Compared with traditional methods that employ inertial sensors or wireless sensors, device-free approaches do not require that people carry devices, and they are considered a useful technique for indoor navigation and posture recognition. However, few existing methods can detect the trajectory and movements of humans at the same time. In this study, we propose a scheme called PADAR for addressing these two problems simultaneously by using passive radio frequency identification (RFID) tags but without attaching them to the human body. The idea is based on the principle of radio tomographic imaging, where the variance in a tag’s backscattered radio frequency signal strength is influenced by human movement. We integrated a commodity off-the-shelf RFID reader with a two-dimensional phased array antenna and a matrix of passive tags to evaluate the performance of our scheme. We conducted experiments in a simulated indoor environment. The experimental results showed that PADAR achieved an accuracy of over 70%.

In RFID systems, how to detect the position precisely is an important and challenging research topic. In this paper, we propose a range-free 2D tag localization method based on phased array antenna, called PATL. This method takes advantage of the adjustable radiation angle of the phased array antenna to scan the surveillance region in turns. By using the statistics of the tags’ number in different antenna beam directions, a weighting algorithm is used to calculate the position of the tag. This method can be applied to real-time location of multiple targets without usage of any reference tags or additional readers. Additionally, we present an optimized weighting method based on RSSI to increase the locating accuracy. We use a Commercial Off-the-Shelf (COTS) UHF RFID reader which is integrated with a phased array antenna to evaluate our method. The experiment results from an indoor office environment demonstrate the average distance error of PATL is about 21 cm and the optimized approach achieves an accuracy of 13 cm. This novel 2D localization scheme is a simple, yet promising, solution that is especially applicable to the smart shelf visualized management in storage or retail area.

RFID is one of the indispensable technologies employed to implement the Internet of Things, where it facilitates the identification and awareness of objects in our daily lives by integrating backscatter tags. The interference synthetic aperture radar (InSAR)-based localization scheme provides high precision in three-dimensional (3D) conditions, although it still has some practical problems such as its strict sampling operation and it is also highly time-consuming. In this study, we developed E3DinSAR as an optimized InSAR-based 3D localization approach for pinpointing tags rapidly and easily with high accuracy. E3DinSAR does not use any other infrastructure or reference tags, and only one movable reader with one antenna is sufficient to implement the localization method, where it measures the phase value of the tag at different positions throughout its movement in order to create multiple holographic images. In contrast to the previously proposed InSAR-based technique, we extend the sampling trajectory of the reader to an arbitrary curve by restricting it to several approximate linear apertures, which allows more flexible operations and increase the accuracy. We also employ a rapid computing method to calculate the results for the holographic images by estimating the direction of arrival for the tag. Experiments were performed where E3DinSAR was implemented with commercial off-the-shelf (COTS) RFID products and our systematic evaluation showed that E3DinSAR achieved a mean locating accuracy of 18.4 cm in 3D space.

This is a task of computational chemistry and pharmaceutical development, with rapidly growing relevance for consumer healthcare electronics: deducing three-dimensional molecular structures from two-dimensional diagrams. In this paper, we introduce a new generative model, DOGMH, specifically tailored for applications on consumer electronic devices, which considerably increases the accuracy in this process. DOGMH borrows from the diffusion mechanisms of classical nonequilibrium thermodynamics and treats atoms as individual particles, excelling in the inversion of the diffusion process-turning noise into organized molecular structures. Our approach leverages a specially tailored bilevel optimization technique that ensures roto-translational invariance and allows for efficient processing on resource-constrained consumer devices. We show that DOGMH outperforms existing methods in terms of both conformational synthesis and distance distribution accuracy. DOGMH also provides the ability to create AI-enabled interactive 3D visualizations of molecular structures for consumer health education. Our work paves the way for advanced molecular informatics capabilities in consumer-grade devices and will potentially revolutionize personal healthcare management through AI-enhanced insights. By bridging the gap between complex molecular modeling and consumer electronics, DOGMH will open up a new frontier in intelligent, molecularly aware health devices accessible to the general public.

With the increasing volume of healthcare data, automated International Classification of Diseases (ICD) has become increasingly relevant and is frequently regarded as a medical multi-label prediction problem. Current methods struggle to accurately classify medical diagnosis texts that represent deep and sparse categories. Unlike these works that model the label with code hierarchy or description for label prediction, we argue that the label generation with structural information can provide more comprehensive knowledge based on the observation that label synonyms and parent-child relationships in vary from their context in clinical contexts. In this study, we introduce \tool, a heterogeneous graph model with improved attention for automated ICD coding. Notably, our approach represents the model to consider this task as a labelled graph generation problem. Our enhanced attention mechanism boosts the model's capacity to learn from multi-relational heterogeneous graph representations. Additionally, we propose a discriminator for labelled graphs (LG) that computes the reward for each ICD code in the labelled graph generator. Our experimental findings demonstrate that our proposed model significantly outperforms all existing strong baseline methods and attains the best performance on three benchmark datasets.

Remote sensing image classification is pivotal in applications ranging from environmental monitoring to urban planning. However, the scarcity of labeled data in target domains often impedes the generalization of models trained on source domains. To address this challenge, we propose Semantic-Aware Contrastive Adaptation (SACA), a novel framework that leverages contrastive learning to construct a discriminative and class-balanced embedding space. SACA emphasizes semantic details by contrasting samples with category centroids and other samples in a distribution-aware manner. It first identifies distinctive features using category centroids as semantic anchors, then expands the sample scope by incorporating statistical information from the labeled source domain. This approach enables the embedding space to better reflect the true distribution of each semantic class. Our rigorous theoretical analysis reveals that SACA implicitly considers an infinite number of similar and dissimilar sample pairs, leading to a tighter upper bound on the contrastive loss compared to existing methods. This not only improves computational efficiency but also enhances the robustness of learned representations. To demonstrate SACA’s effectiveness, we conduct extensive experiments on three challenging remote sensing datasets: Hyperion, National Center for Airborne Laser Mapping, andWorldview-2. The results show that SACA significantly boosts the performance of self-training methods, outperforming state-of-the-art approaches by a large margin. In-depth ablation studies provide insights into the contribution of each SACA component. SACA represents a significant step towards bridging the domain gap in remote sensing image classification. By leveraging semantic-aware contrastive learning, it enables models to capture consistent semantic concepts across domains, enhancing transferability and robustness. This work opens new avenues for research in domain adaptation and has the potential to benefit a wide range of real-world applications, from precision agriculture to disaster response.