Taoping Liu

Taoping Liu
Xidian University · Academy of Advanced Interdisciplinary Research

Doctor of Philosophy

About

16
Publications
2,684
Reads
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137
Citations
Citations since 2017
16 Research Items
137 Citations
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201720182019202020212022202301020304050
Introduction
Taoping Liu received his Ph.D. degree in Biomedical Engineering from the University of Technology Sydney, Ultimo, Australia, in 2021. He is currently working at the Academy of Advanced Interdisciplinary Research, Xidian University, Xi'an, China. His current research interests include machine olfactory systems, machine learning, system identification, and signal processing.

Publications

Publications (16)
Article
In the electronic nose (e-nose), a stable feature representation of the gas sensor's response is a key step to realize subsequent odor identification algorithms. However, the noises in gas sensors hinder the acquisition of such features. In order to solve this problem, this article proposes a stable feature extraction algorithm which takes the impu...
Article
Full-text available
Electronic nose devices consisting of a matrix of sensors to sense the smell of various target gases have received considerable attention during the past two decades. This paper presents an efficient classification algorithm for a self-designed electronic nose, which integrates both genetic algorithms (GAs) and fuzzy support vector machines (FSVMs)...
Article
As with any profitable industry, the whisky market is subject to fraudulent activity, including adulteration. An expert can identify the differences between whiskies, but it is difficult for the majority of consumers to differentiate fraudulent beverages. Complex chemical and analytical analyses have been able to detect the differences between whis...
Article
Full-text available
Electronic noses, or e-noses, refer to systems powered by chemical gas sensors, signal processing, and machine learning algorithms for realizing artificial olfaction. They play a crucial role in various applications for decoding chemical environmental information. Despite decades of advances in gas-sensing technology and artificial intelligence, th...
Article
Globally, bladder cancer (BLC) is one of the most common cancers and has a high recurrence and mortality rate. Current clinical diagnostic approaches are either invasive or inaccurate. Here, we report on a cost-efficient, artificially intelligent chemiresistive sensor array made of polyaniline (PANI) derivatives that can noninvasively diagnose BLC...
Chapter
Non-invasive disease diagnosis with high accuracy is a vital element in healthcare. Detecting the volatile metabolites in breath, body, and wastes via an electronic nose (E-nose) has been demonstrated as a practical, low-cost, and accurate approach to diagnose diseases. This chapter introduces the history and research of disease-related volatolomic...
Article
Flexible devices for capturing anatomical and physiological movements are essential for improving the quality of life in, e.g., disease monitoring, physical rehabilitation, and assistance for people with cognitive disorders. They...
Article
Full-text available
Accurate assessment of fish quality is difficult in practice due to the lack of trusted fish provenance and quality tracking information. Working with Sydney Fish Market (SFM), we develop a Blockchain-enabled Fish provenance And Quality Tracking (BeFAQT) system. A multi-layer Blockchain architecture based on Attribute-Based Encryption (ABE) is prop...
Article
This paper presented an efficient electronic nose (e-nose) system, named “NOS.E”, for odour analysis and assessment. In addition to the reliable hardware and software designs, an airflow intake system is implemented to ensure the precise odour analysis procedure in the NOS.E system. Besides, a particular control logic was introduced to improve the...
Conference Paper
Full-text available
One-Shot Neural architecture search (NAS) has received wide attentions due to its computational efficiency. Most state-of-the-art One-Shot NAS methods use the validation accuracy based on inheriting weights from the supernet as the stepping stone to search for the best performing architecture, adopting a bilevel optimization pattern with assuming t...
Preprint
Full-text available
One-Shot Neural architecture search (NAS) attracts broad attention recently due to its capacity to reduce the computational hours through weight sharing. However, extensive experiments on several recent works show that there is no positive correlation between the validation accuracy with inherited weights from the supernet and the test accuracy aft...
Article
This paper presents a novel electronic nose (E-nose) data pre-processing method, based on a recently developed non-parametric kernel-based modelling (KBM) approach. The proposed method is tested by an automated odour detection and classification system, named “NOS.E” developed by the NOS.E team in University of Technology Sydney. Experimental resul...
Article
Full-text available
This paper presents a smart “e-nose” device to monitor indoor hazardous air. Indoor hazardous odor is a threat for seniors, infants, children, pregnant women, disabled residents, and patients. To overcome the limitations of using existing non-intelligent, slow-responding, deficient gas sensors, we propose a novel artificial-intelligent-based multip...
Conference Paper
Full-text available
We present a practical electronic nose (e-nose) sys-tem, NOS.E, for the rapid detection and identification of human health conditions. By detecting the changes in the composition of an individual's respiratory gases, which have been shown to be linked to changes in metabolism, e-nose systems can be used to characterize the physical health condition...

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Projects

Project (1)
Project
i) Development of fast and automatic electronic nose systems. ii) Development of reliable odor identification algorithms.