Novel flash codes with small average write deficiency are proposed. A flash code is a coding scheme for avoiding the wearing of cells in flash memory. One approach to develop flash codes with large parameters is to make use of slices which are small groups of cells. Preliminary study shows that using small slices brings several favorable characteristics, but naive use of small slices induces a certain overhead. In this study, a new structure which is called a cluster is devised to develop a good slice-based flash code. Two different slice encoding schemes are used in a cluster, which decreases the overhead of using small slices while retaining its advantage. The proposed flash codes show much smaller write deficiency compared to another slice-based flash code.

Route discovery process is a major mechanism in the most routing protocols in Mobile Ad Hoc Network (MANET). Routing overhead is one of the problems caused by broadcasting the route discovery packet. To reduce the routing overhead, the location-based routing schemes have been proposed. In this paper, we propose our scheme called Location-based Routing scheme with Adaptive Request Zone (LoRAReZ). In LoRAReZ scheme, the size of expected zone is set adaptively depending on the distance between source and destination nodes. Computer simulation has been conducted to show the effectiveness of our propose scheme. We evaluate the performances of LoRAReZ scheme in the terms of packet delivery fraction (PDF), routing overhead, average end-to-end delay, throughput, packet collision, average hop count, average route setup time, and power consumption. We compare those performance metrics with those of Location Aided Routing (LAR) and Location Aware Routing Protocol with Dynamic Adaptation of Request Zone (LARDAR) protocols. The simulation results show that LoRAReZ can provide all the better performances among those of LAR and LARDAR schemes.

The optimum generalized partial response (GPR) target for barium ferrite (BaFe) tape systems was studied. The shift in perpendicular magnetic recording technology in HDDs to systems employing single-pole-type (SPT) recording heads and media with a soft under layer (SUL) has been accompanied by a change in the read channel design, whereas current magnetic tape recording systems utilize a combination of a ring-type recording head with a single magnetic layer structured medium. Therefore, the read channel performance of current oriented BaFe particulate tape systems needs to be studied to best exploit the potential of this medium. Toward this end, DC-free, DC-full, and DC-suppressed targets were compared. The results show that assuming a GPRML detector with 16 or more states, a traditional DC-free target exhibits the best bit error rate performance for both longitudinally and perpendicularly oriented BaFe media, suggesting that the current read channel designed for longitudinally oriented media can also be utilized for BaFe particulate tape systems.

Recently, the border security systems attract attention as large-scale monitoring system in wireless sensor networks (WSNs). In the border security systems whose aim is the monitoring of illegal immigrants and the information management in long-period, it deploys a lot of sensor nodes that have the communication and sensing functions in the detection area. Hence, the border security systems are necessary to reduce the power consumption of the whole system in order to extend the system lifetime and accurately monitor the track of illegal immigrants. In this paper, we propose two effective barrier coverage construction methods by switch dynamically operation modes of sensor nodes to reduce the operating time of the sensing function that wastes a lot of power consumption. We carry out performance evaluations by computer simulations to show the effectiveness of two proposed methods and show that the proposed methods are suitable for the border security systems.

A virtual machine (VM) migration is useful for improving flexibility and maintainability in cloud computing environments. However, VM monitor (VMM)-bypass I/O technologies, including PCI passthrough and SR-IOV, in which the overhead of I/O virtualization can be significantly reduced, make VM migration impossible. This paper proposes a novel and practical mechanism, called Symbiotic Virtualization (SymVirt), for enabling migration and checkpoint/restart on a virtualized cluster with VMM-bypass I/O devices, without the virtualization overhead during normal operations. SymVirt allows a VMM to cooperate with a message passing layer on the guest OS, then it realizes VM-level migration and checkpoint/restart by using a combination of a user-level dynamic device configuration and coordination of distributed VMMs. We have implemented the proposed mechanism on top of QEMU/KVM and the Open MPI system. All PCI devices, including Infiniband, Ethernet, and Myrinet, are supported without implementing specific para-virtualized drivers; and it is not necessary to modify either of the MPI runtime and applications. Using the proposed mechanism, we demonstrate reactive and proactive FT mechanisms on a virtualized Infiniband cluster. We have confirmed the effectiveness using both a memory intensive micro benchmark and the NAS parallel benchmark.

