{"title":"A Novel Rapid Bacterial Infection Screening Multisensor System With Feature Selection and Sensor Array Optimization","authors":"Junhui Qian;Yuanyuan Lu;Jinru Zhang;Gaojie Chen","doi":"10.1109/JSEN.2024.3391935","DOIUrl":null,"url":null,"abstract":"In this article, a novel multisensor detection system framework for the rapid screening of bacterial infection is proposed. To capture the dynamic information of sensor response curves, eight features, such as time- and frequency-domain features, are extracted for each sensor. In addition, a novel feature selection algorithm based on adaptive similarity and latent semantics (ASLSFS) is employed to eliminate irrelevant features in the initial feature set. Due to the redundant information and noise introduced by the sensors’ broad-spectrum response characteristics and hardware circuit interference, a dynamical information change weighted array optimization (DICWAO) is developed, which leverages the impact of adding candidate sensor features on the shared information among previously selected sensor features, candidate sensor features, and class label. The experimental results validate the effectiveness of the designed system. Comparative analysis with existing algorithms verifies the effectiveness of the developed feature selection algorithm and array optimization framework.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Sensors Journal","FirstCategoryId":"103","ListUrlMain":"https://ieeexplore.ieee.org/document/10509640/","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
In this article, a novel multisensor detection system framework for the rapid screening of bacterial infection is proposed. To capture the dynamic information of sensor response curves, eight features, such as time- and frequency-domain features, are extracted for each sensor. In addition, a novel feature selection algorithm based on adaptive similarity and latent semantics (ASLSFS) is employed to eliminate irrelevant features in the initial feature set. Due to the redundant information and noise introduced by the sensors’ broad-spectrum response characteristics and hardware circuit interference, a dynamical information change weighted array optimization (DICWAO) is developed, which leverages the impact of adding candidate sensor features on the shared information among previously selected sensor features, candidate sensor features, and class label. The experimental results validate the effectiveness of the designed system. Comparative analysis with existing algorithms verifies the effectiveness of the developed feature selection algorithm and array optimization framework.
期刊介绍:
The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following:
-Sensor Phenomenology, Modelling, and Evaluation
-Sensor Materials, Processing, and Fabrication
-Chemical and Gas Sensors
-Microfluidics and Biosensors
-Optical Sensors
-Physical Sensors: Temperature, Mechanical, Magnetic, and others
-Acoustic and Ultrasonic Sensors
-Sensor Packaging
-Sensor Networks
-Sensor Applications
-Sensor Systems: Signals, Processing, and Interfaces
-Actuators and Sensor Power Systems
-Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting
-Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data)
-Sensors in Industrial Practice