In this correspondence, a method of constructing optimal zero correlation zone (ZCZ) sequence sets over the 16-QAM+ constellation is presented. Based on 16-QAM orthogonal matrices and perfect ternary sequences, 16-QAM+ ZCZ sequence sets are obtained. The resulting ZCZ sequence sets are optimal with respect to the Tang-Fan-Matsufuji bound. Moreover, methods for transforming binary or quaternary orthogonal matrices into 16-QAM orthogonal matrices are proposed. The proposed 16-QAM+ ZCZ sequence sets can be potentially applied to communication systems using a 16-QAM constellation to remove the multiple access interference (MAI) and multi-path interference (MPI).

Electroencephalography (EEG) and magnetoencephalography (MEG) measure the brain signal from spatially-distributed electrodes. In order to detect event-related synchronization and desynchronization (ERS/ERD), which are utilized for brain-computer/machine interfaces (BCI/BMI), spatial filtering techniques are often used. Common spatial potential (CSP) filtering and its extensions which are the spatial filtering methods have been widely used for BCIs. CSP transforms brain signals that have a spatial and temporal index into vectors via a covariance representation. However, the variance-covariance structure is essentially different from the vector space, and not all the information can be transformed into an element of the vector structure. Grassmannian embedding methods, therefore, have been proposed to utilize the variance-covariance structure of variational patterns. In this paper, we propose a metric learning method to classify the brain signal utilizing the covariance structure. We embed the brain signal in the extended Grassmann manifold, and classify it on the manifold using the proposed metric. Due to this embedding, the pattern structure is fully utilized for the classification. We conducted an experiment using an open benchmark dataset and found that the proposed method exhibited a better performance than CSP and its extensions.

In this paper, we propose an undirected model of learning systems, named max-min-degree neural network, to realize centralized-decentralized collaborative computing. The basic idea of the proposal is a max-min-degree constraint which extends a k-degree constraint to improve the communication cost, where k is a user-defined degree of neurons. The max-min-degree constraint is defined such that the degree of each neuron lies between kmin and kmax. Accordingly, the Boltzmann machine is a special case of the proposal with kmin=kmax=n, where n is the full-connected degree of neurons. Evaluations show that the proposal is much better than a state-of-the-art model of deep learning systems with respect to the communication cost. The cost of the above improvement is slower convergent speed with respect to data size, but it does not matter in the case of big data processing.

Recently, the Services Oriented Architectures (SOA) have been recognized as the key to the integration and interoperability of different applications and systems that coexist in an organization. However, even though the use of SOA has increased, some applications are unable to use it. That is the case of mission critical information applications, whose requirements such as high reliability, non-stop operation, high flexibility and high performance are not satisfied by conventional SOA infrastructures. In this article we present a novel approach of combining SOA with Autonomous Decentralized Systems (ADS) in order to provide an infrastructure that can satisfy those requirements. We have named this infrastructure Autonomous Decentralized Service Oriented Architecture (ADSOA). We present the concept and architecture of ADSOA, as well as the Loosely Couple Delivery Transaction and Synchronization Technology for assuring the data consistency and high reliability of the application. Moreover, a real implementation and evaluation of the proposal in a mission critical information system, the Uniqueness Verifying Public Key Infrastructure (UV-PKI), is shown in order to prove its effectiveness.

This paper presents a parallelization method utilizing dead time to implement higher precision feedback control systems in multicore processors. The feedback control system is known to be difficult to parallelize, and it is difficult to deal with the dead time in control systems. In our method, the dead time is explicitly represented as delay elements. Then, these delay elements are distributed to the overall systems with equivalent transformation so that the system can be simulated or executed in parallel pipeline operation. In addition, we introduce a method of delay-element addition for parallelization. For a spring-mass-damper model with a dead time, parallel execution of the model using our technique achieves 3.4 times performance acceleration compared with its sequential execution on an ideal four-core simulation and 1.8 times on a cycle-accurate simulator of a four-core embedded processor as a threaded application on a real-time operating system.

As the electricity rates during peak hours are higher, this paper proposes a design for an ultrabook to automatically shift the charging period to an off-peak period. In addition, this design sets an upper limit for the battery which thus protects the battery and prevents it from remaining in a continued state of both high temperature and high voltage. This design uses both a low-power embedded controller (EC) and the fuzzy logic controller (FLC) control method as the main control techniques together with real time clock (RTC) ICs. The sensing value of the EC and the presetting of parameters are used to control the conversion of the AC/DC module. This user interface design allows the user to set not only the peak/off-peak period but also the upper use limit of the battery.

