Improved inapproximability of lattice and coding problems with preprocessing

We show that the closest vector problem with preprocessing (CVPP) is NP-hard to approximate to within /spl radic/3-/spl epsi/ for any /spl epsi/>0. In addition, we show that the nearest codeword problem with preprocessing (NCPP) is NP-hard to approximate to within 3-/spl epsi/. These results improve previous results of Feige and Micciancio. We also present the first inapproximability result for the relatively nearest codeword problem with preprocessing (RNCP). Finally, we describe an n-approximation algorithm to CVPP.     

In-situ cleaning and passivation of oxidized Cu surfaces by alkanethiols and its application to wire bonding

The treatment of oxidized Cu surfaces using an alkanethiol as a reducing agent has been investigated. Exposure to a dilute solution of 1-decanethiol resulted in the complete removal and/or conversion of CuO and subsequent formation of a passivating thiolate film, a so-called self-assembled monolayer (SAM), on the underlying Cu/Cu₂O surface as evidenced by x-ray photoelectron spectroscopy (XPS) analysis. Morphological changes, monitored by scanning electron microscopy (SEM) and atomic force microscopy (AFM), revealed transformation of the rough, porous CuO layer into a comparatively smooth Cu/Cu₂O surface. Experiments performed on integrated circuit back-end-of-line (BEOL) die structures, comprising Cu/SiO₂ bond pads used as substrates for Cu wire bonding, demonstrate the potential application of a thiol-based in-situ cleaning-passivation procedure in microelectronics.     

Electroluminescence properties of a whispering-gallery-mode laser with coupled disk cavities

Whispering-gallery-mode (WGM) lasers (emission wavelength λ = 2.1–2.4 μm) with coupled disk cavities connected by a bridge are developed on the basis of a GaInAsSb/GaAlAsSb quantum-well nano-heterostructure and their emission spectra and optical far-field patterns are studied. Almost single-mode lasing in one spatial mode is observed in a wide range of supply currents. The emission wavelength grows with increasing current due to heating of the laser, and this phenomenon can be used in diode-laser spectroscopy. An assumption is made that the flux of generated emission can pass in lasers of this kind from one cavity to the other and back along the cavity-connecting bridge. It is found that strong narrowing of the laser emission pattern occurs with increasing current near the plane separating the coupled cavities.     

Asymptotic distribution of the number of isolated nodes in wireless ad hoc networks with Bernoulli nodes

Nodes in wireless ad hoc networks may become inactive or unavailable due to, for example, internal breakdown or being in the sleeping state. The inactive nodes cannot take part in routing/relaying, and thus may affect the connectivity. A wireless ad hoc network containing inactive nodes is then said to be connected, if each inactive node is adjacent to at least one active node and all active nodes form a connected network. This paper is the first installment of our probabilistic study of the connectivity of wireless ad hoc networks containing inactive nodes. We assume that the wireless ad hoc network consists of n nodes which are distributed independently and uniformly in a unit-area disk, and are active (or available) independently with probability p for some constant 0

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In-network performance of handheld mobile terminals

This paper considers a realistic evaluation of the power mobile handsets are able to transmit and receive. It has been suggested to use the so-called total radiated power (TRP) and the total isotropic sensitivity (TIS) for the uplink and downlink, respectively, which may be seen as special cases of the general mean-effective-gain (MEG) measure. These measures are computed from the spherical radiation pattern of the handset and the different measures are obtained by using different models of the mobile propagation environment. In this paper, the results obtained via the spherical radiation patterns are compared with the equivalent performance obtained in a live Global System for Mobile Communications (GSM) network using data from the Abis network interface. This method does not require altering of the handsets and the testing uses normal calls in the network. The investigation is based on measurements with four different commercially available handsets carried out in two different indoor environments and involving 22 test users. In addition, a series of measurements were also made with a phantom simulating the handset user, allowing a test of how well the phantom represents the average user.     

Uncertainty evaluation of an in vivo near-field exposure setup for testing biological effects of cellular phones

In designing an in vivo near-field exposure setup for testing biological effects of cellular phones, one generally uses a small still animal because a plastic holder is used to restrain it. One also takes no account of the exposure box with radio wave absorbers as well as the plastic holder. In this paper, for the in vivo exposure setup developed in the National Institute of Information and Communications Technology (NICT), which was used for testing the promoting effect of 1.439- and 1.95-GHz digital cellular phones on rat brain carcinogenesis, we investigated the effects of the above-mentioned factors on the dosimetry design using the finite-difference time-domain (FDTD) method in conjunction with an anatomical rat model. As a result, we found that the specific absorption rate (SAR) averaged in the brain was 18% higher at maximum than the previously designed level due to the existence of the exposure box and the plastic holder and that the variation due to the rotation of the rat's head inside the plastic holder was within 10%. The backward movement of the rat along the plastic holder was more serious, which yielded a decrease of nearly 20% for the average SAR in the brain.     

Scheduling with interference decoding: Complexity and algorithms

In this article we study the problem of scheduling wireless links in the physical interference model with interference decoding capability. We analyze two models with different decoding strategies that explore the fact that interfering signals should not be treated as random noise, but as well-structured signals. The first model makes use of successive interference cancelation, which allows the strongest signal to be iteratively decoded and subtracted from a collision, thus enabling the decoding of weaker simultaneous signals. The second model explores the fact that routers are able to forward the interfered signal of a pair of nodes that wish to exchange a message and these nodes are able to decode the collided messages by subtracting their own contribution from the interfered signal. We prove that the scheduling problem remains NP-complete in both models. Moreover, we propose a polynomial-time scheduling algorithm that uses successive interference cancelation to compute short schedules for network topologies formed by nodes arbitrarily distributed in the Euclidean plane. We prove that the proposed algorithm is correct in the physical interference model and provide simulation results demonstrating the performance of the algorithm in different network topologies. We compare the results to solutions without successive interference cancelation and observe that considerable throughput gains are obtained in certain scenarios.     

Accelerating Machine-Learning Algorithms on FPGAs using Pattern-Based Decomposition

Machine-learning algorithms are employed in a wide variety of applications to extract useful information from data sets, and many are known to suffer from super-linear increases in computational time with increasing data size and number of signals being processed (data dimension). Certain principal machine-learning algorithms are commonly found embedded in larger detection, estimation, or classification operations. Three such principal algorithms are the Parzen window-based, non-parametric estimation of Probability Density Functions (PDFs), K-means clustering and correlation. Because they form an integral part of numerous machine-learning applications, fast and efficient execution of these algorithms is extremely desirable. FPGA-based reconfigurable computing (RC) has been successfully used to accelerate computationally intensive problems in a wide variety of scientific domains to achieve speedup over traditional software implementations. However, this potential benefit is quite often not fully realized because creating efficient FPGA designs is generally carried out in a laborious, case-specific manner requiring a great amount of redundant time and effort. In this paper, an approach using pattern-based decomposition for algorithm acceleration on FPGAs is proposed that offers significant increases in productivity via design reusability. Using this approach, we design, analyze, and implement a multi-dimensional PDF estimation algorithm using Gaussian kernels on FPGAs. First, the algorithm’s amenability to a hardware paradigm and expected speedups are predicted. After implementation, actual speedup and performance metrics are compared to the predictions, showing speedup on the order of 20× over a 3.2 GHz processor. Multi-core architectures are developed to further improve performance by scaling the design. Portability of the hardware design across multiple FPGA platforms is also analyzed. After implementing the PDF algorithm, the value of pattern-based decomposition to support reuse is demonstrated by rapid development of the K-means and correlation algorithms.