| 1. |
- Adalbjörnsson, Stefan, et al.
(författare)
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Conjugate priors for Gaussian emission plsa recommender systems
- 2016
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Ingår i: 2016 24th European Signal Processing Conference, EUSIPCO 2016. - 9780992862657 ; 2016-November, s. 2096-2100
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Konferensbidrag (refereegranskat)abstract
- Collaborative filtering for recommender systems seeks to learn and predict user preferences for a collection of items by identifying similarities between users on the basis of their past interest or interaction with the items in question. In this work, we present a conjugate prior regularized extension of Hofmann's Gaussian emission probabilistic latent semantic analysis model, able to overcome the over-fitting problem restricting the performance of the earlier formulation. Furthermore, in experiments using the EachMovie and MovieLens data sets, it is shown that the proposed regularized model achieves significantly improved prediction accuracy of user preferences as compared to the latent semantic analysis model without priors.
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| 2. |
- Alickovic, Emina, et al.
(författare)
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A System Identification Approach to Determining Listening Attention from EEG Signals
- 2016
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Ingår i: 2016 24TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO). - : IEEE. - 9780992862657 - 9781509018918 ; , s. 31-35
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Konferensbidrag (refereegranskat)abstract
- We still have very little knowledge about how ourbrains decouple different sound sources, which is known assolving the cocktail party problem. Several approaches; includingERP, time-frequency analysis and, more recently, regression andstimulus reconstruction approaches; have been suggested forsolving this problem. In this work, we study the problem ofcorrelating of EEG signals to different sets of sound sources withthe goal of identifying the single source to which the listener isattending. Here, we propose a method for finding the number ofparameters needed in a regression model to avoid overlearning,which is necessary for determining the attended sound sourcewith high confidence in order to solve the cocktail party problem.
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| 3. |
- Andersson, Fredrik, et al.
(författare)
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On a fixed-point algorithm for structured low-rank approximation and estimation of half-life parameters
- 2016
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Ingår i: 2016 24th European Signal Processing Conference, EUSIPCO. - 9780992862657 ; , s. 326-330
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Konferensbidrag (refereegranskat)abstract
- We study the problem of decomposing a measured signal as a sum of decaying exponentials. There is a direct connection to sums of these types and positive semi-definite (PSD) Hankel matrices, where the rank of these matrices equals the number of exponentials. We propose to solve the identification problem by forming an optimization problem with a misfit function combined with a rank penalty function that also ensures the PSD-constraint. This problem is non-convex, but we show that it is possible to compute the minimum of an explicit closely related convexified problem. Moreover, this minimum can be shown to often coincide with the minimum of the original non-convex problem, and we provide a simple criterion that enables to verify if this is the case.
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| 4. |
- Batstone, Kenneth John, et al.
(författare)
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Robust Time-of-Arrival Self Calibration with Missing Data and Outliers
- 2016
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Ingår i: 2016 24th European Signal Processing Conference (EUSIPCO). - 9780992862657 ; , s. 2370-2374
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Konferensbidrag (refereegranskat)abstract
- The problem of estimating receiver-sender node positionsfrom measured receiver-sender distances is a key issue indifferent applications such as microphone array calibration, radioantenna array calibration, mapping and positioning using ultrawidebandand mapping and positioning using round-trip-timemeasurements between mobile phones and Wi-Fi-units. Thanks torecent research in this area we have an increased understandingof the geometry of this problem. In this paper, we study theproblem of missing information and the presence of outliers inthe data. We propose a novel hypothesis and test frameworkthat efficiently finds initial estimates of the unknown parametersand combine such methods with optimization techniques toobtain accurate and robust systems. The proposed systems areevaluated against current state-of-the-art methods on a large setof benchmark tests. This is evaluated further on Wi-Fi roundtriptime and ultra-wideband measurements to give a realisticexample of self calibration for indoor localization.
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| 5. |
- Biswas, Sinchan, et al.
(författare)
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Sensing Throughput Optimization in Cognitive Fading Multiple Access Channels With Energy Harvesting Secondary Transmitters
- 2016
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Ingår i: 2016 24Th European Signal Processing Conference (EUSIPCO). - 9780992862657 ; , s. 577-581
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Konferensbidrag (refereegranskat)abstract
- The paper investigates the problem of maximizing the expected achievable sum rate in a fading multiple access cognitive radio network when secondary user (SU) transmitters have energy harvesting capability, and perform cooperative spectrum sensing. We formulate the problem as maximization of throughput of the cognitive multiple access network over a finite time horizon subject to a time averaged interference constraint at the primary user (PU) and almost sure energy causality constraints at the SUs. The problem is a mixed integer non-linear program with respect to two decision variables, namely, spectrum access decision and spectrum sensing decision, and the continuous variables sensing time and transmission power. In general, this problem is known to be NP hard. For optimization over these two decision variables, we use an exhaustive search policy when the length of the time horizon is small, and a heuristic policy for longer horizons. For given values of the decision variables, the problem simplifies into a joint optimization on SU transmission power and sensing time, which is non-convex in nature. We present an analytic solution for the resulting optimization problem using an alternating convex optimization problem for non-causal channel state information and harvested energy information patterns at the SU base station (SBS) or fusion center (FC) and infinite battery capacity at the SU transmitters. We formulate the problem with causal information and finite battery capacity as a stochastic control problem and solve it using the technique of dynamic programming. Numerical results are presented to illustrate the performance of the various algorithms.
