Face normalization using multi-scale cortical keypoints

Empirical studies concerning face recognition suggest that faces may be stored in memory by a few canonical representations. Models of visual perception are based on image representations in cortical area V1 and beyond, which contain many cell layers for feature extractions. Simple, complex and end-stopped cells tuned to different spatial frequencies (scales) and/or orientations provide input for line, edge and keypoint detection. This yields a rich, multi-scale object representation that can be stored in memory in order to identify objects. The multi-scale, keypoint-based saliency maps for Focus-of-Attention can be explored to obtain face detection and normalization, after which face recognition can be achieved using the line/edge representation. In this paper, we focus only on face normalization, showing that multi-scale keypoints can be used to construct canonical representations of faces in memory.

Joint turbo equalization and cancelation of nonlinear distortion effects in MC-CDMA signals

In this paper, we consider low-PMEPR (Peak-to-Mean Envelope Power Ratio) MC-CDMA (Multicarrier Coded Division Multiple Access) schemes. We develop frequencydomain turbo equalizers combined with an iterative estimation and cancellation of nonlinear distortion effects. Our receivers have relatively low complexity, since they allow FFT-based (Fast Fourier Transform) implementations. The proposed turbo receivers allow significant performance improvements at low and moderate SNR (Signal-to-Noise Ratio), even when a low-PMEPR MC-CDMA transmission is intended.

An overview of nonlinear identification and control with neural networks.

The aim of this chapter is to introduce background concepts in nonlinear systems identification and control with artificial neural networks. As this chapter is just an overview, with a limited page space, only the basic ideas will be explained here. The reader is encouraged, for a more detailed explanation of a specific topic of interest, to consult the references given throughout the text. Additionally, as general books in the field of neural networks, the books by Haykin [1] and Principe et al. [2] are suggested. Regarding nonlinear systems identification, covering both classical and neural and neuro-fuzzy methodologies, Reference 3 is recommended. References 4 and 5 should be used in the context of B-spline networks.

Turbo equalization with cancellation of nonlinear distortion effects for CP-Assisted and Zero-Padded MC-CDMA signals

We consider MC-CDMA schemes, with reduced envelope fluctuations. Both CP-assisted (cyclic prefix) and ZP (zero-padded) MC-CDMA schemes are addressed. We develop turbo FDE (frequency-domain equalization) schemes, combined with cancelation of nonlinear distortion effects. The proposed turbo receivers allow significant performance improvements at low and moderate SNR, even when the transmitted signals have reduced envelope fluctuations.