An SEU-Tolerant Approach for Space-Borne Viterbi Decoders

In the space environment, Viterbi decoder implemented on SRAM-based FPGA is sensitive to Single event upsets （SEUs）, which may lead to functional failure of the decoder. Conventional SEU mitigation techniques like modular redundancy could not exploit the characters of Viterbi decoders, therefore could not provide optimized SEU tolerance when the device resource utilization cost is a constraint. Leveraging the properties of the decoding algorithm, three effective mitigation techniques are adopted, including structure optimization, Error detection and correction （EDAC） for Block RAM （BRAM） protection, and Partial triple-modular redundancy （PTMR）, which are applied to the modules of the decoder in accordance with their characteristics. Analysis of effectiveness shows that compared with unmitigated design, the SEU induced failure rate in the proposed SEU tolerant decoder can be reduced to 1/4 at the cost of 61.1% extra resource utilization. Error detec- tion and correction （EDAC）.     

An Efficient Causal Structure Learning Algorithm Based on Recursive Simultaneous Equations Model

A new algorithm, the RSEM （Recursive simultaneous equations model） algorithm, is presented for causal structure learning under the LSEM （Linear structural equations model）. The algorithm effectively applies recursive simultaneous equations model to causal structure learning. This paper makes two specific contributions. Firstly, under the assumption that knowing the causal order of the variables, we show that recursive simultaneous equations model can be used for causal structure learning under the LSEM regardless of whether the datasets follow multivariate Gaussian distribution. Secondly, the performance of the RSEM algorithm is compared with the state-of-the-art algorithms on 7 networks. Simulation results show that the RSEM algorithm outperforms existing algorithms in terms of time performance, and has a quite high accuracy for thresholds 0.005 and 0.01.     

Joint Antenna Selection and Robust Beamforming Design in Multi-cell Distributed Antenna System

Most of studies on Distributed Antenna System （DAS） focus on maximizing the sum capacity and perfect channel state information at transmitter （CSIT）. However, CSI is inevitable imperfect in practical wireless networks. Based on the sources of error, there are two models. One assumes error lies in a bounded region, the other assumes random error. Accordingly, we propose two joint antenna selection （AS） and robust- beamforming schemes aiming to minimize the total transmit power at antenna nodes subject to quality of service （QoS） guarantee for all the mobile users （MUs） in multicell DAS. This problem is mathematically intractable. For the bounded error model, we cast it into a semidefinite program （SDP） using semidefinite relaxation （SDR） and S-procedure. For the second, we first design outage constrained robust beamforming and then formulate it as an SDP based on the Bernstein-type inequality, which we generalize it to the multi-cell DAS. Simulation results verify the effectiveness of the proposed methods.     

Amended Truncated Union Bounds on the ML Decoding Performance of Binary Linear Codes over AWGN Channels

In this paper＇s simple upper bounds are derived on the frame and bit error probabilities of binary linear codes over Additive white Gaussian noise （AWGN） channels. The conventional union bound is first truncated and then amended, which can be justified by invoking Gallager＇s first bounding technique （GFBT）. Different from most other works, the ＂good region＂ is specified by a suboptimal list decoding algorithm. The error probability caused by the good region can be upper-bounded by the union bounds using pair-wlse error probability and tripletwise error probability, which can be further tightened by exploiting the independence between the error events and certain components of the receiving signal vector. The proposed bounds are simple since they involve only the Q-function. The proposed bounds improve our recently proposed bounds. Numerical results are also presented to compare the proposed bounds with the Divsalar bound, which is a simple tight upper bound with closed-form, and the Tangential-sphere bound （TSB）, which is considered as one of the tightest upper bounds.     

Interference Mitigation and Joint Decoding for Uplink LDPC-Coded Relay Cooperation

This paper proposes an efficient inter- ference mitigation and joint decoding scheme for uplink LDPC-coded relay cooperation over a Rayleigh fading channel, where a concatenation of Minimum-mean-squared error linear detectors （MMSE） and BP-based joint itera- tire decoding based on the introduced treble-layer Tanner graph are effectively designed to filter and decode the cor- rupted received sequence at a base station. It is demon- strated by theoretical analysis and numerical simulations that the proposed design can well combine the gains from coding and diversity, which consequently leads to a sig- nificant performance improvement over the conventional cooperation system under the same conditions.     

