The simple repetitive control system proposed by Yamada et al. is a type of servomechanism for periodic reference inputs. This system follows a periodic reference input with a small steady-state error, even if there is periodic disturbance or uncertainty in the plant. In addition, simple repetitive control systems ensure that transfer functions from the periodic reference input to the output and from the disturbance to the output have finite numbers of poles. Yamada et al. clarified the parameterization of all stabilizing simple repetitive controllers. Recently, Yamada et al. proposed the parameterization of all stabilizing two-degrees-of-freedom (TDOF) simple repetitive controllers that can specify the input-output characteristic and the disturbance attenuation characteristic separately. However, when using the method of Yamada et al., it is complex to specify the low-pass filter in the internal model for the periodic reference input that specifies the frequency characteristics. This paper extends the results of Yamada et al. and proposes the parameterization of all stabilizing TDOF simple repetitive controllers with specified frequency characteristics in which the low-pass filter can be specified beforehand.

Periodic-finite-type shifts (PFT's) are sofic shifts which forbid the appearance of finitely many pre-specified words in a periodic manner. The class of PFT's strictly includes the class of shifts of finite type (SFT's). The zeta function of a PFT is a generating function for the number of periodic sequences in the shift. For a general sofic shift, there exists a formula, attributed to Manning and Bowen, which computes the zeta function of the shift from certain auxiliary graphs constructed from a presentation of the shift. In this paper, we derive an interesting alternative formula computable from certain “word-based graphs” constructed from the periodically-forbidden word description of the PFT. The advantages of our formula over the Manning-Bowen formula are discussed.

Wireless sensor nodes are becoming more and more common in various settings and require a long battery life for better maintainability. Since most sensor nodes are powered by batteries, energy efficiency is a critical problem. In an experiment, we observed that when peak power consumption is high, battery voltage drops quickly, and the sensor stops working even though some useful charge remains in the battery. We propose three off-line algorithms that extend battery life by scheduling sensors' execution time that is able to reduce peak power consumption as much as possible under a deadline constraint. We also developed a simulator to evaluate the effectiveness of these algorithms. The simulation results showed that one of the three algorithms dramatically can extend battery life approximately three time as long as in simultaneous sensor activation.

A wireless emergency communication network with a fixed allocation of spectrum resources cannot meet the tremendous demand for spectrum access when a crisis occurs. It is necessary to develop an effective spectrum access scheme to improve the performance of emergency communication systems. In this paper, we study a new emergency communication system combines cognitive radio technology and an emergency communication network. Emergency users can utility resources in a general network when traffic becomes congested in an emergency network. Non-reciprocal spectrum access scheme (NRA) and reciprocal spectrum access scheme (RA) for two heterogeneous cognitive networks, namely emergency network and general network are proposed to compare with traditional spectrum access scheme (TA). User behavior with each scheme is modeled by continuous-time Markov chains. Moreover, the blocking and dropping probabilities of users in two heterogeneous cognitive networks are derived as the performance metrics. In addition, the throughput and the spectrum utilization rate of the system are evaluated. Finally, we compare the performance of three dynamic spectrum access schemes. The simulation results show that the RA scheme is an effective scheme to enhance the performance of emergency systems.

We present a multiband LTE SAW-less CMOS transmitter with source-follower-driven passive mixers, envelope-tracked RF-programmable gain amplifiers (RF-PGAs), and Marchand Baluns. A driver stage for passive mixers is realized by a source follower, which enables a quadrature modulator (QMOD) to achieve low noise performance at a 1.2 V supply and contributes to a small-area and low-power transmitter. An envelope-tracking technique is adopted to improve the linearity of RF-PGAs and obtain a better Evolved Universal Terrestrial Radio Access Adjacent Channel Leakage power Ratio (E-UTRA ACLR). The Marchand balun covers more frequency bands than a transformer and is more suitable for multiband operation. The proposed transmitter, which also includes digital-to-analog converters and a phase-locked loop, is implemented in a 65-nm CMOS process. The implemented transmitter achieves E-UTRA ACLR of less than -42 dBc and RX-band noise of less than -158 dBc/Hz in the frequency range of 700 MHz–2.6 GHz. These performances are good enough for multiband LTE and SAW-less operation.

