Formal understanding of the inductive bias behind deep convolutional networks, i.e. the relation between the network’s architectural features and the functions it is able to model, is limited. In this work, we establish a fundamental connection between the fields of quantum physics and deep learning, and use it for obtaining novel theoretical observations regarding the inductive bias of convolu...

Convolutional neural networks (CNNs) have attracted increasing attention in the remote sensing community. Most CNNs only take the last fully-connected layers as features for the classification of remotely sensed images, discarding the other convolutional layer features which may also be helpful for classification purposes. In this paper, we propose a new adaptive deep pyramid matching (ADPM) mo...

Recent years have witnessed the great progress for semantic segmentation using deep convolutional neural networks (DCNNs). This paper presents a novel fully convolutional network for semantic segmentation using multi-scale contextual convolutional features. Since objects in natural images tend to be with various scales and aspect ratios, capturing the rich contextual information is very critica...

Quantum convolutional codes, like their classical counterparts, promise to offer higher error correction performance than block codes of equivalent encoding complexity, and are expected to find important applications in reliable quantum communication where a continuous stream of qubits is transmitted. Grassl and Roetteler devised an algorithm to encode a quantum convolutional code with a “pearl...

The generalized Singleton bound and MDS-convolutional codes are reviewed. For each n, k and 6 an elementary construction of rate k / n MDS convolutional codes of degree 6 is given.

In this work the output-observability of convolutional codes is examined and a decoding algorithm for convolutional codes using linear algebra techniques is presented. Key–Words: Codes, linear systems, output-observability, generalized inverses.

Convolutional neural networks have achieved a great success in the recent years. Although, the way to maximize the performance of the convolutional neural networks still in the beginning. Furthermore, the optimization of the size and the time that need to train the convolutional neural networks is very far away from reaching the researcher's ambition. In this paper, we proposed a new convolutio...

ABSTRA CT— This paper presents F-ary convolutional codes for optimal decoding of pulse-amplitude-modulated (PAM) sequences over noisy finite intersymbol interference (1S1) channels. We refer to this new class of codes as ‘Optimal Convolutional Codes 9 (OCCS) for a channel in the sense of maximizing the minimum Euclidean Distance (ED), drni., between channel output sequences corresponding to dis...

A trellis code encoded by using the encoder of a convolutional code with a short constraint length followed by an additional processing unit is equivalent to a trellis code with a large constraint length. In 1993, Hellstern proposed a trellis coding scheme for which the processing unit consists of a delay processor and a signal mapper. With Hellstern’s scheme, trellis codes with large free dist...

Absfmcf-The paper describes the possible implementation of concatenated coding scheme suitable particularly for high date rate applications. The proposed coding scheme uses a set of n convolutional codes multiplexed into an inner code and a (n, n 1) single-parity-check code serving as the outer code. Each of the inner convolutional codes is decoded independently with maximum-likelihood decoding...