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Traditional implementations of federated learning for preserving data privacy are unsuitable for longitudinal health data. To remedy this, we develop a federated enhanced fuzzy c-means clustering (FeFCM) algorithm that can identify groups of patients based on complex behavioral intervention responses. FeFCM calculates a global cluster model by incorporating data from multiple healthcare institutions without requiring patient observations to be shared. We evaluate FeFCM on simulated clusters as well as empirical data from four different dietary health studies in Massachusetts. Results find that FeFCM converges rapidly and achieves desirable clustering performance. As a result, FeFCM can promote pattern recognition in longitudinal health studies across hundreds of collaborating healthcare institutions while ensuring patient privacy. 

Digital technology has huge potentials in transforming clinical trial research. One common issue in digital clinical trials for long-term behavioral treatments is incomplete longitudinal data, as subjects’ behavior changes over time. In this paper, we aim to improve the fuzzy clustering accuracy and stability of digital clinical trials by intelligently searching for the optimal fuzzifier, which is the key to identify the optimal number of overlapped clusters for incomplete longitudinal data. Our findings showed that integrating optimal fuzzifier searching with cluster validation can streamline the clustering process, thus enabling the intelligent fuzzy clustering procedure. 

This study is motivated by the fact that localization in Vehicle-to-Vehicle communication becomes a more critical problem because both the terminals of the communication link are in motion. The positional awareness merely based on GPS or local sensors has an error margin of around 10 meters, which can worsen in uncertain real-time conditions such as road topology and highway traffic. The paper analyses the relation between beamforming and beam alignment for highly directive antennas. This is more challenging in the events of localization of transceivers. When the subsystem models presented in this paper are taken into consideration, the joint vehicle dynamics-beamforming approach will improve the SNR for a constant power gain. The vehicle dynamics model is designed to be more realistic considering the non-linear acceleration based on the throttle-brake jerks due to internal engine noises as well as external traffic conditions. The prediction subsystem highlights the flaws of the Kalman Filter for non-linear parameters and the need for an Unscented Kalman Filter. The beamforming strategies are supported by the requirements of localization and the hardware constraints on the antenna due to phase shifters and the number of elements to yield more realistic results. 

Vehicle to Vehicle (V2V) communication allows vehicles to wirelessly exchange information on the surrounding environment and enables cooperative perception. It helps prevent accidents, increase the safety of the passengers, and improve the traffic flow efficiency. However, these benefits can only come when the vehicles can communicate with each other in a fast and reliable manner. Therefore, we investigated two areas to improve the communication quality of V2V: First, using beamforming to increase the bandwidth of V2V communication by establishing accurate and stable collaborative beam connection between vehicles on the road; second, ensuring scalable transmission to decrease the amount of data to be transmitted, thus reduce the bandwidth requirements needed for collaborative perception of autonomous driving vehicles. Beamforming in V2V communication can be achieved by utilizing image-based and LIDAR’s 3D data-based vehicle detection and tracking. For vehicle detection and tracking simulation, we tested the Single Shot Multibox Detector deep learning-based object detection method that can achieve a mean Average Precision of 0.837 and the Kalman filter for tracking. For scalable transmission, we simulate the effect of varying pixel resolutions as well as different image compression techniques on the file size of data. Results show that without compression, the file size for only transmitting the bounding boxes containing detected object is up to 10 times less than the original file size. Similar results are also observed when the file is compressed by lossless and lossy compression to varying degrees. Based on these findings using existing databases, the impact of these compression methods and methods of effectively combining feature maps on the performance of object detection and tracking models will be further tested in the real-world autonomous driving system. 

The Intelligent Transportation System has become one of the most globally researched topics, with Connected and Autonomous Vehicles(CAV) at its core. The CAV applications can be improved by the study of vehicle platooning immune to realtime traffic and vehicular network losses. In this work, we explore the need to integrate the Network model and Platooning system model for highway environments. The proposed platoon model is designed to be adaptive in length, providing the node vehicles to merge and exit. This overcomes the assumption that all the platoon nodes should have a common source and destination. The challenges of the existing platoon model, such as relay selection, acceleration threshold, are addressed for highly modular platoon design. The presented algorithm for merge and exit events optimizes the trade-off between network parameters such as communication range and vehicle dynamic parameters such as velocity and acceleration threshold. It considers the network bounds like SINR and link stability and vehicle trajectory parameters like the duration of the vehicle in the platoon. This optimizes the traffic throughput while maintaining stability using the PID controller. The work tries to increase the vehicle inclusion time in the platoon while preserving the overall traffic throughput. 

With 5G networks on the rise, it becomes more and more important to grant researchers access to tools that allow for development and experimentation in the field of 5G transmission. Healthcare can benefit greatly from these developments. In this paper a real-time transmission technique is described and tested that, if implemented, allows wearable devices to transmit multiple streams of data on various frequencies. These tests will be used to explain how this presented platform works, what drawbacks and benefits exist with the proposed scheme, and how to further develop the solution of real-time transmission of sensitive data, such as substance-use data, at higher frequencies.