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Modern patient data tends to be large-scale and multi-dimensional, containing both spatial and temporal features. Learning good spatio-temporal features from large patient data is a challenging task, especially when there are missing observations. In this paper, we propose a spatio-temporal autoencoder (STAE), an unsupervised deep learning scheme, to learn features from large-scale and high-dimensional...
In a MIMO wireless communications system, a space-time block code specifies how the data symbols are transmitted over different antennas at different time instants. A hybrid space-time code attempts to obtain some of the available diversity and multiplexing gains, achieving low error probability and high data rate. The LD StBc-VBLAST hybrid code layers one spatial-multiplexing antenna (to increase...
In this paper we are interested to the decoding of blocks turbo codes constructed from Reed Solomon (RS) codes. We have modified the iterative algorithm proposed by Chase in order to exploit the a priori information provided by a correlated source. Indeed, some sources such as images have a strong correlation. A simple iterative decoding algorithm does not take this feature into account. The choice...
In this paper, we investigate the cryptanalysis of stream ciphers, and evaluate the enhancement to security that can be gained when the ciphertext is error prone by analyzing specific attack algorithm. The stream ciphers that we investigate here have a keystream generator that is based on linear-feedback shift registers (LFSRs). In particular, we characterize the security of these ciphers when the...
In this paper, we study the tradeoff of efficiency and delay of Slepian-Wolf distributed source coding (DSC). In the considered network, Machine-Type Communications (MTC) devices transmit correlated data to one base station, the data sources follow a multi-variate Gaussian distribution. To reduce the consumption of communication resources by massive MTC devices, Slepian-Wolf coding is adopted to eliminate...
With the significant increase of the network heterogeneity and the wide use of emerging video applications such as wireless sensor networks, video surveillance systems or remote sensing, the Distributed Scalable Video Coding (DSVC) is a potential solution for efficiently transmitting and storing video data due to its high compression efficiency and low encoding complexity capabilities. In DSVC framework,...
Plane wave methods for ultrafast ultrasound imaging suffer from a low signal to noise ratio (SNR) and a limited field of view at greater imaging depths. Imaging using multiple focused coded beams in parallel is one strategy for high speed imaging that may improve on these limitations. However, the SNR and resolution of this strategy are degraded by the interference between the beams transmitted in...
In this paper, we consider a Joint Channel Network Coding (JCNC) scheme applied to Multiple Access Relay Channel (MARC) for correlated sources. Then, two correlated sources send their messages to one destination and one relay which performs network coding and provides additional information to the receiver. At the destination, a joint iterative decoding algorithm is developed which takes into account...
Document clustering groups documents of certain similar characteristics in one cluster. Document clustering has shown advantages on organization, retrieval, navigation and summarization of a huge amount of text documents on Internet. This paper presents a novel, unsupervised approach for clustering single-author documents into groups based on authorship. The key novelty is that we propose to extract...
Massive multiple-input multiple-output (MIMO) systems can achieve high data rates due to the large number of antennas at the base station when it serves a small number of users. However, deploying massive MIMO in current mobile networks faces a lot of practical issues such as limited physical space and power consumption. Packing large number of antennas in a limited physical space makes spatial correlation...
In recent years, Deep Learning has been successfully applied to multimodal learning problems, with the aim of learning useful joint representations in data fusion applications. When the available modalities consist of time series data such as video, audio and sensor signals, it becomes imperative to consider their temporal structure during the fusion process. In this paper, we propose the Correlational...
Generative models of 3D human motion are often restricted to a small number of activities and can therefore not generalize well to novel movements or applications. In this work we propose a deep learning framework for human motion capture data that learns a generic representation from a large corpus of motion capture data and generalizes well to new, unseen, motions. Using an encoding-decoding network...
The CNN-RNN design pattern is increasingly widely applied in a variety of image annotation tasks including multi-label classification and captioning. Existing models use the weakly semantic CNN hidden layer or its transform as the image embedding that provides the interface between the CNN and RNN. This leaves the RNN overstretched with two jobs: predicting the visual concepts and modelling their...
Is there a general representation of the information content of human brain, which can be extracted from the functional magnetic resonance imaging (fMRI) data? Is it possible to learn this representation automatically from big data sets by unsupervised learning methods? Is it possible to transfer this representation to learn and decode a set of cognitive states in other fMRI data sets? This study...
A general analytical framework based on generalized mutual information is applied to the analysis of massive multiple-input-multiple-output systems with low-resolution output quantization. For Gaussian codebook ensemble and nearestneighbor decoding rule, an equivalence relationship is established for general nonlinear transceiver distortion, that the effective signal-to-noise ratio based on the generalized...
A new coding technique, based on fixed block-length codes, is proposed for the problem of communicating a pair of correlated sources over a 2-user interference channel. Its performance is analyzed to derive a new set of sufficient conditions. The latter is proven to be strictly less binding than the current known best, which is due to Liu and Chen [1]. Our findings are inspired by Dueck's example...
This paper studies the fundamental limits of caching in a network with two receivers and two files generated by a two-component discrete memoryless source with arbitrary joint distribution. Each receiver is equipped with a cache of equal capacity, and the requested files are delivered over a shared error-free broadcast link. First, a lower bound on the optimal peak rate-memory trade-off is provided...
The problem of characterizing sufficient conditions for communicating correlated sources over a MAC is considered. The technique of inducing source correlation onto channel inputs [1] is enhanced by the use of fixed B-L coding [2]. The performance of the proposed coding technique is characterized via single-letter expressions to derive a new set of sufficient conditions. The latter conditions are...
This paper is concerned with the design of capacity approaching ensembles of Low-Density Parity-Check (LDPC) codes for correlated sources. We consider correlated binary sources where the data is encoded independently at each source through a systematic LDPC encoder and sent over two independent Gaussian channels. At the receiver, a joint iterative decoder consisting of two component LDPC decoders...
The Boolean multireference alignment problem consists in recovering a Boolean signal from multiple shifted and noisy observations. In this paper we obtain an expression for the error exponent of the maximum A posteriori decoder. This expression is used to characterize the number of measurements needed for signal recovery in the low SNR regime, in terms of higher order autocorrelations of the signal...
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