Vol. 21 · No. 6

Many multimedia applications require transmission of streaming video from a server to a client across an internetwork. In many cases loss may be unavoidable due to congestion or heterogeneous nature of the network. We explore how discard policies can be used in order to maximize the quality of service (QoS) perceived by the client. In our model the QoS of a video stream is measured in terms of a cost function, which takes into account the discarded frames. In this paper we consider online policies for selective frame discard and analyze their performance by means of competitive analysis. In competitive analysis the performance of a given online policy is compared with that of an optimal offline policy. In this work we present competitive policies for a wide range of cost functions, describing the QoS of a video stream.

With the ever increasing volume of data in networks, the traditional storage architecture is greatly challenged; more and more people pay attention to network storage. Currently, the main technology of network storage is represented by NAS (Network Attached Storage) and SAN (Storage Area Network). They are different, but mutually complementary and used under different circumstances; however, both NAS and SAN may be needed in the same company. To reduce the TOC (total of cost), for easier implementation, etc., people hope to merge the two technologies. Additionally, the main internetworking technology of SAN is the Fibre Channel; however, the major obstacles are in its poor interoperability, lack of trained staff, high implementation costs, etc. To solve the above-mentioned issues, this paper creatively introduces a novel storage architecture called USN (United Storage Networks), which uses the iSCSI to build the storage network, and merges the NAS and SAN techniques supplying the virtues and overcoming the drawbacks of both, and provides both file I/O and block I/O service simultaneously.

Given three partially overlapping views of the scene from which a set of point or line correspondences have been extracted, 3D structure and camera motion parameters can be represented by the trifocal tensor, which is the key to many problems of computer vision on three views. Unlike in conventional typical methods, the residual value is the only rule to eliminate outliers with large value, we build a Gaussian mixture model assuming that the residuals corresponding to the inliers come from Gaussian distributions different from that of the residuals of outliers. Then Bayesian rule of minimal risk is employed to classify all the correspondences using the parameters computed from GMM. Experiments with both synthetic data and real images show that our method is more robust and precise than other typical methods because it can efficiently detect and delete the bad corresponding points, which include both bad locations and false matches.

We refine the alternating space hierarchy by separating the classes break sgak spa(s(n)) and piak spa(s(n)) from deak spa(s(n)) as well as from break deak+1 spa(s(n)), for each s(n)in Omega(łlgn) cap o(łgn), and k geq 2. We also present unary (tally) sets separating sga2 spa(s(n)) and pia2 spa(s(n)) from break dea2 spa(s(n)) as well as from dea3 spa(s(n)).

We present very sharp separation results for Turing machine sublogarithmic space complexity classes which are of the form: For any, arbitrarily slow growing, recursive nondecreasing and unbounded function s there is a k in N and an unary language L such that L in SPACE(s(n)+k) setminus SPACE(s(n-1)). For a binary L the supposition łims = infty is sufficient. The witness languages differ from each language from the lower classes on infinitely many words. We use so called demon (Turing) machines where the tape limit is given automatically without any construction. The results hold for deterministic and nondeterministic demon machines and also for alternating demon machines with a constant number of alternations, and with unlimited number of alternations. The sharpness of the results is ensured by using a very sensitive measure of space complexity of Turing computations which is defined as the amount of the tape required by the simulation (of the computation in question) on a fixed universal machine. As a proof tool we use a succint diagonalization method.