SwePub - sökning: WFRF:(Venkitaraman Arun)

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1.	Javid, Alireza Mahdavi, et al. (författare) High-dimensional neural feature design for layer-wise reduction of training cost 2020 Ingår i: EURASIP Journal on Advances in Signal Processing. - : Springer Nature. - 1687-6172 .- 1687-6180. ; 2020:1 Tidskriftsartikel (refereegranskat)abstract We design a rectified linear unit-based multilayer neural network by mapping the feature vectors to a higher dimensional space in every layer. We design the weight matrices in every layer to ensure a reduction of the training cost as the number of layers increases. Linear projection to the target in the higher dimensional space leads to a lower training cost if a convex cost is minimized. Anl(2)-norm convex constraint is used in the minimization to reduce the generalization error and avoid overfitting. The regularization hyperparameters of the network are derived analytically to guarantee a monotonic decrement of the training cost, and therefore, it eliminates the need for cross-validation to find the regularization hyperparameter in each layer. We show that the proposed architecture is norm-preserving and provides an invertible feature vector and, therefore, can be used to reduce the training cost of any other learning method which employs linear projection to estimate the target.
2.	Javid, Alireza M., et al. (författare) High-dimensional neural feature using rectified linear unit and random matrix instance 2020 Ingår i: 2020 IEEE international conference on acoustics, speech, and signal processing. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 4237-4241 Konferensbidrag (refereegranskat)abstract We design a ReLU-based multilayer neural network to generate a rich high-dimensional feature vector. The feature guarantees a monotonically decreasing training cost as the number of layers increases. We design the weight matrix in each layer to extend the feature vectors to a higher dimensional space while providing a richer representation in the sense of training cost. Linear projection to the target in the higher dimensional space leads to a lower training cost if a convex cost is minimized. An l(2)-norm convex constraint is used in the minimization to improve the generalization error and avoid overfitting. The regularization hyperparameters of the network are derived analytically to guarantee a monotonic decrement of the training cost and therefore, it eliminates the need for cross-validation to find the regularization hyperparameter in each layer.
3.	Mochaourab, Rami, et al. (författare) Post-hoc Explainability for Time Series Classification: Towards a Signal Processing Perspective 2022 Ingår i: IEEE signal processing magazine (Print). - : Institute of Electrical and Electronics Engineers (IEEE). - 1053-5888 .- 1558-0792. ; 39:4, s. 119-129 Tidskriftsartikel (refereegranskat)abstract Time series data correspond to observations of phenomena that are recorded over time [1]. Such data are encountered regularly in a wide range of applications, such as speech and music recognition, monitoring health and medical diagnosis, financial analysis, motion tracking, and shape identification, to name a few. With such a diversity of applications and the large variations in their characteristics, time series classification is a complex and challenging task. One of the fundamental steps in the design of time series classifiers is that of defining or constructing the discriminant features that help differentiate between classes. This is typically achieved by designing novel representation techniques [2] that transform the raw time series data to a new data domain, where subsequently a classifier is trained on the transformed data, such as one-nearest neighbors [3] or random forests [4]. In recent time series classification approaches, deep neural network models have been employed that are able to jointly learn a representation of time series and perform classification [5]. In many of these sophisticated approaches, the discriminant features tend to be complicated to analyze and interpret, given the high degree of nonlinearity.
4.	Sundin, Martin, 1983-, et al. (författare) A Connectedness Constraint for Learning Sparse Graphs 2017 Ingår i: 2017 25TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO). - : IEEE. - 9780992862671 ; , s. 151-155 Konferensbidrag (refereegranskat)abstract Graphs are naturally sparse objects that are used to study many problems involving networks, for example, distributed learning and graph signal processing. In some cases, the graph is not given, but must be learned from the problem and available data. Often it is desirable to learn sparse graphs. However, making a graph highly sparse can split the graph into several disconnected components, leading to several separate networks. The main difficulty is that connectedness is often treated as a combinatorial property, making it hard to enforce in e.g. convex optimization problems. In this article, we show how connectedness of undirected graphs can be formulated as an analytical property and can be enforced as a convex constraint. We especially show how the constraint relates to the distributed consensus problem and graph Laplacian learning. Using simulated and real data, we perform experiments to learn sparse and connected graphs from data.
