Information Field and Physical Noise Generator Entanglement

In experiments with physical noise generators based on electronic or photonic quantum events, significant deviations from random distributions have been repeatedly observed. To explain these effects, intention-based interactions between consciousness and mind with physical random processes have been suggested, either being caused by individual minds or by a proposed global mind. As these explanations involve physically undefined objects such as “mind” and “consciousness”, an explanatory model based on the concept of an information field is given herein, based on the concept of generalized quantum entanglement, including the entanglement of physical noise processes with information fields and an analogy to quantum teleportation. In addition, the non-random hypothesis of using such a physical noise generator in capturing qualitative characteristics of individuals is tested in a randomized controlled study with 100 participants.

Programmable multi-channel white noise generator

The pseudo-random number is widely applied in digital signal processing. The white noise number is the basis of generating random numbers of desired probability distributions. The design philosophy of a programmable digital multi-channel white noise generator is proposed in this paper. In stead of the conventional shift register, by using high speed static RAM and digital signal processing chip, the multi-channel independent white noise is generated. It satisfies the requirement in the fields of radar, sonar and communication. The analysis of design principle, block diagram and the results of system simulation is illustrated.

Kernel Generalized Noise Clustering Algorithm

To deal with the nonlinear separable problem, the generalized noise clustering （GNC） algorithm is extended to a kernel generalized noise clustering （KGNC） model. Different from the fuzzy c-means （FCM） model and the GNC model which are based on Euclidean distance, the presented model is based on kernel-induced distance by using kernel method. By kernel method the input data are nonlinearly and implicitly mapped into a high-dimensional feature space, where the nonlinear pattern appears linear and the GNC algorithm is performed. It is unnecessary to calculate in high-dimensional feature space because the kernel function can do it just in input space. The effectiveness of the proposed algorithm is verified by experiments on three data sets. It is concluded that the KGNC algorithm has better clustering accuracy than FCM and GNC in clustering data sets containing noisy data.

Non-Stationary and Resonant Passage of a System: A High-Frequency Cutoff Noise

We study non-equilibrium behaviors of a particle subjected to a high-frequency cutoff noise in terms of generalized Langevin equation, where the spectrum of internal noise is considered to be of the generalized Debye form. A closed solution is impossible even if the equation is linear, because the Laplace transform of the memory kernel is a multi-value function. We use a numerical method to calculate the velocity correlation function of a force-free particle and the probability of a particle passing over the top of an inverse harmonic potential. We indicate the nonergodicity of the second type, i.e., the auto-correlation function of the velocity approaches to non-stationary at large times. Applied to the barrier passage problem, we find and analyse a resonant phenomenon that the dependence of the cutoff frequency is nonmonotonic when the initial directional velocity of the particle is less than the critical value, the latter is determined by the passing probability equal to 0.5.

A voltage-controlled chaotic oscillator based on carbon nanotube field-effect transistor for low-power embedded systems

This paper presents a compact and low-power-based discrete-time chaotic oscillator based on a carbon nanotube field-effect transistor implemented using Wong and Deng＇s well-known model. The chaotic circuit is composed of a nonlinear circuit that creates an adjustable chaos map, two sample and hold cells for capture and delay functions, and a voltage shifter that works as a buffer and adjusts the output voltage for feedback. The operation of the chaotic circuit is verified with the SPICE software package, which uses a supply voltage of 0.9 V at a frequency of 20 kHz. The time series, frequency spectra, transitions in phase space, sensitivity with the initial condition diagrams, and bifurcation phenomena are presented. The main advantage of this circuit is that its chaotic signal can be generated while dissipating approximately 7.8 μW of power, making it suitable for embedded systems where many chaos-signal generators are required on a single chip.

ON COLLISION LOCAL TIME OF TWO INDEPENDENT FRACTIONAL ORNSTEIN-UHLENBECK PROCESSES

In this article, we study the existence of collision local time of two indepen- dent d-dimensional fractional Ornstein-Uhlenbeck processes X＋^H1 and Xt^H2 with different parameters Hi ∈ （0, 1）,i = 1, 2. Under the canonical framework of white noise analysis, we characterize the collision local time as a Hida distribution and obtain its＇ chaos expansion. Key words Collision local time; fractional Ornstein-Uhlenbeck processes; generalized white noise functionals; choas expansion

Low phase noise and quasi-tunable millimeter-wave generator using a passively In As/InP mode-locked quantum dot laser

A low phase noise millimeter-wave(MMW) signal generator is proposed and experimentally demonstrated with a C-band passively Fabry-Pérot(F-P) quantum dot mode-locked laser. A novel method is proposed to generate low phase noise MMW signal, which is simply based on a commercial off-the-shelf dual-driven Li Nb O3 Mach-Zehnder modulator and a passively F-P quantum dot mode-locked laser. MMW signal with the frequency of 30 GHz, 45 GHz and 90 GHz respectively is obtained experimentally. Single-sideband phase noise of the 30 GHz and 45 GHz MMW signal is-112 d Bc/Hz and-106 d Bc/Hz at an offset of 1 k Hz, respectively. The linewidth of the 30 GHz and 45 GHz MMW signal is about from 225 Hz and 239 Hz. This is considered a very simple MMW generator with a quasi-tunable broadband and ultra-low phase noise.

An Improved Soft Subspace Clustering Algorithm for Brain MR Image Segmentation

In recent years,the soft subspace clustering algorithm has shown good results for high-dimensional data,which can assign different weights to each cluster class and use weights to measure the contribution of each dimension in various features.The enhanced soft subspace clustering algorithm combines interclass separation and intraclass tightness information,which has strong results for image segmentation,but the clustering algorithm is vulnerable to noisy data and dependence on the initialized clustering center.However,the clustering algorithmis susceptible to the influence of noisydata and reliance on initializedclustering centers andfalls into a local optimum;the clustering effect is poor for brain MR images with unclear boundaries and noise effects.To address these problems,a soft subspace clustering algorithm for brain MR images based on genetic algorithm optimization is proposed,which combines the generalized noise technique,relaxes the equational weight constraint in the objective function as the boundary constraint,and uses a genetic algorithm as a method to optimize the initialized clustering center.The genetic algorithm finds the best clustering center and reduces the algorithm’s dependence on the initial clustering center.The experiment verifies the robustness of the algorithm,as well as the noise immunity in various ways and shows good results on the common dataset and the brain MR images provided by the Changshu First People’s Hospital with specific high accuracy for clinical medicine.