We present an iterative equalization scheme that accounts for both inter-carrier interference (ICI) and inter-symbol interference (ISI) on multicarrier communication systems without a cyclic prefix (CP). We employ two equalizers based on the BCJR algorithm that process data in the two-dimensional (2D) time-frequency space; the equalizers exchange soft-bit information with each other and with a channel decoder. We generalize an existing iterative maximum likelihood equalizer (IMLE) to account for ICI-ISI and compare against it. We also use a 1D ICI-only BCJR equalizer with full CP as a baseline. We show that, in presence of moderate amounts of ICI-ISI, our equalizers improve bit error rate (BER) by exploiting Doppler and time diversity. In fact, our CP-free ICI-ISI equalizers achieve the same performance as the full CP ICI-only equalizer on some channels, which results in a data throughput gain. Furthermore, BERs as low as 10-6are achieved in high-spread channels that surpass the orthogonal time-frequency space (OTFS) crystallization condition for interference-free transmission. Even when perfect channel state information (CSI) is not available, our equalizers are more robust, outperforming the IMLE despite higher CSI estimation error variance.

We propose a three-track detection system for two dimensional magnetic recording (TDMR) in which a local area influence probabilistic (LAIP) detector works with a trellis-based Bahl-Cocke-Jelinek-Raviv (BCJR) detector to remove intersymbol interference (ISI) and intertrack interference (ITI) among coded data bits as well as media noise due to magnetic grain-bit interactions. Two minimum mean-squared error (MMSE) linear equalizers with different response targets are employed before the LAIP and BCJR detectors. The LAIP detector considers local grain-bit interactions and passes coded bit log-likelihood ratios (LLRs) to the channel decoder, whose output LLRs serve as a priori information to the BCJR detector, which is followed by a second channel decoding pass. Simulation results under 1-shot decoding on a grain-flipping-probability (GFP) media model show that the proposed LAIP/BCJR detection system achieves density gains of 10.16% for center-track detection and 3.13% for three-track detection compared to a standard BCJR/1D-PDNP. The proposed system's BCJR detector bit error rates (BERs) are lower than those of a recently proposed two-track BCJR/2D-PDNP system by factors of (0.55, 0.08) for tracks 1 and 2 respectively.

The performance of traditional compressive sensing (CS) architectures has been tempered by dynamically changing real-world data. This paper demonstrates an information-aware compressive sensing (CS) architecture for dynamic artifact detection of biophysiological signals in wearable applications. Artifacts such as long pause, baseline wandering, and saturation often corrupt recorded data due to environmental factors. In wearable applications where power conservation and ultra-low power operation are paramount, this can lead to wasted power. By combining earlier proposed CS based architectures with an efficient analog feature-extraction (FE) and digital decision making, the sampling rate of the ADC and the integration window of the multiplying DAC can be reduced in presence of artifacts to save power. As shown, this technique can reduce the system power consumption by up to 70% in the more extreme cases of signal corruption. A serialized Walsh-Hadamard Transform (WHT) used for FE is proposed that dramatically simplifies the circuit implementation while the digital classifier comprising of quadratic Support Vector Machine (SVM) classifier ensures low power operation with accurate decision outcomes.

We present an extrinsic information transfer (EXIT) chart-based design technique for irregular repeat-accumulate (IRA) codes used in 2-D magnetic recording (TDMR) turbo-equalization systems. The channel model includes Voronoi magnetic grains, 2-D intersymbol interference (2D-ISI) and additive white Gaussian noise (AWGN). The receiver uses a 2D-ISI BCJR equalizer and an IRA decoder. For one outer equalizer-decoder iteration, we propose theory and simulation-based methods for computing EXIT curves. The simulation method calculates experimental EXIT curves for the check node decoder (CND) and the combination of the variable node decoder (VND) and an equalizer. The theoretical approach recursively calculates CND and VND Gaussian mixture model parameters in order to calculate EXIT curves. We then fit the VND and CND EXIT curves to find optimized variable node degree distributions. Simulation results show that the TDMR-optimized IRA codes achieve up to a 6.2% density increase in user bits/grain (U/G) compared with IRA codes designed for AWGN channels. The theory-based code designs achieve the same or better U/G as the simulation-based designs, but require 98% less design computation time. We also derive optimized IRA codes for iterative turbo-equalization; these codes can achieve simultaneous U/G gains and SNR savings compared with AWGN-optimized codes.