5.	Venkitaraman, Arun, et al. (författare) A Technique to Compute Smooth Amplitude, Phase, and Frequency Modulations From the Analytic Signal 2012 Ingår i: IEEE Signal Processing Letters. - : IEEE Signal Processing Society. - 1070-9908 .- 1558-2361. ; 19:10, s. 623-626 Tidskriftsartikel (refereegranskat)abstract Gabor’s analytic signal (AS) is a unique complexsignal corresponding to a real signal, but in general, it admitsinfinitely-many combinations of amplitude and frequency modu-lations (AM and FM, respectively). The standard approach is toenforce a non-negativity constraint on the AM, but this results indiscontinuities in the corresponding phase modulation (PM), andhence, an FM with discontinuities particularly when the under-lying AM-FM signal is over-modulated. In this letter, we analyzethe phase discontinuities and propose a technique to computesmooth AM and FM from the AS, by relaxing the non-negativityconstraint on the AM. The proposed technique iseffective athandling over-modulated signals. We present simulation results tosupport the theoretical calculations.
6.	Venkitaraman, Arun, et al. (författare) Binaural Signal Processing Motivated Generalized Analytic Signal Construction and AM-FM Demodulation 2014 Ingår i: IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING. - 2329-9290. ; 22:6, s. 1023-1036 Tidskriftsartikel (refereegranskat)abstract Binaural hearing studies show that the auditory system uses the phase-difference information in the auditory stimuli for localization of a sound source. Motivated by this finding, we present a method for demodulation of amplitude-modulated-frequency-modulated (AM-FM) signals using a signal and its arbitrary phase-shifted version. The demodulation is achieved using two allpass filters, whose impulse responses are related through the fractional Hilbert transform (FrHT). The allpass filters are obtained by cosine-modulation of a zero-phase flat-top prototype halfband lowpass filter. The outputs of the filters are combined to construct an analytic signal (AS) from which the AM and FM are estimated. We show that, under certain assumptions on the signal and the filter structures, the AM and FM can be obtained exactly. The AM-FM calculations are based on the quasi-eigenfunction approximation. We then extend the concept to the demodulation of multicomponent signals using uniform and non-uniform cosine-modulated filterbank (FB) structures consisting of flat bandpass filters, including the uniform cosine-modulated, equivalent rectangular bandwidth (ERB), and constant-Q filterbanks. We validate the theoretical calculations by considering application on synthesized AM-FM signals and compare the performance in presence of noise with three other multiband demodulation techniques, namely, the Teager-energy-based approach, the Gabor's AS approach, and the linear transduction filter approach. We also show demodulation results for real signals.
7.	Venkitaraman, Arun, et al. (författare) Extreme learning machine for graph signal processing 2018 Ingår i: 2018 26th European Signal Processing Conference (EUSIPCO). - : European Signal Processing Conference, EUSIPCO. - 9789082797015 ; , s. 136-140 Konferensbidrag (refereegranskat)abstract In this article, we improve extreme learning machines for regression tasks using a graph signal processing based regularization. We assume that the target signal for prediction or regression is a graph signal. With this assumption, we use the regularization to enforce that the output of an extreme learning machine is smooth over a given graph. Simulation results with real data confirm that such regularization helps significantly when the available training data is limited in size and corrupted by noise.
8.	Venkitaraman, Arun, et al. (författare) Fractional Hilbert transform extensions and associated analytic signal construction 2014 Ingår i: Signal Processing. - : Elsevier. - 0165-1684 .- 1872-7557. ; 94, s. 359-372 Tidskriftsartikel (refereegranskat)abstract The analytic signal (AS) was proposed by Gabor as a complex signal corresponding to a given real signal. The AS has a one-sided spectrum and gives rise to meaningful spectral averages. The Hilbert transform (HT) is a key component in Gabor's AS construction. We generalize the construction methodology by employing the fractional Hilbert transform (FrHT), without going through the standard fractional Fourier transform (FrFT) route. We discuss some properties of the fractional Hilbert operator and show how decomposition of the operator in terms of the identity and the standard Hilbert operators enables the construction of a family of analytic signals. We show that these analytic signals also satisfy Bedrosian-type properties and that their time-frequency localization properties are unaltered. We also propose a generalized-phase AS (GPAS) using a generalized-phase Hilbert transform (GPHT). We show that the GPHT shares many properties of the FrHT, in particular, selective highlighting of singularities, and a connection with Lie groups. We also investigate the duality between analyticity and causality concepts to arrive at a representation of causal signals in terms of the FrHT and GPHT. On the application front, we develop a secure multi-key single-sideband (SSB) modulation scheme and analyze its performance in noise and sensitivity to security key perturbations.
