Mengenal pasti tahap pengetahuan pelajar tahun akhir Ijazah Sarjana Muda Kejuruteraan di KUiTTHO dalam bidang keusahawanan dari aspek pengurusan modal

Malaysia ialah sebuah negara membangun di dunia. Dalam proses pembangunan ini, hasrat negara untuk melahirkan bakal usahawan beijaya tidak boleh dipandang ringan. Oleh itu, pengetahuan dalam bidang keusahawanan perlu diberi perhatian dengan sewajarnya; antara aspek utama dalam keusahawanan ialah modal. Pengurusan modal yang tidak cekap menjadi punca utama kegagalan usahawan. Menyedari hakikat ini, kajian berkaitan Pengurusan Modal dijalankan ke atas 100 orang pelajar Tahun Akhir Kejuruteraan di KUiTTHO. Sampel ini dipilih kerana pelajar-pelajar ini akan menempuhi alam pekeijaan di mana mereka boleh memilih keusahawanan sebagai satu keijaya. Walau pun mereka bukanlah pelajar dari jurusan perniagaan, namun mereka mempunyai kemahiran dalam mereka cipta produk yang boleh dikomersialkan. Hasil dapatan kajian membuktikan bahawa pelajar-pelajar ini berminat dalam bidang keusahawanan namun masih kurang pengetahuan tentang pengurusan modal terutamanya dalam menentukan modal permulaan, pengurusan modal keija dan caracara menentukan pembiayaan kewangan menggunakan kaedah jualan harian. Oleh itu, satu garis panduan Pengurusan Modal dibina untuk memberi pendedahan kepada mereka

Efficient reconfigurable architectures for 3D medical image compression

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Recently, the more widespread use of three-dimensional (3-D) imaging modalities, such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and ultrasound (US) have generated a massive amount of volumetric data. These have provided an impetus to the development of other applications, in particular telemedicine and teleradiology. In these fields, medical image compression is important since both efficient storage and transmission of data through high-bandwidth digital communication lines are of crucial importance. Despite their advantages, most 3-D medical imaging algorithms are computationally intensive with matrix transformation as the most fundamental operation involved in the transform-based methods. Therefore, there is a real need for high-performance systems, whilst keeping architectures exible to allow for quick upgradeability with real-time applications. Moreover, in order to obtain efficient solutions for large medical volumes data, an efficient implementation of these operations is of significant importance. Reconfigurable hardware, in the form of field programmable gate arrays (FPGAs) has been proposed as viable system building block in the construction of high-performance systems at an economical price. Consequently, FPGAs seem an ideal candidate to harness and exploit their inherent advantages such as massive parallelism capabilities, multimillion gate counts, and special low-power packages. The key achievements of the work presented in this thesis are summarised as follows. Two architectures for 3-D Haar wavelet transform (HWT) have been proposed based on transpose-based computation and partial reconfiguration suitable for 3-D medical imaging applications. These applications require continuous hardware servicing, and as a result dynamic partial reconfiguration (DPR) has been introduced. Comparative study for both non-partial and partial reconfiguration implementation has shown that DPR offers many advantages and leads to a compelling solution for implementing computationally intensive applications such as 3-D medical image compression. Using DPR, several large systems are mapped to small hardware resources, and the area, power consumption as well as maximum frequency are optimised and improved. Moreover, an FPGA-based architecture of the finite Radon transform (FRAT)with three design strategies has been proposed: direct implementation of pseudo-code with a sequential or pipelined description, and block random access memory (BRAM)- based method. An analysis with various medical imaging modalities has been carried out. Results obtained for image de-noising implementation using FRAT exhibits promising results in reducing Gaussian white noise in medical images. In terms of hardware implementation, promising trade-offs on maximum frequency, throughput and area are also achieved. Furthermore, a novel hardware implementation of 3-D medical image compression system with context-based adaptive variable length coding (CAVLC) has been proposed. An evaluation of the 3-D integer transform (IT) and the discrete wavelet transform (DWT) with lifting scheme (LS) for transform blocks reveal that 3-D IT demonstrates better computational complexity than the 3-D DWT, whilst the 3-D DWT with LS exhibits a lossless compression that is significantly useful for medical image compression. Additionally, an architecture of CAVLC that is capable of compressing high-definition (HD) images in real-time without any buffer between the quantiser and the entropy coder is proposed. Through a judicious parallelisation, promising results have been obtained with limited resources. In summary, this research is tackling the issues of massive 3-D medical volumes data that requires compression as well as hardware implementation to accelerate the slowest operations in the system. Results obtained also reveal a significant achievement in terms of the architecture efficiency and applications performance.Ministry of Higher Education Malaysia (MOHE), Universiti Tun Hussein Onn Malaysia (UTHM) and the British Counci

