Program
During the tutorials on Sunday, we have a coffee break from 10:45 am – 11:00 am and 3:15 pm -3:30 pm.
All meals that appear on the program are included
Sunday, December 10
Sunday, December 10, 09:30 - 12:30
T1: Tutorial 1: The Multiport Communication Theory![go to top](icon/top.gif)
Information theory serves well as the mathematical theory of communication. However, it contains no provision that makes sure its theorems are consistent with the physical laws that govern any existing realization of a communication system. Therefore, it may not be surprising that applications of information theory or signal processing, as currently practiced, easily turn out to be inconsistent with fundamental principles of physics, such as the law of conservation of energy. It is the purpose of multiport communication theory to provide the necessary framework ensuring that applications of signal processing and information theory actually do comply with physical law. This framework involves a circuit theoretic approach where the inputs and outputs of the communication system are associated with ports of a multiport black-box. Thanks to each port being described by a pair of two instead of just one variable, consistency with physics can be maintained. The connection to information theory and signal processing is then obtained by means of isomorphisms between mathematical (formal) symbols of the latter and the physical quantities of the multiport model. In this tutorial, the principles of the multiport communication theory are presented and accompanied by a discussion of a number of interesting results of its application to single and multi-antenna radio communications in single- and multi-user contexts. Among these results are a new mapping from uplink to downlink channel matrices of TDD systems based on reciprocity of the physical propagation channel and physical limitations of massive MIMO systems
Sunday, December 10, 14:00 - 17:00
T2: Tutorial 2: Learning Nonlinear and Dynamic Connectivity and Processes over Graphs![go to top](icon/top.gif)
Learning the topology of graphs as well as processes evolving over graphs are tasks emerging in application domains as diverse as gene-regulatory, brain, power, and social networks, to name a few. Scalable approaches to deal with such high-dimensional settings aim to address the unique modeling and computational challenges associated with data-driven science in the modern era of big data analytics. Albeit simple and tractable, linear time-invariant models are limited as they are incapable of modeling changing topologies, as well as nonlinear and dynamic dependencies between nodal processes. To this end, novel approaches are presented to leverage nonlinear counterparts of partial correlation and partial Granger causality, as well as nonlinear structural equations and vector auto-regressions, along with attributes such as low rank, sparsity, and smoothness to capture even directional dependencies with abrupt change points, as well as dynamic processes over possibly time-evolving topologies. The unifying framework inherits the versatility and generality of kernel-based methods, and lends itself to batch and computationally affordable online learning algorithms, which include novel Kalman filters and smoothers over graphs. Real data experiments highlight the impact of the nonlinear and dynamic models on gene-regulatory and functional connectivity of brain networks, where connectivity patterns revealed exhibit discernible differences relative to existing approaches.
Sunday, December 10, 17:00 - 19:00
SC: Student Paper Contest![go to top](icon/top.gif)
- Fundamental Limits of PhaseMax for Phase Retrieval: A Replica Analysis
- Distributed Big-Data Optimization via Block Communications
- Understanding the Role of Positive Constraints in Sparse Bilinear Problems
- Maximally Economic Sparse Arrays and Cantor Arrays
- Correlation-based Ultrahigh-dimensional Variable Screening
- Simultaneous Target State and Sensor Bias Estimation: Is More Better
- Face Recognition as a Kronecker Product Equation
- Nonlinear Dimensionality Reduction on Graphs
- STARK: Structured Dictionary Learning Through Rank-one Tensor Recovery
- Distributed Edge-Variant Graph Filters
Monday, December 11
Monday, December 11, 08:30 - 09:00
OC: Opening & Awards Ceremony![go to top](icon/top.gif)
Monday, December 11, 09:00 - 10:00
PL1: Plenary Talk 1: Sub-Nyquist Sampling without Sparsity and Phase Retrieval![go to top](icon/top.gif)
In recent years there has been an explosion of work on exploiting sparsity in order to reduce sampling rates in a wide-range of applications. In this talk, we consider several examples in which sub-Nyquist sampling is possible without assuming any structure on the signal being sampled. This is possible by careful design of the measurement scheme, together with nonlinear recovery methods. We then show how these concepts of measurement design and optimization-based recovery can be used to tackle a very different set of problems: phase retrieval from Fourier measurements. We begin by considering sampling a signal when we are interested in recovering its power spectrum. Next, we develop the minimal sampling rates required to achieve minimal distortion when representing an arbitrary signal by quantized samples. We then treat sampling of ultrasound signals where the goal is to create a beamformed image from the given samples. Finally, we propose several new measurement techniques in optical imaging that enable phase retrieval even in 1D problems from Fourier measurements.
