In this paper, we establish a novel bimodal emotion database from physiological signals and facial expression, which is named as PSFE. The physiological signals and facial expression of the PSFE database are respectively recorded by the equipment of the BIOPAC MP 150 and the Kinect for Windows in the meantime. The PSFE database altogether records 32 subjects which include 11 women and 21 man, and their age distribution is from 20 to 25. Moreover, the PSFE database records three basic emotion classes containing calmness, happiness and sadness, which respectively correspond to the neutral, positive and negative emotion state. The general sample number of the PSFE database is 288 and each emotion class contains 96 samples.

In this paper, the problem on secure communication based on chaos synchronization is investigated. The dual channel information transmitting technology is proposed to increase the security of secure communication system. Based on chaos synchronization, a new digital secure communication scheme is presented for a class of master-slave systems. Finally some numerical simulation examples are given to demonstrate the effectiveness of the given results.

This paper investigates an adaptive movable access point (AMAP) system and explores its feasibility in a static indoor classroom environment with an applied wireless local area network (WLAN) system. In the AMAP system, the positions of multiple access points (APs) are adaptively moved in accordance with clustered user groups, which ensures effective coverage for non-uniform user distributions over the target area. This enhances the signal to interference and noise power ratio (SINR) performance. In order to derive the appropriate AP positions, we utilize the k-means method in the AMAP system. To accurately estimate the position of each user within the target area for user clustering, we use the general methods of received signal strength indicator (RSSI) or time of arrival (ToA), measured by the WLAN systems. To clarify the basic effectiveness of the AMAP system, we first evaluate the SINR performance of the AMAP system and a conventional fixed-position AP system with equal intervals using computer simulations. Moreover, we demonstrate the quantitative improvement of the SINR performance by analyzing the ToA and RSSI data measured in an indoor classroom environment in order to clarify the feasibility of the AMAP system.

Various applications have been realized on mobile computers such as smart phones and tablet computers. Because mobile computers have smaller monitors than conventional computers, strategies to develop user interfaces differ from conventional computer applications. For example, contents in a window are reduced or divided into multiple windows on mobile computers. To realize usable applications in this situation, usability evaluations are important. Although various usability evaluation methods for mobile computers have been proposed, few evaluate applications and identify problems automatically. Herein we propose a systematic usability evaluation method. In our method, operation histories by users are recorded and analyzed to identify steps with usability problems. Our method automatically analyzes usability problems, allowing usability evaluations in software development to be implemented easily and economically. As a case study, the operation histories were recorded and analyzed when 20 subjects operated an application on a tablet computer. Our method automatically identified many usability problems, confirming its effectiveness.

This paper proposes MRO-PUF, a new architecture for ring-oscillator-based physically unclonable functions (PUFs) with enhanced resistance against machine learning attacks. In the proposed PUF, an instantaneous output value of a ring oscillator is used as a response, whereas the most existing PUFs directly use propagation delays to determine the response. Since the response of the MRO-PUF is non-linear and discontinuous as the delay of the ring oscillator increases, the prediction of the response by machine learning attacks is difficult. Through the performance evaluation of the MRO-PUF with simulations, it achieves 15 times stronger resistance against machine learning attacks using a support vector machine compared to the existing ones such as an arbiter PUF and a bistable ring PUF. The MRO-PUF also achieves a sufficient level of the basic performance of PUFs in terms of uniqueness and robustness.

A penta-band antenna based on the mu-negative transmission line is presented for radio frequency (RF) energy harvesting application. The antenna utilizes five radiation modes; two quarter wavelength resonances, three quarter wavelength resonance, zeroth order resonance, and first order resonance. The parasitic radiating strip antenna generates quarter wavelength resonance radiation. The dual band antenna based on two unit cell mu-negative (MNG) transmission line gives birth to the zeroth order resonance (ZOR) mode and the first order resonance (FOR) mode. The parasitic radiating strip and dual band antenna based on two unit mu-negative (MNG) transmission line are magnetically coupled by a feed monopole with gap. This feed monopole, simultaneously, radiates at quarter and three quarter wavelength resonance frequency to cover the other bands. The multi-mode coupling mechanism of this penta-band antenna is well modeled by our derived equivalent circuit. The measured radiation efficiencies are more than 87% over the entire penta-band.

A vertex subset F ⊆ V(G) is called a cyclic vertex-cut set of a connected graph G if G-F is disconnected such that at least two components in G-F contain cycles. The cyclic vertex connectivity is the cardinality of a minimum cyclic vertex-cut set. In this paper, we show that the cyclic vertex connectivity of the trivalent Cayley graphs TGn is equal to eight for n ≥ 4.

