MLP neural network based gas classification system on Zynq SoC

Systems based on Wireless Gas Sensor Networks (WGSN) offer a powerful tool to observe and analyse data in complex environments over long monitoring periods. Since the reliability of sensors is very important in those systems, gas classification is a critical process within the gas safety precautions. A gas classification system has to react fast in order to take essential actions in case of fault detection. This paper proposes a low latency real-time gas classification service system, which uses a Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) to detect and classify the gas sensor data. An accurate MLP is developed to work with the data set obtained from an array of tin oxide (SnO2) gas sensor, based on convex Micro hotplates (MHP). The overall system acquires the gas sensor data through RFID, and processes the sensor data with the proposed MLP classifier implemented on a System on Chip (SoC) platform from Xilinx. Hardware implementation of the classifier is optimized to achieve very low latency for real-time application. The proposed architecture has been implemented on a ZYNQ SoC using fixed-point format and achieved results have shown that an accuracy of 97.4% has been obtained

An Empirical Study for PCA- and LDA-Based Feature Reduction for Gas Identification

Abstract: Increasing the number of sensors in a gas identification system generally improves its performance as this will add extra features for analysis. However, this affects the computational complexity, especially if the identification algorithm is to be implemented on a hardware platform. Therefore, feature reduction is required to extract the most important information from the sensors for processing. In this paper, linear discriminant analysis (LDA) and principal component analysis (PCA)-based feature reduction algorithms have been analyzed using the data obtained from two different types of gas sensors, i.e., seven commercial Figaro sensors and in-house fabricated 4×4 tin-oxide gas array sensor. A decision tree-based classifier is used to examine the performance of both the PCA and LDA approaches. The software implementation is carried out in MATLAB and the hardware implementation is performed using the Zynq system-on-chip (SoC) platform. It has been found that with the 4×4 array sensor, two discriminant functions (DF) of LDA provide 3.3% better classification than five PCA components, while for the seven Figaro sensors, two principal components and one DF show the same performances. The hardware implementation results on the programmable logic of the Zynq SoC shows that LDA outperforms PCA by using 50% less resources as well as by being 11% faster with a maximum running frequency of 122 MHz

Uncertainty Quantification for Deep Learning in Ultrasonic Crack Characterization

Deep learning for nondestructive evaluation (NDE) has received a lot of attention in recent years for its potential ability to provide human level data analysis. However, little research into quantifying the uncertainty of its predictions has been done. Uncertainty quantification (UQ) is essential for qualifying NDE inspections and building trust in their predictions. Therefore, this article aims to demonstrate how UQ can best be achieved for deep learning in the context of crack sizing for inline pipe inspection. A convolutional neural network architecture is used to size surface breaking defects from plane wave imaging (PWI) images with two modern UQ methods: deep ensembles and Monte Carlo dropout. The network is trained using PWI images of surface breaking defects simulated with a hybrid finite element / ray-based model. Successful UQ is judged by calibration and anomaly detection, which refer to whether in-domain model error is proportional to uncertainty and if out of training domain data is assigned high uncertainty. Calibration is tested using simulated and experimental images of surface breaking cracks, while anomaly detection is tested using experimental side-drilled holes and simulated embedded cracks. Monte Carlo dropout demonstrates poor uncertainty quantification with little separation between in and out-of-distribution data and a weak linear fit ( R=0.84 ) between experimental root-mean-square-error and uncertainty. Deep ensembles improve upon Monte Carlo dropout in both calibration ( R=0.95 ) and anomaly detection. Adding spectral normalization and residual connections to deep ensembles slightly improves calibration ( R=0.98 ) and significantly improves the reliability of assigning high uncertainty to out-of-distribution samples

