Modulation spectrogram is effective for analyzing underwater signals which consist of tonal and modulated components. This method can analyze the acoustic and modulation frequency at the same time, but has the trade-off issue of time-frequency localization. This letter introduces a reassignment method for overcoming the localization issue in conventional spectrograms, and then presents an alignment scheme for implementing modulation spectrogram. Relevant experiments show improvement in acoustic frequency estimation perspective and an increment in analyzable modulation frequency range.

In recent decade, many state-of-the-art algorithms on image classification as well as audio classification have achieved noticeable successes with the development of deep convolutional neural network (CNN). However, most of the works only exploit single type of training data. In this paper, we present a study on classifying bird species by exploiting the combination of both visual (images) and audio (sounds) data using CNN, which has been sparsely treated so far. Specifically, we propose CNN-based multimodal learning models in three types of fusion strategies (early, middle, late) to settle the issues of combining training data cross domains. The advantage of our proposed method lies on the fact that we can utilize CNN not only to extract features from image and audio data (spectrogram) but also to combine the features across modalities. In the experiment, we train and evaluate the network structure on a comprehensive CUB-200-2011 standard data set combing our originally collected audio data set with respect to the data species. We observe that a model which utilizes the combination of both data outperforms models trained with only an either type of data. We also show that transfer learning can significantly increase the classification performance.

The sparse unmixing of hyperspectral data has attracted much attention in recent years because it does not need to estimate the number of endmembers nor consider the lack of pure pixels in a given hyperspectral scene. However, the high mutual coherence of spectral libraries strongly affects the practicality of sparse unmixing. The collaborative sparse unmixing via variable splitting and augmented Lagrangian (CLSUnSAL) algorithm is a classic sparse unmixing algorithm that performs better than other sparse unmixing methods. In this paper, we propose a CLSUnSAL-based hyperspectral unmixing method based on dictionary pruning and reweighted sparse regression. First, the algorithm identifies a subset of the original library elements using a dictionary pruning strategy. Second, we present a weighted sparse regression algorithm based on CLSUnSAL to further enhance the sparsity of endmember spectra in a given library. Third, we apply the weighted sparse regression algorithm on the pruned spectral library. The effectiveness of the proposed algorithm is demonstrated on both simulated and real hyperspectral datasets. For simulated data cubes (DC1, DC2 and DC3), the number of the pruned spectral library elements is reduced by at least 94% and the runtime of the proposed algorithm is less than 10% of that of CLSUnSAL. For simulated DC4 and DC5, the runtime of the proposed algorithm is less than 15% of that of CLSUnSAL. For the real hyperspectral datasets, the pruned spectral library successfully reduces the original dictionary size by 76% and the runtime of the proposed algorithm is 11.21% of that of CLSUnSAL. These experimental results show that our proposed algorithm not only substantially improves the accuracy of unmixing solutions but is also much faster than some other state-of-the-art sparse unmixing algorithms.

This letter presents a computational complexity reduction technique for space diversity based spectrum sensing when the number of receive antennas is greater than three (NR≥3 where NR is the number of receive antenna). The received signals are combined with phase inversion so as to not attenuate the combined signal, and a statistic for signal detection is computed from the combined signal. Because the computation of only one statistic is required regardless of the number of receive antenna, the complexity can be reduced. Numerical examples and simple analysis verify the effectiveness of the presented technique.

Dynamic sharing of limited radio spectrum resources is expected to satisfy the increasing demand for spectrum resources in the upcoming 5th generation mobile communication system (5G) era and beyond. Distributed real-time spectrum sensing is a key enabler of dynamic spectrum sharing, but the costs incurred in observed-data transmission are a critical problem, especially when massive numbers of spectrum sensors are deployed. To cope with this issue, the proposed spectrum sensors learn the ambient radio environment in real-time and create a time-spectral model whose parameters are shared with servers operating in the edge-computing layer. This process makes it possible to significantly reduce the communication cost of the sensors because frequent data transmission is no longer needed while enabling the edge servers to keep up on the current status of the radio environment. On the basis of the created time-spectral model, sharable spectrum resources are dynamically harvested and allocated in terms of geospatial, temporal, and frequency-spectral domains when accepting an application for secondary-spectrum use. A web-based prototype spectrum management system has been implemented using ten servers and dozens of sensors. Measured results show that the proposed approach can reduce data traffic between the sensors and servers by 97%, achieving an average data rate of 10 kilobits per second (kbps). In addition, the basic operation flow of the prototype has been verified through a field experiment conducted at a manufacturing facility and a proof-of-concept experiment of dynamic-spectrum sharing using wireless local-area-network equipment.

Spectrum sensing is a fundamental requirement for cognitive radio, and it is a challenging problem in impulsive noise modeled by symmetric alpha-stable (SαS) distributions. The Gaussian kernelized energy detector (GKED) performs better than the conventional detectors in SαS distributed noise. However, it fails to detect the DC signal and has high computational complexity. To solve these problems, this paper proposes a more efficient and robust detector based on a Gaussian function (GF). The analytical expressions of the detection and false alarm probabilities are derived and the best parameter for the statistic is calculated. Theoretical analysis and simulation results show that the proposed GF detector has much lower computational complexity than the GKED method, and it can successfully detect the DC signal. In addition, the GF detector performs better than the conventional counterparts including the GKED detector in SαS distributed noise with different characteristic exponents. Finally, we discuss the reason why the GF detector outperforms the conventional counterparts.