9.	Venkitaraman, Arun, et al. (författare) Gaussian Processes over Graphs 2020 Ingår i: 2020 IEEE International Conference on Acoustics Speech and Signal Processing ICASSP. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 5640-5644 Konferensbidrag (refereegranskat)abstract Kernel Regression over Graphs (KRG) was recently proposed for predicting graph signals in a supervised learning setting, where the inputs are agnostic to the graph. KRG model predicts targets that are smooth graph signals as over the given graph, given the input when all the signals are deterministic. In this work, we consider the development of a stochastic or Bayesian variant of KRG. Using priors and likelihood functions, our goal is to systematically derive a predictive distribution for the smooth graph signal target given the training data and a new input. We show that this naturally results in a Gaussian process formulation which we call Gaussian Processes over Graphs (GPG). Experiments with real-world datasets show that the performance of GPG is superior to a conventional Gaussian Process (without the graph-structure) for small training data sizes and under noisy training.
10.	Venkitaraman, Arun, et al. (författare) Graph linear prediction results in smaller error than standard linear prediction 2015 Ingår i: 2015 23rd European Signal Processing Conference, EUSIPCO 2015. - : Institute of Electrical and Electronics Engineers (IEEE). - 9780992862633 ; , s. 220-224 Konferensbidrag (refereegranskat)abstract Linear prediction is a popular strategy employed in the analysis and representation of signals. In this paper, we propose a new linear prediction approach by considering the standard linear prediction in the context of graph signal processing, which has gained significant attention recently. We view the signal to be defined on the nodes of a graph with an adjacency matrix constructed using the coefficients of the standard linear predictor (SLP). We prove theoretically that the graph based linear prediction approach results in an equal or better performance compared with the SLP in terms of the prediction gain. We illustrate the proposed concepts by application to real speech signals.
11.	Venkitaraman, Arun, 1988- (författare) Graph Signal Processing Meets Machine Learning 2018 Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract Graph signal processing is an emerging paradigm in signal processing which took birth in the search for a set of consistent mathematical tools to analyze signals which occur over networks or graphs. The viewpoint of signals through graphs is universal and applicable to a large variety of diverse real-world problems. In this thesis, we make contributions to graph signal processing in two different settings: graph signal processing theory and graph signal processing with machine learning. In the first setting, we derive a novel Hilbert transform framework for graph signals in answering the question of whether amplitude and frequency modulations be defined for graph signals. We generalize Gabor’s analytic signal and define amplitude and phase modulations for graph signals via a Hilbert transform which is shown to demonstrate ability to highlight anomalies or singularities over graphs.In the second setting, we bring together some of the popular machine learning approaches to graph signal processing, demonstrating how the two thought pro- cesses can be mutually coupled meaningfully for significant benefits. Specifically, we deal with the problem of predicting vector target signals which are graph signals over an associated graph. The input is taken to be a general quantity associated to the graph signal, but not necessarily the same physical quantity as that of the graph signal. In this way, we make graph signal output predictions with inputs which are agnostic to a graph structure. We apply this line of thought to extend some of the popular and powerful techniques in machine learning to graph signal setting: kernel regression, multi-kernel regression, Gaussian processes, and extreme learning machines. We show that our approach outperforms the conventional versions when the training samples are scarce and noisy: application to many real-world graph signal applications show that similar prediction performance as that of non-graph- aware versions is achieved with much less training data, and that too corrupted with noise. This also includes the extreme cases where data is partly missing or corrupted with large perturbations. This observation in turn points to the efficiency of our approach in terms of both availability of resources and computational complexity, which usually increases as datasize increases. Our approach stands out uniquely in being able to handle cases where the input and output are different physical quantities. It is also interesting to note that our approach performs reasonably well even in cases where the graph exists but is not known to the user.We conclude by addressing the general problem of learning graphs from graph signals in two ways. First, we show that learning of connected graphs can be trans- formed into a convex optimization constraint which can be easily augmented to any of the existing graph learning techniques. Second, we propose a sparsity based approach to learn graphs in a hyperparameter-free manner which is computation- ally efficient. In our first contribution in the context of learning graphs, we are concerned with learning connected graphs which describe the data, whereas in the second part, we focus on learning graphs that are effective in making predictions for the signal value at the different nodes.
