There are different types of social ties among people, and recognizing specialized types of relationship, such as family or friend, has important significance. It can be applied to personal credit, criminal investigation, anti-terrorism and many other business scenarios. So far, some machine learning algorithms have been used to establish social relationship inferencing models, such as Decision Tree, Support Vector Machine, Naive Bayesian and so on. Although these algorithms discover family members in some context, they still suffer from low accuracy, parameter sensitive, and weak robustness. In this work, we develop a Novel Family Relationship Recognition (NFRR) algorithm on telecom dataset for identifying one's family members from its contact list. In telecom dataset, all attributes are divided into three series, temporal, spatial and behavioral. First, we discover the most probable places of residence and workplace by statistical models, then we aggregate data and select the top-ranked contacts as the user's intimate contacts. Next, we establish Relational Spectrum Matrix (RSM) of each user and its intimate contacts to form communication feature. Then we search the user's nearest neighbors in labelled training set and generate its Specialized Family Spectrum (SFS). Finally, we decide family relationship by comparing the similarity between RSM of intimate contacts and the SFS. We conduct complete experiments to exhibit effectiveness of the proposed algorithm, and experimental results also show that it has a lower complexity.

Blind nonlinear compensation for RF receivers is an important research topic in 5G mobile communication, in which higher level modulation schemes are employed more often to achieve high capacity and ultra-broadband services. Since nonlinear compensation circuits must handle intermodulation bandwidths that are more than three times the signal bandwidth, reducing the sampling frequency is essential for saving power consumption. This paper proposes a novel blind nonlinear compensation technique that employs sub-Nyquist sampling analog-to-digital conversion. Although outband distortion spectrum is folded in the proposed sub-Nyquist sampling technique, determination of compensator coefficients is still possible by using the distortion power. Proposed technique achieves almost same compensation performance in EVM as the conventional compensation scheme, while reducing sampling speed of analog to digital convertor (ADC) to less than half the normal sampling frequency. The proposed technique can be applied in concurrent dual-band communication systems and adapt to flat Rayleigh fading environments.

This paper presents a concept of a domain-specific framework for software analytics by enabling querying, modeling, and integration of heterogeneous software repositories. The framework adheres to a multi-layered abstraction mechanism that consists of domain-specific operators. We showcased the potential of this approach by employing a case study.

A diversity strategy is efficient to reduce the fluctuation of communication quality caused by fading. In order to further maintain the communication quality and improve the communication capacity, this paper proposes a two-dimensional diversity approach by serially-concatenating spectral precoding and power normalized-differential space time block coding (PN-DSTBC). Spectral precoding is able to take benefit from a frequency diversity effect without loss in spectral efficiency. In addition, PN-DSTBC is robust against serious phase noise in an extremely high frequency (EHF) band by exploiting a spatial diversity effect. However, there is a problem that a naive concatenation degrades the performance due to the imbalance of equivalent noise variances over transmit frequencies. Thus, we examine an equalized PN-DSTBC decoder as a modified approach to uniform equivalent noise variances over frequencies. The performance evaluation using computer simulations shows that the proposed modified approach yields the performance improvement at any modulation schemes and at any number of transmit frequencies. Furthermore, in the case of 64QAM and two transmit frequencies, the performance gain of the modified approach is 4dB larger than that of PN-DSTBC only at uncoded BER=10-4.

One of essential requirements for the next generation communications is to support higher spectral efficiency (SE) and energy efficiency (EE) than the existing communication system. For increasing the SE, carrier aggregation (CA) has received great attention. In this paper, we propose an energy efficient smart crest factor reduction (E2S-CFR) method for increasing the EE while satisfying the required SE when the CA is applied. The proposed E2S-CFR exploits different weights on each carrier according to the required error vector magnitude (EVM), and efficiently reduces the peak to average power ratio (PAR). Consequently, we can reduce the bias voltage of a power amplifier, and it leads to save total consumed energy. Through performance evaluation, we demonstrate that the proposed E2S-CFR improves the EE by 11.76% compared to the existing schemes.

The gap states of tetratetracontane (C44H90; TTC), which is a model oligomer of polyethylene, was examined by using high-sensitivity UV photoemission spectroscopy (HS-UPS). The high sensitivity enabled us to directly observe the weak gap states distributed in the HOMO-LUMO gap from the valence band top to 3.0 eV below the vacuum level. On the basis of the density-of-states derived from UPS results, the tribocharging nature of polyethylene was discussed in comparison with our previous result for nylon-6,6 film.

Spectral graph theory gives an algebraic approach to the analysis of the dynamics of a network by using the matrix that represents the network structure. However, it is not easy for social networks to apply the spectral graph theory because the matrix elements cannot be given exactly to represent the structure of a social network. The matrix element should be set on the basis of the relationship between persons, but the relationship cannot be quantified accurately from obtainable data (e.g., call history and chat history). To get around this problem, we utilize the universality of random matrices with the feature of social networks. As such a random matrix, we use the normalized Laplacian matrix for a network where link weights are randomly given. In this paper, we first clarify that the universality (i.e., the Wigner semicircle law) of the normalized Laplacian matrix appears in the eigenvalue frequency distribution regardless of the link weight distribution. Then, we analyze the information propagation speed by using the spectral graph theory and the universality of the normalized Laplacian matrix. As a result, we show that the worst-case speed of the information propagation changes up to twice if the structure (i.e., relationship among people) of a social network changes.

