This work presents a general framework for blindly estimating the sensor parameters of decision-fusion systems over wireless sensor networks (WSNs). The sensors report their binary decisions to a fusion center (FC) through parallel binary symmetric channels. Then, the FC makes the final decision by combining the noisy sensor decisions according to a certain fusion rule. We present an algorithm for the FC to blindly estimate the sensor parameters from the noisy sensor decisions received after a number of sensing periods. The algorithm covers a wide variety of situations that may arise in WSNs. For example, the algorithm is applicable when the FC knows in advance some of the parameters of some sensors, when it knows the true hypothesis for a subset of sensing periods, or when only a subset of sensors communicates their decisions in each sensing period. Based on the estimates of the system parameters, optimal channel-aware fusion rules are derived considering the minimum Bayes risk criterion. Simulation results show that, after sufficient sensing periods, the estimates of the WSN parameters are accurate enough for the fusion rule to exhibit near-optimal detection performance.

This work in progress innovative practice paper intends to report on the outline and the ongoing progress of the EU-project GreenChips-EDU, which has been started in October 2023, and intends to fundamentally redesign educational microelectronics programs especially but not limited to students and professionals. One of the major goals is the design of a new microelectronics master program to which six European universities are contributing. The contents of this program will be substantially enhanced with green electronics contents innovative teaching methods. Other work will be done in the field of a new MBA program, self-standing modules for professionals, and a new microelectronics bachelor designed by one university of applied sciences.

Using a subset of observed network links, high-order link prediction (HOLP) infers missing hyperedges, that is links connecting three or more nodes. HOLP emerges in several applications, but existing approaches have not dealt with the associated predictor’s performance. To overcome this limitation, the present contribution develops a Bayesian approach and the relevant predictive distributions that quantify model uncertainty. Gaussian processes model the dependence of each node to the remaining nodes. These nonparametric models yield predictive distributions, which are fused across nodes by means of a pseudo-likelihood based criterion. Performance is quantified by proper measures of dispersion, which are associated with the predictive distributions. Tests on benchmark datasets demonstrate the benefits of the novel approach.

In addressing the challenge posed by noise in actual quantum devices, the application of quantum error mitigation techniques becomes essential. These techniques are resource-efficient, making them viable for implementation in noisy intermediate-scale quantum devices, unlike the resource-intensive quantum error correction codes. A prominent example among these techniques is Clifford Data Regression, which employs a supervised learning approach. This work explores two variants of this technique, both of which add a non-trivial set of gates to the original circuit. The first variant leverages copies of the original circuit, whereas the second approach adds a layer of 1-qubit rotations.

MOTIVATION: Single-nucleotide variants (SNVs) are the most common type of genetic variation in the human genome. Accurate and efficient detection of SNVs from next-generation sequencing (NGS) data is essential for various applications in genomics and personalized medicine. However, SNV calling methods usually suffer from high computational complexity and limited accuracy. In this context, there is a need for new methods that overcome these limitations and provide fast reliable results. RESULTS: We present EMVC-2, a novel method for SNV calling from NGS data. EMVC-2 uses a multi-class ensemble classification approach based on the expectation-maximization algorithm that infers at each locus the most likely genotype from multiple labels provided by different learners. The inferred variants are then validated by a decision tree that filters out unlikely ones. We evaluate EMVC-2 on several publicly available real human NGS data for which the set of SNVs is available, and demonstrate that it outperforms state-of-the-art variant callers in terms of accuracy and speed, on average. AVAILABILITY AND IMPLEMENTATION: EMVC-2 is coded in C and Python, and is freely available for download at: https://github.com/guilledufort/EMVC-2. EMVC-2 is also available in Bioconda.

