With increasing core counts ushering in power-constrained 3-D multiprocessor system-on-chips (MPSoCs), optimizing communication power dissipated by the 3-D network-on-chip (NoC) fabric is critical. At the same time, with increased power densities in 3-D ICs, problems of IR drops in the power delivery network (PDN) as well as thermal hot spots on the 3-D die are…
Category: M.TECH PROJECT IN OMNET
Energy Efficiency Analysis of Cooperative Jamming in Cognitive Radio Networks with Secrecy Constraints
We investigate energy-efficient cooperation for se- crecy in cognitive radio networks. In particular, we consider a four-node cognitive scenario where the secondary receiver is treated as a potential eavesdropper with respect to the primary transmission. The cognitive transmitter should ensure that the primary message is not leaked to the secondary user by using cooperative jamming.…
Sensor Networks Localization: Extending Trilateration via Shadow Edges
Distance-based network localization is known to have solution, in general, if the network is globally rigid. In this paper we relax this condition with reference to unit disk graphs. To this end, shadow edges are introduced to model the fact that selected nodes are not able to sense each other. We provide a localization algorithm…
Robust Restoration Decision-Making Model for Distribution Networks Based on Information Gap Decision Theory
Service restoration is important in distribution networks following an outage. During the restoration process, the system operating conditions will fluctuate, including variation of the load demand and the output from distributed generators (DGs). These variations are hard to be predicted and the load demands are roughly estimated because of absence of real-time measurements, which can…
Multi-valued Neural Network Trained by Differential Evolution for Synthesizing Multiple-Valued Functions
We consider the problem of synthesizing multiple valued logic (MVL) functions by neural networks. A differential evolution algorithm is proposed to train the learnable multiple valued logic network. The optimum window and biasing parameters to be chosen for convergence are derived. Experiments performed on benchmark problems demonstrate the convergence and robustness of the network. Preliminary…