OpenRaiser/Intern-Atlas huggingface dataset:
Crawford-sobel meet Lloyd-Max on the grid
Venue: Conference ICASSP 2014
Year: 2014

The main contribution of this work is twofold. First, we apply, for the first time, a framework borrowed from economics to a problem in the smart grid namely, the design of signaling schemes between a consumer and an electricity aggregator when these have non-aligned objectives. The consumer's objective is to meet its need in terms of power and send a request (a message) to the aggregator which does not correspond, in general, to its actual need. The aggregator, which receives this request, not only wants to satisfy it but also wants to manage the cost induced by the residential electricity distribution network. Second, we establish connections between the exploited framework and the quantization problem. Although the model assumed for the payoff functions for the consumer and aggregator is quite simple, it allows one to extract insights of practical interest from the analysis conducted. This allows us to establish a direct connection with quantization, and more importantly, to open a much more general challenge for source and channel coding.
Decentralized subspace pursuit for joint sparsity pattern recovery
Venue: Conference ICASSP 2014
Year: 2014

To solve the problem of joint sparsity pattern recovery in a decen-tralized network, we propose an algorithm named decentralized and collaborative subspace pursuit (DCSP). The basic idea of DCSP is to embed collaboration among nodes and fusion strategy into each iteration of the standard subspace pursuit (SP) algorithm. In DCSP, each node collaborates with several of its neighbors by sharing high-dimensional coefficient estimates and communicates with other remote nodes by exchanging low-dimensional support set estimates. Experimental evaluations show that, compared with several existing algorithms for sparsity pattern recovery, DCSP produces satisfactory results in terms of accuracy of sparsity pattern recovery with much less communication cost.
Visual reranking with improved image graph
Venue: Conference ICASSP 2014
Year: 2014

This paper introduces an improved reranking method for the Bag-of-Words (BoW) based image search. Built on [1], a directed image graph robust to outlier distraction is proposed. In our approach, the relevance among images is encoded in the image graph, based on which the initial rank list is refined. Moreover, we show that the rank-level feature fusion can be adopted in this reranking method as well. Taking advantage of the complementary nature of various features, the reranking performance is further enhanced. Particularly, we exploit the reranking method combining the BoW and color information. Experiments on two benchmark datasets demonstrate that ourmethod yields significant improvements and the reranking results are competitive to the state-of-the-art methods.
Spectral compressive sensing with model selection
Venue: Conference ICASSP 2014
Year: 2014

The performance of existing approaches to the recovery of frequency-sparse signals from compressed measurements is limited by the coherence of required sparsity dictionaries and the discretization of frequency parameter space. In this paper, we adopt a parametric joint recovery-estimation method based on model selection in spectral compressive sensing. Numerical experiments show that our approach outperforms most state-of-the-art spectral CS recovery approaches in fidelity, tolerance to noise and computation efficiency.
Identification of dynamic functional brain network states through tensor decomposition
Venue: Conference ICASSP 2014
Year: 2014

With the advances in high resolution neuroimaging, there has been a growing interest in the detection of functional brain connectivity. Complex network theory has been proposed as an attractive mathematical representation of functional brain networks. However, most of the current studies of functional brain networks have focused on the computation of graph theoretic indices for static networks, i.e. long-time averages of connectivity networks. It is well-known that functional connectivity is a dynamic process and the construction and reorganization of the networks is key to understanding human cognition. Therefore, there is a growing need to track dynamic functional brain networks and identify time intervals over which the network is quasi-stationary. In this paper, we present a tensor decomposition based method to identify temporally invariant 'network states' and find a common topographic representation for each state. The proposed methods are applied to electroencephalogram (EEG) data during the study of error-related negativity (ERN).
A quasi-Newton method for large scale support vector machines
Venue: Conference ICASSP 2014
Year: 2014

This paper adapts a recently developed regularized stochastic version of the Broyden, Fletcher, Goldfarb, and Shanno (BFGS) quasi-Newton method for the solution of support vector machine classification problems. The proposed method is shown to converge almost surely to the optimal classifier at a rate that is linear in expectation. Numerical results show that the proposed method exhibits a convergence rate that degrades smoothly with the dimensionality of the feature vectors.
Energy consumption in multi-user MIMO systems: Impact of user mobility
Venue: Conference ICASSP 2014
Year: 2014

In this work, we consider the downlink of a single-cell multi-user multiple-input multiple-output system in which zero-forcing precoding is used at the base station (BS) to serve a certain number of user equipments (UEs). A fixed data rate is guaranteed at each UE. The UEs move around in the cell according to a Brownian motion, thus the path losses change over time and the energy consumption fluctuates accordingly. We aim at determining the distribution of the energy consumption. To this end, we analyze the asymptotic regime where the number of antennas at the BS and the number of UEs grow large with a given ratio. It turns out that the energy consumption is asymptotically a Gaussian random variable whose mean and variance are derived analytically. These results can, for example, be used to approximate the probability that a battery-powered BS runs out of energy within a certain time period.
Matching theory for priority-based cell association in the downlink of wireless small cell networks
Venue: Conference ICASSP 2014
Year: 2014

