The high-quality simulation of the penumbra effect in real-time shadows is a challenging problem in shadow mapping. The existing shadow map filtering techniques are prone to aliasing and light leaking artifacts which decrease the shadow visual quality. In this paper, we aim to minimize both problems with the Euclidean distance transform shadow mapping. To reduce the perspective aliasing artifac...

There is one Information Retrieval model that uses geometrical space: it is the Vector Space Model, which is defined in Euclidean Space. The paper shows that it is possible to define a Vector Space Model in non-Euclidean Space, too. Namely, the paper proposes a Vector Space Model over the Cayley-Klein Hyperbolic Geometry using a similarity measure derived from the hyperbolic distance. It is sho...

We obtain an optimal deviation from the mean upper bound D(x) def = sup f∈F μ{f −Eμf ≥ x}, for x ∈ R (0.1) where F is the complete class of integrable, Lipschitz functions on probability metric (product) spaces. As corollaries we get exact solutions of (0.1) for Euclidean unit sphere Sn−1 with a geodesic distance function and a normalized Haar measure, for R equipped with a Gaussian measure and...

In this paper a new synthesis for circuit design of Euclidean distance calculation is presented. The circuit is implemented based on a simple two-quadrant squarer/divider block. The circuit that employs floating gate MOS (FG-MOS) transistors operating in weak inversion region, features low circuit complexity, low power (<20uW), low supply voltage (0.5V), two quadrant input current, wide dyn...

The continuous phase property of Continuous Phase Modulation (CPM) makes it possible to define spectrally efficient digital modulation schemes with a narrow spectral main lobe and small spectral side lobes by using a small modulation index, a non-binary alphabet size and a smooth phase pulse [1]. The constant envelope property of CPM also allows the use of non-linear amplifiers, which have lowe...

In the Euclidean space, the approximate nearest neighbors (ANN) search measures the similarity degree through computing the Euclidean distances, which owns high time complexity and large memory overhead. To address these problems, this paper maps the data from the Euclidean space into the Hamming space, and the normalized distance similarity restriction and the quantization error are required t...

The normalized information distance is a universal distance measure for objects of all kinds. It is based on Kolmogorov complexity and thus uncomputable, but there are ways to utilize it. First, compression algorithms can be used to approximate the Kolmogorov complexity if the objects have a string representation. Second, for names and abstract concepts, page count statistics from the World Wid...

An intuitionistic fuzzy set (IFS) can be helpful in decision-making as a concept to describe uncertainty. This study proposes the application of IFS determining research topics for students mathematics education program using normalized Euclidean distance method. also shows differences analysis results max-min composition method revised by De et al. (2001) with Hamming and The show that determi...

In this paper, we propose the Normalized Freebase Distance (NFD), a new measure for determing semantic concept relatedness that is based on similar principles as the Normalized Web Distance (NWD). We illustrate that the NFD is more effective when comparing ambiguous concepts.