1961988
An Area-Efficient LDPC Decoder Architecture and Implementation for CMMB Systems
This paper presents an area-efficient LDPC decoder architecture for the China Multimedia Mobile Broadcasting (CMMB) standard. Several techniques are adopted to reduce memory size, including the min-sum algorithm (MSA), optimal bit-width quantization of the iterative messages and reduced complexity for the interconnect network. The decoder for the rate-1/2 9216-bit code is implemented using the 90nm 1.0V CMOS technology. It achieves the decoding throughput of 48Mbps at 5 iterations when operating at 60MHz and the power dissipation is only 34mW.
15937167
High-throughput layered decoder implementation for quasi-cyclic LDPC codes
This paper presents a high-throughput decoder design for the Quasi-Cyclic (QC) Low-Density Parity-Check (LDPC) codes. Two new techniques are proposed, including parallel layered decoding architecture (PLDA) and critical path splitting. PLDA enables parallel processing for all layers by establishing dedicated message passing paths among them. The decoder avoids crossbar-based large interconnect network. Critical path splitting technique is based on articulate adjustment of the starting point of each layer to maximize the time intervals between adjacent layers, such that the critical path delay can be split into pipeline stages. Furthermore, min-sum and loosely coupled algorithms are employed for area efficiency. As a case study, a rate-1/2 2304-bit irregular LDPC decoder is implemented using ASIC design in 90 nm CMOS process. The decoder can achieve the maximum decoding throughput of 2.2 Gbps at 10 iterations. The operating frequency is 950 MHz after synthesis and the chip area is 2.9 mm2.
28737704
Feature-Correlation Based Multi-view Detection
A view validation algorithm has been shown to predict whether or not the views are sufficiently compatible for solving a particular learning task. But it only works when a natural split of features exists. If the split does not exist, it will fail to manufacture a feature split to build the best views. In this paper, we present a general algorithm CCFP (Correlation and Compatibility based Feature Partitioner) to automate multi-view detection. CCFP first labels the large amount of unlabeled examples using single view algorithm, then calculates the conditional SU (Symmetric Uncertainty) between every pair of features and the IG (Information Gain) of each feature given the examples labeled previously by single view algorithm with high-confidence predictions. According to the estimated values of SU and IG, all the features will be partitioned into two views that are low correlated, compatible and sufficient enough. The experiment results show that multi-view learner with views generated by CCFP outperforms learner with views generated by other means clearly.
973036
Accelerated Convergence Using Dynamic Mean Shift
Mean shift is an iterative mode-seeking algorithm widely used in pattern recognition and computer vision. However, its convergence is sometimes too slow to be practical. In this paper, we improve the convergence speed of mean shift by dynamically updating the sample set during the iterations, and the resultant procedure is called dynamic mean shift (DMS). When the data is locally Gaussian, it can be shown that both the standard and dynamic mean shift algorithms converge to the same optimal solution. However, while standard mean shift only has linear convergence, the dynamic mean shift algorithm has superlinear convergence. Experiments on color image segmentation show that dynamic mean shift produces comparable results as the standard mean shift algorithm, but can significantly reduce the number of iterations for convergence and takes much less time.
9235487
Queuing Analysis on MIMO Systems with Adaptive Modulation and Coding
The combined MIMO with adaptive modulation and coding (AMC) technology can provide high spectral efficiency and link robustness. While most existing adaptive algorithms focus on physical layer, cross-layer analysis on the queuing behavior of MIMO-AMC systems is necessary, but remains open. In this paper, under the conditions of unsaturated traffic and finite-length buffer, we investigate the queuing characters of two representative categories of MIMO systems, namely the BLAST system and the space-time block coding (STBC) system. We model the service processes of both STBC and BLAST coupled with AMC, which is the most challenging part of the queuing analysis. We observe a new tradeoff between diversity and multiplexing in terms of link layer packet loss rate and queuing delay, based on which we propose a cross-layer design of diversity-multiplexing switching scheme to optimize the QoS of the MIMO-AMC systems.
42520367
Extending metadata with scenarios in adaptive distributed system
The traditional metadata cannot provide enough support for the adaptive distributed systems. In this paper, we propose a metadata extension that uses the scenarios as semantic aspect, describing the expected interaction sequence based on XML Schema. With such constraints, resource waste and further damages can be avoided by preventing the illegal interactions at runtime. This extension is more valid and user-friendly than the majority of the existing proposals, avoiding the potential inconsistency. A prototype has been developed to illustrate the applicability of the extended metadata. The experiments demonstrate that the prototype introduces an average overhead ratio of 6.4% in the inter-process interactions and only 0.27% in the remote interactions. The approach presented in this paper is suitable for the adaptive distributed systems and affords more guarantee of reliability.
10651609
Improved Nyström low-rank approximation and error analysis
Low-rank matrix approximation is an effective tool in alleviating the memory and computational burdens of kernel methods and sampling, as the mainstream of such algorithms, has drawn considerable attention in both theory and practice. This paper presents detailed studies on the Nyström sampling scheme and in particular, an error analysis that directly relates the Nyström approximation quality with the encoding powers of the landmark points in summarizing the data. The resultant error bound suggests a simple and efficient sampling scheme, the k-means clustering algorithm, for Nyström low-rank approximation. We compare it with state-of-the-art approaches that range from greedy schemes to probabilistic sampling. Our algorithm achieves significant performance gains in a number of supervised/unsupervised learning tasks including kernel PCA and least squares SVM.