Higher-order link prediction (HOLP) seeks missing links capturing dependencies among three or more network nodes. Predicting high-order links (HOLs) can for instance reveal hyperlinks in the structure of drug substance and metabolic networks. Existing methods either make restrictive assumptions regarding the emergence of HOLs, or, they rely on reduced dimensionality models of limited expressiveness. To overcome these limitations, the HOLP approach developed here leverages distribution similarities across embeddings as captured by a learnable probability metric. The intuition underpinning the novel approach is that sets of nodes whose embeddings are less similar in distribution, are less likely to be connected by a HOL. Specifically, nonlinear dimensionality reduction is effected through a Gaussian process latent variable model that yields nodal embeddings, and also learns a data-driven similarity function (kernel). This kernel forms the core of a maximum mean discrepancy probability metric. Tests on benchmark datasets illustrate the potential of the proposed approach.

Unsupervised ensemble learning refers to methods devised for a particular task that combine data provided by decision learners taking into account their reliability, which is usually inferred from the data. Here, the variant calling step of the next generation sequencing technologies is formulated as an unsupervised ensemble classification problem. A variant calling algorithm based on the expectation-maximization algorithm is further proposed that estimates the maximum-a-posteriori decision among a number of classes larger than the number of different labels provided by the learners. Experimental results with real human DNA sequencing data show that the proposed algorithm is competitive compared to state-of-the-art variant callers as GATK, HTSLIB, and Platypus.

This letter addresses the problem of order estimation for uniform linear arrays (ULAs) with multi-switch antenna selection in the small-sample regime. Multi-switch antenna selection results in a data matrix with missing entries, a scenario for which existing order estimation methods that build on the eigenvalues of the sample covariance matrix do not perform well. A direct application of the Davis-Kahan theorem allows us to show that the signal subspace is quite robust in the presence of missing entries. Based on this finding, this letter proposes a matrix completion (MC) subspace-based order estimation criterion that exploits the shift-invariance property of ULAs. A recently proposed shift-invariant matrix completion (SIMC) method is used for reconstructing the data matrix, and the proposed order estimation criterion is based on the chordal subspace distance between two submatrices extracted from the reconstructed matrix for increasing values of the dimension of the signal subspace. Our simulation results show that the method provides accurate order estimates with percentages of missing entries higher than 50%.

This paper addresses the problem of source enumeration by an array of sensors in the presence of noise whose spatial covariance structure is a diagonal matrix with possibly different variances, referred to non-iid noise hereafter, when the sources are uncorrelated. The diagonal terms of the sample covariance matrix are removed and, after applying Toeplitz rectification as a denoising step, the signal covariance matrix is reconstructed by using a low-rank matrix completion method adapted to enforce the Toeplitz structure of the sought solution. The proposed source enumeration criterion is based on the Frobenius norm of the reconstructed signal covariance matrix obtained for increasing rank values. As illustrated by simulation examples, the proposed method performs robustly for both small and large-scale arrays with few snapshots, i.e. small-sample regime.

Prior information can be incorporated in matrix completion to improve estimation accuracy and extrapolate the missing entries. Reproducing kernel Hilbert spaces provide tools to leverage the said prior information, and derive more reliable algorithms. This paper analyzes the generalization error of such approaches, and presents numerical tests confirming the theoretical results.

Decision-making procedures, when a set of individual binary labels is processed to produce a unique joint decision, can be approached modeling the individual labels as multivariate independent Bernoulli random variables. This probabilistic model allows an unsupervised solution using EM-based algorithms, which basically estimate the distribution model parameters and take a joint decision using a maximum a posteriori criterion. These methods usually assume that individual decision agents are conditionally independent, an assumption that might not hold in practical setups. Therefore, in this work we formulate and solve the decision-making problem using an EM-based approach, but assuming correlated decision agents. Improved performance is obtained on synthetic and real datasets, compared to classical and state-of-the-art algorithms.

This paper takes on the problem of recovering the missing entries of an incomplete matrix, which is known as matrix completion, when the columns of the matrix are signals that lie on a graph and the available observations are noisy. We solve a version of the problem regularized with the Laplacian quadratic form by means of the proximal gradient method, and derive theoretical bounds on the recovery error. Moreover, in order to speed up the convergence of the proximal gradient, we propose an initialization method that utilizes the structural information contained in the Laplacian matrix of the graph.

