Tag Archive: 'Omnet Download'

Evaluating harmonic voltage distortion in load-variating unbalanced networks using Monte Carlo simulations

This study presents a new methodology for modelling and handling with aggregated harmonic loads and linear loads in the harmonic power flow analysis. This method, so-called variable load method, can be used in balanced and unbalanced systems and for deterministic and stochastic studies. The main types of harmonic loads and their participation coefficients inside the […]

Scaling Limits of MEMS Beam-Steering Switches for Data Center Networks

Transparent optical circuit switching can improve the aggregate bandwidth, scalability, and cost of data center networks provided it can meet the performance requirements on switching speed, port count, and optical efficiency. Here, we examine the theoretical scaling limits of transparent non-blocking optical switches based on MEMS electrostatic tilt mirror devices. Using physical optics and electromechanics, […]

A Potential-Game Approach for Information-Maximizing Cooperative Planning of Sensor Networks

This paper presents a potential-game approach for distributed cooperative selection of informative sensors, when the goal is to maximize the mutual information between the measurement variables and the quantities of interest. It is proved that a local utility function defined by the conditional mutual information of an agent conditioned on the other agents’ sensing decisions […]

A Supervisory Load-Leveling Approach to Improve the Voltage Profile in Distribution Network

This paper suggests a supervisory control for storage units to provide load leveling in distribution networks. This approach coordinates storage units to charge during high generation and discharge during peak load times, while utilized to improve the network voltage profile indirectly. The aim of this control strategy is to establish power sharing on a pro […]

Mixed H-Infinity and Passive Filtering for Discrete Fuzzy Neural Networks With Stochastic Jumps and Time Delays

In this brief, the problems of the mixed H-infinity and passivity performance analysis and design are investigated for discrete time-delay neural networks with Markovian jump parameters represented by Takagi-Sugeno fuzzy model. The main purpose of this brief is to design a filter to guarantee that the augmented Markovian jump fuzzy neural networks are stable in […]

Taming Cross-Technology Interference for WiFi and ZigBee Coexistence Networks

Recent studies show that WiFi interference has been a major problem for low power urban sensing technology ZigBee networks. Existing approaches for dealing with such interferences often modify either the ZigBee nodes or WiFi nodes. However, massive deployment of ZigBee nodes and uncooperative WiFi users call for innovative cross-technology coexistence without intervening legacy systems. In […]

Demonstration of multi-hop optical add-drop network with high frequency granular optical channel defragmentation nodes

Four nodes optical add-drop network with high frequency granular optical channel defragmentation has been demonstrated. All channels presents error-free operation and power penalties are less than 4 dB even after multi-hop transmission.

Engineering Parallel Algorithms for Community Detection in Massive Networks

The amount of graph-structured data has recently experienced an enormous growth in many applications. To transform such data into useful information, fast analytics algorithms and software tools are necessary. One common graph analytics kernel is disjoint community detection (or graph clustering). Despite extensive research on heuristic solvers for this task, only few parallel codes exist, […]

Leveraging the Error Resilience of Neural Networks for Designing Highly Energy Efficient Accelerators

In the recent years, inexact computing has been increasingly regarded as one of the most promising approaches for slashing energy consumption in many applications that can tolerate a certain degree of inaccuracy. Driven by the principle of trading tolerable amounts of application accuracy in return for significant resource savings–the energy consumed, the (critical path) delay […]

Sensor Failure Detection, Identification, and Accommodation Using Fully Connected Cascade Neural Network

Modern control systems rely heavily on their sensors for reliable operation. Failure of a sensor could destabilize the system, which could have serious consequences to the system’s operations. Therefore, there is a need to detect and accommodate such failures, particularly if the system in question is of a safety critical application. In this paper, a […]