Energy parameters of a solar car for Jordan

All parameters related to car energy were investigated in this work, and the power basics of a car design powered by solar energy are laid down. Year-round weather conditions, car weight, road roughness and road inclination were taken as variable parameters used to calculate the car speed and working hours (year round). Fixed parameters taken for the design were: a photovoltaic generator area of 6 m², car length of 5 m, car width of 1.8 m and maximum power output of 480 W/person with a maximum of two persons. These parameters depended on the regulations of “Tour de Sol '91”. Calculations depended on Amman weather conditions. Sixteen ASI 16-200 ARCO SOLAR INC. model photovoltaic modules were used, with three DELCO 1150 lead acid batteries connected to a permanent magnet DC motor of 0.8 hp. This car can run at different speeds 3–6 hours/day on 200 days/year.     

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.     

Modification of polyolefins with silicone copolymers. II. Thermal,mechanical, and tribological behavior of PP and HDPE blended with silicone copolymers

Thermoplastic polyolefin (TPO) films with permanent, silicone‐rich, low‐friction, low‐abrasion surfaces were obtained by melt blending of high‐density polyethylene (HDPE) and polypropylene (PP) with polydimethylsiloxane (PDMS)‐containing block copolymers. Two different block copolymers, a siloxane–urea segmented copolymer and a polycaprolactone‐b‐PDMS triblock copolymer were used as modifiers at levels between 0.1 and 5.0% by weight. Blends were prepared in a twin‐screw extruder. Modified films displayed surfaces with very low friction coefficients and high abrasion resistance, which depended on the type and the level of additive incorporated into the system. Bulk properties of these modified systems, such as crystallization and melting behavior or tensile properties, were not affected. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 84: 535–540, 2002; DOI 10.1002/app.10279     

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.