Ensemble learning is widely used in the field of sensor network monitoring and target identification. To improve the generalization ability and classification precision of ensemble learning, we first propose an approximate attribute reduction algorithm based on rough sets in this paper. The reduction algorithm uses mutual information to measure attribute importance and introduces a correction coefficient and an approximation parameter. Based on a random sampling strategy, we use the approximate attribute reduction algorithm to implement the multi-modal sample space perturbation. To further reduce the ensemble size and realize a dynamic subset of base classifiers that best matches the test sample, we define a similarity parameter between the test samples and training sample sets that takes the similarity and number of the training samples into consideration. We then propose a k-means clustering-based dynamic ensemble selection algorithm. Simulations show that the multi-modal perturbation method effectively selects important attributes and reduces the influence of noise on the classification results. The classification precision and runtime of experiments demonstrate the effectiveness of the proposed dynamic ensemble selection algorithm.

This paper introduces a technique for automatically generating potential training data from sentences in which entity pairs are not apparently presented in a relation extraction. Most previous works on relation extraction by distant supervision ignored cases in which a relationship may be expressed via null-subjects or anaphora. However, natural language text basically has a network structure that is composed of several sentences. If they are closely related, this is not expressed explicitly in the text, which can make relation extraction difficult. This paper describes a new model that augments a paragraph with a “salient entity” that is determined without parsing. The entity can create additional tuple extraction environments as potential subjects in paragraphs. Including the salient entity as part of the sentential input may allow the proposed method to identify relationships that conventional methods cannot identify. This method also has promising potential applicability to languages for which advanced natural language processing tools are lacking.

This paper proposes a method for predicting individuality-preserving gait patterns. Physical rehabilitation can be performed using visual and/or physical instructions by physiotherapists or exoskeletal robots. However, a template-based rehabilitation may produce discomfort and pain in a patient because of deviations from the natural gait of each patient. Our work addresses this problem by predicting an individuality-preserving gait pattern for each patient. In this prediction, the transition of the gait patterns is modeled by associating the sequence of a 3D skeleton in gait with its continuous-value gait features (e.g., walking speed or step width). In the space of the prediction model, the arrangement of the gait patterns are optimized so that (1) similar gait patterns are close to each other and (2) the gait feature changes smoothly between neighboring gait patterns. This model allows to predict individuality-preserving gait patterns of each patient even if his/her various gait patterns are not available for prediction. The effectiveness of the proposed method is demonstrated quantitatively. with two datasets.

A wind-noise suppressor with SNR based wind-noise detection and speech-wind discrimination is proposed. Wind-noise detection is performed in each frame and frequency based on the power ratio of the noisy speech and an estimated stationary noise. The detection result is modified by speech presence likelihood representing spectral smoothness to eliminate speech components. To suppress wind noise with little speech distortion, spectral gains are made smaller in the frame and the frequency where wind-noise is detected. Subjective evaluation results show that the 5-grade MOS for the proposed wind-noise suppressor reaches 3.4 and is 0.56 higher than that by a conventional noise suppressor with a statistically significant difference.

This paper investigates receive power control for multiuser inductive power transfer (IPT) systems with a single-frequency coil array. The primary task is to optimize the transmit coil currents to minimize the total input power, subject to the minimum receive powers required by individual users. Due to the complicated coupling mechanism among all transmit coils and user pickups, the optimization problem is a non-convex quadratically constrained quadratic program (QCQP), which is analytically intractable. This paper solves the problem by applying the semidefinite relaxation (SDR) technique and evaluates the performance by full-wave electromagnetic simulations. Our results show that a single-frequency coil array is capable of power control for various multiuser scenarios, assuming that the number of transmit coils is greater than or equal to the number of users and the transmission conditions for individual users are uncorrelated.

The Jacobi-Davidson method and the Riccati method for eigenvalue problems are studied. In the methods, one has to solve a nonlinear equation called the correction equation per iteration, and the difference between the methods comes from how to solve the equation. In the Jacobi-Davidson/Riccati method the correction equation is solved with/without linearization. In the literature, avoiding the linearization is known as an improvement to get a better solution of the equation and bring the faster convergence. In fact, the Riccati method showed superior convergence behavior for some problems. Nevertheless the advantage of the Riccati method is still unclear, because the correction equation is solved not exactly but with low accuracy. In this paper, we analyzed the approximate solution of the correction equation and clarified the point that the Riccati method is specialized for computing particular solutions of eigenvalue problems. The result suggests that the two methods should be selectively used depending on target solutions. Our analysis was verified by numerical experiments.

