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Background: Anemia is a common extraintestinal manifestation of Inflammatory Bowel Disease (IBD) affecting negatively the patients’ quality of life. The aim of this study was to determine the frequency and real-life management of anemia in IBD patients in Greece. Methods: This study was conducted in 17 Greek IBD referral centers. Demographic, clinical, laboratory, IBD and anemia treatment data were collected and analyzed retrospectively. Results: A total of 1394 IBD patients [560 ulcerative colitis (UC), 834 Crohn’s disease (CD)] were enrolled. Anemia at any time was reported in 687 (49.3%) patients of whom 413 (29.6%) had episodic and 274 (19.7%) had recurrent/persistent anemia. Anemia was diagnosed before IBD in 45 (6.5%), along with IBD in 269 (39.2%) and after IBD in 373 (54.3%) patients. In the multivariate analysis the presence of extraintestinal manifestations (p = 0.0008), IBD duration (p = 0.026), IBD related surgeries and hospitalizations (p = 0.026 and p = 0.004 accordingly) were risk factors of recurrent/persistent anemia. Serum ferritin was measured in 839 (60.2%) IBD patients. Among anemic patients, 535 (77.9%) received treatment. Iron supplementation was administered in 485 (90.6%) patients, oral in 142 (29.3%) and intravenous in 393 (81%). Conclusions: The frequency of anemia in IBD patients, followed at Greek referral centers, is approximately 50%. Development of recurrent/persistent anemia may be observed in 20% of cases and is independently associated with the presence of extraintestinal manifestations, IBD duration, IBD related surgeries and hospitalizations. Anemia treatment is administered in up to [Formula: see text] of anemia IBD patients with the majority of them receiving iron intravenously.

Summarization: In an interdisciplinary, semester-long class, undergraduate students learn how to build a low-cost, multihop wireless sensor network from first principles for a digital garden. This type of course better prepares electrical engineering graduates for the sensor-rich, pervasive computing era. Presented on: IEEE Pervasive Computing

Δημοσίευση σε επιστημονικό περιοδικό Summarization: We describe ways to define and calculate L1-norm signal subspaces that are less sensitive to outlying data than L2-calculated subspaces. We start with the computation of the L1 maximum-projection principal component of a data matrix containing N signal samples of dimension D. We show that while the general problem is formally NP-hard in asymptotically large N, D, the case of engineering interest of fixed dimension D and asymptotically large sample size N is not. In particular, for the case where the sample size is less than the fixed dimension , we present in explicit form an optimal algorithm of computational cost 2N. For the case N ≥ D, we present an optimal algorithm of complexity O(ND). We generalize to multiple L1-max-projection components and present an explicit optimal L1 subspace calculation algorithm of complexity O(NDK-K+1) where K is the desired number of L1 principal components (subspace rank). We conclude with illustrations of L1-subspace signal processing in the fields of data dimensionality reduction, direction-of-arrival estimation, and image Presented on: IEEE Transactions on Signal Processing

Summarization: We describe ways to define and calculate L1-norm signal subspaces which are less sensitive to outlying data than L2-calculated subspaces. We focus on the computation of the L1 maximum-projection principal component of a data matrix containing N signal samples of dimension D and conclude that the general problem is formally NP-hard in asymptotically large N, D. We prove, however, that the case of engineering interest of fixed dimension D and asymptotically large sample support N is not and we present an optimal algorithm of complexity O(N(exp D)). We generalize to multiple L1-max-projection components and present an explicit optimal L1 subspace calculation algorithm in the form of matrix nuclear-norm evaluations. We conclude with illustrations of L1-subspace signal processing in the fields of data dimensionality reduction and direction-of-arrival estimation Παρουσιάστηκε στο: International Symposium on Wireless Communication Systems

Submitted in partial fulfillment of the requirements for the M.Sc. degree in Electronic and Computer Engineering Μη διαθέσιμη περίληψη Not available summarization

Summarization: In this diploma thesis, we study the problem of optimal noncoherent trellis decoding, that is, the maximization of |s(x)^H y| over x, where y is a complex vector, x is a discrete symbol sequence, and s(x) is a vector that is produced by x through a trellis structure. Two example cases of noncoherent trellis decoding are noncoherent detection of a minimum-shift keying (MSK) modulated sequence and noncoherent decoding of convolutionally encoded data. Specifically, MSK is a modulation scheme that limits problems associated with nonlinear distortion and is used in a variety of applications, like signal transmission from satellites and broadcasting. Although the optimal coherent MSK receiver simplifies to constant-complexity symbol-bysymbol detection, optimal noncoherent reception of MSK takes the form of sequence detection (due to channel-induced memory) which has exponential (in the sequence length) complexity when implemented through an exhaustive search among all possible sequences. Convolutional codes are used extensively to achieve reliable data transfer in numerous applications, such as digital video, radio, and satellite communications. They are modeled by a trellis structure and optimal noncoherent reception of convolutionally encoded data also takes the form of sequence detection. In this work, we present an algorithm that performs generalized-likelihood-ratio-test (GLRT) optimal noncoherent sequence detection of MSK signals in flat fading with log-linear (in the sequence length) complexity. Moreover, for Rayleigh fading channels, the proposed algorithm is equivalent to the maximum-likelihood (ML) noncoherent sequence detector. We then discuss how the proposed algorithm can be generalized for use on noncoherent convolutional decoding. To simplify the presentation, we consider a particular convolutional code and modify the proposed algorithm to perform optimal noncoherent trellis decoding with empirically low complexity. Simulation studies indicate that the optimal noncoherent MSK detector attains coherent-detection performance when the sequence length is on the order of 100, offering a 5–6 dB gain over the typical single-symbol detector. Similar results are obtained for the generalized algorithm on convolutional decoding.