The Game of Life, a two-dimensional computationally universal cellular automaton, is known to exhibits 1/f noise in the evolutions starting from random configurations. In this paper we perform the spectral analysis on the computation process by a Turing machine constructed on the array of the Game of Life. As a result, the power spectrum averaged over the whole array has almost flat line at low frequencies and a lot of sharp peaks at high frequencies although some regions in which complicated behavior such as frequent memory rewriting occurs exhibit 1/f noise. This singular power spectrum is, however, easily turned into 1/f by slightly deforming the initial configuration of the Turing machine. These results emphasize the peculiarity of the computation process on the Game of Life that is never shared with the evolutions from random configurations. The Lyapunov exponents have positive values in three out of six trials and zero or negative values in other three trails. That means the computation process is essentially chaotic but it has capable of recovering a slight error in the configuration of the Turing machine.

Hydrophobic DNA (H-DNA) nano-film was formed on a thin glass plate of 50μm thick working as a slab optical waveguide. Bromothymol blue (BTB) molecules were immobilized from aqueous solution with direct contacting to the H-DNA nano-film for 20 minutes. From changes in absorption spectra observed with slab optical wave guide (SOWG) during automated solution exchange (SE) processes for 100 times, it was found that about 95% of bromothymol blue (BTB) molecules was immobilized in the H-DNA nano-film with keeping their functionality of color change responsible to pH change in the solution.

Conventional target recognition methods usually suffer from information-loss and target-aspect sensitivity when applied to radar high resolution range profile (HRRP) recognition. Thus, Effective establishment of robust and discriminatory feature representation has a significant performance improvement of practical radar applications. In this work, we present a novel feature extraction method, based on modified collaborative auto-encoder, for millimeter-wave radar HRRP recognition. The latent frame-specific weight vector is trained for samples in a frame, which contributes to retaining local information for different targets. Experimental results demonstrate that the proposed algorithm obtains higher target recognition accuracy than conventional target recognition algorithms.

In stepped FM radar, the transmitter intermittently transmits narrowband pulse trains of frequencies that are incremented in steps, and the receiver performs phase detection on each pulse and applies the inverse discrete Fourier transform (IDFT) to create ultra-short pulses in the time domain. Furthermore, since the transmitted signal consists of a narrowband pulse train of different frequencies, the transmitter can avoid arbitrary frequency bands while sending the pulse train (spectrum holes), allowing these systems to coexist with other narrowband wireless systems. However, spectrum holes cause degradation in the distance resolution and range sidelobe characteristics of wireless systems. In this paper, we propose a spectrum hole compensation method for stepped FM radars using Khatri-Rao product extended-phase processing to overcome the problem of spectrum holes and investigate the effectiveness of this method through experiments. Additionally, we demonstrate that the proposed method dramatically improves the range sidelobe and distance resolution characteristics.

Generation of secure signatures suitable for spread-spectrum video watermarking is proposed. The method embeds a message, which is a two-dimensional binary pattern, into a three-dimensional volume, such as video, by addition of a signature. The message can be a mark or a logo indicating the copyright information. The signature is generated by shuffling or permuting random matrices along the third or time axis so that the message is extracted when they are accumulated after demodulation by the correct key. In this way, a message is hidden in the signature having equal probability of decoding any variation of the message, where the key is used to determine which one to extract. Security of the proposed method, stemming from the permutation, is evaluated as resistance to blind estimation of secret information. The matrix-based permutation allows the message to survive the spatial down-sampling without sacrificing the security. The downside of the proposed method is that it needs more data or frames to decode a reliable information compared to the conventional spread-spectrum modulation. However this is minimized by segmenting the matrices and applying permutation to sub-matrices independently. Message detectability is theoretically analyzed. Superiority of our method in terms of robustness to blind message estimation and down-sampling is verified experimentally.

For capacity expansion of the Quasi-Zenith Satellite System (QZSS) safety confirmation system, frame slotted ALOHA with flag method has previously been proposed as an access control scheme. While it is always able to communicate in an optimum state, its maximum channel efficiency is only 36.8%. In this paper, we propose adding a reservation channel (R-Ch) to the frame slotted ALOHA with flag method to increase the upper limit of the channel efficiency. With an R-Ch, collision due to random channel selection is decreased by selecting channels in multiple steps, and the channel efficiency is improved up to 84.0%. The time required for accommodating 3 million mobile terminals, each sending one message, when using the flag method only and the flag method with an R-Ch are compared. It is shown that the accommodating time can be reduced to less than half by adding an R-Ch to the flag method.

