Multi-Device Parallel MRI Reconstruction: Efficient Partitioning for Undersampled 5D Cardiac CINE.

Cardiac CINE, a form of dynamic cardiac MRI, is indispensable in the diagnosis and treatment of heart conditions, offering detailed visualization essential for the early detection of cardiac diseases. As the demand for higher-resolution images increases, so does the volume of data requiring processing, presenting significant computational challenges that can impede the efficiency of diagnostic imaging. Our research presents an approach that takes advantage of the computational power of multiple Graphics Processing Units (GPUs) to address these challenges. GPUs are devices capable of performing large volumes of computations in a short period, and have significantly improved the cardiac MRI reconstruction process, allowing images to be produced faster. The innovation of our work resides in utilizing a multi-device system capable of processing the substantial data volumes demanded by high-resolution, five-dimensional cardiac MRI. This system surpasses the memory capacity limitations of single GPUs by partitioning large datasets into smaller, manageable segments for parallel processing, thereby preserving image integrity and accelerating reconstruction times. Utilizing OpenCL technology, our system offers adaptability and cross-platform functionality, ensuring wider applicability. The proposed multi-device approach offers an advancement in medical imaging, accelerating the reconstruction process and facilitating faster and more effective cardiac health assessment.

Predicting post-operative vault and optimal implantable collamer lens size using machine learning based on various ophthalmic device combinations.

BACKGROUND: Implantable Collamer Lens (ICL) surgery has been proven to be a safe, effective, and predictable method for correcting myopia and myopic astigmatism. However, predicting the vault and ideal ICL size remains technically challenging. Despite the growing use of artificial intelligence (AI) in ophthalmology, no AI studies have provided available choices of different instruments and combinations for further vault and size predictions. This study aimed to fill this gap and predict post-operative vault and appropriate ICL size utilizing the comparison of numerous AI algorithms, stacking ensemble learning, and data from various ophthalmic devices and combinations. RESULTS: This retrospective and cross-sectional study included 1941 eyes of 1941 patients from Zhongshan Ophthalmic Center. For both vault prediction and ICL size selection, the combination containing Pentacam, Sirius, and UBM demonstrated the best results in test sets [R2 = 0.499 (95% CI 0.470-0.528), mean absolute error = 130.655 (95% CI 128.949-132.111), accuracy = 0.895 (95% CI 0.883-0.907), AUC = 0.928 (95% CI 0.916-0.941)]. Sulcus-to-sulcus (STS), a parameter from UBM, ranked among the top five significant contributors to both post-operative vault and optimal ICL size prediction, consistently outperforming white-to-white (WTW). Moreover, dual-device combinations or single-device parameters could also effectively predict vault and ideal ICL size, and excellent ICL selection prediction was achievable using only UBM parameters. CONCLUSIONS: Strategies based on multiple machine learning algorithms for different ophthalmic devices and combinations are applicable for vault predicting and ICL sizing, potentially improving the safety of the ICL implantation. Moreover, our findings emphasize the crucial role of UBM in the perioperative period of ICL surgery, as it provides key STS measurements that outperformed WTW measurements in predicting post-operative vault and optimal ICL size, highlighting its potential to enhance ICL implantation safety and accuracy.

A Scheme for Quantum Teleportation and Remote Quantum State Preparation of IoT Multiple Devices.

With the continuous development of the Internet of Things (IoT) technology, the industry's awareness of the security of the IoT is also increasing, and the adoption of quantum communication technology can significantly improve the communication security of various devices in the IoT. This paper proposes a scheme of controlled remote quantum state preparation and quantum teleportation based on multiple communication parties, and a nine-qubit entanglement channel is used to achieve secure communication of multiple devices in the IoT. The channel preparation, measurement operation, and unitary operation of the scheme were successfully simulated on the IBM Quantum platform, and the entanglement degree and reliability of the channel were verified through 8192 shots. The scheme's application in the IoT was analyzed, and the steps and examples of the scheme in the secure communication of multiple devices in the IoT are discussed. By simulating two different attack modes, the effect of the attack on the communication scheme in the IoT was deduced, and the scheme's high security and anti-interference ability was analyzed. Compared with other schemes from the two aspects of principle and transmission efficiency, it is highlighted that the advantages of the proposed scheme are that it overcomes the single fixed one-way or two-way transmission protocol form of quantum teleportation in the past and can realize quantum communication with multiple devices, ensuring both security and transmission efficiency.

