Frequency- domain multiuser detection for highly overloaded DS-CDMA systems

A DS-CDMA (Direct Sequence-Coded Division Multiple Access) system has maximum spectral efficiency if the system is fully loaded (i.e., the number of users is equal to the spreading factor) and we employ signals with bandwidth equal to the chip rate. However, due to implementation constraints we need to employ signals with higher bandwidth, decreasing the system’s spectral efficiency. In this paper we consider prefixassisted DS-CDMA systems with bandwidth that can be significantly above the chip rate. To allow high spectral efficiency we consider highly overloaded systems where the number of users can be twice the spreading factor or even more. To cope with the strong interference levels we present an iterative frequencydomain receiver that takes full advantage of the total bandwidth of the transmitted signals. Our performance results show that the proposed receiver can have excellent performance, even for highly overloaded systems. Moreover, the overall system performance can be close to the maximum theoretical spectral efficiency, even with transmitted signals that have bandwidth significantly above the chip rate.

Modelling and simulation of three-phase power transformers

This paper presents the development and implementation of a digital simulation model of a three-phase, three-leg, power transformer. The proposed model, implemented in MATLAB environment, is based on the physical concept of representing windings as mutually coupled coils, and it is intended to study its adequacy to incorporate, at a later stage, the influences of the occurrence of windings inter- turn short-circuits. Both simulation and laboratory tests results, obtained so far, for a three-phase, 6 kVA transformer, demonstrate the adequacy of the model under normal operating conditions.

Multi-scale cortical keypoints for realtime hand tracking and gesture recognition

Human-robot interaction is an interdisciplinary research area which aims at integrating human factors, cognitive psychology and robot technology. The ultimate goal is the development of social robots. These robots are expected to work in human environments, and to understand behavior of persons through gestures and body movements. In this paper we present a biological and realtime framework for detecting and tracking hands. This framework is based on keypoints extracted from cortical V1 end-stopped cells. Detected keypoints and the cells’ responses are used to classify the junction type. By combining annotated keypoints in a hierarchical, multi-scale tree structure, moving and deformable hands can be segregated, their movements can be obtained, and they can be tracked over time. By using hand templates with keypoints at only two scales, a hand’s gestures can be recognized.