Hydrophobic DNA (H-DNA) nano-film was formed as the surface modifier on a thin glass plate working as a slab optical waveguide (SOWF). Cytochrom c (cytc) molecules were immobilized from aqueous solution with direct contacting to the H-DNA nano-film for 30 minutes. From SOWG absorption spectral changes during automated solution exchange (SE) processes, it was found that about 28.1% of cytc molecules was immobilized in the H-DNA nano-film with keeping their reduction functionality by reducing reagent.

Wireless communication security has been increasingly important nowadays. Directional modulation (DM) is seen as a promising wireless physical layer security technology. Traditional DM is a transmit-side technology that projects digitally modulated information signals in the desired directions (or at the desired locations) while simultaneously distorting the constellation formats of the same signals in other directions (or at all other locations). However, these directly exposed digitally modulated information signals are easily intercepted by eavesdroppers along the desired directions (or around the desired locations). A new DM scheme for secure point-to-multipoint communication based on the spread spectrum assisted orthogonal frequency diverse array (short for SS-OFDA-M-DM) is proposed in this paper. It can achieve point-to-multipoint secure communication for multiple cooperative receivers at different locations. In the proposed SS-OFDA-M-DM scheme, only cooperative users that use specific DM receivers with right spread spectrum parameters can retrieve right symbols. Eavesdroppers without knowledge of spread spectrum parameters cannot intercept useful signals directly at the desired locations. Moreover, they cannot receive normal symbols at other locations either even if the right spread spectrum parameters are known. Numerical simulation results verify the validity of our proposed scheme.

In this paper, we investigate different power allocation optimization problems with interferences for distributed antenna systems (DAS) with and without D2D communication, respectively. The first objective problem is maximizing spectral efficiency (SE) of the DAS with D2D communication under the constraints of the minimum SE requirements of user equipment (UE) and D2D pair, maximum transmit power of each remote access unit (RAU) and maximum transmit power of D2D transmitter. We transform this non-convex objective function into a difference of convex functions (D.C.) then using the concave-convex procedure (CCCP) algorithm to solve the optimization problem. The second objective is maximizing energy efficiency (EE) of the DAS with D2D communication under the same constraints. We first exploit fractional programming theory to obtain the equivalent objective function of the second problem with subtract form, and then transform it into a D.C. problem and use CCCP algorithm to obtain the optimal power allocation. In each part, we summarize the corresponding optimal power allocation algorithms and also use similar method to obtain optimal solutions of the same optimization problems in DAS. Simulation results are provided to demonstrate the effectiveness of the designed power allocation algorithms and illustrate the SE and EE of the DAS by using D2D communication are much better than DAS without D2D communication.

This paper proposes an enhanced affinity graph (EA-graph) for image segmentation. Firstly, the original image is over-segmented to obtain several sets of superpixels with different scales, and the color and texture features of the superpixels are extracted. Then, the similarity relationship between neighborhood superpixels is used to construct the local affinity graph. Meanwhile, the global affinity graph is obtained by sparse reconstruction among all superpixels. The local affinity graph and global affinity graph are superimposed to obtain an enhanced affinity graph for eliminating the influences of noise and isolated regions in the image. Finally, a bipartite graph is introduced to express the affiliation between pixels and superpixels, and segmentation is performed using a spectral clustering algorithm. Experimental results on the Berkeley segmentation database demonstrate that our method achieves significantly better performance compared to state-of-the-art algorithms.

A spectrum-based fault localization technique (SBFL), which identifies fault location(s) in a buggy program by comparing the execution statistics of the program spectra of passed executions and failed executions, is a popular automatic debugging technique. However, the usefulness of SBFL is mainly affected by the following two factors: accuracy and fault understanding in reality. To solve this issue, we propose a SBFL framework to support fault understanding. In the framework, we firstly localize a suspicious fault module to start debugging and then generate a weighted fault propagation graph (WFPG) for the hypothesis fault module, which weights the suspiciousness for the nodes to further perform block-level fault localization. In order to evaluate the proposed framework, we conduct a controlled experiment to compare two different module-level SBFL approaches and validate the effectiveness of WFPG. According to our preliminary experiments, the results are promising.

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.

Spectral graph theory, based on the adjacency matrix or the Laplacian matrix that represents the network topology and link weights, provides a useful approach for analyzing network structure. However, in large scale and complex social networks, since it is difficult to completely know the network topology and link weights, we cannot determine the components of these matrices directly. To solve this problem, we propose a method for indirectly determining the Laplacian matrix by estimating its eigenvalues and eigenvectors using the resonance of oscillation dynamics on networks.

To improve the efficiency of spectrum utilization, cognitive radio systems attempt to use temporarily unoccupied spectrum which is referred to as a spectrum hole. To this end, QoS (Quality of Service) is one of the most important issues in practical cognitive radio systems. In this article, an efficient spectrum management scheme using self-reserving spectrum is proposed to support QoS for cognitive radio users. The self-reservation of a spectrum hole can minimize service dropping probability by using the statistical characteristics of spectrum bands while using optimum amount of resources. In addition, it realizes seamless service for users by eliminating spectrum entry procedure that includes spectrum sensing, spectrum request, and spectrum grant. Performance analysis and intensive system level simulations confirm the efficiency of the proposed algorithms.

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.

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.

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.

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.

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.