For the reconstruction of the feedback polynomial of a synchronous scrambler placed after a convolutional encoder, the existing algorithms require the prior knowledge of a dual word of the convolutional code. To address the case of a dual word being unknown, a new algorithm for the reconstruction of the feedback polynomial based on triple correlation characteristic of an m-sequence is proposed. First, the scrambled convolutional code sequence is divided into bit blocks; the product of the scrambled bit blocks with a dual word is proven to be an m-sequence with the same period as the synchronous scrambler. Second, based on the triple correlation characteristic of the generated m-sequence, a dual word is estimated; the generator polynomial of the generated m-sequence is computed by two locations of the triple correlation peaks. Finally, the feedback polynomial is reconstructed using the generator polynomial of the generated m-sequence. As the received sequence may contain bit errors, a method for detecting triple correlation peaks based on the constant false-alarm criterion is elaborated. Experimental results show that the proposed algorithm is effective. Ulike the existing algorithms available, there is no need to know a dual word a priori and the reconstruction result is more accurate. Moreover, the proposed algorithm is robust to bit errors.

Quadrant silicon avalanche photodiodes (APDs) were fabricated by standard 0.18µm CMOS process, and were characterized at 405nm wavelength for Blu-ray applications. The size of each APD element is 50×50µm2. The dark current was 10pA at low bias voltage, and low crosstalk of about -80dB between adjacent APD elements was achieved. Although the responsivity is less than 0.1A/W at low bias voltage, the responsivity is enhanced to more than 1A/W at less than 10V bias voltage due to avalanche amplification. The wide bandwidth of 1.5GHz was achieved with the responsivity of more than 1A/W, which is limited by the capacitance of the APD. We believe that the fabricated quadrant APD is a promising photodiode for multi-layer Blu-ray system.

Conceptual data modeling is an important activity in database design. However, it is difficult for novice learners to master its skills. In the conceptual data modeling, learners are required to detect and correct errors of their artifacts by themselves because modeling tools do not assist these activities. We call such activities self checking, which is also an important process. However, the previous research did not focus on it and/or the data collection of self checks. The data collection of self checks is difficult because self checking is an internal activity and self checks are not usually expressed. Therefore, we developed a method to help learners express their self checks by reflecting on their artifact making processes. In addition, we developed a system, KIfU3, which implements this method. We conducted an evaluation experiment and showed the effectiveness of the method. From the experimental results, we found out that (1) the novice learners conduct self checks during their conceptual data modeling tasks; (2) it is difficult for them to detect errors in their artifacts; (3) they cannot necessarily correct the errors even if they could identify them; and (4) there is no relationship between the numbers of self checks by the learners and the quality of their artifacts.

Location information is meaningful information for future ITS (Intelligent Transport Systems) world. Especially, the accuracy of the information is required because the accuracy decides the quality of ITS services. For realization of high precision positioning, Kinematic positioning technique has been attracting attention. The Kinematic positioning requires the configuration of many positioning parameters. However, the configuration is difficult because optimal parameter differs according to user's environment. In this paper, we will propose an estimation method of optimal parameter according to the environment. Further, we will propose an elimination method of unreliable positioning results. Hereby, we can acquire extensively only the reliable positioning results. By using the actual vehicle traveling data, the ability and the applicable range of the proposed method will be shown. The result will show that our proposed method improves the acquision rate of reliable positioning results and mitigates the acquision rate of the unreliable positioning results.

Cellular heterogeneous networks (HetNets) with densely deployed small cells can effectively boost network capacity. The co-channel interference and the prominent energy consumption are two crucial issues in HetNets which need to be addressed. Taking the traffic variations into account, this paper proposes a theoretical framework to analyze spectral efficiency (SE) and energy efficiency (EE) considering jointly further-enhanced inter-cell interference coordination (FeICIC) and spectrum allocation (SA) via a stochastic geometric approach for a two-tier downlink HetNet. SE and EE are respectively derived and validated by Monte Carlo simulations. To create spectrum and energy efficient HetNets that can adapt to traffic demands, a non-convex optimization problem with the power control factor, resource partitioning fraction and number of subchannels for the SE and EE tradeoff is formulated, based on which, an iterative algorithm with low complexity is proposed to achieve the sub-optimal solution. Numerical results confirm the effectiveness of the joint FeICIC and SA scheme in HetNets. Meanwhile, a system design insight on resource allocation for the SE and EE tradeoff is provided.

Response property is a kind of liveness property. Response property problem is defined as follows: Given two activities α and β, whenever α is executed, is β always executed after that? In this paper, we tackled the problem in terms of Workflow Petri nets (WF-nets for short). Our results are (i) the response property problem for acyclic WF-nets is decidable, (ii) the problem is intractable for acyclic asymmetric choice (AC) WF-nets, and (iii) the problem for acyclic bridge-less well-structured WF-nets is solvable in polynomial time. We illustrated the usefulness of the procedure with an application example.

There is a strong demand for super-resolution time of arrival (TOA) estimation techniques for radar applications that can that can exceed the theoretical limits on range resolution set by frequency bandwidth. One of the most promising solutions is the use of compressed sensing (CS) algorithms, which assume only the sparseness of the target distribution but can achieve super-resolution. To preserve the reconstruction accuracy of CS under highly correlated and noisy conditions, we introduce a random resampling approach to process the received signal and thus reduce the coherent index, where the frequency-domain-based CS algorithm is used as noise reduction preprocessing. Numerical simulations demonstrate that our proposed method can achieve super-resolution TOA estimation performance not possible with conventional CS methods.