An Intelligent Self-Service Vending System for Smart Retail.

The traditional weighing and selling process of non-barcode items requires manual service, which not only consumes manpower and material resources but is also more prone to errors or omissions of data. This paper proposes an intelligent self-service vending system embedded with a single camera to detect multiple products in real-time performance without any labels, and the system realizes the integration of weighing, identification, and online settlement in the process of non-barcode items. The system includes a self-service vending device and a multi-device data management platform. The flexible configuration of the structure gives the system the possibility of identifying fruits from multiple angles. The height of the system can be adjusted to provide self-service for people of different heights; then, deep learning skill is applied implementing product detection, and real-time multi-object detection technology is utilized in the image-based checkout system. In addition, on the multi-device data management platform, the information docking between embedded devices, WeChat applets, Alipay, and the database platform can be implemented. We conducted experiments to verify the accuracy of the measurement. The experimental results demonstrate that the correlation coefficient R2 between the measured value of the weight and the actual value is 0.99, and the accuracy of non-barcode item prediction is 93.73%. In Yangpu District, Shanghai, a comprehensive application scenario experiment was also conducted, proving that our system can effectively deal with the challenges of various sales situations.

Federated Reinforcement Learning for Training Control Policies on Multiple IoT Devices.

Reinforcement learning has recently been studied in various fields and also used to optimally control IoT devices supporting the expansion of Internet connection beyond the usual standard devices. In this paper, we try to allow multiple reinforcement learning agents to learn optimal control policy on their own IoT devices of the same type but with slightly different dynamics. For such multiple IoT devices, there is no guarantee that an agent who interacts only with one IoT device and learns the optimal control policy will also control another IoT device well. Therefore, we may need to apply independent reinforcement learning to each IoT device individually, which requires a costly or time-consuming effort. To solve this problem, we propose a new federated reinforcement learning architecture where each agent working on its independent IoT device shares their learning experience (i.e., the gradient of loss function) with each other, and transfers a mature policy model parameters into other agents. They accelerate its learning process by using mature parameters. We incorporate the actor-critic proximal policy optimization (Actor-Critic PPO) algorithm into each agent in the proposed collaborative architecture and propose an efficient procedure for the gradient sharing and the model transfer. Using multiple rotary inverted pendulum devices interconnected via a network switch, we demonstrate that the proposed federated reinforcement learning scheme can effectively facilitate the learning process for multiple IoT devices and that the learning speed can be faster if more agents are involved.

The AM4I Architecture and Framework for Multimodal Interaction and Its Application to Smart Environments.

Technologies, such as smart sensors, actuators, and other kinds of devices, are often installed in our environments (e.g., our Homes) and available to integrate our daily lives. Despite their installation being motivated by the pursuit of automation and increased efficiency, making these environments usable, acceptable and enjoyable in a sustainable, energy efficient way is not only a matter of automation. Tackling these goals is a complex task demanding the combination of different perspectives including building and urban Architecture, Ubiquitous Computing and Human-Computer Interaction (HCI) to provide occupants with the means to shape these environments to their needs. Interaction is of paramount relevance in the creation of adequate relations of users with their environments, but it cannot be seen independently from the ubiquitous sensing and computing or the environment's architecture. In this regard, there are several challenges to HCI, particularly in how to integrate this multidisciplinary effort. Although there are several solutions to address some of these challenges, the complexity and dynamic nature of the smart environments and the diversity of technologies involved still present many challenges, particularly for its development. In general, the development is complex, and it is hard to create a dynamic environment providing versatile and adaptive forms of interaction. To participate in the multidisciplinary effort, the development of interaction must be supported by tools capable of facilitating co-design by multidisciplinary teams. In this article, we address the development of interaction for complex smart environments and propose the AM4I architecture and framework, a novel modular approach to design and develop adaptive multiplatform multilingual multi-device multimodal interactive systems. The potential of the framework is demonstrated by proof-of-concept applications in two different smart environment contexts, non-residential buildings and smart homes.

