Spectral graph theory provides an algebraic approach to investigate the characteristics of weighted networks using the eigenvalues and eigenvectors of a matrix (e.g., normalized Laplacian matrix) that represents the structure of the network. However, it is difficult to accurately represent the structures of large-scale and complex networks (e.g., social network) as a matrix. This difficulty can be avoided if there is a universality, such that the eigenvalues are independent of the detailed structure in large-scale and complex network. In this paper, we clarify Wigner's Semicircle Law for weighted networks as such a universality. The law indicates that the eigenvalues of the normalized Laplacian matrix of weighted networks can be calculated from a few network statistics (the average degree, average link weight, and square average link weight) when the weighted networks satisfy a sufficient condition of the node degrees and the link weights.

We consider the bisimilarity control problem for partially observed nondeterministic discrete event systems with deterministic specifications. This problem requires us to synthesize a supervisor that achieves bisimulation equivalence of the supervised system and the deterministic specification under partial observation. We present necessary and sufficient conditions for the existence of such a deterministic supervisor and show that these conditions can be verified polynomially.

Dynamic spectrum access (DSA) exploits vacant frequency resources via distributed wireless access. The two nodes of DSA, master and slave, access different channels, and thus, cannot communicate with each other. To compensate for the access channel mismatch between the two nodes, a rendezvous channel, which exchanges control signals between two nodes, has been considered. The rendezvous channel based on channel-occupancy ratio (COR) adaptively constructs the channel in accordance with the channel occupancy of other systems, and both a high-speed rendezvous channel and high usage efficiency of the frequency resource are accomplished owing to exploitation of the vacant channel. In the rendezvous channel based on COR, the master and slave recognize the channel with minimum measured COR as the superior channel. As the master sends the control signals through the superior channel recognized by the master, the slave accesses to the superior channel recognized by the slave with higher access rate than to the other channels. As a result, the slave can receive the control signals with highly probability and thus high speed rendezvous channel is achieved. If the master and the slave recognize the different channel as the superior channel, the access rate to the other channel should be larger. This is because the slave obtains the opportunity of receiving the control signals through the different channel from the superior channel recognized by slave and thus the high probability that the slave can receive the control signals is maintained. Therefore, the access rate of slave should be constructed in accordance with the recognition of superior channel by master and slave. In this paper, the access rate of slave to the superior channel is optimally constructed using the analyzed probability of completion of rendezvous channel. The analysis of the probability of completion of rendezvous channel includes the recognition of superior channel by master and slave. Even if the master and the slave recognize the different channel, the constructed access rate of slave can maintain the high speed rendezvous channel. From the theoretical analysis and computer simulation, the rendezvous channel based on COR with the optimal access rate to the channel with the lowest COR achieves reduced time for the rendezvous channel.

This paper provides theoretical analyses for maximum cyclic autocorrelation selection (MCAS)-based spectrum sensing techniques in cognitive radio networks. The MCAS-based spectrum sensing techniques are low computational complexity spectrum sensing in comparison with some cyclostationary detection. However, MCAS-based spectrum sensing characteristics have never been theoretically derived. In this study, we derive closed form solutions for signal detection probability and false alarm probability for MCAS-based spectrum sensing. The theoretical values are compared with numerical examples, and the values match well with each other.

