Given the remarkable performance of diffusion models in image generation, recent research has been exploring their adaptation to style transfer. However, current diffusion-based approaches encounter persistent challenges, such as style distortions and the reliance on textual prompts for content preservation. To address these limitations, we introduce StyleFM, a novel training-free diffusion-based style transfer approach that incorporates optimization strategies into both the frequency and temporal domains. The proposed method provides two core innovations: (1) Tripartite Frequency Manipulation: To more precisely tailor frequency manipulation, StyleFM introduces a tripartite frequency design with a buffer band accounting for the overlap of content and style representations. In addition, StyleFM designs a frequency superposition editing method to achieve frequency enhancement. (2) Recursive Attention: StyleFM proposes the recursive attention strategy within the diffusion process, which facilitates the progressive and consistent injection of style information throughout the temporal process without reliance on text guidance. Experiments demonstrate that StyleFM outperforms state-of-the-art methods. It effectively preserves content fidelity while achieving sufficient style embedding.

The objective of arbitrary style transfer is to apply a given artistic or photo-realistic style to a target image. Although current methods have shown some success in transferring style, arbitrary style transfer still has several issues, including content leakage. Embedding an artistic style can result in unintended changes to the image content. This article proposes an iterative framework called Restorable Arbitrary Style Transfer (RAST) to effectively ensure content preservation and mitigate potential alterations to the content information. RAST can transmit both content and style information through multi-restorations and balance the content-style tradeoff in stylized images using the image restoration accuracy. To ensure RAST’s effectiveness, we introduce two novel loss functions: multi-restoration loss and style difference loss. We also propose a new quantitative evaluation method to assess content preservation and style embedding performance. Experimental results show that RAST outperforms state-of-the-art methods in generating stylized images that preserve content and embed style accurately.

Arbitrary style transfer aims to reproduce the target image with the artistic or photo-realistic styles provided. Even though existing approaches can successfully transfer style information, arbitrary style transfer still faces many challenges, such as the content leak issue. Specifically, the embedding of artistic style can lead to content changes. In this paper, we solve the content leak problem from the perspective of image restoration. In particular, an iterative architecture is proposed to achieve the Restorable Arbitrary Style Transfer (RAST), which can realize transmission of both content and style information through multi-restorations. We control the content-style balance in stylized images by the accuracy of image restoration. In order to ensure effectiveness of the proposed RAST architecture, we design two novel loss functions: multi-restoration loss and style difference loss. In addition, we propose a new quantitative evaluation method to measure content preservation performance and style embedding performance. Comprehensive experiments comparing with state-of-the-art methods demonstrate that our proposed architecture can produce stylized images with superior performance on content preservation and style embedding.

Ultrasound is a non-invasive tool that is useful for medical diagnosis and treatment. To reduce long wait times and add convenience to patients, portable ultrasound scanning devices are becoming increasingly popular. These devices can be held in one hand, and are compatible with modern cell phones. However, the quality of ultrasound images captured from the portable scanners is relatively poor compared to standard ultrasound scanning systems in hospitals. To improve the quality of the ultrasound images obtained from portable ultrasound devices, we propose a new neural network architecture called Edge-guided Denoising Convolutional Neural Network (EDCNN), which can preserve significant edge information in ultrasound images when removing noise. We also study and compare the effectiveness of existing deep learning methods and classical filtering approaches in removing speckle noise in these images. Experimental results show that after applying the proposed EDCNN, various organs can be better recognized from ultrasound images. This approach is expected to lead to better accuracy in diagnostics in the future.

Fall is one of great threats affecting people with old age. Aimed at the falling issue of aged, the paper explored a pre-impact fall early warning and protection system. This system consists of an early fall alarm, protection airbags, a remote monitoring platform and a guardian's cellphone app. The early fall alarm and airbags are integrated in a belt, convenient for wearing and hip-protection. The inner of early fall alarm has a MEMS sensor, which collects 3-axis accelerated velocity and 3-axis angular velocity. A fall detection algorithm is applied to recognize falls from activities of daily living (ADL). When there is a dangerous movement approaching fall, the early fall alarm will warn the aged to stop the movement. When the fall happens, the early fall alarm will trigger the airbag system, then the airbags in the belt will inflate as soon as possible to reduce the damage to the aged. In addition, the early fall alarm will ring and send message to the guardian's cellphone for help. Meanwhile, the kinematics of the human body during falling time will be stored in TF card and sent to remote monitoring platform for storage. Then the monitoring platform can show the fall location where the fall incident happens in the electronic map. In order to test the reliability of this early fall alarm and protection system, a series of experiments have been designed. The results show that this system can be relatively accurate to detect falls, accomplishing functions including early fall warning and alarming, airbag inflation, statics transferring and storage, real-time location, which has significant benefit for reducing the direct damage and shortening the aiding time.

In the current electronic industry, embedded systems have become essential components in most electronic devices. The fast growth mobile devices have been used in every sector and brought wide applications in many aspects of our society. These devices involve in internet connections, wireless communications and even cloud computing, which increases the necessity of both hardware and software securities. Therefore there is an urgent need of efficient education in EE, CE and CS in embedded systems, especially in hardware and software co-design with the concepts of modern security methods. In this paper, we present a work-in-progress development of an effective learning tool which benefits from active learning concepts and modern mobile technologies. The contributions of this tool on promoting engineering students' learning of embedded system designs and security are also investigated.