Accessing Video Contents through Key Objects over IP

In order to support content-based video database access over the Internet Protocol (IP), achieving the following objectives are important: (i) video query by a representative object (key object) or some statistical characterization of the target contents, (ii) bandwidth-efficient browsing over IP, and (iii) scalable and user-centric video transmission over a heterogeneous and variable-bandwidth network. We present a video object extraction and scalable coding system designed to meet the above objectives. In our system, key objects of meaning to video database users are generated via a human-computer-interaction procedure, and are tracked across frames. Given a key object, an algorithm classifies a subset of its VOPs as key VOPs. This subset forms the basis of a highly bandwidth-efficient base layer for supporting activities such as browsing and refining queries. Over the base layer, a number of enhancement layers can be defined to progressively increase the spatial and temporal resolutions of retrieved video. It is expected that heterogeneous users can subscribe to different numbers of the enhancement layers according to their own conditions, such as access authorization, available connection bandwidth, and quality preference.     

Is medical research informing professional practice more highly cited? Evidence from AHFS DI Essentials in drugs.com

Scientometrics - Citation-based indicators are often used to help evaluate the impact of published medical studies, even though the research has the ultimate goal of improving human wellbeing. One...     

Exploring a Local Linear Model Tree Approach to Car‐Following

Because car‐following (CF) models are fundamental to replicating traffic flow they have received considerable attention over the last 50 years. They are in a continuous state of improvement due to their significant role in traffic microsimulations, intelligent transportation systems, and safety engineering models. This article uses the local linear model tree (LOLIMOT) approach to model driver's CF behavior to incorporate human perceptual imperfections into a CF model. This model defines some localities in the input space. These localities are fuzzy and have overlaps with each other. Specific models for each of the localities are then defined and combined in a fuzzy manner to predict the final output. The model was developed using real world dynamic data sets. Three different data sets were used for training, testing, and validating the model. The performance of the model was compared to a number of existing CF models. The results showed very close agreement between the real data and the LOLIMOT outputs.     

Robust image watermarking scheme using bit-plane of hadamard coefficients

Multimedia Tools and Applications - Nowadays, due to widespread usage of the Internet, digital contents are distributed quickly and inexpensively throughout the world. Watermarking techniques can...     

A Fixed-Distribution PAC Learning Theory for Neural FIR Models

The PAC learning theory creates a framework to assess the learning properties of static models. This theory has been extended to include learning of modeling tasks with m-dependent data given that the data are distributed according to a uniform distribution. The extended theory can be applied for learning of nonlinear FIR models with the restriction that the data are unformly distributed. In this paper, The PAC learning scheme is extended to deal with any FIR model regardless of the distribution of the data. This fixed-distribution m-dependent extension of the PAC learning theory is then applied to the learning of FIR three-layer feedforward sigmoid neural networks.     

Do Mendeley reader counts reflect the scholarly impact of conference papers? An investigation of computer science and engineering

Scientometrics - Counts of Mendeley readers may give useful evidence about the impact of published research. Although previous studies have found significant positive correlations between counts of...     

Magnetohydrodynamic (MHD) flows of viscoelastic fluids in converging/diverging channels

The present work is a theoretical investigation of the applicability of magnetic fields for controlling hydrodynamic separation in Jeffrey-Hamel flows of viscoelastic fluids. To achieve this goal, a local similarity solution was found for laminar, two-dimensional flow of a viscoelastic fluid obeying second-order/second-grade model as its constitutive equation with the assumption being made that the flow is symmetric and purely radial. These assumptions enabled a third-order nonlinear ODE to be obtained as the single equation governing the MHD flow of this particular fluid in flow through converging/diverging channels. With three physical boundary conditions available, Chebyshev collocation-point method was used to solve this ODE numerically. Results are presented in terms of parameters such as Reynolds number, Weissenberg number, channel half-angle, and the magnetic number. It was found that these parameters all have a profound effect on the velocity profiles in Jeffrey-Hamel flows. The effect of magnetic field was found to be more striking in that it is predicted to force fluid elements near the wall to exceed centerline velocity in converging channels and to suppress separation in diverging channels. Interestingly, the effect of the magnetic field in delaying flow separation is predicted to become more pronounced the higher the fluid’s elasticity.     

Retinal blood vessel segmentation for macula detachment surgery monitoring instruments

In recent years image processing has improved detection and diagnosis in medical application. Image processing applications are now embedded in medical instruments such as MRI and CT. In the case of retinopathy, fast extraction of blood vessels can allow the physician to view injury regions during surgery. Macula detachment surgeries, or computer‐aided intraocular surgeries, require precise and real‐time knowledge of the vasculature during the operation. Use of artificial neural network has produced good results in image processing applications, but its implementation may not be suitable for real‐time applications in small, embedded hardware. Because of error resiliency of the neural network, its structure can be pruned and simplified. In this paper an efficient hardware implementation of neural network for retinal vessel segmentation is proposed. We simplify the neural network structure in such a way that the accuracy of the results is not altered significantly. Simulation results and FPGA implementation show that our proposed network has low complexity and can be applied for segmentation of retinal vessels with acceptable accuracy. This makes the proposed method a good candidate to be implemented in any device such as a binocular indirect ophthalmoscope.