Monday, December 11, 10:15 - 12:15
MM1: Exploiting Structure in Compressed Sensing![go to top](icon/top.gif)
- 10:15 A Constrained Formulation for Compressive Spectral Image Reconstruction Using Linear Mixture Models
- 10:35 Efficient Recovery from Noisy Quantized Compressed Sensing Using Generalized Approximate Message Passing
- 10:55 One-bit Compressive Sampling with Time-Varying Thresholds for Multiple Sinusoids
- 11:15 Extended Defect Localization in Sparsity-based Guided Wave Structural Health Monitoring
- 11:35 ANM-PhaseLift: Structured Line Spectrum Estimation from Quadratic Measurements
- 11:55 Understanding the Role of Positive Constraints in Sparse Bilinear Problems
MM2: Advances in Multi-Sensor Processing With Coarse Quantization![go to top](icon/top.gif)
- 10:15 Analysis of MRC for Mixed-ADC Massive MIMO
- 10:35 Performance Comparison of Hybrid and Digital Beamforming with Transmitter Impairments
- 10:55 On the Achievable Rate of Multi-Antenna Receivers with Oversampled 1-Bit Quantization
- 11:15 Massive MIMO Downlink 1-Bit Precoding for Frequency Selective Channels
- 11:35 Joint CFO and Channel Estimation in Millimeter Wave Systems with One-Bit ADCs
- 11:55 Wideband Source Localization Using One-Bit Quantized Arrays
MM3: Learning Representations for Tensor Data![go to top](icon/top.gif)
- 10:15 Locally Low-Rank Tensor Regularization for High-Resolution Quantitative Dynamic MRI
- 10:35 Image Classification Using Local Tensor Singular Value Decompositions
- 10:55 Nonlinear Least Squares Algorithm for Canonical Polyadic Decomposition Using Low-Rank Weights
- 11:15 Identification of Kronecker-structured Dictionaries: An Asymptotic Analysis
- 11:35 Low-rank Tensor Regression: Scalability and Applications
- 11:55 STARK: Structured Dictionary Learning Through Rank-one Tensor Recovery
Monday, December 11, 15:30 - 16:30
PL2: Plenary Talk 2: A Signal Processing and Optimization Perspective on Financial Engineering![go to top](icon/top.gif)
Financial engineering and electrical engineering are seemingly different areas that share strong underlying connections. Both areas rely on statistical analysis and modeling of systems and the underlaying time series. Either modeling price fluctuations in the financial markets or modeling, say, channel fluctuations in wireless communication systems. Having a model of reality allows us to make predictions and to accordingly optimize the future strategies. It is as important to optimize our investment strategies in a financial market as it is to optimize the signal transmitted by an antenna in a wireless link. The foundations of both areas are the same in disguise and lie on signal processing and optimization. This talk provides a glimpse of financial engineering from a signal processing and optimization perspective, while exploring connections to other engineering disciplines.
