Binary Fingerprints at Fluctuation-Enhanced Sensing

1 Binary Fingerprints at Fluctuation-Enhanced Sensing Hung-Chih Chang1, Laszlo B. Kish1,*, Maria D. King2, and Chiman Kwan3 1 Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843-3128, USA 2 Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843- 3123, USA 3 Signal Processing, Inc., 13619 Valley Oak Circle, Rockville, MD 20850, USA * Author to whom correspondence should be addressed: Laszlo@ece.tamu.edu Abstract: We developed a simple way to generate binary patterns based on spectral slopes in different frequency ranges at fluctuation-enhanced sensing. Such patterns can be considered as binary "fingerprints" of odors. The method has experimentally been demonstrated with a commercial semiconducting metal oxide (Taguchi) sensor exposed to bacterial odors (Escherichia coli and Anthrax-surrogate Bacillus subtilis) and processing their stochastic signals. With a single Taguchi sensor, the situations of empty chamber, tryptic soy agar (TSA) medium, or TSA with bacteria could be distinguished with 100% reproducibility. The bacterium numbers were in the range of 2.5*104 - 106. To illustrate the relevance for ultra-low power consumption, we show that this new type of signal processing and pattern recognition task can be implemented by a simple analog circuitry and a few logic gates with total power consumption in the microWatts range. Keywords: Fluctuation-enhanced sensing; semiconducting metal oxide sensors; nano-sensors; ultra-low power sensor systems. 2 1. Introduction Bacterium detection and identification has an important role in medical, agricultural, environmental, defense, etc. applications. Analyzing their odor [1][2] has good prospects because of high speed, low cost, wide availability, good sensitivity and selectivity, while solid-state electronic noses [3-7] can be applied. Recently, we have carried out an experimental study [8] with commercial Taguchi sensors to test the shape of the power density spectrum of the stochastic component of their signal as a pattern to recognize bacteria. The power density spectrum S( f ) of the spontaneous fluctuations of the sensor signal is one of the easiest and natural tools for Fluctuation-Enhanced Sensing (FES) of chemicals [9-20]. While it is reasonably simple to generate it from the measured data, it contains significant sensing information and it has been shown to enhance sensitivity by a factor of 300, or more [14,16]. It is also relatively straightforward to construct a theory to explain its behavior [18-20]. In the present paper, we show a new method to generate binary patterns from measured spectra, an ultra-low power implementation of such a system including a simple Boolean logic circuit as a microprocessor-free pattern recognizer, see Sections 2, 3 and 6, respectively. In order to demonstrate the feasibility of the method and the nature of binary patterns, we conducted relevant experimental tests/evaluations where we have used some of the spectra published in paper [8] and spectra from new measurements. 2. Binary patterns for low power consumption To achieve ultra-low power consumption, we must avoid the usage of microprocessors and extensive data processing. The sensor signal must be processed in the simplest possible way, presumably with analog circuitry, and the pattern recognition must be a deterministic process based on a few simple logic decisions. Let us make the following notations: n
(local slope) is the average local slope of the power density spectrum S(f) in the n-th frequency sub-band and  = n / N
is the average of
n over the entire measurement band, see Figure 1 as an illustration for logarithmically equidistant sub-band boundaries. The boundaries of sub-bands can be equidistant or any convenient settings. These quantities can easily be generated by a low number of operational amplifiers and filters, see Figures 2 and 3. 3 Figure 1. Illustration of n
and
for logarithmically equidistant sub-band boundaries. S( f ) is the power density spectrum of the fluctuations of the sensor signal. The deviation n
of the local slope is defined for each sub-band as the difference between n
and
in the following equation n n
=    (1) The sign n
of the local deviation n
will be binary bit related to that sub-band: ) signum( n n
=  (2) The quantity
n is a binary pattern that indicates if
n is larger or smaller than
in the n-th sub-band of the spectrum. The advantage of the quantity
n is that it provides a single bit information about the spectral pattern. In the case of N non- overlapping frequency bands, the n
( N n ,..., 1 = ) quantities represent N bit information obtained from a single sensor. Then a simple, deterministic, fast and low- power pattern recognizer can easily be constructed by applying a Boolean logic rule to identify/distinguish the particular spectral patterns with their relevant set of the n
bits. All these t

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