In recent times, wireless Local Area Networks (wireless LANs) based on the IEEE 802.11 standard have been spreading rapidly, and connecting to the Internet using wireless LANs has become more common. In addition, public wireless LAN service areas, such as train stations, hotels, and airports, are increasing and tethering technology has enabled smartphones to act as access points (APs). Consequently, there can be multiple APs in the same area. In this situation, users must select one of many APs. Various studies have proposed and evaluated many AP selection methods; however, existing methods do not consider AP mobility. In this paper, we propose an AP selection method based on cooperation among APs and user movement. Moreover, we demonstrate that the proposed method dramatically improves throughput compared to an existing method.

Network coding on the physical-layer has recently been widely discussed as a potentially promising solution to the wireless access problem in a relay network. However, the existing research on physical-layer network coding (PNC), usually assumes that the symbol timing of the nodes is fully synchronized and hardly investigates the unavoidable symbol timing errors. Similar to many telecommunication systems, symbol timing plays a critical role in PNC and precise alignment has to be provided for the encoding. In this work, we propose a novel symbol timing algorithm with a low oversampling factor (samples per symbol) based on the a priori knowledge of the transmitted pulse shape. The proposed algorithm has the dual advantages of the low oversampling rate and high precision. The mean square error (MSE) performance is verified by simulations to be at least one order of magnitude better than that of the conventional optimum phase (OP) algorithm for a signal noise ratio (SNR) greater than 5dB.

A novel dynamic element matching (DEM) method, called binary-tree random DEM (BTR-DEM), is presented for a Nyquist-rate current-steering digital-to-analog converter (DAC). By increasing or decreasing the number of unit current sources randomly at the same time, the BTR-DEM encoding reduces switch transition glitches. A 5-bit current-steering DAC with the BTR-DEM technique is implemented in a 65-nm CMOS technology. The measured spurious free dynamic range (SFDR) attains 42 dB for a sample rate of 100 MHz and shows little dependence on signal frequency.

This paper addresses the problem of job scheduling in volunteer computing (VC) systems where each computation job is replicated and allocated to multiple participants (workers) to remove incorrect results by a voting mechanism. In the job scheduling of VC, the number of workers to complete a job is an important factor for the system performance; however, it cannot be fixed because some of the workers may secede in real VC. This is the problem that existing methods have not considered in the job scheduling. We propose a dynamic job scheduling method which considers the expected probability of completion (EPC) for each job based on the probability of worker's secession. The key idea of the proposed method is to allocate jobs so that EPC is always greater than a specified value (SPC). By setting SPC as a reasonable value, the proposed method enables to complete jobs without excess allocation, which leads to the higher performance of VC systems. We assume in this paper that worker's secession probability follows Weibull-distribution which is known to reflect more practical situation. We derive parameters for the distribution using actual trace data and compare the performance of the proposed and the previous method under the Weibull-distribution model, as well as the previous constant probability model. Simulation results show that the performance of the proposed method is up to 5 times higher than that of the existing method especially when the time for completing jobs is restricted, while keeping the error rate lower than a required value.

The amount of seasonings used during food preparation is quite important information for modern people to enable them to cook delicious dishes as well as to take care for their health. In this paper, we propose a near real-time automated system for measuring and recording the amount of seasonings used during food preparation. Our proposed system is equipped with two devices: electronic scales and a camera. Seasoning bottles are basically placed on the electronic scales in the proposed system, and the scales continually measure the total weight of the bottles placed on them. When a chef uses a certain seasoning, he/she first picks up the bottle containing it from the scales, then adds the seasoning to a dish, and then returns the bottle to the scales. In this process, the chef's picking and returning actions are monitored by the camera. The consumed amount of each seasoning is calculated as the difference in weight between before and after it is used. We evaluated the performance of the proposed system with experiments in 301 trials in actual food preparation performed by seven participants. The results revealed that our system successfully measured the consumption of seasonings in 60.1% of all the trials.

Orthogonal frequency division multiplexing (OFDM) channel estimation is the key technique used in broadband wireless networks. The Doppler frequency caused by fast mobility environments will cause inter-carrier interference (ICI) and degrade the performance of OFDM systems. Due to the severe ICI, channel estimation becomes a difficult task in higher mobility scenarios. Our aim is to propose a pilot-aided channel estimation method that is robust to high Doppler frequency with low computational complexity and pilot overheads. In this paper, the time duration of each estimate covers multiple consecutive OFDM symbols, named a “window”. A close-form of polynomial channel modeling is derived. The proposed method is initialized to the least squares (LS) estimates of the channels corresponding to the time interval of the pilot symbols within the window. Then, the channel interpolation is performed in the entire window. The results of computer simulations and computation complexity evaluations show that the proposed technique is robust to high Doppler frequency with low computation complexity and low pilot overheads. Compared with the state-of-the-art method and some conventional methods, the new technique proposed here has much lower computational complexity while offering comparable performance.