Monday, December 11, 16:30 - 18:00
MP1: Array Processing![go to top](icon/top.gif)
- Maximally Economic Sparse Arrays and Cantor Arrays
- Cramér-Rao-Induced Bound for Interference-to-Signal Ratio Achievable Through Non-Gaussian Independent Component Extraction
- Independent Low-Rank Matrix Analysis Based on Parametric Majorization-Equalization Algorithm
- A Comparison of Iterative and DFT-Based Polynomial Matrix Eigenvalue Decompositions
- An Adaptive Distributed Asynchronous Algorithm with Application to Target Localization
- Adaptive ADMM in Distributed Radio Interferometric Calibration
MP2: Sparsity![go to top](icon/top.gif)
- Microkicking for Fast Convergence of Sparse Kaczmarz and Sparse LMS
- Compressive Sensing Seismic Acquisition by Using Regular Sampling in an Orthogonal Grid
- Sparse Array Imaging Using Low-Rank Matrix Recovery
- Communication-Efficient Distributed Optimization for Sparse Learning via Two-Way Truncation
- Sparse Bayesian Learning with Dictionary Refinement for Super-Resolution Through Time
- Sparsity-based Cholesky Factorization and Its Application to Hyperspectral Anomaly Detection
- Spectral Image Fusion from Compressive Measurements Using Spectral Unmixing
- Greedy Phase Retrieval with Reference Points and Bounded Sparsity
MP3: Advanced Computational Methods for Photon Limited Imaging and Sensing![go to top](icon/top.gif)
- Proximal-gradient Methods for Poisson Image Reconstruction with BM3D-based Regularization
- Restoration of Depth and Intensity Images Using a Graph Laplacian Regularization
- Photon-Limited Fluorescence Lifetime Imaging Microscopy Signal Recovery with Known Bounds
- Unsupervised Restoration of Subsampled Images Constructed from Geometric and Binomial Data
MP4: Imaging![go to top](icon/top.gif)
- A Fast Algorithm Based on a Sylvester-like Equation for LS Regression with GMRF Prior
- Generative Adversarial Network-Based Restoration of Speckled SAR Images
- Online Deconvolution for Pushbroom Hyperspectral Imaging Systems
- Fast and Accurate Radio Interferometric Imaging Using Krylov Subspaces
- Boolean Approximation of a Phase-Coded Aperture Diffraction Pattern System for X-ray Crystallography
Monday, December 11, 18:00 - 20:00
MA1: Recent Advances in Tensor-Based Signal Processing and Applications![go to top](icon/top.gif)
- 6:00 Under-Determined Tensor Diagonalization for Decomposition of Difficult Tensors
- 6:20 Broadband Beamforming via Frequency Invariance Transformation and PARAFAC Decomposition
- 6:40 Compressed Power Spectrum, Carrier and DOA Estimation via PARAFAC Decomposition
- 7:00 Face Recognition as a Kronecker Product Equation
- 7:20 Intentional Islanding of Power Grids with Data Depth
- 7:40 Generalized Tensor Contraction with Application to Khatri-Rao Coded MIMO OFDM Systems
MA2: Global and Non-convex Optimization Methods for Signal Processing![go to top](icon/top.gif)
- 6:00 Energy Efficient Transmission in MIMO Interference Channels with QoS Constraints
- 6:20 Transmit Beamforming for Minimum Outage via Stochastic Approximation
- 6:40 Optimization Framework for Baseband Functionality Splitting in C-RAN
- 7:00 Local Strong Convexity of Maximum-Likelihood TDOA-Based Source Localization and Its Algorithmic Implications
- 7:20 EE Maximization for Massive MIMO with Fully Connected Hybrid Beamformers
- 7:40 Multi-Agent Asynchronous Nonconvex Large-Scale Optimization
MA3: Sparsity in Sensing and Inference![go to top](icon/top.gif)
- 6:00 Memory-Limited Stochastic Approximation for Poisson Subspace Tracking
- 6:20 Memory Efficient Low-Rank Non-Linear Subspace Tracking
- 6:40 Sparse Sensing for Composite Matched Subspace Detection
- 7:00 Robust Detection of Random Events with Spatially Correlated Data in Wireless Sensor Networks via Distributed Compressive Sensing
- 7:20 Multi-Target Localization in Asynchronous MIMO Radars Using Sparse Sensing
- 7:40 Online Topology Estimation for Vector Autoregressive Processes in Data Networks
Tuesday, December 12
Tuesday, December 12, 09:00 - 10:00
PL3: Plenary Talk 3: Graph Signal Processing Methods and their Application to Sensor Networks![go to top](icon/top.gif)
Information gathering and processing in sensor networks was an early motivating application in the early 2000's for the study of transforms for graph signals. In this talk we start by providing a brief review of early work on signal representations for sensor data. Then we discuss how these methods have been extended to arbitrary graphs and summarize some key recent results in graph signal processing. Finally, we discuss how these results can be applied to sensor networks, focusing on problems such as graph identification, sensor selection, or distributed processing.
