A Novel Sparse Representation Classification Method for Gas Identification Using Self-Adapted Temperature Modulated Gas Sensors.

A novel sparse representation classification method (SRC), namly SRC based on Method of Optimal Directions (SRC_MOD), is proposed for electronic nose system in this paper. By finding both a synthesis dictionary and a corresponding coefficient vector, the i-th class training samples are approximated as a linear combination of a few of the dictionary atoms. The optimal solutions of the synthesis dictionary and coefficient vector are found by MOD. Finally, testing samples are identified by evaluating which class causes the least reconstruction error. The proposed algorithm is evaluated on the analysis of hydrogen, methane, carbon monoxide, and benzene at self-adapted modulated operating temperature. Experimental results show that the proposed method is quite efficient and computationally inexpensive to obtain excellent identification for the target gases.

Metal oxide gas sensor drift compensation using a two-dimensional classifier ensemble.

Sensor drift is the most challenging problem in gas sensing at present. We propose a novel two-dimensional classifier ensemble strategy to solve the gas discrimination problem, regardless of the gas concentration, with high accuracy over extended periods of time. This strategy is appropriate for multi-class classifiers that consist of combinations of pairwise classifiers, such as support vector machines. We compare the performance of the strategy with those of competing methods in an experiment based on a public dataset that was compiled over a period of three years. The experimental results demonstrate that the two-dimensional ensemble outperforms the other methods considered. Furthermore, we propose a pre-aging process inspired by that applied to the sensors to improve the stability of the classifier ensemble. The experimental results demonstrate that the weight of each multi-class classifier model in the ensemble remains fairly static before and after the addition of new classifier models to the ensemble, when a pre-aging procedure is applied.

Metal oxide gas sensor drift compensation using a dynamic classifier ensemble based on fitting.

Sensor drift is currently the most challenging problem in gas sensing. We propose a novel ensemble method with dynamic weights based on fitting (DWF) to solve the gas discrimination problem, regardless of the gas concentration, with high accuracy over extended periods of time. The DWF method uses a dynamic weighted combination of support vector machine (SVM) classifiers trained by the datasets that are collected at different time periods. In the testing of future datasets, the classifier weights are predicted by fitting functions, which are obtained by the proper fitting of the optimal weights during training. We compare the performance of the DWF method with that of competing methods in an experiment based on a public dataset that was compiled over a period of three years. The experimental results demonstrate that the DWF method outperforms the other methods considered. Furthermore, the DWF method can be further optimized by applying a fitting function that more closely matches the variation of the optimal weight over time.

Highly sensitive fiber pressure sensor based on off-center diaphragm reflection.

A fiber pressure sensor with a collimator at the off-center position of a diaphragm is demonstrated. The detection mechanism is incident-angle sensitive rather than traditional working-distance sensitive. Due to the small beam divergence of the collimator, the control on working distance is less stringent, and high sensitivity can be realized because the coupling efficiency of the collimator is very sensitive to the incident angle decided by the off-center diaphragm reflection. Sensitivity of 1.11 and 0.16 dB/KPa can be achieved with silicon diaphragm thicknesses of 100 and 150 µm, respectively. Moreover, the detection range can be continually shifted by changing the pressure in the sealed diaphragm cavity.

Growth and large-scale assembly of InAs/InP core/shell nanowire: effect of shell thickness on electrical characteristics.

InAs/InP core/shell nanowires with different shell thicknesses were grown by a two-step method, and large-scale assembly of single nanowire was realized by using dielectrophoresis alignment and patterned grooves. Thousands of single nanowire field-effect transistors were fabricated on a single chip. The effect of InP shell thickness on the electron mobility and density of InAs nanowires are experimentally investigated and discussed.

A MEMS device capable of measuring near-field thermal radiation between membranes.

For sensors constructed by freestanding membranes, when the gap between a freestanding membrane and the substrate or between membranes is at micron scale, the effects of near-field radiative heat transfer on the sensors' thermal performance should be considered during sensor design. The radiative heat flux is transferred from a membrane to a plane or from a membrane to a membrane. In the current study of the near-field thermal radiation, the scanning probe technology has difficulty in making a membrane separated at micron scale parallel to a plane or another membrane. A novel MEMS (micro electromechanical system) device was developed by sacrificial layer technique in this work to realize a double parallel freestanding membrane structure. Each freestanding membrane has a platinum thin-film resistor and the distance between the two membranes is 1 m. After evaluating the electrical and thermal characteristics of the lower freestanding membrane, experimental measurements of near-field radiative heat transfer between the lower membrane and the upper membrane were carried out by setting the lower membrane as a heat emitter and the upper membrane as a heat receiver. The near-field radiative heat transfer between the two membranes was validated by finding a larger-than-blackbody radiative heat transfer based on the experimental data.