Sub-sample displacement estimation from digitized ultrasound RF signals using multi-dimensional polynomial fitting of the cross-correlation function

A widely used time-domain technique for motion or delay estimation between digitized ultrasound RF signals involves the maximization of a discrete pattern-matching function, usually the cross-correlation. To achieve sub-sample accuracy, the discrete pattern-matching function is interpolated using the values at the discrete maximizer and adjacent samples. In prior work, only 1-D fit, applied separately along the axial, lateral, and elevational axes, has been used to estimate the sub-sample motion in 1-D, 2-D, and 3-D. In this paper, we explore the use of 2-D and 3-D polynomial fitting for this purpose. We quantify the estimation error in noise-free simulations using Field II and experiments with a commercial ultrasound machine. In simulated 2-D translational motions, function fitting with quartic spline polynomials leads to maximum bias of 0.2% of the sample spacing in the axial direction and 0.4% of the sample spacing in the lateral direction, corresponding to 38 nm and 1.31 μm, respectively. The maximum standard deviations were approximately 1% of the sample spacing in both the axial and the lateral directions, corresponding to 193 nm axially and 4.43 μm laterally. In simulated 1% axial strain, the same function fitting leads to mean absolute displacement estimation errors of 255 nm in the axial direction and 4.77 μm in the lateral direction. In experiments with a linear array transducer, 2-D quartic spline fitting leads to maximum bias of 458 nm and 6.27 μm in the axial and the lateral directions, respectively. These results are more than one order of magnitude smaller than those obtained with separate 1-D fit when applied to the same data set. Simulations and experiments in 3-D yield similar results when comparing 3-D polynomial fitting with 1-D fitting along the axial, lateral, and elevational directions.     

SVD-based digital image watermarking using complex wavelet transform

A new robust method of non-blind image watermarking is proposed in this paper. The suggested method is performed by modification on singular value decomposition (SVD) of images in Complex Wavelet Transform (CWT) domain while CWT provides higher capacity than the real wavelet domain. Modification of the appropriate sub-bands leads to a watermarking scheme which favourably preserves the quality. The additional advantage of the proposed technique is its robustness against the most of common attacks. Analysis and experimental results show much improved performance of the proposed method in comparison with the pure SVD-based as well as hybrid methods (e.g. DWT-SVD as the recent best SVD-based scheme).     

Nocturnal blood pressure elevation in patients with type 1 diabetes receiving intensive insulin therapy compared with that in patients receiving conventional insulin therapy

Studies have shown that type 1 diabetic patients may suffer from nocturnal elevation in blood pressure and that this elevation may be related to hyperinsulinemia. In this study we tested the hypothesis that tight type 1 diabetes control, which is usually accompanied by hyperinsulinemia and subclinical nocturnal hypoglycemia, may result in a higher rise in nocturnal blood pressure compared with conventional type 1 diabetes control. Eighteen patients treated with intensive insulin therapy (multiple daily injections; IIT) were compared with 18 patients treated with conventional insulin regimens (twice daily injections of regular and intermediate acting insulin; CIT). Both groups were matched for age, sex, duration of diabetes, body weight, body mass index, baseline daytime blood pressure, and microalbuminuria levels. Hemoglobin A1c was lower in the IIT group compared with that in the CIT group (8.1 +/- 1.2% vs. 11.0 +/- 3.2%; P < 0.01). The amount of insulin/body weight (units per kg) was higher in the IIT group than that in the CIT group (1.0 +/- 0.2 vs. 0.7 +/- 0.2 U/kg; P < 0.05). In all patients, a 24-h ambulatory blood pressure was recorded. The nocturnal diastolic blood pressure was higher in the IIT group (66 +/- 9 mm Hg) than in the CIT group (55 +/- 4 mm Hg; P < 0.01). The nocturnal decline in both systolic and diastolic blood pressure was lower in the IIT group (7 +/- 5 and 6 +/- 4 mm Hg, respectively) compared with that in the CIT group (13 +/- 6 and 16 +/- 6 mm Hg, respectively; P < 0.01). The nocturnal heart rate was higher in IIT group than in the CIT group (81 +/- 12 vs. 67 +/- 9/min; P < 0.05). These findings show that the intensive insulin therapy regimen may have a more deleterious effect than the conventional insulin therapy regimen on the nocturnal blood pressure of patients with type 1 diabetes.     

Immunoconfocal localization of gelatinase B expressed by migrating intrastromal epithelial cells after deep annular excimer keratectomy

PURPOSE: Intrastromal epithelial migration occurs following deep excimer annular keratectomy. Our purpose is to determine the localization of gelatinase B expression and its relationship to migrating intrastromal epithelial cells following deep annular excimer keratectomy. METHODS: Rabbit corneas were treated with deep annular ablation and harvested 3, 5, 7 and 14 days following surgery. Histological examination was performed. Confocal microscopy was used to determine the time and the pattern of gelatinase B expression. This was compared to AE-5, vimentin, and alpha-smooth muscle actin immunolocalization. RESULTS: Histologic examination revealed islands of epithelial cells within the corneal stroma after deep annular excimer ablation, whereas eyes treated with superficial annular keratectomy did not show intrastromal epithelial migration. Immunoconfocal microscopy using monoclonal anti-matrix metalloproteinase-9 (MMP-9) antibody demonstrated the presence of gelatinase B at the outer borders of the intrastromal epithelial islands following deep annular excimer keratectomy. CONCLUSION: Gelatinase B may be involved in the process of intrastromal epithelial migration following deep annular excimer keratectomy.     

