dsPIC-based signal processing techniques and an IT-enabled distributed system for intelligent process monitoring and management

dsPIC Technology employs a powerful 16-bit architecture into single-chip devices that seamlessly integrate the diverse attributes of a microcontroller with the computation and throughput capabilities of a digital signal processor in a single core. The key element of this dissertation is to explore how dsPIC has influenced the applicability of research in e-Monitoring systems. At the same time dsPIC has offered the opportunity to develop methodologies which were previously not even considered. The dsPIC devices are used, in this research, for front end data acquisition, signal processing and communication tools within a proposed monitoring architecture. In this work, novel digital signal processing (DSP) techniques are developed for the monitoring of an example application, namely the challenging one of tool breakage in milling operations. The monitoring regime is implemented on the dsPIC and its capabilities for real-time frequency analysis using overlap FFT and Multiband IIR Filters with dynamic coefficient selection techniques is explored. The developed systems are tested for various cutting conditions using existing machine tool signals and tool breakage is detected reliably in real-time. In attempting to enhance the accuracy of tool monitoring it is evident that the depth of cut (DOC) is an important parameter and achieving its on-line monitoring provides valuable information for condition monitoring. A systematic approach is adopted for the analysis and selection of ultrasonic sensors for distance measurement. A DOC monitoring system is developed using the dsPIC as the data acquisition and processing core. To achieve reliable results, various DSP algorithms are developed, implemented and verified for their effectiveness. The system integration stage combines the above elements for robust and reliable decision making and provides communication of the generated information to support management function using the internet and GSM connectivity. This integration enables an enhanced process management system which is capable of identifying all significant events, for offline analysis and subsequent diagnosis in addition to the real time diagnostic mode

Firmware design of a portable medical device to measure the quadriceps muscle group after a total knee arthroplasty by EMG, LBIA and clinical score methods

El objetivo de este proyecto es el diseño del firmware de un dispositivo médico portátil para mediciones de EMG y LBIA, que se utilizará para la evaluación de pacientes de artroplastia total de rodilla, para estudiar la progresión de diferentes prótesis de rodilla (Medial-Pivot y Ultra-Congruente). En la tesis, se expone el conocimiento actual de los estudios y aplicaciones de EMG y LBIA, junto con los dispositivos comerciales utilizados actualmente. Además, se han estudiado e implementado las diferentes técnicas de filtrado y procesamiento digital para señales de EMG y LBIAs. Adicionalmente, se ha realizado un estudio estadístico preliminar con datos LBIA de 12 pacientes de artroplastia total de rodilla. El diseño del firmware de esta tesis incluye: los procesos de adquisición de datos con el uso de diferentes ADCs (Conversor Analógico a Digital) (de la propia placa y externos, utilizando la interfaz SPI) y un DAC (Conversor Digital a Analógico), el correspondiente procesamiento de la señal y la extracción de sus características, la comunicación con un dispositivo externo utilizando un módulo BLE externo con interfaz UART, el proceso de encriptación de los datos médicos, la funcionalidad de manejo de errores y la aproximación del nivel de batería. En esta tesis, todos los flujos de trabajo de los procesos se exponen y explican mediante diagramas de flujo, mientras que se justifica cada cálculo y configuración. Además, todo el código correspondiente se ha programado en lenguaje C y se expone en los anexos. También se ha revisado la normativa aplicable y se ha analizado tanto el impacto ambiental como el coste económico del producto. Por último, se proponen mejoras para futuros trabajos.The aim of this project is the firmware design for a portable medical device for EMG and LBIA measurements which will be used for the assessment of total knee arthroplasty patients to study the progression of different knee prostheses (Medial-Pivot and Ultra-Congruent). For its realization, the state of the art of the EMG and LBIA studies and applications are exposed, along with the currently used medical devices. In addition, the different digital filtering and processing techniques for these studies have been studied and implemented. Furthermore, a preliminary statistical study has been performed with LBIA data from 12 patients with total knee arthroplasty. The firmware design of this thesis includes: the acquiring data processes with the use of different ADCs (from the actual board and external, using the SPI interface) and a DAC, the corresponding signal processing and feature abstraction, the communication with an external device using an external BLE module with UART interface, the medical data encrypting process, the error handling functionality, and the battery level approximation. In this work, all the process workflows are exposed and explained using flowcharts, while every calculation and configuration is justified. In addition, all the corresponding code has been programmed using C language and exposed in the Annexes. Moreover, the applicable regulation has been reviewed, and both the environmental impact and economic cost of the product have been analyzed. Finally, improvements are proposed for future work.L'objectiu d'aquest projecte és el disseny del microprogramari d'un dispositiu mèdic portàtil per a mesures d'EMG i LBIA. L’aparell mèdic s'utilitzarà per a l'avaluació de pacients d'artroplàstia total de genoll per estudiar la progressió de dues pròtesis de genoll (Medial-Pivot i Ultra- Congruent). En el treball, s'exposa el coneixement actual dels estudis i aplicacions d'EMG i LBIA, juntament amb els dispositius comercials utilitzats actualment. A més, s'han estudiat i implementat les diferents tècniques de filtrat i processament digital dels senyals de EMG i LBIA. Addicionalment, s'ha fet un estudi estadístic preliminar amb dades de LBIA de 12 pacients amb artroplàstia total de genoll. El disseny del microprogramari d'aquesta tesi inclou: els processos d'adquisició de dades fent ús de diferents ADCs (de la pròpia placa i externs, utilitzant la interfície SPI) i un DAC, el processament dels senyals i l'abstracció de les seves característiques, la comunicació amb un dispositiu extern utilitzant un mòdul BLE extern amb interfície UART, el procés d'encriptació de les dades mèdiques, la funcionalitat de l’avaluació d'errors i l'aproximació del nivell de bateria. En aquest treball, totes les funcionalitats del dispositiu s'exposen i s'expliquen mitjançant diagrames de flux i es justifiquen els càlculs i configuracions corresponents. Tot el codi desenvolupat s'ha programat en llenguatge C i s'exposa als annexos. A més, s'ha revisat la normativa aplicable i s'ha analitzat tant l'impacte ambiental com el cost econòmic de l’aparell. Finalment, es proposen millores per a futurs desenvolupaments

