SOI-CMOS compatible low-power gas sensor using sputtered and drop-coated metal-oxide active layers

In this paper, a Silicon-On-Insulator (SOI) solid-state gas-sensor with an original design of a polysilicon loop-shaped microheater fabricated on a thin-stacked dielectric membrane is presented. The microheater ensures high thermal uniformity and very low power consumption (25 mW for heating at 400°C). Sensitive films are based on tin and tungsten oxides deposited either by RF sputtering or drop coating methods. The fabricated sensors are tested to a wide variety of contaminant species and promising results are obtained. The use of completely CMOS compatible TMAH-based bulk micro-machining techniques during the fabrication process, allows easy smart gas sensor integration in SOI-CMOS technology. This makes SOI-based gas-sensing devices particularly attractive for use in handheld battery-operated gas monitors.     

一种基于隐变量模型的聚类算法用于气体传感器阵列数据的模式识别

聚类分析方法是一种无需先验信息即能探索数据内在分类结构信息的模式识别方法,已经被广泛应用到气体传感器阵列的模式识别研究中.该文提出了基于隐变量模型的聚类算法对两组金属氧化物半导体(MOS)传感器阵列数据进行模式识别.数据处理结果表明,该方法能准确的对两组传感器阵列数据中对应不同气体物质的样本进行分类识别.     

变系数PID控温系统在半导体气体传感器中的应用

提出了一种用于半导体气体传感器的温度控制电路,包含键盘输入、液晶显示、温度采集、数控调压等模块,采用变系数PID算法实现对降压模块输出电压的调控,控制气敏元件的温度.实验表明:与恒压加热和常规PID控温相比,变系数PID控温的调节时间短、超调量小,具有较强的抗干扰能力.350℃加热时,与恒压加热相比调节时间从234 s缩短到了6 s,当环境温度从30℃变化到50℃时恒压加热敏感元件温度上升9℃,而改进后温度变化不超过0.3℃,是一种很有潜力的半导体型气体传感器控温系统.     

Design considerations and electro-mechanical simulation of an inertial sensor based on a floating gate metal-oxide semiconductor field-effect transistor as transducer

Microsystem Technologies - In this paper, an analysis on the electrostatic actuation in capacitive complementary metal-oxide semiconductor and micro-electro-mechanical systems (CMOS–MEMS)...     

An automatic parking system using an optimized image-based fuzzy controller by genetic algorithms

An automatic parking system of a car-like mobile robot is an important issue in commercial applications. An image-based fuzzy controller for an automatic parking system of a car-like mobile robot was developed in previous work, where the membership functions were tuned by experimentally. The aim of this paper is to optimize the parameters of the membership functions, which were performed in previous work, using a genetic algorithm against the complicated tuning of the controller. The details of GA implementation, such as the design parameters and choice of fitness function, are described. Simulation results illustrate the effectiveness of the developed schemes.     

Development of an ozone gas sensor using single-walled carbon nanotubes

This study deals with the fabrication of an ozone gas sensor using single-walled carbon nanotubes (SWCNTs) as sensing material. The SWCNTs are dispersed by N,N-dimethylformamide (DMF). The CNT-DMF solution was dropped between interdigitated electrodes’ fingers to fabricate ozone gas sensor. For ozone environment, a commercial ozone generator was introduced. To improve sensor response, the deposited carbon nanotubes network was thermally treated at high temperature in a furnace. The sensor exhibits high sensitivity to ozone gas at concentration as low as 50 ppb, and fast response time, which is promising for future commercialization of carbon nanotubes based ozone gas sensor.     

Design and optimization of heating plate for metal oxide semiconductor gas sensor

A thin film was coated onto the top of the heating electrodes to reduce the power consumption and improve the uniformity of temperature distribution. Finite element simulation software COMSOL was used to simulate the effect of coating materials and dependence of thicknesses of the coating film on the power consumption of the heating plate. On the basis of simulation, the temperature distribution of different heating plates was measured using infrared thermography. Experiments have showed that the power consumption of the heating plate can be significantly reduced and the temperature uniformity is promoted with adding the coating film on the top of the heating electrodes. The response of the gas sensor based on PdO-WO₃ nanoparticles was characterized with analyte of acetone. It was found that the addition of the coating film could enhance the response to acetone. In addition, the response speed of sensors was investigated with coating films and the results indicated that with the coating film sensor response speed became faster.

     

An integrated chemical sensor with multiple ion and gas sensors

An integrated chemical sensor with multiple ion and gas sensors, composed of four ISFETs (pH, Na⁺, K⁺ and Cl⁻) and two gas sensors (P_O2 and P_CO2) on a 4 mm × 4 mm chip, is realized using semiconductor processing. The ISFETs are based on an Si₃N₄-gate ISFET, and use polymeric membrane except for the pH ISFET. The P_O2 sensor is a miniaturized Clark-type sensor, consisting of a Pt cathode and Ag/AgCl anode patterned by the lift-off process. The P_CO2 sensor is a miniaturized Severinghaus-type sensor using a pH ISFET. All of the ISFETs show sensitivities over 50 mV/decade, and a linear range between 1 × 10⁻⁴ and 5 × 10⁻¹ mol/l. The sensitivities of the P_O2 and P_CO2 sensors are 0.35 nA/mmHg and 42 mV/decade, respectively, and their response times are 30 s and 1 min, respectively. The integrated chemical sensor with multiple ion and gas sensors could be used for clinical analysis.     