Substrate coupling of radio frequency (RF) components is represented by equivalent circuits unifying a resistive mesh network with lumped capacitors in connection with the backside of device models. Two-port S-parameter test structures are used to characterize the strength of substrate coupling of resistors, capacitors, inductors, and MOSFETs in a 65 nm CMOS technology with different geometries and dimensions. The consistency is finely demonstrated between simulation with the equivalent circuits and measurements of the test structures, with the deviation of typically less than 3 dB for passive and 6 dB for active components, in the transmission properties for the frequency range of interest up to 8 GHz.

Online resource management of a software system can take advantage of a performance model to predict the effect of proposed changes. However, the prediction accuracy may degrade if the performance model does not adapt to the changes in the system. This work considers the problem of using Kalman filters to track changes in both performance model parameters and system behavior. We propose a method based on the multiple-model Kalman filter. The method runs a set of Kalman filters, each of which models different system behavior, and adaptively fuses the output of those filters for overall estimates. We conducted case studies to demonstrate how to use the method to track changes in various system behaviors: performance modeling, process modeling, and measurement noise. The experiments show that the method can detect changes in system behavior promptly and significantly improve the tracking and prediction accuracy over the single-model Kalman filter. The influence of model design parameters and mode-model mismatch is evaluated. The results support the usefulness of the multiple-model Kalman filter for tracking performance model parameters in systems with time-varying behavior.

This paper presents a digitally error-corrected pipeline analog-to-digital converter (ADC) using linearization of incomplete settling errors. A pre-charging technique is used for residue amplifiers in order to reduce the incomplete settling error itself and linearize the input signal dependency of the incomplete settling error. A technique with charge redistribution of divided capacitors is proposed for pre-charging capacitors without any additional reference sources. This linearized settling error is corrected by a first-order error approximation in digital domain with feasible complexity and cost. Simulation results show that the ADC achieves SNDR of 70 dB, SFDR of 79 dB at nyquist input frequency in a 65 nm CMOS process under 1.2 V power supply voltage for 1.2 Vp-p input signal swing. The estimated power consumption of the 12b 200 MS/s pipeline ADC using the proposed digital error correction of incomplete settling errors is 7.6 mW with a small FOM of 22 fJ/conv-step.

Diffusion-weighted (DW)-functional magnetic resonance imaging (fMRI) is a recently reported technique for measuring neural activities by using diffusion-weighted imaging (DWI). DW-fMRI is based on the property that cortical cells swell when the brain is activated. This approach can be used to observe changes in water diffusion around cortical cells. The spatial and temporal resolutions of DW-fMRI are superior to those of blood-oxygenation-level-dependent (BOLD)-fMRI. To investigate how the DWI signal intensities change in DW-fMRI measurement, we carried out Monte Carlo simulations to evaluate the intensities before and after cell swelling. In the simulations, we modeled cortical cells as two compartments by considering differences between the intracellular and the extracellular regions. Simulation results suggested that DWI signal intensities increase after cell swelling because of an increase in the intracellular volume ratio. The simulation model with two compartments, which respectively represent the intracellular and the extracellular regions, shows that the differences in the DWI signal intensities depend on the ratio of the intracellular and the extracellular volumes. We also investigated the MPG parameters, b-value, and separation time dependences on the percent signal changes in DW-fMRI and obtained useful results for DW-fMRI measurements.

