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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...
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...
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...
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...
State of the art video compression techniques use the motion model to approximate geometric boundaries of moving objects where motion discontinuities occur. Motion hints based inter- frame prediction paradigm moves away from this redundant approach and employs an innovative framework consisting of motion hint fields that are continuous and invertible, at least, over their respective domains. However,...
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...
Surveillance visual systems play an important role in modern life, especially in the Internet of Things (IoTs) era. However, the limitation of bandwidth, energy resources and the heterogeneity of devices, networks and environments have been asking for a more powerful video coding solution, which provides not only the high compression efficiency but also the flexible scalability capability. In this...
In recent years, deep discriminative models have achieved extraordinary performance on supervised learning tasks, significantly outperforming their generative counterparts. However, their success relies on the presence of a large amount of labeled data. How can one use the same discriminative models for learning useful features in the absence of labels? We address this question in this paper, by jointly...
The present paper has considered multithreshold decoders for self-orthogonal codes providing a near-optimal efficiency of the error correction under linear computational complexity. New divergence principle used within construction and decoding convolutional codes has been discussed. The paper has shown that usage of such principle allows significantly approximating an area of the decoder effective...
This work presents images encoding and decoding using the theory of conformal mapping. The conformal mapping theory made changes in the domain of problems without modifying physical characteristics between the domains. Images were utilized and are transported between domains using transformation functions like encrypt keys. Developed method showed to be able to preserve original images characteristics...
This paper presents a method to extract rendering matrix on multi-channel audio signals as an object fed to Moving Picture Expert Group Spatial Audio Object Coding (MPEG SAOC) encoder. This technique allows MPEG SAOC to transmit multiple multi-channel audio objects, instead of only a single multi-channel background object as specified in MPEG SAOC standard. Listening tests show that the proposed method...
In this paper, the principle of normalized minimum-sum (NMS) polar decoding process is explored. It is demonstrated that with one properly chosen parameters for NMS algorithm, performances approach to that of the sum-product (SP) algorithm can be achieved. As well, the complexity reduction is realized by calculating a linear function instead of nonlinear function. Simulation results for successive...
Deep neural networks (DNNs) usually demand a large amount of operations for real-time inference. Especially, fully-connected layers contain a large number of weights, thus they usually need many off-chip memory accesses for inference. We propose a weight compression method for deep neural networks, which allows values of +1 or −1 only at predetermined positions of the weights so that decoding using...
To satisfy the requirements of complex distributed power electronic system (PES) communication, this paper has proposed a single optic-fiber link data communication protocol based on the Manchester code due to an vacancy in the commonly-adopted protocols, analyzed the encoding and decoding principle of Manchester code, defined the formats of command frame and data frame, calculated the communication...
Polar coding is a low-complexity method for communication over noisy classical channels, which is capable of providing highly reliable data transmission. This paper proposes an enhanced polar codec scheme for mission critical applications in train-to-ground wireless communications. Firstly, we develop an enhanced polar coding scheme to support reliable data transmission under time-varying channel...
This paper presents the joint design of network coding and backpressure algorithm for cognitive radio networks and its implementation with software-defined radios (SDRs) in a high fidelity network emulation testbed. The backpressure algorithm is known to provide throughput optimal solutions to joint routing and scheduling for dynamic packet traffic. This solution applies to cognitive radio networks...
To compress large datasets of high-dimensional descriptors, modern quantization schemes learn multiple codebooks and then represent individual descriptors as combinations of codewords. Once the codebooks are learned, these schemes encode descriptors independently. In contrast to that, we present a new coding scheme that arranges dataset descriptors into a set of arborescence graphs, and then encodes...
Virtual reality applications make use of 360-degree panoramic or omnidirectional video with high resolution and high frame rate in order to create the immersive experience to the user. The user views only a portion of the captured 360-degree scene at each time instant, hence streaming the whole omnidirectional video in highest quality is not efficient. In order to alleviate the problem of bandwidth...
Polar codes are a family of capacity-achieving error-correcting codes, and they have been selected as part of the next generation wireless communication standard. Each polar code bit-channel is assigned a reliability value, used to determine which bits transmit information and which parity. Relative reliabilities need to be known by both encoders and decoders: in case of multi-mode systems, where...
The paper is focused on use of Pulse Coupled Neural Network (PCNN) in the image steganography based on the research in the field of invariant image recognition. In general, steganography deals with data concealing in the cover mediums which can be freely accessible or transmitted by various communication channels without any restriction. A suitable position of hidden message is crucial for a successful...
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