Implementation and evaluation of a remote authentication system using touchless palmprint recognition

When a cellular phone is lost or stolen, it may be used improperly or the personal information may be stolen from it by a malicious user. Biometric authentication such as palmprint recognition is the strongest of the personal authentication technologies designed to prevent such misuse. In biometric authentication, when compared with a local authentication model, a remote authentication model has several advantages such as direct authentication and authentication levels. Ito et al. proposed several palmprint recognition schemes using correspondence matching based on the phase-only correlation. However, these schemes require a palmprint image to be captured with the hand touching the dedicated device, while palmprint images must be captured without such physical contact when using cellular phones. Thus, these schemes cannot be applied to cellular phones since there are large positioning gaps and large differences in brightness and distortion between the images. Furthermore, they have not been implemented in cellular phones and their performances have not been evaluated either. In this paper, we adopt a remote authentication model from the two types of biometric authentication incorporating the above advantages and propose a remote system between a cellular phone and an authentication server. We implement the proposed system using two different types of Android terminal as the terminal on the user side. We also show the validity of the proposed system by examining and confirming the accuracy and processing time. We furthermore discuss the problem of an impersonation attack on the proposed system and consider solutions to this problem from the viewpoints of security and usability. Then, we adopt a palmprint recognition scheme as a biometric authentication scheme and, in particular, use a palmprint recognition algorithm that incorporates Yörük et al.’s preprocessing technique to Ito et al.’s and Iitsuka et al.’s schemes.     

An oscillation-driven neural network for the simulation of an olfactory system

Understanding the nonlinear dynamics of an olfactory bulb (OB) is essential for the modelling of the brain and nervous system. We have analysed the nature of odour-receptor interactions and the conditions controlling neural oscillations. This analysis is the basis for the proposed biologically plausible three-tiered model of an oscillation-driven neural network (ODNN) with three non-linearities. The layered architecture of the bulb is viewed as a composition of different processing stages performing specific computational tasks. The presented three-tiered model of the olfactory system (TTOS) contains the sensory, olfactory bulb and anterior nucleus tiers. The number of excitatory (mitral/tufted) cells differs from the number of inhibitory (granule) cells, which improves the cognitive ability of the model. The odour molecules are first received at the sensory layer, where receptor neurons spatio-temporally encode them in terms of spiking frequencies. Neurons expressing a specific receptor project to two or more topographically fixed glomeruli in the OB and create a sensory map. Excitatory postsynaptic potentials are formed in the primary dendrite of mitral cells and are encoded in an exclusive way to present them to the coupled non-linear oscillatory model of the next mitral-granule layer. In a noisy background, our model functions as an associative memory, although it operates in oscillatory mode. While feed-forward networks and recurrent networks with symmetric connections always converge to static states, learning and pattern retrieval in an asymmetrically connected neural network based on oscillations are not well studied. We derive the requirements under which a state is stable and test whether a given equilibrium state is stable against noise. The ODNN demonstrates its capability to discriminate odours by using nonlinear dendro-dendritic interactions between neurons. This model allows us to visualise and analyse how the brain is able to encode information from countless molecules with different odour receptors.     

Computational model for recognition of scene objects with an active sensor

In the paper, a computational model for recognition of objects in a scene image is presented. The model is based on the use of an active sensor. The structure of the object model (OM) is described. This structure is a component that stores different representations of the object and puts at user’s disposal an interface whose operations are used in the scene recognition process. Semen Yu. Sergunin. Born 1980. Graduated from the Faculty of Mathematics and Mechanics of Moscow State University in 2002. Finished postgraduate course of the Department of Computational Mathematics of the same faculty. Scientific interests include image recognition. Author of about 20 papers. Mikhail I. Kumskov. Graduated from the Faculty of Computational Mathematics and Cybernetics of Moscow State University in 1978. Received his candidate’s degree (in Physics and Mathematics) in 1981 and doctoral degree in 1997. In 1981–1997 taught at the Faculty of Computational Mathematics and Cybernetics in the special seminar on Computer Graphics and Image Processing of the Department of Automation of Systems of Computational Complexes. Since 1992 works at the laboratory of Mathematical Chemistry of the Zelinsky Institute of Organic Chemistry of the Russian Academy of Sciences. Since 1997 teaches at the Department of Computational Mathematics of the Faculty of Mathematics and Mechanics of Moscow State University. Author of more than 50 papers. Scientific interests include prediction of properties of chemical compounds, optimization of structural object representation for classification problems, and image understanding.     

Fabrication and evaluation of a drag-force type dual flow sensor with an embedded temperature sensor

Microsystem Technologies - We designed and fabricated a drag-force type dual flow sensor with an embedded temperature sensor. For the wide range detection, two devices in varying length (300 and...     

