We propose an optical fiber connection navigation system that uses visible light communication for an integrated distribution module in a central office. The system realizes an accurate database, requires less skilled work to operate and eliminates human error. This system can achieve a working time reduction of up to 88.0% compared with the conventional work without human error for the connection/removal of optical fiber cords, and is economical as regards installation and operation.

The subset sum problem, which is often called as the knapsack problem, is known as an NP-hard problem, and there are several cryptosystems based on the problem. Assuming an oracle for shortest vector problem of lattice, the low-density attack algorithm by Lagarias and Odlyzko and its variants solve the subset sum problem efficiently, when the “density” of the given problem is smaller than some threshold. When we define the density in the context of knapsack-type cryptosystems, weights are usually assumed to be chosen uniformly at random from the same interval. In this paper, we focus on general subset sum problems, where this assumption may not hold. We assume that weights are chosen from different intervals, and make analysis of the effect on the success probability of above algorithms both theoretically and experimentally. Possible application of our result in the context of knapsack cryptosystems is the security analysis when we reduce the data size of public keys.

In 2008, the authors and Makwakwa demonstrated a close link between variable-length T-codes and cyclic equivalence classes, which introduces a limit on the the number of codewords of a particular length that a T-code can have. This paper presents a collection of new results on the codeword length distribution of T-codes based on this link. In particular, the average and average weighted codeword lengths are investigated for systematic T-codes. Several results are presented on subsets of T-code codewords for which the aforementioned limit is reached, and asymptotic expressions are derived for the variance and the coefficient of variation of codeword length distributions.

We developed a flexible-logic-gate single-electron device (SED) in which logic functions can be selected by changing the voltage applied to the control gate. It consists of an array of nanodots with multiple inputs and multiple outputs. Since the gate electrodes couple capacitively to the many dots underneath, complicated characteristics depending on the combination of the gate voltages yield a selectable logic gate when some of the gate electrodes are used as control gates. One of the important issues is how to design the arrangement of nanodots and gate electrodes. In this study, we fabricated a Si nanodot array with two simple input gates and two output terminals, in which each gate was coupled to half of the nanodot array. Even though the device had a very simple input-gate arrangement and just one control gate, we could create a half-adder function through the use of current maps as functions of the input gate voltages. We found that the nanodots evenly coupled capacitively to both input gates played an important role in getting a basic set of logic functions. Moreover, these results guarantee that a more complicated input-gate structure, in which each gate evenly couples many nanodots, will yield more complicated functions.

In this paper, we analyze current-voltage characteristics of InSb/AlInSb triple-barrier resonant tunneling diodes (TBRTDs) with spin-splitting under zero magnetic fields. The InSb has very small effective mass, thus we can obtain large spin-splitting by Rashba spin-orbit interaction due to asymmetric InSb/AlInSb quantum wells. In our model, broadening of each resonant tunneling level and spin-splitting energy can be considered to calculate spin-polarized resonant tunneling current.

We herein investigate the operation stability of the single-electron-pump (SEP) refrigerator with respect to thermal and dimensional fluctuations. The SEP refrigerator was found to successfully demonstrate single-electron extraction and injection at temperatures up to 2 K. Although the dimensional fluctuation in junction capacitance will seriously affect operation, the effect of the gate capacitance fluctuation is unlikely to be severe.

This paper describes a blind watermarking scheme through cyclic signal processing. Due to various rapid networks, there is a growing demand of copyright protection for multimedia data. As efficient watermarking of images, there exist two major approaches: a quantization-based method and a correlation-based method. In this paper, we proposes a correlation-based watermarking technique of three-dimensional (3-D) polygonal models using the fast Fourier transforms (FFTs). For generating a watermark with desirable properties, similar to a pseudonoise signal, an impulse signal on a two-dimensional (2-D) space is spread through the FFT, the multiplication of a complex sinusoid signal, and the inverse FFT. This watermark, i.e., spread impulse signal, in a transform domain is converted to a spatial domain by an inverse wavelet transform, and embedded into 3-D data aligned by the principle component analysis (PCA). In the detection procedure, after realigning the watermarked mesh model through the PCA, we map the 3-D data on the 2-D space via block segmentation and averaging operation. The 2-D data are processed by the inverse system, i.e., the FFT, the division of the complex sinusoid signal, and the inverse FFT. From the resulting 2-D signal, we detect the position of the maximum value as a signature. For 3-D bunny models, detection rates and information capacity are shown to evaluate the performance of the proposed method.