Fast Prototyping of KNN Based Gas Discrimination System on the Zynq SoC

Electronic noses (EN) or machine olfaction are systems used for the detection and identification of odorous compounds and gas mixtures. The accuracy of such systems is as important as the processing time. Therefore, the choice of the algorithm and the implementation platform are both crucial. In this abstract, a design and implementation of a gas identification system on the Zynq platform which shows promising results is presented. The Zynq-7000 based platforms are increasingly being used in different applications including image and signal processing. The Zynq system on chip (SoC) architecture combines a processing system based on a dual core ARM Cortex processor with a programmable logic (PL) based on a Xilinx 7 series field programmable gate arrays (FPGAs). Using the Zynq platform, real-time hardware acceleration of classification algorithms can be performed on the PL and controlled by a software running on the ARM-based processing system (PS). The gas identification system is based on a 16-Array SnO2 in-house fabricated gas sensor and k-Nearest Neighbors (KNN) for classification. The KNN algorithm is executed on the PL for hardware acceleration. The implementation takes the form of an IP developed in C and synthesized using Vivado High Level Synthesis (HLS), the synthesis includes the conversion from C to register transfer level (RTL). The implementation requires the creation of a hardware design for the entire system that allows the execution of the IP on the PL and the remaining parts of the identification system on the PS. The hardware design is developed in Vivado using IP Integrator. The communication between the PS and PL is performed using advanced extensible interface protocol (AXI). A software application is written and executed on the ARM processor to control the hardware acceleration on the PL of the previously designed IP core and the board is programmed using Software Development Kit (SDK). An overview of the system architecture can be seen in Figure 1. The system is designed to discriminate five types of gases including C6H6, CH2O, CO, NO2 and SO2 at various concentrations, from 0.25 to 5 parts per million (ppm) for C6H6 and CH2O, from 5 to 200 ppm for CO, from 1 to 10 ppm for NO2 and finally from 1 to 25 ppm for SO2. The experimental setup used in the laboratory to collect the data is shown in Figure 2. It consists of a gas chamber where the sensor array is placed. The gas chamber has two orifices, one to serve as an input for the in-flow of gases and the other one as an exhaust to evacuate the gases. Multiple gases are stored in various cylinders and connected to the gas chamber individually through several Mass Flow Controllers (MFCs). A control unit is connected to the MFCs to control the in-flow of gases and to the sensor array via a Data Acquisition (DAQ) system to collect and sample the response of the sensor array. In total, 192 samples are collected, 50% is used for training and the other 50% is used for testing. Simulations were performed in MATLAB environment prior to the implementation on the hardware where different k values have been used. The Euclidean distance has been used as a metric for the computation of distances between various points. The best results were obtained for k = 1 and k = 2 with a classification accuracy of 97.91% and 98.95% respectively. The system implemented on hardware is based on k = 1 since the accuracies are almost similar while the hardware resources required for k = 2 are much higher than for k = 1. This can be explained by the fact that in the case of k = 2 we need to sort the vector of distances to be able to find the nearest two neighbours while in k = 1 we only need to find the smallest distance. The target hardware implementation platform of the proposed KNN is the heterogeneous Zynq SoC. The implementation is based on the use of Vivado HLS. A summary of the design flow is presented in Figure 3. The starting point is Vivado HLS where the KNN block is converted from C/C++ implementation to a RTL based IP core. This allows a considerable gain in development time without scarifying on high parallelism characteristics because Vivado HLS provides a large number of powerful optimization directives. The generated IP-core is then exported and stored in the Xilinx IP Catalog before being used in Vivado IP Integrator to create the hardware block design with all needed components and interconnections. The next step is to export the generated hardware along with IP drivers to the SDK tool. The SDK tool is used to program the Xilinx ZC702 prototyping board via joint test action group (JTAG) interface and the terminal in SDK is used to communicate with the board via universal asynchronous receiver/transmitter (UART) interface. The KNN IP is implemented on the PL of the Zynq SoC and communicates with the PS part via the Xilinx AXI-Interconnect IP. A software is written in C/C++ and executed on the PS to manage the IP present in the PL in terms of sending the input data, waiting for the interrupt and then reading the output data. The block design and the resulting chip layout are shown in Figure 4. It is worth mentioning that the running frequency for the ARM processor is set to the maximum 667MHz while the PL frequency is set to 100 MHz which is the maximum for the KNN IP generated in HLS. The real execution of KNN on the PL side of the ZC702 board shows that one sample can be processed for gas identification in 0.0078 ms while the same sample requires 0.9228 ms if executes on the PS side in the ARM processor in a pure software manner. This means that a speed up of 118 times has been achieved. The main directive in Vivado HLS that helped to reach these performances is the "Loop pipelining" which allows the operations in a loop to be implemented in a concurrent manner. The hardware resources usage can be seen in Figure 5, it shows that 24% of lookup tables (LUT), 12% of flip-flops (FF), 6% of BRAM and 58% DSP have been used. As shown in Figure 6, the total power consumption is 1.895 W, 1.565 W is consumed by the PS and the remaining 0.33W is consumed by the PL.qscienc

