75 research outputs found
Π£ΡΠΎΡΠ½Π΅Π½ΠΈΠ΅ ΠΎΡΠ΅Π½ΠΊΠΈ Π»Π°ΡΠ΅Π½ΡΠ½ΠΎΠ³ΠΎ ΠΏΠ΅ΡΠΈΠΎΠ΄Π° Π΄Π»Ρ Π½Π΅ΠΉΡΠΎΠ½ΠΎΠ² Ρ ΡΠΈΠ½Π°ΠΏΡΠΈΡΠ΅ΡΠΊΠΈΠΌ Π²Π·Π°ΠΈΠΌΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΠ΅ΠΌ
We consider the estimation of the spike delay induced by external sinaptic influence. We propose a first-order estimation for the latent period of synapse coupled spiking neurons.Π Π°ΡΡΠΌΠΎΡΡΠ΅Π½Π° Π·Π°Π΄Π°ΡΠ° Π²ΡΡΠΈΡΠ»Π΅Π½ΠΈΡ Π·Π°Π΄Π΅ΡΠΆΠΊΠΈ Π²ΠΎΠ·Π½ΠΈΠΊΠ½ΠΎΠ²Π΅Π½ΠΈΡ ΠΈΠ½Π΄ΡΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ ΡΠΏΠ°ΠΉΠΊΠ° ΠΏΠΎ ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΡ ΠΊ ΠΌΠΎΠΌΠ΅Π½ΡΡ Π½Π°ΡΠ°Π»Π° ΡΠΈΠ½Π°ΠΏΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π²ΠΎΠ·Π΄Π΅ΠΉΡΡΠ²ΠΈΡ Π½Π° ΠΈΠΌΠΏΡΠ»ΡΡΠ½ΡΠΉ Π½Π΅ΠΉΡΠΎΠ½. ΠΠΎΠ»ΡΡΠ΅Π½Π° Π°ΡΠΈΠΌΠΏΡΠΎΡΠΈΡΠ΅ΡΠΊΠ°Ρ ΠΎΡΠ΅Π½ΠΊΠ°, ΠΈΠΌΠ΅ΡΡΠ°Ρ ΠΏΠ΅ΡΠ²ΡΠΉ ΠΏΠΎΡΡΠ΄ΠΎΠΊ ΡΠΎΡΠ½ΠΎΡΡΠΈ ΠΏΠΎ ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΡ ΠΊ Π²Π΅Π»ΠΈΡΠΈΠ½Π΅, ΠΎΠ±ΡΠ°ΡΠ½ΠΎΠΉ ΠΊ Π·Π½Π°ΡΠ΅Π½ΠΈΡ Π±ΠΎΠ»ΡΡΠΎΠ³ΠΎ ΠΏΠ°Β¬ΡΠ°ΠΌΠ΅ΡΡΠ°
ΠΡΠΈΠ½ΡΠΈΠΏΡ ΠΏΠΎΡΡΡΠΎΠ΅Π½ΠΈΡ ΡΠ»ΠΎΠΈΡΡΡΡ Π½Π΅ΠΉΡΠΎΠ½Π½ΡΡ ΡΠ΅ΡΠ΅ΠΉ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΈΠΌΠΏΡΠ»ΡΡΠ½ΡΡ Π½Π΅ΠΉΡΠΎΠ½ΠΎΠ²
In the article we describe principles of pulse implementation of multilayer neural
networks using biologically plausible neurons. It is shown that the multilayer perceptron can
be modeled with a neural network composed of pulse neurons using impulse information
coding.ΠΠΏΠΈΡΠ°Π½Ρ ΠΏΡΠΈΠ½ΡΠΈΠΏΡ ΠΏΠΎΡΡΡΠΎΠ΅Π½ΠΈΡ ΠΌΠ½ΠΎΠ³ΠΎΡΠ»ΠΎΠΉΠ½ΡΡ
Π½Π΅ΠΉΡΠΎΠ½Π½ΡΡ
ΡΠ΅ΡΠ΅ΠΉ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΈΠΌΠΏΡΠ»ΡΡΠ½ΠΎΠΉ Π½Π΅ΠΉΡΠΎΠ½Π½ΠΎΠΉ ΡΠ΅ΡΠΈ, ΡΠΎΡΡΠ°Π²Π»Π΅Π½Π½ΠΎΠΉ ΠΈΠ· Π±ΠΈΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈ ΠΏΡΠ°Π²Π΄ΠΎΠΏΠΎΠ΄ΠΎΠ±Π½ΡΡ
Π½Π΅ΠΉΡΠΎΠ½ΠΎΠ². ΠΠΎΠΊΠ°Π·Π°Π½ΠΎ, ΡΡΠΎ ΠΌΠ½ΠΎΠ³ΠΎΡΠ»ΠΎΠΉΠ½ΡΠΉ ΠΏΠ΅ΡΡΠ΅ΠΏΡΡΠΎΠ½ ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΡΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ Π½Π΅ΠΉΡΠΎΠ½Π½ΠΎΠΉ ΡΠ΅ΡΡΡ, ΡΠΎΡΡΠΎΡΡΠ΅ΠΉ ΠΈΠ· ΠΈΠΌΠΏΡΠ»ΡΡΠ½ΡΡ
Π½Π΅ΠΉΡΠΎΠ½ΠΎΠ² ΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΡΡΠ΅ΠΉ ΠΈΠΌΠΏΡΠ»ΡΡΠ½ΠΎΠ΅ ΠΊΠΎΠ΄ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ
New approaches to registration and conservation of domestic cultivars of berry crops in the VIR Genebank on the example of red raspberry and black currant
