This paper designs tag antennas to satisfy three key goals: mounting on very small objects, extending the reading range with planar structures, and maintaining stable performance on various materials. First, the size of the tag is reduced up to 17% compared to the half-wavelength dipole without a large reduction in bandwidth and efficiency by introducing an inductively coupled feed structure. Second, the reading range is increased to 1.68 times that of the reference dipole tags while maintaining the planar structure using circular polarization characteristics. Finally, a stable reading range is achieved with a deviation in the reading range of only 30% of that of commercial tags on various objects by employing the capacitively-loaded and T-matching network.

To better support data-intensive workflows which are typically built out of various independently developed executables, this paper proposes extensions to parallel database systems called User-Defined eXecutables (UDX) and collective queries. UDX facilitates the description of workflows by enabling seamless integrations of external executables into SQL statements without any efforts to write programs confirming to strict specifications of databases. A collective query is an SQL query whose results are distributed to multiple clients and then processed by them in parallel, using arbitrary UDX. It provides efficient parallelization of executables through the data transfer optimization algorithms that distribute query results to multiple clients, taking both communication cost and computational loads into account. We implement this concept in a system called ParaLite, a parallel database system based on a popular lightweight database SQLite. Our experiments show that ParaLite has several times higher performance over Hive for typical SQL tasks and has 10x speedup compared to a commercial DBMS for executables. In addition, this paper studies a real-world text processing workflow and builds it on top of ParaLite, Hadoop, Hive and general files. Our experiences indicate that ParaLite outperforms other systems in both productivity and performance for the workflow.

This letter studies the simple dynamic binary neural network characterized by signum activation function and ternary connection parameters. In order to control the sparsity of the connections and the stability of the stored signal, a simple evolutionary algorithm is presented. As a basic example of teacher signals, we consider a binary periodic orbit which corresponds to a control signal of ac-dc regulators. In the numerical experiment, applying the correlation-based learning, the periodic orbit can be stored. The sparsification can be effective to reinforce the stability of the periodic orbit.

As data volumes explode, data storage costs become a large fraction of total IT costs. We can reduce the costs substantially by using compression. However, it is generally known that database compression is not suitable for write-intensive workloads. In this paper, we provide a comprehensive solution to improve the performance of compressed databases for write-intensive OLTP workloads. We find that storing data too densely in compressed pages incurs many future page splits, which require exclusive locks. In order to avoid lock contention, we reduce page splits by sacrificing a couple of percent of space savings. We reserve enough space in each compressed page for future updates of records and prevent page merges that are prone to incur page splits in the near future. The experimental results using TPC-C benchmark and MySQL/InnoDB show that our method gives 1.5 times higher throughput with 33% space savings compared with the uncompressed counterpart and 1.8 times higher throughput with only 1% more space compared with the state-of-the-art compression method developed by Facebook.

Probabilistic range query is an important type of query in the area of uncertain data management. A probabilistic range query returns all the data objects within a specific range from the query object with a probability no less than a given threshold. In this paper, we assume that each uncertain object stored in the database is associated with a multi-dimensional Gaussian distribution, which describes the probability distribution that the object appears in the multi-dimensional space. A query object is either a certain object or an uncertain object modeled by a Gaussian distribution. We propose several filtering techniques and an R-tree-based index to efficiently support probabilistic range queries over Gaussian objects. Extensive experiments on real data demonstrate the efficiency of our proposed approach.

Probabilistic frequent itemset mining, which discovers frequent itemsets from uncertain data, has attracted much attention due to inherent uncertainty in the real world. Many algorithms have been proposed to tackle this problem, but their performance is not satisfactory because handling uncertainty incurs high processing cost. To accelerate such computation, we utilize GPUs (Graphics Processing Units). Our previous work accelerated an existing algorithm with a single GPU. In this paper, we extend the work to employ multiple GPUs. Proposed methods minimize the amount of data that need to be communicated among GPUs, and achieve load balancing as well. Based on the methods, we also present algorithms on a GPU cluster. Experiments show that the single-node methods realize near-linear speedups, and the methods on a GPU cluster of eight nodes achieve up to a 7.1 times speedup.

This study looks at how an e-Health System can reduce morbidity (poor health) in unreached communities. The e-Health system combines affordable sensors and Body Area Networking technology with mobile health concepts and is called a Portable Health Clinic. The health clinic is portable because all the medical devices fit inside a briefcase and are carried to unreached communities by a healthcare assistants. Patient morbidity is diagnosed using software stratification algorithm and categorized according to triage color-coding scheme within the briefcase. Morbid patients are connected to remote doctor in a telemedicine call center using the mobile network coverage. Electronic Health Records (EHR) are used for the medical consultancy and e-Prescription is generated. The effectiveness of the portable health clinic system to target morbidity was tested on 8690 patients in rural and urban areas of Bangladesh during September 2012 to January 2013. There were two phases to the experiment: the first phase identified the intensity of morbidity and the second phase re-examined the morbid patients, two months later. The experiment results show a decrease in patients to identify as morbid among those who participated in telemedicine process.

