This paper presents an algorithm to arrange a large number of antenna elements in the limited space of massive MIMO base station antenna without degrading the communication quality under a street-cell line-of-sight environment in mobile communications. The proposed algorithm works by using mathematical optimization in which the objective function is the correlation coefficient between the channel responses of two elements of the base station antenna, according to an algorithm constructed based on the results obtained through basic examinations of the characteristics of the correlation coefficient between channel responses. The channel responses are computed by using the propagation path information obtained by ray-tracing. The arrangements output by the proposed algorithm are mainly evaluated by channel capacity comparison with uniformly spaced arrangements on the vertical plane in single user and multiuser environments. The evaluation results of these arrangements in downlink demonstrate the superiority of the arrangements generated by the proposed algorithm, especially in term of robustness against an increase in the number of users.

A new design approach of broadband RF power amplifier (PA) is introduced in this work with combination of large signal X-parameter and Real-Frequency Technique (RFT). A theoretical analysis of large signal X-parameter is revisited, and a simplification method is introduced to determine the optimum large signal impedances of a Gallium Nitride HEMT (GaN HEMT) device. With the optimum impedance extraction over the wide frequency range (0.3 to 2.0 GHz), a wideband matching network is constructed employing RFT and the final design is implemented with practical mixed-lumped elements. The prototype broadband RF PA demonstrates an output power of 40 dBm. The average drain efficiency of the PA is found to be more than 60%; while exhibiting acceptable flat gain performance (12±0.25 dB) over the frequency band of (0.3-2.0 GHz). The PA designed using the proposed approach yields in small form factor and relatively lower production cost over those of similar PAs designed with the classical methods. It is expected that the newly proposed design method will be utilized to construct power amplifiers for radio communications applications.

A fully integrated 16-phase 8-branch Dickson charge pump is proposed and implemented to decrease the power dissipation due to parasitic capacitance at the bottom plate of the boost capacitor. By using the charge recycling concept, 87% of the power consumption related to parasitic capacitance is saved. In a 4-stage version of this charge pump, a maximum power efficiency of 41% is achieved at 35µA output current and 11V output voltage from a 3.3V supply voltage. The proposed multi-branch charge pump can also reach a very low output voltage ripple of only 0.146% at a load resistance of 1MΩ, which is attributed to the fact that the 8-branch charge pump can transfer charges to the output node eight times consecutively during one clock period. In addition, a high voltage gain of 4.6 is achieved in the 4-stage charge pump at light load conditions. The total chip area is 0.57mm2 in a 0.35µm HV CMOS technology.

This paper presents a 28-GHz-band highly linear stacked-FET power amplifier (PA) IC. A 4-stacked-FET structure is employed for high output power considering the low breakdown voltage of scaled MOSFET transistors. A novel adaptive bias circuit is proposed to dynamically control the gate-to-source bias voltage for amplification MOSFETs. The novel adaptive bias allows the PA to attain high linearity with high back-off efficiency. In addition, the third-order intermodulation distortion (IM3) is improved by a multi-cascode structure. The PA IC is designed, fabricated and fully tested in 56-nm SOI CMOS technology. At a supply voltage of 4 V, the PA IC has achieved an output power of 20.0 dBm with a PAE as high as 38.1% at the 1-dB gain compression point (P1dB). Moreover, PAEs at 3-dB and 6-dB back-off from P1dB are 36.2% and 28.7%, respectively. The PA IC exhibits an output third-order intercept point (OIP3) of 25.0 dBm.

This study presents a new method to exploit both accent and grouping structures of music in meter estimation. The system starts by extracting autocorrelation-based features that characterize accent periodicities. Based on the local boundary detection model, we construct grouping features that serve as additional cues for inferring meter. After the feature extraction, a multi-layer cascaded classifier based on neural network is incorporated to derive the most likely meter of input melody. Experiments on 7351 folk melodies in MIDI files indicate that the proposed system achieves an accuracy of 95.76% for classification into nine categories of meters.

