A low pressure SWCNT-ENM sandwich membrane system for the removal of PPCPs from water

While carbon nanotubes are known as efficient adsorbents for removal of a number of contaminants from water, the possibility of their leaching into drinking water has prevented their application in water treatment. In this study, single walled carbon nanotubes (SWCNT) were sandwiched between two electrospun nanofibre membranes (ENM). The relatively small pore size of the ENM prevented the mechanically entangled nanotubes from passing through and contaminating the water. The performance of the SWCNT-ENM was evaluated in a lab-scale setup for the removal of PPCPs. For this purpose, a feed solution spiked with known concentrations of six PPCPs was passed through the membrane system. The target substances included acetaminophen (ACT), bezafibrate (BZF), iopromide (IOP), diclofenac (DCF), carbamazepine (CBZ), and sulphamethoxazole (SMX). The same test was also conducted using a single contaminant (ACT). Results demonstrated a decrease in the overall percent removal of PPCPs as feed flow rate and PPCP concentration increased. For multi-component feeds containing equal amounts of the aforementioned PPCPs, the overall percent removal decreased from 90.8% to 71.0% when increasing the feed concentration from 30 to 600 μg/L. Experiments using sandwiched powdered activated carbon (PAC) showed that the dynamic adsorption capacity of PPCPs by SWCNT-ENM was higher than that of PAC-ENM, and remained unaffected by the feed composition. In addition, the high porosity of this novel membrane allowed for flow of water with low resistance such that the trans-membrane pressure was found to be as low as 4 kPa at a pure water flux of 330 L/m²h.     

LSMAT Least Squares Medial Axis Transform

The medial axis transform has applications in numerous fields including visualization, computer graphics, and computer vision. Unfortunately, traditional medial axis transformations are usually brittle in the presence of outliers, perturbations and/or noise along the boundary of objects. To overcome this limitation, we introduce a new formulation of the medial axis transform which is naturally robust in the presence of these artefacts. Unlike previous work which has approached the medial axis from a computational geometry angle, we consider it from a numerical optimization perspective. In this work, we follow the definition of the medial axis transform as ‘the set of maximally inscribed spheres’. We show how this definition can be formulated as a least squares relaxation where the transform is obtained by minimizing a continuous optimization problem. The proposed approach is inherently parallelizable by performing independent optimization of each sphere using Gauss–Newton, and its least‐squares form allows it to be significantly more robust compared to traditional computational geometry approaches. Extensive experiments on 2D and 3D objects demonstrate that our method provides superior results to the state of the art on both synthetic and real‐data.     

Exact stability of a platoon of vehicles by considering time delay and lag

The problem of controlling a string of vehicles moving in one dimension is considered so that they all follow a lead vehicle with constant time headway spacing between successive vehicles. Due to realistic design and execution, the negative effect of the tracking lag parameter and time delay is taken into account. By applying an acceleration feedforward, the distance error of each vehicle will be independent of the behavior of other vehicles. As a result, the vehicle is better able to track a desired trajectory, which improves the string stability. Cluster treatment of characteristic roots (CTCR) paradigm is utilized to reveal the stabilizing parametric regions in the domain of the time delay to render the stability of closed loop system. The string stability analysis is performed to evaluate the disturbance attenuation. Finally, an example of multiple vehicle platoon control is presented, which demonstrates the effectiveness of the proposed method.     

Novel electro-conductive nanocomposites based on electrospun PLGA/CNT for biomedical applications

Electro-conductive nanocomposites have several applications in biomedical field. Development of a biocompatible electro-conductive polymeric materials is therefore of prime importance. In this study, electro-conductive nanofibrous mats of PLGA/CNT were fabricated through different methods including blend electrospinning, simultaneous PLGA electrospinning and CNT electrospraying and ultrasound-induced adsorption of CNTs on the electrospun PLGA nanofibers. The morphology and diameter of fibers were characterized by SEM and TEM, showing the lowest average diameters of 477?±?136?nm for PLGA/MWCNT blend nanocomposites. MWCNT-sprayed PLGA specimens showed significant lower water contact angle (83°), electrical resistance (3.0?×?10⁴?Ω) and higher mechanical properties (UTS: 5.50?±?0.46?MPa) compared to the untreated PLGA scaffolds. Also, results of PC12 cell study demonstrated highest viability percentage on the MWCNT-sprayed PLGA nanofibers. We propose that the conductive nanocomposites have capability to use as tool for the neural regeneration and biosensors.