12.	Venkitaraman, Arun, et al. (författare) KERNEL REGRESSION FOR GRAPH SIGNAL PREDICTION IN PRESENCE OF SPARSE NOISE 2019 Ingår i: 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP). - : IEEE. - 9781479981311 ; , s. 5426-5430 Konferensbidrag (refereegranskat)abstract In presence of sparse noise we propose kernel regression for predicting output vectors which are smooth over a given graph. Sparse noise models the training outputs being corrupted either with missing samples or large perturbations. The presence of sparse noise is handled using appropriate use of l(1)-norm along-with use of l(2)-norm in a convex cost function. For optimization of the cost function, we propose an iteratively reweighted least-squares (IRLS) approach that is suitable for kernel substitution or kernel trick due to availability of a closed form solution. Simulations using real-world temperature data show efficacy of our proposed method, mainly for limited-size training datasets.
13.	Venkitaraman, Arun, et al. (författare) Learning Sparse Graphs for Prediction of Multivariate Data Processes 2019 Ingår i: IEEE Signal Processing Letters. - : IEEE. - 1070-9908 .- 1558-2361. ; 26:3, s. 495-499 Tidskriftsartikel (refereegranskat)abstract We address the problem of prediction of multivariate data process using an underlying graph model. We develop a method that learns a sparse partial correlation graph in a tuning-free and computationally efficient manner. Specifically, the graph structure is learned recursively without the need for cross validation or parameter tuning by building upon a hyperparameter-free framework. Our approach does not require the graph to be undirected and also accommodates varying noise levels across different nodes. Experiments using real-world datasets show that the proposed method offers significant performance gains in prediction, in comparison with the graphs frequently associated with these datasets.
14.	Venkitaraman, Arun, et al. (författare) Learning sparse linear dynamic networks in a hyper-parameter free setting 2020 Ingår i: IFAC PAPERSONLINE. - : ELSEVIER. - 2405-8963. ; , s. 82-86 Konferensbidrag (refereegranskat)abstract We address the issue of estimating the topology and dynamics of sparse linear dynamic networks in a hyperparameter-free setting. We propose a method to estimate the network dynamics in a computationally efficient and parameter tuning-free iterative framework known as SPICE (Sparse Iterative Covariance Estimation). Our approach does not assume that the network is undirected and is applicable even with varying noise levels across the modules of the network. We also do not assume any explicit prior knowledge on the network dynamics. Numerical experiments with realistic dynamic networks illustrate the usefulness of our method.
15.	Venkitaraman, Arun, et al. (författare) Motion-based segmentation of chest and abdomen region of neonates from videos 2015 Ingår i: ICAPR 2015 - 2015 8th International Conference on Advances in Pattern Recognition. - 9781479974580 Konferensbidrag (refereegranskat)abstract Respiration rate (RR) is one of the important vital signs used for clinical monitoring of neonates in intensive care units. Due to the fragile skin of the neonates, it is preferable to have monitoring systems with minimal contact with the neonate. Recently, several methods have been proposed for contact-free monitoring of vital signs using a video camera. Detection of the chest-and-abdomen region of the neonate is crucial to determining the respiration rate accurately. We propose a technique for automatic selection of the region of interest (ROI) in neonates using motion. Our approach is based on the observation that points on the chest-and-abdomen region, and hence, the corresponding optic flow vectors, exhibit coherency in the motion caused by breathing. The motion induced due to the movement of the neonate (e.g., hands and legs) is not coherent and hence does not exhibit the characteristics of respiratory motion. We evaluate the proposed technique using several videos of neonates and demonstrate that it picks up the ROI accurately in spite of the movement of the neonate. We compare its performance with that of the standard motion history image (MHI) framework, using different metrics. Results indicate that our method can be profitably employed in RR studies.
16.	Venkitaraman, Arun, et al. (författare) MULTI-KERNEL REGRESSION FOR GRAPH SIGNAL PROCESSING 2018 Ingår i: 2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP). - : IEEE. ; , s. 4644-4648 Konferensbidrag (refereegranskat)abstract We develop a multi-kernel based regression method for graph signal processing where the target signal is assumed to be smooth over a graph. In multi-kernel regression, an effective kernel function is expressed as a linear combination of many basis kernel functions. We estimate the linear weights to learn the effective kernel function by appropriate regularization based on graph smoothness. We show that the resulting optimization problem is shown to be convex and propose an accelerated projected gradient descent based solution. Simulation results using real-world graph signals show efficiency of the multi-kernel based approach over a standard kernel based approach.