Distributed embedded system with internet GSM connectivity for intelligent e-monitoring of machine tools

Machining is one of the most important operations in many industrial environments. To prosper in today's competitive industrial world any machining system should be able to deliver the highest possible quality at the lowest possible costs, with very high reliability and flexibility. To fulfil these requirements the idea of e-Monitoring an industrial process was introduced by the Intelligent Process Monitoring and Management (IPMM) Centre at Cardiff University. It has considerable potential applications in industrial systems to not only monitor the health of the machines but also for data management and presentation for future decision making. The research presented in this thesis considers the evolution of two different low complexity signal analysis techniques which can be used for e-Monitoring the health of the cutters used in milling machine tools. The researched techniques are based in the time and frequency domains. The frequency domain analysis technique is based on the idea of using switched capacitor filters and microcontrollers to monitor the frequencies of interest in existing machine tool signals (spindle load and speed) thus avoiding the need for external sensors. The results of frequency domain analysis are used to assess the health of the cutter. The time domain analysis technique uses the same signals to analyse any variations within a tool rotation period and relate these to the health of the cutter. The results are integrated before final decision making which helps in reducing false alarms. The thesis goes on to logically describe the design and development of an on-line microcontroller based distributed intelligent e-Monitoring system for a milling machine tool model Kondia B500, using the proposed signal analysis techniques. Some additional features such as internet and GSM connectivity have also been added to the designed system. The designed system was interfaced to the machine tool and tested for its reliability which was found to be competitive with many other very expensive systems. The designed system can be fitted into a machine tool at the manufacturing stage or it could be interfaced to an existing machine tool for automatically detecting a tooth breakage

Técnicas de Adquisición y Procesamiento de Señales Electrocardiográficas en la Detección de Arritmias Cardíacas