Controlling fluid simulation is one of the important research topics in computer graphics. In this paper, we focus on controlling the simulation of cumuliform cloud formation. Using a previously proposed method for controlling cloud simulation the convergence speed is very slow; therefore, it takes a long time before the clouds form the desired shapes. We improved the method and accelerated the convergence by introducing a new mechanism for controlling the amount of water vapor added. We demonstrate the effectiveness of the proposed method by several examples.

Due to its on-demand and pay-as-you-go properties, cloud computing has become an attractive alternative for HPC applications. However, communication-intensive applications with complex communication patterns still cannot be performed efficiently on cloud platforms, which are equipped with MapReduce technologies, such as Hadoop and Spark. In particular, one major obstacle is that MapReduce's simple programming model cannot explicitly manipulate data transfers between compute nodes. Another obstacle is cloud's relatively poor network performance compared with traditional HPC platforms. The traditional Strassen's algorithm of square matrix multiplication has a recursive and complex pattern on the HPC platform. Therefore, it cannot be directly applied to the cloud platform. In this paper, we demonstrate how to make Strassen's algorithm with complex communication patterns “cloud-friendly”. By reorganizing Strassen's algorithm in an iterative pattern, we completely separate its computations and communications, making it fit to MapReduce programming model. By adopting a novel data/task parallel strategy, we solve Strassen's data dependency problems, making it well balanced. This is the first instance of Strassen's algorithm in MapReduce-style systems, which also matches Strassen's communication lower bound. Further experimental results show that it achieves a speedup ranging from 1.03× to 2.50× over the classical Θ(n3) algorithm. We believe the principle can be applied to many other complex scientific applications.

With the growth of cloud-based services, cloud data centers are experiencing large growth. A key component in a cloud data center is the network technology deployed. In particular, Ethernet technology, commonly deployed in cloud data centers, is already envisioned for 10 Tbps Ethernet. In this paper, we study and analyze the makespan, workload execution times, and virtual machine migrations as the network speed increases. In particular, we consider homogeneous and heterogeneous data centers, virtual machine scheduling algorithms, and workload scheduling algorithms. Results obtained from our study indicate that the increase in a network's speed reduces makespan and workloads execution times, while aiding in the increase of the number of virtual machine migrations. We further observed that the number of migrations' behaviors in relation to the speed of the networks also depends on the employed virtual machines scheduling algorithm.

Isosurface extraction is one of the most popular techniques for visualizing scalar volume data. However, volume data contains infinitely many isosurfaces. Furthermore, a single isosurface might contain many connected components, or contours, with each representing a different object surface. Hence, it is often a tedious and time-consuming manual process to find and extract contours that are interesting to users. This paper describes a novel method for automatically extracting salient contours from volume data. For this purpose, we propose a contour gradient tree (CGT) that contains the information of salient contours and their saliency magnitude. We organize the CGT in a hierarchical way to generate a sequence of contours in saliency order. Our method was applied to various medical datasets. Experimental results show that our method can automatically extract salient contours that represent regions of interest in the data.

Viterbi decoding is commonly used for several protocols, but computational cost is quite high and thus it is necessary to implement it effectively. This paper describes GPU implementation of Viterbi decoder utilizing three-point Viterbi decoding algorithm (TVDA), in which the received bits are divided into multiple chunks and several chunks are decoded simultaneously. Coalesced access and Warp Shuffle, which is new instruction introduced are also utilized in order to improve decoder performance. In addition, iterative execution of parallel chunks decoding reduces the latency of proposed Viterbi decoder in order to utilize the decoder as a part of GPU-based SDR transceiver. As the result, the throughput of proposed Viterbi decoder is improved by 23.1%.

A cognitive radio user (CU) can get assistance from sensor nodes (SN) to perform spectrum sensing. However, the SNs are often powered by a finite-capacity battery, which can maintain operations of the SNs over a short time. Therefore, energy-efficiency of the SNs becomes a crucial problem. In this paper, an SN is considered to be a device with an energy harvester that can harvest energy from a non-radio frequency (non-RF) energy resource while performing other actions concurrently. In any one time slot, in order to maintain the required sensing accuracy of the CR network and to conserve energy in the SNs, only a small number of SNs are required to sense the primary user (PU) signal, and other SNs are kept silent to save energy. For this, an algorithm to divide all SNs into groups that can satisfy the required sensing accuracy of the network, is proposed. In a time slot, each SN group can be assigned one of two actions: stay silent, or be active to perform sensing. The problem of determining the optimal action for all SN groups to maximize throughput of the CR network is formulated as a framework of a partially observable Markov decision process (POMDP), in which the effect of the current time slot's action on the throughput of future time slots is considered. The solution to the problem, that is the decision mode of the SN groups (i.e., active or silent), depends on the residual energy and belief of absence probability of the PU signal. The simulation results show that the proposed scheme can improve energy efficiency of CR networks compared with other conventional schemes.

