BER performance study for optical OFDM of optical camera communication

In this article, different forms of optical orthogonal frequency division multiplexing (OFDM) were observed which were suitable for optical camera communication (OCC) systems. This research aims to establish the bit error rate (BER) versus signal-to-noise ratio (SNR) of the OCC system. This research will focus on OCC systems and the design that produces the noise of the clipping but will gain SNR as a whole if an optimum clipping factor is chosen. The BER versus SNR analysis was investigated for the different clipping factors 0.7, 1.4, and 2.6. The BER performance of the asymmetrically clipped optical OFDM (ACO-OFDM) was also compared with the direct current optical OFDM (DCO-OFDM) to show the suitable effectiveness of the proposed approach. ACO-OFDM was considered to be better due to lower bit loading, but DCO-OFDM was efficient for higher SNR values. This was because the DC bias used was inefficient in terms of optical capacity, while ACO-OFDM used only half of the subcarriers to transmit the information. Moreover, ACO-OFDM two-dimensional half-subcarriers of mapping rule would introduce the clipping noise to its unused 2D subcarriers, although further data can be provided by the 2D DCO-OFDM mapping rule.

Fast Logic Function Extraction of LUT from Bitstream in Xilinx FPGA

This paper presents a fast method to extract logic functions of look-up tables (LUTs) from a bitstream in Xilinx FPGAs. In general, FPGAs utilize LUTs as a primary resource to realize a logic function, and a typical N-input LUT comprises 2N 1-bit SRAM and N – 1 multiplexers. Whereas the previous research demands 2N exhaustive processing to find a mapping rule between an LUT and a bitstream, the proposed method decreases the processing to 2N by eliminating unnecessary processing. Experimental results show that the proposed method can reduce reversing time by more than 57% and 85% for Xilinx Spartan-3 and Virtex-5 compared to the previous exhaustive algorithm. It is noticeable that the reduction time becomes more significant as a commercial Xilinx FPGA tends to include a more tremendous number of LUTs.

Online Handwritten Gujarati Word Recognition

In this article, an online handwritten word recognition system for the Gujarati language is presented by combining strokes, characters, punctuation marks, and diacritics. The authors have used a support vector machine classification algorithm with a radial basis function kernel. The authors used a hybrid features set. The hybrid feature set consists of directional features with curvature data. The authors have used a normalized chain code and zoning-based chain code features. Words are a combination of characters and diacritics. Recognized strokes require post-processing to form a word. The authors have used location-based and mapping rule-based post-processing methods. The authors have achieved an accuracy of 95.3% for individual characters, 91.5% for individual words, and 83.3% for sentences. The average processing time for individual characters is 0.071 seconds.

Reusing Source Task Knowledge via Transfer Approximator in Reinforcement Transfer Learning

Transfer Learning (TL) has received a great deal of attention because of its ability to speed up Reinforcement Learning (RL) by reusing learned knowledge from other tasks. This paper proposes a new transfer learning framework, referred to as Transfer Learning via Artificial Neural Network Approximator (TL-ANNA). It builds an Artificial Neural Network (ANN) transfer approximator to transfer the related knowledge from the source task into the target task and reuses the transferred knowledge with a Probabilistic Policy Reuse (PPR) scheme. Specifically, the transfer approximator maps the state of the target task symmetrically to states of the source task with a certain mapping rule, and activates the related knowledge (components of the action-value function) of the source task as the input of the ANNs; it then predicts the quality of the actions in the target task with the ANNs. The target learner uses the PPR scheme to bias the RL with the suggested action from the transfer approximator. In this way, the transfer approximator builds a symmetric knowledge path between the target task and the source task. In addition, two mapping rules for the transfer approximator are designed, namely, Full Mapping Rule and Group Mapping Rule. Experiments performed on the RoboCup soccer Keepaway task verified that the proposed transfer learning methods outperform two other transfer learning methods in both jumpstart and time to threshold metrics and are more robust to the quality of source knowledge. In addition, the TL-ANNA with the group mapping rule exhibits slightly worse performance than the one with the full mapping rule, but with less computation and space cost when appropriate grouping method is used.

FROM SOCCER TEAMS TO PRESIDENTIAL ELECTIONS: A NEW APPROACH TO UNDERSTAND AFFECTIVE RIVALRY

Conde et al (2011) reported a modulation of the spatial compatibility effect by the affective valence of soccer team figures. For Favorite team, it was faster to respond by pressing the key located on the stimulus side than the opposite key (ipsi- and contralateral keys, respectively). For Rival team, this pattern was reversed. These findings were interpreted as being due to approach and avoidance reactions which facilitate both the ipsilateral response to a positive stimulus and the contralateral response to a negative one and vice-versa. This hypothesis was challenged by arguing that there is no spatial compatibility effect when a mixed-rule task was used and that approach/avoidance reactions are not elicited when a keyboard was employed to execute the responses. Alternatively, it was proposed that Conde et al. (2011) results were due to task-set effects. Here, manual responses were selected according to the volunteer’s Preference for the candidates to Presidential election in Brazil. The names of the Favorite and Rival candidates were presented left or right to the center of a screen and the responses should be chosen according to two different mapping-rules. In Mapping-rule 1, the instruction was to press the key located on the same side of the Favorite stimulus (compatible response) and to press the opposite key for the Rival (incompatible response). In Mapping-rule 2, the instruction was reversed. The order of the mapping-rules was counterbalanced. It was found that the Mapping-rule 1 responses were faster than Mapping-rule 2 ones. This Mapping-rule (task-set) effect may be due to Approach and Avoidance reactions to Favorite and Rival candidates, respectively. These automatic reactions facilitate the compatible responses for the Favorite and incompatible ones for the Rival (Mapping 1) and delay the incompatible response for the Favorite and the compatible ones for the Rival (Mapping 2). A further analysis of the interaction between Preference and Compatibility showed that there is a compatibility effect for the Favorite but not for the Rival, indicating that a task-set effect due to the mapping-rules is not enough to explain the findings in this experiment. It is proposed an alternative hypothesis based on facilitatory and inhibitory effects of positive and negative affective stimuli.