In this paper, the problem of target detection and tracking utilizing the single frequency network (SFN) is addressed. Specifically, by exploiting the characteristics of the signal in SFN, a novel likelihood model which avoids the measurement origin uncertain problem in the point measurement model is proposed. The particle filter based track-before-detect (PF-TBD) algorithm is adopted for the proposed SFN likelihood to detect and track the possibly existed target. The advantage of using TBD algorithm is that it is suitable for the condition of low SNR, and specially, in SFN, it can avoid the data association between the measurement and the transmitters. The performance of the adopted algorithm is examined via simulations.

This letter presents numerical results of lossy source coding for non-uniformly distributed binary source with trellis codes. The results show how the performance of trellis codes approaches the rate-distortion function in terms of the number of states.

An FPGA-based hardware hand sign recognition system was proposed in our previous work. The hand sign recognition system consisted of a preprocessing and a self-organizing map (SOM)-Hebb classifier. The training of the SOM-Hebb classifier was carried out by an off-chip computer using training vectors given by the system. The recognition performance was reportedly improved by adding perturbation to the training data. The perturbation was added manually during the process of image capture. This paper proposes a new off-chip training method with automatic performance improvement. To improve the system's recognition performance, the off-chip training system adds artificially generated perturbation to the training feature vectors. Advantage of the proposed method compared to additive scale perturbation to image is its low computational cost because the number of feature vector elements is much less than that of pixels contained in image. The feasibility of the proposed off-chip training was tested in simulations and experiments using American sign language (ASL). Simulation results showed that the proposed perturbation computation alters the feature vector so that it is same as the one obtained by a scaled image. Experimental results revealed that the proposed off-chip training improved the recognition accuracy from 78.9% to 94.3%.

SUMPLE, one of important signal combining approaches, its combining loss increases when a sensor in an array fails. A novel failure detection circuit for SUMPLE is proposed by using variability index. This circuit can effectively judge whether a sensor fails or not. Simulation results validate its effectiveness with respect to the existing algorithms.

In this paper, we propose a novel phrase-based model for Korean morphological analysis by considering a phrase as the basic processing unit, which generalizes all the other existing processing units. The impetus for using phrases this way is largely motivated by the success of phrase-based statistical machine translation (SMT), which convincingly shows that the larger the processing unit, the better the performance. Experimental results using the SEJONG dataset show that the proposed phrase-based models outperform the morpheme-based models used as baselines. In particular, when combined with the conditional random field (CRF) model, our model leads to statistically significant improvements over the state-of-the-art CRF method.

In this paper, we study a control problem of a concurrent discrete event system, where several subsystems are partially synchronized via shared events, under local and global constraints described by linear temporal logic formulas. We propose a hierarchical control architecture consisting of local supervisors and a coordinator. While the supervisors ensure the local requirements, the coordinator decides which shared events to be disabled so as to satisfy the global specification. First, we construct Rabin games to obtain local supervisors. Next, we reduce them based on shared transitions. Finally, we construct a global Rabin game from the reduced supervisors and a deterministic Rabin automaton that accepts every run satisfying the global specification. By solving it, we obtain a coordinator that disables shared events to guarantee the global requirement. Moreover, the concurrent system controlled by the coordinator and the local supervisors is deadlock-free.

Considering that the traditional local-world evolving network model cannot fully reflect the characteristics of real-world power grids, this Letter proposes a new evolving model based on geographical location clusters. The proposed model takes into account the geographical locations and degree values of nodes, and the growth process is in line with the characteristics of the power grid. Compared with the characteristics of real-world power grids, the results show that the proposed model can simulate the degree distribution of China's power grids when the number of nodes is small. When the number of nodes exceeds 800, our model can simulate the USA western power grid's degree distribution. And the average distances and clustering coefficients of the proposed model are close to that of the real world power grids. All these properties confirm the validity and rationality of our model.

Multiple Subcarrier Multiple Access (MSMA) enables concurrent sensor data streamings from multiple wireless and batteryless sensors using the principle of subcarrier backscatter used extensively in passive RFID. Since the interference cancellation performance of MSMA depends on the Signal to Interference plus Noise Ratio of each subcarrier, the choice of channel allocation scheme is essential. Since the channel allocation is a combinatorial problem, obtaining the true optimal allocation requires a vast amount of examinations which is impracticable in a system where we have tens of sensor RF tags. It is particularly true when we have variable distance and variable bandwidth sensor RF tags. This paper proposes a channel allocation scheme in the variable distance and variable bandwidth MSMA system based on a newly introduced performance index, total contamination power, to prioritize indecision cases. The performance of the proposal is evaluated with existing methods in terms of average communication capacity and system fairness using MATLAB Monte Carlo simulation to reveal its advantage. The accuracy of the simulation is also verified with the result obtained from the brute force method.