The development of ambulatory monitoring systems and its electrocardiographic (ECG) signal processing techniques has become an important field of investigation, due to its relevance in the early detection of cardiovascular diseases such as the arrhythmias. The current trend of this technology is oriented to the use of portable equipment and mobile devices such as Smartphones, which have been widely accepted due to the technical characteristics and common integration in daily life. A fundamental characteristic of these systems is their ability to reduce the most common types of noise by means of digital signal processing techniques.  Among the most used techniques are the adaptive filters and the Discrete Wavelet Transform (DWT) which have been successfully implemented in several studies. There are systems that integrate classification stages based on artificial intelligence, which increases the performance in the process of arrhythmias detection. These techniques are not only evaluated for their functionality but for their computational cost, since they will be used in real-time applications, and implemented in embedded systems. This paper shows a review of each of the stages in the construction of a standard ambulatory monitoring system, for the contextualization of the reader in this type of technology.El desarrollo de sistemas de  monitoreo  ambulatorio  y  sus  técnicas  de  procesamiento  de  la  señal  electrocardiográfica (ECG) se han convertido en un importante campo de investigación, debido a su relevancia en la detección temprana de enfermedades cardiovasculares, tales como arritmias. La tendencia actual de esta tecnología está orientada al uso de equipos portátiles y dispositivos móviles como los Smartphones, que han sido ampliamente aceptados debido a sus características técnicas y a su integración, cada vez más común, en la vida diaria. Una característica fundamental de estos sistemas es su capacidad de reducir los tipos más comunes de ruido mediante técnicas de procesamiento de señales digitales. Entre las técnicas más utilizadas se encuentran los filtros adaptativos y la Transformada Discreta Wavelet (DWT, por sus siglas en inglés), los cuales han sido implementados exitosamente en diversos estudios. Así mismo, se reportan sistemas que integran etapas de clasificación basadas en inteligencia artificial, con lo cual se aumenta el rendimiento en el proceso de detección de arritmias. En este sentido, estas técnicas no solo son evaluadas por su funcionalidad, sino por su costo computacional, debido a que deben ser utilizadas en aplicaciones en tiempo real, e implementadas en sistemas embebidos. Este documento presenta una revisión del estado del arte de cada una de las etapas en la construcción de un sistema de monitoreo ambulatorio estándar, para la contextualización del lector en este tipo de tecnologías

Real-time membrane puncture detection using force sensors for micro-injections in phantoms

Micro-manipulators provide tools for researchers to improve workflow in common preclinical and clinical applications. Following drug delivery injections where drugs did not reach their target will squander research time, experimental animals and other resources. An ultrasound-guided robot developed at Robarts Research Institute was revised to implement closed-loop force feedback to compensate for tissue deformation during micro-interventions. Force sensors can detect puncture events as the needle penetrates tissue membranes, thereby reducing damage to surrounding tissues by preventing the needle from overshooting its target. Changing the angle of injection determined that the range of detectable forces during injections into tissue-mimicking phantoms suggests that sensors accurately measure projection of the needle force onto the vertical direction and are sensitive to puncture events through relatively thick (0.15 mm) membranes. Injections into mouse tissue yielded low success rates, suggesting different experimental designs are necessary to provide safer and less traumatic procedures, thus accelerating preclinical research

Novel Digital Alias-Free Signal Processing Approaches to FIR Filtering Estimation

