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-input compound AND-OR gates. In the second stage by two ROM Encoders, 2-of-M codes determine n-1 MSB bits and one LSB bit. The advantages of the proposed Encoder rather than other similar works are high speed, low power consumption, low active area, and low latency with same bubble error removing capability. To demonstrate the mention specifications, 5-bit flash ADCs with conventional and proposed Encoders in their Encoder blocks, are simulated at 2-GS/s and 3. 5-GS/s sampling rates in 0. 18-μ m CMOS process. Simulation results show that the ENOB of flash ADCs with conventional and proposed Encoders are equal. In this case, the proposed Encoder outputs are determined almost 30-ps faster rather than the conventional Encoder at 2-GS/s. Also, the power consumptions of the conventional and proposed Encoders were 17. 94-mW and 11. 74-mW at 3. 5-GS/s sampling rate from a 1. 8-V supply, respectively. Corresponding, latencies of the conventional and proposed Encoders were 3 and 2 clock cycles. In this case, number of TSPC D-FFs and logic gates of the proposed Encoder is decreased almost 39% compared to the conventional Encoder.

Nowadays, the use of hardware/software codesign has grown dramatically in the design of embedded systems since it can improve the processing power, system efficiency, and the total cost of production. In this method, some parts of the system are implemented in hardware and the other parts are implemented in software in order to satisfy the system constraints, including power consumption, area and processing time. This paper proposes a parallel architecture for motion estimation in HEVC Encoder. In the proposed method, the motion estimation part of the Encoder, which has a high computational complexity, is implemented in hardware, and the computational complexity of this part is improved using parallel processing. The hardware implementation of motion estimation part is much less complex than the adopted HM reference software, making it more suitable for embedded systems. Experimental results show a significant improvement over software implementation.

Recent researches have determined that regularized auto-Encoders can provide a good representation of data which improves the performance of data classification. These type of auto-Encoders provides a representation of data that has some degree of sparsity and is robust against variation of data to extract useful information and reveal the underlying structure of data. The present study aimed to propose a novel approach to generate sparse, robust, and discriminative features through supervised regularized auto-Encoders, in which unlike most existing auto-Encoders, the data labels are used during feature extraction to improve discrimination of the representation and also, the sparsity ratio of the representation is completely adaptive with data distribution. Results reveal that this method has better performance in comparison to other regularized auto-Encoders regarding data classification.

In this paper, a photonic crystal structure composed of the silicon rods is proposed for an all-optical 4*2 Encoder. Four input ports are connected to two outputs port via the cross-connections. Different radii of rods as defects are placed in the cross-connection region for coupling the optical waves from the input waveguides to the desired outputs. The total size of the device is about 133 μm2. Plane-wave expansion and finite difference time domain methods are used to calculate the band diagram and simulation of the optical wave propagation inside the structure, respectively. The maximum rise time of the device for all possible states is just about 205 fs which is less than one in the previous works. No need to a bias port and using the same power at input ports are other advantages of this work. The normalized output power margins for logic 0 and 1 are calculated by 2% and 34%, respectively. The simulation results demonstrate the presented structure is capable of using in optical integrated circuits.

Angular displacement measuring Encoder is one of the most important measuring tools in the automated systems، industries and machine tools. In many of manufacturing and production processes، rotary Encoders are used as reliable tools for precise positioning. In this study، a new capacitive-type rotary Encoder with un-tethered rotor is designed. The main components are made of printed circuit films. Hence، the Encoder can be set up in thin inter spaces. The Encoder consists of a receiving stator and a transmitter rotor، respectively containing four-phase and two-phase electrodes. In order to designs an un-tethered rotor; the Encoder employs a unique approach. Electrical power is supplied to the transmitter rotor by electrostatic induction without any physical contact. This un-tethered rotor can facilitate sensitive applications that a mechanical disturbance caused by an electric wire can be a problem. In this study the Encoder was built and its performance was evaluated. The result of experimental evaluation shows that the Encoder has max ± 0. 09 degree error.

Web Application Firewall (WAF) is known as one of the Intrusion Detection System (IDS) solutions for protecting web servers from HTTP attacks. WAF is a tool to identify and prevent many types of attacks, such as XSS and SQL-injection. In this paper, deep machine learning algorithms are used for enriching the WAF based on the anomaly detection method. Firstly, we construct attributes from HTTP data, to do so we consider two models namely n-gram and one-hot. Then, according to Auto-Encoder LSTM (AE-LSTM) as an unsupervised deep leaning method, we should extract informative features and then reduce them. Finally, we use ensemble isolation forest to train only normal data for the classifier. We apply the proposed model on CSIC 2010 and ECML/ PKDD 2007 datasets. The results show AE-LSTM has higher performance in terms of accuracy and generalization compared with naï ve methods on CSIC dataset; the proposed method also have acceptable detection rate on ECML/PKDD dataset using n-gram model.

In this paper, an improved sample of capacitive-type linear Encoder with untethered slider is presented that has higher resolution and lower error. This Encoder has a simple structure consisting of two main parts, stator and slider. Both parts are made of hard PCB. The stator has a sequence of four-phase receiver electrodes and the slider contains two-phase transmitter electrodes. The electrical energy is supplied to transmitter electrodes by the electrostatic induction. Therefore, there is no need to connect any wire to the slider. These features make the Encoder to use in limited spaces. By improving resolution, smooth and precise movement of motors is facilitated in the closed loop systems. Thus, the improved Encoder is equipped with two ground electrodes between the receiver electrodes and induction electrodes on the stator board to reduce electrostatic interface between these electrodes. As, output error goes down. In addition, the step of electrodes are reduced to increase resolution. Making an experimental sample of improved Encoder and testing it, showed 15μ m error in 1μ m resolution. This indicates, the error and resolution are improved than the previous one.

Linear and angular displacement measuring Encoders are the most important measuring tools in the industry. Linear Encoders are widely used in various positioning applications, such as numerical controlled (NC) machine tools and factory automation, since they are essential for precision positioning systems. In this study, a capacitive-type linear Encoder with un-tethered slider is designed. The main components are made of printed circuit films. Hence, the Encoder can be set up in thin inter spaces or on curved surfaces. The Encoder consists of a long receiver film and a short transmitter film, respectively containing four-phase and two-phase electrodes. The transmitter is used as a slider and the receiver as a stator. In order to designs an unconstrained slider; the Encoder employs a unique approach. Electrical power is supplied to the transmitter film by electrostatic induction which removes electric wires from the slider. In this study the Encoder was built using a new signal processing circuit and its performance was evaluated. The new signal processing circuit is more compact and facilitates using this Encoder for trade purposes. The result of experimental evaluation shows that the Encoder has ±20 micrometers error.

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.