Crowdsourcing approaches rely on the collection of multiple individuals to solve problems that require analysis of large data sets in a timely accurate manner. The inexperience of participants or annotators motivates well robust techniques. Focusing on clustering setups, the data provided by all annotators is suitably modeled here as a mixture of Gaussian components plus a uniformly distributed random variable to capture outliers. The proposed algorithm is based on the expectation-maximization algorithm and allows for soft assignments of data to clusters, to rate annotators according to their performance, and to estimate the number of Gaussian components in the non-Gaussian/Gaussian mixture model, in a jointly manner.

This paper focuses on the problem of positioning a source using angle-of-arrival measurements taken by a wireless sensor network in which some of the nodes experience non line-of-sight (LOS) propagation conditions. In order to mitigate the errors induced by the nodes in NLOS, we derive an algorithm that combines the expectation-maximization algorithm with a weighted least-squares estimation of the source position so that the nodes in NLOS are eventually identified and discarded. Moreover, a distributed version of this algorithm based on a diffusion strategy that iteratively refines the position estimate while driving the network to a consensus is presented.

This paper addresses the problem of distributed estimation of a parameter vector in the presence of noisy input and noisy output data, as well as data faults, performed by a wireless sensor network in which only local interactions among the nodes are allowed. In the presence of unreliable observations, standard estimators become biased and perform poorly in low signal-to-noise ratios. We propose therefore two different distributed approaches based on the Expectation-Maximization algorithm: in the first one the regressors are estimated at each iteration, whereas the second one does not require explicit regressor estimation. Numerical results show that the proposed methods approach the performance of a clairvoyant scheme with knowledge of the random data faults.

We consider Total Least Squares (TLS) estimation in a network in which each node has access to a subset of equations of an overdetermined linear system. Previous distributed approaches require that the number of equations at each node be larger than the dimension L of the unknown parameter. We present novel distributed TLS estimators which can handle as few as a single equation per node. In the first scheme, the network computes an extended correlation matrix via standard iterative average consensus techniques, and the TLS estimate is extracted afterwards by means of an eigenvalue decomposition (EVD). The second scheme is EVD-free, but requires that a linear system of size L be solved at each iteration by each node. Replacing this step by a single Gauss-Seidel subiteration is shown to be an effective means to reduce computational cost without sacrificing performance.

Distributed implementations of the Expectation-Maximization (EM) algorithm reported in literature have been proposed for applications to solve specific problems. In general, a primary requirement to derive a distributed solution is that the structure of the centralized version enables the computation involving global information in a distributed fashion. This paper treats the problem of distributed estimation of Gaussian densities by means of the EM algorithm in wireless sensor networks using diffusion strategies, where the information is gradually diffused across the network for the computation of the global functions. The low-complexity implementation presented here is based on a two time scale operation for information averaging and diffusion. The convergence to a fixed point of the centralized solution has been studied and the appealing results motivates our choice for this model. Numerical examples provided show that the performance of the distributed EM is, in practice, equal to that of the centralized scheme.

We address the problem of distributed estimation of a parameter from a set of noisy observations collected by a sensor network, assuming that some sensors may be subject to data failures and report only noise. In such scenario, simple schemes such as the Best Linear Unbiased Estimator result in an error floor in moderate and high signal-to-noise ratio (SNR), whereas previously proposed methods based on hard decisions on data failure events degrade as the SNR decreases. Aiming at optimal performance within the whole range of SNRs, we adopt a Maximum Likelihood framework based on the Expectation-Maximization (EM) algorithm. The statistical model and the iterative nature of the EM method allow for a diffusion-based distributed implementation, whereby the information propagation is embedded in the iterative update of the parameters. Numerical examples show that the proposed algorithm practically attains the Cramer-Rao Lower Bound at all SNR values and compares favorably with other approaches.

