Showing papers by "George Tzagkarakis published in 2013"

Joint reconstruction of compressively sensed ultrasound RF echoes by exploiting temporal correlations

Abstract: In this paper, the principles of compressive sensing are exploited for the joint reconstruction of an ensemble of biomedical ultrasound RF echoes, using a highly reduced set of random measurements. Temporal correlations between the distinct RF echoes are taken into account during the reconstruction, which results in a reduction of the required number of measurements, while also increasing the reconstruction quality. The efficiency of recent state-of-the-art methods is evaluated on a set of real ultrasound data, to highlight the importance of accounting for temporal correlations during reconstruction. Our experimental evaluation reveals an improved performance, both visually and in terms of quality metrics, such as the SSIM and PSNR, when such correlations are extracted during the joint reconstruction of RF echoes, compared with previous methods based on the separate recovery of each RF echo.

Compressed gated range sensing

Abstract: Active Range Imaging (ARI) has recently sparked an enthusiastic interest due to the numerous applications that can benefit from the high quality depth maps that ARI systems offer. One of the most successful ARI techniques employs Time-of-Flight (ToF) cameras which emit and subsequently record laser pulses in order to estimate the distance between the camera and objects in a scene. A limitation of this type of ARI is the requirement for a large number of frames that have to be captured in order to generate high resolution depth maps. In this work, we introduce Compressed Gated Range Sensing (CGRS), a novel approach for ToF-based ARI that utilizes the recently proposed framework of Compressed Sensing (CS) to dramatically reduce the number of necessary frames. The CGRS technique employs a random gating function along with state-of-the-art reconstruction in order to estimate the timing of a returning laser pulse and infer the depth map. To validate our method, software simulations were carried out using a realistic system model. Simulated results suggest that low error reconstruction of a depth map is possible using approximately 20% of the frames that traditional ToF cameras require, while 30% sampling rates can achieve very high fidelity reconstruction.

WLAN-based indoor path tracking using compressive RSS measurements

Abstract: In this paper, a hybrid path-tracking system is introduced, which exploits the power of compressive sensing (CS) to recover accurately sparse signals, in conjunction with the efficiency of a Kalman filter to update the states of a dynamical system. The proposed method first employs a hierarchical region-based approach to constrain the area of interest, by modeling the signal-strength values received from a set of wireless access points using the statistics of multivariate Gaussian models. Then, based on the inherent spatial sparsity of indoor localization, CS is applied as a refinement of the estimated position by recovering an appropriate sparse position-indicator vector. The experimental evaluation with real data reveals that the proposed approach achieves increased localization accuracy when compared with previous methods, while maintaining a low computational complexity, thus, satisfying the constraints of mobile devices with limited resources.

Exploiting Market Integration for Pure Alpha Investments via Probabilistic Principal Factors Analysis

Abstract: In this paper, a long-short beta neutral portfolio strategy is proposed based on earnings yields forecasts, where positions are modified by accounting for time-varying risk budgeting by employing an appropriate integration measure. In contrast to previous works, which primarily rely on a standard principal component analysis (PCA), here we exploit the advantages of a probabilistic PCA (PPCA) framework to extract the factors to be used for designing an efficient integration measure, as well as relating these factors to an asset-pricing model. Our experimental evaluation with a dataset of 12 developed equity market indexes reveals certain improvements of our proposed approach, in terms of an increased representation capability of the underlying principal factors, along with an increased robustness to noisy and/or missing data in the original dataset.