High sensitivity ethanol gas sensor integrated with a solid-state heater and thermal isolation improvement structure for legal drink-drive limit detecting

The paper reports the successful fabrication of ethanol gas sensors with tin-dioxide (SnO₂) thin films integrated with a solid-state heater, which is realized with technologies of micro-electro-mechanical systems (MEMS), and are compatible with VLSI processes. The main sensing part with dimensions of 450×400 μm² in this developed device is composed of a sensing SnO₂ film, which is fabricated by electron-gun evaporation with proper annealing in ambient oxygen gas to yield fine particles and good structure. An integrated solid-state heater with a 4.5 μm-thick cantilever bridge (1000×500 μm²) structure is made of silicon carbide (SiC) material by MEMS technologies. The sensitivity for 1000 ppm ethanol gas reaches as high as 90 with 10 s and 2 min for the response and recovery time, respectively, at an operating temperature of 300°C. Those experimental results also exhibit a much superior performance to that of a popular commercial ethanol gas sensor TGS-822. Therefore, the developed sensor with high performance is a good candidate for some specific application in automobile to detect drink-drive limit and allows an array integration available with various films for controlling each element at separate resistance.     

基于PC机的气敏元件筛选检测系统

阐述了气敏元件批量化规模性生产中的微机测试手段,利用单片机采集瞬时测试数据,并传送给PC机软件系统进行数据编译,能够精确地进行数据分析和处理,提高了气敏元件在生产过程中的筛选效率。     

An expert system for automated recognition of patients with obstructive sleep apnea using electrocardiogram recordings

Obstructive sleep apnea (OSA) is a highly prevalent sleep disorder. The traditional diagnosis methods of the disorder are cumbersome and expensive. The ability to automatically identify OSA from electrocardiogram (ECG) recordings is important for clinical diagnosis and treatment. In this study, we proposed an expert system based on discrete wavelet transform (DWT), fast-Fourier transform (FFT) and least squares support vector machine (LS-SVM) for the automatic recognition of patients with OSA from nocturnal ECG recordings. Thirty ECG recordings collected from normal subjects and subjects with sleep apnea, each of approximately 8 h in duration, were used throughout the study. The proposed OSA recognition system comprises three stages. In the first stage, an algorithm based on DWT was used to analyze ECG recordings for the detection of heart rate variability (HRV) and ECG-derived respiration (EDR) changes. In the second stage, an FFT based power spectral density (PSD) method was used for feature extraction from HRV and EDR changes. Then, a hill-climbing feature selection algorithm was used to identify the best features that improve classification performance. In the third stage, the obtained features were used as input patterns of the LS-SVM classifier. Using the cross-validation method, the accuracy of the developed system was found to be 100% for using a subset of selected combination of HRV and EDR features. The results confirmed that the proposed expert system has potential for recognition of patients with suspected OSA by using ECG recordings.     

Validation of an industrial analytical sensor procedure realized with a SAW-based sensor system

Mass-sensitive sensors based on surface acoustic waves (Suface Acoustic Wave Devices, SAWs) can be used for qualitative as well as for quantitative determination of volatile organic compounds (VOCs). An analytical procedure for a SAW-based sensor system used for on-line process control of VOCs in pharmaceutical industry was developed. Analytical procedures applied in pharmaceutical development and production have to meet the requirements of good manufacturing practice (GMP) and therefore a validation in compliance with the International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH)-guidelines had to be performed. It is shown that these procedures can be successfully validated as non-chromatographic procedures according to ICH-guidelines. As a typical example, the on-line control of a HPLC gradient former was investigated. In this particular case, the sensor system has to determine the actual composition of the mobile phase and furthermore it has to discriminate possible avoidance. The following analytical performance parameters are investigated to prove the validation of the analytical procedure: Specificity, Linearity, Limit of Detection and Quantitation, Accuracy, Precision, and Robustness.     

Multiple model approach to nonlinear system identification with an uncertain scheduling variable using EM algorithm

This paper deals with system identification of general nonlinear dynamical systems with an uncertain scheduling variable. A multi model approach is developed; wherein, a set of local auto regressive exogenous (ARX) models are first identified at different process operating points, and are then combined to describe the complete dynamics of a nonlinear system. An expectation-maximization (EM) algorithm is used for simultaneous identification of local ARX models, and for computing the probability associated with each of the local ARX models taking effect. A smoothing algorithm is used to estimate the distribution of the hidden scheduling variables in the EM algorithm. If the dynamics of the scheduling variables are linear, Kalman smoother is used; whereas, if the dynamics are nonlinear, sequential Monte-Carlo (SMC) method is used. Several simulation examples, including a continuous stirred tank reactor (CSTR) and a distillation column, are considered to illustrate the efficacy of the proposed method. Furthermore, to highlight the practical utility of the developed identification method, an experimental study on a pilot-scale hybrid tank system is also provided.     

Optical system design for light detection and ranging sensor with an ultra-wide field-of-view using a micro actuator

Microsystem Technologies - Light detection and ranging (LiDAR) sensors can measure the three-dimensional environment by irradiating near-infrared light and measuring the flight time of light...