It has been an important issue to deal with risks in business processes for achieving companies' goals. This paper introduces a method for applying a formal method to analysis of risks and control activities in business processes in order to evaluate control activities consistently, exhaustively, and to give us potential to have scientific discussion on the result of the evaluation. We focus on document flows in business activities and control activities and risks related to documents because documents play important roles in business. In our method, document flows including control activities are modeled and it is verified by OTS/CafeOBJ Method that risks about falsification of documents are avoided by control activities in the model. The verification is done by interaction between humans and CafeOBJ system with theorem proving, and it raises potential to discuss the result scientifically because the interaction gives us rigorous reasons why the result is derived from the verification.

The proliferation of many-core architectures has led to the explosive development of parallel applications using programming models, such as OpenMP, TBB, and Cilk/Cilk++. With increasing number of cores, however, it becomes even harder to efficiently schedule parallel applications on these resources since current many-core runtime systems still lack effective mechanisms to support collaborative scheduling of these applications. In this paper, we study feedback-driven adaptive scheduling based on work stealing, which provides an efficient solution for concurrently executing a set of applications on many-core systems. To dynamically estimate the number of cores desired by each application, a stable feedback-driven adaptive algorithm, called SAWS, is proposed using active workers and the length of active deques, which well captures the runtime characteristics of the applications. Furthermore, a prototype system is built by extending the Cilk runtime system, and the experimental results, which are obtained on a Sun Fire server, show that SAWS has more advantages for scheduling concurrent parallel applications. Specifically, compared with existing algorithms A-Steal and WS-EQUI, SAWS improves the performances by up to 12.43% and 21.32% with respect to mean response time respectively, and 25.78% and 46.98% with respect to processor utilization, respectively.

Bittorrent is one of the most popular and successful applications in the current Internet. However, we still have little knowledge about the topology of real Bittorrent swarms, how dynamic the topology is, and how it affects overall behavior. This paper describes an experimental study of the overlay topologies of real-world Bittorrent networks, focusing on the activity of the nodes of its P2P topology and especially their dynamic relationships. Peer Exchange Protocol (PEX) messages are analyzed to infer topologies and their properties, capturing the variations of their behavior. Our measurements, verified using the Kolmogorov-Smirnov goodness of fit test and the likelihood ratio test and confirmed via simulation, show that a power-law with exponential cutoff is a more plausible model than a pure power-law distribution. We also found that the average clustering coefficient is very low, supporting this observation. Bittorrent swarms are far more dynamic than has been recognized previously, potentially impacting attempts to optimize the performance of the system as well as the accuracy of simulations and analyses.

A receiver architecture and a digital IQ imbalance compensation method for dual-carrier reception are newly proposed. The impact of IQ imbalance on the baseband signal is mathematically analyzed. Based on the analysis, IQ imbalance parameters are estimated and the coupling effect of IQ imbalance is compensated using digital baseband processing alone. Simulation results show that the proposed IQ imbalance compensation successfully removes IQ imbalance. The deviation from the ideal performance is less than 1 dB when it is applied to the 3GPP-LTE carrier aggregation.

In this paper, we propose a method for supervised single-channel speech separation through sparse decomposition using periodic signal models. The proposed separation method employs sparse decomposition, which decomposes a signal into a set of periodic signals under a sparsity penalty. In order to achieve separation through sparse decomposition, the decomposed periodic signals have to be assigned to the corresponding sources. For the assignment of the periodic signal, we introduce clustering using a K-means algorithm to group the decomposed periodic signals into as many clusters as the number of speakers. After the clustering, each cluster is assigned to its corresponding speaker using preliminarily learnt codebooks. Through separation experiments, we compare our method with MaxVQ, which performs separation on the frequency spectrum domain. The experimental results in terms of signal-to-distortion ratio show that the proposed sparse decomposition method is comparable to the frequency domain approach and has less computational costs for assignment of speech components.

In this paper, we propose integration of multimodal features using conditional random fields (CRFs) for the segmentation of broadcast news stories. We study story boundary cues from lexical, audio and video modalities, where lexical features consist of lexical similarity, chain strength and overall cohesiveness; acoustic features involve pause duration, pitch, speaker change and audio event type; and visual features contain shot boundaries, anchor faces and news title captions. These features are extracted in a sequence of boundary candidate positions in the broadcast news. A linear-chain CRF is used to detect each candidate as boundary/non-boundary tags based on the multimodal features. Important interlabel relations and contextual feature information are effectively captured by the sequential learning framework of CRFs. Story segmentation experiments show that the CRF approach outperforms other popular classifiers, including decision trees (DTs), Bayesian networks (BNs), naive Bayesian classifiers (NBs), multilayer perception (MLP), support vector machines (SVMs) and maximum entropy (ME) classifiers.