Tuesday, December 12, 10:15 - 12:15
TM1: Graph Signal Processing![go to top](icon/top.gif)
- 10:15 Graph Signal Processing: Filter Design and Spectral Statistics
- 10:35 Distributed Edge-Variant Graph Filters
- 10:55 Design of Weighted Median Graph Filters
- 11:15 Nonlinear Dimensionality Reduction on Graphs
- 11:35 Graph Topology Recovery for Regular and Irregular Graphs
- 11:55 Graph Recursive Least Squares Filter for Topology Inference in Causal Data Processes
TM2: Biomedical Signal Processing![go to top](icon/top.gif)
- 10:15 Statistical Modeling and Classification of Reflectance Confocal Microscopy Images
- 10:35 Parameter Estimation in Block Term Decomposition for Noninvasive Atrial Fibrillation Analysis
- 10:55 Joint MEG-EEG Signal Decomposition Using the Coupled SECSI Framework: Validation on a Controlled Experiment
- 11:15 Optical Flow Estimation in Ultrasound Images Using a Sparse Representation
- 11:35 Coded Aperture Design for Super-Resolution Compressive X-ray Tomography
- 11:55 Efficient Sparsity-Based Algorithm for Parameter Estimation of the Tri-Exponential Intra Voxel Incoherent Motion (IVIM) Model: Application to Diffusion-Weighted MR Imaging in the Liver
TM3: Advances in Processing Faulty High-Dimensional Data![go to top](icon/top.gif)
- 10:15 Reconstruction of Compressively Sampled Images Using a Nonlinear Bayesian Prior
- 10:35 Bi-Linear Modeling of Manifold-Data Geometry for Dynamic-MRI Recovery
- 10:55 Distributed Sketched Subspace Clustering for Large-scale Datasets
- 11:15 Computational Advances in Sparse L1-norm Principal-Component Analysis of Multi-Dimensional Data
- 11:35 On Canonical Polyadic Decomposition of Overcomplete Tensors of Arbitrary Even Order
- 11:55 L1-PCA Signal Subspace Identification for Non-sphered Data Under the ICA Model
Tuesday, December 12, 15:30 - 16:30
PL4: Plenary Talk 4: Data Fusion through Matrix and Tensor Factorizations: Uniqueness, Diversity, and Interpretability![go to top](icon/top.gif)
Fusion of multiple sets of data, either of the same type as in multiset data or of different types and nature as in multi-modality data, is inherent to many problems in engineering and computer science. In data fusion, since most often, very little is known about the relationship of the underlying processes that give rise to such data, it is desirable to minimize the modeling assumptions, and at the same time, to maximally exploit the interactions within and across the multiple sets of data. This is one of the reasons for the growing importance of data-driven methods in data fusion tasks. Models based on matrix or tensor decompositions allow data sets to remain in their most explanatory form while admitting a broad range of assumptions among their elements. This talk will provide an overview of the main approaches that have been successfully applied for fusion of multiple datasets with a focus on the interrelated concepts of uniqueness, diversity, and interpretability. Diversity refers to any structural, numerical, or statistical inherent property or assumption on the data that contributes to the identifiability of the model, and for multiple datasets, provides the link among these datasets. Hence, diversity enables uniqueness, which is key to interpretability, the ability to attach a physical meaning to the final decomposition. The importance of these concepts as well as the challenges that remain are highlighted through a number of practical examples.
