BATCH SEQUENCING

Consider the single machine scheduling problem where there are a number of part types to be processed. A part type is defined as follows: Two parts are of the same part type if the machine does not require a setup in between the processing of these parts. The problem investigated in this paper is to find a sequence of batches of parts (if there are any) where all the requirements for parts are met. A heuristic and an exact algorithm are developed, and computational analysis is performed to measure the performance of the heuristic. The time complexity function of the heuristic is O(n²), and the exact algorithm runs in polynomial time given a fixed upper bound on the number of setups.     

Robust estimation approach for blind denoising.

This work develops a new robust statistical framework for blind image denoising. Robust statistics addresses the problem of estimation when the idealized assumptions about a system are occasionally violated. The contaminating noise in an image is considered as a violation of the assumption of spatial coherence of the image intensities and is treated as an outlier random variable. A denoised image is estimated by fitting a spatially coherent stationary image model to the available noisy data using a robust estimator-based regression method within an optimal-size adaptive window. The robust formulation aims at eliminating the noise outliers while preserving the edge structures in the restored image. Several examples demonstrating the effectiveness of this robust denoising technique are reported and a comparison with other standard denoising filters is presented.     

Tissue segmentation in ultrasound images by using genetic algorithms

This paper presents a genetic based incremental neural network (GINeN) for the segmentation of tissues in ultrasound images. Performances of the GINeN and the Kohonen network are investigated for tissue segmentation in ultrasound images. Feature extraction is carried out by using continuous wavelet transform. Pixel intensities at the same spatial location on 12 wavelet planes and on the original image are considered as features, leading to 13-dimensional feature vectors. The same training set is used for the training of the Kohonen network and the GINeN.

This paper proposes the use of wavelet transform and genetic based incremental neural network together in order to increase the segmentation performance. It is observed that genetic based incremental neural network gives satisfactory segmentation performance for ultrasound images.     

Medical image segmentation with transform and moment based features and incremental supervised neural network

In this study, a novel incremental supervised neural network (ISNN) is proposed for the segmentation of medical images. Performance of the ISNN is investigated for tissue segmentation in medical images obtained from various imaging modalities. Two feature extraction methods based on transform and moments are comparatively investigated to segment the tissues in medical images. Two-dimensional (2D) continuous wavelet transform (CWT) and the moments of the gray-level histogram (MGH) are computed in order to form the feature vectors of ultrasound (US) bladder and phantom images, X-ray computerized tomography (CT) and magnetic resonance (MR) head images. In the 2D-CWT method, feature vectors are formed by the intensity of one pixel of each wavelet-plane of different energy bands. The MGH represents the tissues within the sub-windows by using the spatial variation of image intensities. In this study, the ISNN and Grow and Learn (GAL) network are employed for the segmentation task. It is observed that the ISNN has significantly eliminated the disadvantages of the GAL network in the segmentation of the medical images.     

Hybrid intelligent techniques for MRI brain images classification

This paper presents a hybrid technique for the classification of the magnetic resonance images (MRI). The proposed hybrid technique consists of three stages, namely, feature extraction, dimensionality reduction, and classification. In the first stage, we have obtained the features related to MRI images using discrete wavelet transformation (DWT). In the second stage, the features of magnetic resonance images have been reduced, using principal component analysis (PCA), to the more essential features. In the classification stage, two classifiers have been developed. The first classifier based on feed forward back-propagation artificial neural network (FP-ANN) and the second classifier is based on k-nearest neighbor (k-NN). The classifiers have been used to classify subjects as normal or abnormal MRI human images. A classification with a success of 97% and 98% has been obtained by FP-ANN and k-NN, respectively. This result shows that the proposed technique is robust and effective compared with other recent work.     

Risk-sensitive control of Markov jump linear systems: Caveats and difficulties

In this technical note, we revisit the risk-sensitive optimal control problem for Markov jump linear systems (MJLSs). We first demonstrate the inherent difficulty in solving the risk-sensitive optimal control problem even if the system is linear and the cost function is quadratic. This is due to the nonlinear nature of the coupled set of Hamilton-Jacobi-Bellman (HJB) equations, stemming from the presence of the jump process. It thus follows that the standard quadratic form of the value function with a set of coupled Riccati differential equations cannot be a candidate solution to the coupled HJB equations. We subsequently show that there is no equivalence relationship between the problems of risk-sensitive control and H ^∞ control of MJLSs, which are shown to be equivalent in the absence of any jumps. Finally, we show that there does not exist a large deviation limit as well as a risk-neutral limit of the risk-sensitive optimal control problem due to the presence of a nonlinear coupling term in the HJB equations.

     

An incremental neural network for tissue segmentation in ultrasound images

This paper presents an incremental neural network (INeN) for the segmentation of tissues in ultrasound images. The performances of the INeN and the Kohonen network are investigated for ultrasound image segmentation. The elements of the feature vectors are individually formed by using discrete Fourier transform (DFT) and discrete cosine transform (DCT). The training set formed from blocks of 4x4 pixels (regions of interest, ROIs) on five different tissues designated by an expert is used for the training of the Kohonen network. The training set of the INeN is formed from randomly selected ROIs of 4x4 pixels in the image. Performances of both 2D-DFT and 2D-DCT are comparatively examined for the segmentation of ultrasound images.