The few microcontroller based active/semi-active prosthetic knee joints available commercially are extremely expensive and do not consider the uncertainties of inputs sensory information. Progressing in the controller of the current prosthetic devices and creating artificial lower limbs compatible with different users may lead to more effective and low-cost prostheses. This can affect the life style of lots of amputees specially the land-mine victims in developing war-torn countries who are unable to partake in the advancement of the current intelligent prosthetic knees. The purpose of the proposed Active Prosthetic Knee (APK) design is to investigate a new schema that allows the device to provide the full necessary torque at the knee joint based on echoing the state of the intact leg. This study involves the design features of the mechanical aspects, sensing system, communication, and knowledge-based controller to implement a cost-effective APK. The proposed microcontroller based prosthesis utilizes a ball screw system accompanied by a high-speed brushed servomotor to provide one degree of freedom for the fabricated prototype. Moreover, a modular test-bed is manufactured to mimic the lower limb motion which contributes investigating different controllers for the prototype. Thus, the test bed allows assessing the primary performance of the APK before testing on a human subject. Different types of sensing systems (electromyography and lower limb inclination angles) are investigated to extract signals from the user’s healthy leg and send the captured data to the APK controller. The methodology to measure each type of signal is described, and comparison analyses are provided. Wireless communication between the sensory part and actuator is established. A knowledge-based control mechanism is developed that takes advantage of an Adaptive-Network-based Fuzzy Inference System (ANFIS) to determine knee torque as a function of the echoing angular state of the able leg considering the uncertainty of inputs. Therefore, the developed controller can make the APK serviceable for different users. The fuzzy membership function’s parameters and rules define the knowledge-base of the system. This knowledge is based on existing experience and known facts about the walking cycle.

In hands-free telephony and in teleconference systems, the main aim is to provide agood free voice quality when two or more people communicate from different places.The problem often arises during the conversation is the creation of acoustic echo. Thisproblem will cause the bad quality of voice signal and thus talkers could not hearclearly the content of the conversation, even thought lost the important information.This acoustic echo is actually the noise which is created by the reflection of soundwaves by the wall of the room and the other things exist in the room. The mainobjective for engineers is the cancellation of this acoustic echo and provides an echofree environment for speakers during conversation. For this purpose, scientists designdifferent adaptive filter algorithms. Our thesis is also to study and simulate theacoustics echo cancellation by using different adaptive algorithms.

A channel board was designed for a DRFM circuit. The DRFM is implemented in a Virtex-4 FPGA from Xilinx. In the future a similar channel board is intended to be used for target echo generation in ELSI which is an electronic warfare simulator at Saab Bofors Dynamics in Linköping. Besides the DRFM circuit the channel board consists of analog-to-digital converters, digital-to-analog converters, Ethernet plug-in board with a microcontroller, voltage regulators, FPGA configuration memory, voltage amplifiers, current amplifiers, oscillator, buffers/drivers and bus transceivers. The sample rate is 200 MHz and LVDS signalling standard is used between the DRFM circuit and the converters. The channel board has a JTAG interface which enables in-system programming of the FPGA. This implies that the DRFM can easily be redesigned. An external computer can manage the channel board via Ethernet. Software was developed for the microcontroller on the channel board and for the external computer. The function of the channel board is heavily dependent on the DRFM circuit. The channel board design resulted in the assembly of a prototype circuit board. Measurements were performed in a lab and the channel board was approved to be integrated in ELSI for further tests.

The work of my MFA thesis exhibition comes directly out of the physical processes that constitute my studio practice. It is work that embodies the labour of my hands and the decisions that guided them in their struggle with unfamiliar materials. Drawing inspiration from subjects as diverse as physics and fiction to create a sculptural arrangement, the work is intended to engage with its audience in an active way through acoustic appropriation and physical presence. As an artist, I set up an environment to be explored, establishing boundaries and also possibilities.