This thesis aims at developing a new methodology of filtering continuous-time bandlimited signals and piecewise-continuous signals from their discrete-time samples. Unlike the existing state-of-the-art filters, my filters are not adversely affected by aliasing, allowing the designers to flexibly select the sampling rates of the processed signal to reach the required accuracy of signal filtering rather than meeting stiff and often demanding constraints imposed by the classical theory of digital signal processing (DSP). The impact of this thesis is cost reduction of alias-free sampling, filtering and other digital processing blocks, particularly when the processed signals have sparse and unknown spectral support. Novel approaches are proposed which can mitigate the negative effects of aliasing, thanks to the use of nonuniform random/pseudorandom sampling and processing algorithms. As such, the proposed approaches belong to the family of digital alias-free signal processing (DASP). Namely, three main approaches are considered: total random (ToRa), stratified (StSa) and antithetical stratified (AnSt) random sampling techniques. First, I introduce a finite impulse response (FIR) filter estimator for each of the three considered techniques. In addition, a generalised estimator that encompasses the three filter estimators is also proposed. Then, statistical properties of all estimators are investigated to assess their quality. Properties such as expected value, bias, variance, convergence rate, and consistency are all inspected and unveiled. Moreover, closed-form mathematical expression is devised for the variance of each single estimator. Furthermore, quality assessment of the proposed estimators is examined in two main cases related to the smoothness status of the filter convolution’s integrand function, \u1d454(\u1d461,\u1d70f)∶=\u1d465(\u1d70f)ℎ(\u1d461−\u1d70f), and its first two derivatives. The first main case is continuous and differentiable functions \u1d454(\u1d461,\u1d70f), \u1d454′(\u1d461,\u1d70f), and \u1d454′′(\u1d461,\u1d70f). Whereas in the second main case, I cover all possible instances where some/all of such functions are piecewise-continuous and involving a finite number of bounded discontinuities. Primarily obtained results prove that all considered filter estimators are unbiassed and consistent. Hence, variances of the estimators converge to zero after certain number of sample points. However, the convergence rate depends on the selected estimator and which case of smoothness is being considered. In the first case (i.e. continuous \u1d454(\u1d461,\u1d70f) and its derivatives), ToRa, StSa and AnSt filter estimators converge uniformly at rates of \u1d441−1, \u1d441−3, and \u1d441−5 respectively, where 2\u1d441 is the total number of sample points. More interestingly, in the second main case, the convergence rates of StSa and AnSt estimators are maintained even if there are some discontinuities in the first-order derivative (FOD) with respect to \u1d70f of \u1d454(\u1d461,\u1d70f) (for StSa estimator) or in the second-order derivative (SOD) with respect to \u1d70f of \u1d454(\u1d461,\u1d70f) (for AnSt). Whereas these rates drop to \u1d441−2 and \u1d441−4 (for StSa and AnSt, respectively) if the zero-order derivative (ZOD) (for StSa) and FOD (for AnSt) are piecewise-continuous. Finally, if the ZOD of \u1d454(\u1d461,\u1d70f) is piecewise-continuous, then the uniform convergence rate of the AnSt estimator further drops to \u1d441−2. For practical reasons, I also introduce the utilisation of the three estimators in a special situation where the input signal is pseudorandomly sampled from otherwise uniform and dense grid. An FIR filter model with an oversampled finite-duration impulse response, timely aligned with the grid, is proposed and meant to be stored in a lookup table of the implemented filter’s memory to save processing time. Then, a synchronised convolution sum operation is conducted to estimate the filter output. Finally, a new unequally spaced Lagrange interpolation-based rule is proposed. The so-called composite 3-nonuniform-sample (C3NS) rule is employed to estimate area under the curve (AUC) of an integrand function rather than the simple Rectangular rule. I then carry out comparisons for the convergence rates of different estimators based on the two interpolation rules. The proposed C3NS estimator outperforms other Rectangular rule estimators on the expense of higher computational complexity. Of course, this extra cost could only be justifiable for some specific applications where more accurate estimation is required

APPROXIMATE COMPUTING BASED PROCESSING OF MEA SIGNALS ON FPGA

The Microelectrode Array (MEA) is a collection of parallel electrodes that may measure the extracellular potential of nearby neurons. It is a crucial tool in neuroscience for researching the structure, operation, and behavior of neural networks. Using sophisticated signal processing techniques and architectural templates, the task of processing and evaluating the data streams obtained from MEAs is a computationally demanding one that needs time and parallel processing.This thesis proposes enhancing the capability of MEA signal processing systems by using approximate computing-based algorithms. These algorithms can be implemented in systems that process parallel MEA channels using the Field Programmable Gate Arrays (FPGAs). In order to develop approximate signal processing algorithms, three different types of approximate adders are investigated in various configurations. The objective is to maximize performance improvements in terms of area, power consumption, and latency associated with real-time processing while accepting lower output accuracy within certain bounds. On FPGAs, the methods are utilized to construct approximate processing systems, which are then contrasted with the precise system. Real biological signals are used to evaluate both precise and approximative systems, and the findings reveal notable improvements, especially in terms of speed and area. Processing speed enhancements reach up to 37.6%, and area enhancements reach 14.3% in some approximate system modes without sacrificing accuracy. Additional cases demonstrate how accuracy, area, and processing speed may be traded off. Using approximate computing algorithms allows for the design of real-time MEA processing systems with higher speeds and more parallel channels. The application of approximate computing algorithms to process biological signals on FPGAs in this thesis is a novel idea that has not been explored before