Jewish settlements in the Odessa region

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Plan U.S.R.R. Melitopol'skogo okruga Genicheskogo raiona poluostrova Chongar vymezhevannogo v poriadke chastichnogo izmeneniia ustroistva kolfonda s ustroistvom poselkov, razbivkoiu na polia sevooborota, gony soglasno nakazu N.K.Z dnia 1927, n 52-2825. Izmerenie v nature proizvedeno i plan etot sostovlen X. partieiu po ustroistvu kolfondov

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Skhematicheskii plan uchastkov pereselencheskogo fonda v Krymu

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Explanations of the general sketches of settlements on the Chonhar peninsula

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Adaptive frequency scaled wavelet packet decomposition for frog call classification

Environmental changes have put great pressure on biological systems leading to the rapid decline of biodiversity. To monitor this change and protect biodiversity, animal vocalizations have been widely explored by the aid of deploying acoustic sensors in the field. Consequently, large volumes of acoustic data are collected. However, traditional manual methods that require ecologists to physically visit sites to collect biodiversity data are both costly and time consuming. Therefore it is essential to develop new semi-automated and automated methods to identify species in automated audio recordings. In this study, a novel feature extraction method based on wavelet packet decomposition is proposed for frog call classification. After syllable segmentation, the advertisement call of each frog syllable is represented by a spectral peak track, from which track duration, dominant frequency and oscillation rate are calculated. Then, a k-means clustering algorithm is applied to the dominant frequency, and the centroids of clustering results are used to generate the frequency scale for wavelet packet decomposition (WPD). Next, a new feature set named adaptive frequency scaled wavelet packet decomposition sub-band cepstral coefficients is extracted by performing WPD on the windowed frog calls. Furthermore, the statistics of all feature vectors over each windowed signal are calculated for producing the final feature set. Finally, two well-known classifiers, a k-nearest neighbour classifier and a support vector machine classifier, are used for classification. In our experiments, we use two different datasets from Queensland, Australia (18 frog species from commercial recordings and field recordings of 8 frog species from James Cook University recordings). The weighted classification accuracy with our proposed method is 99.5% and 97.4% for 18 frog species and 8 frog species respectively, which outperforms all other comparable methods.

Sostoianie posevov v raionakh evreiskogo zemledeliia na 15-oe iiunia 1927 goda

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Raiony sortov ozimnoi pshenitsy

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Old colonies and new settlements in Southern Ukraine and Crimea

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Kukuruza Raĭony vozdelyvanii︠a︡ kukuruzy : nailuchshie sorta i sredni︠a︡i︠a︡ urozhaĭnost' sukhogo zerna na 1 des. po dannym seti opytnykh uchastkov iuga Rossii i postoi︠a︡nnykh opytnykh uchrezhdenniĭ

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Cover page of a binder for maps of Crimea and Ukraine

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Negative map of Jewish settlements in Ukraine and Crimea

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Proportion of Jews to general population of the European provinces of Russia

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Population density of the European provinces of Russia

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Meditsinskie uchastki v evreiskikh koloniiakh Khersonskogo u Kr.Rozhskogo raionov

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Evreiskie kolonii Guliaipol'skogo raiona

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