Concurrent transmission (CT) is a revolutionary multi-hop protocol that significantly improves the MAC- and network-layer efficiency by allowing synchronized packet collisions. Although its superiority has been empirically verified, there is still a lack of studies on how the receiver survives such packet collisions, particularly in the presence of the carrier frequency offsets (CFO) between the transmitters. This work rectifies this omission by providing a comprehensive evaluation of the physical-layer receiver performance under CT, and a theoretical analysis on the fading duration of the beating effect resulting from the CFO. The main findings from our evaluations are the following points. (1) Beating significantly affects the receiver performance, and an error correcting mechanism is needed to combat the beating. (2) In IEEE 802.15.4 systems, the direct sequence spread spectrum (DSSS) plays such a role in combatting the beating. (3) However, due to the limited length of DSSS, the receiver still suffers from the beating if the fading duration is too long. (4) On the other hand, the basic M-ary FSK mode of IEEE 802.15.4g is vulnerable to CT due to the lack of error correcting mechanism. In view of the importance of the fading duration, we further theoretically derive the closed form of the average fading duration (AFD) of the beating under CT in terms of the transmitter number and the standard deviation of the CFO. Moreover, we prove that the receiver performance can be improved by having higher CFO deviations between the transmitters due to the shorter AFD. Finally, we estimate the AFD in the real system by actually measuring the CFO of a large number of sensor nodes.

This paper studies a simultaneous wireless information and power transfer (SWIPT) system in which the transmitter not only sends data and energy to many types of wireless users, such as multiple information decoding users, multiple hybrid power-splitting users (i.e., users with a power-splitting structure to receive both information and energy), and multiple energy harvesting users, but also prevents information from being intercepted by a passive eavesdropper. The transmitter is equipped with multiple antennas, whereas all users and the eavesdropper are assumed to be equipped with a single antenna. Since the transmitter does not have any channel state information (CSI) about the eavesdropper, artificial noise (AN) power is maximized to mask information as well as to interfere with the eavesdropper as much as possible. The non-convex optimization problem is formulated to minimize the transmit power satisfying all signal-to-interference-plus-noise (SINR) and harvested energy requirements for all users so that the remaining power for generating AN is maximized. With perfect CSI, a semidefinite relaxation (SDR) technique is applied, and the optimal solution is proven to be tight. With imperfect CSI, SDR and a Gaussian randomization algorithm are proposed to find the suboptimal solution. Finally, numerical performance with respect to the maximum SINR at the eavesdropper is determined by a Monte-Carlo simulation to compare the proposed AN scenario with a no-AN scenario, as well as to compare perfect CSI with imperfect CSI.

The problem of evaluating the matrix polynomial I+A+A2+…+AN-1 with a reduced number of matrix multiplications has long been considered. Several algorithms have been proposed for this problem, which find a procedure requiring O(log N) matrix multiplications for a given N. Among them, the hybrid algorithm based on the double-base representation of N, i.e., using mixed radices 2 and 3, proposed by Dimitrov and Cooklev is most efficient. It has been suggested by them that the use of higher radices would not bring any more efficient algorithms. In this paper, we show that we can derive more efficient algorithms by using higher radices, and propose several efficient algorithms.

A random forest (RF) is a kind of ensemble machine learning algorithm used for a classification and a regression. It consists of multiple decision trees that are built from randomly sampled data. The RF has a simple, fast learning, and identification capability compared with other machine learning algorithms. It is widely used for application to various recognition systems. Since it is necessary to un-balanced trace for each tree and requires communication for all the ones, the random forest is not suitable in SIMD architectures such as GPUs. Although the accelerators using the FPGA have been proposed, such implementations were based on HDL design. Thus, they required longer design time than the soft-ware based realizations. In the previous work, we showed the high-level synthesis design of the RF including the fully pipelined architecture and the all-to-all communication. In this paper, to further reduce the amount of hardware, we use k-means clustering to share comparators of the branch nodes on the decision tree. Also, we develop the krange tool flow, which generates the bitstream with a few number of hyper parameters. Since the proposed tool flow is based on the high-level synthesis design, we can obtain the high performance RF with short design time compared with the conventional HDL design. We implemented the RF on the Xilinx Inc. ZC702 evaluation board. Compared with the CPU (Intel Xeon (R) E5607 Processor) and the GPU (NVidia Geforce Titan) implementations, as for the performance, the FPGA realization was 8.4 times faster than the CPU one, and it was 62.8 times faster than the GPU one. As for the power consumption efficiency, the FPGA realization was 7.8 times better than the CPU one, and it was 385.9 times better than the GPU one.

System scenarios that derived from system design specification play an important role in the reliability engineering of component-based software systems. Several scenario-based approaches have been proposed to predict the reliability of a system at the design time, most of them adopt flat construction of scenarios, which doesn't conform to software design specifications and is subject to introduce state space explosion problem in the large systems. This paper identifies various challenges related to scenario modeling at the early design stages based on software architecture specification. A novel scenario-based reliability modeling and prediction approach is introduced. The approach adopts hierarchical scenario specification to model software reliability to avoid state space explosion and reduce computational complexity. Finally, the evaluation experiment shows the potential of the approach.