Due to network users' different time-preference, network traffic load usually significantly differs at different time. In traffic peak time, network congestion may happen, which make the quality of service for network users deteriorate. There are essentially two ways to improve the quality of services in this case: (1) Network service providers (NSPs) over-provision network capacity by investment; (2) NSPs use time-dependent pricing (TDP) to reduce the traffic at traffic peak time. However, over-provisioning network capacity can be costly. Therefore, some researchers have proposed TDP to control congestion as well as improve the revenue of NSP. But to the best of our knowledge, all of the literature related time-dependent pricing scheme only consider the monopoly NSP case. In this paper, a duopoly NSP case is studied. The NSPs try to maximize their overall revenue by setting time-dependent price, while users choose NSP by considering their own preference, congestion status in the networks and the price set by the NSPs. Analytical and experimental results show that the TDP benefits the NSPs, but the revenue improvement is limited due to the competition effect.

In this paper, we propose a novel voice activity detection (VAD) algorithm based on the generalized normal-Laplace (GNL) distribution to provide enhanced performance in adverse noise environments. Specifically, the probability density function (PDF) of a noisy speech signal is represented by the GNL distribution; the variance of the speech and noise of the GNL distribution are estimated using higher-order moments. After in-depth analysis of estimated variances, a feature that is useful for discrimination between speech and noise at low SNRs is derived and compared to a threshold to detect speech activity. To consider the inter-frame correlation of speech activity, the result from the previous frame is employed in the decision rule of the proposed VAD algorithm. The performance of our proposed VAD algorithm is evaluated in terms of receiver operating characteristics (ROC) and detection accuracy. Results show that the proposed method yields better results than conventional VAD algorithms.

The use of computing resources on network is becoming active in the Internet and private networks. OpenFlow/Software-Defined Networking (SDN) is drawing attention as a method to control network virtualization for the cloud computing services and other carrier services. This paper introduces examples of OpenFlow/SDN technologies applied to commercial cloud services. Various activities to expand coverage over commercial carrier networks are also mentioned.

The uniform magnetic field of various strength was applied to the perpendicularly and in-plane demagnetized media, and the change in each magnetic cluster state was investigated as the fundamental investigation of the influence of demagnetization method on noise during signal recording on the stacked perpendicular recording media. The results showed that the in-plane demagnetization can achieve lower noise level if the recording field is not very high. In other words, the in-plane demagnetization is an effective way to achieve lower noise in transition area, near track edge of recorded bit, and in high-density bit. In addition, the simulation clarified that this noise reduction can be explained using the idea of sub-domain structure in the in-plane demagnetized media.

To achieve high recording density in a bit-patterned media recording system, the spacing between data bit islands in both the along-track and the across-track directions must be decreased, thus leading to the increase of two-dimensional (2D) interference. One way to reduce the 2D interference is to apply a 2D coding scheme on a data sequence before recording; however, this method usually requires many redundant bits, thus lowering a code rate. Therefore, we propose a novel 2D coding scheme referred to as a recorded-bit patterning (RBP) scheme to mitigate the 2D interference, which requires no redundant bits at the expense of using more buffer memory. Specifically, an input data sequence is first split into three tracks in which will then be rotated to find the best 3-track data pattern based on a look-up table before recording, such that the shifted data tracks yield the least effect of 2D interference in the readback signal. Numerical results indicate that the proposed RBP scheme provides a significant performance improvement if compared to a conventional system (without 2D coding), especially when the recording density is high and/or the position jitter noise is large.

To mitigate the impact of the frequency selectivity of the wireless channel on the initial ranging (IR) process in 802.16 based WiMax systems, several well known pre-equalization techniques applied in the IR are first analyzed in detail, and the optimal pre-equalization scheme is further improved for the IR by overcoming its weaknesses. A numerical simulation shows that the proposed pre-equalization scheme significantly improves the performance of multiuser detection and parameter estimation in the IR process.

It is not always easy for an Android user to choose the most suitable application for a particular task from the great number of applications available. In this paper, we propose a semi-automatic approach to extract feature names from Android applications. The case study verifies that we can associate common sequences of Android API calls with feature names.

In this paper, joint transmit/receive frequency-domain equalization (FDE) is proposed for analog network coded (ANC) single-carrier (SC) bi-directional multi-antenna relay. In the proposed scheme, diversity transmission using transmit FDE is performed at relay station (RS) equipped with multiple antennas while receive FDE is carried out at base station (BS) and mobile terminal (MT) both equipped with single antenna. The transmit and receive FDE weights are jointly optimized so as to minimize the end-to-end mean square error (MSE). We evaluate, by computer simulation, the throughput performance and show that the joint transmit/receive FDE obtains the spatial and frequency diversity gains and accordingly achieve better throughput performance compared to either the transmit FDE only or the receive FDE only. It is also shown that ANC SC bi-directional multi-antenna relay can extend the communication coverage area for the given required throughput compared to conventional direct transmission.