Despite the benefits of the Gustafson-Kessel (GK) clustering algorithm, it becomes computationally inefficient when applied to high-dimensional data. In this letter, a parallel implementation of the GK algorithm on the GPU with CUDA is proposed. Using an optimized matrix multiplication algorithm with fast access to shared memory, the CUDA version achieved a maximum 240-fold speedup over the single-CPU version.

A recursive and efficient method for generating binary vectors in non-increasing order of their likelihood for a set of all binary vectors is proposed. Numerical results on experiments show the effectiveness of this method. Efficient decoding algorithms with simulation results are also proposed as applications of the method.

In this paper, we study the problem of distributed spectrum allocation under a vertical spectrum sharing scenario in a cognitive radio network. The secondary users share the spectrum licensed to the primary user by observing the activity statistics of the primary users, and regulate their transmission strategy in order to abide by the spectrum sharing etiquette. When the primary user is inactive in a subset of the available frequency bands, from the perspective of the secondary users the problem reduces to a distributed horizontal spectrum sharing. For a specific class of networks, the latter problem is addressed by the recently proposed GADIA algorithm [1]. In this paper, we present analytical and numerical results on the performance of the GADIA algorithm in conjunction with the above-mentioned vertical spectrum sharing scenario. These results reveal near-optimal performance guarantees for the overall vertical spectrum sharing scenario.

Cognitive radio is an emerging technology to further improve the efficiency of spectrum use. Due to the nature of the technology, it has many facets, including its enabling technologies, its implementation issues and its regulatory implications. In ITU-R (International Telecommunications Union – Radiocommunication sector), cognitive radio systems are currently being studied so that ITU-R can have a clear picture on this new technology and its potential regulatory implications, from a viewpoint of global spectrum management. This paper introduces the recent results of the ITU-R studies on cognitive radio on both regulatory and technical aspects. This paper represents a personal opinion of the author, but not an official view of the ITU-R.

This letter considers a dual-hop multiuser MIMO amplify-and-forward relay broadcast system with multi-antenna nodes. A unified scheme is addressed to jointly optimize the linear transceiver based on the sum mean-square error (MSE) and the sum rate criterion. The solutions are iteratively obtained by deriving the gradients of the objective functions for a gradient descent algorithm. Simulation results demonstrate the performance improvements in terms of the BER and the sum rate.

A gated sense amplifier (GSA) consisting of a low-Vt gated preamplifier (LGA) and a high-Vt sense amplifier (SA) is proposed. The gating scheme of the LGA enables quick amplification of an initial cell signal voltage (vS0) because of its low Vt and prevents the cell signal from degrading due to interference noise between data lines. As for a conventional sense amplifier (CSA), this new type of noise causes sensing error, and the noise-generation mechanism was clarified for the first time by analysis of vS0. The high-Vt SA holds the amplified signal and keeps subthreshold current low. Moreover, the gating scheme of the low-Vt MOSFETs in the LGA drives the I/O line quickly. The GSA thus simultaneously achieves fast sensing, low-leakage data holding, and fast I/O driving, even for sub-1-V mid-point sensing. The GSA is promising for future sub-1-V gigabit dynamic random-access memory (DRAM) because of reduced variations in the threshold voltage of MOSFETs; thus, the offset voltage of the LGA is reduced. The effectiveness of the GSA was verified with a 70-nm 512-Mbit DRAM chip. It demonstrated row access time (tRCD) of 16.4 ns and read access (tAA) of 14.3 ns at array voltage of 0.9 V.

Along with the miniaturization of CMOS-LSIs, control methods for LSIs have been extensively developed. The most predominant method is to digitize observed values as early as possible and to use digital control. Thus, many types of analog-to-digital converters (ADCs) have been developed such as temperature, time, delay, and frequency converters. ADCs are the easiest circuits into which digital correction methods can be introduced because their outputs are digital. Various types of calibration method have been developed, which has markedly improved the figure of merits by alleviating margins for device variations. The above calibration and correction methods not only overcome a circuit's weak points but also give us the chance to develop quite new circuit topologies and systems. In this paper, several digital calibration and correction methods for major analog-to-digital converters are described, such as pipelined ADCs, delta-sigma ADCs, and successive approximation ADCs.