Tuesday, December 12, 16:30 - 18:00
TP1: Radar![go to top](icon/top.gif)
- Illuminator of Opportunity Selection for Passive Radar
- Multi-scale Histogram Tone Mapping Algorithm for Display of Wide Dynamic Range Images
- Joint Design of Co-Existing Communication System and Pulsed Radar
- A General Class of Recursive Minimum Variance Distortionless Response Estimators
- Operational Characteristics of Wigner-Ville Accelerating Target Detector
- A Bootstrapped Sequential Probability Ratio Test for Signal Processing Applications
TP2: Signal and information processing over networks![go to top](icon/top.gif)
- Byzantine-Resilient Locally Optimum Detection Using Collaborative Autonomous Networks
- Efficient Sensor Selection with Application to Time Varying Graphs
- Regularized LMS and Diffusion Adaptation LMS with Graph Filters for Non-Stationary Data
- Diffusion in Networks by Cooperative Particle Filtering
- Distributed Mirror Descent for Stochastic Learning over Rate-limited Networks
- Penalty-Based Multitask Estimation with Non-Local Linear Equality Constraints
TP3: Tensor Signal Processing![go to top](icon/top.gif)
- Block Term Decomposition with Rank Estimation Using Group Sparsity
- Nonlinear System Identification: Finding Structure in Nonlinear Black-Box Models
- Performance Analysis of Least-Squares Khatri-Rao Factorization
TP4: Direction of Arrival Estimation![go to top](icon/top.gif)
- Low Rank Matrix Recovery for Joint Array Self-Calibration and Sparse Model DoA Estimation
- Adaptive Channel Selection for DOA Estimation in MIMO Radar
- Multi-Mode Antenna Specific Direction-of-Arrival Estimation Schemes
- Improved DOA Estimators Using Partial Relaxation Approach
- DOA Estimation and Beamforming Using Spatially Under-Sampled AVS Arrays
- Localization of Multiple Simultaneously Active Sources in Acoustic Sensor Networks Using ADP
- Performance Analysis of ESPRIT-Type Algorithms for Co-Array Structures
- The Mean-Squared-Error of Autocorrelation Sampling in Coprime Arrays
Tuesday, December 12, 18:00 - 19:40
TA1: Algorithms for Big Data Analytics![go to top](icon/top.gif)
- 6:00 Robust Low-Complexity Methods for Matrix Column Outlier Identification
- 6:20 Correlation-based Ultrahigh-dimensional Variable Screening
- 6:40 A Distributed Algorithm for Partitioned Robust Submodular Maximization
- 7:00 Distributed Big-Data Optimization via Block Communications
- 7:20 Recurrent Generative Adversarial Neural Networks for Compressive Imaging
TA2: Advances in Multi-Sensor Adaptive Processing for GNSS![go to top](icon/top.gif)
- 6:00 Design of Optimum Sparse Array for Robust MVDR Beamforming Against DOA Mismatch
- 6:20 Time-Delay Estimation via CPD-GEVD Applied to Tensor-based GNSS Arrays with Errors
- 6:40 Multipath Mitigation Using OMP and Newton's Method for Multi-Antenna GNSS Receivers
- 7:00 Estimation Bounds for GNSS Synthetic Aperture Techniques
- 7:20 On the Delay-Doppler Tracking Error for Sub-Nyquist Satellite-Based Synchronization
TA3: Information Geometry Approaches for Signal Processing![go to top](icon/top.gif)
- 6:00 Information Distances for Radar Resolution Analysis
- 6:20 Multivariate Time-Series Analysis via Diffusion Maps
- 6:40 Stochastic EM Algorithm for Mixture Estimation on Manifolds
- 7:00 Optimisation Geometry and Its Implications for Optimisation Algorithms
- 7:20 Restricted Update Sequential Matrix Diagonalisation for Parahermitian Matrices
Wednesday, December 13
Wednesday, December 13, 09:00 - 10:00
PL5: Plenary Talk 5: Artificial Intelligence for 5G : Challenges and Opportunities![go to top](icon/top.gif)
Mobile cellular networks are becoming increasingly complex to manage while classical deployment/optimization techniques are cost-ineffective and thus seen as stopgaps. This is all the more difficult considering the extreme constraints of 5G networks in terms of data rate (more than 10 Gb/s), massive connectivity (more than 1000000 devices per km2), latency (under 1ms) and energy efficiency (a reduction by a factor of 100 with respect to 4G network). Unfortunately, the development of adequate solutions is severely limited by the scarcity of the actual resources (energy, bandwidth and space). Recently, the community has turned to a new resource known as Artificial Intelligence at all layers of the network to exploit the increasing computing power afforded by the improvement in Moore's law in combination with the availability of huge data in 5G networks. This is an important paradigm shift which considers the increasing data flood/huge number of nodes as an opportunity rather than a curse. In this talk, we will discuss through various examples how the recent advances in big data algorithms can provide an efficient framework for the design of 5G Intelligent Networks.
