907 resultados para Random graphs


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This paper proposes an ultra-low power CMOS random number generator (RING), which is based on an oscillator-sampling architecture. The noisy oscillator consists of a dual-drain MOS transistor, a noise generator and a voltage control oscillator. The dual-drain MOS transistor can bring extra-noise to the drain current or the output voltage so that the jitter of the oscillator is much larger than the normal oscillator. The frequency division ratio of the high-frequency sampling oscillator and the noisy oscillator is small. The RNG has been fabricated in a 0.35 mu m CMOS process. It can produce good quality bit streams without any post-processing. The bit rate of this RNG could be as high as 100 kbps. It has a typical ultra-low power dissipation of 0.91 mu W. This novel circuit is a promising unit for low power system and communication applications. (c) 2007 Elsevier Ltd. All rights reserved.

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In this paper, we redefine the sample points set in the feature space from the point of view of weighted graph and propose a new covering model - Multi-Degree-of-Freedorn Neurons (MDFN). Base on this model, we describe a geometric learning algorithm with 3-degree-of-freedom neurons. It identifies the sample points secs topological character in the feature space, which is different from the traditional "separation" method. Experiment results demonstrates the general superiority of this algorithm over the traditional PCA+NN algorithm in terms of efficiency and accuracy.

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We have investigated random telegraph noise in the photoluminescence from InGaAs quantum dots in GaAs. Dots switching among two and three levels have been measured. The experiments show that the switching InGaAs dots behave very similarly to switching InP dots in GaInP. but differently from the more commonly investigated colloidal dots. The switching is attributed to defects, and we show that the switching can be used as a monitor of the defect.

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In this paper, we redefine the sample points set in the feature space from the point of view of weighted graph and propose a new covering model - Multi-Degree-of-Freedorn Neurons (MDFN). Base on this model, we describe a geometric learning algorithm with 3-degree-of-freedom neurons. It identifies the sample points secs topological character in the feature space, which is different from the traditional "separation" method. Experiment results demonstrates the general superiority of this algorithm over the traditional PCA+NN algorithm in terms of efficiency and accuracy.

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Submitted by 阎军 (yanj@red.semi.ac.cn) on 2010-06-07T01:33:41Z No. of bitstreams: 1 ApplPhysLett_96_213505.pdf: 1153920 bytes, checksum: 69931d8deb797813dd478b5dd0e292c0 (MD5)

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Numerical simulations of freak wave generation are studied in random oceanic sea states described by JONSWAP spectrum. The evolution of initial random wave trains is numerically carried out within the framework of the modified four-order nonlinear Schroedinger equation (mNLSE), and some involved influence factors are also discussed. Results show that if the sideband instability is satisfied, a random wave train may evolve into a freak wave train, and simultaneously the setting of the Phillips parameter and enhancement coefficient of JONSWAP spectrum and initial random phases is very important for the formation of freak waves. The way to increase the generation efficiency of freak waves though changing the involved parameters is also presented.

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This review paper summarises briefly some important achievements of our recent research on the synthesis and novel applications of nanostructure ZnO such as honeycomb shaped 3-D (dimension) nano random-walls. A chemical reaction/vapour transportation deposition technique was employed to fabricate this structure on ZnO/SiO2/Si substrate without any catalyst and additive in a simple tube furnace to aim the low-cost and high qualified samples. Random laser action with strong coherent feedback at the wavelength between 375 nm and 395 nm has been firstly observed under 355 nm optical excitation with threshold pumping intensity of 0.38 MW/cm(2).

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This paper proposes a novel single electron random number generator (RNG). The generator consists of multiple tunneling junctions (MTJ) and a hybrid single electron transistor (SET)/MOS output circuit. It is an oscillator-based RNG. MTJ is used to implement a high-frequency oscillator,which uses the inherent physical randomness in tunneling events of the MTJ to achieve large frequency drift. The hybrid SET and MOS output circuit is used to amplify and buffer the output signal of the MTJ oscillator. The RNG circuit generates high-quality random digital sequences with a simple structure. The operation speed of this circuit is as high as 1GHz. The circuit also has good driven capability and low power dissipation. This novel random number generator is a promising device for future cryptographic systems and communication applications.

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The isoscalar giant monopole resonance (ISGMR) in nuclei is studied in the framework of a fully consistent relativistic continuum random phase approximation (RCRPA). In this method the contribution of the continuum spectrum to nuclear excitations is treated exactly by the single particle Green's function technique. The negative energy states in the Dirac sea are also included in the single particle Green's function in the no-sea approximation. The single particle Green's function is calculated numerically by a proper product of the regular and irregular solutions of the Dirac equation. The strength distributions in the RCRPA calculations, the inverse energy-weighted sum rule m(-1) and the centroid energy of the ISGMR in Sn-120 and Pb-208 are analysed. Numerical results of the RCRPA are checked with the constrained relativistic mean field model and relativistic random phase approximation with a discretized spectrum in the continuum. Good agreement between them is achieved.

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This paper presents the vulnerabilities of single event effects (SEEs) simulated by heavy ions on ground and observed oil SJ-5 research satellite in space for static random access memories (SRAMs). A single event upset (SEU) prediction code has been used to estimate the proton-induced upset rates based oil the ground test curve of SEU cross-section versus heavy ion linear energy transfer (LET). The result agrees with that of the flight data.