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Underwater acoustic sensor networks are characterized by limited transmit power and bandwidth, and a harsh communication environment. Different techniques for reliable communication in underwater networks have been studied in the literature at different layers of the network protocol stack. Among these, the use of fountain codes as a way for improving the quality of the communication over a highly...
Widely-used Rate Control (RC) algorithms, such as those in the H.264 encoder, have certain shortcomings for time-sensitive applications such as High Definition Video Conferencing (HDVC): they either respond too slowly to available bandwidth variations, causing degradation in the perceived quality of the video session, or do not optimize video quality for a given available bandwidth. To overcome these...
The JPEG committee (formally, ISO SC29 WG1) is currently standardizing a lightweight mezzanine codec for video over IP transport under the name JPEG XS. A particular challenging design constraint of this codec is multi-generation robustness, that is the necessity to minimize the error built-up under multiple re-compression cycles. In this paper, we discuss the sources of such errors, how they are...
Recently, deep learning has enjoyed a great deal of success for computer vision problems due to its capability to model highly complex tasks, such as image classification, object detection, face recognition, among many others. Although these neural networks are nowadays very powerful, there is a huge amount of parameters (i.e. the model) that need to be learned and require considerable storage space...
This paper introduces an open-source HEVC video call application called Kvazzup. This academic proposal is the first HEVC-based end-to-end video call system with a user-friendly Graphical User Interface for call management. Kvazzup is built on the Qt framework and it makes use of four open-source tools: Kvazaar for HEVC encoding, OpenHEVC for HEVC decoding, Opus codec for audio coding, and Live555...
This paper demonstrates the usage of Kvazaar open-source HEVC intra encoder in 4K real-time video encoding. In this setup, a raw 4K video is shot by an action camera, captured by an HDMI capture card, encoded in real-time by Kvazaar ultrafast preset on a 22-core Intel Xeon processor, sent to a laptop, and decoded by OpenHEVC decoder for playback. The encoding process is visualized on the fly by Kvazaar...
Given the significant industrial growth of demand for virtual reality (VR), 360º video streaming is one of the most important VR applications that require cost-optimal solutions to achieve widespread proliferation of VR technology. Because of its inherent variability of data-intensive content types and its tiled-based encoding and streaming, 360º video requires new encoding ladders in adaptive streaming...
Content Based Image Retrieval (CBIR) systems that search similar images in a large database are attracting more and more research interests recently, and have been applied to medical image characterization for expert's experience sharing. One challenging task in CBIR is how to extract features for effective image representation. Therein sparse coding technique has been proven to be an effective way...
Video summarization is an important multimedia task for applications such as video indexing and retrieval, video surveillance, human-computer interaction and video "storyboarding". In this paper, we present a new approach for automatic summarization of video collections that leverages a structured minimum-risk classifier and efficient submodular inference. To test the accuracy of the predicted...
This paper develops a general framework of image retrieval, named A3, by introducing an auxiliary set of samples (object references), each of which is annotated with semantic attributes (tags). Given a query image (without tags), we first map it into the references by a non-convex sparse coding formulation, which jointly optimizes appearance reconstruction of the query and semantics consistency among...
This paper presents a novel local posture orientation-context descriptor, and proposes a FDDL(Fisher discriminant dictionary learning) method based on local orientation-preserving(LOP-FDDL) for sparse coding in action recognition task. To take full use of the information about the position of the local body-part related to the center of the torso, ant the spatial-temporal shape changes of the human...
As high-throughput sequencing technologies are generating vast amounts of data, there is urgent need to develop efficient algorithms for sequencing data compression. Existing methods usually dispatch the similar sequences into the same bucket based on their same minimizer, that is the lexicographical smallest k-mer within the sequence, for data compression. However, when the sequencing error existed...
Many rare and common genetic variants, including SNPs and CNVs, are reported to be associated with mental disorders, yet more remain to be discovered. However, despite the large amount of high-throughput genomics data, there is a lack of integrative methods to systematically prioritize variants that confer susceptibility to mental disorders in personal genomes. Here, we developed a computational tool:...
It is a grand challenge to reveal the causal effects of DNA variants in complex phenotypes. Although statistical techniques can establish correlations between genotypes and phenotypes in Genome-Wide Association Studies (GWAS), they often fail when the variant is rare. The emerging Network-based Association Studies aim to address this shortcoming in statistical analysis, but are mainly applied to coding...
In this paper, we introduce a novel method to discover common and distinct structural connectivity patterns between SZP and MDD via a Cluster-Driven Nonnegative Matrix Factorization (called CD-NMF). Specifically, CD-NMF is applied to decompose the joint structural connectivity map into common and distinct parts, and each part is further factorized into two sub-matrices (i.e. common/distinct basis...
Thanks to recent advances in the field of genomics, it is now possible to create a comprehensive atlas of the basic units of life—cells. In this paper, we present a frame work for single cell genomics research which employs several new machine learning models such as convolutional neural networks, deep auto-encoder, recurrent neural networks etc. With these effective learning models on multi-source...
In biomedical research, events revealing complex relations between entities play an important role. Event trigger identification is a crucial and prerequisite step in the pipeline process of biomedical event extraction. There exist two main problems in the previous work: (1) Traditional feature-based methods often rely on human ingenuity, which is a time-consuming process. Though most representation-based...
In this paper, we propose a new discriminative dictionary learning framework, called robust Label Embedding Projective Dictionary Learning (LE-PDL), for data classification. LE-PDL can learn a discriminative dictionary and the blockdiagonal representations without using the l0-norm or l1-norm sparsity regularization, since the l0 or l1-norm constraint on the coding coefficients used in the existing...
Given an undirected network where some of the nodes are labeled, how can we classify the unlabeled nodes with high accuracy? Loopy Belief Propagation (LBP) is an inference algorithm widely used for this purpose with various applications including fraud detection, malware detection, web classification, and recommendation. However, previous methods based on LBP have problems in modeling complex structures...
This paper proposes a predictive power control for a converter connected to the grid. The power references are sent by a wireless channel with the focus to control renewable sources. The three-phase converter controls the power injected to the grid using a predictive control technique. Simulation results are presented to validate the system operation.
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