Classification with rejection is well understood for classifiers which provide explicit class probabilities. The situation is more complicated for popular deterministic classifiers such as learning vector quantisation schemes: albeit reject options using simple distance-based geometric measures were proposed [4], their local scaling behaviour is unclear for complex problems. Here, we propose a ...

Most perceptual quantization algorithms use some kind of activity measure such as variance in determining the quantization step size for local macroblocks in MPEG coding. In this paper, we use directional total variation as a new kind of activity measure to develop a novel adaptive quantization algorithm. The scheme is contrasted with a method proposed by Puri and Aravind. Simulation result sug...

This paper presents a quantization noise cancellation technique for frequency-to-digital converter-based fractional-N phase-locked loops (FDC-PLLs). The technique cancels quantization noise prior to the loop filter so the PLL bandwidth can be increased without a significant phase noise penalty. The paper also presents an FDC-PLL architecture enhancement that achieves the effect of a charge pump...

We present a combination of an extended vector quantization (VQ) algorithm for training a speaker model and a gaussian interpretation of the VQ speaker model in the veri cation phase. This leads to a large decrease of the error rates compared to normal vector quantization and only a slight deterioration compared to full Gaussian mixture model (GMM) training. The training costs of the new method...

Vector-quantized local features frequently used in bag-ofvisual-words approaches are the backbone of popular visual recognition systems due to both their simplicity and their performance. Despite their success, bag-of-words-histograms basically contain low-level image statistics (e.g., number of edges of different orientations). The question remains how much visual information is lost in quanti...

In 3D object retrieval it is very important to define reliable shape descriptors, which compactly characterize geometric properties of the underlying surface. To this aim two main approaches are considered: global, and local ones. Global approaches are effective in describing the whole object, while local ones are more suitable to characterize small parts of the shape. Some strategies to combin...

Phase Space is the framework best suited for quantizing superintegrable systems— systems with more conserved quantities than degrees of freedom. In this quantization method, the symmetry algebras of the hamiltonian invariants are preserved most naturally, as illustrated on nonlinear σ -models, specifically for Chiral Models and de Sitter N-spheres. Classically, the dynamics of superintegrable m...

Spectrum sensing remains a challenge in the context of cognitive radio networks (CRNs). Compared with traditional single-user sensing, cooperative spectrum sensing (CSS) exploits multiuser diversity to overcome channel fading, shadowing, and hidden terminal problems, which can effectively enhance the sensing performance and protect licensed users from harmful interference. However, for a large ...

Since digital images require a large space on the storage devices and the network bandwidth, many compression methods have been used to solve this problem. Actually, these methods have, more or less, good results in terms of compression ratio and the quality of the reconstructed images. There are two main types of compression: the lossless compression which is based on the scalar quantization a...