17.	Venkitaraman, Arun, et al. (författare) On Computing Amplitude, Phase, and Frequency Modulations Using a Vector Interpretation of the Analytic Signal 2013 Ingår i: IEEE Signal Processing Letters. - : IEEE Signal Processing Society. - 1070-9908 .- 1558-2361. ; 20:12, s. 1187-1190 Tidskriftsartikel (refereegranskat)abstract The amplitude-modulation (AM) and phase-modulation (PM) of an amplitude-modulated frequency-modulated (AM-FM) signal are defined as the modulus and phase angle, respectively, of the analytic signal (AS). The FM is defined as the derivative of the PM. However, this standard definition results in a PM with jump discontinuities in cases when the AM index exceeds unity, resulting in an FM that contains impulses. We propose a new approach to define smooth AM, PM, and FM for the AS, where the PM is computed as the solution to an optimization problem based on a vector interpretation of the AS. Our approach is directly linked to the fractional Hilbert transform (FrHT) and leads to an eigenvalue problem. The resulting PM and AM are shown to be smooth, and in particular, the AM turns out to be bipolar. We show an equivalence of the eigenvalue formulation to the square of the AS, and arrive at a simple method to compute the smooth PM. Some examples on synthesized and real signals are provided to validate the theoretical calculations.
18.	Venkitaraman, Arun, et al. (författare) On Estimation of Covariance Matrices Modeled as a Sum of Kronecker Products 2014 Konferensbidrag (refereegranskat)
19.	Venkitaraman, Arun, et al. (författare) On Hilbert transform, analytic signal, and modulation analysis for signals over graphs 2019 Ingår i: Signal Processing. - : Elsevier. - 0165-1684 .- 1872-7557. ; 156, s. 106-115 Tidskriftsartikel (refereegranskat)abstract We propose Hilbert transform and analytic signal construction for signals over graphs. This is motivated by the popularity of Hilbert transform, analytic signal, and modulation analysis in conventional signal processing, and the observation that complementary insight is often obtained by viewing conventional signals in the graph setting. Our definitions of Hilbert transform and analytic signal use a conjugate symmetry-like property exhibited by the graph Fourier transform (GFT), resulting in a 'one-sided' spectrum for the graph analytic signal. The resulting graph Hilbert transform is shown to possess many interesting mathematical properties and also exhibit the ability to highlight anomalies/discontinuities in the graph signal and the nodes across which signal discontinuities occur. Using the graph analytic signal, we further define amplitude, phase, and frequency modulations for a graph signal. We illustrate the proposed concepts by showing applications to synthesized and real-world signals. For example, we show that the graph Hilbert transform can indicate presence of anomalies and that graph analytic signal, and associated amplitude and frequency modulations reveal complementary information in speech signals.
20.	Venkitaraman, Arun, et al. (författare) Predicting Graph Signals Using Kernel Regression Where the Input Signal is Agnostic to a Graph 2019 Ingår i: IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2373-776X. ; 5:4, s. 698-710 Tidskriftsartikel (refereegranskat)abstract We propose a kernel regression method to predict a target signal lying over a graph when an input observation is given. The input and the output could be two different physical quantities. In particular, the input may not be a graph signal at all or it could be agnostic to an underlying graph. We use a training dataset to learn the proposed regression model by formulating it as a convex optimization problem, where we use a graph-Laplacian based regularization to enforce that the predicted target is a graph signal. Once the model is learnt, it can be directly used on a large number of test data points one-by-one independently to predict the corresponding targets. Our approach employs kernels between the various input observations, and as a result the kernels are not restricted to be functions of the graph adjacency/Laplacian matrix. We show that the proposed kernel regression exhibits a smoothing effect, while simultaneously achieving noise-reduction and graph-smoothness. We then extend our method to the case when the underlying graph may not be known apriori, by simultaneously learning an underlying graph and the regression coefficients. Using extensive experiments, we show that our method provides a good prediction performance in adverse conditions, particularly when the training data is limited in size and is noisy. In graph signal reconstruction experiments, our method is shown to provide a good performance even for a highly under-determined subsampling.
21.	Venkitaraman, Arun, et al. (författare) Recursive Prediction of Graph Signals with Incoming Nodes 2020 Ingår i: 2020 IEEE International Conference on Acoustics, Speech, And Signal Processing. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 5565-5569 Konferensbidrag (refereegranskat)abstract Kernel and linear regression have been recently explored in the prediction of graph signals as the output, given arbitrary input signals that are agnostic to the graph. In many real-world problems, the graph expands over time as new nodes get introduced. Keeping this premise in mind, we propose a method to recursively obtain the optimal prediction or regression coefficients for the recently proposed Linear Regression over Graphs (LRG), as the graph expands with incoming nodes. This comes as a natural consequence of the structure of the regression problem, and obviates the need to solve a new regression problem each time a new node is added. Experiments with real-world graph signals show that our approach results in a good prediction performance which tends to be close to that obtained from knowing the entire graph apriori.
