Teruyuki Baba

Multi-root Share of Single-Root I/O Virtualization (SR-IOV) Compliant PCI Express Device

We have achieved sharing a single-root I/O virtualization (SR-IOV) compliant PCI Express (PCIe) I/O device among multiple computers. A device share not only inside a single computer among virtual machines, but also among multiple computers attracts a great interest because it provides efficient utilization of computer resources. Because PCIe is originally a single-root system, realizing multi-root I/O virtualization is much more difficult than SR-IOV. We allocate virtual instances called VF of an SR-IOV-compliant I/O device to an individual computer by virtualizing IOV configuration and translating memory address of VF to that of the allocated computer. With the FPGA implementation, we have achieved sharing a commercially-available network interface card among three computers without modification in OS/driver and device itself. In addition, the performance reaches 99% of the device in the best case whereas the implementation is in the early stage, indicating this method provides not only MR sharing but high performance at the same time.

Algorithm for detecting significant locations from raw GPS data

We present a fast algorithm for probabilistically extracting significant locations from raw GPS data based on data point density. Extracting significant locations from raw GPS data is the first essential step of algorithms designed for location-aware applications. Assuming that a location is significant if users spend a certain time around that area, most current algorithms compare spatial/temporal variables, such as stay duration and a roaming diameter, with given fixed thresholds to extract significant locations. However, the appropriate threshold values are not clearly known in priori and algorithms with fixed thresholds are inherently error-prone, especially under high noise levels. Moreover, for N data points, they are generally O(N2) algorithms since distance computation is required. We developed a fast algorithm for selective data point sampling around significant locations based on density information by constructing random histograms using locality sensitive hashing. Evaluations show competitive performance in detecting significant locations even under high noise levels.

Frequency-shifted multiwavelength FBG laser sensor

We propose and demonstrate a novel frequency-shifted FBG (fiber Bragg grating) laser sensor system. It is composed of an EDFA (erbium-doped fiber amplifier) broadband light source, 3-FBG array, single mode fiber, circulator, acousto-optic modulator (AOM) and an output 3dB fiber coupler. The frequency-shifted feedback by the AOM allows the laser to operate at multiwavelength at room temperature. Compared to a conventional passive system, the optical signal power and SN ratio were enhanced by 20.1 dB, and 17.0 dB, respectively, and the linewidth was reduced to 1/5.3.

Frequency-Shifted Multiwavelength Fiber Bragg Grating Laser Sensor

We propose and demonstrate a novel active multiwavelength fiber Bragg grating (FBG) laser sensor system. Simultaneous multiwavelength oscillation by the frequency-shifted feedback technique markedly enhances output power and signal-to-noise ratio (SNR) compared with a conventional passive FBG sensor system. The principle of the simultaneous measurement of the dynamic strain signal from wavelength multiplexed FBGs is experimentally confirmed by employing an arrayed waveguide grating (AWG) as a wavelength demodulator.

Multiwavelength fiber lasers with tunable wavelength spacing

We propose and demonstrate multiwavelength fiber lasers with tunable wavelength spacing. A tunable spacing filter consists of polarizers and a PMF. By changing the stress position on the PMF, the spacing can be controlled.

Detecting Significant Locations from Raw GPS Data Using Random Space Partitioning

We present a fast algorithm for probabilistically extracting significant locations from raw GPS data based on data point density. Extracting significant locations from raw GPS data is the first essential step of algorithms designed for location-aware applications. Most current algorithms compare spatial/temporal variables with given fixed thresholds to extract significant locations. However, the appropriate threshold values are not clearly known in priori, and algorithms with fixed thresholds are inherently error-prone, especially under high noise levels. Moreover, they do not often scale in response to increase in system size since direct distance computation is required. We developed a fast algorithm for selective data point sampling around significant locations based on density information by constructing random histograms using locality-sensitive hashing. Theoretical analysis and evaluations show that significant locations are accurately detected with a loose parameter setting even under high noise levels.

Information Dissemination Network for Dynamic Target Location Pursuit

We study a network structure for efficient information dissemination that leverages location-based real-time applications operating in a distributed system. Gossip-based information dissemination is a promising method for a large-scale system to keep nodes in the network quickly updated with new information. The information dissemination efficiency depends strongly on the underlying network structure, so it is important to analyze how the network structure is optimized to achieve agile information dissemination, especially for time-sensitive applications, e.g., for dynamic pursuit of a moving target that requires frequent information updating. To this end, we construct a continuous-space network model extended from Kleinbergs small-world model so that it is applicable to location-based services. Analytical and simulation-based study shows that excellent information dissemination efficiency is achieved when the shortcuts overlaying a uniform cluster network are created nearly uniformly to nodes at all distance scales. We apply our model to the dynamic pursuit problem where a pursuer searches for a moving target. Monte-Carlo simulation shows that the network with well-structured shortcuts surpasses other types of networks in finding a moving target because the pursuer can steadily approach the target regardless of its location, and the network structure greatly influences the dynamic search ability in distributed systems.