Comparing the distribution of the electronic gap of an organic molecule with its photoluminescence spectrum

The electronic gap structure of the organic molecule N,N′-diphenyl-N,N′-bis(3-methylphenyl)-(1,1′-biphenyl)-4,4′-diamine, also known as TPD, has been studied by means of a Scanning Tunneling Microscope (STM) and by Photoluminescence (PL) analysis. Hundreds of current-voltage characteristics measured at different spots of the sample show the typical behavior of a semiconductor. The analysis of the curves allows to construct a gap distribution histogram which reassembles the PL spectrum of this compound. This analysis demonstrates that STM can give relevant information, not only related to the expected value of a semiconductor gap but also on its distribution which affects its physical properties such as its PL.

An Electronic Reconfigurable Neural Architecture for Intrusion Detection

The explosive growth of the traffic in computer systems has made it clear that traditional control techniques are not adequate to provide the system users fast access to network resources and prevent unfair uses. In this paper, we present a reconfigurable digital hardware implementation of a specific neural model for intrusion detection. It uses a specific vector of characterization of the network packages (intrusion vector) which is starting from information obtained during the access intent. This vector will be treated by the system. Our approach is adaptative and to detecting these intrusions by using a complex artificial intelligence method known as multilayer perceptron. The implementation have been developed and tested into a reconfigurable hardware (FPGA) for embedded systems. Finally, the Intrusion detection system was tested in a real-world simulation to gauge its effectiveness and real-time response.