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The Bit-Flipping (BF) algorithm is considered as a hard decoding method for LDPC codes. It is much simpler than the probabilistic methods like Sum Product Algorithm (SPA), and can be efficiently implemented by electronic circuits. In this paper, we propose a new Bit Flipping algorithm for Low-Density Parity-Check codes (LDPC) called Single Bit-Flipping (SBF). Compared to the Gallager Bit-Flipping...
In dynamic manipulation, robots can manipulate objects without grasping by utilizing inertia effect. However, the trajectory planning for dynamic manipulation is a difficult issue due to dynamic constraint. Trajectory deformation considering dynamic constraint after original trajectories are generated is necessary for the issue. To realize such deformation methods, we introduce on sequence-to-sequence...
Existing methods for layer-based backward compatible high dynamic range (HDR) image and video coding mostly focus on the rate-distortion optimization of base layer while neglecting the encoding of the residue signal in the enhancement layer. Although some recent studies handle residue coding by designing function based fixed global mapping curves for 8-bit conversion and exploiting standard codecs...
Lattice codes used under the Compute-and-Forward paradigm suggest an alternative strategy for the standard Gaussian multiple-access channel (MAC). It has been proven that decoding an integer linear combination of the transmitted codewords enables higher data rates compared to decoding the messages individually. Recent work of Gasptar et al. proposed a new multiple access channel technique that they...
In this paper a method to control the output current of a three-phase grid connected two-level converter is presented. The current control approach is a finite control set model predictive control with sphere decoding. Sphere decoding algorithm gives a computationally efficient solution of the problem for long horizons. The goal of the control algorithm is to minimize the grid current tracking error...
Translation of natural language text using statistical machine translation (SMT) is a supervised machine learning problem. SMT algorithms are trained to learn how to translate by providing many translations produced by human language experts. The field SMT has gained momentum in recent three decades. New techniques are constantly introduced by the researchers. This is survey paper presenting an introduction...
In this paper, we propose an advanced polar encoding scheme with successive cancellation list (SCL) decoder for visible light communication (VLC). As much high-profile channel coding methods, polar codes have achieved the symmetric capacity of binary-input discrete memoryless channels (B-DMCs) based on channel polarization. By taking advantages of simple recursive encoding structure and confirmed...
This study proposes a novel classification method for sequential data invoving human trial and error. The classification of sequential data obtained from human experiments has become an important tool that supports the data analytics of the modern society. For example, several algorithms for motion recognition, voice recognition, etc. have been recently developed. The hidden Markov model (HMM) is...
The development of a deep (stacked) convolutional auto-encoder in the Caffe deep learning framework is presented in this paper. We describe simple principles which we used to create this model in Caffe. The proposed model of convolutional auto-encoder does not have pooling/unpooling layers yet. The results of our experimental research show comparable accuracy of dimensionality reduction in comparison...
The possibility of describing the encoding and decoding procedures Bose-Chaudhuri-Hocquenghem codes using the summation operations modulo 2 is researched. This allows you to simplify these procedures and improve efficiency by correcting the error of the triplicate. There identified coding equations and triangular match tables: the syndrome — the distortion of the position.
In this paper a log-MAP turbo decoding algorithm which incorporates reliability threshold based trellis branch elimination together with mean based early iteration termination has been proposed. The proposed algorithm reduces computational complexity by eliminating branches in trellis. Since the minimum Log Likelihood Ratio (LLR) obtained for each iteration varies with channel conditions, an early...
In contemporary digital communications design, two major challenges should be addressed: adaptability and flexibility. The system should be capable of flexible and efficient use of all available spectrums and should be adaptable to provide efficient support for the diverse set of service characteristics. These needs imply the necessity of limit-achieving and flexible channel coding techniques, to...
Increasing soft error rate and decreasing technological nodes sizes pave a way for Error Correcting Codes (ECC) widespread use in embedded systems. Depending on application safety goals and acceptable performance and area overhead, different codes can be selected. The goal of this paper is to investigate the efficiency and expediency of two of the most prominent ECC codes, Hamming and Hsiao, in the...
The given paper presents a method for constructing a QC-LDPC code of shorter length by length adaption from a given QC-LDPC code of maximal length. The proposed method can be considered as a generalization of floor lifting. Making some offline calculation it is possible to construct a sequence of QC-LDPC codes with different circulant sizes generated from a single exponent matrix of QC-LDPC code which...
Deep Neural Networks(DNNs) outperform previous works in many fields such as in natural language processing. Neural Machine Translation(NMT) also outperforms Statistical Machine Translation(SMT) which has complex features and rules. However, NMT requires a large corpus and a long calculation time. In order to suppress calculation cost, recent researches replaced low frequency words with symbols. However,...
Image matting is a fundamental computer vision problem and has many applications. Previous algorithms have poor performance when an image has similar foreground and background colors or complicated textures. The main reasons are prior methods 1) only use low-level features and 2) lack high-level context. In this paper, we propose a novel deep learning based algorithm that can tackle both these problems...
We focus on the non-Lambertian object-level intrinsic problem of recovering diffuse albedo, shading, and specular highlights from a single image of an object. Based on existing 3D models in the ShapeNet database, a large-scale object intrinsics database is rendered with HDR environment maps. Millions of synthetic images of objects and their corresponding albedo, shading, and specular ground-truth...
Attention-based neural encoder-decoder frameworks have been widely adopted for image captioning. Most methods force visual attention to be active for every generated word. However, the decoder likely requires little to no visual information from the image to predict non-visual words such as the and of. Other words that may seem visual can often be predicted reliably just from the language model e...
Two classes of perfect codes for single balanced adjacent deletions (BADs) are provided. These classes are inspired by Levenshtein's work on binary perfect codes for single standard deletions. One of the classes is defined via inversion numbers and the other is defined via Levenshtein codes. The first half of this paper is devoted to the proof of perfectness and the second half is devoted to discussion...
We propose a new partial decoding algorithm for m-interleaved Reed-Solomon (IRS) codes that can decode, with high probability, a random error of relative weight 1 − Rm/m+1 at all code rates R, in time polynomial in the code length n. For m > 2, this is an asymptotic improvement over the previous state-of-the-art for all rates, and the first improvement for R > 1/3 in the last 20 years. The method...
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