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Ivan Simeonov

Publications and source records attributed to Ivan Simeonov.

2 recordsLinked to original sources

Multi-Sensor Edge Angle Detection for Performance Analysis in Ski Jumping

In ski jumping, performance during the gliding phase depends on achieving an aerodynamic posture that maximizes the lift-to-drag ratio. In the V-style technique, the ski edge angle is a key determinant. Reducing the edge angle flattens the skis, increases their effective surface area, and improves aerodynamic lift, ultimately contributing to longer flight distances. Ski edge angles are biomechanically constrained by the limited range of ankle inversion. Current sensing solutions widely quantify these angles using multi-system approaches that combine sensor signals through geometric relations. Such configurations require instrumentation on both the boot and the ski, altering mass distribution, affecting balance during flight, and increasing system complexity. To overcome these limitations, this work presents a wearable sensing system that measures both boot inclination and ski edge angle without modifying the ski surface. Two ultrasonic Time of Flight (ToF) sensors and an in-shoe Inertial Measurement Unit (IMU) are integrated into a single boot-mounted unit. Edge angles are estimated by combining ultrasonic distance measurements with IMU data through geometric reconstruction of the boot-ski configuration. Laboratory experiments demonstrate an angle resolution of 0.4500°, a Mean Absolute Error (MAE) of 0.2640°, and a coefficient of determination exceeding 99\% when compared with reference measurements, indicating strong linear agreement between the two modalities. The system achieves an end-to-end latency of 30.31 ms, enabling real-time feedback suitable for athlete training, while consuming 1.28 mW of power. With a total weight of only 18.6 g the proposed system enables unobtrusive measurement of ski edge angle and boot orientation.

eess.SP

An attraction-repulsion point process model for respiratory syncytial virus infections

How is the progression of a virus influenced by properties intrinsic to individual cells? We address this question by studying the susceptibility of cells infected with two strains of the human respiratory syncytial virus (RSV-A and RSV-B) in an in vitro experiment. Spatial patterns of infected cells give us insight into how local conditions influence susceptibility to the virus. We observe a complicated attraction and repulsion behavior, a tendency for infected cells to lump together or remain apart. We develop a new spatial point process model to describe this behavior. Inference on spatial point processes is difficult because the likelihood functions of these models contain intractable normalizing constants; we adapt an MCMC algorithm called double Metropolis-Hastings to overcome this computational challenge. Our methods are computationally efficient even for large point patterns consisting of over 10,000 points. We illustrate the application of our model and inferential approach to simulated data examples and fit our model to various RSV experiments. Because our model parameters are easy to interpret, we are able to draw meaningful scientific conclusions from the fitted models.

stat.ME