A Harmonic Approach to Handwriting Style Synthesis Using Deep Learning

The challenging task of handwriting style synthesis requires capturing the individuality and diversity of human handwriting.The majority of currently available methods use either a generative adversarial network(GAN)or a recurrent neural network(RNN)to generate new handwriting styles.This is why these techniques frequently fall short of producing diverse and realistic text pictures,particularly for terms that are not commonly used.To resolve that,this research proposes a novel deep learning model that consists of a style encoder and a text generator to synthesize different handwriting styles.This network excels in generating conditional text by extracting style vectors from a series of style images.The model performs admirably on a range of handwriting synthesis tasks,including the production of text that is out-of-vocabulary.It works more effectively than previous approaches by displaying lower values on key Generative Adversarial Network evaluation metrics,such Geometric Score(GS)(3.21×10^(-5))and Fréchet Inception Distance(FID)(8.75),as well as text recognition metrics,like Character Error Rate(CER)and Word Error Rate(WER).A thorough component analysis revealed the steady improvement in image production quality,highlighting the importance of specific handwriting styles.Applicable fields include digital forensics,creative writing,and document security.

Study of word length selection for DDCs used in ultra-low symbol rate receivers

The relationship between the hardware requirement of digital down converters（DDCs）in ultra-low symbol rate receivers and the word length is studied.Through analyzing the impact of word length selection to the system performance,a modified scheme is presented to decline the resource consumption without too much degradation on the signal to noise ratio（SNR）.Theoretical analysis and numerical results demonstrate that compared to the traditional design,the proposed scheme could save dozens of memory resources.The scheme also includes some selectable parameters to achieve desired performance in various circumstances.Different from previous work in DDCs that concentrates mostly on the structure design,this paper considers special applications such as ultra-low symbol rate receivers.

Enhanced Marathi Speech Recognition Facilitated by Grasshopper Optimisation-Based Recurrent Neural Network

Communication is a significant part of being human and living in the world.Diverse kinds of languages and their variations are there;thus,one person can speak any language and cannot effectively communicate with one who speaks that language in a different accent.Numerous application fields such as education,mobility,smart systems,security,and health care systems utilize the speech or voice recognition models abundantly.Though,various studies are focused on the Arabic or Asian and English languages by ignoring other significant languages like Marathi that leads to the broader research motivations in regional languages.It is necessary to understand the speech recognition field,in which the major concentrated stages are feature extraction and classification.This paper emphasis developing a Speech Recognition model for the Marathi language by optimizing Recurrent Neural Network(RNN).Here,the preprocessing of the input signal is performed by smoothing and median filtering.After preprocessing the feature extraction is carried out using MFCC and Spectral features to get precise features from the input Marathi Speech corpus.The optimized RNN classifier is used for speech recognition after completing the feature extraction task,where the optimization of hidden neurons in RNN is performed by the Grasshopper Optimization Algorithm(GOA).Finally,the comparison with the conventional techniques has shown that the proposed model outperforms most competing models on a benchmark dataset.

EFFICIENT DESIGN OF ANTI-JAMMING RATELESS CODING IN COGNITIVE OFDM

Efficient anti-jamming rateless coding based on cognitive Orthogonal Frequency Division Multiplexing (OFDM) modulation in Cognitive Radio Network (CRN) is mainly discussed. Rateless coding with small redundancy and low complexity is presented, and the optimal design methods of building rateless codes are also proposed. In CRN, anti-jamming rateless coding could recover the lost packets in parallel channels of cognitive OFDM, thus it protects Secondary Users (SUs) from the in-terference by Primary Users (PUs) efficiently. Frame Error Rate (FER) and throughput performance of SU employing anti-jamming rateless coding are analyzed in detail. Performance comparison between rateless coding and piecewise coding are also presented. It is shown that, anti-jamming rateless coding provides low FER and Word Error Rate (WER) performance with uniform sub-channel selection. Meanwhile, it is also verified that, in higher jamming rate and longer code redundancy scenario, rateless coding method could achieve better FER and throughput performance than another anti-jamming coding schemes.

Energy efficient HARQ scheme for wireless communication over block fading channel

Energy efficiency （EE） can be enhanced by retransmissions and combining in hybrid automatic repeat request （HARQ） system. However, it is difficult to optimize the transmit power of each retransmission when the accurate retransmission number and future channel state information （CSI） cannot be obtained. This paper proposes a simple energy efficient HARQ scheme for point-to-point wireless communication. In the proposed scheme, the conditional word error rate （WER） of each retransmission is fixed and the transmit power is adapted correspondingly. Three performance metrics are analyzed including average transmission number, throughput and EE. Compared with the conventional equal power HARQ scheme, the proposed scheme can significantly improve the EE and other two metrics under the same constraint of average transmit power or average energy consumption. Furthermore, it is found that, selecting a conditional WER which is slightly smaller than the optimal one is sufficient for practical implementation.