In this paper, the issue of network topology control in wireless networks using a fully distributed algorithm is considered. Whereas the proposed distributed algorithm is designed applying game theory concepts to design a non-cooperative game, network connectivity is guaranteed based on asymptotic results of network connectivity. Simulations show that for a relatively low node density, the probability that the proposed algorithm leads to a connected network is close to one.

The average consensus in wireless sensor networks is achieved under assumptions of symmetric or balanced topology at every time instant. However, communication and/or node failures, as well as node mobility or changes in the environment make the topology vary in time, and instantaneous symmetry of the links is not guaranteed unless an acknowledgment protocol or an equivalent approach is implemented. In this paper, we evaluate the convergence in the mean square sense of a well-known consensus algorithm assuming a random topology and asymmetric communication links. A closed form expression for the mean square error of the state is derived as well as the optimum choice of parameters to guarantee fastest convergence of the mean square error.

Following the seminal work of Zheng and Tse, this paper investigates the fundamental diversity and multiplexing tradeoff of multiple-input-multiple-output (MIMO) systems in which knowledge of the channel state at both sides of the link is employed to transmit independent data streams through the channel eigenmodes. First, the fundamental diversity and multiplexing tradeoff of each of the individual substreams is obtained and this result is then used to derive a tradeoff optimal scheme for rate allocation along channel eigenmodes. The tradeoff of spatial multiplexing is finally compared to the fundamental tradeoff of the MIMO channel and to the one of both space only codes and V-BLAST which do not require channel state information (CSI) at the transmit side.

Linear MIMO transceivers (composed of a linear precoder at the transmitter and a linear equalizer at the receiver) are a low-complexity approach to optimize the spectral efficiency and/or the reliability of the communication, when perfect channel state information is available at both sides of the link. The design of linear transceivers has been extensively studied in the literature with a variety of cost functions. In this paper we focus on the minimum BER design, and show that the common practice of fixing a priori the number of transmitted data symbols per channel use inherently limits the diversity gain of the system. Finally, we propose a minimum BER linear precoding scheme that achieves the full diversity of the MIMO channel.

Mobile location using time of arrival (TOA), time difference of arrival (TDOA) or angle of arrival (AOA) measurements has received considerable attention over the last years. Several closed-form algorithms have been presented for the TOA and TDOA case based on approximations of the maximum-likelihood (ML) estimator. In the case of AOA measurements, only ad-hoc estimators have been presented in order to avoid the classical linearization solution that needs an initial guess. This paper presents an approximation of the ML position estimator based on AOA measurements applying the divide-and-conquer approach dividing the ML estimation in smaller problems each one with a closed-form solution. Numerical simulations show that the proposed algorithm outperforms the previous contributions and presents a generic way to combine AOA and TDOA measurements

Following the pioneering work of Zheng and Tse, this paper derives the diversity and spatial multiplexing gain tradeoff for MIMO systems employing a multiple beamforming scheme. This result is obtained looking at the channel performance limits when both the SNR and the transmission rate tend to infinity, such that the relation between the rate and the capacity is constant. Assuming a uniform power allocation among eigenbeams, the optimal rate allocation policy in the sense of the best diversity and multiplexing tradeoff is obtained

The work considers the problem of channel estimation in the iterative reception of pilot-aided signals in DS-CDMA systems. The performance of classical training-based schemes is severely degraded in highly frequency selective channels due to the code-multiplexing of traffic and pilot signals. Thus, estimation algorithms that rely on the presence of the pilot signal, but also consider the information signal structure, are preferred. We present a Bayesian channel estimation algorithm for turbo receivers that effectively exploits the available soft information about the symbols (to model the traffic signal) in order to improve the channel estimation iteratively. Simulation results in realistic frequency selective test cases reveal only a moderate degradation compared to the perfect channel knowledge case.

This work considers a high rate, multiple input multiple output (MIMO) systems using multiple codes, as well as channel coding and space time (ST) coding. The transmitter consists of a channel encoder followed by parallel linear dispersion codes (LDC) ST encoders using different spreading codes. The iterative receiver consists of a soft input and soft output (SISO) demodulator, followed by a SISO detector. Simulation results in realistic frequency selective third generation partnership project test cases reveal good performance even for high rate HSDPA services.

