The diffusion of GPS-equipped devices has resulted in the generation of vast amounts of spatio-temporal data. This data represents a fundamental resource to conduct analysis on transportation networks. It is therefore of great interest to identify models capable of distinguishing and classifying trajectories to facilitate decision-making processes, congestion prediction and emissions monitoring. However, many existing algorithms necessitate a complex feature engineering process and domain knowledge. In this context, this work proposes a neural network-based approach, which eliminates the need for complicated hand-crafted features, using Gramian angular fields and leveraging possibly pre-trained convolutional neural networks. Therefore, we combine these tools to tackle the challenge of multiclass trajectory classification. We demonstrate the effectiveness of our method on an imbalanced dataset simulated with SUMO by classifying different means of transportation–private car, taxi, bus, pedestrian, motorcycle, bicycle–achieving good results in terms of accuracy and F1 score.

Heterogeneous networks have emerged as a popular solution for accommodating the growing number of connected devices and increasing traffic demands in cellular networks. While offering broader coverage, higher capacity, and lower latency, the escalating energy consumption poses sustainability challenges. In this paper a novel optimization approach for orthogonal heterogeneous networks is proposed to minimize transmission power while respecting individual users’ throughput constraints. The problem is formulated as a mixed integer geometric program, and optimizes at once multiple system variables such as user association, working bandwidth, and base stations transmission powers. Crucially, the proposed approach becomes a convex optimization problem when user-base station associations are provided. Evaluations in multiple realistic scenarios from the production mobile network of a major European operator and based on precise channel gains and throughput requirements from measured data validate the effectiveness of the proposed approach. Overall, our original solution paves the road for greener connectivity by reducing the energy footprint of heterogeneous mobile networks, hence fostering more sustainable communication systems.

The increasing amount of data generated by space applications poses several challenges due to limited resources available onboard: power, memory, computation, data rate. In this paper, we propose Compressed Sensing (CS) as the key tool to face those challenges via compressive imaging. This signal processing technique, only recently applied to space applications, dramatically simplifies the image acquisition featuring native compression/encryption and enabling onboard image analysis, allowing to design simpler and lighter optical systems. In this paper, we try to answer the following question: To what extent are the potential benefits of CS going to materialize in a realistic “space big data” application scenario? To this purpose, we first review compressive imaging techniques and already existing prototypes and concepts, critically discussing the technological issues involved. Then, we propose a set of instrument concepts in the application domains of space science, planetary exploration and earth observation, most suitable for a CS-based application. For the most promising of them, we go deeper into the analysis showing preliminary reconstruction performance tests.

In this paper, we propose new gradient-based methods for image reconstruction from partial Fourier measurements, which are commonly used in magnetic resonance imaging (MRI) or synthetic aperture radar. Compared to classical gradient recovery methods, a key improvement is obtained by formulating the gradient recovery problem as a compressed sensing problem with the additional constraint that the curl of the gradient field must be zero. Moreover, we formulate the image recovery problem as an inverse problem on graphs. Iteratively reweighted ℓ1recovery methods are proposed to recover these relative differences and the structure of the similarity graph. Finally, the image is recovered from the compressed Fourier measurements using least squares estimation. Numerical experiments demonstrate that the proposed approach outperforms the state-of-the-art image recovery methods.

In this paper, we propose a new method for the recovery of a sparse signal from few linear measurements using a reference signal as side information. Modeling the signal coefficients with a double Laplace mixture model, and assuming that the distribution of the components of the prior information differs slightly from the unknown signal, the problem is formulated as a weighted ℓ1minimization problem. We derive sufficient conditions for perfect recovery and we show that our method is able to reduce significantly the number of measurements required for reconstruction. Numerical experiments demonstrate that the proposed approach outperforms the best algorithms for compressed sensing with prior information and is robust in imperfect scenarios.

