Generating Arabic text in multilingual speech-to-speech machine translation framework

The interlingual approach to machine translation (MT) is used successfully in multilingual translation. It aims to achieve the translation task in two independent steps. First, meanings of the source-language sentences are represented in an intermediate language-independent (Interlingua) representation. Then, sentences of the target language are generated from those meaning representations. Arabic natural language processing in general is still underdeveloped and Arabic natural language generation (NLG) is even less developed. In particular, Arabic NLG from Interlinguas was only investigated using template-based approaches. Moreover, tools used for other languages are not easily adaptable to Arabic due to the language complexity at both the morphological and syntactic levels. In this paper, we describe a rule-based generation approach for task-oriented Interlingua-based spoken dialogue that transforms a relatively shallow semantic interlingual representation, called interchange format (IF), into Arabic text that corresponds to the intentions underlying the speaker’s utterances. This approach addresses the handling of the problems of Arabic syntactic structure determination, and Arabic morphological and syntactic generation within the Interlingual MT approach. The generation approach is developed primarily within the framework of the NESPOLE! (NEgotiating through SPOken Language in E-commerce) multilingual speech-to-speech MT project. The IF-to-Arabic generator is implemented in SICStus Prolog. We conducted evaluation experiments using the input and output from the English analyzer that was developed by the NESPOLE! team at Carnegie Mellon University. The results of these experiments were promising and confirmed the ability of the rule-based approach in generating Arabic translation from the Interlingua taken from the travel and tourism domain.     

Effect of Chewing on the Expression of Salivary Protein Composition: A Systematic Review

Salivary proteins have an imperative role in the maintenance of oral health and repairing mechanisms of injured tissues. However, there is paucity of information reported in the literature about the influence of chewing activities on the secretion or expression of salivary proteins. The purpose of this systematic review is to evaluate the effect of chewing on the expression of salivary proteins composition in healthy individuals. A thorough systematic search shows 14 eligible studies for the review. The results of the systematic review show the effect of chewing on total protein concentration, alpha‐amylase (α‐amylase), peroxidase, lysozyme, immunoglobulin A (IgA), and mucin. Six papers concluded that chewing has a little or no effect on total protein concentration, α‐amylase, peroxidase, lysozyme, and IgA activities. Five papers reported a negative (decreasing) effect of chewing on the function of total protein, α‐amylase, IgA, and mucin. Only two papers showed an increase in total protein and IgA function upon chewing stimulation. The results of this systematic review indicate that more standardized evidence‐based research is required for better assessment of chewing effects on salivary proteins. Within the limitations of this review, the existing evidence suggests that chewing in healthy people has minimum effect on the expression and activities of salivary proteins.     

Design of novel circular lattice photonic crystal fiber suitable for transporting 48 OAM modes

In this paper, we propose and numerically investigate a novel circular lattice photonic crystal fiber (CL-PCF) using controllable GeO2 doped silica, suitable for modes carrying quantized orbital angular momentum (OAM). Large effective index separations between 25 supported vector modes (≥10-4) are confirmed over large bandwidth (C+L bands) leading to 48 OAM modes bearing information. The simulations show that the modes in the proposed CL-PCF have good features including low and flat dispersion (within 51.82 ps/km/nm), low confinement loss (lower than 0.002 dB/m), high effective mode area (88.5 μm2) and low nonlinearity (1.22 W-1.km-1). These promising results show that the proposed CL-PCF could be an arguably candidate in fiber-based OAM multiplexing or other applications using OAM states.     

Multi-constraints self-adaptation for reconfigurable multimedia embedded systems

The application of multimedia in embedded systems (ES), such as Virtual reality and 3-D imaging, represents the current trend in ES development. Coupling multimedia with ES has raised new multimedia-related challenges that have been added to the common ES constraints. These challenges deal with the real-time, quality, performance and efficient processing requirements of multimedia applications. The integration of self-adaptation in ES development has been, for many years, a paramount solution to cope with these issues. Although there has been extensive research on the topic of ES self-adaptation, the related works still lack global approaches that better deal with multimedia-related constraints. Coordinating different adaptation mechanisms, monitoring multiple system constraints and supporting multi-application contexts are still underexplored. The aim of the present work is to fill in these gaps by providing a global adaptation approach that offers better adaptation decisions with fair resource sharing among competing multimedia applications. With the above challenges in mind, we propose a multi-constraints combined adaptation approach that targets multimedia ES. It addresses four critical system constraints: maximizing the overall system‘s Quality of Application (QoA) under the real-time constraint, the remaining system energy and the available network bandwidth. It coordinates the adaptation at both application and architecture levels. To test and validate the proposed technique, a videophone system is designed on a Xilinx FPGA development board. It executes two complex multimedia applications. The validation results show the aptitude of the proposed system to successfully reconfigure itself at run-time in response to its constraints.

     

A physics-informed dynamic deep autoencoder for accurate state-of-health prediction of lithium-ion battery

Neural Computing and Applications - Lithium-ion batteries (LIBs) are currently the standard for energy storage in portable consumer electronic devices. They are also used in electric vehicles and...     