This paper investigates the asymptotic uniform power allocation capacity of frequency nonselective multiple-input multiple-output channels with fading correlation at either the transmitter or the receiver. We consider the asymptotic situation, where the number of inputs and outputs increase without bound at the same rate. A simple uniparametric model for the fading correlation function is proposed and the asymptotic capacity per antenna is derived in closed form. Although the proposed correlation model is introduced only for mathematical convenience, it is shown that its shape is very close to an exponentially decaying correlation function. The asymptotic expression obtained provides a simple and yet useful way of relating the actual fading correlation to the asymptotic capacity per antenna from a purely analytical point of view. For example, the asymptotic expressions indicate that fading correlation is more harmful when arising at the side with less antennas. Moreover, fading correlation does not influence the rate of growth of the asymptotic capacity per receive antenna with high Eb/N/sub 0/.

This paper deals with the problem of estimating the position of a user equipment operating in a wireless communication network. We present a new positioning method based on the angles of arrival (AOA) measured in several radio links between that user equipment and different base stations. The proposed AOA-based method leads us to a non-iterative closed-form solution of the positioning problem, and an statistical analysis of that solution is also included. The comparison between this method and the classical AOA-based positioning technique is discussed in terms of computational load, convergence of the solution and also in terms of the bias and variance of the position estimate.

In a wireless communication network, the position estimation of a given user equipment is usually obtained by the combination of angle and delay measurements of one or more radio links between that user equipment and one or several base stations. This paper focuses on the performance of an approximate maximum likelihood position estimator in the presence of non-zero cross-correlations between measurements. This issue becomes specially relevant in hybrid positioning techniques because angles and delays of the same radio link are combined and, in consequence, the usual assumption of uncorrelation between measurements does not hold.

A description of the signal processing stage of an on-board integrated VLSI multi-carrier demodulator at the demultiplexing level is presented, along with a description of the optimization procedure that has been developed for the signal processing functions. The varying adjacent carrier interference and channel noise distribution are modeled to provide the best performing demultiplexing scheme under the given carrier distribution with minimum complexity.

The estimation of probability density functions (PDFs) of a given random variable (r.v.) is involved in topics related to codification, speech or whenever a short record of data is available but a greater amount is needed. Existing methods go from the so-called minimum description-length method, up to others based on the maximisation of the differential entropy imposing constraints on the moments of the r.v. In this paper we propose to estimate a PDF function by means of spectral estimate methods, since the positiveness and the real character of any PDF function allow us to deal with it as a power spectrum density function. Particularly, the minimum variance method is focused on because it can be generalised to multidimensional problems, being used in this paper to estimate the joint-PDF function of a multidimensional r.v.

This paper shows a new architecture specially thought to model non-linear systems (NLSs). At first, it was applied only to memoryless systems but then it developed to solve a more general problem, NLSs with memory. The result is a new filter, based on the Fourier transform, that the authors have named "K-filter". Important features of the K-filter are its nonlinear behaviour and second, that it profits from a temporal diversity of the input signal in order to provide itself with memory. At the end of the paper, the K-filter is used to solve an identification problem of a communication system which behaves nonlinearly due to the response of the amplifiers and which also has memory introduced basically by the channel response. The simulation results will provide an evaluation of the K-filter.>

A multitone tracker is described using two basic principles in optimum frequency estimation: processing bandwidth depending on the distance from the estimate to the actual frequency values, and parallel estimates with inhibitory paths to ensure orthogonality between the enhanced tones. The first feature is provided by extended Kalman filters (EKFs), and the second one is achieved by a high-order rule for the learning of the inhibitory cells. It is shown that the independence between signals is linked to the high-order function of the learning process. The resulting multitone tracker seems to be a potential alternative to adaptive high-resolution methods or time-frequency tools.>