Digital image watermarking is the process of embedding and extracting a watermark covertly on a cover-image. To dynamically adapt image watermarking algorithms, deep learning—based image watermarking schemes have attracted increased attention during recent years. However, existing deep learning—based watermarking methods neither fully apply the fitting ability to learn and automate the embedding and extracting algorithms, nor achieve the properties of robustness and blindness simultaneously. In this paper, a robust and blind image watermarking scheme based on deep learning neural networks is proposed. To minimize the requirement of domain knowledge, the fitting ability of deep neural networks is exploited to learn and generalize an automated image watermarking algorithm. A deep learning architecture is specially designed for image watermarking tasks, which will be trained in an unsupervised manner to avoid human intervention and annotation. To facilitate flexible applications, the robustness of the proposed scheme is achieved without requiring any prior knowledge or adversarial examples of possible attacks. A challenging case of watermark extraction from phone camera—captured images demonstrates the robustness and practicality of the proposal. The experiments, evaluation, and application cases confirm the superiority of the proposed scheme.

During the last decades, many studies have been dedicated to improving the performance of neural networks, for example, the network architectures, initialization, and activation. However, investigating the importance and effects of learnable padding methods in deep learning remains relatively open. To mitigate the gap, this paper proposes a novel trainable Padding Module that can be placed in a deep learning model. The Padding Module can optimize itself without requiring or influencing the model’s entire loss function. To train itself, the Padding Module constructs a ground truth and a predictor from the inputs by leveraging the underlying structure in the input data for supervision. As a result, the Padding Module can learn automatically to pad pixels to the border of its input images or feature maps. The padding contents are realistic extensions to its input data and simultaneously facilitate the deep learning model’s downstream task. Experiments have shown that the proposed Padding Module outperforms the state-of-the-art competitors and the baseline methods. For example, the Padding Module has 1.23% and 0.44% more classification accuracy than the zero padding when tested on the VGG16 and ResNet50.

Smart Digital Twins (SDTs) are being increasingly used to virtually replicate and predict the behaviors of complex physical systems through continual data assimilation, enabling the optimization of the performance of these systems by controlling the actions of systems. Recently, the Deep Learning (DL) models have significantly enhanced the capabilities of SDTs, particularly for tasks such as predictive maintenance, anomaly detection, and optimization. In many domains, including medicine, engineering, and education, SDTs use image data (image-based SDTs) to observe, learn, and control system behaviors. This paper focuses on various approaches and associated challenges in developing image-based SDTs by continually assimilating image data from physical systems. The paper also discusses the challenges in designing and implementing DL models for SDTs, including data acquisition, processing, and interpretation. In addition, insights into the future directions and opportunities for developing new image-based DL approaches to develop robust SDTs are provided. This includes the potential for using generative models for data augmentation, developing multi-modal DL models, and exploring the integration of DL models with other technologies, including Fifth Generation (5 G), edge computing, and the Internet of Things (IoT). In this paper, we describe the image-based SDTs, which enable broader adoption of the Digital Twins (DTs) paradigms across a broad spectrum of areas and the development of new methods to improve the abilities of SDTs in replicating, predicting, and optimizing the behavior of complex systems.

Scanning electron microscopy (SEM) techniques have been extensively performed to image and study bacterial cells with high-resolution images. Bacterial image segmentation in SEM images is an essential task to distinguish an object of interest and its specific region. These segmentation results can then be used to retrieve quantitative measures (e.g., cell length, area, cell density) for the accurate decision-making process of obtaining cellular objects. However, the complexity of the bacterial segmentation task is a barrier, as the intensity and texture of foreground and background are similar, and also, most clustered bacterial cells in images are partially overlapping with each other. The traditional approaches for identifying cell regions in microscopy images are labor intensive and heavily dependent on the professional knowledge of researchers. To mitigate the aforementioned challenges, in this study, we tested a U-Net-based semantic segmentation architecture followed by a post-processing step of morphological over-segmentation resolution to achieve accurate cell segmentation of SEM-acquired images of bacterial cells grown in a rotary culture system. The approach showed an 89.52% Dice similarity score on bacterial cell segmentation with lower segmentation error rates, validated over several cell overlapping object segmentation approaches with significant performance improvement.

Task planning for a construction robot requires systematically integrating diverse elements, such as building components, construction processes, user input, and robot software. Conventional robot programming complicates this by requiring precise entity naming, relationship definitions, unstructured language interpretation, and accurate action selection. Existing research has focused on isolated components, such as natural language processing, hardcoded data linkages, or BIM data extraction. We introduce a novel framework using an LLM as the cognitive core for autonomous construction robots, encompassing both data preparation and task planning phases. Leveraging OpenAI’s ChatGPT-4, we demonstrate how LLMs can process structured BIM data and unstructured human inputs to generate robot instructions. A prototype tested in a simulated environment with a mobile painting robot adaptively executed tasks through real-time dialogues with ChatGPT-4, reducing reliance on hardcoded logic. Results suggest that LLMs can serve as the cognitive core for construction robots, with potential for extension to more complex operations.

The development of intelligent medical service robots for patient care presents significant challenges, particularly in integrating diverse knowledge sources and enabling robots to autonomously perform tasks in dynamic and unpredictable healthcare environments. This study introduces a novel framework that combines large language models with healthcare-specific knowledge and robotic operations to enhance autonomous task execution for a Robotic Health Attendant. Utilizing OpenAI’s ChatGPT, the system processes structured information about patient care protocols and unstructured human inputs to generate context-aware robot actions. A prototype system was tested in a simulated patient room where the robot successfully performed both simple individual actions and complex tasks involving the execution of multiple actions, based on real-time dialogues with the language model and predefined task specifications. The results demonstrate the potential of language models to reduce the reliance on hardcoded logic and provide healthcare professionals with the ability to interact with robotic systems through natural language.