In this paper we give a compact presentation of the theory of abstract spaces for convolutional codes and convolutional encoders, and show a connection between them that seems to be missing in the literature. We use it for a short proof of two facts: the size of a convolutional encoder of a polynomial matrix is at least its inner degree, and the minimal encoder has the size of the external degr...

AhtracrThis paper investigates encoders optimization for Hamming weight after periodic puncturing, rind discusses minimality issues that may affect the performanoe of the punctured encoders. Periodically puncturing a minimal encoder produces a higher rate encoder that may or may not be minimal. If it is not minimal, it may have a zero-output loop and it may be Catastrophic. A code search can us...

This paper presents several optimization algorithms for a High Efficiency Video Coding (HEVC) encoder based on single instruction multiple data (SIMD) operations and data-level parallelism. Based on the analysis of the computational complexity of HEVC encoder, we found that interpolation filter, cost function, and transform take around 68% of the total computation, on average. In this paper, se...

In this programmatic paper, we articulate a minimalist conception of linguistic composition, syntactic and semantic, with the aim of identifying fundamental operations invoked by the human faculty of language (HFL). 1. Introduction For these purposes, we take it as given that humans have such a faculty, stable states of which can be described as I-languages in Chomsky's (1986) sense. Idealizing...

In this study, we employ skip connections into a deep recurrent neural network for modeling basic dance steps using audio as input. Our model consists of two blocks, one encodes the audio input sequences, and another generates the motion. The encoder uses a configuration called convolutional, long short-term memory deep neural network (CLDNN) which handle the power features of audio. Furthermor...

We propose a general Convolutional Neural Network (CNN) encoder model for machine translation that fits within in the framework of Encoder-Decoder models proposed by Cho, et. al. [1]. A CNN takes as input a sentence in the source language, performs multiple convolution and pooling operations, and uses a fully connected layer to produce a fixed-length encoding of the sentence as input to a Recur...

This paper develops a differential encoder for differentially demodulated continuous phase frequency shift keying (CPFSK). CPFSK schemes with modulation index , where and are relatively prime positive integers, can be represented by a decomposed model consisting of a continuous phase encoder (CPE) and a memoryless modulator (MM). The differential encoder is shown to fit well with the CPE and fo...

Image denoising is always a challenging task in the field of computer vision and image processing. In this paper we have proposed an encoder-decoder model with direct attention, which is capable of denoising and reconstruct highly corrupted images. Our model is consisted of an encoder and a decoder, where encoder is a convolutional neural network and decoder is a multilayer Long Short-Term memo...

This paper presents a high-speed, low-power and low area encoder for implementation of flash ADCs. Key technique for design of this encoder is performed by convert the conventional 1-of-N thermometer code to 2-of-M codes (M = ¾ N). The proposed encoder is composed from two-stage; in the first stage, thermometer code are converted to 2-of-M codes by used 2-input AND and 4-i...