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Articles 1 - 4 of 4
Full-Text Articles in Controls and Control Theory
Scheduling Architectures For Diffserv Networks With Input Queuing Switches, Mei Yang, Henry Selvaraj, Enyue Lu, Jianping Wang, S. Q. Zheng, Yingtao Jiang
Scheduling Architectures For Diffserv Networks With Input Queuing Switches, Mei Yang, Henry Selvaraj, Enyue Lu, Jianping Wang, S. Q. Zheng, Yingtao Jiang
Electrical & Computer Engineering Faculty Research
ue to its simplicity and scalability, the differentiated services (DiffServ) model is expected to be widely deployed across wired and wireless networks. Though supporting DiffServ scheduling algorithms for output-queuing (OQ) switches have been widely studied, there are few DiffServ scheduling algorithms for input-queuing (IQ) switches in the literaure. In this paper, we propose two algorithms for scheduling DiffServ DiffServ networks with IQ switches: the dynamic DiffServ scheduling (DDS) algorithm and the hierarchical DiffServ scheduling (HDS) algorithm. The basic idea of DDS and HDS is to schedule EF and AF traffic According to Their minimum service rates with the reserved bandwidth …
Free Regions Of Sensor Nodes, Laxmi P. Gewali, Navin Rongatana, Henry Selvaraj, Jan B. Pedersen
Free Regions Of Sensor Nodes, Laxmi P. Gewali, Navin Rongatana, Henry Selvaraj, Jan B. Pedersen
Electrical & Computer Engineering Faculty Research
We introduce the notion of free region of a node in a sensor network. Intuitively, a free region of a node is the connected set of points R in its neighborhood such that the connectivity of the network remains the same when the node is moved to any point in R. We characterize several properties of free regions and develop an efficient algorithm for computing them. We capture free region in terms of related notions called in-free region and out-free region. We present an O(n2) algorithm for constructing the free region of a node, where n is the number of …
Implementation Of Large Neural Networks Using Decomposition, Henry Selvaraj, H. Niewiadomski, P. Buciak, M. Pleban, Piotr Sapiecha, Tadeusz Luba, Venkatesan Muthukumar
Implementation Of Large Neural Networks Using Decomposition, Henry Selvaraj, H. Niewiadomski, P. Buciak, M. Pleban, Piotr Sapiecha, Tadeusz Luba, Venkatesan Muthukumar
Electrical & Computer Engineering Faculty Research
The article presents methods of dealing with huge data in the domain of neural networks. The decomposition of neural networks is introduced and its efficiency is proved by the authors’ experiments. The examinations of the effectiveness of argument reduction in the above filed, are presented. Authors indicate, that decomposition is capable of reducing the size and the complexity of the learned data, and thus it makes the learning process faster or, while dealing with large data, possible. According to the authors experiments, in some cases, argument reduction, makes the learning process harder.
Variable Structure End Point Control Of A Flexible Manipulator, Shailaja Chenumalla, Sahjendra N. Singh
Variable Structure End Point Control Of A Flexible Manipulator, Shailaja Chenumalla, Sahjendra N. Singh
Electrical & Computer Engineering Faculty Research
We treat the question of control and stabilization of the elastic multibody system developed in the Phillips Laboratory, Edwards Air Force Base, California. The controlled output is judiciously chosen such that the zero dynamics are stable or almost stable. A variable structure control (VSC) law is derived for the end point trajectory control. Although, the VSC law accomplishes precise end point tracking, elastic modes are excited during the maneuver of the arm. A Linear stabilizer is designed for the final capture of the terminal state.