The deployment of small cells, overlaid on existing cellular infrastructure, is seen as a key feature in next-generation cellular systems. In this paper, the problem of user association in the downlink of small cell networks (SCNs) is considered. The problem is formulated as a many-to-one matching game in which the users and SCBSs rank one another based on utility functions that account for both the achievable performance, in terms of rate and fairness to cell edge users, as captured by newly proposed priorities. To solve this game, a novel distributed algorithm that can reach a stable matching is proposed. Simulation results show that the proposed approach yields an average utility gain of up to 65% compared to a common association algorithm that is based on received signal strength. Compared to the classical deferred acceptance algorithm, the results also show a 40% utility gain and a more fair utility distribution among the users.
Efficient unimodality test in clustering by signature testing
Venue: Conference ICASSP 2014
Year: 2014

This paper provides a new unimodality test with application in hierarchical clustering methods. The proposed method denoted by signature test (Sigtest), transforms the data based on its statistics. The transformed data has much smaller variation compared to the original data and can be evaluated in a simple proposed unimodality test. Compared with the existing unimodality tests, Sigtest is more accurate in detecting the overlapped clusters and has a much less computational complexity. Simulation results demonstrate the efficiency of this statistic test for both real and synthetic data sets.
Distributed quantization for compressed sensing
Venue: Conference ICASSP 2014
Year: 2014

We study distributed coding of compressed sensing (CS) measurements using vector quantizer (VQ). We develop a distributed framework for realizing optimized quantizer that enables encoding CS measurements of correlated sparse sources followed by joint decoding at a fusion center. The optimality of VQ encoder-decoder pairs is addressed by minimizing the sum of mean-square errors between the sparse sources and their reconstruction vectors at the fusion center. We derive a lower-bound on the end-to-end performance of the studied distributed system, and propose a practical encoder-decoder design through an iterative algorithm.
An accurate and efficient analysis of a MBSFN network
Venue: Conference ICASSP 2014
Year: 2014

A new accurate analysis is presented for an OFDM-based multicast-broadcast single-frequency network (MBSFN). The topology of the network is modeled by a constrained random spatial model involving a fixed number of base stations placed over a finite area with a minimum separation. The analysis is driven by a new closed-form expression for the conditional outage probability at each location of the network, where the conditioning is with respect to the network realization. The analysis accounts for the diversity combining of signals transmitted by different base stations of a given MBSFN area, and also accounts for the interference caused by the base stations of other MBSFN areas. The analysis features a flexible channel model, accounting for path loss, Nakagami fading, and correlated shadowing. The analysis is used to investigate the influence of the minimum base-station separation and provides insight regarding the optimal size of the MBSFN areas. In order to highlight the percentage of the network that will fail to successfully receive the broadcast, the area below an outage threshold (ABOT) is here used and defined as the fraction of the network that provides an outage probability (averaged over the fading) that meets a threshold.
Projection onto the cosparse set is NP-hard
Venue: Conference ICASSP 2014
Year: 2014

The computational complexity of a problem arising in the context of sparse optimization is considered, namely, the projection onto the set of $k$-cosparse vectors w.r.t. some given matrix $\Omeg$. It is shown that this projection problem is (strongly) \NP-hard, even in the special cases in which the matrix $\Omeg$ contains only ternary or bipolar coefficients. Interestingly, this is in contrast to the projection onto the set of $k$-sparse vectors, which is trivially solved by keeping only the $k$ largest coefficients.
On the degrees of freedom of the K-user MISO interference channel with imperfect delayed CSIT
Venue: Conference ICASSP 2014
Year: 2014

This work investigates the degrees of freedom (DoF) of the K-user multiple-input single-output (MISO) interference channel (IC) with imperfect delayed channel state information at the transmitters (dCSIT). For this setting, new DoF inner bonds are provided, and benchmarked with cooperation-based outer bounds. The achievability result is based on a precoding scheme that aligns the interfering received signals through time, exploiting the concept of Retrospective Interference Alignment (RIA). The proposed approach outperforms all previous known schemes. Furthermore, we study the proposed scheme under channel estimation errors (CEE) on the reported dCSIT, and derive a closed-form expression for the achievable DoF with imperfect dCSIT.
Retrospective Interference Alignment for the 3-user MIMO interference channel with delayed CSIT
Venue: Conference ICASSP 2014
Year: 2014

