# Report of the 2017 IEEE Cyber Science and Technology Congress
## 3 Cyber Physical Computing and Systems  
Because twelve papers were accepted on this topic, the presentation of these papers were divided into two sessions during the congress. The six papers in the first session are all related to transportation, with five of them presenting research that is fundamental to intelligent transportation system [8; 9; 10; 11; 12], and the remaining one on the clustering of trajectory datasets [13]. In [8], [PERSON] et al. reported their work using machine learning to make trajectory predictions of vehicles. More accurate vehicle trajectory prediction could improve situational awareness, which is important for forward collision warning and cooperative adaptive cruise control. The authors proposed to use a two-layer neural network-based system to predict values of vehicle parameters including velocity, acceleration, and yaw rate in the first layer, and based on the output of the first layer, to predict the longitudinal and lateral trajectory points. In [9], [PERSON] and [PERSON] presented their research on the development of a simulation framework to perform vehicle safety analysis based on human behavior in emergency situations. They specifically studied the modeling of driver braking behavior upon receiving a warning from the collision warning system. In [10], [PERSON] et al. reported their work on the time-of-arrival estimation for Buses based on Global Positioning System (GPS) data. To overcome the low precision of bus-mounted GPS system and the lack of real-time traffic information, they proposed a novel GPS calibration method and a hybrid dynamic prediction model that takes into account traffic flow evaluation results and GPS position calibration for better time-of-arrival estimation. In [11], [PERSON] et al. presented their work on tracking vehicle information via wireless networks for better understanding of the traffic flow and for detecting hazardous situations. By using several public datasets, they studied the impact of different data sampling strategies, including periodic beaconing and error-dependent sampling, and combined with constant-speed and constant-acceleration estimation. In [12], [PERSON] et al. proposed a genetic algorithm for more efficient vehicle routing in the context of multimodel transportation logistics. They introduced adaptive crossover probability and mutation probability to enhance the global search performance. In [13], [PERSON] et al. presented their work on the clustering of large trajectory datasets. This is typically the first step in extracting knowledge from trajectory data. They experimented with two types of hash functions, namely, locality-sensitive and distance-based hash functions, to cluster trajectory data. They showed that the locality-sensitive hashes lead to higher accuracy, but not higher bucket balance. The six papers in the second session cover a variety of topics. We provide an overview of five of them here. Two papers are related to smart health with one on the coordination of health related data from multiple different sources [14], and the other on anomaly detection based on physiological data [15]. The remaining three papers are about high-performance computing [16], improving mobile user experience [17], and clustering for big data [18]. In [14], [PERSON] et al. studied the issue of coordinating data collection from multiple wearable devices for health monitoring purposes. They proposed two statistical methods to take into account the time discrepancy between different data sources and its distribution. They verified the accuracy of their models by a set of experiments, and demonstrated the usability of the proposed models with a case study using the adaptive frequency strategy. They showed that their models help improve the completeness of the data collected and minimize the redundancy in the data compared with the static frequency method. In [15], [PERSON] et al. proposed an anomaly detection algorithm on physiological data collected via wearable devices.