In this paper, we introduce new gradient-based methods for image recovery from a small collection of spectral coefficients of the Fourier transform, which is of particular interest for several scanning technologies, such as magnetic resonance imaging. Since gradients of a medical image are much more sparse or compressible than the corresponding image, classical ℓ1-minimization methods have been used to recover these relative differences. The image values can then be obtained by integration algorithms imposing boundary constraints. Compared with classical gradient recovery methods, we propose two new techniques that improve reconstruction. First, we cast the gradient recovery problem as a compressed sensing problem taking into account that the curl of the gradient field should be zero. Second, inspired by the emerging field of signal processing on graphs, we formulate the gradient recovery problem as an inverse problem on graphs. Iteratively reweighted ℓ1recovery methods are proposed to recover these relative differences and the structure of the similarity graph. Once the gradient field is estimated, the image is recovered from the compressed Fourier measurements using least squares estimation. Numerical experiments show that the proposed approach outperforms the state-of-the-art image recovery methods.

In this paper, we propose a method for estimating the sparsity of a signal from its noisy linear projections without recovering it. The method exploits the property that linear projections acquired using a sparse sensing matrix are distributed according to a mixture distribution whose parameters depend on the signal sparsity. Due to the complexity of the exact mixture model, we introduce an approximate two-component Gaussian mixture model whose parameters can be estimated via expectation-maximization techniques. We demonstrate that the above model is accurate in the large system limit for a proper choice of the sensing matrix sparsifying parameter. Moreover, experimental results demonstrate that the method is robust under different signal-to-noise ratios and outperforms existing sparsity estimation techniques.

Iterative shrinkage-thresholding algorithms provide simple methods to recover sparse signals from compressed measurements. In this paper, we propose a new class of iterative shrinkage-thresholding algorithms which preserve the computational simplicity and improve iterative estimation by incorporating a soft support detection. Indeed, at each iteration, by learning the components that are likely to be nonzero from the current signal estimation using Bayesian techniques, the shrinkage-thresholding step is adaptively tuned and optimized. Unlike other adaptive methods, we are able to prove, under suitable conditions, the convergence of the proposed methods. Moreover, we show through numerical experiments that the proposed methods outperform classical shrinkage-thresholding in terms of rate of convergence and of sparsity-undersampling tradeoff.

In this paper, we analyze a new class of iterative re-weighted least squares (IRLS) algorithms and their effectiveness in signal recovery from incomplete and inaccurate linear measurements. These methods can be interpreted as the constrained maximum likelihood estimation under a two-state Gaussian scale mixture assumption on the signal. We show that this class of algorithms, which performs exact recovery in noiseless scenarios under suitable assumptions, is robust even in presence of noise. Moreover these methods outperform classical IRLS for ℓτ-minimization with τ ∈ (0; 1] in terms of accuracy and rate of convergence.

In this paper, we consider the problem of RSS-fingerprinting localization in wireless sensor networks. In particular, inspired by the recent advances in sparse approximation and compressive sensing theory, we propose a localization scheme based on the dictionary design of block-sparse signals. We show via numerical simulations and real experiments that the proposed technique outperforms traditional fingerprinting methods.

The ℓ0/ℓ1-regularized least-squares approach is used to deal with linear inverse problems under sparsity constraints, which arise in mathematical and engineering fields. In particular, multiagent models have recently emerged in this context to describe diverse kinds of networked systems, ranging from medical databases to wireless sensor networks. In this paper, we study methods for solving ℓ0/ℓ1-regularized leastsquares problems in such multiagent systems. We propose a novel class of distributed protocols based on iterative thresholding and input driven consensus techniques, which are well-suited to work in-network when the communication to a central processing unit is not allowed. Estimation is performed by the agents themselves, which typically consist of devices with limited computational capabilities. This motivates us to develop low-complexity and low-memory algorithms that are feasible in real applications. Our main result is a rigorous proof of the convergence of these methods in regular networks. We introduce a suitable distributed, regularized, least-squares functional, and we prove that our algorithms reach their minima using results from dynamical systems theory. Furthermore, we propose numerical comparisons with the alternating direction method of multipliers and the distributed subgradient methods, in terms of performance, complexity, and memory usage. We conclude that our techniques are preferable for their good memory-accuracy tradeoff.