We propose a speech analysis-synthesis and deep neural network (DNN)-based text-to-speech (TTS) synthesis framework using Gaussian mixture model (GMM)-based approximation of full-band spectral envelopes. GMMs have excellent properties as acoustic features in statistic parametric speech synthesis. Each Gaussian function of a GMM fits the local resonance of the spectrum. The GMM retains the fine spectral envelope and achieve high controllability of the structure. However, since conventional speech analysis methods (i.e., GMM parameter estimation) have been formulated for a narrow-band speech, they degrade the quality of synthetic speech. Moreover, a DNN-based TTS synthesis method using GMM-based approximation has not been formulated in spite of its excellent expressive ability. Therefore, we employ peak-picking-based initialization for full-band speech analysis to provide better initialization for iterative estimation of the GMM parameters. We introduce not only prediction error of GMM parameters but also reconstruction error of the spectral envelopes as objective criteria for training DNN. Furthermore, we propose a method for multi-task learning based on minimizing these errors simultaneously. We also propose a post-filter based on variance scaling of the GMM for our framework to enhance synthetic speech. Experimental results from evaluating our framework indicated that 1) the initialization method of our framework outperformed the conventional one in the quality of analysis-synthesized speech; 2) introducing the reconstruction error in DNN training significantly improved the synthetic speech; 3) our variance-scaling-based post-filter further improved the synthetic speech.

This paper proposes a noise reduction technology for a specific frequency band that uses the pulse coding controlled method to automatically set the notch frequency in DC-DC switching converters of communication equipment. For reducing the power levels at the frequency and its harmonics in the switching converter, we often use a frequency-modulated clock. This paper investigates a technology that prevents modulated clock frequency noise from spreading into protected frequency bands; this proposed noise reduction technology does not distribute the switching noise into some specified frequency bands. The notch in the spectrum of the switching pulses is created by the Pulse Width Coding (PWC) method. In communication devices, the noise in the receiving signal band must be as small as possible. The notch frequency is automatically set to the frequency of the received signal by adjusting the clock frequency using the equation Fn = (P+0.5)Fck. Here Fn is the notch frequency, Fck is the clock frequency, and P is a positive integer that determines the noise spectrum location. Therefore, simply be setting the notch frequency to the received signal frequency can suppress the noise present. We confirm with simulations that the proposed technique is effective for noise reduction and notch generation. Also we implement a method of automatic switching between two receiving channels. The conversion voltage ratio in the pulse width coding method switching converter is analyzed and full automatic notch frequency generation is realized. Experiments on a prototype circuit confirm notch frequency generation.

A high degree of freedom in spectral domain allows us to accommodate additional optical signal processing for wavelength division multiplexing in photonic analog-to-digital conversion. We experimentally verified a spectral compression to save a necessary bandwidth for soliton self-frequency shift based optical quantization through the cascade of the four-wave mixing based and the sum-frequency generation based spectral compression. This approach can realize 0.03 nm individual bandwidth correspond to save up to more than 85 percent of bandwidth for 7-bit optical quantization in C-band.

Mode-by-mode impulse responses, or spectral transfer matrix (STM) of birefringent fibers are measured by using linear optical sampling, with assist of polarization multiplexed probe pulse. By using the eigenvalue analysis of the STM, the differential mode delay and PMD vector of polarization-maintaining fiber are analyzed as a function of optical frequency over 1THz. We show that the amplitude averaging of the complex impulse responses is effective for enhancing the signal-to-noise ratio of the measurement, resulting in improving the accuracy and expanding the bandwidth of the measurement.

A spectrum suppressed transmission that increases the frequency utilization efficiency, defined as throughput/bandwidth, by suppressing the required bandwidth has been proposed. This is one of the most effective schemes to solve the exhaustion problem of frequency bandwidths. However, in spectrum suppressed transmission, its transmission quality potentially degrades due to the ISI making the bandwidth narrower than the Nyquist bandwidth. In this paper, in order to improve the transmission quality degradation, we propose the spectrum suppressed transmission applying both FEC (forward error correction) and LE (linear equalization). Moreover, we also propose a new channel allocation scheme for the spectrum suppressed transmission, in multi-channel environments over a satellite transponder. From our computer simulation results, we clarify that the proposed schemes are more effective at increasing the system throughput than the scheme without spectrum suppression.