The degrees of freedom (DoF) of the 3-user multiple input multiple output interference channel (3-user MIMO IC) are investigated where there is delayed channel state information at the transmitters (dCSIT). We generalize the ideas of Maleki et al. about {\it Retrospective Interference Alignment (RIA)} to be applied to the MIMO IC, where transmitters and receivers are equipped with $(M,N)$ antennas, respectively. We propose a two-phase transmission scheme where the number of slots per phase and number of transmitted symbols are optimized by solving a maximization problem. Finally, we review the existing achievable DoF results in the literature as a function of the ratio between transmitting and receiving antennas $ρ=M/N$. The proposed scheme improves all other strategies when $ρ\in \left(\frac{1}{2}, \frac{31}{32} \right]$.
Compressive signal processing with circulant sensing matrices
Venue: Conference ICASSP 2014
Year: 2014

Compressive sensing achieves effective dimensionality reduction of signals, under a sparsity constraint, by means of a small number of random measurements acquired through a sensing matrix. In a signal processing system, the problem arises of processing the random projections directly, without first reconstructing the signal. In this paper, we show that circulant sensing matrices allow to perform a variety of classical signal processing tasks such as filtering, interpolation, registration, transforms, and so forth, directly in the compressed domain and in an exact fashion, \emph{i.e.}, without relying on estimators as proposed in the existing literature. The advantage of the techniques presented in this paper is to enable direct measurement-to-measurement transformations, without the need of costly recovery procedures.
Finite-horizon optimal transmission policies for energy harvesting sensors
Venue: Conference ICASSP 2014
Year: 2014

In this paper, we derive optimal transmission policies for energy harvesting sensors to maximize the utility obtained over a finite horizon. First, we consider a single energy harvesting sensor, with discrete energy arrival process, and a discrete energy consumption policy. Under this model, we show that the optimal finite horizon policy is a threshold policy, and explicitly characterize the thresholds, and the thresholds can be precomputed using a recursion. Next, we address the case of multiple sensors, with only one of them allowed to transmit at any given time to avoid interference, and derive an explicit optimal policy for this scenario as well.
Integrating energy storage into the smart grid: A prospect theoretic approach
Venue: Conference ICASSP 2014
Year: 2014

In this paper, the interactions and energy exchange decisions of a number of geographically distributed storage units are studied under decision-making involving end-users. In particular, a noncooperative game is formulated between customer-owned storage units where each storage unit's owner can decide on whether to charge or discharge energy with a given probability so as to maximize a utility that reflects the tradeoff between the monetary transactions from charging/discharging and the penalty from power regulation. Unlike existing game-theoretic works which assume that players make their decisions rationally and objectively, we use the new framework of prospect theory (PT) to explicitly incorporate the users' subjective perceptions of their expected utilities. For the two-player game, we show the existence of a proper mixed Nash equilibrium for both the standard game-theoretic case and the case with PT considerations. Simulation results show that incorporating user behavior via PT reveals several important insights into load management as well as economics of energy storage usage. For instance, the results show that deviations from conventional game theory, as predicted by PT, can lead to undesirable grid loads and revenues thus requiring the power company to revisit its pricing schemes and the customers to reassess their energy storage usage choices.
Energy beamforming with one-bit feedback
Venue: Conference ICASSP 2014
Year: 2014

Wireless energy transfer (WET) has attracted significant attention recently for providing energy supplies wirelessly to electrical devices without the need of wires or cables. Among different types of WET techniques, the radio frequency (RF) signal enabled far-field WET is most practically appealing to power energy constrained wireless networks in a broadcast manner. To overcome the significant path loss over wireless channels, multi-antenna or multiple-input multiple-output (MIMO) techniques have been proposed to enhance the transmission efficiency and distance for RF-based WET. However, in order to reap the large energy beamforming gain in MIMO WET, acquiring the channel state information (CSI) at the energy transmitter (ET) is an essential task. This task is particularly challenging for WET systems, since existing channel training and feedback methods used for communication receivers may not be implementable at the energy receiver (ER) due to its hardware limitation. To tackle this problem, in this paper we consider a multiuser MIMO system for WET, where a multiple-antenna ET broadcasts wireless energy to a group of multiple-antenna ERs concurrently via transmit energy beamforming. By taking into account the practical energy harvesting circuits at the ER, we propose a new channel learning method that requires only one feedback bit from each ER to the ET per feedback interval. The feedback bit indicates the increase or decrease of the harvested energy by each ER between the present and previous intervals, which can be measured without changing the existing hardware at the ER. Based on such feedback information, the ET adjusts transmit beamforming in different training intervals and at the same time obtains improved estimates of the MIMO channels to ERs by applying a new approach termed analytic center cutting plane method (ACCPM).
Distributions of upper PAPR and lower PAPR of OFDM signals in visible light communications
Venue: Conference ICASSP 2014
Year: 2014

