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This paper proposes a new estimation algorithm for the parameters of an HMM as to best account for the observed data. In this model, in addition to the observation sequence, we have partial and noisy access to the hidden state sequence as side information. This access can be seen as “partial labeling” of the hidden states. Furthermore, we model possible(More)
This paper describes the ROMEO architecture for live 3D multi-view video delivery over peer-to-peer networks. The scope of the European founded Integration Project ROMEO encompasses addressing the challenges of live multiview video delivery over heterogeneous networks (WiFi, LTE, DVB, etc.) and diverse user equipment (mobile, portable, fixed). In order to(More)
The delivery of 3D immersive media to individual users remains a highly challenging problem due to the large amount of data involved, diverse network characteristics and user terminal requirements, as well as users' context such as their preferences and location. As the number of visual views increases, current systems struggle to meet the demanding(More)
In this paper, we study the binary classification problem in machine learning and introduce a novel classification algorithm based on the “Context Tree Weighting Method”. The introduced algorithm incrementally learns a classification model through sequential updates in the course of a given data stream, i.e., each data point is processed only(More)
It has been a challenging task to deliver high-volume 3D multi-view media with spatial audio to users. The challenge stems from the availability of a large number of user equipment with different capabilities, range of viewing preferences, as well as diverse network characteristics. With the target of facilitating 3D media delivery to multiple users(More)
In this paper, the iterative Expectation-Maximization equations are mathematically derived for Hidden Markov Models (HMM), when there is partial and noisy access to the hidden states. Since the standard HMM is recovered when this partial and noisy access is turned off, our study provides a generalized observation model; and proposes a new model training(More)
This paper proposes a new estimation algorithm for the parameters of an HMM as to best account for the observed data. In this model, in addition to the observation sequence, we have partial and noisy access to the hidden state sequence as side information. This access can be seen as “partial labeling” of the hidden states. Furthermore, we model possible(More)
This paper proposes a novel estimation algorithm for the parameters of an HMM as to best account for the observed data. In this model, in addition to the observation sequence, we have partial and noisy access to the hidden state sequence as side information. This access can be seen as “partial labeling” of the hidden states. Furthermore, we(More)
In this paper, using “context tree weighting method”, a novel classification algorithm is proposed for real time machine learning applications, which is mathematically shown to be “competitive” with respect to a certain class of algorithms. The computational complexity of our algorithm is independent with the amount of data to be(More)
In this paper, we propose a distributed, self-organizing, adaptive and semi-static Soft Frequency Reuse based downlink Inter-Cell Interference Coordination scheme for an LTE system to increase the throughput of cell-edge users. Cells are able to self-organize the allocation of frequency sub-bands for cell-edge users using a distributed algorithm without(More)
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