Efficient Algorithms for Computing a Complete Visibility Region in Three-Dimensional Space

Given a set P of polygons in three-dimensional space, two points p and q are said to be visible from each other with respect to P if the line segment joining them does not intersect any polygon in P . A point p is said to be completely visible from an area source S if p is visible from every point in S . The completely visible region CV(S, P) from S with respect to P is defined as the set of all points in three-dimensional space that are completely visible from S . We present two algorithms for computing CV(S, P) for P with a total of n vertices and a convex polygonal source S with m vertices. Our first result is a divide-and-conquer algorithm which runs in O(m ² n ² α(mn)) time and space, where α(mn) is the inverse of Ackermann's function. We next give an incremental algorithm for computing CV(S,P) in O(m ² n+mn ² α(n)) time and O(mn+n ² ) space. We also prove that CV(S,P) consists of Θ(mn+n ² ) surface elements such as vertices, edges, and faces. Received November 16, 1995; revised November 11, 1996.     

Towards an understanding of the consumer acceptance of mobile wallet

This study seeks to validate a comprehensive model of consumer acceptance in the context of mobile payment. It uses the unified theory of acceptance and use of technology (UTAUT) model with constructs of security, trust, social influence, and self-efficacy. Structural equation modeling is used to construct a predictive model of attitudes toward the mobile wallet. Individuals’ responses to questions about attitude and intention to adopt/use a mobile wallet were collected and analyzed with various factors modified from UTAUT. While the model confirms the classical role of technology acceptance factors (i.e., perceived usefulness and ease of use are key antecedents to users’ attitude), the results also show that users’ attitudes and intentions are influenced by perceived security and trust. In the extended model, the moderating effects of demographics on the relations among the variables were found to be significant. The proposed model brings together extant research on mobile payment and provides an important cluster of antecedents to eventual technology acceptance via constructs of behavioral intention to use and actual system usage.     

Multiscale Bayesian texture segmentation using neural networks and Markov random fields

This paper presents a wavelet-based texture segmentation method using multilayer perceptron (MLP) networks and Markov random fields (MRF) in a multi-scale Bayesian framework. Inputs and outputs of MLP networks are constructed to estimate a posterior probability. The multi-scale features produced by multi-level wavelet decompositions of textured images are classified at each scale by maximum a posterior (MAP) classification and the posterior probabilities from MLP networks. An MRF model is used in order to model the prior distribution of each texture class, and a factor, which fuses the classification information through scales and acts as a guide for the labeling decision, is incorporated into the MAP classification of each scale. By fusing the multi-scale MAP classifications sequentially from coarse to fine scales, our proposed method gets the final and improved segmentation result at the finest scale. In this fusion process, the MRF model serves as the smoothness constraint and the Gibbs sampler acts as the MAP classifier. Our texture segmentation method was applied to segmentation of gray-level textured images. The proposed segmentation method shows better performance than texture segmentation using the hidden Markov trees (HMT) model and the HMTseg algorithm, which is a multi-scale Bayesian image segmentation algorithm.     

Empirical data modeling in software engineering using radial basisfunctions

Many empirical studies in software engineering involve relationships between various process and product characteristics derived via linear regression analysis. We propose an alternative modeling approach using radial basis functions (RBFs) which provide a flexible way to generalize linear regression function. Further, RBF models possess strong mathematical properties of universal and best approximation. We present an objective modeling methodology for determining model parameters using our recent SG algorithm, followed by a model selection procedure based on generalization ability. Finally, we describe a detailed RBF modeling study for software effort estimation using a well-known NASA dataset     

Real-time transport of MPEG video with a statistically guaranteedloss ratio in ATM networks

Unlike deterministic real-time communication in which excessive resources may be required for “absolute” performance guarantees, statistical real-time communication seeks to achieve both probabilistic performance guarantees and efficient resource sharing. This paper presents a framework for statistical real-time communication in ATM networks, providing delay-guaranteed transport of MPEG-coded video traffic with a statistically-guaranteed cell-loss ratio. Delay-guaranteed communication is achieved with a modified version of Traffic-Controlled Rate-Monotonic Priority Scheduling (TCRM). A set of statistical real-time channels that share similar traffic characteristics are multiplexed into a common macrochannel. Those statistical real-time channels which are multiplexed together share the resources of a macrochannel, and individual statistical real-time channels are given timeliness and probabilistic cell-loss guarantees. A macrochannel is serviced by the modified TCRM which improves link utilization and makes channel management simpler. Based on the analysis of an M/D/1/N queueing system, we propose a procedure for determining the transmission capacity of a macrochannel necessary to statistically guarantee a cell-loss ratio bound. Our extensive trace-driven simulation has shown the superiority of the proposed framework to the other approaches. The overall cell-loss ratios for multihop statistical real-time channels are shown to be smaller than the predetermined bounds, thus verifying our analytical results     

Extraction of low-toxicity nanodiamonds from carbonaceous wastes

Being innocuous, nanodiamonds find huge applications in bioimaging, drug delivery and biomedical sensing. Their synthesis is generally carried out by detonation or shock wave methods. This article reports novel, economical and risk-free extraction of nanodiamonds from carbonaceous waste (smoke deposits of Hawan kund). Structural and morphological studies of extracted material revealed that the nanodiamonds have a size range of 4–5 nm and consists of sp³ core and sp² shell. Extracted nanodiamonds contain various functional groups on its surface which make it suitable for various applications. Cytotoxicity assay revealed very less toxicity of extracted nanodiamonds, which makes them suitable candidate for biological applications. The proposed method offers the possibility of economical and detonation free production of nanodiamonds.