The current orthogonal cooperative system (OCS) achieves diversity through the use of relays and the consumption of an additional time slot (TS). To guarantee the orthogonality of the received signal and avoid the mutual interference at the destination, the source has to be mute in the second TS. Consequently, the spectral efficiency (SE) is halved. In this paper, linear constellation precoded orthogonal frequency division multiplexing with index modulation (LCP-OFDM-IM) based OCS is proposed, where the source activates the complementary subcarriers to convey the symbols over two TSs. Hence the source can consecutively transmit information to the destination without the mutual interference. Compared with the current OFDM based OCS, the LCP-OFDM-IM based OCS can achieve a higher SE, since the subcarrier activation patterns (SAPs) can be exploited to convey additional information. Furthermore, the optimal precoder, in the sense of maximizing the minimum Euclidean distance of the symbols conveyed on each subcarrier over two TSs, is provided. Simulation results show the superiority of the LCP-OFDM-IM based OCS over the current OFDM based OCS.

In this paper, we propose a method to reduce the labeling cost while acquiring training data for a malicious domain name detection system using supervised machine learning. In the conventional systems, to train a classifier with high classification accuracy, large quantities of benign and malicious domain names need to be prepared as training data. In general, malicious domain names are observed less frequently than benign domain names. Therefore, it is difficult to acquire a large number of malicious domain names without a dedicated labeling method. We propose a method based on active learning that labels data around the decision boundary of classification, i.e., in the gray area, and we show that the classification accuracy can be improved by using approximately 1% of the training data used by the conventional systems. Another disadvantage of the conventional system is that if the classifier is trained with a small amount of training data, its generalization ability cannot be guaranteed. We propose a method based on ensemble learning that integrates multiple classifiers, and we show that the classification accuracy can be stabilized and improved. The combination of the two methods proposed here allows us to develop a new system for malicious domain name detection with high classification accuracy and generalization ability by labeling a small amount of training data.

Currently, mobile terminals face serious security threats. A Trusted Execution Environment (TEE) which can provide an isolated execution environment for sensitive workloads, is seen as a trusted relay for providing security services for any mobile application. However, mobile TEE's architecture design and implementation strategy are not unbreakable at present. The existing researches lack of detect mechanisms for attack behaviour and malicious software. This paper proposes a Malicious code Detection scheme for Trusted Execution Environment based on Homomorphic Encryption (HE-TEEMD), which is a novel detection mechanism for data and code in the trusted execution environment. HE-TEEMD uses the Paillier additive homomorphic algorithm to implement the signature matching and transmits the ciphertext information generated in the TEE to the normal world for detection by the homomorphism and randomness of the homomorphic encryption ciphertext. An experiment and security analysis proves that our scheme can achieve malicious code detection in the secure world with minimal cost. Furthermore, evaluation parameters are introduced to address the known plaintext attack problem of privileged users.

In small cell deployments, the combined usage of user association and inter-cell interference coordination (ICIC) is inevitable. This paper investigates the joint optimization of user association and ICIC in the downlink. We first formulate the joint optimization problem as a utility maximization problem. We then employ the logarithmic utility function known as the proportional fair criteria. The optimum user association and the ICIC are derived by solving a convex optimization problem based on the average spectral efficiencies of all users. We propose an iterative algorithm to obtain the optimum solution to this problem. We evaluate the performance of the proposed algorithm for the small cell deployments and shows that the proposed algorithm works well. We also compare the performance of the proposed algorithm based on utility maximization user association with the CRE, and show the superiority of the utility maximization. Furthermore, we show that intra-tier ICIC and inter-tier ICIC can effectively improve the throughput performance according to the conditions. It is also shown that the combined usage of inter-tier ICIC and intra-tier ICIC enhances the throughput performance compared to schemes employing either the inter- or intra-tier ICIC scheme.

