Journal of China Universities of Posts and Telecommunications最新文献

A compact common-mode filter is proposed to suppress common-mode noise for application of high-speed differential signal traces. The filter adopts one big C-shaped defected ground structure (DGS) cell in the left of ground plane and two small C-shaped DGS cells with opposite direction in the right of ground plane. Because these DGS cells have different dimensions, the filter has three adjacent equivalent resonant points, which can suppress wideband common-mode noise effectively. The left C-shaped DGS and its adjacent C-shaped DGS cell form an approximate closed structure, which can efficiently reduce the influence of the mutual capacitance. The filter provides a common-mode suppression from 3.6 GHz to 14.4 GHz over 15 dB while it has a small size of 10 mm10 mm. The fractional bandwidth of the filter is 120%, and the differential signals still keep good signal integrity. The experimental results are in good agreement with the simulated results.

NAND flash chips have been innovated from two-dimension (2D) design which is based on planar NAND cells to three-dimension (3D) design which is based on vertical NAND cells. Two types of NAND flash technologies–charge-trap (CT) and floating-gate (FG) are presented in this paper to introduce NAND flash designs in detail. The physical characteristics of CT-based and FG-based 3D NAND flashes are analyzed. Moreover, the advantages and disadvantages of these two technologies in architecture, manufacture, interference and reliability are studied and compared.

High efficiency video coding (HEVC) transform algorithm for residual coding uses 2-dimensional (2D) 4×4 transforms with higher precision than H.264's 4×4 transforms, resulting in increased hardware complexity. In this paper, we present a shared architecture that can compute the 4×4 forward discrete cosine transform (DCT) and inverse discrete cosine transform (IDCT) of HEVC using a new mapping scheme in the video processor array structure. The architecture is implemented with only adders and shifts to an area-efficient design. The proposed architecture is synthesized using ISE14.7 and implemented using the BEE4 platform with the Virtex-6 FF1759 LX550T field programmable gate array (FPGA). The result shows that the video processor array structure achieves a maximum operation frequency of 165.2 MHz. The architecture and its implementation are presented in this paper to demonstrate its programmable and high performance.

For the time-frequency overlapped signals, a low-complexity single-channel blind source separation (SBSS) algorithm is proposed in this paper. The algorithm does not only introduce the Gibbs sampling theory to separate the mixed signals, but also adopts the orthogonal triangle decomposition-M (QRD-M) to reduce the computational complexity. According to analysis and simulation results, we demonstrate that the separation performance of the proposed algorithm is similar to that of the per-survivor processing (PSP) algorithm, while its computational complexity is sharply reduced.

Accurate modeling and recognition of the brain activity patterns for reliable communication and interaction are still a challenging task for the motor imagery (MI) brain-computer interface (BCI) system. In this paper, we propose a common spatial pattern (CSP) and chaotic particle swarm optimization (CPSO) twin support vector machine (TWSVM) scheme for classification of MI electroencephalography (EEG). The self-adaptive artifact removal and CSP were used to obtain the most distinguishable features. To improve the recognition results, CPSO was employed to tune the hyper-parameters of the TWSVM classifier. The usefulness of the proposed method was evaluated using the BCI competition IV-IIa dataset. The experimental results showed that the mean recognition accuracy of our proposed method was increased by 5.35%, 4.33%, 0.78%, 1.45%, and 9.26% compared with the CPSO support vector machine (SVM), particle swarm optimization (PSO) TWSVM, linear discriminant analysis LDA), back propagation (BP) and probabilistic neural network (PNN), respectively. Furthermore, it achieved a faster or comparable central processing unit (CPU) running time over the traditional SVM methods.

Content-centric networking (CCN) proposes a content-centric paradigm which changes the waist hourglass from Internet protocol (IP) to content chunk. In this paper, based on content chunks, an optimization model of minimizing the total delay time in information centric networking (ICN) is established, and branch-and-bound method and greedy (BG) algorithm is proposed to get the content placement method. As the multipath is natural supported in CCN, chunk-based content placement can decline delay time obviously, even it would increase the calculation amount which can be solved easily by the node's capacity. Simulation results indicate that the chunk-based content placement scheme is better than the single-based cache policy on the network total delay time, and the best number of each content chunk split is decided by the link density and the number of the nodes in the network.

In this paper, an iterative carrier recovery algorithm of Gaussian filtered minimum shift keying (GMSK) in burst-mode was designed. The data utilization rate and precision of the traditional demodulation method for recovering carrier signal in burst data packet with limited-length is poor. In order to solve this problem, this paper proposed an iterative carrier recovery algorithm. This algorithm can improve the estimated precision of carrier recovery and data utilization rate of burst data packet in a large extent by performing multiple forward and backward iterations. And the algorithm can be implemented in Simulink environment. As the communication mode of automatic identification system (AIS) is abrupt, the algorithm is especially suitable for satellite-based AIS.

User interactive behaviors play a dual role during the hypertext transfer protocol (HTTP) video service: reflection and influence. However, they are seldom taken into account in practices. To this end, this paper puts forward the user interactive behaviors, as subjective factors of quality of experience (QoE) from viewer level, to structure a comprehensive multilayer evaluation model based on classic network quality of service (QoS) and application QoS. First, dual roles of user behaviors are studied and the characteristics are extracted where the user experience is correlated with user interactive behaviors. Furthermore, we categorize QoE factors into three dimensions and build the metric system. Then we perform the subjective tests and investigate the relationships among network path quality, user behaviors, and QoE. Ultimately, we employ the back propagation neural network (BPNN) to validate our analysis and model. Through the simulation experiment of mathematical and BPNN, the dual effects of user interaction behaviors on the reflection and influence of QoE in the video stream are analyzed, and the QoE metric system and evaluation model are established.

The unique characteristics of opportunistic networks (ONs), such as intermittent connectivity and limited network resources, makes it difficult to support quality of service (QoS) provisioning, particularly to guarantee delivery ratio and delivery delay. In this paper, we propose a QoS-oriented packet scheduling scheme (QPSS) to make decisions for bundle transmissions to satisfy the needs for the delivery ratio and delivery delay constraints of bundles. Different from prior works, a novel bundle classification method based on the reliability and latency requirements is utilized to decide the traffic class of bundles. A scheduling algorithm of traffic class and bundle redundancy is used to maintain a forwarding and dropping priority queue and allocate network resources in QPSS. Simulation results indicate that our scheme not only achieves a higher overall delivery ratio but also obtains an approximate 14% increase in terms of the amount of eligible bundles.

To improve the accuracy of the network security situation, a security situation automatic prediction model based on accumulative data preprocess and support vector machine (SVM) optimized by covariance matrix adaptive evolutionary strategy (CMA-ES) is proposed. The proposed model adopts SVM which has strong nonlinear ability. Also, the hyper parameters for SVM are optimized through the CMA-ES which owns good performance in finding optimization automatically. Considering the irregularity of network security situation values, we accumulate the original sequence, so that the internal rules of discrete data can be revealed and it is easy to model. Simulation experiments show that the proposed model has faster convergence-speed and higher prediction accuracy than other extant prediction models.