Nonnegative component representation (NCR) is a mid-level representation based on nonnegative matrix factorization (NMF). Recently, it has attached much attention and achieved encouraging result for action recognition. In this paper, we propose a novel hierarchical dictionary learning strategy (HDLS) for NMF to improve the performance of NCR. Considering the variability of action classes, HDLS clusters the similar classes into groups and forms a two-layer hierarchical class model. The groups in the first layer are disjoint, while in the second layer, the classes in each group are correlated. HDLS takes account of the differences between two layers and proposes to use different dictionary learning methods for this two layers, including the discriminant class-specific NMF for the first layer and the discriminant joint dictionary NMF for the second layer. The proposed approach is extensively tested on three public datasets and the experimental results demonstrate the effectiveness and superiority of NCR with HDLS for large-scale action recognition.

Due to the periodic recovery of virtual machines regardless of whether malicious intrusions exist, proactive recovery-based Intrusion Tolerant Systems (ITSs) are being considered for mission-critical applications. However, the virtual replicas can easily be exposed to attacks during their working period, and additionally, proactive recovery-based ITSs are ineffective in eliminating the vulnerability of exposure time, which is closely related to service availability. To address these problems, we propose a novel hybrid recovery-based ITS in this paper. The proposed method utilizes availability-driven recovery and dynamic cluster resizing. The availability-driven recovery method operates the recovery process by both proactive and reactive ways for the system to gain shorter exposure times and higher success rates. The dynamic cluster resizing method reduces the overhead of the system that occurs from dynamic workload fluctuations. The performance of the proposed ITS with various synthetic and real workloads using CloudSim showed that it guarantees higher availability and reliability of the system, even under malicious intrusions such as DDoS attacks.

Changing attitudes toward energy security and energy conservation have led to the introduction of distributed power systems such as photovoltaic, gas-cogeneration, biomass, water, and wind power generators. The mass installation of distributed energy generators often causes instability in the voltage and frequency of the power grid. Moreover, the power quality of distributed power grids can become degraded when system faults or the activation of highly loaded machines cause rapid changes in power load. To avoid such problems and maintain an acceptable power quality, it is important to detect the source of these rapid changes. To address these issues, next-generation power grids that can detect the fault location have been proposed. Fault location demands accurate time synchronization. Conventional techniques use the Global Positioning System (GPS) and/or IEEE 1588v2 for time synchronization. However, both methods have drawbacks — GPS cannot be used in indoor situations, and the installation cost of IEEE 1588v2 devices is high. In this paper, a time synchronization technique using the broadcast function of an Ethernet Passive Optical Network (EPON) system is proposed. Experiments show that the proposed technique is low-cost and useful for smart grid applications that use time synchronization in EPON-based next-generation power grids.

We propose an impact and high-pitch noise-suppression method based on spectral entropy. Spectral entropy takes a large value for flat spectral amplitude and a small value for spectra with several lines. We model the impact noise as a flat spectral signal and its damped oscillation as a high-pitch periodic signal consisting of spectra with several lines. We discriminate between the current noise situations by using spectral entropy and adaptively change the noise-suppression parameters used in a zero phase-based impact-noise-suppression method. Simulation results show that the proposed method can improve the perceptual evaluation of the speech quality and speech-recognition rate compared to conventional methods.

Recently, a high dimensional classification framework has been proposed to introduce spatial and anatomical priors in classical single kernel support vector machine optimization scheme, wherein the sequential minimal optimization (SMO) training algorithm is adopted, for brain image analysis. However, to satisfy the optimization conditions required in the single kernel case, it is unreasonably assumed that the spatial regularization parameter is equal to the anatomical one. In this letter, this approach is improved by combining SMO algorithm with multiple kernel learning to avoid that assumption and optimally estimate two parameters. The improvement is comparably demonstrated by experimental results on classification of Alzheimer patients and elderly controls.

In recent years, society has experienced several changes in its ways and methods of consuming. Nowadays, the diversity and the customization of products and services have provoked that the consumer needs continuously change. Hence, the database systems support e-business processes are required to be timeliness and adaptable to the changing preferences. Autonomous Decentralized Database System (ADDS), has been proposed in order to satisfy the enhanced requirements of current on-line e-business applications. Autonomy and decentralization of subsystems help to achieve short response times in highly competitive situations and an autonomous Coordination Mobile Agent (CMA) has been proposed to achieve flexibility in a highly dynamic environment. However, a problem in ADDS is as the number of sites increases, the distribution and harmonization of product information among the sites are turning difficult. Therefore, many users cannot be satisfied quickly. As a result, system timeliness is inadequate. To solve this problem, a self configuration technology is proposed. This technology can configure the system to the evolving situation dynamically for achieving high response. A simulation shows the effectiveness of the proposed technology in a large-scale system. Finally, an implementation of this technology is presented.