A collection of nomenclatural standards is being created at VIR for domestic cultivars of various crops in accordance with the International Code of Nomenclature for Cultivated Plants (ICNCP). A new complex strategy was proposed for vegetatively propagated crops for registering domestic cultivars received from their authors in the VIR genebank. In addition to the creation of nomenclature standards, the strategy includes the development of a genetic passport of a cultivar and the use of biotechnological methods to preserve explants (buds, meristems) isolated from plant material transferred by breeders to the VIR Herbarium. This approach can be used for any vegetatively propagated crop applying a protocol developed specifically for an individual crop. For raspberry and black currant varieties, the collecting of plant material, its preparation for the registration of nomenclature standards and the preservation of viable samples under controlled inΒ vitro conditions have specific features. This article provides detailed protocols for performing the mentioned work for raspberry and black currant varieties. In addition, the article summarizes the first results of the implementation of our proposed strategy on the example of domestic raspberry and black currant varieties created in various breeding centers of Russia. Three years of joint work of VIR researchers and breeders from four breeding centers in five regions of the country have resulted in creation of nomenclature standards for 20 raspberry varieties, as well as for five black currant varieties bred at VIR. Thirteen samples of raspberry varieties and four of black currant varieties, genetically identical to nomenclature standards, were introduced into inΒ vitro culture; four raspberry cultivars have been placed in the VIR cryobank for the long-term cryopreservation
ΠΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΈ Π΄ΠΎΠΏΠΎΠ»Π½Π΅Π½ΠΈΡ ΠΊ ΡΡΠ°ΡΡΠ΅ Β«ΠΠΎΠΏΡΠ°Π²ΠΊΠ° ΠΊ ΠΏΠ΅ΡΠΈΠΎΠ΄Ρ ΡΠ΅ΡΠ΅Π½ΠΈΡ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ, ΠΌΠΎΠ΄Π΅Π»ΠΈΡΡΡΡΠ΅Π³ΠΎ Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΡ ΠΌΠ΅ΠΌΠ±ΡΠ°Π½Π½ΠΎΠ³ΠΎ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»Π°
In this paper we present a corrected version of the theorem from the article "A Correction For Period Of Oscillation In The Model Of Spiking Neuron", in which a new first-order estimation for the oscillation period in the difference-differential model of the spiking neuron was obtained.ΠΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ ΠΈΡΠΏΡΠ°Π²Π»Π΅Π½Π½ΡΠΉ Π²Π°ΡΠΈΠ°Π½Ρ ΡΠΎΡΠΌΡΠ»ΠΈΡΠΎΠ²ΠΊΠΈ ΠΈ Π΄ΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΡΡΡΠ²Π° ΡΠ΅ΠΎΡΠ΅ΠΌΡ ΠΈΠ· ΡΡΠ°ΡΡΠΈ Β«ΠΠΎΠΏΡΠ°Π²ΠΊΠ° ΠΊ ΠΏΠ΅ΡΠΈΠΎΠ΄Ρ ΡΠ΅ΡΠ΅Π½ΠΈΡ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ, ΠΌΠΎΠ΄Π΅Π»ΠΈΡΡΡΡΠ΅Π³ΠΎ Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΡ ΠΌΠ΅ΠΌΠ±ΡΠ°Π½Π½ΠΎΠ³ΠΎ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»Π° Π½Π΅ΠΉΡΠΎΠ½Π°Β», Π² ΠΊΠΎΡΠΎΡΠΎΠΉ Π±ΡΠ»Π° ΠΏΠΎΠ»ΡΡΠ΅Π½Π° ΠΎΡΠ΅Π½ΠΊΠ° ΠΏΠ΅ΡΠ²ΠΎΠ³ΠΎ ΠΏΠΎΡΡΠ΄ΠΊΠ° ΡΠΎΡΠ½ΠΎΡΡΠΈ Π΄Π»Ρ ΠΏΠ΅ΡΠΈΠΎΠ΄Π° ΡΠ΅ΡΠ΅Π½ΠΈΡ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ Ρ Π·Π°ΠΏΠ°Π·Π΄ΡΠ²Π°Π½ΠΈΠ΅ΠΌ, ΠΌΠΎΠ΄Π΅Π»ΠΈΡΡΡΡΠ΅Π³ΠΎ Π±ΠΈΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠΉ Π½Π΅ΠΉΡΠΎΠ½
Cryopreservation of raspberry cultivar accessions bred in Russia from the VIR <i>in vitro</i> collection
Cryobanks use plant cryocollections for long-term preservation of crops which cannot be preserved in seed collections. These are vegetatively propagated crops, accessions of species which form either a small amount of seeds, or recalcitrant seeds. Shoot tips (apexes) of inΒ vitro plants are used for cryopreservation for most berry crops, therefore maintenance of inΒ vitro collections is very important. The VIR inΒ vitro collection includes 150 accessions of RubusΒ L. species, 85 of them are raspberry cultivars, 59 of which were bred in Russia. These cultivars reflect a wide ecogeographic diversity. Among them, there are raspberry cultivars created at the end of the 19thΒ β first half of the 20th centuries, including cultivars bred by I.V.Β Michurin and by the pioneer of northern horticulture V.V.Β Spirin. More than half of national raspberry varieties (33) are listed in the State Register for Selection Achievements Admitted for Usage. Raspberry cultivars from Russian breeding programs have a very limited representation in foreign genebanks. The first aim of the present work was cryopreservation of mostly folk and old Russian raspberry cultivars received by VIR from 1925 till 1950 and their transfer into the cryobank. The second aim of the work was to monitor post-cryogenic regeneration of raspberry cultivars transferred to the cryobank earlier. A modified protocol of the droplet vitrification method by βDV-biotechβ was used for cryopreservation of shoot tips of inΒ vitro plants of 10 raspberry cultivars (7 of which are folk and old Russian ones) from the VIR inΒ vitro collection. Post-cryogenic regeneration was evaluated for 17 raspberry cultivars preserved in the cryobank from one to five years.