A Low-Power Hardware-Friendly Binary Decision Tree Classifier for Gas Identification

Li, Qingzheng; Bermak, Amine

doi:10.3390/jlpea1010045

Cited by 29 publications

(10 citation statements)

References 14 publications

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“…Regarding concrete applications of hardware boosted decision tree classifiers, [13,14] combine silicon and highly optimized VLSI circuits for gas identification. These works do not make it use of FPGAs since the resulting circuit must be embodied in a small sensor, and size and power consumption are the main driving factors.…”

Section: Related Workmentioning

confidence: 99%

“…These works do not make it use of FPGAs since the resulting circuit must be embodied in a small sensor, and size and power consumption are the main driving factors. The architecture of the solution presented in [13] is optimized for power consumption by means of the elimination of costly computations in the decision process. The work in [14] describes a parameterizable, fault tolerant hardware implementation of trees classifiers based on a custom VLSI chip and a CPLD chip for the same application.…”

Section: Related Workmentioning

confidence: 99%

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FPGA acceleration of semantic tree reasoning algorithms

Barba

Santofimia

Dondo

et al. 2015

Journal of Systems Architecture

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

FPGA acceleration of semantic tree reasoning algorithms

Barba

Santofimia

Dondo

et al. 2015

Journal of Systems Architecture

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“…It is worth mentioning that a classification accuracy of 88.6% is achieved with this technique. The work presented in [20] is another example of a pure hardware implementation of an EN on FPGA. The EN uses decision tree (DT) as a classifier, linear (Axis-Parallel) and non-linear (oblique) DT are evaluated.…”

Section: Related Workmentioning

confidence: 99%

Embedded Platform for Gas Applications Using Hardware/Software Co-Design and RFID

et al. 2018

Self Cite

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This paper presents the development of a wireless low power reconfigurable self-calibrated multi-sensing platform for gas sensing applications. The proposed electronic nose (EN) system monitors gas temperatures, concentrations and mixtures wirelessly using the radio-frequency identification (RFID) technology. The EN takes the form of a set of gas and temperature sensors as well as multiple pattern recognition algorithms implemented on the Zynq system on chip (SoC) platform. The gas and temperature sensors are integrated on a semi-passive RFID tag to reduce the consumed power. Various gas sensors are tested including an in-house fabricated 4 × 4 SnO2 based sensor and 7 commercial Figaro sensors. The Data is transmitted to the Zynq based processing unit using a RFID reader where it is processed using multiple pattern recognition algorithms for dimensionality reduction and classification. Multiple algorithms are explored for optimum performance including principal component analysis (PCA) and linear discriminant analysis (LDA) for dimensionality reduction while decision tree (DT) and k-nearest neighbors (KNN) are assessed for classification purpose. Different gases are targeted at diverse concentration including carbon monoxide (CO), ethanol (C2H6O), carbon dioxide (CO2), propane (C3H8), ammonia (N H3) and hydrogen (H2). An accuracy of 100% is achieved in many cases with an overall accuracy above 90% in most scenarios. Finally, the hardware/software heterogeneous solution to implementation PCA, LDA, DT and KNN on the Zynq SoC shows promising results in terms of resources usage, power consumption and processing time.

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“…BDT-based classifier is selected because of its simplicity in terms of software and hardware implementation [38]. BDT is a supervised learning technique with a set of class labelled data as the input of the learning algorithm and a binary tree as its output.…”

Section: Binary Decision Treementioning

confidence: 99%

Compressive sensing based electronic nose platform

Djelouat

Ali

Amira

et al. 2017

Digital Signal Processing

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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 inhouse fabricated 4×4 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.

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A Low-Power Hardware-Friendly Binary Decision Tree Classifier for Gas Identification

Cited by 29 publications

References 14 publications

FPGA acceleration of semantic tree reasoning algorithms

FPGA acceleration of semantic tree reasoning algorithms

Embedded Platform for Gas Applications Using Hardware/Software Co-Design and RFID

Compressive sensing based electronic nose platform

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