This paper shows accuracy of using azimuth-variable path-loss fitting in white-space (WS) boundary-estimation. We perform experiments to evaluate this method, and demonstrate that the required number of sensors can be significantly reduced. We have proposed a WS boundary-estimation framework that utilizes sensors to not only obtain spectrum sensing data, but also to estimate the boundaries of the incumbent radio system (IRS) coverage. The framework utilizes the transmitter position information and pathloss fitting. The pathloss fitting describes the IRS coverage by approximating the well-known pathloss prediction formula from the received signal power data, which is measured using sensors, and sensor-transmitter separation distances. To enhance its accuracy, we have further proposed a pathloss-fitting method that employs azimuth variables to reflect the azimuth dependency of the IRS coverage, including the antenna directivity of the transmitter and propagation characteristics.

This letter proposes a situation-adaptive detection algorithm for the improved efficiency of the detection performance and complexity in the MIMO-OFDM system. The proposed algorithm adaptively uses the QRD-M, DFE, and iterative detection scheme in according to the detection environment. Especially, the proposed algorithm effectively reduces the occurrence probability of error in the successive interference cancellation procedure by the unit of the spatial stream. The simulations demonstrate that the adaptive detection method using the proposed algorithm provides a better trade-off between detection performance and complexity in the MIMO-OFDM system.

Conventional localization techniques such as triangulation and multilateration are not reliable in non-line-of-sight (NLOS) environments such as dense urban areas. Although fingerprint-based localization techniques have been proposed to solve this problem, we may face difficulties because we do not know the parameters of the illegal radio when creating the fingerprint database. This paper proposes a novel technique to localize illegal radios in an urban environment by interpolating the channel impulse responses stored as fingerprints in a database. The proposed interpolation technique consists of interpolation in the bandwidth (delay), frequency and spatial domains. A localization algorithm that minimizes the squared error criterion is employed in this paper, and the proposed technique is evaluated through Monte Carlo simulations using location fingerprints obtained from ray-tracing simulations. Results show that utilizing an interpolated fingerprint database is advantageous in such scenarios.

This paper proposes the MIMO MC-CDMA channel estimation method for the various mobile environments. The distinctive feature of the proposed method is possible to robustly estimate with respect to the mobile velocity using the Kalman filter with the colored driving source. Effectiveness of the proposed method are shown by computer simulations.

Dynamic analysis is frail and insufficient to find hidden paths in environment-intensive program. By analyzing a broad spectrum of different concolic testing systems, we conclude that a number of them cannot handle programs that interact with the environment or require a complete working model. This paper addresses this problem by automatically identifying and modifying outputs of the data input interface function(DIIF). The approach is based on fine-grained taint analysis for detecting and updating the data that interacts with the environment to generate a new set of inputs to execute hidden paths. Moreover, we developed a prototype and conducted extensive experiments using a set of complex and environmentally intensive programs. Finally, the result demonstrates that our approach could identify the DIIF precisely and discover hidden path obviously.

We discuss software reliability assessment considering multiple changes of software fault-detection phenomenon. The testing-time when the characteristic of the software failure-occurrence or fault-detection phenomenon changes notably in the testing-phase of a software development process is called change-point. It is known that the occurrence of the change-point influences the accuracy for the software reliability assessment based on a software reliability growth models, which are mainly divided into software failure-occurrence time and fault counting models. This paper discusses software reliability growth modeling frameworks considering with the effect of the multiple change-point occurrence on the software reliability growth process in software failure-occurrence time and fault counting modeling. And we show numerical illustrations for the software reliability analyses based on our models by using actual data.

In indoor localization using sensor networks, performance improvements are required for non-line-of-sight (NLOS) environments in which the estimation error is high. NLOS mitigation schemes involve the detection and elimination of the NLOS measurements. The iterative minimum residual (IMR) scheme, which is often applied to the localization scheme using the time of arrival (TOA), is commonly employed for this purpose. The IMR scheme is a low-complexity scheme and its NLOS detection performance is relatively high. However, when there are many NLOS nodes in a sensor field, the NLOS detection error of the IMR scheme increases and the estimation accuracy deteriorates. Therefore, we propose a new scheme that exploits coarse NLOS detection based on stochastic characteristics prior to the application of the IMR scheme to improve the localization accuracy. Improved performances were confirmed in two NLOS channel models by performing numerical simulations.