Β Ten raspberry cultivars (900 apexes) with an average mean post-cryoregenic regeneration value of 38.2Β±3.0% determined in control tests, were placed in the cryobank for long-term storage. A statistically significant effect of the genotype on the viability of explants after cryopreservation was noted, while the post-cryogenic regeneration was genotype insensitive. Additionally, levels of post-cryogenic regeneration were evaluated for 17 raspberry cultivars (296 apexes) preserved in the cryobank from one to five years. Post-cryogenic regeneration within the 20-70% range was displayed by four raspberry cultivars preserved in the cryobank for one year, and for 8 cultivars conserved there from three to five years post-cryogenic regeneration was within the 10-50% range. According to the results of monitoring, regeneration displayed by 12 raspberry cultivars was within the 10-70% range, which can be considered as a reliable rate of apex preservation in liquid nitrogen vapors in the VIR cryobank. Monitoring of the post-cryogenic regeneration of the raspberry accessions preserved in the VIR cryobank and cryopreservation of new raspberry cultivars will be continued
Molecular markers in the genetic diversity studies of representatives of the genus <i>Rubus</i> L. and prospects of their application in breeding
According to estimates of various taxonomists, the genus Rubus L. (Rosaceae Juss.) consists of 12-16 subgenera comprising ~750 species. The two largest subgenera are Idaeobatus (Focke) Focke, which includes raspberries, and the type subgenus Rubus (=Eubatus Focke), which contains blackberry species. Representatives of the genus Rubus have high nutritional and economic values, as well as medicinal properties. Breeding programs are aimed at broadening genetic diversity and creating new varieties of raspberries and blackberries that are resistant to biotic and abiotic stressors and have high fruit quality. Modern breeding and genetic programs increasingly use the achievements of molecular genetics and genomics. This paper reviews the literature data on the application of molecular markers in fundamental and applied research aimed at studying the genetic diversity of cultivated and wild species of the genus Rubus. The review describes the main types of molecular markers (RFLP, RAPD, SCoT, SSR, ISSR, AFLP, SCAR, SSCP) and their application for studying the species of the genus Rubus. The results of the work on the use of DNA markers for solving different tasks are presented, including: studying the phylogenetic relationships of species, clarifying controversial issues of taxonomy, analyzing interspecific and intraspecific diversity, genotyping and pedigree analysis of raspberry and