In this paper, we present a new recovery algorithm for innetwork compressed sensing from measurements acquired in multi-agent systems. Each agent has to recover a common signal taking advantage of local communication and simple computations. Such distributed problem typically incurs a high energy cost due to inter-node communications. In this paper we propose an iterative distributed algorithm to address this problem, featuring pairwise gossip communications and updates. We propose some theoretical results on its dynamics and numerical comparisons with the most recent approaches proposed in literature. The performance turns out to be competitive in terms of reconstruction accuracy, complexity, and energy consumption required for convergence.

In this paper, we address the problem of distributed sparse recovery of signals acquired via compressed measurements in a sensor network. We propose a new class of distributed algorithms to solve Lasso regression problems, when the communication to a fusion center is not possible, e.g., due to communication cost or privacy reasons. More precisely, we introduce a distributed iterative soft thresholding algorithm (DISTA) that consists of three steps: an averaging step, a gradient step, and a soft thresholding operation. We prove the convergence of DISTA in networks represented by regular graphs, and we compare it with existing methods in terms of performance, memory, and complexity.

We present PISTA, a GPU-accelerated Iterative Soft Thresholding (IST) algorithm for sparse image recovery in Compressive Sensing applications. As the time required to recover an image increases with the number of pixels, GPU-acceleration enables to recover even large images in reasonable time. With respect to equivalent methods, IST-like algorithms have lower computational complexity per-iteration and lower memory requirements, plus the operations are inherently suitable for parallelization. Our experiments show that our algorithm enables a significant reduction in the time required to recover an image even over a highly-optimized CPU-only reference.

In this paper, exact formul\ae of the input-output weight distribution function and its exponential growth rate are derived for truncated convolutional encoders. In particular, these weight distribution functions are expressed in terms of generating functions of error events associated with a minimal realization of the encoder. Although explicit analytic expressions can be computed for relatively small truncation lengths, the explicit expressions become prohibitively complex to compute as the truncation lengths and the weights increase. Fortunately, a very accurate asymptotic expansion can be derived using the multidimensional saddle-point method (MSP method). This approximation is substantially easier to evaluate and is used to obtain an expression of the asymptotic spectral function, and to prove continuity and concavity in its domain (convex and closed). Finally, this approach is able to guarantee that the sequence of exponential growth rates converges uniformly to the asymptotic limit, and to estimate the speed of this convergence.

In this paper we derive exact formulae of the input-output weight enumerators for truncated convolutional encoders. Although explicit analytic expressions can be computed for relatively small code lengths, they become prohibitively complex to calculate as the truncation length increases. By applying Hayman-like techniques, we present an accurate and easy to compute approximation of the weight enumerators. One of our main results is the proof that the sequence of their exponential growths converges uniformly to the asymptotic growth rate. Finally, we estimate the speed of this convergence.

In this paper, the ensembles of repeat multiple- accumulate codes (RAm), which are obtained by interconnecting a repeater with a cascade of m accumulate codes through uniform random interleavers, are analyzed. It is proved that the average spectral shapes of these code ensembles are equal to 0 below a threshold distance epsivmand, moreover, they form a nonincreasing sequence in m converging uniformly to the maximum between the average spectral shape of the linear random ensemble and 0. Consequently the sequence epsivmconverges to the Gilbert-Varshamov (GV) distance. A further analysis allows to conclude that if m ges 2 the RAmare asymptotically good and that epsivmis the typical normalized minimum distance when the interleaver length goes to infinity. Combining the two results it is possible to conclude that the typical distance of the ensembles RAmconverges to the Gilbert-Varshamov bound.