The few microcontroller based active/semi-active prosthetic knee joints available commercially are extremely expensive and do not consider the uncertainties of inputs sensory information. Progressing in the controller of the current prosthetic devices and creating artificial lower limbs compatible with different users may lead to more effective and low-cost prostheses. This can affect the life style of lots of amputees specially the land-mine victims in developing war-torn countries who are unable to partake in the advancement of the current intelligent prosthetic knees. The purpose of the proposed Active Prosthetic Knee (APK) design is to investigate a new schema that allows the device to provide the full necessary torque at the knee joint based on echoing the state of the intact leg. This study involves the design features of the mechanical aspects, sensing system, communication, and knowledge-based controller to implement a cost-effective APK. The proposed microcontroller based prosthesis utilizes a ball screw system accompanied by a high-speed brushed servomotor to provide one degree of freedom for the fabricated prototype. Moreover, a modular test-bed is manufactured to mimic the lower limb motion which contributes investigating different controllers for the prototype. Thus, the test bed allows assessing the primary performance of the APK before testing on a human subject. Different types of sensing systems (electromyography and lower limb inclination angles) are investigated to extract signals from the user’s healthy leg and send the captured data to the APK controller. The methodology to measure each type of signal is described, and comparison analyses are provided. Wireless communication between the sensory part and actuator is established. A knowledge-based control mechanism is developed that takes advantage of an Adaptive-Network-based Fuzzy Inference System (ANFIS) to determine knee torque as a function of the echoing angular state of the able leg considering the uncertainty of inputs. Therefore, the developed controller can make the APK serviceable for different users. The fuzzy membership function’s parameters and rules define the knowledge-base of the system. This knowledge is based on existing experience and known facts about the walking cycle.

Corrosion often occurs in the inner aluminum lining of the HB-53 helicopter external fuel tank, resulting in fuel leaks. This project centers on developing an in-situ ultrasonic inspection technique to detect corroded areas inside the fuel tank. Due to the complexity of the composite structure of the tank, the ultrasonic inspection is carried out from inside the tank using a monostatic backscattering technique. The backscattered field contains information related to the insonified surface properties (surface roughness scales). Numerical predictions are implemented with a simplified model of backscattered intensity (Ogilvy, 1991). Experimental results are obtained on artificially corroded plates, and on the actual fuel tank of the HB-53 helicopter. Signal processing techniques (Envelope Correlation and Inverse Technique) are used to detect corroded surfaces with data obtained with a focused 10 MHz pulsed transducer.

In this thesis, optimum current injection strategy for Magnetic Resonance Electrical Impedance Tomography (MREIT) is studied. Distinguishability measure based on magnetic flux density is defined for MREIT. Limit of distinguishability is analytically derived for an infinitely long cylinder with concentric and eccentric inhomogeneities. When distinguishability limits of MREIT and Electrical Impedance Tomography (EIT) are compared, it is found that MREIT is capable of detecting smaller perturbations than EIT. When conductivities of inhomogeneity and background object are equal to 0.8S and 1S respectively, MREIT provides improvement of %74 in detection capacity. Optimum current injection pattern is found based on the distinguishability definition. For 2-D cylindrical body with concentric and eccentric inhomogeneities, opposite drive provides best result. As for the 3-D case, a sphere with azimuthal symmetry is considered. Distinguishability limit expression is obtained and optimum current injection pattern is again opposite drive. Based these results, optimum current injection principles are provided and Regional Image Reconstruction (RIR) using optimum currents is proposed. It states that conductivity distribution should be reconstructed for a region rather than for the whole body. Applying current injection principles and RIR provides reasonable improvement in image quality when there is noise in the measurement data. For the square geometry, when SNR is 13dB, RIR provides decrement of nearly %50 in conductivity error rate of small inhomogeneity. Pulse sequence optimization is done for Gradient Echo (GE) and it is compared with Spin Echo (SE) in terms of their capabilities for MREIT.

La séquence d'Imagerie Echo Planaire est largement utilisée pour l'acquisition des séries temporelles d'images nécessaires aux études d'imagerie fonctionnelle cérébrale. Cette séquence permet d'acquérir une trentaine de coupes couvrant le cerveau entier, avec une résolution spatiale de 2 à 4 mm et une résolution temporelle de 1 à 2 s. Elle est donc bien adaptée à l'analyse exploratoire des aires cérébrales activées, mais ne permet pas d'étudier précisément la dynamique temporelle de l'activation. Par ailleurs, une interpolation temporelle des données est nécessaire pour tenir compte des délais inter-coupes et l'acquisition 2D est source d'artéfacts d'origine vasculaire, en particulier à bas champs magnétiques. Afin d'améliorer l'estimation de la réponse cérébrale, cette thèse a eu pour objet le développement d'une séquence d'acquisition 3D à haute résolution temporelle, à 1.5T. Pour cela, la séquence d'Imagerie Echo Volume (EVI) a été combinée avec l'utilisation de l'imagerie parallèle et l'acquisition de champs de vue réduits. L'EVI permet l'acquisition d'un volume de l'espace de Fourier après une unique impulsion d'excitation, mais requiert des trains d'échos très longs. L'imagerie parallèle et la réduction des champs de vue permettent de réduire la durée des trains d'échos et de réaliser l'acquisition d'un volume de cerveau, avec peu de distorsions géométriques et de pertes de signal, en 200 ms. Tous les paramètres d'acquisition ont été optimisés afin de maximiser le rapport signal sur bruit de l'EVI localisé parallèle et de pouvoir détecter les activations cérébrales de manière robuste. La détection des activations cérébrales a été mise en évidence avec des paradigmes de stimulation visuels et auditifs, et des fonctions de réponses hémodynamiques à haute résolution temporelle ont pu être extraites. Afin d'améliorer le rapport signal sur bruit, les inversions matricielles nécessaires à la reconstruction parallèle ont été régularisées et l'influence du niveau de régularisation sur la détection des activations a été étudiée. Finalement, quelques applications potentielles de l'EVI parallèle ont été expérimentées, telles que l'étude des non-stationnarités de la réponse BOLD.