This paper presents a bistatic remote sensing system to efficiently estimate the characteristics of sea swell near a harbor by receiving and processing global navigation satellite system signals transmitted in line-of-sight channels and fading multipath channels. The new system is designed to measure and monitor sea swell to improve the safety of mooring and navigation services in or around harbors, and long-term measurement also will provide valuable hydrologic data for harbor construction or reconstruction. The system uses two sets of antennas. One is a conventional antenna to receive line-of-sight signal and mitigate the disturbances from multiple propagation paths, and the other is a left hand circular polarization arrayed antenna to receive reflected signals from sea-surface. In particular, a wide bandwidth RF/IF front-end is designed to process reflected signals with high sampling frequency. A software receiver is developed to provide information from satellites and line-of-sight signals, and a wave characteristic estimator is also developed to process reflected signals. More specifically, correlators and Teager-Kaiser energy operator are combined to detect and depict reflected signals. Wave propagation of sea swell can be accurately mapped using intensity and relative time delays of reflected signals. The operational performance of the remote sensing system was also evaluated by numerical simulations. The results confirm that wavelength and wave period can be measured precisely by the proposed bistatic ocean wave remote sensing system.

In this paper, we propose a pedestrian detection algorithm based on both appearance and motion features to achieve high detection accuracy when applied to complex scenes. Here, a pedestrian's appearance is described by a histogram of oriented spatial gradients, and his/her motion is represented by another histogram of temporal gradients computed from successive frames. Since pedestrians typically exhibit not only their human shapes but also unique human movements generated by their arms and legs, the proposed algorithm is particularly powerful in discriminating a pedestrian from a cluttered situation, where some background regions may appear to have human shapes, but their motion differs from human movement. Unlike the algorithm based on a co-occurrence feature descriptor where significant generalization errors may arise owing to the lack of extensive training samples to cover feature variations, the proposed algorithm describes the shape and motion as unique features. These features enable us to train a pedestrian detector in the form of a spatio-temporal histogram of oriented gradients using the AdaBoost algorithm with a relatively small training dataset, while still achieving excellent detection performance. We have confirmed the effectiveness of the proposed algorithm through experiments on several public datasets.

Application-level routing that chooses an end-to-end traffic route that relays other end hosts can improve user-perceived performance metrics such as end-to-end latency and available bandwidth. However, selfish route selection performed by each end user can lead to a decrease in path performance due to overload by route overlaps, as well as an increase in the inter-ISP transit cost as a result of utilizing more transit links compared with native IP routing. In this paper, we first strictly define an optimization problem for selecting application-level traffic routes with the aim of maximizing end-to-end network performance under a transit cost constraint. We then propose an application-level traffic routing method based on distributed simulated annealing to obtain good solutions to the problem. We evaluate the performance of the proposed method by assuming that PlanetLab nodes utilize application-level traffic routing. We show that the proposed routing method can result in considerable improvement of network performance without increasing transit cost. In particular, when using end-to-end latency as a routing metric, the number of overloaded end-to-end paths can be reduced by about 65%, as compared with that when using non-coordinated methods. We also demonstrate that the proposed method can react to dynamic changes in traffic demand and select appropriate routes.

Scale Invariant Feature Transform (SIFT) algorithm is a very excellent approach for feature detection. It is characterized by data intensive computation. The current studies of accelerating SIFT algorithm are mainly reflected in three aspects: optimizing the parallel parts of the algorithm based on general-purpose multi-core processors, designing the customized multi-core processor dedicated for SIFT, and implementing it based on the FPGA platform. The real-time performance of SIFT has been highly improved. However, the factors such as the input image size, the number of octaves and scale factors in the SIFT algorithm are restricted for some solutions, the flexibility that ensures the high execution performance under variable factors should be improved. This paper proposes a reconfigurable solution to solve this problem. We fully exploit the algorithm and adopt several techniques, such as full parallel execution, block computation and CORDIC transformation, etc., to improve the execution efficiency on a REconfigurable MUltimedia System called REMUS. Experimental results show that the execution performance of the SIFT is improved by 33%, 50% and 8 times comparing with that executed in the multi-core platform, FPGA and ASIC separately. The scheme of dynamic reconfiguration in this work can configure the circuits to meet the computation requirements under different input image size, different number of octaves and scale factors in the process of computing.

This paper presents the stability analysis for continuous-time Takagi-Sugeno fuzzy systems using a fuzzy Lyapunov function. The proposed fuzzy Lyapunov function involves the time derivatives of states to include new free matrices in the LMI stability conditions. These free matrices extend the solution space for Linear Matrix Inequalities (LMIs) problems. Numerical examples illustrate the effectiveness of the proposed methods.