blackberry varieties, studying somaclonal variation and others. The most important applied result is the development of molecular genetic maps for raspberry and blackberry species, on which numerous genes and QTLs conferring various valuable traits have been mapped. At the same time, the number of markers that are promising for effective molecular screening is still insufficient
ΠΠΌΠΏΡΠ»ΡΡΠ½ΡΠΉ Π½Π΅ΠΉΡΠΎΠ½ ΠΈ Π½Π΅ΠΉΡΠΎΠ½Π½ΡΠΉ ΠΊΠ»Π΅ΡΠΎΡΠ½ΡΠΉ Π°Π²ΡΠΎΠΌΠ°Ρ Π°ΡΠΈΠΌΠΏΡΠΎΡΠΈΡΠ΅ΡΠΊΠΈ ΡΠΊΠ²ΠΈΠ²Π°Π»Π΅Π½ΡΠ½Ρ
In the article, it is established the asymptotic equivalence of dynamics of the neural networks consisting of the impulse neurons and the neural networks built of the neural cellular automata of different kinds (autogenerators and detectors). Such an equivalence takes place for appropriate parameters values and non-intersection of input impacts for each neuron. In the first chapter, we describe the model of impulse autogenerator neuron and two different models of impulse detector neuron. For these models, we prove statements about duration of the latent period for a neuron under impact. In the second chapter, we describe the model of a neural cellular automaton with autogenerator dynamics and a modification of this model with detector dynamics. For these models, we also prove statements about the latent period for a neural automaton under impact. In the third chapter, we prove some statements about asymptotic equivalence of the impulse neurons and neural cellular automata of different kinds.Π ΡΠ°Π±ΠΎΡΠ΅ ΡΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½Π° Π°ΡΠΈΠΌΠΏΡΠΎΡΠΈΡΠ΅ΡΠΊΠ°Ρ ΡΠΊΠ²ΠΈΠ²Π°Π»Π΅Π½ΡΠ½ΠΎΡΡΡ Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠΈ Π½Π΅ΠΉΡΠΎΠ½Π½ΡΡ
ΡΠ΅ΡΠ΅ΠΉ, ΡΠΎΡΡΠΎΡΡΠΈΡ
ΠΈΠ· ΠΈΠΌΠΏΡΠ»ΡΡΠ½ΡΡ
Π½Π΅ΠΉΡΠΎΠ½ΠΎΠ² ΠΈ Π½Π΅ΠΉΡΠΎΠ½Π½ΡΡ
ΠΊΠ»Π΅ΡΠΎΡΠ½ΡΡ
Π°Π²ΡΠΎΠΌΠ°ΡΠΎΠ² ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΡΠΈΠΏΠΎΠ² (Π°Π²ΡΠΎΠ³Π΅Π½Π΅ΡΠ°ΡΠΎΡΠΎΠ² ΠΈ Π΄Π΅ΡΠ΅ΠΊΡΠΎΡΠΎΠ²) ΠΏΡΠΈ ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΡΡΡΠ΅ΠΌ Π²ΡΠ±ΠΎΡΠ΅ ΠΈΡ
ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ² ΠΈ ΠΏΡΠΈ ΡΡΠ»ΠΎΠ²ΠΈΠΈ Π½Π΅Π½Π°Π»ΠΎΠΆΠ΅Π½ΠΈΡ Π²Ρ
ΠΎΠ΄Π½ΡΡ
Π²ΠΎΠ·Π΄Π΅ΠΉΡΡΠ²ΠΈΠΉ Π½Π° ΠΊΠ°ΠΆΠ΄ΡΠΉ Π½Π΅ΠΉΡΠΎΠ½. Π ΠΏΠ΅ΡΠ²ΠΎΠΌ ΡΠ°Π·Π΄Π΅Π»Π΅ ΠΎΠΏΠΈΡΠ°Π½Ρ ΠΌΠΎΠ΄Π΅Π»Ρ ΠΈΠΌΠΏΡΠ»ΡΡΠ½ΠΎΠ³ΠΎ Π½Π΅ΠΉΡΠΎΠ½Π°-Π°Π²ΡΠΎΠ³Π΅Π½Π΅ΡΠ°ΡΠΎΡΠ° ΠΈ Π΄Π²Π΅ ΡΠ°Π·Π»ΠΈΡΠ½ΡΠ΅ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΠΈΠΌΠΏΡΠ»ΡΡΠ½ΠΎΠ³ΠΎ Π½Π΅ΠΉΡΠΎΠ½Π°-Π΄Π΅ΡΠ΅ΠΊΡΠΎΡΠ°. ΠΠ»Ρ ΡΡΠΈΡ
ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ Π΄ΠΎΠΊΠ°Π·ΡΠ²Π°ΡΡΡΡ ΡΡΠ²Π΅ΡΠΆΠ΄Π΅Π½ΠΈΡ ΠΎΡΠ½ΠΎΡΠΈΡΠ΅Π»ΡΠ½ΠΎ Π²Π΅Π»ΠΈΡΠΈΠ½Ρ Π»Π°ΡΠ΅Π½ΡΠ½ΠΎΠ³ΠΎ ΠΏΠ΅ΡΠΈΠΎΠ΄Π° Π½Π΅ΠΉΡΠΎΠ½Π° ΠΏΡΠΈ Π΅Π΄ΠΈΠ½ΠΈΡΠ½ΠΎΠΌ Π²Π½Π΅ΡΠ½Π΅ΠΌ Π²ΠΎΠ·Π΄Π΅ΠΉΡΡΠ²ΠΈΠΈ. ΠΠΎ Π²ΡΠΎΡΠΎΠΌ ΡΠ°Π·Π΄Π΅Π»Π΅ ΠΎΠΏΠΈΡΠ°Π½Π° ΠΌΠΎΠ΄Π΅Π»Ρ Π½Π΅ΠΉΡΠΎΠ½Π½ΠΎΠ³ΠΎ ΠΊΠ»Π΅ΡΠΎΡΠ½ΠΎΠ³ΠΎ Π°Π²ΡΠΎΠΌΠ°ΡΠ° Ρ Π°Π²ΡΠΎΠ³Π΅Π½Π΅ΡΠ°ΡΠΎΡΠ½ΠΎΠΉ Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠΎΠΉ ΠΈ ΠΌΠΎΠ΄ΠΈΡΠΈΠΊΠ°ΡΠΈΡ ΡΡΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ, ΠΈΠΌΠ΅ΡΡΠ°Ρ Π΄Π΅ΡΠ΅ΠΊΡΠΎΡΠ½ΡΡ Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΡ. ΠΠ»Ρ ΡΡΠΈΡ
ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ΡΠ°ΠΊΠΆΠ΅ Π΄ΠΎΠΊΠ°Π·ΡΠ²Π°ΡΡΡΡ ΡΡΠ²Π΅ΡΠΆΠ΄Π΅Π½ΠΈΡ ΠΎΡΠ½ΠΎΡΠΈΡΠ΅Π»ΡΠ½ΠΎ Π»Π°ΡΠ΅Π½ΡΠ½ΠΎΠ³ΠΎ ΠΏΠ΅ΡΠΈΠΎΠ΄Π° Π½Π΅ΠΉΡΠΎΠ½Π½ΠΎΠ³ΠΎ Π°Π²ΡΠΎΠΌΠ°ΡΠ°, Π½Π°Ρ
ΠΎΠ΄ΡΡΠ΅Π³ΠΎΡΡ ΠΏΠΎΠ΄ Π²Π½Π΅ΡΠ½ΠΈΠΌ Π²ΠΎΠ·Π΄Π΅ΠΉΡΡΠ²ΠΈΠ΅ΠΌ. Π ΡΡΠ΅ΡΡΠ΅ΠΌ ΡΠ°Π·Π΄Π΅Π»Π΅ Π΄Π»Ρ ΠΈΠΌΠΏΡΠ»ΡΡΠ½ΡΡ
Π½Π΅ΠΉΡΠΎΠ½ΠΎΠ² ΠΈ Π½Π΅ΠΉΡΠΎΠ½Π½ΡΡ
ΠΊΠ»Π΅ΡΠΎΡΠ½ΡΡ
Π°Π²ΡΠΎΠΌΠ°ΡΠΎΠ² ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΡΠΈΠΏΠΎΠ² ΡΡΠΎΡΠΌΡΠ»ΠΈΡΠΎΠ²Π°Π½Ρ ΠΈ Π΄ΠΎΠΊΠ°Π·Π°Π½Ρ ΡΡΠ²Π΅ΡΠΆΠ΄Π΅Π½ΠΈΡ ΠΎΡΠ½ΠΎΡΠΈΡΠ΅Π»ΡΠ½ΠΎ ΡΡΠ»ΠΎΠ²ΠΈΠΉ ΠΈΡ
Π°ΡΠΈΠΌΠΏΡΠΎΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠΊΠ²ΠΈΠ²Π°Π»Π΅Π½ΡΠ½ΠΎΡΡΠΈ
Π‘Π΅Π³ΠΌΠ΅Π½ΡΠ°ΡΠΈΡ ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΈΡ ΡΠ½Π΄ΠΎΡΠΊΠΎΠΏΠΈΡΠ΅ΡΠΊΠΈΡ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ, ΠΎΡΠ½ΠΎΠ²Π°Π½Π½Π°Ρ Π½Π° ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ Π²Π΅ΠΊΡΠΎΡΠ½ΡΡ ΡΠΎΠΏΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ²
In this work, we describe a prototype of an automatic segmentation system and annotation of endoscopy images. The used algorithm is based on the classification of vectors of the topological features of the original image. We use the image processing scheme which includes image preprocessing, calculation of vector descriptors defined for every point of the source image and the subsequent classification of descriptors. Image preprocessing includes finding and selecting artifacts and equalizating the image brightness. In this work, we give the detailed algorithm of the construction of topological descriptors and the classifier creating procedure based on mutual sharing the AdaBoost scheme and a naive Bayes classifier. In the final section, we show the results of the classification of real endoscopic images.Π ΡΠ°Π±ΠΎΡΠ΅ ΠΎΠΏΠΈΡΠ°Π½ ΠΏΡΠΎΡΠΎΡΠΈΠΏ ΡΠΈΡΡΠ΅ΠΌΡ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠ΅Π³ΠΌΠ΅Π½ΡΠ°ΡΠΈΠΈ ΠΈ Π°Π½Π½ΠΎΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠ½Π΄ΠΎΡΠΊΠΎΠΏΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ. ΠΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΠΌΡΠΉ Π°Π»Π³ΠΎΡΠΈΡΠΌ ΠΎΡΠ½ΠΎΠ²Π°Π½ Π½Π° ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ Π²Π΅ΠΊΡΠΎΡΠΎΠ² ΡΠΎΠΏΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ² ΠΈΡΡ