Layered Dynamic Schedulings for BP Decoding of LDPC Codes over GF（q）

A Layered dynamic scheduling （LDS） for Belief-propagation （BP） decoding of LDPC codes over GF（q） is presented, which is derived from the dynamic scheduling for the BP decoding of binary LDPC codes. In order to restrain the LDS from cycling in certain checknodes, a life-index for each check-node is adopted and the optimal value of the life-index is analyzed. Furthermore, in consideration of hardware implementation and decoding latency, a strategy, which allows many more checknodes to be updated in parallel, is introduced. Simulations show that the LDS with life-index speeds up the convergence rate and greatly improves the performance of the BP decoding at medium to high signal-to-noise ratio value, and the algorithm employing the LDS with life-index and the new strategy offers good trade-off between the performance and the decoding latency.     

Performance Analysis of Regular Low-Density Erasure Codes

We propose a simple method for calculating the thresholds of regular low-density erasure codes under erasure recovery algorithm. Based on this conclusion, we prove that among （l, r）-regular low-density erasure codes, （3, r）-regular codes perform best, for the given integer l ≥ 3 and any rate R with 1/4 〈R 〈 1. Numerical results show the correctness of this conclusion.     

Exploring Semantic Association Rules Between Keyword and Visual Feature Clusters for Web Image Retrieval

The semantic gap is a big challenge in image retrieval area. Previous studies in web image retrieval have mainly focused on Relevance feedback （RF） and Latent semantic indexing （LSI） to alleviate the gap. This paper proposes an approach base on Frequent itemset mining （FIM） and Association rule （AR） techniques, which explores the semantic association rule between the two modalities that are represented by keyword and visual feature clusters. The rules are obtained oftline based on the inverted file, and utilized in query process online to realize the integration of the two modalities of web im- ages. Our approach improves the retrieval performance and is scalable well, as well as satisfies the requirement of the web users with no additional interactions. The exper- iments are carried out in our web image retrieval system named VAST （VisuAl ＆ SemanTic image search）, and the results show the effectiveness of the proposed approach.     

Multi-Feature Based Bayesian Segmentation of Cerebrovascular from Time-of-flight Magnetic Resonance Angiography

Vessel analysis in medical images is important both for diagnostic and intervention planning purposes, especially, a three-dimensional representation of vasculature can be extremely important in image-guided neurosurgery and pre-surgical planning. In this paper, a Bayesian approach is proposed to aggregating geometric shape and intensity features for whole cerebrovascular tree extraction from Time-of-flight Magnetic resonance angiography （TOF-MRA）, while most of the current segmentation methods solely deal with the latter. In this method, we first utilige scale space analysis to get shape feature of blood vessels, then both shape and speed features are incorporated into a Bayesian segmentation framework. Maximum a posterior （MAP） method is used to estimate the posterior probabilities of vessel and background for classi- fication. The experimental results show that the proposed method can obtain a better quality of segmentation than those sole feature utilized methods.     

Parameterized Integrated Power and Performance （PIPP） Model for Ultra High-Performance of TOPS level DSP

Amdahl＇s law is a simple and fundamen- tal tool for understanding the evolution of performance as a function of parallelism. Following a recent trend on timing and power analysis of general purpose many-core chip using this law, we develop a novel PIPP analytical model for evaluating the performance and power of hier- archical on-chip large-scale parallel architectures with the core number, super-node size, processing element number, and function unit number taken into consideration. We thereby investigate the influence of workload characteris- tics （Thread-level parallel TLP, Instruction-level parallel ILP and Data-level parallel DLP） on resource allocation with the restriction of performance and power. The re- sults provide some feasible options to design TOPS level DSP architecture as well as a theoretical basis for making the design more scalable.     

