Relaxation procedure for phase retrieval of nonnegative signals

The paper presents an iterative procedure called the relaxation of autocorrelation equations (RAE) for solving the phase retrieval problem for nonnegative signals. First, the phase retrieval problem is formulated in the spatial domain as a set of polynomial equations with autocorrelations as known data and signal values as unknowns. Then, the RAE procedure solves these equations by recognizing one unknown at a time. While other unknowns are held constant at previously estimated values, a single unknown is varied inside the nonnegative region to globally minimize the sum of squared residuals of the equations with respect to the unknown. In every iteration, this procedure is repeated for each signal value. Since the sum of squared residuals is nonincreasing, the algorithm will either converge to a solution or stagnate; ways to overcome stagnation are suggested. The key feature of the RAE procedure is that unlike iterative transform algorithms, it allows direct control over bounding values of the signal at all times. Several numerical examples illustrate the RAE procedure     

基于边信息改进的分布式信源编码方案

针对现有的非对称分布式信源编码(DSC)方案均存在的在误比特率(BER)以及压缩率方面的不足，该文提出基于边信息改进的DSC(DSCUISI)方案。发送方对信源序列进行抽样，将序列分为抽样与未抽样子序列，利用算术编码器对未抽样子序列进行压缩，同时计算抽样子序列的伴随式。接收方利用边信息序列与未抽样子序列之间的相关性，对抽样符号进行估计，估计出的序列与原始抽样子序列的相关性得到改进。最后利用原始抽样子序列的伴随式与估计出的序列进行联合译码以重建原始抽样子序列。实验结果表明:与基于低密度奇偶校验码和算术码的DSC方案相比，该文所提方案在信源内部相关性较强时具有压缩率高、在信源间相关度不高时则有重建错误率低的特点，是一种高效、实用且易于实现的DSC方案。     

Differential privacy protection scheme based on edge betweenness model

With the continuous development of social network application,user’s personal social data is so sensitive that the problem of privacy protection needs to be solved urgently.In order to reduce the network data sensitivity,a differential privacy protection scheme BCPA based on edge betweenness model was proposed.The 2K sequence corresponding to the graph structure based on the dK model was captured,and 2K sequences based on the edge betweenness centrality were reordered.According to the result of reordering,the 2K sequence was grouped into several sub-sequences,and each sub-sequence was respectively added with noise by a dK perturbation algorithm.Finally,a social network graph satisfying differential privacy was generated according to the new 2K sequences after integration.Based on the real datasets,the scheme was compared with the classical schemes through simulation experiments.The results demonstrate that it improves the accuracy and usability of data while ensuring desired privacy protection level.     

一种自适应数据逐层分解的Reed-Solomon码迭代纠错方法及应用

该文针对Reed-Solomon码纠错算法计算复杂度较高、运算时间较长等问题,提出一种自适应数据逐层分解的Reed-Solomon码的迭代译码纠错方法。首先,接收码通过逐层分解将随机错误或突发错误分散于不同的子序列中,缩小突发或随机错误的查找范围;其次,制定约束规则确定错误数目,同时根据不同的伴随矩阵维数自适应选择迭代求解关键方程的方法,定位子序列中误码的位置;最后,计算正确码字,结束纠错。实验测试表明,该算法在保证不漏检误码的前提下,能够有效简化计算多项式的维数,减少计算量和复杂度,纠错时效优于DFT(Discrete Fourier Transform)算法和BM(Berlekamp-Massey)算法。特别是对2维码数据的纠错测试中,与传统算法相比,该算法纠错时效可提升一个数量级。     

An Algorithm for Hardware/Software Partitioning Using Mixed Integer Linear Programming

One of the key problems in hardware/software codesign is hardware/software partitioning. This paper describes a new approach to hardware/software partitioning using integer programming (IP). The advantage of using IP is that optimal results are calculated for a chosen objective function. The partitioning approach works fully automatic and supports multi-processor systems, interfacing and hardware sharing. In contrast to other approaches where special estimators are used, we use compilation and synthesis tools for cost estimation. The increased time for calculating values for the cost metrics is compensated by an improved quality of the values. Therefore, fewer iteration steps for partitioning are needed. The paper presents an algorithm using integer programming for solving the hardware/software partitioning problem leading to promising results.     