The QC-MDPC McEliece scheme was considered one of the most promising public key encryption schemes for efficient post-quantum secure encryption. As a variant of the McEliece scheme, it is based on the syndrome decoding problem, which is a hard problem from Coding Theory. Its key sizes are competitive with the ones of the widely used RSA cryptosystem, and it came with an apparently strong security reduction. For three years, the scheme has not suffered major threats, until the end of 2016, at the Asiacrypt, when Guo, Johansson, and Stankovski presented a reaction attack on the QC-MDPC that exploits one aspect that was not considered in the security reduction: the probability of a decoding failure to occur is lower when the secret key and the error used for encryption share certain properties. Recording the decoding failures, the attacker obtains information about the secret key and then use the information gathered to reconstruct the key. Guo et al. presented an algorithm for key reconstruction for which we can point two weaknesses. The first one is that it cannot deal with partial information about the secret key, resulting in the attacker having to send a large number of decoding challenges. The second one is that it does not scale well for higher security levels. To improve the attack, we propose a key reconstruction algorithm that runs faster than Guo's et al. algorithm, even using around 20% less interactions with the secret key holder than used by their algorithm, considering parameters suggested for 80 bits of security. It also has a lower asymptotic complexity which makes it scale much better for higher security parameters. The algorithm can be parallelized straightforwardly, which is not the case for the one by Guo et al.

Dual-motor driving servo systems are widely used in many military and civil fields. Since backlash nonlinearity affects the dynamic performance and steady-state tracking accuracy of these systems, it is necessary to study a control strategy to reduce its adverse effects. We first establish the state-space model of a system. To facilitate the design of the controller, we simplify the model based on the state-space model. Then, we design an adaptive controller combining a projection algorithm with dynamic surface control applied to a dual-motor driving servo system, which we believe to be the first, and analyze its stability. Simulation results show that projection algorithm-based dynamic surface control has smaller tracking error, faster tracking speed, and better robustness and stability than mere dynamic surface control. Finally, the experimental analysis validates the effectiveness of the proposed control algorithm.

As one functionality of the wireless distributed network (WDN) enabling flexible wireless networks, it is supposed that a dynamic spectrum access is applied to OFDM systems for superior radio resource management. As a basic technology for such WDN, our study deals with the OFDM signal detection based on its cyclostationary feature. Previous relevant studies mainly relied on software simulations based on the Monte Carlo method. This paper analytically clarifies the relationship between the design parameters of the detector and its detection performance. The detection performance is formulated by using multiple design parameters including the transfer function of the receive filter. A hardware experiment with radio frequency (RF) signals is also carried out by using the detector consisting of an RF unit and FPGA. Thereby, it is verified that the detection characteristics represented by the false-alarm and non-detection probabilities calculated by the analytical formula agree well with those obtained by the hardware experiment. Our analysis and experiment results are useful for the parameter design of the signal detector to satisfy required performance criteria.

For more flexible and efficient use of radio spectrum, reconfigurable RF devices have important roles in the future wireless systems. In 5G mobile communications, concurrent multi-band operation using new SHF bands is considered. This paper presents a new configuration of dual-band SHF BPF consisting of a low SHF three-bit reconfigurable BPF and a high SHF BPF. The proposed dual-band BPF employs direct parallel connection without additional divider/combiner to reduce circuit elements and simplify the BPF. In order to obtain a good isolation between two passbands while achieving a wide center frequency range in the low SHF BPF, input/output impedances and external Qs of BPFs are analyzed and feedbacked to the design. A high SHF BPF design method with tapped transmission line resonators and lumped-element coupling is also presented to make the BPF compact. Two types of prototypes; all inductor-coupled dual-band BPF and C-L-C coupled dual-band BPF were designed and fabricated. Both prototypes have low SHF reconfigurable center frequency range from 3.5 to 5 GHz as well as high SHF center frequency of 8.5 GHz with insertion loss below 2.0 dB.

Zero-difference balanced (ZDB) functions, which have many applications in coding theory and sequence design, have received a lot of attention in recent years. In this letter, based on two known classes of ZDB functions, a new class of ZDB functions, which is defined on the group (Z2e-1×Zn,+) is presented, where e is a prime and n=p1m1p2m2…pkmk, pi is odd prime satisfying that e|(pi-1) for any 1≤i≤k . In the case of gcd(2e-1,n)=1, the new constructed ZDB functions are cyclic.

The research on inertial sensor based human action detection and recognition (HADR) is a new area in machine learning. We propose a novel time sequence based interval convolutional neutral networks framework for HADR by combining interesting interval proposals generator and interval-based classifier. Experiments demonstrate the good performance of our method.

In this letter, a new semi-blind approach incorporating the bounded nature of communication sources with the distance between the equalizer outputs and the training sequence is proposed. By utilizing the sparsity property of l1-norm cost function, the proposed algorithm can outperform the semi-blind method based on higher-order statistics (HOS) criterion especially for transmitting sources with non-constant modulus. Experimental results demonstrate that the proposed method shows superior performance over the HOS based semi-blind method and the classical training-based method for QPSK and 16QAM sources equalization. While for 64QAM signal inputs, the proposed algorithm exhibits its superiority in low signal-to-noise-ratio (SNR) conditions compared with the training-based method.

