Recently, we proposed an interference-aware channel segregation based dynamic channel assignment (IACS-DCA). In IACS-DCA, each base station (BS) measures the instantaneous co-channel interference (CCI) power on each available channel, computes the moving average CCI power using past CCI measurement results, and selects the channel having the lowest moving average CCI power. In this way, the CCI-minimized channel reuse pattern can be formed. In this paper, we introduce the autocorrelation function of channel reuse pattern, the fairness of channel reuse, and the minimum co-channel BS distance to quantitatively examine the channel reuse pattern formed by the IACS-DCA. It is shown that the IACS-DCA can form a CCI-minimized channel reuse pattern in a distributed manner and that it improves the signal-to-interference ratio (SIR) compared to the other channel assignment schemes.

The capacity maximization of line-of-sight (LoS) two-input and multiple-output (TIMO) channels in indoor environments is investigated in this paper. The 3×2 TIMO channel is mainly studied. First, the capacity fluctuation number (CFN) which reflects the variation of channel capacity is proposed. Then, the expression of the average capacity against the CFN is derived. The CFN is used as a criterion for optimization of the capacity by changing inter-element spacings of transmit and receive antenna arrays. Next, the capacity sensitivity of the 3×2 TIMO channel to the orientation and the frequency variation is studied and compared with those of 2×2 and 4×2 TIMO channels. A small capacity sensitivity of the 3×2 TIMO channel is achieved and verified by both simulation and measurement results. Furthermore, the CFN can also be used as a criterion for optimization of average capacity and the proposed optimization method is validated through numerical results.

We propose a systematic method for improving the response time of forward collision warning (FCW) on vehicles. First, a performance metric, called the warning lag time, is introduced. We use the warning lag time because its measurement is practical in real driving situations. Next, we discuss two ideas to improve this warning lag time, vertical and horizontal methods. The vertical method gives an additional warning, derived from the cause of a car crash, to a normal FCW system. The experiment showed that it can improve the warning lag time by an average of 0.31sec. compared with a traditional FCW system. The horizontal method uses distributed sensing among vehicles, which helps the vehicle see farther. It can also improve the warning lag time by an average of 1.08sec. compared with a single vehicle FCW.

Ni nanodots (NDs) used as nano-scale top electrodes were formed on a 10-nm-thick Si-rich oxide (SiO$_{mathrm{x}}$)/Ni bottom electrode by exposing a 2-nm-thick Ni layer to remote H$_{2}$-plasma (H$_{2}$-RP) without external heating, and the resistance-switching behaviors of SiO$_{mathrm{x}}$ were investigated from current-voltage ( extit{I--V}) curves. Atomic force microscope (AFM) analyses confirmed the formation of electrically isolated Ni NDs as a result of surface migration and agglomeration of Ni atoms promoted by the surface recombination of H radicals. From local extit{I--V} measurements performed by contacting a single Ni ND as a top electrode with a Rh coated Si cantilever, a distinct uni-polar type resistance switching behavior was observed repeatedly despite an average contact area between the Ni ND and the SiO$_{mathrm{x}}$ as small as $sim$ 1.9 $ imes$ 10$^{-12}$cm$^{2}$. This local extit{I--V} measurement technique is quite a simple method to evaluate the size scalability of switching properties.

Fast simulation techniques of large scale RLC networks with nonlinear devices are presented. Generally, when scale of nonlinear part in a circuit is much less than the linear part, matrix or circuit partitioning approach is known to be efficient. In this paper, these partitioning techniques are used for the conventional transient analysis using an implicit numerical integration and the circuit-based finite-difference time-domain (FDTD) method, whose efficiency and accuracy are evaluated developing a prototype simulator. It is confirmed that the matrix and circuit partitioning approaches do not degrade accuracy of the transient simulations that is compatible to SPICE, and that the circuit partitioning approach is superior to the matrix one in efficiency. Moreover, it is demonstrated that the circuit-based FDTD method can be efficiently combined with the matrix or circuit partitioning approach, compared with the transient analysis using an implicit numerical integration.

An on-channel repeater (OCR) performing simultaneous reception and transmission at the same frequency is beneficial to improve spectral efficiency and coverage. In an OCR, it is important to cancel the feedback interference caused by imperfect isolation between the transmit and receive antennas, and least mean square (LMS) based adaptive filters are commonly used for this purpose. In this paper, we analyze the performance of the LMS based adaptive feedback canceller in terms of its transient behavior and the steady-state mean square error (MSE). Through a theoretical analysis, we derive iterative equations to compute transient MSEs and provide a procedure to simply evaluate steady-state MSEs for the adaptive feedback canceller. Simulation results performed to verify the theoretical MSEs show good agreement between the proposed theoretical analysis and the empirical results.

Accuracy of a method for analyzing the interface defect properties; time-domain charge pumping method, is evaluated. The method monitors the charge pumping (CP) current in time domain, and thus we expect that it gives us a noble way to investigate the interface state properties. In this study, for the purpose of evaluating the accuracy of the method, the interface state density extracted from the time-domain data is compared with that measured using the conventional CP method. The results show that they are equal to each other for all measured devices with various defect densities, demonstrating that the time-domain CP method is sufficiently accurate for the defect density evaluation.