<p>In hands-free telephony and in teleconference systems, the main aim is to provide agood free voice quality when two or more people communicate from different places.The problem often arises during the conversation is the creation of acoustic echo. Thisproblem will cause the bad quality of voice signal and thus talkers could not hearclearly the content of the conversation, even thought lost the important information.This acoustic echo is actually the noise which is created by the reflection of soundwaves by the wall of the room and the other things exist in the room. The mainobjective for engineers is the cancellation of this acoustic echo and provides an echofree environment for speakers during conversation. For this purpose, scientists designdifferent adaptive filter algorithms. Our thesis is also to study and simulate theacoustics echo cancellation by using different adaptive algorithms.</p>

One of the most important parts of engineered and biological systems is the ability to acquire and interpret information from the surrounding world accurately and in time-scales relevant to the tasks critical to system performance. This classical concept of efficient signal acquisition has been a cornerstone of signal processing research, spawning traditional sampling theorems (e.g. Shannon-Nyquist sampling), efficient filter designs (e.g. the Parks-McClellan algorithm), novel VLSI chipsets for embedded systems, and optimal tracking algorithms (e.g. Kalman filtering). Traditional techniques have made minimal assumptions on the actual signals that were being measured and interpreted, essentially only assuming a limited bandwidth. While these assumptions have provided the foundational works in signal processing, recently the ability to collect and analyze large datasets have allowed researchers to see that many important signal classes have much more regularity than having finite bandwidth. One of the major advances of modern signal processing is to greatly improve on classical signal processing results by leveraging more specific signal statistics. By assuming even very broad classes of signals, signal acquisition and recovery can be greatly improved in regimes where classical techniques are extremely pessimistic. One of the most successful signal assumptions that has gained popularity in recet hears is notion of sparsity. Under the sparsity assumption, the signal is assumed to be composed of a small number of atomic signals from a potentially large dictionary. This limit in the underlying degrees of freedom (the number of atoms used) as opposed to the ambient dimension of the signal has allowed for improved signal acquisition, in particular when the number of measurements is severely limited. While techniques for leveraging sparsity have been explored extensively in many contexts, typically works in this regime concentrate on exploring static measurement systems which result in static measurements of static signals. Many systems, however, have non-trivial dynamic components, either in the measurement system's operation or in the nature of the signal being observed. Due to the promising prior work leveraging sparsity for signal acquisition and the large number of dynamical systems and signals in many important applications, it is critical to understand whether sparsity assumptions are compatible with dynamical systems. Therefore, this work seeks to understand how dynamics and sparsity can be used jointly in various aspects of signal measurement and inference. Specifically, this work looks at three different ways that dynamical systems and sparsity assumptions can interact. In terms of measurement systems, we analyze a dynamical neural network that accumulates signal information over time. We prove a series of bounds on the length of the input signal that drives the network that can be recovered from the values at the network nodes~[1--9]. We also analyze sparse signals that are generated via a dynamical system (i.e. a series of correlated, temporally ordered, sparse signals). For this class of signals, we present a series of inference algorithms that leverage both dynamics and sparsity information, improving the potential for signal recovery in a host of applications~[10--19]. As an extension of dynamical filtering, we show how these dynamic filtering ideas can be expanded to the broader class of spatially correlated signals. Specifically, explore how sparsity and spatial correlations can improve inference of material distributions and spectral super-resolution in hyperspectral imagery~[20--25]. Finally, we analyze dynamical systems that perform optimization routines for sparsity-based inference. We analyze a networked system driven by a continuous-time differential equation and show that such a system is capable of recovering a large variety of different sparse signal classes~[26--30].