RESEARCH TOWARDS THE DESIGN OF A NOVEL SMART FLUID DAMPER USING A MCKIBBEN ACTUATOR

Vibration reducing performance of many mechanical systems, decreasing the quality of manufactured products, producing noise, generating fatigue in mechanical components, and producing an uncomfortable environment for human bodies. Vibration control is categorized as: active, passive, or semi-active, based on the power consumption of the control system and feedback or feed forward based on whether sensing is used to control vibration. Semi-active vibration control is the most attractive method; one method of semi-active vibration control could be designed by using smart fluid. Smart fluids are able to modify their effective viscosity in response to an external stimulus such as a magnetic field. This unique characteristic can be utilised to build semi-active dampers for a wide variety of vibration control systems. Previous work has studied the application of smart fluids in semi-active dampers, where the kinetic energy of a vibrating structure can be dissipated in a controllable fashion. A McKibben actuator is a device that consists of a rubber tube surrounded by braided fibre material. It has different advantages over a piston/cylinder actuator such as: a high power to weight ratio, low weight and less cost. Recently McKibben actuator has appeared in some semi-active vibration control devise. This report investigates the possibility of designing a Magnetorheological MR damper that seeks to reduce the friction in the device by integrating it with a McKibben actuator. In this thesis the concept of both smart fluid and McKibben actuator have been reviewed in depth, and methods of modelling and previous applications of devices made using these materials are also presented. The experimental part of the research includes: designing and modelling a McKibben actuator (using water) under static loads, and validating the model experimentally. The research ends by presenting conclusions and future work

Acoustic, psychophysical, and neuroimaging measurements of the effectiveness of active cancellation during auditory functional magnetic resonance imaging

Functional magnetic resonance imaging (fMRI) is one of the principal neuroimaging techniques for studying human audition, but it generates an intense background sound which hinders listening performance and confounds measures of the auditory response. This paper reports the perceptual effects of an active noise control (ANC) system that operates in the electromagnetically hostile and physically compact neuroimaging environment to provide significant noise reduction, without interfering with image quality. Cancellation was first evaluated at 600 Hz, corresponding to the dominant peak in the power spectrum of the background sound and at which cancellation is maximally effective. Microphone measurements at the ear demonstrated 35 dB of acoustic attenuation [from 93 to 58 dB sound pressure level (SPL)], while masked detection thresholds improved by 20 dB (from 74 to 54 dB SPL). Considerable perceptual benefits were also obtained across other frequencies, including those corresponding to dips in the spectrum of the background sound. Cancellation also improved the statistical detection of sound-related cortical activation, especially for sounds presented at low intensities. These results confirm that ANC offers substantial benefits for fMRI research

Body MRI artifacts in clinical practice: a physicist\u27s and radiologist\u27s perspective.

The high information content of MRI exams brings with it unintended effects, which we call artifacts. The purpose of this review is to promote understanding of these artifacts, so they can be prevented or properly interpreted to optimize diagnostic effectiveness. We begin by addressing static magnetic field uniformity, which is essential for many techniques, such as fat saturation. Eddy currents, resulting from imperfect gradient pulses, are especially problematic for new techniques that depend on high performance gradient switching. Nonuniformity of the transmit radiofrequency system constitutes another source of artifacts, which are increasingly important as magnetic field strength increases. Defects in the receive portion of the radiofrequency system have become a more complex source of problems as the number of radiofrequency coils, and the sophistication of the analysis of their received signals, has increased. Unwanted signals and noise spikes have many causes, often manifesting as zipper or banding artifacts. These image alterations become particularly severe and complex when they are combined with aliasing effects. Aliasing is one of several phenomena addressed in our final section, on artifacts that derive from encoding the MR signals to produce images, also including those related to parallel imaging, chemical shift, motion, and image subtraction

Speech perception in MRI scanner noise by persons with aphasia

Purpose. To examine reductions in performance on auditory tasks by aphasic and neurologically-intact individuals as a result of concomitant MRI scanner noise. Methods. Four tasks together forming a continuum of linguistic complexity were developed. They included complex-tone pitch discrimination, same/different discrimination of minimal pair syllables, lexical decision, and sentence plausibility. Each task was performed by persons with aphasia (PWA) and by controls. The stimuli were presented in silence and also in the noise recorded from within the bore of a 3T MRI scanner at three signal-to-noise ratios (S/Ns). Results. Across the four tasks, the PWA scored lower than the controls and performance fell as a function of decreased S/N. However, the rate at which performance fell was not different across the two listener groups in any task. Conclusions. Depending upon the relative levels of the signals and noise, the intense noise accompanying MRI scanning has the potential to severely disrupt performance. However, PWA are no more susceptible to the disruptive influence of this noise than are unimpaired individuals usually employed as controls. Thus, fMRI data from aphasic and control individuals may be interpreted without complications associated with large interactions between scanner noise and performance reduction

The free energy cost of reducing noise while maintaining a high sensitivity

Living systems need to be highly responsive, and also to keep fluctuations low. These goals are incompatible in equilibrium systems due to the Fluctuation Dissipation Theorem (FDT). Here, we show that biological sensory systems, driven far from equilibrium by free energy consumption, can reduce their intrinsic fluctuations while maintaining high responsiveness. By developing a continuum theory of the E. coli chemotaxis pathway, we demonstrate that adaptation can be understood as a non-equilibrium phase transition controlled by free energy dissipation, and it is characterized by a breaking of the FDT. We show that the maximum response at short time is enhanced by free energy dissipation. At the same time, the low frequency fluctuations and the adaptation error decrease with the free energy dissipation algebraically and exponentially, respectively

Magnetoresistive biosensors with on-chip pulsed excitation and magnetic correlated double sampling.

Giant magnetoresistive (GMR) sensors have been shown to be among the most sensitive biosensors reported. While high-density and scalable sensor arrays are desirable for achieving multiplex detection, scalability remains challenging because of long data acquisition time using conventional readout methods. In this paper, we present a scalable magnetoresistive biosensor array with an on-chip magnetic field generator and a high-speed data acquisition method. The on-chip field generators enable magnetic correlated double sampling (MCDS) and global chopper stabilization to suppress 1/f noise and offset. A measurement with the proposed system takes only 20 ms, approximately 50× faster than conventional frequency domain analysis. A corresponding time domain temperature correction technique is also presented and shown to be able to remove temperature dependence from the measured signal without extra measurements or reference sensors. Measurements demonstrate detection of magnetic nanoparticles (MNPs) at a signal level as low as 6.92 ppm. The small form factor enables the proposed platform to be portable as well as having high sensitivity and rapid readout, desirable features for next generation diagnostic systems, especially in point-of-care (POC) settings

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Active Noise Control using Variable step-size Griffiths’ LMS (VGLMS) algorithm on Real-Time platform

This paper proposes implementation of Griffith’s Variable step-size algorithm for Active Noise Control (ANC) on ADSP-TS201 EZ-Kit Lite. The dual computational units and execution of up to four instructions per cycle which are special features over other processors are best utilized to generate an optimized code. The VGLMS provides improved secondary path estimation and computations involved are marginal as the same gradient is used for step-size computation and coefficient adaptation. The improved secondary path estimate, in turn improves the ANC performance. Further, variable step-size algorithm is used for the main-path to achieve faster convergence. Both for narrowband (fundamental and its harmonics) and broadband noise fields, for a duct the attenuation achieved is 25 dB and 15 dB respectively. The program execution time was only 1.25% for an input sampling rate of 1 KHz which indicates the utility of the special features of the processor considered. Further these features have enabled in bringing down the program memory requirement in the implementation of the algorithm