This paper considers turbo equalization for 2-D magnetic recording. Magnetic grains are modeled as Voronoi regions of randomly distributed nuclei. Bits read from the magnetic grain model flow into a 2-D intersymbol interference (2D-ISI) model including additive white Gaussian noise. At high bit densities, some bits are not written on any grain, and hence are effectively “overwritten” by surrounding bits. The proposed system iteratively exchanges log-likelihood ratios (LLRs) between a 2D-ISI equalizer based on the forward-backward algorithm and an irregular repeat-accumulate (IRA) decoder. To combat bit overwrites, the system employs a non-linear function to map 2D-ISI extrinsic output LLRs to IRA decoder input LLRs. To pass back LLRs from the IRA decoder to the 2D-ISI equalizer, we design a simple likelihood-ratio-based LLR estimator. Simulations of the proposed system that employ the perturbed-bit-centers grain model proposed in a 2010 IEEE Transactions on Magnetics paper show a 6.5% increase in user bits per grain (U/G) and a 16.4 dB signal-to-noise ratio (SNR) gain compared with the previous paper, without iterative turbo equalization. Utilizing the LLR estimator to do iterative detection results in SNR gains of up to 1.7 dB compared with non-iterative detection. The random Voronoi model employed in this paper appears to be more difficult to equalize than the grain model in the 2010 paper. The proposed system with random Voronoi model achieves 0.4422 U/G at SNR =11.6 dB, i.e., about 8.8 Tb/in2at (typically assumed future grain density) 20 Tgr/in2; this is almost ten times the density of current systems at 10 Tgr/in2.

We present a novel iterative soft decision feedback zig-zag algorithm for detection of binary images corrupted by two dimensional intersymbol interference and additive white Gaussian noise. The algorithm exchanges soft information between maximum-a-posteriori detectors employing different zigzag scan directions. Each detector exploits soft-decision feedback from the other zig-zag detectors. Simulation results for the 2 × 2 averaging mask channel show that, at low signal-to-noise ratios, the new algorithm gains about 1 dB over an iterative row column soft decision feedback algorithm and over a separable mask algorithm, two of the best previously published schemes. When the zig-zag algorithm is concatenated with the row-column algorithm, the concatenated system performs as well as or better than four of the best previously published algorithms, at both low and high signal-to-noise ratios, for a variety of 2 × 2 and 3 × 3 convolution masks; in several cases, the system performs within less than 0.1 dB of the maximum-likelihood performance bound.

We consider BCJR-like soft-input soft-output (SISO) iterative detection algorithms for 1D and 2D binary-input ISI channels with AWGN. The complexity of BCJR algorithms grows exponentially with the size of the ISI mask and is an important concern with their implementation. We consider new techniques to reduce the complexity of BCJR algorithms by decreasing the effective number of states in the trellis. The proposed state reduction technique does particularly well for mixed phase sequence ISI masks, which have higher weights for the center taps and lower weights for the peripheral taps. Other complexity reduction techniques proposed in the literature perform poorly for such masks. Moreover, the complexity of the proposed state reduction technique is comparable to other reduced complexity techniques reported in the literature. Experimental results are provided to demonstrate the advantages of the proposed state reduction technique.

Data transmission in ad hoc networks involves interactions between medium access control (MAC)-layer protocols and data forwarding along network-layer paths. These interactions have been shown to have a significant impact on the performance of a system. This impact on multipath data transmission over multihop IEEE 802.11 MAC-based ad hoc networks is assessed; analysis is from a cross-layer perspective. Both MAC layer protocols and network-layer data forwarding are taken into account in the system models. The frame service time at source in a 802.11 MAC-based multipath data transmission system under unsaturated conditions is studied. Analytical models are developed for two packet generation schemes (round robin and batch) with a Poisson frame arrival process. Moreover, an analytical model is developed to investigate the throughput of a multipath transmission system in 802.11-based multihop wireless networks. Two methods are proposed to estimate the impact of cross-layer interactions on the frame service time in such a system. Two bounds of the system throughput are obtained based on these estimation methods. These models are validated by means of simulation under various scenarios.