22.	Venkitaraman, Arun, et al. (författare) Temporal Envelope Fit of Transient Audio Signals 2013 Ingår i: IEEE Signal Processing Letters. - : IEEE Signal Processing Society. - 1070-9908 .- 1558-2361. ; 20:12, s. 1191-1194 Tidskriftsartikel (refereegranskat)abstract We address the problem of temporal envelope modeling for transient audio signals. We propose the Gamma distribution function (GDF) as a suitable candidate for modeling the envelope keeping in view some of its interesting properties such as asymmetry, causality, near-optimal time-bandwidth product, controllability of rise and decay, etc. The problem of finding the parameters of the GDF becomes a nonlinear regression problem. We overcome the hurdle by using a logarithmic envelope fit, which reduces the problem to one of linear regression. The logarithmic transformation also has the feature of dynamic range compression. Since temporal envelopes of audio signals are not uniformly distributed, in order to compute the amplitude, we investigate the importance of various loss functions for regression. Based on synthesized data experiments, wherein we have a ground truth, and real-world signals, we observe that the least-squares technique gives reasonably accurate amplitude estimates compared with other loss functions.
23.	Winqvist, Rebecka, et al. (författare) Learning Models of Model Predictive Controllers using Gradient Data 2021 Ingår i: IFAC PAPERSONLINE. - : Elsevier BV. - 2405-8963. ; , s. 7-12 Konferensbidrag (refereegranskat)abstract This paper investigates the problem of controller identification given the data from a linear quadratic Model Predictive Controller (MPC) with constraints. We propose an approach for learning MPC that explicitly uses the gradient information in the training process. This is motivated by the observation that recent differentiable convex optimization MPC solvers can provide both the optimal feedback law from the state to control input as well as the corresponding gradient. As a proof of concept, we apply this approach to explicit MPC (eMPC), for which the feedback law is a piece-wise affine function of the state, but the number of pieces grows rapidly with the state dimension. Controller identification can here be used to find an approximate low complexity functional approximation of the controller. The eMPC is modelled using a Neural Network (NN) with Rectified Linear Units (ReLUs), since such NNs can represent any piece-wise affine function. A key motivation is to replace on-line solvers with neural networks to implement MPC and to simplify the evaluation of the function in larger input dimensions. We also study experimental design and model evaluation in this framework, and propose a hit-and-run sampling algorithm for input design. The proposed algorithms are illustrated and numerically evaluated on a second order MPC problem.
24.	Zaki, Ahmed, et al. (författare) Distributed Greedy Sparse Learning over Doubly Stochastic Networks 2017 Ingår i: 2017 25TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO). - : IEEE. - 9780992862671 ; , s. 361-364 Konferensbidrag (refereegranskat)abstract In this paper, we develop a greedy algorithm for sparse learning over a doubly stochastic network. In the proposed algorithm, nodes of the network perform sparse learning by exchanging their individual intermediate variables. The algorithm is iterative in nature. We provide a restricted isometry property (RIP)-based theoretical guarantee both on the performance of the algorithm and the number of iterations required for convergence. Using simulations, we show that the proposed algorithm provides good performance.
25.	Zaki, Ahmed, et al. (författare) Greedy Sparse Learning Over Network 2018 Ingår i: IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2373-776X. ; 4:3, s. 424-435 Tidskriftsartikel (refereegranskat)abstract In this paper, we develop a greedy algorithm for solving the problem of sparse learning over a right stochastic network in a distributed manner. The nodes iteratively estimate the sparse signal by exchanging a weighted version of their individual intermediate estimates over the network. We provide a restricted-isometry-property (RIP)-based theoretical performance guarantee in the presence of additive noise. In the absence of noise, we show that under certain conditions on the RIP-constant of measurement matrix at each node of the network, the individual node estimates collectively converge to the true sparse signal. Furthermore, we provide an upper bound on the number of iterations required by the greedy algorithm to converge. Through simulations, we also show that the practical performance of the proposed algorithm is better than other state-of-the-art distributed greedy algorithms found in the literature.

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