Security plays an important role in several ZigBee applications such as Smart Energy and medical sensor applications. For a secure communication among ZigBee devices, a secret key should be shared among any two ZigBee devices using the Key Distribution protocol. Recently, Yüksel and Nielson proposed a new Key Distribution protocol for ZigBee addressing the security weaknesses of the original ZigBee Key Distribution protocol. In this letter, it is shown that their protocol is not secure against a key de-synchronization attack, and a security-enhanced Key Distribution protocol is newly proposed and analyzed in terms of security.

Compressive sensing is a promising technique in data acquisition field. A central problem in compressive sensing is that for a given sparse signal, we wish to recover it accurately, efficiently and stably from very few measurements. Inspired by mathematical analysis, we introduce a combining scheme between stability and robustness in reconstruction problems using compressive sensing. By choosing appropriate parameters, we are able to construct a condition for reconstruction map to perform properly.

Faced with social problems such as rapidly aging society, the solutions have been expected in sports medicine. Humans became widely distributed on the earth from their birth by acquiring abilities to walk in an upright position and to adapt themselves to various natural environments. However, seeking a ‘comfortable environment’ in modern civilization has deteriorated these genetic characteristics of humans, and the consumption of resources and energy to acquire such a ‘comfortable environment’ has induced global warming-associated natural disasters and the destruction of social order. To halt this vicious cycle, we may reactivate the genetic characteristics in humans by doing exercise. To do this, we have developed a health promotion program for middle aged and older people, Jukunen Taiikudaigaku Program, in cooperation with the Japanese government, developed high-intensity interval walking training (IWT), and examined the physical and mental effects on 5,400 people for these 10 years. We found that IWT for 4 months increased physical fitness by 10-20%, decreased the indices of life-style related diseases by 10-20%. Since a prescription of IWT can be conducted by using an IT network system called e-Health Promotion System, the participants in the program were able to receive the prescription even if they lived remote from trainers, enabling them to perform IWT at their favored places and times, and also at low cost. Moreover, we found some single nucleotide polymorphisms closely related to inter-individual differences in the responses to IWT. Further, the system enables us to assess the inactivation/activation of genes for inflammatory responses which has been suggested to be involved in life-style related diseases. Also, the system enables us to search foods to promote health when they are consumed during exercise training. Thus, the system would have strong potential to promote health of middle-aged and older people in advanced aging society.

Methods for in-network joins of sensing data with tuples, in partitioned condition tables stored in sensor nodes, have been studied for efficient event detection. A recently proposed method performs the join operation after distributing the tuples of a condition table evenly among homogeneous sensor nodes with the same storage capacity. In the method, the condition table is horizontally partitioned, and each partition is allocated to the corresponding node, along the path from the highest level to the leaf level. If the path length is larger than the number of partitions, the second round distribution of the partitions resumes from the node at the next level, and so on. Thus, the last node at each round can be assigned the partition that is smaller than the others, which would otherwise cause wasted internal fragmentation. Further, little research has been conducted on methods for the cases of heterogeneous sensor nodes with different available spaces, as well as the vertical partitioning of condition table. In this study, we propose a method of partitioning a condition table that utilizes the internal fragmentation, by treating the tuples of a condition table as a circular list. The proposed method is applicable to the case in which nodes have different available spaces. Furthermore, a new method for vertically partitioning a condition table is suggested. Experiments verify the reduction in the data transmission amount offered by the proposed methods, as compared to existing methods.

Due to the recent progress made in camera and network environments, on-line video services enable people around the world to watch or share high-quality HD videos that can record a wider angle without losing objects' details in each image. As a result, users of these services can watch videos in different ways with different ROIs (Regions of Interest), especially when there are multiple objects in a scene, and thus there are few common ways for them to transfer their impressions for each scene directly. Posting messages is currently the usual way but it does not sufficiently enable all users to transfer their impressions. To transfer a user's impressions directly and provide users with a richer video watching experience, we propose a system that enables them to extract their favorite parts of videos as ROI trajectories through simple and intuitive manipulation of their tablet device. It also enables them to share a recorded trajectory with others after stabilizing it in a manner that should be satisfactory to every user. Using statistical analysis of user manipulations, we have demonstrated an approach to trajectory stabilization that can eliminate undesirable or uncomfortable elements due to tablet-specific manipulations. The system's validity has been confirmed by subjective evaluations.

This paper is concerned with exploring an extended approach for the stability analysis and synthesis for Markovian jump nonlinear systems (MJNLSs) via fuzzy control. The Takagi-Sugeno (T-S) fuzzy model is employed to represent the MJNLSs with incomplete transition description. In this paper, not all the elements of the rate transition matrices (RTMs), or probability transition matrices (PTMs) are assumed to be known. By fully considering the properties of the RTMs and PTMs, sufficient criteria of stability and stabilization is obtained in both continuous and discrete-time. Stabilization conditions with a mode-dependent fuzzy controller are derived for Markovian jump fuzzy systems in terms of linear matrix inequalities (LMIs), which can be readily solved by using existing LMI optimization techniques. Finally, illustrative numerical examples are provided to demonstrate the effectiveness of the proposed approach.

