We propose a system to provide accurate data and timely medical services, unconstrained by location, through the use of a platform that can utilize mobile devices, interface with sensors and medical information systems. As the application of integrated platform, we aim to develop medical services that manage thrombolytic therapy for emergent stroke patients.

Sensor networks, in which many small terminals are wirelessly connected, have recently received considerable interest according to the development of wireless technology and electronic circuit. To provide advanced applications and services by the sensor networks, data collection including node location is essential. Hence the location estimation is important and many localization schemes have been proposed. Time of arrival (TOA) localization is one of the popular schemes because of its high estimation accuracy in ultra wide-band (UWB) sensor networks. However, a non-line-of-sight (NLOS) environment between the target and the anchor nodes causes a serious estimation error because the time is delayed more than its true one. Thus, the NLOS nodes should be detected and eliminated for estimation. As a well-known NLOS detection scheme, an iterative minimum residual (IMR) scheme which has low calculation complexity is used for detection. However, the detection error exists in IMR scheme due to the measurement error. Therefore, in this paper, we propose a new IMR-based NLOS detection scheme and show its performance improvement by computer simulations.

We propose a deception mechanism to combat a compromised station in IEEE 802.11 channel hopping systems. A compromised station can follow the hopping channels and continuously attack them, since it recognizes the channel-hopping sequence. The key concept of the deception mechanism is that an access point notifies a new hopping seed but not to the jammer, while a deception station deceives the jammer. Simulations show that the proposed scheme increases network throughput compared to conventional channel hopping schemes when they are under compromised station attacks.

For an adaptive system identification filter with a stochastic input signal, a coefficient vector updated with an NLMS algorithm converges in the sense of ensemble average and the expected convergence vector has been revealed. When the input signal is periodic, the convergence of the adaptive filter coefficients has also been proved. However, its convergence vector has not been revealed. In this paper, we derive the convergence vector of adaptive filter coefficients updated with the NLMS algorithm in system identification for deterministic sinusoidal inputs. Firstly, we derive the convergence vector when a disturbance does not exist. We show that the derived convergence vector depends only on the initial vector and the sinusoidal frequencies, and it is independent of the step-size for adaptation, sinusoidal amplitudes, and phases. Next, we derive the expected convergence vector when the disturbance exists. Simulation results support the validity of the derived convergence vectors.

In this letter, the amount of interference and an analytic methodology from a combination of Korea's LTE system and Japan's digital terrestrial TV broadcasting system using the 700 MHz frequency band are established when considering a practical deployment of both systems. We performed Monte-Carlo simulations on the throughput loss to evaluate how much interference radiating from Japan's DTV is imposed on the Korean LTE system. The results of the established methodology can be used as a guideline for allowing the deployed LTE system to avoid an unacceptable amount of interference.

The energy consumption is always a serious problem for mobile devices powered by battery. As the capacity and density of off-chip memory continuous to scale, its energy consumption accounts for a considerable amount of the whole system energy. There are therefore strong demands for energy efficient techniques towards memory system. Different from previous works, we explore the different power management modes of the off-chip memory by process scheduling for the multi-core mobile devices. In particular, we schedule the processes based on their memory access characteristics to maximize the number of the memory banks being in low power mode. We propose a fast approximation algorithm to solve the scheduling process problem for the dual-core mobile device. And for those equipped with more than two cores, we prove that the scheduling process problem is NP-Hard, and propose two heuristic algorithms. The proposed algorithms are evaluated through a series of experiments, for which we have encouraging results.

In this paper, we present a new fault-tolerant, large-scale star network scheme called Scalable Autonomous Fault-tolerant Ethernet (SAFE). The primary goal of a SAFE scheme is to provide network scalability and autonomous fault detection and recovery. SAFE divides a large-scale, mission-critical network, such as the naval combatant network, into several subnets by limiting the number of nodes in each subnet. This network can be easily configured as a star network in order to meet fault recovery time requirements. For SAFE, we developed a novel mechanism for inter-subnet fault detection and recovery; a conventional Ethernet-based heartbeat mechanism is used in each subnet. Theoretical and experimental performance analyses of SAFE in terms of fail-over time were conducted under various network failure scenarios. The results validate our scheme.