Wednesday, December 13, 10:15 - 12:15
WM1: Signal Processing for mmWave Communication in Freqency Selective Channels![go to top](icon/top.gif)
- 10:15 Position Aided Beam Alignment for Millimeter Wave Backhaul Systems with Large Phased Arrays
- 10:35 Learning-based Pilot Precoding and Combining for Wideband Millimeter-wave Networks
- 10:55 A Compressive Sensing-Maximum Likelihood Approach for Off-Grid Wideband Channel Estimation at mmWave
- 11:15 Channel Estimation for Hybrid Multi-Carrier MmWave MIMO Systems Using Three-Dimensional Unitary ESPRIT in DFT Beamspace
- 11:35 Wideband Channel Tracking for mmWave MIMO System with Hybrid Beamforming Architecture
- 11:55 Tensor-Based Compressed Estimation of Frequency-Selective mmWave MIMO Channels
WM2: Low-dimension Dynamical Systems in Signal Processing and Data Analysis![go to top](icon/top.gif)
- 10:15 Earth-Mover's Distance as a Tracking Regulaizer
- 10:35 Sequential Detection of Low-Rank Changes Using Extreme Eigenvalues
- 10:55 Data-Driven Discovery of Governing Physical Laws and Their Parametric Dependencies in Engineering, Physics and Biology
- 11:15 Simultaneous Recovery of A Series of Low-rank Matrices by Locally Weighted Matrix Smoothing
- 11:35 Structure-Exploiting Variational Inference for Recurrent Switching Linear Dynamical Systems
- 11:55 Network Estimation via Poisson Autoregressive Models
WM3: Ill-Posed Inverse Problems in High Resolution Imaging![go to top](icon/top.gif)
- 10:15 Super-Resolution of Complex Exponentials from Modulations with Known Waveforms
- 10:35 Joint Low-Rank and Sparse Based Image Reconstruction for Through-the-Wall Radar Imaging
- 10:55 Soft Extrapolation of Bandlimited Functions
- 11:15 Performance of Free-Space Tomographic Imaging Approximation for Shallow-Buried Target Detection
- 11:35 On the Role of Sampling and Sparsity in Phase Retrieval for Optical Coherence Tomography
- 11:55 Fundamental Limits of PhaseMax for Phase Retrieval: A Replica Analysis
Wednesday, December 13, 15:30 - 16:30
PL6: Plenary Talk 6: Tensors and Probability: An Intriguing Union![go to top](icon/top.gif)
We reveal an interesting link between tensors and multivariate statistics. The rank of a multivariate probability tensor can be interpreted as a nonlinear measure of statistical dependence of the associated random variables. Rank equals one when the random variables are independent, and complete statistical dependence corresponds to full rank; but we show that rank as low as two can already model strong statistical dependence. In practice we usually work with random variables that are neither independent nor fully dependent -- partial dependence is typical, and can be modeled using a low-rank multivariate probability tensor. Directly estimating such a tensor from sample averages is impossible even for as few as ten random variables taking ten values each -- yielding a billion unknowns; but we often have enough data to estimate lower-order marginalized distributions. We prove that it is possible to identify the higher-order joint probabilities from lower order ones, provided that the higher-order probability tensor has low-enough rank, i.e., the random variables are only partially dependent. We also provide a computational identification algorithm that is shown to work well on both simulated and real data. The insights and results have numerous applications in estimation, hypothesis testing, completion, machine learning, and system identification. Low-rank tensor modeling thus provides a `universal' non-parametric (model-free) alternative to probabilistic graphical models.
