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.

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.

This paper proposes a matched myriad filter based detector for MSK signal under symmetric alpha-stable (SαS) noise. As shown in the previous literatures, SαS distribution is more accurate to characterize the atmospheric noise, which is the main interference in VLF communication. MSK modulation is widely used in VLF communication for its high spectral efficiency and constant envelope properties. However, the optimal detector for MSK under SαS noise is rarely reported due to its memory modulation characteristic. As MSK signal can be viewed as a sinusoidal pulse weighted offset QPSK (OQPSK), a matched myriad filter is proposed to derive a near-optimal detection performance for the in-phase and quadrature components, respectively. Simulations for MSK demodulation under SαS noise with different α validate the effectiveness of the proposed method.

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 this paper, operator-based reset control for a class of nonlinear systems with unknown bounded disturbance is considered using right coprime factorization approach. In detail, firstly, for dealing with the unknown bounded disturbance of the nonlinear systems, operator-based reset control framework is proposed based on right coprime factorization. By the proposed framework, robust stability of the nonlinear systems with unknown bounded disturbance is guaranteed by using the proposed reset controller. Secondly, under the reset control framework, an optimal design scheme is discussed for minimizing the error norm based on the proposed operator-based reset controller. Finally, for conforming effectiveness of the proposed design scheme, a simulation example is given.

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.

A family of quaternary sequences over Z4 is defined based on the Ding-Helleseth generalized cyclotomic classes modulo pq for two distinct odd primes p and q. The linear complexity is determined by computing the defining polynomial of the sequences, which is in fact connected with the discrete Fourier transform of the sequences. The results show that the sequences possess large linear complexity and are “good” sequences from the viewpoint of cryptography.

With the aim of characterizing the thermal conductivity for nanometer-scale thermoelectric materials, we have constructed a new measurement system based on ac calorimetry. Analysis of the obtained data requires time-evolution of temperature distribution in nanometer-scale material under periodic heating. In this study, we made a simulation using a C#-program for time-dependent temperature distribution, based on 2-dimensional heat-diffusion equation including the influence of heat emission from material edges. The simulation was applied to AlN with millimeter-scale dimensions for confirming the validity and accuracy. The simulated thermal diffusivity for 10×75-mm2-area AlN was 1.3×10-4 m2/s, which was larger than the value set in the heat-diffusion equation. This overestimation was also observed in the experiment. Therefore, our simulation can reproduce the unsteady heat conduction and be used for analyzing the ac calorimetry experiment.

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.

Radar is expected in advanced driver-assistance systems for environmentally robust measurements. In this paper, we propose a novel radar signal segmentation method by using a complex-valued fully convolutional network (CvFCN) that comprises complex-valued layers, real-valued layers, and a bidirectional conversion layer between them. We also propose an efficient automatic annotation system for dataset generation. We apply the CvFCN to two-dimensional (2D) complex-valued radar signal maps (r-maps) that comprise angle and distance axes. An r-maps is a 2D complex-valued matrix that is generated from raw radar signals by 2D Fourier transformation. We annotate the r-maps automatically using LiDAR measurements. In our experiment, we semantically segment r-map signals into pedestrian and background regions, achieving accuracy of 99.7% for the background and 96.2% for pedestrians.

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.

The concept of Gaussian integer sequence pair is generalized from a single Gaussian integer sequence. In this letter, by adopting cyclic difference set pairs, a new construction method for perfect Gaussian integer sequence pairs is presented. Furthermore, the necessary and sufficient conditions for constructing perfect Gaussian integer sequence pairs are given. Through the research in this paper, a large number of perfect Gaussian integer sequence pairs can be obtained, which can greatly extend the existence of perfect sequence pairs.

In this letter, a novel transmission scheme is proposed to eliminate the polarization dependent loss (PDL) effect in dual-polarized satellite systems. In fact, the PDL effect is the key problem that limits the performance of the systems based on the PM technique, while it is naturally eliminated in the proposed scheme since we transmit the two components of the polarized signal in turn in two symbol periods. Moreover, a simple and effective detection method based on the signal's power is proposed to distinguish the polarization characteristic of the transmit antenna. In addition, there is no requirement on the channel state information at the transmitter, which is popular in satellite systems. Finally, superiorities are validated by the theoretical analysis and simulation results in the dual-polarized satellite systems.