In this paper, we present a novel regression-based robust locality preserving projections (RRLPP) method to effectively deal with the issue of noise and occlusion in facial expression recognition. Similar to robust principal component analysis (RPCA) and robust regression (RR) approach, the basic idea of the presented RRLPP approach is also to lead in the low-rank term and the sparse term of facial expression image sample matrix to simultaneously overcome the shortcoming of the locality preserving projections (LPP) method and enhance the robustness of facial expression recognition. However, RRLPP is a nonlinear robust subspace method which can effectively describe the local structure of facial expression images. The test results on the Multi-PIE facial expression database indicate that the RRLPP method can effectively eliminate the noise and the occlusion problem of facial expression images, and it also can achieve better or comparative facial expression recognition rate compared to the non-robust and robust subspace methods meantime.

This paper proposes a method for reducing the peak-to-average power ratio (PAPR) at the output signal of a digital predistortion linearizer (DPDL) that compensates for frequency dependent intermodulation distortion (IMD) components. The proposed method controls the amplitude and phase values of the frequency components corresponding to the transmission bandwidth of the output signal. A DPDL employing the proposed method simultaneously provides IMD component cancellation of out-of-band components and PAPR reduction at the output signal. This paper identifies the amplitude and phase conditions to minimize the PAPR. Experimental results based on a 2-GHz band 1-W class power amplifier show that the proposed method improves the drain efficiency of the power amplifier when degradation is allowed in the error vector magnitude. To the best knowledge of the authors, this is the first PAPR reduction method for DPDL that reduces the PAPR while simultaneously compensating for IMD components.

To design antennas for ingestible capsule endoscope systems, the transmission factors of dipole and loop antennas placed in the torso-shaped phantom filled with deionized water or human body equivalent liquid (HBEL) are investigated by numerical and experimental study. The S-parameter method is used to evaluate transmission characteristics through a torso-shaped phantom in a broadband frequency range. Good agreement of S-parameters between measured results and numerical analysis is observed and the transmission factors for both cases are obtained. Comparison of the transmission factors between HBEL and deionized water is presented to explain the relation between conductivity and the transmission characteristics. Two types of antennas, dipole antenna and loop antenna are compared. In the case of a dipole antenna placed in deionized water, it is observed that the transmission factor decreases as conductivity increases. On the other hand, there is a local maximum in the transmission factor at 675 MHz in the case of HBEL. This phenomenon is not observed in the case of a loop antenna. The transmission factor of capsule dipole antenna and capsule loop antenna are compared and the guideline in designing capsule antennas by using transmission factor is also proposed.

Event-triggered control is a method that the control input is updated only when a certain triggering condition is satisfied. In networked control systems, quantization errors via A/D conversion should be considered. In this paper, a new method for quantized event-triggered control with switching triggering conditions is proposed. For a discrete-time linear system, we consider the problem of finding a state-feedback controller such that the closed-loop system is uniformly ultimately bounded in a certain ellipsoid. This problem is reduced to an LMI (Linear Matrix Inequality) optimization problem. The volume of the ellipsoid may be adjusted. The effectiveness of the proposed method is presented by a numerical example.

This paper describes analytical results obtained for floor penetration loss characteristics and their frequency dependency by measurements in multiple frequency bands, including those above 6GHz, in an indoor office environment. Measurement and analysis results confirm that the floor penetration loss depends on two dominant components: the transmission path through floors, and the path traveling through the outside building. We also clarify that these dominant paths have different path loss characteristics and frequency dependency. The transmission path through floors rapidly attenuates with large inter-floor offsets and in high frequency bands. On the other hand, the path traveling through outside of the building attenuates monotonically as the frequency increases. Therefore, the transmission path is dominant at short inter-floor offsets and low frequencies, and the path traveling through the outside is dominant at high number of floors or high frequency. Finally, we clarify that the floor penetration loss depends on the frequency dependency of the dominant path on the basis of the path loss characteristics of each dominant path.

This paper proposes a genetic algorithm (GA) based design method for arbitrarily-shaped resonant elements that offer enhanced reflectarray antenna performance. All elements have the specified phase property over the range of 360°, and also have dual-polarization and low cross-polarization properties for better reflectarray performance. In addition, the proposal is suitable for linear-to-circular polarization conversion elements. Thus, polarizer reflectarray elements are also presented in this paper. The proposed elements are validated using both numerical simulations and experiments.

An area-efficient FPGA-based annealing processor that is based on Ising model is proposed. The proposed processor eliminates random number generators (RNGs) and temperature schedulers, which are the key components in the conventional annealing processors and occupying a large portion of the design. Instead, a shift-register-based spin flipping scheme successfully helps the Ising model from stucking in the local optimum solutions. An FPGA implementation and software-based evaluation on max-cut problems of 2D-grid torus structure demonstrate that our annealing processor solves the problems 10-104 times faster than conventional optimization algorithms to obtain the solution of equal accuracy.