In this letter, we propose a distributed Turbo Product Code (DTPC) with soft information relaying over cooperative network using block Extended Bose Chaudhuri Hochquenghem (EBCH) codes as component codes. The source b...
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In this letter, we propose a distributed Turbo Product Code (DTPC) with soft information relaying over cooperative network using block Extended Bose Chaudhuri Hochquenghem (EBCH) codes as component codes. The source broadcasts extended EBCH coded frames to the destination and to a preassigned relay. After soft-decoding, the received sequences and obtaining the Log-Likelihood Ratio (LLR) values, the relay constructs a product code by arranging the decoded bit sequences in rows and re-encoding them along the columns using a novel soft block encoding technique to obtain soft parity bits with different reliabilities that can be used as soft Incremental Redundancy (IR) that is forwarded to the destination. We compared the simulation results in Additive White Gaussian Noise (AWGN) channel with our previous work for Decode and Forward (DF) DTPC and with noncooperative case. The proposed system shows better Bit Error Rate (BER) performance and less sensitivity to the relay's position.
Applied research of an automated method to quickly emerge better encoding profiles, using Genetic Algorithms with many source video samples to converge to the specified limits of processing time, video quality and fil...
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ISBN:
(纸本)9788576691990
Applied research of an automated method to quickly emerge better encoding profiles, using Genetic Algorithms with many source video samples to converge to the specified limits of processing time, video quality and file size.
Although previous studies have implicated a diverse set of brain regions in reward-related decision making, it is not yet known which of these regions contain information that directly reflects a decision. Here, we me...
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Although previous studies have implicated a diverse set of brain regions in reward-related decision making, it is not yet known which of these regions contain information that directly reflects a decision. Here, we measured brain activity using functional MRI in a group of subjects while they performed a simple reward-based decision-making task: probabilistic reversal-learning. We recorded brain activity from nine distinct regions of interest previously implicated in decision making and separated out local spatially distributed signals in each region from global differences in signal. Using a multivariate analysis approach, we determined the extent to which global and local signals could be used to decode subjects' subsequent behavioral choice, based on their brain activity on the preceding trial. We found that subjects' decisions could be decoded to a high level of accuracy on the basis of both local and global signals even before they were required to make a choice, and even before they knew which physical action would be required. Furthermore, the combined signals from three specific brain areas (anterior cingulate cortex, medial prefrontal cortex, and ventral striatum) were found to provide all of the information sufficient to decode subjects' decisions out of all of the regions we studied. These findings implicate a specific network of regions in encoding information relevant to subsequent behavioral choice.
In this paper, we ignore transmission issues and focus on the total number of bits to transmit to the collector to form a reconstruction of the field with a given MSE. We assume that all sensors can transmit bits to t...
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ISBN:
(纸本)0780376293
In this paper, we ignore transmission issues and focus on the total number of bits to transmit to the collector to form a reconstruction of the field with a given MSE. We assume that all sensors can transmit bits to the collector without error. With this assumption, with total number of bits as the cost measure, and with the style of coding, it can be argued that sensor-to-sensor relaying offers no advantages. This problem is similar to image coding and transmission, except that the quantization, encoding and transmission are constrained to take place separately at each sensor (pixel location), in contrast to traditional image coding and transmission, wherein the entire image is available for quantization, encoding, and transmission. Due to the need to separately encode values from separate sensors, we pursue a Slepian-Wolf style coding approach.
A neural classifier of planar trajectories is presented. There already exist a large variety of classifiers that are specialized in particular invariants contained in a trajectory classification task such as position-...
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A neural classifier of planar trajectories is presented. There already exist a large variety of classifiers that are specialized in particular invariants contained in a trajectory classification task such as position-invariance, rotation-invariance, and size-invariance. That is, there exist classifiers specialized in recognizing trajectories, eg., independently of their position. The neural classifier presented in this paper is not restricted to certain invariants in a task: The neural network itself extracts the invariants contained in a classification task by assessing only the trajectories. The trajectories need to be given as a set of points. No additional information must be available for training, which saves the designer from determining the needed invariants by himself Besides its applicability to real-world problems, such a more general classifier is also cognitively plausible: In assessing trajectories for classification, human beings are able to find class specific features no matter what kinds of invariants they are confronted with, Invariants are easily handled by ignoring unspecific features.
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