A Robust Novel Technique for SPICE Simulation of ESD Snap-back Characteristics

This paper presents a robust and novel technique for the circuit simulation of ESD （ElectroStatic discharge） snap-back characteristics. A new linearization scheme by introducing current as independent variable for the avalanche current model in ESD evaluation shows a good convergence behavior during ESD stress simulation. This technique is compatible with the traditional circuit simulator based on the Modified nodal analysis （MNA） like SPICE. We have implemented the well known Amerasekera＇s ESD MOSFET model in SPICE3fS. The commonly used ESD protection configurations such as GGNMOS （Gate-grounded NMOS） and GCNMOS （Gatecoupled NMOS） are simulated and the simulation results demonstrated the good convergence behavior of this new technique.     

Analysis and Design of Multi-aspect SAR System for Compressive Sensing-Based 3D Imaging

This paper focuses on the analysis and design of Multl-aspect SAR （MuSAR） system for Compressive sensing-based （CS-based） 3D imaging. For this purpose,the Point ambiguous function （PAF） is proposed to analyze the factors that dominate the Mutual coherence （MC） of MuSAR sensing matrix. The PAF contacts with the parameters and configuration of MuSAR system directly and is easy to manipulate. With PAF, the present study analyzes the factors that dominate the performance of CS-based MuSAR 3D imaging. First of all, the stochastic waveform is an excellent selection. Second, the angular-frequency-diversity can improve the robustness of 3D imaging. Finally, the finer sampling of received data could improve the robustness of MuSAR 3D imaging. Simulation experiments show the validity of conclusion.     

On l-th NMDS Codes

Linear codes with Singleton defects equivalent to 0, 1, 2 are studied, and they have good properties. They are called MDS codes, NMDS codes, NNMDS cdoes, respectively. In this paper, we study linear codes with large Singleton defects and these codes have same good properties with MDS codes, NMDS codes, NNMDS codes. We call them l-th NNIDS codes. A series of iff con- ditions of l-th NMDS codes is presented. And we give an upper bound on length of l-th NMDS codes. In the last, some examples of l-th NMDS codes are given.     

Channel Mismatches Correction in Time-Interleaved ADCs Based on Hybrid Filter Bank Reconstruction

Time-interleaved analog-to-digital con- verter （TIADC） is an efficient way to achieve higher sam- pling rate for medium-to-high resolution applications. The performance of a TIADC suffers from the mismatch errors among the sub-channels. This paper presents a method to estimate the channel mismatches using the sub-channels output data. The proposed method introduces an equiv- alent transfer function for each channel to model and es- timate the mismatch errors. A Hybrid filter bank （HFB） structure is used to both model the TIADC and recon- struct the desired uniformly sampled sequence based on the perfect reconstruction conditions of the HFB system. A four-channel 12-bit 400MHz TIADC has been imple- mented in hardware to verify the proposed calibration method. The measured results show that the Spurious- free dynamic range （SFDR） can be improved up to 74dB after being corrected with 64-tap Finite-impulse response （FIR） filters.     

The Price of Anarchy of Network Coding and Routing Based on an ACS Pricing Mechanism

To emphasize the decisions of all users, and the total number of users sharing the same technique, we propose a novel Average cost sharing （ACS） pricing mechanism to study the game between Network coding （NC） and routing flows sharing a single link when users are price anticipating. We characterize the worst-case efficiency bounds of the game compared with the optimal （i.e., the Price of anarchy （PoA））, which can be as low as 4/9 when the number of routing users becomes sufficiently large. NC cannot improve the PoA significantly under ACS. However, to achieve a more efficient use of limited resources, this approach indicates the sharing users have a greater tendency to choose NC. However, the users will follow the majority users＇ choice of data transmission technique.     