Densification of mmWave smallcells overlaid on the conventional macro cell is considered to be an essential technology for enhanced mobile broadband services and future IoT applications requiring high data rate e.g. automated driving in 5G communication networks. Taking into account actual measurement mobile traffic data which reveal dynamicity in both time and space, this paper proposes a joint optimization of user association and smallcell base station (BS)'s ON/OFF status. The target is to improve the system's energy efficiency while guaranteeing user's satisfaction measured through e.g. delay tolerance. Numerical analyses are conducted to show the effectiveness of the proposed algorithm against dynamic traffic variation.

Differential microphone arrays have been widely used in hands-free communication systems because of their frequency-invariant beampatterns, high directivity factors and small apertures. Considering the position of acoustic source always moving within a certain range in real application, this letter proposes an approach to construct the steerable first-order differential beampattern by using four omnidirectional microphones arranged in a non-orthogonal circular geometry. The theoretical analysis and simulation results show beampattern constructed via this method achieves the same direction factor (DF) as traditional DMAs and higher white noise gain (WNG) within a certain angular range. The simulation results also show the proposed method applies to processing speech signal. In experiments, we show the effectiveness and small computation amount of the proposed method.

With the development of wireless network technology and popularization of mobile devices, the Wireless Local Area Network (WLAN) has become an indispensable part of our daily life. Although the 802.11-based WLAN provides enormous convenience for users to access the Internet, it also gives rise to a number of security issues. One of the most severe threat encountered by Wi-Fi users is the evil twin attacks. The evil twin, a kind of rogue access points (RAPs), masquerades as a legitimate access point (AP) to lure users to connect it. Due to the characteristics of strong concealment, high confusion, great harmfulness and easy implementation, the evil twin has led to significant loss of sensitive information and become one of the most prominent security threats in recent years. In this paper, we propose a passive client-based detection solution that enables users to independently identify and locate evil twins without any assistance from a wireless network administrator. Because of the forwarding behavior of evil twins, proposed method compares 802.11 data frames sent by target APs to users to determine evil twin attacks. We implemented our detection technique in a Python tool named ET-spotter. Through implementation and evaluation in our study, our algorithm achieves 96% accuracy in distinguishing evil twins from legitimate APs.

This paper proposes a directional noise suppressor with a specified constant beamwidth for directional interferences and diffuse noise. A directional gain is calculated based on interchannel phase difference and combined with a spectral gain commonly used in single-channel noise suppressors. The beamwidth can be specified as passband edges of the directional gain. In order to implement frequency-independent constant beamwidth, frequency-proportionate directional gains are defined for different frequencies as a constraint. Evaluation with signals recorded by a commercial PC demonstrates good agreement between the theoretical and the measured directivity. The signal-to-noise ratio improvement and the PESQ score for the enhanced signal are improved by 24.4dB and 0.3 over a conventional noise suppressor. In a speech recognition scenario, the proposed directional noise suppressor outperforms both the conventional nondirectional noise suppressor and the conventional directional noise suppressor based on phase based T/F filtering with a negligible degradation in the word error rate for clean speech.

The energy of a threshold circuit C is defined to be the maximum number of gates outputting ones for an input assignment, where the maximum is taken over all the input assignments. In this paper, we study computational power of threshold circuits of energy at most two. We present several results showing that the computational power of threshold circuits of energy one and the counterpart of energy two are remarkably different. In particular, we give an explicit function which requires an exponential size for threshold circuits of energy one, but is computable by a threshold circuit of size just two and energy two. We also consider MOD functions and Generalized Inner Product functions, and show that these functions also require exponential size for threshold circuits of energy one, but are computable by threshold circuits of substantially less size and energy two.

Review rating prediction is an important problem in machine learning and data mining areas and has attracted much attention in recent years. Most existing methods for review rating prediction on Location-Based Social Networks only capture the semantics of texts, but ignore user information (social links, geolocations, etc.), which makes them less personalized and brings down the prediction accuracy. For example, a user's visit to a venue may be influenced by their friends' suggestions or the travel distance to the venue. To address this problem, we develop a review rating prediction framework named TSG by utilizing users' review Text, Social links and the Geolocation information with machine learning techniques. Experimental results demonstrate the effectiveness of the framework.

Association rule mining discovers relationships among variables in a data set, representing them as rules. These are expected to often have predictive abilities, that is, to be able to predict future events, but commonly used rule interestingness measures, such as support and confidence, do not directly assess their predictive power. This paper proposes a cross-validation -based metric that quantifies the predictive power of such rules for characterizing software defects. The results of evaluation this metric experimentally using four open-source data sets (Mylyn, NetBeans, Apache Ant and jEdit) show that it can improve rule prioritization performance over conventional metrics (support, confidence and odds ratio) by 72.8% for Mylyn, 15.0% for NetBeans, 10.5% for Apache Ant and 0 for jEdit in terms of SumNormPre(100) precision criterion. This suggests that the proposed metric can provide better rule prioritization performance than conventional metrics and can at least provide similar performance even in the worst case.