, have been detailed. The machine learning algorithms have been performed with labeled output for prediction classification. The activity traffic between ICS components is analyzed and packet inspection of the dataset is performed for the ICS network. The features of flow-based network traffic are extracted for behavior analysis with port-wise profiling based on the data baseline, and anomaly detection classification and prediction using machine learning algorithms are performed.     

N-Body Simulation Inspired by Metaheuristics Optimization

The N-body problem in classical physics, is the calculation of force of gravitational attraction of heavenly bodies towards each other. Solving this problem for many heavenly bodies has always posed a challenge to physicists and mathematicians. Large number of bodies, huge masses, long distances and exponentially increasing number of equations of motion of the bodies have been the major hurdles in solving this problem for large and complex galaxies. Advent of high performance computational machines have mitigated the problem to much extent, but still for large number of bodies it consumes huge amount of resources and days for computation. Conventional algorithms have been able to reduce the computational complexity from to by splitting the space into a tree or mesh network, researchers are still looking for improvements. In this research work we propose a novel solution to N-body problem inspired by metaheuristics algorithms. The proposed algorithm is simulated for various time periods of selected heavenly bodies and analyzed for speed and accuracy. The results are compared with that of conventional algorithms. The outcomes show about 50% time saving with almost no loss in accuracy. The proposed approach being a metaheuristics optimization technique, attempts to find optimal solution to the problem, searching the entire space in a unique and efficient manner in a very limited amount of time.     

Chemical composition and antimicrobial activity of the essential oils from four Ruta species growing in Algeria

Antimicrobial properties of plants essential oils have been investigated in order to suggest them as potential tools to overcome the microbial drug resistance and the increasing incidence of food borne diseases problems. The aim of this research is to study the antibacterial and antifungal effects of four traditional plants essential oils, Ruta angustifolia, Ruta chalepensis, Ruta graveolens and Ruta tuberculata, against standard bacterial and fungal strains. The chemical compounds of the oils were examined by GC/MS. Results revealed a powerful antifungal activity against filamentous fungi. Aspergillus fumigatus and Cladosporium herbarum are the most sensitive strains to these oils with MIC values less than 3.5 μg ml⁻¹ for certain oils, reaching 7.8 μg ml⁻¹ for other. GC/MS essay exhibited ketones as the most abundant constituent of these oils except for R. tuberculata essential oil which has a completely different composition, monoterpenes alcohols being the most abundant. These compositions explain their potential antifungal activity.