All you need is data preparation: A systematic review of image harmonization techniques in Multi-center/device studies for medical support systems.

BACKGROUND AND OBJECTIVES: Artificial intelligence (AI) models trained on multi-centric and multi-device studies can provide more robust insights and research findings compared to single-center studies. However, variability in acquisition protocols and equipment can introduce inconsistencies that hamper the effective pooling of multi-source datasets. This systematic review evaluates strategies for image harmonization, which standardizes appearances to enable reliable AI analysis of multi-source medical imaging. METHODS: A literature search using PRISMA guidelines was conducted to identify relevant papers published between 2013 and 2023 analyzing multi-centric and multi-device medical imaging studies that utilized image harmonization approaches. RESULTS: Common image harmonization techniques included grayscale normalization (improving classification accuracy by up to 24.42 %), resampling (increasing the percentage of robust radiomics features from 59.5 % to 89.25 %), and color normalization (enhancing AUC by up to 0.25 in external test sets). Initially, mathematical and statistical methods dominated, but machine and deep learning adoption has risen recently. Color imaging modalities like digital pathology and dermatology have remained prominent application areas, though harmonization efforts have expanded to diverse fields including radiology, nuclear medicine, and ultrasound imaging. In all the modalities covered by this review, image harmonization improved AI performance, with increasing of up to 24.42 % in classification accuracy and 47 % in segmentation Dice scores. CONCLUSIONS: Continued progress in image harmonization represents a promising strategy for advancing healthcare by enabling large-scale, reliable analysis of integrated multi-source datasets using AI. Standardizing imaging data across clinical settings can help realize personalized, evidence-based care supported by data-driven technologies while mitigating biases associated with specific populations or acquisition protocols.

Association between Media Use and Bedtime Delays in Young Children: An Adjunct Study of the Japan Environment and Children's Study.

Excessive screen media use has been reported to cause shorter sleep; however, the types of media environments that affect early childhood sleep are less known. This study examined the association of multiple media use, screen time for each device, and the purpose of smartphone and tablet use with delayed bedtime among 4-8-year-olds. Participants were recruited from the Japan Environment and Children's Study, a nationwide birth cohort study. Mothers of 1837 children reported screen media use and bedtime in a questionnaire. The association between delayed bedtimes (after 22:00 h) and media device use (smartphones, tablets, portable and console games, and TV/DVDs) was examined by logistic regression analysis. Children who used three or more devices besides TV/DVDs were more likely to have delayed bedtimes. Delayed bedtimes were associated with smartphone use, even with a 0.1-1 h/day screen time, and with prolonged screen time for tablets, portable games, and console games, but not for TV/DVDs. Gaming on smartphones and tablets was also associated with delayed bedtime. To ensure adequate sleep for young children, families must develop feasible measures to discourage children's use of multiple devices and prolonged device use, especially for games, and a social environment that supports such measures.

Multi-device trust transfer: Can trust be transferred among multiple devices?

Recent advances in automation technology have increased the opportunity for collaboration between humans and multiple autonomous systems such as robots and self-driving cars. In research on autonomous system collaboration, the trust users have in autonomous systems is an important topic. Previous research suggests that the trust built by observing a task can be transferred to other tasks. However, such research did not focus on trust in multiple different devices but in one device or several of the same devices. Thus, we do not know how trust changes in an environment involving the operation of multiple different devices such as a construction site. We investigated whether trust can be transferred among multiple different devices, and investigated the effect of two factors: the similarity among multiple devices and the agency attributed to each device, on trust transfer among multiple devices. We found that the trust a user has in a device can be transferred to other devices and that attributing different agencies to each device can clarify the distinction among devices, preventing trust from transferring.