DCT-based objective quality assessment metric of 2D/3D image

With the increasing growth of multimedia applications over the networking in recent years, users have put forward much higher requirements for multimedia quality of experience (QoE) than before. One of the representative requirements is the image quality. Therefore, the image quality assessment ranging from two-dimension (2D) image to three-dimension (3D) image has been getting much attention. In this paper, an efficient objective image quality assessment metric in block-based discrete cosine transform (DCT) coding is proposed. The metric incorporates properties of human visual system (HVS) to improve its validity and reliability in evaluating the quality of stereoscopic image. This is fulfilled by calculating the local pixel-based distortions in frequency domain, combining the simplified models of local visibility properties embodied in frequency domain, which consist of region of interest (ROI) mechanism (visual sensitivity), contrast sensitivity function (CSF) and contrast masking effect. The performance of the proposed metric is compared with other currently state-of-the-art objective image quality assessment metrics. The experimental results have demonstrated that the proposed metric is highly consistent with the subjective test scores. Moreover, the performance of the metric is also confirmed with the popular IRCCyN/IVC database. Therefore, the proposed metric is promising in term of the practical efficiency and reliability for real-life multimedia applications.     

Virtual learning environments as sociomaterial agents in the network of teaching practice

This article presents findings related to the sociomaterial agency of educators and their practice in Norwegian education. Using actor-network theory, we ask how Virtual Learning Environments (VLEs) negotiate the agency of educators and how they shape their teaching practice. Since the same kinds of VLE tools have been widely implemented throughout Norwegian education, it is interesting to study how practices are formed in different parts of the educational system. This research is therefore designed as a case study of two different teaching contexts representing lecturers from a higher education institution and teachers from primary schools. Data are collected by means of interviews, online logging of VLE activities and self-reported personal logs. From the analysis of the data, three main networks of aligned interests can be identified. In each of those, the sociomaterial agency of the teaching practice with VLE is crucial in shaping and consolidating the network.     

Scalable Parallel Algorithms for Geometric Pattern Recognition

This paper considers a variety of geometric pattern recognition problems on input sets of size n using a coarse grained multicomputer model consisting of p processors with Ω(n/p) local memory each (i.e., Ω(n/p) memory cells of Θ(log n) bits apiece), where the processors are connected to an arbitrary interconnection network. It introduces efficient scalable parallel algorithms for a number of geometric problems including the rectangle finding problem, the maximal equally spaced collinear points problem, and the point set pattern matching problem. All of the algorithms presented are scalable in that they are applicable and efficient over a very wide range of ratios of problem size to number of processors. In addition to the practicality imparted by scalability, these algorithms are easy to implement in that all required communications can be achieved by a small number of calls to standard global routing operations.     

A single solution method for converting 2D assembly drawings to 3D part drawings

Although solid models play a central role in modern CAD systems, 2D CAD systems are still commonly used for designing products without complex curved faces. Therefore, an important task is to convert 2D drawings to solid models, and this is usually carried out manually even in present CAD systems. Many methods have been proposed to automatically convert orthographic part drawings of solid objects to solid models. Unfortunately, products are usually drawn as 2D assembly drawings, and therefore, these methods cannot be applied. A further problem is the difficult and time-consuming task of decomposing 2D assembly drawings into 2D part drawings. In previous work, the authors proposed a method to automatically decompose 2D assembly drawings into 3D part drawings, from which 2D part drawings can be easily generated. However, one problem with the proposed method was that the number of solutions could easily explode if the 2D assembly drawings became complex. Building on this work, here we describe a new method to automatically convert 2D assembly drawings to 3D part drawings, generating a unique solution for designers regardless of the complexity of the original 2D assembly drawings. The only requirement for the approach is that the assembly drawings consist of standard parts such as bars and plates. In 2D assembly drawings, the dimensions, part numbers and parts lists are usually drawn, and the proposed method utilizes these to obtain a unique solution.     

Properties of Digital Homotopy

Several recent papers have adapted notions of geometric topology to the emerging field of digital topology. An important notion is that of digital homotopy. In this paper, we study a variety of digitally-continuous functions that preserve homotopy types or homotopy-related properties such as the digital fundamental group.Laurence Boxer is Professor of Computer and Information Sciences at Niagara University, and Research Professor of Computer Science and Engineering at the State University of New York at Buffalo. He received his Ph.D. in Mathematics from the University of Illinois at Urbana-Champaign. His research interests are computational geometry, parallel algorithms, and digital topology. Dr. Boxer is co-author, with Russ Miller, of Algorithms Sequential and Parallel, A Unified Approach, a recent textbook published by Prentice Hall.     