Domain Adapted Deep-Learning for Improved Ultrasonic Crack Characterization Using Limited Experimental Data

Deep learning is an effective method for ultrasonic crack characterization due to its high level of automation and accuracy. Simulating the training set has been shown to be an effective method of circumventing the lack of experimental data common to nondestructive evaluation (NDE) applications. However, a simulation can neither be completely accurate nor capture all variability present in the real inspection. This means that the experimental and simulated data will be from different (but related) distributions, leading to inaccuracy when a deep learning algorithm trained on simulated data is applied to experimental measurements. This article aims to tackle this problem through the use of domain adaptation (DA). A convolutional neural network (CNN) is used to predict the depth of surface-breaking defects, with in-line pipe inspection as the targeted application. Three DA methods across varying sizes of experimental training data are compared to two non-DA methods as a baseline. The performance of the methods tested is evaluated by sizing 15 experimental notches of length (1–5 mm) and inclined at angles of up to 20° from the vertical. Experimental training sets are formed with between 1 and 15 notches. Of the DA methods investigated, an adversarial approach is found to be the most effective way to use the limited experimental training data. With this method, and only three notches, the resulting network gives a root-mean-square error (RMSE) in sizing of 0.5 ± 0.037 mm, whereas with only experimental data the RMSE is 1.5 ± 0.13 mm and with only simulated data it is 0.64 ± 0.044 mm

Generalised and Versatile Connected Health Solution on the Zynq SoC

This chapter presents a generalized and versatile connected health solution for patient monitoring. It consists of a mobile system that can be used at home, an ambulance and a hospital. The system uses the Shimmer sensor device to collect three axes (x, y and z) accelerometer data as well as electrocardiogram signals. The accelerometer data is used to implement a fall detection system using the k-Nearest Neighbors classifier. The classification algorithm is implemented on various platform including a PC and the Zynq system on chip platform where both programmable logic and processing system of the Zynq are explored. In addition, the electrocardiogram signals are used to extract vital information, the signals are also encrypted using the Advanced Encryption Standard and sent wirelessly using Wi-Fi for further processing. Implementation results have shown that the best overall accuracy reaches 90% for the fall detection while meeting real-time performances when implemented on the Zynq and while using only 48% of Look-up Tables and 22% of Flip-Flops available on chip

Deep Learning for Ultrasonic Crack Characterization in NDE

Machine learning for nondestructive evaluation (NDE) has the potential to bring significant improvements in defect characterization accuracy due to its effectiveness in pattern recognition problems. However, the application of modern machine learning methods to NDE has been obstructed by the scarcity of real defect data to train on. This article demonstrates how an efficient, hybrid finite element (FE) and ray-based simulation can be used to train a convolutional neural network (CNN) to characterize real defects. To demonstrate this methodology, an inline pipe inspection application is considered. This uses four plane wave images from two arrays and is applied to the characterization of cracks of length 1-5 mm and inclined at angles of up to 20° from the vertical. A standard image-based sizing technique, the 6-dB drop method, is used as a comparison point. For the 6-dB drop method, the average absolute error in length and angle prediction is ±1.1 mm and ±8.6°, respectively, while the CNN is almost four times more accurate at ±0.29 mm and ±2.9°. To demonstrate the adaptability of the deep learning approach, an error in sound speed estimation is included in the training and test set. With a maximum error of 10% in shear and longitudinal sound speed, the 6-dB drop method has an average error of ±1.5 mmm and ±12°, while the CNN has ±0.45 mm and ±3.0°. This demonstrates far superior crack characterization accuracy by using deep learning rather than traditional image-based sizing

