Amine Ait Si Ali

MLP Neural Network Based Gas Classification System on Zynq SoC

Systems based on wireless gas sensor networks offer a powerful tool to observe and analyze 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 the 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 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. The overall system acquires the gas sensor data through radio-frequency identification (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 the achieved results have shown that an accuracy of 97.4% has been obtained.

Hardware PCA for gas identification systems using high level synthesis on the Zynq SoC

One of the significant stages in a gas identification system is dimensionality reduction to speed up the processing part. This is even more important when the system is implemented on a hardware platform where the resources are limited. This paper presents the design and the implementation of the learning and testing phases of principal component analysis (PCA) that can be used in a gas identification system on the heterogeneous Zynq platform. All steps of PCA starting from the mean computation to the projection of data onto the new space, passing by the normalization process, covariance matrix and the eigenvectors computation are developed in C and synthesized using the new Xilinx VIVADO high level synthesis (HLS). The computation of the eigenvectors was based on the iterative Jacobi method. The designed hardware for computing the learning part of PCA on the Zynq system on chip showed that it can be faster than its 64-bit Intel i7-3770 processor counterpart with a speed up of 1.41. Optimization techniques using HLS directives were also utilised in the hardware implementation of the testing part of the PCA to speed up the design and reduce its latency.

Compressive sensing based electronic nose platform

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 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.

HLS based hardware acceleration on the zynq SoC: A case study for fall detection system

Fall detection is a major problem in healthcare systems, especially for elderly people who are the most vulnerable. It is important to design and implement not only an accurate fall detection system (FDS) but also a system with a real-time response. The achievement of high accuracy and fast response time together allows the development of a system that helps saving lives, time and money in healthcare industry. This paper presents the design, simulation and implementation of a novel FDS using the Shimmer wearable sensor. The discrete wavelet transform (DWT) is applied for preprocessing the data coming from the Shimmer platform, principal component analysis (PCA) is used for dimensionality reduction and feature extraction and finally, a binary decision tree (DT) is utilized for classification purpose. The system is simulated in MATLAB prior to the implementation on the Zynq system-on-chip (SoC) for hardware acceleration. DWT is executed on the processing system (PS) of the Zynq platform in a software manner while PCA and DT are both implemented on the programmable logic (PL) for hardware acceleration. PCA and DT are developed in C and synthesized in Vivado high level synthesis (HLS) tool to transform the C based designed into a register transfer level (RTL) implementation. Various optimization techniques are explored in Vivado HLS. The performance of the FDS in terms of accuracy of the classifier is 88.4% while the overall resources used in PL of the Zynq vary between 2% and 23% depending on the running frequency and optimization technique used.

ECG encryption and identification based security solution on the Zynq SoC for connected health systems

Connected health is a technology that associates medical devices, security devices and communication technologies. It enables patients to be monitored and treated remotely from their home. Patients data and medical records within a connected health system should be securely stored and transmitted for further analysis and diagnosis. This paper presents a set of security solutions that can be deployed in a connected health environment, which includes the advanced encryption standard (AES) algorithm and electrocardiogram (ECG) identification system. Efficient System-on-Chip (SoC) implementations for the proposed algorithms have been carried out on the Xilinx ZC702 prototyping board. The Achieved hardware implementation results have shown that the proposed AES and ECG identification based system met the real-time requirements and outperformed existing field programmable gate array (FPGA)-based systems in different key performance metrics such as processing time, hardware resources and power consumption. The proposed systems can process an ECG sample in 10.71ms and uses only 30% of the available hardware resources with a power consumption of 107mW. An ECG based solution for connected health systems.ECG based solution on a reconfigurable hardware.Implementation of ECG algorithms on Zynq SoC.

HW/SW co-design based implementation of Gas discrimination

A gas discrimination system is mainly made of two parts, the sensing part and the processing part. As an alternative solution to pure software or hardware implementation of the processing part of a gas identification system, this paper proposes a gas discrimination system and its implementation on the Zynq system on chip platform using hardware/software co-design approach. In addition, the recommended system uses principal component analysis for dimensionality reduction, binary decision tree for classification and a 4×4 in-house gas sensor array for sensing. Moreover, k-nearest neighbors classifier is also used and compared with decision tree. MATLAB is used for simulation and validation before the final implementation on the Zynq. Algorithms are implemented using high level synthesis and different optimization directives are applied. Hardware implementation results on the Zynq show that real-time performances can be achieved for proposed e-nose system using hardware/software co-design approach with a single ARM processor running at 667 MHz and the programmable logic running at 142 MHz.