Multiscale comparison of remote-sensing data for linear woody vegetation mapping

Wooded hedgerows do not cover large areas but perform many functions that are beneficial to water quality and biodiversity. A broad range of remotely sensed data is available to map these small linear elements in rural landscapes, but only a few of them have been evaluated for this purpose. In this study, we evaluate and compare various optical remote-sensing data including high and very high spatial resolution, active and passive, and airborne and satellite data to produce quantitative information on the hedgerow network structure and to analyse qualitative information from the maps produced in order to estimate the true value of these maps. We used an object-based image analysis that proved to be efficient for detecting and mapping thin elements in complex landscapes. The analysis was performed at two scales, the hedgerow network scale and the tree canopy scale, on a study site that shows a strong landscape gradient of wooded hedgerow density. The results (1) highlight the key role of spectral resolution on the detection and mapping of wooded elements with remotely sensed data; (2) underline the fact that every satellite image provides relevant information on wooded network structures, even in closed landscape units, whatever the spatial resolution; and (3) indicate that light detection and ranging data offer important insights into future strategies for monitoring hedgerows.     

Design and implementation of a trusted monitoring framework for cloud platforms

Virtualization is a pillar technology in cloud computing for multiplexing computing resources on a single cloud platform for multiple cloud tenants. Monitoring the behavior of virtual machines (VMs) on a cloud platform is a critical requirement for cloud tenants. Existing monitoring mechanisms on virtualized platforms either takes a complete VM as the monitoring granularity, such that they cannot capture the malicious behaviors within individual VMs, or they focus on specific monitoring functions that cannot be used for heterogeneous VMs concurrently running on a single cloud node. Furthermore, the existing monitoring mechanisms have made an assumption that the privileged domain is trusted to act as expected, which causes the cloud tenants’ concern about security because the privileged domain in fact could not act as the tenants’ expectation. We design a trusted monitoring framework, which provides a chain of trust that excludes the untrusted privileged domain, by deploying an independent guest domain for the monitoring purpose, as well as utilizing the trusted computing technology to ensure the integrity of the monitoring environment. Moreover, the feature of fine-grained and general monitoring is also provided. We have implemented the proposed monitoring framework on Xen, and integrated it into OpenNebula. Our experimental results show that it can offer expected functionality, and bring moderate performance overhead.     

Coping with uncertainty: police strategies for resilient decision-making and action implementation

This study uses a hostage negotiation setting to demonstrate how a team of strategic police officers can utilize specific coping strategies to minimize uncertainty at different stages of their decision-making in order to foster resilient decision-making to effectively manage a high-risk critical incident. The presented model extends the existing research on coping with uncertainty by (1) applying the RAWFS heuristic (Lipshitz and Strauss in Organ Behav Human Decis Process 69:149–163, 1997) of individual decision-making under uncertainty to a team critical incident decision-making domain; (2) testing the use of various coping strategies during “in situ” team decision-making by using a live simulated hostage negotiation exercise; and (3) including an additional coping strategy (“reflection-in-action”; Schön in The reflective practitioner: how professionals think in action. Temple Smith, London, 1983) that aids naturalistic team decision-making. The data for this study were derived from a videoed strategic command meeting held within a simulated live hostage training event; these video data were coded along three themes: (1) decision phase; (2) uncertainty management strategy; and (3) decision implemented or omitted. Results illustrate that, when assessing dynamic and high-risk situations, teams of police officers cope with uncertainty by relying on “reduction” strategies to seek additional information and iteratively update these assessments using “reflection-in-action” (Schön 1983) based on previous experience. They subsequently progress to a plan formulation phase and use “assumption-based reasoning” techniques in order to mentally simulate their intended courses of action (Klein et al. 2007), and identify a preferred formulated strategy through “weighing the pros and cons” of each option. In the unlikely event that uncertainty persists to the plan execution phase, it is managed by “reduction” in the form of relying on plans and standard operating procedures or by “forestalling” and intentionally deferring the decision while contingency planning for worst-case scenarios.     

A low distortion image enhancement scheme based on multi-resolutions analysis in next generation network

With the advent of Next Generation Network (NGN), services that are currently provided by multiple specific network-centric architectures. NGN provides AAA (Anytime, Anywhere and Always on) access to users from different service providers with consistent and ubiquitous provision of services as necessary. This special issue of NGN includes pervasive, grid, and peer-to-peer computing to provide computing and communication services at anytime and anywhere. In fact, the application of NGN includes digital image processing, multimedia systems/services, and so on. Here we focus on the digital image processing technology in NGN environments. Low-contrast structure and heavy noise in NGN environments can be found in many kinds of digital images, which makes the images vague and uncertainly, especially in x-ray images. As result, some useful tiny characteristic are weakened—which are difficult to distinguish even by naked eyes. Based on the combination of no-linear grad-contrast operator and multi-resolution wavelet analysis, a kind of image enhancement processing algorithm for useful tiny characters is presented. The algorithm can enhance the tiny characters while confine amplifying noise. The analysis of the results shows that local regions of the image are enhanced by using the concept of the grad contrast to make image clearer adaptively. Experiments were conducted on real pictures, and the results show that the algorithm is flexible and convenient.