Two problems in convex conic optimization

Master'sMASTER OF SCIENC

Automatic Number Plate Recognition on FPGA

Automatic Number Plate Recognition (ANPR) systems have become one of the most important components in the current Intelligent Transportation Systems (ITS). In this paper, a FPGA implementation of a complete ANPR system which consists of Number Plate Localisation (NPL), Character Segmentation (CS), and Optical Character Recognition (OCR) is presented. The Mentor Graphics RC240 FPGA development board was used for the implementation, where only 80% of the available on-chip slices of a Virtex-4 LX60 FPGA have been used. The whole system runs with a maximum frequency of 57.6 MHz and is capable of processing one image in 11ms with a successful recognition rate of 93%

Virtual machine-based task scheduling algorithm in a cloud computing environment

Virtualization technology has been widely used to virtualize single server into multiple servers, which not only creates an operating environment for a virtual machine-based cloud computing platform but also potentially improves its efficiency. Currently, most task scheduling-based algorithms used in cloud computing environments are slow to convergence or easily fall into a local optimum. This paper introduces a Greedy Particle Swarm Optimization (G&PSO) based algorithm to solve the task scheduling problem. It uses a greedy algorithm to quickly solve the initial particle value of a particle swarm optimization algorithm derived from a virtual machine-based cloud platform. The archived experimental results show that the algorithm exhibits better performance such as a faster convergence rate, stronger local and global search capabilities, and a more balanced workload on each virtual machine. Therefore, the G&PSO algorithm demonstrates improved virtual machine efficiency and resource utilization compared with the traditional particle swarm optimization algorithm

A Complex Event Processing-Based Online Shopping User Risk Identification System

Online shopping is an important part of the development of the Internet and plays a critical role in the current and future economy. However, there are many risks in the trading process. In order to reduce the hidden risks, it is necessary to study the method of risk identification. This paper proposes user risk identification method of online shopping system based on Complex Event Process (CEP). In this paper, we use the Esper as the CEP engine and the risk behavior patterns are defined as the event pattern language. Firstly, the CEP system captures event streams by analyzing data streams in real-time. Secondly, the captured event streams are sent to the CEP's engine. Finally, the Esper intelligently analyzes user's online shopping risk behaviors in real-time according to the event pattern languages. User risk identification effectively guarantees the fund and account security of the shopping users

Efficient Data-Processing Algorithms for Wireless-Sensor-Networks-Based Planetary Exploration

The Space Wireless Sensor Networks for Planetary Exploration project aims to design a wireless sensor network that consists of small wireless sensor nodes dropped onto the moon surface to collect scientific measurements. Data gathered from the sensors will be processed and aggregated for uploading to a lunar orbiter and subsequent transmission to Earth. In this paper, efficient data-processing/fusion algorithms are proposed, the purpose of which is to integrate the scientific sensor data collected by the wireless sensor network, reducing the data volume without sacrificing the data quality to satisfy energy constraints of wireless-sensor-network nodes operating in the extreme moon environment. The results of an extensive simulation experiment targeted at the Space Wireless Sensor Networks for Planetary Exploration lunar exploration mission are reported, which quantify the performance efficiency of the data-processing scheme. It is shown that the proposed data-processing algorithms can reduce the wireless-sensor-network node energy consumption significantly, decreasing the data transmission energy up to 91%. In addition, it is shown that up to 99% of the accuracy of the original data can be preserved in the final reconstructed data

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

Inequality Indexes as Sparsity Measures Applied to Ventricular Ectopic Beats Detection and its Efficient Hardware Implementation

Meeting application requirements under a tight power budget is of a primary importance to enable connected health internet of things applications. This paper considers using sparse representation and well-defined inequality indexes drawn from the theory of inequality to distinguish ventricular ectopic beats (VEBs) from non-VEBs. Our approach involves designing a separate dictionary for each arrhythmia class using a set of labeled training QRS complexes. Sparse representation, based on the designed dictionaries of each new test QRS complex is then calculated. Following this, its class is predicted using the winner-takes-all principle by selecting the class with the highest inequality index. The experiments showed promising results ranging between 80% and 100% for the detection of VEBs considering the patient-specific approach, 80% using cross validation and 70% on unseen data using independent sets for training and testing, respectively. An efficient hardware implementation of the alternating direction method of multipliers algorithm is also presented. The results show that the proposed hardware implementation can classify a QRS complex in 69.3 ms that use only 0.934 W energy

System-on-Chip Solution for Patients Biometric: A Compressive Sensing-Based Approach

IEEE The ever-increasing demand for biometric solutions for the internet of thing (IoT)-based connected health applications is mainly driven by the need to tackle fraud issues, along with the imperative to improve patient privacy, safety and personalized medical assistance. However, the advantages offered by the IoT platforms come with the burden of big data and its associated challenges in terms of computing complexity, bandwidth availability and power consumption. This paper proposes a solution to tackle both privacy issues and big data transmission by incorporating the theory of compressive sensing (CS) and a simple, yet, efficient identification mechanism using the electrocardiogram (ECG) signal as a biometric trait. Moreover, the paper presents the hardware implementation of the proposed solution on a system on chip (SoC) platform with an optimized architecture to further reduce hardware resource usage. First, we investigate the feasibility of compressing the ECG data while maintaining a high identification quality. The obtained results show a 98.88% identification rate using only a compression ratio of 30%. Furthermore, the proposed system has been implemented on a Zynq SoC using heterogeneous software/hardware solution, which is able to accelerate the software implementation by a factor of 7.73 with a power consumption of 2.318 W

Image analysis and machine learning based malaria assessment system

Malaria is an important and worldwide fatal disease that has been widely reported by the World Health Organization (WHO), and it has about 219 million cases worldwide, with 435,000 of those mortal. The common malaria diagnosis approach is heavily reliant on highly trained experts, who use a microscope to examine the samples. Therefore, there is a need to create an automated solution for the diagnosis of malaria. One of the main objectives of this work is to create a design tool that could be used to diagnose malaria from the image of a blood sample. In this paper, we firstly developed a graphical user interface that could be used to help segment red blood cells and infected cells and allow the users to analyze the blood samples. Secondly, a Feed-forward Neural Network (FNN) is designed to classify the cells into two classes. The achieved results show that the proposed techniques can be used to detect malaria, as it has achieved 92% accuracy with a database that contains 27,560 benchmark images

A Dual-Band Microwave Filter Design for Modern Wireless Communication Systems

Nowadays, modern communication system relies on the designs of high-performance devices to enhance communication effect for a high quality of life and smart city system. As a crucial signal processing step, microwave filter removes unwanted frequency components away from the received signal and enhances the useful ones. However, large loss, bulky size, and single-band greatly limit the practical applications in urban computing. Therefore, the filters with dual-band characteristic are highly desirable for modern wireless communication, such as device-to-device communication, environment monitoring, and automatic driving. In this paper, a dual-band microwave filter is designed and fabricated based on the theory of Mie-resonance extraordinary transmission. An electromagnetic wave cannot propagate through a subwavelength aperture drilled in a metallic film. By adding two dielectric cuboids of different sizes into the two apertures, two passbands appear in the frequency range of 10.0-12.0 GHz. In this range, the insertion loss is less than 0.4 dB, and 3-dB bandwidth is more than 48 MHz. Particularly, the two passband frequencies can be tuned by adjusting the size of the dielectric cuboids. This approach opens a way for designing tunable dual-band microwave bandpass filter, which is benefit for enhancing spectrum resource utilization