Automatic disassembly of railway fasteners is of great significance for improving the efficiency of replacing rails. The accurate positioning of fastener is the key factor to realize automatic disassembling. However, most of the existing literature mainly focuses on fastener region positioning and the literature on accurate positioning of fasteners is scarce. Therefore, this paper constructed a visual inspection system for accurate positioning of fastener (VISP). At first, VISP acquires railway image by image acquisition subsystem, and then the subimage of fastener can be obtained by coarse-to-fine method. Subsequently, the accurate positioning of fasteners can be completed by three steps, including contrast enhancement, binarization and spike region extraction. The validity and robustness of the VISP were verified by vast experiments. The results show that VISP has competitive performance for accurate positioning of fasteners. The single positioning time is about 260ms, and the average positioning accuracy is above 90%. Thus, it is with theoretical interest and potential industrial application.

In the age of big data, recommendation systems provide users with fast access to interesting information, resulting to a significant commercial value. However, the extreme sparseness of user assessment data is one of the key factors that lead to the poor performance of recommendation algorithms. To address this problem, we propose a spectral clustering recommendation scheme with low-rank matrix completion and spectral clustering. Our scheme exploits spectral clustering to achieve the division of a similar user group. Meanwhile, the low-rank matrix completion is used to effectively predict un-rated items in the sub-matrix of the spectral clustering. With the real dataset experiment, the results show that our proposed scheme can effectively improve the prediction accuracy of un-rated items.

Femtocell structures can offer better voice and data exchange in cellular networks. However, interference in such networks poses a major challenge in the practical development of cellular communication. To tackle this issue, an advanced interference mitigation scheme for Line-Of-Sight (LOS) femtocell networks in indoor environments is proposed in this paper. Using a femtocell management system (FMS) that controls all femtocells in a service area, the aggressor femtocells are identified and then the transmitted beam patterns are adjusted using the linear array antenna equipped in each femtocell to mitigate the interference contribution to the neighbouring femtocells. Prior to that, the affected users are switched to the femtocells that provide better throughput levels to avoid increasing the outage probability. This paper considers different femtocell deployment indexes to verify and justifies the feasibility of the findings in different density areas. Relative to fixed and adaptive power control schemes, the proposed scheme achieves approximately 5% spectral efficiency (SE) improvement, about 10% outage probability reduction, and about 7% Mbps average user throughput improvement.

This paper presents a novel statistic computation technique for energy detection-based spectrum sensing with multiple antennas. The presented technique computes the statistic for signal detection after combining all the signals. Because the computation of the statistic for all the received signals is not required, the presented technique reduces the computational complexity. Furthermore, the absolute value of all the received signals are combined to prevent the attenuation of the combined signals. Because the statistic computations are not required for all the received signals, the reduction of the computational complexity for signal detection can be expected. Furthermore, the presented technique does not need to choose anything, such as the binary phase rotator in the conventional technique, and therefore, the performance degradation due to wrong choices can be avoided. Numerical examples indicate that the spectrum sensing performances of the presented technique are almost the same as those of conventional techniques despite the complexity of the presented technique being less than that of the conventional techniques.

This paper presents a novel spectrum sensing technique based on selection diversity combining in cognitive radio networks. In general, a selection diversity combining scheme requires a period to select an optimal element, and spectrum sensing requires a period to detect a target signal. We consider that both these periods are required for the spectrum sensing based on selection diversity combining. However, conventional techniques do not consider both the periods. Furthermore, spending a large amount of time in selection and signal detection increases their accuracy. Because the required period for spectrum sensing based on selection diversity combining is the summation of both the periods, their lengths should be considered while developing selection diversity combining based spectrum sensing for a constant period. In reference to this, we discuss the spectrum sensing technique based on selection diversity combining. Numerical examples are shown to validate the effectiveness of the presented design techniques.

