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Current State of Deep Ocean Bathymetric Exploration

Zwolak K., Felski A.

Annual of Navigation

2017

No. 24

257--267

The paper presents current state of bathymetric survey concerning deep ocean rather than shallow areas, which are better surveyed due to safety of navigation concerns. Rules and requirements of the new challenge, called the Shell Ocean Discovery XPRIZE, became a starting point for a discussion about the possibilities of mapping large areas of the ocean using up-to-date and new technology. The amount of bathymetric data available nowadays and the current state of ocean map compilations are also discussed in the paper as a motivation to inspire the new initiatives in the deep ocean.

W artykule przedstawiono obecny stan pomiarów batymetrycznych głębokowodnych obszarów oceanicznych. Zasady najnowszego konkursu Shell Ocean Discovery XPRIZE stały się punktem wyjścia do dyskusji o obecnych możliwościach pozyskiwania danych niezbędnych do tworzenia map oceanów w oparciu o aktualnie dostępne technologie. W artykule poruszono również zagadnienie ilości i cech danych batymetrycznych znajdujących się i udostępnianych w bazach danych gromadzących tego typu informacje.

An improved ant colony optimization algorithm and its application to text-independent speaker verification system

Aghdam M. H.

Journal of Artificial Intelligence and Soft Computing Research

2012

Vol. 2, No. 4

301--315

With the growing trend toward remote security verification procedures for telephone banking, biometric security measures and similar applications, automatic speaker verification (ASV) has received a lot of attention in recent years. The complexity of ASV system and its verification time depends on the number of feature vectors, their dimensionality, the complexity of the speaker models and the number of speakers. In this paper, we concentrate on optimizing dimensionality of feature space by selecting relevant features. At present there are several methods for feature selection in ASV systems. To improve performance of ASV system we present another method that is based on ant colony optimization (ACO) algorithm. After feature selection phase, feature vectors are applied to a Gaussian mixture model universal background model (GMM-UBM) which is a text-independent speaker verification model. The performance of proposed algorithm is compared to the performance of genetic algorithm on the task of feature selection in TIMIT corpora. The results of experiments indicate that with the optimized feature set, the performance of the ASV system is improved. Moreover, the speed of verification is significantly increased since by use of ACO, number of features is reduced over 80% which consequently decrease the complexity of our ASV system.