Arbitrary ROI-based wavelet video coding

This paper presents a novel arbitrary shape region of interest (ROI) coding for scalable wavelet video codec. The motion information of the ROIs is estimated by macroblock padding and polygon matching. The derived motion vectors are set as the motion trajectory of the samples to generate a one-dimensional temporal signal. This signal is then filtered to reduce the temporal redundancy using motion compensated temporal filtering for arbitrary shape ROI. Compared to traditional non-ROI coding, the reconstructed quality of the ROI coding can be significantly improved at low bit-rates. The efficiency of motion compensated temporal filtering (MCTF) based on arbitrary ROI is also compared with that of the video object coding in MPEG-4. Based on large number of experiments, the ability of the MCTF to reduce the temporal redundancy is demonstrated as better than (or at least comparable to) that of MPEG-4.     

A fast fault-identification algorithm for bijective connection graphs using the PMC model

Diagnosis of reliability is an important topic for interconnection networks. Under the classical PMC model, Dahura and Masson [5] proposed a polynomial time algorithm with time complexity O(N^2.5) to identify all faulty nodes in an N-node network. This paper addresses the fault diagnosis of so called bijective connection (BC) graphs including hypercubes, twisted cubes, locally twisted cubes, crossed cubes, and Möbius cubes. Utilizing a helpful structure proposed by Hsu and Tan [20] that was called the extending star by Lin et al. [24], and noting the existence of a structured Hamiltonian path within any BC graph, we present a fast diagnostic algorithm to identify all faulty nodes in O(N) time, where N = 2ⁿ, n ? 4, stands for the total number of nodes in the n-dimensional BC graph. As a result, this algorithm is significantly superior to Dahura–Masson’s algorithm when applied to BC graphs.     

A rational model of the surface swept by a curve*

This paper shows how to construct a rational Bezier model of a swept surface that interpolates N frames (i.e., N position/orientation pairs) of a fixed rational space curve c(s) and maintains the shape of the curve at all intermediate points of the sweep. Thus, the surface models an exact sweep of the curve, consistent with the given data. The primary novelty of the method is that this exact modeling of the sweep is achieved without sacrificing a rational representation for the surface. Through a simple extension, we also allow the sweeping curve to change its size through the sweep. The position, orientation, and size of the sweeping curve can change with arbitrary continuity (we use C² continuity in this paper). Our interpolation between frames has the classical properties of Bezier interpolation, such as the convex hull property and linear precision. This swept surface is a useful primitive for geometric design. It encompasses the surface of revolution and extruded surface, but extends them to arbitrary sweeps. It is a useful modeling primitive for robotics and CAD/CAM, using frames generated automatically by a moving robot or tool.     

New least-squares algorithm for model parameters estimation using self-potential anomalies

We have developed a new least-squares minimization approach to depth determination from self-potential (SP) data. By defining the anomaly value at the origin and at any two symmetrical points around the origin on the profile, the problem of depth determination from the residual SP anomaly has been transformed into finding a solution to a nonlinear equation of the form f(z)=0. Procedures are also formulated to estimate the polarization angle, amplitude coefficient and the shape of the buried structure (shape factor). The method is simple and can be used as a rapid method to estimate parameters that produced SP anomalies. The method is tested on synthetic data with and without random errors. It is also applied to a field example from Turkey. In all cases, the model parameters obtained are in good agreement with actual ones.     

Quantum simultaneous secret distribution with dense coding by using cluster states

A new communication mode, quantum simultaneous secret distribution (QSSD) is put forward, where one sender distributes different classical secret message to multiparty receivers simultaneously. Based on the properties of the one-dimensional four-qubit cluster states, a three-party QSSD protocol is proposed, and then it is extended to the case that there are many receivers. Owing to the idea of quantum dense coding, each receiver can receive two bits of classical message by the sender only using a cluster state. In order to check security of quantum channels, a strategy which can prevent common attacks efficiently is put forward. QSSD is distinct from quantum secret sharing (QSS) and quantum broadcast communication (QBC), but it can be easily converted into QSS and QBC. QSSD is also different from the multiple-QKD communication mode where the sender shares a private key with each receiver at first, while in QSSD the sender doesn’t; in addition, only one round of one-to-many communication is performed in QSSD, while in multiple-QKD communication mode many rounds of one-to-one communication are performed.     

Parametric-based brain Magnetic Resonance Elastography using a Rayleigh damping material model

The three-parameter Rayleigh damping (RD) model applied to time-harmonic Magnetic Resonance Elastography (MRE) has potential to better characterise fluid-saturated tissue systems. However, it is not uniquely identifiable at a single frequency. One solution to this problem involves simultaneous inverse problem solution of multiple input frequencies over a broad range. As data is often limited, an alternative elegant solution is a parametric RD reconstruction, where one of the RD parameters (μ_I or ρ_I) is globally constrained allowing accurate identification of the remaining two RD parameters. This research examines this parametric inversion approach as applied to in vivo brain imaging.