Multilevel codes for unequal error protection

Two combined unequal error protection (UEP) coding and modulation schemes are proposed. The first method multiplexes different coded signal constellations, with each coded constellation providing a different level of error protection. In this method, a codeword specifies the multiplexing rule and the choice of the codeword from a fixed codebook is used to convey additional important information. The decoder determines the multiplexing rule before decoding the rest of the data. The second method is based on partitioning a signal constellation into disjoint subsets in which the most important data sequence is encoded, using most of the available redundancy, to specify a sequence of subsets. The partitioning and code construction is done to maximize the minimum Euclidean distance between two different valid subset sequences. This leads to ways of partitioning the signal constellations into subsets. The less important data selects a sequence of signal points to be transmitted from the subsets. A side benefit of the proposed set partitioning procedure is a reduction in the number of nearest neighbors, sometimes even over the uncoded signal constellation     

A column generation and partitioning approach for multi-commodity flow problems

Multi-commodity network flow problems, prevalent in transportation, production and communication systems, can be characterized by a set of commodities and an underlying network. The objective is to flow the commodities through the network at minimum cost without exceeding arc capacities. In this paper, we present a partitioning solution procedure for large-scale multi-commodity flow problems with many commodities, such as those encountered in the telecommunications industry. Using a cycle-based multi-commodity formulation and column generation techniques, we solve a series of reduced-size linear programs in which a large number of constraints are relaxed. Each solution to a reduced-size problem is an improved basic dual feasible solution to the original problem and, after a finite number of steps, an optimal multi-commodity flow solution is determined. Computational experience is gained in solving randomly generated test problems and message routing problems in the communications industry. The tests show that the procedure solves large-scale multi-commodity flow problems significantly faster than existing linear programming or column generation solution procedures.     

A modified trellis coding technique for partial response channels

The problem of trellis coding for multilevel baseband transmission over partial response channels with transfer polynomials of the form (1±D^N) is addressed. The novel method presented here accounts for the channel memory by using multidimensional signal sets and partitioning the signal set present at the noiseless channel output. It is shown that this coding technique can be viewed as a generalization of a well-known procedure for binary signaling: the concatenation of convolutional codes and inner block codes that are tuned to the channel polynomial. It results in high coding gains with moderate complexity if some bandwidth expansion is accepted     

Image motion estimation algorithms using cumulants

A class of algorithms is presented that estimates the displacement vector from two successive image frames consisting of signal plus noise. In the model, the signals are assumed to be either non-Gaussian or (quasistationary) deterministic; and, via a consistency result for cumulant estimators, the authors unify the stochastic and deterministic signal viewpoints. The noise sources are assumed to be Gaussian (perhaps spatially and temporally correlated) and of unknown covariance. Viewing image motion estimation as a 2D time delay estimation problem, the displacement vector of a moving object is estimated by solving linear equations involving third-order auto-cumulants and cross-cumulants. Additionally, a block-matching algorithm is developed that follows from a cumulant-error optimality criterion. Finally, the displacement vector for each pel is estimated using a recursive algorithm that minimizes a mean 2D fourth-order cumulant criterion. Simulation results are presented and discussed.     