Recently, Intelligent Transport Systems (ITS) are being researched and developed briskly. As a part of ITS, detecting vehicles from images taken by a camera loaded on a vehicle are conducted. From such backgrounds, authors have been conducting vehicle detection in nighttime. To evaluate the accuracy of this detection, gold standards of the detection are required. At present, gold standards are created manually, but manually detecting vehicles take time. Accordingly, a system which detects vehicles accurately without human help is needed to evaluate the accuracy of the vehicle detection in real time. Therefore the purpose of this study is to automatically detect vehicles in nighttime images, taken by an in-vehicle camera, with high accuracy in offline processing. To detect vehicles we focused on the brightness of the headlights and taillights, because it is difficult to detect vehicles from their shape in nighttime driving scenes. The method we propose uses Center Surround Extremas, called CenSurE for short, to detect blobs. CenSurE is a method that uses the difference in brightness between the lights and the surroundings. However, blobs obtained by CenSurE will also include objects other than headlights and taillights. For example, streetlights and delineators would be detected. To distinguish such blobs, they are tracked in inverse time and vehicles are detected using tags based on the characteristics of each object. Although every object appears from the same point in forward time process, each object appears from different places in images in inverse time processing, allowing it to track and tag blobs easily. To evaluate the effectiveness of this proposed method, experiment of detecting vehicles was conducted using nighttime driving scenes taken by a camera loaded on a vehicle. Experimental results of the proposed method were nearly equivalent to manual detection.

In recent years, various applications based on propagation characteristics have been developed. They generally utilize the locality of the fading characteristics of multipath environments. On the other hand, if a received signal at a remote location can be estimated beyond the correlation distance of the multipath fading environment, a wide variety of new applications can be possible. In this paper, we attempt to estimate a received signal at a remote location using the MUSIC method and the least squares method. Based on the plane wave assumption for each arriving wave, multipath environment is analyzed through estimation of the directions of arrival by the MUISC method and the complex amplitudes of the received signals by the least squares method, respectively. We present evaluation results on the estimation performance of the method by computer simulations.

We present an algorithm for efficient rendering of animated hair under a dynamic, low-frequency lighting environment. We use spherical harmonics (SH) to represent the environmental light. The transmittances between a point on a hair strand and the light sources are also represented by SH functions. Then, a convolution of SH functions and the scattering function of a hair strand is precomputed. This allows us to efficiently compute the intensity at a point on the hair. However, the computation of the transmittance is very time-consuming. We address this problem by using a voxel-based approach: the transmittance is computed by using a voxelized hair model. We further accelerate the computation by sampling the voxels. By using our method, we can render a hair model consisting of tens of thousands of hair strands at interactive frame rates.

Although multi-user multiple-input multiple-output (MI-MO) systems provide high data rate transmission, they may suffer from interference. Block diagonalization and eigenbeam-space division multiplexing (E-SDM) can suppress interference. The transmitter needs to determine beamforming weights from channel state information (CSI) to use these techniques. However, MIMO channels change in time-varying environments during the time intervals between when transmission parameters are determined and actual MIMO transmission occurs. The outdated CSI causes interference and seriously degrades the quality of transmission. Channel prediction schemes have been developed to mitigate the effects of outdated CSI. We evaluated the accuracy of prediction of autoregressive (AR)-model-based prediction and Lagrange extrapolation in the presence of channel estimation error. We found that Lagrange extrapolation was easy to implement and that it provided performance comparable to that obtained with the AR-model-based technique.

This paper proposes a method of improving the performance of blind reverberation time (RT) estimation in noisy environments. RT estimation is conducted using a maximum likelihood (ML) method based on the autocorrelation function of the linear predictive residual signal. To reduce the effect of environmental noise, a noise reduction technique is applied to the noisy speech signal. In addition, a frequency coefficient selection is performed to eliminate signal components with low signal-to-noise ratio (SNR). Experimental results confirm that the proposed method improves the accuracy of RT measures, particularly when the speech signal is corrupted by a colored noise with a narrow bandwidth.

Sound source localization (SSL), with a binaural input in practical environments, is a challenging task due to the effects of noise and reverberation. In psychoacoustic research field, one of the theories to explain the mechanism of human perception in such environments is the well-known equalization-cancellation (EC) model. Motivated by the EC theory, this paper investigates a binaural SSL method by integrating EC procedures into a beamforming technique. The principle idea is that the EC procedures are first utilized to eliminate the sound signal component at each candidate direction respectively; direction of sound source is then determined as the direction at which the residual energy is minimal. The EC procedures applied in the proposed method differ from those in traditional EC models, in which the interference signals in rooms are accounted in E and C operations based on limited prior known information. Experimental results demonstrate that our proposed method outperforms the traditional SSL algorithms in the presence of noise and reverberation simultaneously.