This report describes a robust method of instantaneous heart rate (IHR) extraction from noisy electrocardiogram (ECG) signals. Generally, R-waves are extracted from ECG using a threshold to calculate the IHR from the interval of R-waves. However, noise increases the incidence of misdetection and false detection in wearable healthcare systems because the power consumption and electrode distance are limited to reduce the size and weight. To prevent incorrect detection, we propose a short-time autocorrelation (STAC) technique. The proposed method extracts the IHR by determining the search window shift length which maximizes the correlation coefficient between the template window and the search window. It uses the similarity of the QRS complex waveform beat-by-beat. Therefore, it has no threshold calculation process. Furthermore, it is robust against noisy environments. The proposed method was evaluated using MIT-BIH arrhythmia and noise stress test databases. Simulation results show that the proposed method achieves a state-of-the-art success rate of IHR extraction in a noise stress test using a muscle artifact and a motion artifact.

Recently, the ratio of probability density functions was demonstrated to be useful in solving various machine learning tasks such as outlier detection, non-stationarity adaptation, feature selection, and clustering. The key idea of this density ratio approach is that the ratio is directly estimated so that difficult density estimation is avoided. So far, parametric and non-parametric direct density ratio estimators with various loss functions have been developed, and the kernel least-squares method was demonstrated to be highly useful both in terms of accuracy and computational efficiency. On the other hand, recent study in pattern recognition exhibited that deep architectures such as a convolutional neural network can significantly outperform kernel methods. In this paper, we propose to use the convolutional neural network in density ratio estimation, and experimentally show that the proposed method tends to outperform the kernel-based method in outlying image detection.

Measurements of 60GHz proximity channels are performed in desktop environments with a digital camera, a laptop PC, a tablet, a smartphone, and a DVD player. The results are characterized by a statistical channel model. All measured channels are found to be similar to conventional exponential decay profiles that have a relatively large first path due to line-of-sight components. We also show that the power difference between the first path and the delay paths is related to randomization of radio wave polarization by internal reflections in the devices, whereas this is conventionally dependent on only a Rice factor. To express this effect, the conventional model is modified by adding one parameter. Computer simulations confirm that RMS delay spreads of the modeled channels are a good fit to measured channels under most conditions.

Tonal signals are shown as spectral peaks in the frequency domain. When the number of spectral peaks is small and the spectral signal is sparse, Compressive Sensing (CS) can be adopted to locate the peaks with a low-cost sensing system. In the CS scheme, a time domain signal is modelled as $oldsymbol{y}=Phi F^{-1}oldsymbol{s}$, where y and s are signal vectors in the time and frequency domains. In addition, F-1 and $Phi$ are an inverse DFT matrix and a random-sampling matrix, respectively. For a given y and $Phi$, the CS method attempts to estimate s with l0 or l1 optimization. To generate the peak candidates, we adopt the frequency-domain information of $ esmile{oldsymbol{s}}$ = $oldsymbol{F} esmile{oldsymbol{y}}$, where $ esmile{y}$ is the extended version of y and $ esmile{oldsymbol{y}}left(oldsymbol{n} ight)$ is zero when n is not elements of CS time instances. In this paper, we develop Gaussian statistics of $ esmile{oldsymbol{s}}$. That is, the variance and the mean values of $ esmile{oldsymbol{s}}left(oldsymbol{k} ight)$ are examined.

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.

In this study, Si(100) surface flattening process was investigated utilizing sacrificial oxidation method to improve Metal--Insulator--Semiconductor (MIS) diode characteristics. By etching of the 100,nm-thick sacrificial oxide formed by thermal oxidation at 1100$^{circ}$C, the surface roughness of Si substrate was reduced. The obtained Root-Mean-Square (RMS) roughness was decreased from 0.15,nm (as-cleaned) to 0.07,nm in the case of sacrificial oxide formed by wet oxidation, while it was 0.10,nm in the case of dry oxidation. Furthermore, time-dependent dielectric breakdown (TDDB) characteristic of Al/SiO$_{2}$(10,nm)/p-Si(100) MIS diode structures was found to be improved by the reduction of Si surface RMS roughness.

Tracking algorithms for arbitrary objects are widely researched in the field of computer vision. At the beginning, an initialized bounding box is given as the input. After that, the algorithms are required to track the objective in the later frames on-the-fly. Tracking-by-detection is one of the main research branches of online tracking. However, there still exist two issues in order to improve the performance. 1) The limited processing time requires the model to extract low-dimensional and discriminative features from the training samples. 2) The model is required to be able to balance both the prior and new objectives' appearance information in order to maintain the relocation ability and avoid the drifting problem. In this paper, we propose a real-time tracking algorithm called coupled randomness tracking (CRT) which focuses on dealing with these two issues. One randomness represents random projection, and the other randomness represents online random forests (ORFs). In CRT, the gray-scale feature is compressed by a sparse measurement matrix, and ORFs are used to train the sample sequence online. During the training procedure, we introduce a tree discarding strategy which helps the ORFs to adapt fast appearance changes caused by illumination, occlusion, etc. Our method can constantly adapt to the objective's latest appearance changes while keeping the prior appearance information. The experimental results show that our algorithm performs robustly with many publicly available benchmark videos and outperforms several state-of-the-art algorithms. Additionally, our algorithm can be easily utilized into a parallel program.