The degradation of a SiC-MOSFET-based DC-AC converter-circuit efficiency due to aging of the electrically active devices is investigated. The newly developed compact aging model HiSIM_HSiC for high-voltage SiC-MOSFETs is used in the investigation. The model considers explicitly the carrier-trap-density increase in the solution of the Poisson equation. Measured converter characteristics during a 3-phase line-to-ground (3LG) fault is correctly reproduced by the model. It is verified that the MOSFETs experience additional stress due to the high biases occurring during the fault event, which translates to severe MOSFET aging. Simulation results predict a 0.5% reduction of converter efficiency due to a single 70ms-3LG, which is equivalent to a year of operation under normal conditions, where no additional stress is applied. With the developed compact model, prediction of the efficiency degradation of the converter circuit under prolonged stress, for which measurements are difficult to obtain and typically not available, is also feasible.

Three-dimensional (3D) reconstruction and scene depth estimation from 2-dimensional (2D) images are major tasks in computer vision. However, using conventional 3D reconstruction techniques gets challenging in participating media such as murky water, fog, or smoke. We have developed a method that uses a continuous-wave time-of-flight (ToF) camera to estimate an object region and depth in participating media simultaneously. The scattered light observed by the camera is saturated, so it does not depend on the scene depth. In addition, received signals bouncing off distant points are negligible due to light attenuation, and thus the observation of such a point contains only a scattering component. These phenomena enable us to estimate the scattering component in an object region from a background that only contains the scattering component. The problem is formulated as robust estimation where the object region is regarded as outliers, and it enables the simultaneous estimation of an object region and depth on the basis of an iteratively reweighted least squares (IRLS) optimization scheme. We demonstrate the effectiveness of the proposed method using captured images from a ToF camera in real foggy scenes and evaluate the applicability with synthesized data.

This paper proposes a generative moment matching network (GMMN)-based post-filtering method for providing inter-utterance pitch variation to singing voices and discusses its application to our developed mixing method called neural double-tracking (NDT). When a human singer sings and records the same song twice, there is a difference between the two recordings. The difference, which is called inter-utterance variation, enriches the performer's musical expression and the audience's experience. For example, it makes every concert special because it never recurs in exactly the same manner. Inter-utterance variation enables a mixing method called double-tracking (DT). With DT, the same phrase is recorded twice, then the two recordings are mixed to give richness to singing voices. However, in synthesized singing voices, which are commonly used to create music, there is no inter-utterance variation because the synthesis process is deterministic. There is also no inter-utterance variation when only one voice is recorded. Although there is a signal processing-based method called artificial DT (ADT) to layer singing voices, the signal processing results in unnatural sound artifacts. To solve these problems, we propose a post-filtering method for randomly modulating synthesized or natural singing voices as if the singer sang again. The post-filter built with our method models the inter-utterance pitch variation of human singing voices using a conditional GMMN. Evaluation results indicate that 1) the proposed method provides perceptible and natural inter-utterance variation to synthesized singing voices and that 2) our NDT exhibits higher double-trackedness than ADT when applied to both synthesized and natural singing voices.

Near real-time periodic tasks, which are popular in multimedia streaming applications, have deadline periods that are longer than the input intervals thanks to buffering. For such applications, the conventional frame-based schedulings cannot realize the optimal scheduling due to their shortsighted deadline assumptions. To realize globally energy-efficient executions of these applications, we propose a novel task scheduling algorithm, which takes advantage of the long deadline period. We confirm our approach can take advantage of the longer deadline period and reduce the average power consumption by up to 18%.

When a unit repeats some works over again and undergoes minimal repairs at failures, it is more practical to replace it preventively at the end of working cycles or at its failure times. In this case, it would be an interesting problem to know which is better to replace the unit at a number of working cycles or at random failures from the point of cost. For this purpose, we give models of the expected cost rates for the following replacement policies: (1) The unit is replaced at a working cycle N and at a failure number K, respectively; (2) Replacement first and last policies with working cycle N and failure number K, respectively; (3) Replacement overtime policies with working cycle N and failure number K, respectively. Optimizations and comparisons of the policies for N and K are made analytically and numerically.

