Set in a whimsical world, this collection features enchanting stories that blend traditional fairy tale elements with unique cultural twists. Each tale introduces captivating characters and imaginative plots, inviting readers to explore themes of adventure, friendship, and moral lessons. The vibrant storytelling and rich illustrations create a magical experience, making it perfect for readers of all ages who appreciate the charm of fairy tales reimagined through a fresh lens.
Immerse yourself in a captivating collection of enchanting stories that blend traditional fairy tale elements with unique cultural twists. Each tale transports readers to a whimsical world filled with magical creatures, moral lessons, and vibrant settings. The narratives celebrate imagination and creativity while offering insights into values and life lessons that resonate across generations. Perfect for readers of all ages, this anthology invites you to explore the wonders of storytelling from a fresh perspective.
Blending traditional Chinese storytelling with Western fantasy, the book features vivid language and enchanting narratives. The poignant fairy tales explore universal themes such as friendship, family, loyalty, and loss, creating a magical experience that resonates deeply with readers.
The requirement of high-quality image compression methods is becoming more and more critical with the flourishing of multimedia applications. The development of the cellular computing, e. g. cellular automata (CA), Cellular Neural Networks (CNN), in the past few decades has revealed various feasibility to implement these kinds of computational frameworks for signal processing. Due to their massive parallel nature, CNN have been proven well suitable for image processing. In this thesis, inspired by Dogaru’s work, a wavelet-based image compression method is proposed. The CNN paradigm is implemented in an image compression scheme as far as possible. In this thesis, the nonlinear dynamics and the parallel computing capability of CNN have been investigated. Different CNN-based algorithms of operations involved in the image compression have been developed. The proposed method has proven a comparable performance in both objective quality and the perceptual quality to that of the JPEG 2000 standard for image compression applications where a high compression ratio is required. The results obtained in this thesis show that the application of a CNN-based image compression method can lead to a high-quality while retaining a low system complexity by taking advantage of the CNN paradigm.
