Linearity of the Faraday-rotation-type ac magnetic-field sensor with a ferrimagnetic or ferromagnetic rotator film

We analyze the linearity and modulation depth of ac magnetic-field sensors or current sensors, using a ferrimagnetic or ferromagnetic film as the Faraday rotator and employing the detection of only the zeroth-order optical diffraction component from the rotator. It is theoretically shown that for this class of sensor the condition of a constant modulation depth and that of a constant ratio error give an identical series of curves for the relationship between Faraday rotation angle Θ and polarizer/analyzer relative angle Φ. We give some numerical examples to demonstrate the usefulness of the result with reference to a rare-earth iron garnet film as the rotator.     

Superconductivity and spin correlation study by Fe3+ - ESR in (La1−xSrx)2Cu1−yFeyO4−z

Fe³⁺-ESR measurements are carried out for the samples of (La_1–xSr_x)₂Cu_1–yFe_yO_4–z. Peak-peak width H_pp of the signals decreases with falling temperature until minimum value and rises sharply with further decreasing temperature, which is approximated by H_pp = C₀ +C₁/T + BT. The H_pp behavior at high temperature and at low temperature can be analyzed by Korringa mechanism and slowing down of Fe₃₊ spin fluctuation, respectively. From the analysis of coefficient B's of Korringa terms, C₀ and g-shift, it is revealed that the magnetic interaction of Fe³⁺ with hole carriers and Ce²⁺ spins depends strongly on hole density.     

Slicing the metric space to provide quick indexing of complex data in the main memory

Searching in a dataset for elements that are similar to a given query element is a core problem in applications that manage complex data, and has been aided by metric access methods (MAMs). A growing number of applications require indices that must be built faster and repeatedly, also providing faster response for similarity queries. The increase in the main memory capacity and its lowering costs also motivate using memory-based MAMs. In this paper, we propose the Onion-tree, a new and robust dynamic memory-based MAM that slices the metric space into disjoint subspaces to provide quick indexing of complex data. It introduces three major characteristics: (i) a partitioning method that controls the number of disjoint subspaces generated at each node; (ii) a replacement technique that can change the leaf node pivots in insertion operations; and (iii) range and k-NN extended query algorithms to support the new partitioning method, including a new visit order of the subspaces in k-NN queries. Performance tests with both real-world and synthetic datasets showed that the Onion-tree is very compact. Comparisons of the Onion-tree with the MM-tree and a memory-based version of the Slim-tree showed that the Onion-tree was always faster to build the index. The experiments also showed that the Onion-tree significantly improved range and k-NN query processing performance and was the most efficient MAM, followed by the MM-tree, which in turn outperformed the Slim-tree in almost all the tests.     

Optimizing time histograms for non-Poissonian spike trains

The time histogram is a fundamental tool for representing the inhomogeneous density of event occurrences such as neuronal firings. The shape of a histogram critically depends on the size of the bins that partition the time axis. In most neurophysiological studies, however, researchers have arbitrarily selected the bin size when analyzing fluctuations in neuronal activity. A rigorous method for selecting the appropriate bin size was recently derived so that the mean integrated squared error between the time histogram and the unknown underlying rate is minimized (Shimazaki & Shinomoto, 2007 ). This derivation assumes that spikes are independently drawn from a given rate. However, in practice, biological neurons express non-Poissonian features in their firing patterns, such that the spike occurrence depends on the preceding spikes, which inevitably deteriorate the optimization. In this letter, we revise the method for selecting the bin size by considering the possible non-Poissonian features. Improvement in the goodness of fit of the time histogram is assessed and confirmed by numerically simulated non-Poissonian spike trains derived from the given fluctuating rate. For some experimental data, the revised algorithm transforms the shape of the time histogram from the Poissonian optimization method.     

Probabilistic nearest neighbor query processing on distributed uncertain data

A nearest neighbor (NN) query, which returns the most similar object to a user-specified query object, plays an important role in a wide range of applications and hence has received considerable attention. In many such applications, e.g., sensor data collection and location-based services, objects are inherently uncertain. Furthermore, due to the ever increasing generation of massive datasets, the importance of distributed databases, which deal with such data objects, has been growing. One emerging challenge is to efficiently process probabilistic NN queries over distributed uncertain databases. The straightforward approach, that each local site forwards its own database to the central server, is communication-expensive, so we have to minimize communication cost for the NN object retrieval. In this paper, we focus on two important queries, namely top-k probable NN queries and probabilistic star queries, and propose efficient algorithms to process them over distributed uncertain databases. Extensive experiments on both real and synthetic data have demonstrated that our algorithms significantly reduce communication cost.     

Temperature-controlled molecular dynamics study on velocity-dependent threshold behavior of dynamic nano-friction

Laws of dynamic nano-friction (i.e., continuous wearless friction) were searched for under steady spatial distributions of the local quasi-temperature, by molecular dynamics (MD) simulations. The temperature control of the non-conservative model was carried out by extending the isothermal MD method using the Nosé–Poincaré thermostat. The results suggested that the threshold phenomenon characterizes sliding-velocity dependence of the nano-frictional force between crystal lattices constituting a nano-electromechanical system (NEMS). This phenomenon was turned out to be a universal feature, whether heat transfer to the environment exists or not.     

Design and implementation of a vehicle interface protocol using an IEEE 1394 network

A wide variety of in-vehicle devices such as camera sensors, navigation systems, telematics and communication equipments have been incorporated into a vehicle to realize Intelligent Transport Systems (ITS) applications. Because an efficient standardized network is required, ITS Data Bus (IDB) has been discussed to carry high-speed multimedia data for audio, video and other real-time ITS applications. For connecting devices in a standardized manner, the IDB network has architecture with a gateway called vehicle interface which is located between automaker’s proprietary network and the standardized IDB network. IEEE 1394 (also known as iLink or FireWire), which can transport multimedia data for consumer electronics, is a good candidate for IDB network. In this paper, we analyze the issues for existing AV/C protocol (application layer protocol over IEEE 1394) to comprise the IDB network. In addition, we designed and implemented the vehicle interface protocol as a higher layer of IEEE 1394 to address the AV/C protocol issues for realizing the whole IDB network architecture.     

Preconditioned iterative solver on the coarsest level of a multi-grid method for high frequency time harmonic electromagnetic field analyses

A multi-grid method is one of the most powerful linear solvers for finite element electromagnetic field analysis. However, as the discretized model has recently been enlarged, a solution process for a linear system arising on the coarsest level tends to be problematic in a complete multi-grid solution process. Whereas a linear system on the coarsest level is generally solved by a direct solver, we solve it here by means of an iterative solver to reduce the memory requirements. Since a conventional preconditioning technique is not effective for such a linear system, we introduce preconditioning techniques based on Arnold, Falk, and Winther’s and on Hiptmair’s smoothers. Numerical tests show that the newly installed preconditioning technique greatly improves the convergence rate.     

A study on a quantum-inspired evolutionary algorithm based on pair swap

A quantum-inspired evolutionary algorithm (QEA) is proposed as a stochastic algorithm to perform combinatorial optimization problems. The QEA is evolutionary computation that uses quantum bits and superposition states in quantum computation. Although the QEA is a coarse-grained parallel algorithm, it involves many parameters that must be adjusted manually. This paper proposes a new method, named pair swap, which exchanges each best solution information between two individuals instead of migration in the QEA. Experimental results show that our proposed method is a simpler algorithm and can find a high quality solution in the 0-1 knapsack problem. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007