S-shaped nonlinearity is found in the electrical resistance-length relationship in an electroactive supercoiled polymer artificial muscle. The modulation of the electrical resistance is mainly caused by the change in the contact condition of coils in the artificial muscle upon deformation. A mathematical model based on logistic function fairly reproduces the experimental data of electrical resistance-length relationship.

This paper studies the joint optimization of precoding, transmit power and data rate allocation for energy-efficient full-duplex (FD) cloud radio access networks (C-RANs). A new nonconvex problem is formulated, where the ratio of total sum rate to total power consumption is maximized, subject to the maximum transmit powers of remote radio heads and uplink users. An iterative algorithm based on successive convex programming is proposed with guaranteed convergence to the Karush-Kuhn-Tucker solutions of the formulated problem. Numerical examples confirm the effectiveness of the proposed algorithm and show that the FD C-RANs can achieve a large gain over half-duplex C-RANs in terms of energy efficiency at low self-interference power levels.

In wireless networks, efficient topology improves the performance of network protocols. The previous research mainly focuses on how to construct a cost-efficient network structure from a static and connected topology. Due to lack of continuous connectivity in the underlying topology, most traditional topology control methods are not applicable to the delay or disruption tolerant networks (DTNs). In this paper, we consider the topology control problem in a predictable DTN where the dynamic topology is known a priori or can be predicted over time. First, this dynamic topology is modeled by a directed space-time graph that includes spatial and temporal information. Second, the topology control problem of the predictable DTN is formulated as building a sparse structure. For any pair devices, there is an efficient path connecting them to improve the efficiency of the generated structure. Then, a topology control strategy is proposed for this optimization problem by using a kth shortest paths algorithm. Finally, simulations are conducted on random networks and a real-world DTN tracing date. The results demonstrate that the proposed method can significantly improve the efficiency of the generated structure and reduce the total cost.

Recently, many studies have applied gamification to software engineering education and software development to enhance work results. Gamification is defined as “the use of game design elements in non-game contexts.” When applying gamification, we make various game rules, such as a time limit. However, it is not clear whether the rule affects working time or not. For example, if we apply a time limit to impatient developers, the working time may become shorter, but the rule may negatively affect because of pressure for time. In this study, we analyze with subjective experiments whether the rules affects work results such as working time. Our experimental results suggest that for the coding tasks, working time was shortened when we applied a rule that made developers aware of working time by showing elapsed time.

In-class species classification based on animal sounds is a highly challenging task even with the latest deep learning technique applied. The difficulty of distinguishing the species is further compounded when the number of species is large within the same class. This paper presents a novel approach for fine categorization of animal species based on their sounds by using pre-trained CNNs and a new self-attention module well-suited for acoustic signals The proposed method is shown effective as it achieves average species accuracy of 98.37% and the minimum species accuracy of 94.38%, the highest among the competing baselines, which include CNN's without self-attention and CNN's with CBAM, FAM, and CFAM but without pre-training.

To implement the parallel acceleration of convolution operation of Convolutional Neural Networks (CNNs) on field programmable gate array (FPGA), large quantities of the logic resources will be consumed, expecially DSP cores. Many previous researches fail to make a well balance between DSP and LUT6. For better resource efficiency, a typical convolution structure is implemented with LUT6s in this paper. Besides, a novel convolution structure is proposed to further reduce the LUT6 resource consumption by modifying the typical convolution structure. The equations to evaluate the LUT6 resource consumptions of both structures are presented and validated. The theoretical evaluation and experimental results show that the novel structure can save 3.5-8% of LUT6s compared with the typical structure.