We propose an estimation method of pedestrian detectability considering the driver's visual adaptation to drastic illumination change, which has not been studied in previous works. We assume that driver's visual characteristics change in proportion to the elapsed time after illumination change. In this paper, as a solution, we construct multiple estimators corresponding to different elapsed periods, and estimate the detectability by switching them according to the elapsed period. To evaluate the proposed method, we construct an experimental setup to present a participant with illumination changes and conduct a preliminary simulated experiment to measure and estimate the pedestrian detectability according to the elapsed period. Results show that the proposed method can actually estimate the detectability accurately after a drastic illumination change.

We consider device-to-device (D2D) direct communication underlying cellular networks where the D2D link reuses the frequency resources of the cellular downlink. In this paper, we propose a linear precoder design scheme for a base station (BS) and D2D transmitter using the weighted sum-rate of the cellular downlink and D2D link as a cost function. Because the weighted sum-rate maximization problem is not convex on the precoding matrices of BS and D2D transmitters, an equivalent mean-squared error (MSE) minimization problem which is convex on the precoding matrices is proposed by introducing auxiliary matrices. We show that the two optimization problems have the same optimal solution for the precoding matrices. Then, an iterative algorithm for solving the equivalent MSE minimization problem is presented. Through a computer simulation, we show that the proposed scheme offers better weighted sum-rate performance that a conventional scheme.

We propose a novel object recognition system that is able to (i) work in real-time while reconstructing segmented 3D maps and simultaneously recognize objects in a scene, (ii) manage various kinds of objects, including those with smooth surfaces and those with a large number of categories, utilizing a CNN for feature extraction, and (iii) maintain high accuracy no matter how the camera moves by distributing the viewpoints for each object uniformly and aggregating recognition results from each distributed viewpoint as the same weight. Through experiments, the advantages of our system with respect to current state-of-the-art object recognition approaches are demonstrated on the UW RGB-D Dataset and Scenes and on our own scenes prepared to verify the effectiveness of the Viewpoint-Class-based approach.

We report the characterization of hysteresis in SOI-based super-steep subthreshold slope FETs, which are conventional floating body and body-tied, and newly proposed PN-body-tied structures. We found that the hysteresis widths of the PN-body-tied structures are smaller than that of the conventional floating body and body-tied structures; this means that they are feasible for switching devices. Detailed characterizations of the hysteresis widths of each device are also reported in the study, such as dependency on the gate length and the impurity concentration.

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.

In this paper, a dual-polarized phased array based polarization state modulation method is proposed to enhance the physical-layer security in millimeter-wave (mm-wave) communication systems. Indeed, we utilize two polarized beams to transmit the two components of the polarized signal, respectively. By randomly selecting the transmitting antennas, both the amplitude and the phase of two beams vary randomly in undesired directions, which lead to the PM constellation structure distortion in side lobes, thus the transmission security is enhanced since the symbol error rate increases at the eavesdropper side. To enhance the security performance when the eavesdropper is close to the legitimate receiver and located in main beam, the artificial noise based on the orthogonal vector approach is inserted randomly between two polarized beams, which can further distort the constellation structure in undesired directions and improve the secrecy capacity in main beam as well. Finally, theoretical analysis and simulation results demonstrate the proposed method can improve the transmission security in mm-wave communication systems.

Determining the validity of knowledge triples and filling in the missing entities or relationships in the knowledge graph are the crucial tasks for large-scale knowledge graph completion. So far, the main solutions use machine learning methods to learn the low-dimensional distributed representations of entities and relationships to complete the knowledge graph. Among them, translation models obtain excellent performance. However, the proposed translation models do not adequately consider the indirect relationships among entities, affecting the precision of the representation. Based on the long short-term memory neural network and existing translation models, we propose a multiple-module hybrid neural network model called TransP. By modeling the entity paths and their relationship paths, TransP can effectively excavate the indirect relationships among the entities, and thus, improve the quality of knowledge graph completion tasks. Experimental results show that TransP outperforms state-of-the-art models in the entity prediction task, and achieves the comparable performance with previous models in the relationship prediction task.