In this letter, by using Whiteman's generalized cyclotomy of order 2 over Zpq, where p, q are twin primes, we construct new perfect Gaussian integer sequences of period pq.

The paper introduces a novel pheromone update strategy to improve the functionality of ant colony optimization algorithms. This modification tries to extend the search area by an optimistic reinforcement strategy in which not only the most desirable sub-solution is reinforced in each step, but some of the other partial solutions with acceptable levels of optimality are also favored. therefore, it improves the desire for the other potential solutions to be selected by the following artificial ants towards a more exhaustive algorithm by increasing the overall exploration. The modifications can be adopted in all ant-based optimization algorithms; however, this paper focuses on two static problems of travelling salesman problem and classification rule mining. To work on these challenging problems we considered two ACO algorithms of ACS (Ant Colony System) and AntMiner 3.0 and modified their pheromone update strategy. As shown by simulation experiments, the novel pheromone update method can improve the behavior of both algorithms regarding almost all the performance evaluation metrics.

The abundance of information published on the Internet makes filtering of hazardous Web pages a difficult yet important task. Supervised learning methods such as Support Vector Machines (SVMs) can be used to identify hazardous Web content. However, scalability is a big challenge, especially if we have to train multiple classifiers, since different policies exist on what kind of information is hazardous. We therefore propose two different strategies to train multiple SVMs for personalized Web content filters. The first strategy identifies common data clusters and then performs optimization on these clusters in order to obtain good initial solutions for individual problems. This initialization shortens the path to the optimal solutions and reduces the training time on individual training sets. The second approach is to train all SVMs simultaneously. We introduce an SMO-based kernel-biased heuristic that balances the reduction rate of individual objective functions and the computational cost of kernel matrix. The heuristic primarily relies on the optimality conditions of all optimization problems and secondly on the pre-calculated part of the whole kernel matrix. This strategy increases the amount of information sharing among learning tasks, thus reduces the number of kernel calculation and training time. In our experiments on inconsistently labeled training examples, both strategies were able to predict hazardous Web pages accurately (> 91%) with a training time of only 26% and 18% compared to that of the normal sequential training.

This article introduces the Coincidence Algorithm (COIN) to solve several multimodal puzzles. COIN is an algorithm in the category of Estimation of Distribution Algorithms (EDAs) that makes use of probabilistic models to generate solutions. The model of COIN is a joint probability table of adjacent events (coincidence) derived from the population of candidate solutions. A unique characteristic of COIN is the ability to learn from a negative sample. Various experiments show that learning from a negative example helps to prevent premature convergence, promotes diversity and preserves good building blocks.

To understand human emotion, it is necessary to be aware of the surrounding situation and individual personalities. In most previous studies, however, these important aspects were not considered. Emotion recognition has been considered as a classification problem. In this paper, we attempt new approaches to utilize a person's situational information and personality for use in understanding emotion. We propose a method of extracting situational information and building a personalized emotion model for reflecting the personality of each character in the text. To extract and utilize situational information, we propose a situation model using lexical and syntactic information. In addition, to reflect the personality of an individual, we propose a personalized emotion model using KBANN (Knowledge-based Artificial Neural Network). Our proposed system has the advantage of using a traditional keyword-spotting algorithm. In addition, we also reflect the fact that the strength of emotion decreases over time. Experimental results show that the proposed system can more accurately and intelligently recognize a person's emotion than previous methods.

Localization in wireless sensor networks is the problem of estimating the geographical locations of wireless sensor nodes. We propose a framework to utilizing multiple data sources for localization scheme based on support vector machines. The framework can be used with both classification and regression formulation of support vector machines. The proposed method uses only connectivity information. Multiple hop count data sources can be generated by adjusting the transmission power of sensor nodes to change the communication ranges. The optimal choice of communication ranges can be determined by evaluating mutual information. We consider two methods for integrating multiple data sources together; unif method and align method. The improved localization accuracy of the proposed framework is verified by simulation study.

In communication networks, congestion control, routing, and multiple access schemes for scheduling transmissions are typically regulated by distributed algorithms. Engineers designed these algorithms using clever heuristics that they refined in the light of simulation results and experiments. Over the last two decades, a deeper understanding of these algorithms emerged through the work of researchers. This understanding has a real potential for improving the design of protocols for data centers, cloud computing, and even wireless networks. Since protocols tend to be standardized by engineers, it is important that they become familiar with the insights that emerged in research. We hope that this paper might appeal to practitioners and make the research results intuitive and useful. The methods that the paper describes may be useful for many other resource allocation problems such as in call centers, manufacturing lines, hospitals and the service industry.