This paper presents a method to seamlessly extend the coverage of energy supply field for wireless sensor networks in order to free sensors from wires and batteries, where the multi-point scheme is employed to overcome path-loss attenuation, while the carrier shift diversity is introduced to mitigate the effect of interference between multiple wave sources. As we focus on the energy transmission part, sensor or communication schemes are out of scope of this paper. To verify the effectiveness of the proposed wireless energy transmission, this paper conducts indoor experiments in which we compare the power distribution and the coverage performance of different energy transmission schemes including conventional single-point, simple multi-point and our proposed multi-point scheme. To easily observe the effect of the standing-wave caused by multipath and interference between multiple wave sources, 3D measurements are performed in an empty room. The results of our experiments together with those of a simulation that assumes a similar antenna setting in free space environment show that the coverage of single-point and multi-point wireless energy transmission without carrier shift diversity are limited by path-loss, standing-wave created by multipath and interference between multiple wave sources. On the other hand, the proposed scheme can overcome power attenuation due to the path-loss as well as the effect of standing-wave created by multipath and interference between multiple wave sources.

We present the adaptation of the acoustic models of hidden Markov models (HMMs) to the target speaker and noise environment using bilinear models. Acoustic models trained from various speakers and noise conditions are decomposed to build the bases that capture the interaction between the two factors. The model for the target speaker and noise is represented as a product of bases and two weight vectors. In experiments using the AURORA4 corpus, the bilinear model outperforms the linear model.

This study examines the dialog-based language learning game (DB-LLG) realized in a 3D environment built with game contents. We designed the DB-LLG to communicate with users who can conduct interactive conversations with game characters in various immersive environments. From the pilot test, we found that several technologies were identified as essential in the construction of the DB-LLG such as dialog management, hint generation, and grammar error detection and feedback. We describe the technical details of our system POSTECH immersive English study (Pomy). We evaluated the performance of each technology using a simulator and field tests with users.

A decentralized secure protocol for casting trust rating in reputation systems (StR protocol) is lately proposed by Dimitriou and Michalas, and the StR protocol is verified to be faster than the previous work providing anonymous feedback. In this letter, we present new enhanced scheme of StR. Compared with StR protocol, our new approach attains the exactly same security, but requires less processing time and about half communication overheads. Therefore, we improve the performance without sacrificing any security, especially the communication delay is dramatically reduced.

As a consequence of recent developments in communications, the parameters of communication signals, such as the modulation parameter values, are becoming unstable because of time-varying SNR under electromagnetic conditions. In general, it is difficult to classify target signals that have time-varying parameters using traditional signal recognition methods. To overcome this problem, this study proposes a novel recognition method that works well even for such time-dependent communication signals. This method is mainly composed of feature extraction and classification processes. In the feature extraction stage, we adopt Shannon entropy and index entropy to obtain the stable features of modulated signals. In the classification stage, the interval gray relation theory is employed as suitable for signals with time-varying parameter spaces. The advantage of our method is that it can deal with time-varying SNR situations, which cannot be handled by existing methods. The results from numerical simulation show that the proposed feature extraction algorithm, based on entropy characteristics in time-varying SNR situations,offers accurate clustering performance, and the classifier, based on interval gray relation theory, can achieve a recognition rate of up to 82.9%, even when the SNR varies from -10 to -6 dB.

An on-chip SIDO DC-DC boost converter core that can be used for both battery and solar cell operating applications is proposed. The converter is able to supply a current of up to around 30mA with an on-chip driver and more than 100mA by using an off-chip power MOS driver. The cross regulation problem was solved by inserting an extra cycle. Efficiencies of 85% and 84% were achieved for each driving mode. Complicated maximum power point tracking (MPPT) controls are available for a solar cell operation. An embedded micro-computer can be used to calculate a complicated algorithm. The converter exploits 99% of the expected maximum power of the solar cell. The converter protects the leak current that flows through the solar cell when there is no light. The proposed protection circuits reduce the leak current by three orders of magnitude without any performance loss.

An S-band GaN on Si based 1kW-class SSPA system for space wireless applications is proposed. Since high-efficiency and high-reliability amplifier is one of the most important technologies for power and communication systems in a future space base station on a planet, compact, high-power, and high-efficiency SSPA is strongly requested instead of TWTA. Thus, we adopt GaN on Si based amplifier due to its remarkable material properties. At the beginning, thermal vacuum and radiation test of GaN on Si are conducted so as to confirm the space applicability. Fabricated SSPA system consists of eight 200W HPAs and coaxial waveguide power combiner. It achieves high efficiency such as 57% of drain efficiency and 87% of combining efficiency when RF output power achieves more than 60dBm. Furthermore, long-term stable operation and good phase noise characteristics are also confirmed.