Multiple ping sonar accuracy improvement using robust motion estimation and ping fusion

Noise degrades the accuracy of sonar systems. We demonstrate a practical method for increasing the effective signal-to-noise ratio (SNR) by fusing time delay information from a burst of multiple sonar pings. This approach can be useful when there is no relative motion between the sonar and the target during the burst of sonar pinging. Otherwise, the relative motion degrades the fusion and therefore, has to be addressed before fusion can be used. In this paper, we present a robust motion estimation algorithm which uses information from multiple receivers to estimate the relative motion between pings in the burst. We then compensate for motion, and show that the fusion of information from the burst of motion compensated pings improves both the resilience to noise and sonar accuracy, consequently increasing the operating range of the sonar system.     

Transition of MHD kink-stability properties between line-tied and non-line-tied boundary conditions

Magnetic flux tubes or flux ropes in plasmas are important in nature and the laboratory. Axial boundary conditions strongly affect flux rope behavior, but this has never been systematically investigated. We experimentally demonstrate for the first time axial boundary conditions that are continuously varied between ideal magnetohydrodynamic (MHD) line-tied (fixed) and non-line-tied (free). In contrast with the usual interpretation that mechanical plasma motion is MHD line-tied to a conducting boundary, we constrain boundary plasma motion to cause the line-tied condition.     

Variational multiple-tensor fitting of fiber-ambiguous diffusion-weighted magnetic resonance imaging voxels

Partial volume effects are often experienced in diffusion-weighted MRI of biologic tissue. This is when the signal attenuation reflects a mixture of diffusion processes, originating from different tissue compartments, residing in the same voxel. Decomposing the mixture requires elaborated models that account for multiple compartments, yet the fitting problem for those models is usually ill posed. We suggest a novel approach for stabilizing the fitting problem of the multiple-tensors model by a variational framework that adds biologically oriented assumption of neighborhood alignments. The framework is designed to address fiber ambiguity caused by a number of neuronal fiber compartments residing in the same voxel. The method requires diffusion data acquired by common, clinically feasible MRI sequences, and is able to derive familiar tensor quantities for each compartment. Neighborhood alignment is performed by adding piece-wise smooth regularization constraints to an energy function. Minimization with the gradient descent method produces a set of diffusion-reaction partial differential equations that describe a tensor-preserving flow towards a best approximation of the data while maintaining the constraints. We analyze fiber compartment separation capabilities on a synthetic model of crossing fibers and on brain areas known to have crossing fibers. We compare the results with diffusion tensor imaging analysis and discuss applications for the framework.     

Evaluation of an auditory model for echo delay accuracy in wideband biosonar

In a psychophysical task with echoes that jitter in delay, big brown bats can detect changes as small as 10-20 ns at an echo signal-to-noise ratio of approximately 49 dB and 40 ns at approximately 36 dB. This performance is possible to achieve with ideal coherent processing of the wideband echoes, but it is widely assumed that the bat's peripheral auditory system is incapable of encoding signal waveforms to represent delay with the requisite precision or phase at ultrasonic frequencies. This assumption was examined by modeling inner-ear transduction with a bank of parallel bandpass filters followed by low-pass smoothing. Several versions of the filterbank model were tested to learn how the smoothing filters, which are the most critical parameter for controlling the coherence of the representation, affect replication of the bat's performance. When tested at a signal-to-noise ratio of 36 dB, the model achieved a delay acuity of 83 ns using a second-order smoothing filter with a cutoff frequency of 8 kHz. The same model achieved a delay acuity of 17 ns when tested with a signal-to-noise ratio of 50 dB. Jitter detection thresholds were an order of magnitude worse than the bat for fifth-order smoothing or for lower cutoff frequencies. Most surprising is that effectively coherent reception is possible with filter cutoff frequencies well below any of the ultrasonic frequencies contained in the bat's sonar sounds. The results suggest that only a modest rise in the frequency response of smoothing in the bat's inner ear can confer full phase sensitivity on subsequent processing and account for the bat's fine acuity or delay.     

On the combination of supervised and unsupervised learning

The bias/variance dilemma is addressed in the context of neural networks. A bias constraint based on prior knowledge about the underlying distribution of the data is discussed as a means for reducing the overall error measure of a classifier.     

Langmuir probe characteristics in RF glow discharges

The current-voltage characteristics of Langmuir probes in rf glow discharges can be misinterpreted by the effects of rf time averaging, ionization near a probe, and expansion of the probe sheath. This paper presents a new Langmuir probe technique which can be used to determine the plasma parameters in rf glow discharges. Simple expressions for the time averaged I-V characteristics are derived for the cases of fully sinusoidal and partially rectified plasma potential waveforms. Examples of corresponding I-V characteristics obtained from argon rf glow discharges are also illustrated.     

Semi-coherent time of arrival estimation using regression

Time of arrival (ToA) estimation is essential for many types of remote sensing applications including radar, sonar, and underground exploration. The standard method for ToA estimation employs a matched filter for computing the maximum likelihood estimator (MLE) for ToA. The accuracy of the MLE decreases rapidly whenever the amount of noise in a received signal rises above a certain threshold. This well-known threshold effect is unavoidable in several important applications due to various limitations on the power and the spectrum of a narrowband source pulse. A measurement performed in the presence of the threshold effect employs a receiver which operates in the semi-coherent state. Therefore, the conventional methods assuming a coherent state receiver should be adapted to the semi-coherent case. In this paper, a biosonar-inspired method for the semi-coherent ToA estimation is described. The method abandons the exploration of an echo signal by a single matched filter in favor of the analysis by multiple phase-shifted unmatched filters. Each phase-shifted unmatched filter gives rise to a biased ToA estimator. The described method uses regression for combining these estimators into a single unbiased ToA estimator that outperform the MLE in the presence of the threshold effect.