Skyline queries are usually associated with user preferences which are dependent on his/her current contexts. For most contexts are from sensing devices, uncertainty is along with the contexts. In this paper, asymptotic-efficient skyline query processing algorithms over uncertain contexts are proposed. First possible world semantics model is utilized to model uncertain contexts as well as uncertain contextual preferences. Since exact skyline algorithm is a #P-hard problem, two heuristic skyline algorithms LHSA, CHSA are proposed to reduce the number of possible worlds not contributing to final skyline query results. To improve the efficiency of skyline query processing under user specified precision, two Monte Carlo Sampling based approximation algorithms 2PMA, S2PMA are proposed. Finally, extensive experimental results show that LHSA and CHSA algorithms can reduce the number of possible worlds to a large extent and the proposed 2PMA and S2PMA algorithms perform more efficiently than heuristic algorithms while S2PMA algorithm is prior to 2PMA with smaller absolute errors.

In this paper, we investigate the problem of determining the routing that minimizes the maximum/average delivery time or the maximum/average delivery delay for a set of packets in a deterministic Delay Tolerant Network, i.e. in a network for which all the nodes' transmission opportunities are known in advance. While the general problem with multiple sources and multiple destinations is NP-hard, we present a polynomial time algorithm that can efficiently compute the optimal routing in the case of a single destination or of a single packet that needs to be routed to multiple destinations.

In this paper, we investigate the benefits of applying a form of network coding known as random linear coding (RLC) to unicast applications in disruption-tolerant networks (DTNs). Under RLC, nodes store and forward random linear combinations of packets as they encounter each other. For the case of a single group of packets originating from the same source and destined for the same destination, we prove a lower bound on the probability that the RLC scheme achieves the minimum time to deliver the group of packets. Although RLC significantly reduces group delivery delays, it fares worse in terms of average packet delivery delay and network transmissions. When replication control is employed, RLC schemes reduce group delivery delays without increasing the number of transmissions. In general, the benefits achieved by RLC are more significant under stringent resource (bandwidth and buffer) constraints, limited signaling, highly dynamic networks, and when applied to packets in the same flow. For more practical settings with multiple continuous flows in the network, we show the importance of deploying RLC schemes with a carefully tuned replication control in order to achieve reduction in average delay, which is observed to be as large as 20% when buffer space is constrained.

New wavelet techniques are designed to improve the perceptual quality of images/signal, enhance and detect the detail features in the region of interest (ROI). Distributed approximating functionals (DAFs) are used to construct a new class of smooth wavelets, which enable better signal processing performance. This paper is focused on improvements in DAF wavelet signal processing. The combined perceptual techniques (such as regularization, visual group normalization and contrast nonlinear enhancement) produce natural high-quality images based on the human vision system. The underlying technologies significantly facilitate the creation of generic signal processing and computer-aided diagnostic (CAD) systems.

New wavelet techniques are employed to improve the perceptual quality of images, and to enhance and detect the detail features in the region of interest (ROI). Distributed approximating functionals (DAFs) are used to construct a new class of interpolating wavelets, which enable better image processing performance. This paper is focused on previous improvements in DAF wavelet image processing. The combined perceptual techniques (such as visual group normalization and contrast nonlinear enhancement) produce natural high-quality images adapted to the human vision system. The underlying technologies significantly facilitate the creation of generic image processing and computer-aided diagnostic (CAD) systems.

The alpha-trimmed mean (ATM) filter is introduced and a median-weighted ATM (MWATM) filter is proposed to detect and remove impulse noise from corrupted images by combining them with a modified iterative switching scheme. It is shown that although the ATM filter may not perform better than the median filter in impulse noise removal, it is a better impulse detector. The MWATM filter is generated by set the weight of the median value in the ATM filter as a filtering parameter instead of one. Simulations show that the optimum weight of the median value does not equal to one in general. We also noticed that the iterative switching scheme used by Zhang and Wang (see Optical Engineering, vol.37, p.1275-82, 1998) is not optimized. A modified version of it is therefore proposed.