Nanoscale spectrum analyzer based on spin-wave interference.

We present the design of a spin-wave-based microwave signal processing device. The microwave signal is first converted into spin-wave excitations, which propagate in a patterned magnetic thin-film. An interference pattern is formed in the film and its intensity distribution at appropriate read-out locations gives the spectral decomposition of the signal. We use analytic calculations and micromagnetic simulations to verify and to analyze the operation of the device. The results suggest that all performance figures of this magnetoelectric device at room temperature (speed, area, power consumption) may be significantly better than what is achievable in a purely electrical system. We envision that a new class of low-power, high-speed, special-purpose signal processors can be realized by spin-waves.

A computational model for label-free detection of non-fluorescent biochromophores by stimulated emission.

We created a computational model to investigate the characteristics of label-free molecular detection by stimulated emission, which is the fundamental process of stimulated emission microscopy proposed and experimentally demonstrated by Min et al. In our model the molecule is considered to be a two-state quantum system with finite number of vibrational states. The laser excitations are modelled as zero order Gaussian beams. The field-molecule interaction is considered to be an electric dipole interaction. Based on these assumptions we constructed a Liouville-von Neumann master equation for the reduced density operator. The numerical solution of the master equation determines the expectation value of additional photons produced by stimulated emission. Based on this model algorithms are proposed to evaluate relative excitations. Linear dependence in concentration and quadratic dependence in space resolution were obtained at weak excitations. Time delay dependent relative excitation can be evaluated by taking into account only a single vibrational mode. However, to calculate the spectrum of relative excitation two entangled vibrational modes are necessary. An algorithm is proposed that overcomes the problem of computational complexity and enables to evaluate the spectrum on a high-end computer. High correlation between calculated and measured data of time delay and frequency dependent relative excitation, confirm the validity of the proposed model.

Fluorescence in two-photon-excited diffusible samples exposed to photobleaching: a simulation-based study.

We created a simulation model to investigate the characteristics of fluorescence in two-photon-excited samples. In the model, the sample is a diffusible solution of fluorophore molecules, which is divided into cubic cells and illuminated by a train of focused laser pulses described as a Gaussian beam. Simulating the state transitions according to a multilevel photodynamic model (also including photobleaching and intersystem crossing), the simulator provides the expected number and the spatial distribution of emitted photons over time. Our simulations demonstrated how the illumination laser power, diffusion, and the photodynamic parameters of the fluorophore affect fluorescence. We revealed the unusual fluorescent profile that evolves as photobleaching progresses: the most photons are not emitted from the focus (where a "dark hole" appears) but from an ellipsoid around the focus. The model could be adapted to several fluorescent techniques (such as two-photon microscopy and fluorescence recovery after photobleaching). Furthermore, it might help to optimize the operating parameters of the measurement devices (e.g., in order to reach higher image quality and lower photobleaching).

Bio-inspired nano-sensor-enhanced CNN visual computer.

Nanotechnology opens new ways to utilize recent discoveries in biological image processing by translating the underlying functional concepts into the design of CNN (cellular neural/nonlinear network)-based systems incorporating nanoelectronic devices. There is a natural intersection joining studies of retinal processing, spatio-temporal nonlinear dynamics embodied in CNN, and the possibility of miniaturizing the technology through nanotechnology. This intersection serves as the springboard for our multidisciplinary project. Biological feature and motion detectors map directly into the spatio-temporal dynamics of CNN for target recognition, image stabilization, and tracking. The neural interactions underlying color processing will drive the development of nanoscale multispectral sensor arrays for image fusion. Implementing such nanoscale sensors on a CNN platform will allow the implementation of device feedback control, a hallmark of biological sensory systems. These biologically inspired CNN subroutines are incorporated into the new world of analog-and-logic algorithms and software, containing also many other active-wave computing mechanisms, including nature-inspired (physics and chemistry) as well as PDE-based sophisticated spatio-temporal algorithms. Our goal is to design and develop several miniature prototype devices for target detection, navigation, tracking, and robotics. This paper presents an example illustrating the synergies emerging from the convergence of nanotechnology, biotechnology, and information and cognitive science.