Abstract-Cognitive Radios is emerging in research laboratories as a promising wireless paradigm, which will integrate benefits of software defined radio with a complete aware communication behavior. To reach this goal many issues remain still open, such as powerful algorithms for sensing the external environment. This paper presents a further step in the direction of allowing cooperative spectrum sensing in peer-to-peer cognitive networks by using distributed detection theory. The approach aims at improving the radio awareness with respect to stand alone scenario as it is shown with theoretical and experimental results.

The use of time-frequency distributions is proposed as a nonlinear signal processing technique that is combined with a pattern recognition approach to identify superimposed transmission modes in a reconfigurable wireless terminal based on software-defined radio techniques. In particular, a software-defined radio receiver is described aiming at the identification of two coexistent communication modes: frequency hopping code division multiple access and direct sequence code division multiple access. As a case study, two standards, based on the previous modes and operating in the same band (industrial, scientific, and medical), are considered: IEEE WLAN 802.11b (direct sequence) and Bluetooth (frequency hopping). Neural classifiers are used to obtain identification results. A comparison between two different neural classifiers is made in terms of relative error frequency

The use of Time Frequency (TF) analysis is proposed as signal processing technique combined with a pattern recognition approach, for identifying the transmission modes in indoor wireless environment with a reconfigurable mobile terminal based on Software Radio techniques. In particular, a Software Radio device is considered aiming at the identification of the presence of two coexistent communication modes as Bluetooth, based on Frequency Hopping -Code Division Multiple Access (FH-CDMA), and IEEE WLAN 802.11b, based on Direct Sequence -Code Division Multiple Access (DS-CDMA). A pattern recognition approach will be presented, where TF analysis is employed for feature extraction, and a multi hypotheses k nearest neighbors (k-NN) non parametric classifier is used. Results in terms of error classification probability, expressed as relative error frequency, will be provided.

Abstract

This paper proposes a novel architecture for multisensor data fusion in the context of Ambient Intelligence (AmI). The proposed system integrates an heterogeneous network of sensors with CCD cameras and computational units working together in a LAN. Activities of humans interacting in the monitored area are detected and classified by combining sensors data output with a neural method.