In wireless networks, interference from adjacent nodes that are concurrently transmitting can cause packet reception failures and thus a significant throughput degradation. The interference can be simply avoided by assigning different orthogonal channels to each interfering node. However, if the number of orthogonal channels is smaller than that of interfering nodes, some adjacent nodes have to share the same channel and may interfere with each other. This interference can be mitigated by reducing the transmit power of the interfering nodes. In this paper, we propose to jointly coordinate the transmit power and the multi-channel allocation to maximize the network throughput performance by fully exploiting multi-channel availability. This coordination enables each node to use high transmission power as long as different orthogonal channels can be assigned to its adjacent nodes. Then, we propose a simple multi-channel media access control (MAC) protocol that allows the nodes on different channels to perform efficient data exchanges without interference in multi-channel networks. We show that the proposed scheme improves the network throughput performance in comparison with other existing schemes.

Ontology mapping is important in many areas, such as information integration, semantic web and knowledge management. Thus the effectiveness of ontology mapping needs to be further studied. This paper puts forward a mapping method between different ontology concepts in the same field. Firstly, the algorithms of calculating four individual similarities (the similarities of concept name, property, instance and structure) between two concepts are proposed. The algorithm features of four individual similarities are as follows: a new WordNet-based method is used to compute semantic similarity between concept names; property similarity algorithm is used to form property similarity matrix between concepts, then the matrix will be processed into a numerical similarity; a new vector space model algorithm is proposed to compute the individual similarity of instance; structure parameters are added to structure similarity calculation, structure parameters include the number of properties, instances, sub-concepts, and the hierarchy depth of two concepts. Then similarity of each of ontology concept pairs is represented by a vector. Finally, Support Vector Machine (SVM) is used to accomplish mapping discovery by training and learning the similarity vectors. In this algorithm, Harmony and reliability are used as the weights of the four individual similarities, which increases the accuracy and reliability of the algorithm. Experiments achieve good results and the results show that the proposed method outperforms many other methods of similarity-based algorithms.

Appearance changes conform to certain rules for a same person,while for different individuals the changes are uncontrolled. Hence, this paper studies the age progression rules to tackle face verification task. The age progression rules are discovered in the difference space of facial image pairs. For this, we first represent an image pair as a matrix whose elements are the difference of a set of visual words. Thereafter, the age progression rules are trained using Support Vector Machine (SVM) based on this matrix representation. Finally, we use these rules to accomplish the face verification tasks. The proposed approach is tested on the FGnet dataset and a collection of real-world images from identification card. The experimental results demonstrate the effectiveness of the proposed method for verification of identity.

A renal biopsy is a procedure to get a small piece of kidney for microscopic examination. With the development of tissue sectioning and medical imaging techniques, microscope renal biopsy image sequences are consequently obtained for computer-aided diagnosis. This paper proposes a new context-based segmentation algorithm for acquired image sequence, in which an improved genetic algorithm (GA) patching method is developed to segment different size target. To guarantee the correctness of first image segmentation and facilitate the use of context information, a boundary fusion operation and a simplified scale-invariant feature transform (SIFT)-based registration are presented respectively. The experimental results show the proposed segmentation algorithm is effective and accurate for renal biopsy image sequence.

Developing a practical and accurate statistical parser for low-resourced languages is a hard problem, because it requires large-scale treebanks, which are expensive and labor-intensive to build from scratch. Unsupervised grammar induction theoretically offers a way to overcome this hurdle by learning hidden syntactic structures from raw text automatically. The accuracy of grammar induction is still impractically low because frequent collocations of non-linguistically associable units are commonly found, resulting in dependency attachment errors. We introduce a novel approach to building a statistical parser for low-resourced languages by using language parameters as a guide for grammar induction. The intuition of this paper is: most dependency attachment errors are frequently used word orders which can be captured by a small prescribed set of linguistic constraints, while the rest of the language can be learned statistically by grammar induction. We then show that covering the most frequent grammar rules via our language parameters has a strong impact on the parsing accuracy in 12 languages.

Parameter variations in nanometer process technology are one of the major design challenges. They cause delay to be increased on the critical path and may change the logic level of internal nodes. The basic concept to solve these problems at the circuit level, design-for-variability (DFV), is to add an error handling circuit to the conventional circuits so that they are robust to nanometer related variations. The state-of-the-art variation-aware flip flops are mainly evolved from aggressive dynamic voltage and frequency scaling (DVFS) -based low-power application systems which handle errors due to the scaled supply voltage. However, they only detect the timing errors and cannot correct the errors. We propose a variation-aware flip flop which can detect and correct the timing error efficiently. The experimental results show that the proposed variation-aware flip flop is more robust and lower power than the existing approaches.