PCA IP-core for gas applications on the heterogenous zynq platform

Principal component analysis (PCA) is a commonly used technique for data reduction in general as well as for dimensionality reduction in gas identification systems when a sensor array is being used. This paper presents the design and implementation of a complete PCA IP core for gas application on the Zynq programmable system on chip (SoC). All steps of PCA starting from the mean computation to the projection of data onto the new space, passing by the normalization process, covariance matrix and the eigenvectors computation are developed in C and synthesized using the new Xilinx VIVADO high level synthesis (HLS). The Jacobi method is used to find the eigenvectors and different approaches for the implementation of the PCA core on the heterogeneous Zynq platform are proposed. The hardware implementation of the presented PCA algorithm for a 16 × 30 matrix is faster than the software one with a speed up of 1.41 times when executed on a desktop running a 64-bit Intel i7-3770 processor at 3.40GHz. It was achieved using an average of 23% of all resources.

Electronic nose system on the Zynq SoC platform

Abstract Electronic nose or machine olfaction are systems used for detection and identification of odorous compounds and gas mixtures. An electronic nose system is mainly made of two parts, the sensing part which takes the form of a single or a set of sensors and the processing part which takes the form of some pattern recognition algorithms. As an alternative solution to pure software or hardware implementation of the processing part of a gas identification system, this paper proposes a hardware/software co-design approach using the Zynq platform for the implementation of an electronic nose system based on principal component analysis as a dimensionality reduction technique and decision tree as a classification algorithm using two different sensors array, a 4 × 4 in-house fabricated sensor and a commercial one based on 7 Figaro sensors, for comparison purpose. The system was successfully trained and simulated in MATLAB environment prior to the implementation on the Zynq platform. Various scenarios were explored and discussed including the investigation of different combination of principal components as well as the utilization of drift compensation technique to improve the identification accuracy. High level synthesis was carried out on the proposed designs using different optimization directives including loop unrolling, array partitioning and pipelining. Hardware implementation results on the Zynq system on chip show that real-time performances can be achieved for proposed electronic nose systems using hardware/software co-design approach with a single ARM processor running at 667 MHz and the programmable logic running at 142 MHz. In addition, using the designed IP cores and for the best scenarios, a gas can be identified in 3.46  μ s using the 4 × 4 sensor and 0.55  μ s using the Figaro sensors. Furthermore, it has been noticed that the choice of the sensor array has an important impact on performances in terms of accuracy and processing time. Finally, it has been demonstrated that the programmable logic of the Zynq platform consumes much less power than the processing system.

Heterogeneous Implementation of ECG Encryption and Identification on the Zynq SoC

This paper presents an innovative and safe connected health solution for human identification. The system consists of the encryption and decryption of ECG signals using the advanced encryption standard (AES) as well as the recognition of individuals based on ECG biometrics. Heterogeneous and efficient implementation of the proposed system has been performed on a Xilinx ZC702 Zynq based prototyping board. Various IP-cores have been created based on the high level synthesis (HLS) implementation of the AES cipher, AES decipher and ECG identification blocks. The proposed hardware implementation has shown promising results since it met the real-time requirements and outclassed current field programmable gate array (FPGA) based systems in multiple key metrics including power consumption, processing time and hardware resources usage. The implemented system needs 10.71 ms to process one ECG sample and consumes 107mW while using only 30% of all available on-chip resources.

Versatile Gas Monitoring System on the Heterogeneous Zynq SoC Platform

Abstract This paper presents a gas identification system which is part of an ongoing research project aiming to develop a low power reconfigurable self-calibrated multi-sensing platform for gas applications where real time parameters such as temperatures and gas concentrations are monitored. The gas identification system is based on decision tree classifier; training is performed in MATLAB environment. It is first carried out using the steady states extracted from the raw data obtained from the sensors, and then using transformed data by applying principal component analysis. The data used for training is collected from a 16-Array SnO2 gas sensor. The sensor array is exposed to three types of gases (CO, C2H6O and H2) at ten different concentrations (20, 40, 60, 80, 100, 120, 140, 160, 180 and 200ppm). The resulting models are implemented in C and synthesized using Vivado High Level Synthesis tool for rapid prototyping on the heterogeneous Zynq platform.