| 1. |
- Chen, Hsien-Pu, et al.
(författare)
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Do Electromagnetic Waves Exist in a Short Cable at Low Frequencies? : What Does Physics Say?
- 2014
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Ingår i: Fluctuation and Noise Letters. - 0219-4775 .- 1793-6780. ; 13:2, s. 1450016-
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Tidskriftsartikel (refereegranskat)abstract
- 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, Boltzmann's energy equipartition theorem, and Planck's 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 GAA's experimental interpretation is incorrect too.
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| 2. |
- Kish, Laszlo B., et al.
(författare)
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Fluctuation-Enhanced Sensing for Biological Agent Detection and Identification
- 2011
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Ingår i: IEEE transactions on nanotechnology. - 1536-125X .- 1941-0085. ; 10:6, s. 1238-1242
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 3. |
- Kwan, Chiman, et al.
(författare)
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Advanced agent identification with fluctuation-enhanced sensing
- 2008
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Ingår i: IEEE Sensors Journal. - 1530-437X .- 1558-1748. ; 8:5-6, s. 706-713
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Tidskriftsartikel (refereegranskat)abstract
- Conventional agent sensing methods normally use the steady state sensor values for agent classification. Many sensing elements (Hines et al., 1999, Ryan et al., 2004, Young et al.,, 2003, Qian et al., 2004, Qian et al.,, 2006, Carmel et ad., 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.
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| 4. |
- Schmera, Gabor, et al.
(författare)
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Separating Chemical Signals of Adsorption-Desorption and Diffusive Processes
- 2010
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Ingår i: IEEE Sensors Journal. - 1530-437X .- 1558-1748. ; 10:3, s. 461-464
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Tidskriftsartikel (refereegranskat)abstract
- We present a new sensor signal processing method that improves selectivity, sensitivity, and processing speed in systems, using fluctuation-enhanced sensing. We consider the output signal of a symmetric two-sensor arrangement and generate two independent output spectra by separating the adsorption-desorption signal component from the diffusion signal component. We demonstrate the key features of our method by computer modeling and simulation.
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