The paper is aimed at an investigation of the features of a tubular-linear synchronous quasi-Halbach machine (T-LSM) where the radially-magnetized PMs are substituted by four equal segments with parallel magnetization. Such a substitution is done in an attempt to improve the machine cost-effectiveness which makes it a viable candidate to equip free-piston engine-based series


The dataset consists of tracking data of over 23,000 vehicles travelling though five different roundabouts in Sydney, Australia. This data was collected by a vehicle outfitted with a ibeo.HAD Feature Fusion detection and tracking system. This system uses 6 ibeo LUX 4 beam, 25 Hz Lidar scanners to identify road users at a range of up to 200m, and has an on-board computer for classification and tracking, in real time.


see full paper publication:

A. Zyner, S. Worrall, and E. Nebot, “ACFR Five Roundabouts Dataset: NaturalisticDriving at Unsignalised Intersections,” IEEE Intelligent Transportation Systems Magazine, Early access:


The paper deals with fractional-slot permanent magnet synchronous machines

(FSPMSMs) equipped with phases made up of one coil parallel branches, with emphasis on their

faculty to reject the harmonic currents circulating in the loops yielded by the phase parallel branches.

These exhibit attractive potentialities, especially their enhanced open-circuit fault tolerance capability.

Furthermore, these topologies are suitably-adapted for low-voltage power supply that makes them


Car-hailing order data are a rich source to study the human mobility patterns, which could contribute to transporation planning and policy-making. In general, a orginal car-hailing order record includes information such as origin, destination, pick-up time, drop-off time, and travel distance. Beijing car-hailing order dataset stored the discretized order data at a traffic analysis zone(TAZ) scale, including the dataset for training and test. 



we extract human trips from Call Records Detail data. Combining traffic analysis zone dataset, we map each trip record to the zones with the same origin zones and destination zones. After  this, we can obtain this dataset. This dataset stores the hourly number of departure and arrival trips in each traffic analysis zone.


These datasets include the results from the comparison of different traffic-free path planning strategies presented in the work entitled "A primitive comparison for traffic-free path planning",  Antonio Artuñedo, Jorge Godoy, Jorge Villagra.


The dataset files are named as follows: p'x'.csv, where 'x' is the percentile used to filter data included in the file. Each file contains data of both considered scenarios.


The content of the datasets is organized in set a columns that represent the concrete test cases setup included in each row.

The columns order is:

  1. ID: Test case identifier.
  2. ID_num: Test case number.
  3. Scenario: Scenario number.
  4. RP select method: Referenc points selection method
  5. Primitive: Primitive used in the test case
  6. RP opt. method: Reference points optimization method
  7. RP opt. algorithm: Reference points optimization algorithm
  8. RP cost fcn.: Cost function used in reference points optimization
  9. SP opt. method: Seeding points optimization method
  10. SP opt. algorithm: Seeding points optimization algorithm
  11. SP cost fcn.: Cost function used in seeding points optimization
  12. Init. heading: Initial heading setting
  13. Final heading: Final heading setting
  14. Init. curv.: Initial curvature setting.
  15. Final curv.: Final curvature setting.
  16. K_t (exc. time): time KPI 
  17. K_kmax: Maximum curvature KPI
  18. K_k0: KPI related to curvature along the path
  19. K_k1: KPI related to the first derivative of curvature along the path
  20. K_k2: KPI related to the second derivative of curvature along the path
  21. K_cl: KPI related to centreline offset.

For further details please find the paper in this link:, or contact the corresponding author at


Recognition of human activities is one of the most promising research areas in artificial intelligence. This has come along with the technological advancement in sensing technologies as well as the high demand for applications that are mobile, context-aware, and real-time. We have used a smart watch (Apple iWatch) to collect sensory data for 14 ADL activities (Activities of Daily Living).