Papers by Konstantinos Diamantaras
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Education Sciences, Jun 16, 2023
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Data technologies and applications, Feb 27, 2023
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Zenodo (CERN European Organization for Nuclear Research), Jan 25, 2018
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The adaptive blind source separation problem has been traditionally dealt mith the use of nonline... more The adaptive blind source separation problem has been traditionally dealt mith the use of nonlinear neural models implementing higher-order statistical methods. In this paper we show that second order Cross-Coupled Hebbian rille used for Asymmetric Principal Component Analysis (APCA) is capable blindly and adaptively separating uncorrelated sources. Our method en- joys the following advantages over similar higher-order models such as those performing Independent Component Analysis (ICA) : (a) the strong indepen- dence assumption about the source signals is reduced to the weaker uncor- relation assumption, (b) there is no constraint on the sources pdf's, i.e. we rctmove the assumption that at most one signal is Gaussian, and (c) the higher order statistical optimization methods are replaced with second order methods with no local minima, and(d) the kurtosis of the sources becomes ir- riblevant. Simulation experiments shows that the model successfully separates source images with kurtoses of different signs.
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Journal of Signal Processing Systems, Dec 18, 2009
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IEEE Transactions on Signal Processing, 2022
We consider joint beamforming and relay motion control in mobile relay beamforming networks, oper... more We consider joint beamforming and relay motion control in mobile relay beamforming networks, operating in a spatio-temporally varying channel environment. A time slotted approach is adopted, where in each slot, the relays implement optimal beamforming and estimate their optimal positions for the next slot. We place the problem of relay motion control in a sequential decision-making framework. We employ Reinforcement Learning (RL) to guide the relay motion, with the goal of maximizing the cumulative Signal-to-Interference+Noise Ratio (SINR) at the destination. First, we present a model based RL approach, which predictively estimates the SINR and accordingly determines the relay motion, based on partial knowledge of the channel model along with channel measurements at the current relay positions. Second, we propose a model-free deep Q-learning approach, which does not rely on channel models. For the deep Q-learning approach, we propose two modified Multilayer Perceptron Neural Networks (MLPs) for approximating the value function Q. The first modification applies a Fourier feature mapping of the state before passing it through the MLP. The second modification constitutes a different neural network architecture that uses sinusoids as activations between layers. Both modifications enable the MLP to better learn the high frequency value function and have a profound effect on convergence speed and SINR performance. Finally, we conduct a comparative analysis of all the presented approaches and provide insights on advantages and drawbacks.
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2021 55th Asilomar Conference on Signals, Systems, and Computers, Oct 31, 2021
The paper considers the discrete 2D motion control of mobile relays implementing distributed beam... more The paper considers the discrete 2D motion control of mobile relays implementing distributed beamforming in a spatiotemporally correlated channel environment. A time-slotted scenario is considered where the relays implement optimal beamforming, while standing still, then estimate the optimal positions for the next slot and move to those selected positions to beamform again. The goal is to maximize the cumulative Signal-To-Interference+Noise Ratio (SINR) at the destination. We employ double deep Q learning to construct the motion policies. The method is completely model free and agnostic of channels statistics. A Fourier feature mapping is applied on the state before passing it to the Q networks, which enables the learning of a richer representation of the Q function in terms of its frequency spectrum. We propose a strategy to bias the neural network gradient updates. In the initial stages of training, our approach induces bias towards easier experiences (experiences that correspond to relatively low loss) from a relay trajectory, while later, it gradually places the bias towards harder examples. This bias transition is controlled by a temperature parameter, that we change through the course of training. The proposed approach provides significant improvement both in reward accumulation and speed of convergence.
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DOAJ (DOAJ: Directory of Open Access Journals), Sep 1, 2022
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The paper addresses motion control of mobile relays implementing cooperative beamforming in a tim... more The paper addresses motion control of mobile relays implementing cooperative beamforming in a time- and space- varying channel environment. The relays move in a time-slotted fashion and movement is confined within a 2D rectangular plane, discretized on a fine grid. In each time slot, the relays optimally beamform to maximize the Signal-to-Interference+Noise Ratio (SINR) at the destination, subject to power constraints, and determine their optimal next slot positions to which they move by the end of the slot. Prior works have assumed the availability of statistical channel models, which were used to predictively compute the optimal next slot relay positions. In this paper, we propose a novel, model-free, deep Q learning approach to govern relay motion policies, which drops all assumptions on channel model statistics and allows relays to learn solely from experience. Due to the randomness of the channels, the Q function is highly varying with respect to state and action. To facilitate the learning of Q, we propose to apply Fourier mapping of the state with a Gaussian matrix. Via simulations, we show that this approach leads to significant improvement in convergence and SINR performance, as compared to using the state directly.