Carrier Frequency Offset Estimation for MIMO over Spatially Correlated Flat-fading Channels

This paper deals with Carrier frequency offset （CFO） estimation for Multiple-input multiple-output （MIMO） systems. A Rayleigh fiat-fading channel model is considered and spatial correlation among channels corresponding to different pairs of transmit and receive antennas is taken into account. A Data-aided （DA） Maximum likelihood （ML）CFO estimator based on the marginal likeli- hood function is proposed. It can exploit spatial diversity and make use of the knowledge of spatial correlation by averaging the conditional likelihood function over all realizations of the channel. The Cramer-Rao bound （CRB） for the problem is derived as a benchmark. Simulation results are given to illustrate the performance of the pro- posed estimator. It is shown that its performance is close to the CRB at high Signal-to-noise ratio （SNR） region and improved with decrease of the spatial correlation and in- crease of the number of receive antennas.     

An Enhanced IRC Algorithm for LTE Downlink Receiver in Multi-cell Environment

This paper proposes an enhanced Interfer- ence rejection combining （IRC） algorithm for Long term evolution （LTE） downlink receiver in multi-cell communi= cation systems. In this algorithm, a proper Multiple input multiple output （MIMO） receive method is adopted ac- cording to Generalized likelihood ratio test （GLRT） inter- cell interference detection. Iteration between channel es- timation and data detection is carried out to improve the performance of IRC algorithm. Simulation results show that this proposed algorithm can ei~ectively detect inter- cell interference and improve Block error rate （BLER） performance and channel estimation Mean squared error （MSE） compared to non-iterative IRC algorithm, making it suitable for LTE downlink receiver in multi-cell cellular systems.     

A New Weighted Least Squares Support Vector Machines and Its Sequential Minimal Optimization Algorithm

Least squares support vector machines （LSSVM） is widely used in pattern recognition and artificial intelligence domain in recent years for its efficiency in classification and regression. The solution of LSSVM is an optimization problem of a Sum squared error （SSE） cost function with only equality constraints and can be solved in a simple linear system. However, its generalization performance is sensitive to noise points and outliers that are often existent in training dataset. In order to endow robustness to LSSVM, a new method for computing weight vector of error is proposed and the substituting of weighted error vector for original error vector in LSSVM gives birth to a new weighted LSSVM. The method gets weight factor by computing distance between sample and its corresponding class center. Sequential minimal optimization （SMO） algorithm is also extended to the new method for its efficient application. Comparison experiments show superiority of the new method in terms of generalization performance, robust property and sparse approximation. Especially, the new method is much faster than the other method for large number of samples.     

Distributed Compressive Video Sensing with Adaptive Measurements Based on Structural Similarity

This paper presents a Distributed compressive video sensing scheme with Adaptive measurements （DCVS-AM）. In this approach, the key frame in each Group of pictures （GOP） is coded by Compressive sensing （CS） with a fixed measurement rate; whereas other frames in the same GOP are compressed by an adaptive random projection in two stages, yielding the Adaptive compressive sensing （ACS） frames. The first stage uses a small and fixed measurement rate and recovers a coarse version. In the second stage, each coarse-version ACS-frame together with its proceeding and following key frames will go through a joint analysis at the decoder side and the analysis result - Structural similarity （SSIM） that is based on a motion-guided interpolation and calculated in a multilevel discrete wavelet transform domain - is sent back to the encoder side to facilitate a re-sampling of the ACS-frame with an adaptive measurement rate. Experimental results show that our proposed DCVS-AM consistently outperforms the state-of-the-art DCVS with a fixed measurement.     

Multiuser Frequency Synchronization for Interleaved-OFDMA Uplink Systems

This paper investigates the frequency synchronization in the uplink of the Orthogonal frequency- division multiple access （OFDMA） system with interleaved subcarrier assignment. In such a system, one of the key problems is the multiuser frequency synchronization, which focuses on the multiple-parameter estimation of the Carrier frequency offsets （CFOs）. In this paper, we propose a two-stage frequency offset estimation algorithm. The main advantage of the proposed method is that it can obtain the CFOs of all users simultaneously using only one OFDMA block. In addition, a novel CFO-compensation method is presented. Based on the inner signal structure of interleaved-OFDMA uplink, the new scheme uses adaptive beamformer to compensate the CFO of each user and isolate the signals of all users at the same time. Compared to the previously known methods, the proposed algorithm can provide accurate frequency synchronization and data detection without feeding CFO estimates back to active users for frequency adjustment.