Most conventional research on target motion analysis (TMA) based on least squares (LS) has focused on performing asymptotically unbiased estimation with inaccurate measurements. However, such research may often yield inaccurate estimation results when only a small set of measurement data is used. In this paper, we propose an accurate TMA method even with a small set of bearing measurements. First, a subset of measurements is selected by a random sample consensus (RANSAC) algorithm. Then, LS is applied to the selected subset to estimate target motion. Finally, to increase accuracy, the target motion estimation is refined through a bias compensation algorithm. Simulated results verify the effectiveness of the proposed method.

Heterogeneous Small Cell Network (HSCN) will have wide application given its ability to improve system capacity and hot spot coverage. In order to increase the efficiency of spectrum and energy, a great deal of research has been carried out on radio resource management in HSCN. However, it is a remarkable fact that the user experience in terms of traffic rate demands has been neglected in existing research with excessive concentration on network capacity and energy efficiency. In this paper, we redefined the energy efficiency (EE) and formulate the joint optimization problem of user experience and energy efficiency maximization into a mixed integer non-linear programming (MINLP) problem. After reformulating the optimization problem, the joint subchannel (SC) allocation and power control algorithm is proposed with the help of cluster method and genetic algorithm. Simulation results show that the joint SC allocation and power control algorithm proposed has better performance in terms of user experience and energy consumption than existing algorithms.

This paper proposes a novel access technique that enables uplink multiuser multiple input multiple output (MU-MIMO) access with small overhead in distributed wireless networks. The proposed access technique introduces a probe packet that is sent to all terminals to judge whether they have the right to transmit their signals or not. The probe packet guarantees high quality MU-MIMO signal transmission when a minimum mean square error (MMSE) filter is applied at the access point, which results in high frequency utilization efficiency. Computer simulation reveals that the proposed access achieves more than twice of the capacity obtained by the traditional carrier sense multiple access/collision avoidance (CSMA/CA) with a single user MIMO, when the access point with 5 antennas is surrounded by the terminals with 2 antennas.

In this paper, we propose a method of salient object detection based on distributed seeds and a co-propagation of seed information. Salient object detection is a technique which estimates important objects for human by calculating saliency values of pixels. Previous salient object detection methods often produce incorrect saliency values near salient objects in the case of images which have some objects, called the leakage of saliencies. Therefore, a method based on a co-propagation, the scale invariant feature transform, the high dimensional color transform, and machine learning is proposed to reduce the leakage. Firstly, the proposed method estimates regions clearly located in salient objects and the background, which are called as seeds and resultant seeds, are distributed over images. Next, the saliency information of seeds is simultaneously propagated, which is then referred as a co-propagation. The proposed method can reduce the leakage caused because of the above methods when the co-propagation of each information collide with each other near the boundary. Experiments show that the proposed method significantly outperforms the state-of-the-art methods in mean absolute error and F-measure, which perceptually reduces the leakage.

In this paper, we consider Bayesian image denoising based on a Gaussian Markov random field (GMRF) model, for which we propose an new algorithm. Our method can solve Bayesian image denoising problems, including hyperparameter estimation, in O(n)-time, where n is the number of pixels in a given image. From the perspective of the order of the computational time, this is a state-of-the-art algorithm for the present problem setting. Moreover, the results of our numerical experiments we show our method is in fact effective in practice.

Automatic detection of heart cycle abnormalities in a long duration of ECG data is a crucial technique for diagnosing an early stage of heart diseases. Concretely, Paroxysmal stage of Atrial Fibrillation rhythms (ParAF) must be discriminated from Normal Sinus rhythms (NS). The both of waveforms in ECG data are very similar, and thus it is difficult to completely detect the Paroxysmal stage of Atrial Fibrillation rhythms. Previous studies have tried to solve this issue and some of them achieved the discrimination with a high degree of accuracy. However, the accuracies of them do not reach 100%. In addition, no research has achieved it in a long duration, e.g. 12 hours, of ECG data. In this study, a new mechanism to tackle with these issues is proposed: “Door-to-Door” algorithm is introduced to accurately and quickly detect significant peaks of heart cycle in 12 hours of ECG data and to discriminate obvious ParAF rhythms from NS rhythms. In addition, a quantitative method using Artificial Neural Network (ANN), which discriminates unobvious ParAF rhythms from NS rhythms, is investigated. As the result of Door-to-Door algorithm performance evaluation, it was revealed that Door-to-Door algorithm achieves the accuracy of 100% in detecting the significant peaks of heart cycle in 17 NS ECG data. In addition, it was verified that ANN-based method achieves the accuracy of 100% in discriminating the Paroxysmal stage of 15 Atrial Fibrillation data from 17 NS data. Furthermore, it was confirmed that the computational time to perform the proposed mechanism is less than the half of the previous study. From these achievements, it is concluded that the proposed mechanism can practically be used to diagnose early stage of heart diseases.