We have demonstrated tunable extit{n}-channel fullerene and extit{p}-channel pentacene OFETs and CMOS inverter circuit based on a bilayer-dielectric structure of CYTOP (poly(perfluoroalkenyl vinyl ether)) electret and SiO$_{2}$. For both OFET types, the $V_{mathrm{th}}$ can be electrically tuned thanks to the charge-trapping at the interface of CYTOP and SiO$_{2}$. The stability of the shifted $V_{mathrm{th}}$ was investigated through monitoring a change in transistor current. The measured transistor current versus time after programming fitted very well with a stretched-exponential distribution with a long time constant up to 10$^{6}$ s. For organic CMOS inverter, after applying the program gate voltages for extit{n}-channel fullerene or extit{p}-channel pentacene elements, the voltage transfer characteristics were shifted toward more positive values, resulting in a modulation of the noise margin. We realized that at a program gate voltage of 60,V for extit{p}-channel OFET, the circuit switched at 4, 8,V, that is close to half supply voltage $V_{mathrm{DD}}$, leading to the maximum electrical noise immunity of the inverter circuit.

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.

Memory leak occurs when useless objects cannot be released for a long time during program execution. Memory leaked objects may cause memory overflow, system performance degradation and even cause the system to crash when they become serious. This paper presents a dynamic approach for detecting and measuring memory leaked objects in Java programs. First, our approach tracks the program by JDI and records heap information to find out the potentially leaked objects. Second, we present memory leaking confidence to measure the influence of these objects on the program. Finally, we select three open-source programs to evaluate the efficiency of our approach. Furthermore, we choose ten programs from DaCapo 9.12 benchmark suite to reveal the time overhead of our approach. The experimental results show that our approach is able to detect and measure memory leaked objects efficiently.

Topic features are useful in improving text summarization. However, independency among topics is a strong restriction on most topic models, and alleviating this restriction can deeply capture text structure. This paper proposes a hybrid topic model to generate multi-document summaries using a combination of the Hidden Topic Markov Model (HTMM), the surface texture model and the topic transition model. Based on the topic transition model, regular topic transition probability is used during generating summary. This approach eliminates the topic independence assumption in the Latent Dirichlet Allocation (LDA) model. Meanwhile, the results of experiments show the advantage of the combination of the three kinds of models. This paper includes alleviating topic independency, and integrating surface texture and shallow semantic in documents to improve summarization. In short, this paper attempts to realize an advanced summarization system.

This paper presents a novel peer-to-peer protocol to efficiently distribute virtual machine images in a datacenter. A primary idea of it is to improve the performance of peer-to-peer content delivery by employing deduplication to take advantage of similarity both among and within VM images in cloud datacenters. The efficacy of the proposed scheme is validated through an evaluation that demonstrates substantial performance gains.

Robust crater recognition is a research focus on deep space exploration mission, and sparse representation methods can achieve desirable robustness and accuracy. Due to destruction and noise incurred by complex topography and varied illumination in planetary images, a robust crater recognition approach is proposed based on dictionary learning with a low-rank error correction model in a sparse representation framework. In this approach, all the training images are learned as a compact and discriminative dictionary. A low-rank error correction term is introduced into the dictionary learning to deal with gross error and corruption. Experimental results on crater images show that the proposed method achieves competitive performance in both recognition accuracy and efficiency.

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.

Millimeter-wave (mm-wave) radio is attracting attention as one of the key enabling physical layer technologies for the fifth-generation (5G) mobile access and backhaul. This paper aims at clarifying possible roles of mm-wave radio in the 5G development and performing a comprehensive literature survey on mm-wave radio channel modeling essential for the feasibility study. Emphasis in the literature survey is laid on grasping the typical behavior of mm-wave channels, identifying missing features in the presently available channel models for the design and evaluation of the mm-wave radio links within the 5G context, and exemplifying different channel modeling activities through analyses performed in the authors' group. As a key technological element of the mm-wave radios, reduced complexity beamforming is also addressed. Design criteria of the beamforming are developed based on the spatial multipath characteristics of measured indoor mm-wave channels.

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.

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.

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.

