An embedded system supporting dynamic partial reconfiguration of hardware resources for morphological image processing

Processors for high-performance computing applications are generally designed with a focus on high clock rates, parallelism of operations and high communication bandwidth, often at the expense of large power consumption. However, the emphasis of many embedded systems and untethered devices is on minimal hardware requirements and reduced power consumption. With the incessant growth of computational needs for embedded applications, which contradict chip power and area needs, the burden is put on the hardware designers to come up with designs that optimize power and area requirements. This thesis investigates the efficient design of an embedded system for morphological image processing applications on Xilinx FPGAs (Field Programmable Gate Array) by optimizing both area and power usage while delivering high performance. The design leverages a unique capability of FPGAs called dynamic partial reconfiguration (DPR) which allows changing the hardware configuration of silicon pieces at runtime. DPR allows regions of the FPGA to be reprogrammed with new functionality while applications are still running in the remainder of the device. The main aim of this thesis is to design an embedded system for morphological image processing by accounting for real time and area constraints as compared to a statically configured FPGA. IP (Intellectual Property) cores are synthesized for both static and dynamic time. DPR enables instantiation of more hardware logic over a period of time on an existing device by time-multiplexing the hardware realization of functions. A comparison of power consumption is presented for the statically and dynamically reconfigured designs. Finally, a performance comparison is included for the implementation of the respective algorithms on a hardwired ARM processor as well as on another general-purpose processor. The results prove the viability of DPR for morphological image processing applications

Extraction of operation characteristics in mechanical systems using genetic morphological filter

Operation characteristics such as rotating speed are of great importance in condition monitoring and fault diagnosis of rotating machineries. Since different components in the mechanical system are often correlated and interacted, the acquired signals are highly coupled and contaminated by lots of high-frequency noises. As a result, the frequency and phase of the observed signal cannot reflect actual condition of the mechanical component. In this paper, we propose a genetic morphological filter to purify the operation characteristics of the mechanical system in the time domain. Firstly, an average weighted combination of open-closing and close-opening morphological operator, which eliminates statistical deflection of amplitude, is utilized to remove stochastic noises from the original signal. Then, according to the geometric characteristic of the noises, the structure elements are constructed with two parabolas and four parameters of the structure elements are synchronously optimized with genetic algorithm. The combination of Hurst exponent and Kurtosis is selected as the fitness function of the genetic algorithm and the optimal parameters of the structure elements correspond to the maximum of fitness function. The proposed method is evaluated by simulated signals with different frequencies, vibration signals measured on condensate pump and sound signals acquired from motor engine, respectively. Results show that with genetic morphological filter, the operation characteristics such as rotating speed and phase can be extracted in the time domain efficiently

Texture Analysis with Arbitrarily Oriented Morphological Opening and Closing

13 pagesThis paper presents a fast, streaming algorithm for 1-D morphological opening on 2-D support. The algorithm is further extended to compute the complete size distribution during a single image run. The Structuring Element (SE) can be oriented under arbitrary angle that allows us to perform different orientation-involved image analysis, such as local angle extraction, directional granulometries, \etc The algorithm processes an image in constant time irrespective of the SE orientation and size, with a minimal latency and very low memory requirements. Regardless the SE orientation, it reads and writes data strictly sequentially in the horizontal scan order. Aforementioned properties allow an efficient implementation in embedded hardware platforms that opens a new opportunity of a parallel computation, and consequently, a significant speed-up

