In this paper we combined an optical mixer via photonic crystal ring resonator to propose an all optical 1 to 2 decoder. The main idea used in this paper is based on controlling the optical behavior of the resonant ring via optical power intensity. We know that resonant wavelength of the photonic crystal ring resonator is very sensitive upon the refractive index of dielectric rods, on the other had the refractive index of dielectric materials depend on the optical power intensity. Therefore we can change the resonant wavelength of the resonator by increasing the optical power intensity up to adequate amount. The final structure has two output ports whose working states can be controlled by one input port. When I is OFF, the O1 port will turn ON and O2 is OFF, when I is ON, O1 turns OFF and O2 turns ON. The proposed structure works completely in optical domain without any electronics.

All-optical decoders play a key role in the design and implementation of all optical processing systems. Switching speed, power and structural dimensions are the most important parameters that should be considered in the design of all optical devices. In this research, by using two nonlinear resonance cavities based on photonic crystals, a structure for design and implementing an all optical decoder is presented. Nonlinear resonance cavities are created by adding nonlinear rods made of contaminated glass inside the cavity. The proposed structure has one activation port, one input port and two output ports. If the activator port is off, both outputs will be off regardless of the status of the input port situation. In design of this structure, we need an activator input port and a control input. The activator input port is connected directly to the main waveguide using a side waveguide. The control input of the decoder, which is the same as the main input port of the decoder 1 to 2, is connected to the main waveguide by means of an optical power divider. Also to improve the efficiency of the light transmission from the activator port to the main waveguide, two point defects with a radius of 60 nm have been installed at the junction of the input waveguides to the main waveguide. In the designed structure, the maximum delay time is 5 ps.

AbstractNew design in photonic crystal structures has provided a great opportunity to employ optical decoders with high contrast ratio and low response time. Based on the last published articles, photonic crystal-based decoders result in a contrast ratio higher than 20 dB and a response time of 200 fs which is proper to use in optical circuits. Changing the path of a signal may occur with the help of a resonator in the form of a cavity or ring. Dropping a signal at a wavelength makes a possible to direct it toward the desired path. We present an introduction to the decoding operation and its advantages in different applications, followed by an explanation of the excellent features of photonic crystals including forbidden bandgap and the optical Kerr effect. Furthermore, we discuss the key role of the resonance phenomenon in photonic crystal structures. Then, we investigate the valuable designs in approaching the decoding. It has been tried to present an algorithm to design photonic crystal-based decoders which is so needed for research.

The importance and demand of visual scene understanding have been increasing because of autonomous systems development. optical flow is known as an important tool for scene understanding. Current optical flow methods present general assumptions and spatial homogeneous for spatial structure of flow. In fact, the optical flow in an image depends on object class and the type of object movement. The first assumption in many methods in this field is the brightness constancy during movements of pixels between frames. This assumption is proven to be inaccurate in general. In this paper, we use recent development of deep convolutional networks in semantic segmentation of static scenes to divide an image in to different objects and also depends on type of the object different movement patterns are defined. Next, estimation of the optical flow is performed by using deep neural network for initial image which has been semantically segmented. The proposed method provides minimum error in optical flow measures for KITTI-2015 database and results in more accurate segmentation compared to state-of-the-art methods for several natural videos.

In this paper, a hybrid Machine Translation (MT) system is proposed by combining the result of a rule-based machine translation (RBMT) system with a statistical approach. The RBMT uses a set of linguistic rules for translation, which leads to better translation results in terms of word ordering and syntactic structure. On the other hand, SMT works better in lexical choice. Therefore, in our system, an initial translation is generated using RBMT. Then the proper lexical for the resulted sentence is chosen by using a decoder algorithm which is inspired by SMT architecture. In the pure SMT approach, decoder is responsible for selecting proper final lexical during the translation procedure. Normally this method deals with lexical choice as well as reordering and required exponential order in time complexity. By fixing the word order in the output, a polynomial version of this method, named monotone decoding, is used in this paper. Monotone decoder algorithm selects the best lexical from a candidate list by maximizing the language model of resulted sentence. The candidate list is gathered from the outputs of both pure RBMT and pure SMT systems. The experiments of proposed hybrid method on English-Persian language pair show significant improvements over both RBMT and SMT results. The results show that the proposed hybrid method gains an improvement of almost +5 units over RBMT and about one unit over SMT in BLEU score.

This paper investigates the multiplicative spread spectrum watermarking method for the image. The information bit is spreaded into middle-frequency Discrete Cosine Transform (DCT) coefficients of each block of an image using a generated pseudo-random sequence. Unlike the conventional signal modeling, we suppose that both signal and noise are distributed with Laplacian distribution, because the sample loss of digital media can be better modeled with this distribution than the Gaussian one. We derive the optimum decoder for the proposed embedding method thanks to the maximum likelihood decoding scheme. We also analyze our watermarking system in the presence of noise and provide analytical evaluations and several simulations. The results show that it has the suitable performance and transparency required for watermarking applications.

The quality of the extracted features from a long-term sequence of raw prices of the instruments greatly affects the performance of the trading rules learned by machine learning models. Employing a neural encoder-decoder structure to extract informative features from complex input time-series has proved very effective in other popular tasks like neural machine translation and video captioning. In this paper, a novel end-to-end model based on the neural encoder-decoder framework combined with deep reinforcement learning is proposed to learn single instrument trading strategies from a long sequence of raw prices of the instrument. In addition, the effects of different structures for the encoder and various forms of the input sequences on the performance of the learned strategies are investigated. Experimental results showed that the proposed model outperforms other state-of-the-art models in highly dynamic environments.

Image captioning is an interdisciplinary research field in machine vision and natural language processing. Most of the proposed methods for generating image captions follow an encoder-decoder framework. In this way, each word is generated based on the image features and previously generated words. Recently the attention mechanism, which usually creates a spatial map that highlights the image regions associated with each word, has been widely used in research. In this paper, we propose a new method that integrates the encoder-decoder framework with the attention on attention mechanism. The encoder part of the model uses ResNet to extract global features of the image, and the decoder consists of three important parts: Attention-LSTM, Language-LSTM, and Attention on attention-layer. The attention mechanism uses local evidence to enhance the demonstration of the features and reasoning in the generation of image descriptions. The method was able to improve the generation of captions and improve METEOR, ROUGH evaluation metric well. And also it generates better captions compared to modern methods on the Flickr8k, dataset.

In this research, using graphene nano-ribbons on silicon dioxide, a plasmonic channel with high confinement has been designed for guiding surface plasmon polaritons. By adjusting the chemical potential of graphene, the channel's conductivity can be controlled. Simulation results show that by applying voltages of 1.5 and 8.3 volts to graphene nano-ribbons can obtained chemical potentials of 0.1 and 0.5 electron volts, and change the channel losses from 88.23 to 0.91 dB/μm. Accordingly, two logical states of zero and one and key switching operation can be realized. The figure-of-merit of 975.43 shows that there is a good ratio between the confinement of surface plasmons and their propagation loss. The coupling length of 99.1 μm shows that the power leakage to adjacent channel can be controlled and the small size of the proposed decoder, which is equal to 1.92 μm2 shows the importance of power leakage control. The discrimination ratio of the decoder is 45.73 dB, demonstrating the ability of the device to distinguish logical levels of one and zero. Comparison of the structure obtained in this research with other works confirms that the proposed design has been able to improve the performance of the optical decoder.