We experimentally demonstrated polarization-mode dispersion (PMD) compensation by distributing polarizers with only 1 degree of freedom (DOF) along the transmission line. The average power penalty was measured to be 0.4 dB by inserting four compensators, where average differential group delay was 47% of bit period.

We performed 10 Gbit/s optical soliton transmission experiment over 2,000 km with bit error rate of < 10-9 in a comb-like dispersion profiled fiber (CDPF) loop of 80 km amplifier spacing which corresponds to 1.8 times of dispersion distance. By reducing the average dispersion of the CDPF, error free distance of 3,000 km was obtained.

With the acceptance of social sharing, public bike sharing services have become popular worldwide. One of the most important tasks in operating a bike sharing system is managing the bike supply at each station to avoid either running out of bicycles or docks to park them. This requires the system operator to redistribute bicycles from overcrowded stations to under-supplied ones. Trip demand prediction plays a crucial role in improving redistribution strategies. Predicting trip demand is a highly challenging problem because it is influenced by multiple levels of factors, both environmental and individual, e.g., weather and user characteristics. Although several existing studies successfully address either of them in isolation, no framework exists that can consider all factors simultaneously. This paper starts by analyzing trip data from real-world bike-sharing systems. The analysis reveals the interplay of the multiple levels of the factors. Based on the analysis results, we develop a novel form of the point process; it jointly incorporates multiple levels of factors to predict trip demand, i.e., predicting the pick-up and drop-off levels in the future and when over-demand is likely to occur. Our extensive experiments on real-world bike sharing systems demonstrate the superiority of our trip demand prediction method over five existing methods.

Front video and sensor data captured by vehicle-mounted event recorders are used for not only traffic accident evidence but also safe-driving education as near-miss traffic incident data. However, most event recorder (ER) data shows only regular driving events. To utilize near-miss data for safe-driving education, we need to be able to easily and rapidly locate the appropriate data from large amounts of ER data through labels attached to the scenes/events of interest. This paper proposes a method that can automatically identify near-misses with objects such as pedestrians and bicycles by processing the ER data. The proposed method extracts two deep feature representations that consider car status and the environment surrounding the car. The first feature representation is generated by considering the temporal transitions of car status. The second one can extract the positional relationship between the car and surrounding objects by processing object detection results. Experiments on actual ER data demonstrate that the proposed method can accurately identify and tag near-miss events.

By sacrificing approximately ten percent of the transmission speed, ultra-high speed optical time division multiplexed network can be fully operatable by the use of currently available electrical switches. The network utilizes dispersion managed quasi-solitons and transmits TDM packet which comprises of ATM cells that are introduced from a gateway through bit compression to match to the ultra-high speed traffics. The network can provide flexible bandwidth and bit on demand at burst rate of the maximum LAN speed.