کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4973963 | 1451720 | 2017 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Compressive sensing based electronic nose platform
ترجمه فارسی عنوان
پلت فرم بینی مبتنی بر حساسیت فشاری
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کلمات کلیدی
سنجش فشاری، سنجش فشاری توزیع شده، الگوریتم های بازسازی، طبقه بندی، سنسورهای گاز،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
پردازش سیگنال
چکیده انگلیسی
Electronic nose (EN) systems play a significant role for gas monitoring and identification in gas plants. Using an EN system which consists of an array of sensors provides a high performance. Nevertheless, this performance is bottlenecked by the high system complexity incorporated with the high number of sensors. In this paper a new EN system is proposed using data sets collected from an in-house fabricated 4Ã4 tin-oxide gas array sensor. The system exploits the theory of compressive sensing (CS) and distributed compressive sensing (DCS) to reduce the storage capacity and power consumption. The obtained results have shown that compressing the transmitted data to 20% of its original size will preserve the information by achieving a high reconstruction quality. Moreover, exploiting DCS will maintain the same reconstruction quality for just 15% of the original size. This high quality of reconstruction is explored for classification using several classifiers such as decision tree (DT), K-nearest neighbour (KNN) and extended nearest neighbour (ENN) along with linear discrimination analysis (LDA) as feature reduction technique. CS-based reconstructed data has achieved a 95% classification accuracy. Furthermore, DCS-based reconstructed data achieved a 98.33% classification accuracy which is the same as using original data without compression.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Digital Signal Processing - Volume 60, January 2017, Pages 350-359
Journal: Digital Signal Processing - Volume 60, January 2017, Pages 350-359
نویسندگان
Hamza Djelouat, Amine Ait Si Ali, Abbes Amira, Faycal Bensaali,