다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

Neural networks

Search for Unidentified Maritime Objects (SUMO) is an algorithm for ship detection in satellite Synthetic Aperture Radar (SAR) images. It has been developed over the course of more than 15 years, using a large amount of SAR images from almost all available SAR satellites operating in L-, C- and X-band. As validated by benchmark tests, it performs very well on a wide range of SAR image modes (from Spotlight to ScanSAR) and resolutions (from 1–100 m) and for all types and sizes of ships, within the physical limits imposed by the radar imaging. This paper describes, in detail, the algorithmic approach in all of the steps of the ship detection: land masking, clutter estimation, detection thresholding, target clustering, ship attribute estimation and false alarm suppression. SUMO is a pixel-based CFAR (Constant False Alarm Rate) detector for multi-look radar images. It assumes a K distribution for the sea clutter, corrected however for deviations of the actual sea clutter from this distribution, implementing a fast and robust method for the clutter background estimation. The clustering of detected pixels into targets (ships) uses several thresholds to deal with the typically irregular distribution of the radar backscatter over a ship. In a multi-polarization image, the different channels are fused. Azimuth ambiguities, a common source of false alarms in ship detection, are removed. A reliability indicator is computed for each target. In post-processing, using the results of a series of images, additional false alarms from recurrent (fixed) targets including range ambiguities are also removed. SUMO can run in semi-automatic mode, where an operator can verify each detected target. It can also run in fully automatic mode, where batches of over 10,000 images have successfully been processed in less than two hours. The number of satellite SAR systems keeps increasing, as does their application to maritime surveillance. The open data policy of the EU’s Copernicus program, which includes the Sentinel-1 satellite, has hugely increased the availability of SAR images. This paper aims to cater to the consequently expected wider demand for knowledge about SAR ship detectors.

The SUMO Ship Detector Algorithm for Satellite Radar Images

In this paper, we present a novel feature-based neural network (NN) approach for estimation of blood pressure (BP) from wrist oscillometric measurements. Unlike previous methods that use the raw oscillometric waveform envelope (OMWE) as input to the NN, in this paper, we propose to use features extracted from the envelope. The OMWE is mathematically modeled as a sum of two Gaussian functions. The optimum parameters of this model are found by minimizing the least squares error between the model and the OMWE using the Levenberg-Marquardt algorithm and are used as features. Two separate feed-forward NNs (FFNNs) are then designed to estimate the systolic and diastolic BPs using these features. The FFNNs are trained using the resilient backpropagation learning algorithm and tested on a data set of BP measurements recorded from 85 subjects. The performance is then compared with that of the conventional maximum amplitude algorithm, adaptive neuro-fuzzy inference system, and already published NN-based methods. It is found that the proposed approach achieves lower values of mean absolute error and standard deviation of error in the estimation of BP. In addition, the proposed approach has the following advantages: lower complexity with respect to the design parameters, smaller training data set, and lower computational load.

Feature-Based Neural Network Approach for Oscillometric Blood Pressure Estimation

The detection of Gaussian signals with an unknown correlation coefficient rhos is considered. Solutions based on neural networks (NNs) are studied, and a strategy for designing committee machines in a composite hypothesis test is proposed. A single multilayer perceptron (MLP) has been trained with rhos uniformly varying in [0, 1]. Considering the decision boundaries for rhos = 0 and rhos = 1 and how an MLP approximates them, a detection scheme composed of two MLPs has been proposed. One of them MLP1 has been trained with rhos uniformly varying in [0, 0.5], and the other one MLP2 has been trained with rhos uniformly varying in [0.5, 1]. For making a decision, the higher output is compared to a threshold for each false-alarm probability (P FA). This strategy simplifies the task of finding a compromise solution between the computational cost and the approximation error and outperforms the single-MLP detector. When MLP1 is substituted with a radial basis function NN (RBFNN), a new combination strategy of the outputs is required. We propose separately thresholding the outputs and applying them to an or logic function. The performance of this detector is slightly better than the two-MLP one, and the computational cost is significantly reduced.

Combining MLPs and RBFNNs to Detect Signals With Unknown Parameters

Space-borne commercial Opportunity Illuminators present interesting characteristics (global coverage and availability). An analysis of these illuminators' imaging capabilities has been carried out. Resolution requirements for classification purposes has been considered and the impact of the desired target size and dynamics studied. Small-medium ships, light aircrafts and UAVs have been considered in a maritime surveillance scenario. Geostationary communication satellites and Global Positioning Systems have been analysed, whose central frequency, signal bandwidth and orbits are quite different. Results show the feasibility of both illuminators for the considered application.

Passive radar imaging capabilities using space-borne commercial illuminators in surveillance applications

This paper deals with the reduction of the ambiguity peaks that appear in the Cross Ambiguity Function used in a passive radar system, which uses DVB-T signals as Illuminator of Opportunity. Three methods are proposed, consisting in replacing the elements that are responsible of the ambiguity peaks, by other elements that do not give rise to periodic components. Ideal 64-QAM symbols, zero power components, and random segments with mean power equal to the acquired signal power, are used for this purpose. The obtained results show that a significant reduction of the ambiguity peaks is obtained, but with an undesired reduction in detection capabilities, due to the decrement in the main peak to pedestal level ratio.

DVB-T ambiguity peaks reduction in passive radar applications based on signal reconstruction

Approximating the Neyman-Pearson detector with 2C-SVMs. Application to radar detection

A novel method for detecting ships in marine environments is presented in this paper. For this purpose, the information contained in the marine images obtained by a measuring and monitoring marine system is used. The ship detection is done by multilayer perceptrons (MLPs). In the first approach, the MLP processes the information extracted from the images using horizontal or vertical integration modes. However, if a suitable combination of these integration modes is done, better detection performances are achieved. Therefore, the use of an improved integration mode is proposed, which is based on a square shape. These modes are also used in a commonly used detector, the cell averaging constant false alarm rate (CA-CFAR) detector, which is taken as reference in our experiments. The comparison of the performances of both detectors shows how the MLP-based detector outperforms the CA-CFAR detector in all the cases under study. This comparison is based on objective (probabilities of false alarm and detection) and subjective estimations of their performances. The MLP-based detector also presents another advantage, particularly when the square integration mode is considered: high-performance robustness against changes in the marine environmental conditions.

M. P. Jarabo-Amores

Papers

Combining MLPs and RBFNNs to Detect Signals With Unknown Parameters

Passive radar imaging capabilities using space-borne commercial illuminators in surveillance applications

DVB-T ambiguity peaks reduction in passive radar applications based on signal reconstruction

Approximating the Neyman-Pearson detector with 2C-SVMs. Application to radar detection

Detection of Ships in Marine Environments by Square Integration Mode and Multilayer Perceptrons