This paper summarizes the current status of regulations, standardization efforts and trials around the world regarding white space (WS) communications, especially television band WS (TVWS). After defining WS communication systems configurations and function and the categories of white space database, the TVWS regulations in United States, United Kingdom, and Japan are summarized. Then regarding status of standardization for TVWS devices, IEEE 802 and IEEE 1900 standards are summarized. Finally ongoing pilot projects and trials of WS communications in the world are summarized, and trends and future direction of research on WS communication systems are summarized.

In this paper, we propose a robust speech/music classification algorithm to improve the performance of speech/music classification in the selectable mode vocoder (SMV) of 3GPP2 using deep belief networks (DBNs), which is a powerful hierarchical generative model for feature extraction and can determine the underlying discriminative characteristic of the extracted features. The six feature vectors selected from the relevant parameters of the SMV are applied to the visible layer in the proposed DBN-based method. The performance of the proposed algorithm is evaluated using the detection accuracy and error probability of speech and music for various music genres. The proposed algorithm yields better results when compared with the original SMV method and support vector machine (SVM) based method.

In this paper, a new digital true random number generator based on Cross Feedback Ring Oscillators (CFRO) is proposed. The random sources of CFRO lie in delay variations (jitter), unpredictable transition behaviors as well as metastability. The CFRO is proved to be truly random by restarting from the same initial states. Compared with the so-called Fibonacci Ring Oscillator (FIRO) and Galois Ring Oscillator (GARO), the CFRO needs less than half of their time to accumulate relatively high entropy and enable extraction of one random bit. Only a simple XOR corrector is used to reduce the bias of output sequences. TRNG based on CFRO can be run continuously at a constant high speed of 150Mbps. For higher security, the TRNG can be set in stateless mode at a cost of slower speed of 10Mbps. The total logical resources used are relatively small and no special placement and routing is needed. The TRNG both in continuous mode and in stateless mode can pass the NIST tests and the DIEHARD tests.

In recent years, there has been rapid developments in radio-frequency identification (RFID) systems, and their industrial applications include logistics management, automatic object identification, access and parking management, etc. Moreover, RFID systems have also been introduced for the management of medical instruments in medical applications to improve the quality of medical services. In recent years, the combination of such a system with a biological monitoring system through permanent implantation in the human body has been suggested to reduce malpractice events and ameliorate the patient suffering. This paper presents an implantable RFID tag antenna design that can match the conjugate impedance of most integrated circuit (IC) chips (9.3-j55.2Ω at 2.45GHz. The proposed antenna can be injected into the human body through a biological syringe, owing to its compact size of 9.3mm × 1.0mm × 1.0mm. The input impedance, transmission coefficient, and received power are simulated by a finite element method (FEM). A three-layered phantom is used to confirm antenna performance.

We introduce a new nonlinear control method to globally asymptotically stabilize a class of uncertain nonlinear systems. First, we provide a system reconfiguration method which reconfigures the nonlinear systems with smooth positive functions. Then, we provide a nonlinear controller design method to globally asymptotically stabilize the reconfigured systems by utilizing Lyapunov equations. As a result, a class of uncertain nonlinear systems which have not been treated in the existing results can be globally asymptotically stabilized by our control method. Examples are given for easy following and illustration.

Recently, various research efforts have been conducted to develop strategies for accelerating multi-dimensional query processing using the graphics processing units (GPUs). However, well-known multi-dimensional access methods such as the R-tree, B-tree, and their variants are hardly applicable to GPUs in practice, mainly due to the characteristics of a hierarchical index structure. More specifically, the hierarchical structure not only causes frequent transfers of small volumes of data but also provides limited opportunity to exploit the advanced data parallelism of GPUs. To address these problems, we propose an approach that uses GPUs as a buffer. The main idea is that object entries in recently visited leaf nodes are buffered in the global memory of GPUs and processed by massive parallel threads of the GPUs. Through extensive performance studies, we observed that the proposed approach achieved query performance up to five times higher than that of the original R-tree.

This paper describes a near-optimal allocation method for web-based multi-player online role-playing games (MORPGs), which must be able to cope with a large number of users and high frequency of user requests. Our previous work introduced a dynamic data reallocation method. It uses multiple web servers and divides the entire game world into small blocks. Each ownership of block is allocated to a web server. Additionally, the ownership is reallocated to the other web server according to the user's requests. Furthermore, this block allocation was formulated as a combinational optimization problem. And a simulation based experiment with an exact algorithm showed that our system could achieve 31% better than an ad-hoc approach. However, the exact algorithm takes too much time to solve a problem when the problem size is large. This paper proposes a meta-heuristic approach based on a tabu search to solve a problem quickly. A simulation result shows that our tabu search algorithm can generate solutions, whose average correctness is only 1% different from that of the exact algorithm. In addition, the average calculation time for 50 users on a system with five web servers is about 25.67 msec while the exact algorithm takes about 162 msec. An evaluation for a web-based MORPG system with our tabu search shows that it could achieve 420 users capacity while 320 for our previous system.