We normally hold a lot of confidential information in hard disk drives and solid-state drives. When we want to erase such information to prevent the leakage, we have to overwrite the sequence of information with a sequence of symbols independent of the information. The overwriting is needed only at places where overwritten symbols are different from original symbols. Then, the cost of overwrites such as the number of overwritten symbols to erase information is important. In this paper, we clarify the minimum cost such as the minimum number of overwrites to erase information under weak and strong independence criteria. The former (resp. the latter) criterion represents that the mutual information between the original sequence and the overwritten sequence normalized (resp. not normalized) by the length of the sequences is less than a given desired value.

Crack detection is a vital task to maintain a bridge's health and safety condition. Traditional computer-vision based methods easily suffer from disturbance of noise and clutters for a real bridge inspection. To address this limitation, we propose a two-stage crack detection approach based on Convolutional Neural Networks (CNN) in this letter. A predictor of small receptive field is exploited in the first detection stage, while another predictor of large receptive field is used to refine the detection results in the second stage. Benefiting from data fusion of confidence maps produced by both predictors, our method can predict the probability belongs to cracked areas of each pixel accurately. Experimental results show that the proposed method is superior to an up-to-date method on real concrete surface images.

In the successive refinement problem, a fixed-length sequence emitted from an information source is encoded into two codewords by two encoders in order to give two reconstructions of the sequence. One of two reconstructions is obtained by one of two codewords, and the other reconstruction is obtained by all two codewords. For this coding problem, we give non-asymptotic inner and outer bounds on pairs of numbers of codewords of two encoders such that each probability that a distortion exceeds a given distortion level is less than a given probability level. We also give a general formula for the rate-distortion region for general sources, where the rate-distortion region is the set of rate pairs of two encoders such that each maximum value of possible distortions is less than a given distortion level.

Compressed Sensing (CS) is known to provide better channel estimation performance than the Least Square (LS) method for channel estimation. However, multipath delays may not be resolved if they span between the grids. This grid problem of CS is an obstacle to super resolution channel estimation. An Atomic Norm (AN) minimization is one of the methods for estimating continuous parameters. The AN minimization can successfully recover a spectrally sparse signal from a few time-domain samples even though the dictionary is continuous. There are studies showing that the AN minimization method has better resolution than conventional CS methods. In this paper, we propose a channel estimation method based on the AN minimization for Spread Spectrum (SS) systems. The accuracy of the proposed channel estimation is compared with the conventional LS method and Dantzig Selector (DS) of the CS. In addition to the application of channel estimation in wireless communication, we also show that the AN minimization can be applied to Global Positioning System (GPS) using Gold sequence.

In 1973, Arimoto proved the strong converse theorem for the discrete memoryless channels stating that when transmission rate R is above channel capacity C, the error probability of decoding goes to one as the block length n of code word tends to infinity. He proved the theorem by deriving the exponent function of error probability of correct decoding that is positive if and only if R > C. Subsequently, in 1979, Dueck and Körner determined the optimal exponent of correct decoding. Recently the author determined the optimal exponent on the correct probability of decoding have the form similar to that of Dueck and Körner determined. In this paper we give a rigorous proof of the equivalence of the above exponet function of Dueck and Körner to a exponent function which can be regarded as an extention of Arimoto's bound to the case with the cost constraint on the channel input.

In this paper, we propose a design of reversible adder/subtractor blocks and arithmetic logic units (ALUs). The main concept of our approach is different from that of the existing related studies; we emphasize the function design. Our approach of investigating the reversible functions includes (a) the embedding of irreversible functions into incompletely-specified reversible functions, (b) the operation assignment, and (c) the permutation of function outputs. We give some extensions of these techniques for further improvements in the design of reversible functions. The resulting reversible circuits are smaller than that of the existing design in terms of the number of multiple-control Toffoli gates. To evaluate the quantum cost of the obtained circuits, we convert the circuits to reduced quantum circuits for experiments. The results also show the superiority of our realization of adder/subtractor blocks and ALUs in quantum cost.

Spectrum sensing is the first task performed by cognitive radio (CR) networks. In this paper we propose a spectrum sensing algorithm for orthogonal frequency division multiplex (OFDM) signal based on deep learning and covariance matrix graph. The advantage of deep learning in image processing is applied to the spectrum sensing of OFDM signals. We start by building the spectrum sensing model of OFDM signal, and then analyze structural characteristics of covariance matrix (CM). Once CM has been normalized and transformed into a gray level representation, the gray scale map of covariance matrix (GSM-CM) is established. Then, the convolutional neural network (CNN) is designed based on the LeNet-5 network, which is used to learn the training data to obtain more abstract features hierarchically. Finally, the test data is input into the trained spectrum sensing network model, based on which spectrum sensing of OFDM signals is completed. Simulation results show that this method can complete the spectrum sensing task by taking advantage of the GSM-CM model, which has better spectrum sensing performance for OFDM signals under low SNR than existing methods.