This letter deals with a system identification problem with unknown model order, which can be formulated as the matrix rank minimization problem by applying the subspace identification method. A sequential rank minimization algorithm is provided by modifying the null space based alternating optimization (NSAO) algorithm, and a model order identification algorithm is proposed. Numerical examples show that the proposed sequential algorithm can adaptively identify the model order of switched systems whose model order changes.

Radio frequency identification (RFID) system has gained attention as a new identification source that achieves a ubiquitous environment. Each RFID tag has a unique ID and is attached to an object. A user reads the unique ID of an RFID tag by using RFID readers and obtains the information on the object. One of the important technologies that use the RFID systems is the position estimation of RFID tags. Position estimation means estimating the location of the object with the RFID tag. Acquiring the location information of the RFID tag can be very useful. If a user can know the position of the RFID tag, the position estimation can be applied to a navigation system for walkers. In this paper, we propose a new position estimation method named Swift Communication Range Recognition (S-CRR) as an extended improvement on previous CRR that shortens the estimation delay. In this method, the position of an RFID tag is estimated by selecting the communication area model that corresponds to its boundary angles. We evaluated its performance by experiments and simulations of the RFID system. As the results, we found that S-CRR can estimate the position of an RFID tag comparatively accurately and quickly.

In this paper, we propose a hardware solution for overcoming the problem of high computational demands in a nearest neighbor (NN) based multi-prototype learning system. The multiple prototypes are obtained by a high-speed K-means clustering algorithm utilizing a concept of software-hardware cooperation that takes advantage of the flexibility of the software and the efficiency of the hardware. The one nearest neighbor (1-NN) classifier is used to recognize an object by searching for the nearest Euclidean distance among the prototypes. The major deficiency in conventional implementations for both K-means and 1-NN is the high computational demand of the nearest neighbor searching. This deficiency is resolved by an FPGA-implemented coprocessor that is a VLSI circuit for searching the nearest Euclidean distance. The coprocessor requires 12.9% logic elements and 58% block memory bits of an Altera Stratix III E110 FPGA device. The hardware communicates with the software by a PCI Express (4) local-bus-compatible interface. We benchmark our learning system against the popular case of handwritten digit recognition in which abundant previous works for comparison are available. In the case of the MNIST database, we could attain the most efficient accuracy rate of 97.91% with 930 prototypes, the learning speed of 1.310-4 s/sample and the classification speed of 3.9410-8 s/character.

In this letter, a quantization error-aware Tomlinson-Harashinma Precoding (THP) is proposed based on the equivalent zero-forcing (ZF) criterion in Multiuser Multiple-Input Single-Output (MU-MISO) systems with limited feedback, where the transmitter has only quantized channel direction information (CDI). This precoding scheme is robust to the channel uncertainties arising from the quantization error and the lack of channel magnitude information (CMI). Our simulation results show that the new THP scheme outperforms the conventional precoding scheme in limited feedback systems with respect to Bit Error Ratio (BER).

This letter proposes a simple heuristic to identify the discrete-time switched autoregressive exogenous (SARX) systems. The goal of the identification is to identify the switching sequence and the system parameters of all submodels simultaneously. In this letter the SARX system identification problem is formulated as the l0 norm minimization problem, and an iterative algorithm is proposed by applying the reweighted least squares technique. Although the proposed algorithm is heuristic, the numerical examples show its efficiency and robustness for noise.

The optimal way to build speech understanding modules depends on the amount of training data available. When only a small amount of training data is available, effective allocation of the data is crucial to preventing overfitting of statistical methods. We have developed a method for allocating a limited amount of training data in accordance with the amount available. Our method exploits rule-based methods for when the amount of data is small, which are included in our speech understanding framework based on multiple model combinations, i.e., multiple automatic speech recognition (ASR) modules and multiple language understanding (LU) modules, and then allocates training data preferentially to the modules that dominate the overall performance of speech understanding. Experimental evaluation showed that our allocation method consistently outperforms baseline methods that use a single ASR module and a single LU module while the amount of training data increases.