Sistema de Diagnóstico Distribuido de Motores de Inducción basado en Redes Inalámbricas de Sensores y Protocolo ZigBee

La inclusión de las redes inalámbricas de sensores y tecnologías IoT (Internet of Things) en ambientes industriales busca el monitoreo y registro autónomo de una mayor cantidad de variables del proceso industrial con una alta confiabilidad y resiliencia, además, procuran realizar un análisis previó para obtener parámetros de las señales que puedan dar a conocer una mejor descripción del estado del sistema y su condición de operación. Esto permite reducir el consumo de energía al disminuir la transmisión de datos netos medidos con paquetes hasta mil veces más largas que un parámetro calculado desde el sensor hacia los centros de control. La finalidad del monitoreo propuesto es el análisis para la identificación de anomalías que puedan afectar la disponibilidad de la planta o incrementar los costos de producción, y mejorar los procesos de mantenimiento. En este proyecto se desarrolló un sistema de monitoreo y diagnóstico remoto basado en una red de sensores cuyos nodos remotos se encarguen de la recolección de datos y su posterior análisis para la identificación de anomalías que representen fallas críticas para el proceso o sistema industrial. El sistema propuesto se enfocó en el diagnóstico de falla de motores de inducción debido a que representan el mayor porcentaje de equipos en aplicaciones industriales. El proyecto se limitó a la identificación de falla entre espiras (2, 4 y 6 espiras), como un antecedente de fallas críticas, corto circuito fase-fase y corto circuito fase-tierra al presentarse como un deterioro del aislamiento. Se empleo el método de análisis de corriente de estator (MCSA). El nodo remoto inteligente se implementó con MCU LPCXpresso54114 con conexión a una red inalámbrica de sensores basada en protocolo ZigBee mediante tarjetas de comunicación XBee. El nodo concentrador (gateway) está compuesto de una tarjeta Raspberrry PI con comunicación mediante protocolo HTTP y formato JSON (PI Web API) a la base de datos del sistema de monitoreo industrial PI System El diagnóstico se ejecuta de manera remota por medio de un análisis preliminar para el cálculo de indicadores de falla y luego mediante SVM (Support Vector Machine) se clasifican los datos en comportamientos conocidos de condiciones de falla. Se plantearon indicadores basados en la medición neta de las corrientes, FFT (Fast Fourier Transform) y DWT (Discrete Wavelet Transform). Se realizó validación en laboratorio de la clasificación en tiempo real de fallas entre espiras aplicadas a un motor de inducción tipo jaula de ardilla, comparando diferentes configuraciones del diagnóstico, del análisis para la extracción de indicadores y de los indicadores de falla empleados; permitiendo plantear mejoras para la reducción de los porcentajes de error por falsa detección de falla, o no detección de falla. Estos avances finalmente se traducen a incrementar la confiabilidad del diagnóstico, la observabilidad de la falla, la diferenciación entre condiciones de falla, la precisión de la clasificación, la tolerancia a transitorios, sensibilidad, entre otros.The inclusion of sensors wireless networks and Internet of Things (IoT) technologies in industrial environments seeks an autonomous monitoring and storage with high reliability and resilience of a greater number of industrial process variables, in addition, they attempt to perform a preliminary analysis to obtain parameters of the signals that can give a better description of system state and its operation condition. This allows reducing energy consumption by decreasing the transmission of raw data, a parameter calculated from the sensor to the control centers in change of a thousand times longer package. The purpose of the proposed monitoring is the analysis for the identification of anomalies that may affect the availability of the plant or increase production costs and improve maintenance processes. In this project, a remote fault diagnosis and monitoring system based on wireless sensor networks was developed whose remote nodes are responsible for data collection and analysis for the identification of anomalies over industrial process or system, previously to critical faults. The proposed system was focused on the induction motor fault diagnosis because these represent the highest percentage of equipment in industrial applications. This project was based on identify interturn faults (2, 4 and 6 turns) using Motor Current Signature Analysis (MCSA), because of the Interturn faults are produced by insulation deterioration and evolve in critical faults, phase to phase short-circuit and ground fault. The developed intelligent remote node was implemented with MCU LPCXpresso54114 with connection to a ZigBee protocol wireless sensor network through XBee communication module. The gateway node is a Raspberrry PI with communication through HTTP protocol and JSON format (PI Web API) to the PI System database (industrial monitoring system). The diagnosis is remotely executed, where a preliminary analysis is applied to calculate fault indicators. Then, with a SVM (Support Vector Machine), the data are classified in known behavior of fault conditions. Different fault indicators were proposed based on current measurement’s raw data, FFT (Fast Fourier Transform) and DWT (Discrete Wavelet Transform). Real time interturn fault classification was validated in laboratory with a squirrel cage induction motor comparing different settings and configuration of diagnosis, analysis for indicators extraction and testing diversified fault indicators. This allowed proposing improvements to reduce of error percentage by false detection or missing detection. The progress finally are reflected in increase the diagnosis reliability, the observability of the failure, the differentiation between fault conditions, classification accuracy, tolerance to transients, sensitivity, etc.Magíster en Ingeniería Eléctrica.Maestrí