Performance of the maximum likelihood constant frequency estimatorfor frequency tracking

The performance of maximum likelihood (ML) estimators for an important frequency estimation problem is considered when the signal model assumptions are not valid. The motivation for this problem is to understand the robustness of the hidden Markov model-maximum likelihood (HMM-ML) tandem frequency estimator, where the signal is divided into time blocks, and the frequency in each time block is estimated using the ML approach under the assumption that the signal has a constant frequency in each time block. In order to analyze the sensitivity of ML estimators to the model assumptions, the mean frequency of a discrete complex tone that has a time-varying (ramp) frequency is estimated under the incorrect assumption that it has a constant frequency. In particular, the behavior of the threshold region with respect to different chirp rates is analyzed, and a simple rule is given. The mean squared error above the threshold region is shown to be constant even at very high SNR levels. These results are supported by simulations     

Propagation delay estimation in asynchronous direct-sequencecode-division multiple access systems

In an asynchronous direct-sequence code-division multiple access (DS-CDMA) communication system, the parameter estimation problem, i.e., estimating the propagation delay, attenuation and phase shift of each user's transmitted signal, may be complicated by the so-called near-far problem. The near-far problem occurs when the amplitudes of the users received signals are very dissimilar, as the case might be in many important applications. In particular, the standard method for estimating the propagation delays will fail in a near-far situation. Several new estimators, the maximum likelihood, an approximative maximum likelihood and a subspace-based estimator, are therefore proposed and are shown to be robust against the near-far problem. No knowledge of the transmitted bits is assumed, and the proposed estimators can thus be used for both acquisition and tracking. In addition, the Cramer-Rao bound is derived for the parameter estimation problem     

Blind symbol-timing and frequency-offset estimation in OFDM systems with real data symbols

In this letter, the problem of blind symbol-timing and frequency-offset estimation in orthogonal frequency-division multiplexing (OFDM) systems with real data symbols is considered. Maximum-likelihood (ML) estimators for the parameters of interest have been derived by modeling the OFDM signal as a proper complex Gaussian random process. However, when data symbols belong to a real constellation, the received signal becomes an improper complex random process, and hence, previously mentioned estimators, termed cyclic prefix (CP) estimators, are not ML estimators. In this letter, minimum mean-squared error estimators exploiting the conjugate-symmetry property exhibited by the OFDM signal with real data symbols, termed MSR estimators, that greatly outperform CP estimators in additive noise channels, are derived. Moreover, a modified MSR symbol-timing estimator that can assure satisfactory performance in multipath fading channels is proposed.     

Region-based image coding using polynomial intensity functions

The vast majority of coded images are real-world images. These images consist of distinct objects within a scene, where each object has its own reflective, textural and lighting characteristics. Region-based image coding encodes these images by partitioning the scene into objects, and then describing each object's characteristics using a set of parameters. The paper uses orthonormal polynomial functions to describe the lighting and reflective characteristics of each object. The coefficients of these polynomials are coded with linear quantisers that have their decision boundaries spaced according to rate-distortion considerations. The textural component of each object is coded using vector quantisation of the autocorrelation coefficients of the residual. The partitioning of the image into distinct objects is achieved with a segmentation algorithm which attempts to maximise the rate-distortion performance of the encoding procedure as a whole. In doing so, the segmentation algorithm partitions the image into distinct objects as well as providing estimates for the optimal bit allocations among the polynomial coefficients. Results generated by this method show reconstructions with quality superior to other region-based methods, both objectively and subjectively     

Communication and Coordination in Wireless Sensor and Actor Networks

In this paper, coordination and communication problems in wireless sensor and actor networks (WSANs) are jointly addressed in a unifying framework. A sensor-actor coordination model is proposed based on an event-driven partitioning paradigm. Sensors are partitioned into different sets, and each set is constituted by a data-delivery tree associated with a different actor. The optimal solution for the partitioning strategy is determined by mathematical programming, and a distributed solution is proposed. In addition, a new model for the actor-actor coordination problem is introduced. The actor coordination is formulated as a task assignment optimization problem for a class of coordination problems in which the area to be acted upon needs to be optimally split among different actors. An auction-based distributed solution of the problem is also presented. Performance evaluation shows how global network objectives, such as compliance with real-time constraints and minimum energy consumption, can be achieved in the proposed framework with simple interactions between sensors and actors that are suitable for large-scale networks of energy-constrained devices.     

