A 1-V 2.6-mW Environmental Compensated Fully Integrated Nose-on-a-Chip
Convolutional neural networks (CNNs) play a key role in deep learning applications. However, the large storage overheads and the substantial computational cost of CNNs are problematic in hardware accelerators. Computing-inmemory (CIM) architecture has demonstrated great potential to effectively compute large-scale matrix–vector multiplication. However, the intensive multiply and accumulation (MAC) operations executed on CIM macros remain bottlenecks for further improvement of energy efficiency and throughput. To reduce computational costs, ......
Fake Alcohol Detection
Fake Alchohol Classification - methanol & whisky mixture
Fish Spoilage Detection
Spoilage Degree Detection
Gas Source Tracking
Wheeled Mobile Robot
Concentration, Wind Direction, Wind Speed
Enhance Efficiency & Success Rate
Explosive, Toxic Gas, Fire Exploration
Development of a Dual MOS Electronic Nose/Camera System for Improving Fruit Ripeness Classification
Electronic nose (E-nose) systems have become popular in food and fruit quality evaluation because of their rapid and repeatable availability and robustness. In this paper, we propose an E-nose system that has potential as a non-destructive system for monitoring variation in the volatile organic compounds produced by fruit during the maturing process. In addition to the E-nose system, we also propose a camera system to monitor the peel color of fruit as another feature for identification. By incorporating E-nose and camera systems together, we propose a non-destructive solution for fruit maturity monitoring. The dual E-nose/camera system presents the best Fisher class separability measure and shows a perfect classification of the four maturity stages of a banana: Unripe, half-ripe, fully ripe, and overripe.......
24.5 A 0.5 V 1.27 mW nose-on-a-chip for rapid diagnosis of ventilator-associated pneumonia
Ventilator-associated pneumonia (VAP) is the most frequently acquired infection among patients that receive mechanical ventilation in the intensive-care unit (ICU). The mortality rate for VAP lies in the 20-to-50% range and could be even higher in some ICUs. A standard operation procedure to VAP treatment includes a sequence of chest radiography, sputum gram stain, sputum culture, and empiric therapy, initially with antibiotics covering broad pathogens. However, collection of the gram stain and culture of lower respiratory tract specimen is usually not time-efficient (up to 5 days), delaying the initiation of therapy and unacceptable for critically ill patients. A rapid and accurate diagnosis for VAP is therefore crucial, but still unavailable........
Design of a 0.5 V 1.68 mW nose-on-a-chip for rapid screen of chronic obstructive pulmonary disease
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........
Development of a breath detection method based E-nose system for lung cancer identification
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%.......
An Electronic Nose System for Rapid Detection of Ketamine Smoke
Ketamine severely endangers health and dwelling quality. In Taiwan, one of the common methods of taking ketamine is by smoking ketamine cigarettes. By heating up ketamine cigarettes, it produces an odor that smells similar to burning plastic. There are no effective and convenient methods for immediately detecting and identifying the smell of ketamine cigarettes. Therefore, this research proposed to develop an electronic nose system to identify the gas of ketamine cigarettes. In this study, standard operating procedures were established for collecting ketamine gas and ketamine cigarette gas at constant flow to quantitatively collect those gas samples least affected by environmental factors. After injecting the gas samples, the variation signal of sensor array was noted. By feature extraction and dimensionality reduction algorithms, the complexity of data decreased. The classification results were also analyzed through linear regression and classification algorithms, and the classification accuracy was up to 95.92%........
Tea Leaves Stage Classification
Tea Leaves Stages Monitoring
Process Time Determination
Air Pollution Monitoring
Air Quality Monitoring Module
Detection Limitation (Ammonia: 0.25ppm)
Recording Distribution in NTHU