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| 6. |
- Elvander, Filip, et al.
(författare)
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Robust Non-Negative Least Squares Using Sparsity
- 2016
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Ingår i: 2016 24th European Signal Processing Conference (EUSIPCO). - 2076-1465. - 9780992862657 ; , s. 61-65
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Konferensbidrag (refereegranskat)abstract
- Sparse, non-negative signals occur in many applications. To recover such signals, estimation posed as non-negative least squares problems have proven to be fruitful. Efficient algorithms with high accuracy have been proposed, but many of them assume either perfect knowledge of the dictionary generating the signal, or attempts to explain deviations from this dictionary by attributing them to components that for some reason is missing from the dictionary. In this work, we propose a robust non-negative least squares algorithm that allows the generating dictionary to differ from the assumed dictionary, introducing uncertainty in the setup. The proposed algorithm enables an improved modeling of the measurements, and may be efficiently implemented using a proposed ADMM implementation. Numerical examples illustrate the improved performance as compared to the standard non-negative LASSO estimator.
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| 7. |
- Kronvall, Ted, et al.
(författare)
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Multi-pitch estimation via fast group sparse learning
- 2016
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Ingår i: 2016 24th European Signal Processing Conference (EUSIPCO). - 2076-1465. - 9780992862657 ; , s. 1093-1097
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Konferensbidrag (refereegranskat)abstract
- In this work, we consider the problem of multi-pitch estimation using sparse heuristics and convex modeling. In general, this is a difficult non-linear optimization problem, as the frequencies belonging to one pitch often overlap the frequencies belonging to other pitches, thereby causing ambiguity between pitches with similar frequency content. The problem is further complicated by the fact that the number of pitches is typically not known. In this work, we propose a sparse modeling framework using a generalized chroma representation in order to remove redundancy and lower the dictionary's block-coherency. The found chroma estimates are then used to solve a small convex problem, whereby spectral smoothness is enforced, resulting in the corresponding pitch estimates. Compared with previously published sparse approaches, the resulting algorithm reduces the computational complexity of each iteration, as well as speeding up the overall convergence.
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| 8. |
- Leong, Alex S., et al.
(författare)
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Optimal Transmission Policies for Variance Based Event Triggered Estimation With an Energy Harvesting Sensor
- 2016
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Ingår i: 2016 24Th European Signal Processing Conference (EUSIPCO). - 9780992862657 ; , s. 225-229
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Konferensbidrag (refereegranskat)abstract
- This paper considers a remote state estimation problem where a sensor observes a dynamical process, and transmits local state estimates over an independent and identically distributed (i.i.d.) packet dropping channel to a remote estimator. The sensor is equipped with energy harvesting capabilities. At every discrete time instant, provided there is enough battery energy, the sensor decides whether it should transmit or not, in order to minimize the expected estimation error covariance at the remote estimator. For transmission schedules dependent only on the estimation error covariance at the remote estimator, the energy available at the sensor, and the harvested energy, we establish structural results on the optimal scheduling which show that for a given battery energy level and a given harvested energy, the optimal policy is a threshold policy on the error covariance, i.e. transmit if and only if the error covariance exceeds a certain threshold. Similarly, for a given error covariance and a given harvested energy, the optimal policy is a threshold policy on the battery level. Numerical studies confirm the qualitative behaviour predicted by our structural results.
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| 9. |
- Ozcelikkale, Ayca, 1982, et al.
(författare)
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Transmission Strategies for Remote Estimation under Energy Harvesting Constraints
- 2016
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Ingår i: European Signal Processing Conference. - 2219-5491. - 9780992862657 ; 2016-November, s. 572-576
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Konferensbidrag (refereegranskat)abstract
- We consider the remote estimation of a time- correlated field using an energy harvesting (EH) sensor. The sensor observes the unknown field and communicates its observa- tions to a remote fusion center using an amplify-forward strategy. We consider the design of optimal transmission strategies in order to minimize the mean-square error (MSE) at the fusion center. Contrary to traditional approaches, the degree of correlation between the field values constitutes an important aspect of our formulation. We provide the optimal power allocation strategies for a number of illustrative scenarios, including the circularly wide-sense stationary (c.w.s.s.) signals with static correlation coefficient and the sampled low-pass c.w.s.s. signals. Based on these results, we propose low-complexity policies for the general case. Numerical evaluations illustrate the performance of the optimal and the low-complexity policies.
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| 10. |
- Tarighati, Alla, et al.
(författare)
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Decentralized Detection in Energy Harvesting Wireless Sensor Networks
- 2016
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Ingår i: 2016 24TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO). - : IEEE conference proceedings. - 9780992862657 ; , s. 567-571
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Konferensbidrag (refereegranskat)abstract
- We consider a decentralized hypothesis testing problem in which several peripheral energy harvesting sensors are arranged in parallel. Each sensor makes a noisy observation of a time varying phenomenon, and sends a message about the present hypothesis towards a fusion center at each time instance t. The fusion center, using the aggregate of the received messages during the time instance t, makes a decision about the state of the present hypothesis. We assume that each sensor is an energy harvesting device and is capable of harvesting all the energy it needs to communicate from its environment. Our contribution is to formulate and analyze the decentralized detection problem when the energy harvesting sensors are allowed to form a long term energy usage policy. Our analysis is based on a queuing-theoretic model for the battery. Then, by using numerical simulations, we show how the resulting performance differs from the energy-unconstrained case.
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