We propose a quasi-linear trellis-coded modulation (TCM) using nonbinary convolutional codes for quadrature amplitude modulation (QAM). First, we study a matched mapping which is able to reduce the computational complexity of the Euclidean distances between signal points of MQAM. As an example, we search for rate R=1/2 convolutional codes for coded 64QAM by this method. The symbol error rates of the proposed codes are estimated by the distance properties theoretically and they are verified by simulation. In addition, we compare the minimum free Euclidean distances of these new codes with their upper bounds. Finally, the bit error probabilitiy of the proposed coded modulation is compared with uncoded signal constellations and a conventional TCM code proposed by Ungerboeck. The result shows the proposed scheme outperform them on the AWGN channels.

Distributed file systems, which manage large amounts of data over multiple commercially available machines, have attracted attention as management and processing systems for Big Data applications. A distributed file system consists of multiple data nodes and provides reliability and availability by holding multiple replicas of data. Due to system failure or maintenance, a data node may be removed from the system, and the data blocks held by the removed data node are lost. If data blocks are missing, the access load of the other data nodes that hold the lost data blocks increases, and as a result, the performance of data processing over the distributed file system decreases. Therefore, replica reconstruction is an important issue to reallocate the missing data blocks to prevent such performance degradation. The Hadoop Distributed File System (HDFS) is a widely used distributed file system. In the HDFS replica reconstruction process, source and destination data nodes for replication are selected randomly. We find that this replica reconstruction scheme is inefficient because data transfer is biased. Therefore, we propose two more effective replica reconstruction schemes that aim to balance the workloads of replication processes. Our proposed replication scheduling strategy assumes that nodes are arranged in a ring, and data blocks are transferred based on this one-directional ring structure to minimize the difference in the amount of transfer data for each node. Based on this strategy, we propose two replica reconstruction schemes: an optimization scheme and a heuristic scheme. We have implemented the proposed schemes in HDFS and evaluate them on an actual HDFS cluster. We also conduct experiments on a large-scale environment by simulation. From the experiments in the actual environment, we confirm that the replica reconstruction throughputs of the proposed schemes show a 45% improvement compared to the HDFS default scheme. We also verify that the heuristic scheme is effective because it shows performance comparable to the optimization scheme. Furthermore, the experimental results on the large-scale simulation environment show that while the optimization scheme is unrealistic because a long time is required to find the optimal solution, the heuristic scheme is very efficient because it can be scalable, and that scheme improved replica reconstruction throughput by up to 25% compared to the default scheme.

In this paper, a novel synchronization method is proposed for a heterogeneous cognitive radio that combines public safety mobile communication systems (PMCSs) with commercial mobile wireless communication systems (CMWCSs). The proposed method enables self-synchronization of the PMCSs as well as co-synchronization of PMCSs and CMWCSs. In this paper, the self-synchronization indicates that each system obtains own timing synchronization. The co-synchronization indicates that a system recognizes data transmitted from other systems correctly. In our research, we especially focus on PMCS self-synchronization because it is one of the most difficult parts of our proposed cognitive radio that improves PMCS's communication quality. The proposed method is utilized for systems employing differentially encoded π/4 shift QPSK modulation. The synchronization can be achieved by correlating envelopes calculated from a PMCS's received signals with subsidiary information (SI) sent via a CMWCS. In this paper, the performance of the proposed synchronization method is evaluated by computer simulation. Moreover, because this SI can also be used to improve the bit error rate (BER) of PMCSs, BER improvement and efficient SI sending methods are derived, after which their performance is evaluated.

A dictionary attack against SSH is a common security threat. Many methods rely on network traffic to detect SSH dictionary attacks because the connections of remote login, file transfer, and TCP/IP forwarding are visibly distinct from those of attacks. However, these methods incorrectly judge the connections of automated operation tasks as those of attacks due to their mutual similarities. In this paper, we propose a new approach to identify user authentication methods on SSH connections and to remove connections that employ non-keystroke based authentication. This approach is based on two perspectives: (1) an SSH dictionary attack targets a host that provides keystroke based authentication; and (2) automated tasks through SSH need to support non-keystroke based authentication. Keystroke based authentication relies on a character string that is input by a human; in contrast, non-keystroke based authentication relies on information other than a character string. We evaluated the effectiveness of our approach through experiments on real network traffic at the edges in four campus networks, and the experimental results showed that our approach provides high identification accuracy with only a few errors.

