Wireless Electromagnetic Tracking (WEMT) enables non-line-of-sight (NLoS) pose estimation in robotics but faces accuracy limitations from restricted operational range and environmental interference. This paper proposes a WEMT-inertial fusion system enhanced by a learning-based Interacting Multiple Model (IMMNN) to address these challenges. The framework integrates a multi-transmitter array with a WEMT-IMU fusion tracker, leveraging IMMNN to mitigate performance degradation caused by nonlinear spatial noise and motion uncertainty in dynamic, array-based environments. IMMNN employs a graph attention network to dynamically model spatial correlations among array units, adaptively optimizing state transition probabilities across motion models. A gated recurrent framework further enhances robustness by analyzing residual sequences to suppress transient noise and outliers. Experimental results demonstrate that the proposed system achieves a root-mean-square error (RMSE) of 30.4 mm over an expanded 1.9×1.9 m2operational area. The graph attention mechanism enables adaptive spatial noise suppression and ensures stable tracking under rapid motion and electromagnetic disturbances. By synergizing model-driven filtering with data-driven learning, IMMNN effectively improves accuracy and robustness, advancing high-precision WEMT solutions for complex robotic applications.

BACKGROUND: Monitoring spine kinematics is crucial for applications like disease evaluation and ergonomics analysis. However, the small scale of vertebrae and the number of degrees of freedom present significant challenges for noninvasive and convenient spine kinematics estimation. METHODS: This study developed a dynamic optimization framework for wearable spine motion tracking at the intervertebral joint level by integrating smartphone videos and Inertia Measurement Units (IMUs) with dynamic constraints from a thoracolumbar spine model. Validation involved motion data from 10 healthy males performing static standing, dynamic upright trunk rotations, and gait. This data included rotations of ten IMUs on vertebrae and virtual landmarks from three smartphone videos preprocessed by OpenCap, an application leveraging computer vision for pose estimation. The kinematic measures derived from the optimized solution were compared against simultaneously collected infrared optical marker-based measurements and in vivo literature data. Solutions only based on IMUs or videos were also compared for accuracy evaluation. RESULTS: The proposed optimization approach closely matched the reference data in the intervertebral or segmental rotation range, demonstrating minimal angular differences across all motions and the highest correlation in 3D rotations (maximal Pearson and intraclass correlation coefficients of 0.92 and 0.94, respectively). Time-series changes of joint angles also aligned well with the optical-marker reference. CONCLUSION: Dynamic optimization of the spine simulation that integrates IMUs and computer vision outperforms the single-modality method. SIGNIFICANCE: This markerless 3D spine motion capture method holds potential for spinal health assessment in large cohorts in real-world settings without dedicated laboratories.

In this paper, we propose a concept for AI and machine vision for the observation and assessment of induced pluripotent stem (iPS) cell culture based on a differentiation of the two major monitoring aspects of cell expansion-cell health and cell density. The proposed concept is part of a more holistic approach to automating cell cultures in smaller laboratories. The concept embedded the broader automation of basic cell culture procedures using iPS cells and expansion protocols, as well as implementing mechanisms and a multipurpose gripper for discarding old and refilling fresh culture media and handling individual vessels.Based on phase contrast microscopy imaging our approach involves utilizing machine vision algorithms to calculate cell density and decide whether to split the culture or not. We implemented an image algorithm to achieve this and used threshold values derived from the working experiences with iPS cells. We concluded that our approach’s achieved accuracy is sufficient to automate this task of cell assessment. The process is modular so that the proposed algorithms can be implemented with manually taken images or fully automated imaging.A trained image-based AI can be used to obtain a decision if the shown phase contrast image only shows healthy cells. If there are any not trained formations, the cells will be discarded. Future work includes completing the set of images for training the AI to recognize any deviation i.e. contamination, premature differentiation, or cell death for the health aspect of cell expansion monitoring. We aim to streamline and automate basic iPS cell culture procedures and make them more accessible to smaller laboratories. The proposed algorithms for cell health and available growing space assessment seem promising and may help with decision-making and ensure the health and growth of the iPS cells.

The user interface (UI) of web applications is usually the entry point of web attacks against enterprises and organizations. Finding the UI elements utilized by the intruders is of great importance both for attack interception and web application fixing. Current attack investigation methods targeting web UI either provide rough analysis results or have poor performance in high concurrency scenarios, which leads to heavy manual analysis work. In this paper, we propose TeSec, an accurate attack investigation method for web UI applications. TeSec makes use of two kinds of correlations. The first one, built from annotated audit log partitioned by PID/TID and delimiter-logs, captures the correspondence between audit log entries and web requests. The second one, modeled by an Aho-Corasick automaton built during system testing period, captures the correspondence between requests and the UI elements/events. Leveraging these two correlations, TeSec can accurately and automatically locate the UI elements/events (i.e., the root cause of the alarm) from an alarm, even in high concurrency scenarios. Furthermore, TeSec only needs to be deployed in the server and does not need to collect logs from the client-side browsers. We evaluate TeSec on 12 web applications. The experimental results show that the matching accuracy between UI events/elements and the alarm is above 99.6%. And security analysts only need to check no more than 2 UI elements on average for each individual forensics analysis. The maximum overhead of average response time and audit log space overhead are low (4.3% and 4.6% respectively).

Conventional electric motors usually provide rotary or linear motions to actuate mechatronic systems. In order to accomplish complex tasks, many research studies have been conducted to develop intelligent actuation mechanisms capable of converting simple input motions into complex target motions. In this paper, we propose a novel type of curvilinear actuator to achieve non-linear output motions, which is based on a scissor mechanism with off-centered link position in each unit. A kinematic modeling framework is also presented to compute the motion trajectories of the proposed scissor-mechanism-based curvilinear actuators (SMCA). Simulation results have demonstrated that, by changing the link position of each scissor unit, the created SMCA can achieve different types of output curves and poses.