Orthogonal frequency-division multiplexing (OFDM) in visible light communications (VLC) inherits the disadvantage of high peak-to-average power ratio (PAPR) from OFDM in radio frequency (RF) communications. The upper peak power and lower peak power of real-valued VLC-OFDM signals are both limited by the dynamic constraints of light emitting diodes (LEDs). The efficiency and transmitted electrical power are directly related with the upper PAPR (UPAPR) and lower PAPR (LPAPR) of VLC-OFDM. In this paper, we will derive the complementary cumulative distribution function (CCDF) of UPAPR and LPAPR, and investigate the joint distribution of UPAPR and LPAPR.
Group-sparse matrix recovery
Venue: Conference ICASSP 2014
Year: 2014

We apply the OSCAR (octagonal selection and clustering algorithms for regression) in recovering group-sparse matrices (two-dimensional---2D---arrays) from compressive measurements. We propose a 2D version of OSCAR (2OSCAR) consisting of the $\ell_1$ norm and the pair-wise $\ell_{\infty}$ norm, which is convex but non-differentiable. We show that the proximity operator of 2OSCAR can be computed based on that of OSCAR. The 2OSCAR problem can thus be efficiently solved by state-of-the-art proximal splitting algorithms. Experiments on group-sparse 2D array recovery show that 2OSCAR regularization solved by the SpaRSA algorithm is the fastest choice, while the PADMM algorithm (with debiasing) yields the most accurate results.
Robust binary fused compressive sensing using adaptive outlier pursuit
Venue: Conference ICASSP 2014
Year: 2014

We propose a new method, {\it robust binary fused compressive sensing} (RoBFCS), to recover sparse piece-wise smooth signals from 1-bit compressive measurements. The proposed method is a modification of our previous {\it binary fused compressive sensing} (BFCS) algorithm, which is based on the {\it binary iterative hard thresholding} (BIHT) algorithm. As in BIHT, the data term of the objective function is a one-sided $\ell_1$ (or $\ell_2$) norm. Experiments show that the proposed algorithm is able to take advantage of the piece-wise smoothness of the original signal and detect sign flips and correct them, achieving more accurate recovery than BFCS and BIHT.
Interference alignment (IA) and coordinated multi-point (CoMP) with IEEE802.11AC feedback compression: Testbed results
Venue: Conference ICASSP 2014
Year: 2014

We have implemented interference alignment (IA) and joint transmission coordinated multipoint (CoMP) on a wireless testbed using the feedback compression scheme of the new 802.11ac standard. The performance as a function of the frequency domain granularity is assessed. Realistic throughput gains are obtained by probing each spatial modulation stream with ten different coding and modulation schemes. The gain of IA and CoMP over TDMA MIMO is found to be 26% and 71%, respectively under stationary conditions. In our dense indoor office deployment, the frequency domain granularity of the feedback can be reduced down to every 8th subcarrier (2.5MHz), without sacrificing performance.
A deep representation for invariance and music classification
Venue: Conference ICASSP 2014
Year: 2014

Representations in the auditory cortex might be based on mechanisms similar to the visual ventral stream; modules for building invariance to transformations and multiple layers for compositionality and selectivity. In this paper we propose the use of such computational modules for extracting invariant and discriminative audio representations. Building on a theory of invariance in hierarchical architectures, we propose a novel, mid-level representation for acoustical signals, using the empirical distributions of projections on a set of templates and their transformations. Under the assumption that, by construction, this dictionary of templates is composed from similar classes, and samples the orbit of variance-inducing signal transformations (such as shift and scale), the resulting signature is theoretically guaranteed to be unique, invariant to transformations and stable to deformations. Modules of projection and pooling can then constitute layers of deep networks, for learning composite representations. We present the main theoretical and computational aspects of a framework for unsupervised learning of invariant audio representations, empirically evaluated on music genre classification.
Large Deformation Diffeomorphic Metric Mapping and Fast-Multipole Boundary Element Method provide new insights for Binaural acoustics
Venue: Conference ICASSP 2014
Year: 2014

This paper describes how Large Deformation Diffeomorphic Metric Mapping (LDDMM) can be coupled with a Fast Multipole (FM) Boundary Element Method (BEM) to investigate the relationship between morphological changes in the head, torso, and outer ears and their acoustic filtering (described by Head Related Transfer Functions, HRTFs). The LDDMM technique provides the ability to study and implement morphological changes in ear, head and torso shapes. The FM-BEM technique provides numerical simulations of the acoustic properties of an individual's head, torso, and outer ears. This paper describes the first application of LDDMM to the study of the relationship between a listener's morphology and a listener's HRTFs. To demonstrate some of the new capabilities provided by the coupling of these powerful tools, we examine the classical question of what it means to ``listen through another individual's outer ears.'' This work utilizes the data provided by the Sydney York Morphological and Acoustic Recordings of Ears (SYMARE) database.