ΠΎΠ΄Π½ΠΎΠ³ΠΎ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ. ΠΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΠΌ ΡΡ
Π΅ΠΌΡ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ, ΠΊΠΎΡΠΎΡΠ°Ρ Π²ΠΊΠ»ΡΡΠ°Π΅Ρ Π² ΡΠ΅Π±Ρ ΠΏΡΠ΅Π΄ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΡ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ, Π²ΡΡΠΈΡΠ»Π΅Π½ΠΈΠ΅ Π²Π΅ΠΊΡΠΎΡΠ½ΡΡ
Π΄Π΅ΡΠΊΡΠΈΠΏΡΠΎΡΠΎΠ², ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΡΡ
Π΄Π»Ρ ΠΊΠ°ΠΆΠ΄ΠΎΠΉ ΡΠΎΡΠΊΠΈ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ, ΠΈ Π΄Π°Π»ΡΠ½Π΅ΠΉΡΡΡ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΡ Π΄Π΅ΡΠΊΡΠΈΠΏΡΠΎΡΠΎΠ². ΠΡΠΎΡΠ΅Π΄ΡΡΠ° ΠΏΡΠ΅Π΄ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ Π²ΠΊΠ»ΡΡΠ°Π΅Ρ Π²ΡΠ΄Π΅Π»Π΅Π½ΠΈΠ΅ Π°ΡΡΠ΅ΡΠ°ΠΊΡΠΎΠ² ΠΈ Π²ΡΡΠ°Π²Π½ΠΈΠ²Π°Π½ΠΈΠ΅ ΡΡΠΊΠΎΡΡΠΈ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ. Π ΡΠ°Π±ΠΎΡΠ΅ ΠΏΠΎΠ΄ΡΠΎΠ±Π½ΠΎ ΠΎΠΏΠΈΡΠ°Π½ Π°Π»Π³ΠΎΡΠΈΡΠΌ ΠΏΠΎΡΡΡΠΎΠ΅Π½ΠΈΡ ΡΠΎΠΏΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
Π΄Π΅ΡΠΊΡΠΈΠΏΡΠΎΡΠΎΠ² ΠΈ ΠΏΡΠΎΡΠ΅Π΄ΡΡΠ° ΠΏΠΎΡΡΡΠΎΠ΅Π½ΠΈΡ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΎΡΠ°, ΠΎΡΠ½ΠΎΠ²Π°Π½Π½Π°Ρ Π½Π° ΡΠΎΠ²ΠΌΠ΅ΡΡΠ½ΠΎΠΌ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠΈ ΡΡ
Π΅ΠΌΡ AdaBoost ΠΈ Π½Π°ΠΈΠ²Π½ΠΎΠ³ΠΎ Π±Π°ΠΉΠ΅ΡΠΎΠ²Π° ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΎΡΠ°. Π Π·Π°ΠΊΠ»ΡΡΠΈΡΠ΅Π»ΡΠ½ΠΎΠΌ ΡΠ°Π·Π΄Π΅Π»Π΅ ΠΏΡΠΈΠ²Π΅Π΄Π΅Π½Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΡΠ΅Π°Π»ΡΠ½ΡΡ
ΡΠ½Π΄ΠΎΡΠΊΠΎΠΏΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ
Towards a More Complete and Accurate Experimental Nuclear Reaction Data Library (EXFOR): International Collaboration Between Nuclear Reaction Data Centres (NRDC)
The International Network of Nuclear Reaction Data Centres (NRDC) coordinated
by the IAEA Nuclear Data Section (NDS) is successfully collaborating in the
maintenance and development of the EXFOR library. As the scope of published
data expands (e.g., to higher energy, to heavier projectile) to meet the needs
from the frontier of sciences and applications, it becomes nowadays a hard and
challenging task to maintain both completeness and accuracy of the whole EXFOR
library. The paper describes evolution of the library with highlights on recent
developments.Comment: 4 pages, 2 figure
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