Iterative multiuser detector-decoding for nonbinary LDPC coded multicarrier MFSK systems

Telecommunication Systems - This paper investigates an iterative multiuser detection for nonbinary low density parity check (LDPC) coded multicarrier multiple level frequency shift keying system to...     

An HVS-Directed Neural-Network-Based Image Resolution Enhancement Scheme for Image Resizing

In this paper, a novel human visual system (HVS)-directed neural-network-based adaptive interpolation scheme for natural image is proposed. A fuzzy decision system built from the characteristics of the HVS is proposed to classify pixels of the input image into human perception nonsensitive class and sensitive class. Bilinear interpolation is used to interpolate the nonsensitive regions and a neural network is proposed to interpolate the sensitive regions along edge directions. High-resolution digital images along with supervised learning algorithms are used to automatically train the proposed neural network. Simulation results demonstrate that the proposed new resolution enhancement algorithm can produce a higher visual quality for the interpolated image than the conventional interpolation methods.     

Priority visual secret sharing of random grids for threshold access structures

Conventional (k, n)-threshold visual secret sharing of random grids (VSSRG) schemes generate n shares having the same average light transmission from a secret image to be shared, and any information related to the secret image cannot be identified externally from a single share held by one participant. In addition, the secret image can be recovered only by collecting k shares individually held by participants, and every share has the same capability of recovering the secret image. In fact, participants’ priority levels vary in certain conditions, and therefore their shares’ capabilities to recover the secret image are different. The priority-based (k, n)-threshold VSSRG scheme proposed in this study enables the assignment of different priority weights to each share to create different priority levels. During decryption, the stacking of shares with different priority levels recovers the secret image at different levels. Moreover, shares individually held by each participant have the same average light transmission, and consequently the shares’ priority levels cannot be identified externally.     

Genetic algorithm-based neural fuzzy decision tree for mixed scheduling in ATM networks

Future broadband integrated services networks based on asynchronous transfer mode (ATM) technology are expected to support multiple types of multimedia information with diverse statistical characteristics and quality of service (QoS) requirements. To meet these requirements, efficient scheduling methods are important for traffic control in ATM networks. Among general scheduling schemes, the rate monotonic algorithm is simple enough to be used in high-speed networks, but does not attain the high system utilization of the deadline driven algorithm. However, the deadline driven scheme is computationally complex and hard to implement in hardware. The mixed scheduling algorithm is a combination of the rate monotonic algorithm and the deadline driven algorithm; thus it can provide most of the benefits of these two algorithms. In this paper, we use the mixed scheduling algorithm to achieve high system utilization under the hardware constraint. Because there is no analytic method for schedulability testing of mixed scheduling, we propose a genetic algorithm-based neural fuzzy decision tree (GANFDT) to realize it in a real-time environment. The GANFDT combines a GA and a neural fuzzy network into a binary classification tree. This approach also exploits the power of the classification tree. Simulation results show that the GANFDT provides an efficient way of carrying out mixed scheduling in ATM networks.     

Design of face-centred orthorhombic filter banks

In this paper, a method to design the two-channel FIR linear-phase (LP) face-centred orthorhombic (FCO) filter banks with equiripple magnitude responses and perfect-reconstruction (PR) is presented. The necessary conditions of lengths of LP FCO filter banks satisfying the PR constraint are derived. An interior-point algorithm is utilized to optimize the peak ripples of the analysis filters and a first-order approximation skill is introduced to satisfy the PR constraint. The simulation example is presented to illustrate the effectiveness of this proposed design technique.     

Adaptive Fractal Image Sequence Coding by Variable Shape Decomposition

A two-phase fractal image sequence compression system is proposed. In the classification phase, according to the texture attribution a testing solid image block is assigned to its corresponding texture class. The texture attribution is derived from the tomographic block projection classification for the finite projection directions at the three-dimensional (3D) space. In the adaptive coding phase, both the algorithm of the 3D projection classification and the 3D variable shape decomposition are incorporated into the variable shape block transformation for image sequence. By applying this variable shape block transformation algorithm to fractal image sequence coding scheme, we can obtain a promising performance.