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Selecting effective features on prediction of delay in servicing ships arriving to ports using a combination of Clonal Selection and Grey Wolf Optimization algorithms—Case study: Shahid Rajaee port in Bandar Abbas

Predicting the delay in servicing incoming ships to ports is crucial for maritime transportation. In this study, we use support vector regression (SVR) in order to accurately predict this delay for ships arriving to the terminal No. 1 of Shahid Rajaee's port in Bandar Abbas. To achieve this goal, a combination of Clonal Selection and Grey Wolf Optimization algorithms (named as CLOGWO) is used for two purposes: (i) selecting the most important features among the features that affect prediction of this delay and (ii) optimizing SVR parameters for a more accurate prediction. Performance of the proposed method was compared with Genetic Algorithm (GA), Clonal Selection (CS), Grey Wolf Optimization (GWO), and Particle Swarm Optimization (PSO) algorithms on the following metrics: correlation, rate of feature reduction, root mean square error (RMSE), and normalized RMSE (NRMSE). Evaluations on Shahid Rajaee dataset showed that the mean value of these metrics in 10 independent runs of the proposed method were 0.867, 74.45%, 0.080, and 9.02, respectively. These results and evaluations on standard datasets indicate that the proposed method provides competitive results with other evolutionary algorithms.     

Robust image watermarking scheme using bit-plane of hadamard coefficients

Multimedia Tools and Applications - Nowadays, due to widespread usage of the Internet, digital contents are distributed quickly and inexpensively throughout the world. Watermarking techniques can...     

Neural network solution for a real-time no-reference video quality assessment of H.264/AVC video bitstreams

The ever-growing video streaming services require accurate quality assessment with often no reference to the original media. One primary challenge in developing no-reference (NR) video quality metrics is achieving real-timeliness while retaining the accuracy. A real-time no-reference video quality assessment (VQA) method is proposed for videos encoded by H.264/AVC codec. Temporal and spatial features are extracted from the encoded bit-stream and pixel values to train and validate a fully connected neural network. The hand-crafted features and network dynamics are designed in a manner to ensure a high correlation with human judgment of quality as well as minimizing the computational complexities. Proof-of-concept experiments are conducted via comparison with: 1) video sequences rated by a full-reference quality metric, and 2) H.264-encoded sequences from the LIVE video dataset which are subjectively evaluated through differential mean opinion scores (DMOS). The performance of the proposed method is verified by correlation measurements with the aforementioned objective and subjective scores. The framework achieves real-time execution while outperforming state-of-art full-reference and no-reference video quality assessment methods.

     

A Survey on Traffic Management in Software-Defined Networks: Challenges,Effective Approaches,and Potential Measures

Software-defined networks (SDNs), as an emerging paradigm by separating the control plane from the data plane, increases flexibility and network utilization and reduces redundancy and operational cost. Traffic management of software-defined networks can be defined as network traffic monitoring and analyzing measures to improve network performance and quality of service metrics. Traffic management as an effective instrument for optimizing network traffic can offer the appropriate services according to network situation. Due to the inherent characteristics of SDN, special techniques are required to analyze, predict, and adapt the network traffic in order to achieve an efficient traffic management mechanism. This paper surveys traffic management techniques of SDN in four distinct categories including, routing, load balancing, congestion control, and flow control to cover the impressible issues. Moreover, the differences between SDN and traditional networks are analyzed in terms of traffic management necessities across the various groups to further determine the dimensions affecting research in this area. Furthermore, the available algorithms in each group and their role in traffic management are reviewed as well as the research challenges and future trends.

     

A second-order blind source separation method for bilinear mixtures

In this paper, we are interested in the problem of Blind Source Separation using a Second-order Statistics (SOS) method in order to separate autocorrelated and mutually independent sources mixed according to a bilinear (BL) model. In this context, we propose a new approach called Bilinear Second-order Blind Source Separation, which is an extension of linear SOS methods, devoted to separate sources present in BL mixtures. These sources, called extended sources, include the actual sources and their products. We first study the statistical properties of the different extended sources, in order to verify the assumption of identifiability when the actual sources are zero-mean and when they are not. Then, we present the different steps performed in order to estimate these actual centred sources and to extract the actual mixing parameters. The obtained results using artificial mixtures of synthetic and real sources confirm the effectiveness of the new proposed approach.     

SDN-based wireless user management system

The degradation of end-to-end quality of service (QoS) on mobile users is becoming a common issue for IEEE 802.11 infrastructure-based networks in crowded areas. This research tackles the issue by employing an SDN framework on an integrated wireless/wired environment. Thereby, we present the development and implementation of a system that performs user management by analyzing the network load from the OpenFlow statistics, as well as the wireless information collected from the associated users. In order to analyse the behaviour of the proposed user migration algorithm, we evaluate the system under scenarios with different traffic load and user session duration. From the experiments, we observed that in several cases wireless users get a considerable QoS improvement at the application layer (up to 30% improvement in throughput and up to 20% in delay in our simulations) once the system is activated. Based on the results, we present an analysis on how and when user migration in multi-access point IEEE 802.11 networks can be most effective.