Gabor Schmera

Detecting harmful gases using fluctuation-enhanced sensing with Taguchi sensors

Sensing techniques are often required to not only be versatile and portable, but also to be able to enhance sensor information. This paper describes and demonstrates a new approach to chemical signal analysis that we call fluctuation-enhanced sensing. It utilizes the entire bandwidth of the sensor signal in contrast to more conventional approaches that rely on the dc response. The new principle holds prospects for significantly reducing the necessary number of sensors in artificial noses and tongues, and it can provide improved sensitivity.

FLUCTUATION-ENHANCED GAS SENSING BY SURFACE ACOUSTIC WAVE DEVICES

This paper presents a new method for the detection, identification and quantitative analysis of gas molecules and their mixtures. The enhanced sensitivity and selectivity of this method allows the detection and identification of gases in amounts not previously detectable. This method is based on the spectral analysis of the dynamics of adsorbed molecules on surface acoustic wave (SAW) delay lines and resonators.

1/f noise in systems showing stochastic resonance

Stochastic resonator systems with input and/or output 1/f noise have been studied. Disordered magnets/dielectrics serve as examples for the case of output 1/f noise with white noise (thermal excitation) at the input of the resonators. Due to the fluctuation-dissipation theorem, the output noise is related to the out-of-phase component of the periodic peak of the output spectrum. Spin glasses and ferromagnets serve as interesting examples of coupled stochastic resonators. A proper coupling can lead to an extremely large signal-to-noise ratio. As a model system, a l/f-noise-driven Schmitt trigger has been investigated experimentally to study stochastic resonance with input 1/f noise. Under proper conditions, we have found several new nonlinearity effects, such as peaks at even harmonics, holes at even harmonics, and 1/f noise also in the output spectrum.

Advanced Agent Identification With Fluctuation-Enhanced Sensing

Conventional agent sensing methods normally use the steady state sensor values for agent classification. Many sensing elements (Hines , 1999; Ryan, 2004; Young, 2003;Qian, 2004; Qian, 2006; Carmel, 2003) are needed in order to correctly classify multiple agents in mixtures. Fluctuation-enhanced sensing (FES) looks beyond the steady-state values and extracts agent information from spectra and bispectra. As a result, it is possible to use a single sensor to perform multiple agent classification. This paper summarizes the application of some advanced algorithms that can classify and estimate concentrations of different chemical agents. Our tool involves two steps. First, spectral and bispectral features will be extracted from the sensor signals. The features contain unique agent characteristics. Second, the features are fed into a hyperspectral signal processing algorithm for agent classification and concentration estimation. The basic idea here is to use the spectral/bispectral shape information to perform agent classification. Extensive simulations have been performed by using simulated nanosensor data, as well as actual experimental data using commercial sensor (Taguchi). It was observed that our algorithms are able to accurately classify different agents, and also can estimate the concentration of the agents. Bispectra contain more information than spectra at the expense of high-computational costs. Specific nanostructured sensor model data yielded excellent performance because the agent responses are additive with this type of sensor. Moreover, for measured conventional sensor outputs, our algorithms also showed reasonable performance in terms of agent classification.

Single effective neuron: dendritic coupling effects and stochastic resonance

We consider a model of a neuron coupled with a surrounding dendritic network subject to Langevin noise and a weak periodic modulation. Through an adiabatic elimination procedure, the single-neuron dynamics are extracted from the coupled stochastic differential equations describing the network of dendrodendritic interactions.Our approach yields a“reduced neuron” model whose dynamics may correspond to neurophysiologically realistic behavior for certain ranges of soma and bath parameters. Cooperative effects (e.g., stochastic resonance) arising from the interplay between the noise and modulation are discussed in detail.

Surface diffusion enhanced chemical sensing by surface acoustic waves

Abstract A new chemical sensing method that enhances sensitivity and selectivity is proposed and verified by theoretical analysis. This method is based on the spectral analysis of the dynamics of adsorbed molecules on surface acoustic wave (SAW) delay lines and resonators. Various sources of noise, including diffusion and adsorption–desorption noise is considered.

Do Electromagnetic Waves Exist in a Short Cable at Low Frequencies? What Does Physics Say?

We refute a physical model, recently proposed by Gunn, Allison and Abbott (GAA) [http://arxiv.org/pdf/1402.2709v2.pdf], to utilize electromagnetic waves for eavesdropping on the Kirchhoff-law–Johnson-noise (KLJN) secure key distribution. Their model, and its theoretical underpinnings, is found to be fundamentally flawed because their assumption of electromagnetic waves violates not only the wave equation but also the second law of thermodynamics, the principle of detailed balance, Boltzmanns energy equipartition theorem, and Plancks formula by implying infinitely strong blackbody radiation. We deduce the correct mathematical model of the GAA scheme, which is based on impedances at the quasi-static limit. Mathematical analysis and simulation results confirm our approach and prove that GAAs experimental interpretation is incorrect too.

Fluctuation-Enhanced Sensing: Status and Perspectives

Both selectivity and sensitivity of chemical sensors can be considerably improved by exploiting the information contained in microfluctuations present in the sensor system. We call our collection of methods and algorithms to extract information from these microfluctuations, fluctuation enhanced sensing. In this paper, we present a short survey of results with Taguchi sensors, surface acoustic wave devices, MOSFET-based sensors, and nanosensors.

Fluctuation-Enhanced Sensing for Biological Agent Detection and Identification

We survey and show our earlier results about three different ways of fluctuation-enhanced sensing of bio agent, 1) the phage-based method for bacterium detection published earlier; 2) sensing and evaluating the odors of microbes; and 3) spectral and amplitude distribution analysis of noise in light scattering to identify spores based on their diffusion coefficient.

Application of nonlinearity measures to chemical sensor signals

The stochastic component of chemical sensor signal contains valuable information that can be visualized not only by spectral analysis but also by using nonlinear characteristic components. The analysis of nonlinear stochastic components enables the extraction of physically interesting and useful features and may lead to significant improvements in selectivity and sensitivity. Various measures of nonlinearity are presented and estimated for sample sensor data obtained from commercial chemical sensors. Particular attention was paid to the bispectrum function that detects nonlinear and non-stationary components in the analyzed noise. The results suggest that bispectrum measurements provide valuable information about the nature of noise generation in chemical sensors. Moreover, we have found, by analyzing skewness and kurtosis distributions, that the measured time series were stationary.