We propose efficient algorithms for Sorting k-Sets in Bins. The Sorting k-Sets in Bins problem can be described as follows. We are given numbered n bins with k balls in each bin. Balls in the i-th bin are numbered n-i+1. We can only swap balls between adjacent bins. Our task is to move all of the balls to the same numbered bins. For this problem, we give an efficient greedy algorithm with $rac{k+1}{4}n^2+O(k+n)$ swaps and provide a detailed analysis for k=3. In addition, we give a more efficient recursive algorithm using $rac{15}{16}n^2+O(n)$ swaps for k=3.

In this paper, synchronization for uncertain fractional order chaotic systems is investigated. By using the fractional order extension of the Lyapunov stability criterion, a linear feedback controller and an adaptive controller are designed for synchronizing uncertain fractional order chaotic systems without and with unknown external disturbance, respectively. Quadratic Lyapunov functions are used in the stability analysis of fractional-order systems, and fractional order adaptation law is constructed to update design parameter. The proposed methods can guarantee that the synchronization error converges to zero asymptotically. Finally, illustrative examples are given to confirm the theoretical results.

In this letter, a new subband adaptive filter (SAF) which is robust against impulsive noise in system identification is presented. To address the vulnerability of adaptive filters based on the L2-norm optimization criterion to impulsive noise, the robust SAF (R-SAF) comes from the L1-norm optimization criterion with a constraint on the energy of the weight update. Minimizing L1-norm of the a posteriori error in each subband with a constraint on minimum disturbance gives rise to robustness against impulsive noise and the capable convergence performance. Simulation results clearly demonstrate that the proposal, R-SAF, outperforms the classical adaptive filtering algorithms when impulsive noise as well as background noise exist.

In this study, we investigate whether copula modeling contributes to the improvement of reliability evaluation in a cascading failure-occurrence environment. In particular, as a basic problem, we focus on a 2-unit parallel system whose units may fail dependently each other. As a result, the reliability assessment of the system by using the maximal copula provides more accurate evaluation than the traditional Weibull analysis, if the degree of dependency between two units are high. We show this result by using several simulation studies.

We present a power-saving method for large-scale storage systems of cloud data sharing services, particularly those providing media (video and photograph) sharing services. The idea behind our method is to periodically rearrange stored data in a disk array, so that the workload is skewed toward a small subset of disks, while other disks can be sent to standby mode. This idea is borrowed from the Popular Data Concentration (PDC) technique, but to avoid an increase in response time caused by the accesses to disks in standby mode, we introduce a function that predicts future access frequencies of the uploaded files. This function uses the correlation of potential future accesses with the combination of elapsed time after upload and the total number of accesses in the past. We obtain this function in statistical analysis of the real access patterns of 50,000 randomly selected publicly available photographs on Flickr over 7,000 hours (around 10 months). Moreover, to adapt to a constant massive influx of data, we propose a mechanism that effectively packs the continuously uploaded data into the disk array in a storage system based on the PDC. To evaluate the effectiveness of our method, we measured the performance in simulations and a prototype implementation. We observed that our method consumed 12.2% less energy than the static configuration (in which all disks are in active mode). At the same time, our method maintained a preferred response time, with 0.23% of the total accesses involving disks in standby mode.

We experimentally investigated the impact of the mode filtering technique on the performances of pulse amplification in a fiber with a large core diameter. The technique was applied to a femtosecond pulse amplifier, and was based on a large area double-clad Yb-doped fiber. The mode filtering enabled selective excitation of the lowest transverse mode with minimal contamination of higher order modes. The output pulses with 110 fs duration, > 30 nJ pulse energy (> 3 W average power), and clean spatial/temporal profiles were successfully generated. Benefits of this technique are also discussed.

A signal-model-based SAR image formation algorithm is proposed in this paper. A model is used to describe the received signal, and each scatterer can be characterized by a set of its parameters. Two parameter estimation methods via atomic decomposition are presented: (1) applying 1-D matching pursuit to azimuthal projection data; (2) applying 2-D matching pursuit to raw data. The estimated parameters are mapped to form a SAR image, and the mapping procedure can be implemented under application guidelines. This algorithm requires no prior information about the relative motion between the platform and the target. The Cramer-Rao bounds of parameter estimation are derived, and the root mean square errors of the estimates are close to the bounds. Experimental results are given to validate the algorithm and indicate its potential applications.

A simple robust finite-time convergent observer is presented in the presence of unknown input disturbance and measurement noise. In order to achieve the robust estimation and ensure the finite-time convergence, the proposed observer is constructed by using a multiple integral observer scheme in a hybrid system framework. Comparative computer simulations and laboratory experiments have been performed to test the effectiveness of the proposed observer.