Design techniques for smart and energy-efficient wireless body sensor networks

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Arquitectura de Computadores y Automática, leída el 26/10/2012Las redes inalámbricas de sensores corporales (en inglés: "wireless body sensor networks" o WBSNs) para monitorización, diagnóstico y detección de emergencias, están ganando popularidad y están llamadas a cambiar profundamente la asistencia sanitaria en los próximos años. El uso de estas redes permite una supervisión continua, contribuyendo a la prevención y el diagnóstico precoz de enfermedades, al tiempo que mejora la autonomía del paciente con respecto a otros sistemas de monitorización actuales. Valiéndose de esta tecnología, esta tesis propone el desarrollo de un sistema de monitorización de electrocardiograma (ECG), que no sólo muestre continuamente el ECG del paciente, sino que además lo analice en tiempo real y sea capaz de dar información sobre el estado del corazón a través de un dispositivo móvil. Esta información también puede ser enviada al personal médico en tiempo real. Si ocurre un evento peligroso, el sistema lo detectará automáticamente e informará de inmediato al paciente y al personal médico, posibilitando una rápida reacción en caso de emergencia. Para conseguir la implementación de dicho sistema, se desarrollan y optimizan distintos algoritmos de procesamiento de ECG en tiempo real, que incluyen filtrado, detección de puntos característicos y clasificación de arritmias. Esta tesis también aborda la mejora de la eficiencia energética de la red de sensores, cumpliendo con los requisitos de fidelidad y rendimiento de la aplicación. Para ello se proponen técnicas de diseño para reducir el consumo de energía, que permitan buscar un compromiso óptimo entre el tamaño de la batería y su tiempo de vida. Si el consumo de energía puede reducirse lo suficiente, sería posible desarrollar una red que funcione permanentemente. Por lo tanto, el muestreo, procesamiento, almacenamiento y transmisión inalámbrica tienen que hacerse de manera que se suministren todos los datos relevantes, pero con el menor consumo posible de energía, minimizando así el tamaño de la batería (que condiciona el tamaño total del nodo) y la frecuencia de recarga de la batería (otro factor clave para su usabilidad). Por lo tanto, para lograr una mejora en la eficiencia energética del sistema de monitorización y análisis de ECG propuesto en esta tesis, se estudian varias soluciones a nivel de control de acceso al medio y sistema operativo.Depto. de Arquitectura de Computadores y AutomáticaFac. de InformáticaTRUEunpu

Computationally efficient, one-pass algorithm for morphological filters

International audienceMany useful morphological filters are built as long concatenations of erosions and dilations: openings, closings, size distributions, sequential filters, etc. This paper proposes a new algorithm implementing morphological dilation and erosion of functions. It supports rectangular structuring element, runs in linear time w.r.t. the image size and constant time w.r.t. the structuring element size, and has minimal memory usage. It has zero algorithm latency and processes data in stream. These properties are inherited by operators composed by concatenation, and allow their efficient implementation. We show how to compute in one pass an Alternate Sequential Filter (ASF(n)) regardless the number of stages n. This algorithm opens the way to such time-critical applications where the complexity and memory requirements of serial morphological operators represented a bottleneck limiting their usability. (C) 2011 Elsevier Inc. All rights reserved

Real-Time Vision System for License Plate Detection and Recognition on FPGA

Rapid development of the Field Programmable Gate Array (FPGA) offers an alternative way to provide acceleration for computationally intensive tasks such as digital signal and image processing. Its ability to perform parallel processing shows the potential in implementing a high speed vision system. Out of numerous applications of computer vision, this paper focuses on the hardware implementation of one that is commercially known as Automatic Number Plate Recognition (ANPR).Morphological operations and Optical Character Recognition (OCR) algorithms have been implemented on a Xilinx Zynq-7000 All-Programmable SoC to realize the functions of an ANPR system. Test results have shown that the designed and implemented processing pipeline that consumed 63 % of the logic resources is capable of delivering the results with relatively low error rate. Most importantly, the computation time satisfies the real-time requirement for many ANPR applications

Spectral-spatial classification of n-dimensional images in real-time based on segmentation and mathematical morphology on GPUs

The objective of this thesis is to develop efficient schemes for spectral-spatial n-dimensional image classification. By efficient schemes, we mean schemes that produce good classification results in terms of accuracy, as well as schemes that can be executed in real-time on low-cost computing infrastructures, such as the Graphics Processing Units (GPUs) shipped in personal computers. The n-dimensional images include images with two and three dimensions, such as images coming from the medical domain, and also images ranging from ten to hundreds of dimensions, such as the multiand hyperspectral images acquired in remote sensing. In image analysis, classification is a regularly used method for information retrieval in areas such as medical diagnosis, surveillance, manufacturing and remote sensing, among others. In addition, as the hyperspectral images have been widely available in recent years owing to the reduction in the size and cost of the sensors, the number of applications at lab scale, such as food quality control, art forgery detection, disease diagnosis and forensics has also increased. Although there are many spectral-spatial classification schemes, most are computationally inefficient in terms of execution time. In addition, the need for efficient computation on low-cost computing infrastructures is increasing in line with the incorporation of technology into everyday applications. In this thesis we have proposed two spectral-spatial classification schemes: one based on segmentation and other based on wavelets and mathematical morphology. These schemes were designed with the aim of producing good classification results and they perform better than other schemes found in the literature based on segmentation and mathematical morphology in terms of accuracy. Additionally, it was necessary to develop techniques and strategies for efficient GPU computing, for example, a block–asynchronous strategy, resulting in an efficient implementation on GPU of the aforementioned spectral-spatial classification schemes. The optimal GPU parameters were analyzed and different data partitioning and thread block arrangements were studied to exploit the GPU resources. The results show that the GPU is an adequate computing platform for on-board processing of hyperspectral information