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Elsevier eBooks, 2018
Abstract Kernel methods are nonparametric feature extraction techniques that attempt to boost the... more Abstract Kernel methods are nonparametric feature extraction techniques that attempt to boost the learning capability of machine learning algorithms using nonlinear transformations. However, one major challenge in its basic form is that the computational complexity and the memory requirement do not scale well with respect to the training size. Kernel approximation is commonly employed to resolve this issue. Essentially, kernel approximation is equivalent to learning an approximated subspace in the high-dimensional feature vector space induced and characterized by the kernel function. With streaming data acquisition, approximated subspaces can be constructed adaptively. Explicit feature vectors are then extracted by a transformation onto the approximated subspace and linear learning techniques can be subsequently applied. From a computational point of view, operations in kernel methods can easily be parallelized and modern infrastructures can be utilized to achieve efficient computing. Moreover, the extracted explicit feature vectors can easily be interfaced with other learning techniques.
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2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Jul 11, 2022
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EAI endorsed transactions on bioengineering and bioinformatics, Feb 18, 2021
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Various internet services, including cloud providers and social networks collect large amounts of... more Various internet services, including cloud providers and social networks collect large amounts of information that needs to be processed for statistical or other reasons without breaching user privacy. We present a novel approach where privacy protection can be viewed as a data transformation problem. The problem is formulated as a pair of classification tasks, (a) a privacy-insensitive and (b) a privacy-sensitive task. Then privacy protection is the requirement that, given the transformed data, no classification algorithm may perform well on the sensitive task while hurting the performance on the insensitive task as little as possible. To that end, we introduce a novel criterion called Multiclass Discriminant Ratio which is optimized using the generalized eigenvalue decomposition of a pair of between class scatter matrices. We then formulate a nonlinear extension of this approach using the kernel GED method. Our proposed methods are evaluated using the Human Activity Recognition data set. Using the kernel projected data the performance of the User recognition task is reduced by 89% while the Activity recognition task is reduced only by 7.8%.
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Papers by Konstantinos Diamantaras
Είναι ιδανικό για φοιτητές του χώρου, ερευνητές και προγραμματιστές, με μόνη προϋπόθεση ορισμένες βασικές γνώσεις αλγοριθμικής και προγραμματισμού.
Στο βιβλίο μελετάται ο σχεδιασμός παράλληλων συστημάτων, τόσο σε επίπεδο αρχιτεκτονικής υπολογιστών όσο και σε επίπεδο προγραμματισμού. Εξηγούνται βασικές έννοιες, όπως οι πολύ-υπολογιστές και οι πολυ-επεξεργαστές, παρουσιάζονται οι κύριες μετρικές αξιολόγησης της επίδοσης των παράλληλων αλγορίθμων, και περιγράφονται βασικές αρχιτεκτονικές δικτύων παράλληλης επεξεργασίας. Επιπλέον, γίνεται εκτενής αναφορά στην υλοποίηση παράλληλων αλγορίθμων σε αρχιτεκτονικές παράλληλης επεξεργασίας κοινής χρήσης, όπως στις κάρτες γραφικών (GPU) μέσω των προτύπων CUDA και τη γλώσσα OpenCL.
Περιεχόμενα:
Αρχιτεκτονικές παράλληλης επεξεργασίας
Δίκτυα διασύνδεσης
Γενικά ζητήματα παραλληλοποίησης
Παραλληλοποίηση εργασιών
Ένθετοι βρόχοι
Εξαρτήσεις σε ένθετους βρόχους
Χρονοδρομολόγηση
Απεικόνιση
Υπολογισμοί στην GPU: Παράλληλη επεξεργασία σε κάρτες γραφικών
Μαθηματικά και αλγοριθμικά εργαλεία
Απεικόνιση ένθετων βρόχων
Συστολικές συστοιχίες επεξεργαστών