In this paper, we demonstrate how to subtract the intertrack interference (ITI) before the decoding process in multi-track multi-head bit-patterned media recording (BPMR) system, which can obtain a better bit error rate (BER) performance. We focus on the three-track/three-head BPMR channel and propose the ITI subtraction technique that performs together with a rate-5/6 two dimensional (2D) modulation code. Since the coded system can provide the estimated recorded bit sequence with a high reliability rate for the center track. However, the upper and lower data sequences still be interfered with their sidetracks, which results to have a low reliability rate. Therefore, we propose to feedback the data from the center and upper tracks for subtracting the ITI effect of the lower track. Meanwhile, the feedback data from the center and lower tracks will be also used to subtract the ITI effect of the upper track. The use of our proposed technique can effectively reduce the severity of ITI effect which caused from the two sidetracks. The computer simulation results in the presence of position and size fluctuations show that the proposed system yields better BER performance than a conventional coded system, especially when an areal density (AD) is ultra high.

In social networks, predicting user behavior under social hotspots can aid in understanding the development trend of a topic. In this paper, we propose a retweeting prediction method for social hotspots based on tensor decomposition, using user information, relationship and behavioral data. The method can be used to predict the behavior of users and analyze the evolvement of topics. Firstly, we propose a tensor-based mechanism for mining user interaction, and then we propose that the tensor be used to solve the problem of inaccuracy that arises when interactively calculating intensity for sparse user interaction data. At the same time, we can analyze the influence of the following relationship on the interaction between users based on characteristics of the tensor in data space conversion and projection. Secondly, time decay function is introduced for the tensor to quantify further the evolution of user behavior in current social hotspots. That function can be fit to the behavior of a user dynamically, and can also solve the problem of interaction between users with time decay. Finally, we invoke time slices and discretization of the topic life cycle and construct a user retweeting prediction model based on logistic regression. In this way, we can both explore the temporal characteristics of user behavior in social hotspots and also solve the problem of uneven interaction behavior between users. Experiments show that the proposed method can improve the accuracy of user behavior prediction effectively and aid in understanding the development trend of a topic.

A rich and robust representation for scene characters plays a significant role in automatically understanding the text in images. In this letter, we focus on the issue of feature representation, and propose a novel encoding method named bilateral convolutional activations encoded with Fisher vectors (BCA-FV) for scene character recognition. Concretely, we first extract convolutional activation descriptors from convolutional maps and then build a bilateral convolutional activation map (BCAM) to capture the relationship between the convolutional activation response and the spatial structure information. Finally, in order to obtain the global feature representation, the BCAM is injected into FV to encode convolutional activation descriptors. Hence, the BCA-FV can effectively integrate the prominent features and spatial structure information for character representation. We verify our method on two widely used databases (ICDAR2003 and Chars74K), and the experimental results demonstrate that our method achieves better results than the state-of-the-art methods. In addition, we further validate the proposed BCA-FV on the “Pan+ChiPhoto” database for Chinese scene character recognition, and the experimental results show the good generalization ability of the proposed BCA-FV.

Cell phones with GPS function as well as GPS loggers are widely used and users' geographic information can be easily obtained. However, still battery consumption in these mobile devices is main concern and then obtaining GPS positioning data so frequently is not allowed. In this paper, a stayed location estimation method for sparse GPS positioning information is proposed. After generating initial clusters from a sequence of measured positions, the effective radius is set for every cluster based on positioning accuracy and the clusters are merged effectively using it. After that, short-time clusters are removed temporarily but measured positions included in them are not removed. Then the clusters are merged again, taking all the measured positions into consideration. This process is performed twice, in other words, two-stage short-time cluster removal is performed, and finally accurate stayed location estimation is realized even when the GPS positioning interval is five minutes or more. Experiments demonstrate that the total distance error between the estimated stayed location and the true stayed location is reduced by more than 33% and also the proposed method much improves F1 measure compared to conventional state-of-the-art methods.

Public safety networks need to more effectively meet the increasing demands for images or videos to be shared among first responders and incident commanders. Long term evolution (LTE) networks are considered to be candidates to achieve such broadband services. Capital expenditures in deploying base stations need to be decreased to introduce LTE for public safety. However, out-of-coverage areas tend to occur in cell edge areas or inside buildings because the cell areas of base stations for public safety networks are larger than those for commercial networks. The 3rd Generation Partnership Program (3GPP) in Release 13 has investigated device-to-device (D2D) based relay communication as a means to fill out-of-coverage areas in public safety LTE (PS-LTE). This paper proposes a relay selection scheme based on effective path throughput from an out-of-coverage terminal to a base station via an in-coverage relay terminal, which enables the optimal relay terminal to be selected. System level simulation results assuming on radii of 20km or less revealed that the proposed scheme could provide better user ratios that satisfied the throughput requirements for video transmission than the scheme standardized in 3GPP. Additionally, an evaluation that replicates actual group of fire-fighters indicated that the proposed scheme enabled 90% of out-of-coverage users to achieve the required throughput, i.e., 1.0Mbps, to transmit video images.

