In order to optimize the performance of thermoelectric devices, we have fabricated and characterized the micrometer-scaled Si thermopile preserving the phonon-drag effect, where the Si thermopile consists of p- and n-type Si wire pairs. The measured Seebeck coefficient of the p-type Si wire was found to be higher than the theoretical value calculated only from the carrier transport, which indicates the contribution of phonon-drag part. Moreover, the measured Seebeck coefficient increased with increasing the width of Si wire. This fact is considered due to dependency of phonon-drag part on the wire width originating from the reduction of phonon-boundary scattering. These contributions were observed also in measured output voltage of Si-wire thermopile. Hence, the output voltage of Si-wire thermopile is expected can be enhanced by utilizing the phonon-drag effect in Si wire by optimizing its size and carrier concentration.

We propose an analytical model for IEEE 802.11 wireless local area networks (WLANs). The analytical model uses macroscopic descriptions of the distributed coordination function (DCF): the backoff process is described by a few macroscopic states (medium-idle, transmission, and medium-busy), which obviates the need to track the specific backoff counter/backoff stages. We further assume that the transitions between the macroscopic states can be characterized as a continuous-time Markov chain under the assumption that state persistent times are exponentially distributed. This macroscopic description of DCF allows us to utilize a two-dimensional continuous-time Markov chain for simplifying DCF performance analysis and queueing processes. By comparison with simulation results, we show that the proposed model accurately estimates the throughput performance and average queue length under light, heavy, or asymmetric traffic.

One of the advantages of the kernel methods is that they can deal with various kinds of objects, not necessarily vectorial data with a fixed number of attributes. In this paper, we develop kernels for time series data using dynamic time warping (DTW) distances. Since DTW distances are pseudo distances that do not satisfy the triangle inequality, a kernel matrix based on them is not positive semidefinite, in general. We use semidefinite programming (SDP) to guarantee the positive definiteness of a kernel matrix. We present neighborhood preserving embedding (NPE), an SDP formulation to obtain a kernel matrix that best preserves the local geometry of time series data. We also present an out-of-sample extension (OSE) for NPE. We use two applications, time series classification and time series embedding for similarity search, to validate our approach.