Set theoretic signal restoration using an error in variablescriterion

The restoration of a signal degraded by a stochastic impulse response is formulated as a problem with uncertainties in both the measurements and the impulse response. The method of total least squares, and variants thereof, are effective techniques for solving this class of problems. However, unlike set theoretic estimation schemes, these methods do not allow the incorporation of other a priori information in the estimate. In this correspondence, two new sets motivated by total least squares are introduced for set theoretic estimation. The convexity of these sets is established and the projection operators onto these sets are given. Through simulations, the advantages of the new technique over conventional and older set theoretic schemes for restoration are demonstrated.     

Efficient variable partitioning and scheduling for DSP processors with multiple memory modules

Multiple on-chip memory modules are attractive to many high-performance digital signal processing (DSP) applications. This architectural feature supports higher memory bandwidth by allowing multiple data memory accesses to be executed in parallel. However, making effective use of multiple memory modules remains difficult. The performance gain in this kind of architecture strongly depends on variable partitioning and scheduling techniques. In this paper, we propose a graph model known as the variable independence graph (VIG) and algorithms to tackle the variable partitioning problem. Our results show that VIG is more effective than interference graph for solving variable partitioning problem. Then, we present a scheduling algorithm known as the rotation scheduling with variable repartition (RSVR) to improve the schedule lengths efficiently on a multiple memory module architecture. This algorithm adjusts the variable partitions during scheduling and generates a compact schedule based on retiming and software pipelining. The experimental results show that the average improvement on schedule lengths is 44.8% by using RSVR with VIG. We also propose a design space exploration algorithm using RSVR to find the minimum number of memory modules and functional units satisfying a schedule length requirement. The algorithm produces more feasible solutions with equal or fewer number of functional units compared with the method using interference graph.     

Maximum minimal distance partitioning of the /spl Zopf//sup 2/ lattice

We study the problem of dividing the /spl Zopf//sup 2/ lattice into partitions so that minimal intra-partition distance between the points is maximized. We show that this problem is analogous to the problem of sphere packing. An upper bound on the achievable intra-partition distances for a given number of partitions follows naturally from this observation, since the optimal sphere packing in two dimensions is achieved by the hexagonal lattice. Specific instances of this problem, when the number of partitions is 2/sup m/, were treated in trellis-coded modulation (TCM) code design by Ungerboeck (1982) and others. It is seen that methods previously used for set partitioning in TCM code design are asymptotically suboptimal as the number of partitions increases. We propose an algorithm for solving the /spl Zopf//sup 2/ lattice partitioning problem for an arbitrary number of partitions.     

A Systematic Set Partitioning for Unitary Space-Time Signal Sets

Proper partitioning of a unitary space-time (UST) signal set is essential for the optimal design of trellis coded unitary space-time modulation (TC-USTM). The unique properties of these UST signals have necessitated a different partitioning methodology from that of the conventional two dimensional constellations. In this letter, we propose a systematic set partitioning through a novel subset-pairing strategy, for an arbitrary UST signal set. This approach leads to a geometrically congruent partitioning, i.e., subsets of the same size (order) have identical intra-distance profiles. Based on this partitioning, the resulting TC-USTM can achieve a minimum bit error probability.     

Resource constrained scheduling of hierarchically structured designactivity networks

In this paper, a new approach to the problem of scheduling of design activities with precedence and multiple resource constraints is proposed. In addition to the AND type relationship, OR and EXCLUSIVE OR relationships may also exist between design activities. In order to handle these logical relationships, IDEF3 is used for network representation. A large network of design activities can be arranged in different levels of abstraction. A procedure is proposed to transform an IDEF3 model into a set of alternative precedence networks. In the networks selected, the activities that are resource independent are grouped with a partitioning procedure. In order to increase the efficiency of the search for the best schedule, a procedure based on the Christofides et al. (1987) reduction procedure is introduced to determine a lower bound on the completion time of the hierarchically structured design activity network