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Partial transmit sequence (PTS) is an attractive technique for PAPR reduction without distortion, but to obtain preferable PAPR performance it needs many inverse fast Fourier transforms (IFFTs), which results in high computational complexity. In order to reduce the complexity, modified PTS technique, with real and imaginary parts, separately multiplied with phase factors is considered in this paper. To reduce PAPR further the forward error-correcting codes (FECs) such as Golay codes and Turbo codes are employed in the modified PTS radix FFT, where, the PAPR is jointly optimized in both the real and imaginary part. The simulation results show that the combined FEC with modified PTS technique significantly provides better PAPR reduction with reduced computational complexity compared to ordinary PTS with FEC. General TermsOrthogonal frequency division multiplexing (OFDM), peak-toaverage power ratio (PAPR), partial transmit sequence (PTS).

Abstract-Orthogonal Frequency-Division Multiplexing (OFDM) is a striking technique for achieving high-bit-rate wireless data transmission as it has the ability to cope with severe channel conditions. However, the potentially large Peak-To-Average Power Ratio (PAPR) has limited its application. Partial Transmit Sequence (PTS) is an attractive scheme for PAPR reduction without distortion, but to obtain preferable PAPR performance it needs many Inverse Fast Fourier Transforms (IFFTs) which results in high complexity. In this paper, a modified PTS scheme combined with interleaving for PAPR reduction using Quadrature Phase Shift Keying Modulation (QPSK) has been presented. The scheme is very efficient and avoids the use of any extra IFFTs as was done in PAPR reduction by ordinary PTS technique. The simulation result shows that PAPR performance is improved with the increase in number of subblocks.

The development of hand gesture recognition systems has gained more attention in recent days, due to its support of modern human-computer interfaces. Moreover, sign language recognition is mainly developed for enabling communication between deaf and dumb people. In conventional works, various image processing techniques like segmentation, optimization, and classification are deployed for hand gesture recognition. Still, it limits the major problems of inefficient handling of large dimensional datasets and requires more time consumption, increased false positives, error rate, and misclassification outputs. Hence, this research work intends to develop an efficient hand gesture image recognition system by using advanced image processing techniques. During image segmentation, skin color detection and morphological operations are performed for accurately segmenting the hand gesture portion. Then, the Heuristic Manta-ray Foraging Optimization (HMFO) technique is employed for optimally selecting the features by computing the best fitness value. Moreover, the reduced dimensionality of features helps to increase the accuracy of classification with a reduced error rate. Finally, an Adaptive Extreme Learning Machine (AELM) based classification technique is employed for predicting the recognition output. During results validation, various evaluation measures have been used to compare the proposed model's performance with other classification approaches.