Titlebook: Stochastic Resonance; Theory and Applicati Bruno Andò,Salvatore Graziani Book 2000 Springer Science+Business Media Dordrecht 2000 Experimen

Jaundice

書目名稱Stochastic Resonance影響因子(影響力)




書目名稱Stochastic Resonance影響因子(影響力)學(xué)科排名




書目名稱Stochastic Resonance網(wǎng)絡(luò)公開度




書目名稱Stochastic Resonance網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Stochastic Resonance被引頻次




書目名稱Stochastic Resonance被引頻次學(xué)科排名




書目名稱Stochastic Resonance年度引用




書目名稱Stochastic Resonance年度引用學(xué)科排名




書目名稱Stochastic Resonance讀者反饋




書目名稱Stochastic Resonance讀者反饋學(xué)科排名

ELATE

d with BCG release NO, which is cytostatic or cytolytic for tumor cells [.],[.]. This in vitro cytostatic and/or cytolytic activity is also observed after activation of the macrophages by cytokines [.],[.]. Interferon γ (INFγ) is important for the priming of macrophages, and tumor necrosis factor α

Occlusion

存在主義

Bruno Andò,Salvatore Grazianis very basic synchronization strategy is unfortunately restricted to only few special cases. More recent synchronization approaches to optimizing transportation networks suggest a mutual adjustment of the service periods of neighboring facilities by exclusively local information transfer, yielding s

后退

Bruno Andò,Salvatore Graziani on-site personnel in terms of extended sensing ranges, sensing of modalities such as IR and ultrasound not normally open to humans, and cooperative control of UAV/UGV to extend the range of human team members. Also Automated decision assistance (e.g., handheld PDAs) should be provided to onsite per

Allodynia

樹木中

Armada

Bruno Andò,Salvatore Grazianin a range of systems is still not yet well-understood (e.g. system models with time varying coefficients, immune systems, swarm intelligent systems, chaotic and fractal systems, stochastic systems, self-organized systems, etc.). This is due amongst others to the challenging task of establishing a pr

親愛

Bruno Andò,Salvatore Grazianint conditions obtained that guarantee the synchronization are directly related to several matrix quantities describing the coupling topology. Furthermore, these conditions are expressed in terms of several linear matrix inequalities (LMIs) which can be easily verified by utilizing the numerically ef

寬容

re able to analytically explore the resulting dynamics and in particular to calculate the resulting consensual state. The ultimate consensual state is topology network-independent. However, the interplay between the graph connectivity and the local dynamics does strongly influence the learning rate.