A novel numerical modeling paradigm for bio particle tracing in non-inertial microfluidics devices

Microsystem Technologies - In this work, we report on the design and implementation of a new method for the two dimensional (2D) simulation of rigid spherical particles trajectory which are to be...     

Synthesis of a novel sulfonated poly(amide‐imide) and its application in preparation of ultrafiltration PES membranes as a modifier

Sulfonated poly(amide‐imide) (SPAI) copolymer was synthesized, characterized, and blended into poly(ether sulfone) (PES)/dimethylacetamide casting solutions to prepare ultrafiltration membranes. Different weight ratios of the copolymer (0–10 wt %) were mixed in the PES casting solution. The analyses of contact angle and attenuated total reflection‐Fourier transform infrared spectra were used to study hydrophilicity and physicochemical properties of the membrane surface, respectively. The membranes were further characterized by scanning electron microscopy images, ultrafiltration performance, and fouling analyses. The outcomes showed that addition of the SPAI in the PES matrix improved considerably the membranes hydrophilicity. Moreover, with increasing SPAI concentration, the porosity, flux recovery ratio, and pure water permeability of the modified membranes were improved. The pure water flux was increased from 3.6 to 12.4 kg/m² h by increasing 2 wt % SPAI. The antifouling property of the modified PES membranes against bovine serum albumin, tested by a dead‐end filtration setup revealed that bovine serum albumin rejection of the obtained membrane was also enhanced and the antifouling properties of the blending membranes were improved. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018 , 135, 46477.     

Super peer selection strategy in peer‐to‐peer networks based on learning automata

A super peer is a peer that has the highest capacity in comparison with other peers in the network. It is trying to reduce the load of the rest of the peers and improve network performance. Selecting a super peer in a peer‐to‐peer–based network is a very crucial challenge. As the ability of peers are very different, by considering capacity of each peer and selecting a proper role, we can use network components much more efficiently. Because of the dynamicity of these networks, comparative methods of selecting super peers is of special importance. Comparative selection is continuously trying to select proper super peer. In recent studies, learning automata was introduced as a powerful learning model to solve this issue. In most of the studies, learning automata with an S model is employed. In this article, another selection method of learning automata with a P model environment is presented and its capability for super peer selection is shown. Moreover, simulation results show that removing some of the super peers would result in better performance in terms of inversion time in the high level of super‐peer capacity, required time for selecting proper super peer, and super peer tolerance.     

An efficient contrast enhancement method using repulsive force of edges

Multidimensional Systems and Signal Processing - This paper presents a novel algorithm which employs edge information to automatically improve the quality of low contrast images. This algorithm...     

TSLBS: A Time-Sensitive and Load Balanced Scheduling Approach to Wireless Sensor Actor Networks

Existing works on scheduling in Wireless Sensor Actor Networks (WSANs) are mostly concerned with energy savings and ignore time constraints and thus increase the make-span of the network. Moreover, these algorithms usually do not consider balance of workloads on the actor nodes and hence, sometimes some of the actors are busy when some others are idle. These problem causes the actors are not utilized properly and the actors’ lifetime is reduced. In this paper we take both time awareness and balance of workloads on the actor in WSANs into account and propose a convex optimization model (TAMMs) to minimize make-span. We also propose a protocol called LIBP to improve load balancing that allocates tasks to actors according to their measured capabilities in such a way to enhance balances of workloads on the actors. Finally, by combination of TAMMs and LIBP, a time-sensitive and load balanced scheduling approach (TSLBS) is proposed. TSLBS considers both local and global tasks and the distribution requirements of WSANs (i.e. WSANs with hybrid architecture). The results of simulations on typical scenarios shows that TSLBs is more efficient in terms of both the make-span and load balancing compared to stochastic task scheduling algorithm (STSA). We also show that TSLBs performs significantly better than STSA in terms of actor’s lifetime.     

Presenting a traffic management and control system in driver assistance form based on vehicular networks

Vehicular Networks is considered a major step in the field of Intelligent Transportation System (ITS). In this technology, some equipment will be installed on vehicles and special places at roadsides which will enable the wireless communication between vehicles with each other and will provide the communication between the vehicles and roadside equipment. One of the ITS application is Traffic monitoring system. Such system enables accessing traffic videos by traffic monitoring centers to make traffic decision. However, providing traffic video for the vehicles can be appealing. This paper addresses a new application in vehicular networks and ITS which can provide this videos for drivers in a city. Each driver request timely traffic video of a location from a web server and the web server forward this request to a stream management server. This server based on current location of the requester vehicle, its speed and its direction calculates appropriate video chunks for each RSU along vehicle destination. This study aims to present a system which can bring a high accessibility for content and can provide it with an appropriate bandwidth and quality for vehicles. Due to the scalability and bandwidth limitations for its content and streaming, vehicular networks are used in this system.     

Using a recurrent artificial neural network for dynamic self-adaptation of cluster-based web-server systems

To process huge requests issued from web users, web servers often set up a cluster using switches and gateways where a switch directs users’ requests to some gateway. Each gateway, which is connected to some servers, is considered for processing a specific type of request such as fttp or http service. When servers of a gateway are saturated and the gateway is not able to process more requests, adaptation is performed by borrowing a server from another gateway. However, such a reactive adaptation causes some problems. However, due to problem of the reactive techniques, predictive ones have been paid attention. While a reactive adaptation aims to redress the system after incurring a bottleneck, a predictive adaptation tries to prevent the system from entering the bottleneck. In this article, we improved our previous predictive framework using a Recurrent Artificial Neural Network (RANN) called Nonlinear Autoregressive with eXogenous (external) inputs (NARX). We employed our new framework for adaptation of a web-based cluster where each cluster is meant for a specific service and self-adaptation is used for load balancing clusters. To show the improvement, we used the case study presented in our previous study.