We present a novel iterative row-column soft decision feedback algorithm (IRCSDFA) for detection of binary images corrupted by 2-D intersymbol interference and additive white Gaussian noise. The algorithm exchanges weighted soft information between row and column maximuma posteriori(MAP) detectors. Each MAP detector exploits soft-decision feedback from previously processed rows or columns. The new algorithm gains about 0.3 dB over the previously best published results for the 2times2 averaging mask. For a non-separable 3times3 mask, the IRCSDFA gains 0.8 dB over a previous soft-input/soft-output iterative algorithm which decomposes the 2-D convolution into 1-D row and column operations.

This paper considers the problem of learning the parameters of a Bayesian network, assuming the structure of the network is given, from a privacy-sensitive dataset that is distributed between multiple parties. For a binary-valued dataset, we show that the count information required to estimate the conditional probabilities in a Bayesian network can be obtained as a solution to a set of linear equations involving some inner product between the relevant different feature vectors. We consider a random projection-based method that was proposed elsewhere to securely compute the inner product (with a modified implementation of that method).

Defending the safety of an open society from terrorism or other similar threats requires intelligent but careful ways to monitor different types of activities and transactions in the electronic media. Data mining techniques are playing an increasingly important role in sifting through large amount of data in search of useful patterns that might help us in securing our safety. Although the objective of this class of data mining applications is very well justified, they also open up the possibility of misusing personal information by malicious people with access to the sensitive data. This brings up the following question: Can we design data mining techniques that are sensitive to privacy? Several researchers are currently working on a class of data mining algorithms that work without directly accessing the sensitive data in their original form. This paper considers the problem of mining distributed data in a privacy-sensitive manner. It first points out the problems of some of the existing privacy-sensitive data mining techniques that make use of additive random noise to hide sensitive information. Next it briefly reviews some new approaches that make use of random projection matrices for computing statistical aggregates from sensitive data.

Privacy is becoming an increasingly important issue in many data mining applications. This has triggered the development of many privacy-preserving data mining techniques. A large fraction of them use randomized data distortion techniques to mask the data for preserving the privacy of sensitive data. This methodology attempts to hide the sensitive data by randomly modifying the data values often using additive noise. We question the utility of the random value distortion technique in privacy preservation. We note that random objects (particularly random matrices) have "predictable" structures in the spectral domain and it develops a random matrix-based spectral filtering technique to retrieve original data from the dataset distorted by adding random values. We present the theoretical foundation of this filtering method and extensive experimental results to demonstrate that in many cases random data distortion preserve very little data privacy. We also point out possible avenues for the development of new privacy-preserving data mining techniques like exploiting multiplicative and colored noise for preserving privacy in data mining applications.

We propose a collective method to address the problem of learning the structure of a Bayesian network from a distributed heterogeneous data sources. In this case, the dataset is distributed among several sites, with different features at each site. The collective method has four steps: local learning, sample selection, cross learning, and combination of the results. The parents of local nodes can be correctly identified in local learning. The main task of cross learning is to identify the links whose vertices are in different sites (cross links). This is done by transmitting a small subset of samples from each local site to a central site. The combination step involves removing extra links from local Bayesian networks that may be introduced during local learning due to the well known hidden variable problem. The sample selection step selects samples, based on a likelihood criterion, that are possibly evidence of cross links. The overall procedure is called collective learning. Experimental results verify that, for sparsely connected networks, the collective learning method can learn the same structure as that obtained by a centralized learning method (which simply aggregates data from all local sites into a single site).

We present a novel approach for learning parameters of a Bayesian network from distributed heterogeneous dataset. In this case, the whole dataset is distributed in several sites and each site contains observations for a different subset of features. The new method uses the collective learning approach proposed in our earlier work and substantially reduces the computational and transmission overhead. Theoretical analysis is given and experimental results are provided to illustrate the accuracy and efficiency of our method.

We present a collective approach to mining Bayesian networks from distributed heterogenous Web-log data streams. In this approach we first learn a local Bayesian network at each site using the local data. Then each site identifies the observations that are most likely to be evidence of coupling between local and non-local variables and transmits a subset of these observations to a central site. Another Bayesian network is learnt at the central site using the data transmitted from the local site. The local and central Bayesian networks are combined to obtain a collective Bayesian network that models the entire data. We applied this technique to mining multiple data streams, where data centralization is difficult because of large response time and scalability issues. Experimental results and theoretical justification that demonstrate the feasibility of our approach are presented.