A Weighted Spatially Constrained Finite Mixture Model for Image Segmentation

Spatially Constrained Mixture Model (SCMM) is an image segmentation model that works over the framework of maximum a-posteriori and Markov Random Field (MAP-MRF). It developed its own maximization step to be used within this framework. This research has proposed an improvement in the SCMM’s maximization step for segmenting simulated brain Magnetic Resonance Images (MRIs). The improved model is named as the Weighted Spatially Constrained Finite Mixture Model (WSCFMM). To compare the performance of SCMM and WSCFMM, simulated T1-Weighted normal MRIs were segmented. A region of interest (ROI) was extracted from segmented images. The similarity level between the extracted ROI and the ground truth (GT) was found by using the Jaccard and Dice similarity measuring method. According to the Jaccard similarity measuring method, WSCFMM showed an overall improvement of 4.72%, whereas the Dice similarity measuring method provided an overall improvement of 2.65% against the SCMM. Besides, WSCFMM significantly stabilized and reduced the execution time by showing an improvement of 83.71%. The study concludes that WSCFMM is a stable model and performs better as compared to the SCMM in noisy and noise-free environments.     

Deep Learning Control for Autonomous Robot

Several applications of machine learning and artificial intelligence, have acquired importance and come to the fore as a result of recent advances and improvements in these approaches. Autonomous cars are one such application. This is expected to have a significant and revolutionary influence on society. Integration with smart cities, new infrastructure and urban planning with sophisticated cyber-security are some of the current ramifications of self-driving automobiles. The autonomous automobile, often known as self-driving systems or driverless vehicles, is a vehicle that can perceive its surroundings and navigate predetermined routes without human involvement. Cars are on the verge of evolving into autonomous robots, thanks to significant breakthroughs in artificial intelligence and related technologies, and this will have a wide range of socio-economic implications. However, in order for these automobiles to become a reality, they must be endowed with the perception and cognition necessary to deal with high-pressure real-life events and make proper judgments and take appropriate action. The majority of self-driving car technologies are based on computer systems that automate vehicle control parts. From forward-collision warning and antilock brakes to lane-keeping and adaptive drive control, to fully automated driving, these technological components have a wide range of capabilities. A self-driving car combines a wide range of sensors, actuators, and cameras. Recent researches on computer vision and deep learning are used to control autonomous driving systems. For self-driving automobiles, lane-keeping is crucial. This study presents a deep learning approach to obtain the proper steering angle to maintain the robot in the lane. We propose an advanced control for a self-driving robot by using two controllers simultaneously. Convolutional neural networks (CNNs) are employed, to predict the car’ and a proportional-integral-derivative (PID) controller is designed for speed and steering control. This study uses a Raspberry PI based camera to control the robot car.     

Radiation shielding features for various tellurium-based alloys: a comparative study

We investigate the radiation shielding properties for four Te-based alloys. X-ray diffraction patterns revealed pure phases in all studied samples; however, a secondary phase is detected in the CrTe sample in good agreement with the literature. All samples’ densities were measured using the Archimedes principle. The mass attenuation coefficient (MAC) was calculated using Geant4 MC Toolkit and then compared with the XCOM data. Many photon-shielding properties were computed for all investigated samples based on the MAC. The Phy-X and SRIM were used to determine the fast neutron removal cross-section (Σ_R) and projected range, respectively. As a result, PbTe shows superior shielding features compared to the rest of the investigated samples to use this sample in different shielding applications.

     

A novel technique for residence time distribution (RTD) measurements in solids unit operations

This work presents a novel technique with fast response for Residence Time Distribution (RTD) measurements in gas-solid unit operations (e.g., fluidized bed reactors). This technique is based on an optical method which eliminates the requirement of knowing the velocity and concentration profiles at the exit section of the system. Experiments were carried out with SiC particles and a phosphorescent pigment used as a tracer. A concentration measurement system was developed to measure the tracer concentration in SiC/pigment mixtures. The corresponding pigment concentrations were evaluated at the bottom of this system using a photomultiplier. The pigment concentration was derived from the integral of the signal intensity received by the photomultiplier. Then, a calibration curve was established which provided the empirical relationship between the integral and pigment concentration. In order to validate this RTD measurement technique, a series of experiments was performed in a bubbling fluidized bed and the effect of the bed height was studied. It was shown that the experimental RTD curves were in good agreement with the theoretical RTD of bubbling fluidized beds. This solids RTD measurement technique can be used to provide a better understanding of the hydrodynamics of complex solids unit operations.     

Eu3+, Sm3+ Deep‐Red Phosphors as Novel Materials for White Light‐Emitting Diodes and Simultaneous Performance Enhancement of Organic–Inorganic Perovskite Solar Cells

The luminous efficiency of inorganic white light‐emitting diodes, to be used by the next generation as light initiators, is continuously progressing and is an emerging interest for researchers. However, low color‐rendering index (Ra), high correlated color temperature (CCT), and poor stability limit its wider application. Herein, it is reported that Sm³⁺‐ and Eu³⁺‐doped calcium scandate (CaSc₂O₄ (CSO)) are an emerging deep‐red‐emitting material with promising light absorption, enhanced emission properties, and excellent thermal stability that make it a promising candidate with potential applications in emission display, solid‐state white lighting, and the device performance of perovskite solar cells (PSCs). The average crystal structures of Sm³⁺‐doped CSO are studied by synchrotron X‐ray data that correspond to an extremely rigid host structure. Samarium ion is incorporated as a sensitizer that enhances the emission intensity up to 30%, with a high color purity of 88.9% with a 6% increment. The impacts of hosting the sensitizer are studied by quantifying the lifetime curves. The CaSc₂O₄:0.15Eu³⁺,0.03Sm³⁺ phosphor offers significant resistance to thermal quenching. The incorporation of lanthanide ion‐doped phosphors CSOE into PSCs is investigated along with their potential applications. The CSOE‐coated PSCs devices exhibit a high current density and a high power conversion efficiency (15.96%) when compared to the uncoated control devices.