http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Implementing Innovative Routing Using Software Defined Networking (SDN)
Adnan Shahid,Jinan Fiaidhi,Sabah Mohammed 보안공학연구지원센터 2016 International Journal of Multimedia and Ubiquitous Vol.11 No.2
Software Defined Networking (SDN) is an open source networking framework recently introduced. It allows developers to program and reprogram the network so that intelligence and new features can be integrated to optimize and enhance the performance of the network. This paper is focused on optimizing the routing implementation of SDN (i.e. SDN Controller). We have used the Floodlight Open Source SDN Controller1 in our experimentation. The Floodlight controller provide source Java libraries and APIs.. It uses Dijkstra’s algorithm to calculate the shortest path between any source and any destination within the network. However, the default routing implementation of Floodlight Controller is such that, while calculating any path, it ignores the actual bandwidth of the link as it takes a unit value for each link. The resultant calculated path becomes a least hop path. This least hop path may be an optimal path where all the links in the network have equal bandwidth and may not be optimal where the networks have unequal link bandwidth. However, today’s networks are mostly consisting of unequal link bandwidth. The goal of this paper is to re-structure the Floodlight Controller so that it can collect the actual bandwidth of all the links in the network and use this information to calculate
Implications of deep learning for the automation of design patterns organization
Hussain, Shahid,Keung, Jacky,Khan, Arif Ali,Ahmad, Awais,Cuomo, Salvatore,Piccialli, Francesco,Jeon, Gwanggil,Akhunzada, Adnan Elsevier 2018 Journal of parallel and distributed computing Vol.117 No.-
<P><B>Abstract</B></P> <P>Though like other domains such as email filtering, web page classification, sentiment analysis, and author identification, the researchers have employed the text categorization approach to automate organization and selection of design patterns. However, there is a need to bridge the gap between the semantic relationship between design patterns (i.e. Documents) and the features which are used for the organization of design patterns. In this study, we propose an approach by leveraging a powerful deep learning algorithm named Deep Belief Network (DBN) which learns on the semantic representation of documents formulated in the form of feature vectors. We performed a case study in the context of a text categorization based automated system used for the classification and selection of software design patterns. In the case study, we focused on two main research objectives: 1) to empirically investigate the effect of feature sets constructed through the global filter-based feature selection methods besides the proposed approach, and 2) to evaluate the significant improvement in the classification decision (i.e. Pattern organization) of classifiers using the proposed approach. The adjustment of DBN parameters such as a number of hidden layers, nodes and iteration can aid a developer to construct a more illustrative feature set. The experimental promising results suggest the significance of the proposed approach to construct a more representative feature set and improve the classifier’s performance in terms of organization of design patterns.</P> <P><B>Highlights</B></P> <P> <UL> <LI> There is a need to bridge the gap between the semantic relationship between patterns. </LI> <LI> We propose an approach by leveraging a powerful deep learning algorithm named Deep Belief Network (DBN). </LI> <LI> The DBN learns on the semantic representation of documents formulated in the form of feature vectors. </LI> <LI> We performed a case study in the context of a text categorization based automated system. </LI> <LI> The experimental promising results suggest the significance of the proposed approach to construct a more representative feature set. </LI> </UL> </P>
Aslam, Saleem,Shahid, Adnan,Lee, Kyung Geun Hindawi Limited 2013 Mathematical problems in engineering Vol.2013 No.-
<P>This paper presents a centralized control-channel selection scheme for cognitive radio networks (CRNs) by exploiting the variation in the spectrum across capacity, occupancy, and error rate. We address the fundamental challenges in the design of the control-channel for CRNs: (1) random licensed users (LUs) activity and (2) the economical and vulnerability concerns for a dedicated control-channel. We develop a knapsack problem (KP) based reliable, efficient, and power optimized (REPO) control-channel selection scheme with an optimal data rate, bit error rate (BER), and idle time. Moreover, we introduce the concept of the backup channels in the context of control-channel selection, which assists the CRs to quickly move on to the next stable channel in order to cater for the sudden appearance of LUs. Based on the KP and its dynamic programming solution, simulation results show that the proposed scheme is highly adaptable and resilient to random LU activity. The REPO scheme reduces collisions with the LUs, minimizes the alternate channel selection time, and reduces the bit error rate (BER). Moreover, it reduces the power consumed during channel switching and provides a performance, that is, competitive with those schemes that are using a static control-channel for the management of control traffic in CRNs.</P>