Lately, an increasing number of wireless local area network (WLAN) access points (APs) are deployed to serve an ever increasing number of mobile stations (STAs). Due to the limited frequency spectrum, more and more AP and STA nodes try to access the same channel. Spatial spectrum reuse is promoted by the IEEE 802.11ax task group through dynamic sensitivity control (DSC), which permits cochannel operation when the received signal power at the prospective transmitting node (PTN) is lower than an adjusted carrier sensing threshold (CST). Previously-proposed DSC approaches typically calculate the CST without node grouping by using a margin parameter that remains fixed during operation. Setting the margin has previously been done heuristically. Finding a suitable value has remained an open problem. Therefore, herein, we propose a DSC approach that employs a node grouping method for adaptive calculation of the CST at the PTN with a channel-aware and margin-free formula. Numerical simulations for dense residential WLAN scenario reveal total throughput and Jain's fairness index gains of 8.4% and 7.6%, respectively, vs. no DSC (as in WLANs deployed to present).

In this letter, we propose a more secure modeling and simulation approach that can systematically detect state variable corruptions caused by buffer overflows in simulation models. Using our approach, developers may not consider secure coding practices related to the corruptions. We have implemented a prototype of the approach based on a modeling and simulation formalism and an open source simulator. Through optimization, the prototype could show better performance, compared to the original simulator, and detect state variable corruptions.

Software fault localization, as one of the essential activities in program debugging, aids to software developers to identify the locations of faults in a program, thus reducing the cost of program debugging. Spectrum-based fault localization (SBFL), as one of the representative localization techniques, has been intensively studied. The localization technique calculates the probability of each program entity that is faulty by a certain suspiciousness formula. The accuracy of SBFL is not always as satisfactory as expected because it neglects the contextual information of statement executions. Therefore, we proposed 5 rules, i.e., random, the maximum coverage, the minimum coverage, the maximum distance, and the minimum distance, to improve the accuracy of SBFL for further. The 5 rules can effectively use the contextual information of statement executions. Moreover, they can be implemented on the traditional SBFL techniques using suspiciousness formulas with little effort. We empirically evaluated the impacts of the rules on 17 suspiciousness formulas. The results show that all 5 rules can significantly improve the ranking of faulty statements. Particularly, for the faults difficult to locate, the improvement is more remarkable. Generally, the rules can effectively reduce the number of statements examined by an average of more than 19%. Compared with other rules, the minimum coverage rule generates better results. This indicates that the application of the test case having the minimum coverage capability for fault localization is more effective.

A unified decision scheme for the classification and localization of cable faults is proposed based on a two-step procedure. Having basis in the time domain reflectometry (TDR), the proposed scheme is capable of determining not only the locations but also types of faults in a cable without an excessive additional computational burden compared to other TDR-based schemes. Results from simulation and experiments with measured real data demonstrate that the proposed scheme exhibits a higher rate of correct decision than the conventional schemes in localizing and classifying faults over a wide range of the location of faults.

To improve the likability of speech, we propose a voice conversion algorithm by controlling the fundamental frequency (F0) and the spectral envelope and carry out a subjective evaluation. The subjects can manipulate these two speech parameters. From the result, the subjects preferred speech with a parameter related to higher brightness.

This paper presents an automated patient-specific ECG classification algorithm, which integrates long short-term memory (LSTM) and convolutional neural networks (CNN). While LSTM extracts the temporal features, such as the heart rate variance (HRV) and beat-to-beat correlation from sequential heartbeats, CNN captures detailed morphological characteristics of the current heartbeat. To further improve the classification performance, adaptive segmentation and re-sampling are applied to align the heartbeats of different patients with various heart rates. In addition, a novel clustering method is proposed to identify the most representative patterns from the common training data. Evaluated on the MIT-BIH arrhythmia database, our algorithm shows the superior accuracy for both ventricular ectopic beats (VEB) and supraventricular ectopic beats (SVEB) recognition. In particular, the sensitivity and positive predictive rate for SVEB increase by more than 8.2% and 8.8%, respectively, compared with the prior works. Since our patient-specific classification does not require manual feature extraction, it is potentially applicable to embedded devices for automatic and accurate arrhythmia monitoring.