On-Line and Off-Line Approximation Algorithms for Vector Covering Problems

This paper deals with vector covering problems in d -dimensional space. The input to a vector covering problem consists of a set X of d -dimensional vectors in [0,1] ^d . The goal is to partition X into a maximum number of parts, subject to the constraint that in every part the sum of all vectors is at least one in every coordinate. This problem is known to be NP-complete, and we are mainly interested in its on-line and off-line approximability. For the on-line version, we construct approximation algorithms with worst case guarantee arbitrarily close to 1/(2d) in d≥ 2 dimensions. This result contradicts a statement of Csirik and Frenk in [5] where it is claimed that, for d≥ 2 , no on-line algorithm can have a worst case ratio better than zero. Moreover, we prove that, for d≥ 2 , no on-line algorithm can have a worst case ratio better than 2/(2d+1) . For the off-line version, we derive polynomial time approximation algorithms with worst case guarantee Θ(1/ log d) . For d=2 , we present a very fast and very simple off-line approximation algorithm that has worst case ratio 1/2 . Moreover, we show that a method from the area of compact vector summation can be used to construct off-line approximation algorithms with worst case ratio 1/d for every d≥ 2 . Received November 1996; revised March 1997.     

Experimental Study of Phased Array Beam Steering Characteristics

The influence of several geometric parameters of linear phased arrays was studied. A systematic approach using an automated testing assembly was used to assess the steering performance of the array in a solid medium. In addition to calibrating the transducer with respect to its steering accuracy, this arrangement provided a detailed study of the effects of steering angle, number of elements, inter-element spacing and array aperture on the beam directivity. The experimental results show good agreement quantitatively with the predicted steering characteristics.     

Minimax PAC bounds on the sample complexity of reinforcement learning with a generative model

We consider the problems of learning the optimal action-value function and the optimal policy in discounted-reward Markov decision processes (MDPs). We prove new PAC bounds on the sample-complexity of two well-known model-based reinforcement learning (RL) algorithms in the presence of a generative model of the MDP: value iteration and policy iteration. The first result indicates that for an MDP with N state-action pairs and the discount factor γ∈[0,1) only O(Nlog(N/δ)/((1?γ)³ ε ²)) state-transition samples are required to find an ε-optimal estimation of the action-value function with the probability (w.p.) 1?δ. Further, we prove that, for small values of ε, an order of O(Nlog(N/δ)/((1?γ)³ ε ²)) samples is required to find an ε-optimal policy w.p. 1?δ. We also prove a matching lower bound of Θ(Nlog(N/δ)/((1?γ)³ ε ²)) on the sample complexity of estimating the optimal action-value function with ε accuracy. To the best of our knowledge, this is the first minimax result on the sample complexity of RL: the upper bounds match the lower bound in terms of N, ε, δ and 1/(1?γ) up to a constant factor. Also, both our lower bound and upper bound improve on the state-of-the-art in terms of their dependence on 1/(1?γ).     

Frequency-locked pulse sequencer for high-frame-rate monochromatic tissue motion imaging

To overcome the inherent low frame rate of conventional ultrasound, we have previously presented a system that can be implemented on conventional ultrasound scanners for high-frame-rate imaging of monochromatic tissue motion. The system employs a sector subdivision technique in the sequencer to increase the acquisition rate. To eliminate the delays introduced during data acquisition, a motion phase correction algorithm has also been introduced to create in-phase displacement images. Previous experimental results from tissue- mimicking phantoms showed that the system can achieve effective frame rates of up to a few kilohertz on conventional ultrasound systems. In this short communication, we present a new pulse sequencing strategy that facilitates high-frame-rate imaging of monochromatic motion such that the acquired echo signals are inherently in-phase. The sequencer uses the knowledge of the excitation frequency to synchronize the acquisition of the entire imaging plane to that of an external exciter. This sequencing approach eliminates any need for synchronization or phase correction and has applications in tissue elastography, which we demonstrate with tissue-mimicking phantoms.     

Feature selection using swarm-based relative reduct technique for fetal heart rate

Fetal heart rate helps in diagnosing the well-being and also the distress of fetal. Cardiotocograph (CTG) monitors the fetal heart activity to estimate the fetal tachogram based on the evaluation of ultrasound pulses reflected from the fetal heart. It consists in a simultaneous recording and analysis of fetal heart rate signal, uterine contraction activity and fetal movements. Generally CTG comprises more number of features. Feature selection also called as attribute selection is a process of selecting a subset of highly relevant features which is responsible for future analysis. In general, medical datasets require more number of features to predict an activity. This paper aims at identifying the relevant and ignores the redundant features, consequently reducing the number of features to assess the fetal heart rate. The features are selected by using unsupervised particle swarm optimization (PSO)-based relative reduct (US-PSO-RR) and compared with unsupervised relative reduct and principal component analysis. The proposed method is then tested by applying various classification algorithms such as single decision tree, multilayer perceptron neural network, probabilistic neural network and random forest for maximum number of classes and clustering accuracies like root mean square error, mean absolute error, Davies–Bouldin index and Xie–Beni index for minimum number of classes. Empirical results show that the US-PSO-RR feature selection technique outperforms the existing methods by producing sensitivity of 72.72 %, specificity of 97.66 %, F-measure of 74.19 % which is remarkable, and clustering results demonstrate error rate produced by US-PSO-RR is less as well.