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Potential application of a metal oxide semiconductor based electronic nose (E-Nose) as a non-destructive system for monitoring the change in volatile organic production of fruit during the maturing process. Using PCA analysis, it was possible to distinguish and to classify the different stages. The result showed that this method yielded highest average accuracies greater than 90% in classifying fruit maturity.

In this paper, we focused on the method of lung cancer identification by breath. Lung cancer had occupied the first place in the top ten leading causes of death. When lung cancer patients were diagnosed, most of the patients had lost the opportunity of cure. However, physicians determined the lung cancer cases in complicated steps. Therefore, the purpose of this breath detection system was to help physicians to quickly screen for rapid screening lung cancer. We used KNN and SVM with leave-one-out cross validation to analyze. Finally, we got good accuracy that was 84.4%.

Metal-oxide-semiconductor gas sensors are widely used for gas concentration estimation and identification because of high sensitivity, good robustness and low price. To achieve good estimated performance, the feature extraction method is crucial for gas detection. However, the feature extracted from the transient part of gas response is required for fast gas concentrations estimation. In this paper, we proposed a new transient feature extraction method to reduce the estimated time of gas concentration. The linear regression was utilized to estimate gas concentrations. The result showed the estimation error of gas concentrations was less than 28.6%, and the average completion time was reduced up to 19 times. The proposed method showed superior performance to the conventional features.

This paper presents a CMOS capacitance-to-digital converter (CDC) for capacitive sensors applications. Off-chip capacitive sensors are fabricated by coating sensing materials on interdigitated electrode (IDE) structure. A CDC consists of a capacitance-to-voltage front-end and 12-bit successive approximation register analog-to-digital converter (SAR-ADC) in a single chip is used for converting sensor data into digital codes. The proposed capacitance-to-voltage front-end circuit extends the dynamic range by a select logic for combinations of reference capacitances and voltages. The SAR-ADC uses a monotonic capacitor switching procedure and the input common-mode voltage gradually converges to supply voltage. This chip is fabricated with the TSMC 0.18-um 1P6M CMOS process and the core occupies area of 719 X 276 um2. The CDC achieves measurement interval of 1.5-78 pF and 8.35-bit ENOB with 220 nW consumption at a 0.7 V supply voltage and 0.25 ms measurement time.

Chronic obstructive pulmonary disease (COPD) still lacks a rapid diagnosis strategy. In this paper, we propose a low-power nose-on-a-chip for rapid COPD screening. This chip is designed for implementation in a personal handheld device that detects patient breath for COPD diagnosis. The chip has 36 on-chip sensors, a 36-channel adaptive interface with an integrated programmable amplifier, a four-channel frequency readout interface, one on-chip temperature sensor, a two-channel successive approximation analog-to-digital converter, a scalable learning kernel cluster, and a reduced instruction set computing core with low-voltage static random-access memory. This chip is fabricated in 90 nm CMOS and consumes 1.68 mW at 0.5 V. In simulation, the system distinguished between undiseased and diseased patients with 90.82% accuracy for a set of diseases including COPD and asthma and exhibited 92.31% accuracy for identifying patients with COPD or asthma. The system classified severity levels of COPD under four labels (normal, mild, moderate, and severe) with 92.00% accuracy. Accordingly, this work provides a promising solution for the unmet medical need of rapid COPD screening.

This paper presents a signal acquisition and processing chip. The chip connects with a chemiresistive sensor array and directly acquires array data. A built-in neural network cluster enhances the signal processing ability of the proposed chip and enlarges the distance between various odour groups. When noise is induced in the training phase, the cluster is robust against background interference. The chip is space-efficient and features a flexible hardware structure. The chip was fabricated using the TSMC 90-nm CMOS process. The chip area is 2.0 × 2.6 mm2. The supply voltage is 1.0 V. The power consumption is approximately 4 mW. The measurements revealed that the chip excellently identified the odours of methanol, ethanol, and a mixture of 50% methanol + 50% ethanol. Gas-sensing systems based on this signal acquisition and processing chip can be extensively implemented in diverse applications.

Food quality control is an important issue all over the world. Odor identification is one of the non-invasive methods to identify food quality. However, in most cases, only well trained experts are able to distinguish the difference between foods with different qualities. Therefore, both the business and individuals require an objective, reliable, and rather simple device to qualify food. As a result, electronic nose (E-Nose) is applied to this application.

Electronic noses (e-nose) have been studied for several years and extensively applied; however, they are limited by their volume and high manufacturing cost. Portable devices have become popular in recent years; therefore, it is crucial to integrate e-noses in portable devices (e.g., mobile phones). This study used TSMC 90nm 1P9M CMOS MSG technology to develop a front-end system-on-chip (SoC) for an electronic nose. The SoC contained interdigitated electrodes, multi-channel sensor interface circuits, an analog to digital converter, and a digital continuous restricted Boltzmann machine (CRBM). Various conducting-polymer materials were titrated on the interdigitated electrodes to form an on-chip sensor array. This SoC was controlled through an external microcontroller to perform odor identification and analysis. The simulation results of the SoC and gas classification show that this chip is suitable for portable applications and further integration.

The Electronic nose (E-Nose) system has the potential for personal healthcare applications but is restricted by size and cost. This study reports the world's first E-Nose system-on-chip (SoC), which fully integrates the sensor array and signal processing circuit. The E-Nose SoC has been designed and fabricated using TSMC 0.18µm 1P6M CMOS technology. The sensor array is coated with multiple-walled carbon nanotubes (MWNTs) conducting polymers. The signal processing circuit is composed of interface circuitry, analog-to-digital converter, memory, and microprocessor embedded with a pattern recognition algorithm. The power consumption of the chip was measured at 1.05mW, making it very suitable for a wearable device. The E-Nose SoC has been integrated in a prototype, and successfully recognized eight odors emulating decayed fish, liver damage, careinogen and industrial solvent.

We report an electronic-nose sensor node based on polymer-coated surface acoustic wave (SAW) sensor array for environmental monitoring applications. The sensor node consists of a SAW sensor array, its readout circuitry and a Wireless Sensor Network (WSN) platform. The 2 × 2 non-continuous gas SAW sensor array is coated with different polymer composite materials for different gas detection. The frequency signals from the SAW array are processed by the frequency readout circuitry to obtain frequency shift information. The sensor data is transmitted from sensor nodes by Octopus II WSN platform. Experimental results have shown good performance of gas detection and recognition. In order to achieve a high efficient environmental monitoring sensing network, the decision supporting system by implementing weighted voting system (WVS) has also been considered.