We have studied on thermally assisted nano-structured transistors made of superconductor ultra-thin films. These transistors potentially work as interface devices for Josephson-CMOS (complementary metal oxide semiconductor) hybrid memory systems, because they can generate a high output voltage of sub-V enough to drive a CMOS transistor. In addition, our superconductor transistors are formed with very fine lines down to several tens of nm in widths, leading to very small foot print enabling us to make large capacity hybrid memories. Our superconductor transistors are made with niobium titanium nitride (NbTiN) thin films deposited on thermally-oxidized silicon substrates, on which other superconductor circuits or semiconductor circuits can be formed. The NbTiN thickness dependence of the critical temperature and of resistivity suggest thermally activated vortex or anti-vortex behavior in pseudo-two-dimensional superconducting films plays an important role for the operating principle of the transistors. To show the potential that the transistors can drive MOS transistors, we analyzed the driving ability of the superconductor transistors with HSPICE simulation. We also showed the turn-on behavior of a MOS transistor used for readout of a CMOS memory cell experimentally. These results showed the high potential of superconductor transistors for Josephson-CMOS hybrid memories.

This paper presents a self-calibrating dynamic latched comparator with a stochastic offset voltage detector that can be realized by using simple digital circuitry. An offset voltage of the comparator is compensated by using a statistical calibration scheme, and the offset voltage detector uses the uncertainty in the comparator output. Thanks to the simple offset detection technique, all the calibration circuitry can be synthesized using only standard logic cells. This paper also gives a design methodology that can provide the optimal design parameters for the detector on the basis of fundamental statistics, and the correctness of the design methodology was statistically validated through measurement. The proposed self-calibrating comparator system was fabricated in a 180 nm 1P6M CMOS process. The prototype achieved a 38 times improvement in the three-sigma of the offset voltage from 6.01 mV to 158 µV.

Our goal is to use a single passive moving sensor to determine the locations of multiple radio stations. The conventional method uses only direction-of-arrival (DOA) measurements, and its performance is poor when emitters are located closely in the lateral direction, even if they are not close in the range direction, or in the far field from the moving sensor, resulting in similar DOAs for several emitters. This paper proposes a new method that uses the power of the received signals as well as DOA. The received signal power is a function of the inverse of the squared distance between an emitter and the moving sensor. This has the advantage of providing additional information in the range direction; therefore, it can be used for data association as additional information when emitter ranges are different from each other. Simulations showed that the success rate of the conventional method is 73%, whereas that of the proposed method is 97%, an overall 24-percentage-point improvement. The localization error of the proposed method is also reduced to half that of the conventional method. We further investigated its performance with different emitter and sensor configurations. In all cases, the proposed method proved superior to the conventional method.

In the era of e-commerce, purchase behavior prediction is one of the most important issues to promote both online companies' sales and the consumers' experience. The previous researches usually use the feature engineering and ensemble machine learning algorithms for the prediction. The performance really depends on designed features and the scalability of algorithms because the large-scale data and a lot of categorical features lead to huge samples and the high-dimensional feature. In this study, we explore an alternative to use tree-based Feature Transformation (FT) and simple machine learning algorithms (e.g. Logistic Regression). Random Forest (RF) and Gradient Boosting decision tree (GB) are used for FT. Then, the simple algorithm, rather than ensemble algorithms, is used to predict purchase behavior based on transformed features. Tree-based FT regards the leaves of trees as transformed features, and can learn high-order interactions among original features. Compared with RF, if GB is used for FT, simple algorithms are enough to achieve better performance.