A Low Power Reconfigurable Multi-sensing Platform For Gas Application

Presence of toxic gases and accidental explosions in gas industries have turned the researcher to innovate an electronic nose system which can indicate the nature and the parameters of the gas passing through different vessels. Therefore, in this research we propose a low power Radio Frequency Identification (RFID) based gas sensor tag which can monitor the parameters and indicate the type of gas. The research work is divided in to three main parts. The first two parts cover the design and analysis of low power multi-sensors and processing unit, while the last part focuses on a passive RFID module which can provide communication between the sensor and the processing unit, as shown in Fig. 1. In passive RFID applications, power consumption is one of the most prominent parameter because most of the power is harvested from the coming RF signal. Therefore a ring-oscillator based low power temperature sensor is designed to measure the gas thermodynamic conditions. The oscillator is designed using the Thyristor based delay element [7], in which the current source present for temperature compensation has been displaced to make the delay element as temperature dependent. The proposed temperature sensor consumes 47nW power at 27 °C, which increases linearly with temperature. Moreover, a 4x4 array of tin-oxide gas sensor based on convex Micro hotplates (MHP), is also utilized to identify the type of gas. The array is designed such that each sensor of an array provide different pattern for the same gas. The power consumption caused by the temperature and gas sensor is in the order of few µW's. The prime advantage of MHP can be visualized by the 950 °C annealed MHP, which exhibit the thermal efficiency of 13 °C /mW. Moreover it requires a driving voltage of only 2.8V to reach 300 °C in less than 5ms, which make it compatible with power supplies required by CMOS ICs. The gas sensor will provide 16 feature points at a time, which can results in hardware complexity and throughput degradation of the processing unit. Therefore, a principle component analysis (PCA) algorithm is implemented to reduce the number of feature points. Thereafter, a binary decision tree algorithm is adopted to classify the gases. We implemented both algorithms on heterogeneous Zynq platform. It is observed that the execution of PCA on Zynq programmable SoC is 1.41 times faster than the corresponding software execution, with a resource utilization of only 23% . Finally, a passive ultrahigh-frequency (UHF) RFID transponder is developed for communicating between the sensing block and processing unit. The designed module is responsible to harvest the power from the coming RF signal and accomplish the power requirement of both sensors. The designed transponder IC achieved minimum sensitivity of -17dBm with a minimum operational power of 2.6µW.qscienc

Deep learning for ultrasonic crack characterization in NDE

Machine learning for nondestructive evaluation (NDE) has the potential to bring significant improvements in defect characterization accuracy due to its effectiveness in pattern recognition problems. However, the application of modern machine learning methods to NDE has been obstructed by the scarcity of real defect data to train on. This article demonstrates how an efficient, hybrid finite element (FE) and ray-based simulation can be used to train a convolutional neural network (CNN) to characterize real defects. To demonstrate this methodology, an inline pipe inspection application is considered. This uses four plane wave images from two arrays and is applied to the characterization of cracks of length 1-5 mm and inclined at angles of up to 20° from the vertical. A standard image-based sizing technique, the 6-dB drop method, is used as a comparison point. For the 6-dB drop method, the average absolute error in length and angle prediction is ±1.1 mm and ±8.6°, respectively, while the CNN is almost four times more accurate at ±0.29 mm and ±2.9°. To demonstrate the adaptability of the deep learning approach, an error in sound speed estimation is included in the training and test set. With a maximum error of 10% in shear and longitudinal sound speed, the 6-dB drop method has an average error of ±1.5 mmm and ±12°, while the CNN has ±0.45 mm and ±3.0°. This demonstrates far superior crack characterization accuracy by using deep learning rather than traditional image-based sizing