In this letter, we propose a GRid-based Indexing scheme on Multiple channels (GRIM) for processing spatial window queries in non-flat wireless spatial data broadcasting. When the clients access both popular and regular items simultaneously, GRIM provides the clients with improved access time by broadcasting popular items separately from regular ones in units of grid cells over multiple channels. Simulations show that the proposed GRIM outperforms the existing indexing scheme in terms of the access time.

In this paper, the optimal control problem of a probabilistic Boolean network (PBN), which is one of the significant models in gene regulatory networks, is discussed. In the existing methods of optimal control for PBNs, it is necessary to compute state transition diagrams with 2n nodes for a given PBN with n states. To avoid this computation, a polynomial optimization approach is proposed. In the proposed method, a PBN is transformed into a polynomial system, and the optimal control problem is reduced to a polynomial optimization problem. Since state transition diagrams are not computed, the proposed method is convenient for users.

In this paper, we discuss the stochastic modeling for operational software reliability measurement, assuming that the testing environment is originally different from the user operation one. In particular, we introduce the concept of systemability which is defined as the reliability characteristic subject to the uncertainty of the field operational environment into the model. First we introduce the environmental factor to consistently bridge the gap between the software failure-occurrence characteristics during the testing and the operation phases. Then we consider the randomness of the environmental factor, i.e., the environmental factor is treated as a random-distributed variable. We use the Markovian imperfect debugging model to describe the software reliability growth phenomena in the testing and the operation phases. We derive the analytical solutions of the several operational software reliability assessment measures which are given as the functions of time and the number of debuggings. Finally, we show several numerical illustrations to investigate the impacts of the consideration of systemability on the field software reliability evaluation.

Based on a reverse converter algorithm derived from the New Chinese Remainder Theorem I, an algorithm for sign detection of RNS {2n-1, 2n, 2n+1} is presented in this paper. The hardware of proposed algorithm can be implemented using two n-bit additions and one (n+1)-bit comparator. Comparing with the previous paper, the proposed algorithm has reduced the number of additions used in the circuit. The experimental results show that the proposed circuit achieves 17.3% savings in area for small moduli and 10.5% savings in area for large moduli on an average, with almost the same speed. The power dissipations obtain 12.6% savings in average.

We propose linear precoders which jointly minimize the mean-squared error of estimated symbol at the destination node for cooperative multiple-input multiple-output communication systems. Unlike the existing precoders which require high computational complexity to solve the optimization problem on the direct link, the proposed precoder is expressed in a closed-form. Simulation results show that the proposed precoder outperforms the existing precoders in the low SNR region. Moreover, we observe that the proposed iterative algorithm is not sensitive to the initial matrices.

Ultra-wideband (UWB) technology has attracted much attention recently due to its high data rate and low emission power. Its media access control (MAC) protocol, WiMedia MAC, promises a lot of facilities for high-speed and high-quality wireless communication. However, these benefits in turn involve a large amount of computational load, which challenges the traditional uniprocessor architecture based implementation method to provide the required performance. However, the constrained cost and power budget, on the other hand, makes using commercial multiprocessor solutions unrealistic. In this paper, a low-cost and energy-efficient multiprocessor system-on-chip (MPSoC), which tackles at once the aspects of system design, software migration and hardware architecture, is presented for the implementation of UWB MAC layer. Experimental results show that the proposed MPSoC, based on four simple RISC processors and shared-memory infrastructure, achieves up to 45% performance improvement and 65% power saving, but takes 15% less area than the uniprocessor implementation.

This letter investigates an iterative learning control with advanced output data (ADILC) scheme for non-minimum phase (NMP) systems when the number of NMP zeros is unknown. ADILC has a simple learning structure that can be applied to both minimum phase and NMP systems. However, in the latter case, it is assumed that the number of NMP zeros is already known. In this paper, we propose an ADILC scheme in which the number of NMP zeros is unknown. Based on input-to-output mapping, the learning starts from the relative degree. When the input becomes larger than a certain upper bound, we redesign the input update law which consists of the relative degree and the estimated value for the number of NMP zeros.