Distributed antenna systems (DASs) combined with multi-user multiple-input multiple-output (MU-MIMO) transmission techniques have recently attracted significant attention. To establish MU-MIMO DASs that have wide service areas, the use of a dynamic clustering scheme (CS) is necessary to reduce computation in precoding. In the present study, we propose a simple method for dynamic clustering to establish a single cell large-scale MU-MIMO DAS and investigate its performance. We also compare the characteristics of the proposal to those of other schemes such as exhaustive search, traditional location-based adaptive CS, and improved norm-based CS in terms of sum rate improvement. Additionally, to make our results more universal, we further introduce spatial correlation to the considered system. Computer simulation results indicate that the proposed CS for the considered system provides better performance than the existing schemes and can achieve a sum rate close to that of exhaustive search but at a lower computational cost.

East Japan Railway Company has created new businesses such as life-style business and information technology business on the basis of railway business for sustainable growth. These businesses generate and provide synergy to one another effectively because each business is autonomous decentralized system based on diversified infrastructure. The infrastructure includes not just structure but management, technology, operation and maintenance: we call this “MTOMI Model.” The MTOMI Model is the key concept of JR East's businesses and can generate JR East's ecosystem.

The growing trends in Internet usage for data and knowledge sharing calls for dynamic classification of web contents, particularly at the edges of the Internet. Rather than considering Linked Data as an integral part of Big Data, we propose Autonomous Decentralized Semantic-based Content Classifier (ADSCC) for dynamic classification of unstructured web contents, using Linked Data and web metadata in Content Delivery Network (CDN). The proposed framework ensures efficient categorization of URLs (even overlapping categories) by dynamically mapping the changing user-defined categories to ontologies' category/classes. This dynamic classification is performed by the proposed system that mainly involves three main algorithms/modules: Dynamic Mapping algorithm, Autonomous coordination-based Inference algorithm, and Context-based disambiguation. Evaluation results show that the proposed system achieves (on average), the precision, recall and F-measure within the 93-97% range.

An on-chip monitoring circuit using a sub-sampling scheme, which consists of a 6-bit flash analog-to-digital converter (ADC) and a 51-phase phase-locked loop (PLL)-based frequency synthesizer, is proposed to analyze the signal integrity of a single-ended 8-Gb/s octal data rate (ODR) chip-to-chip interface with a source synchronous clocking scheme.

A partitioning parallelization of the multi-objective evolutionary algorithm based on decomposition, pMOEA/D, is proposed in this letter to achieve significant time reductions for expensive bi-objective optimization problems (BOPs) on message-passing clusters. Each sub-population of pMOEA/D resides on a separate processor in a cluster and consists of a non-overlapping partition and some extra overlapping individuals for updating neighbors. Additionally, sub-populations cooperate across separate processors by the hybrid migration of elitist individuals and utopian points. Experimental results on two benchmark BOPs and the wireless sensor network layout problem indicate that pMOEA/D achieves satisfactory performance in terms of speedup and quality of solutions on message-passing clusters.

Blood pressure measurement by auscultatory method is a compulsory skill that is required by all healthcare practitioners. During the measurement, they must concentrate on recognizing the Korotkoff sounds, looking at the sphygmomanometer scale, and constantly deflating the cuff pressure simultaneously. This complex operation is difficult for the new learners and they need a lot of practice with the supervisor in order to guide them on their measurements. However, the supervisor is not always available and consequently, they always face the problem of lack of enough training. In order to help them mastering the skill of measuring blood pressure by auscultatory method more efficiently and effectively, we propose using a sensor device to capture the signals of Korotkoff sounds and cuff pressure during the measurement, and display the signal changes on a visualization tool through wireless connection. At the end of the measurement, the learners can verify their skill on deflation speed and recognition of Korotkoff sounds using the graphical view, and compare their measurements with the machine instantly. By using this device, the new learners do not need to wait for their supervisor for training but can practice with their colleagues more frequently. As a result, they will be able to acquire the skill in a shorter time and be more confident with their measurements.

This paper proposes a rate adjustment scheme for inbound data traffic on a virtualized host. Most prior studies on network virtualization have only focused on outbound traffic, yet many cloud applications rely on inbound traffic performance. The proposed scheme adjusts the inbound rates of virtual network interfaces dynamically in proportion to the bandwidth demands of the virtual machines.

Wheeze is a general sign for obstructive airway diseases whose clinical diagnosis mainly depends on auscultating or X-ray imaging with subjectivity or harm. Therefore, this paper introduces an automatic, noninvasive method to detect wheeze which consists of STFT decomposition, preprocessing of the spectrogram, correlation-coefficients calculating and duration determining. In particular, duration determining takes the Haas effect into account, which facilitates us to achieve a better determination. Simulation result shows that the sensibility (SE), the specificity (SP) and the accuracy (AC) are 88.57%, 97.78% and 93.75%, respectively, which indicates that this method could be an efficient way to detect wheeze.