Video conferencing and, more generally, real-time communications (RTC) applications have become indispensable across domains such as remote work or telemedicine, with new platforms competing for performance and market share. Much of the research to date has focused on understanding how these systems operate and perform under varying network conditions. However, the scale of the ecosystem for RTC applications beyond a handful of key players remains largely understudied and unquantified. Frameworks such as WebRTC have democratized the ability to easily extend an application to support RTC capabilities, resulting in a diverse ecosystem beyond the most commonly studied applications.

The determination of step length, an important gait parameter, has been a challenging task. Although unobtrusive sensors (inertial measurement units) have been developed recently, they cannot facilitate the automatic estimation of step length. In this article, we use a model-based technique to determine the step length using the Unscented Kalman Filter with angular velocity from a gyroscope inside the thigh pocket. We then propose a novel covariance estimation algorithm based on a screening technique that performs a search for the optimal Process Noise Covariance matrix. Upon implementing the Unscented Kalman Filter, the step length is found using the horizontal position of the foot relative to the hip using a patient-independent robust peak detection algorithm. This research article paves the way for algorithms that are computationally much faster than black box methods, with more scope for the development of better algorithms for covariance estimation using the one proposed in this article as a foundation.

As software-defined networking deployments mature, operators need to manage and compose multiple resource-management applications, such as traffic engineering and service chaining. Today such applications' resource management algorithms run separately and composition approaches are output-driven, e.g., running each application on a statically provisioned slice of the network and then combining the flow rules output for each slice. Such approaches result in inefficient resource utilization and unfairness. Instead, we argue for intent-driven composition, where a unified resource optimization formulation is composed from applications' high-level intents and the solution to this problem formulation is realized in the network. We design Chopin1, an intent-driven framework for composing SDN resource-management applications. Chopin's design addresses key robustness challenges with regard to efficiency and fairness that arise in realizing such an intent-driven approach. We have integrated Chopin with the ONOS controller and show that it substantially improves efficiency and fairness over existing composition approaches.