Adaptive beamforming methods for ultrasound imaging have been studied to improve image resolution and contrast. The most common approach is the minimum variance (MV) beamformer which minimizes the power of the beamformed output while maintaining the response from the direction of interest constant. The method achieves higher resolution and better contrast than the delay-and-sum (DAS) beamformer, but it suffers from high computational cost. This cost is mainly due to the computation of the spatial covariance matrix and its inverse, which requires O(L(3)) computations, where L denotes the subarray size. In this study, we propose a computationally efficient MV beamformer based on QR decomposition. The idea behind our approach is to transform the spatial covariance matrix to be a scalar matrix σI and we subsequently obtain the apodization weights and the beamformed output without computing the matrix inverse. To do that, QR decomposition algorithm is used and also can be executed at low cost, and therefore, the computational complexity is reduced to O(L(2)). In addition, our approach is mathematically equivalent to the conventional MV beamformer, thereby showing the equivalent performances. The simulation and experimental results support the validity of our approach.
Flyback converters (FBCs) have been widely used in portable fuel-cell power systems since they feature step up/ down ability, galvanic isolation between the input and output, and low cost. As the input/output voltage of the FBC varies, it may operate either in discontinuous-conduction mode (DCM) or in continuous-conduction mode (CCM). Since different modes of the FBC result in different dynamics, it is difficult to stabilise the converter operating in both modes. To solve the problem, the authors propose a controller that can control a dual-mode FBC whether it operates in DCM or in CCM. The proposed controller is composed of a dual-mode feed-forward control unit and a feedback control unit based on the common Lyapunov function. The dual-mode feed-forward control signal is utilised to reduce the burden on the common Lyapunov function-based feedback controller that must function well despite variable system dynamics. The closed-loop system guarantees global exponential stability and provides a fast transient response as the operating mode of the FBC switches between DCM and CCM. When constructing the proposed controller, they use the large-signal nonlinear averaged model of the dual-mode FBC and consider the parasitic components. Experimental tests were conducted to validate the proposed control scheme.
Recently, a new noninvasive ultrasonic technique called the coronary Doppler vibrometry (CDV) was developed to detect the audio-frequency vibrations in the vessel wall and surrounding tissues generated by the turbulence flow associated with the stenosed artery. Inspiring clinical data for diagnosing coronary artery stenosis (CAS) were obtained from CDV with high sensitivity and specificity. However, there still exists a significant limitation, one of which is the long examination time. In estimating myocardial vibrations, we must extract the myocardial tissue Doppler data from the multiple segments that are adjacent to the coronary artery in each echocardiography view and hence we need to acquire multiple data that are as many as the number of segments. To deal with this problem, we consider the use of unfocused transmission which is called the flash imaging. By using the flash imaging technique, we can interrogate all segments associated the coronary artery by using only one unfocused beam in each echocardiography view, and we need less data acquisitions. As a result, the data acquisition time is reduced significantly and so is the examination time. Vibrations were characterized by the vibration index (VI) computed from the tissue velocity spectrum for diagnosing coronary artery stenosis. The feasibility of the proposed approach was confirmed through a series of the clinical testing.
Most of available results in adaptive learning controllers (ALCs) with input learning technique have considered the single-input single-output nonlinear systems. This paper presents an ALC for MIMO uncertain feedback linearizable systems whose uncertainty is in their linear parameters. Since only an output signal is available for measurement, a high gain observer is used to estimate the unmeasurable state. The estimated state is then utilized to implement the ALC. The proposed ALC learns the input gain parameters of the state equation as well as the internal parameters. In addition, the desired input is also learned using an input learning rule to track the whole command history. In the proposed ALC, the tracking errors are bounded and the mean-square tracking error is O( ) as the task is repeated. Single-link and two-link manipulators are presented as simulation examples to confirm the feasibility and the performance of the proposed ALC.
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