Technology 2003: The Fourth National Technology Transfer Conference and Exposition, volume 2

Proceedings from symposia of the Technology 2003 Conference and Exposition, Dec. 7-9, 1993, Anaheim, CA, are presented. Volume 2 features papers on artificial intelligence, CAD&E, computer hardware, computer software, information management, photonics, robotics, test and measurement, video and imaging, and virtual reality/simulation

Robot Manipulators

Robot manipulators are developing more in the direction of industrial robots than of human workers. Recently, the applications of robot manipulators are spreading their focus, for example Da Vinci as a medical robot, ASIMO as a humanoid robot and so on. There are many research topics within the field of robot manipulators, e.g. motion planning, cooperation with a human, and fusion with external sensors like vision, haptic and force, etc. Moreover, these include both technical problems in the industry and theoretical problems in the academic fields. This book is a collection of papers presenting the latest research issues from around the world

Robust linear and non-linear control of magnetically levitated systems

The two most advanced applications of contactless magnetic levitation are high-speed magnetic bearings and magnetically levitated vehicles (Maglev) for ground transportation using superconducting magnets and controlled d.c. electromagnets. The repulsion force from superconducting magnets provide stable levitation with low damping, while the suspension force generated by electromagnets is inherently unstable. This instability, due to the in verse force-distance relationship, requires the addition of feedback controllers to sustain stable suspension. The problem of controlling magnetically levitated systems using d.c. electromagnets under different operating conditions has been studied in this thesis with a design process primarily driven by experimental results from a representative single-magnet test rig and a multi-magnet vehicle. The controller-design stages are presented in detail and close relationships have been constructed between selection of performance criteria for the derivation process and desired suspension characteristics. Both linear and nonlinear stabilising compensators have been developed. Simulation and experimental results have been studied in parallel to assess operational stability and the main emphasis has been given to assessing performance under different operational conditions. For the experimental work, a new digital signal processor-based hardware platform has been designed, built with interface to Matlab/Simulink. The controller design methods and algorithmic work presented in this thesis can be divided into: non-adaptive, adaptive, optimal linear and nonlinear. Adaptive algorithms based on model reference control have been developed to improve the performance of the suspension system in the presence of considerable variations in external payload and force disturbances. New design methods for Maglev suspension have been developed using robust control theory (%oo and fi synthesis). Single- and multi-magnet control problems have been treated using the same framework. A solution to the Hoo controller-optimisation problem has been derived and applied to Maglev control. The sensitivity to robustness has been discussed and tools for assessing the robustness of the closed-loop system in terms of sustaining stability and performance in the presence of uncertainties in the suspension model have been presented. Multivariable controllers based on %00 and /i synthesis have been developed for a laboratory scale experimental vehicle weighing 88 kg with four suspension magnets, and experimental results have been derived to show superiority of the proposed design methods in terms of ability to deal with external disturbances. The concept of Hoo control has been extended to the nonlinear setting using the concepts of energy and dissipativity, and nonlinear state-feedback and out put-feed back controllers for Maglev have been developed and reported. Simulation and experimental results have been presented to show the improved performance of these controllers to attenuate guideway-induced disturbances while maintaining acceptable suspension qualities and larger operational bandwidth

Proceedings of the Fifth NASA/NSF/DOD Workshop on Aerospace Computational Control

The Fifth Annual Workshop on Aerospace Computational Control was one in a series of workshops sponsored by NASA, NSF, and the DOD. The purpose of these workshops is to address computational issues in the analysis, design, and testing of flexible multibody control systems for aerospace applications. The intention in holding these workshops is to bring together users, researchers, and developers of computational tools in aerospace systems (spacecraft, space robotics, aerospace transportation vehicles, etc.) for the purpose of exchanging ideas on the state of the art in computational tools and techniques

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes

Technology 2002: The Third National Technology Transfer Conference and Exposition, volume 2

Proceedings from symposia of the Technology 2002 Conference and Exposition, December 1-3, 1992, Baltimore, MD. Volume 2 features 60 papers presented during 30 concurrent sessions