Recently, Hadoop has attracted much attention from engineers and researchers as an emerging and effective framework for Big Data. HDFS (Hadoop Distributed File System) can manage a huge amount of data with high performance and reliability using only commodity hardware. However, HDFS requires a single master node, called a NameNode, to manage the entire namespace (or all the i-nodes) of a file system. This causes the SPOF (Single Point Of Failure) problem because the file system becomes inaccessible when the NameNode fails. This also causes a bottleneck of efficiency since all the access requests to the file system have to contact the NameNode. Hadoop 2.0 resolves the SPOF problem by introducing manual failover based on two NameNodes, Active and Standby. However, it still has the efficiency bottleneck problem since all the access requests have to contact the Active in ordinary executions. It may also lose the advantage of using commodity hardware since the two NameNodes have to share a highly reliable sophisticated storage. In this paper, we propose a new HDFS architecture to resolve all the problems mentioned above.

Traceability links between requirements and source code are helpful in software reuse and maintenance tasks. However, manually recovering links in a large group of products requires significant costs and some links may be overlooked. Here, we propose a semi-automatic method to recover traceability links between requirements and source code in the same series of large software products. In order to support differences in representation between requirements and source code, we recover links by using the configuration management log as an intermediary. We refine the links by classifying requirements and code elements in terms of whether they are common to multiple products or specific to one. As a result of applying our method to real products that have 60KLOC, we have recovered valid traceability links within a reasonable amount of time. Automatic parts have taken 13 minutes 36 seconds, and non-automatic parts have taken about 3 hours, with a recall of 76.2% and a precision of 94.1%. Moreover, we recovered some links that were unknown to engineers. By recovering traceability links, software reusability and maintainability will be improved.

Implicit feedback is an approach that utilizes uplink channel state information (CSI) for downlink transmit beamforming on multiple-input multiple-output (MIMO) systems, relying on over-the-air channel reciprocity. The implicit feedback improves throughput efficiency because overhead of CSI feedback for change of over-the-air channel responses is omitted. However, it is necessary for the implicit feedback to calibrate circuitry responses that uplink CSI includes, because actual downlink and uplink channel responses do not match due to different transmit and receive circuitry chains. This paper presents our proposed calibration scheme, weighted-combining calibration (WCC); it offers improved calibration accuracy. In WCC, an access point (AP) calculates multiple calibration coefficients from ratios of downlink and uplink CSI, and then combines coefficients with minimum mean square error (MMSE) weights. The weights are derived using a linear approximation in the high signal to noise power ratio (SNR) regime. Analytical mean square error (MSE) of calibration coefficients with WCC and calibration schemes for comparison is expressed based on the linear approximation. Computer simulations show that the analytical MSE matches simulated one if the linear approximation holds, and that WCC improves the MSE and signal to interference plus noise power ratio (SINR). Indoor experiments are performed on a multiuser MIMO system with implicit feedback based on orthogonal frequency division multiplexing (OFDM), built using measurement hardware. Experimental results verify that the channel reciprocity can be exploited on the developed multiuser MIMO-OFDM system and that WCC is also effective in indoor environments.

A new exemplar-based inpainting method which effectively preserves global structures and textures in the restored region driven by feature vectors is presented. Exemplars that belong to the source region are segmented based on their features. To express characteristics of exemplars such as shapes of structures and smoothness of textures, the Harris corner response and the variance of pixel values are employed as a feature vector. Enhancements on restoration plausibility and processing speedup are achieved as shown in the experiments.

TTFF (Time-To-First-Fix) is an important indicator of GPS receiver performance, and must be reduced as much as possible. Bit synchronization is the pre-condition of positioning, which affects TTFF. The frequency error leads to power loss, which makes it difficult to find the bit edge. The conventional bit synchronization methods only work well when there is no or very small frequency error. The bit synchronization process is generally carried out after the pull-in stage, where the carrier loop is already stable. In this paper, a new bit synchronization method based on frequency compensation is proposed. Through compensating the frequency error, the new method reduces the signal power loss caused by the accumulation of coherent integration. The performances of the new method in different frequency error scenarios are compared. The parameters in the proposed method are analyzed and optimized to reduce the computational complexity. Simulation results show that the new method has good performance when the frequency error is less than 25Hz. Test results show that the new method can tolerate dynamic frequency errors, and it is possible to move the bit synchronization to the pull-in process to reduce the TTFF.