In this paper, we propose a generic construction of one-round attribute-based (implicitly) authenticated key exchange (ABAKE). The construction is based on a chosen-ciphertext (CCA) secure attribute-based KEM and the decisional Diffie-Hellman (DDH) assumption. If an underlying attribute-based KEM scheme allows expressive access controls and is secure in the standard model (StdM), an instantiated ABAKE scheme also achieves them. Our scheme enjoys the best of both worlds: efficiency and security. The number of rounds is one (optimal) while the known secure scheme in the StdM is not one-round protocol. Our scheme is comparable in communication complexity with the most efficient known scheme that is not proved in the StdM. Also, our scheme is proved to satisfy security against advanced attacks like key compromise impersonation.

Given a binary image I and a threshold t, the size-thresholded binary image I(t) defined by I and t is the binary image after removing all connected components consisting of at most t pixels. This paper presents space-efficient algorithms for computing a size-thresholded binary image for a binary image of n pixels, assuming that the image is stored in a read-only array with random-access. With regard to the problem, there are two cases depending on how large the threshold t is, namely, Relatively large threshold where t = Ω(), and Relatively small threshold where t = O(). In this paper, a new algorithmic framework for the problem is presented. From an algorithmic point of view, the problem can be solved in O() time and O() work space. We propose new algorithms for both the above cases which compute the size-threshold binary image for any binary image of n pixels in O(nlog n) time using only O() work space.

This paper presents an efficient algorithm for reporting all intersections among n given segments in the plane using work space of arbitrarily given size. More exactly, given a parameter s which is between Ω(1) and O(n) specifying the size of work space, the algorithm reports all the segment intersections in roughly O(n2/+ K) time using O(s) words of O(log n) bits, where K is the total number of intersecting pairs. The time complexity can be improved to O((n2/s) log s + K) when input segments have only some number of different slopes.

Forward secrecy (FS) is a central security requirement of authenticated key exchange (AKE). Especially, strong FS (sFS) is desirable because it can guarantee security against a very realistic attack scenario that an adversary is allowed to be active in the target session. However, most of AKE schemes cannot achieve sFS, and currently known schemes with sFS are only proved in the random oracle model. In this paper, we propose a generic construction of AKE protocol with sFS in the standard model against a constrained adversary. The constraint is that session-specific intermediate computation results (i.e., session state) cannot be revealed to the adversary for achieving sFS, that is shown to be inevitable by Boyd and González Nieto. However, our scheme maintains weak FS (wFS) if session state is available to the adversary. Thus, our scheme satisfies one of strongest security definitions, the CK+ model, which includes wFS and session state reveal. The main idea to achieve sFS is to use signcryption KEM while the previous CK+ secure construction uses ordinary KEM. We show a possible instantiation of our construction from Diffie-Hellman problems.

In this paper, we propose an identity-based authenticated key exchange (ID-AKE) protocol that is secure in the identity-based extended Canetti-Krawczyk (id-eCK) model in the random oracle model under the gap Bilinear Diffie-Hellman assumption. The proposed ID-AKE protocol is the most efficient among the existing ID-AKE protocols that is id-eCK secure, and it can be extended to use in asymmetric pairing.

Corpus-based concatenative speech synthesis has been widely investigated and deployed in recent years since it provides a highly natural synthesized speech quality. The amount of computation required in the run time, however, can often be quite large. In this paper, we propose early stopping schemes for Viterbi beam search in the unit selection, with which we can stop early in the local Viterbi minimization for each unit as well as in the exploration of candidate units for a given target. It takes advantage of the fact that the space of the acoustic parameters of the database units is fixed and certain lower bounds of the concatenation costs can be precomputed. The proposed method for early stopping is admissible in that it does not change the result of the Viterbi beam search. Experiments using probability-based concatenation costs as well as distance-based costs show that the proposed methods of admissible stopping effectively reduce the amount of computation required in the Viterbi beam search while keeping its result unchanged. Furthermore, the reduction effect of computation turned out to be much larger if the available lower bound for concatenation costs is tighter.