In this study, the effect of nitrogen-doped (N-doped) LaB6 bottom-contact electrodes and interfacial layer (IL) on n-type pentacene-based organic field-effect transistor (OFET) was investigated. The scaled OFET was fabricated by using photolithography for bottom-contact electrodes. A 20-nm-thick N-doped LaB6 bottom-contact electrodes were formed on SiO2/n+-Si(100) substrate by RF sputtering followed by the surface treatment with sulfuric acid and hydrogen peroxide mixture (SPM) followed by diluted hydrofluoric acid (DHF; 1% HF) at room temperature (RT). Then, a 1.2-nm-thick N-doped LaB6 IL was deposited at RT. Finally, a 10-nm-thick pentacene film was deposited at 100°C followed by the Al back-gate electrode formation by using thermal evaporation. The current of electron injection was observed in the air due to the effect of surface treatment and N-doped LaB6 IL.

We present a human point of gaze estimation system using corneal surface reflection and omnidirectional image taken by spherical panorama cameras, which becomes popular recent years. Our system enables to find where a user is looking at only from an eye image in a 360° surrounding scene image, thus, does not need gaze mapping from partial scene images to a whole scene image that are necessary in conventional eye gaze tracking system. We first generate multiple perspective scene images from an omnidirectional (equirectangular) image and perform registration between the corneal reflection and perspective images using a corneal reflection-scene image registration technique. We then compute the point of gaze using a corneal imaging technique leveraged by a 3D eye model, and project the point to an omnidirectional image. The 3D eye pose is estimate by using the particle-filter-based tracking algorithm. In experiments, we evaluated the accuracy of the 3D eye pose estimation, robustness of registration and accuracy of PoG estimations using two indoor and five outdoor scenes, and found that gaze mapping error was 5.546 [deg] on average.

We experimentally demonstrate transistor operation in a vertical p+-MoS2/n-HfS2 van der Waals (vdW) heterostructure configuration for the first time. The HfS2/MoS2 heterojunction transistor exhibits an ON/OFF ratio of 104 and a maximum drain current of 20 nA. These values are comparable with the corresponding reported values for vdW heterojunction TFETs. Moreover, we study the effect of atmospheric exposure on the subthreshold slope (SS) of the HfS2/MoS2 transistor. Unpassivated and passivated devices are compared in terms of their SS values and IDS-VGS hysteresis. While the unpassivated HfS2/MoS2 heterojunction transistor exhibits a minimum SS value of 2000 mV/dec, the same device passivated with a 20-nm-thick HfO2 film exhibits a significantly lower SS value of 700 mV/dec. HfO2 passivation protects the device from contamination caused by atmospheric moisture and oxygen and also reduces the effect of surface traps. We believe that our findings will contribute to the practical realization of HfS2-based vdW heterojunction TFETs.

This paper addresses the issue of text matching for plagiarism detection. This task aims at identifying the matching plagiarism segments in a pair of suspicious document and its plagiarism source document. All the time, heuristic-based methods are mainly utilized to resolve this problem. But the heuristics rely on the experts' experiences and fail to integrate more features to detect the high obfuscation plagiarism matches. In this paper, a statistical machine learning approach, named the Ranking-based Text Matching Approach for Plagiarism Detection, is proposed to deal with the issues of high obfuscation plagiarism detection. The plagiarism text matching is formalized as a ranking problem, and a pairwise learning to rank algorithm is exploited to identify the most probable plagiarism matches for a given suspicious segment. Especially, the Meteor evaluation metrics of machine translation are subsumed by the proposed method to capture the lexical and semantic text similarity. The proposed method is evaluated on PAN12 and PAN13 text alignment corpus of plagiarism detection and compared to the methods achieved the best performance in PAN12, PAN13 and PAN14. Experimental results demonstrate that the proposed method achieves statistically significantly better performance than the baseline methods in all twelve document collections belonging to five different plagiarism categories. Especially at the PAN12 Artificial-high Obfuscation sub-corpus and PAN13 Summary Obfuscation plagiarism sub-corpus, the main evaluation metrics PlagDet of the proposed method are even 22% and 43% relative improvements than the baselines. Moreover, the efficiency of the proposed method is also better than that of baseline methods.

This letter considers a legitimate proactive eavesdropping scenario, where a half-duplex legitimate monitor hires a third-party jammer for jamming the suspicious communication to improve the eavesdropping performance. The interaction between the third-party jammer and the monitor is modeled as a Stackelberg game, where the jammer moves first and sets the price for jamming the suspicious communication, and then the legitimate monitor moves subsequently and determines the requested transmit power of the jamming signals. We derive the optimal jamming price and the optimal jamming transmit power. It is shown that the proposed price-based proactive eavesdropping scheme is effective in improving the successful eavesdropping probability compared to the case without jamming. It is also shown that the proposed scheme outperforms the existing full-duplex scheme when the residual self-interference cannot be neglected.