Wednesday, December 13, 16:30 - 18:00
WP1: Communication Systems![go to top](icon/top.gif)
- Constellation Shaping for Rate Maximization in AWGN Channels with Non-linear Distortion
- Weighted Sum Rate Maximization for Non-Regenerative Multi-Way Relay Channels with Multi-User Decoding
- Harvested Power Maximization in QoS-constrained MIMO SWIPT with Generic RF Harvesting Model
- Fast Converging Decentralized WSRMax for MIMO IBC with Low Computational Complexity
- Tracking Abruptly Changing Channels in mmWave Systems Using Overlaid Data and Training
- GenS: A New Conflict-Free Link Scheduler for Next Generation of Wireless Systems
WP2: Beamforming![go to top](icon/top.gif)
- Energy-Efficient Distributed Amplify-and-Forward Beamforming for Wireless Sensor Networks
- Bias-Compensated MPDR Beamformer for Small Number of Samples
- Network Beamforming for Asynchronous MIMO Two-Way Relay Networks
- An Improved Design of Robust Adaptive Beamforming Based on Steering Vector Estimation
- An ADMM Approach to Distributed Coordinated Beamforming in Dynamic TDD Networks
- Robust MDDR Beamforming for Sub-Gaussian Signals in the Presence of Fast-moving Interferences
WP3: Computer-intensive methods in signal processing![go to top](icon/top.gif)
- Rapid System Identification for Jump Markov Non-Linear Systems
- Bayesian Bhattacharyya Bound for Discrete-Time Filtering Revisited
- A Particle-based Approach for Topology Estimation of Gene Networks
- Bayesian Selection of Models of Network Formation
- Deep Robust Regression
- Robust-COMET for Covariance Estimation in Convex Structures: Algorithm and Statistical Properties
- Recursive Estimation of Time-Varying RSS Fields Based on Crowdsourcing and Gaussian Processes
- A Fast Model for Solving the ECG Forward Problem Based on an Evolutionary Algorithm
Wednesday, December 13, 18:00 - 19:40
WA1: Signal Processing for Smart Grids![go to top](icon/top.gif)
- 6:00 Distributed Optimal Power Flow Using Feasible Point Pursuit
- 6:20 Going Beyond Linear Dependencies to Unveil Connectivity of Meshed Grids
- 6:40 Multi-Channel Missing Data Recovery by Exploiting the Low-rank Hankel Structures
- 7:00 Predicting Voltage Stability Margin via Learning Stability Region Boundary
- 7:20 Power Grid Probing for Load Learning: Identifiability over Multiple Time Instances
WA2: Advances in Monte Carlo Methods for Optimization and Inference in High-dimensional Systems![go to top](icon/top.gif)
- 6:00 Multiple Sigma-point Kalman Smoothers for High-dimensional State-Space Models
- 6:20 Population Monte Carlo Schemes with Reduced Path Degeneracy
- 6:40 Simulated Convergence Rates with Application to an Intractable $\alpha$-Stable Inference Problem
- 7:00 Gaussian Sum Particle Flow Filter
- 7:20 Adaptive Noisy Importance Sampling for Stochastic Optimization
WA3: Target Tracking![go to top](icon/top.gif)
- 6:00 Adaptive Target Tracking Using Multistatic Sensor with Unknown Moving Transmitter Positions
- 6:20 Performance of Range-Only TMA
- 6:40 Algorithms for the Multi-Sensor Assignment Problem in the Delta-Generalized Labeled multi-Bernoulli Filter
- 7:00 A Classify-While-Track Approach Using Dynamical Tensors
- 7:20 Simultaneous Target State and Sensor Bias Estimation: Is More Better