The problem of parameter estimation for a binary Markov random field, from a given realization, is addressed. A strongly consistent estimator for the clique potentials, based on morphological erosions, is proposed. Experimental results demonstrate the effectiveness of the proposed approach.

The authors consider the problem of estimating the parameters of a binary Markov random field from a given realization. They propose a consistent estimator of the clique potentials that can be implemented by means of morphological erosions. Experimental results demonstrate the effectiveness of the proposed approach.

A new class of Gibbs random fields (GRFs) is proposed capable of modeling geometrical constraints in images by means of mathematical morphology. The proposed approach, known as morphologically constrained GRFs, models images by means of their size density. Since the size density is a multiresolution statistical summary, morphologically constrained GRFs explicitly incorporate multiresolution information into image modeling. Important properties are studied and their implication to texture synthesis and analysis is discussed. Statistical inference can be easily implemented by means of mathematical morphology. This allows the design of a computationally simple morphological Bayes classifier which produces excellent results in classifying natural textures.

Presents a comprehensive discussion on the segmentation of mammograms using morphological texture features. These features are derived from morphological granulometries with various structuring elements. Each structuring element captures a specific texture content. The segmentation is carried out in an unsupervised manner by applying the KL (Karhunen-Loeve) transform feature reduction and Voronoi clustering on the extracted morphological texture features. The evaluation of the segmentation outcome by a trained radiologist is provided.

We revisit the problem of non-linear image filtering by means of maximum-a-posteriori (MAP) estimation and mathematical morphology. Based on "mild" modeling assumptions, we bound the optimal MAP estimate from above and below and provide explicit morphological expressions for such bounds. The derived bounds can be easily calculated and effectively used to design a fast MAP algorithm.

We propose a class of Gibbs random fields which incorporate geometric information into stochastic image modeling by means of morphological constraints. This class is shown to be capable of modeling shapes with given morphological size density. Simulation examples illustrate some model properties.

We present new results on the problem of morphologically sampling binary images and image operators, thus enhancing the morphological sampling theory of Heijmans and Toet. These include sampling composite operators and approximating continuous translation invariant erosions, openings, and closings by discrete erosions, openings, and closings, respectively. The results presented in this paper have been already used in a theory of morphologically sampling random binary images modeled as random closed sets.

We theoretically formulate the problem of processing continuous-space binary random fields by means of mathematical morphology. This may allow us to employ mathematical morphology to develop new statistical techniques for the analysis of binary random images. Since morphological transformations of continuous-space binary random fields are not measurable in general, we are naturally forced to employ intermediate steps that require generation of an equivalent random closed set. The relationship between continuous-space binary random fields and random closed sets is thoroughly investigated. As a byproduct of this investigation, a number of useful new results, regarding separability of random closed sets, are presented. Our plan, however, suffers from a few technical problems that are prominent in the continuous case. As an alternative, we suggest morphological discretization of binary random fields, random closed sets, and morphological operators, thereby effectively implementing our problem in the discrete domain.

Modeling image data by means of random fields and developing statistical techniques for the processing and analysis of these random fields is an important problem in image processing and analysis. We theoretically formulate the problem of processing binary random fields by means of mathematical morphology. This may allow us employ mathematical morphology in order to develop new statistical techniques for the processing and analysis of random shapes modeled as binary random fields. Since morphological transformations of continuous space binary random fields are not measurable in general, we employ intermediate steps which require generation of an equivalent random closed set. The relationship between binary random fields and random closed sets is thoroughly investigated. As a by-product of this investigation, a number of useful new results, regarding separability of random closed sets, are presented.

This paper presents a unifying approach to the problem of morphologically processing image sequences (or, equivalently, vector-valued images) by means of lattice theory, thus providing a mathematical foundation for vector morphology. Lattice theory is an abstract algebraic tool that has been extensively used as a theoretical framework for scalar morphology (i.e., mathematical morphology applied on single images). Two approaches to vector morphology are discussed. According to the first approach, vector morphology is viewed as a natural extension of the well-known scalar morphology. This approach formalizes and generalizes Wilson's matrix morphology and shows that the latter is a direct consequence of marginal vector ordering. The derivation of the second approach is more delicate and requires careful treatment. This approach is a direct consequence of a vector transformation followed by marginal ordering. When the vector transformation is the identity transformation, the two approaches are equivalent. A number of examples demonstrate the applicability of the proposed theory in a number of image processing and analysis problems.