A logo is a symbolic presentation that is designed not only to identify a product manufacturer but also to attract the attention of shoppers. Shoe logos are a challenging subject for automatic extraction and recognition using image analysis techniques because they have characteristics that distinguish them from those of other products; that is, there is much within-class variation in the appearance of shoe logos. In this paper, we propose an automatic extraction and recognition method for shoe logos with a wide variety of appearance using a limited number of training samples. The proposed method employs maximally stable extremal regions for the initial region extraction, an iterative algorithm for region grouping, and gradient features and a support vector machine for logo recognition. The results of performance evaluation experiments using a logo dataset that consists of a wide variety of appearances show that the proposed method achieves promising performance for both logo extraction and recognition.

Predicting the service performance of Internet applications is important in service selection, especially for video services. In order to design a predictor for forecasting video service performance in third-party application, two famous service providers in China, Iqiyi and Letv, are monitored and analyzed. The study highlights that the measured performance in the observation period is time-series data, and it has strong autocorrelation, which means it is predictable. In order to combine the temporal information and map the measured data to a proper feature space, the authors propose a predictor based on a Conditional Restricted Boltzmann Machine (CRBM), which can capture the potential temporal relationship of the historical information. Meanwhile, the measured data of different sources are combined to enhance the training process, which can enlarge the training size and avoid the over-fit problem. Experiments show that combining the measured results from different resolutions for a video can raise prediction performance, and the CRBM algorithm shows better prediction ability and more stable performance than the baseline algorithms.

The physics and applications of superconducting phase shifts and their control in superconducting systems are reviewed herein. The operation principle of almost all superconducting devices is related to the superconducting phase, and an efficient control of the phase is crucial for improving the performance and scalability. Furthermore, employing new methods to shift or control the phase may lead to the development of novel superconducting device applications, such as cryogenic memory and quantum computing devices. Recently, as a result of the progress in nanofabrication techniques, superconducting phase shifts utilizing π states have been realized. In this review, following a discussion of the basic physics of phase propagation and shifts in hybrid superconducting structures, interesting phenomena and device applications in phase-shifted superconducting systems are presented. In addition, various possibilities for developing electrically and magnetically controllable 0 and π junctions are presented; these possibilities are expected to be useful for future devices.

Channel capacity is a useful numerical index not only for grasping the upper limit of the transmission bit rate but also for comparing the abilities of various digital transmission schemes commonly used in radio-wave propagation environments because the channel capacity does not depend on specific communication methods such as modulation/demodulation schemes or error correction schemes. In this paper, modeling of the noncoherent capacity in a highly underspread WSSUS channel is investigated using a new approach. Unlike the conventional method, namely, the information theoretic method, a very straightforward formula can be obtained in a statistical manner. Although the modeling in the present study is carried out using a somewhat less rigorous approach, the result obtained is useful for roughly understanding the channel capacity in doubly selective fading environments. We clarify that the radio wave propagation parameter of the spread factor, which is the product of the Doppler spread and the delay spread, can be related quantitatively to the effective maximum signal-to-interference ratio by a simple formula. Using this model, the physical limit of wireless digital transmission is discussed from a radio wave propagation perspective.

In this paper, we propose a novel self-supervised learning of video representation which is capable to anticipate the video category by only reading its short clip. The key idea is that we employ the Siamese convolutional network to model the self-supervised feature learning as two different image matching problems. By using frame encoding, the proposed video representation could be extracted from different temporal scales. We refine the training process via a motion-based temporal segmentation strategy. The learned representations for videos can be not only applied to action anticipation, but also to action recognition. We verify the effectiveness of the proposed approach on both action anticipation and action recognition using two datasets namely UCF101 and HMDB51. The experiments show that we can achieve comparable results with the state-of-the-art self-supervised learning methods on both tasks.

We have investigated the influence of the ferromagnet magnetization on the transport properties of intrinsic Josephson junctions in Co/Au/BSCCO/Au/Co hybrid structure under applied magnetic fields. The current-voltage characteristic at 77K in a zero-field showed the multiple quasiparticle branches with hysteresis similar to that of conventional intrinsic Josephson junctions. On the other hand, it was observed that the critical current shows a clear asymmetric field dependence with respect to the direction of the field sweep, resulting in hysteretic behavior. By comparing the field dependence of critical current with magnetization curve of the sample, we found that the critical current is strongly suppressed in the antiparallel configuration of the relative magnetization orientation of two Co layers due to the accumulation of spin-polarized quasiparticles in intrinsic Josephson junctions. The observed suppression of the critical current is as large as more than 20%.